diff --git a/.gitattributes b/.gitattributes index bed0738c7eeb449bca98b5d2f33c89a1ee56349a..dd985fce3dcef6cc24f6362a34e47c182bfbad4d 100644 --- a/.gitattributes +++ b/.gitattributes @@ -58,3 +58,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text # Video files - compressed *.mp4 filter=lfs diff=lfs merge=lfs -text *.webm filter=lfs diff=lfs merge=lfs -text +gpudrive-main/papers/LLM评估场景.pdf filter=lfs diff=lfs merge=lfs -text +gpudrive-main/server_resource/event_traces.jsonl filter=lfs diff=lfs merge=lfs -text diff --git a/gpudrive-main/.dockerignore b/gpudrive-main/.dockerignore new file mode 100644 index 0000000000000000000000000000000000000000..abd089961281e41fb462008b4ac5bdd472f6e2a2 --- /dev/null +++ b/gpudrive-main/.dockerignore @@ -0,0 +1,6 @@ +Dockerfile +.* +!.git +!.gitignore +!.gitmodules +!.python-version diff --git a/gpudrive-main/.env.template b/gpudrive-main/.env.template new file mode 100644 index 0000000000000000000000000000000000000000..431883d8b6f638b985fbabfc8bc37b2b17e6d0c9 --- /dev/null +++ b/gpudrive-main/.env.template @@ -0,0 +1,13 @@ +# .env template + +# Path for logs +LOG_FOLDER= + +# Your HPC account code +NYU_HPC_ACCOUNT= + +# NYU ID +USERNAME= + +SINGULARITY_IMAGE= +OVERLAY_FILE= \ No newline at end of file diff --git a/gpudrive-main/.github/workflows/ci.yml b/gpudrive-main/.github/workflows/ci.yml new file mode 100644 index 0000000000000000000000000000000000000000..1c4943393a818a839919ae4a09f2ccbfc1a98345 --- /dev/null +++ b/gpudrive-main/.github/workflows/ci.yml @@ -0,0 +1,42 @@ +name: Continuous Integration + +on: + push: + branches: [main] + pull_request: + +jobs: + build-and-smoketest: + runs-on: ubuntu-latest + + steps: + - name: Checkout repository + uses: actions/checkout@v4 + + - name: Set up Docker Buildx + uses: docker/setup-buildx-action@v2 + + - name: Build Docker image + run: docker build --tag gpudrive:latest . + + - name: Run smoke test inside Docker container (using a here document) + run: | + docker run --rm gpudrive:latest /bin/bash -c "$(cat << 'EOF' + #!/bin/bash + set -e + set -x + export DEBIAN_FRONTEND=noninteractive + echo 'Dummy cuda' + ln -s /usr/local/cuda/lib64/stubs/libcuda.so /usr/local/cuda/lib64/stubs/libcuda.so.1 + export LD_LIBRARY_PATH=/usr/local/cuda/lib64/stubs/:$LD_LIBRARY_PATH + echo 'Modifications to run without extra data' + sed -i 's|train_path: \"data/processed/training\"|train_path: \"data/processed/examples\"|g' examples/experimental/config/visualization_config.yaml + sed -i '/# Load policy/{N;N;N;N;N;N;s|# Load policy\n policy = load_policy(\n path_to_cpt=config.cpt_path,\n model_name=config.cpt_name,\n device=config.device,\n env=env,\n )|from gpudrive.networks.late_fusion import NeuralNet\n policy = NeuralNet.from_pretrained(\"daphne-cornelisse/policy_S10_000_02_27\")|}' examples/experimental/viz_rollouts.py + echo 'Modifications to run without GPU' + sed -i 's/device=\"cuda\"/device=\"cpu\"/g' gpudrive/datatypes/observation.py + export PYTHONFAULTHANDLER=1 + ulimit -c unlimited + echo 'Run smoketest' + /gpudrive/.venv/bin/python examples/experimental/viz_rollouts.py + EOF + )" diff --git a/gpudrive-main/.gitignore b/gpudrive-main/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..6695207c99c305a2f532423e4ea0d67b768a3308 --- /dev/null +++ b/gpudrive-main/.gitignore @@ -0,0 +1,258 @@ +/build +/out +/build-debug +/.vs +/CMakeSettings.json +/scripts/__pycache__ +/train_src/madrona_escape_room_learn/__pycache__ +.vscode/launch.json +.vscode/settings.json +.vscode/tasks.json +.uv_cache + +/examples/benchmarks/results/ +/baselines/ppo/logs/* +*.sif +/cloudpickle +/cloudpickle-3.0.0.dist-info +*madrona.diff +/bin +/zipp* +/google-cloud-sdk/* +*.gz + +# Data +hpc/overlay* +data/raw/* +data/processed/validation/* +data/processed/training/* +data/processed/testing/* +data/processed/sampled/* +data/processed/hand_designed/* +analyze/figures/* + +# Logging +/wandb +/wandb* +events.out.tfevents.* +/runs* + +### C++ ### +# Prerequisites +*.d + +# Compiled Object files +*.slo +*.lo +*.o + +# Precompiled Headers +*.gch +*.pch + +# Compiled Dynamic libraries +*.so +*.dylib +*.dll + +# Fortran module files +*.mod +*.smod + +# Compiled Static libraries +*.lai +*.la +*.a +*.lib + +# Executables +*.exe +*.out +*.app + +### Python ### +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST +*.csv + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/#use-with-ide +.pdm.toml + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.venv +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ + +### Python Patch ### +# Poetry local configuration file - https://python-poetry.org/docs/configuration/#local-configuration +poetry.toml + +# ruff +.ruff_cache/ + +# LSP config files +pyrightconfig.json + +*~ + +# Environment variables +# To be manually created using .env.template +.env + +# Logs +examples/experimental/logs/* + +# Sbatch scripts +*.sh + +# Videos +videos/ +output_videos_larger_dataset/ +output_videos_small_dataset/ + +# Trained models +models/ + +# End of https://www.toptal.com/developers/gitignore/api/python,c++ diff --git a/gpudrive-main/.gitmodules b/gpudrive-main/.gitmodules new file mode 100644 index 0000000000000000000000000000000000000000..75c1ccb24771954dc9cec7b07f217043af4068ca --- /dev/null +++ b/gpudrive-main/.gitmodules @@ -0,0 +1,6 @@ +[submodule "external/madrona"] + path = external/madrona + url = https://github.com/m-naumann/madrona.git +[submodule "external/json"] + path = external/json + url = https://github.com/nlohmann/json.git diff --git a/gpudrive-main/.pre-commit-config.yaml b/gpudrive-main/.pre-commit-config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..fdbd58940d8b7e2dd181466ff68d86106e052033 --- /dev/null +++ b/gpudrive-main/.pre-commit-config.yaml @@ -0,0 +1,18 @@ +repos: + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.0.1 # Use the latest version + hooks: + - id: trailing-whitespace + - id: end-of-file-fixer + - id: check-yaml + - id: check-added-large-files + + - repo: https://github.com/pycqa/flake8 + rev: 3.9.2 # Use the latest version + hooks: + - id: flake8 + - repo: https://github.com/psf/black + rev: 22.3.0 # Use the latest version + hooks: + - id: black + args: [--line-length, "79"] diff --git a/gpudrive-main/.python-version b/gpudrive-main/.python-version new file mode 100644 index 0000000000000000000000000000000000000000..2c0733315e415bfb5e5b353f9996ecd964d395b2 --- /dev/null +++ b/gpudrive-main/.python-version @@ -0,0 +1 @@ +3.11 diff --git a/gpudrive-main/CMakeLists.txt b/gpudrive-main/CMakeLists.txt new file mode 100644 index 0000000000000000000000000000000000000000..240fcd3c23adb9477ff787ac5ae77e0b89bd2b4d --- /dev/null +++ b/gpudrive-main/CMakeLists.txt @@ -0,0 +1,17 @@ +cmake_minimum_required(VERSION 3.24 FATAL_ERROR) # for madrona + +include("${CMAKE_CURRENT_SOURCE_DIR}/external/madrona/cmake/madrona_init.cmake") + +project(Madrona3DExample LANGUAGES C CXX) + +include(setup) +include(dependencies) + +add_subdirectory(external) +option(GPUDRIVE_BUILD_TESTS "Build GPUDrive C++ tests" ON) + +add_subdirectory(src) +if (GPUDRIVE_BUILD_TESTS) + enable_testing() + add_subdirectory(tests) +endif() diff --git a/gpudrive-main/Dockerfile b/gpudrive-main/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..1bd95dd35efafdd957ef94af64c20bea3d4eb8cf --- /dev/null +++ b/gpudrive-main/Dockerfile @@ -0,0 +1,52 @@ +# Base image with CUDA and cuDNN support +FROM nvidia/cuda:12.4.1-cudnn-devel-ubuntu22.04 + +ARG DEBIAN_FRONTEND=noninteractive + +# Install essential packages +RUN apt-get update && apt-get install -y -q --no-install-recommends \ + software-properties-common \ + build-essential \ + cmake \ + git \ + curl \ + vim \ + ca-certificates \ + libjpeg-dev \ + libpng-dev \ + wget \ + libx11-dev \ + libxrandr-dev \ + libxinerama-dev \ + libxcursor-dev \ + libxi-dev \ + mesa-common-dev \ + libc++1 \ + openssh-client \ + ffmpeg \ + && apt-get clean \ + && rm -rf /var/lib/apt/lists/* + +# Install uv +RUN curl -LsSf https://astral.sh/uv/install.sh | env UV_INSTALL_DIR="/usr/bin" sh + +# Copy the gpudrive repository +COPY . /gpudrive +WORKDIR /gpudrive +RUN git submodule update --init --recursive + +# Install python part using uv +RUN uv sync --frozen + +ENV MADRONA_MWGPU_KERNEL_CACHE=./gpudrive_cache + +RUN mkdir build +WORKDIR /gpudrive/build +RUN uv run cmake .. -DCMAKE_BUILD_TYPE=Release -DCMAKE_POLICY_VERSION_MINIMUM=3.5 && find external -type f -name "*.tar" -delete +RUN ln -s /usr/local/cuda/lib64/stubs/libcuda.so /usr/local/cuda/lib64/stubs/libcuda.so.1 +RUN LD_LIBRARY_PATH=/usr/local/cuda/lib64/stubs/:$LD_LIBRARY_PATH uv run make -j +RUN rm /usr/local/cuda/lib64/stubs/libcuda.so.1 +WORKDIR /gpudrive + +CMD ["/bin/bash"] +LABEL org.opencontainers.image.source=https://github.com/Emerge-Lab/gpudrive diff --git a/gpudrive-main/LICENSE b/gpudrive-main/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..1ee9d3f643d4dd308a84023a798da26eb61623fb --- /dev/null +++ b/gpudrive-main/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2024 Saman Kazemkhani, Aarav Pandya, Daphne Cornelisse, Brennan Shacklett, Eugene Vinitsky + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/gpudrive-main/README.md b/gpudrive-main/README.md new file mode 100644 index 0000000000000000000000000000000000000000..7d87e0557ff1130d102ecc0c20d746754d471b92 --- /dev/null +++ b/gpudrive-main/README.md @@ -0,0 +1,383 @@ +GPUDrive +======== + +[![Paper](https://img.shields.io/badge/arXiv-2408.01584-b31b1b.svg)](https://arxiv.org/abs/2408.01584) +[![GitHub CI](https://github.com/Emerge-Lab/gpudrive/actions/workflows/ci.yml/badge.svg)](https://github.com/Emerge-Lab/gpudrive/actions/workflows/ci.yml) +[![License](https://img.shields.io/github/license/Emerge-Lab/gpudrive)](LICENSE) +![Python version](https://img.shields.io/badge/Python-3.11-blue) + +An extremely fast, data-driven driving simulator written in C++. + +## Highlights + +- ⚡️ Fast simulation for agent development and evaluation at 1 million FPS through the [Madrona engine](https://madrona-engine.github.io/). +- 🐍 Provides Python bindings and `gymnasium` wrappers in `torch` and `jax`. +- 🏃‍➡️ Compatible with the [Waymo Open Motion Dataset](https://github.com/waymo-research/waymo-open-dataset), featuring over 100K scenarios with human demonstrations. +- 📜 Readily available PPO implementations via [SB3](https://github.com/DLR-RM/stable-baselines3) and [CleanRL](https://github.com/vwxyzjn/cleanrl) / [Pufferlib](https://puffer.ai/). +- 👀 Easily configure the simulator and agent views. +- 🎨 Diverse agent types: Vehicles, cyclists and pedestrians. + +
+ +| Simulator state | Agent observation | +| ---------------------------------------------------------------- | ---------------------------------------------------------------- | +| | | +| | | + +
+ +For details, see our [paper](https://arxiv.org/abs/2408.01584) and the [introduction tutorials](https://github.com/Emerge-Lab/gpudrive/tree/main/examples/tutorials), which guide you through the basic usage. + +## Installation + +To build GPUDrive, ensure you have all the required dependencies listed [here](https://github.com/shacklettbp/madrona#dependencies) including CMake, Python, and the CUDA Toolkit. See the details below. + +
Dependencies + +- CMake >= 3.24 +- Python >= 3.11 +- CUDA Toolkit >= 12.2 and <= 12.4 (We do not support CUDA versions 12.5+ at this time. Verify your CUDA version using nvcc --version.) +- On macOS and Windows, install the required dependencies for XCode and Visual Studio C++ tools, respectively. + +
+ +After installing the necessary dependencies, clone the repository (don't forget the --recursive flag!): + +```bash +git clone --recursive https://github.com/Emerge-Lab/gpudrive.git +cd gpudrive +``` + +Then, there are two options for building the simulator: + +--- + +
+ 🔧 Option 1. Manual install + +For Linux and macOS, use the following commands: + +```bash +mkdir build +cd build +cmake .. -DCMAKE_BUILD_TYPE=Release +make -j # cores to build with, e.g. 32 +cd .. +``` + +For Windows, open the cloned repository in Visual Studio and build the project using the integrated `cmake` functionality. + +Next, set up a Python environment + +#### With uv (Recommended) + +Create a virtual environment and install the Python components of the repository: + +```bash +uv sync --frozen +``` + +#### With pyenv + + Create a virtual environment: + + ```bash + pyenv virtualenv 3.11 gpudrive + pyenv activate gpudrive + ``` + + Set it for the current project directory (optional): + + ```bash + pyenv local gpudrive + ``` + +#### With conda + +```bash +conda env create -f ./environment.yml +conda activate gpudrive +``` + +### Install Python package + +Finally, install the Python components of the repository using pip (this step is not required for the `uv` installation): + +```bash +# macOS and Linux. +pip install -e . +``` + +Dependency-groups include `pufferlib`, `sb3`, `vbd`, and `tests`. + +```bash +# On Windows. +pip install -e . -Cpackages.madrona_escape_room.ext-out-dir= +``` + +
+ +--- + +--- + +
+ 🐳 Option 2. Docker + +To get started quickly, we provide a Dockerfile in the root directory. + +### Prerequisites +Ensure you have the following installed: +- [Docker](https://docs.docker.com/get-docker/) +- [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html) + +### Building the Docker mage +Once installed, you can build the container with: + +```bash +DOCKER_BUILDKIT=1 docker build --build-arg USE_CUDA=true --tag gpudrive:latest --progress=plain . +``` + +### Running the Container +To run the container with GPU support and shared memory: + +```bash +docker run --gpus all -it --rm --shm-size=20G -v ${PWD}:/workspace gpudrive:latest /bin/bash +``` + +
+ +--- + +Test whether the installation was successful by importing the simulator: + +```Python +import madrona_gpudrive +``` + +To avoid compiling on GPU mode everytime, the following environment variable can be set with any custom path. For example, you can store the compiled program in a cache called `gpudrive_cache`: + +```bash +export MADRONA_MWGPU_KERNEL_CACHE=./gpudrive_cache +``` + +Please remember that if you make any changes in C++, you need to delete the cache and recompile. + +--- + +
+ Optional: If you want to use the Madrona viewer in C++ + +#### Extra dependencies to use Madrona viewer + + To build the simulator with visualization support on Linux (`build/viewer`), you will need to install X11 and OpenGL development libraries. Equivalent dependencies are already installed by Xcode on macOS. For example, on Ubuntu: + +```bash + sudo apt install libx11-dev libxrandr-dev libxinerama-dev libxcursor-dev libxi-dev mesa-common-dev libc++1 +``` + +
+ +--- + +## Integrations + + +| What | Info | Run | Training SPS | +| ------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------- | ------------------------------ | +| **IPPO** implementation [SB3](https://github.com/DLR-RM/stable-baselines3/tree/master) | [IPPO](https://proceedings.neurips.cc/paper_files/paper/2022/file/9c1535a02f0ce079433344e14d910597-Paper-Datasets_and_Benchmarks.pdf), [PufferLib](https://arxiv.org/pdf/2406.12905), [Implementation](https://github.com/Emerge-Lab/gpudrive/blob/main/integrations/ppo/puffer) | `python baselines/ppo/ppo_sb3.py` | 25 - 50K | +| **IPPO** implementation [PufferLib](https://github.com/PufferAI/PufferLib) 🐡 | [PPO](https://arxiv.org/pdf/2406.12905) | `python baselines/ppo/ppo_pufferlib.py` | 100 - 300K | + + +## Getting started + +To get started, see these entry points: + +- Our [intro tutorials](https://github.com/Emerge-Lab/gpudrive/tree/main/examples/tutorials). These tutorials take approximately 30-60 minutes to complete and will guide you through the dataset, simulator, and how to populate the simulator with different types of actors. +- The [environment docs](https://github.com/Emerge-Lab/gpudrive/tree/main/gpudrive/env) provide detailed info on environment settings and supported features. + + + + + +## Pre-trained policies + +Several pre-trained policies are available via the `PyTorchModelHubMixin` class on 🤗 huggingface_hub. + +- **Best Policy (10,000 Scenarios).** The best policy from [Building reliable sim driving agents by scaling self-play](https://arxiv.org/abs/2502.14706) is available here [here](https://huggingface.co/daphne-cornelisse/policy_S10_000_02_27). This policy was trained on 10,000 randomly sampled scenarios from the WOMD training dataset. + +- **Alternative Policy (1,000 Scenarios).** A policy trained on 1,000 scenarios can be found [here](https://huggingface.co/daphne-cornelisse/policy_S1000_02_27) + +--- + +> Note: These models were trained with the environment configurations defined in `examples/experimental/config/reliable_agents_params.yaml`, changing environment/observation configurations will affect performance. + +--- + +### Usage + +To load a pre-trained policy, use the following: +```Python +from gpudrive.networks.late_fusion import NeuralNet + +# Load pre-trained model via huggingface_hub +agent = NeuralNet.from_pretrained("daphne-cornelisse/policy_S10_000_02_27") +``` + +See [tutorial 04](https://github.com/Emerge-Lab/gpudrive/tree/main/examples/tutorials/04_use_pretrained_sim_agent.ipynb) for all the details. + +## Dataset + +### Download the dataset + +- Two versions of the dataset are available, a [mini version](https://huggingface.co/datasets/EMERGE-lab/GPUDrive_mini) with a 1000 training files and 300 test/validation files, and a [large dataset](https://huggingface.co/datasets/EMERGE-lab/GPUDrive) with 100k unique scenes. +- Replace 'GPUDrive_mini' with 'GPUDrive' below if you wish to download the full dataset. + +
+ Download the dataset + +To download the dataset you need the huggingface_hub library + +```bash +pip install huggingface_hub +``` + +Then you can download the dataset using python or just `huggingface-cli`. + +- **Option 1**: Using Python + +```python +>>> from huggingface_hub import snapshot_download +>>> snapshot_download(repo_id="EMERGE-lab/GPUDrive_mini", repo_type="dataset", local_dir="data/processed") +``` + +- **Option 2**: Use the huggingface-cli + +1. Log in to your Hugging Face account: + +```bash +huggingface-cli login +``` + +2. Download the dataset: + +```bash +huggingface-cli download EMERGE-lab/GPUDrive_mini --local-dir data/processed --repo-type "dataset" +``` + +- **Option 3**: Manual Download + +1. Visit https://huggingface.co/datasets/EMERGE-lab/GPUDrive_mini +2. Navigate to the Files and versions tab. +3. Download the desired files/directories. + +_NOTE_: If you downloaded the full-sized dataset, it is grouped to subdirectories of 10k files each (according to hugging face constraints). In order for the path to work with GPUDrive, you need to run + +```python +python data_utils/post_processing.py #use --help if you've used a custom download path +``` + +
+ +### Re-build the dataset + +If you wish to manually generate the dataset, GPUDrive is compatible with the complete [Waymo Open Motion Dataset](https://github.com/waymo-research/waymo-open-dataset), which contains well over 100,000 scenarios. To download new files and create scenarios for the simulator, follow the steps below. + +
+ Re-build the dataset in 3 steps + +1. First, head to [https://waymo.com/open/](https://waymo.com/open/) and click on the "download" button a the top. After registering, click on the files from `v1.2.1 March 2024`, the newest version of the dataset at the time of wrting (10/2024). This will lead you a Google Cloud page. From here, you should see a folder structure like this: + +``` +waymo_open_dataset_motion_v_1_2_1/ +│ +├── uncompressed/ +│ ├── lidar_and_camera/ +│ ├── scenario/ +│ │ ├── testing_interactive/ +│ │ ├── testing/ +│ │ ├── training_20s/ +│ │ ├── training/ +│ │ ├── validation_interactive/ +│ │ └── validation/ +│ └── tf_example/ +``` + +2. Now, download files from testing, training and/or validation in the **`scenario`** folder. An easy way to do this is through `gsutil`. First register using: + +```bash +gcloud auth login +``` + +...then run the command below to download the dataset you prefer. For example, to download the validation dataset: + +```bash +gsutil -m cp -r gs://waymo_open_dataset_motion_v_1_2_1/uncompressed/scenario/validation/ data/raw +``` + +where `data/raw` is your local storage folder. Note that this can take a while, depending on the size of the dataset you're downloading. + +3. The last thing we need to do is convert the raw data to a format that is compatible with the simulator using: + +```bash +python data_utils/process_waymo_files.py '' '' '' +``` + +Note: Due to an open [issue](https://github.com/waymo-research/waymo-open-dataset/issues/868), installation of `waymo-open-dataset-tf-2.12.0` fails for Python 3.11. To use the script, in a separate Python 3.10 environment, run + +```bash +pip install waymo-open-dataset-tf-2-12-0 trimesh[easy] python-fcl +``` + +Then for example, if you want to process the validation data, run: + +```bash +python data_utils/process_waymo_files.py 'data/raw/' 'data/processed/' 'validation' +>>> +Processing Waymo files: 100%|████████████████████████████████████████████████████████████████| 150/150 [00:05<00:00, 28.18it/s] +INFO:root:Done! +``` + +and that's it! + +> **🧐 Caveat**: A single Waymo tfrecord file contains approximately 500 traffic scenarios. Processing speed is about 250 scenes/min on a 16 core CPU. Trying to process the entire validation set for example (150 tfrecords) is a LOT of time. + +
+ +### Post-processing +- Running `python data_utils/postprocessing.py` filters out corrupted files and undoes hugging face directory grouping. + +## 📜 Citing GPUDrive + +If you use GPUDrive in your research, please cite our ICLR 2025 paper +```bibtex +@inproceedings{kazemkhani2025gpudrive, + title={GPUDrive: Data-driven, multi-agent driving simulation at 1 million FPS}, + author={Saman Kazemkhani and Aarav Pandya and Daphne Cornelisse and Brennan Shacklett and Eugene Vinitsky}, + booktitle={Proceedings of the International Conference on Learning Representations (ICLR)}, + year={2025}, + url={https://arxiv.org/abs/2408.01584}, + eprint={2408.01584}, + archivePrefix={arXiv}, + primaryClass={cs.AI}, +} +``` + +## Contributing + +If you encounter a bug, notice a missing feature, or want to contribute, feel free to create an issue or reach out! We'd be excited to have you involved in the project. diff --git a/gpudrive-main/README_SHOW.md b/gpudrive-main/README_SHOW.md new file mode 100644 index 0000000000000000000000000000000000000000..126ae955c605858d5059fffcf99515714d6391aa --- /dev/null +++ b/gpudrive-main/README_SHOW.md @@ -0,0 +1,700 @@ +# Long-Tail Scenario Generation 展示说明 + +本文档用于展示当前 GPUDrive 长尾场景生成系统的闭环流程、每个阶段的产物路径、文件内容结构，以及这些文件在系统中的作用。 + +## 1. 整体闭环 + +当前系统可以概括为四个阶段： + +```text +Search Agent + -> 搜索并保存候选危险场景 + +Converter Agent + -> 将仿真轨迹转换成 LLM 可读案例 + +Evaluator Agent + -> 评估场景危险性、自然性、交互合理性和价值 + +Optimizer Agent + -> 根据评估结果推荐下一轮搜索参数 +``` + +核心思想是：不是只追求“碰撞数量”，而是追求“自然、合理、危险、可解释、有价值”的长尾场景。 + +## 2. Search Agent 产物 + +### 文件路径 + +每一轮搜索会写入一个 round 目录，例如第二轮： + +```text +search_outputs/round_002/search/ +``` + +多卡并行时，每张 GPU 对应一个 shard： + +```text +search_outputs/round_002/search/shard_000/ +search_outputs/round_002/search/shard_001/ +search_outputs/round_002/search/shard_002/ +search_outputs/round_002/search/shard_003/ +``` + +每个 shard 下的主要文件： + +```text +events.jsonl +event_traces.jsonl +summary.json +``` + +### events.jsonl + +`events.jsonl` 是轻量级事件索引，一行代表一个候选长尾场景。 + +典型结构： + +```json +{ + "shard_id": 0, + "batch_idx": 0, + "world_idx": 15, + "scenario_file": "tfrecord-00475-of-01000_425.json", + "controlled_agents": 5, + "risk_agents": [1, 4, 38], + "collision": true, + "near_miss": true, + "offroad": true, + "first_collision_step": 61, + "first_offroad_step": 38, + "min_distance_m": 1.3015, + "min_distance_step": 63, + "min_distance_risk_agent": 4, + "min_distance_partner_agent": 21, + "collided_agents": [4], + "offroad_agents": [4], + "goal_agents": 5 +} +``` + +作用： + +- 快速记录哪些 world 出现了 collision、near-miss、offroad 等事件。 +- 快速统计搜索阶段产生了多少候选危险场景。 +- 作为后续可视化和人工定位的索引。 + +它回答的是：“这一轮搜到了哪些候选事件？” + +### event_traces.jsonl + +`event_traces.jsonl` 是更完整的轨迹记录，一行代表一个候选事件。 + +主要内容包括： + +```text +source: + shard_id, batch_idx, world_idx, scenario_file + +outcome: + collision, near_miss, offroad + first_collision_step + first_offroad_step + min_distance_m + +risk_agents: + 当前 world 中被赋予 risk_taker 风格的车辆 id + +agent_traces: + 每个相关 agent 的逐帧状态 + 包括 x, y, heading, speed, valid, done 等 + +pairwise_trace: + 关键车辆对之间的逐帧距离、相对速度、closing speed 等 + +map_context: + 事件附近的道路元素、车道线、边界等 + +quality_metrics: + 初步自然性和交互质量指标 +``` + +作用： + +- 保存真实评估所需的轨迹数据。 +- 后续不依赖视频判断场景，而是基于轨迹和交互指标进行结构化评估。 +- 为 Converter Agent 提供完整输入。 + +它回答的是：“这个候选事件具体是怎么发生的？” + +### summary.json + +`summary.json` 是当前 shard 的搜索统计。 + +常见内容： + +```text +num_worlds +num_batches +num_events +collision_count +near_miss_count +offroad_count +event_rate +``` + +作用： + +- 快速查看单个 shard 的搜索结果。 +- 检查搜索任务是否正常完成。 +- 粗略比较不同参数下候选事件数量。 + +它回答的是：“这一张卡跑出的总体情况如何？” + +## 3. Converter Agent 产物 + +### 文件路径 + +Converter Agent 将 `event_traces.jsonl` 转换成 LLM 可读案例。 + +路径示例： + +```text +search_outputs/round_002/llm_cases/shard_000/ +search_outputs/round_002/llm_cases/shard_001/ +search_outputs/round_002/llm_cases/shard_002/ +search_outputs/round_002/llm_cases/shard_003/ +``` + +主要文件： + +```text +llm_cases.jsonl +case_cards/ +summary.json +``` + +### llm_cases.jsonl + +`llm_cases.jsonl` 是 Evaluator Agent 的主要输入，一行代表一个结构化场景案例。 + +主要结构： + +```text +case_id: + 唯一案例 id + +source: + 来自哪个 shard、batch、world、scenario_file + +outcome: + 是否 collision、near_miss、offroad + 首次事件发生 step + +primary_pair: + 当前事件中最关键的一对车辆 + 包括 risk agent、partner agent、选择原因 + +primary_interaction_metrics: + min_clean_distance_m + min_ttc_s + time_ttc_below_1s + event_relative_speed_mps + event_closing_speed_mps + +timeline: + 事件前后若干关键时间点的车辆状态 + +context_agents: + 周围车辆的简化状态 + +naturalness_checks: + max_speed_mps + max_accel_mps2 + max_yaw_rate_radps + large_displacement_flags + +map_context: + 事件附近道路环境 +``` + +作用： + +- 将复杂的多车逐帧轨迹压缩成 LLM 和规则系统都能理解的结构化文本/JSON。 +- 过滤 `done=true` 后的污染帧，并要求 `valid=true`，避免仿真结束后的无效位置影响评估。 +- 自动选择 primary pair，让评估聚焦于最关键的交互车辆对。 + +它回答的是：“如何把仿真轨迹变成可评估的交通事件描述？” + +### primary pair 的意义 + +一个 world 中可能有很多车，但真正决定场景价值的通常是一对关键交互车辆。 + +primary pair 的选择优先级： + +```text +1. risk agent 直接参与碰撞的车辆对 +2. 最小距离对应的 risk-agent 车辆对 +3. 碰撞车辆对 +4. 其他最接近的交互车辆对 +``` + +作用： + +- 让 Evaluator Agent 聚焦主要冲突对象。 +- 避免被无关车辆干扰。 +- 让 LLM 评估时更容易判断这个事件是否自然、合理、有价值。 + +### case_cards/ + +`case_cards/` 保存 Markdown 版案例卡。 + +示例路径： + +```text +search_outputs/round_002/llm_cases/shard_000/case_cards/ +``` + +作用： + +- 给人快速浏览。 +- 展示单个事件的关键指标、时间线、车辆关系。 +- 可用于人工抽查 Evaluator Agent 的判断是否合理。 + +### summary.json + +记录转换统计，例如： + +```text +input_events +converted_cases +written_cards +skipped_cases +``` + +作用： + +- 检查转换是否完整。 +- 对比 Search Agent 产出的事件数量和最终可评估 case 数量。 + +## 4. Evaluator Agent 产物 + +### 文件路径 + +Evaluator Agent 对 `llm_cases.jsonl` 进行规则评估和 LLM 评估。 + +路径示例： + +```text +search_outputs/round_002/evaluator_agent/shard_000/ +search_outputs/round_002/evaluator_agent/shard_001/ +search_outputs/round_002/evaluator_agent/shard_002/ +search_outputs/round_002/evaluator_agent/shard_003/ +``` + +主要文件： + +```text +all_evaluations.jsonl +accepted_cases.jsonl +rejected_cases.jsonl +summary.json +optimizer_metrics.json +raw_responses/ +``` + +### all_evaluations.jsonl + +`all_evaluations.jsonl` 保存所有案例的完整评估结果。 + +主要结构： + +```text +case: + 原始 llm case + +local_eval: + 本地规则评估结果 + +llm_eval: + DeepSeek / LLM 返回的评估结果 + +final_eval: + 融合后的最终评估结果 +``` + +`final_eval` 中的重要字段： + +```text +accepted: + 是否接受该场景 + +high_value: + 是否为高价值场景 + +criticality_score: + 危险程度 + +naturalness_score: + 运动自然性 + +interaction_score: + 交互合理性 + +scenario_value_score: + 场景价值 + +danger_level: + 危险等级 + +scenario_type: + 场景类型 + +artifact_flags: + 伪影标记 + +rejection_reasons: + 拒绝原因 + +optimization_feedback: + 给 Optimizer Agent 的反馈信号 +``` + +作用： + +- 给每个场景一个结构化评估结果。 +- 判断场景是否真正危险、自然、合理。 +- 为后续筛选和优化提供数据。 + +它回答的是：“这个场景到底有没有价值？” + +### accepted_cases.jsonl + +保存通过评估的案例。 + +通过条件不是简单的“发生碰撞”，而是综合考虑： + +```text +危险性 +运动自然性 +交互合理性 +risk agent 是否真正参与 +是否存在明显伪影 +场景是否有分析价值 +``` + +作用： + +- 作为最终可展示、可保存、可进一步分析的高质量候选集。 +- 用于后续可视化或构建长尾数据集。 + +### rejected_cases.jsonl + +保存被拒绝的案例。 + +常见拒绝原因： + +```text +车辆运动不自然 +速度或加速度异常 +车辆轨迹有跳变 +risk agent 没有真正参与冲突 +offroad 早于 collision，场景价值较低 +done 后状态污染 +碰撞缺少交互因果 +``` + +作用： + +- 帮助分析当前搜索策略的缺陷。 +- 给 Optimizer Agent 提供负反馈。 +- 避免系统只追求碰撞数量而产生大量低质量事件。 + +### optimizer_metrics.json + +这是 Evaluator Agent 输出给 Optimizer Agent 的核心文件。 + +路径示例： + +```text +search_outputs/round_002/evaluator_agent/shard_000/optimizer_metrics.json +``` + +核心结构： + +```text +current_search_parameters: + risk_collision_weight + risk_goal_weight + risk_offroad_weight + risk_agents_per_world + normal_mode + deterministic + +objective_metrics: + total_evaluated + accepted_count + rejected_count + high_value_count + accepted_rate + high_value_rate + natural_critical_rate + risk_direct_involved_rate + risk_direct_accept_rate + hard_artifact_record_rate + semantic_diversity_entropy + composite_objective + +score_stats: + criticality_score + naturalness_score + interaction_score + scenario_value_score + +metric_stats: + clean_min_distance_m + min_ttc_s + event_relative_speed_mps + event_closing_speed_mps + max_speed_mps + max_accel_mps2 + max_yaw_rate_radps + +counts: + scenario_type + artifact_flags + rejection_reasons + optimization_feedback + +optimizer_ready_records: + top cases used as compact feedback examples +``` + +作用： + +- 汇总一个 shard 的整体质量。 +- 作为 Optimizer Agent 的训练样本。 +- 衡量一轮搜索是否比上一轮更好。 + +它回答的是：“当前这组搜索参数整体表现怎么样？” + +## 5. Optimizer Agent 产物 + +### 文件路径 + +Optimizer Agent 读取一轮或多轮的 `optimizer_metrics.json`，学习参数到质量指标的映射，并推荐下一轮搜索参数。 + +路径示例： + +```text +search_outputs/round_002/optimizer_agent/mlp_next/ +``` + +主要文件： + +```text +optimizer_plan.json +candidate_predictions.jsonl +best_recommendation.env +recommended_search_commands.sh +``` + +### optimizer_plan.json + +完整优化计划。 + +主要内容： + +```text +metrics_paths: + 使用了哪些 optimizer_metrics.json + +history_samples: + 历史搜索参数和对应质量指标 + +candidate_grid: + 当前可搜索的参数空间 + +training: + MLP surrogate model 的训练状态 + +recommendations: + 推荐的下一轮参数 +``` + +作用： + +- 记录 Optimizer Agent 的完整决策过程。 +- 可解释为什么推荐下一组参数。 +- 支持复盘每一轮闭环优化。 + +### candidate_predictions.jsonl + +保存每个候选参数组合的预测结果。 + +典型字段： + +```text +risk_collision_weight +risk_goal_weight +risk_offroad_weight +risk_agents_per_world +normal_mode +deterministic +predicted_composite_objective +predicted_accepted_rate +predicted_high_value_rate +predicted_hard_artifact_rate +``` + +作用： + +- 查看 MLP surrogate model 对不同参数组合的预测。 +- 分析哪些参数更可能生成高质量长尾场景。 +- 支持 top-k 推荐和 exploration。 + +### best_recommendation.env + +下一轮搜索可直接加载的参数文件。 + +示例： + +```bash +RISK_COLLISION_WEIGHT=-0.2 +RISK_GOAL_WEIGHT=1.6 +RISK_OFFROAD_WEIGHT=-0.8 +RISK_AGENTS_PER_WORLD=1 +NORMAL_MODE=expert +DETERMINISTIC=0 +``` + +作用： + +- 直接用于下一轮 Search Agent。 +- 闭环系统中最直接的优化输出。 + +### recommended_search_commands.sh + +自动生成的搜索命令。 + +作用： + +- 方便直接提交下一轮实验。 +- 避免手工复制参数出错。 + +## 6. 每轮之间如何比较是否变好 + +不要只看碰撞数量。碰撞数量变多只说明候选池变大，不代表场景质量一定更好。 + +核心比较文件： + +```text +search_outputs/round_001/evaluator_agent/shard_*/optimizer_metrics.json +search_outputs/round_002/evaluator_agent/shard_*/optimizer_metrics.json +``` + +推荐展示指标： + +```text +composite_objective + 主指标，越高越好 + +accepted_rate + Evaluator Agent 接受比例，越高越好 + +high_value_rate + 高价值场景比例，越高越好 + +natural_critical_rate + 同时危险且自然的场景比例，越高越好 + +risk_direct_accept_rate + risk agent 直接参与并被接受的比例，越高越好 + +hard_artifact_record_rate + 明显伪影比例，越低越好 + +semantic_diversity_entropy + 场景类型多样性，过低说明模式坍塌 +``` + +如果第二轮出现： + +```text +LLM case 数量增加 +accepted_rate 不下降或上升 +high_value_rate 上升 +natural_critical_rate 上升 +hard_artifact_record_rate 不上升或下降 +composite_objective 上升 +``` + +则可以说明闭环优化在产生更高质量的长尾场景。 + +## 7. 展示时推荐讲法 + +可以这样概括： + +```text +我们首先基于 GPUDrive 并行运行大量世界，并使用 reward-conditioned policy +给车辆赋予不同驾驶风格。Search Agent 会搜索 collision、near-miss、 +offroad 等候选长尾事件，并保存轻量事件索引 events.jsonl 和完整轨迹 +event_traces.jsonl。 + +随后，Converter Agent 将复杂的多车逐帧轨迹转换为 LLM 可读的结构化案例 +llm_cases.jsonl。转换过程中会清理 done 后的无效帧，并自动选择最关键的 +primary pair。 + +Evaluator Agent 进一步从危险性、运动自然性、交互合理性、场景价值和伪影率 +等维度评估每个案例，输出 accepted/rejected cases 和 optimizer_metrics.json。 + +最后，Optimizer Agent 使用这些评估指标训练一个 MLP surrogate model， +预测不同 reward-condition 参数的效果，并推荐下一轮搜索参数。这样系统形成 +自动生成、自动评估、自动优化的闭环多 Agent 长尾数据生成流程。 +``` + +一句话总结： + +```text +我们的目标不是生成更多碰撞，而是自动生成更多自然、合理、危险且有价值的长尾交互场景。 +``` + +## 8. 各文件一句话速查 + +```text +events.jsonl + 轻量事件索引，记录搜到了哪些候选危险事件。 + +event_traces.jsonl + 完整轨迹记录，保存评估所需的多车状态和交互数据。 + +llm_cases.jsonl + LLM 可读案例，将复杂轨迹压缩成结构化交通事件。 + +case_cards/ + Markdown 案例卡，方便人工快速浏览。 + +all_evaluations.jsonl + 所有案例的完整评估结果。 + +accepted_cases.jsonl + 通过评估的高质量候选场景。 + +rejected_cases.jsonl + 被拒绝的低质量或伪影场景。 + +optimizer_metrics.json + 当前搜索参数的质量汇总，是 Optimizer Agent 的输入。 + +optimizer_plan.json + Optimizer Agent 的完整优化计划。 + +candidate_predictions.jsonl + 每个候选参数组合的预测效果。 + +best_recommendation.env + 下一轮搜索直接加载的推荐参数。 + +recommended_search_commands.sh + 下一轮可直接提交的搜索命令。 +``` diff --git a/gpudrive-main/assets/GPUDrive_eval_with_humans_control_6.gif b/gpudrive-main/assets/GPUDrive_eval_with_humans_control_6.gif new file mode 100644 index 0000000000000000000000000000000000000000..52490a64f55f36b88e9eb7eb023dbd096f29750c --- /dev/null +++ b/gpudrive-main/assets/GPUDrive_eval_with_humans_control_6.gif @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:adfb9238dd9da73a75a54dfc0f7009e21c343d44c2fc0b1a43261280abb343e2 +size 2973687 diff --git a/gpudrive-main/assets/Lidar120.gif b/gpudrive-main/assets/Lidar120.gif new file mode 100644 index 0000000000000000000000000000000000000000..b844b5c146d57f0c36205f414592d6d22e6d7cb3 --- /dev/null +++ b/gpudrive-main/assets/Lidar120.gif @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:498ed5d1c0ace2b4ec027a0cdb58bf0ff9022acb4d5cdfb7e78d4d8d74f39401 +size 202114 diff --git a/gpudrive-main/assets/Lidar360.gif b/gpudrive-main/assets/Lidar360.gif new file mode 100644 index 0000000000000000000000000000000000000000..8ad6ba2c65aac8ffad9b462887e64c631259ce58 --- /dev/null +++ b/gpudrive-main/assets/Lidar360.gif @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:02597335daf33456428325f91cd3415eedc7c234d11498e5b2e46915df716ba8 +size 395291 diff --git a/gpudrive-main/assets/agent_collision.obj b/gpudrive-main/assets/agent_collision.obj new file mode 100644 index 0000000000000000000000000000000000000000..898989d46ccc66a8925238cbc867d6b1b94076ce --- /dev/null +++ b/gpudrive-main/assets/agent_collision.obj @@ -0,0 +1,212 @@ +# Blender 3.6.0 +# 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486/441/492 559/659/676 726/698/706 +f 646/696/705 726/698/706 559/659/676 diff --git a/gpudrive-main/assets/cube_collision.obj b/gpudrive-main/assets/cube_collision.obj new file mode 100644 index 0000000000000000000000000000000000000000..5f8e954ae24b1bc2c4356c4dd558f6a74bb9c20f --- /dev/null +++ b/gpudrive-main/assets/cube_collision.obj @@ -0,0 +1,18 @@ +# Blender 3.3.1 +# www.blender.org +o Cube +v 1.000000 1.000000 1.000000 +v 1.000000 1.000000 -1.000000 +v 1.000000 -1.000000 1.000000 +v 1.000000 -1.000000 -1.000000 +v -1.000000 1.000000 1.000000 +v -1.000000 1.000000 -1.000000 +v -1.000000 -1.000000 1.000000 +v -1.000000 -1.000000 -1.000000 +s 0 +f 1 5 7 3 +f 4 3 7 8 +f 8 7 5 6 +f 6 2 4 8 +f 2 1 3 4 +f 6 5 1 2 diff --git a/gpudrive-main/assets/cube_render.obj b/gpudrive-main/assets/cube_render.obj new file mode 100644 index 0000000000000000000000000000000000000000..9c659c0c5497c2864675e1114f82bed879282f6f --- /dev/null +++ b/gpudrive-main/assets/cube_render.obj @@ -0,0 +1,44 @@ +# Blender 3.3.1 +# www.blender.org +o Cube +v 1.000000 1.000000 1.000000 +v 1.000000 1.000000 -1.000000 +v 1.000000 -1.000000 1.000000 +v 1.000000 -1.000000 -1.000000 +v -1.000000 1.000000 1.000000 +v -1.000000 1.000000 -1.000000 +v -1.000000 -1.000000 1.000000 +v -1.000000 -1.000000 -1.000000 +vn -0.0000 -0.0000 1.0000 +vn -0.0000 -1.0000 -0.0000 +vn -1.0000 -0.0000 -0.0000 +vn -0.0000 -0.0000 -1.0000 +vn 1.0000 -0.0000 -0.0000 +vn -0.0000 1.0000 -0.0000 +vt 0.625000 0.500000 +vt 0.375000 0.500000 +vt 0.625000 0.750000 +vt 0.375000 0.750000 +vt 0.875000 0.500000 +vt 0.625000 0.250000 +vt 0.125000 0.500000 +vt 0.375000 0.250000 +vt 0.875000 0.750000 +vt 0.625000 1.000000 +vt 0.625000 0.000000 +vt 0.375000 0.000000 +vt 0.375000 1.000000 +vt 0.125000 0.750000 +s 0 +f 5/5/1 3/3/1 1/1/1 +f 3/3/2 8/13/2 4/4/2 +f 7/11/3 6/8/3 8/12/3 +f 2/2/4 8/14/4 6/7/4 +f 1/1/5 4/4/5 2/2/5 +f 5/6/6 2/2/6 6/8/6 +f 5/5/1 7/9/1 3/3/1 +f 3/3/2 7/10/2 8/13/2 +f 7/11/3 5/6/3 6/8/3 +f 2/2/4 4/4/4 8/14/4 +f 1/1/5 3/3/5 4/4/5 +f 5/6/6 1/1/6 2/2/6 diff --git a/gpudrive-main/assets/cylinder_collision.obj b/gpudrive-main/assets/cylinder_collision.obj new file mode 100644 index 0000000000000000000000000000000000000000..61bdf64618d2b57def69390db1d251832e0e500b --- /dev/null +++ b/gpudrive-main/assets/cylinder_collision.obj @@ -0,0 +1,54 @@ +# Blender 3.3.1 +# www.blender.org +o Cylinder +v 0.000000 1.000000 -1.500000 +v 0.000000 1.000000 1.500000 +v 0.382683 0.923880 -1.500000 +v 0.382683 0.923880 1.500000 +v 0.707107 0.707107 -1.500000 +v 0.707107 0.707107 1.500000 +v 0.923880 0.382683 -1.500000 +v 0.923880 0.382683 1.500000 +v 1.000000 0.000000 -1.500000 +v 1.000000 0.000000 1.500000 +v 0.923880 -0.382683 -1.500000 +v 0.923880 -0.382683 1.500000 +v 0.707107 -0.707107 -1.500000 +v 0.707107 -0.707107 1.500000 +v 0.382683 -0.923880 -1.500000 +v 0.382683 -0.923880 1.500000 +v 0.000000 -1.000000 -1.500000 +v 0.000000 -1.000000 1.500000 +v -0.382683 -0.923880 -1.500000 +v -0.382683 -0.923880 1.500000 +v 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sha256:d65792b2930fe4e60d7c8cfd26e7418f64652c891044a1256cc0842a4bf39a34 +size 28397 diff --git a/gpudrive-main/assets/elongated_collision.obj b/gpudrive-main/assets/elongated_collision.obj new file mode 100644 index 0000000000000000000000000000000000000000..b56cf477cab7027ac5a0767332ab95087a25764f --- /dev/null +++ b/gpudrive-main/assets/elongated_collision.obj @@ -0,0 +1,18 @@ +# Blender 3.3.1 +# www.blender.org +o Cube +v -4.000000 -0.750000 -1.000000 +v -4.000000 -0.750000 1.000000 +v -4.000000 0.750000 -1.000000 +v -4.000000 0.750000 1.000000 +v 4.000000 -0.750000 -1.000000 +v 4.000000 -0.750000 1.000000 +v 4.000000 0.750000 -1.000000 +v 4.000000 0.750000 1.000000 +s 0 +f 1 2 4 3 +f 3 4 8 7 +f 7 8 6 5 +f 5 6 2 1 +f 3 7 5 1 +f 8 4 2 6 diff --git a/gpudrive-main/assets/elongated_render.obj b/gpudrive-main/assets/elongated_render.obj new file mode 100644 index 0000000000000000000000000000000000000000..7a4baa967b5c4cca7dc64e20417b135b8e9ac14f --- /dev/null +++ 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a/gpudrive-main/assets/obs_video_clear_example.gif b/gpudrive-main/assets/obs_video_clear_example.gif new file mode 100644 index 0000000000000000000000000000000000000000..bc7b087678deac297997cad0de1ec97210959875 --- /dev/null +++ b/gpudrive-main/assets/obs_video_clear_example.gif @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:26c00bc2f69eadafe558a8a56bbe79301dbedd0a8ef8601bab0d7e7537002a66 +size 2587013 diff --git a/gpudrive-main/assets/pink_agent.svg b/gpudrive-main/assets/pink_agent.svg new file mode 100644 index 0000000000000000000000000000000000000000..1a6c4015676fa6792e7539b61d92433a60b068a9 --- /dev/null +++ b/gpudrive-main/assets/pink_agent.svg @@ -0,0 +1,108 @@ + + + + + + + + + + + + + image/svg+xml + + + + + + + + + + + diff --git a/gpudrive-main/assets/plane.obj b/gpudrive-main/assets/plane.obj new file mode 100644 index 0000000000000000000000000000000000000000..0d2cbfe500322f8a55a7db11c972afd6020e2cd1 --- /dev/null +++ b/gpudrive-main/assets/plane.obj @@ -0,0 +1,15 @@ +# Blender 3.3.1 +# www.blender.org +o Plane +v -10000.0 -10000.0 0.000000 +v 10000.0 -10000.0 0.000000 +v -10000.0 10000.0 0.000000 +v 10000.0 10000.0 0.000000 +vn -0.0000 -0.0000 1.0000 +vt 0.000000 0.000000 +vt 1000.000000 0.000000 +vt 0.000000 1000.000000 +vt 1000.000000 1000.000000 +s 0 +f 2/2/1 3/3/1 1/1/1 +f 2/2/1 4/4/1 3/3/1 diff --git a/gpudrive-main/assets/ramp_collision.obj b/gpudrive-main/assets/ramp_collision.obj new file mode 100644 index 0000000000000000000000000000000000000000..8b36d21a609759d0e6a7c8120d0a048ece31d911 --- /dev/null +++ b/gpudrive-main/assets/ramp_collision.obj @@ -0,0 +1,15 @@ +# Blender 3.3.1 +# www.blender.org +o Cube +v 1.000000 1.000000 1.000000 +v 1.000000 1.000000 -1.000000 +v 1.000000 -2.000000 -1.000000 +v -1.000000 1.000000 1.000000 +v -1.000000 1.000000 -1.000000 +v -1.000000 -2.000000 -1.000000 +s 0 +f 5 2 3 6 +f 5 4 1 2 +f 3 1 4 6 +f 2 1 3 +f 6 4 5 diff --git 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+f 2/2/1 3/3/1 6/7/1 +f 4/4/2 1/1/2 2/2/2 +f 7/8/3 8/9/3 6/7/3 +l 1 7 +l 4 8 diff --git a/gpudrive-main/assets/sim_video_0_10.gif b/gpudrive-main/assets/sim_video_0_10.gif new file mode 100644 index 0000000000000000000000000000000000000000..b92072690ac5a7ad86bc4b66162374f1118b9c40 --- /dev/null +++ b/gpudrive-main/assets/sim_video_0_10.gif @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4d64ad6b972ba4ea7725ed10d5cfd64649c52edd64305eee7cee4ac1311731eb +size 6206688 diff --git a/gpudrive-main/assets/sim_video_7.gif b/gpudrive-main/assets/sim_video_7.gif new file mode 100644 index 0000000000000000000000000000000000000000..6e04c59ef79af87e89899b29ff3ab91c8ade0fdc --- /dev/null +++ b/gpudrive-main/assets/sim_video_7.gif @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1aa9f02b1f65dc9c43aec8ef4673dfcfb612ab445d5f5be69896af212b035fe5 +size 2085000 diff --git a/gpudrive-main/assets/sim_video_clear_example.gif 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@@ + + + + + + + + + + + + + image/svg+xml + + + + + + + + + + + diff --git a/gpudrive-main/baselines/__init__.py b/gpudrive-main/baselines/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/gpudrive-main/baselines/imitation_data_generation.py b/gpudrive-main/baselines/imitation_data_generation.py new file mode 100644 index 0000000000000000000000000000000000000000..c194e9bb60b299e632bac19d1eb837f507ff1e80 --- /dev/null +++ b/gpudrive-main/baselines/imitation_data_generation.py @@ -0,0 +1,279 @@ +"""Extract expert states and actions from Waymo Open Dataset.""" +import torch +import numpy as np +import imageio +import logging +import argparse + +from pygpudrive.env.config import EnvConfig, RenderConfig, SceneConfig +from pygpudrive.env.env_torch import GPUDriveTorchEnv + +logging.getLogger(__name__) + + +def parse_args(): + parser = argparse.ArgumentParser("Select the dynamics model that you use") + parser.add_argument( + "--dynamics-model", + "-d", + type=str, + default="delta_local", + choices=["delta_local", "bicycle", "classic"], + ) + args = parser.parse_args() + return args + + +def map_to_closest_discrete_value(grid, cont_actions): + """ + Find the nearest value in the action grid for a given expert action. + """ + # Calculate the absolute differences and find the indices of the minimum values + abs_diff = torch.abs(grid.unsqueeze(0) - cont_actions.unsqueeze(-1)) + indx = torch.argmin(abs_diff, dim=-1) + + # Gather the closest values based on the indices + closest_values = grid[indx] + + return closest_values, indx + + +def generate_state_action_pairs( + env, + device, + action_space_type="discrete", + use_action_indices=False, + make_video=False, + render_index=[0], + save_path="output_video.mp4", +): + """Generate pairs of states and actions from the Waymo Open Dataset. + + Args: + env (GPUDriveTorchEnv): Initialized environment class. + device (str): Where to run the simulation (cpu or cuda). + action_space_type (str): discrete, multi-discrete, continuous + use_action_indices (bool): Whether to return action indices instead of action values. + make_video (bool): Whether to save a video of the expert trajectory. + render_index (int): Index of the world to render (must be <= num_worlds). + + Returns: + expert_actions: Expert actions for the controlled agents. An action is a + tuple with (acceleration, steering, heading). + obs_tensor: Expert observations for the controlled agents. + """ + frames = [[] for _ in range(render_index[1] - render_index[0])] + + logging.info( + f"Generating expert actions and observations for {env.num_worlds} worlds \n" + ) + + # Reset the environment + obs = env.reset() + + # Get expert actions for full trajectory in all worlds + expert_actions, expert_speeds, expert_positions, expert_yaws = env.get_expert_actions() + if action_space_type == "discrete": + # Discretize the expert actions: map every value to the closest + # value in the action grid. + disc_expert_actions = expert_actions.clone() + if env.config.dynamics_model == "delta_local": + disc_expert_actions[:, :, :, 0], _ = map_to_closest_discrete_value( + grid=env.dx, cont_actions=expert_actions[:, :, :, 0] + ) + disc_expert_actions[:, :, :, 1], _ = map_to_closest_discrete_value( + grid=env.dy, cont_actions=expert_actions[:, :, :, 1] + ) + disc_expert_actions[:, :, :, 2], _ = map_to_closest_discrete_value( + grid=env.dyaw, cont_actions=expert_actions[:, :, :, 2] + ) + else: + # Acceleration + disc_expert_actions[:, :, :, 0], _ = map_to_closest_discrete_value( + grid=env.accel_actions, cont_actions=expert_actions[:, :, :, 0] + ) + # Steering + disc_expert_actions[:, :, :, 1], _ = map_to_closest_discrete_value( + grid=env.steer_actions, cont_actions=expert_actions[:, :, :, 1] + ) + + if use_action_indices: # Map action values to joint action index + logging.info("Mapping expert actions to joint action index... \n") + expert_action_indices = torch.zeros( + expert_actions.shape[0], + expert_actions.shape[1], + expert_actions.shape[2], + 1, + dtype=torch.int32, + ).to(device) + for world_idx in range(disc_expert_actions.shape[0]): + for agent_idx in range(disc_expert_actions.shape[1]): + for time_idx in range(disc_expert_actions.shape[2]): + action_val_tuple = tuple( + round(x, 3) + for x in disc_expert_actions[ + world_idx, agent_idx, time_idx, : + ].tolist() + ) + if not env.config.dynamics_model == "delta_local": + action_val_tuple = ( + action_val_tuple[0], + action_val_tuple[1], + 0.0, + ) + + action_idx = env.values_to_action_key.get( + action_val_tuple + ) + expert_action_indices[ + world_idx, agent_idx, time_idx + ] = action_idx + + expert_actions = expert_action_indices + else: + # Map action values to joint action index + expert_actions = disc_expert_actions + elif action_space_type == "multi_discrete": + """will be update""" + pass + else: + logging.info("Using continuous expert actions... \n") + + # Storage + expert_observations_lst = [] + expert_actions_lst = [] + expert_next_obs_lst = [] + expert_dones_lst = [] + + # Initialize dead agent mask + + dead_agent_mask = ~env.cont_agent_mask.clone() + alive_agent_mask = env.cont_agent_mask.clone() + for time_step in range(env.episode_len): + + # Step the environment with inferred expert actions + env.step_dynamics(expert_actions[:, :, time_step, :]) + + next_obs = env.get_obs() + + dones = env.get_dones() + infos = env.get_infos() + + # Unpack and store (obs, action, next_obs, dones) pairs for controlled agents + expert_observations_lst.append(obs[~dead_agent_mask, :]) + expert_actions_lst.append( + expert_actions[~dead_agent_mask][:, time_step, :] + ) + + expert_next_obs_lst.append(next_obs[~dead_agent_mask, :]) + expert_dones_lst.append(dones[~dead_agent_mask]) + + # Update + obs = next_obs + dead_agent_mask = torch.logical_or(dead_agent_mask, dones) + + # Render + if make_video: + for render in range(render_index[0], render_index[1]): + frame = env.render(world_render_idx=render) + frames[render].append(frame) + if (dead_agent_mask == True).all(): + break + + is_collision = infos[:, :, :3].sum(dim=-1) + is_goal = infos[:, :, 3] + collision_mask = is_collision != 0 + goal_mask = is_goal != 0 + valid_collision_mask = collision_mask & alive_agent_mask + valid_goal_mask = goal_mask & alive_agent_mask + collision_rate = ( + valid_collision_mask.sum().float() / alive_agent_mask.sum().float() + ) + goal_rate = valid_goal_mask.sum().float() / alive_agent_mask.sum().float() + + print(f"Collision {collision_rate} Goal {goal_rate}") + + if make_video: + for render in range(render_index[0], render_index[1]): + imageio.mimwrite( + f"{save_path}_world_{render}.mp4", + np.array(frames[render]), + fps=30, + ) + + flat_expert_obs = torch.cat(expert_observations_lst, dim=0) + flat_expert_actions = torch.cat(expert_actions_lst, dim=0) + flat_next_expert_obs = torch.cat(expert_next_obs_lst, dim=0) + flat_expert_dones = torch.cat(expert_dones_lst, dim=0) + + return ( + flat_expert_obs, + flat_expert_actions, + flat_next_expert_obs, + flat_expert_dones, + goal_rate, + collision_rate, + ) + + +if __name__ == "__main__": + import argparse + + args = parse_args() + torch.set_printoptions(precision=3, sci_mode=False) + NUM_WORLDS = 10 + MAX_NUM_OBJECTS = 128 + + # Initialize lists to store results + num_actions = [] + goal_rates = [] + collision_rates = [] + + # Set the environment and render configurations + # Action space (joint discrete) + + render_config = RenderConfig(draw_obj_idx=True) + scene_config = SceneConfig( + "/data/formatted_json_v2_no_tl_train/", NUM_WORLDS + ) + env_config = EnvConfig( + dynamics_model=args.dynamics_model, + steer_actions=torch.round(torch.linspace(-0.3, 0.3, 7), decimals=3), + accel_actions=torch.round(torch.linspace(-6.0, 6.0, 7), decimals=3), + dx=torch.round(torch.linspace(-3.0, 3.0, 100), decimals=3), + dy=torch.round(torch.linspace(-3.0, 3.0, 100), decimals=3), + dyaw=torch.round(torch.linspace(-1.0, 1.0, 300), decimals=3), + ) + + env = GPUDriveTorchEnv( + config=env_config, + scene_config=scene_config, + max_cont_agents=MAX_NUM_OBJECTS, # Number of agents to control + device="cpu", + render_config=render_config, + action_type="continuous", + ) + # Generate expert actions and observations + ( + expert_obs, + expert_actions, + next_expert_obs, + expert_dones, + goal_rate, + collision_rate, + ) = generate_state_action_pairs( + env=env, + device="cpu", + action_space_type="continuous", # Discretize the expert actions + use_action_indices=True, # Map action values to joint action index + make_video=True, # Record the trajectories as sanity check + render_index=[0, 1], # start_idx, end_idx + save_path="use_discr_actions_fix", + ) + env.close() + del env + del env_config + + # Uncommment to save the expert actions and observations + # torch.save(expert_actions, "expert_actions.pt") + # torch.save(expert_obs, "expert_obs.pt") \ No newline at end of file diff --git a/gpudrive-main/baselines/ppo/README.md b/gpudrive-main/baselines/ppo/README.md new file mode 100644 index 0000000000000000000000000000000000000000..8678a197a074cb070c6a554248d7273ad305ebcc --- /dev/null +++ b/gpudrive-main/baselines/ppo/README.md @@ -0,0 +1,106 @@ +# Using IPPO in GPUDrive + +## PufferLib implementation + +### Dependencies + +This implementation is compatible with the `gpudrive` branch of [PufferLib](https://github.com/PufferAI/PufferLib/tree/gpudrive/pufferlib/environments/gpudrive). To install, run: + +``` +pip install git+https://github.com/PufferAI/PufferLib.git@gpudrive +``` + +### Example + +- Launch a run: + +```bash +python baselines/ippo/ippo_pufferlib.py +``` + +- Configs are in `baselines/ippo/config/ippo_ff_puffer.yaml` +- A small feedforward network is implemented in `integrations/rl/puffer/utils.py` + +## Stable baselines 3 implementation + +### Example + +- Launch a run: + +``` +python baselines/ippo/ippo_sb3.py +``` + +- Configurations are found in `baselines/ippo/config/ippo_ff_sb3.yaml` + +### Details + +#### Resampling the data + +The configuration for resampling traffic scenarios includes: + +- **`resample_scenarios`**: A boolean that enables or disables traffic scenario resampling when set to `True`. +- **`resample_criterion`**: Set to `"global_step"`, indicating resampling occurs based on the global step count. +- **`resample_freq`**: Specifies resampling frequency at `50,000` steps, recommended to align with `num_worlds * n_steps`. +- **`resample_mode`**: Set to `"random"` for random selection of new scenarios. + +``` +# RESAMPLE TRAFFIC SCENARIOS +resample_scenarios: bool = True +resample_criterion: str = "global_step" # Options: "global_step" +resample_freq: int = 100_000 # Resample every k steps (recommended to be a multiple of num_worlds * n_steps) +resample_mode: str = "random" # Options: "random" +``` + +#### Implemented networks + +- Classic Observations + +For classic observations (e.g., `ego_state`), there is support for a permutation equivariant network (recommended). In `baselines/ippo/config.py`, set the following: + +```python +# NETWORK +mlp_class = LateFusionNet +policy = LateFusionPolicy +``` + +The default settings for classic observations are: + +```python +ego_state: bool = True # Use ego vehicle state +road_map_obs: bool = True # Use road graph data +partner_obs: bool = True # Include partner vehicle information +norm_obs: bool = True # Normalize observations +``` + +- LiDAR Observations + +For only LiDAR-based observations, set the following options: + +```python +ego_state: bool = False # Use ego vehicle state +road_map_obs: bool = False # Use road graph data +partner_obs: bool = False # Include partner vehicle information +norm_obs: bool = False # Normalize observations +disable_classic_obs: bool = True # Disable classic observations for faster sim +lidar_obs: bool = True # Use LiDAR in observations +``` + +You can also **mix** classic and LiDAR observations by setting: + +```python +ego_state: bool = True # Include ego vehicle state in observations +road_map_obs: bool = True # Include road graph in observations +partner_obs: bool = True # Include partner vehicle info in observations +norm_obs: bool = True # Normalize observations +disable_classic_obs: bool = False # Keep classic observations +lidar_obs: bool = True # Add LiDAR to observations +``` + +In both cases, you can use a feedforward network from `networks/basic_ffn.py`: + +```python +# NETWORK +mlp_class = FFN +policy = FeedForwardPolicy +``` diff --git a/gpudrive-main/baselines/ppo/__init__.py b/gpudrive-main/baselines/ppo/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/gpudrive-main/baselines/ppo/config/ppo_base_puffer.yaml b/gpudrive-main/baselines/ppo/config/ppo_base_puffer.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9f985667a286dcf82c929c3641d611837167fc1d --- /dev/null +++ b/gpudrive-main/baselines/ppo/config/ppo_base_puffer.yaml @@ -0,0 +1,107 @@ +mode: "train" +use_rnn: false +eval_model_path: null +baseline: false +data_dir: data/processed/training +continue_training: false +model_cpt: null + +environment: # Overrides default environment configs (see pygpudrive/env/config.py) + name: "gpudrive" + num_worlds: 75 # Number of parallel environments + k_unique_scenes: 75 # Number of unique scenes to sample from + max_controlled_agents: 64 # Maximum number of agents controlled by the model. Make sure this aligns with the variable kMaxAgentCount in src/consts.hpp + ego_state: true + road_map_obs: true + partner_obs: true + norm_obs: true + remove_non_vehicles: true # If false, all agents are included (vehicles, pedestrians, cyclists) + lidar_obs: false # NOTE: Setting this to true currently turns of the other observation types + reward_type: "weighted_combination" + collision_weight: -0.75 + off_road_weight: -0.75 + goal_achieved_weight: 1.0 + dynamics_model: "classic" + collision_behavior: "ignore" # Options: "remove", "stop", "ignore" + dist_to_goal_threshold: 2.0 + polyline_reduction_threshold: 0.1 # Rate at which to sample points from the polyline (0 is use all closest points, 1 maximum sparsity), needs to be balanced with kMaxAgentMapObservationsCount + sampling_seed: 42 # If given, the set of scenes to sample from will be deterministic, if None, the set of scenes will be random + obs_radius: 50.0 # Visibility radius of the agents + action_space_steer_disc: 13 + action_space_accel_disc: 7 + # Versatile Behavior Diffusion (VBD): This will slow down training + use_vbd: false + vbd_model_path: "gpudrive/integrations/vbd/weights/epoch=18.ckpt" + init_steps: 11 + vbd_trajectory_weight: 0.1 # Importance of distance to the vbd trajectories in the reward function + vbd_in_obs: false + +wandb: + entity: "" + project: "gpudrive" + group: "test" + mode: "online" # Options: online, offline, disabled + tags: ["ppo", "ff"] + +train: + exp_id: PPO # Set dynamically in the script if needed + seed: 42 + cpu_offload: false + device: "cuda" # Dynamically set to cuda if available, else cpu + bptt_horizon: 1 + compile: false + compile_mode: "reduce-overhead" + + # # # Data sampling # # # + resample_scenes: false + resample_dataset_size: 10_000 # Number of unique scenes to sample from + resample_interval: 2_000_000 + sample_with_replacement: true + shuffle_dataset: false + + # # # PPO # # # + torch_deterministic: false + total_timesteps: 1_000_000_000 + batch_size: 131_072 + minibatch_size: 8192 + learning_rate: 3e-4 + anneal_lr: false + gamma: 0.99 + gae_lambda: 0.95 + update_epochs: 4 + norm_adv: true + clip_coef: 0.2 + clip_vloss: false + vf_clip_coef: 0.2 + ent_coef: 0.0001 + vf_coef: 0.3 + max_grad_norm: 0.5 + target_kl: null + log_window: 1000 + + # # # Network # # # + network: + input_dim: 64 # Embedding of the input features + hidden_dim: 128 # Latent dimension + dropout: 0.01 + class_name: "NeuralNet" + num_parameters: 0 # Total trainable parameters, to be filled at runtime + + # # # Checkpointing # # # + checkpoint_interval: 400 # Save policy every k iterations + checkpoint_path: "./runs" + + # # # Rendering # # # + render: false # Determines whether to render the environment (note: will slow down training) + render_3d: true # Render simulator state in 3d or 2d + render_interval: 1 # Render every k iterations + render_k_scenarios: 10 # Number of scenarios to render + render_format: "mp4" # Options: gif, mp4 + render_fps: 15 # Frames per second + zoom_radius: 50 + +vec: + backend: "native" # Only native is currently supported + num_workers: 1 + env_batch_size: 1 + zero_copy: false diff --git a/gpudrive-main/baselines/ppo/config/ppo_base_sb3.yaml b/gpudrive-main/baselines/ppo/config/ppo_base_sb3.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d601b6e61e091039b71f0a4323dd061db6771c75 --- /dev/null +++ b/gpudrive-main/baselines/ppo/config/ppo_base_sb3.yaml @@ -0,0 +1,66 @@ +data_dir: "data/processed/examples" +num_worlds: 100 +k_unique_scenes: 4 +device: "cuda" # or "cpu" + +reward_type: "weighted_combination" +remove_non_vehicles: false +polyline_reduction_threshold: 0.1 +observation_radius: 50.0 +collision_behavior: "ignore" # Options: "remove", "stop", "ignore" + +resample_scenes: false +resample_dataset_size: 4 # Number of unique scenes to sample from +resample_interval: 2_000_000 +sample_with_replacement: true +shuffle_dataset: false + +track_time_to_solve: false + +sync_tensorboard: true +logging_collection_window: 100 +log_freq: 100 +project_name: "gpudrive" +group_name: "my_experiment" +entity: " " +tags: + - "ppo" +wandb_mode: "online" # Options: online, offline, disabled + +episode_len: 91 # Length of an episode in the simulator + +save_policy: true +save_policy_freq: 200 + +seed: 42 +gamma: 0.99 +gae_lambda: 0.95 +clip_range: 0.2 +clip_range_vf: null +vf_coef: 0.5 +n_steps: 91 # Number of steps per rollout +num_minibatches: 5 # Used to determine the minibatch size +verbose: 0 +total_timesteps: 100_000_000 +ent_coef: 0.0001 +lr: 0.0003 +n_epochs: 5 + +mlp_class: "late_fusion" +policy: "late_fusion_policy" +ego_state_layers: + - 64 + - 32 +road_object_layers: + - 64 + - 64 +road_graph_layers: + - 64 + - 64 +shared_layers: + - 64 + - 64 +act_func: "tanh" +dropout: 0.0 +last_layer_dim_pi: 64 +last_layer_dim_vf: 64 diff --git a/gpudrive-main/baselines/ppo/config/ppo_mini_official_sanity.yaml b/gpudrive-main/baselines/ppo/config/ppo_mini_official_sanity.yaml new file mode 100644 index 0000000000000000000000000000000000000000..3938fdcb12d5256c24fc99dc8bef43eb298543a1 --- /dev/null +++ b/gpudrive-main/baselines/ppo/config/ppo_mini_official_sanity.yaml @@ -0,0 +1,101 @@ +mode: "train" +use_rnn: false +eval_model_path: null +baseline: false +data_dir: /mingli01/data/GPUDrive_mini/training +continue_training: false +model_cpt: null + +environment: + name: "gpudrive" + num_worlds: 75 + k_unique_scenes: 75 + max_controlled_agents: 64 + ego_state: true + road_map_obs: true + partner_obs: true + norm_obs: true + remove_non_vehicles: true + lidar_obs: false + reward_type: "weighted_combination" + collision_weight: -0.75 + off_road_weight: -0.75 + goal_achieved_weight: 1.0 + dynamics_model: "classic" + collision_behavior: "ignore" + dist_to_goal_threshold: 2.0 + polyline_reduction_threshold: 0.1 + sampling_seed: 42 + obs_radius: 50.0 + action_space_steer_disc: 13 + action_space_accel_disc: 7 + use_vbd: false + vbd_model_path: "gpudrive/integrations/vbd/weights/epoch=18.ckpt" + init_steps: 11 + vbd_trajectory_weight: 0.1 + vbd_in_obs: false + +wandb: + entity: "" + project: "gpudrive" + group: "mini_official_sanity" + mode: "offline" + tags: ["ppo", "puffer", "mini", "official_sanity"] + +train: + exp_id: PPO_MINI_OFFICIAL_SANITY + seed: 42 + cpu_offload: false + device: "cuda" + bptt_horizon: 1 + compile: false + compile_mode: "reduce-overhead" + + resample_scenes: false + resample_dataset_size: 10_000 + resample_interval: 2_000_000 + sample_with_replacement: true + shuffle_dataset: false + + torch_deterministic: false + total_timesteps: 10_000_000 + batch_size: 131_072 + minibatch_size: 8192 + learning_rate: 3e-4 + anneal_lr: false + gamma: 0.99 + gae_lambda: 0.95 + update_epochs: 4 + norm_adv: true + clip_coef: 0.2 + clip_vloss: false + vf_clip_coef: 0.2 + ent_coef: 0.0001 + vf_coef: 0.3 + max_grad_norm: 0.5 + target_kl: null + log_window: 1000 + + network: + input_dim: 64 + hidden_dim: 128 + dropout: 0.01 + class_name: "NeuralNet" + num_parameters: 0 + + checkpoint_interval: 400 + checkpoint_path: "/mingli01/project/zihanliu/gpudrive-main/runs" + + render: false + render_3d: true + render_interval: 1 + render_k_scenarios: 10 + render_format: "mp4" + render_fps: 15 + zoom_radius: 50 + +vec: + backend: "native" + num_workers: 1 + env_batch_size: 1 + zero_copy: false diff --git a/gpudrive-main/baselines/ppo/config/ppo_mini_reward_conditioned.yaml b/gpudrive-main/baselines/ppo/config/ppo_mini_reward_conditioned.yaml new file mode 100644 index 0000000000000000000000000000000000000000..262eddb4d39f32d0d79ee5e7c9ead1b72425d410 --- /dev/null +++ b/gpudrive-main/baselines/ppo/config/ppo_mini_reward_conditioned.yaml @@ -0,0 +1,112 @@ +mode: "train" +use_rnn: false +eval_model_path: null +baseline: false +data_dir: /mingli01/data/GPUDrive_mini/training +continue_training: false +model_cpt: null + +environment: + name: "gpudrive" + num_worlds: 600 + k_unique_scenes: 600 + max_controlled_agents: 64 + ego_state: true + road_map_obs: true + partner_obs: true + norm_obs: true + remove_non_vehicles: true + lidar_obs: false + + reward_type: "reward_conditioned" + condition_mode: "random" + collision_weight_lb: -1.0 + collision_weight_ub: 0.0 + goal_achieved_weight_lb: 1.0 + goal_achieved_weight_ub: 2.0 + off_road_weight_lb: -1.0 + off_road_weight_ub: 0.0 + + collision_weight: -0.75 + off_road_weight: -0.75 + goal_achieved_weight: 1.0 + + dynamics_model: "classic" + collision_behavior: "ignore" + dist_to_goal_threshold: 2.0 + polyline_reduction_threshold: 0.1 + sampling_seed: 42 + obs_radius: 50.0 + action_space_steer_disc: 13 + action_space_accel_disc: 7 + + use_vbd: false + vbd_model_path: "gpudrive/integrations/vbd/weights/epoch=18.ckpt" + init_steps: 0 + vbd_trajectory_weight: 0.1 + vbd_in_obs: false + +wandb: + entity: "" + project: "gpudrive" + group: "mini_reward_conditioned" + mode: "offline" + tags: ["ppo", "puffer", "mini", "reward_conditioned"] + +train: + exp_id: PPO_MINI_RC + seed: 42 + cpu_offload: false + device: "cuda" + bptt_horizon: 1 + compile: false + compile_mode: "reduce-overhead" + + resample_scenes: true + resample_dataset_size: 864 + resample_interval: 2_000_000 + sample_with_replacement: true + shuffle_dataset: true + + torch_deterministic: false + total_timesteps: 10_000_000 + batch_size: 524_288 + minibatch_size: 8192 + learning_rate: 3e-4 + anneal_lr: false + gamma: 0.99 + gae_lambda: 0.95 + update_epochs: 4 + norm_adv: true + clip_coef: 0.2 + clip_vloss: false + vf_clip_coef: 0.2 + ent_coef: 0.0001 + vf_coef: 0.3 + max_grad_norm: 0.5 + target_kl: null + log_window: 1000 + + network: + input_dim: 64 + hidden_dim: 128 + dropout: 0.01 + class_name: "NeuralNet" + num_parameters: 0 + + checkpoint_interval: 10 + checkpoint_path: "/mingli01/project/zihanliu/gpudrive-main/runs" + + render: false + render_3d: true + render_interval: 1 + render_k_scenarios: 10 + render_format: "mp4" + render_fps: 15 + zoom_radius: 50 + +vec: + backend: "native" + num_workers: 1 + env_batch_size: 1 + zero_copy: false diff --git a/gpudrive-main/baselines/ppo/config/ppo_nuscenes_reward_conditioned.yaml b/gpudrive-main/baselines/ppo/config/ppo_nuscenes_reward_conditioned.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b7b48b2e5075f69ada777d943cbec83699e58a32 --- /dev/null +++ b/gpudrive-main/baselines/ppo/config/ppo_nuscenes_reward_conditioned.yaml @@ -0,0 +1,113 @@ +mode: "train" +use_rnn: false +eval_model_path: null +baseline: false +data_dir: data/processed/nuscenes +continue_training: false +model_cpt: null + +environment: + name: "gpudrive" + num_worlds: 75 + k_unique_scenes: 75 + max_controlled_agents: 64 + ego_state: true + road_map_obs: true + partner_obs: true + norm_obs: true + remove_non_vehicles: true + lidar_obs: false + + reward_type: "reward_conditioned" + condition_mode: "random" + collision_weight_lb: -1.0 + collision_weight_ub: -0.1 + goal_achieved_weight_lb: 0.5 + goal_achieved_weight_ub: 2.0 + off_road_weight_lb: -1.0 + off_road_weight_ub: -0.1 + + # Kept for non reward-conditioned fallbacks and logging compatibility. + collision_weight: -0.75 + off_road_weight: -0.75 + goal_achieved_weight: 1.0 + + dynamics_model: "classic" + collision_behavior: "ignore" + dist_to_goal_threshold: 2.0 + polyline_reduction_threshold: 0.1 + sampling_seed: 42 + obs_radius: 50.0 + action_space_steer_disc: 13 + action_space_accel_disc: 7 + + use_vbd: false + vbd_model_path: "gpudrive/integrations/vbd/weights/epoch=18.ckpt" + init_steps: 0 + vbd_trajectory_weight: 0.1 + vbd_in_obs: false + +wandb: + entity: "" + project: "gpudrive" + group: "nuscenes_reward_conditioned" + mode: "offline" + tags: ["ppo", "puffer", "nuscenes", "reward_conditioned"] + +train: + exp_id: PPO_NUSCENES_RC + seed: 42 + cpu_offload: false + device: "cuda" + bptt_horizon: 1 + compile: false + compile_mode: "reduce-overhead" + + resample_scenes: true + resample_dataset_size: 10_000 + resample_interval: 2_000_000 + sample_with_replacement: true + shuffle_dataset: true + + torch_deterministic: false + total_timesteps: 10_000_000 + batch_size: 131_072 + minibatch_size: 8192 + learning_rate: 3e-4 + anneal_lr: false + gamma: 0.99 + gae_lambda: 0.95 + update_epochs: 4 + norm_adv: true + clip_coef: 0.2 + clip_vloss: false + vf_clip_coef: 0.2 + ent_coef: 0.0001 + vf_coef: 0.3 + max_grad_norm: 0.5 + target_kl: null + log_window: 1000 + + network: + input_dim: 64 + hidden_dim: 128 + dropout: 0.01 + class_name: "NeuralNet" + num_parameters: 0 + + checkpoint_interval: 50 + checkpoint_path: "./runs" + + render: false + render_3d: true + render_interval: 1 + render_k_scenarios: 10 + render_format: "mp4" + render_fps: 15 + zoom_radius: 50 + +vec: + backend: "native" + num_workers: 1 + env_batch_size: 1 + zero_copy: false diff --git a/gpudrive-main/baselines/ppo/ppo_pufferlib.py b/gpudrive-main/baselines/ppo/ppo_pufferlib.py new file mode 100644 index 0000000000000000000000000000000000000000..e1e072be761c9676b5ccd8b360078e6e3bdf6159 --- /dev/null +++ b/gpudrive-main/baselines/ppo/ppo_pufferlib.py @@ -0,0 +1,331 @@ +""" +This implementation is adapted from the demo in PufferLib by Joseph Suarez, +which in turn is adapted from Costa Huang's CleanRL PPO + LSTM implementation. +Links +- PufferLib: https://github.com/PufferAI/PufferLib/blob/dev/demo.py +- Cleanrl: https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ppo.py +""" + +import os +from typing import Optional +from typing_extensions import Annotated +import yaml +from datetime import datetime +import torch +import numpy as np +import wandb +from box import Box + +from gpudrive.integrations.puffer import ppo +from gpudrive.env.env_puffer import PufferGPUDrive + +from gpudrive.networks.late_fusion import NeuralNet +from gpudrive.env.dataset import SceneDataLoader + +import pufferlib +import pufferlib.vector +from rich.console import Console + +import typer +from typer import Typer + +app = Typer() + + +def get_model_parameters(policy): + """Helper function to count the number of trainable parameters.""" + params = filter(lambda p: p.requires_grad, policy.parameters()) + return sum([np.prod(p.size()) for p in params]) + + +def load_config(config_path): + """Load the configuration file.""" + with open(config_path, "r") as f: + config = Box(yaml.safe_load(f)) + return pufferlib.namespace(**config) + + +def make_agent(env, config): + """Create a policy based on the environment.""" + + if config.continue_training: + print("Loading checkpoint...") + # Load checkpoint + saved_cpt = torch.load( + f=config.model_cpt, + map_location=config.train.device, + weights_only=False, + ) + policy = NeuralNet( + input_dim=saved_cpt["model_arch"]["input_dim"], + action_dim=saved_cpt["action_dim"], + hidden_dim=saved_cpt["model_arch"]["hidden_dim"], + config=config.environment, + ) + + # Load the model parameters + policy.load_state_dict(saved_cpt["parameters"]) + + return policy + + else: + # Start from scratch + return NeuralNet( + input_dim=config.train.network.input_dim, + action_dim=env.single_action_space.n, + hidden_dim=config.train.network.hidden_dim, + dropout=config.train.network.dropout, + config=config.environment, + ) + + +def train(args, vecenv): + """Main training loop for the PPO agent.""" + policy = make_agent(env=vecenv.driver_env, config=args).to( + args.train.device + ) + + args.train.network.num_parameters = get_model_parameters(policy) + args.train.env = args.environment.name + + args.wandb = init_wandb(args, args.train.exp_id, id=args.train.exp_id) + args.train.__dict__.update(dict(args.wandb.config.train)) + + data = ppo.create(args.train, vecenv, policy, wandb=args.wandb) + while data.global_step < args.train.total_timesteps: + try: + ppo.evaluate(data) # Rollout + ppo.train(data) # Update policy + except KeyboardInterrupt: + ppo.close(data) + os._exit(0) + except Exception as e: + print(f"An error occurred: {e}") # Log the error + Console().print_exception() + os._exit(1) # Exit with a non-zero status to indicate an error + + ppo.evaluate(data) + ppo.close(data) + + +def init_wandb(args, name, id=None, resume=True): + wandb.init( + id=id or wandb.util.generate_id(), + project=args.wandb.project, + entity=args.wandb.entity, + group=args.wandb.group, + mode=args.wandb.mode, + tags=args.wandb.tags, + config={ + "environment": dict(args.environment), + "train": dict(args.train), + "vec": dict(args.vec), + }, + name=name, + save_code=True, + resume=False, + ) + + return wandb + + +def sweep(args, project="PPO", sweep_name="my_sweep"): + """Initialize a WandB sweep with hyperparameters.""" + sweep_id = wandb.sweep( + sweep=dict( + method="random", + name=sweep_name, + metric={"goal": "maximize", "name": "environment/episode_return"}, + parameters={ + "learning_rate": { + "distribution": "log_uniform_values", + "min": 1e-4, + "max": 1e-1, + }, + "batch_size": {"values": [512, 1024, 2048]}, + "minibatch_size": {"values": [128, 256, 512]}, + }, + ), + project=project, + ) + wandb.agent(sweep_id, lambda: train(args), count=100) + + +@app.command() +def run( + config_path: Annotated[ + str, typer.Argument(help="The path to the default configuration file") + ] = "baselines/ppo/config/ppo_base_puffer.yaml", + *, + # fmt: off + data_dir: Annotated[Optional[str], typer.Option(help="Directory containing GPUDrive JSON scenes")] = None, + # Environment options + num_worlds: Annotated[Optional[int], typer.Option(help="Number of parallel envs")] = None, + k_unique_scenes: Annotated[Optional[int], typer.Option(help="The number of unique scenes to sample")] = None, + collision_weight: Annotated[Optional[float], typer.Option(help="The weight for collision penalty")] = None, + off_road_weight: Annotated[Optional[float], typer.Option(help="The weight for off-road penalty")] = None, + goal_achieved_weight: Annotated[Optional[float], typer.Option(help="The weight for goal-achieved reward")] = None, + dist_to_goal_threshold: Annotated[Optional[float], typer.Option(help="The distance threshold for goal-achieved")] = None, + polyline_reduction_threshold: Annotated[Optional[float], typer.Option(help="Road polyline reduction threshold")] = None, + sampling_seed: Annotated[Optional[int], typer.Option(help="The seed for sampling scenes")] = None, + obs_radius: Annotated[Optional[float], typer.Option(help="The radius for the observation")] = None, + collision_behavior: Annotated[Optional[str], typer.Option(help="The collision behavior; 'ignore' or 'remove'")] = None, + remove_non_vehicles: Annotated[Optional[int], typer.Option(help="Remove non-vehicles from the scene; 0 or 1")] = None, + use_vbd: Annotated[Optional[bool], typer.Option(help="Use VBD model for trajectory predictions")] = False, + vbd_model_path: Annotated[Optional[str], typer.Option(help="Path to VBD model checkpoint")] = None, + vbd_trajectory_weight: Annotated[Optional[float], typer.Option(help="Weight for VBD trajectory deviation penalty")] = 0.1, + vbd_in_obs: Annotated[Optional[bool], typer.Option(help="Include VBD predictions in the observation")] = False, + init_steps: Annotated[Optional[int], typer.Option(help="Environment warmup steps")] = 0, + # Train options + seed: Annotated[Optional[int], typer.Option(help="The seed for training")] = None, + learning_rate: Annotated[Optional[float], typer.Option(help="The learning rate for training")] = None, + anneal_lr: Annotated[Optional[int], typer.Option(help="Whether to anneal the learning rate over time; 0 or 1")] = None, + resample_scenes: Annotated[Optional[int], typer.Option(help="Whether to resample scenes during training; 0 or 1")] = None, + resample_interval: Annotated[Optional[int], typer.Option(help="The interval for resampling scenes")] = None, + resample_dataset_size: Annotated[Optional[int], typer.Option(help="The size of the dataset to sample from")] = None, + total_timesteps: Annotated[Optional[int], typer.Option(help="The total number of training steps")] = None, + ent_coef: Annotated[Optional[float], typer.Option(help="Entropy coefficient")] = None, + update_epochs: Annotated[Optional[int], typer.Option(help="The number of epochs for updating the policy")] = None, + batch_size: Annotated[Optional[int], typer.Option(help="The batch size for training")] = None, + minibatch_size: Annotated[Optional[int], typer.Option(help="The minibatch size for training")] = None, + gamma: Annotated[Optional[float], typer.Option(help="The discount factor for rewards")] = None, + vf_coef: Annotated[Optional[float], typer.Option(help="Weight for vf_loss")] = None, + # Wandb logging options + project: Annotated[Optional[str], typer.Option(help="WandB project name")] = None, + entity: Annotated[Optional[str], typer.Option(help="WandB entity name")] = None, + group: Annotated[Optional[str], typer.Option(help="WandB group name")] = None, + render: Annotated[Optional[int], typer.Option(help="Whether to render the environment; 0 or 1")] = None, +): + """Run PPO training with the given configuration.""" + # fmt: on + + # Load default configs + config = load_config(config_path) + + # Override configs with command-line arguments + if data_dir is not None: + config.data_dir = data_dir + + env_config = { + "num_worlds": num_worlds, + "k_unique_scenes": k_unique_scenes, + "collision_weight": collision_weight, + "off_road_weight": off_road_weight, + "goal_achieved_weight": goal_achieved_weight, + "dist_to_goal_threshold": dist_to_goal_threshold, + "polyline_reduction_threshold": polyline_reduction_threshold, + "sampling_seed": sampling_seed, + "obs_radius": obs_radius, + "collision_behavior": collision_behavior, + "remove_non_vehicles": None + if remove_non_vehicles is None + else bool(remove_non_vehicles), + "use_vbd": use_vbd, + "vbd_model_path": vbd_model_path, + "vbd_trajectory_weight": vbd_trajectory_weight, + "vbd_in_obs": vbd_in_obs, + "init_steps": init_steps, + } + config.environment.update( + {k: v for k, v in env_config.items() if v is not None} + ) + + train_config = { + "seed": seed, + "learning_rate": learning_rate, + "anneal_lr": None if anneal_lr is None else bool(anneal_lr), + "resample_scenes": None + if resample_scenes is None + else bool(resample_scenes), + "resample_interval": resample_interval, + "resample_dataset_size": resample_dataset_size, + "total_timesteps": total_timesteps, + "ent_coef": ent_coef, + "update_epochs": update_epochs, + "batch_size": batch_size, + "minibatch_size": minibatch_size, + "render": None if render is None else bool(render), + "gamma": gamma, + "vf_coef": vf_coef, + } + config.train.update( + {k: v for k, v in train_config.items() if v is not None} + ) + + wandb_config = { + "project": project, + "entity": entity, + "group": group, + } + config.wandb.update( + {k: v for k, v in wandb_config.items() if v is not None} + ) + + datetime_ = datetime.now().strftime("%m_%d_%H_%M_%S_%f")[:-3] + + if config["continue_training"]: + cont_train = "C" + else: + cont_train = "" + + if config["train"]["resample_scenes"]: + if config["train"]["resample_scenes"]: + dataset_size = config["train"]["resample_dataset_size"] + config["train"][ + "exp_id" + ] = f'{config["train"]["exp_id"]}__{cont_train}__R_{dataset_size}__{datetime_}' + else: + dataset_size = str(config["environment"]["k_unique_scenes"]) + config["train"][ + "exp_id" + ] = f'{config["train"]["exp_id"]}__{cont_train}__S_{dataset_size}__{datetime_}' + + config["environment"]["dataset_size"] = dataset_size + config["train"]["device"] = config["train"].get( + "device", "cpu" + ) # Default to 'cpu' if not set + if torch.cuda.is_available(): + config["train"]["device"] = "cuda" # Set to 'cuda' if available + + # Make dataloader + train_loader = SceneDataLoader( + root=config.data_dir, + batch_size=config.environment.num_worlds, + dataset_size=config.train.resample_dataset_size + if config.train.resample_scenes + else config.environment.k_unique_scenes, + sample_with_replacement=config.train.sample_with_replacement, + shuffle=config.train.shuffle_dataset, + seed=config.train.seed, + ) + print( + "[GPUDrive] Data loader ready: " + f"data_dir={train_loader.root}, " + f"scenes={len(train_loader.dataset)}, " + f"batch_size={train_loader.batch_size}, " + f"sample_with_replacement={train_loader.sample_with_replacement}, " + f"seed={train_loader.seed}", + flush=True, + ) + + # Make environment + print("[GPUDrive] Creating PufferGPUDrive environment...", flush=True) + vecenv = PufferGPUDrive( + data_loader=train_loader, + **config.environment, + **config.train, + ) + print( + "[GPUDrive] Environment ready: " + f"num_worlds={vecenv.num_worlds}, " + f"controlled_agents={vecenv.num_agents}, " + f"max_controlled_agents_per_world={vecenv.max_cont_agents_per_env}", + flush=True, + ) + + train(config, vecenv) + + +if __name__ == "__main__": + + app() diff --git a/gpudrive-main/baselines/ppo/ppo_sb3.py b/gpudrive-main/baselines/ppo/ppo_sb3.py new file mode 100644 index 0000000000000000000000000000000000000000..e0c25e1a3019cb61c1be24bc187c672f957cea5c --- /dev/null +++ b/gpudrive-main/baselines/ppo/ppo_sb3.py @@ -0,0 +1,123 @@ +import wandb +import yaml +from box import Box +from typing import Callable +from datetime import datetime +import dataclasses +from gpudrive.integrations.sb3.ppo import IPPO +from gpudrive.integrations.sb3.callbacks import MultiAgentCallback +from gpudrive.env.config import EnvConfig +from gpudrive.env.wrappers.sb3_wrapper import SB3MultiAgentEnv + +from gpudrive.networks.perm_eq_late_fusion import ( + LateFusionNet, + LateFusionPolicy, +) +from gpudrive.networks.basic_ffn import FFN, FeedForwardPolicy + + +def linear_schedule(initial_value: float) -> Callable[[float], float]: + """Linear learning rate schedule.""" + + def func(progress_remaining: float) -> float: + return progress_remaining * initial_value + + return func + + +def load_config(config_path): + """Load the configuration file.""" + with open(config_path, "r") as f: + return Box(yaml.safe_load(f)) + + +def train(exp_config: Box): + """Run PPO training with stable-baselines3.""" + + env_config = dataclasses.replace( + EnvConfig(), + reward_type=exp_config.reward_type, + episode_len=exp_config.episode_len, + remove_non_vehicles=exp_config.remove_non_vehicles, + polyline_reduction_threshold=exp_config.polyline_reduction_threshold, + obs_radius=exp_config.observation_radius, + collision_behavior=exp_config.collision_behavior, + ) + + # Select model + if exp_config.mlp_class == "late_fusion": + exp_config.mlp_class = LateFusionNet + exp_config.policy = LateFusionPolicy + elif exp_config.mlp_class == "feed_forward": + exp_config.mlp_class = FFN + exp_config.policy = FeedForwardPolicy + else: + raise NotImplementedError( + f"Unsupported MLP class: {exp_config.mlp_class}" + ) + + # Make environment + env = SB3MultiAgentEnv( + config=env_config, + exp_config=exp_config, + max_cont_agents=env_config.max_num_agents_in_scene, + device=exp_config.device, + ) + + exp_config.batch_size = ( + exp_config.num_worlds * exp_config.n_steps + ) // exp_config.num_minibatches + + datetime_ = datetime.now().strftime("%m_%d_%H_%S") + run_id = f"{datetime_}" + run = wandb.init( + project=exp_config.project_name, + name=run_id, + id=run_id, + group=exp_config.group_name, + sync_tensorboard=exp_config.sync_tensorboard, + tags=exp_config.tags, + mode=exp_config.wandb_mode, + config={**exp_config, **env_config.__dict__}, + ) + + custom_callback = MultiAgentCallback( + config=exp_config, + wandb_run=run if run_id is not None else None, + ) + + model = IPPO( + n_steps=exp_config.n_steps, + batch_size=exp_config.batch_size, + env=env, + seed=exp_config.seed, + verbose=exp_config.verbose, + device=exp_config.device, + tensorboard_log=f"runs/{run_id}" if run_id is not None else None, + mlp_class=exp_config.mlp_class, + policy=exp_config.policy, + gamma=exp_config.gamma, + gae_lambda=exp_config.gae_lambda, + vf_coef=exp_config.vf_coef, + clip_range=exp_config.clip_range, + learning_rate=linear_schedule(exp_config.lr), + ent_coef=exp_config.ent_coef, + n_epochs=exp_config.n_epochs, + env_config=env_config, + exp_config=exp_config, + ) + + model.learn( + total_timesteps=exp_config.total_timesteps, + callback=custom_callback, + ) + + run.finish() + env.close() + + +if __name__ == "__main__": + + exp_config = load_config("baselines/ppo/config/ppo_base_sb3.yaml") + + train(exp_config) diff --git a/gpudrive-main/build_gpudrive.py b/gpudrive-main/build_gpudrive.py new file mode 100644 index 0000000000000000000000000000000000000000..84a004fa1210647f98f56df94f478414f91a063f --- /dev/null +++ b/gpudrive-main/build_gpudrive.py @@ -0,0 +1,32 @@ +import subprocess +import os +import logging + +logging.basicConfig(level=logging.INFO) + + +def main(): + # Cloning the repository, although typically you would not do this in the build step + # as the code should already be present. Including it just for completeness. + subprocess.check_call( + ["git", "submodule", "update", "--init", "--recursive", "--force"] + ) + + # Create and enter the build directory + if not os.path.exists("build"): + os.mkdir("build") + os.chdir("build") + + # Run CMake and Make + subprocess.check_call(["cmake", "..", "-DCMAKE_BUILD_TYPE=Release"]) + subprocess.check_call( + ["make", f"-j{os.cpu_count()}"] + ) # Utilize all available cores + + # Going back to the root directory + os.chdir("..") + + +if __name__ == "__main__": + logging.info("Building the C++ code and installing the Python package") + main() diff --git a/gpudrive-main/conftest.py b/gpudrive-main/conftest.py new file mode 100644 index 0000000000000000000000000000000000000000..0666a2717ef669923222e4b5eba6b2eea78d4aef --- /dev/null +++ b/gpudrive-main/conftest.py @@ -0,0 +1,21 @@ +import subprocess +import pytest +import os + +@pytest.fixture(scope="session", autouse=True) +def run_ctest(): + build_dir = "build" # Replace this with the path to your build directory + print("Running ctest in", os.path.abspath(build_dir)) + + # Run ctest in the build directory + try: + result = subprocess.run(["ctest"], check=True, cwd=build_dir, capture_output=True, text=True) + print("ctest stdout:") + print(result.stdout) + print("ctest stderr:") + print(result.stderr) + except subprocess.CalledProcessError as e: + print(f"ctest failed with error code {e.returncode}") + print("Error output:") + print(e.output) + raise # Re-raise the exception to make pytest fail diff --git a/gpudrive-main/data/processed/.gitattributes b/gpudrive-main/data/processed/.gitattributes new file mode 100644 index 0000000000000000000000000000000000000000..1ef325f1b111266a6b26e0196871bd78baa8c2f3 --- /dev/null +++ b/gpudrive-main/data/processed/.gitattributes @@ -0,0 +1,59 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.lz4 filter=lfs diff=lfs merge=lfs -text +*.mds filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text +# Audio files - uncompressed +*.pcm filter=lfs diff=lfs merge=lfs -text +*.sam filter=lfs diff=lfs merge=lfs -text +*.raw filter=lfs diff=lfs merge=lfs -text +# Audio files - compressed +*.aac filter=lfs diff=lfs merge=lfs -text +*.flac filter=lfs diff=lfs merge=lfs -text +*.mp3 filter=lfs diff=lfs merge=lfs -text +*.ogg filter=lfs diff=lfs merge=lfs -text +*.wav filter=lfs diff=lfs merge=lfs -text +# Image files - uncompressed +*.bmp filter=lfs diff=lfs merge=lfs -text +*.gif filter=lfs diff=lfs merge=lfs -text +*.png 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"metadata": {"sdc_track_index": 14, "objects_of_interest": [], "tracks_to_predict": [{"track_index": 1, "difficulty": 0}, {"track_index": 5, "difficulty": 0}]}} \ No newline at end of file diff --git a/gpudrive-main/data/raw/.gitkeep b/gpudrive-main/data/raw/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/gpudrive-main/data_utils/README.md b/gpudrive-main/data_utils/README.md new file mode 100644 index 0000000000000000000000000000000000000000..c0649d19dbd58128fdcf5a5993b180b349a77ae6 --- /dev/null +++ b/gpudrive-main/data_utils/README.md @@ -0,0 +1,43 @@ +## Why do we need to process Waymo files? +- GPUDrive works with a subset of information given in a Waymo scene, in json format. +- Processing script deserializes Waymo tfrecords according to their protobuf format and generates json files compatible with GPUDrive. + +## What exactly happens when processing a Waymo tfrecord? +- Each Waymo tfrecord contains about 500 scenes, with all assiciated information regarding road objects, vehicles, traffic lights, etc. +- [process_waymo_files.py](process_waymo_files.py) generates a json file for each scene in the tfrecord in parallel. +- During processing, we mark certain vehicles as "expert" meaning they cannot be controlled in the sim. + +## Why do we mark some vehicles as experts? +- In the ground truth Waymo vehicle trajectories, a small minority of them involve crossing a road edge entity. +- GPUDrive interprets this as a collision and subsequently going offroad. Hence these vehicles fail to make it to their goals. +- This leads to inaccuracies in evaluating policies with respect to rate of reaching goals. +- Marking them as "expert" makes them uncontrolled in the sim, and hence are not considered in evaluation. + +## How do we check if a vehicle should be marked expert? +- For each Waymo scene we process, we construct all road edges, and all vehicle trajectories. +- For each vehicle trajectory, if it intersects with a road edge, we set an expert flag in the json. +- Then when a scene is loaded into the sim, it checks for this flag and behaves accordingly. + +## How many of these experts even exist in the first place? +- In the entire training set (>100k scenes), we found 31837 vehicles (roughly 0.35% of all vehicles) marked expert. +![scene_distribution](../assets/distribution.png) + +## NuScenes adapter + +GPUDrive still consumes the same JSON schema as the Waymo converter. For +NuScenes, use the adapter to convert keyframe annotations and map layers into +that schema: + +```bash +python data_utils/process_nuscenes_files.py \ + --dataroot /path/to/nuscenes \ + --version v1.0-mini \ + --split mini_train \ + --output-dir data/processed/nuscenes +``` + +The converter synthesizes an ego vehicle from NuScenes ego poses, maps supported +dynamic categories to `vehicle`, `pedestrian`, and `cyclist`, and interpolates +2 Hz keyframe tracks to GPUDrive's default 10 Hz, 91-step trajectory format. The +default output prefix is `tfrecord-nuscenes`, so `SceneDataLoader` can discover +the files with its default `file_prefix="tfrecord"`. diff --git a/gpudrive-main/data_utils/__init__.py b/gpudrive-main/data_utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/gpudrive-main/data_utils/datatypes.py b/gpudrive-main/data_utils/datatypes.py new file mode 100644 index 0000000000000000000000000000000000000000..467f1d4f57f9af2f0d80b9d3ce6a8d24371bde61 --- /dev/null +++ b/gpudrive-main/data_utils/datatypes.py @@ -0,0 +1,34 @@ +# Const# Constants defined for map drawing parameters. +import enum + + +class MapElementIds(enum.IntEnum): + """Ids for different map elements to be mapped into a tensor to be consistent with + https://github.com/waymo-research/waymax/blob/main/waymax/datatypes/roadgraph.py. + + These integers represent the ID of these specific types as defined in: + https://waymo.com/open/data/motion/tfexample. + """ + + LANE_UNDEFINED = 0 + LANE_FREEWAY = 1 + LANE_SURFACE_STREET = 2 + LANE_BIKE_LANE = 3 + # Original definition skips 4. + ROAD_LINE_UNKNOWN = 5 + ROAD_LINE_BROKEN_SINGLE_WHITE = 6 + ROAD_LINE_SOLID_SINGLE_WHITE = 7 + ROAD_LINE_SOLID_DOUBLE_WHITE = 8 + ROAD_LINE_BROKEN_SINGLE_YELLOW = 9 + ROAD_LINE_BROKEN_DOUBLE_YELLOW = 10 + ROAD_LINE_SOLID_SINGLE_YELLOW = 11 + ROAD_LINE_SOLID_DOUBLE_YELLOW = 12 + ROAD_LINE_PASSING_DOUBLE_YELLOW = 13 + ROAD_EDGE_UNKNOWN = 14 + ROAD_EDGE_BOUNDARY = 15 + ROAD_EDGE_MEDIAN = 16 + STOP_SIGN = 17 + CROSSWALK = 18 + SPEED_BUMP = 19 + DRIVEWAY = 20 # New datatype in v1.2.0: Driveway entrances + UNKNOWN = -1 diff --git a/gpudrive-main/data_utils/detect_3d.ipynb b/gpudrive-main/data_utils/detect_3d.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..d4b94e4b7d2efec38eba7d31e36d5d0bdab28416 --- /dev/null +++ b/gpudrive-main/data_utils/detect_3d.ipynb @@ -0,0 +1,1510 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Notebook to Detect 3D Structures" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import trimesh\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Meshes for collision checking\n", + "def _filter_small_segments(segments, min_length=1e-6):\n", + " \"\"\"Filter out segments that are too short.\"\"\"\n", + " valid_segments = []\n", + " for segment in segments:\n", + " start, end = segment\n", + " length = np.linalg.norm(np.array(end) - np.array(start))\n", + " if length >= min_length:\n", + " valid_segments.append(segment)\n", + " return valid_segments\n", + "\n", + "\n", + "def _generate_mesh(segments, height=2.0, width=0.2, color=None):\n", + " segments = np.array(segments, dtype=np.float64)\n", + " starts, ends = segments[:, 0, :], segments[:, 1, :]\n", + " directions = ends - starts\n", + " lengths = np.linalg.norm(directions, axis=1, keepdims=True)\n", + " unit_directions = directions / lengths\n", + " \n", + " # Create the base box mesh with the height along the z-axis\n", + " base_box = trimesh.creation.box(extents=[1.0, width, height])\n", + " base_box.apply_translation([0.5, 0, 0]) # Align box's origin to its start\n", + " \n", + " # Prepare rotation matrices around the z-axis\n", + " z_axis = np.array([0, 0, 1]) # The desired vertical axis\n", + " angles = np.arctan2(unit_directions[:, 1], unit_directions[:, 0]) # Rotation in the XY plane\n", + "\n", + " rectangles = []\n", + " lengths = lengths.flatten()\n", + "\n", + " for i, (start, length, angle) in enumerate(zip(starts, lengths, angles)):\n", + " # Copy the base box and scale to match segment length\n", + " scaled_box = base_box.copy()\n", + " scaled_box.apply_scale([length, 1.0, 1.0])\n", + " \n", + " # Apply rotation around the z-axis\n", + " rotation_matrix = trimesh.transformations.rotation_matrix(angle, z_axis)\n", + " scaled_box.apply_transform(rotation_matrix)\n", + " \n", + " # Translate the box to the segment's starting point\n", + " scaled_box.apply_translation(start)\n", + " if color is not None:\n", + " scaled_box.visual.face_colors = color\n", + " rectangles.append(scaled_box)\n", + "\n", + " # Concatenate all boxes into a single mesh\n", + " mesh = trimesh.util.concatenate(rectangles)\n", + " return mesh\n", + "\n", + "\n", + "def _create_agent_box_mesh(position, heading, length, width, height, color=None):\n", + " # Create box centered at origin\n", + " box = trimesh.creation.box(extents=[length, width, height])\n", + " \n", + " # Rotate box to align with heading\n", + " z_axis = np.array([0, 0, 1])\n", + " rotation_matrix = trimesh.transformations.rotation_matrix(heading, z_axis)\n", + " box.apply_transform(rotation_matrix)\n", + " \n", + " # Move box to position\n", + " box.apply_translation(position)\n", + " \n", + " if color is not None:\n", + " box.visual.face_colors = color\n", + " \n", + " return box" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def check_3d_structures(scene_path, tolerance=0.1):\n", + " \"\"\"Check for 3D structures using the same logic as process_waymo_files.py\"\"\"\n", + " with open(scene_path, 'r') as f:\n", + " scene = json.load(f)\n", + "\n", + " # Collect edge points like in the processing script\n", + " edge_points = []\n", + " edge_segments = []\n", + " for road in scene['roads']:\n", + " if road[\"type\"] == \"road_edge\":\n", + " edge_vertices = [[r[\"x\"], r[\"y\"], r[\"z\"]] for r in road[\"geometry\"]]\n", + " edge_points.extend(edge_vertices)\n", + " edge_segments.extend([\n", + " [edge_vertices[i], edge_vertices[i + 1]]\n", + " for i in range(len(edge_vertices) - 1)\n", + " ])\n", + "\n", + " # Check for 3D structures using same logic as processing script\n", + " has_3d = False\n", + " if len(edge_points) > 0:\n", + " edge_points = np.array(edge_points)\n", + " xy_points = edge_points[:, :2]\n", + " \n", + " # Process in chunks like in processing script\n", + " chunk_size = 1000\n", + " for i in range(0, len(xy_points), chunk_size):\n", + " chunk = xy_points[i:i + chunk_size]\n", + " # Calculate distances between current chunk and all points\n", + " dists = np.linalg.norm(chunk[:, np.newaxis] - xy_points, axis=2)\n", + " potential_pairs = np.where((dists < tolerance) & (dists > 0))\n", + " \n", + " # Check z-values for identified pairs\n", + " for p1, p2 in zip(*potential_pairs):\n", + " p1_idx = i + p1 # Adjust index for chunking\n", + " if abs(edge_points[p1_idx, 2] - edge_points[p2, 2]) > tolerance:\n", + " has_3d = True\n", + " print(f\"Found 3D structure between points:\")\n", + " print(f\"Point 1: {edge_points[p1_idx]}\")\n", + " print(f\"Point 2: {edge_points[p2]}\")\n", + " print(f\"Z difference: {abs(edge_points[p1_idx, 2] - edge_points[p2, 2])}\")\n", + " break\n", + " \n", + " if has_3d:\n", + " break\n", + "\n", + " # Create visualization identical to processing script\n", + " edge_segments = _filter_small_segments(edge_segments)\n", + " edge_mesh = _generate_mesh(edge_segments)\n", + "\n", + " # Add objects just like in processing script\n", + " object_meshes = []\n", + " trajectory_meshes = []\n", + " for object in scene['objects']:\n", + " if object['type'] not in ['vehicle', 'cyclist']:\n", + " continue\n", + "\n", + " first_valid_idx = next((i for i, valid in enumerate(object[\"valid\"]) if valid), None)\n", + " if first_valid_idx is not None:\n", + " initial_pos = [\n", + " object[\"position\"][first_valid_idx][\"x\"],\n", + " object[\"position\"][first_valid_idx][\"y\"],\n", + " object[\"position\"][first_valid_idx][\"z\"]\n", + " ]\n", + " initial_heading = object[\"heading\"][first_valid_idx]\n", + " color = (180, 0, 0, 15) if object[\"mark_as_expert\"] else (0, 180, 0, 15)\n", + " \n", + " initial_box = _create_agent_box_mesh(\n", + " initial_pos,\n", + " initial_heading,\n", + " object[\"length\"],\n", + " object[\"width\"],\n", + " object[\"height\"],\n", + " color=color\n", + " )\n", + " object_meshes.append(initial_box)\n", + "\n", + " # Create trajectory visualization\n", + " if False in object[\"valid\"]:\n", + " trajectory_segments = []\n", + " for i in range(len(object[\"position\"]) - 1):\n", + " if object[\"valid\"][i] and object[\"valid\"][i + 1]:\n", + " trajectory_segments.append([\n", + " [object[\"position\"][i][\"x\"], object[\"position\"][i][\"y\"], object[\"position\"][i][\"z\"]],\n", + " [object[\"position\"][i+1][\"x\"], object[\"position\"][i+1][\"y\"], object[\"position\"][i+1][\"z\"]]\n", + " ])\n", + " else:\n", + " object_vertices = [[pos[\"x\"], pos[\"y\"], pos[\"z\"]] for pos in object[\"position\"]]\n", + " trajectory_segments = [[object_vertices[i], object_vertices[i+1]] for i in range(len(object_vertices) - 1)]\n", + " \n", + " trajectory_segments = _filter_small_segments(trajectory_segments)\n", + " if len(trajectory_segments) > 0:\n", + " color = (180,0,0,15) if object[\"mark_as_expert\"] else (0,180,0,15)\n", + " trajectory_mesh = _generate_mesh(trajectory_segments, color=color)\n", + " trajectory_meshes.append(trajectory_mesh)\n", + "\n", + " # Create scene\n", + " view = trimesh.Scene()\n", + " view.add_geometry(edge_mesh, node_name=\"Road Edges\")\n", + " for i, mesh in enumerate(trajectory_meshes):\n", + " view.add_geometry(mesh, node_name=f\"Vehicle {i} Path\")\n", + " for i, mesh in enumerate(object_meshes):\n", + " view.add_geometry(mesh, node_name=f\"Vehicle {i} Initial Position\")\n", + "\n", + " print(f\"\\nHas 3D structures: {has_3d}\")\n", + " return view, has_3d" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Has 3D structures: False\n" + ] + }, + { + "data": { + "text/html": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Example usage:\n", + "scene_path = '../data/processed/examples/tfrecord-00000-of-01000_222.json'\n", + "view, has_3d = check_3d_structures(scene_path, tolerance=0.2)\n", + "view.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "json_gen", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/gpudrive-main/data_utils/detect_behavior.py b/gpudrive-main/data_utils/detect_behavior.py new file mode 100644 index 0000000000000000000000000000000000000000..1e8d727e99892cfdc0c16f408a0dd678df2c9b3e --- /dev/null +++ b/gpudrive-main/data_utils/detect_behavior.py @@ -0,0 +1,221 @@ +import os +import json +import logging +import psutil +import argparse +import math +from pathlib import Path +import numpy as np +from multiprocessing import Pool, cpu_count +from tqdm import tqdm + +logging.basicConfig(level=logging.INFO) + +def check_uturn(headings, valid_mask): + """ + Check if a vehicle makes a U-turn by comparing heading angles. + Args: + headings: List of heading angles in radians + valid_mask: List of boolean values indicating valid timesteps + Returns: + bool: True if U-turn detected + """ + # Convert 150 degrees to radians (150 * pi/180) + angle_threshold = 2.618 # approximately 150 degrees in radians + + # Get first valid heading + valid_indices = [i for i, v in enumerate(valid_mask) if v] + if not valid_indices: + return False + + first_valid_idx = valid_indices[0] + first_heading = headings[first_valid_idx] + + # Check subsequent valid headings + for i in valid_indices[1:]: + angle_diff = abs(headings[i] - first_heading) + # Normalize angle difference to [-pi, pi] + angle_diff = (angle_diff + math.pi) % (2 * math.pi) - math.pi + if abs(angle_diff) > angle_threshold: + return True + + return False + +def check_reversing(headings, velocities, valid_mask, min_timesteps=10): + """ + Check if a vehicle reverses by comparing velocity direction with heading. + Args: + headings: List of heading angles in radians + velocities: List of dictionaries containing 'x' and 'y' velocities + valid_mask: List of boolean values indicating valid timesteps + min_timesteps: Minimum number of consecutive timesteps required for reversing + Returns: + bool: True if sustained reversing detected + """ + # Convert angle range to radians (150 to 210 degrees) + min_angle = 2.618 # 150 degrees + + consecutive_reverse = 0 + + for i, valid in enumerate(valid_mask): + if not valid: + consecutive_reverse = 0 + continue + + # Calculate velocity direction + vx = velocities[i]['x'] + vy = velocities[i]['y'] + + # Skip stationary moments + if abs(vx) < 0.1 and abs(vy) < 0.1: + consecutive_reverse = 0 + continue + + velocity_angle = math.atan2(vy, vx) + heading = headings[i] + + # Calculate angle between velocity and heading + angle_diff = velocity_angle - heading + # Normalize to [-pi, pi] + angle_diff = (angle_diff + math.pi) % (2 * math.pi) - math.pi + + # Check if velocity is in the reverse cone + if min_angle <= abs(angle_diff): + consecutive_reverse += 1 + if consecutive_reverse >= min_timesteps: + return True + else: + consecutive_reverse = 0 + + return False + +def process_scene(args): + """Process a single scene file.""" + filepath, min_reverse_timesteps = args + try: + with open(filepath, 'r') as f: + scene = json.load(f) + + uturn_count = np.int64(0) + reverse_count = np.int64(0) + total_agents = np.int64(0) + + # Process each object + for obj in scene['objects']: + # Check if object is a vehicle or cyclist and not an expert + if (obj['type'] in ['vehicle', 'cyclist'] and + not obj.get('mark_as_expert', False)): + + total_agents += 1 + + # Get valid mask and corresponding headings/velocities + valid_mask = obj['valid'] + headings = obj['heading'] + velocities = obj['velocity'] + + # Check for U-turn + if check_uturn(headings, valid_mask): + uturn_count += 1 + + # Check for reversing + if check_reversing(headings, velocities, valid_mask, min_reverse_timesteps): + reverse_count += 1 + + return filepath, (total_agents, uturn_count, reverse_count) + + except Exception as e: + logging.error(f"Error processing {filepath}: {e}") + return filepath, None + +def process_directory(args): + """Process all JSON files in directory.""" + input_dir = Path(args.input_dir) + num_workers = args.num_workers + + # Get all JSON files + json_files = list(input_dir.glob("*.json")) + if not json_files: + logging.error(f"No JSON files found in {input_dir}") + return + + logging.info(f"Found {len(json_files)} JSON files to process") + + # Calculate batch size based on available memory + mem_info = psutil.virtual_memory() + available_memory = mem_info.available / (1024**3) # Convert to GB + usable_memory = int(available_memory * 0.9) # Use 90% of available memory + batch_size = min(1000 * usable_memory, len(json_files)) + + # Initialize counters using numpy int64 to handle large numbers + total_processed = np.int64(0) + total_agents = np.int64(0) + total_uturns = np.int64(0) + total_reverses = np.int64(0) + + # Process files in batches + for i in range(0, len(json_files), int(batch_size)): + batch = json_files[i:i + int(batch_size)] + + # Process batch in parallel + with Pool(num_workers) as pool: + results = list(tqdm( + pool.imap(process_scene, [(str(f), args.min_reverse_timesteps) for f in batch]), + total=len(batch), + desc=f"Processing batch {i//int(batch_size) + 1}" + )) + + # Count results + for filepath, counts in results: + if counts is not None: + agents, uturns, reverses = counts + total_processed += 1 + total_agents += agents + total_uturns += uturns + total_reverses += reverses + + # Calculate percentages using float64 for precision + uturn_percentage = (float(total_uturns) / float(total_agents) * 100) if total_agents > 0 else 0.0 + reverse_percentage = (float(total_reverses) / float(total_agents) * 100) if total_agents > 0 else 0.0 + + logging.info(f"Processing complete!") + logging.info(f"Total files processed: {total_processed:,d}") + logging.info(f"Total non-expert agents: {total_agents:,d}") + logging.info(f"Total U-turns: {total_uturns:,d} ({uturn_percentage:.2f}%)") + logging.info(f"Total reversing: {total_reverses:,d} ({reverse_percentage:.2f}%)") + + # Also save results to a JSON file for future reference + results = { + "total_files_processed": int(total_processed), + "total_non_expert_agents": int(total_agents), + "total_uturns": int(total_uturns), + "total_reversing": int(total_reverses), + "uturn_percentage": float(uturn_percentage), + "reverse_percentage": float(reverse_percentage) + } + + with open('vehicle_behavior_results.json', 'w') as f: + json.dump(results, f, indent=4) + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + description="Analyze vehicle behaviors in JSON files" + ) + parser.add_argument( + "input_dir", + help="Directory containing JSON files to process" + ) + parser.add_argument( + "--num_workers", + type=int, + default=cpu_count(), + help="Number of worker processes (default: number of CPU cores)" + ) + parser.add_argument( + "--min_reverse_timesteps", + type=int, + default=3, + help="Minimum number of consecutive timesteps required for reversing (default: 3)" + ) + + args = parser.parse_args() + process_directory(args) \ No newline at end of file diff --git a/gpudrive-main/data_utils/nuscenes_adapter.py b/gpudrive-main/data_utils/nuscenes_adapter.py new file mode 100644 index 0000000000000000000000000000000000000000..a6919140f7df4a9ddb147753c2ebec6c903fd64c --- /dev/null +++ b/gpudrive-main/data_utils/nuscenes_adapter.py @@ -0,0 +1,812 @@ +"""NuScenes to GPUDrive JSON conversion utilities. + +The adapter keeps NuScenes-specific logic at the dataset boundary and emits the +same JSON schema consumed by GPUDrive's existing C++ map reader. +""" + +from __future__ import annotations + +from collections import defaultdict +from dataclasses import dataclass +import json +import logging +import math +from pathlib import Path +from typing import Any, Iterable, Optional + +import numpy as np + +try: + from .datatypes import MapElementIds +except ImportError: # pragma: no cover - supports running as a script. + from datatypes import MapElementIds + + +ERR_VAL = -1e4 + + +@dataclass +class NuScenesAdapterConfig: + """Configuration for converting NuScenes scenes into GPUDrive scenarios.""" + + target_hz: float = 10.0 + num_steps: int = 91 + max_annotation_gap_sec: float = 1.1 + min_valid_steps: int = 2 + require_start_valid: bool = True + require_full_window: bool = True + include_ego: bool = True + ego_width: float = 2.0 + ego_length: float = 4.8 + ego_height: float = 1.8 + include_map: bool = True + fail_on_missing_map: bool = False + map_radius_m: float = 120.0 + map_discretization_m: float = 1.0 + max_road_elements: int = 900 + max_geometry_points: int = 512 + + @property + def dt(self) -> float: + return 1.0 / self.target_hz + + @property + def horizon_sec(self) -> float: + return (self.num_steps - 1) * self.dt + + +def load_nuscenes(version: str, dataroot: str, verbose: bool = True): + """Load the NuScenes SDK object with a clear optional-dependency error.""" + + try: + from nuscenes.nuscenes import NuScenes + except ImportError as exc: # pragma: no cover - depends on local install. + raise ImportError( + "NuScenes conversion requires the optional NuScenes devkit. " + "Install it with `pip install nuscenes-devkit`." + ) from exc + + return NuScenes(version=version, dataroot=dataroot, verbose=verbose) + + +def wrap_yaw(yaw: float | np.ndarray) -> float | np.ndarray: + """Wrap yaw angles to [-pi, pi].""" + + return (yaw + np.pi) % (2 * np.pi) - np.pi + + +def yaw_from_quaternion(rotation: Iterable[float]) -> float: + """Return z-yaw from a NuScenes quaternion stored as [w, x, y, z].""" + + w, x, y, z = rotation + siny_cosp = 2.0 * (w * z + x * y) + cosy_cosp = 1.0 - 2.0 * (y * y + z * z) + return wrap_yaw(math.atan2(siny_cosp, cosy_cosp)) + + +def category_to_gpudrive(category_name: str) -> Optional[str]: + """Map NuScenes categories to GPUDrive object types.""" + + if category_name.startswith("pedestrian."): + return "pedestrian" + if category_name in {"vehicle.bicycle", "vehicle.motorcycle"}: + return "cyclist" + if category_name.startswith("vehicle."): + return "vehicle" + return None + + +def _dedupe_states_by_time(states: list[dict[str, Any]]) -> list[dict[str, Any]]: + deduped: dict[float, dict[str, Any]] = {} + for state in states: + deduped[float(state["time_sec"])] = state + return [deduped[t] for t in sorted(deduped)] + + +def _empty_series(num_steps: int) -> tuple[list[dict[str, float]], list[float], list[dict[str, float]], list[bool]]: + positions = [{"x": ERR_VAL, "y": ERR_VAL, "z": ERR_VAL} for _ in range(num_steps)] + headings = [ERR_VAL for _ in range(num_steps)] + velocities = [{"x": ERR_VAL, "y": ERR_VAL} for _ in range(num_steps)] + valid = [False for _ in range(num_steps)] + return positions, headings, velocities, valid + + +def _interpolate_value( + raw_t: np.ndarray, + raw_values: np.ndarray, + target_t: float, + right_idx: int, +) -> Optional[np.ndarray]: + if right_idx < len(raw_t) and abs(raw_t[right_idx] - target_t) <= 1e-6: + return raw_values[right_idx] + if right_idx == 0 or right_idx >= len(raw_t): + return None + + left_idx = right_idx - 1 + denom = raw_t[right_idx] - raw_t[left_idx] + if denom <= 0: + return None + + alpha = (target_t - raw_t[left_idx]) / denom + return raw_values[left_idx] * (1.0 - alpha) + raw_values[right_idx] * alpha + + +def _is_interpolation_gap_valid( + raw_t: np.ndarray, + target_t: float, + right_idx: int, + max_gap_sec: float, +) -> bool: + if right_idx < len(raw_t) and abs(raw_t[right_idx] - target_t) <= 1e-6: + return True + if right_idx == 0 or right_idx >= len(raw_t): + return False + return raw_t[right_idx] - raw_t[right_idx - 1] <= max_gap_sec + + +def _compute_velocities( + xy: np.ndarray, + valid: np.ndarray, + dt: float, +) -> list[dict[str, float]]: + velocities = [{"x": ERR_VAL, "y": ERR_VAL} for _ in range(len(valid))] + valid_indices = np.flatnonzero(valid) + + for idx in valid_indices: + prev_valid = idx > 0 and valid[idx - 1] + next_valid = idx + 1 < len(valid) and valid[idx + 1] + if prev_valid and next_valid: + vel = (xy[idx + 1] - xy[idx - 1]) / (2.0 * dt) + elif next_valid: + vel = (xy[idx + 1] - xy[idx]) / dt + elif prev_valid: + vel = (xy[idx] - xy[idx - 1]) / dt + else: + vel = np.array([0.0, 0.0]) + + velocities[idx] = {"x": float(vel[0]), "y": float(vel[1])} + + return velocities + + +def interpolate_track( + states: list[dict[str, Any]], + target_times_sec: np.ndarray, + config: NuScenesAdapterConfig, +) -> Optional[dict[str, Any]]: + """Interpolate one NuScenes track to GPUDrive's fixed time grid.""" + + states = _dedupe_states_by_time(states) + num_steps = len(target_times_sec) + if not states: + return None + + raw_t = np.array([float(s["time_sec"]) for s in states], dtype=np.float64) + raw_xyz = np.array([s["translation"] for s in states], dtype=np.float64) + raw_yaw = np.unwrap(np.array([s["yaw"] for s in states], dtype=np.float64)) + + positions, headings, _, valid_list = _empty_series(num_steps) + interp_xy = np.full((num_steps, 2), ERR_VAL, dtype=np.float64) + valid = np.zeros(num_steps, dtype=bool) + + for out_idx, target_t in enumerate(target_times_sec): + right_idx = int(np.searchsorted(raw_t, target_t, side="left")) + if not _is_interpolation_gap_valid( + raw_t, target_t, right_idx, config.max_annotation_gap_sec + ): + continue + + xyz = _interpolate_value(raw_t, raw_xyz, target_t, right_idx) + yaw = _interpolate_value(raw_t, raw_yaw, target_t, right_idx) + if xyz is None or yaw is None: + continue + + positions[out_idx] = { + "x": float(xyz[0]), + "y": float(xyz[1]), + "z": float(xyz[2]), + } + headings[out_idx] = float(wrap_yaw(float(yaw))) + interp_xy[out_idx] = xyz[:2] + valid[out_idx] = True + + if config.require_start_valid and not valid[0]: + return None + if int(valid.sum()) < config.min_valid_steps: + return None + + valid_indices = np.flatnonzero(valid) + last_valid_idx = int(valid_indices[-1]) + velocities = _compute_velocities(interp_xy, valid, config.dt) + + valid_list = [bool(v) for v in valid] + size = np.median(np.array([s["size"] for s in states], dtype=np.float64), axis=0) + return { + "position": positions, + "width": float(size[0]), + "length": float(size[1]), + "height": float(size[2]), + "heading": headings, + "velocity": velocities, + "valid": valid_list, + "goalPosition": positions[last_valid_idx], + } + + +def _sample_time_sec(sample: dict[str, Any]) -> float: + return float(sample["timestamp"]) / 1e6 + + +def collect_scene_samples(nusc: Any, scene: dict[str, Any]) -> list[dict[str, Any]]: + """Collect all keyframe samples for one NuScenes scene.""" + + samples = [] + sample_token = scene["first_sample_token"] + while sample_token: + sample = nusc.get("sample", sample_token) + samples.append(sample) + sample_token = sample["next"] + return samples + + +def collect_annotation_tracks( + nusc: Any, + samples: list[dict[str, Any]], +) -> dict[str, list[dict[str, Any]]]: + """Collect NuScenes sample annotations into per-instance tracks.""" + + tracks: dict[str, list[dict[str, Any]]] = defaultdict(list) + for sample in samples: + time_sec = _sample_time_sec(sample) + for ann_token in sample["anns"]: + ann = nusc.get("sample_annotation", ann_token) + gpudrive_type = category_to_gpudrive(ann["category_name"]) + if gpudrive_type is None: + continue + + tracks[ann["instance_token"]].append( + { + "time_sec": time_sec, + "translation": ann["translation"], + "yaw": yaw_from_quaternion(ann["rotation"]), + "size": ann["size"], + "category_name": ann["category_name"], + "gpudrive_type": gpudrive_type, + } + ) + + return tracks + + +def collect_ego_track( + nusc: Any, + samples: list[dict[str, Any]], + config: NuScenesAdapterConfig, +) -> list[dict[str, Any]]: + """Collect ego poses as a synthetic vehicle track.""" + + ego_track = [] + ego_size = [config.ego_width, config.ego_length, config.ego_height] + for sample in samples: + lidar_token = sample["data"].get("LIDAR_TOP") + if lidar_token is None: + continue + + sample_data = nusc.get("sample_data", lidar_token) + pose = nusc.get("ego_pose", sample_data["ego_pose_token"]) + ego_track.append( + { + "time_sec": _sample_time_sec(sample), + "translation": pose["translation"], + "yaw": yaw_from_quaternion(pose["rotation"]), + "size": ego_size, + "category_name": "vehicle.ego", + "gpudrive_type": "vehicle", + } + ) + return ego_track + + +def iter_window_starts( + samples: list[dict[str, Any]], + config: NuScenesAdapterConfig, + window_stride_sec: Optional[float] = None, +) -> list[float]: + """Return conversion window starts in absolute NuScenes seconds.""" + + if not samples: + return [] + + first_time = _sample_time_sec(samples[0]) + last_time = _sample_time_sec(samples[-1]) + latest_start = last_time - config.horizon_sec + + if config.require_full_window and latest_start < first_time: + return [] + + if window_stride_sec is None or window_stride_sec <= 0: + return [first_time] + + latest_allowed_start = latest_start if config.require_full_window else last_time + starts = [] + cur = first_time + while cur <= latest_allowed_start + 1e-6: + starts.append(cur) + cur += window_stride_sec + return starts + + +def _geometry_point(x: float, y: float, z: float = 0.0) -> dict[str, float]: + return {"x": float(x), "y": float(y), "z": float(z)} + + +def _dedupe_points(points: list[dict[str, float]]) -> list[dict[str, float]]: + if not points: + return points + + deduped = [points[0]] + for point in points[1:]: + prev = deduped[-1] + if abs(point["x"] - prev["x"]) > 1e-6 or abs(point["y"] - prev["y"]) > 1e-6: + deduped.append(point) + + if len(deduped) > 2: + first = deduped[0] + last = deduped[-1] + if abs(first["x"] - last["x"]) <= 1e-6 and abs(first["y"] - last["y"]) <= 1e-6: + deduped.pop() + return deduped + + +def _thin_points( + points: list[dict[str, float]], + max_points: int, +) -> list[dict[str, float]]: + if len(points) <= max_points: + return points + + keep = np.linspace(0, len(points) - 1, max_points).round().astype(int) + return [points[int(i)] for i in keep] + + +def _coords_to_geometry( + coords: Iterable[Any], + max_points: int, +) -> list[dict[str, float]]: + points = [] + for coord in coords: + x = coord[0] + y = coord[1] + points.append(_geometry_point(x, y, 0.0)) + return _thin_points(_dedupe_points(points), max_points) + + +def _add_road( + roads: list[dict[str, Any]], + geometry: list[dict[str, float]], + road_type: str, + map_element_id: int, + road_id: int, + config: NuScenesAdapterConfig, +) -> int: + if len(roads) >= config.max_road_elements: + return road_id + + min_points = 4 if road_type in {"crosswalk", "speed_bump"} else 2 + if road_type == "stop_sign": + min_points = 1 + if len(geometry) < min_points: + return road_id + + roads.append( + { + "geometry": geometry, + "type": road_type, + "map_element_id": int(map_element_id), + "id": road_id, + } + ) + return road_id + 1 + + +def _get_records_in_radius( + nusc_map: Any, + center_xy: tuple[float, float], + radius_m: float, + layer_names: list[str], +) -> dict[str, list[str]]: + try: + return nusc_map.get_records_in_radius( + center_xy[0], + center_xy[1], + radius_m, + layer_names, + mode="intersect", + ) + except TypeError: # pragma: no cover - SDK-version compatibility. + return nusc_map.get_records_in_radius( + center_xy[0], + center_xy[1], + radius_m, + layer_names, + ) + + +def _discretize_lanes( + nusc_map: Any, + lane_tokens: list[str], + discretization_m: float, +) -> dict[str, Any]: + try: + return nusc_map.discretize_lanes( + lane_tokens, resolution_meters=discretization_m + ) + except TypeError: # pragma: no cover - SDK-version compatibility. + return nusc_map.discretize_lanes( + lane_tokens, discretization_meters=discretization_m + ) + + +def _extract_polygon_exterior(nusc_map: Any, polygon_token: str) -> list[Any]: + polygon = nusc_map.extract_polygon(polygon_token) + return list(polygon.exterior.coords) + + +def _extract_line_coords(nusc_map: Any, line_token: str) -> list[Any]: + line = nusc_map.extract_line(line_token) + return list(line.coords) + + +def _road_center_from_objects(objects: list[dict[str, Any]]) -> tuple[float, float]: + starts = [] + for obj in objects: + for idx, valid in enumerate(obj["valid"]): + if valid: + pos = obj["position"][idx] + starts.append((pos["x"], pos["y"])) + break + + if not starts: + return 0.0, 0.0 + arr = np.array(starts, dtype=np.float64) + return float(arr[:, 0].mean()), float(arr[:, 1].mean()) + + +def load_nuscenes_map(dataroot: str, map_name: str): + """Load a NuScenes map object.""" + + try: + from nuscenes.map_expansion.map_api import NuScenesMap + except ImportError as exc: # pragma: no cover - depends on local install. + raise ImportError( + "NuScenes map conversion requires `nuscenes-devkit`." + ) from exc + + return NuScenesMap(dataroot=dataroot, map_name=map_name) + + +def get_scene_map_name(nusc: Any, scene: dict[str, Any]) -> str: + log = nusc.get("log", scene["log_token"]) + return log["location"] + + +def extract_roads( + nusc_map: Any, + center_xy: tuple[float, float], + config: NuScenesAdapterConfig, +) -> list[dict[str, Any]]: + """Extract nearby NuScenes map layers as GPUDrive road objects.""" + + layer_names = [ + "lane", + "lane_connector", + "road_divider", + "lane_divider", + "drivable_area", + "ped_crossing", + "stop_line", + ] + records = _get_records_in_radius( + nusc_map, center_xy, config.map_radius_m, layer_names + ) + + roads: list[dict[str, Any]] = [] + road_id = 0 + + lane_tokens = records.get("lane", []) + records.get("lane_connector", []) + if lane_tokens: + lane_paths = _discretize_lanes( + nusc_map, lane_tokens, config.map_discretization_m + ) + for points in lane_paths.values(): + geometry = _coords_to_geometry(points, config.max_geometry_points) + road_id = _add_road( + roads, + geometry, + "lane", + MapElementIds.LANE_SURFACE_STREET, + road_id, + config, + ) + + for layer_name, map_id in [ + ("road_divider", MapElementIds.ROAD_LINE_SOLID_SINGLE_WHITE), + ("lane_divider", MapElementIds.ROAD_LINE_BROKEN_SINGLE_WHITE), + ("stop_line", MapElementIds.ROAD_LINE_UNKNOWN), + ]: + for token in records.get(layer_name, []): + record = nusc_map.get(layer_name, token) + line_token = record.get("line_token") + if not line_token: + continue + geometry = _coords_to_geometry( + _extract_line_coords(nusc_map, line_token), + config.max_geometry_points, + ) + road_id = _add_road( + roads, + geometry, + "road_line", + map_id, + road_id, + config, + ) + + for token in records.get("drivable_area", []): + record = nusc_map.get("drivable_area", token) + for polygon_token in record.get("polygon_tokens", []): + geometry = _coords_to_geometry( + _extract_polygon_exterior(nusc_map, polygon_token), + config.max_geometry_points, + ) + road_id = _add_road( + roads, + geometry, + "road_edge", + MapElementIds.ROAD_EDGE_BOUNDARY, + road_id, + config, + ) + + for token in records.get("ped_crossing", []): + record = nusc_map.get("ped_crossing", token) + polygon_token = record.get("polygon_token") + if not polygon_token: + continue + geometry = _coords_to_geometry( + _extract_polygon_exterior(nusc_map, polygon_token), + config.max_geometry_points, + ) + road_id = _add_road( + roads, + geometry, + "crosswalk", + MapElementIds.CROSSWALK, + road_id, + config, + ) + + return roads + + +def _make_gpudrive_object( + obj_id: int, + gpudrive_type: str, + states: list[dict[str, Any]], + target_times_sec: np.ndarray, + config: NuScenesAdapterConfig, + mark_as_expert: bool = False, +) -> Optional[dict[str, Any]]: + obj = interpolate_track(states, target_times_sec, config) + if obj is None: + return None + + obj["type"] = gpudrive_type + obj["id"] = obj_id + obj["mark_as_expert"] = mark_as_expert + return obj + + +def scene_to_gpudrive_scenarios( + nusc: Any, + scene: dict[str, Any], + dataroot: str, + config: NuScenesAdapterConfig, + file_prefix: str = "tfrecord-nuscenes", + window_stride_sec: Optional[float] = None, + map_cache: Optional[dict[str, Any]] = None, +) -> list[dict[str, Any]]: + """Convert a NuScenes scene into one or more GPUDrive scenario dicts.""" + + samples = collect_scene_samples(nusc, scene) + window_starts = iter_window_starts(samples, config, window_stride_sec) + if not window_starts: + return [] + + annotation_tracks = collect_annotation_tracks(nusc, samples) + ego_track = collect_ego_track(nusc, samples, config) if config.include_ego else [] + map_cache = map_cache if map_cache is not None else {} + scenarios = [] + + for window_idx, window_start in enumerate(window_starts): + target_times = window_start + np.arange(config.num_steps, dtype=np.float64) * config.dt + objects = [] + next_id = 0 + + if config.include_ego: + ego_obj = _make_gpudrive_object( + obj_id=next_id, + gpudrive_type="vehicle", + states=ego_track, + target_times_sec=target_times, + config=config, + ) + if ego_obj is not None: + objects.append(ego_obj) + next_id += 1 + + sorted_tracks = sorted( + annotation_tracks.items(), + key=lambda item: (item[1][0]["time_sec"], item[0]), + ) + for _, states in sorted_tracks: + gpudrive_type = states[0]["gpudrive_type"] + obj = _make_gpudrive_object( + obj_id=next_id, + gpudrive_type=gpudrive_type, + states=states, + target_times_sec=target_times, + config=config, + ) + if obj is None: + continue + objects.append(obj) + next_id += 1 + + if not objects: + continue + + center_xy = _road_center_from_objects(objects) + roads: list[dict[str, Any]] = [] + if config.include_map: + try: + map_name = get_scene_map_name(nusc, scene) + if map_name not in map_cache: + map_cache[map_name] = load_nuscenes_map(dataroot, map_name) + roads = extract_roads(map_cache[map_name], center_xy, config) + except Exception as exc: # pragma: no cover - SDK/data dependent. + if config.fail_on_missing_map: + raise + logging.warning( + "Skipping map extraction for scene %s window %d: %s", + scene.get("name", scene.get("token")), + window_idx, + exc, + ) + + sdc_track_index = 0 if config.include_ego and objects and objects[0]["id"] == 0 else -1 + tracks_to_predict = [ + {"track_index": idx, "difficulty": 0} + for idx, obj in enumerate(objects) + if obj["type"] in {"vehicle", "cyclist", "pedestrian"} + and not obj.get("mark_as_expert", False) + ] + objects_of_interest = [ + obj["id"] + for obj in objects + if obj["type"] in {"vehicle", "cyclist"} + and not obj.get("mark_as_expert", False) + ] + + output_name = f"{file_prefix}_{scene['name']}_{window_idx:03d}.json" + scenarios.append( + { + "name": output_name, + "scenario_id": f"{scene['token'][:24]}_{window_idx:03d}", + "objects": objects, + "roads": roads, + "tl_states": {}, + "metadata": { + "sdc_track_index": sdc_track_index, + "objects_of_interest": objects_of_interest, + "tracks_to_predict": tracks_to_predict, + "source": { + "dataset": "nuscenes", + "scene_name": scene["name"], + "scene_token": scene["token"], + "window_start_sec": float(window_start), + "target_hz": config.target_hz, + "num_steps": config.num_steps, + }, + }, + } + ) + + return scenarios + + +def select_scenes( + nusc: Any, + split: Optional[str] = None, + scene_names: Optional[set[str]] = None, + max_scenes: Optional[int] = None, +) -> list[dict[str, Any]]: + """Select NuScenes scenes by split/name.""" + + selected_names = scene_names + if split: + try: + from nuscenes.utils.splits import create_splits_scenes + except ImportError as exc: # pragma: no cover - depends on local install. + raise ImportError( + "NuScenes split selection requires `nuscenes-devkit`." + ) from exc + + splits = create_splits_scenes() + if split not in splits: + valid = ", ".join(sorted(splits)) + raise ValueError(f"Unknown NuScenes split '{split}'. Valid splits: {valid}") + selected_names = set(splits[split]) + + scenes = [ + scene + for scene in nusc.scene + if selected_names is None or scene["name"] in selected_names + ] + if max_scenes is not None: + scenes = scenes[:max_scenes] + return scenes + + +def convert_nuscenes_dataset( + dataroot: str, + version: str, + output_dir: str | Path, + config: NuScenesAdapterConfig, + split: Optional[str] = None, + scene_names: Optional[set[str]] = None, + max_scenes: Optional[int] = None, + file_prefix: str = "tfrecord-nuscenes", + window_stride_sec: Optional[float] = None, + verbose: bool = True, +) -> dict[str, int]: + """Convert NuScenes scenes and write GPUDrive JSON files.""" + + nusc = load_nuscenes(version=version, dataroot=dataroot, verbose=verbose) + scenes = select_scenes( + nusc, + split=split, + scene_names=scene_names, + max_scenes=max_scenes, + ) + + output_path = Path(output_dir) + output_path.mkdir(parents=True, exist_ok=True) + map_cache: dict[str, Any] = {} + written = 0 + skipped = 0 + + for scene in scenes: + scenarios = scene_to_gpudrive_scenarios( + nusc=nusc, + scene=scene, + dataroot=dataroot, + config=config, + file_prefix=file_prefix, + window_stride_sec=window_stride_sec, + map_cache=map_cache, + ) + if not scenarios: + skipped += 1 + continue + + for scenario in scenarios: + with (output_path / scenario["name"]).open("w", encoding="utf-8") as f: + json.dump(scenario, f) + written += 1 + + return { + "scenes_selected": len(scenes), + "scenes_without_outputs": skipped, + "files_written": written, + } diff --git a/gpudrive-main/data_utils/post_processing.py b/gpudrive-main/data_utils/post_processing.py new file mode 100644 index 0000000000000000000000000000000000000000..94d3ccea8c962954a6e4c6d3919aba97923bde24 --- /dev/null +++ b/gpudrive-main/data_utils/post_processing.py @@ -0,0 +1,214 @@ +import argparse +import json +import shutil +from pathlib import Path +from multiprocessing import Pool, cpu_count +import tqdm + +def is_valid_json_structure(file_path): + """Check if the JSON file has the expected structure.""" + try: + with open(file_path, "r") as file: + data = json.load(file) + + # Check if required keys exist in the top-level structure + if not all(key in data for key in ["name", "objects", "roads", "tl_states"]): + return False + + # Check if "objects" is a list and contains dictionaries with the correct structure + if not isinstance(data["objects"], list) or not all( + isinstance(obj, dict) and "position" in obj and "type" in obj + for obj in data["objects"] + ): + return False + + # Check if "roads" is a list of dictionaries with required "geometry" + if not isinstance(data["roads"], list) or not all( + isinstance(road, dict) and "geometry" in road + for road in data["roads"] + ): + return False + + # Check that each "geometry" in "roads" has valid "x" and "y" coordinates + for road in data["roads"]: + if not all( + isinstance(geo, dict) and "x" in geo and "y" in geo + for geo in road.get("geometry", []) + ): + return False + + return True + except (json.JSONDecodeError, ValueError, IOError): + return False + +def process_file(args): + """ + Validate JSON file and handle it according to the operation mode. + + Args: + args (tuple): (source_path, target_dir, should_move) + - source_path: Path to the source file + - target_dir: Path to target directory (if moving) + - should_move: Boolean indicating if file should be moved if valid + Returns: + tuple: (str, bool) - (file path, whether file was valid) + """ + source_path, target_dir, should_move = args + + # First validate the JSON + if not is_valid_json_structure(source_path): + try: + source_path.unlink() # Delete invalid file + return str(source_path), False + except Exception as e: + print(f"Error deleting invalid file {source_path}: {e}") + return str(source_path), False + + # If valid and should_move is True, move the file + if should_move and target_dir: + try: + target_path = Path(target_dir) / source_path.name + shutil.move(str(source_path), str(target_path)) + except Exception as e: + print(f"Error moving file {source_path}: {e}") + return str(source_path), False + + return str(source_path), True + +def process_directory(dataset_dir, num_workers=None): + """ + Process all JSON files in a directory, automatically handling group extraction if needed. + + Args: + dataset_dir (str): Path to the dataset directory + num_workers (int, optional): Number of processes to use. Defaults to CPU count. + Returns: + tuple: (int, int) - (valid_files, invalid_files) + """ + dataset_path = Path(dataset_dir) + + if not dataset_path.is_dir(): + print(f"Directory {dataset_dir} does not exist, skipping...") + return 0, 0 + + # Check for group directories + group_dirs = [d for d in dataset_path.iterdir() + if d.is_dir() and d.name.startswith("group_")] + + # Collect all files that need to be processed + all_files = [] + + if group_dirs: + # Found group directories - will extract files from them + print(f"\nFound {len(group_dirs)} group directories in {dataset_dir}") + for group_dir in sorted(group_dirs): + files = list(group_dir.glob("*.json")) + all_files.extend([(file, dataset_path, True) for file in files]) + + # Always check for JSON files in the main directory as well + main_dir_files = [f for f in dataset_path.glob("*.json") + if not any(g.name in str(f) for g in group_dirs)] + all_files.extend([(file, None, False) for file in main_dir_files]) + + total_files = len(all_files) + if total_files == 0: + print(f"No JSON files found in {dataset_dir}") + return 0, 0 + + print(f"Total files to process: {total_files}") + + # Use all available CPUs if num_workers is not specified + if num_workers is None: + num_workers = cpu_count() + + # Track statistics + valid_files = 0 + invalid_files = 0 + + # Create a pool of workers and process files in parallel + with Pool(processes=num_workers) as pool: + # Use tqdm to show progress bar + results = list(tqdm.tqdm( + pool.imap_unordered(process_file, all_files), + total=total_files, + desc=f"Processing files from {dataset_dir}" + )) + + # Count valid and invalid files + for _, is_valid in results: + if is_valid: + valid_files += 1 + else: + invalid_files += 1 + + # If we found group directories, try to remove empty ones after processing + if group_dirs: + for group_dir in group_dirs: + try: + group_dir.rmdir() + except OSError: + print(f"Warning: Could not remove directory {group_dir} - it may not be empty") + + print(f"\nCompleted processing {dataset_dir}") + print(f"Valid files: {valid_files}") + print(f"Invalid files deleted: {invalid_files}") + + return valid_files, invalid_files + +def process_all_directories(num_workers=None): + """Process all dataset directories (training, testing, validation).""" + directories = [ + "data/processed/training", + "data/processed/testing", + "data/processed/validation" + ] + + total_valid = 0 + total_invalid = 0 + + for directory in directories: + print(f"\nProcessing directory: {directory}") + valid, invalid = process_directory(directory, num_workers) + total_valid += valid + total_invalid += invalid + + print("\nOverall Statistics:") + print(f"Total valid files across all directories: {total_valid}") + print(f"Total invalid files deleted: {total_invalid}") + print(f"Total files processed: {total_valid + total_invalid}") + +def main(): + parser = argparse.ArgumentParser( + description="Process JSON files in dataset directories, validating their structure and " + "automatically extracting from group directories if they exist. " + "Invalid files are deleted. " + 'Use "all" to process training, testing, and validation directories.' + ) + parser.add_argument( + "dataset_dir", + nargs="?", + default="all", + help='Path to the dataset directory or "all" for processing all directories' + ) + parser.add_argument( + "--num_workers", + type=int, + help="Number of processes to use (defaults to number of CPU cores)", + default=cpu_count() + ) + + args = parser.parse_args() + + try: + if args.dataset_dir.lower() == "all": + process_all_directories(args.num_workers) + else: + process_directory(args.dataset_dir, args.num_workers) + except Exception as e: + print(f"Error: {e}") + return 1 + + return 0 + +if __name__ == "__main__": + exit(main()) diff --git a/gpudrive-main/data_utils/process_nuscenes_files.py b/gpudrive-main/data_utils/process_nuscenes_files.py new file mode 100644 index 0000000000000000000000000000000000000000..90b66488606ebaad0ff8d6eb8b3bcf93e4345b66 --- /dev/null +++ b/gpudrive-main/data_utils/process_nuscenes_files.py @@ -0,0 +1,191 @@ +"""Convert NuScenes scenes to GPUDrive-compatible JSON files.""" + +from __future__ import annotations + +import argparse +import logging + +try: + from .nuscenes_adapter import ( + NuScenesAdapterConfig, + convert_nuscenes_dataset, + ) +except ImportError: # pragma: no cover - supports running as a script. + from nuscenes_adapter import NuScenesAdapterConfig, convert_nuscenes_dataset + + +def _parse_scene_names(value: str | None) -> set[str] | None: + if not value: + return None + return {item.strip() for item in value.split(",") if item.strip()} + + +def build_arg_parser() -> argparse.ArgumentParser: + parser = argparse.ArgumentParser( + description=( + "Convert NuScenes scenes into the Waymo-style JSON schema consumed " + "by GPUDrive. Trajectories are interpolated to a fixed 10 Hz, " + "91-step grid by default." + ) + ) + parser.add_argument( + "--dataroot", + required=True, + help="Path to the NuScenes dataset root.", + ) + parser.add_argument( + "--version", + default="v1.0-mini", + help="NuScenes version, e.g. v1.0-mini or v1.0-trainval.", + ) + parser.add_argument( + "--output-dir", + default="data/processed/nuscenes", + help="Directory where GPUDrive JSON files will be written.", + ) + parser.add_argument( + "--split", + default=None, + help="Optional NuScenes split name, e.g. mini_train, train, val.", + ) + parser.add_argument( + "--scene-names", + default=None, + help="Optional comma-separated scene names, e.g. scene-0061,scene-0103.", + ) + parser.add_argument( + "--max-scenes", + type=int, + default=None, + help="Optional cap on the number of scenes to convert.", + ) + parser.add_argument( + "--file-prefix", + default="tfrecord-nuscenes", + help=( + "Output file prefix. Keep the default if you want SceneDataLoader's " + "default file_prefix='tfrecord' to discover the files." + ), + ) + parser.add_argument( + "--target-hz", + type=float, + default=10.0, + help="Target interpolation rate in Hz.", + ) + parser.add_argument( + "--num-steps", + type=int, + default=91, + help="Number of timesteps per generated GPUDrive scenario.", + ) + parser.add_argument( + "--max-annotation-gap-sec", + type=float, + default=1.1, + help="Do not interpolate across raw annotation gaps larger than this.", + ) + parser.add_argument( + "--window-stride-sec", + type=float, + default=None, + help=( + "If set, export multiple 9-second windows per scene using this " + "stride. If omitted, only the first full window is exported." + ), + ) + parser.add_argument( + "--allow-partial-window", + action="store_true", + help="Allow windows that extend beyond the final NuScenes keyframe.", + ) + parser.add_argument( + "--no-ego", + action="store_true", + help="Do not synthesize an ego vehicle from NuScenes ego poses.", + ) + parser.add_argument( + "--no-map", + action="store_true", + help="Do not export NuScenes map layers.", + ) + parser.add_argument( + "--fail-on-missing-map", + action="store_true", + help="Fail conversion if map extraction fails for a scene.", + ) + parser.add_argument( + "--map-radius-m", + type=float, + default=120.0, + help="Radius around the scenario center for extracting map features.", + ) + parser.add_argument( + "--map-discretization-m", + type=float, + default=1.0, + help="Lane centerline discretization spacing in meters.", + ) + parser.add_argument( + "--max-road-elements", + type=int, + default=900, + help="Maximum road records to write per scenario.", + ) + parser.add_argument( + "--max-geometry-points", + type=int, + default=512, + help="Maximum points to keep per road geometry.", + ) + parser.add_argument( + "--quiet", + action="store_true", + help="Reduce NuScenes SDK output.", + ) + return parser + + +def main() -> int: + logging.basicConfig(level=logging.INFO) + parser = build_arg_parser() + args = parser.parse_args() + + config = NuScenesAdapterConfig( + target_hz=args.target_hz, + num_steps=args.num_steps, + max_annotation_gap_sec=args.max_annotation_gap_sec, + require_full_window=not args.allow_partial_window, + include_ego=not args.no_ego, + include_map=not args.no_map, + fail_on_missing_map=args.fail_on_missing_map, + map_radius_m=args.map_radius_m, + map_discretization_m=args.map_discretization_m, + max_road_elements=args.max_road_elements, + max_geometry_points=args.max_geometry_points, + ) + + stats = convert_nuscenes_dataset( + dataroot=args.dataroot, + version=args.version, + output_dir=args.output_dir, + config=config, + split=args.split, + scene_names=_parse_scene_names(args.scene_names), + max_scenes=args.max_scenes, + file_prefix=args.file_prefix, + window_stride_sec=args.window_stride_sec, + verbose=not args.quiet, + ) + + logging.info( + "NuScenes conversion complete: selected=%d, skipped=%d, written=%d", + stats["scenes_selected"], + stats["scenes_without_outputs"], + stats["files_written"], + ) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/gpudrive-main/data_utils/process_waymo_files.py b/gpudrive-main/data_utils/process_waymo_files.py new file mode 100644 index 0000000000000000000000000000000000000000..6428099b1afc2db43baa8283656c7b2820f7a37f --- /dev/null +++ b/gpudrive-main/data_utils/process_waymo_files.py @@ -0,0 +1,701 @@ +""" +Convert Waymo Open Dataset TFRecord files to JSON format. +See https://waymo.com/open/data/motion/tfexample for the tfrecord structure; and +https://github.com/waymo-research/waymo-open-dataset/blob/master/src/waymo_open_dataset/protos/map.proto +https://github.com/waymo-research/waymo-open-dataset/blob/master/src/waymo_open_dataset/protos/scenario.proto +for the proto structure. +""" +from collections import defaultdict +import os +import json +import argparse +import logging +import psutil +from pathlib import Path +import warnings +from typing import Any, Dict, Optional, List +from pdb import set_trace as T +from tqdm import tqdm +from waymo_open_dataset.protos import scenario_pb2, map_pb2 +from datatypes import MapElementIds +import trimesh +from multiprocessing import Pool, cpu_count +import numpy as np + +# To filter out warnings before tensorflow is imported +os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2" +import tensorflow as tf + +warnings.filterwarnings("ignore") +logging.getLogger("tensorflow").setLevel(logging.ERROR) +logging.basicConfig(level=logging.INFO) + + +def wrap_yaws(yaws): + """Wraps yaw angles between pi and -pi radians.""" + return (yaws + np.pi) % (2 * np.pi) - np.pi + + +ERR_VAL = -1e4 + +_WAYMO_OBJECT_STR = { + scenario_pb2.Track.TYPE_UNSET: "unset", + scenario_pb2.Track.TYPE_VEHICLE: "vehicle", + scenario_pb2.Track.TYPE_PEDESTRIAN: "pedestrian", + scenario_pb2.Track.TYPE_CYCLIST: "cyclist", + scenario_pb2.Track.TYPE_OTHER: "other", +} + +_WAYMO_ROAD_STR = { + map_pb2.TrafficSignalLaneState.LANE_STATE_UNKNOWN: "unknown", + map_pb2.TrafficSignalLaneState.LANE_STATE_ARROW_STOP: "arrow_stop", + map_pb2.TrafficSignalLaneState.LANE_STATE_ARROW_CAUTION: "arrow_caution", + map_pb2.TrafficSignalLaneState.LANE_STATE_ARROW_GO: "arrow_go", + map_pb2.TrafficSignalLaneState.LANE_STATE_STOP: "stop", + map_pb2.TrafficSignalLaneState.LANE_STATE_CAUTION: "caution", + map_pb2.TrafficSignalLaneState.LANE_STATE_GO: "go", + map_pb2.TrafficSignalLaneState.LANE_STATE_FLASHING_STOP: "flashing_stop", + map_pb2.TrafficSignalLaneState.LANE_STATE_FLASHING_CAUTION: "flashing_caution", +} + +_WAYMO_LANE_TYPES = { + map_pb2.LaneCenter.TYPE_UNDEFINED: MapElementIds.LANE_UNDEFINED, + map_pb2.LaneCenter.TYPE_FREEWAY: MapElementIds.LANE_FREEWAY, + map_pb2.LaneCenter.TYPE_SURFACE_STREET: MapElementIds.LANE_SURFACE_STREET, + map_pb2.LaneCenter.TYPE_BIKE_LANE: MapElementIds.LANE_BIKE_LANE, +} + +_WAYMO_ROAD_LINE_TYPES = { + map_pb2.RoadLine.TYPE_UNKNOWN: MapElementIds.ROAD_LINE_UNKNOWN, + map_pb2.RoadLine.TYPE_BROKEN_SINGLE_WHITE: MapElementIds.ROAD_LINE_BROKEN_SINGLE_WHITE, + map_pb2.RoadLine.TYPE_SOLID_SINGLE_WHITE: MapElementIds.ROAD_LINE_SOLID_SINGLE_WHITE, + map_pb2.RoadLine.TYPE_SOLID_DOUBLE_WHITE: MapElementIds.ROAD_LINE_SOLID_DOUBLE_WHITE, + map_pb2.RoadLine.TYPE_BROKEN_SINGLE_YELLOW: MapElementIds.ROAD_LINE_BROKEN_SINGLE_YELLOW, + map_pb2.RoadLine.TYPE_BROKEN_DOUBLE_YELLOW: MapElementIds.ROAD_LINE_BROKEN_DOUBLE_YELLOW, + map_pb2.RoadLine.TYPE_SOLID_SINGLE_YELLOW: MapElementIds.ROAD_LINE_SOLID_SINGLE_YELLOW, + map_pb2.RoadLine.TYPE_SOLID_DOUBLE_YELLOW: MapElementIds.ROAD_LINE_SOLID_DOUBLE_YELLOW, + map_pb2.RoadLine.TYPE_PASSING_DOUBLE_YELLOW: MapElementIds.ROAD_LINE_PASSING_DOUBLE_YELLOW, +} + +_WAYMO_ROAD_EDGE_TYPES = { + map_pb2.RoadEdge.TYPE_UNKNOWN: MapElementIds.ROAD_EDGE_UNKNOWN, + map_pb2.RoadEdge.TYPE_ROAD_EDGE_BOUNDARY: MapElementIds.ROAD_EDGE_BOUNDARY, + map_pb2.RoadEdge.TYPE_ROAD_EDGE_MEDIAN: MapElementIds.ROAD_EDGE_MEDIAN, +} + + +def feature_class_to_map_id(map_feature): + """ + Converts the map feature types defined in the proto to the ones + defined in the datatypes.py, to ensure consistency with Waymax. + """ + if map_feature.HasField("lane"): + map_element_id = _WAYMO_LANE_TYPES.get(map_feature.lane.type) + elif map_feature.HasField("road_line"): + map_element_id = _WAYMO_ROAD_LINE_TYPES.get(map_feature.road_line.type) + elif map_feature.HasField("road_edge"): + map_element_id = _WAYMO_ROAD_EDGE_TYPES.get(map_feature.road_edge.type) + elif map_feature.HasField("stop_sign"): + map_element_id = MapElementIds.STOP_SIGN + elif map_feature.HasField("crosswalk"): + map_element_id = MapElementIds.CROSSWALK + elif map_feature.HasField("speed_bump"): + map_element_id = MapElementIds.SPEED_BUMP + # New in WOMD v1.2.0: Driveway entrances + elif map_feature.HasField("driveway"): + map_element_id = MapElementIds.DRIVEWAY + else: + map_element_id = MapElementIds.UNKNOWN + + return int(map_element_id) + + +def _parse_object_state( + states: scenario_pb2.ObjectState, final_state: scenario_pb2.ObjectState +) -> Dict[str, Any]: + """Construct a dict representing the trajectory and goals of an object. + + Args: + states (scenario_pb2.ObjectState): Protobuf of object state + final_state (scenario_pb2.ObjectState): Protobuf of last valid object state. + + Returns + ------- + Dict[str, Any]: Dict representing an object. + """ + return { + "position": [ + {"x": state.center_x, "y": state.center_y, "z": state.center_z} + if state.valid + else {"x": ERR_VAL, "y": ERR_VAL, "z": ERR_VAL} + for state in states + ], + "width": final_state.width, + "length": final_state.length, + "height": final_state.height, + "heading": [ # In radians between [-pi, pi] + (state.heading + np.pi) % (2 * np.pi) - np.pi if state.valid else ERR_VAL + for state in states + ], + "velocity": [ + {"x": state.velocity_x, "y": state.velocity_y} + if state.valid + else {"x": ERR_VAL, "y": ERR_VAL} + for state in states + ], + "valid": [state.valid for state in states], + "goalPosition": { + "x": final_state.center_x, + "y": final_state.center_y, + "z": final_state.center_z, + }, + } + + +def _init_tl_object(mapstate: scenario_pb2.DynamicMapState) -> Dict[int, Any]: + """Construct a dict representing the traffic light states. + + Args: + mapstate (scenario_pb2.DynamicMapState) : protobuf of map state (traffic lights) + + Returns: + Dict[int, Any] : Dict representing map state + """ + returned_dict = {} + for lane_state in mapstate.lane_states: + returned_dict[lane_state.lane] = { + "state": _WAYMO_ROAD_STR[lane_state.state], + "x": lane_state.stop_point.x, + "y": lane_state.stop_point.y, + "z": lane_state.stop_point.z, + } + return returned_dict + + +def _init_object(track: scenario_pb2.Track) -> Optional[Dict[str, Any]]: + """Construct a dict representing the state of the object (vehicle, cyclist, pedestrian). + + Args: + track (scenario_pb2.Track): protobuf representing the scenario + + Returns + ------- + Optional[Dict[str, Any]]: dict representing the trajectory and velocity of an object. + """ + final_valid_index = 0 + for i, state in enumerate(track.states): + if state.valid: + final_valid_index = i + + obj = _parse_object_state(track.states, track.states[final_valid_index]) + obj["type"] = _WAYMO_OBJECT_STR[track.object_type] + obj["id"] = track.id + return obj + + +def _init_road(map_feature: map_pb2.MapFeature) -> Optional[Dict[str, Any]]: + """Convert an element of the map protobuf to a dict representing its coordinates and type.""" + feature = map_feature.WhichOneof("feature_data") + if feature == "stop_sign": + p = getattr( + map_feature, map_feature.WhichOneof("feature_data") + ).position + geometry = [{"x": p.x, "y": p.y, "z": p.z}] + elif ( + feature != "crosswalk" + and feature != "speed_bump" + and feature != "driveway" + ): # For road points + geometry = [ + {"x": p.x, "y": p.y, "z": p.z} + for p in getattr( + map_feature, map_feature.WhichOneof("feature_data") + ).polyline + ] + else: + geometry = [ + {"x": p.x, "y": p.y, "z": p.z} + for p in getattr( + map_feature, map_feature.WhichOneof("feature_data") + ).polygon + ] + return { + "geometry": geometry, + "type": map_feature.WhichOneof("feature_data"), + "map_element_id": feature_class_to_map_id(map_feature), + "id": map_feature.id, + } + + +# Meshes for collision checking +def _filter_small_segments(segments, min_length=1e-6): + """Filter out segments that are too short.""" + valid_segments = [] + for segment in segments: + start, end = segment + length = np.linalg.norm(np.array(end) - np.array(start)) + if length >= min_length: + valid_segments.append(segment) + return valid_segments + + +def _generate_mesh(segments, height=2.0, width=0.2): + segments = np.array(segments, dtype=np.float64) + starts, ends = segments[:, 0, :], segments[:, 1, :] + directions = ends - starts + lengths = np.linalg.norm(directions, axis=1, keepdims=True) + unit_directions = directions / lengths + + # Create the base box mesh with the height along the z-axis + base_box = trimesh.creation.box(extents=[1.0, width, height]) + base_box.apply_translation([0.5, 0, 0]) # Align box's origin to its start + z_axis = np.array([0, 0, 1]) + angles = np.arctan2( + unit_directions[:, 1], unit_directions[:, 0] + ) # Rotation in the XY plane + + rectangles = [] + lengths = lengths.flatten() + + for i, (start, length, angle) in enumerate(zip(starts, lengths, angles)): + # Copy the base box and scale to match segment length + scaled_box = base_box.copy() + scaled_box.apply_scale([length, 1.0, 1.0]) + + # Apply rotation around the z-axis + rotation_matrix = trimesh.transformations.rotation_matrix( + angle, z_axis + ) + scaled_box.apply_transform(rotation_matrix) + + # Translate the box to the segment's starting point + scaled_box.apply_translation(start) + + rectangles.append(scaled_box) + + # Concatenate all boxes into a single mesh + mesh = trimesh.util.concatenate(rectangles) + return mesh + + +def _create_agent_box_mesh(position, heading, length, width, height): + """Create a box mesh for an agent at a given position and orientation. + + Args: + position (list): [x, y, z] position + heading (float): yaw angle in radians + length (float): length of the box + width (float): width of the box + height (float): height of the box + + Returns: + trimesh.Trimesh: Box mesh positioned and oriented correctly + """ + # Create box centered at origin + box = trimesh.creation.box(extents=[length, width, height]) + + # Rotate box to align with heading + z_axis = np.array([0, 0, 1]) + rotation_matrix = trimesh.transformations.rotation_matrix(heading, z_axis) + box.apply_transform(rotation_matrix) + + # Move box to position + box.apply_translation(position) + + return box + + +def waymo_to_scenario( + scenario_path: str, protobuf: scenario_pb2.Scenario +) -> None: + """Dump a JSON File containing the protobuf parsed into the right format. + See https://waymo.com/open/data/motion/tfexample for the tfrecord structure. + + Args + ---- + scenario_path (str): path to dump the json file + protobuf (scenario_pb2.Scenario): the protobuf we are converting + no_tl (bool, optional): If true, environments with traffic lights are not dumped. + """ + # read the protobuf file to get the right state + # write the json file + # construct the road geometries + # place the initial position of the vehicles + + # Get unique ID string for a scenario + scenario_id = protobuf.scenario_id + + # Construct the traffic light states + tl_dict = defaultdict( + lambda: {"state": [], "x": [], "y": [], "z": [], "time_index": []} + ) + all_keys = ["state", "x", "y", "z"] + i = 0 + for dynamic_map_state in protobuf.dynamic_map_states: + traffic_light_dict = _init_tl_object(dynamic_map_state) + # there is a traffic light but we don't want traffic light scenes so just return + if len(traffic_light_dict) > 0: + return + for id, value in traffic_light_dict.items(): + for key in all_keys: + tl_dict[id][key].append(value[key]) + tl_dict[id]["time_index"].append(i) + i += 1 + + # Construct the map states + roads = [] + edge_points = [] + edge_segments = [] + + for map_feature in protobuf.map_features: + road = _init_road(map_feature) + if road is not None: + roads.append(road) + if road["type"] == "road_edge": + # Collect points for 3D structure detection + edge_vertices = [[r["x"], r["y"], r["z"]] for r in road["geometry"]] + edge_points.extend(edge_vertices) + # Collect edge segments for collision checking + edge_segments.extend([ + [edge_vertices[i], edge_vertices[i + 1]] + for i in range(len(edge_vertices) - 1) + ]) + + # Check for 3D structures + if len(edge_points) > 0: + edge_points = np.array(edge_points) + if len(edge_points) > 0: + # Calculate pairwise distances in xy plane efficiently + xy_points = edge_points[:, :2] + # Use broadcasting for memory efficiency + tolerance = 0.2 + has_3d = False + + # Process in chunks to avoid memory issues + chunk_size = 1000 + for i in range(0, len(xy_points), chunk_size): + chunk = xy_points[i:i + chunk_size] + # Calculate distances between current chunk and all points + dists = np.linalg.norm(chunk[:, np.newaxis] - xy_points, axis=2) + potential_pairs = np.where((dists < tolerance) & (dists > 0)) + + # Check z-values for identified pairs + for p1, p2 in zip(*potential_pairs): + p1_idx = i + p1 # Adjust index for chunking + if abs(edge_points[p1_idx, 2] - edge_points[p2, 2]) > tolerance: + has_3d = True + break + + if has_3d: + break + + # Skip this scenario if it has 3D structures + if has_3d: + return + + # Construct road edges for collision checking + edge_segments = _filter_small_segments(edge_segments) + edge_mesh = _generate_mesh(edge_segments) + + # Create collision managers + road_collision_manager = trimesh.collision.CollisionManager() + road_collision_manager.add_object("road_edges", edge_mesh) + agent_collision_manager = trimesh.collision.CollisionManager() + trajectory_collision_manager = trimesh.collision.CollisionManager() + + + # Construct object states + objects = [] + for track in protobuf.tracks: + obj = _init_object(track) + if obj is not None: + if obj["type"] not in ["vehicle", "cyclist"]: + obj["mark_as_expert"] = False + objects.append(obj) + continue + + # Find first valid position + first_valid_idx = next((i for i, valid in enumerate(obj["valid"]) if valid), None) + if first_valid_idx is not None: + # Create agent at initial position + initial_pos = [ + obj["position"][first_valid_idx]["x"], + obj["position"][first_valid_idx]["y"], + obj["position"][first_valid_idx]["z"] + ] + initial_heading = obj["heading"][first_valid_idx] + initial_box = _create_agent_box_mesh( + initial_pos, + initial_heading, + obj["length"], + obj["width"], + obj["height"] + ) + agent_collision_manager.add_object(str(obj["id"]), initial_box) + + # Create trajectory mesh + if False in obj["valid"]: + # Create trajectory segments of only valid positions + trajectory_segments = [] + for i in range(len(obj["position"]) - 1): + if obj["valid"][i] and obj["valid"][i + 1]: + trajectory_segments.append( + [ + [ + obj["position"][i]["x"], + obj["position"][i]["y"], + obj["position"][i]["z"], + ], + [ + obj["position"][i + 1]["x"], + obj["position"][i + 1]["y"], + obj["position"][i + 1]["z"], + ], + ] + ) + else: + obj_vertices = [ + [pos["x"], pos["y"], pos["z"]] for pos in obj["position"] + ] + trajectory_segments = [ + [obj_vertices[i], obj_vertices[i + 1]] + for i in range(len(obj_vertices) - 1) + ] + + trajectory_segments = _filter_small_segments(trajectory_segments) + if len(trajectory_segments) > 0: + trajectory_mesh = _generate_mesh(trajectory_segments) + trajectory_collision_manager.add_object(str(obj["id"]), trajectory_mesh) + + objects.append(obj) + + # Check collisions between all init agent positions + _, agent_collision_pairs = agent_collision_manager.in_collision_internal(return_names=True) + + # Check collisions between init agent positions and road edges + _, road_collision_pairs = agent_collision_manager.in_collision_other( + road_collision_manager, return_names=True + ) + + # Check trajectory collisions with road edges + _, trajectory_collision_pairs = trajectory_collision_manager.in_collision_other( + road_collision_manager, return_names=True + ) + + # Create sets of colliding agent IDs + colliding_agents = set() + + # Add agents that collide with each other at first step + for agent1, agent2 in agent_collision_pairs: + colliding_agents.add(agent1) + colliding_agents.add(agent2) + + # Add agents that collide with road edges + road_colliding_agents = set(agent_id for agent_id, _ in road_collision_pairs) + colliding_agents.update(road_colliding_agents) + + # Add agents whose trajectories collide with road edges + trajectory_colliding_agents = set(agent_id for agent_id, _ in trajectory_collision_pairs) + colliding_agents.update(trajectory_colliding_agents) + + # Update mark_as_expert based on initial collisions + for index, obj in enumerate(objects): + if obj["type"] in ["vehicle", "cyclist"]: + if str(obj["id"]) in colliding_agents: + objects[index]["mark_as_expert"] = True + else: + objects[index]["mark_as_expert"] = False + + # Parse metadata + sdc_track_index = protobuf.sdc_track_index + objects_of_interest = list(protobuf.objects_of_interest) + tracks_to_predict = [ + { + "track_index": track.track_index, + "difficulty": track.difficulty + } + for track in protobuf.tracks_to_predict +] + metadata = { + "sdc_track_index" : sdc_track_index, + "objects_of_interest" : objects_of_interest, + "tracks_to_predict" : tracks_to_predict + } + + scenario_dict = { + "name": scenario_path.split("/")[-1], + "scenario_id": scenario_id, + "objects": objects, + "roads": roads, + "tl_states": tl_dict, + "metadata": metadata + } + + with open(scenario_path, "w") as f: + json.dump(scenario_dict, f) + + +def as_proto_iterator(tf_dataset): + """Parse the tfrecord dataset into a protobuf format.""" + for tfrecord in tf_dataset: + # Parse the scenario protobuf + scene_proto = scenario_pb2.Scenario() + scene_proto.ParseFromString(bytes(tfrecord.numpy())) + yield scene_proto + + +def process_scene(args): + scene_proto, output_dir, file_prefix, scene_count, id_as_filename = args + try: + scenario_id = scene_proto.scenario_id + file_suffix = ( + f"{scenario_id}.json" if id_as_filename else f"{scene_count}.json" + ) + waymo_to_scenario( + scenario_path=os.path.join( + output_dir, f"{file_prefix}{file_suffix}" + ), + protobuf=scene_proto, + ) + except Exception as e: + logging.error( + f"Error processing scene {file_prefix}{scene_count}: {e}" + ) + + +# Scenario-level parallelization +def process_file(args): + """Process a single TFRecord file.""" + filename, output_dir, id_as_filename, num_workers = args + + # Read the records in batches + mem_info = psutil.virtual_memory() + available_memory = mem_info.available / (1024**3) + usable_memory = int(available_memory * 0.9) + # 10 scenes take 1 Gb at max + batch_size = 12 * usable_memory + tfrecord_dataset = tf.data.TFRecordDataset(filename, compression_type="") + tf_dataset_iter = as_proto_iterator(tfrecord_dataset) + + scene_count = 0 + file_prefix = f"{str(filename).split('.')[-1]}_" + scene_batch = [] + + for scene_proto in tf_dataset_iter: + scene_batch.append((scene_proto, scene_count)) + scene_count += 1 + if len(scene_batch) == batch_size: + # Process the batch + with Pool(num_workers) as pool: + pool.map( + process_scene, + [ + ( + scene_proto, + output_dir, + file_prefix, + count, + id_as_filename, + ) + for scene_proto, count in scene_batch + ], + ) + scene_batch = [] + + # Process any remaining scenes + if scene_batch: + with Pool(num_workers) as pool: + pool.map( + process_scene, + [ + ( + scene_proto, + output_dir, + file_prefix, + count, + id_as_filename, + ) + for scene_proto, count in scene_batch + ], + ) + + +def process_data(args): + + if args.dataset == "all": + datasets = ["training", "validation", "testing"] + elif args.dataset == "training": + datasets = ["training"] + elif args.dataset == "validation": + datasets = ["validation"] + elif args.dataset == "testing": + datasets = ["testing"] + else: + raise ValueError( + "Invalid dataset name. Must be one of: 'all', 'training', 'validation', or 'testing'" + ) + + for dataset in datasets: + + input_dir = os.path.join(args.tfrecord_dir, dataset) + output_dir = os.path.join(args.output_dir, dataset) + + if not os.path.exists(output_dir): + os.makedirs(output_dir) + + filenames = [ + p for p in Path(input_dir).iterdir() if "tfrecord" in p.suffix + ] + + assert len(filenames) > 0, f"No TFRecords found in {input_dir}" + + logging.info( + f"Processing {dataset} data. Found {len(filenames)} files. \n \n" + ) + # Process the files one at a time + for filename in tqdm(filenames, unit="file"): + process_file( + ( + str(filename), + output_dir, + args.id_as_filename, + args.num_workers, + ) + ) + logging.info("Done!") + + +if __name__ == "__main__": + + parser = argparse.ArgumentParser( + description="Convert TFRecord files to JSON. \ + Note: This takes about 45 seconds per tfrecord file (=500 traffic scenes)." + ) + parser.add_argument( + "tfrecord_dir", help="Path to the directory containing TFRecord files" + ) + parser.add_argument( + "output_dir", + help="Directory where JSON files will be saved", + ) + parser.add_argument( + "dataset", + type=str, + help="Dataset to process: training, validation, testing, or all", + ) + parser.add_argument( + "--id_as_filename", + default=False, + action="store_true", + help="Use the unique scenario id as the filename", + ) + parser.add_argument( + "--num_workers", + type=int, + default=cpu_count(), + help="Number of worker processes to use", + ) + + args = parser.parse_args() + + process_data(args) diff --git a/gpudrive-main/debug.log b/gpudrive-main/debug.log new file mode 100644 index 0000000000000000000000000000000000000000..a17f8b119c7470809b041920f5c7bece8efc4a0a --- /dev/null +++ b/gpudrive-main/debug.log @@ -0,0 +1,27 @@ +[0527/161110.983:ERROR:third_party\crashpad\crashpad\util\win\registration_protocol_win.cc:108] CreateFile: 系统找不到指定的文件。 (0x2) +[0527/161111.134:ERROR:third_party\crashpad\crashpad\util\win\registration_protocol_win.cc:108] CreateFile: 系统找不到指定的文件。 (0x2) +[0527/161111.376:ERROR:third_party\crashpad\crashpad\util\win\registration_protocol_win.cc:108] CreateFile: 系统找不到指定的文件。 (0x2) +[0527/161111.525:ERROR:third_party\crashpad\crashpad\util\win\registration_protocol_win.cc:108] CreateFile: 系统找不到指定的文件。 (0x2) +[0527/161111.655:ERROR:third_party\crashpad\crashpad\util\win\registration_protocol_win.cc:108] CreateFile: 系统找不到指定的文件。 (0x2) 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0000000000000000000000000000000000000000..9b73f3ebac0548022768a9e094b65d81fc7ade57 --- /dev/null +++ b/gpudrive-main/environment.yml @@ -0,0 +1,70 @@ +name: gpudrive +channels: + - defaults +dependencies: + - bzip2=1.0.8 + - ca-certificates=2024.3.11 + - libffi=3.4.4 + - libuuid=1.41.5 + - ncurses=6.4 + - openssl=3.0.13 + - pip=23.3.1 + - python=3.11.8 + - readline=8.2 + - setuptools=68.2.2 + - sqlite=3.41.2 + - tk=8.6.12 + - tzdata=2024a + - wheel=0.41.2 + - xz=5.4.6 + - zlib=1.2.13 + - pip: + - build==1.2.1 + - cachecontrol==0.14.0 + - certifi==2024.2.2 + - cffi==1.16.0 + - charset-normalizer==3.3.2 + - cleo==2.1.0 + - crashtest==0.4.1 + - cryptography==42.0.5 + - distlib==0.3.8 + - dulwich==0.21.7 + - fastjsonschema==2.19.1 + - filelock==3.13.3 + - idna==3.6 + - importlib-metadata==7.1.0 + - installer==0.7.0 + - jaraco-classes==3.4.0 + - jeepney==0.8.0 + - keyring==24.3.1 + - more-itertools==10.2.0 + - msgpack==1.0.8 + - packaging==24.0 + - pexpect==4.9.0 + - pkginfo==1.10.0 + - platformdirs==4.2.0 + - poetry==1.8.2 + - poetry-core==1.9.0 + - poetry-plugin-export==1.7.1 + - ptyprocess==0.7.0 + - pycparser==2.22 + - pyproject-hooks==1.0.0 + - rapidfuzz==3.7.0 + - requests==2.31.0 + - requests-toolbelt==1.0.0 + - secretstorage==3.3.3 + - shellingham==1.5.4 + - stable-baselines3==2.3.2 + - tensorboard + - tomlkit==0.12.4 + - trove-classifiers==2024.3.25 + - urllib3==2.2.1 + - virtualenv==20.25.1 + - zipp==3.18.1 + - huggingface_hub==0.26.5 + - wandb==0.19.1 + - python-box==7.3.0 + - python-dotenv==1.0.1 + - jax==0.4.0 + - typer==0.9.0 + - matplotlib==3.9 diff --git a/gpudrive-main/examples/experimental/config/eval_config.yaml b/gpudrive-main/examples/experimental/config/eval_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..48202ab2b578fbe559ce17530af7565106056265 --- /dev/null +++ b/gpudrive-main/examples/experimental/config/eval_config.yaml @@ -0,0 +1,35 @@ +res_path: examples/experimental/dataframes # Store dataframes here +test_dataset_size: 10_000 # Number of test scenarios to evaluate on + +# Environment settings +train_dir: data/processed/training +test_dir: data/processed/validation + +num_worlds: 50 # Number of parallel environments for evaluation +max_controlled_agents: 64 # Maximum number of agents controlled by the model. +ego_state: true +road_map_obs: true +partner_obs: true +norm_obs: true +remove_non_vehicles: true # If false, all agents are included (vehicles, pedestrians, cyclists) +lidar_obs: false # NOTE: Setting this to true currently turns of the other observation types +reward_type: "weighted_combination" +collision_weight: -0.75 +off_road_weight: -0.75 +goal_achieved_weight: 1.0 +dynamics_model: "classic" +collision_behavior: "ignore" # Options: "remove", "stop" +dist_to_goal_threshold: 2.0 +polyline_reduction_threshold: 0.1 # Rate at which to sample points from the polyline (0 is use all closest points, 1 maximum sparsity), needs to be balanced with kMaxAgentMapObservationsCount +sampling_seed: 42 # If given, the set of scenes to sample from will be deterministic, if None, the set of scenes will be random +obs_radius: 50.0 # Visibility radius of the agents +init_roadgraph: False +render_3d: True + +# Number of discretizations in the action space +# Note: Make sure that this equals the discretizations that the policy +# has been trained with +action_space_steer_disc: 13 +action_space_accel_disc: 7 + +device: "cuda" # Options: "cpu", "cuda" diff --git a/gpudrive-main/examples/experimental/config/hand_designed_experiments.yaml b/gpudrive-main/examples/experimental/config/hand_designed_experiments.yaml new file mode 100644 index 0000000000000000000000000000000000000000..959bfad0c00ed561dd6e8f6ad3bfabaf26f28347 --- /dev/null +++ b/gpudrive-main/examples/experimental/config/hand_designed_experiments.yaml @@ -0,0 +1,38 @@ +save_results_path: examples/experiments/dataframes/hand_designed + +data_path_original: data/processed/hand_designed/original +data_path_altered: data/processed/hand_designed/altered + +cpt_path: examples/experiments/models/ +cpt_name: model_PPO__C__R_10000__01_28_20_57_35_873_011426 + +num_worlds: 13 +dataset_size: 13 + +device: cuda +deterministic: false +render_sim_state: true +zoom_radius: 90 + +# Environment settings +max_controlled_agents: 64 +ego_state: true +road_map_obs: true +partner_obs: true +norm_obs: true +remove_non_vehicles: true +lidar_obs: false +reward_type: "weighted_combination" +collision_weight: -0.5 +off_road_weight: -0.5 +goal_achieved_weight: 1.0 +dynamics_model: "classic" +collision_behavior: "ignore" +dist_to_goal_threshold: 2.0 +polyline_reduction_threshold: 0.1 +sampling_seed: 42 +obs_radius: 50.0 + +# Number of discretizations in the action space +action_space_steer_disc: 41 # 11 in smaller act space +action_space_accel_disc: 17 # 7 in smaller act space diff --git a/gpudrive-main/examples/experimental/config/model_config.yaml b/gpudrive-main/examples/experimental/config/model_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0f23a2432002a06b960f2ea274d63ae3459a8ccf --- /dev/null +++ b/gpudrive-main/examples/experimental/config/model_config.yaml @@ -0,0 +1,7 @@ +models_path: examples/experimental/models + +models: + - name: model_PPO____R_10000__02_27_09_19_10_626_003200 + train_dataset_size: 10_000 + wandb: null + trained_on: null \ No newline at end of file diff --git a/gpudrive-main/examples/experimental/config/reliable_agents_params.yaml b/gpudrive-main/examples/experimental/config/reliable_agents_params.yaml new file mode 100644 index 0000000000000000000000000000000000000000..004e8e504fdea87c9b9a88ca901381b6411de1ec --- /dev/null +++ b/gpudrive-main/examples/experimental/config/reliable_agents_params.yaml @@ -0,0 +1,22 @@ +# Overrides default environment configs (see pygpudrive/env/config.py) +max_controlled_agents: 64 # Maximum number of agents controlled by the model. Make sure this aligns with the variable kMaxAgentCount in src/consts.hpp +ego_state: true +road_map_obs: true +partner_obs: true +norm_obs: true +remove_non_vehicles: true # If false, all agents are included (vehicles, pedestrians, cyclists) +lidar_obs: false # NOTE: Setting this to true currently turns of the other observation types +reward_type: "weighted_combination" +collision_weight: -0.75 +off_road_weight: -0.75 +goal_achieved_weight: 1.0 +dynamics_model: "classic" +collision_behavior: "ignore" # Options: "remove", "stop", "ignore" +dist_to_goal_threshold: 2.0 +polyline_reduction_threshold: 0.1 # Rate at which to sample points from the polyline (0 is use all closest points, 1 maximum sparsity), needs to be balanced with kMaxAgentMapObservationsCount +sampling_seed: 42 # If given, the set of scenes to sample from will be deterministic, if None, the set of scenes will be random +obs_radius: 50.0 # Visibility radius of the agents +action_space_steer_disc: 13 +action_space_accel_disc: 7 +init_mode: all_non_trivial +vbd_in_obs: false # If true, the VBD is included in the observation diff --git a/gpudrive-main/examples/experimental/config/scene_manipulation_config.yaml b/gpudrive-main/examples/experimental/config/scene_manipulation_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..1ae969300adbbfbe4098dde56b0ea7ad1ab75055 --- /dev/null +++ b/gpudrive-main/examples/experimental/config/scene_manipulation_config.yaml @@ -0,0 +1,33 @@ +save_results_path: examples/experimental/dataframes/scene_manipulation +train_path: "" + +cpt_path: examples/experimental/models/sota_paper +cpt_name: model_PPO__R_10000__01_19_17_27_13_052_007500 + +perc_to_rmv_per_scene: 0.5 + +num_worlds: 100 +dataset_size: 1000 + +device: cuda +deterministic: false +render_sim_state: false + +# Environment settings +max_controlled_agents: 64 +ego_state: true +road_map_obs: true +partner_obs: true +norm_obs: true +remove_non_vehicles: true +lidar_obs: false +reward_type: "weighted_combination" +collision_weight: -0.5 +off_road_weight: -0.5 +goal_achieved_weight: 1.0 +dynamics_model: "classic" +collision_behavior: "ignore" +dist_to_goal_threshold: 2.0 +polyline_reduction_threshold: 0.1 +sampling_seed: 42 +obs_radius: 50.0 \ No newline at end of file diff --git a/gpudrive-main/examples/experimental/config/visualization_config.yaml b/gpudrive-main/examples/experimental/config/visualization_config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..5d4dc92a6b37c86d01a86e55aa46282a1732e348 --- /dev/null +++ b/gpudrive-main/examples/experimental/config/visualization_config.yaml @@ -0,0 +1,35 @@ +save_results_path: examples/experimental/visualizations +train_path: "data/processed/training" + +cpt_path: examples/experimental/models +cpt_name: model_PPO__C__R_10000__01_28_20_57_35_873_011426 + +num_worlds: 1 +dataset_size: 3 + +device: cpu +deterministic: false +render_sim_state: true + +# Environment settings +max_controlled_agents: 64 +ego_state: true +road_map_obs: true +partner_obs: true +norm_obs: true +remove_non_vehicles: true +lidar_obs: false +reward_type: "weighted_combination" +collision_weight: -0.75 +off_road_weight: -0.75 +goal_achieved_weight: 1.0 +dynamics_model: "classic" +collision_behavior: "ignore" +dist_to_goal_threshold: 2.0 +polyline_reduction_threshold: 0.1 +sampling_seed: 42 +obs_radius: 50.0 + +# Number of discretizations in the action space +action_space_steer_disc: 41 # 11 in smaller act space +action_space_accel_disc: 17 # 7 in smaller act space \ No newline at end of file diff --git a/gpudrive-main/examples/experimental/eval_utils.py b/gpudrive-main/examples/experimental/eval_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..675f9afe1a4c9c44d196c5a0934dc4b49973fd34 --- /dev/null +++ b/gpudrive-main/examples/experimental/eval_utils.py @@ -0,0 +1,365 @@ +import torch +import pandas as pd +from tqdm import tqdm +import yaml +from box import Box +import numpy as np +import dataclasses + +from gpudrive.env.config import EnvConfig, RenderConfig +from gpudrive.env.env_torch import GPUDriveTorchEnv +from gpudrive.env.dataset import SceneDataLoader +from gpudrive.visualize.utils import img_from_fig +from gpudrive.datatypes.observation import GlobalEgoState + +from gpudrive.networks.late_fusion import NeuralNet + +import logging +import torch + +logging.basicConfig(level=logging.INFO) + +import pdb + + +class RandomPolicy: + def __init__(self, action_space_n): + self.action_space_n = action_space_n + + def __call__(self, obs, deterministic=False): + """Generate random actions.""" + # Uniformly sample integers from the action space for each observation + batch_size = obs.shape[0] + random_action = torch.randint( + 0, self.action_space_n, (batch_size,), dtype=torch.int64 + ) + return random_action, None, None, None + + +def load_policy(path_to_cpt, model_name, device, env=None): + """Load a policy from a given path.""" + + # Load the saved checkpoint + if model_name == "random_baseline": + return RandomPolicy(env.action_space.n) + + else: # Load a trained model + saved_cpt = torch.load( + f=f"{path_to_cpt}/{model_name}.pt", + map_location=device, + weights_only=False, + ) + + logging.info(f"Load model from {path_to_cpt}/{model_name}.pt") + + # Create policy architecture from saved checkpoint + policy = NeuralNet( + input_dim=saved_cpt["model_arch"]["input_dim"], + action_dim=saved_cpt["action_dim"], + hidden_dim=saved_cpt["model_arch"]["hidden_dim"], + ).to(device) + + # Load the model parameters + policy.load_state_dict(saved_cpt["parameters"]) + + logging.info("Load model parameters") + + return policy.eval() + +def rollout( + env, + policy, + device, + deterministic: bool = False, + render_sim_state: bool = False, + render_every_n_steps: int = 1, + zoom_radius: int = 100, + return_agent_positions: bool = False, + center_on_ego: bool = False, +): + """ + Perform a rollout of a policy in the environment. + + Args: + env: The simulation environment. + policy: The policy to be rolled out. + device: The device to execute computations on (CPU/GPU). + deterministic (bool): Whether to use deterministic policy actions. + render_sim_state (bool): Whether to render the simulation state. + + Returns: + tuple: Averages for goal achieved, collisions, off-road occurrences, + controlled agents count, and simulation state frames. + """ + # Initialize storage + sim_state_frames = {env_id: [] for env_id in range(env.num_worlds)} + num_worlds = env.num_worlds + max_agent_count = env.max_agent_count + episode_len = env.config.episode_len + agent_positions = torch.zeros((env.num_worlds, env.max_agent_count, episode_len, 2)) + + # Reset episode + next_obs = env.reset() + + # Storage + goal_achieved = torch.zeros((num_worlds, max_agent_count), device=device) + collided = torch.zeros((num_worlds, max_agent_count), device=device) + off_road = torch.zeros((num_worlds, max_agent_count), device=device) + active_worlds = np.arange(num_worlds).tolist() + episode_lengths = torch.zeros(num_worlds) + + control_mask = env.cont_agent_mask + live_agent_mask = control_mask.clone() + + for time_step in range(episode_len): + + print(f't: {time_step}') + + # Get actions for active agents + if live_agent_mask.any(): + action, _, _, _ = policy( + next_obs[live_agent_mask], deterministic=deterministic + ) + + # Insert actions into a template + action_template = torch.zeros( + (num_worlds, max_agent_count), dtype=torch.int64, device=device + ) + action_template[live_agent_mask] = action.to(device) + + # Step the environment + env.step_dynamics(action_template) + + # Render + if render_sim_state and len(active_worlds) > 0: + + has_live_agent = torch.where( + live_agent_mask[active_worlds, :].sum(axis=1) > 0 + )[0].tolist() + + if time_step % render_every_n_steps == 0: + if center_on_ego: + agent_indices = torch.argmax(control_mask.to(torch.uint8), dim=1).tolist() + else: + agent_indices = None + + sim_state_figures = env.vis.plot_simulator_state( + env_indices=has_live_agent, + time_steps=[time_step] * len(has_live_agent), + zoom_radius=zoom_radius, + center_agent_indices=agent_indices, + ) + for idx, env_id in enumerate(has_live_agent): + sim_state_frames[env_id].append( + img_from_fig(sim_state_figures[idx]) + ) + + # Update observations, dones, and infos + next_obs = env.get_obs() + dones = env.get_dones().bool() + infos = env.get_infos() + + off_road[live_agent_mask] += infos.off_road[live_agent_mask] + collided[live_agent_mask] += infos.collided[live_agent_mask] + goal_achieved[live_agent_mask] += infos.goal_achieved[live_agent_mask] + + # Update live agent mask + live_agent_mask[dones] = False + + # Process completed worlds + num_dones_per_world = (dones & control_mask).sum(dim=1) + total_controlled_agents = control_mask.sum(dim=1) + done_worlds = (num_dones_per_world == total_controlled_agents).nonzero( + as_tuple=True + )[0] + + for world in done_worlds: + if world in active_worlds: + active_worlds.remove(world) + episode_lengths[world] = time_step + + if return_agent_positions: + global_agent_states = GlobalEgoState.from_tensor(env.sim.absolute_self_observation_tensor()) + agent_positions[:, :, time_step, 0] = global_agent_states.pos_x + agent_positions[:, :, time_step, 1] = global_agent_states.pos_y + + + if not active_worlds: # Exit early if all worlds are done + break + + # Aggregate metrics to obtain averages across scenes + controlled_per_scene = control_mask.sum(dim=1).float() + + # Counts + goal_achieved_count = (goal_achieved > 0).float().sum(axis=1) + collided_count = (collided > 0).float().sum(axis=1) + off_road_count = (off_road > 0).float().sum(axis=1) + not_goal_nor_crash_count = torch.logical_and( + goal_achieved == 0, # Didn't reach the goal + torch.logical_and( + collided == 0, # Didn't collide + torch.logical_and( + off_road == 0, # Didn't go off-road + control_mask, # Only count controlled agents + ), + ), + ).float().sum(dim=1) + + # Fractions per scene + frac_goal_achieved = goal_achieved_count / controlled_per_scene + frac_collided = collided_count / controlled_per_scene + frac_off_road = off_road_count / controlled_per_scene + frac_not_goal_nor_crash_per_scene = not_goal_nor_crash_count / controlled_per_scene + + return ( + goal_achieved_count, + frac_goal_achieved, + collided_count, + frac_collided, + off_road_count, + frac_off_road, + not_goal_nor_crash_count, + frac_not_goal_nor_crash_per_scene, + controlled_per_scene, + sim_state_frames, + agent_positions, + episode_lengths, + ) + +def load_config(cfg: str) -> Box: + """Load configurations as a Box object. + Args: + cfg (str): Name of config file. + + Returns: + Box: Box representation of configurations. + """ + with open(f"{cfg}.yaml", "r") as stream: + config = Box(yaml.safe_load(stream)) + return config + + +def make_env(config, train_loader, render_3d=False): + """Make the environment with the given config.""" + + # Override any default environment settings + env_config = dataclasses.replace( + EnvConfig(), + ego_state=config.ego_state, + road_map_obs=config.road_map_obs, + partner_obs=config.partner_obs, + reward_type=config.reward_type, + norm_obs=config.norm_obs, + dynamics_model=config.dynamics_model, + collision_behavior=config.collision_behavior, + dist_to_goal_threshold=config.dist_to_goal_threshold, + polyline_reduction_threshold=config.polyline_reduction_threshold, + remove_non_vehicles=config.remove_non_vehicles, + lidar_obs=config.lidar_obs, + disable_classic_obs=True if config.lidar_obs else False, + obs_radius=config.obs_radius, + steer_actions = torch.round( + torch.linspace(-torch.pi, torch.pi, config.action_space_steer_disc), decimals=3 + ), + accel_actions = torch.round( + torch.linspace(-4.0, 4.0, config.action_space_accel_disc), decimals=3 + ), + ) + + render_config = RenderConfig() + render_config.render_3d = render_3d + + env = GPUDriveTorchEnv( + config=env_config, + data_loader=train_loader, + max_cont_agents=config.max_controlled_agents, + device=config.device, + render_config=render_config + ) + + return env + + +def evaluate_policy( + env, + policy, + data_loader, + dataset_name, + device="cuda", + deterministic=False, + render_sim_state=False, +): + """Evaluate policy in the environment.""" + + res_dict = { + "scene": [], + "goal_achieved_count": [], + "goal_achieved_frac": [], + "collided_count": [], + "collided_frac": [], + "off_road_count": [], + "off_road_frac": [], + "other_count": [], + "other_frac": [], + "controlled_agents_in_scene": [], + "episode_lengths": [], + } + + for batch in tqdm( + data_loader, + desc=f"Processing {dataset_name} batches", + total=len(data_loader), + colour="blue", + ): + + # Update simulator with the new batch of data + env.swap_data_batch(batch) + + # Rollout policy in the environments + ( + goal_achieved_count, + goal_achieved_frac, + collided_count, + collided_frac, + off_road_count, + off_road_frac, + other_count, + other_frac, + controlled_agents_in_scene, + sim_state_frames, + agent_positions, + episode_lengths, + ) = rollout( + env=env, + policy=policy, + device=device, + deterministic=deterministic, + render_sim_state=render_sim_state, + ) + + # Get names from env + scenario_to_worlds_dict = env.get_env_filenames() + + res_dict["scene"].extend(scenario_to_worlds_dict.values()) + res_dict["goal_achieved_count"].extend(goal_achieved_count.cpu().numpy()) + res_dict["goal_achieved_frac"].extend(goal_achieved_frac.cpu().numpy()) + + res_dict["collided_count"].extend(collided_count.cpu().numpy()) + res_dict["collided_frac"].extend(collided_frac.cpu().numpy()) + + res_dict["off_road_count"].extend(off_road_count.cpu().numpy()) + res_dict["off_road_frac"].extend(off_road_frac.cpu().numpy()) + + res_dict["other_count"].extend(other_count.cpu().numpy()) + res_dict["other_frac"].extend(other_frac.cpu().numpy()) + res_dict["controlled_agents_in_scene"].extend( + controlled_agents_in_scene.cpu().numpy() + ) + res_dict["episode_lengths"].extend(episode_lengths.cpu().numpy()) + + # Convert to pandas dataframe + df_res = pd.DataFrame(res_dict) + df_res["dataset"] = dataset_name + + return df_res + diff --git a/gpudrive-main/examples/experimental/extended_goals.py b/gpudrive-main/examples/experimental/extended_goals.py new file mode 100644 index 0000000000000000000000000000000000000000..073adddcf51641d0fc29725d893d08c144d6bae5 --- /dev/null +++ b/gpudrive-main/examples/experimental/extended_goals.py @@ -0,0 +1,92 @@ +import os +import logging +from pathlib import Path +from tqdm import tqdm +import torch +from eval_utils import load_policy, rollout, load_config, make_env +from pygpudrive.env.dataset import SceneDataLoader + +def visualize_extended_goals( + env, + data_loader, + config, + save_path, + num_scenes=None, +): + """ + Visualize scenes with extended goals for controlled agents. + + Args: + env: Environment instance + data_loader: SceneDataLoader instance + config: Configuration object + save_path: Path to save visualizations + num_scenes: Number of scenes to visualize. If None, processes all scenes. + """ + + # Create save directory if it doesn't exist + os.makedirs(save_path, exist_ok=True) + + # Limit number of scenes if specified + if num_scenes is not None: + total_batches = min(num_scenes // config.num_worlds + 1, len(data_loader)) + else: + total_batches = len(data_loader) + + scene_count = 0 + + for batch_idx, batch in tqdm( + enumerate(data_loader), + desc="Processing scenes", + total=total_batches, + colour="green", + ): + if num_scenes is not None and scene_count >= num_scenes: + break + + # Set new data batch with simulator + env.swap_data_batch(batch) + + # Reset environment to start state + _ = env.reset() + + # Visualize state with extended goals + extended_states = env.vis.plot_simulator_state( + env_indices=list(range(len(batch))), + time_steps=[0] * len(batch), + zoom_radius=150, + extend_goals=True + ) + + # Save extended goal states + for i, fig in enumerate(extended_states): + scene_name = Path(batch[i]).stem + print(f"Scene: {scene_name}\n") + fig.savefig(os.path.join(save_path, f"{scene_name}_extended.png")) + + scene_count += len(batch) + +if __name__ == "__main__": + # Load configuration + config = load_config("examples/experimental/config/visualization_config") + + # Initialize data loader + data_loader = SceneDataLoader( + root=config.train_path, + batch_size=config.num_worlds, + dataset_size=config.dataset_size, + sample_with_replacement=False, + ) + + # Create environment + env = make_env(config, data_loader) + + # Run visualization + visualize_extended_goals( + env=env, + data_loader=data_loader, + config=config, + save_path=config.save_results_path, + ) + + logging.info(f"Saved visualizations at {config.save_results_path}") \ No newline at end of file diff --git a/gpudrive-main/examples/experimental/get_model_performance.py b/gpudrive-main/examples/experimental/get_model_performance.py new file mode 100644 index 0000000000000000000000000000000000000000..70443d852900adcb6009a6f6c68ececd0edf19b0 --- /dev/null +++ b/gpudrive-main/examples/experimental/get_model_performance.py @@ -0,0 +1,143 @@ +import torch +import pandas as pd +from box import Box +import numpy as np +import os +import logging +from gpudrive.env.dataset import SceneDataLoader +from eval_utils import ( + load_config, + make_env, + load_policy, + evaluate_policy, +) + +import random +import torch +import numpy as np + +def set_seed(seed: int): + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) # If using CUDA + torch.backends.cudnn.deterministic = True + +logging.basicConfig(level=logging.INFO) +SEED = 42 # Set to any fixed value +set_seed(SEED) + +if __name__ == "__main__": + + # Load configurations + eval_config = load_config("examples/experimental/config/eval_config") + model_config = load_config("examples/experimental/config/model_config") + + train_loader = SceneDataLoader( + root=eval_config.train_dir, + batch_size=eval_config.num_worlds, + dataset_size=eval_config.num_worlds, + sample_with_replacement=False, + ) + + # Make environment + env = make_env(eval_config, train_loader) + + for model in model_config.models: + + logging.info(f"Evaluating model {model.name}") + + # Load policy + policy = load_policy( + path_to_cpt=model_config.models_path, + model_name=model.name, + device=eval_config.device, + env=env, + ) + + # Create dataloaders for train and test sets + train_loader = SceneDataLoader( + root=eval_config.train_dir, + batch_size=eval_config.num_worlds, + dataset_size=model.train_dataset_size + if model.name != "random_baseline" + else 1000, + sample_with_replacement=False, + shuffle=False, + ) + + test_loader = SceneDataLoader( + root=eval_config.test_dir, + batch_size=eval_config.num_worlds, + dataset_size=eval_config.test_dataset_size + if model.name != "random_baseline" + else 1000, + sample_with_replacement=False, + shuffle=True, + ) + + # Rollouts + logging.info( + f"Rollouts on {len(set(train_loader.dataset))} train scenes / {len(set(test_loader.dataset))} test scenes" + ) + + df_res_train = evaluate_policy( + env=env, + policy=policy, + data_loader=train_loader, + dataset_name="train", + deterministic=False, + render_sim_state=False, + ) + + df_res_test = evaluate_policy( + env=env, + policy=policy, + data_loader=test_loader, + dataset_name="test", + deterministic=False, + render_sim_state=False, + ) + + # Concatenate train/test results + df_res = pd.concat([df_res_train, df_res_test]) + + # Add metadata + df_res["model_name"] = model.name + df_res["train_dataset_size"] = model.train_dataset_size + + # Store + if not os.path.exists(eval_config.res_path): + os.makedirs(eval_config.res_path) + + tab_agg_perf = df_res.groupby("dataset")[ + [ + "goal_achieved_frac", + "collided_frac", + "off_road_frac", + "other_frac", + ] + ].agg(["mean", "std"]) + tab_agg_perf = tab_agg_perf * 100 + tab_agg_perf = tab_agg_perf.round(1) + + print("Scene-based metrics \n") + print(tab_agg_perf) + print("") + + print("Agent-based metrics \n") + total_agents = df_res["controlled_agents_in_scene"].sum() + collision_rate = (df_res["collided_count"].sum() / total_agents) * 100 + offroad_rate = (df_res["off_road_count"].sum() / total_agents) * 100 + goal_rate = (df_res["goal_achieved_count"].sum() / total_agents) * 100 + other_rate = (df_res["other_count"].sum() / total_agents) * 100 + + print(f"Total agents: {total_agents} in {df_res.shape[0]} scenes") + print(f"Collision rate: {collision_rate}") + print(f"Offroad rate: {offroad_rate}") + print(f"Goal rate: {goal_rate}") + print(f"Other rate: {other_rate}") + + df_res.to_csv(f"{eval_config.res_path}/{model.name}.csv", index=False) + + logging.info(f"Saved at {eval_config.res_path}/{model.name}.csv \n") diff --git a/gpudrive-main/examples/experimental/hand_designed_experiments.py b/gpudrive-main/examples/experimental/hand_designed_experiments.py new file mode 100644 index 0000000000000000000000000000000000000000..4edf8df9c37462f6df445f997828784ef91fd4cb --- /dev/null +++ b/gpudrive-main/examples/experimental/hand_designed_experiments.py @@ -0,0 +1,150 @@ +import os +import logging +from PIL import Image +from tqdm import tqdm +import pandas as pd +from pathlib import Path +import matplotlib.pyplot as plt +import mediapy +import numpy as np +import torch +from eval_utils import load_policy, rollout, load_config, make_env, evaluate_policy + +from pygpudrive.env.dataset import SceneDataLoader +from pygpudrive.datatypes.observation import LocalEgoState +import pdb + + +if __name__ == "__main__": + + config = load_config("examples/experimental/config/hand_designed_experiments") + + # Load original scenes + data_loader_orig = SceneDataLoader( + root=config.data_path_original, + batch_size=config.num_worlds, + dataset_size=config.dataset_size, + sample_with_replacement=False, + ) + + # Load altered scenes + data_loader_altered = SceneDataLoader( + root=config.data_path_altered, + batch_size=config.num_worlds, + dataset_size=config.dataset_size, + sample_with_replacement=False, + ) + + # Make env + env = make_env(config, data_loader_orig) + + # Load policy + policy = load_policy( + path_to_cpt=config.cpt_path, + model_name=config.cpt_name, + device=config.device, + env=env, + ) + + # Run tests + df_perf_original = evaluate_policy( + env=env, + policy=policy, + data_loader=data_loader_orig, + dataset_name="test", + deterministic=False, + render_sim_state=False, + ) + + df_perf_altered = evaluate_policy( + env=env, + policy=policy, + data_loader=data_loader_altered, + dataset_name="test", + deterministic=False, + render_sim_state=False, + ) + + # Concatenate all three dataframes with a new column to identify the scenario + df_perf_original['Class'] = 'Original' + df_perf_altered['Class'] = 'Altered' + + df = pd.concat([df_perf_original, df_perf_altered]) + + metrics = ['goal_achieved_frac', 'collided_frac', 'off_road_frac', 'other_frac'] + + tab_agg_perf = df.groupby('Class')[metrics].agg(['mean', 'std']) + tab_agg_perf = tab_agg_perf * 100 + tab_agg_perf = tab_agg_perf.round(1) + + print('') + print(tab_agg_perf) + print('') + + # Save + if not os.path.exists(config.save_results_path): + os.makedirs(config.save_results_path) + + df.to_csv(f"{config.save_results_path}/combined_results_ood.csv", index=False) + + logging.info(f"Saved results at {config.save_results_path}") + + # # Make videos + # videos_dir = Path(f"videos/{config.cpt_name}/hand_designed") + # videos_dir.mkdir(parents=True, exist_ok=True) + + # for data_loader in [data_loader_orig]: #data_loader_altered + + # for batch in tqdm( + # data_loader, + # desc=f"Making videos", + # total=len(data_loader), + # colour="MAGENTA", + # ): + + # env.swap_data_batch(batch) + + # ( + # goal_achieved_count, + # goal_achieved_frac, + # collided_count, + # collided_frac, + # off_road_count, + # off_road_frac, + # other_count, + # other_frac, + # controlled_agents_in_scene, + # sim_state_frames, + # agent_positions, + # episode_lengths, + # ) = rollout( + # env=env, + # policy=policy, + # device=config.device, + # deterministic=config.device, + # render_sim_state=config.render_sim_state, + # render_every_n_steps=1, + # zoom_radius=config.zoom_radius, + # ) + + # filenames = env.get_env_filenames() + + # sim_state_arrays = {k: np.array(v) for k, v in sim_state_frames.items()} + + # # Save videos locally + # for env_id, frames in sim_state_arrays.items(): + + # filename = filenames[env_id] + + # video_path = videos_dir / f"{filename}.mp4" + + # mediapy.write_video( + # str(video_path), + # frames, + # fps=15, + # ) + + # logging.info(f"Saved video to {video_path}") + + + \ No newline at end of file diff --git a/gpudrive-main/examples/experimental/notebooks/00_simple_model_test.ipynb b/gpudrive-main/examples/experimental/notebooks/00_simple_model_test.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..b2f2a4b97c6248659cc778a9385eef74bfb85185 --- /dev/null +++ b/gpudrive-main/examples/experimental/notebooks/00_simple_model_test.ipynb @@ -0,0 +1,221 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from pathlib import Path\n", + "import mediapy\n", + "import matplotlib.pyplot as plt\n", + "\n", + "working_dir = Path.cwd()\n", + "while working_dir.name != 'gpudrive':\n", + " working_dir = working_dir.parent\n", + " if working_dir == Path.home():\n", + " raise FileNotFoundError(\"Base directory 'gpudrive' not found\")\n", + "os.chdir(working_dir)\n", + "\n", + "from gpudrive.env.dataset import SceneDataLoader\n", + "from gpudrive.env.config import EnvConfig\n", + "from gpudrive.env.env_torch import GPUDriveTorchEnv\n", + "from gpudrive.visualize.utils import img_from_fig\n", + "\n", + "import logging\n", + "logging.basicConfig(level=logging.INFO)\n", + "\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "train_loader = SceneDataLoader(\n", + " root=\"data/processed/examples\",\n", + " batch_size=4, # Number of worlds\n", + " dataset_size=1000,\n", + " sample_with_replacement=False,\n", + " shuffle=False,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Model\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from examples.experimental.eval_utils import load_policy, rollout\n", + "\n", + "policy = load_policy(\n", + " path_to_cpt='examples/experimental/models',\n", + " model_name='',\n", + " device='cpu'\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# Check that the model weights are not random\n", + "# for name, param in policy.state_dict().items():\n", + "# print(f\"{name} - Mean: {param.mean():.4f}, Std: {param.std():.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "policy" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### GPUDriveTorchEnv" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "env = GPUDriveTorchEnv(\n", + " config=EnvConfig(),\n", + " data_loader=train_loader,\n", + " max_cont_agents=64, \n", + " device=\"cpu\",\n", + ")\n", + "\n", + "print(env.data_batch)\n", + "\n", + "obs = env.reset()[env.cont_agent_mask]\n", + "\n", + "print(f'observation_space: {env.observation_space}')\n", + "print(f'obs shape: {obs.shape}')\n", + "print(f'obs dtype: {obs.dtype} \\n')\n", + "\n", + "print(f'action_space: {env.action_space}')\n", + "\n", + "plt.hist(obs.flatten());" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Show simulator to make sure we're at the same state\n", + "env.vis.figsize = (5, 5)\n", + "sim_states = env.vis.plot_simulator_state(\n", + " env_indices=[0],\n", + " zoom_radius=100,\n", + " time_steps=[0],\n", + ")\n", + "\n", + "sim_states[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "( \n", + " goal_achieved_count,\n", + " frac_goal_achieved,\n", + " collided_count,\n", + " frac_collided,\n", + " off_road_count,\n", + " frac_off_road,\n", + " not_goal_nor_crash_count,\n", + " frac_not_goal_nor_crash_per_scene,\n", + " controlled_agents_per_scene,\n", + " sim_state_frames,\n", + " agent_positions,\n", + " episode_lengths\n", + ") = rollout(\n", + " env=env, \n", + " policy=policy, \n", + " device='cpu', \n", + " render_sim_state=True,\n", + " zoom_radius=100,\n", + " deterministic=True,\n", + ")\n", + "\n", + "print(f'\\n Results: \\n')\n", + "print(f'Goal achieved: {frac_goal_achieved}')\n", + "print(f'Collided: {frac_collided}')\n", + "print(f'Off road: {frac_off_road}')\n", + "print(f'Not goal nor crashed: {frac_not_goal_nor_crash_per_scene}')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Show rollout videos\n", + "mediapy.show_videos(sim_state_frames, fps=15, codec='gif')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "gpudrive", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/gpudrive-main/examples/experimental/notebooks/01_performance_and_scaling.ipynb b/gpudrive-main/examples/experimental/notebooks/01_performance_and_scaling.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..85c33ab4caab2904e2bfb6ef86dc630e109ca05f --- /dev/null +++ b/gpudrive-main/examples/experimental/notebooks/01_performance_and_scaling.ipynb @@ -0,0 +1,2423 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import warnings\n", + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt\n", + "import datetime\n", + "\n", + "sns.set(\"notebook\", font_scale=1.05, rc={\"figure.figsize\": (10, 5)})\n", + "sns.set_style(\"ticks\", rc={\"figure.facecolor\": \"none\", \"axes.facecolor\": \"none\"})\n", + "%config InlineBackend.figure_format = 'svg'\n", + "warnings.filterwarnings(\"ignore\")\n", + "plt.set_loglevel(\"WARNING\")\n", + "mpl.rcParams[\"lines.markersize\"] = 8\n", + "\n", + "now = datetime.datetime.now()\n", + "now_str = now.strftime(\"%m_%d_%H_%M\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Helper functions" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def make_barplot(data, df, df_random_baseline=None, save_path=None):\n", + " \n", + " fig, axs = plt.subplots(1, 3, figsize=(10, 3))\n", + " fig.suptitle(f'Policy performance on the {data} set', y=1.05)\n", + "\n", + " # Goal achieved\n", + " sns.barplot(data=df[df['dataset'] == data], errorbar=\"sd\", x='train_dataset_size', y='goal_achieved', ax=axs[0])\n", + " axs[0].set_ylim(0, 1)\n", + " axs[0].grid(True, alpha=0.3)\n", + "\n", + " # Collided\n", + " sns.barplot(data=df[df['dataset'] == data], errorbar=\"sd\", x='train_dataset_size', y='collided', ax=axs[1])\n", + " axs[1].grid(True, alpha=0.3)\n", + "\n", + " # Off road\n", + " sns.barplot(data=df[df['dataset'] == data], errorbar=\"sd\", x='train_dataset_size', y='off_road', ax=axs[2])\n", + " axs[2].grid(True, alpha=0.3)\n", + " \n", + " # Random policy performance\n", + " if df_random_baseline is not None:\n", + " rand_pi_mean_gr = df_random_baseline[df_random_baseline['dataset'] == data]['goal_achieved'].mean()\n", + " rand_pi_mean_col = df_random_baseline[df_random_baseline['dataset'] == data]['collided'].mean()\n", + " rand_pi_mean_off = df_random_baseline[df_random_baseline['dataset'] == data]['off_road'].mean()\n", + " \n", + " axs[0].axhline(rand_pi_mean_gr, color='r', linestyle='--', linewidth=2)\n", + " axs[1].axhline(rand_pi_mean_col, color='r', linestyle='--', linewidth=2)\n", + " axs[2].axhline(rand_pi_mean_off, color='r', linestyle='--', linewidth=2, label='Random policy')\n", + " \n", + " axs[1].set_ylim(0, rand_pi_mean_col + 0.05)\n", + " axs[2].set_ylim(0, rand_pi_mean_off + 0.05)\n", + " \n", + " fig.legend()\n", + " plt.tight_layout()\n", + "\n", + " sns.despine()\n", + " if save_path:\n", + " plt.savefig(save_path)\n", + " \n", + " \n", + "def make_histplot(data, df, bins=None, save_path=None):\n", + " \n", + " fig, axs = plt.subplots(1, 3, figsize=(10, 3))\n", + " fig.suptitle(f'Policy performance on the {data} set', y=1.05)\n", + "\n", + " # Goal achieved\n", + " sns.histplot(data=df[df['dataset'] == data], x='goal_achieved', bins=bins, ax=axs[0])\n", + " axs[0].grid(True, alpha=0.3)\n", + "\n", + " # Collided\n", + " sns.histplot(data=df[df['dataset'] == data], x='collided', bins=bins, ax=axs[1])\n", + " axs[1].grid(True, alpha=0.3)\n", + "\n", + " # Off road\n", + " sns.histplot(data=df[df['dataset'] == data], x='off_road', bins=bins, ax=axs[2])\n", + " axs[2].grid(True, alpha=0.3)\n", + " \n", + " fig.legend()\n", + " plt.tight_layout()\n", + "\n", + " sns.despine()\n", + " if save_path:\n", + " plt.savefig(save_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Configs" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "BASE_DIR = \"...\"\n", + "FIGURE_DIR = \"...\"\n", + "\n", + "# Constants\n", + "GRID_ALPHA = 0.4\n", + "ERRORBAR = 'ci'\n", + "DATA = 'test'\n", + "\n", + "colors = sns.color_palette(\"tab10\").as_hex()\n", + "blue = colors[0]\n", + "orange = colors[1]\n", + "green = colors[2]\n", + "red = colors[3]\n", + "purple = colors[4]\n", + "\n", + "SCENE_BASED_METRICS = ['goal_achieved_frac', 'collided_frac', 'off_road_frac', 'other_frac']\n", + "AGENT_BASED_METRICS = ['goal_achieved_count', 'collided_count', 'off_road_count', 'other_count']" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "['#1f77b4',\n", + " '#ff7f0e',\n", + " '#2ca02c',\n", + " '#d62728',\n", + " '#9467bd',\n", + " '#8c564b',\n", + " '#e377c2',\n", + " '#7f7f7f',\n", + " '#bcbd22',\n", + " '#17becf']" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sns.color_palette(\"tab10\").as_hex()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load the results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Scaling laws dataframes\n", + "df_100 = pd.read_csv(BASE_DIR + 'model_PPO__R_100__02_23_21_28_06_645_006500.csv')\n", + "df_1000 = pd.read_csv(BASE_DIR + 'model_PPO__R_1000__02_23_22_34_47_084_007500.csv')\n", + "df_10_000 = pd.read_csv(BASE_DIR + 'model_PPO____R_10000__02_26_11_02_54_752_002800.csv')\n", + "\n", + "#df_rand_baseline = pd.read_csv(BASE_DIR + \"random_baseline.csv\")\n", + "\n", + "# Best model\n", + "df_10_000 = pd.read_csv(BASE_DIR + \"model_PPO__C__R_10000__01_28_20_57_35_873_011426.csv\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Aggregate performance" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# tab_rand_baseline = df_rand_baseline.groupby('dataset')[['goal_achieved', 'collided', 'off_road']].agg(['mean', 'std'])\n", + "# tab_rand_baseline = tab_rand_baseline * 100 \n", + "# tab_rand_baseline = tab_rand_baseline.round(2) \n", + "\n", + "# tab_rand_baseline" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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goal_achieved_fraccollided_fracoff_road_fracother_frac
meanstdmeanstdmeanstdmeanstd
dataset
test99.811.530.443.170.312.590.141.1
\n", + "
" + ], + "text/plain": [ + " goal_achieved_frac collided_frac off_road_frac \\\n", + " mean std mean std mean std \n", + "dataset \n", + "test 99.81 1.53 0.44 3.17 0.31 2.59 \n", + "\n", + " other_frac \n", + " mean std \n", + "dataset \n", + "test 0.14 1.1 " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_10_000 = df_10_000[df_10_000['controlled_agents_in_scene'] != 0]\n", + "\n", + "tab_10_000 = df_10_000.groupby('dataset')[SCENE_BASED_METRICS].agg(['mean', 'std'])\n", + "tab_10_000 = tab_10_000 * 100 \n", + "tab_10_000 = tab_10_000.round(2) \n", + "\n", + "tab_10_000" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
goal_achieved_countcollided_countoff_road_countother_count
dataset
test99.810.320.180.17
\n", + "
" + ], + "text/plain": [ + " goal_achieved_count collided_count off_road_count other_count\n", + "dataset \n", + "test 99.81 0.32 0.18 0.17" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Compute the sum of controlled agents\n", + "df = df_10_000[df_10_000['dataset'] == 'test']\n", + "\n", + "total_controlled_agents = df['controlled_agents_in_scene'].sum()\n", + "\n", + "# Group by 'dataset' and compute normalized values\n", + "tab_agent_based_10_000 = (df.groupby('dataset')[AGENT_BASED_METRICS].sum() / total_controlled_agents)*100\n", + "\n", + "tab_agent_based_10_000.round(2) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Error distribution" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "8.959999999999999" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_error = df_10_000[df_10_000['dataset'] == 'test']\n", + "\n", + "df_error = df_10_000[\n", + " (df_10_000['collided_frac'] > 0) |\n", + " (df_10_000['off_road_frac'] > 0) |\n", + " (df_10_000['other_frac'] > 0)\n", + " \n", + "]\n", + "\n", + "(df_error.shape[0] / df_10_000.shape[0]) * 100" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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scenegoal_achieved_countgoal_achieved_fraccollided_countcollided_fracoff_road_countoff_road_fracother_countother_fraccontrolled_agents_in_sceneepisode_lengthsdatasetmodel_nametrain_dataset_size
4tfrecord-00048-of-00150_243.json3.01.0000000.00.0000001.00.3333330.00.0000003.044.0testmodel_PPO__C__R_10000__01_28_20_57_35_873_01142610000
8tfrecord-00029-of-00150_80.json44.01.0000001.00.0227270.00.0000000.00.00000044.027.0testmodel_PPO__C__R_10000__01_28_20_57_35_873_01142610000
15tfrecord-00096-of-00150_283.json36.00.9729730.00.0000000.00.0000001.00.02702737.090.0testmodel_PPO__C__R_10000__01_28_20_57_35_873_01142610000
16tfrecord-00061-of-00150_178.json5.00.8333330.00.0000000.00.0000001.00.1666676.090.0testmodel_PPO__C__R_10000__01_28_20_57_35_873_01142610000
24tfrecord-00115-of-00150_93.json16.00.9411760.00.0000001.00.0588240.00.00000017.090.0testmodel_PPO__C__R_10000__01_28_20_57_35_873_01142610000
\n", + "
" + ], + "text/plain": [ + " scene goal_achieved_count goal_achieved_frac \\\n", + "4 tfrecord-00048-of-00150_243.json 3.0 1.000000 \n", + "8 tfrecord-00029-of-00150_80.json 44.0 1.000000 \n", + "15 tfrecord-00096-of-00150_283.json 36.0 0.972973 \n", + "16 tfrecord-00061-of-00150_178.json 5.0 0.833333 \n", + "24 tfrecord-00115-of-00150_93.json 16.0 0.941176 \n", + "\n", + " collided_count collided_frac off_road_count off_road_frac other_count \\\n", + "4 0.0 0.000000 1.0 0.333333 0.0 \n", + "8 1.0 0.022727 0.0 0.000000 0.0 \n", + "15 0.0 0.000000 0.0 0.000000 1.0 \n", + "16 0.0 0.000000 0.0 0.000000 1.0 \n", + "24 0.0 0.000000 1.0 0.058824 0.0 \n", + "\n", + " other_frac controlled_agents_in_scene episode_lengths dataset \\\n", + "4 0.000000 3.0 44.0 test \n", + "8 0.000000 44.0 27.0 test \n", + "15 0.027027 37.0 90.0 test \n", + "16 0.166667 6.0 90.0 test \n", + "24 0.000000 17.0 90.0 test \n", + "\n", + " model_name train_dataset_size \n", + "4 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(1, 3, figsize=(9, 3), sharex=True)\n", + "metrics = ['collided_frac', 'off_road_frac', 'other_frac']\n", + "titles = ['Collided in scene', 'Off-road in scene', 'Other in scene']\n", + "colors = [red, orange, 'grey']\n", + "\n", + "for ax, metric, title, color in zip(axes, metrics, titles, colors):\n", + " sns.histplot(df_error, stat='percent', color=color, x=metric, kde=False, bins=10, ax=ax)\n", + " #ax.set_title(title, fontsize=14)\n", + " ax.set_xlabel('')\n", + " ax.grid(True, alpha=0.2)\n", + " ax.set_ylim(0, 100)\n", + " ax.set_xlabel(title, fontsize=12)\n", + " sns.despine()\n", + "\n", + "# Improve layout\n", + "#plt.xlabel(\"Scene-based metric (%)\")\n", + "plt.tight_layout()\n", + "plt.show()\n", + "sns.despine()\n", + "fig.savefig(f'{FIGURE_DIR}eror_distribution_{now_str}.pdf', bbox_inches='tight', format='pdf');" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.stats import pearsonr\n", + "\n", + "# Compute correlation and p-value\n", + "corr, p_value = pearsonr(df_error['collided_frac'], df_error['off_road_frac'])\n", + "\n", + "print(f'Correlation: {corr:.4f}')\n", + "print(f'P-value: {p_value:.4f}')\n", + "\n", + "alpha = 0.05 # Typical significance level\n" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "# THE_TABLE = tab_train_10_000_cont\n", + "\n", + "# # Combine mean and std into \"mean ± std\"\n", + "# def format_mean_std(row):\n", + "# formatted = {}\n", + "# for col in row.index.get_level_values(0).unique(): # Iterate over the metric names\n", + "# mean = row[(col, 'mean')]\n", + "# std = row[(col, 'std')]\n", + "# formatted[col] = f\"{mean:.2f} ± {std:.2f}\"\n", + "# return formatted\n", + "\n", + "# # Apply formatting to all rows\n", + "# tab_formatted = THE_TABLE.apply(format_mean_std, axis=1)\n", + "\n", + "# # Flatten the result\n", + "# tab_formatted = pd.DataFrame(tab_formatted.tolist(), index=tab_formatted.index)\n", + "\n", + "# # Generate LaTeX table\n", + "# latex_table = tab_formatted.to_latex(\n", + "# index=True,\n", + "# caption=\"Aggregate performance on WOMD (mean ± std)\",\n", + "# label=\"tab:aggregate_performance\",\n", + "# )\n", + "\n", + "# # Generate LaTeX table code with \\resizebox to fit within a single column\n", + "# latex_code = \"\"\"\n", + "# \\\\begin{table}[htbp]\n", + "# \\\\centering\n", + "# \\\\resizebox{\\\\columnwidth}{!}{\n", + "# \\\\begin{tabular}{@{}lcccc@{}} \n", + "# \\\\toprule\n", + "# \\\\textbf{Dataset} & \\\\textbf{Goal achieved} & \\\\textbf{Collided} & \\\\textbf{Off-road} & \\\\textbf{Other} \\\\\\\\\n", + "# \\\\midrule\n", + "# \"\"\"\n", + "\n", + "# # Add rows to the table\n", + "# for idx, row in tab_formatted.iterrows():\n", + "# latex_code += f\"\\\\textbf{{{idx}}} & \" # dataset name in bold\n", + "# latex_code += \" & \".join([f\"{value}\" for value in row]) # values for each metric\n", + "# latex_code += \" \\\\\\\\ \\n\"\n", + "\n", + "# # Closing part of the LaTeX table\n", + "# latex_code += \"\"\"\n", + "# \\\\bottomrule\n", + "# \\\\end{tabular}\n", + "# }\n", + "# \\\\caption{Aggregate performance on train and 10,000 test traffic scenarios from the Waymo Open Motion Dataset (mean $\\\\pm$ std)}\n", + "# \\\\label{tab:aggregate_perf_best_policy}\n", + "# \\\\end{table}\n", + "# \"\"\"\n", + "# print(latex_code)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Scaling laws\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.concat([df_100, df_1000, df_10_000]).reset_index(drop=True)\n", + "categories = ['goal_achieved_frac','collided_frac', 'off_road_frac']\n", + "df[categories] = df[categories] * 100\n", + "df['train_dataset_size'] = df['train_dataset_size'].apply(lambda x: f\"{int(x):,}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ERRORBAR = 'se'\n", + "MARKERSIZE = 6\n", + "MARKER = 's'\n", + "\n", + "fig, axs = plt.subplots(1, 3, figsize=(9.3, 3))\n", + "\n", + "# Goal achieved\n", + "sns.pointplot(\n", + " data=df[df['dataset'] == DATA], \n", + " errorbar=ERRORBAR, \n", + " x='train_dataset_size', \n", + " y='goal_achieved_frac', \n", + " marker='o',\n", + " markersize=MARKERSIZE,\n", + " color=blue, \n", + " ax=axs[0]\n", + ")\n", + "axs[0].set_ylim(70, 105)\n", + "axs[0].grid(axis='y', linestyle='-', color='lightgrey', alpha=0.3)\n", + "axs[0].set_xlabel(\"Training dataset size\", labelpad=10)\n", + "# Add horizontal line at 100%\n", + "axs[0].axhline(100, color='#aac0ea', linestyle='--', linewidth=2, zorder=0)\n", + "axs[0].set_ylabel(\"Goal achieved [%]\")\n", + "\n", + "# Collided\n", + "sns.pointplot(\n", + " data=df[df['dataset'] == DATA], \n", + " errorbar=ERRORBAR, \n", + " x='train_dataset_size', \n", + " y='collided_frac', \n", + " marker='s',\n", + " markersize=MARKERSIZE,\n", + " color=red, \n", + " ax=axs[1]\n", + ")\n", + "\n", + "axs[1].set_ylim(-2, 15)\n", + "axs[1].grid(axis='y', linestyle='-', color='lightgrey', alpha=0.3)\n", + "axs[1].axhline(0, color='#ffc7c4', linestyle='--', linewidth=2, zorder=0)\n", + "axs[1].set_xlabel(\"Training dataset size\", labelpad=10)\n", + "axs[1].set_ylabel(\"Collided [%]\")\n", + "\n", + "# Off road\n", + "sns.pointplot(\n", + " data=df[df['dataset'] == DATA], \n", + " errorbar=ERRORBAR, \n", + " x='train_dataset_size', \n", + " y='off_road_frac', \n", + " marker=\"D\",\n", + " markersize=MARKERSIZE,\n", + " color=orange, \n", + " ax=axs[2]\n", + ")\n", + "\n", + "axs[2].set_ylim(-2, 15)\n", + "axs[2].grid(axis='y', linestyle='-', color='lightgrey', alpha=0.3)\n", + "axs[2].set_xlabel(\"Training dataset size\", labelpad=10)\n", + "axs[2].axhline(0, color=\"#ffb995\", linestyle='--', linewidth=2, zorder=0)\n", + "axs[2].set_ylabel(\"Off-road [%]\")\n", + "\n", + "plt.tight_layout()\n", + "sns.despine()\n", + "plt.savefig(f'{FIGURE_DIR}scaling_laws_point_{now_str}_simple.pdf', bbox_inches='tight', format='pdf')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "gpudrive", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/gpudrive-main/examples/experimental/notebooks/02_training_curves.ipynb b/gpudrive-main/examples/experimental/notebooks/02_training_curves.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..1b4b8e3d2f80352f0fcfa3155c1338635dcf7d4a --- /dev/null +++ b/gpudrive-main/examples/experimental/notebooks/02_training_curves.ipynb @@ -0,0 +1,9917 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# Dependencies\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import warnings\n", + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt\n", + "import datetime\n", + "\n", + "sns.set(\"notebook\", font_scale=1.05, rc={\"figure.figsize\": (10, 5)})\n", + "sns.set_style(\"ticks\", rc={\"figure.facecolor\": \"none\", \"axes.facecolor\": \"none\"})\n", + "%config InlineBackend.figure_format = 'svg'\n", + "warnings.filterwarnings(\"ignore\")\n", + "plt.set_loglevel(\"WARNING\")\n", + "mpl.rcParams[\"lines.markersize\"] = 8\n", + "\n", + "# generate datetime string\n", + "now = datetime.datetime.now()\n", + "now_str = now.strftime(\"%m_%d_%H_%M\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "WINDOW_SIZE = 250\n", + "\n", + "PATH = \"...\"\n", + "\n", + "# Load data\n", + "df_goal = pd.read_csv(PATH + \"perc_goal_uptime.csv\")\n", + "df_collisions = pd.read_csv(PATH + \"perc_collisions_step.csv\")\n", + "df_reward = pd.read_csv(PATH + \"mean_rew_step.csv\")\n", + "\n", + "df_goal" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "global_step = df_reward['global_step'].values\n", + "time = df_goal['performance/uptime'].values / 3600\n", + "\n", + "reward = df_reward['PPO____S_1000__02_26_08_54_58_289 - metrics/mean_episode_reward_per_agent'].rolling(window=WINDOW_SIZE).mean().values\n", + "offroad = df_collisions['PPO____S_1000__02_26_08_54_58_289 - metrics/perc_off_road'].rolling(window=WINDOW_SIZE).mean().values * 100\n", + "collision = df_collisions['PPO____S_1000__02_26_08_54_58_289 - metrics/perc_veh_collisions'].rolling(window=WINDOW_SIZE).mean().values * 100\n", + "goal_achieved = df_goal['PPO____S_1000__02_26_08_54_58_289 - metrics/perc_goal_achieved'].rolling(window=WINDOW_SIZE).mean().values * 100\n" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2025-02-27T11:52:47.530985\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.9.0, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n", + "import matplotlib.ticker as ticker\n", + "\n", + "# Define constant for the number of steps to zoom in\n", + "ZOOM_STEPS = 2000\n", + "\n", + "fig, axs = plt.subplots(1, 3, figsize=(12, 3.5))\n", + "\n", + "# First axis (Average reward)\n", + "axs[0].plot(time, reward, color='g', linewidth=2)\n", + "axs[0].grid(True, color='lightgrey', alpha=0.3)\n", + "axs[0].set_xlabel(\"Wall-clock-time [h]\", labelpad=10)\n", + "axs[0].set_ylabel(\"Average reward\")\n", + "axs[0].set_xlim([0, 24])\n", + "axs[0].set_ylim([0.79, 1])\n", + "\n", + "# Inset for the first axis (zoom-in on last ZOOM_STEPS steps)\n", + "axins0 = inset_axes(axs[0], width=\"30%\", height=\"35%\", loc=\"center right\") # Slightly increased height\n", + "axins0.plot(time[-ZOOM_STEPS:], reward[-ZOOM_STEPS:], color='g', linewidth=2)\n", + "axins0.grid(True, color='lightgrey', alpha=0.3)\n", + "axins0.set_xlim(time[-ZOOM_STEPS], time[-1])\n", + "axins0.set_ylim(min(reward[-ZOOM_STEPS:]), max(reward[-ZOOM_STEPS:]))\n", + "axins0.set_yscale('log')\n", + "\n", + "# Label for the zoom-in plot\n", + "axins0.text(0.05, 1.15, 'Zoom-in', transform=axins0.transAxes, fontsize=10, color='black', ha='left', va='top')\n", + "\n", + "# Hide y-axis labels and ticks\n", + "axins0.get_yaxis().set_visible(False)\n", + "\n", + "# Annotate the min and max values in red\n", + "min_value = min(reward[-ZOOM_STEPS:])\n", + "max_value = max(reward[-ZOOM_STEPS:])\n", + "\n", + "# Annotating min and max values\n", + "axins0.annotate(f'{min_value:.3f}', xy=(time[-ZOOM_STEPS], min_value), xytext=(-20, -2),\n", + " textcoords='offset points', color='red', fontsize=10, ha='center', va='bottom')\n", + "axins0.annotate(f'{max_value:.3f}', xy=(time[-ZOOM_STEPS], max_value), xytext=(67, 15),\n", + " textcoords='offset points', color='red', fontsize=10, ha='center', va='top')\n", + "\n", + "# Second axis (Goal achieved)\n", + "axs[1].plot(global_step, goal_achieved, color='b', linewidth=2)\n", + "axs[1].grid(True, color='lightgrey', alpha=0.3)\n", + "axs[1].set_xlabel(\"Global step\", labelpad=10)\n", + "axs[1].set_ylabel(\"Perc. goal achieved [%]\")\n", + "\n", + "# Inset for the second axis (zoom-in on last ZOOM_STEPS steps)\n", + "axins1 = inset_axes(axs[1], width=\"30%\", height=\"35%\", loc=\"center right\") # Slightly increased height\n", + "axins1.plot(global_step[-ZOOM_STEPS:], goal_achieved[-ZOOM_STEPS:], color='b', linewidth=2)\n", + "axins1.grid(True, color='lightgrey', alpha=0.3)\n", + "axins1.set_xlim(global_step[-ZOOM_STEPS], global_step[-1])\n", + "axins1.set_ylim(min(goal_achieved[-ZOOM_STEPS:]), max(goal_achieved[-ZOOM_STEPS:]))\n", + "axins1.set_yscale('log')\n", + "\n", + "# Label for the zoom-in plot\n", + "axins1.text(0.05, 1.15, 'Zoom-in', transform=axins1.transAxes, fontsize=10, color='black', ha='left', va='top')\n", + "\n", + "# Format y-axis labels of the inset plot\n", + "for tick in axins1.get_yticklabels():\n", + " tick.set_rotation(0)\n", + " tick.set_fontsize(1)\n", + "\n", + "axins1.get_yaxis().set_visible(False)\n", + "\n", + "# Annotate the min and max values in red\n", + "min_value = min(goal_achieved[-ZOOM_STEPS:])\n", + "max_value = max(goal_achieved[-ZOOM_STEPS:])\n", + "\n", + "# Annotating min and max values\n", + "axins1.annotate(f'{min_value:.3f}', xy=(global_step[-ZOOM_STEPS], min_value), xytext=(-20, -2),\n", + " textcoords='offset points', color='red', fontsize=10, ha='center', va='bottom')\n", + "axins1.annotate(f'{max_value:.3f}', xy=(global_step[-ZOOM_STEPS], max_value), xytext=(67, 15),\n", + " textcoords='offset points', color='red', fontsize=10, ha='center', va='top')\n", + "\n", + "\n", + "# Third axis (Collisions and Off-road)\n", + "axs[2].plot(global_step, collision, color='r', label='Collided', linewidth=2)\n", + "axs[2].plot(global_step, offroad, color='orange', label='Off-road', linewidth=2)\n", + "axs[2].grid(True, color='lightgrey', alpha=0.3)\n", + "axs[2].set_xlabel(\"Global step\", labelpad=10)\n", + "axs[2].set_ylabel(\"Metric [%]\")\n", + "#axs[2].set_yscale('log')\n", + "axs[2].legend(facecolor='white', framealpha=1, fontsize=10)\n", + "\n", + "# Inset for the third axis (zoom-in on last ZOOM_STEPS steps)\n", + "axins2 = inset_axes(axs[2], width=\"30%\", height=\"35%\", loc=\"center right\") # Slightly increased height\n", + "axins2.plot(global_step[-ZOOM_STEPS:], collision[-ZOOM_STEPS:], color='r', linewidth=2)\n", + "axins2.plot(global_step[-ZOOM_STEPS:], offroad[-ZOOM_STEPS:], color='orange', linewidth=2)\n", + "axins2.grid(True, color='lightgrey', alpha=0.3)\n", + "axins2.set_xlim(global_step[-ZOOM_STEPS], global_step[-1])\n", + "axins2.set_yscale('log')\n", + "\n", + "# Label for the zoom-in plot\n", + "axins2.text(0.05, 1.15, 'Zoom-in', transform=axins2.transAxes, fontsize=10, color='black', ha='left', va='top')\n", + "\n", + "# Format y-axis labels of the inset plot\n", + "for tick in axins2.get_yticklabels():\n", + " tick.set_rotation(0)\n", + " tick.set_fontsize(6)\n", + "\n", + "# Disable scientific notation for y-axis\n", + "axins2.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, _: f'{x:.2f}'))\n", + "\n", + "sns.despine()\n", + "plt.tight_layout()\n", + "plt.savefig(f'training_curves.pdf', bbox_inches='tight', format='pdf')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "gpudrive", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/gpudrive-main/examples/experimental/notebooks/03_finetuning.ipynb b/gpudrive-main/examples/experimental/notebooks/03_finetuning.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..1625c16782f81396bd82ea08abdbebd717b396cd --- /dev/null +++ b/gpudrive-main/examples/experimental/notebooks/03_finetuning.ipynb @@ -0,0 +1,6535 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [], + "source": [ + "# Dependencies\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "import warnings\n", + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt\n", + "import datetime\n", + "\n", + "sns.set(\"notebook\", font_scale=1.05, rc={\"figure.figsize\": (10, 5)})\n", + "sns.set_style(\"ticks\", rc={\"figure.facecolor\": \"none\", \"axes.facecolor\": \"none\"})\n", + "%config InlineBackend.figure_format = 'svg'\n", + "warnings.filterwarnings(\"ignore\")\n", + "plt.set_loglevel(\"WARNING\")\n", + "mpl.rcParams[\"lines.markersize\"] = 8\n", + "\n", + "# generate datetime string\n", + "now = datetime.datetime.now()\n", + "now_str = now.strftime(\"%m_%d_%H_%M\")" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [], + "source": [ + "WINDOW_SIZE = 120\n", + "\n", + "\n", + "df = pd.read_csv(\"/home/emerge/daphne/gpudrive/examples/benchmarks/dataframes/finetuning/goal_wallclock.csv\")\n", + "df2 = pd.read_csv(\"/home/emerge/daphne/gpudrive/examples/benchmarks/dataframes/finetuning/offroad_wallclock.csv\")\n", + "df3 = pd.read_csv(\"/home/emerge/daphne/gpudrive/examples/benchmarks/dataframes/finetuning/collision_wallclock.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "wallclock_time = df['performance/uptime'].rolling(window=WINDOW_SIZE).mean().dropna().values[:X_LIMIT]\n", + "perc_goal_achieved = df['finetune - metrics/perc_goal_achieved'].rolling(window=WINDOW_SIZE).mean().dropna().values[:X_LIMIT] *100\n", + "\n", + "perc_offroad = df2['finetune - metrics/perc_off_road'].rolling(window=WINDOW_SIZE).mean().dropna().values[:X_LIMIT] *100\n", + "perc_collided = df3['finetune - metrics/perc_veh_collisions'].rolling(window=WINDOW_SIZE).mean().dropna().values[:X_LIMIT] * 100\n", + "\n", + "\n", + "wallclock_time = wallclock_time / 60" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2025-01-30T21:40:22.093007\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.9.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(5, 4))\n", + "ax.plot(wallclock_time, perc_goal_achieved, label=\"Goal achieved [%]\", linewidth=2)\n", + "ax.plot(wallclock_time, perc_collided, label=\"Collided [%]\", color='r', linewidth=2)\n", + "ax.plot(wallclock_time, perc_offroad, label=\"Offroad [%]\", color='orange', linewidth=2)\n", + "ax.grid(axis='y', linestyle='-', color='lightgrey', alpha=0.3)\n", + "ax.set_xlabel(\"Wallclock time (minutes)\")\n", + "ax.set_ylabel(\"Performance\")\n", + "ax.set_xlim([0, 15])\n", + "ax.legend(loc=\"upper left\", fontsize=10, facecolor=\"white\", framealpha=1)\n", + "sns.despine()\n", + "\n", + "plt.savefig(f'finetuning_backwards_driving.pdf', bbox_inches='tight', format='pdf')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Log " + ] + }, + { + "cell_type": "code", + "execution_count": 115, + "metadata": {}, + "outputs": [], + "source": [ + "df_ga_wallclock = pd.read_csv(\"/home/emerge/daphne/gpudrive/examples/benchmarks/dataframes/finetuning/reward.csv\")\n", + "df_rew_step = pd.read_csv(\"/home/emerge/daphne/gpudrive/examples/benchmarks/dataframes/finetuning/goal_achieved.csv\")\n", + "df_offroad = pd.read_csv(\"/home/emerge/daphne/gpudrive/examples/benchmarks/dataframes/finetuning/offroad.csv\")\n", + "df_collided = pd.read_csv(\"/home/emerge/daphne/gpudrive/examples/benchmarks/dataframes/finetuning/collided.csv\")\n", + "\n", + "wallclock_time = df_ga_wallclock['performance/uptime'].rolling(window=WINDOW_SIZE).mean().dropna().values / 60 / 60\n", + "global_step = df_rew_step['global_step'].rolling(window=WINDOW_SIZE).mean().dropna().values\n", + "\n", + "reward_over_time = df_rew_step['PPO__R_10000__01_23_21_02_58_770 - metrics/mean_episode_reward_per_agent'].rolling(window=WINDOW_SIZE).mean().dropna().values\n", + "goal_achieved = df_ga_wallclock['PPO__R_10000__01_23_21_02_58_770 - metrics/perc_goal_achieved'].rolling(window=WINDOW_SIZE).mean().dropna().values * 100\n", + "offroad = df_offroad['PPO__R_10000__01_23_21_02_58_770 - metrics/perc_off_road'].rolling(window=WINDOW_SIZE).mean().dropna().values * 100\n", + "collided = df_collided['PPO__R_10000__01_23_21_02_58_770 - metrics/perc_veh_collisions'].rolling(window=WINDOW_SIZE).mean().dropna().values * 100" + ] + }, + { + "cell_type": "code", + "execution_count": 153, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2025-01-31T02:15:02.759964\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.9.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(1, 3, figsize=(12, 3.5))\n", + "axs[0].plot(global_step, goal_achieved, color='b', linewidth=2)\n", + "axs[0].grid(True, color='lightgrey', alpha=0.3)\n", + "axs[0].set_xlabel(\"Global step\", labelpad=10)\n", + "axs[0].set_ylabel(\"Goal achieved [%]\")\n", + "axs[0].set_yscale('log')\n", + "axs[0].set_xscale('log')\n", + "axs[0].set_xlim([3e8, 1e9])\n", + "axs[0].set_ylim([97, 100])\n", + "plt.gca().yaxis.set_major_formatter(FuncFormatter(format_func))\n", + "\n", + "axs[1].plot(global_step, offroad, color='orange', linewidth=2)\n", + "axs[1].grid(True, color='lightgrey', alpha=0.3)\n", + "axs[1].set_xlabel(\"Global step\", labelpad=10)\n", + "axs[1].set_ylabel(\"Off-road [%]\")\n", + "axs[1].set_yscale('log')\n", + "axs[1].set_xscale('log')\n", + "axs[1].set_xlim([3e8, 1e9])\n", + "\n", + "\n", + "axs[2].plot(global_step, collided, color='r', linewidth=2)\n", + "axs[2].grid(True, color='lightgrey', alpha=0.3)\n", + "axs[2].set_xlabel(\"Global step\", labelpad=10)\n", + "axs[2].set_ylabel(\"Collided [%]\")\n", + "axs[2].set_yscale('log')\n", + "axs[2].set_xscale('log')\n", + "axs[2].set_xlim([3e8, 1e9])\n", + "axs[2].set_ylim([2, 4])\n", + "\n", + "sns.despine()\n", + "plt.tight_layout()\n", + "plt.savefig(f'loglog_train_performance.pdf', bbox_inches='tight', format='pdf')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "gdrive", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/gpudrive-main/examples/experimental/scene_manipulation.py b/gpudrive-main/examples/experimental/scene_manipulation.py new file mode 100644 index 0000000000000000000000000000000000000000..d81a24c60ebba0b136f25cf9317c3731d340182e --- /dev/null +++ b/gpudrive-main/examples/experimental/scene_manipulation.py @@ -0,0 +1,215 @@ +import os +import logging +from PIL import Image +from tqdm import tqdm +import pandas as pd +from pathlib import Path +import matplotlib.pyplot as plt +import numpy as np +import torch +from eval_utils import load_policy, rollout, load_config, make_env + +from pygpudrive.env.dataset import SceneDataLoader +from pygpudrive.datatypes.observation import LocalEgoState + +import pdb + +def test_policy_robustness( + env, + policy, + data_loader, + config, + remove_random_agents=False, + remove_controlled_agents=True, + plot_trajectories=False +): + + res_dict = { + "scene": [], + "goal_achieved": [], + "collided": [], + "off_road": [], + "not_goal_nor_crashed": [], + "controlled_agents_in_scene": [], + } + + for batch in tqdm( + data_loader, + desc=f"Processing; remove_random_agents = {remove_random_agents}", + total=len(data_loader), + colour="red", + ): + # Set new data batch with simulator + env.swap_data_batch(batch) + + # Sanity check: plot scenes before removing agents + sim_state_figs_before = env.vis.plot_simulator_state( + env_indices=[0, 1], + time_steps=[0, 0], + ) + sim_state_figs_before[0].savefig(f"sim_state_before_0.png") + sim_state_figs_before[1].savefig(f"sim_state_before_1.png") + + if remove_random_agents: + env.remove_agents_by_id( + config.perc_to_rmv_per_scene, + remove_controlled_agents = remove_controlled_agents + ) + + # Check after + sim_state_figs_after = env.vis.plot_simulator_state( + env_indices=[0, 1], + time_steps=[0, 0], + ) + sim_state_figs_after[0].savefig(f"sim_state_after_0.png") + sim_state_figs_after[1].savefig(f"sim_state_after_1.png") + + # Rollout policy + ( + goal_achieved, + collided, + off_road, + controlled_agents_in_scene, + not_goal_nor_crashed, + _, + agent_positions, + ) = rollout( + env=env, + policy=policy, + device=config.device, + deterministic=config.deterministic, + render_sim_state=config.render_sim_state, + return_agent_positions=plot_trajectories + ) + + # Save last timestep rollout + _ = env.reset() + last_sim_state = env.vis.plot_simulator_state( + env_indices=[0, 1], + time_steps=[-1, -1], + agent_positions=agent_positions + ) + last_sim_state[0].savefig(f"last_sim_state_0.png") + last_sim_state[1].savefig(f"last_sim_state_1.png") + + # Store results for the current batch + scenario_names = [Path(path).stem for path in batch] + res_dict["scene"].extend(scenario_names) + res_dict["goal_achieved"].extend(goal_achieved.cpu().numpy()) + res_dict["collided"].extend(collided.cpu().numpy()) + res_dict["off_road"].extend(off_road.cpu().numpy()) + res_dict["not_goal_nor_crashed"].extend( + not_goal_nor_crashed.cpu().numpy() + ) + res_dict["controlled_agents_in_scene"].extend( + controlled_agents_in_scene.cpu().numpy() + ) + + df_res = pd.DataFrame(res_dict) + if remove_random_agents: + df_res["deleted_agents"] = True + else: + df_res["deleted_agents"] = False + + return df_res + +if __name__ == "__main__": + + config = load_config("examples/experimental/config/scene_manipulation_config") + + train_loader = SceneDataLoader( + root=config.train_path, + batch_size=config.num_worlds, + dataset_size=config.dataset_size, + sample_with_replacement=False, + ) + + # Make env + env = make_env(config, train_loader) + + # Load policy + policy = load_policy( + path_to_cpt=config.cpt_path, + model_name=config.cpt_name, + device=config.device, + env=env, + ) + + # Run tests + df_perf_original = test_policy_robustness( + env=env, + policy=policy, + data_loader=train_loader, + config=config, + remove_random_agents=False, + plot_trajectories=True + ) + + df_perf_perturbed_controlled = test_policy_robustness( + env=env, + policy=policy, + data_loader=train_loader, + config=config, + remove_random_agents=True, + plot_trajectories=True + ) + + df_perf_perturbed_static = test_policy_robustness( + env=env, + policy=policy, + data_loader=train_loader, + config=config, + remove_random_agents=True, + remove_controlled_agents=False, + plot_trajectories=True + ) + + # Concatenate all three dataframes with a new column to identify the scenario + df_perf_original['Class'] = 'Original' + df_perf_perturbed_controlled['Class'] = 'Removed controlled' + df_perf_perturbed_static['Class'] = 'Removed other' + + df = pd.concat([df_perf_original, df_perf_perturbed_controlled, df_perf_perturbed_static]) + + # # Calculate mean values for each metric grouped by deleted_agents + # metrics = ['goal_achieved', 'collided', 'off_road', 'not_goal_nor_crashed'] + + # # Convert boolean columns to float for averaging + # for col in metrics: + # df[col] = df[col].astype(float) + + # # Now calculate means + # means_by_group = df.groupby('scenario')[metrics].mean() + + # # Set up the plot + # fig, ax = plt.subplots(figsize=(12, 6)) + # x = np.arange(len(metrics)) + # width = 0.25 + + # # Plot bars for each group + # ax.bar(x - width, means_by_group.loc['Original'], width, + # label='Original', color='skyblue') + # ax.bar(x, means_by_group.loc['Removed Controlled'], width, + # label=f'Removed {int(config.perc_to_rmv_per_scene*100)}% Controlled', color='lightcoral') + # ax.bar(x + width, means_by_group.loc['Removed Uncontrolled'], width, + # label=f'Removed {int(config.perc_to_rmv_per_scene*100)}% Uncontrolled', color='lightgreen') + + # # Customize the plot + # ax.set_ylabel('Proportion') + # ax.set_title('Policy Performance Comparison') + # ax.set_xticks(x) + # ax.set_xticklabels(metrics, rotation=45) + # ax.legend() + + # # Adjust layout and save + # plt.tight_layout() + # plt.savefig(f"{config.save_results_path}/metrics_comparison_{int(config.perc_to_rmv_per_scene*100)}pct.png") + # plt.close() + + # Save + if not os.path.exists(config.save_results_path): + os.makedirs(config.save_results_path) + + df.to_csv(f"{config.save_results_path}/combined_results_{int(config.perc_to_rmv_per_scene*100)}pct.csv", index=False) + + logging.info(f"Saved results at {config.save_results_path}") \ No newline at end of file diff --git a/gpudrive-main/examples/experimental/show_agent_behaviors.py b/gpudrive-main/examples/experimental/show_agent_behaviors.py new file mode 100644 index 0000000000000000000000000000000000000000..a88767a8d08eb13a62538515b3438707a8b2a004 --- /dev/null +++ b/gpudrive-main/examples/experimental/show_agent_behaviors.py @@ -0,0 +1,180 @@ +import pandas as pd +from box import Box +import numpy as np +from pathlib import Path +import mediapy + +from gpudrive.env.dataset import SceneDataLoader +from eval_utils import load_policy, load_config, make_env, rollout + +import logging + +logging.basicConfig(level=logging.INFO) + +import pdb +import random +import torch +import numpy as np + +def set_seed(seed: int): + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) # If using CUDA + torch.backends.cudnn.deterministic = True + +logging.basicConfig(level=logging.INFO) +SEED = 42 +set_seed(SEED) + +def make_videos( + policy, + eval_config, + df_results=None, + dataset="train", + sort_by=None, # Options: 'goal_achieved', + show_top_k=100, + device="cuda", + zoom_radius=100, + deterministic=False, + render_every_n_steps=10, + center_on_ego=False, + render_3d=True, +): + """Make videos policy rollouts environment. + Args: + df_results (pd.DataFrame): Dataframe with the results of the policy rollout. + policy (torch.nn.Module): Policy to select actions. + eval_config (Box): Configuration for the evaluation. + sort_by (str): Sample scenarios from dataframe sorted by this column. Options: + - goal_achieved_frac: Sort by goal_achieved in descending order (successes first). + - collided_frac: Sort by collided in descending order. + - off_road_frac: Sort by off_road in descending order. + - other_frac: Sort by not_goal_nor_crashed in descending order. + - controlled_agents_in_scene: Sort by controlled_agents_in_scene in descending order. + """ + + base_data_path = ( + eval_config.train_dir if dataset == "train" + else eval_config.test_dir + ) + + if df_results is not None: + df_results = df_results[df_results["dataset"] == dataset] + + # Make environment + train_loader = SceneDataLoader( + root=base_data_path, + batch_size=show_top_k, + dataset_size=show_top_k, + sample_with_replacement=False, + shuffle=False, + ) + + env = make_env(eval_config, train_loader, render_3d) + + # Select data batch to + if sort_by is not None and sort_by in df_results.columns: + df_top_k = df_results.sort_values(by=sort_by, ascending=False).head( + show_top_k + ) + data_batch = ( + base_data_path + "/" + df_top_k.scene.values + ).tolist() + + elif sort_by is None: + data_batch = env.data_batch + + env.swap_data_batch(data_batch) + + # Rollout policy in the environments + _, _, _, _, _, _, _, _, _, sim_state_frames, global_agent_states, _ = rollout( + env=env, + policy=policy, + device=device, + deterministic=deterministic, + render_sim_state=True, + render_every_n_steps=render_every_n_steps, + zoom_radius=zoom_radius, + center_on_ego=center_on_ego, + ) + + return sim_state_frames, env.get_env_filenames() + + +if __name__ == "__main__": + + # Specify which model to load and the dataset to evaluate + MODEL_TO_LOAD = "model_PPO____S_1000__02_26_08_54_58_359_009200" + DATASET = "train" + SORT_BY = None #"goal_achieved_frac" + SHOW_TOP_K = 50 # Render this many scenes + + # Configurations + eval_config = load_config("examples/experimental/config/eval_config") + model_config = load_config("examples/experimental/config/model_config") + + # Load policy + policy = load_policy( + path_to_cpt=model_config.models_path, + model_name=MODEL_TO_LOAD, + device=eval_config.device, + ) + + # Load results dataframe + if SORT_BY is not None: + df_res = pd.read_csv(f"{eval_config.res_path}/{MODEL_TO_LOAD}.csv") + df_res = df_res[df_res['controlled_agents_in_scene'] > 3] + else: + df_res = None + + logging.info( + f"Loaded policy {MODEL_TO_LOAD} and corresponding results df." + ) + + # Rollout policy and make videos + sim_state_frames, filenames = make_videos( + df_results=df_res, + policy=policy, + eval_config=eval_config, + sort_by=SORT_BY, # Options: 'goal_achieved', 'collided', 'off_road', 'not_goal_nor_crashed', 'controlled_agents_in_scene' + show_top_k=SHOW_TOP_K, + dataset=DATASET, + device=eval_config.device, + zoom_radius=40, + deterministic=False, + render_every_n_steps=1, + #center_on_ego=True, + render_3d=True, + ) + + sim_state_arrays = {k: np.array(v) for k, v in sim_state_frames.items()} + + SORT_BY = SORT_BY if not None else "random" + videos_dir = Path(f"videos/{MODEL_TO_LOAD}/project_page") # Path(f"videos/{MODEL_TO_LOAD}/{SORT_BY}_top_{SHOW_TOP_K}") + videos_dir.mkdir(parents=True, exist_ok=True) + + # Save videos locally + for env_id, frames in sim_state_arrays.items(): + + filename = filenames[env_id] + + if df_res: + scene_stats = df_res[df_res["scene"] == filename] + goal_achieved = scene_stats.goal_achieved_frac.values.item() + collided = scene_stats.collided_frac.values.item() + off_road = scene_stats.off_road_frac.values.item() + other = scene_stats.other_frac.values.item() + + video_path = videos_dir / f"{filename}_ga_{goal_achieved:.2f}__cr_{collided:.2f}__or_{off_road:.2f}__ot_{other:.2f}.gif" + else: + video_path = videos_dir / f"{filename}.gif" + + mediapy.write_video( + str(video_path), + frames, + fps=17, + codec='gif', + ) + + logging.info(f"Saved video to {video_path}") diff --git a/gpudrive-main/examples/experimental/viz_rollouts.py b/gpudrive-main/examples/experimental/viz_rollouts.py new file mode 100644 index 0000000000000000000000000000000000000000..f0b22850a935c34538f6322c10ac626cc589f6c7 --- /dev/null +++ b/gpudrive-main/examples/experimental/viz_rollouts.py @@ -0,0 +1,137 @@ +import os +import logging +from pathlib import Path +from tqdm import tqdm +import torch +import mediapy +import numpy as np + +from eval_utils import load_policy, rollout, load_config, make_env +from gpudrive.env.dataset import SceneDataLoader + +def visualize_rollouts( + env, + policy, + data_loader, + config, + save_path, + num_scenes=None, + make_videos=True, +): + """ + Visualize policy rollouts for specified number of scenes. + + Args: + env: Environment instance + policy: Loaded policy model + data_loader: SceneDataLoader instance + config: Configuration object + save_path: Path to save visualizations + num_scenes: Number of scenes to visualize. If None, processes all scenes. + """ + + # Create save directory if it doesn't exist + os.makedirs(save_path, exist_ok=True) + + # Limit number of scenes if specified + if num_scenes is not None: + total_batches = min(num_scenes // config.num_worlds + 1, len(data_loader)) + else: + total_batches = len(data_loader) + + scene_count = 0 + + for batch_idx, batch in tqdm( + enumerate(data_loader), + desc="Processing scenes", + total=total_batches, + colour="green", + ): + if num_scenes is not None and scene_count >= num_scenes: + break + + # Set new data batch with simulator + env.swap_data_batch(batch) + # Rollout policy and get agent positions + ( + _, _, # goal_achieved + _, _, # collided + _, _, # off_road + _, _, # not_goal_nor_crashed + _,# controlled_agents_in_scene + sim_state_frames, # sim state frames + agent_positions, + _, # episode lengths + ) = rollout( + env=env, + policy=policy, + device=config.device, + deterministic=config.deterministic, + render_sim_state=config.render_sim_state if make_videos else False, + return_agent_positions=True, + zoom_radius=40 + ) + + # Reset environment to visualize final states with trajectories + _ = env.reset(env.cont_agent_mask) + final_states = env.vis.plot_simulator_state( + env_indices=list(range(len(batch))), + time_steps=[-1] * len(batch), + zoom_radius = 40, + agent_positions=agent_positions + ) + + # Save final states with trajectories + for i, fig in enumerate(final_states): + scene_name = Path(batch[i]).stem + fig.savefig(os.path.join(save_path, f"{scene_name}_3d.png")) + + if make_videos: + # Save videos + sim_state_arrays = {k: np.array(v) for k, v in sim_state_frames.items()} + filenames = env.get_env_filenames() + for env_id, frames in sim_state_arrays.items(): + + filename = filenames[env_id] + + mediapy.write_video( + os.path.join(save_path, f"{filename}_3d.gif"), + frames, + fps=15, + codec='gif', + ) + +if __name__ == "__main__": + # Load configuration + config = load_config("examples/experimental/config/visualization_config") + + # Initialize data loader + data_loader = SceneDataLoader( + root=config.train_path, + batch_size=config.num_worlds, + dataset_size=config.dataset_size, + sample_with_replacement=False, + ) + + # Create environment + env = make_env(config, data_loader, render_3d=True) + + # Load policy + policy = load_policy( + path_to_cpt=config.cpt_path, + model_name=config.cpt_name, + device=config.device, + env=env, + ) + + # Run visualization + visualize_rollouts( + env=env, + policy=policy, + data_loader=data_loader, + config=config, + save_path=config.save_results_path, + num_scenes=None, # Set to None to process all scenes + ) + + logging.info(f"Saved visualizations at {config.save_results_path}") \ No newline at end of file diff --git a/gpudrive-main/examples/tutorials/01_scenario_loading.ipynb b/gpudrive-main/examples/tutorials/01_scenario_loading.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..6813a2084a524c888ebb98e342b006f46b31c28d --- /dev/null +++ b/gpudrive-main/examples/tutorials/01_scenario_loading.ipynb @@ -0,0 +1,4318 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Scenario loading and structure \n", + "\n", + "`GPUDrive` is a multi-agent driving simulator built on top of the [Waymo Open Motion Dataset (WOMD)](https://waymo.com/open/) (See also [Ettinger et al., 2021](https://arxiv.org/abs/2104.10133)). \n", + "\n", + "In this tutorial, we explain the structure of a traffic scenario and show use processed scenario data with `GPUDrive`.\n", + "\n", + "**Useful links to learn more**:\n", + "- [`waymo-open-dataset`](https://github.com/waymo-research/waymo-open-dataset): Official dataset repo\n", + "- [tf.Example proto format](https://waymo.com/open/data/motion/tfexample): Data dictionary for a raw WOMD scenario\n", + "- [GPUDrive `data_utils`](https://github.com/Emerge-Lab/gpudrive/tree/main/data_utils): Docs and code we use to process the WOMD scenarios" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "# Dependencies\n", + "import json\n", + "import matplotlib.pyplot as plt\n", + "import os\n", + "from pathlib import Path\n", + "import seaborn as sns\n", + "import pandas as pd\n", + "\n", + "# Set working directory to the base directory 'gpudrive'\n", + "working_dir = Path.cwd()\n", + "while working_dir.name != 'gpudrive':\n", + " working_dir = working_dir.parent\n", + " if working_dir == Path.home():\n", + " raise FileNotFoundError(\"Base directory 'gpudrive' not found\")\n", + "os.chdir(working_dir)\n", + "\n", + "cmap = [\"r\", \"g\", \"b\", \"y\", \"c\"]\n", + "%config InlineBackend.figure_format = 'svg'\n", + "sns.set(\"notebook\", font_scale=1.1, rc={\"figure.figsize\": (8, 3)})\n", + "sns.set_style(\"ticks\", rc={\"figure.facecolor\": \"none\", \"axes.facecolor\": \"none\"})" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Iterating through the WOMD dataset\n", + "\n", + "We upload a folder containing three scenarios in the `data/processed/examples` directory that you can work with. The full dataset can be downloaded [here](https://github.com/Emerge-Lab/gpudrive/tree/main?tab=readme-ov-file#dataset). \n", + "\n", + "\n", + "Notice that the data folder is structured as follows:\n", + "\n", + "```bash\n", + "data/\n", + " - tfrecord-xxxxx-of-xxxxx\n", + " - ....\n", + " - tfrecord-xxxxx-of-xxxxx\n", + "```\n", + "\n", + "Every file beginning with `tfrecord` is a unique traffic scenario.\n", + "\n", + "To use the dataset with the simulator, we use the conventions from [PyTorch dataloaders](https://pytorch.org/tutorials/beginner/basics/data_tutorial.html). \n", + "\n", + "\n", + "Here is example of how to set up a dataloader:" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "from gpudrive.env.dataset import SceneDataLoader\n", + "\n", + "data_loader = SceneDataLoader(\n", + " root=\"data/processed/examples\", # Path to the dataset\n", + " batch_size=10, # Batch size, you want this to be equal to the number of worlds (envs) so that every world receives a different scene\n", + " dataset_size=4, # Total number of different scenes we want to use\n", + " sample_with_replacement=True, \n", + " seed=42, \n", + " shuffle=True, \n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['data/processed/examples/tfrecord-00002-of-01000_407.json',\n", + " 'data/processed/examples/tfrecord-00002-of-01000_407.json',\n", + " 'data/processed/examples/tfrecord-00000-of-01000_402.json',\n", + " 'data/processed/examples/tfrecord-00000-of-01000_222.json',\n", + " 'data/processed/examples/tfrecord-00000-of-01000_325.json',\n", + " 'data/processed/examples/tfrecord-00000-of-01000_402.json',\n", + " 'data/processed/examples/tfrecord-00000-of-01000_325.json',\n", + " 'data/processed/examples/tfrecord-00000-of-01000_222.json',\n", + " 'data/processed/examples/tfrecord-00000-of-01000_222.json',\n", + " 'data/processed/examples/tfrecord-00000-of-01000_325.json']" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# The full dataset that we will be using\n", + "data_loader.dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'data/processed/examples/tfrecord-00000-of-01000_222.json',\n", + " 'data/processed/examples/tfrecord-00000-of-01000_325.json',\n", + " 'data/processed/examples/tfrecord-00000-of-01000_402.json',\n", + " 'data/processed/examples/tfrecord-00002-of-01000_407.json'}" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Notice that it only has 4 unique scenes, since we set the dataset_size to 4\n", + "set(data_loader.dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'data/processed/examples/tfrecord-00002-of-01000_407.json'" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_files = next(iter(data_loader))\n", + "\n", + "data_files[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "from gpudrive.env.env_torch import GPUDriveTorchEnv\n", + "from gpudrive.env.config import EnvConfig" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "# Pass the data_loader to the environment \n", + "env = GPUDriveTorchEnv(\n", + " config=EnvConfig(),\n", + " data_loader=data_loader,\n", + " max_cont_agents=64, \n", + " device=\"cpu\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Deep dive: What is inside a traffic scenario? 🤔🔬" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Though every scenario in the WOMD is unique, they all share the same basic data structure. Traffic scenarios are essentially dictionaries, which you can inspect using tools like [JSON Formatter](https://jsonformatter.org/json-viewer). We'll also look at one in this notebook. In a nutshell, traffic scenarios contain a few key elements:\n", + "\n", + "- **Road map**: The layout and structure of the roads.\n", + "- **Human driving (expert) demonstrations**: Examples of human driving behavior.\n", + "- **Road objects**: Elements such as stop signs and other traffic signals." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['name', 'scenario_id', 'objects', 'roads', 'tl_states', 'metadata'])" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Take an example scene\n", + "data_path = \"data/processed/examples/tfrecord-00000-of-01000_222.json\"\n", + "\n", + "with open(data_path) as file:\n", + " traffic_scene = json.load(file)\n", + "\n", + "traffic_scene.keys()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "We will show you how to render a scene in ⏭️ tutorial `03`, which introduces the gym environment wrapper. Let's first take a closer look at the data structure." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Global Overview\n", + "\n", + "A traffic scene includes the following key elements:\n", + "\n", + "- **`name`**: The name of the traffic scenario. \n", + "- **`scenario_id`**: Unique identifier for every scenario.\n", + "- **`objects`**: Dynamic entities such as vehicles or other moving elements in the scene. \n", + "- **`roads`**: Stationary elements, including road points and fixed objects. \n", + "- **`tl_states`**: Traffic light states (currently not included in processing). \n", + "- **`metadata`**: Additional details about the traffic scenario, such as the index of the self-driving car (SDC) and details for the WOSAC Challenge." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "traffic_scene[\"tl_states\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'tfrecord-00000-of-00150_78.json'" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "traffic_scene[\"name\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'sdc_track_index': 11,\n", + " 'objects_of_interest': [14, 41],\n", + " 'tracks_to_predict': [{'track_index': 7, 'difficulty': 0},\n", + " {'track_index': 10, 'difficulty': 0},\n", + " {'track_index': 1, 'difficulty': 0}]}" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "traffic_scene[\"metadata\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'fcb5b4973f486e78'" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "traffic_scene[\"scenario_id\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-12-13T09:26:24.508544\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.9.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 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\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pd.Series(\n", + " [\n", + " traffic_scene[\"objects\"][idx][\"type\"]\n", + " for idx in range(len(traffic_scene[\"objects\"]))\n", + " ]\n", + ").value_counts().plot(kind=\"bar\", rot=45, color=cmap)\n", + "plt.title(\n", + " f'Distribution of road objects in traffic scene. Total # objects: {len(traffic_scene[\"objects\"])}'\n", + ")\n", + "plt.show()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This traffic scenario only contains vehicles and pedestrians, some scenes have cyclists as well." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-12-13T09:26:25.188340\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.9.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" + ], + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pd.Series(\n", + " [traffic_scene[\"roads\"][idx][\"type\"] for idx in range(len(traffic_scene[\"roads\"]))]\n", + ").value_counts().plot(kind=\"bar\", rot=45, color=cmap)\n", + "plt.title(\n", + " f'Distribution of road points in traffic scene. Total # points: {len(traffic_scene[\"roads\"])}'\n", + ")\n", + "plt.show()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### In-Depth: Road Objects\n", + "\n", + "This is a list of different road objects in the traffic scene. For each road object, we have information about its position, velocity, size, in which direction it's heading, whether it's a valid object, the type, and the final position of the vehicle." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['position', 'width', 'length', 'height', 'heading', 'velocity', 'valid', 'goalPosition', 'type', 'id', 'mark_as_expert'])" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Take the first object\n", + "idx = 0\n", + "\n", + "# For each object, we have this information:\n", + "traffic_scene[\"objects\"][idx].keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[\n", + " {\n", + " \"x\": 1769.906982421875,\n", + " \"y\": -4880.38330078125,\n", + " \"z\": -142.78407973639492\n", + " },\n", + " {\n", + " \"x\": 1769.90283203125,\n", + " \"y\": -4880.6923828125,\n", + " \"z\": -142.7745534732751\n", + " },\n", + " {\n", + " \"x\": 1769.9061279296875,\n", + " \"y\": -4880.966796875,\n", + " \"z\": -142.76056932836318\n", + " },\n", + " {\n", + " \"x\": 1769.903076171875,\n", + " \"y\": -4881.26220703125,\n", + " \"z\": -142.7467465274731\n", + " },\n", + " {\n", + " \"x\": 1769.893798828125,\n", + " \"y\": -4881.5322265625,\n", + " \"z\": -142.75370648323303\n", + " },\n", + " {\n", + " \"x\": 1769.889892578125,\n", + " \"y\": -4881.7978515625,\n", + " \"z\": -142.73502097033202\n", + " },\n", + " {\n", + " \"x\": 1769.886962890625,\n", + " \"y\": -4882.04833984375,\n", + " \"z\": -142.73322709385124\n", + " },\n", + " {\n", + " \"x\": 1769.8853759765625,\n", + " \"y\": -4882.24951171875,\n", + " \"z\": -142.7076711328151\n", + " },\n", + " {\n", + " \"x\": 1769.8785400390625,\n", + " \"y\": -4882.4990234375,\n", + " \"z\": -142.70739824649579\n", + " },\n", + " {\n", + " \"x\": 1769.8828125,\n", + " \"y\": -4882.708984375,\n", + " \"z\": -142.71031548280774\n", + " }\n", + "]\n" + ] + } + ], + "source": [ + "# Position contains the (x, y) coordinates for the vehicle at every time step\n", + "print(json.dumps(traffic_scene[\"objects\"][idx][\"position\"][:10], indent=4))" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2.1302132606506348, 4.532253742218018)" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Width and length together make the size of the object, and is used to see if there is a collision\n", + "traffic_scene[\"objects\"][idx][\"width\"], traffic_scene[\"objects\"][idx][\"length\"]" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "An object's heading refers to the direction it is pointing or moving in. The default coordinate system in Nocturne is right-handed, where the positive x and y axes point to the right and downwards, respectively. In a right-handed coordinate system, 0 degrees is located on the x-axis and the angle increases counter-clockwise.\n", + "\n", + "Because the scene is created from the viewpoint of an ego driver, there may be instances where the heading of certain vehicles is not available. These cases are represented by the value `-10_000`, to indicate that these steps should be filtered out or are invalid." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-12-13T09:26:29.374952\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.9.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" + ], + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Heading is the direction in which the vehicle is pointing\n", + "plt.plot(traffic_scene[\"objects\"][idx][\"heading\"])\n", + "plt.xlabel(\"Time step\")\n", + "plt.ylabel(\"Heading\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[\n", + " {\n", + " \"x\": 0.040283203125,\n", + " \"y\": -2.8857421875\n", + " },\n", + " {\n", + " \"x\": -0.04150390625,\n", + " \"y\": -3.0908203125\n", + " },\n", + " {\n", + " \"x\": 0.032958984375,\n", + " \"y\": -2.744140625\n", + " },\n", + " {\n", + " \"x\": -0.030517578125,\n", + " \"y\": -2.9541015625\n", + " },\n", + " {\n", + " \"x\": -0.0927734375,\n", + " \"y\": -2.7001953125\n", + " },\n", + " {\n", + " \"x\": -0.0390625,\n", + " \"y\": -2.65625\n", + " },\n", + " {\n", + " \"x\": -0.029296875,\n", + " \"y\": -2.5048828125\n", + " },\n", + " {\n", + " \"x\": -0.015869140625,\n", + " \"y\": -2.01171875\n", + " },\n", + " {\n", + " \"x\": -0.068359375,\n", + " \"y\": -2.4951171875\n", + " },\n", + " {\n", + " \"x\": 0.042724609375,\n", + " \"y\": -2.099609375\n", + " }\n", + "]\n" + ] + } + ], + "source": [ + "# Velocity shows the velocity in the x- and y- directions\n", + "print(json.dumps(traffic_scene[\"objects\"][idx][\"velocity\"][:10], indent=4))" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2024-12-13T09:26:29.895229\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.9.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" + ], + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Valid indicates if the state of the vehicle was observed for each timepoint\n", + "plt.xlabel(\"Time step\")\n", + "plt.ylabel(\"IS VALID\")\n", + "plt.plot(traffic_scene[\"objects\"][idx][\"valid\"], \"_\", lw=5)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'x': 1769.8800048828125, 'y': -4882.9111328125, 'z': -142.70326633499866}" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Each object has a goalPosition, an (x, y) position within the scene\n", + "traffic_scene[\"objects\"][idx][\"goalPosition\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'vehicle'" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Finally, we have the type of the vehicle\n", + "traffic_scene[\"objects\"][idx][\"type\"]" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### In-Depth: Road Points\n", + "\n", + "Road points are static objects in the scene." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['geometry', 'type', 'map_element_id', 'id'])" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "traffic_scene[\"roads\"][idx].keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'road_line'" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# This point represents the edge of a road\n", + "traffic_scene[\"roads\"][idx][\"type\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[\n", + " {\n", + " \"x\": 1774.635980601552,\n", + " \"y\": -4778.721456255188,\n", + " \"z\": -145.05792806647807\n", + " },\n", + " {\n", + " \"x\": 1774.6294258416444,\n", + " \"y\": -4779.218941088273,\n", + " \"z\": -145.05028100765452\n", + " },\n", + " {\n", + " \"x\": 1774.6245077883877,\n", + " \"y\": -4779.716444838109,\n", + " \"z\": -145.042633948831\n", + " },\n", + " {\n", + " \"x\": 1774.6210581920316,\n", + " \"y\": -4780.213960978309,\n", + " \"z\": -145.03498689000747\n", + " },\n", + " {\n", + " \"x\": 1774.6189087713544,\n", + " \"y\": -4780.711484469765,\n", + " \"z\": -145.02733983118395\n", + " },\n", + " {\n", + " \"x\": 1774.6178912287696,\n", + " \"y\": -4781.209011595056,\n", + " \"z\": -145.0196927723604\n", + " },\n", + " {\n", + " \"x\": 1774.6178372641725,\n", + " \"y\": -4781.706539785734,\n", + " \"z\": -145.01204571353688\n", + " },\n", + " {\n", + " \"x\": 1774.6185785636114,\n", + " \"y\": -4782.204067448844,\n", + " \"z\": -145.00439865471336\n", + " },\n", + " {\n", + " \"x\": 1774.6199468194288,\n", + " \"y\": -4782.701593801315,\n", + " \"z\": -144.99675159588983\n", + " },\n", + " {\n", + " \"x\": 1774.6217737176728,\n", + " \"y\": -4783.199118693311,\n", + " \"z\": -144.98910453706628\n", + " }\n", + "]\n" + ] + } + ], + "source": [ + "# Geometry contains the (x, y) position(s) for a road point\n", + "# Note that this will be a list for road lanes and edges but a single (x, y) tuple for stop signs and alike\n", + "print(json.dumps(traffic_scene[\"roads\"][idx][\"geometry\"][:10], indent=4));" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "gpudrive", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/gpudrive-main/examples/tutorials/02_simulator_demo.ipynb b/gpudrive-main/examples/tutorials/02_simulator_demo.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..edee9197843e9ab8d891fc20eb23d6d278e20768 --- /dev/null +++ b/gpudrive-main/examples/tutorials/02_simulator_demo.ipynb @@ -0,0 +1,652 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## `GPUDrive` simulator concepts\n", + "\n", + "In this notebook, we demonstrate how to work with the `GPUDrive` simulator and access its basic attributes in Python. The simulator, written in C++, is built on top of the [Madrona Engine](https://madrona-engine.github.io/)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import torch\n", + "from pathlib import Path\n", + "import madrona_gpudrive\n", + "\n", + "# Set working directory to the base directory 'gpudrive_madrona'\n", + "working_dir = Path.cwd()\n", + "while working_dir.name != 'gpudrive':\n", + " working_dir = working_dir.parent\n", + " if working_dir == Path.home():\n", + " raise FileNotFoundError(\"Base directory 'gpudrive_madrona' not found\")\n", + "os.chdir(working_dir)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Summary\n", + "\n", + "- `GPUDrive` simulations are discretized traffic scenarios. A scenario is a constructed snapshot of traffic situation at a particular timepoint.\n", + "- The state of the vehicle of focus is referred to as the **ego state**. Each vehicle has their own partial view of the traffic scene; and a visible state is constructed by parameterizing the view distance of the driver. \n", + "- The `step()` method advances the simulation with a desired step size. By default, the dynamics of vehicles are driven by a kinematic bicycle model. If a vehicle is not controlled (that is, we do not give it actions), its position, heading, and speed will be updated according to a the log replay demonstrations.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Instantiating a sim object with default parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "params = madrona_gpudrive.Parameters()\n", + "params.polylineReductionThreshold = 0.1\n", + "\n", + "sim = madrona_gpudrive.SimManager(\n", + " exec_mode=madrona_gpudrive.madrona.ExecMode.CUDA\n", + " if device == \"cuda\"\n", + " else madrona_gpudrive.madrona.ExecMode.CPU,\n", + " gpu_id=0,\n", + " scenes=[\"data/processed/examples/tfrecord-00000-of-01000_325.json\"],\n", + " params=params, # Environment parameters\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The simulator provides the following functions:\n", + "- `reset(world_idx)` resets a specific world or environment at the given index." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "sim.reset([0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- `step()` advances the dynamics of all worlds." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "sim.step()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exporting tensors\n", + "\n", + "To retrieve a tensor from the simulator, call the specific `tensor()` method, followed by either `to_torch()` or `to_jax()`.\n", + "\n", + "For example, here is how to access the ego state, or self-observation tensor:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(torch.Size([1, 64, 8]), device(type='cpu'))" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "observation_tensor = sim.self_observation_tensor().to_torch()\n", + "\n", + "observation_tensor.shape, observation_tensor.device" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Or alternatively:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "observation_tensor_jax = sim.self_observation_tensor().to_jax()\n", + "\n", + "observation_tensor_jax.shape, observation_tensor_jax.devices()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here are all available tensor exports and methods on the sim object:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "absolute_self_observation_tensor\n", + "action_tensor\n", + "agent_roadmap_tensor\n", + "controlled_state_tensor\n", + "deleteAgents\n", + "deleted_agents_tensor\n", + "depth_tensor\n", + "done_tensor\n", + "expert_trajectory_tensor\n", + "info_tensor\n", + "lidar_tensor\n", + "map_name_tensor\n", + "map_observation_tensor\n", + "metadata_tensor\n", + "partner_observations_tensor\n", + "reset\n", + "response_type_tensor\n", + "reward_tensor\n", + "rgb_tensor\n", + "self_observation_tensor\n", + "set_maps\n", + "shape_tensor\n", + "step\n", + "steps_remaining_tensor\n", + "valid_state_tensor\n", + "world_means_tensor\n" + ] + } + ], + "source": [ + "for attr in dir(sim):\n", + " if not attr.startswith(\"_\"):\n", + " print(attr)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Inspect valid and controlled agents" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To check the number of agents and road points in each world, you can use the `shape_tensor`.\n", + "\n", + "The shape tensor is a 2D tensor where the first dimension represents the number of worlds, and the second dimension represents the shape of each world." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "shape_tensor = sim.shape_tensor().to_jax()\n", + "print(f\"Shape tensor has a shape of (Num Worlds, 2): {shape_tensor.shape}\")\n", + "\n", + "for world_idx in range(shape_tensor.shape[0]):\n", + " print(\n", + " f\"World {world_idx} has {shape_tensor[world_idx][0]} VALID agents and {shape_tensor[world_idx][1]} VALID road objects\"\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The number of **valid** agents indicates the number of controllable agents (vehicles). Some vehicles or bicycles may be initialized in incorrect positions or remain static; these are marked as **invalid** and cannot be controlled.\n", + "\n", + "The sim comes with a mask that indicates which agents can be controlled. Entries are `1` for agents that can be controlled, and `0` otherwise." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Controlled state tensor has a shape of (num_worlds, max_num_agents_in_scene, 1): torch.Size([1, 64, 1])\n" + ] + } + ], + "source": [ + "controlled_state_tensor = sim.controlled_state_tensor().to_torch()\n", + "print(\n", + " \"Controlled state tensor has a shape of (num_worlds, max_num_agents_in_scene, 1): \",\n", + " controlled_state_tensor.shape,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=torch.int32)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# We can control 3 agents in this world\n", + "controlled_state_tensor.squeeze()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "controlled_state_tensor.sum().item()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Actions\n", + "\n", + "The action space is determined by the chosen dynamics_model. By default, it uses a bicycle model with the following tuple of actions:\n", + "\n", + "- **Acceleration**: Continuous float values representing the acceleration applied to the agents. This affects how quickly an agent speeds up or slows down.\n", + "- **Steering Angle**: Continuous float values representing the steering angle, following the bicycle kinematic model. This determines how sharply an agent turns.\n", + "- **Heading Angle**: Continuous float values for the heading angle, which control the direction an agent is facing.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The action tensor stores the current actions for all agents across all worlds:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Action tensor has a shape of (num_worlds, max_num_agents_in_scene, 3): torch.Size([1, 64, 10])\n" + ] + } + ], + "source": [ + "action_tensor = sim.action_tensor().to_torch()\n", + "print(\n", + " f\"Action tensor has a shape of (num_worlds, max_num_agents_in_scene, 3): {action_tensor.shape}\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To set the actions for all controlled agents, we use the `copy_()` method:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Actions tensor after setting all actions to 1: tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])\n" + ] + } + ], + "source": [ + "actions_tensor = sim.action_tensor().to_torch()\n", + "\n", + "actions = torch.full(actions_tensor.shape, 1.0)\n", + "actions_tensor.copy_(actions)\n", + "\n", + "print(f\"Actions tensor after setting all actions to 1: {actions_tensor[0][0]}\")\n", + "\n", + "# Call step() to apply the actions\n", + "sim.step()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Inspecting the simulator settings" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Parameters:\n", + "IgnoreNonVehicles : False\n", + "collisionBehaviour : madrona_gpudrive.CollisionBehaviour.AgentStop\n", + "disableClassicalObs : False\n", + "dynamicsModel : madrona_gpudrive.DynamicsModel.Classic\n", + "enableLidar : False\n", + "initOnlyValidAgentsAtFirstStep: True\n", + "isStaticAgentControlled: False\n", + "maxNumControlledAgents: 10000\n", + "observationRadius : 0.0\n", + "polylineReductionThreshold: 0.0\n", + "rewardParams : \n", + "Reward parameters:\n", + " distanceToExpertThreshold: 0.0\n", + " distanceToGoalThreshold: 0.0\n", + " rewardType : madrona_gpudrive.RewardType.DistanceBased\n", + "roadObservationAlgorithm: madrona_gpudrive.FindRoadObservationsWith.KNearestEntitiesWithRadiusFiltering\n" + ] + } + ], + "source": [ + "params = madrona_gpudrive.Parameters()\n", + "\n", + "print(\"Parameters:\")\n", + "for attr in dir(params):\n", + " if not attr.startswith(\"__\"):\n", + " value = getattr(params, attr)\n", + " print(f\"{attr:20}: {value}\")\n", + " if attr == \"rewardParams\":\n", + " print(\"Reward parameters:\")\n", + " reward_params = getattr(params, attr)\n", + " for attr2 in dir(reward_params):\n", + " if not attr2.startswith(\"__\"):\n", + " value2 = getattr(reward_params, attr2)\n", + " print(f\" {attr2:18}: {value2}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Configuring the simulator " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To set the parameters of the simulator, fill in the values for each attribute of the parameter object as below. This allows you to customize the simulation settings.\n", + "\n", + "The params object can be passed to the sim constructor like this:\n", + "\n", + "```Python\n", + "sim = gpudrive_madrona.SimManager(\n", + " ...\n", + " params=params \n", + ")\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "reward_params = madrona_gpudrive.RewardParams()\n", + "reward_params.rewardType = madrona_gpudrive.RewardType.DistanceBased\n", + "reward_params.distanceToGoalThreshold = 1.0\n", + "reward_params.distanceToExpertThreshold = 1.0\n", + "\n", + "# Initialize Parameters\n", + "params = madrona_gpudrive.Parameters()\n", + "params.polylineReductionThreshold = 1.0\n", + "params.observationRadius = 100.0\n", + "params.collisionBehaviour = madrona_gpudrive.CollisionBehaviour.Ignore\n", + "params.maxNumControlledAgents = 10\n", + "params.rewardParams = reward_params" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step simulator and access information\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "actions_shape = sim.action_tensor().to_torch().shape\n", + "dones = sim.done_tensor().to_torch()\n", + "\n", + "\n", + "obs, rews, dones = (\n", + " sim.self_observation_tensor().to_torch(),\n", + " sim.reward_tensor().to_torch(),\n", + " sim.done_tensor().to_torch(),\n", + ")\n", + "actions = torch.rand(actions_shape)\n", + "sim.action_tensor().to_torch().copy_(actions)\n", + "sim.step()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here’s your text with all the headings formatted as lowercase (except for the first letter):\n", + "\n", + "---\n", + "\n", + "## Simulator configurations and objects\n", + "\n", + "This outlines the key components of the simulator, including observation tensors, rewards, road reduction algorithms, collision behavior, and miscellaneous parameters.\n", + "\n", + "### Observation space\n", + "\n", + "- You can use the datatypes in [`gpudrive/datatypes/observations.py`](https://github.com/Emerge-Lab/gpudrive/blob/main/pygpudrive/datatypes/observation.py) to easily index the tensors below. More documentation is provided there.\n", + "\n", + "#### **SelfObservation**\n", + "\n", + "The `SelfObservation` tensor of shape `(6,)` for each agent represents the agent's state. The respective values are:\n", + "\n", + "- `SelfObservation[0]`: Current *speed* of the agent.\n", + "- `SelfObservation[1]`: *Length* of the agent.\n", + "- `SelfObservation[2]`: *Width* of the agent.\n", + "- `SelfObservation[3:4]`: *Coordinates (x, y)* of the goal relative to the agent.\n", + "- `SelfObservation[5]`: Collision state of the agent (`0` for no collision, `1` for collision).\n", + "\n", + "#### **MapObservation**\n", + "\n", + "The `MapObservation` tensor of shape `(8,)` provides absolute position data for map objects. Values include:\n", + "\n", + "- `MapObservation[0:1]`: Position `(x, y)` of the map object.\n", + "- `MapObservation[2:4]`: Scale `(length, width, height)` of the map object.\n", + "- `MapObservation[5]`: Heading angle of the map object.\n", + "- `MapObservation[6]`: Type of the map object.\n", + "- `MapObservation[7]`: Road ID (`>0` for valid ID, `-1` for unassigned, `0` for missing data).\n", + "\n", + "#### **PartnerObservation**\n", + "\n", + "The `PartnerObservation` tensor of shape `(num_agents-1, 7)` provides data about other agents relative to the ego agent within the `observationRadius`:\n", + "\n", + "- `PartnerObservation[0]`: *Speed* of the neighboring agent.\n", + "- `PartnerObservation[1:2]`: *Relative position (x, y)* of the neighboring agent.\n", + "- `PartnerObservation[3]`: *Orientation* of the neighboring agent.\n", + "- `PartnerObservation[4:5]`: *Dimensions (length, width)* of the neighboring agent.\n", + "- `PartnerObservation[6]`: Type of the neighboring agent.\n", + "\n", + "#### **AgentMapObservations**\n", + "\n", + "The `AgentMapObservations` tensor of shape `(num_road_objs, 8)` provides road object data relative to the ego agent. The values are identical to `MapObservation`.\n", + "\n", + "### Rewards\n", + "\n", + "- **Reward types**:\n", + " - `DistanceBased`: Distance from the agent to the goal.\n", + " - `OnGoalAchieved`: Binary reward (`1` for reaching goal, `0` otherwise) (recommended).\n", + " - `Dense`: Distance from the expert trajectory.\n", + "- **Thresholds**:\n", + " - `distanceToGoalThreshold`: Default: `0.0`.\n", + " - `distanceToExpertThreshold`: Default: `0.0`.\n", + "\n", + "### Road graph \n", + "\n", + "- You can use the datatypes in [`gpudrive/datatypes/roadgraph.py`](https://github.com/Emerge-Lab/gpudrive/blob/main/gpudrive/datatypes/roadgraph.py) to easily index the tensors below. More documentation is provided there.\n", + "\n", + "#### Road reduction algorithm\n", + "\n", + "The **Visvalingam-Whyatt Algorithm** is applied to simplify road lines and edges.\n", + "\n", + "- **Parameter**: \n", + " `polylineReductionThreshold`: Ranges from `0` (no reduction) to `1`. Default: `0.5`.\n", + "\n", + "### Collision behavior\n", + "\n", + "Three options for the collision behavior are available. These determine what happens when an agent touches a road edge or other agents' bounding box.\n", + "\n", + "- `AgentStop`: Agents stop upon collision.\n", + "- `AgentRemoved`: Agents are removed from the scene upon collision.\n", + "- `Ignore`: Collision is logged but does not affect the agent's movement.\n", + "\n", + "### Miscellaneous parameters\n", + "\n", + "- `ObservationRadius`: Radius around every agent that determines visible view. Elements outside this radius are zeroed out.\n", + "- `MaxNumControlledVehicles`: Maximum number of controlled agents. Default: First valid agents are controlled.\n", + "- `IgnoreNonVehicles`: Excludes non-vehicle entities (e.g., pedestrians, cyclists). Default: `false`.\n", + "- `roadObservationAlgorithm`: Options:\n", + " - `KNearestEntitiesWithRadiusFiltering`: Limits observations to the nearest points within `observationRadius`.\n", + " - `AllEntitiesWithRadiusFiltering`: Searches all points within `observationRadius`. Default: `KNearestEntitiesWithRadiusFiltering`.\n", + "- `initOnlyValidAgentsAtFirstStep`: Initializes only valid agents. Default: `true`.\n", + "- `isStaticAgentControlled`: Controls if parked agents can be set static. Default: `false`.\n", + "- `enableLidar`: Enables lidar observations.\n", + "- `disableClassicalObs`: Disables `PartnerObservations` and `AgentMapObservations` for performance. Default: `false`.\n", + "\n", + "### Object types\n", + "\n", + "**Road types**:\n", + "- `RoadEdge`, `RoadLine`, `RoadLane`, `CrossWalk`, `SpeedBump`, `StopSign`.\n", + "\n", + "**Agent types**:\n", + "- `Vehicle`, `Pedestrian`, `Cyclist`.\n", + "\n", + "**Other types**:\n", + "- `Padding`: Ensures consistent output shapes.\n", + "- `None`: Marks entities as invalid (e.g., out of `ObservationRadius` or removed due to collision).\n", + "\n", + "For any further details or indexing references, please explore [`gpudrive/datatypes`](https://github.com/Emerge-Lab/gpudrive/tree/main/gpudrive/datatypes) module." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Other exported tensors\n", + "\n", + "- By default, the x, y, z coordinates are de-meaned to center everything at 0. To revert back to the original coordinates, we export the `world_means_tensor`. \n", + " - Shape: (`num_worlds`, 3)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.10" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/gpudrive-main/examples/tutorials/03_gym_env_demo.ipynb b/gpudrive-main/examples/tutorials/03_gym_env_demo.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..fdde72de7a76dd008712e1b0d00b2a778472142c --- /dev/null +++ b/gpudrive-main/examples/tutorials/03_gym_env_demo.ipynb @@ -0,0 +1,244 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Gymnasium environments\n", + "\n", + "This Section shows how you can make and use the `gym` environments that interface with the simulator." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from pathlib import Path\n", + "import torch\n", + "import mediapy\n", + "\n", + "# Set working directory to the base directory 'gpudrive'\n", + "working_dir = Path.cwd()\n", + "while working_dir.name != 'gpudrive':\n", + " working_dir = working_dir.parent\n", + " if working_dir == Path.home():\n", + " raise FileNotFoundError(\"Base directory 'gpudrive' not found\")\n", + "os.chdir(working_dir)\n", + "\n", + "from gpudrive.env.config import EnvConfig\n", + "from gpudrive.env.env_torch import GPUDriveTorchEnv\n", + "from gpudrive.visualize.utils import img_from_fig\n", + "from gpudrive.env.dataset import SceneDataLoader" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Settings" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "MAX_NUM_OBJECTS = 64 # Maximum number of objects in the scene we control\n", + "NUM_WORLDS = 2 # Number of parallel environments\n", + "UNIQUE_SCENES = 2 # Number of unique scenes\n", + "\n", + "device = 'cpu' # for simplicity purposes in notebook we use cpu, note that the simulator is optimized for GPU so use cuda if possible" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Initializing environments\n", + "\n", + "- We provide both a torch and jax gymnasium interface with the simulator. Most functionality is specified in the `GPUDriveGymEnv` class in the `base_env`, `torch_env` and `jax_env` both inherit from the `GPUDriveGymEnv`, the only difference between these is that one exports torch tensors and the other jax arrays.\n", + "- All environment settings are defined in the `EnvConfig` dataclass. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "env_config = EnvConfig(\n", + " steer_actions = torch.round(\n", + " torch.linspace(-1.0, 1.0, 3), decimals=3),\n", + " accel_actions = torch.round(\n", + " torch.linspace(-3, 3, 3), decimals=3\n", + " )\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "> 💡 **See the gym environment docs at [`pygpudrive/env`](https://github.com/Emerge-Lab/gpudrive/tree/main/pygpudrive/env)**\n", + "\n", + "---" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Make dataloader\n", + "data_loader = SceneDataLoader(\n", + " root=\"data/processed/examples\", # Path to the dataset\n", + " batch_size=NUM_WORLDS, # Batch size, you want this to be equal to the number of worlds (envs) so that every world receives a different scene\n", + " dataset_size=UNIQUE_SCENES, # Total number of different scenes we want to use\n", + " sample_with_replacement=False, \n", + " seed=42, \n", + " shuffle=True, \n", + ")\n", + "\n", + "# Make environment\n", + "env = GPUDriveTorchEnv(\n", + " config=env_config,\n", + " data_loader=data_loader,\n", + " max_cont_agents=MAX_NUM_OBJECTS, # Maximum number of agents to control per scenario\n", + " device=device,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['data/processed/examples/tfrecord-00000-of-01000_325.json',\n", + " 'data/processed/examples/tfrecord-00000-of-01000_222.json']" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "env.data_batch" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Rollout \n", + "\n", + "A single rollout (one episode) is implemented as follows. We:\n", + "- step through N worlds simultaneously.\n", + "- render the simulator state using `plot_simulator_state()`" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "obs = env.reset()\n", + "\n", + "frames = {f\"env_{i}\": [] for i in range(NUM_WORLDS)}\n", + "\n", + "for t in range(env_config.episode_len):\n", + " \n", + " # Sample random actions\n", + " rand_action = torch.Tensor(\n", + " [[env.action_space.sample() for _ in range(MAX_NUM_OBJECTS * NUM_WORLDS)]]\n", + " ).reshape(NUM_WORLDS, MAX_NUM_OBJECTS)\n", + "\n", + " # Step the environment\n", + " env.step_dynamics(rand_action)\n", + "\n", + " obs = env.get_obs()\n", + " reward = env.get_rewards()\n", + " done = env.get_dones()\n", + "\n", + " # Render the environment \n", + " if t % 5 == 0:\n", + " imgs = env.vis.plot_simulator_state(\n", + " env_indices=list(range(NUM_WORLDS)),\n", + " time_steps=[t]*NUM_WORLDS,\n", + " zoom_radius=70,\n", + " )\n", + " \n", + " for i in range(NUM_WORLDS):\n", + " frames[f\"env_{i}\"].append(img_from_fig(imgs[i])) \n", + " \n", + " if done.all():\n", + " break" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Display videos of agents taking random actions" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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"cells": [ + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import dataclasses\n", + "import mediapy\n", + "from huggingface_hub import PyTorchModelHubMixin\n", + "from huggingface_hub import ModelCard\n", + "from gpudrive.networks.late_fusion import NeuralNet\n", + "\n", + "from gpudrive.env.config import EnvConfig\n", + "from gpudrive.env.env_torch import GPUDriveTorchEnv\n", + "from gpudrive.visualize.utils import img_from_fig\n", + "from gpudrive.env.dataset import SceneDataLoader\n", + "from gpudrive.utils.config import load_config" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Configs" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'max_controlled_agents': 64, 'ego_state': True, 'road_map_obs': True, 'partner_obs': True, 'norm_obs': True, 'remove_non_vehicles': True, 'lidar_obs': False, 'reward_type': 'weighted_combination', 'collision_weight': -0.75, 'off_road_weight': -0.75, 'goal_achieved_weight': 1.0, 'dynamics_model': 'classic', 'collision_behavior': 'ignore', 'dist_to_goal_threshold': 2.0, 'polyline_reduction_threshold': 0.1, 'sampling_seed': 42, 'obs_radius': 50.0, 'action_space_steer_disc': 13, 'action_space_accel_disc': 7, 'init_mode': 'all_non_trivial'}\n" + ] + } + ], + "source": [ + "# Configs model has been trained with\n", + "config = load_config(\"../../examples/experimental/config/reliable_agents_params\")\n", + "\n", + "print(config)\n", + "\n", + "max_agents = config.max_controlled_agents\n", + "num_envs = 2\n", + "device = \"cpu\" # cpu just because we're in a notebook" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load pre-trained agent via Hugging Face hub\n" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "sim_agent = NeuralNet.from_pretrained(\"daphne-cornelisse/policy_S10_000_02_27\")" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "91" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Agent has an action dimension of 91: 13 steering wheel angle discretizations x 9 acceleration discretizations\n", + "sim_agent.action_dim" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2984" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Size of flattened observation vector\n", + "sim_agent.obs_dim" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['ffn', 'model_hub_mixin', 'pytorch_model_hub_mixin']" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Some other info\n", + "card = ModelCard.load(\"daphne-cornelisse/policy_S10_000_02_27\")\n", + "card.data.tags" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "# Model architecture\n", + "#agent" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "# Weights \n", + "#agent.state_dict()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Make environment" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "# Create data loader\n", + "train_loader = SceneDataLoader(\n", + " root='../../data/processed/examples',\n", + " batch_size=num_envs,\n", + " dataset_size=100,\n", + " sample_with_replacement=False,\n", + ")\n", + "\n", + "# Set params\n", + "env_config = dataclasses.replace(\n", + " EnvConfig(),\n", + " ego_state=config.ego_state,\n", + " road_map_obs=config.road_map_obs,\n", + " partner_obs=config.partner_obs,\n", + " reward_type=config.reward_type,\n", + " norm_obs=config.norm_obs,\n", + " dynamics_model=config.dynamics_model,\n", + " collision_behavior=config.collision_behavior,\n", + " dist_to_goal_threshold=config.dist_to_goal_threshold,\n", + " polyline_reduction_threshold=config.polyline_reduction_threshold,\n", + " remove_non_vehicles=config.remove_non_vehicles,\n", + " lidar_obs=config.lidar_obs,\n", + " disable_classic_obs=config.lidar_obs,\n", + " obs_radius=config.obs_radius,\n", + " steer_actions = torch.round(\n", + " torch.linspace(-torch.pi, torch.pi, config.action_space_steer_disc), decimals=3 \n", + " ),\n", + " accel_actions = torch.round(\n", + " torch.linspace(-4.0, 4.0, config.action_space_accel_disc), decimals=3\n", + " ),\n", + ")\n", + "\n", + "# Make env\n", + "env = GPUDriveTorchEnv(\n", + " config=env_config,\n", + " data_loader=train_loader,\n", + " max_cont_agents=config.max_controlled_agents,\n", + " device=device,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['../../data/processed/examples/tfrecord-00000-of-01000_325.json',\n", + " '../../data/processed/examples/tfrecord-00000-of-01000_4.json']" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "env.data_batch" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Use the agent" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([2, 64, 2984])" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "next_obs = env.reset()\n", + "\n", + "control_mask = env.cont_agent_mask\n", + "\n", + "next_obs.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "action, logprob, entropy, value = sim_agent(\n", + " next_obs[control_mask], deterministic=False\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(torch.Size([13]), torch.Size([13]), torch.Size([13]), torch.Size([13, 1]))" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "action.shape, logprob.shape, entropy.shape, value.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Rollout" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([2, 64, 2984])\n", + "Step: 54" + ] + } + ], + "source": [ + "next_obs = env.reset()\n", + "\n", + "control_mask = env.cont_agent_mask\n", + "\n", + "print(next_obs.shape)\n", + "\n", + "frames = {f\"env_{i}\": [] for i in range(num_envs)}\n", + "\n", + "for time_step in range(env.episode_len):\n", + " print(f\"\\rStep: {time_step}\", end=\"\", flush=True)\n", + "\n", + " # Predict actions\n", + " action, _, _, _ = sim_agent(\n", + " next_obs[control_mask], deterministic=False\n", + " )\n", + " action_template = torch.zeros(\n", + " (num_envs, max_agents), dtype=torch.int64, device=device\n", + " )\n", + " action_template[control_mask] = action.to(device)\n", + "\n", + " # Step\n", + " env.step_dynamics(action_template)\n", + "\n", + " # Render \n", + " sim_states = env.vis.plot_simulator_state(\n", + " env_indices=list(range(num_envs)),\n", + " time_steps=[time_step]*num_envs,\n", + " zoom_radius=70,\n", + " )\n", + " \n", + " for i in range(num_envs):\n", + " frames[f\"env_{i}\"].append(img_from_fig(sim_states[i])) \n", + "\n", + " next_obs = env.get_obs()\n", + " reward = env.get_rewards()\n", + " done = env.get_dones()\n", + " info = env.get_infos()\n", + " \n", + " if done.all():\n", + " break\n", + "\n", + "env.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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For details, [see the docs.](https://github.com/Emerge-Lab/gpudrive/tree/main/pygpudrive/env)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "control_mask = env.cont_agent_mask\n", + "\n", + "obs = env.reset(control_mask)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([3, 64, 91, 3])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Extract full expert trajectory\n", + "expert_actions, _, _, _ = env.get_expert_actions()\n", + "\n", + "expert_actions.shape # Shape: (num_envs, num_steps, num_agents, num_actions)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Step through an episode" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Reset environment\n", + "obs = env.reset(control_mask)\n", + "done_envs = []\n", + "\n", + "frames = {f\"env_{i}\": [] for i in range(NUM_ENVS)}\n", + "\n", + "# Step through the scene\n", + "for t in range(env.episode_len):\n", + "\n", + " env.step_dynamics(expert_actions[:, :, t, :])\n", + " \n", + " dones = env.get_dones()\n", + " \n", + " # Render the scenes\n", + " env_indices = [i for i in range(NUM_ENVS) if i not in done_envs]\n", + " figs = env.vis.plot_simulator_state(\n", + " env_indices=env_indices,\n", + " time_steps=[t]*NUM_ENVS,\n", + " zoom_radius=100,\n", + " #center_agent_indices=[0]*NUM_ENVS,\n", + " )\n", + " for i, env_id in enumerate(env_indices):\n", + " frames[f\"env_{env_id}\"].append(img_from_fig(figs[i])) \n", + " \n", + " # Check if done\n", + " for env_id in range(NUM_ENVS):\n", + " if dones[env_id].all():\n", + " done_envs.append(env_id)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Show videos" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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"metadata": {}, + "source": [ + "## Visualizer demo" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from pathlib import Path\n", + "import matplotlib.pyplot as plt\n", + "from PIL import Image\n", + "\n", + "# Set working directory to the base directory 'gpudrive'\n", + "working_dir = Path.cwd()\n", + "while working_dir.name != 'gpudrive':\n", + " working_dir = working_dir.parent\n", + " if working_dir == Path.home():\n", + " raise FileNotFoundError(\"Base directory 'gpudrive' not found\")\n", + "os.chdir(working_dir)\n", + "\n", + "from gpudrive.env.config import EnvConfig, RenderConfig\n", + "from gpudrive.env.dataset import SceneDataLoader\n", + "from gpudrive.visualize.utils import img_from_fig\n", + "\n", + "from gpudrive.env.env_torch import GPUDriveTorchEnv\n", + "# Increase the resolution of the figure\n", + "#plt.rcParams['figure.dpi'] = 100 # Higher DPI for better resolution" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "DYNAMICS_MODEL = \"delta_local\" # \"delta_local\" / \"state\" / \"classic\"\n", + "NUM_ENVS = 2\n", + "\n", + "# Configs\n", + "render_config = RenderConfig(render_3d=True)\n", + "env_config = EnvConfig(dynamics_model=DYNAMICS_MODEL)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Create data loader\n", + "train_loader = SceneDataLoader(\n", + " root=\"data/processed/examples\",\n", + " batch_size=NUM_ENVS,\n", + " dataset_size=NUM_ENVS,\n", + " sample_with_replacement=True,\n", + ")\n", + "\n", + "\n", + "env = GPUDriveTorchEnv(\n", + " config=env_config,\n", + " data_loader=train_loader,\n", + " max_cont_agents=64,\n", + " device=\"cpu\",\n", + " render_config=render_config,\n", + " action_type=\"continuous\" # \"continuous\" or \"discrete\"\n", + ")\n", + "\n", + "_ = env.reset(env.cont_agent_mask)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Plot a bird's eye view of the environment\n", + "sim_state_figs = env.vis.plot_simulator_state(\n", + " env_indices=[0],\n", + " zoom_radius=50,\n", + " #center_agent_indices=[1],\n", + " time_steps=[0],\n", + ")\n", + "Image.fromarray(img_from_fig(sim_state_figs[0]))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/jpeg": 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RRQAjKrY3AHFN8pP7op9FADPKT+6KPKT+6KfkHoelFADPKT+6KPKT+6KfRQAzyk/uijyk/uin0UAM8pP7oo8pP7op9FAEL2scjAkEY9O9P8pP7op9FADPKT+6KPKj/uin0UAM8qP+6KPKj/uin0UAM8pP7oo8pP7op9FADPKT+6KPKT+6KfRQA0RopyFANOozSFlUElgAOpJoAWiq9tf2l6he1uoZ0U4LRyBgD+FTb0/vL+dADqKb5if31/Ok82P/AJ6L+dAD6KZ50X/PRP8AvoU5WV1DKwZT0IOaAFooooAKKKKACiiigAopjzRRtteVFbGcFgDik+0Qf89o/wDvoUASUVXmv7O2iMs91DHGOrPIABTo7u2ljWSO4idGGVZXBBFAE1FM86L/AJ6p/wB9ClV0fO1lbHXBzigAMaMclcmk8qP+6KfRQAzyo/7oo8qP+6KfRQBU8pP7QxtH+q/rVjyo/wC6KiBB1BuRxEP5mo9S1KDS7UTz7iC20BRkk1lzxhFyk7JFWbaSLPlR/wB0VXtokLT/ACj/AFp/kKji1rT5YLeVrqKL7RxGkrhWY5xgA9eantOk3/XVqfMpOLjsws1dMl8mP+6KPJj/ALop9FaElaK0WNySdw7CpvKj/uin0UAM8qP+6KPKj/uin0UAM8qP+6KPKj/uin0UAM8qP+6KPKj/ALop+RnGeaKAGeTH/do8mP8Au0+igBFUKuFGBS0UUAFFFFABRRRQAUVXgvrS6lkjt7qGWSI4kVHDFT7gdKnyPUUALRSblzjcMntmloARlDjDDIqE2sRkD4P07VPRQA1UVM7RjNKQD1AP1paKAGJDHGxZVwTT6KKACkIBBB6GlooAZ5Mf92jyY/7tPooAZ5Mf92jyY/7tPooAZ5Mf92kEcRzgA496huBPIcRggA884zU8SbEA43Y59zQA0W0Q/gB+tElujoVAwT3qWigCCK1REw3zHOc05kiTG4AZOKlpCAwwelADfKj/ALoo8mP+6KrwW8sMucgqeDzVugBnkx/3RR5Mf90U+igBnkx/3RSeTGf4RUlFABRRRQAU1kVwQwByMU6igCGK2ji6Ak5zk1MQCCD0NFFAFaW0WRgQdoHBFWFVUGFGBS0UAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAc34848G3r/883hk/wC+Zkb+lFL4/wD+Sfa+/wDzzsZZP++V3f0ooAseDYvI8D6BF/c023X8o1rbrL8NDHhXSB6WUP8A6AK1KACiiigAooooAKzJtKmluZZRqd3GrnIRW4XjGBWnRQBk/wBjTf8AQWvf++6P7Fl/6Ct9/wB/K1qKAMn+xJP+gpff9/KP7EfH/ITvv+/la1FAHEaD4K1DTtRup7rU28uQEAwOwaQ5zls//X610P8AYX/URvv+/ta1FAGT/YQ/6CF9/wB/aP7BT/n/AL7/AL+//WrWooAyf7Bj/wCf6+/7/f8A1qt2FglgkipNNLvbcTK+4jjHH5VbooAKKKKACiiigAooooAKKKKACsp/D9jJNLK3nbpHLtiQgZNatFAGT/wjth6Tf9/DR/wjmn/3Zf8Av4a1qKAMn/hHNO/55yf9/DUdx4X02e2lh2yL5iFdwkORkda2qKAOU0PwNaaUk32idrp5CMEAoFA9gfetb/hHdM/54N/323+NatFAGX/wj2mf8+5/77b/ABo/4R7S/wDn2/8AH2/xrUooAzP+Ee0v/n1H/fbf41dtbaGztkt7dNkScKuScd+9TUUAFFFFABRRRQAUUUUAU7rSrK8nE1xAJJAuwMSemc4/Wof7B0v/AJ9F/wC+j/jWlRQBzmt+DrHVbEQQf6LKHDLIAW/Agn3qbTfCem2Gnw20kfnugO6ViQWJOegPFbtFAGZ/wj+l/wDPoP8Avtv8as2enWuniQWsXl+YQW+YnJAx3q1RQAUUUUAFFFFAGGPD+ntfTAxvggOf3h6kkmnTeFtKnTZJFIV6/wCsNaUf/H9P/uoP51YrOKTi0/P8ynozjdT+Htle3MD291JbxIu10xvyM54JPB5PrWtbaBZOjkmbiRgMSHoDW5Vez/1BPq7/APoRptWkkg6MoN4csmUjfcAEY/1prTgiWCCOFCSsahRk5OAMVJXHfErULzTvDMMljdS20sl0kZeJtrbSrHGR9BXTh6Lr1Y0k7XMa1RUoOb6HY0Vwv/CC67/0O+pfk3/xyj/hBdd/6HfUvyb/AOOV0/VsN/z/AF90v8jH21b/AJ9v71/md1UN3bLd2sluzugkGCyHBH0NcX/wguu/9DvqX5N/8co/4QXXf+h31L8m/wDjlH1bDf8AP9fdL/IPbVv+fb+9f5nSDw/bY/4+Lv8A7+0f8I/bf8/F3/39rm/+EF13/od9S/Jv/jlO+H9zqB1DxBp99qM98LK4WJJJmJPVwTyTjO0cZpTwlP2UqlOopctr6Nbu3VBHET51CcGr+aC08D3cPieW+lvybNixG1z5rA9FPGOP6fl0X/CP23/Pxd/9/a1qK4TqMk+H7fHFzdg9j5vStUDAApaZNNHbwSTTOqRRqXd2OAoAySaEr6IB9FV7O+tNRt1uLO5iuIW6PGwYVYptOLsxJpq6Cqt9YQ6hCkUxcKrhxsbByM/41aopDMn/AIR2x9Z/+/po/wCEcsD1E3/f01rUUAcfpHgC0028mnlu5J0YbY0xs2jOeSDyfyrb/wCEc03/AJ5P/wB/DWrRQBnQaHYW1zHcRRMJYySp3k9Rj+RrRoooAKKKKACim713bdw3DtmlyPWgBaKKjkmSMHceR2FAElFRRXCSkBQc+4p7lgvyAE+9ADqKqCK4ac7mIQ9watAYAHXFAFae6ZACi8HuRU8W7ywWOSefpT6KACiiigAoopGYKMsQB6mgBaKAQRkHIooAKa671xkj6U6igCoslwkxDKzIPardFB5FABRVSWGZXXyWbb9elWU34+cgnPagB1FRvKIzyDjGcgU2K5jlOBkH0NAE1FFISB1IFAC0Um5f7w/OkV0YfKwP0NADqKM0UAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAYnjKLz/A/iCL+/ptwv5xtRU/iUZ8K6wPWym/8AQDRQAaGvm+H9NkBKB7WJtq8AZQcCtDyv+mj/AJ1n+GjnwrpB9bKH/wBAFalAEflf9NH/ADo8r/po/wCdSUUAR+V/00f86PK/6aP+dSUUAR+V/wBNH/Ojyv8Apo/51JRQBH5X/TR/zqBrVzOHEnHrnmrdFAEflf8ATR/zo8r/AKaP+dSUUAR+V/00f86PK/6aP+dSUUAR+V/00f8AOjyv+mj/AJ1JRQBH5X+2/wCdHk/7b/nUlFAEfk/7b/nSpHsJO4nPrUU99aWsscVxdQxSSnEayOFLH2B61YoAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooozQAUVXnv7O1ljjuLqGKSU4jV5ApY+wPWp8j1FAC0hGQR60ZHqKNw9RQBH5I/vv+dL5P+2/50/cvqPzoDAnAIz9aAGeSP77/nR5I/vv+dSUUAR+SP77/nR5I/vv+dSUUAR+SP77/nR5I/vv+dSUUAR+SP77/nR5I/vv+dSUUAR+SP77/nR5I/vv+dSUUAZptWe+mKvwNvU+1XvJH99/zqOH/j7ufqv8qmd1jUs7BVHUk4FRDb5v8xy3G+SP7z/nVeyhBtVO5uWbv/tGrH2iD/ntH/30Kr2VxALSPM0YPP8AEPWh/GvR/oHQseSP7z/nXDfFSMJ4XtiCx/02Pqf9l67j7Tb/APPeL/vsVwvxWuYW8L2wWRXP21DhWB/gevSyz/fKfqcmN/3efod35I/vP+dHkj+8/wCdcR/wtjQf+fXUv+/K/wDxVH/C2NB/59dS/wC/K/8AxVH9mYz/AJ9sPruH/nR0Osw+IN8P9htp23B837aZM54xt2/j1rL8nx7/AHvDn5z1S/4WxoP/AD66l/35X/4qgfFnQD0ttR/79J/8VXRDCYyEeX2KfqjKWIw8nf2n4l3yfHv97w5+c9cr4Vj8Ttr3iMaedJ+0C6H2rzzLs37n+5jnGc9fat3/AIWxoP8Az66l/wB+V/8AiqrfDzULe51fxNf7vJhurpZIvOIUkEufXryK6FGtRw9WVWko6LpvqjFunUrQUJt79fI1vJ8e/wB7w7+c9PRPHqDAPhs8558+uj+3Wn/P1B/38FH2+z/5+4P+/gryvrX9yP3Hd7D+8/vOe/4r7/qWv/I9UNb/AOE3/sDUftX/AAj32f7LL5vledv2bTnbnjOM4zXYfb7P/n7g/wC/g/xqOa6064gkhmuLZ4pFKOjSKQwIwQauGM5ZJuEfuJlh7prmf3nz34e/tz+0R/YP2r7TxnyM4x/tdsfXive/D39tf2Uv9vfZ/tmf+WP93A+923Zz046VPpy6Xaxiz037LGqjd5UBXp64H86vV05lmaxjsoJefX7zHB4J4dayb/IKKKK8k7wooooAKKKKACiiigAqIQJjv+dQyapYxSvFJdRLIhwylsEd6b/a+nf8/sP/AH2KAJFs1EpfcSPSpliRWyBg1mXnifRrBFa4v4wGOAFBY/kAasLrWmugdb6AqwyDu6igC9TWjRwQyg561U/tjTv+f2H/AL6qe3uoLuMyW8qyIGKkqcjPpQBIkaxrtQYFKyh1KnOD6UtFAEfkr6t+dHkr6t+dSUUAR+Qvq350eSvq351JRQBH5K+rfnR5K+rfnTLi8trUoLieOIvnbvbGcdaj/tSw/wCfyD/vsUAT+Svq350yS1WRcBiD+dR/2pYf8/kH/fYpf7Tsf+fyD/v4KAJEtlRAuScU7yV9W/Oof7Ssf+fyD/v4KpyeJtGi1BLF9QiE7dByV56At0B/GgDS8hfVvzo8lfU/nUX9o2X/AD+Qf9/BR/aFl/z92/8A38H+NAEvkr6n86PJX1P51H9vs/8An7g/7+D/ABo+32f/AD9wf9/BQBJ5K+p/OkMCH1/OiK5gnZlhmjkK43BHBx+VS0ABAIIPINMSGOP7qgU+igApGVXHzDOKWigCNoI2UjbjIqOK0WMHcdxP4VYooAYsao2VGDjFPoooAKKiNzAGKmeMMpwQXGRR9qt/+e8X/fYoAloqL7TB/wA94/8AvsUfaYP+e0f/AH2KAJaKi+0Qf89o/wDvoVFDqdhcSOkN7byPGcOqSqSv15oAtUVH9oh/57R/99Cjz4f+esf/AH0KAJKKj8+H/nqn/fQo8+L/AJ6p/wB9CgCSimq6uMowYdMg5p1AGb4i/wCRZ1b/AK85v/QDRTfEpx4V1g+llN/6AaKAIPBsvn+B9Al/v6bbt+ca1t1zvgpzd+CdGnl/1jWiBtvAyBg4H4VveQnv+dAElFR+Qnv+dHkJ7/nQBJRUfkJ7/nR5Ce/50ASUVH5Ce/50eQnv+dAElFR+Qnv+dHkJ7/nQBJRUfkJ7/nR5Ce/50ASVUv7GG/hRJ2dVjfeCjbecEf1qfyE9/wA6a9sroVyRnvmgDOXQLJhlZZyPUSml/wCEes/79x/39NX4rVY1wSSfyp/kp7/nQBm/8I9Z/wB+4/7+mj/hHrP+/cf9/TWl5Ke/50eSnv8AnQBymr+AbbU7yGdL2aFVG11Ybywzng54PPvWz/wjtlj79x/39NaXkp7/AJ0eSnv+dAGb/wAI9Zf3p/8Av6aP+Eesv70//f01peSnv+dHkp7/AJ0AZv8Awj1l/en/AO/pqS30O0trqK5jM2+MkrukJHII/rV7yU9/zo8lPf8AOgCSio/JT3/OjyU9/wA6AJKKj8lPf86PJT3/ADoAkoqPyU9/zo8lPf8AOgCSio/JT3/OjyU9/wA6AJKpX2l22ovE1wHJizt2sR1xn+VWfJT3/OjyU9/zoAzf+Ec0/wDuy/8Afw0f8I5p39yX/v4a0vJT3/OjyU9/zoAzf+Ec07+5J/38NH/COad/zzk/7+GtLyU9/wA6PJT3/OgDk9X8AWmpXkM8V1Jboo2yJjfuGc8Eng/nW1/wjmm/885P+/hrS8lPf86PJT3/ADoAzv8AhHNN/wCeT/8Afw0f8I5pv/PJ/wDv4a0fJT3/ADo8lPf86AM7/hHNN/55P/38P+NTWmjWVlc/aII2WTaUyXJ4OP8ACrRhQdSR+NL5Ke/50ASUVH5Ke/50eSnv+dAElFR+Qnv+dSUAFFFFABRRRQAUUUUAV7f/AI+Lk/7Y/wDQRTrq1hvLdre4TfE+Ny5Izg57fSm23+suT/01/oKsVFP4fv8AzHLczf7B0v8A59F/76P+NRWmiaa9pEzWqliuScn/ABrWY4Qn2qGzGLKH/cFD+Nej/QOhV/sLTP8An0T8z/jS/wBh6Z/z6J+Z/wAa0KKsRn/2Hpn/AD6J+Zo/sPTf+fSP9a0KKAM/+xNN/wCfOP8AWs3TfBOj6bdSzpHJKZBgLK2QgznA/wDr5raS+tJLt7RLqFrlBlog4Lge461YoAof2Lpv/PnH+VH9i6b/AM+cX5VfooAo/wBjab/z5xflR/Y2nf8APnF/3zV6igCj/Y2nf8+cP/fNH9j6d/z5Q/8AfNXqKAK0Gn2drKZYLeOOQrtLKMHHpVmiigAooooAKKKKACiiigAooooArvYWckjSPaws7HLMUBJpP7Osv+fOD/v2Ks0UAYWs+EtL1pIlljaBoiSGgwpIPUHj2rQg0jT7e3jhjs4dkahVygJwBjk96u0UAVv7Osv+fOD/AL9ipYYIbdCkMSRqTkhFAGfXipKKACioLq9tbFFe7uYYEY7Q0rhQT6c1IyrKoOcjqCDQA+io/IT3/OjyE9/zoAkoqPyE9/zo8hPf86AGz2lvdFDPBHKUzt3rnGetRf2XYf8APnB/3wKn8hPf86PIT3/OgCD+yrD/AJ84P++BTW0zTVGWtLcD3UVZ8hPf86jltFkAwxUj8aAI/wCydPP/AC5wf98CsmfwVo8+sJqLRurKQTCpAjYjoSMf1rdW3QKBzx70vkJ7/nQBW/sjTv8Anyh/75pP7H07/nyh/wC+ateQnv8AnR5Ce/50AVf7G07/AJ8of++aP7G03/nzh/75q15Ce/50eQnv+dAEVtYWtm7vbwJGzgBio646fzqzUfkJ7/nR5KAg88HPWgCSiiigAooooAKKKKACiiigChJounSzSSvaq0kjbmYk8n86b/YWmf8APon5n/GtGigDO/sLTP8An0T8z/jR/YOmf8+i/mf8a0aKAM06DpZH/Hov/fR/xrD0bwFZ6XezTzXDXSMpWONl27RnPJB5PHtXXZB6HpRQBm/2Bpf/AD6L/wB9N/jR/YGl/wDPov8A303+NaVFAGb/AGBpf/Pov/fTf40n/CP6X/z6L/303+NadFAEFpZW9hCYbaPy4yxbGSeT161PRRQBieMpfI8D+IJf7mm3DflG1FV/H/8AyT7X0/56WMsf/fS7f60UAJ4D48G2Sf8APN5o/wDvmZ1/pXSVzvhILBpd3awN5iwajdqSRjaWnd9v4b8Zre3S/wDPMfnQBJRUe+X/AJ5j86N8v/PMfnQBJRUe+X/nmPzo3y/88x+dAElFV5ppY03eWBz1zmljlleMN5Y596AJ6Kj3y/8APMfnRvl/55j86AJKKj3y/wDPMfnRvl/55j86ACcSNbyLCwSUqQjEZAbHBrMFtrm0Zv7fOOf3Vae+X/nmPzo3y/8APMfnQBm/Ztb/AOghB/36o+y61/0EIP8Av1Wlvl/55j86N8v/ADzH50AZv2XWv+gjD/35FU9WsvEcml3CWmoRmcr8oRAhPqA3Y4rd3S/88x+dS0Acp4f0/wATQ6WEvr8JJvO1ZQJWC+7c+/etX7JrX/QTi/78itaigDJ+ya1/0E4v+/Ao+x6z/wBBSP8A78CtaigDHay1oowXVYwxHB8gcVrRhliQO25wAGbGMn1p1FABRRRQAUUUUAFFFFABRRRQAUUUUAFZtxp13NdSyx6nNEj42xqvC4GP/r1pUUAZP9lXn/QXuP8AvkUf2Td/9Be5/IVrUUAZP9k3X/QXuaP7Iuv+gtdVrUHpQBk/2Pc/9Ba7/Oj+x7j/AKC15/31WkGlx/qx+dG6X+4PzoA5HxL4Q1DVYYFt9TaTy2JZLlzt57jAPP8AjWvbaDcRWsUcmsXrOiBWYP1IFa+6X+4Pzo3S/wBwfnQBm/2LJ/0FL7/v5R/Yj/8AQTvv+/laW6X+4Pzo3S/3B+dAGb/YZ/6CV9/38o/sIf8AQRvv+/taW6X+4Pzo3S/3B+dAGb/YS97++/7+0f2DH/z/AF9/3+/+tWlul/uD86N0v9wfnQAyztls7WO3WSSQIMBpDlj9TU9R7pf7g/OjdL/cH50ASUVHul/uD86hhuJZHZdg4/DFADjZQl2YbwWO44kYc/nR9jTtJMPpKak3S/3B+dG6X+4PzqPZw7D5n3IzZggjzp+f+mhqWKMRRJGpJCKFBPXik3S/3B+dG6X+4PzpqEU7oG2ySobu2jvLWS3lLBJF2ttODTt0v9wfnRul/uD86oRm/wDCO2P96f8A7+mj/hHbH1n/AO/prS3S/wBwfnRul/uD86AOTt/AFrBrr35vJWhJZlhxggn1bPI5Patr/hHbD/pt/wB/DWlul/uD86N0v9wfnQBnJ4esY5opVEu6Nw65kPUVq1Hul/uD86N0v9wfnQBJRTFZy2GUAeuafQAUUUUAZniGVodBunRirYAyDg8sBWbaeHDLZwTDUbtGkjViA/AJGaueKWxoE49WUf8AjwrTs122UC+kaj9K5JU41K7UuiX5s7oVZ0sOnB2u3+SMb/hHbtfua3eL+JP9aP7E1Vfua7Mf95D/AI1v0Vf1Wn5/e/8AMz+uVurX3L/IwP7M15fuawp/3k/+tR9j8TL01K2b/eQD/wBlrfoo+rx6N/ew+ty6xj/4CjlNTk1+006Z7qW1eErsfaOcHj0HrWhbaBZSWkLnzgWRSQJT6UeLGxoTj1dR+ta8C7beJfRAP0pUk41ZRu2rLf5lVmp0Yz5Und7K3Yzf+EdsfWf/AL+mj/hHbH+9P/39Na1FdJxmT/wj1l/en/7+mj/hHrL+9P8A9/TWtRQBk/8ACPWX9+4/7+mj/hHrP+/cf9/TWtRQBk/8I9Z/37j/AL+mj/hHrP8A56XH/f01rUUAcprfga31VIfKvJYXjJ5ceYCD7ZHpWjb+GLO3tooRNckRoFB8zrgVtU1ywHyjJoAy/wDhHrT/AJ63P/f00f8ACPWn/PW5/wC/prS3S/8APMfnRul/55j86AM3/hH7T/nrc/8Af00f8I/a/wDPa6/7+1pbpf8AnmPzo3S/88x+dAGb/wAI/a/89rr/AL+0f8I/a/8APa6/7+1pbpf+eY/OjdL/AM8x+dAGb/wj9r/z3uv+/tH/AAj9t/z3uv8Av7Wlul/55j86jmmljTdsHXrnNAFa20aC1ukuEmuGZQQA8mRz7Vo1BHLK8Ybyxz70/dL/AM8x+dAElFR7pf8AnmPzo3S/88x+dAElFR7pf+eY/OjdL/zzH50ASUVHul/55j86QtNjiMfnQBLRRRQAUUUUAFFFFABWZNocM1zLObq7UyNuKrLgDjHAx7Vp0UAZP9gQf8/d7/3+/wDrUf8ACPwf8/l7/wB/v/rVrUUAZP8Awj8H/P5e/wDf7/61H/CPw4/4/L3/AL/f/WrWooA4jQfBF5p+o3M15qDGJwQvkOQznOdzZH+PWuh/sCH/AJ/L3/v9/wDWrWooAyf7Ah/5/L7/AL/f/Wo/sCH/AJ/L7/v9/wDWrWooAyf7Ah/5/L7/AL/f/Wq/Z2q2VqlukkkipnDSNljznk1PRQAUUUUAc34858G3qf8APR4Y/wDvqZF/rRR41/eaTY2v/Pxqtih+guEc/ohooAPDH7rWPFNqf+WeqCRfo9vC38y1bcuoWUMzRS3lvHKoBKNKoIB6cZrE0r91498RQ9PMtrO5HvkSx/8AtIVcvfC2iajfSXt3p8ctzIFVpGJyQOnegC7/AGpp/wDz/wBr/wB/l/xo/tXTv+f+1/7/AC/41mf8IX4c/wCgVD+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", + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Plot an agent's POV\n", + "agent_obs_fig = env.vis.plot_agent_observation(\n", + " env_idx=0,\n", + " agent_idx=7,\n", + " figsize=(10, 10),\n", + ")\n", + "Image.fromarray(img_from_fig(agent_obs_fig))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "gpudrive", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/gpudrive-main/examples/tutorials/07_agent_behavior_diversity.md b/gpudrive-main/examples/tutorials/07_agent_behavior_diversity.md new file mode 100644 index 0000000000000000000000000000000000000000..c56749d6298ab2b79f98d81b55f8396f2f7fe50e --- /dev/null +++ b/gpudrive-main/examples/tutorials/07_agent_behavior_diversity.md @@ -0,0 +1,120 @@ +# Agent behavior diversity through reward conditioning + +## Introduction + +This tutorial explains how to use reward conditioning to create diverse agent behaviors in the GPUDrive environment. Inspired by [Robust autonomy emerges from self-play](https://arxiv.org/abs/2502.03349) (Appendix B.3), we condition agents on different reward weights to produce a spectrum of behaviors ranging from cautious to aggressive, all using a single policy network. + +## Setting up the environment + +First, let's set up the GPUDrive environment: + +```python +from gpudrive.env.config import EnvConfig, RenderConfig +from gpudrive.env.dataset import SceneDataLoader +from gpudrive.env.torch_env import GPUDriveTorchEnv + +# Create environment config +env_config = EnvConfig( + # Change this to "reward_conditioned" to enable reward conditioning + reward_type="reward_conditioned", + + # Add reward bounds for each component + collision_weight_lb=-1.0, + collision_weight_ub=-0.1, + goal_achieved_weight_lb=0.5, + goal_achieved_weight_ub=2.0, + off_road_weight_lb=-1.0, + off_road_weight_ub=-0.1, + + # Default conditioning mode + condition_mode="random" +) + +render_config = RenderConfig() + +# Create data loader +train_loader = SceneDataLoader( + root="data/processed/examples", + batch_size=2, + dataset_size=100, + sample_with_replacement=True, + shuffle=False, +) + +# Create environment +env = GPUDriveTorchEnv( + config=env_config, + data_loader=train_loader, + max_cont_agents=64, # Number of agents to control + device="cuda", # Use GPU if available +) + +# Get controlled agent mask +control_mask = env.cont_agent_mask + +# Reset the environment +obs = env.reset() +``` + +To enable reward conditioning, set `reward_type="reward_conditioned"` in the environment config. + +## Conditioning Modes + +We currently support three conditioning modes: + +1. **Random mode**: Each agent receives random reward weights within the specified bounds +2. **Preset mode**: Agents use predefined behavior profiles (cautious, aggressive, etc.) +3. **Fixed mode**: Custom reward weights are provided directly + +### 1. Random mode (default for training) + +During training, you'll typically use random mode to create a diverse set of agent behaviors: + +```python +# Random mode is the default, but you can explicitly set it +obs = env.reset(condition_mode="random") + +# Alternatively, set it in the config +env_config.condition_mode = "random" +env = GPUDriveTorchEnv(config=env_config, data_loader=train_loader, max_cont_agents=64) +``` + +In random mode, each agent receives unique reward weights sampled uniformly within the bounds specified in the config. This creates a diverse population of agents with different behaviors, enabling the policy to learn to handle a wide range of scenarios. + +### 2. Preset mode (particular agent profiles) + +For testing or evaluation, you might want consistent behavior patterns. Preset mode provides predefined agent personalities: + +```python +# All agents use the cautious behavior profile +obs = env.reset(condition_mode="preset", agent_type="cautious") + +# Available preset types: +# - "cautious": Strong penalties for risk, moderate goal reward +# - "aggressive": Lower penalties, higher goal reward +# - "balanced": Middle ground between cautious and aggressive +# - "risk_taker": Minimal penalties, maximum goal reward +``` + +This is useful for evaluating policy performance under specific conditions or for demonstrations. + +### 3. Fixed mode (for custom behaviors) + +For complete control over agent behavior, you can specify exact reward weights: + +```python +# Define custom weights [collision, goal, off_road] +custom_weights = torch.tensor([-0.75, 1.5, -0.3]) +obs = env.reset(condition_mode="fixed", agent_type=custom_weights) +``` + +This gives you precise control over agent behavior for specific testing scenarios. + + +## Incorporating rewards into the observation + +For the policy to adapt its behavior based on reward conditioning, the reward weights should be included in the agent's observation. This allows the policy to "know" it's type. By default, if `reward_type == 'reward_conditioned'`, the weights for each of the 3 reward components are added to the `ego_state`. + +## Conclusion + +Reward conditioning is a simple but powerful technique for creating diverse agent behaviors in simulation. By conditioning agents on different reward weights, we can create a spectrum of behaviors from a single policy, resulting in more varied simulations. We showed how you can get started with reward conditioning in `gpudrive`. diff --git a/gpudrive-main/examples/tutorials/08_multiple_policies.ipynb b/gpudrive-main/examples/tutorials/08_multiple_policies.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..7b7bbd13f84adca6d868b1739b0065b9c2a7f944 --- /dev/null +++ b/gpudrive-main/examples/tutorials/08_multiple_policies.ipynb @@ -0,0 +1,197 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import dataclasses\n", + "import mediapy\n", + "from huggingface_hub import PyTorchModelHubMixin\n", + "from huggingface_hub import ModelCard\n", + "from gpudrive.networks.late_fusion import NeuralNet\n", + "from gpudrive.env.config import EnvConfig\n", + "from gpudrive.env.env_torch import GPUDriveTorchEnv\n", + "from gpudrive.env.dataset import SceneDataLoader\n", + "from gpudrive.utils.config import load_config \n", + "import sys\n", + "import imageio\n", + "import numpy as np\n", + "import os\n", + "from gpudrive.utils.multi_policy_rollout import multi_policy_rollout\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def create_policy_masks(env, num_sim_agents=2, num_worlds=10):\n", + " policy_mask = torch.zeros_like(env.cont_agent_mask, dtype=torch.int)\n", + " agent_indices = env.cont_agent_mask.nonzero(as_tuple=True)\n", + "\n", + " for i, (world_idx, agent_idx) in enumerate(zip(*agent_indices)):\n", + " policy_mask[world_idx, agent_idx] = (i % num_sim_agents) + 1\n", + "\n", + " policy_masks = {f'pi_{int(policy.item())}': torch.zeros_like(env.cont_agent_mask, dtype=torch.bool,device=device) \n", + " for policy in policy_mask.unique() if policy.item() != 0}\n", + "\n", + " for p in range(1, num_sim_agents + 1):\n", + " policy_masks[f'pi_{p}'] = (policy_mask == p).reshape(num_worlds, -1)\n", + "\n", + " return policy_masks\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# Configs model has been trained with\n", + "config = load_config(\"../../examples/experimental/config/reliable_agents_params\")\n", + "max_agents = config.max_controlled_agents\n", + "NUM_ENVS = 2\n", + "device = \"cpu\" # cpu just because we're in a notebook\n", + "NUM_SIM_AGENTS = 2\n", + "FPS = 5\n", + "\n", + "sim_agent1 = NeuralNet.from_pretrained(\"daphne-cornelisse/policy_S10_000_02_27\")\n", + "sim_agent2 = NeuralNet.from_pretrained(\"daphne-cornelisse/policy_S1000_02_27\")\n", + "\n", + "# Some other info\n", + "card = ModelCard.load(\"daphne-cornelisse/policy_S10_000_02_27\")\n", + "\n", + "\n", + "\n", + "\n", + "sim_agent1 = NeuralNet.from_pretrained(\"daphne-cornelisse/policy_S10_000_02_27\")\n", + "sim_agent2 = NeuralNet.from_pretrained(\"daphne-cornelisse/policy_S1000_02_27\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "train_loader = SceneDataLoader(\n", + " root='../../data/processed/examples',\n", + " batch_size=NUM_ENVS,\n", + " dataset_size=100,\n", + " sample_with_replacement=False,\n", + ")\n", + "\n", + "# Set params\n", + "env_config = dataclasses.replace(\n", + " EnvConfig(),\n", + " ego_state=config.ego_state,\n", + " road_map_obs=config.road_map_obs,\n", + " partner_obs=config.partner_obs,\n", + " reward_type=config.reward_type,\n", + " norm_obs=config.norm_obs,\n", + " dynamics_model=config.dynamics_model,\n", + " collision_behavior=config.collision_behavior,\n", + " dist_to_goal_threshold=config.dist_to_goal_threshold,\n", + " polyline_reduction_threshold=config.polyline_reduction_threshold,\n", + " remove_non_vehicles=config.remove_non_vehicles,\n", + " lidar_obs=config.lidar_obs,\n", + " disable_classic_obs=config.lidar_obs,\n", + " obs_radius=config.obs_radius,\n", + " steer_actions = torch.round(\n", + " torch.linspace(-torch.pi, torch.pi, config.action_space_steer_disc), decimals=3 \n", + " ),\n", + " accel_actions = torch.round(\n", + " torch.linspace(-4.0, 4.0, config.action_space_accel_disc), decimals=3\n", + " ),\n", + ")\n", + "\n", + "\n", + "\n", + "env = GPUDriveTorchEnv(\n", + " config=env_config,\n", + " data_loader=train_loader,\n", + " max_cont_agents=max_agents,\n", + " device=device,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "next_obs = env.reset()\n", + "\n", + "\n", + "control_mask = env.cont_agent_mask\n", + "\n", + "policy_mask = create_policy_masks(env, 2,NUM_ENVS)\n", + "\n", + "policies_set = {'pi_1': (sim_agent1,policy_mask['pi_1']),\n", + " 'pi_2': (sim_agent2, policy_mask['pi_2'])\n", + " } \n", + " \n", + "\n", + "\n", + "metrics,frames=multi_policy_rollout(\n", + "env,\n", + "policies_set, \n", + "device,\n", + "deterministic=False,\n", + "render_sim_state = True,\n", + "render_every_n_steps= 5\n", + ")\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "env.close()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "mediapy.show_videos(frames, fps=15, width=500, height=500, columns=2, codec='gif')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "gpudriveenv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.11" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/gpudrive-main/external/CMakeLists.txt b/gpudrive-main/external/CMakeLists.txt new file mode 100644 index 0000000000000000000000000000000000000000..93ced8e32ab3c357946694763a21d0d804a93f59 --- /dev/null +++ b/gpudrive-main/external/CMakeLists.txt @@ -0,0 +1,5 @@ +set(MADRONA_ENABLE_TESTS ON) +add_subdirectory("${MADRONA_DIR}" madrona EXCLUDE_FROM_ALL) +add_subdirectory(json) + + diff --git a/gpudrive-main/gpudrive/__init__.py b/gpudrive-main/gpudrive/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..2acb98f31e7afcba11e6635dfe215d1f600257f4 --- /dev/null +++ b/gpudrive-main/gpudrive/__init__.py @@ -0,0 +1,4 @@ +from pathlib import Path + +GPU_DRIVE_DATA_ROOT = Path('/'.join(__path__[0].split('/')[:-1])) +GPU_DRIVE_DATA_DIR = GPU_DRIVE_DATA_ROOT / 'data/processed/training' diff --git a/gpudrive-main/gpudrive/agents/__init__.py b/gpudrive-main/gpudrive/agents/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/gpudrive-main/gpudrive/agents/core.py b/gpudrive-main/gpudrive/agents/core.py new file mode 100644 index 0000000000000000000000000000000000000000..7fbe2ddb3b2e42fae69b80bac6bb46af0dada386 --- /dev/null +++ b/gpudrive-main/gpudrive/agents/core.py @@ -0,0 +1,39 @@ +import torch + + +def merge_actions( + actor_actions_dict, + actor_ids_dict, + reference_action_tensor, + verbose=False, + device="cuda", +): + """Combines multiple actor_outputs into one action instance. + + Args: + actions (dict of ints): A dictionary of actor outputs. + actor_ids (dict of ints): A dictionary of actor ids. + reference_action_tensor (torch.Tensor): A reference tensor of shape (num_worlds, max_num_controllable_agents) to map the actor outputs to. + + Return: + torch.Tensor: Tensor of shape (num_worlds, max_num_controllable_agents) filled with actor actions. + """ + + action_tensor = ( + torch.zeros(reference_action_tensor.shape) + .type(torch.LongTensor) + .to(device) + ) + + for actor_name in actor_actions_dict.keys(): + for world_idx in range(len(actor_ids_dict[actor_name])): + if verbose: + print( + f"{actor_name} is controlling vehicles: {actor_ids_dict[actor_name][world_idx]} in world {world_idx} \n with actions: {actor_ids_dict[actor_name]}" + ) + actor_indices_in_world = actor_ids_dict[actor_name][world_idx] + action_tensor[ + world_idx, actor_indices_in_world + ] = actor_actions_dict[actor_name][world_idx].long() + + return action_tensor diff --git a/gpudrive-main/gpudrive/agents/policy_actor.py b/gpudrive-main/gpudrive/agents/policy_actor.py new file mode 100644 index 0000000000000000000000000000000000000000..163708bf2a86bf41d77dd9a085cf4517b88b2fef --- /dev/null +++ b/gpudrive-main/gpudrive/agents/policy_actor.py @@ -0,0 +1,103 @@ +import torch +from pathlib import Path +from integrations.rl.sb3.ppo import IPPO + + +class PolicyActor: + """Policy actor that selects actions based on a learned policy. + + This class is compatible with policies trained with stable-baselines 3, such as PPO. + + Args: + is_controlled_func (torch.Tensor): Determines which agents are controlled by this actor (across worlds). + valid_agent_mask (torch.Tensor): Mask that determines which agents are valid, and thus controllable, in the environment. Shape: (num_worlds, num_agents). + saved_model_path (str): Path to the saved model. + model_class: Model class to use. + deterministic (bool): Whether to use deterministic actions. + device (str): Device to run the policy on. + """ + + def __init__( + self, + is_controlled_func, + valid_agent_mask, + saved_model_path, + model_class=IPPO, + deterministic=True, + device="cuda", + ): + self.is_controlled_func = is_controlled_func + self.device = device + self.deterministic = deterministic + self.model_class = model_class + self.policy = self.load_model(saved_model_path) + self.valid_and_controlled_mask = self.get_valid_actor_mask( + is_controlled_func, valid_agent_mask + ) + self.actor_ids = [ + torch.where(self.valid_and_controlled_mask[world_idx, :])[0] + for world_idx in range(valid_agent_mask.shape[0]) + ] + + def load_model(self, saved_model_path): + """Load a learned policy.""" + model_file = Path(saved_model_path) + if not model_file.is_file(): + raise FileNotFoundError(f"File not found: {saved_model_path}") + else: + policy = self.model_class.load( + path=saved_model_path, + device=self.device, + ).policy + return policy + + def select_action(self, obs): + """Use learned policy to select actions. + + obs (torch.Tensor): Observation tensor. + """ + + assert ( + obs.dim() == 3 + ), f"Expected obs to be of shape (num_worlds, max_agents, obs_dim), but got {obs.dim()}." + + action_lists = [] + for world_idx in range(len(self.actor_ids)): + observations = obs[world_idx, self.actor_ids[world_idx], :] + if ( + len(observations) == 0 + ): # Append empty tensor if no agents in this world are controlled + actions = torch.tensor([]).to(self.device) + else: + actions = self.policy._predict( + obs[world_idx, self.actor_ids[world_idx], :], + deterministic=self.deterministic, + ) + action_lists.append(actions) + + return action_lists + + def get_distribution(self, obs): + """Get policy distribution for given observation.""" + return self.policy.get_distribution(obs) + + def evaluate_actions(self, obs, actions): + """Evaluate actions.""" + values, log_prob, entropy = self.policy.evaluate_actions(obs, actions) + return values, log_prob, entropy + + def get_valid_actor_mask(self, is_controlled_func, valid_agent_mask): + """Returns a boolean mask across worlds that indicates which agents + are valid and controlled by this actor. + """ + num_worlds = valid_agent_mask.shape[0] + + is_controlled_func = is_controlled_func.expand((num_worlds, -1)) + + assert ( + is_controlled_func.shape == valid_agent_mask.shape + ), f"is_controlled_func and valid_agent_mask must match but are not: {is_controlled_func.shape} vs {valid_agent_mask.shape}" + + return is_controlled_func.to(self.device) & valid_agent_mask.to( + self.device + ) diff --git a/gpudrive-main/gpudrive/agents/random_actor.py b/gpudrive-main/gpudrive/agents/random_actor.py new file mode 100644 index 0000000000000000000000000000000000000000..6873c858b34f36ff35f02b6eaad5e96bac1dc72d --- /dev/null +++ b/gpudrive-main/gpudrive/agents/random_actor.py @@ -0,0 +1,55 @@ +import torch + + +class RandomActor: + """Random actor. + + Args: + env: Environment. + is_controlled_func (torch.Tensor): Determines which agents are controlled by this actor. Shape: (max_num_agents,). + valid_agent_mask (torch.Tensor): Mask that determines which agents are valid, and thus controllable, in the environment. Shape: (num_worlds, max_num_agents). + device (str): Device to put the actions on. + """ + + def __init__( + self, env, is_controlled_func, valid_agent_mask, device="cuda" + ): + self.env = env + self.is_controlled_func = is_controlled_func + self.device = device + self.valid_and_controlled_mask = self.get_valid_actor_mask( + is_controlled_func, valid_agent_mask + ) + self.actor_ids = [ + torch.where(self.valid_and_controlled_mask[world_idx, :])[0] + for world_idx in range(valid_agent_mask.shape[0]) + ] + + def select_action(self): + """Select random actions.""" + + action_lists = [] + for world_idx in range(len(self.actor_ids)): + + actions = torch.Tensor( + [ + self.env.action_space.sample() + for _ in range(len(self.actor_ids[world_idx])) + ] + ).to(self.device) + action_lists.append(actions) + + return action_lists + + def get_valid_actor_mask(self, is_controlled_func, valid_agent_mask): + """Returns a boolean mask across worlds that indicates which agents + are valid _and_ controlled by this actor. + """ + + num_worlds = valid_agent_mask.shape[0] + + is_controlled_func = is_controlled_func.expand((num_worlds, -1)) + + return is_controlled_func.to(self.device) & valid_agent_mask.to( + self.device + ) diff --git a/gpudrive-main/gpudrive/agents/sim_agent.py b/gpudrive-main/gpudrive/agents/sim_agent.py new file mode 100644 index 0000000000000000000000000000000000000000..5de084b0af234769e11d75d82d1645c9dfb900e9 --- /dev/null +++ b/gpudrive-main/gpudrive/agents/sim_agent.py @@ -0,0 +1,49 @@ +import torch + + +class SimAgentActor: + """Base class for GPUDrive torch simulation agents. + + Args: + is_controlled_func (torch.Tensor): Determines which agents are controlled by this actor (across worlds). + valid_agent_mask (torch.Tensor): Mask that determines which agents are valid, and thus controllable, in the environment. Shape: (num_worlds, num_agents). + device (str): The device. + """ + + def __init__(self, is_controlled_func, valid_agent_mask, device="cuda"): + self.is_controlled_func = is_controlled_func + self.device = device + self.valid_and_controlled_mask = self.get_valid_actor_mask( + is_controlled_func, valid_agent_mask + ) + self.actor_ids = [ + torch.where(self.valid_and_controlled_mask[world_idx, :])[0] + for world_idx in range(valid_agent_mask.shape[0]) + ] + + def select_action(self, obs) -> torch.Tensor: + """Select an action based on an observation. + + Args: + obs (torch.Tensor): Batch of observations of shape (num_samples, observation_dim). + + Returns: + torch.Tensor: _description_ + """ + raise NotImplementedError + + def get_valid_actor_mask(self, is_controlled_func, valid_agent_mask): + """Returns a boolean mask across worlds that indicates which agents + are valid and controlled by this actor. + """ + num_worlds = valid_agent_mask.shape[0] + + is_controlled_func = is_controlled_func.expand((num_worlds, -1)) + + assert ( + is_controlled_func.shape == valid_agent_mask.shape + ), f"is_controlled_func and valid_agent_mask must match but are not: {is_controlled_func.shape} vs {valid_agent_mask.shape}" + + return is_controlled_func.to(self.device) & valid_agent_mask.to( + self.device + ) diff --git a/gpudrive-main/gpudrive/datatypes/__init__.py b/gpudrive-main/gpudrive/datatypes/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/gpudrive-main/gpudrive/datatypes/action.py b/gpudrive-main/gpudrive/datatypes/action.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/gpudrive-main/gpudrive/datatypes/control.py b/gpudrive-main/gpudrive/datatypes/control.py new file mode 100644 index 0000000000000000000000000000000000000000..7def9175190f059ca4ee26c2177f657ba8b9d704 --- /dev/null +++ b/gpudrive-main/gpudrive/datatypes/control.py @@ -0,0 +1,29 @@ +import madrona_gpudrive +import torch + + +class ResponseType: + """A class to represent the response type of an agent. + Initialized from response_type_tensor. + Shape: (num_worlds, max_controlled_agents). + """ + + def __init__(self, tensor: torch.Tensor): + """Initializes the ego state with an observation tensor.""" + self.moving = (tensor == 0).squeeze(-1) # Agents that are moving + self.kinematic = (tensor == 1).squeeze(-1) # Kinematic (not used) + self.static = (tensor == 2).squeeze(-1) # Static and padding agents + + @classmethod + def from_tensor( + cls, tensor: madrona_gpudrive.madrona.Tensor, backend="torch", device="cuda" + ): + if backend == "torch": + return cls(tensor.to_torch().clone().to(device)) + elif backend == "jax": + raise NotImplementedError("JAX backend not implemented yet.") + + @property + def shape(self) -> tuple[int, ...]: + """Shape (num_worlds, num_agents) of the ego state tensor.""" + return self.controlled.shape diff --git a/gpudrive-main/gpudrive/datatypes/info.py b/gpudrive-main/gpudrive/datatypes/info.py new file mode 100644 index 0000000000000000000000000000000000000000..3556294df8b1b4db39dcd36f441b16b8857ffc65 --- /dev/null +++ b/gpudrive-main/gpudrive/datatypes/info.py @@ -0,0 +1,33 @@ +import torch +import madrona_gpudrive + + +class Info: + """A class to represent the information about the state of the environment. + Initialized from info_tensor (src/bindings) of shape (num_worlds, max_agents_in_scene, 5). + For details, see `Info` in src/types.hpp. + """ + + def __init__(self, info_tensor: torch.Tensor): + """Initializes the ego state with an observation tensor.""" + self.off_road = info_tensor[:, :, 0] + self.collided = info_tensor[:, :, 1:3].sum(axis=2) + self.goal_achieved = info_tensor[:, :, 3] + + @classmethod + def from_tensor( + cls, + info_tensor: madrona_gpudrive.madrona.Tensor, + backend="torch", + device="cuda", + ): + """Creates an LocalEgoState from the agent_observation_tensor.""" + if backend == "torch": + return cls(info_tensor.to_torch().clone().to(device)) + elif backend == "jax": + raise NotImplementedError("JAX backend not implemented yet.") + + @property + def shape(self): + """Returns the shape of the info tensor (num_worlds, max_agents_in_scene).""" + return self.off_road.shape diff --git a/gpudrive-main/gpudrive/datatypes/metadata.py b/gpudrive-main/gpudrive/datatypes/metadata.py new file mode 100644 index 0000000000000000000000000000000000000000..147ab0efa14ab10fe8d56efb8c8c36563eca2a79 --- /dev/null +++ b/gpudrive-main/gpudrive/datatypes/metadata.py @@ -0,0 +1,39 @@ +import torch +from dataclasses import dataclass +import madrona_gpudrive + + +class Metadata: + """A class to represent object metadata (eg: sdc_flag) + Initialized from metadata_tensor (src/bindings). For details, see + `MetaData` in src/types.hpp + + Attributes (all masks are 0/1 int of shape (NumWorlds, NumAgents)): + id: Unique ID of the agent (non-negative int). + isSdc: Whether agent is self-driving car (1/0, -1 padding). + isOfInterest: IDs of agents interested (1/0, -1 padding). + isModeled: Whether agent's trajectory needs to be predicted for WOSAC (1/0, -1 padding). + difficulty: Difficulty of the agent's trajectory to be predicted (0/1/2 if isModeled, 0 if !isModeled, -1 padding). + """ + + def __init__(self, metadata_tensor: torch.Tensor): + """Initializes the Metadata with the metadata tensor.""" + self.is_sdc = metadata_tensor[:, :, 0] + self.objects_of_interest = metadata_tensor[:, :, 1] + self.tracks_to_predict = metadata_tensor[:, :, 2] + self.difficulty = metadata_tensor[:, :, 3] + + @classmethod + def from_tensor( + cls, metadata_tensor: madrona_gpudrive.madrona.Tensor, backend="torch" + ): + """Creates a Metadata object from the metadata_tensor.""" + if backend == "torch": + return cls(metadata_tensor.to_torch().clone()) + elif backend == "jax": + raise NotImplementedError("JAX backend not implemented yet.") + + @property + def shape(self) -> tuple[int, ...]: + """Shape (num_worlds, num_agents) of each metadata mask.""" + return self.is_sdc.shape \ No newline at end of file diff --git a/gpudrive-main/gpudrive/datatypes/observation.py b/gpudrive-main/gpudrive/datatypes/observation.py new file mode 100644 index 0000000000000000000000000000000000000000..6aaaab864a32df17dbc8de1c0dae18ee159d6316 --- /dev/null +++ b/gpudrive-main/gpudrive/datatypes/observation.py @@ -0,0 +1,356 @@ +import torch +from dataclasses import dataclass +from gpudrive.env import constants +from gpudrive.utils.geometry import ( + normalize_min_max, + normalize_min_max_inplace, +) +import madrona_gpudrive + +AGENT_SCALE = madrona_gpudrive.vehicleScale + + +class LocalEgoState: + """A class to represent the ego state of the agent in relative coordinates. + Initialized from self_observation_tensor (src/bindings). For details, see + `SelfObservation` in src/types.hpp. + + Attributes: + speed: Speed of the agent in relative coordinates. + vehicle_length: Length of the agent's bounding box. + vehicle_width: Width of the agent's bounding box. + vehicle_height: Height of the agent's bounding box. + rel_goal_x: Relative x-coordinate to the goal. + rel_goal_y: Relative y-coordinate to the goal. + is_collided: Whether the agent is in collision with another object. + id: Unique identifier of the agent. + """ + + def __init__(self, self_obs_tensor: torch.Tensor, mask=None): + """Initializes the ego state with an observation tensor.""" + if mask is not None: + self_obs_tensor = self_obs_tensor[mask] + self.speed = self_obs_tensor[:, 0] + self.vehicle_length = self_obs_tensor[:, 1] * AGENT_SCALE + self.vehicle_width = self_obs_tensor[:, 2] * AGENT_SCALE + self.vehicle_height = self_obs_tensor[:, 3] + self.rel_goal_x = self_obs_tensor[:, 4] + self.rel_goal_y = self_obs_tensor[:, 5] + self.is_collided = self_obs_tensor[:, 6] + self.id = self_obs_tensor[:, 7] + else: + self.speed = self_obs_tensor[:, :, 0] + self.vehicle_length = self_obs_tensor[:, :, 1] * AGENT_SCALE + self.vehicle_width = self_obs_tensor[:, :, 2] * AGENT_SCALE + self.vehicle_height = self_obs_tensor[:, :, 3] + self.rel_goal_x = self_obs_tensor[:, :, 4] + self.rel_goal_y = self_obs_tensor[:, :, 5] + self.is_collided = self_obs_tensor[:, :, 6] + self.id = self_obs_tensor[:, :, 7] + + @classmethod + def from_tensor( + cls, + self_obs_tensor: madrona_gpudrive.madrona.Tensor, + backend="torch", + device="cuda", + mask=None, + ): + """ + Creates an LocalEgoState from the agent_observation_tensor. + """ + if backend == "torch": + tensor = self_obs_tensor.to_torch().clone().to(device) + obj = cls(tensor, mask=mask) + return obj + + elif backend == "jax": + raise NotImplementedError("JAX backend not implemented yet.") + + def normalize(self): + """Normalizes the ego state to be between -1 and 1.""" + self.speed /= constants.MAX_SPEED + self.vehicle_length /= constants.MAX_VEH_LEN + self.vehicle_width /= constants.MAX_VEH_WIDTH + self.vehicle_height /= constants.MAX_VEH_HEIGHT + + self.rel_goal_x = normalize_min_max( + tensor=self.rel_goal_x, + min_val=constants.MIN_REL_GOAL_COORD, + max_val=constants.MAX_REL_GOAL_COORD, + ) + self.rel_goal_y = normalize_min_max( + tensor=self.rel_goal_y, + min_val=constants.MIN_REL_GOAL_COORD, + max_val=constants.MAX_REL_GOAL_COORD, + ) + + @property + def shape(self) -> tuple[int, ...]: + """Shape (num_worlds, num_agents) of the ego state tensor.""" + return self.speed.shape + + +class GlobalEgoState: + """A class to represent the ego state of the agent in global coordinates. + Initialized from absolute_self_observation_tensor (src/bindings). For details, see + `AbsoluteSelfObservation` in src/types.hpp. Shape: (num_worlds, max_agents, 14). + + Attributes: + pos_x: Global x-coordinate of the agent. + pos_y: Global y-coordinate of the agent. + rotation_as_quaternion (4D float): Represents a quaternion, a 3D rotation. + rotation_from_axis (1D float): Represents the angular distance + from the x-axis (2D rotation). + goal_x: Global x-coordinate of the goal. + goal_y: Global y-coordinate of the goal. + vehicle_length: Length of the agent's bounding box. + vehicle_width: Width of the agent's bounding box. + vehicle_height: Height of the agent's bounding box. + id: Unique identifier of the agent. + """ + + def __init__(self, abs_self_obs_tensor: torch.Tensor): + """Initializes the ego state with an observation tensor.""" + self.pos_x = abs_self_obs_tensor[:, :, 0] + self.pos_y = abs_self_obs_tensor[:, :, 1] + self.pos_z = abs_self_obs_tensor[:, :, 2] + self.rotation_as_quaternion = abs_self_obs_tensor[:, :, 3:7] + self.rotation_angle = abs_self_obs_tensor[:, :, 7] + self.goal_x = abs_self_obs_tensor[:, :, 8] + self.goal_y = abs_self_obs_tensor[:, :, 9] + self.vehicle_length = abs_self_obs_tensor[:, :, 10] * AGENT_SCALE + self.vehicle_width = abs_self_obs_tensor[:, :, 11] * AGENT_SCALE + self.vehicle_height = abs_self_obs_tensor[:, :, 12] + self.id = abs_self_obs_tensor[:, :, 13] + + @classmethod + def from_tensor( + cls, + abs_self_obs_tensor: madrona_gpudrive.madrona.Tensor, + backend="torch", + device="cuda", + ): + """Creates an GlobalEgoState from a tensor.""" + if backend == "torch": + return cls(abs_self_obs_tensor.to_torch().clone().to(device)) + elif backend == "jax": + raise NotImplementedError("JAX backend not implemented yet.") + + @property + def shape(self) -> tuple[int, ...]: + """Shape (num_worlds, num_agents) of the ego state tensor.""" + return self.pos_x.shape + + def restore_mean(self, mean_x, mean_y): + """Reapplies the mean to revert back to the original coordinates. + - self.pos_x and self.pos_y are modified in place are of shape (num_worlds, num_agents). + - mean_x and mean_y are expected to be of shape (num_worlds, 1). + """ + # Reshape the mean to broadcast + mean_x_reshaped = mean_x.view(-1, 1) + mean_y_reshaped = mean_y.view(-1, 1) + + self.pos_x += mean_x_reshaped + self.pos_y += mean_y_reshaped + + +@dataclass +class PartnerObs: + speed: torch.Tensor + rel_pos_x: torch.Tensor + rel_pos_y: torch.Tensor + orientation: torch.Tensor + vehicle_length: torch.Tensor + vehicle_width: torch.Tensor + vehicle_height: torch.Tensor + agent_type: torch.Tensor + ids: torch.Tensor + + """ + A dataclass that represents information about other agents in the + scenario, as viewed from the perspective of the ego agent + (in relative coordinates). Initialized from partner_obs_tensor (src/bindings). For details, see + `PartnerObservations` in src/types.hpp. Shape: (num_worlds, num_agents, num_agents-1, 8). + """ + + def __init__(self, partner_obs_tensor: torch.Tensor, mask=None): + """Initializes the partner observation from a tensor.""" + self.mask = mask + if self.mask is not None: # Used for training + self.data = partner_obs_tensor[self.mask][:, :, :6] + self.data[:, :, 4] *= AGENT_SCALE + self.data[:, :, 5] *= AGENT_SCALE + else: + self.speed = partner_obs_tensor[:, :, :, 0].unsqueeze(-1) + self.rel_pos_x = partner_obs_tensor[:, :, :, 1].unsqueeze(-1) + self.rel_pos_y = partner_obs_tensor[:, :, :, 2].unsqueeze(-1) + self.orientation = partner_obs_tensor[:, :, :, 3].unsqueeze(-1) + self.vehicle_length = ( + partner_obs_tensor[:, :, :, 4].unsqueeze(-1) * AGENT_SCALE + ) + self.vehicle_width = ( + partner_obs_tensor[:, :, :, 5].unsqueeze(-1) * AGENT_SCALE + ) + self.vehicle_height = partner_obs_tensor[:, :, :, 6].unsqueeze(-1) + self.agent_type = ( + partner_obs_tensor[:, :, :, 7].unsqueeze(-1).long() + ) + self.ids = partner_obs_tensor[:, :, :, 8].unsqueeze(-1) + + @classmethod + def from_tensor( + cls, + partner_obs_tensor: madrona_gpudrive.madrona.Tensor, + backend="torch", + device="cuda", + mask=None, + ): + """Creates an PartnerObs from a tensor.""" + if backend == "torch": + tensor = partner_obs_tensor.to_torch().clone().to(device) + obj = cls(tensor, mask=mask) + obj.norm = torch.tensor( + [ + constants.MAX_ORIENTATION_RAD, + constants.MAX_VEH_LEN, + constants.MAX_VEH_WIDTH, + ], + device=device, + ) + return obj + + elif backend == "jax": + raise NotImplementedError("JAX backend not implemented yet.") + + def normalize(self): + """Normalizes the partner observation.""" + if self.mask is not None: + self.data[:, :, 0] /= constants.MAX_SPEED + normalize_min_max_inplace( + tensor=self.data[:, :, 1], + min_val=constants.MIN_REL_GOAL_COORD, + max_val=constants.MAX_REL_GOAL_COORD, + ) + normalize_min_max_inplace( + tensor=self.data[:, :, 2], + min_val=constants.MIN_REL_GOAL_COORD, + max_val=constants.MAX_REL_GOAL_COORD, + ) + self.data[:, :, 3:6] /= self.norm + else: + self.speed = self.speed / constants.MAX_SPEED + self.rel_pos_x = normalize_min_max( + tensor=self.rel_pos_x, + min_val=constants.MIN_REL_GOAL_COORD, + max_val=constants.MAX_REL_GOAL_COORD, + ) + self.rel_pos_y = normalize_min_max( + tensor=self.rel_pos_y, + min_val=constants.MIN_REL_GOAL_COORD, + max_val=constants.MAX_REL_GOAL_COORD, + ) + self.orientation = self.orientation / constants.MAX_ORIENTATION_RAD + self.vehicle_length = self.vehicle_length / constants.MAX_VEH_LEN + self.vehicle_width = self.vehicle_width / constants.MAX_VEH_WIDTH + self.vehicle_height = ( + self.vehicle_height / constants.MAX_VEH_HEIGHT + ) + + def one_hot_encode_agent_types(self): + """One-hot encodes the agent types. This operation increases the + number of features by 3. + """ + self.agent_type = self.agent_type.squeeze(-1) + # Map to classes 0-3 + self.agent_type[ + self.agent_type == int(madrona_gpudrive.EntityType.Vehicle) + ] = 1 + self.agent_type[ + self.agent_type == int(madrona_gpudrive.EntityType.Pedestrian) + ] = 2 + self.agent_type[ + self.agent_type == int(madrona_gpudrive.EntityType.Cyclist) + ] = 3 + + self.agent_type = torch.nn.functional.one_hot( + self.agent_type, num_classes=4 + ) + + @property + def shape(self) -> tuple[int, ...]: + """Shape: (num_worlds, num_agents, num_agents-1).""" + return self.speed.shape + + +@dataclass +class LidarObs: + """Dataclass representing the scenario view through LiDAR sensors. + - Shape: (num_worlds, num_agents, 3, num_lidar_points, 4). + - Axis 2 represents the agent samples, road edge samples, and road line samples. + - Axis 3 represents the lidar points per type, which can be configured in src/consts.hpp as `numLidarSamples`. + - Axis 4 represents the depth, type and x, y, values of the lidar points. + Initialized from lidar_tensor (src/bindings). + For details, see `Lidar` and `LidarSample` in src/types.hpp. + """ + + def __init__(self, lidar_tensor: torch.Tensor): + self.all_lidar_samples = lidar_tensor + self.agent_samples = lidar_tensor[:, :, 0, :, :] + self.road_edge_samples = lidar_tensor[:, :, 1, :, :] + self.road_line_samples = lidar_tensor[:, :, 2, :, :] + + @classmethod + def from_tensor( + cls, + lidar_tensor: madrona_gpudrive.madrona.Tensor, + backend="torch", + device="cuda", + ): + if backend == "torch": + return cls(lidar_tensor.to_torch().clone().to(device)) + elif backend == "jax": + raise NotImplementedError("JAX backend not implemented yet.") + + @property + def shape(self) -> tuple[int, ...]: + """Shape: (num_worlds, num_agents, 3, num_lidar_points, 4).""" + return self.all_lidar_samples.shape + + +@dataclass +class BevObs: + """Dataclass representing the scenario view through LiDAR sensors. + - Shape: (num_worlds, num_agents, 200, 200, num_classes). + Initialized from bev_observation_tensor (src/bindings). + For details, see `BevObservation` and `BevObservations` in src/types.hpp. + """ + + def __init__(self, bev_observation_tensor: torch.Tensor): + self.bev_segmentation_map = bev_observation_tensor + + @classmethod + def from_tensor( + cls, + bev_tensor: madrona_gpudrive.madrona.Tensor, + backend="torch", + device="cuda", + ): + if backend == "torch": + return cls(bev_tensor.to_torch().clone().to(device)) + elif backend == "jax": + raise NotImplementedError("JAX backend not implemented yet.") + + @property + def shape(self) -> tuple[int, ...]: + """Shape: (num_worlds, num_agents, resolution, resolution, 1).""" + return self.bev_segmentation_map.shape + + def one_hot_encode_bev_map(self): + """One-hot encodes the agent types. This operation increases the + number of features by 10. + """ + self.bev_segmentation_map = torch.nn.functional.one_hot( + self.bev_segmentation_map.long(), + num_classes=constants.NUM_MADRONA_ENTITY_TYPES, # From size of Madrona EntityType + ) diff --git a/gpudrive-main/gpudrive/datatypes/roadgraph.py b/gpudrive-main/gpudrive/datatypes/roadgraph.py new file mode 100644 index 0000000000000000000000000000000000000000..729d8161ace11baf92c364091b2671a745d16a4f --- /dev/null +++ b/gpudrive-main/gpudrive/datatypes/roadgraph.py @@ -0,0 +1,368 @@ +from dataclasses import dataclass +import numpy as np +import torch +import enum +import madrona_gpudrive +from gpudrive.utils.geometry import normalize_min_max +from gpudrive.env import constants + + +class MapElementIds(enum.IntEnum): + """Ids for different map elements to be mapped into a tensor to be consistent with + https://github.com/waymo-research/waymax/blob/main/waymax/datatypes/roadgraph.py. + + These integers represent the ID of these specific types as defined in: + https://waymo.com/open/data/motion/tfexample. + """ + + LANE_UNDEFINED = 0 + LANE_FREEWAY = 1 + LANE_SURFACE_STREET = 2 + LANE_BIKE_LANE = 3 + # Original definition skips 4. + ROAD_LINE_UNKNOWN = 5 + ROAD_LINE_BROKEN_SINGLE_WHITE = 6 + ROAD_LINE_SOLID_SINGLE_WHITE = 7 + ROAD_LINE_SOLID_DOUBLE_WHITE = 8 + ROAD_LINE_BROKEN_SINGLE_YELLOW = 9 + ROAD_LINE_BROKEN_DOUBLE_YELLOW = 10 + ROAD_LINE_SOLID_SINGLE_YELLOW = 11 + ROAD_LINE_SOLID_DOUBLE_YELLOW = 12 + ROAD_LINE_PASSING_DOUBLE_YELLOW = 13 + ROAD_EDGE_UNKNOWN = 14 + ROAD_EDGE_BOUNDARY = 15 + ROAD_EDGE_MEDIAN = 16 + STOP_SIGN = 17 + CROSSWALK = 18 + SPEED_BUMP = 19 + DRIVEWAY = 20 # New datatype in v1.2.0: Driveway entrances + UNKNOWN = -1 + + +@dataclass +class GlobalRoadGraphPoints: + """A class to represent global road graph points. All information is + global but demeaned, that is, centered at zero. Takes in + map_observation_tensor of shape (num_worlds, num_road_points, 9). + + Attributes: + x: x-coordinate of the road point. + y: y-coordinate of the road point. + segment_length: Length of the road segment. + segment_width: Scale of the road segment. + segment_height: Height of the road segment. + orientation: Orientation of the road segment. + type: Type of road point (e.g., intersection, straight road). + id: Unique identifier of the road point (road id). + """ + + def __init__(self, roadgraph_tensor: torch.Tensor): + """Initializes the global road graph points with a tensor.""" + self.x = roadgraph_tensor[:, :, 0] + self.y = roadgraph_tensor[:, :, 1] + self.xy = torch.stack((self.x, self.y), dim=-1) + self.segment_length = roadgraph_tensor[:, :, 2] + self.segment_width = roadgraph_tensor[:, :, 3] + self.segment_height = roadgraph_tensor[:, :, 4] + self.orientation = roadgraph_tensor[:, :, 5] + self.type = roadgraph_tensor[:, :, 6] # Original GPUDrive road types, used for plotting + self.id = roadgraph_tensor[:, :, 7] + self.vbd_type = roadgraph_tensor[:, :, 8] # VBD map types aligned with Waymax + self.num_points = roadgraph_tensor.shape[1] + + @classmethod + def from_tensor( + cls, + roadgraph_tensor: madrona_gpudrive.madrona.Tensor, + backend="torch", + device="cuda", + ): + """Creates a GlobalRoadGraphPoints instance from a tensor.""" + if backend == "torch": + return cls(roadgraph_tensor.to_torch().clone().to(device)) + elif backend == "jax": + raise NotImplementedError("JAX backend not implemented yet.") + + def normalize(self): + """Normalizes the road graph points to [-1, 1].""" + self.x = normalize_min_max( + self.x, + min_val=constants.MIN_RG_COORD, + max_val=constants.MAX_RG_COORD, + ) + self.y = normalize_min_max( + self.y, + min_val=constants.MIN_RG_COORD, + max_val=constants.MAX_RG_COORD, + ) + self.segment_length = self.segment_length / constants.MAX_ROAD_SCALE + self.segment_width = self.segment_width / constants.MAX_ROAD_SCALE + self.segment_height = self.segment_height / constants.MAX_ROAD_SCALE + self.orientation = self.orientation / constants.MAX_ORIENTATION_RAD + self.id = self.id + + def one_hot_encode_road_point_types(self): + """One-hot encodes the type of road point.""" + self.type = torch.nn.functional.one_hot(self.type, num_classes=21) + + def restore_mean(self, mean_x, mean_y): + """Reapplies the mean to revert back to the original coordinates.""" + # Reshape for broadcasting + mean_x_reshaped = mean_x.view(-1, 1) + mean_y_reshaped = mean_y.view(-1, 1) + + self.x += mean_x_reshaped + self.y += mean_y_reshaped + + def restore_xy(self): + """Shifts x, y from the midpoint to the starting point of a segment, along the heading angle.""" + self.x -= self.segment_length * np.cos(self.orientation) + self.y -= self.segment_length * np.sin(self.orientation) + + # Get the dimensions + num_worlds, num_road_points = self.x.shape + device = self.x.device + + # Lists to collect new tensors for each batch + new_x_batches = [] + new_y_batches = [] + new_segment_length_batches = [] + new_orientation_batches = [] + new_id_batches = [] + new_type_batches = [] + + # Process each world in the batch + for batch_idx in range(num_worlds): + x_batch = self.x[batch_idx] + y_batch = self.y[batch_idx] + segment_length_batch = self.segment_length[batch_idx] + orientation_batch = self.orientation[batch_idx] + id_batch = self.id[batch_idx] + type_batch = self.type[batch_idx] + + # Find the indices where ids change + id_shifted = torch.cat( + [id_batch[1:], id_batch.new_tensor([id_batch[-1] + 1])] + ) + id_change = id_shifted != id_batch + last_indices = torch.nonzero(id_change).squeeze(-1) + + # Lists to collect new tensors for the current batch + new_x_list = [] + new_y_list = [] + new_segment_length_list = [] + new_orientation_list = [] + new_id_list = [] + new_type_list = [] + + prev_idx = 0 + for idx in last_indices: + idx = idx.item() + # Get the slices up to idx+1 (inclusive) + x_slice = x_batch[prev_idx : idx + 1] + y_slice = y_batch[prev_idx : idx + 1] + segment_length_slice = segment_length_batch[prev_idx : idx + 1] + orientation_slice = orientation_batch[prev_idx : idx + 1] + id_slice = id_batch[prev_idx : idx + 1] + type_slice = type_batch[prev_idx : idx + 1] + + # Compute end_x and end_y for the last point in this id + start_x_last = x_slice[-1] + start_y_last = y_slice[-1] + segment_length_last = segment_length_slice[-1] + orientation_last = orientation_slice[-1] + + end_x = start_x_last + 2 * segment_length_last * torch.cos( + orientation_last + ) + end_y = start_y_last + 2 * segment_length_last * torch.sin( + orientation_last + ) + + # Orientation is set to zero for the final point + end_orientation = torch.tensor(0.0, device=device) + # Segment length is zero for the final point + end_segment_length = torch.tensor(0.0, device=device) + # Id remains the same + end_id = id_slice[-1] + # Type remains the same + end_type = type_slice[-1] + + # Append the slices and the new point to the lists + new_x_list.append(x_slice) + new_y_list.append(y_slice) + new_segment_length_list.append(segment_length_slice) + new_orientation_list.append(orientation_slice) + new_id_list.append(id_slice) + new_type_list.append(type_slice) + + # Append the new point + new_x_list.append(end_x.unsqueeze(0)) + new_y_list.append(end_y.unsqueeze(0)) + new_segment_length_list.append(end_segment_length.unsqueeze(0)) + new_orientation_list.append(end_orientation.unsqueeze(0)) + new_id_list.append(end_id.unsqueeze(0)) + new_type_list.append(end_type.unsqueeze(0)) + + prev_idx = idx + 1 + + # Concatenate the lists to form the new tensors for the current batch + new_x_batch = torch.cat(new_x_list) + new_y_batch = torch.cat(new_y_list) + new_segment_length_batch = torch.cat(new_segment_length_list) + new_orientation_batch = torch.cat(new_orientation_list) + new_id_batch = torch.cat(new_id_list) + new_type_batch = torch.cat(new_type_list) + + # Ensure that the tensors have size num_points by padding or truncating + total_points = new_x_batch.size(0) + if total_points < self.num_points: + # Pad with zeros to reach num_points + pad_size = self.num_points - total_points + pad_tensor = lambda t: torch.cat( + [t, torch.zeros(pad_size, device=device)] + ) + new_x_batch = pad_tensor(new_x_batch) + new_y_batch = pad_tensor(new_y_batch) + new_segment_length_batch = pad_tensor(new_segment_length_batch) + new_orientation_batch = pad_tensor(new_orientation_batch) + new_id_batch = pad_tensor(new_id_batch) + new_type_batch = pad_tensor(new_type_batch) + elif total_points > self.num_points: + # Truncate to num_points + new_x_batch = new_x_batch[: self.num_points] + new_y_batch = new_y_batch[: self.num_points] + new_segment_length_batch = new_segment_length_batch[ + : self.num_points + ] + new_orientation_batch = new_orientation_batch[ + : self.num_points + ] + new_id_batch = new_id_batch[: self.num_points] + new_type_batch = new_type_batch[: self.num_points] + + # Collect the new batch tensors + new_x_batches.append(new_x_batch) + new_y_batches.append(new_y_batch) + new_segment_length_batches.append(new_segment_length_batch) + new_orientation_batches.append(new_orientation_batch) + new_id_batches.append(new_id_batch) + new_type_batches.append(new_type_batch) + + # Stack the new tensors across the batch dimension + self.x = torch.stack(new_x_batches, dim=0) + self.y = torch.stack(new_y_batches, dim=0) + self.xy = torch.stack((self.x, self.y), dim=-1) + self.segment_length = torch.stack(new_segment_length_batches, dim=0) + self.orientation = torch.stack(new_orientation_batches, dim=0) + self.id = torch.stack(new_id_batches, dim=0) + self.type = torch.stack(new_type_batches, dim=0) + + +@dataclass +class LocalRoadGraphPoints: + """A class to represent local (relative) road graph points. Takes in + `agent_roadmap_tensor`. Shape: (num_worlds, num_agents, num_road_points, 9). + Note that num_road_points is set in src/consts.hpp and indicates the K + closest road points to each agent (`kMaxAgentMapObservationsCount` in src/consts.hpp). The + selection of these points is configured using `road_obs_algorithm`. + + Attributes: + x: x-coordinate of the road point relative to each agent. + y: y-coordinate of the road point relative to each agent. + segment_length: Length of the road segment. + segment_width: Scale of the road segment. + segment_height: Height of the road segment. + orientation: Orientation of the road segment. + id: Unique identifier of the road point (road id). + type: Type of road point (e.g., edge, lane). + """ + + def __init__(self, local_roadgraph_tensor: torch.Tensor, mask=None): + """Initializes the global road graph points with a tensor.""" + self.mask = mask + if self.mask is not None: + local_roadgraph_tensor = local_roadgraph_tensor[mask] + self.x = local_roadgraph_tensor[:, :, 0] + self.y = local_roadgraph_tensor[:, :, 1] + self.segment_length = local_roadgraph_tensor[:, :, 2] + self.segment_width = local_roadgraph_tensor[:, :, 3] + self.segment_height = local_roadgraph_tensor[:, :, 4] + self.orientation = local_roadgraph_tensor[:, :, 5] + self.id = local_roadgraph_tensor[:, :, 7] + # Note: To use waymax map type take index 8 instead of 6 + self.data = local_roadgraph_tensor[:, :, :6] + self.type = local_roadgraph_tensor[:, :, 6].long() + else: + self.x = local_roadgraph_tensor[:, :, :, 0] + self.y = local_roadgraph_tensor[:, :, :, 1] + self.segment_length = local_roadgraph_tensor[:, :, :, 2] + self.segment_width = local_roadgraph_tensor[:, :, :, 3] + self.segment_height = local_roadgraph_tensor[:, :, :, 4] + self.orientation = local_roadgraph_tensor[:, :, :, 5] + # Note: To use waymax map type take index 8 instead of 6 + self.type = local_roadgraph_tensor[:, :, :, 6].long() + self.id = local_roadgraph_tensor[:, :, :, 7] + + @classmethod + def from_tensor( + cls, + local_roadgraph_tensor: madrona_gpudrive.madrona.Tensor, + backend="torch", + device="cuda", + mask=None, + ): + """Creates a GlobalRoadGraphPoints instance from a tensor.""" + if backend == "torch": + tensor = local_roadgraph_tensor.to_torch().clone().to(device) + obj = cls(tensor, mask=mask) + obj.norm = torch.Tensor([ + constants.MAX_ROAD_LINE_SEGMENT_LEN, + constants.MAX_ROAD_SCALE, + constants.MAX_ROAD_SCALE, + constants.MAX_ORIENTATION_RAD + ]).to(device) + return obj + elif backend == "jax": + raise NotImplementedError("JAX backend not implemented yet.") + + def normalize(self): + """Normalizes the road graph points to [-1, 1].""" + + if self.mask is not None: + self.data[:, :, 0] = normalize_min_max( + self.data[:, :, 0], + min_val=constants.MIN_RG_COORD, + max_val=constants.MAX_RG_COORD, + ) + self.data[:, :, 1] = normalize_min_max( + self.data[:, :, 1], + min_val=constants.MIN_RG_COORD, + max_val=constants.MAX_RG_COORD, + ) + self.data[:, :, 2:6] /= self.norm + else: + self.x = normalize_min_max( + self.x, + min_val=constants.MIN_RG_COORD, + max_val=constants.MAX_RG_COORD, + ) + self.y = normalize_min_max( + self.y, + min_val=constants.MIN_RG_COORD, + max_val=constants.MAX_RG_COORD, + ) + self.segment_length = ( + self.segment_length / constants.MAX_ROAD_LINE_SEGMENT_LEN + ) + self.segment_width = self.segment_width / constants.MAX_ROAD_SCALE + self.segment_height = self.segment_height / constants.MAX_ROAD_SCALE + self.orientation = self.orientation / constants.MAX_ORIENTATION_RAD + + def one_hot_encode_road_point_types(self): + """One-hot encodes the type of road point.""" + self.type = torch.nn.functional.one_hot(self.type, num_classes=7) + + def shape(self): + """Returns the shape of the local road graph tensor.""" + return self.x.shape diff --git a/gpudrive-main/gpudrive/datatypes/trajectory.py b/gpudrive-main/gpudrive/datatypes/trajectory.py new file mode 100644 index 0000000000000000000000000000000000000000..aeeffed793a5e6fd7d4e160e2bf6b92b9044b7b0 --- /dev/null +++ b/gpudrive-main/gpudrive/datatypes/trajectory.py @@ -0,0 +1,65 @@ +import torch +from dataclasses import dataclass +import madrona_gpudrive + +TRAJ_LEN = 91 # Length of the logged trajectory + + +@dataclass +class LogTrajectory: + """A class to represent the logged human trajectories. Initialized from `expert_trajectory_tensor` (src/bindings.cpp). + Shape: (num_worlds, max_agents, TRAJ_LEN, action_space) + + Attributes: + pos_xy: Global (but demeaned) positions of the agent(s) across the trajectory. + vel_xy: Global (but demeaned) velocity of the agent(s) across the trajectory. + yaw: Headings (yaw angles) of the agent(s) across the trajectory. + valids: Valid flag for each timestep in the trajectory. + actions: Expert actions performed by the agent(s) across the trajectory. + """ + + def __init__( + self, raw_logs: torch.Tensor, num_worlds: int, max_agents: int + ): + """Initializes the expert trajectory with an observation tensor.""" + self.pos_xy = raw_logs[:, :, : 2 * TRAJ_LEN].view( + num_worlds, max_agents, TRAJ_LEN, -1 + ) + self.vel_xy = raw_logs[:, :, 2 * TRAJ_LEN : 4 * TRAJ_LEN].view( + num_worlds, max_agents, TRAJ_LEN, -1 + ) + self.yaw = raw_logs[:, :, 4 * TRAJ_LEN: 5 * TRAJ_LEN].view( + num_worlds, max_agents, TRAJ_LEN, -1 + ) + self.valids = raw_logs[:, :, 5 * TRAJ_LEN:6 * TRAJ_LEN].view( + num_worlds, max_agents, TRAJ_LEN, -1 + ).to(torch.int32) + self.inferred_actions = raw_logs[:, :, 6 * TRAJ_LEN: 16 * TRAJ_LEN].view( + num_worlds, max_agents, TRAJ_LEN, -1 + ) + + @classmethod + def from_tensor( + cls, + expert_traj_tensor: madrona_gpudrive.madrona.Tensor, + num_worlds: int, + max_agents: int, + backend="torch", + ): + """Creates an LogTrajectory from a tensor.""" + if backend == "torch": + return cls( + expert_traj_tensor.to_torch().clone(), num_worlds, max_agents + ) # Pass the entire tensor + elif backend == "jax": + raise NotImplementedError("JAX backend not implemented yet.") + + def restore_mean(self, mean_x, mean_y): + """Reapplies the mean to revert back to the original coordinates.""" + # Reshape for broadcasting: [num_worlds, agents, time_steps] + mean_x_reshaped = mean_x.view(-1, 1, 1) + mean_y_reshaped = mean_y.view(-1, 1, 1) + + # Apply to x and y coordinates + self.pos_xy[:, :, :, 0] += mean_x_reshaped + self.pos_xy[:, :, :, 1] += mean_y_reshaped \ No newline at end of file diff --git a/gpudrive-main/gpudrive/env/README.md b/gpudrive-main/gpudrive/env/README.md new file mode 100644 index 0000000000000000000000000000000000000000..2febd2ade3f5dcb61e5f4fb5167c7b5e813ebebf --- /dev/null +++ b/gpudrive-main/gpudrive/env/README.md @@ -0,0 +1,140 @@ +# `gpudrive` gym environments + +## Quick Start + +To get started, you can use the example data available in the `data/processed/examples` folder. For instructions on downloading more traffic scenarios, refer to [this link](https://github.com/Emerge-Lab/gpudrive/tree/main?tab=readme-ov-file#dataset-------%EF%B8%8F-). + +Configure the environment using the default settings in `config`: + +```Python +config = EnvConfig() +``` + +The `config` object holds all environment parameters. A key configuration is the dynamics model, which determines how the successor state (e.g., position, yaw, velocity) is computed for one or more objects given an action (e.g., steering, acceleration). + +The following dynamics models are available: + +- **Classic**: A kinematic bicycle model using the center of gravity as the reference point, with 2D actions (acceleration, steering curvature). Used in [Nocturne](https://arxiv.org/pdf/2206.09889). +- **InvertibleBicycleModel**: A kinematically realistic model using 2D actions (acceleration, steering curvature) based on [this source](https://github.com/waymo-research/waymax/tree/main/waymax/dynamics). +- **DeltaLocal**: A position-based model using a 3D action (dx, dy, dyaw) to represent displacement relative to current position and orientation. This model doesn't check for infeasible actions, and large displacements can cause unrealistic behavior. Based on [this source](https://github.com/waymo-research/waymax/tree/main/waymax/dynamics). +- **StateDynamics**: A position-based model using a 10D action (x, y, z, yaw, velocities, angular velocities) that directly sets global coordinates. This model doesn't check for infeasible actions, as referenced [here](https://github.com/waymo-research/waymax/tree/main/waymax/dynamics). + +Example of creating an environment with one world and a maximum of three controllable agents per scenario: + +```Python +env = GPUDriveTorchEnv( + config=config, + num_worlds=1, # Number of parallel environments + max_cont_agents=3, # Maximum number of agents to control per scene + data_dir="data/processed/examples", # Path to data folder +) +``` + +To step through the environment: + +```Python +env.step_dynamics(actions) + +# Extract information +obs = env.get_obs() +reward = env.get_rewards() +done = env.get_dones() +``` + +For additional configuration details, see `config.py`. + +--- + +> **Note:** You can filter the information from the agents you control using `env.cont_agent_mask`. This boolean mask has the shape `(num_worlds, max_agents_in_scene)` where `kMaxAgentCount` (default 64) is set in `consts.hpp`. It marks `True` for agents under your control and `False` for all others. + +--- + +## Action Space + +### Discrete (default: `action_type='discrete'`) + +This generates a grid of possible actions, with the action space depending on the `dynamics_model`. + +For instance, with `dynamics_model: str = "classic"`, the default action space is: + +```Python +# Action space (joint discrete) +head_tilt_actions: torch.Tensor = torch.Tensor([0]) +steer_actions: torch.Tensor = torch.round(torch.linspace(-1.0, 1.0, 13), decimals=3) +accel_actions: torch.Tensor = torch.round(torch.linspace(-3, 3, 7), decimals=3) +``` + +### Continuous + +To use a continuous action space, set `action_type='continuous'`. + +## Observation Space + +Key observation flags include: + +``` +ego_state: bool = True # Indicates ego vehicle state +road_map_obs: bool = True # Provides road graph data +partner_obs: bool = True # Includes partner vehicle information +norm_obs: bool = True # Normalizes observations if true +lidar_obs: bool = True # Use LiDAR data +``` + +| Observation Feature | Shape | Description | Features | +| --------------------------------------- | ------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| **ego_state** 🚘 | `(max_num_agents_in_scene, 7)` | Basic ego vehicle information. | Speed, length, width, relative goal position (xy), collision state (1 if collided, 0 otherwise), agent ID | +| **partner_obs** 🚗 🚴🏻‍♀️ 🚶 | `(max_num_agents_in_scene, max_num_objects - 1, 11)` | Information about other agents (vehicles, pedestrians, cyclists) within a certain visibility radius. | Speed, relative position (xy), relative orientation, length and width, type (0: None, 1: Vehicle, 2: Pedestrian, 3: Cyclist), agent ID | +| **road_map_obs** 🛣️ 🛑 | `(max_num_agents_in_scene, top_k_road_points, 13)` | Information about the road graph and static road objects. | Road segment position (xy), segment length, road point scale (xy), orientation, point type (0: None, 1: RoadLine, 2: RoadEdge, 3: RoadLane, 4: CrossWalk, 5: SpeedBump, 6: StopSign) | +| **lidar_obs** | `(max_num_agents_in_scene, 3, num_lidar_samples, 4)` | LiDAR data | LiDAR rays; number of points can be set by adjusting `numLidarSamples` in `src/consts.hpp`. Default is 30 points. | + +--- + +### Data Structures + +For detailed structures, refer to: + +- **Agent Observations**: [`gpudrive/datatypes/observation.py`](https://github.com/Emerge-Lab/gpudrive/blob/main/gpudrive/datatypes/observation.py) +- **Roadgraph**: [`gpudrive/datatypes/roadgraph.py`](https://github.com/Emerge-Lab/gpudrive/blob/main/gpudrive/datatypes/roadgraph.py) + +These structures are used in `env_torch.py`. + +### LiDAR Usage + +> **Using LiDAR only**: If you want to use LiDAR data exclusively as the observation, set `disable_classic_obs = True` to improve simulation performance by disabling classical observations. To ensure only LiDAR observations are returned, set all other flags to `False` in `gpudrive/env/config.py`: + +```Python +ego_state: bool = False +road_map_obs: bool = False +partner_obs: bool = False # Do not include partner vehicle info +norm_obs: bool = False # Disable observation normalization + +# NOTE: If disable_classic_obs is True, the other flags will be ignored. +disable_classic_obs: bool = True # Disable classical observations +lidar_obs: bool = True # Use LiDAR observations +``` + +## Rewards + +A reward of +1 is given when an agent is within the `dist_to_goal_threshold` from the goal, marking the end of the expert trajectory for that vehicle. + +## Starting State + +Each vehicle begins at the start of the expert trajectory when the environment is initialized. + +## Dataset + +For detailed instructions, refer to tutorial `01`. + +### Iterating Through the Waymo Open Motion Dataset + +The `swap_data_batch()` method in `gpudrive/env/env_torch.py` reinitializes the simulator with new traffic scenarios: + +1. **Scene Re-initialization**: This function updates the simulation maps by calling `self.sim.set_maps(dataset)`, replacing the current scenes with those provided in `dataset`, a list of paths to traffic scenarios. +2. **Controlled Agent Mask Re-initialization**: The controlled agents' mask is updated using `self.get_controlled_agents_mask()`, marking which agents are user-controlled. This depends on the selected traffic scenarios. +3. **Agent Count Update**: The function updates `self.max_agent_count` to reflect the number of controlled agents, recalculating `self.num_valid_controlled_agents_across_worlds`, which indicates the active controlled agents across all scenarios. + +For an example of how to use this with IPPO, see the `resample_scenario_batch()` method in `gpudrive/env/wrappers/sb3_wrapper.py`. + +## Visualization + +Refer to the visualizer tutorial for more information. \ No newline at end of file diff --git a/gpudrive-main/gpudrive/env/__init__.py b/gpudrive-main/gpudrive/env/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/gpudrive-main/gpudrive/env/base_env.py b/gpudrive-main/gpudrive/env/base_env.py new file mode 100644 index 0000000000000000000000000000000000000000..f67703abda20e1ffe1a735100fe03de39086e830 --- /dev/null +++ b/gpudrive-main/gpudrive/env/base_env.py @@ -0,0 +1,248 @@ +import gymnasium as gym +from gpudrive.env.config import RenderMode +import madrona_gpudrive +import abc + +class GPUDriveGymEnv(gym.Env, metaclass=abc.ABCMeta): + def __init__(self, backend="torch"): + super().__init__() + self.backend = backend + if self.backend not in ["torch", "jax"]: + raise ValueError("Unsupported backend; use 'torch' or 'jax'") + + def to_tensor(self, x): + """Convert simulator data to the correct tensor type for the specified backend.""" + if self.backend == "torch": + return x.to_torch() + elif self.backend == "jax": + return x.to_jax() + + @abc.abstractmethod + def reset(self): + """Reset the dynamics to inital state. + Args: + scenario: Scenario used to generate the initial state. + rng: Optional random number generator for stochastic environments. + + Returns: + The initial observations. + """ + + @abc.abstractmethod + def step_dynamics(self, actions): + """Advance the dynamics by one step. + + Args: + actions: The actions to apply to the environment. + + Returns: None. + """ + + @abc.abstractmethod + def get_dones(): + """Returns the done flags for the environment.""" + + @abc.abstractmethod + def get_infos(): + """Returns the info tensor for the environment.""" + + @abc.abstractmethod + def get_rewards(): + """Returns the reward tensor for the environment.""" + + def _set_reward_params(self): + """Configures the reward parameters based on environment settings. + + Returns: + object: Configured reward parameters. + """ + reward_params = madrona_gpudrive.RewardParams() + + if ( + self.config.reward_type == "sparse_on_goal_achieved" + or self.config.reward_type == "weighted_combination" + or self.config.reward_type == "distance_to_logs" + or self.config.reward_type == "reward_conditioned" + ): + reward_params.rewardType = madrona_gpudrive.RewardType.OnGoalAchieved + else: + raise ValueError(f"Invalid reward type: {self.config.reward_type}") + + reward_params.distanceToGoalThreshold = ( + self.config.dist_to_goal_threshold + ) + return reward_params + + def _set_road_reduction_params(self, params): + """Configures the road reduction parameters. + + Args: + params (object): Parameters object to be modified. + + Returns: + object: Updated parameters object with road reduction settings. + """ + params.observationRadius = self.config.obs_radius + if self.config.road_obs_algorithm == "k_nearest_roadpoints": + params.roadObservationAlgorithm = ( + madrona_gpudrive.FindRoadObservationsWith.KNearestEntitiesWithRadiusFiltering + ) + else: # Default to linear algorithm + params.roadObservationAlgorithm = ( + madrona_gpudrive.FindRoadObservationsWith.AllEntitiesWithRadiusFiltering + ) + return params + + def _setup_environment_parameters(self): + """Sets up various parameters required for the environment simulation. + + Returns: + object: Configured parameters for the simulation. + """ + # Dict with supported dynamics models + self.dynamics_model_dict = dict( + classic=madrona_gpudrive.DynamicsModel.Classic, + delta_local=madrona_gpudrive.DynamicsModel.DeltaLocal, + bicycle=madrona_gpudrive.DynamicsModel.InvertibleBicycle, + state=madrona_gpudrive.DynamicsModel.State, + ) + + params = madrona_gpudrive.Parameters() + + params.polylineReductionThreshold = ( + self.config.polyline_reduction_threshold + ) + params.rewardParams = self._set_reward_params() + params.maxNumControlledAgents = self.max_cont_agents + if self.config.init_mode == "womd_tracks_to_predict": + # Bypasses all gpudrive initialization rules and directly reads from the tracks_to_predict + # flag in the WOMD dataset metadata + params.readFromTracksToPredict = True + elif self.config.init_mode == "all_objects": + params.isStaticAgentControlled = True + params.initOnlyValidAgentsAtFirstStep = False + params.IgnoreNonVehicles = False + elif self.config.init_mode == "all_valid": + params.isStaticAgentControlled = True + params.initOnlyValidAgentsAtFirstStep = True + params.IgnoreNonVehicles = self.config.remove_non_vehicles + elif self.config.init_mode == "all_non_trivial": + params.isStaticAgentControlled = False + params.initOnlyValidAgentsAtFirstStep = True + params.IgnoreNonVehicles = self.config.remove_non_vehicles + else: + raise ValueError(f"Invalid init mode: {self.config.init_mode}") + + params.dynamicsModel = self.dynamics_model_dict[ + self.config.dynamics_model + ] + if self.config.dynamics_model not in self.dynamics_model_dict: + raise ValueError( + f"Invalid dynamics model: {self.config.dynamics_model}" + ) + + if self.config.lidar_obs: + if not self.config.lidar_obs and self.config.disable_classic_obs: + raise ValueError( + "Lidar observations must be enabled if classic observations are disabled." + ) + + else: + params.enableLidar = self.config.lidar_obs + params.disableClassicalObs = self.config.disable_classic_obs + self.config.ego_state = False + self.config.road_map_obs = False + self.config.partner_obs = False + params = self._set_collision_behavior(params) + params = self._set_road_reduction_params(params) + + return params + + def _initialize_simulator(self, params, data_batch): + """Initializes the simulation with the specified parameters. + + Args: + params (object): Parameters for initializing the simulation. + + Returns: + SimManager: A simulation manager instance configured with given parameters. + """ + exec_mode = ( + madrona_gpudrive.madrona.ExecMode.CPU + if self.device == "cpu" + else madrona_gpudrive.madrona.ExecMode.CUDA + ) + + sim = madrona_gpudrive.SimManager( + exec_mode=exec_mode, + gpu_id=0, + scenes=data_batch, + params=params, + enable_batch_renderer=self.render_config + and self.render_config.render_mode + in {RenderMode.MADRONA_RGB, RenderMode.MADRONA_DEPTH}, + batch_render_view_width=self.render_config.resolution[0] + if self.render_config + else None, + batch_render_view_height=self.render_config.resolution[1] + if self.render_config + else None, + ) + + return sim + + def _setup_action_space(self, action_type): + """Sets up the action space based on the specified type. + + Args: + action_type (str): Type of action space to set up. + + Raises: + ValueError: If the specified action type is not supported. + """ + if action_type == "discrete": + self.action_space = self._set_discrete_action_space() + elif action_type == "continuous": + self.action_space = self._set_continuous_action_space() + else: + raise ValueError(f"Action space not supported: {action_type}") + + def _set_collision_behavior(self, params): + """Defines the behavior when a collision occurs. + + Args: + params (object): Parameters object to update based on collision behavior. + + Returns: + object: Updated parameters with collision behavior settings. + """ + if self.config.collision_behavior == "ignore": + params.collisionBehaviour = madrona_gpudrive.CollisionBehaviour.Ignore + elif self.config.collision_behavior == "remove": + params.collisionBehaviour = ( + madrona_gpudrive.CollisionBehaviour.AgentRemoved + ) + elif self.config.collision_behavior == "stop": + params.collisionBehaviour = madrona_gpudrive.CollisionBehaviour.AgentStop + else: + raise ValueError( + f"Invalid collision behavior: {self.config.collision_behavior}" + ) + return params + + def close(self): + """Destroy the simulator and visualizer.""" + del self.sim + + def normalize_tensor(self, x, min_val, max_val): + """Normalizes an array of values to the range [-1, 1]. + + Args: + x (np.array): Array of values to normalize. + min_val (float): Minimum value for normalization. + max_val (float): Maximum value for normalization. + + Returns: + np.array: Normalized array of values. + """ + return 2 * ((x - min_val) / (max_val - min_val)) - 1 diff --git a/gpudrive-main/gpudrive/env/config.py b/gpudrive-main/gpudrive/env/config.py new file mode 100644 index 0000000000000000000000000000000000000000..4ef00999610b6f049e41ed02ff86163bd96b93ea --- /dev/null +++ b/gpudrive-main/gpudrive/env/config.py @@ -0,0 +1,217 @@ +# pylint: skip-file +"""Configuration classes and enums for GPUDrive Environments.""" + +from dataclasses import dataclass +from enum import Enum +from typing import Tuple, Optional +import torch + +import madrona_gpudrive + + +@dataclass +class EnvConfig: + """Configuration settings for the GPUDrive gym environment. + + This class contains both Python-specific configurations and settings that + are shared between Python and C++ components of the simulator. + + To modify simulator settings shared with C++, follow these steps: + 1. Navigate to `src/consts.hpp` in the C++ codebase. + 2. Locate and modify the constant (e.g., `kMaxAgentCount`). + 3. Save the changes to `src/consts.hpp`. + 4. Recompile the code to apply changes across both C++ and Python. + """ + + # Python-specific configurations + # Observation space settings + ego_state: bool = True # Include ego vehicle state in observations + road_map_obs: bool = True # Include road graph in observations + partner_obs: bool = True # Include partner vehicle info in observations + bev_obs: bool = False # Include rasterized Bird's Eye View observations centered on ego vehicle + norm_obs: bool = True # Normalize observations + + # Maximum number of controlled agents in the scene + max_controlled_agents: int = madrona_gpudrive.kMaxAgentCount + num_worlds: int = 1 # Number of worlds in the environment + + # NOTE: If disable_classic_obs is True, ego_state, road_map_obs, + # and partner_obs are invalid. This makes the sim 2x faster + disable_classic_obs: bool = False # Disable classic observations + lidar_obs: bool = False # Use LiDAR in observations + + # Set the weights for the reward components + # R = a * collided + b * goal_achieved + c * off_road + collision_weight: float = 0.0 + goal_achieved_weight: float = 1.0 + off_road_weight: float = 0.0 + + # Road observation algorithm settings + road_obs_algorithm: str = "linear" # Algorithm for road observations + obs_radius: float = 50.0 # Radius for road observations + polyline_reduction_threshold: float = ( + 0.1 # Threshold for polyline reduction + ) + + # Dynamics model + dynamics_model: str = ( + "classic" # Options: "classic", "bicycle", "delta_local", or "state" + ) + + # Action space settings (if discretized) + # Classic or Invertible Bicycle dynamics model + steer_actions: torch.Tensor = torch.round( + torch.linspace(-torch.pi, torch.pi, 13), decimals=3 + ) + accel_actions: torch.Tensor = torch.round( + torch.linspace(-4.0, 4.0, 7), decimals=3 + ) + head_tilt_actions: torch.Tensor = torch.Tensor([0]) + + # Delta Local dynamics model + dx: torch.Tensor = torch.round(torch.linspace(-2.0, 2.0, 20), decimals=3) + dy: torch.Tensor = torch.round(torch.linspace(-2.0, 2.0, 20), decimals=3) + dyaw: torch.Tensor = torch.round( + torch.linspace(-3.14, 3.14, 20), decimals=3 + ) + + # Global action space settings if StateDynamicsModel is used + x: torch.Tensor = torch.round( + torch.linspace(-100.0, 100.0, 10), decimals=3 + ) + y: torch.Tensor = torch.round( + torch.linspace(-100.0, 100.0, 10), decimals=3 + ) + yaw: torch.Tensor = torch.round( + torch.linspace(-3.14, 3.14, 10), decimals=3 + ) + vx: torch.Tensor = torch.round(torch.linspace(-10.0, 10.0, 10), decimals=3) + vy: torch.Tensor = torch.round(torch.linspace(-10.0, 10.0, 10), decimals=3) + + # Collision behavior settings + collision_behavior: str = "remove" # Options: "remove", "stop", "ignore" + + # Scene configuration + remove_non_vehicles: bool = True # Remove non-vehicle entities from scene + + # Initialization steps: Number of steps to take before the episode starts + init_steps: int = 0 + + # Reward settings + reward_type: str = "sparse_on_goal_achieved" + # Alternatively, "weighted_combination", "distance_to_logs", "distance_to_vdb_trajs", "reward_conditioned" + + condition_mode: str = "random" # Options: "random", "fixed", "preset" + + # Define upper and lower bounds for reward components if using reward_conditioned + collision_weight_lb: float = -1.0 + collision_weight_ub: float = 0.0 + goal_achieved_weight_lb: float = 1.0 + goal_achieved_weight_ub: float = 2.0 + off_road_weight_lb: float = -1.0 + off_road_weight_ub: float = 0.0 + + dist_to_goal_threshold: float = ( + 2.0 # Radius around goal considered as "goal achieved" + ) + + # C++ and Python shared settings (modifiable via C++ codebase) + max_num_agents_in_scene: int = ( + madrona_gpudrive.kMaxAgentCount + ) # Max number of objects in simulation + max_num_rg_points: int = ( + madrona_gpudrive.kMaxRoadEntityCount + ) # Max number of road graph segments + roadgraph_top_k: int = ( + madrona_gpudrive.kMaxAgentMapObservationsCount + ) # Top-K road graph segments agents can view + episode_len: int = ( + madrona_gpudrive.episodeLen + ) # Length of an episode in the simulator + num_lidar_samples: int = madrona_gpudrive.numLidarSamples + # Agent size scale factor (0.0-1.0) + # Controls the visual and collision size of vehicles in the simulation. + agent_size_scale: float = madrona_gpudrive.vehicleScale + + # Initialization mode + init_mode: str = ( + "all_non_trivial" # Options: all_non_trivial, all_objects, all_valid + ) + + # VBD model settings + use_vbd: bool = False + vbd_model_path: str = None + vbd_trajectory_weight: float = 0.01 + vbd_in_obs: bool = False + + +class SelectionDiscipline(Enum): + """Enum for selecting scenes discipline in dataset configuration.""" + + FIRST_N = 0 + RANDOM_N = 1 + PAD_N = 2 + EXACT_N = 3 + K_UNIQUE_N = 4 + + +@dataclass +class SceneConfig: + """Configuration for selecting scenes from a dataset. + + Attributes: + path (str): Path to the dataset. + num_scenes (int): Number of scenes to select. + discipline (SelectionDiscipline): Method for selecting scenes. + k_unique_scenes (Optional[int]): Number of unique scenes if using + K_UNIQUE_N discipline. + seed (Optional[int]): Seed for random scene selection. + """ + + batch_size: int # Number of scenes per batch (should be equal to number of worlds in the env). + dataset_size: int # Maximum number of files to include in the dataset. + path: str = None + num_scenes: int = None + discipline: SelectionDiscipline = SelectionDiscipline.PAD_N + k_unique_scenes: Optional[int] = None + seed: Optional[int] = None + + +class RenderMode(Enum): + """Enum for specifying rendering mode.""" + + MATPLOTLIB = "matplotlib" + MADRONA_RGB = "madrona_rgb" + MADRONA_DEPTH = "madrona_depth" + + +class MadronaOption(Enum): + """Enum for Madrona rendering options.""" + + AGENT_VIEW = "agent_view" + TOP_DOWN = "top_down" + + +@dataclass +class RenderConfig: + """ + Configuration settings for rendering the environment. + Attributes: + render_mode (RenderMode): The mode used for rendering the environment. + view_option (Enum): Rendering view option (e.g., RGB, human view). + resolution (Tuple[int, int]): Resolution of the rendered image. + draw_expert_trajectories (bool): Whether to draw expert trajectories. + draw_only_controllable_veh (bool): Whether to draw only the trajectories of controllable vehicles. + obj_idx_font_size (int): Font size for object indices. + render_3d (bool): Whether to render the scene in 3D. + vehicle_height (float): Height of the vehicle for 3D rendering. + """ + + render_mode: RenderMode = RenderMode.MATPLOTLIB + view_option: Enum = None + resolution: Tuple[int, int] = (1024, 1024) + draw_expert_trajectories: bool = False + draw_only_controllable_veh: bool = False + obj_idx_font_size: int = 9 + render_3d: bool = False + vehicle_height: float = 0.06 diff --git a/gpudrive-main/gpudrive/env/constants.py b/gpudrive-main/gpudrive/env/constants.py new file mode 100644 index 0000000000000000000000000000000000000000..29736ddb88a64ac8feae1353401afc36bc0777fb --- /dev/null +++ b/gpudrive-main/gpudrive/env/constants.py @@ -0,0 +1,32 @@ +import numpy as np + +"""Predefined constants for the environment.""" + +# Observation space constants +MAX_SPEED = 100 +MAX_VEH_LEN = 30 +MAX_VEH_WIDTH = 15 +MAX_VEH_HEIGHT = 10 +MIN_REL_GOAL_COORD = -1000 +MAX_REL_GOAL_COORD = 1000 +MIN_REL_AGENT_POS = -1000 +MAX_REL_AGENT_POS = 1000 +MAX_ORIENTATION_RAD = 2 * np.pi + +# Road graph constants +MIN_RG_COORD = -1000 +MAX_RG_COORD = 1000 +MAX_ROAD_LINE_SEGMENT_LEN = 100 +MAX_ROAD_SCALE = 100 + +# Feature shape constants +EGO_FEAT_DIM = 6 +PARTNER_FEAT_DIM = 6 +ROAD_GRAPH_FEAT_DIM = 13 + +# Dataset constants +LOG_TRAJECTORY_LEN = 91 + +#BEV observation constants +BEV_RASTERIZATION_RESOLUTION = 200 +NUM_MADRONA_ENTITY_TYPES = 11 \ No newline at end of file diff --git a/gpudrive-main/gpudrive/env/dataset.py b/gpudrive-main/gpudrive/env/dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..68d7b2eefa6fca23947065cc747d7042deb90948 --- /dev/null +++ b/gpudrive-main/gpudrive/env/dataset.py @@ -0,0 +1,158 @@ +from dataclasses import dataclass +from typing import Iterator, List +import os +from pathlib import Path +import random + + +@dataclass +class SceneDataLoader: + root: str + batch_size: int + dataset_size: int + sample_with_replacement: bool = False + file_prefix: str = "tfrecord" + seed: int = 42 + shuffle: bool = False + + """ + A data loader for sampling batches of traffic scenarios from a directory of files. + + Attributes: + root (str): Path to the directory containing scene files. + batch_size (int): Number of scenes per batch (usually equal to number of worlds in the env). + dataset_size (int): Maximum number of files to include in the dataset. + sample_with_replacement (bool): Whether to sample files with replacement. + file_prefix (str): Prefix for scene files to include in the dataset. + seed (int): Seed for random number generator to ensure reproducibility. + shuffle (bool): Whether to shuffle the dataset before batching. + """ + + def __post_init__(self): + self.root = str(Path(self.root).expanduser().resolve()) + if self.seed is None: + self.seed = 42 + + # Validate the path + if not os.path.exists(self.root): + raise FileNotFoundError( + f"The specified path does not exist: {self.root}" + ) + + # Set the random seed for reproducibility + self.random_gen = random.Random(self.seed) + + # Create the dataset from valid files in the directory + self.dataset = [ + os.path.join(self.root, scene) + for scene in sorted(os.listdir(self.root)) + if scene.startswith(self.file_prefix) + ] + + # Adjust dataset size based on the provided dataset_size + self.dataset = self.dataset[ + : min(self.dataset_size, len(self.dataset)) + ] + + # If dataset_size < batch_size, repeat the dataset until it matches the batch size + if self.dataset_size < self.batch_size: + repeat_count = (self.batch_size // self.dataset_size) + 1 + self.dataset *= repeat_count + self.dataset = self.dataset[: self.batch_size] + + # Shuffle the dataset if required + if self.shuffle: + self.random_gen.shuffle(self.dataset) + + # Initialize state for iteration + self._reset_indices() + + def _reset_indices(self): + """Reset indices for sampling.""" + if self.sample_with_replacement: + self.indices = [ + self.random_gen.randint(0, len(self.dataset) - 1) + for _ in range(len(self.dataset)) + ] + else: + self.indices = list(range(len(self.dataset))) + self.current_index = 0 + + def __iter__(self) -> Iterator[List[str]]: + self._reset_indices() + return self + + def __len__(self): + """Get the number of batches in the dataloader.""" + return len(self.dataset) // self.batch_size + + def __next__(self) -> List[str]: + if self.sample_with_replacement: + # Ensure deterministic behavior + random_gen = random.Random( + self.seed + self.current_index + ) # Changing the seed per batch + + # Determine the batch size using the random generator to shuffle the indices + batch_indices = [ + random_gen.randint(0, len(self.dataset) - 1) + for _ in range(self.batch_size) + ] + + # Move to the next batch + self.current_index += 1 + + # Retrieve the corresponding scenes + batch = [self.dataset[i] for i in batch_indices] + else: + if self.current_index >= len(self.indices): + raise StopIteration + + end_index = min( + self.current_index + self.batch_size, len(self.indices) + ) + batch_indices = self.indices[self.current_index : end_index] + self.current_index = end_index + batch = [self.dataset[i] for i in batch_indices] + + return batch + + +# Example usage +if __name__ == "__main__": + from pprint import pprint + + data_loader = SceneDataLoader( + root="data/processed/training", + batch_size=5, + dataset_size=15, + sample_with_replacement=True, # Sampling with replacement + shuffle=False, # Shuffle the dataset before batching + ) + + unique_files_sampled = set() + for idx, batch in enumerate(data_loader): + unique_files_sampled.update(batch) + coverage = len(unique_files_sampled) / len(data_loader.dataset) * 100 + print(f"coverage: {coverage:.2f}%") + if idx > 4: + break + + pprint(unique_files_sampled) + + # Now without replacement + data_loader = SceneDataLoader( + root="data/processed/training", + batch_size=5, + dataset_size=15, + sample_with_replacement=False, # Sampling with replacement + shuffle=False, # Shuffle the dataset before batching + ) + + unique_files_sampled = set() + for idx, batch in enumerate(data_loader): + print(idx) + pprint(batch) + + unique_files_sampled.update(batch) + coverage = len(unique_files_sampled) / len(data_loader.dataset) * 100 diff --git a/gpudrive-main/gpudrive/env/env_jax.py b/gpudrive-main/gpudrive/env/env_jax.py new file mode 100644 index 0000000000000000000000000000000000000000..446700a75e915c1760c0cedbe03df0cc8f41b951 --- /dev/null +++ b/gpudrive-main/gpudrive/env/env_jax.py @@ -0,0 +1,451 @@ +from gymnasium.spaces import Box, Discrete, Tuple +import numpy as np +import torch +import jax +import jax.numpy as jnp + +from itertools import product +import mediapy +import gymnasium + +from gpudrive.env.config import EnvConfig, RenderConfig +from gpudrive.env.base_env import GPUDriveGymEnv + +from gpudrive.visualize.core import MatplotlibVisualizer +from gpudrive.visualize.utils import img_from_fig +from gpudrive.env.dataset import SceneDataLoader +from gpudrive.env import constants +from gpudrive.env.config import EnvConfig, RenderConfig, SceneConfig + + +class GPUDriveJaxEnv(GPUDriveGymEnv): + """Jax Gym Environment that interfaces with the GPU Drive simulator.""" + + def __init__( + self, + config, + data_loader, + max_cont_agents, + device="cuda", + action_type="discrete", + render_config: RenderConfig = RenderConfig(), + backend="jax", + ): + # Initialization of environment configurations + self.config = config + self.data_loader = data_loader + self.num_worlds = data_loader.batch_size + self.max_cont_agents = max_cont_agents + self.device = device + self.render_config = render_config + self.backend = backend + + # Environment parameter setup + params = self._setup_environment_parameters() + + # Initialize the iterator once + self.data_iterator = iter(self.data_loader) + + # Get the initial data batch (set of traffic scenarios) + self.data_batch = next(self.data_iterator) + + # Initialize simulator + self.sim = self._initialize_simulator(params, self.data_batch) + + # Controlled agents setup + self.cont_agent_mask = self.get_controlled_agents_mask() + self.max_agent_count = self.cont_agent_mask.shape[1] + self.num_valid_controlled_agents_across_worlds = ( + self.cont_agent_mask.sum().item() + ) + + # Setup action and observation spaces + self.observation_space = Box( + low=-1.0, high=1.0, shape=(self.get_obs().shape[-1],) + ) + + self.single_observation_space = gymnasium.spaces.Box( + low=-1.0, + high=1.0, + shape=(self.observation_space.shape[-1],), + dtype=np.float32, + ) + + self._setup_action_space(action_type) + self.single_action_space = self.action_space + + self.num_agents = self.cont_agent_mask.sum().item() + self.episode_len = self.config.episode_len + + # Rendering setup + self.vis = MatplotlibVisualizer( + sim_object=self.sim, + controlled_agent_mask=self.cont_agent_mask, + goal_radius=self.config.dist_to_goal_threshold, + backend=self.backend, + num_worlds=self.num_worlds, + render_config=self.render_config, + env_config=self.config, + ) + + def reset(self): + """Reset the worlds and return the initial observations.""" + self.sim.reset(list(range(self.num_worlds))) + return self.get_obs() + + def get_dones(self): + """Get dones for all agents.""" + return self.sim.done_tensor().to_jax().squeeze(axis=2) + + def get_infos(self): + """Get info for all agents.""" + return self.sim.info_tensor().to_jax() + + def get_rewards( + self, + off_road_weight=0.75, + collision_weight=0.75, + goal_achieved_weight=1.0, + ): + """Get rewards for all agents.""" + if self.config.reward_type == "sparse_on_goal_achieved": + self.sim.reward_tensor().to_jax().squeeze(axis=2) + + elif self.config.reward_type == "weighted_combination": + infos = self.get_infos() + + # Indicators + off_road = infos[:, :, 0] + agent_collision = infos[:, :, 1:3].sum(axis=2) + goal_achieved = infos[:, :, 3] + + # Weighted combination + rewards = ( + goal_achieved_weight * goal_achieved + - collision_weight * agent_collision + - off_road_weight * off_road + ) + + return rewards + + def step_dynamics(self, actions): + """Step the simulator.""" + if actions is not None: + self._apply_actions(actions) + self.sim.step() + + def _apply_actions(self, actions): + """Apply the actions to the simulator.""" + + if self.config.dynamics_model in {"classic", "bicycle", "delta_local"}: + if actions.ndim == 2: # (num_worlds, max_agent_count) + # Map action indices to action values if indices are provided + actions = jnp.nan_to_num(actions, nan=0).astype(jnp.int32) + action_value_tensor = self.action_keys_tensor[actions] + + elif actions.ndim == 3: + if actions.shape[2] == 1: + actions = actions.squeeze(axis=2) + action_value_tensor = self.action_keys_tensor[actions] + elif ( + actions.shape[2] == 3 + ): # Assuming actual action values are given + action_value_tensor = actions + else: + raise ValueError(f"Invalid action shape: {actions.shape}") + else: + action_value_tensor = actions + + # Feed the action values to gpudrive + self._copy_actions_to_simulator(action_value_tensor) + + def _copy_actions_to_simulator(self, actions): + """Copy the provided actions to the simulator.""" + + # Convert to torch Tensor (tmp solution) + actions = torch.from_numpy(np.array(actions)) + + if self.config.dynamics_model in {"classic", "bicycle", "delta_local"}: + # Action space: (acceleration, steering, heading) or (dx, dy, dyaw) + self.sim.action_tensor().to_torch()[:, :, :3].copy_(actions) + elif self.config.dynamics_model == "state": + # Following the StateAction struct in types.hpp + # Need to provide: (x, y, z, yaw, velocity x, vel y, vel z, ang_vel_x, ang_vel_y, ang_vel_z) + self.sim.action_tensor().to_torch()[:, :, :10].copy_(actions) + else: + raise ValueError( + f"Invalid dynamics model: {self.config.dynamics_model}" + ) + + def _set_discrete_action_space(self) -> None: + """Configure the discrete action space.""" + + self.steer_actions = jnp.asarray(self.config.steer_actions) + self.accel_actions = jnp.asarray(self.config.accel_actions) + self.head_actions = jnp.array([0]) + + # Create a mapping from action indices to action values + self.action_key_to_values = {} + + for action_idx, (accel, steer, head) in enumerate( + product(self.accel_actions, self.steer_actions, self.head_actions) + ): + self.action_key_to_values[action_idx] = [ + accel.item(), + steer.item(), + head.item(), + ] + + self.action_keys_tensor = jnp.array( + [ + self.action_key_to_values[key] + for key in sorted(self.action_key_to_values.keys()) + ] + ) + + return Discrete(n=int(len(self.action_key_to_values))) + + def _get_ego_state(self): + """Get the ego state.""" + if self.config.ego_state: + ego_states = self.sim.self_observation_tensor().to_jax() + if self.config.norm_obs: + ego_states = self.process_ego_state(ego_states) + else: + ego_states = jnp.array() + return ego_states + + def _get_partner_obs(self): + """Get the partner observation.""" + if self.config.partner_obs: + partner_observations = ( + self.sim.partner_observations_tensor().to_jax() + ) + if self.config.norm_obs: + partner_observations = self.process_partner_obs( + partner_observations + ) + else: + partner_observations = partner_observations.flatten( + start_dim=2 + ) + else: + partner_observations = jnp.array() + return partner_observations + + def _get_road_map_obs(self): + """Get the road map observation.""" + if self.config.road_map_obs: + road_map_observations = self.sim.agent_roadmap_tensor().to_jax() + + if self.config.norm_obs: + road_map_observations = self.process_roadgraph( + road_map_observations + ) + else: + road_map_observations = road_map_observations.flatten( + start_dim=2 + ) + else: + road_map_observations = jnp.array() + return road_map_observations + + def get_controlled_agents_mask(self): + """Get the control mask.""" + return (self.sim.controlled_state_tensor().to_jax() == 1).squeeze( + axis=2 + ) + + def get_obs(self): + """Get observation: Aggregate multi-modal environment information into + a single flattened tensor. All information is in the shape of + (num_worlds, max_agent_count, num_features). + + Returns: + jnp.array: (num_worlds, max_agent_count, num_features) + """ + + ego_states = self._get_ego_state() + partner_observations = self._get_partner_obs() + road_map_observations = self._get_road_map_obs() + + obs_filtered = jnp.concatenate( + ( + ego_states, + partner_observations, + road_map_observations, + ), + axis=-1, + ) + return obs_filtered + + def process_ego_state(self, state): + """Normalize ego state features.""" + indices = jnp.array([0, 1, 2, 4, 5, 6]) + + # Speed, vehicle length, vehicle width + state = state.at[:, :, 0].divide(constants.MAX_SPEED) + state = state.at[:, :, 1].divide(constants.MAX_VEH_LEN) + state = state.at[:, :, 2].divide(constants.MAX_VEH_WIDTH) + + # Skip vehicle height (3) + + # Relative goal coordinates + state = state.at[:, :, 4].set( + self.normalize_tensor( + state[:, :, 4], + constants.MIN_REL_GOAL_COORD, + constants.MAX_REL_GOAL_COORD, + ) + ) + + state = state.at[:, :, 5].set( + self.normalize_tensor( + state[:, :, 5], + constants.MIN_REL_GOAL_COORD, + constants.MAX_REL_GOAL_COORD, + ) + ) + indices = jnp.array([0, 1, 2, 4, 5, 6]) + state = state[:, :, indices] + + return state + + def process_partner_obs(self, obs): + """Normalize partner state features. + Args: + obs: jnp.array of shape (num_worlds, kMaxAgentCount, kMaxAgentCount - 1, num_features) + """ + + # Speed + obs = obs.at[:, :, :, 0].divide(constants.MAX_SPEED) + + # Relative position + obs = obs.at[:, :, :, 1].set( + self.normalize_tensor( + obs[:, :, :, 1], + constants.MIN_REL_AGENT_POS, + constants.MAX_REL_AGENT_POS, + ) + ) + obs = obs.at[:, :, :, 2].set( + self.normalize_tensor( + obs[:, :, :, 2], + constants.MIN_REL_AGENT_POS, + constants.MAX_REL_AGENT_POS, + ) + ) + + # Orientation (heading) + obs = obs.at[:, :, :, 3].divide(constants.MAX_ORIENTATION_RAD) + + # Vehicle length and width + obs = obs.at[:, :, :, 4].divide(constants.MAX_VEH_LEN) + obs = obs.at[:, :, :, 5].divide(constants.MAX_VEH_WIDTH) + + obs = obs[:, :, :, :6] + + return obs.reshape(self.num_worlds, self.max_agent_count, -1) + + def process_roadgraph(self, obs): + """Normalize map observation features.""" + + # Road point (x, y) coordinates + obs = obs.at[:, :, :, 0].set( + self.normalize_tensor( + obs[:, :, :, 0], + constants.MIN_RG_COORD, + constants.MAX_RG_COORD, + ) + ) + + obs = obs.at[:, :, :, 1].set( + self.normalize_tensor( + obs[:, :, :, 1], + constants.MIN_RG_COORD, + constants.MAX_RG_COORD, + ) + ) + + # Road line segment length + obs = obs.at[:, :, :, 2].divide(constants.MAX_ROAD_LINE_SEGMENT_LEN) + + # Road scale (width and height) + obs = obs.at[:, :, :, 3].divide(constants.MAX_ROAD_SCALE) + obs = obs.at[:, :, :, 4].divide(constants.MAX_ROAD_SCALE) + + # Road point orientation + obs = obs.at[:, :, :, 5].divide(constants.MAX_ROAD_SCALE) + + # Road types: one-hot encode them + one_hot_road_type = jax.nn.one_hot(obs[:, :, :, 6], num_classes=7) + + # Concatenate the one-hot encoding with the rest of the features (exclude index 3 and 4) + obs = jnp.concatenate((obs[:, :, :, :6], one_hot_road_type), axis=-1) + + return obs.reshape(self.num_worlds, self.max_agent_count, -1) + + +if __name__ == "__main__": + + env_config = EnvConfig( + dynamics_model="classic", reward_type="weighted_combination" + ) + render_config = RenderConfig() + + num_worlds = 2 + max_agents = 64 + + # Create data loader + train_loader = SceneDataLoader( + root="data/processed/examples", + batch_size=num_worlds, + dataset_size=100, + sample_with_replacement=True, + shuffle=False, + ) + + # Make env + env = GPUDriveJaxEnv( + config=env_config, + data_loader=train_loader, + max_cont_agents=max_agents, # Number of agents to control + device="cuda", + ) + + sim_frames = [] + + control_mask = env.cont_agent_mask + + next_obs = env.reset() + + for time_step in range(env.episode_len): + print(f"Time step: {time_step}") + + sim_states = env.vis.plot_simulator_state( + env_indices=[0], + zoom_radius=50, + time_steps=[time_step], + ) + sim_frames.append(img_from_fig(sim_states[0])) + + rand_action = jax.random.randint( + key=jax.random.PRNGKey(0), + shape=(num_worlds, max_agents), + minval=0, + maxval=env.action_space.n, + ) + + # Step the environment + env.step_dynamics(rand_action) + + # Get info + next_obs = env.get_obs() + reward = env.get_rewards() + done = env.get_dones() + + env.close() + + mediapy.write_video( + "sim_video.gif", np.array(sim_frames), fps=20, codec="gif" + ) diff --git a/gpudrive-main/gpudrive/env/env_puffer.py b/gpudrive-main/gpudrive/env/env_puffer.py new file mode 100644 index 0000000000000000000000000000000000000000..448868f6dfb99315ea8f549940980ae4a80c853d --- /dev/null +++ b/gpudrive-main/gpudrive/env/env_puffer.py @@ -0,0 +1,525 @@ +import os +import numpy as np +from pathlib import Path +import torch +import wandb +import gymnasium +from collections import Counter +from gpudrive.env.config import EnvConfig, RenderConfig + +from gpudrive.env.env_torch import GPUDriveTorchEnv +from gpudrive.datatypes.observation import ( + LocalEgoState, +) + +from gpudrive.visualize.utils import img_from_fig +from gpudrive.env.dataset import SceneDataLoader + +from pufferlib.environment import PufferEnv +from gpudrive import GPU_DRIVE_DATA_DIR + + +def env_creator(name="gpudrive", **kwargs): + return lambda: PufferGPUDrive(**kwargs) + + +class PufferGPUDrive(PufferEnv): + """PufferEnv wrapper for GPUDrive.""" + + def __init__( + self, + data_loader=None, + data_dir=GPU_DRIVE_DATA_DIR, + loader_batch_size=128, + loader_dataset_size=3, + loader_sample_with_replacement=True, + loader_shuffle=False, + device=None, + num_worlds=64, + max_controlled_agents=64, + dynamics_model="classic", + action_space_steer_disc=13, + action_space_accel_disc=7, + ego_state=True, + road_map_obs=True, + partner_obs=True, + norm_obs=True, + lidar_obs=False, + bev_obs=False, + reward_type="weighted_combination", + condition_mode="random", + collision_weight_lb=-1.0, + collision_weight_ub=-0.1, + goal_achieved_weight_lb=0.5, + goal_achieved_weight_ub=2.0, + off_road_weight_lb=-1.0, + off_road_weight_ub=-0.1, + collision_behavior="ignore", + collision_weight=-0.5, + off_road_weight=-0.5, + goal_achieved_weight=1, + dist_to_goal_threshold=2.0, + polyline_reduction_threshold=0.1, + remove_non_vehicles=True, + obs_radius=50.0, + use_vbd=False, + vbd_model_path=None, + vbd_trajectory_weight=0.1, + render=False, + render_3d=True, + render_interval=50, + render_k_scenarios=3, + render_agent_obs=False, + render_format="mp4", + render_fps=15, + zoom_radius=50, + buf=None, + **kwargs, + ): + assert buf is None, "GPUDrive set up only for --vec native" + + if data_loader is None: + data_loader = SceneDataLoader( + root=data_dir, + batch_size=loader_batch_size, + dataset_size=loader_dataset_size, + sample_with_replacement=loader_sample_with_replacement, + shuffle=loader_shuffle, + ) + + if device is None: + device = "cuda" if torch.cuda.is_available() else "cpu" + + self.device = device + self.num_worlds = num_worlds + self.max_cont_agents_per_env = max_controlled_agents + self.collision_weight = collision_weight + self.off_road_weight = off_road_weight + self.goal_achieved_weight = goal_achieved_weight + + self.render = render + self.render_interval = render_interval + self.render_k_scenarios = render_k_scenarios + self.render_agent_obs = render_agent_obs + self.render_format = render_format + self.render_fps = render_fps + self.zoom_radius = zoom_radius + + # VBD + self.vbd_model_path = vbd_model_path + self.vbd_trajectory_weight = vbd_trajectory_weight + self.use_vbd = use_vbd + self.vbd_trajectory_weight = vbd_trajectory_weight + + # Total number of agents across envs, including padding + self.total_agents = self.max_cont_agents_per_env * self.num_worlds + + # Madrona loads runtime assets relative to the gpudrive package dir. + working_dir = Path(__file__).resolve().parents[1] + os.chdir(working_dir) + + # Make env + env_config = EnvConfig( + ego_state=ego_state, + road_map_obs=road_map_obs, + partner_obs=partner_obs, + reward_type=reward_type, + norm_obs=norm_obs, + bev_obs=bev_obs, + dynamics_model=dynamics_model, + collision_behavior=collision_behavior, + condition_mode=condition_mode, + collision_weight_lb=collision_weight_lb, + collision_weight_ub=collision_weight_ub, + goal_achieved_weight_lb=goal_achieved_weight_lb, + goal_achieved_weight_ub=goal_achieved_weight_ub, + off_road_weight_lb=off_road_weight_lb, + off_road_weight_ub=off_road_weight_ub, + dist_to_goal_threshold=dist_to_goal_threshold, + polyline_reduction_threshold=polyline_reduction_threshold, + remove_non_vehicles=remove_non_vehicles, + lidar_obs=lidar_obs, + disable_classic_obs=True if lidar_obs else False, + obs_radius=obs_radius, + steer_actions=torch.round( + torch.linspace(-torch.pi, torch.pi, action_space_steer_disc), + decimals=3, + ), + accel_actions=torch.round( + torch.linspace(-4.0, 4.0, action_space_accel_disc), decimals=3 + ), + use_vbd=use_vbd, + vbd_model_path=vbd_model_path, + vbd_trajectory_weight=vbd_trajectory_weight, + ) + + render_config = RenderConfig( + render_3d=render_3d, + ) + + self.env = GPUDriveTorchEnv( + config=env_config, + render_config=render_config, + data_loader=data_loader, + max_cont_agents=max_controlled_agents, + device=device, + ) + + self.obs_size = self.env.observation_space.shape[-1] + self.single_action_space = self.env.action_space + self.single_observation_space = self.env.single_observation_space + + self.controlled_agent_mask = self.env.cont_agent_mask.clone() + + # Number of controlled agents across all worlds + self.num_agents = self.controlled_agent_mask.sum().item() + + # This assigns a bunch of buffers to self. + # You can't use them because you want torch, not numpy + # So I am careful to assign these afterwards + super().__init__() + + # Reset the environment and get the initial observations + self.observations = self.env.reset(self.controlled_agent_mask) + + self.masks = torch.ones(self.num_agents, dtype=bool) + self.actions = torch.zeros( + (self.num_worlds, self.max_cont_agents_per_env), dtype=torch.int64 + ).to(self.device) + + # Setup rendering storage + self.rendering_in_progress = { + env_idx: False for env_idx in range(render_k_scenarios) + } + self.was_rendered_in_rollout = { + env_idx: True for env_idx in range(render_k_scenarios) + } + self.frames = {env_idx: [] for env_idx in range(render_k_scenarios)} + + self.global_step = 0 + self.iters = 0 + + # Data logging storage + self.file_to_index = { + file: idx for idx, file in enumerate(self.env.data_loader.dataset) + } + self.cumulative_unique_files = set() + + def close(self): + """There is no point in closing the env because + Madrona doesn't close correctly anyways. You will want + to cache this copy for later use. Cuda errors if you don't""" + self.env.close() + + def reset(self, seed=None): + self.rewards = torch.zeros(self.num_agents, dtype=torch.float32).to( + self.device + ) + self.terminals = torch.zeros(self.num_agents, dtype=torch.bool).to( + self.device + ) + self.truncations = torch.zeros(self.num_agents, dtype=torch.bool).to( + self.device + ) + self.episode_returns = torch.zeros( + self.num_agents, dtype=torch.float32 + ).to(self.device) + self.agent_episode_returns = torch.zeros( + (self.num_worlds, self.max_cont_agents_per_env), + dtype=torch.float32, + ).to(self.device) + self.episode_lengths = torch.zeros( + (self.num_worlds, self.max_cont_agents_per_env), + dtype=torch.float32, + ).to(self.device) + self.live_agent_mask = self.controlled_agent_mask.clone() + self.collided_in_episode = torch.zeros( + (self.num_worlds, self.max_cont_agents_per_env), + dtype=torch.float32, + ).to(self.device) + self.offroad_in_episode = torch.zeros( + (self.num_worlds, self.max_cont_agents_per_env), + dtype=torch.float32, + ).to(self.device) + + return self.observations, [] + + def step(self, action): + """ + Step the environment with the given actions. Note that we reset worlds + asynchronously when they are done. + Args: + action: A numpy array of actions for the controlled agents. Shape: + (num_worlds, max_cont_agents_per_env) + """ + + # Set the action for the controlled agents + self.actions[self.controlled_agent_mask] = action + + # Step the simulator with controlled agents actions + self.env.step_dynamics(self.actions) + + # Get rewards, terminal (dones) and info + reward = self.env.get_rewards( + collision_weight=self.collision_weight, + off_road_weight=self.off_road_weight, + goal_achieved_weight=self.goal_achieved_weight, + world_time_steps=self.episode_lengths[:, 0].long(), + ) + # Flatten rewards; only keep rewards for controlled agents + reward_controlled = reward[self.controlled_agent_mask] + terminal = self.env.get_dones().bool() + + self.render_env() if self.render else None + + # Check if any worlds are done (terminal or truncated) + controlled_per_world = self.controlled_agent_mask.sum(dim=1) + done_worlds = torch.where( + (terminal * self.controlled_agent_mask).sum(dim=1) + == controlled_per_world + )[0] + done_worlds_cpu = done_worlds.cpu().numpy() + + # Add rewards for living agents + self.agent_episode_returns[self.live_agent_mask] += reward[ + self.live_agent_mask + ] + self.episode_returns += reward_controlled + self.episode_lengths += 1 + + # Log off road and collision events + info = self.env.get_infos() + self.offroad_in_episode += info.off_road + self.collided_in_episode += info.collided + + # Mask used for buffer + self.masks = self.live_agent_mask[self.controlled_agent_mask] + + # Set the mask to False for _agents_ that are terminated for the next step + # Shape: (num_worlds, max_cont_agents_per_env) + self.live_agent_mask[terminal] = 0 + + # Truncated is defined as not crashed nor goal achieved + truncated = torch.logical_and( + ~self.offroad_in_episode.bool(), + torch.logical_and( + ~self.collided_in_episode.bool(), + ~info.goal_achieved.bool(), + ), + ) + + # Flatten + terminal = terminal[self.controlled_agent_mask] + + info_lst = [] + if len(done_worlds) > 0: + + if self.render: + for render_env_idx in range(self.render_k_scenarios): + self.log_video_to_wandb(render_env_idx, done_worlds) + + # Log episode statistics + controlled_mask = self.controlled_agent_mask[ + done_worlds, : + ].clone() + + num_finished_agents = controlled_mask.sum().item() + + # Collision rates are summed across all agents in the episode + off_road_rate = ( + torch.where( + self.offroad_in_episode[done_worlds, :][controlled_mask] + > 0, + 1, + 0, + ).sum() + / num_finished_agents + ) + collision_rate = ( + torch.where( + self.collided_in_episode[done_worlds, :][controlled_mask] + > 0, + 1, + 0, + ).sum() + / num_finished_agents + ) + goal_achieved_rate = ( + info.goal_achieved[done_worlds, :][controlled_mask].sum() + / num_finished_agents + ) + + total_collisions = self.collided_in_episode[done_worlds, :].sum() + total_off_road = self.offroad_in_episode[done_worlds, :].sum() + + agent_episode_returns = self.agent_episode_returns[done_worlds, :][ + controlled_mask + ] + + num_truncated = ( + truncated[done_worlds, :][controlled_mask].sum().item() + ) + + if num_finished_agents > 0: + # fmt: off + info_lst.append( + { + "mean_episode_reward_per_agent": agent_episode_returns.mean().item(), + "perc_goal_achieved": goal_achieved_rate.item(), + "perc_off_road": off_road_rate.item(), + "perc_veh_collisions": collision_rate.item(), + "total_controlled_agents": self.num_agents, + "control_density": self.num_agents / self.controlled_agent_mask.numel(), + "episode_length": self.episode_lengths[done_worlds, :].mean().item(), + "perc_truncated": num_truncated / num_finished_agents, + "num_completed_episodes": len(done_worlds), + "total_collisions": total_collisions.item(), + "total_off_road": total_off_road.item(), + } + ) + # fmt: on + + # Terminal-value bootstrapping is disabled in the PPO loop, so avoid + # rebuilding a full observation tensor for terminal worlds here. + self.last_obs = None + + # Asynchronously reset the done worlds and empty storage + self.env.reset(env_idx_list=done_worlds_cpu) + self.episode_returns[done_worlds] = 0 + self.agent_episode_returns[done_worlds, :] = 0 + self.episode_lengths[done_worlds, :] = 0 + # Reset the live agent mask so that the next alive mask will mark + # all agents as alive for the next step + self.live_agent_mask[done_worlds] = self.controlled_agent_mask[ + done_worlds + ] + self.offroad_in_episode[done_worlds, :] = 0 + self.collided_in_episode[done_worlds, :] = 0 + + # Get the next observations. Note that we do this after resetting + # the worlds so that we always return a fresh observation + next_obs = self.env.get_obs(self.controlled_agent_mask) + + self.observations = next_obs + self.rewards = reward_controlled + self.terminals = terminal + self.truncations = truncated[self.controlled_agent_mask] + + return ( + self.observations, + self.rewards, + self.terminals, + self.truncations, + info_lst, + ) + + def render_env(self): + """Render the environment based on conditions. + - If the episode has just started, start a new rendering. + - If the episode is in progress, continue rendering. + - If the episode has ended, log the video to WandB. + - Only render env once per rollout + """ + for render_env_idx in range(self.render_k_scenarios): + # Start a new rendering if the episode has just started + if (self.iters - 1) % self.render_interval == 0: + if ( + self.episode_lengths[render_env_idx, :][0] == 0 + and not self.was_rendered_in_rollout[render_env_idx] + ): + self.rendering_in_progress[render_env_idx] = True + + envs_to_render = list( + np.where(np.array(list(self.rendering_in_progress.values())))[0] + ) + time_steps = list(self.episode_lengths[envs_to_render, 0]) + + if len(envs_to_render) > 0: + sim_state_figures = self.env.vis.plot_simulator_state( + env_indices=envs_to_render, + time_steps=time_steps, + zoom_radius=self.zoom_radius, + ) + + for idx, render_env_idx in enumerate(envs_to_render): + self.frames[render_env_idx].append( + img_from_fig(sim_state_figures[idx]) + ) + + def resample_scenario_batch(self): + """Sample and set new batch of WOMD scenarios.""" + + # Swap the data batch + self.env.swap_data_batch() + + # Update controlled agent mask and other masks + self.controlled_agent_mask = self.env.cont_agent_mask.clone() + self.num_agents = self.controlled_agent_mask.sum().item() + self.masks = torch.ones(self.num_agents, dtype=bool) + self.agent_ids = np.arange(self.num_agents) + + self.reset() # Reset storage + # Get info from new worlds + self.observations = self.env.reset(self.controlled_agent_mask) + + self.log_data_coverage() + + def clear_render_storage(self): + """Clear rendering storage.""" + for env_idx in range(self.render_k_scenarios): + self.frames[env_idx] = [] + self.rendering_in_progress[env_idx] = False + self.was_rendered_in_rollout[env_idx] = False + + def log_video_to_wandb(self, render_env_idx, done_worlds): + """Log arrays as videos to wandb.""" + if ( + render_env_idx in done_worlds + and len(self.frames[render_env_idx]) > 0 + ): + frames_array = np.array(self.frames[render_env_idx]) + self.wandb_obj.log( + { + f"vis/state/env_{render_env_idx}": wandb.Video( + np.moveaxis(frames_array, -1, 1), + fps=self.render_fps, + format=self.render_format, + caption=f"global step: {self.global_step:,}", + ) + } + ) + # Reset rendering storage + self.frames[render_env_idx] = [] + self.rendering_in_progress[render_env_idx] = False + self.was_rendered_in_rollout[render_env_idx] = True + + def log_data_coverage(self): + """Data coverage statistics.""" + + scenario_counts = list(Counter(self.env.data_batch).values()) + scenario_unique = len(set(self.env.data_batch)) + + batch_idx = {self.file_to_index[file] for file in self.env.data_batch} + + # Check how many new files are in the batch + new_idx = batch_idx - self.cumulative_unique_files + + # Update the cumulative set (coverage) + self.cumulative_unique_files.update(new_idx) + + if self.wandb_obj is not None: + self.wandb_obj.log( + { + "data/new_files_in_batch": len(new_idx), + "data/unique_scenarios_in_batch": scenario_unique, + "data/scenario_counts_in_batch": wandb.Histogram( + scenario_counts + ), + "data/coverage": ( + len(self.cumulative_unique_files) + / len(set(self.file_to_index)) + ) + * 100, + }, + step=self.global_step, + ) diff --git a/gpudrive-main/gpudrive/env/env_torch.py b/gpudrive-main/gpudrive/env/env_torch.py new file mode 100644 index 0000000000000000000000000000000000000000..b8481c9116ae24bd2fa9912d22d97e305c0e175a --- /dev/null +++ b/gpudrive-main/gpudrive/env/env_torch.py @@ -0,0 +1,1545 @@ +"""Torch Gym Environment that interfaces with the GPU Drive simulator.""" + +from gymnasium.spaces import Box, Discrete, Tuple +import numpy as np +import torch +from itertools import product +import mediapy as media +import gymnasium + +from gpudrive.datatypes.observation import ( + LocalEgoState, + GlobalEgoState, + PartnerObs, + LidarObs, + BevObs, +) + +from gpudrive.env import constants +from gpudrive.env.config import EnvConfig, RenderConfig +from gpudrive.env.base_env import GPUDriveGymEnv +from gpudrive.datatypes.trajectory import LogTrajectory +from gpudrive.datatypes.roadgraph import ( + LocalRoadGraphPoints, + GlobalRoadGraphPoints, +) +from gpudrive.datatypes.metadata import Metadata +from gpudrive.datatypes.info import Info + +from gpudrive.visualize.core import MatplotlibVisualizer +from gpudrive.visualize.utils import img_from_fig +from gpudrive.env.dataset import SceneDataLoader +from gpudrive.utils.geometry import normalize_min_max + +from gpudrive.integrations.vbd.data_utils import process_scenario_data + + +class GPUDriveTorchEnv(GPUDriveGymEnv): + """Torch Gym Environment that interfaces with the GPU Drive simulator.""" + + def __init__( + self, + config, + data_loader, + max_cont_agents, + device="cuda", + action_type="discrete", + render_config: RenderConfig = RenderConfig(), + backend="torch", + ): + # Initialization of environment configurations + self.config = config + self.data_loader = data_loader + self.num_worlds = data_loader.batch_size + self.max_cont_agents = max_cont_agents + self.device = device + self.render_config = render_config + self.backend = backend + self.max_num_agents_in_scene = self.config.max_num_agents_in_scene + self.world_time_steps = torch.zeros( + self.num_worlds, dtype=torch.short, device=self.device + ) + + # Initialize reward weights tensor if using reward_conditioned + self.reward_weights_tensor = None + if ( + hasattr(self.config, "reward_type") + and self.config.reward_type == "reward_conditioned" + ): + # Use default condition_mode from config or fall back to "random" + condition_mode = getattr(self.config, "condition_mode", "random") + agent_type = getattr(self.config, "agent_type", None) + self._set_reward_weights( + condition_mode=condition_mode, agent_type=agent_type + ) + + # Environment parameter setup + params = self._setup_environment_parameters() + + # Initialize the iterator once + self.data_iterator = iter(self.data_loader) + + # Get the initial data batch (set of traffic scenarios) + self.data_batch = next(self.data_iterator) + + # Initialize simulator + self.sim = self._initialize_simulator(params, self.data_batch) + + # Controlled agents setup + self.cont_agent_mask = self.get_controlled_agents_mask() + self.max_agent_count = self.cont_agent_mask.shape[1] + self.num_valid_controlled_agents_across_worlds = ( + self.cont_agent_mask.sum().item() + ) + + self.episode_len = self.config.episode_len + + # Initialize VBD model if used + self._initialize_vbd() + + # Setup action and observation spaces + self.observation_space = Box( + low=-1.0, + high=1.0, + shape=(self.get_obs(self.cont_agent_mask).shape[-1],), + ) + + self.single_observation_space = gymnasium.spaces.Box( + low=-1.0, + high=1.0, + shape=(self.observation_space.shape[-1],), + dtype=np.float32, + ) + + self._setup_action_space(action_type) + self.single_action_space = self.action_space + + self.num_agents = self.cont_agent_mask.sum().item() + + # Rendering setup + self.vis = MatplotlibVisualizer( + sim_object=self.sim, + controlled_agent_mask=self.cont_agent_mask, + goal_radius=self.config.dist_to_goal_threshold, + backend=self.backend, + num_worlds=self.num_worlds, + render_config=self.render_config, + env_config=self.config, + ) + + def _initialize_vbd(self): + """ + Initialize the Versatile Behavior Diffusion (VBD) model and related + components. Link: https://arxiv.org/abs/2404.02524. + + Args: + config: Configuration object containing VBD settings. + """ + self.use_vbd = self.config.use_vbd + self.vbd_trajectory_weight = self.config.vbd_trajectory_weight + + # Set initialization steps - ensure minimum steps for VBD + if self.use_vbd: + self.init_steps = max( + self.config.init_steps, 10 + ) # Minimum 10 steps for VBD + else: + self.init_steps = self.config.init_steps + + if ( + self.use_vbd + and hasattr(self.config, "vbd_model_path") + and self.config.vbd_model_path + ): + self.vbd_model = self._load_vbd_model(self.config.vbd_model_path) + + self.vbd_trajectories = torch.zeros( + ( + self.num_worlds, + self.max_agent_count, + self.episode_len - self.init_steps, + 5, + ), + device=self.device, + dtype=torch.float32, + ) + + self._generate_vbd_trajectories() + else: + self.vbd_model = None + self.vbd_trajectories = None + + def _load_vbd_model(self, model_path): + """Load the Versatile Behavior Diffusion (VBD) model from checkpoint.""" + from gpudrive.integrations.vbd.sim_agent.sim_actor import VBDTest + + model = VBDTest.load_from_checkpoint( + model_path, torch.device(self.device) + ) + _ = model.eval() + return model + + def _generate_sample_batch(self, init_steps=10): + """Generate a sample batch for the VBD model.""" + means_xy = ( + self.sim.world_means_tensor().to_torch()[:, :2].to(self.device) + ) + + # Get the logged trajectory and restore the mean + log_trajectory = LogTrajectory.from_tensor( + self.sim.expert_trajectory_tensor(), + self.num_worlds, + self.max_agent_count, + backend=self.backend, + ) + log_trajectory.restore_mean( + mean_x=means_xy[:, 0], mean_y=means_xy[:, 1] + ) + + # Get global road graph and restore the mean + global_road_graph = GlobalRoadGraphPoints.from_tensor( + roadgraph_tensor=self.sim.map_observation_tensor(), + backend=self.backend, + device=self.device, + ) + global_road_graph.restore_mean( + mean_x=means_xy[:, 0], mean_y=means_xy[:, 1] + ) + global_road_graph.restore_xy() + + # Get global agent observations and restore the mean + global_agent_obs = GlobalEgoState.from_tensor( + abs_self_obs_tensor=self.sim.absolute_self_observation_tensor(), + backend=self.backend, + device=self.device, + ) + global_agent_obs.restore_mean( + mean_x=means_xy[:, 0], mean_y=means_xy[:, 1] + ) + metadata = Metadata.from_tensor( + metadata_tensor=self.sim.metadata_tensor(), + backend=self.backend, + ) + sample_batch = process_scenario_data( + max_controlled_agents=self.max_cont_agents, + controlled_agent_mask=self.cont_agent_mask, + global_agent_obs=global_agent_obs, + global_road_graph=global_road_graph, + log_trajectory=log_trajectory, + episode_len=self.episode_len, + init_steps=init_steps, + raw_agent_types=self.sim.info_tensor().to_torch()[:, :, 4], + metadata=metadata, + ) + return sample_batch + + def _set_reward_weights( + self, env_idx_list=None, condition_mode="random", agent_type=None + ): + """Set agent reward weights for all or specific environments. + + Args: + env_idx_list: List of environment indices to generate new weights for. + If None, all environments are updated. + condition_mode: Determines how reward weights are sampled: + - "random": Random sampling within bounds (default for training) + - "fixed": Use predefined agent_type weights (for testing) + - "preset": Use a specific preset from agent_type parameter + agent_type: Specifies which preset weights to use if condition_mode is "preset" or "fixed" + If condition_mode is "preset", can be one of: "cautious", "aggressive", "balanced" + If condition_mode is "fixed", should be a tensor of shape [3] with weight values + """ + if self.reward_weights_tensor is None: + self.reward_weights_tensor = torch.zeros( + self.num_worlds, + self.max_cont_agents, + 3, # collision, goal_achieved, off_road + device=self.device, + ) + + # Read bounds for the three reward components + lower_bounds = torch.tensor( + [ + self.config.collision_weight_lb, + self.config.goal_achieved_weight_lb, + self.config.off_road_weight_lb, + ], + device=self.device, + ) + + upper_bounds = torch.tensor( + [ + self.config.collision_weight_ub, + self.config.goal_achieved_weight_ub, + self.config.off_road_weight_ub, + ], + device=self.device, + ) + bounds_range = upper_bounds - lower_bounds + + # Preset agent personality types + agent_presets = { + "cautious": torch.tensor( + [ + self.config.collision_weight_lb + * 0.9, # Strong collision penalty + self.config.goal_achieved_weight_ub + * 0.7, # Moderate goal reward + self.config.off_road_weight_lb + * 0.9, # Strong off-road penalty + ], + device=self.device, + ), + "aggressive": torch.tensor( + [ + self.config.collision_weight_lb + * 0.5, # Lower collision penalty + self.config.goal_achieved_weight_ub + * 0.9, # Higher goal reward + self.config.off_road_weight_lb + * 0.6, # Moderate off-road penalty + ], + device=self.device, + ), + "balanced": torch.tensor( + [ + ( + self.config.collision_weight_lb + + self.config.collision_weight_ub + ) + / 2, + ( + self.config.goal_achieved_weight_lb + + self.config.goal_achieved_weight_ub + ) + / 2, + ( + self.config.off_road_weight_lb + + self.config.off_road_weight_ub + ) + / 2, + ], + device=self.device, + ), + "risk_taker": torch.tensor( + [ + self.config.collision_weight_lb + * 0.3, # Minimal collision penalty + self.config.goal_achieved_weight_ub, # Maximum goal reward + self.config.off_road_weight_lb + * 0.4, # Low off-road penalty + ], + device=self.device, + ), + } + + # Determine which environments to update + if env_idx_list is None: + env_idx_list = list(range(self.num_worlds)) + + env_indices = torch.tensor(env_idx_list, device=self.device) + num_envs = len(env_indices) + + if condition_mode == "random": + # Traditional random sampling within bounds + random_values = torch.rand( + num_envs, self.max_cont_agents, 3, device=self.device + ) + scaled_values = lower_bounds + random_values * bounds_range + + elif condition_mode == "preset": + # Use a predefined agent type + if agent_type not in agent_presets: + raise ValueError( + f"Unknown agent_type: {agent_type}. Available types: {list(agent_presets.keys())}" + ) + + # Create a tensor with the preset weights for all agents in the specified environments + preset_weights = agent_presets[agent_type] + scaled_values = ( + preset_weights.unsqueeze(0) + .unsqueeze(0) + .expand(num_envs, self.max_cont_agents, 3) + ) + + elif condition_mode == "fixed": + # Use custom provided weights + if agent_type is None or not isinstance(agent_type, torch.Tensor): + raise ValueError( + "For condition_mode='fixed', agent_type must be a tensor of shape [3]" + ) + + custom_weights = agent_type.to(device=self.device) + if custom_weights.shape != (3,): + raise ValueError( + f"agent_type tensor must have shape [3], got {custom_weights.shape}" + ) + + scaled_values = ( + custom_weights.unsqueeze(0) + .unsqueeze(0) + .expand(num_envs, self.max_cont_agents, 3) + ) + + else: + raise ValueError(f"Unknown condition_mode: {condition_mode}") + + # Update the weights tensor for the specified environments + self.reward_weights_tensor[env_indices.cpu()] = scaled_values + + return self.reward_weights_tensor + + def reset( + self, + mask=None, + env_idx_list=None, + condition_mode=None, + agent_type=None, + ): + """Reset the worlds and return the initial observations. + + Args: + mask: Optional mask indicating which agents to return observations for + env_idx_list: Optional list of environment indices to reset + condition_mode: Determines how reward weights are sampled: + - "random": Random sampling within bounds (default for training) + - "fixed": Use predefined agent_type weights (for testing) + - "preset": Use a specific preset from agent_type parameter + agent_type: Specifies which preset weights to use or custom weights + """ + if env_idx_list is not None: + self.sim.reset(env_idx_list) + else: + env_idx_list = list(range(self.num_worlds)) + self.sim.reset(env_idx_list) + + # Re-initialize reward weights if using reward_conditioned + if ( + hasattr(self.config, "reward_type") + and self.config.reward_type == "reward_conditioned" + ): + # Use the specified condition_mode or default to the config setting + mode = ( + condition_mode + if condition_mode is not None + else getattr(self.config, "condition_mode", "random") + ) + self._set_reward_weights( + env_idx_list, condition_mode=mode, agent_type=agent_type + ) + + self.world_time_steps.zero_() + + # Advance the simulator with log playback if warmup steps are provided + if self.init_steps > 0: + self.advance_sim_with_log_playback( + init_steps=self.init_steps, + # render_init=self.render_config.render_init, + ) + + return self.get_obs(mask) + + def get_dones(self): + return ( + self.sim.done_tensor() + .to_torch() + .clone() + .squeeze(dim=2) + .to(torch.float) + ) + + def get_infos(self): + return Info.from_tensor( + self.sim.info_tensor(), + backend=self.backend, + device=self.device, + ) + + def get_rewards( + self, + collision_weight=-0.5, + goal_achieved_weight=1.0, + off_road_weight=-0.5, + world_time_steps=None, + log_distance_weight=0.01, + ): + """Obtain the rewards for the current step. + By default, the reward is a weighted combination of the following components: + - collision + - goal_achieved + - off_road + + The importance of each component is determined by the weights. + """ + + # Return the weighted combination of the reward components + info_tensor = self.sim.info_tensor().to_torch().clone() + off_road = info_tensor[:, :, 0].to(torch.float) + + # True if the vehicle is in collision with another road object + # (i.e. a cyclist or pedestrian) + collided = info_tensor[:, :, 1:3].to(torch.float).sum(axis=2) + goal_achieved = info_tensor[:, :, 3].to(torch.float) + + if self.config.reward_type == "sparse_on_goal_achieved": + return self.sim.reward_tensor().to_torch().clone().squeeze(dim=2) + + elif self.config.reward_type == "weighted_combination": + weighted_rewards = ( + collision_weight * collided + + goal_achieved_weight * goal_achieved + + off_road_weight * off_road + ) + + return weighted_rewards + + elif self.config.reward_type == "reward_conditioned": + # Extract individual weight components from the tensor + # Shape: [num_worlds, max_agents, 3] + if self.reward_weights_tensor is None: + self._set_reward_weights() + + # Apply the weights in a vectorized manner + # Each index in dimension 2 corresponds to a specific weight: + # 0: collision, 1: goal_achieved, 2: off_road + weighted_rewards = ( + self.reward_weights_tensor[:, :, 0] * collided + + self.reward_weights_tensor[:, :, 1] * goal_achieved + + self.reward_weights_tensor[:, :, 2] * off_road + ) + + return weighted_rewards + + elif self.config.reward_type == "distance_to_vdb_trajs": + # Reward based on distance to VBD predicted trajectories + # (i.e. the deviation from the predicted trajectory) + weighted_rewards = ( + collision_weight * collided + + goal_achieved_weight * goal_achieved + + off_road_weight * off_road + ) + + agent_states = GlobalEgoState.from_tensor( + self.sim.absolute_self_observation_tensor(), + self.backend, + self.device, + ) + + agent_pos = torch.stack( + [agent_states.pos_x, agent_states.pos_y], dim=-1 + ) + + # Extract VBD positions at current time steps for each world + vbd_pos = [] + for i in range(self.num_worlds): + current_time = ( + self.world_time_steps[i].item() - self.init_steps + ) + # Make sure we don't exceed trajectory length + current_time = min( + current_time, self.vbd_trajectories.shape[2] - 1 + ) + vbd_pos.append(self.vbd_trajectories[i, :, current_time, :2]) + vbd_pos_tensor = torch.stack(vbd_pos) + + # Compute euclidean distance between agent and logs + dist_to_vbd = torch.norm(vbd_pos_tensor - agent_pos, dim=-1) + + # Add reward based on inverse distance to logs + weighted_rewards += self.vbd_trajectory_weight * torch.exp( + -dist_to_vbd + ) + + return weighted_rewards + + elif self.config.reward_type == "distance_to_logs": + # Reward based on distance to logs and penalty for collision + weighted_rewards = ( + collision_weight * collided + + goal_achieved_weight * goal_achieved + + off_road_weight * off_road + ) + + log_trajectory = LogTrajectory.from_tensor( + self.sim.expert_trajectory_tensor(), + self.num_worlds, + self.max_agent_count, + backend=self.backend, + ) + + # Index log positions at current time steps + log_traj_pos = [] + for i in range(self.num_worlds): + log_traj_pos.append( + log_trajectory.pos_xy[i, :, world_time_steps[i], :] + ) + log_traj_pos_tensor = torch.stack(log_traj_pos) + + agent_state = GlobalEgoState.from_tensor( + self.sim.absolute_self_observation_tensor(), + self.backend, + ) + + agent_pos = torch.stack( + [agent_state.pos_x, agent_state.pos_y], dim=-1 + ) + + # compute euclidean distance between agent and logs + dist_to_logs = torch.norm(log_traj_pos_tensor - agent_pos, dim=-1) + + # add reward based on inverse distance to logs + weighted_rewards += log_distance_weight * torch.exp(-dist_to_logs) + + return weighted_rewards + + def step_dynamics(self, actions): + if actions is not None: + self._apply_actions(actions) + self.sim.step() + not_done_worlds = ~self.get_dones().any( + dim=1 + ) # Check if any agent in world is done + self.world_time_steps[not_done_worlds] += 1 + + def _apply_actions(self, actions): + """Apply the actions to the simulator.""" + + if ( + self.config.dynamics_model == "classic" + or self.config.dynamics_model == "bicycle" + or self.config.dynamics_model == "delta_local" + ): + if actions.dim() == 2: # (num_worlds, max_agent_count) + # Map action indices to action values if indices are provided + actions = ( + torch.nan_to_num(actions, nan=0).long().to(self.device) + ) + action_value_tensor = self.action_keys_tensor[actions] + + elif actions.dim() == 3: + if actions.shape[2] == 1: + actions = actions.squeeze(dim=2).to(self.device) + action_value_tensor = self.action_keys_tensor[actions] + else: # Assuming we are given the actual action values + action_value_tensor = actions.to(self.device) + else: + raise ValueError(f"Invalid action shape: {actions.shape}") + + else: + action_value_tensor = actions.to(self.device) + + # Feed the action values to gpudrive + self._copy_actions_to_simulator(action_value_tensor) + + def _copy_actions_to_simulator(self, actions): + """Copy the provided actions to the simulator.""" + if ( + self.config.dynamics_model == "classic" + or self.config.dynamics_model == "bicycle" + ): + # Action space: (acceleration, steering, heading) + self.sim.action_tensor().to_torch()[:, :, :3].copy_(actions) + elif self.config.dynamics_model == "delta_local": + # Action space: (dx, dy, dyaw) + self.sim.action_tensor().to_torch()[:, :, :3].copy_(actions) + elif self.config.dynamics_model == "state": + # Following the StateAction struct in types.hpp + # Need to provide: + # (x, y, z, yaw, vel x, vel y, vel z, ang_vel_x, ang_vel_y, ang_vel_z) + self.sim.action_tensor().to_torch()[:, :, :10].copy_(actions) + else: + raise ValueError( + f"Invalid dynamics model: {self.config.dynamics_model}" + ) + + def _set_discrete_action_space(self) -> None: + """Configure the discrete action space based on dynamics model.""" + products = None + + if self.config.dynamics_model == "delta_local": + self.dx = self.config.dx.to(self.device) + self.dy = self.config.dy.to(self.device) + self.dyaw = self.config.dyaw.to(self.device) + products = product(self.dx, self.dy, self.dyaw) + elif ( + self.config.dynamics_model == "classic" + or self.config.dynamics_model == "bicycle" + ): + self.steer_actions = self.config.steer_actions.to(self.device) + self.accel_actions = self.config.accel_actions.to(self.device) + self.head_actions = self.config.head_tilt_actions.to(self.device) + products = product( + self.accel_actions, self.steer_actions, self.head_actions + ) + elif self.config.dynamics_model == "state": + self.x = self.config.x.to(self.device) + self.y = self.config.y.to(self.device) + self.yaw = self.config.yaw.to(self.device) + self.vx = self.config.vx.to(self.device) + self.vy = self.config.vy.to(self.device) + + else: + raise ValueError( + f"Invalid dynamics model: {self.config.dynamics_model}" + ) + + # Create a mapping from action indices to action values + self.action_key_to_values = {} + self.values_to_action_key = {} + if products is not None: + for action_idx, (action_1, action_2, action_3) in enumerate( + products + ): + self.action_key_to_values[action_idx] = [ + action_1.item(), + action_2.item(), + action_3.item(), + ] + self.values_to_action_key[ + round(action_1.item(), 5), + round(action_2.item(), 5), + round(action_3.item(), 5), + ] = action_idx + + self.action_keys_tensor = torch.tensor( + [ + self.action_key_to_values[key] + for key in sorted(self.action_key_to_values.keys()) + ] + ).to(self.device) + + return Discrete(n=int(len(self.action_key_to_values))) + else: + return Discrete(n=1) + + def _set_continuous_action_space(self) -> None: + """Configure the continuous action space.""" + if self.config.dynamics_model == "delta_local": + self.dx = self.config.dx.to(self.device) + self.dy = self.config.dy.to(self.device) + self.dyaw = self.config.dyaw.to(self.device) + action_1 = self.dx.clone().cpu().numpy() + action_2 = self.dy.clone().cpu().numpy() + action_3 = self.dyaw.clone().cpu().numpy() + elif self.config.dynamics_model == "classic": + self.steer_actions = self.config.steer_actions.to(self.device) + self.accel_actions = self.config.accel_actions.to(self.device) + self.head_actions = torch.tensor([0], device=self.device) + action_1 = self.steer_actions.clone().cpu().numpy() + action_2 = self.accel_actions.clone().cpu().numpy() + action_3 = self.head_actions.clone().cpu().numpy() + else: + raise ValueError( + f"Continuous action space is currently not supported for dynamics_model: {self.config.dynamics_model}." + ) + + action_space = Tuple( + ( + Box(action_1.min(), action_1.max(), shape=(1,)), + Box(action_2.min(), action_2.max(), shape=(1,)), + Box(action_3.min(), action_3.max(), shape=(1,)), + ) + ) + return action_space + + def _get_ego_state(self, mask=None) -> torch.Tensor: + """Get the ego state.""" + + if not self.config.ego_state: + return torch.Tensor().to(self.device) + + ego_state = LocalEgoState.from_tensor( + self_obs_tensor=self.sim.self_observation_tensor(), + backend=self.backend, + device=self.device, + mask=mask, + ) + if self.config.norm_obs: + ego_state.normalize() + + if mask is None: + if self.config.reward_type == "reward_conditioned": + return torch.stack( + [ + ego_state.speed, + ego_state.vehicle_length, + ego_state.vehicle_width, + ego_state.rel_goal_x, + ego_state.rel_goal_y, + ego_state.is_collided, + self.reward_weights_tensor[:, :, 0], + self.reward_weights_tensor[:, :, 1], + self.reward_weights_tensor[:, :, 2], + ] + ).permute(1, 2, 0) + + else: + return torch.stack( + [ + ego_state.speed, + ego_state.vehicle_length, + ego_state.vehicle_width, + ego_state.rel_goal_x, + ego_state.rel_goal_y, + ego_state.is_collided, + ] + ).permute(1, 2, 0) + + else: + if self.config.reward_type == "reward_conditioned": + return torch.stack( + [ + ego_state.speed, + ego_state.vehicle_length, + ego_state.vehicle_width, + ego_state.rel_goal_x, + ego_state.rel_goal_y, + ego_state.is_collided, + self.reward_weights_tensor[mask][:, 0], + self.reward_weights_tensor[mask][:, 1], + self.reward_weights_tensor[mask][:, 2], + ] + ).permute(1, 0) + else: + return torch.stack( + [ + ego_state.speed, + ego_state.vehicle_length, + ego_state.vehicle_width, + ego_state.rel_goal_x, + ego_state.rel_goal_y, + ego_state.is_collided, + ] + ).permute(1, 0) + + def _get_partner_obs(self, mask=None): + """Get partner observations.""" + + if not self.config.partner_obs: + return torch.Tensor().to(self.device) + + partner_obs = PartnerObs.from_tensor( + partner_obs_tensor=self.sim.partner_observations_tensor(), + backend=self.backend, + device=self.device, + mask=mask, + ) + + if self.config.norm_obs: + partner_obs.normalize() + + if mask is not None: + return partner_obs.data.flatten(start_dim=1) + else: + return torch.concat( + [ + partner_obs.speed, + partner_obs.rel_pos_x, + partner_obs.rel_pos_y, + partner_obs.orientation, + partner_obs.vehicle_length, + partner_obs.vehicle_width, + ], + dim=-1, + ).flatten(start_dim=2) + + def _get_road_map_obs(self, mask=None): + """Get road map observations.""" + if not self.config.road_map_obs: + return torch.Tensor().to(self.device) + + roadgraph = LocalRoadGraphPoints.from_tensor( + local_roadgraph_tensor=self.sim.agent_roadmap_tensor(), + backend=self.backend, + device=self.device, + mask=mask, + ) + roadgraph.one_hot_encode_road_point_types() + + if self.config.norm_obs: + roadgraph.normalize() + + if mask is not None: + return torch.cat( + [ + roadgraph.data, + roadgraph.type, + ], + dim=-1, + ).flatten(start_dim=1) + else: + return torch.cat( + [ + roadgraph.x.unsqueeze(-1), + roadgraph.y.unsqueeze(-1), + roadgraph.segment_length.unsqueeze(-1), + roadgraph.segment_width.unsqueeze(-1), + roadgraph.segment_height.unsqueeze(-1), + roadgraph.orientation.unsqueeze(-1), + roadgraph.type, + ], + dim=-1, + ).flatten(start_dim=2) + + def _get_lidar_obs(self, mask=None): + """Get lidar observations.""" + + if not self.config.lidar_obs: + return torch.Tensor().to(self.device) + + lidar = LidarObs.from_tensor( + lidar_tensor=self.sim.lidar_tensor(), + backend=self.backend, + device=self.device, + ) + + if mask is not None: + return [ + lidar.agent_samples[mask], + lidar.road_edge_samples[mask], + lidar.road_line_samples[mask], + ] + else: + return torch.cat( + [ + lidar.agent_samples, + lidar.road_edge_samples, + lidar.road_line_samples, + ], + dim=-1, + ).flatten(start_dim=2) + + def _get_bev_obs(self, mask=None): + """Get BEV segmentation map observation. + + Returns: + torch.Tensor: (num_worlds, max_agent_count, resolution, resolution, 1) + """ + if not self.config.bev_obs: + return torch.Tensor().to(self.device) + + bev = BevObs.from_tensor( + bev_tensor=self.sim.bev_observation_tensor(), + backend=self.backend, + device=self.device, + ) + bev.one_hot_encode_bev_map() + + if mask is not None: + return bev.bev_segmentation_map[mask].flatten(start_dim=1) + else: + return bev.bev_segmentation_map.flatten(start_dim=2) + + def _get_vbd_obs(self, mask=None): + """ + Get ego-centric VBD trajectory observations for controlled agents using matrix operations. + + Args: + mask: Optional mask to filter agents + + Returns: + Tensor of ego-centric VBD trajectories + """ + if not self.use_vbd or self.vbd_model is None: + return torch.Tensor().to(self.device) + + # Get current agent positions and orientations + agent_state = GlobalEgoState.from_tensor( + abs_self_obs_tensor=self.sim.absolute_self_observation_tensor(), + backend=self.backend, + device=self.device, + ) + + # Initialize output tensor + traj_feature_dim = ( + self.vbd_trajectories.shape[2] * self.vbd_trajectories.shape[3] + ) + + if mask is not None: + # Count valid agents for output tensor size + valid_count = mask.sum().item() + ego_vbd_trajectories = torch.zeros( + (valid_count, traj_feature_dim), device=self.device + ) + + # Track which output index we're filling + out_idx = 0 + + # Process each world + for w in range(self.num_worlds): + # Get valid agent indices for this world + world_mask = mask[w] + agent_indices = torch.where(world_mask)[0] + + if len(agent_indices) == 0: + continue + + # Extract ego positions and yaws for these agents + ego_pos_x = agent_state.pos_x[w, agent_indices] + ego_pos_y = agent_state.pos_y[w, agent_indices] + ego_yaw = agent_state.rotation_angle[w, agent_indices] + + # Process each agent in this world + for i, agent_idx in enumerate(agent_indices): + # Get global trajectory for this agent + global_traj = self.vbd_trajectories[w, agent_idx] + + # Create 2D rotation matrix for this agent + cos_yaw = torch.cos(ego_yaw[i]) + sin_yaw = torch.sin(ego_yaw[i]) + rotation_matrix = torch.tensor( + [[cos_yaw, sin_yaw], [-sin_yaw, cos_yaw]], + device=self.device, + ) + + # Transform positions using matrix multiplication + pos_xy = global_traj[:, :2] + ego_pos = torch.tensor( + [ego_pos_x[i], ego_pos_y[i]], device=self.device + ).reshape(1, 2) + translated_pos = ( + pos_xy - ego_pos + ) # Broadcasting to subtract from all timesteps + rotated_pos = torch.matmul( + translated_pos, rotation_matrix.T + ) + + # Transform velocities (only rotation, no translation) + vel_xy = global_traj[:, 3:5] + rotated_vel = torch.matmul(vel_xy, rotation_matrix.T) + + # Create transformed trajectory + transformed_traj = torch.zeros_like(global_traj) + transformed_traj[:, :2] = rotated_pos + transformed_traj[:, 2] = ( + global_traj[:, 2] - ego_yaw[i] + ) # Adjust heading + transformed_traj[:, 3:5] = rotated_vel + + # Flatten and add to output + ego_vbd_trajectories[out_idx] = transformed_traj.reshape( + -1 + ) + out_idx += 1 + + if self.config.norm_obs: + traj_len = self.vbd_trajectories.shape[2] + ego_vbd_trajectories = self._normalize_vbd_obs( + ego_vbd_trajectories, traj_len + ) + + return ego_vbd_trajectories + + else: + # Without mask, process all agents in all worlds + ego_vbd_trajectories = torch.zeros( + (self.num_worlds, self.max_agent_count, traj_feature_dim), + device=self.device, + ) + + # Process each world + for w in range(self.num_worlds): + # Get controlled agent indices for this world + valid_mask = self.cont_agent_mask[w] + world_agent_indices = torch.where(valid_mask)[0] + + if len(world_agent_indices) == 0: + continue + + # Extract ego positions and yaws + ego_pos_x = agent_state.pos_x[w] + ego_pos_y = agent_state.pos_y[w] + ego_yaw = agent_state.rotation_angle[w] + + # Process each agent in this world + for agent_idx in world_agent_indices: + # Get global trajectory + global_traj = self.vbd_trajectories[w, agent_idx] + + # Create 2D rotation matrix for this agent + cos_yaw = torch.cos(ego_yaw[agent_idx]) + sin_yaw = torch.sin(ego_yaw[agent_idx]) + rotation_matrix = torch.tensor( + [[cos_yaw, sin_yaw], [-sin_yaw, cos_yaw]], + device=self.device, + ) + + # Transform positions + pos_xy = global_traj[:, :2] + ego_pos = torch.tensor( + [ego_pos_x[agent_idx], ego_pos_y[agent_idx]], + device=self.device, + ).reshape(1, 2) + translated_pos = pos_xy - ego_pos + rotated_pos = torch.matmul( + translated_pos, rotation_matrix.T + ) + + # Transform velocities + vel_xy = global_traj[:, 3:5] + rotated_vel = torch.matmul(vel_xy, rotation_matrix.T) + + # Create transformed trajectory + transformed_traj = torch.zeros_like(global_traj) + transformed_traj[:, :2] = rotated_pos + transformed_traj[:, 2] = ( + global_traj[:, 2] - ego_yaw[agent_idx] + ) + transformed_traj[:, 3:5] = rotated_vel + + # Flatten and add to output + ego_vbd_trajectories[ + w, agent_idx + ] = transformed_traj.reshape(-1) + + if self.config.norm_obs: + traj_len = self.vbd_trajectories.shape[2] + ego_vbd_trajectories = self._normalize_vbd_obs( + ego_vbd_trajectories, traj_len + ) + + return ego_vbd_trajectories + + def _normalize_vbd_obs(self, trajectories_flat, traj_len): + """ + Normalize flattened VBD trajectory values to be between -1 and 1, with clipping. + + Args: + trajectories_flat: Flattened tensor containing trajectory data + traj_len: Number of trajectory steps + + Returns: + Normalized flattened trajectories tensor + """ + # Get original shape for proper reshaping + original_shape = trajectories_flat.shape + + # Calculate feature dimension + feature_dim = 5 # x, y, yaw, vel_x, vel_y + + # Reshape to separate the features + if len(original_shape) == 2: # (num_agents, flattened_features) + traj_features = trajectories_flat.reshape( + -1, traj_len, feature_dim + ) + else: # (num_worlds, max_agents, flattened_features) + traj_features = trajectories_flat.reshape( + original_shape[0], original_shape[1], traj_len, feature_dim + ) + + # Normalize each feature + # x, y positions + traj_features[..., 0] = normalize_min_max( + tensor=traj_features[..., 0], + min_val=constants.MIN_REL_GOAL_COORD, + max_val=constants.MAX_REL_GOAL_COORD, + ) + traj_features[..., 1] = normalize_min_max( + tensor=traj_features[..., 1], + min_val=constants.MIN_REL_GOAL_COORD, + max_val=constants.MAX_REL_GOAL_COORD, + ) + + # Normalize yaw angle + traj_features[..., 2] = ( + traj_features[..., 2] / constants.MAX_ORIENTATION_RAD + ) + + # Normalize velocities + traj_features[..., 3] = traj_features[..., 3] / constants.MAX_SPEED + traj_features[..., 4] = traj_features[..., 4] / constants.MAX_SPEED + + # Clip all values to the [-1, 1] range + traj_features = torch.clamp(traj_features, min=-1.0, max=1.0) + + # Reshape back to original format + return traj_features.reshape(original_shape) + + def get_obs(self, mask=None): + """Get observation: Combine different types of environment information into a single tensor. + Returns: + torch.Tensor: (num_worlds, max_agent_count, num_features) + """ + # Base observations + ego_states = self._get_ego_state(mask) + partner_observations = self._get_partner_obs(mask) + road_map_observations = self._get_road_map_obs(mask) + + if ( + self.use_vbd + and self.vbd_model is not None + and self.config.vbd_in_obs + ): + # Add ego-centric VBD trajectories + vbd_observations = self._get_vbd_obs(mask) + + obs = torch.cat( + ( + ego_states, + partner_observations, + road_map_observations, + vbd_observations, + ), + dim=-1, + ) + else: + obs = torch.cat( + ( + ego_states, + partner_observations, + road_map_observations, + ), + dim=-1, + ) + + return obs + + def get_controlled_agents_mask(self): + """Get the control mask. Shape: [num_worlds, max_agent_count]""" + return ( + self.sim.controlled_state_tensor().to_torch().clone() == 1 + ).squeeze(axis=2) + + def advance_sim_with_log_playback(self, init_steps=0): + """Advances the simulator by stepping the objects with the logged human trajectories. + + Args: + init_steps (int): Number of warmup steps. + """ + if init_steps >= self.config.episode_len: + raise ValueError( + "The length of the expert trajectory is 91," + f"so init_steps = {init_steps} should be < than 91." + ) + + self.init_frames = [] + + self.log_playback_traj, _, _, _ = self.get_expert_actions() + + for time_step in range(init_steps): + self.step_dynamics( + actions=self.log_playback_traj[:, :, time_step, :] + ) + + def remove_agents_by_id( + self, perc_to_rmv_per_scene, remove_controlled_agents=True + ): + """Delete random agents in scenarios. + + Args: + perc_to_rmv_per_scene (float): Percentage of agents to remove per scene + remove_controlled_agents (bool): If True, removes controlled agents. If False, removes uncontrolled agents + """ + # Obtain agent ids + agent_ids = LocalEgoState.from_tensor( + self_obs_tensor=self.sim.self_observation_tensor(), + backend="torch", + device=self.device, + ).id + + # Choose the appropriate mask based on whether we're removing controlled or uncontrolled agents + if remove_controlled_agents: + agent_mask = self.cont_agent_mask + else: + # Create inverse mask for uncontrolled agents + agent_mask = ~self.cont_agent_mask + + for env_idx in range(self.num_worlds): + # Get all relevant agent IDs (controlled or uncontrolled) for the current environment + scene_agent_ids = agent_ids[env_idx, :][agent_mask[env_idx]].long() + + if ( + scene_agent_ids.numel() > 0 + ): # Ensure there are agents to sample + # Determine the number of agents to sample (X% of the total agents) + num_to_sample = max( + 1, int(perc_to_rmv_per_scene * scene_agent_ids.size(0)) + ) + + # Randomly sample agent IDs to remove using torch + sampled_indices = torch.randperm(scene_agent_ids.size(0))[ + :num_to_sample + ] + sampled_agent_ids = scene_agent_ids[sampled_indices] + + # Delete the sampled agents from the environment + self.sim.deleteAgents({env_idx: sampled_agent_ids.tolist()}) + + # Reset controlled agent mask and visualizer + self.cont_agent_mask = self.get_controlled_agents_mask() + self.max_agent_count = self.cont_agent_mask.shape[1] + self.num_valid_controlled_agents_across_worlds = ( + self.cont_agent_mask.sum().item() + ) + + # Reset static scenario data for the visualizer + self.vis.initialize_static_scenario_data(self.cont_agent_mask) + + def swap_data_batch(self, data_batch=None): + """ + Swap the current data batch in the simulator with a new one + and reinitialize dependent attributes. + """ + + if data_batch is None: # Sample new data batch from the data loader + self.data_batch = next(self.data_iterator) + else: + self.data_batch = data_batch + + # Validate that the number of worlds (envs) matches the batch size + if len(self.data_batch) != self.num_worlds: + raise ValueError( + f"Data batch size ({len(self.data_batch)}) does not match " + f"the expected number of worlds ({self.num_worlds})." + ) + + # Update the simulator with the new data + self.sim.set_maps(self.data_batch) + + # Reinitialize the mask for controlled agents + self.cont_agent_mask = self.get_controlled_agents_mask() + self.max_agent_count = self.cont_agent_mask.shape[1] + self.num_valid_controlled_agents_across_worlds = ( + self.cont_agent_mask.sum().item() + ) + + # Generate VBD trajectories for the new batch if VBD is enabled + if self.use_vbd and self.vbd_model is not None: + self._generate_vbd_trajectories() + + # Reset static scenario data for the visualizer + self.vis.initialize_static_scenario_data(self.cont_agent_mask) + + def _generate_vbd_trajectories(self): + """Generate and store trajectory predictions for all scenes using VBD model.""" + if not self.use_vbd or self.vbd_model is None: + return + + _ = self.reset() + + # Generate sample batch using the limited mask + sample_batch = self._generate_sample_batch(init_steps=self.init_steps) + + # VBD model prediction + predictions = self.vbd_model.sample_denoiser(sample_batch) + vbd_trajectories = ( + predictions["denoised_trajs"].to(self.device).numpy() + ) + agent_indices = sample_batch["agents_id"] + + self.vbd_trajectories.zero_() + # Process each world separately + for world_idx in range(self.num_worlds): + world_agent_indices = agent_indices[world_idx] + + # Filter out negative indices (they're our padding) + valid_mask = ( + world_agent_indices >= 0 + ) # Boolean mask of valid indices + valid_agent_indices = world_agent_indices[ + valid_mask + ] # Filtered tensor + + if len(valid_agent_indices) > 0: + # Update vbd_trajectories(x, y, yaw, vel_x, vel_y) for this world's agents + self.vbd_trajectories[ + world_idx, valid_agent_indices, :, :2 + ] = torch.Tensor( + vbd_trajectories[ + world_idx, : len(valid_agent_indices), :, :2 + ] + ) + self.vbd_trajectories[ + world_idx, valid_agent_indices, :, :2 + ] -= self.sim.world_means_tensor().to_torch()[ + world_idx, :2 + ] # subtract mean + self.vbd_trajectories[ + world_idx, valid_agent_indices, :, 2 + ] = torch.Tensor( + vbd_trajectories[ + world_idx, : len(valid_agent_indices), :, 2 + ] + ) + self.vbd_trajectories[ + world_idx, valid_agent_indices, :, 3: + ] = torch.Tensor( + vbd_trajectories[ + world_idx, : len(valid_agent_indices), :, 3:5 + ] + ) + + def get_expert_actions(self): + """Get expert actions for the full trajectories across worlds. + + Returns: + expert_actions: Inferred or logged actions for the agents. + expert_speeds: Speeds from the logged trajectories. + expert_positions: Positions from the logged trajectories. + expert_yaws: Heading from the logged trajectories. + """ + + log_trajectory = LogTrajectory.from_tensor( + self.sim.expert_trajectory_tensor(), + self.num_worlds, + self.max_agent_count, + backend=self.backend, + ) + + if self.config.dynamics_model == "delta_local": + inferred_actions = log_trajectory.inferred_actions[..., :3] + inferred_actions[..., 0] = torch.clamp( + inferred_actions[..., 0], -6, 6 + ) + inferred_actions[..., 1] = torch.clamp( + inferred_actions[..., 1], -6, 6 + ) + inferred_actions[..., 2] = torch.clamp( + inferred_actions[..., 2], -torch.pi, torch.pi + ) + elif self.config.dynamics_model == "state": + # Extract (x, y, yaw, velocity x, velocity y) + inferred_actions = torch.cat( + ( + log_trajectory.pos_xy, + torch.ones( + (*log_trajectory.pos_xy.shape[:-1], 1), + device=self.device, + ), + log_trajectory.yaw, + log_trajectory.vel_xy, + torch.zeros( + (*log_trajectory.pos_xy.shape[:-1], 4), + device=self.device, + ), + ), + dim=-1, + ) + elif ( + self.config.dynamics_model == "classic" + or self.config.dynamics_model == "bicycle" + ): + inferred_actions = log_trajectory.inferred_actions[..., :3] + inferred_actions[..., 0] = torch.clamp( + inferred_actions[..., 0], -6, 6 + ) + inferred_actions[..., 1] = torch.clamp( + inferred_actions[..., 1], -0.3, 0.3 + ) + + return ( + inferred_actions, + log_trajectory.pos_xy, + log_trajectory.vel_xy, + log_trajectory.yaw, + ) + + def get_env_filenames(self): + """Obtain the tfrecord filename for each world, mapping world indices to map names.""" + + map_name_integers = self.sim.map_name_tensor().to_torch() + filenames = {} + # Iterate through the number of worlds + for i in range(self.num_worlds): + tensor = map_name_integers[i] + # Convert ints to characters, ignoring zeros + map_name = "".join([chr(i) for i in tensor.tolist() if i != 0]) + filenames[i] = map_name + + return filenames + + def get_scenario_ids(self): + """Obtain the scenario ID for each world.""" + scenario_id_integers = self.sim.scenario_id_tensor().to_torch() + scenario_ids = {} + + # Iterate through the number of worlds + for i in range(self.num_worlds): + tensor = scenario_id_integers[i] + # Convert ints to characters, ignoring zeros + scenario_id = "".join([chr(i) for i in tensor.tolist() if i != 0]) + scenario_ids[i] = scenario_id + + return scenario_ids + + +if __name__ == "__main__": + + env_config = EnvConfig( + dynamics_model="delta_local", + ) + render_config = RenderConfig() + + # Create data loader + train_loader = SceneDataLoader( + root="data/processed/examples", + batch_size=2, + dataset_size=100, + sample_with_replacement=True, + shuffle=False, + ) + + # Make env + env = GPUDriveTorchEnv( + config=env_config, + data_loader=train_loader, + max_cont_agents=64, # Number of agents to control + device="cpu", + ) + + control_mask = env.cont_agent_mask + + # Rollout + obs = env.reset() + + sim_frames = [] + agent_obs_frames = [] + + expert_actions, _, _, _ = env.get_expert_actions() + + env_idx = 0 + + for t in range(10): + print(f"Step: {t}") + + # Step the environment + expert_actions, _, _, _ = env.get_expert_actions() + env.step_dynamics(expert_actions[:, :, t, :]) + + highlight_agent = torch.where(env.cont_agent_mask[env_idx, :])[0][ + -1 + ].item() + + # Make video + sim_states = env.vis.plot_simulator_state( + env_indices=[env_idx], + zoom_radius=50, + time_steps=[t], + center_agent_indices=[highlight_agent], + ) + + agent_obs = env.vis.plot_agent_observation( + env_idx=env_idx, + agent_idx=highlight_agent, + figsize=(10, 10), + ) + + sim_frames.append(img_from_fig(sim_states[0])) + agent_obs_frames.append(img_from_fig(agent_obs)) + + obs = env.get_obs() + reward = env.get_rewards() + done = env.get_dones() + info = env.get_infos() + + if done[0, highlight_agent].bool(): + break + + env.close() + + media.write_video( + "sim_video.gif", np.array(sim_frames), fps=10, codec="gif" + ) + media.write_video( + "obs_video.gif", np.array(agent_obs_frames), fps=10, codec="gif" + ) diff --git a/gpudrive-main/gpudrive/env/scene_selector.py b/gpudrive-main/gpudrive/env/scene_selector.py new file mode 100644 index 0000000000000000000000000000000000000000..82443d0796e01f1efd5ec4cc0d702b16392d3177 --- /dev/null +++ b/gpudrive-main/gpudrive/env/scene_selector.py @@ -0,0 +1,87 @@ +import random +import os +import numpy as np +from math import ceil +from gpudrive.env.config import SelectionDiscipline + + +def select_scenes(config): + """Selects a number of traffic scenes from the data directory based on + the specified discipline. + + Args: + config: Data configuration object containing the path to the data + directory and the selection discipline. + + Raises: + ValueError: If the data directory does not exist or is empty. + ValueError: If the data directory does not contain any traffic scenes. + + Returns: + list: A list of paths to the selected traffic scenes. + """ + assert os.path.exists(config.path) and os.listdir( + config.path + ), "The data directory does not exist or is empty." + + all_scenes = sorted(os.listdir(config.path)) + # Remove elements that are not tfrecord files (traffic scenes) + # NOTE: This uses the naming convention of the traffic scenes as a filter + all_scenes = [ + scene for scene in all_scenes if scene.startswith("tfrecord") + ] + + selected_scenes = None + if not any(scene.startswith("tfrecord") for scene in all_scenes): + raise ValueError( + "The data directory does not contain any traffic scenes." + "Maybe you specified a path to the wrong folder?" + ) + + def random_sample(k): + seed = config.seed if config.seed is not None else 0x5CA1AB1E + rand = random.Random(seed) + return rand.sample(all_scenes, k) + + def repeat_to_N(scenes): + repeat_count = ceil(config.num_scenes / len(scenes)) + return (scenes * repeat_count)[: config.num_scenes] + + match config.discipline: + case SelectionDiscipline.FIRST_N: + selected_scenes = all_scenes[: config.num_scenes] + selected_scenes = all_scenes[: config.num_scenes] + case SelectionDiscipline.RANDOM_N: + selected_scenes = random_sample(config.num_scenes) + case SelectionDiscipline.PAD_N: + selected_scenes = repeat_to_N(all_scenes) + case SelectionDiscipline.EXACT_N: + assert len(all_scenes) == config.num_scenes + selected_scenes = all_scenes + case SelectionDiscipline.K_UNIQUE_N: + assert ( + config.k_unique_scenes > 0 or config.k_unique_scenes is None + ), "K_UNIQUE_N discipline requires specifying positive value for K" + + selected_scenes = repeat_to_N( + random_sample(config.k_unique_scenes) + ) + if ( + not any(scene.startswith("tfrecord") for scene in selected_scenes) + or len(selected_scenes) == 0 + ): + raise ValueError( + "The selected scene is not a traffic scenario." + "Please check your data path." + ) + scene_paths = [ + os.path.join(os.path.abspath(config.path), selected_scene) + for selected_scene in selected_scenes + ] + + print( + f"\n--- Ratio unique scenes / number of worls = \ + {len(np.unique(scene_paths))} / {len(scene_paths)} ---\n" + ) + + return scene_paths diff --git a/gpudrive-main/gpudrive/env/wrappers/__init__.py b/gpudrive-main/gpudrive/env/wrappers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/gpudrive-main/gpudrive/env/wrappers/jaxmarl_wrapper.py b/gpudrive-main/gpudrive/env/wrappers/jaxmarl_wrapper.py new file mode 100644 index 0000000000000000000000000000000000000000..e0d4a2ca98d55ea404cc8f1ca86e4db26dd3dcb9 --- /dev/null +++ b/gpudrive-main/gpudrive/env/wrappers/jaxmarl_wrapper.py @@ -0,0 +1,215 @@ +""" +Abstract base class for multi agent gym environments with JAX +Based on the Gymnax and PettingZoo APIs + +""" + +import jax +import jax.numpy as jnp +from typing import Dict +import chex +from functools import partial +from flax import struct +from typing import Tuple, Optional + +from gpudrive.env.config import EnvConfig, RenderConfig, SceneConfig +from gpudrive.env.env_jax import GPUDriveJaxEnv + + +@struct.dataclass +class State: + done: chex.Array + step: int + + +class GPUDriveToJaxMARL(object): + """ + Wrapper to make the GPUDrive base environment class compatible with JaxMARL. + """ + + def __init__( + self, + env: GPUDriveJaxEnv, + ) -> None: + self.env = env + self.max_agents = ( + env.max_agent_count + ) # Max number of POSSIBLE agents in the environment + self.controlled_agent_mask = ( + env.cont_agent_mask + ) # Agents across all worlds that are controlled + self.episode_len = 90 # Maximum number of steps in an episode + self.controlled_agents_across_worlds = ( + env.cont_agent_mask.sum().item() + ) # All controlled agents + self.observation_spaces = { + i: env.observation_space + for i in range(self.controlled_agents_across_worlds) + } + self.action_spaces = { + i: env.action_space + for i in range(self.controlled_agents_across_worlds) + } + # Note (dc): The ordering of the agents will remain the same, even when agents die + self.agents = [ + f"agent_{i}" for i in range(self.controlled_agents_across_worlds) + ] + + @partial(jax.jit, static_argnums=(0,)) + def reset(self, key) -> Tuple[Dict[str, chex.Array], State]: + """Performs resetting of the environment.""" + # Note (dc): There is currently no randomness when we reset, so we don't need a key + obs = self.env.reset() + + masked_dict_obs = self.get_obs(obs) + + # Initialize the dead agent mask + self.dead_agent_mask = self.controlled_agent_mask.copy() + + # obs is of shape (num_agents, obs_dim) + # Note (dc): we're not returning the whole simulator state, only the observation + # for every agent in the environment + return masked_dict_obs, None + + @partial(jax.jit, static_argnums=(0,)) + def step( + self, + key: chex.PRNGKey, + state: None, + actions: Dict[str, chex.Array], + ) -> Tuple[ + Dict[str, chex.Array], State, Dict[str, float], Dict[str, bool], Dict + ]: + """Performs step transitions in the environment.""" + + key, key_reset = jax.random.split(key) + obs_st, _, rewards, dones, infos = self.step_env(key, state, actions) + obs_re, _ = self.reset(key_reset) + + states = None + + # Auto-reset environment based on termination + obs = jax.tree_map( + lambda x, y: jax.lax.select(dones["__all__"], x, y), obs_re, obs_st + ) + + return obs, states, rewards, dones, infos + + def step_env( + self, key: chex.PRNGKey, state: State, actions: Dict[str, chex.Array] + ) -> Tuple[ + Dict[str, chex.Array], State, Dict[str, float], Dict[str, bool], Dict + ]: + """Environment-specific step transition.""" + + # Note (dc): Convert input action dictionary to array of actions + action_arr = jnp.stack( + [actions[agent_name] for agent_name in self.agents] + ).reshape(self.controlled_agents_across_worlds) + + # Step the environment dynamics + # Note (dc): We could filter out the actions for invalid agents for correctness, + # however, the simulator internally ignores actions for dead or invalid agents + # so this is not strictly necessary + self.env.step_dynamics(action_arr) + + # Get the observations for all agents + obs = self.get_obs(self.env.get_obs()) + reward = self.env.get_rewards() + done = self.env.get_dones() + info = self.env.get_infos() + + # Mask the observations, rewards, dones, and infos for agents and map to dicts + # that are not controlled (padding agents) OR are done + rewards_masked = reward[self.controlled_agent_mask] + rewards = { + agent: rewards_masked[agent_idx] + for agent_idx, agent in enumerate(self.agents) + } + rewards["__all__"] = jnp.sum(rewards_masked) + + # Note: done is 1 from the moment the agent is done and all subsequent time steps (before we reset) + # The maximum episode length is 90 steps + dones_masked = done[self.controlled_agent_mask] + dones = { + agent: dones_masked[agent_idx] + for agent_idx, agent in enumerate(self.agents) + } + dones["__all__"] = jnp.all(dones_masked) + + infos_masked = info[self.controlled_agent_mask] + infos = { + agent: infos_masked[agent_idx] + for agent_idx, agent in enumerate(self.agents) + } + + # Update the dead agent mask + # self.dead_agent_mask = jnp.logical_or(self.dead_agent_mask, done) + + return obs, None, rewards, dones, infos + + def get_obs(self, state) -> Dict[str, chex.Array]: + """Applies observation function to state.""" + # Flatten along agent x world dimensions + valid_obs = state[self.controlled_agent_mask].reshape( + self.controlled_agents_across_worlds, -1 + ) + + agent_obs = {} + for agent_idx, agent_name in enumerate(self.agents): + agent_obs[agent_name] = valid_obs[agent_idx, :] + + return agent_obs + + def observation_space(self, agent: str): + """Observation space for a given agent.""" + return self.observation_spaces[agent] + + def action_space(self, agent: str): + """Action space for a given agent.""" + return self.action_spaces[agent] + + @property + def name(self) -> str: + """Environment name.""" + return type(self).__name__ + + @property + def agent_classes(self) -> dict: + """Returns a dictionary with agent classes, used in environments with hetrogenous agents. + + Format: + agent_base_name: [agent_base_name_1, agent_base_name_2, ...] + """ + raise NotImplementedError + + +if __name__ == "__main__": + + env_config = EnvConfig() + render_config = RenderConfig() + scene_config = SceneConfig(path="data", num_scenes=3) + + # MAKE ENV + base_env = GPUDriveJaxEnv( + config=env_config, + scene_config=scene_config, + max_cont_agents=128, # Number of agents to control + device="cuda", + render_config=render_config, + ) + + jaxmarl_env = GPUDriveToJaxMARL(base_env) + + # RESET + jaxmarl_env.reset(key=jax.random.PRNGKey(0)) + + for step in range(10): + print(f"step: {step}") + + # STEP + jaxmarl_env.step( + key=jax.random.PRNGKey(0), + state=None, + actions={agent: jnp.zeros(1) for agent in jaxmarl_env.agents}, + ) diff --git a/gpudrive-main/gpudrive/env/wrappers/sb3_wrapper.py b/gpudrive-main/gpudrive/env/wrappers/sb3_wrapper.py new file mode 100644 index 0000000000000000000000000000000000000000..25d29e5559bef59f2ab470475f7d8455173336f2 --- /dev/null +++ b/gpudrive-main/gpudrive/env/wrappers/sb3_wrapper.py @@ -0,0 +1,409 @@ +"""Vectorized environment wrapper for multi-agent environments.""" +import logging +import wandb +from typing import Optional, Sequence +import torch +import os +import gymnasium as gym +import random +import numpy as np +from stable_baselines3.common.vec_env.base_vec_env import ( + VecEnv, + VecEnvStepReturn, +) + +from gpudrive.env.env_torch import GPUDriveTorchEnv +from gpudrive.env.dataset import SceneDataLoader +from gpudrive.env.config import RenderConfig +from gpudrive.visualize.utils import img_from_fig + +logging.basicConfig(level=logging.INFO) + + +class SB3MultiAgentEnv(VecEnv): + """Casts multi-agent environments as vectorized environments. + + Args: + ----- + VecEnv (SB3 VecEnv): SB3 VecEnv base class. + """ + + def __init__( + self, + config, + exp_config, + max_cont_agents, + device, + render_mode="rgb_array", + collision_weight=-.5, + goal_achieved_weight=1, + off_road_weight=-.5, + log_distance_weight=.01, + render = False, + render_3d=True, + render_interval=20, + render_k_scenarios=3, + render_agent_obs=False, + render_format="mp4", + render_fps=15, + zoom_radius=50, + wandb_obj=None, + ): + #for rendering + self.wandb_obj = wandb_obj + self.render = render + if self.render: + assert self.wandb_obj != None + self.render_interval = render_interval + self.render_k_scenarios = render_k_scenarios + self.render_agent_obs = render_agent_obs + self.render_format = render_format + self.render_fps = render_fps + self.zoom_radius = zoom_radius + self.iters = 0 + + render_config = RenderConfig( + render_3d=render_3d, + ) + + data_loader = SceneDataLoader( + root=exp_config.data_dir, + batch_size=exp_config.num_worlds, + dataset_size=exp_config.resample_dataset_size, + sample_with_replacement=exp_config.sample_with_replacement, + shuffle=exp_config.shuffle_dataset, + ) + self._env = GPUDriveTorchEnv( + config=config, + render_config=render_config, + data_loader=data_loader, + max_cont_agents=max_cont_agents, + device=device, + ) + self.config = config + self.exp_config = exp_config + self.all_scene_paths = [ + os.path.join(self.exp_config.data_dir, scene) + for scene in sorted(os.listdir(self.exp_config.data_dir)) + if scene.startswith("tfrecord") + ] + self.unique_scene_paths = list(set(self.all_scene_paths)) + self.num_worlds = self._env.num_worlds + self.max_agent_count = self._env.max_agent_count + self.num_envs = self._env.cont_agent_mask.sum().item() + self.device = device + self.controlled_agent_mask = self._env.cont_agent_mask.clone() + self.action_space = gym.spaces.Discrete(self._env.action_space.n) + self.observation_space = gym.spaces.Box( + -np.inf, np.inf, self._env.observation_space.shape, np.float32 + ) + + self.obs_dim = self._env.observation_space.shape[-1] + self.info_dim = 5 + self.render_mode = render_mode + self.agent_step = torch.zeros( + (self.num_worlds, self.max_agent_count) + ).to(self.device) + self.actions_tensor = torch.zeros( + (self.num_worlds, self.max_agent_count) + ).to(self.device) + # Storage: Fill buffer with nan values + self.buf_rews = torch.full( + (self.num_worlds, self.max_agent_count), fill_value=float("nan") + ).to(self.device) + self.buf_dones = torch.full( + (self.num_worlds, self.max_agent_count), fill_value=float("nan") + ).to(self.device) + self.buf_obs = torch.full( + (self.num_envs, self.obs_dim), + fill_value=float("nan"), + ).to(self.device) + + self.num_episodes = 0 + + self.collision_weight = collision_weight + self.goal_achieved_weight = goal_achieved_weight + self.off_road_weight = off_road_weight + self.log_distance_weight = log_distance_weight + + # Setup rendering storage + self.rendering_in_progress = { + env_idx: False for env_idx in range(render_k_scenarios) + } + self.was_rendered_in_rollout = { + env_idx: True for env_idx in range(render_k_scenarios) + } + self.frames = {env_idx: [] for env_idx in range(render_k_scenarios)} + + def _reset_seeds(self) -> None: + """Reset all environments' seeds.""" + self._seeds = None + + def reset(self, world_idx=None, seed=None): + """Reset environment and return initial observations. + + Returns: + -------- + torch.Tensor (max_agent_count * num_worlds, obs_dim): + Initial observation. + """ + if world_idx is None: + self.episode_lengths = torch.zeros(self.num_worlds, dtype=torch.int32) + self._env.reset() + obs = self._env.get_obs() + + # Make dead agent mask (True for dead or invalid agents) + self.dead_agent_mask = ~self.controlled_agent_mask.clone() + # Flatten over num_worlds and max_agent_count + obs = obs[self.controlled_agent_mask].reshape( + self.num_envs, self.obs_dim + ) + return obs + else: + self._env.sim.reset(world_idx.item()) + self.episode_lengths[world_idx] = 0 + + def step(self, actions) -> VecEnvStepReturn: + """ + Returns: + -------- + torch.Tensor (max_agent_count * num_worlds, obs_dim): Next obs. + torch.Tensor (max_agent_count * num_worlds): Rewards. + torch.Tensor (max_agent_count * num_worlds): Dones. + torch.Tensor (max_agent_count * num_worlds, info_dim): Info. + + Note: + ------- + In multi-agent settings some agents may be done before others. + To handle this, we return done is 1 at the first time step the + agent is done. After that, we return nan for the rewards, infos + and done for that agent until the end of the episode. + """ + + # Reset the info dict + self.info_dict = {} + + # Unsqueeze action tensor to a shape the gpudrive env expects + self.actions_tensor[self.controlled_agent_mask] = actions + + # Step the environment + self._env.step_dynamics(self.actions_tensor) + + reward = self._env.get_rewards(collision_weight=self.collision_weight, + goal_achieved_weight=self.goal_achieved_weight, + off_road_weight=self.off_road_weight, + log_distance_weight=self.log_distance_weight).clone() + done = self._env.get_dones().clone() + info = self._env.sim.info_tensor().to_torch() + + # CHECK IF A WORLD IS DONE -> RESET + done_worlds = torch.where( + (done.nan_to_num(0) * self.controlled_agent_mask).sum(dim=1) + == self.controlled_agent_mask.sum(dim=1) + )[0] + self.render_env() if self.render else None + if len(done_worlds) > 0: + if self.render: + for render_env_idx in range(self.render_k_scenarios): + self.log_video_to_wandb(render_env_idx, done_worlds) + self._update_info_dict(info, done_worlds) + self.num_episodes += len(done_worlds) + self._env.sim.reset(done_worlds.tolist()) + self.episode_lengths[done_worlds] = -1 + + if self.render: + self.episode_lengths += 1 + # Override nan placeholders for alive agents + self.buf_rews[self.dead_agent_mask] = torch.nan + self.buf_rews[~self.dead_agent_mask] = reward[~self.dead_agent_mask] + self.buf_dones[self.dead_agent_mask] = torch.nan + self.buf_dones[~self.dead_agent_mask] = done[~self.dead_agent_mask].to( + torch.float32 + ) + + # Store running total reward across worlds + self.agent_step += 1 + + # Update dead agent mask: Set to True if agent is done before + # the end of the episode + self.dead_agent_mask = torch.logical_or(self.dead_agent_mask, done) + + # Now override the dead agent mask for the reset worlds + if len(done_worlds) > 0: + for world_idx in done_worlds: + self.dead_agent_mask[ + world_idx, : + ] = ~self.controlled_agent_mask[world_idx, :].clone() + self.agent_step[done_worlds] = 0 + + # Construct the next observation + next_obs = self._env.get_obs() + self.obs_alive = next_obs[~self.dead_agent_mask] + + # RETURN NEXT_OBS, REWARD, DONE, INFO + return ( + next_obs[self.controlled_agent_mask] + .reshape(self.num_envs, self.obs_dim) + .clone(), + self.buf_rews[self.controlled_agent_mask] + .reshape(self.num_envs) + .clone(), + self.buf_dones[self.controlled_agent_mask] + .reshape(self.num_envs) + .clone(), + info[self.controlled_agent_mask] + .reshape(self.num_envs, self.info_dim) + .clone(), + ) + + def close(self) -> None: + """Close the environment.""" + self._env.close() + + def seed(self, seed=None): + """Set the random seeds for all environments.""" + if seed is None: + # To ensure that subprocesses have different seeds, + # we still populate the seed variable when no argument is passed + seed = int( + np.random.randint(0, np.iinfo(np.uint32).max, dtype=np.uint32) + ) + + self._seeds = [seed + idx for idx in range(self.num_envs)] + return self._seeds + + def resample_scenario_batch(self): + """Swap out the dataset.""" + + self._env.swap_data_batch() + + # Update controlled agent mask + self.controlled_agent_mask = self._env.cont_agent_mask.clone() + self.max_agent_count = self._env.max_agent_count + self.num_valid_controlled_agents_across_worlds = ( + self._env.num_valid_controlled_agents_across_worlds + ) + self.num_envs = self.controlled_agent_mask.sum().item() + + def _update_info_dict(self, info, indices) -> None: + """Update the info logger.""" + + # Select info for controlled agents + controlled_agent_info = info[indices][ + self.controlled_agent_mask[indices] + ] + + self.info_dict["off_road"] = controlled_agent_info[:, 0].sum().item() + self.info_dict["veh_collisions"] = ( + controlled_agent_info[:, 1].sum().item() + ) + self.info_dict["non_veh_collision"] = ( + controlled_agent_info[:, 2].sum().item() + ) + self.info_dict["goal_achieved"] = ( + controlled_agent_info[:, 3].sum().item() + ) + self.info_dict["num_controlled_agents"] = self.controlled_agent_mask[ + indices + ].sum() + + # Log the agents that are done but did not receive any reward + self.info_dict["truncated"] = ( + ( + (self.agent_step[indices] == self.config.episode_len - 1) + * ~self.dead_agent_mask[indices] + ) + .sum() + .item() + ) + + def get_attr(self, attr_name, indices=None): + raise NotImplementedError() + + def set_attr(self, attr_name, value, indices=None) -> None: + raise NotImplementedError() + + def env_method( + self, method_name, *method_args, indices=None, **method_kwargs + ): + if "method" == "render": + return self._env.render() + raise NotImplementedError() + + def env_is_wrapped(self, wrapper_class, indices=None): + raise NotImplementedError() + + def step_async(self, actions: np.ndarray) -> None: + raise NotImplementedError() + + def step_wait(self) -> VecEnvStepReturn: + raise NotImplementedError() + + def get_images(self, policy=None) -> Sequence[Optional[np.ndarray]]: + frames = [self._env.render()] + return frames + + def render_env(self): + """Render the environment based on conditions. + - If the episode has just started, start a new rendering. + - If the episode is in progress, continue rendering. + - If the episode has ended, log the video to WandB. + - Only render env once per rollout + """ + for render_env_idx in range(self.render_k_scenarios): + # Start a new rendering if the episode has just started + if (self.iters - 1) % self.render_interval == 0: + if ( + self.episode_lengths[render_env_idx] == 0 + and not self.was_rendered_in_rollout[render_env_idx] + ): + self.rendering_in_progress[render_env_idx] = True + + envs_to_render = list( + np.where(np.array(list(self.rendering_in_progress.values())))[ + 0 + ] + ) + time_steps = list(self.episode_lengths[envs_to_render]) + + if len(envs_to_render) > 0: + sim_state_figures = self._env.vis.plot_simulator_state( + env_indices=envs_to_render, + time_steps=time_steps, + zoom_radius=self.zoom_radius, + ) + + for idx, render_env_idx in enumerate(envs_to_render): + self.frames[render_env_idx].append( + img_from_fig(sim_state_figures[idx]) + ) + + def clear_render_storage(self): + """Clear rendering storage.""" + for env_idx in range(self.render_k_scenarios): + self.frames[env_idx] = [] + self.rendering_in_progress[env_idx] = False + self.was_rendered_in_rollout[env_idx] = False + + def log_video_to_wandb(self, render_env_idx, done_worlds): + """Log arrays as videos to wandb.""" + if ( + render_env_idx in done_worlds + and len(self.frames[render_env_idx]) > 0 + ): + frames_array = np.array(self.frames[render_env_idx]) + self.wandb_obj.log( + { + f"vis/state/env_{render_env_idx}": wandb.Video( + np.moveaxis(frames_array, -1, 1), + fps=self.render_fps, + format=self.render_format, + caption=f"iteration: {self.iters}", + ) + } + ) + + # Reset rendering storage + self.frames[render_env_idx] = [] + self.rendering_in_progress[render_env_idx] = False + self.was_rendered_in_rollout[render_env_idx] = True \ No newline at end of file diff --git a/gpudrive-main/gpudrive/integrations/__init__.py b/gpudrive-main/gpudrive/integrations/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/gpudrive-main/gpudrive/integrations/puffer/__init__.py b/gpudrive-main/gpudrive/integrations/puffer/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/gpudrive-main/gpudrive/integrations/puffer/logging.py b/gpudrive-main/gpudrive/integrations/puffer/logging.py new file mode 100644 index 0000000000000000000000000000000000000000..2f3b68fd4079ce8efd998dc576faf4479cf8bb52 --- /dev/null +++ b/gpudrive-main/gpudrive/integrations/puffer/logging.py @@ -0,0 +1,168 @@ +import rich +from rich.console import Console +from rich.table import Table +import numpy as np + +ROUND_OPEN = rich.box.Box( + "╭──╮\n" "│ │\n" "│ │\n" "│ │\n" "│ │\n" "│ │\n" "│ │\n" "╰──╯\n" +) + +c1 = "[bright_cyan]" +c2 = "[white]" +c3 = "[cyan]" +b1 = "[bright_cyan]" +b2 = "[bright_white]" + + +def mean_or_zero(values): + return float(np.mean(values)) if len(values) > 0 else 0.0 + + +def abbreviate(num): + if num < 1e3: + return f"{b2}{num:.0f}" + elif num < 1e6: + return f"{b2}{num/1e3:.1f}{c2}k" + elif num < 1e9: + return f"{b2}{num/1e6:.1f}{c2}m" + elif num < 1e12: + return f"{b2}{num/1e9:.1f}{c2}b" + else: + return f"{b2}{num/1e12:.1f}{c2}t" + + +def duration(seconds): + seconds = int(seconds) + h = seconds // 3600 + m = (seconds % 3600) // 60 + s = seconds % 60 + return ( + f"{b2}{h}{c2}h {b2}{m}{c2}m {b2}{s}{c2}s" + if h + else f"{b2}{m}{c2}m {b2}{s}{c2}s" + if m + else f"{b2}{s}{c2}s" + ) + + +def fmt_perf(name, time, uptime): + percent = 0 if uptime == 0 else int(100 * time / uptime - 1e-5) + return f"{c1}{name}", duration(time), f"{b2}{percent:2d}%" + + +def print_dashboard( + env_name, + utilization, + global_step, + epoch, + profile, + losses, + stats, + msg, + clear=False, + max_stats=[0], +): + console = Console() + if clear: + console.clear() + + dashboard = Table( + box=ROUND_OPEN, + expand=True, + show_header=False, + border_style="bright_cyan", + ) + + table = Table(box=None, expand=True, show_header=False) + dashboard.add_row(table) + cpu_percent = mean_or_zero(utilization.cpu_util) + dram_percent = mean_or_zero(utilization.cpu_mem) + gpu_percent = mean_or_zero(utilization.gpu_util) + vram_percent = mean_or_zero(utilization.gpu_mem) + table.add_column(justify="left", width=30) + table.add_column(justify="center", width=12) + table.add_column(justify="center", width=12) + table.add_column(justify="center", width=13) + table.add_column(justify="right", width=13) + table.add_row( + f":blowfish: {c1}PufferLib {b2}1.0.0", + f"{c1}CPU: {c3}{cpu_percent:.1f}%", + f"{c1}GPU: {c3}{gpu_percent:.1f}%", + f"{c1}DRAM: {c3}{dram_percent:.1f}%", + f"{c1}VRAM: {c3}{vram_percent:.1f}%", + ) + + s = Table(box=None, expand=True) + s.add_column(f"{c1}Summary", justify="left", vertical="top", width=16) + s.add_column(f"{c1}Value", justify="right", vertical="top", width=8) + s.add_row(f"{c2}Environment", f"{b2}{env_name}") + s.add_row(f"{c2}Agent Steps", abbreviate(global_step)) + s.add_row(f"{c2}SPS", abbreviate(profile.controlled_agent_sps)) + s.add_row(f"{c2}Epoch", abbreviate(epoch)) + s.add_row(f"{c2}Uptime", duration(profile.uptime)) + s.add_row(f"{c2}Remaining", duration(profile.remaining)) + + p = Table(box=None, expand=True, show_header=False) + p.add_column(f"{c1}Performance", justify="left", width=10) + p.add_column(f"{c1}Time", justify="right", width=8) + p.add_column(f"{c1}%", justify="right", width=4) + p.add_row(*fmt_perf("Evaluate", profile.eval_time, profile.uptime)) + p.add_row( + *fmt_perf(" Forward", profile.eval_forward_time, profile.uptime) + ) + p.add_row(*fmt_perf(" Env", profile.env_time, profile.uptime)) + p.add_row(*fmt_perf(" Misc", profile.eval_misc_time, profile.uptime)) + p.add_row(*fmt_perf("Train", profile.train_time, profile.uptime)) + p.add_row( + *fmt_perf(" Forward", profile.train_forward_time, profile.uptime) + ) + p.add_row(*fmt_perf(" Learn", profile.learn_time, profile.uptime)) + p.add_row(*fmt_perf(" Misc", profile.train_misc_time, profile.uptime)) + + l = Table( + box=None, + expand=True, + ) + l.add_column(f"{c1}Losses", justify="left", width=16) + l.add_column(f"{c1}Value", justify="right", width=8) + for metric, value in losses.items(): + l.add_row(f"{c2}{metric}", f"{b2}{value:.3f}") + + monitor = Table(box=None, expand=True, pad_edge=False) + monitor.add_row(s, p, l) + dashboard.add_row(monitor) + + table = Table(box=None, expand=True, pad_edge=False) + dashboard.add_row(table) + left = Table(box=None, expand=True) + right = Table(box=None, expand=True) + table.add_row(left, right) + left.add_column(f"{c1}User Stats", justify="left", width=20) + left.add_column(f"{c1}Value", justify="right", width=10) + right.add_column(f"{c1}User Stats", justify="left", width=20) + right.add_column(f"{c1}Value", justify="right", width=10) + i = 0 + for metric, value in stats.items(): + try: # Discard non-numeric values + int(value) + except: + continue + + u = left if i % 2 == 0 else right + u.add_row(f"{c2}{metric}", f"{b2}{value:.3f}") + i += 1 + + for i in range(max_stats[0] - i): + u = left if i % 2 == 0 else right + u.add_row("", "") + + max_stats[0] = max(max_stats[0], i) + + table = Table(box=None, expand=True, pad_edge=False) + dashboard.add_row(table) + table.add_row(f" {c1}Message: {c2}{msg}") + + with console.capture() as capture: + console.print(dashboard) + + print("\033[0;0H" + capture.get()) diff --git a/gpudrive-main/gpudrive/integrations/puffer/ppo.py b/gpudrive-main/gpudrive/integrations/puffer/ppo.py new file mode 100644 index 0000000000000000000000000000000000000000..bdc65ed45a5627b063692af5d444c6c9c2a4b946 --- /dev/null +++ b/gpudrive-main/gpudrive/integrations/puffer/ppo.py @@ -0,0 +1,749 @@ +""" +This implementation is adapted from the demo in PufferLib by Joseph Suarez, +which in turn is adapted from Costa Huang's CleanRL PPO + LSTM implementation. +Links +- PufferLib: https://github.com/PufferAI/PufferLib/blob/dev/demo.py +- Cleanrl: https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/ppo.py +""" + +from pdb import set_trace as T +import numpy as np +import os +import random +import psutil +import time + +from threading import Thread +from collections import defaultdict, deque + +import torch + +import pufferlib +import pufferlib.utils +import pufferlib.pytorch + +torch.set_float32_matmul_precision("high") + +# Fast Cython GAE implementation +import pyximport + +pyximport.install(setup_args={"include_dirs": np.get_include()}) + +from c_gae import compute_gae +from gpudrive.integrations.puffer.logging import print_dashboard, abbreviate + + +def create(config, vecenv, policy, optimizer=None, wandb=None): + seed_everything(config.seed, config.torch_deterministic) + profile = Profile() + losses = make_losses() + + utilization = Utilization() + msg = f"Model Size: {abbreviate(count_params(policy))} parameters" + if vecenv.use_vbd: + msg += f" | Using VBD" + print_dashboard( + config.env, utilization, 0, 0, profile, losses, {}, msg, clear=True + ) + + vecenv.async_reset(config.seed) + obs_shape = vecenv.single_observation_space.shape + obs_dtype = vecenv.single_observation_space.dtype + atn_shape = vecenv.single_action_space.shape + total_agents = vecenv.num_agents + + # Log initial data coverage + vecenv.wandb_obj = wandb + vecenv.log_data_coverage() + + lstm = policy.lstm if hasattr(policy, "lstm") else None + + # Rollout buffer + experience = Experience( + config.batch_size, + config.bptt_horizon, + config.minibatch_size, + obs_shape, + obs_dtype, + atn_shape, + config.cpu_offload, + config.device, + lstm, + total_agents, + ) + + uncompiled_policy = policy + + if config.compile: + policy = torch.compile(policy, mode=config.compile_mode) + + optimizer = torch.optim.Adam( + policy.parameters(), lr=float(config.learning_rate), eps=1e-5 + ) + + return pufferlib.namespace( + config=config, + vecenv=vecenv, + policy=policy, + uncompiled_policy=uncompiled_policy, + optimizer=optimizer, + experience=experience, + profile=profile, + losses=losses, + wandb=wandb, + global_step=0, + global_step_pad=0, + resample_buffer=0, + resample_counter=0, + epoch=0, + stats={}, + infos=defaultdict(list), + msg=msg, + last_log_time=0, + utilization=utilization, + ) + + +@pufferlib.utils.profile +def evaluate(data): + + # Sample new batch of scenarios before start of rollout + if ( + data.config.resample_scenes + and data.resample_buffer >= data.config.resample_interval + and data.config.resample_dataset_size > data.vecenv.num_worlds + ): + print(f"Resampling scenarios at global step {data.global_step}") + data.vecenv.resample_scenario_batch() + data.resample_buffer = 0 + + data.vecenv.clear_render_storage() + + config, profile, experience = data.config, data.profile, data.experience + + with profile.eval_misc: + policy = data.policy + lstm_h, lstm_c = experience.lstm_h, experience.lstm_c + + # Rollout loop + while not experience.full: + + with profile.env: + # Receive data from current timestep + ( + obs, + reward, + terminal, + truncated, + info, + env_id, + mask, + ) = data.vecenv.recv() + env_id = env_id.tolist() + + with profile.eval_misc: + total_alive = mask.sum().item() + + data.global_step += total_alive + data.global_step_pad += data.vecenv.total_agents + data.resample_buffer += total_alive + + data.vecenv.global_step = data.global_step + data.vecenv.iters += 1 + + obs_device = obs.to(config.device) + + with profile.eval_forward, torch.no_grad(): + if lstm_h is not None: + h = lstm_h[:, env_id] + c = lstm_c[:, env_id] + actions, logprob, _, value, (h, c) = policy(obs_device, (h, c)) + lstm_h[:, env_id] = h + lstm_c[:, env_id] = c + else: + actions, logprob, _, value = policy(obs_device) + + if config.device == "cuda": + torch.cuda.synchronize() + + with profile.env: + # Step the environment and reset if done + data.vecenv.send(actions) + + with profile.eval_misc: + value = value.flatten() + obs_device = obs_device if config.cpu_offload else obs_device + + # Use the terminal observation value to better estimate the reward + # done_but_truncated = truncated & terminal + # if done_but_truncated.any(): + # terminal_obs = data.vecenv.last_obs[done_but_truncated] + + # # Get terminal (truncated) observation value + # with torch.no_grad(): + # _, _, _, terminal_value = policy(terminal_obs) + + # # Add discounted value to reward + # reward[done_but_truncated] += config.gamma * terminal_value.squeeze(-1) + + # Add to rollout buffer + experience.store( + obs_device, + value, + actions, + logprob, + reward, + terminal, + env_id, + mask, + ) + + # Add metrics for logging + for i in info: + for k, v in pufferlib.utils.unroll_nested_dict(i): + data.infos[k].append(v) + + with profile.eval_misc: + data.stats = {} + + # Store the average across K done worlds across last N rollouts + # ensure we are logging an unbiased estimate of the performance + if sum(data.infos["num_completed_episodes"]) > data.config.log_window: + for k, v in data.infos.items(): + try: + if "num_completed_episodes" in k: + data.stats[k] = np.sum(v) + else: + data.stats[k] = np.mean(v) + + # Log variance for goal and collision metrics + if "goal" in k: + data.stats[f"std_{k}"] = np.std(v) + except: + continue + + # Reset info dict + data.infos = defaultdict(list) + + return data.stats, data.infos + + +@pufferlib.utils.profile +def train(data): + config, profile, experience = data.config, data.profile, data.experience + data.losses = make_losses() + losses = data.losses + + with profile.train_misc: + idxs = experience.sort_training_data() + dones_np = experience.dones_np[idxs] + values_np = experience.values_np[idxs] + rewards_np = experience.rewards_np[idxs] + advantages_np = compute_gae( + dones_np, values_np, rewards_np, config.gamma, config.gae_lambda + ) + experience.flatten_batch(advantages_np) + + # Optimizing the policy and value network + num_update_iters = config.update_epochs * experience.num_minibatches + for epoch in range(config.update_epochs): + lstm_state = None + for mb in range(experience.num_minibatches): + with profile.train_misc: + obs = experience.b_obs[mb] + obs = obs.to(config.device) + atn = experience.b_actions[mb] + log_probs = experience.b_logprobs[mb] + val = experience.b_values[mb] + adv = experience.b_advantages[mb] + ret = experience.b_returns[mb] + + with profile.train_forward: + if experience.lstm_h is not None: + _, newlogprob, entropy, newvalue, lstm_state = data.policy( + obs, state=lstm_state, action=atn + ) + lstm_state = ( + lstm_state[0].detach(), + lstm_state[1].detach(), + ) + else: + _, newlogprob, entropy, newvalue = data.policy( + obs.reshape( + -1, *data.vecenv.single_observation_space.shape + ), + action=atn, + ) + + if config.device == "cuda": + torch.cuda.synchronize() + + with profile.train_misc: + logratio = newlogprob - log_probs.reshape(-1) + ratio = logratio.exp() + + with torch.no_grad(): + # calculate approx_kl http://joschu.net/blog/kl-approx.html + old_approx_kl = (-logratio).mean() + approx_kl = ((ratio - 1) - logratio).mean() + clipfrac = ( + ((ratio - 1.0).abs() > config.clip_coef).float().mean() + ) + + adv = adv.reshape(-1) + if config.norm_adv: + adv = (adv - adv.mean()) / (adv.std() + 1e-8) + + # Policy loss + pg_loss1 = -adv * ratio + pg_loss2 = -adv * torch.clamp( + ratio, 1 - config.clip_coef, 1 + config.clip_coef + ) + pg_loss = torch.max(pg_loss1, pg_loss2).mean() + + # Value loss + newvalue = newvalue.view(-1) + if config.clip_vloss: + v_loss_unclipped = (newvalue - ret) ** 2 + v_clipped = val + torch.clamp( + newvalue - val, + -config.vf_clip_coef, + config.vf_clip_coef, + ) + v_loss_clipped = (v_clipped - ret) ** 2 + v_loss_max = torch.max(v_loss_unclipped, v_loss_clipped) + v_loss = 0.5 * v_loss_max.mean() + else: + v_loss = 0.5 * ((newvalue - ret) ** 2).mean() + + entropy_loss = entropy.mean() + loss = ( + pg_loss + - config.ent_coef * entropy_loss + + v_loss * config.vf_coef + ) + + with profile.learn: + data.optimizer.zero_grad() + loss.backward() + torch.nn.utils.clip_grad_norm_( + data.policy.parameters(), config.max_grad_norm + ) + data.optimizer.step() + if config.device == "cuda": + torch.cuda.synchronize() + + with profile.train_misc: + losses.policy_loss += pg_loss.item() / num_update_iters + losses.value_loss += v_loss.item() / num_update_iters + losses.entropy += entropy_loss.item() / num_update_iters + losses.old_approx_kl += old_approx_kl.item() / num_update_iters + losses.approx_kl += approx_kl.item() / num_update_iters + losses.clipfrac += clipfrac.item() / num_update_iters + + if config.target_kl is not None: + if approx_kl > config.target_kl: + break + + with profile.train_misc: + if config.anneal_lr: + frac = 1.0 - data.global_step / config.total_timesteps + lrnow = float(frac) * float(config.learning_rate) + data.optimizer.param_groups[0]["lr"] = lrnow + + y_pred = experience.values_np + y_true = experience.returns_np + var_y = np.var(y_true) + explained_var = ( + np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y + ) + losses.explained_variance = explained_var + data.epoch += 1 + + done_training = data.global_step >= config.total_timesteps + + # Logging + if profile.update(data) or done_training: + print_dashboard( + config.env, + data.utilization, + data.global_step, + data.epoch, + profile, + data.losses, + data.stats, + data.msg, + ) + + # fmt: off + if ( + data.wandb is not None + and data.global_step > 0 + and time.perf_counter() - data.last_log_time > 3.0 + ): + + data.last_log_time = time.perf_counter() + data.wandb.log( + { + "performance/controlled_agent_sps": profile.controlled_agent_sps, + "performance/controlled_agent_sps_env": profile.controlled_agent_sps_env, + "performance/pad_agent_sps": profile.pad_agent_sps, + "performance/pad_agent_sps_env": profile.pad_agent_sps_env, + "global_step": data.global_step, + "performance/epoch": data.epoch, + "performance/uptime": profile.uptime, + "train/learning_rate": data.optimizer.param_groups[0]["lr"], + **{f"metrics/{k}": v for k, v in data.stats.items()}, + **{f"train/{k}": v for k, v in data.losses.items()}, + } + ) + + if bool(data.stats): + data.wandb.log({ + **{f"metrics/{k}": v for k, v in data.stats.items()}, + }) + + # fmt: on + + if data.epoch % config.checkpoint_interval == 0 or done_training: + save_checkpoint(data) + data.msg = f"Checkpoint saved at update {data.epoch}" + + +def close(data): + data.vecenv.close() + data.utilization.stop() + config = data.config + if data.wandb is not None: + artifact_name = f"{config.exp_id}_model" + artifact = data.wandb.Artifact(artifact_name, type="model") + model_path = save_checkpoint(data) + artifact.add_file(model_path) + data.wandb.run.log_artifact(artifact) + data.wandb.finish() + + +class Profile: + controlled_agent_sps: ... = 0 + controlled_agent_sps_env: ... = 0 + pad_agent_sps: ... = 0 + pad_agent_sps_env: ... = 0 + uptime: ... = 0 + remaining: ... = 0 + eval_time: ... = 0 + env_time: ... = 0 + eval_forward_time: ... = 0 + eval_misc_time: ... = 0 + train_time: ... = 0 + train_forward_time: ... = 0 + learn_time: ... = 0 + train_misc_time: ... = 0 + + def __init__(self): + self.start = time.perf_counter() + self.env = pufferlib.utils.Profiler() + self.eval_forward = pufferlib.utils.Profiler() + self.eval_misc = pufferlib.utils.Profiler() + self.train_forward = pufferlib.utils.Profiler() + self.learn = pufferlib.utils.Profiler() + self.train_misc = pufferlib.utils.Profiler() + self.prev_steps = 0 + self.prev_steps_pad = 0 + self.prev_env_elapsed = 0 + + def __iter__(self): + yield "controlled_agent_sps", self.controlled_agent_sps + yield "controlled_agent_sps_env", self.controlled_agent_sps_env + yield "pad_agent_sps", self.pad_agent_sps + yield "pad_agent_sps_env", self.pad_agent_sps_env + yield "uptime", self.uptime + yield "remaining", self.remaining + yield "eval_time", self.eval_time + yield "env_time", self.env_time + yield "eval_forward_time", self.eval_forward_time + yield "eval_misc_time", self.eval_misc_time + yield "train_time", self.train_time + yield "train_forward_time", self.train_forward_time + yield "learn_time", self.learn_time + yield "train_misc_time", self.train_misc_time + + @property + def epoch_time(self): + return self.train_time + self.eval_time + + def update(self, data, interval_s=1): + global_step = data.global_step + global_step_pad = data.global_step_pad + if global_step == 0: + return True + + uptime = time.perf_counter() - self.start + if uptime - self.uptime < interval_s: + return False + + # SPS = delta global step / delta time (s) + self.controlled_agent_sps = (global_step - self.prev_steps) / ( + uptime - self.uptime + ) + self.controlled_agent_sps_env = (global_step - self.prev_steps) / ( + self.env.elapsed - self.prev_env_elapsed + ) + + self.pad_agent_sps = (global_step_pad - self.prev_steps_pad) / ( + uptime - self.uptime + ) + self.pad_agent_sps_env = (global_step_pad - self.prev_steps_pad) / ( + self.env.elapsed - self.prev_env_elapsed + ) + + self.prev_steps = global_step + self.prev_steps_pad = global_step_pad + self.prev_env_elapsed = self.env.elapsed + self.uptime = uptime + + self.remaining = ( + data.config.total_timesteps - global_step + ) / self.controlled_agent_sps + self.eval_time = data._timers["evaluate"].elapsed + self.eval_forward_time = self.eval_forward.elapsed + self.env_time = self.env.elapsed + self.eval_misc_time = self.eval_misc.elapsed + self.train_time = data._timers["train"].elapsed + self.train_forward_time = self.train_forward.elapsed + self.learn_time = self.learn.elapsed + self.train_misc_time = self.train_misc.elapsed + return True + + +def make_losses(): + return pufferlib.namespace( + policy_loss=0, + value_loss=0, + entropy=0, + old_approx_kl=0, + approx_kl=0, + clipfrac=0, + explained_variance=0, + ) + + +class Experience: + """Flat tensor storage (buffer) and array views for faster indexing.""" + + def __init__( + self, + batch_size, + bptt_horizon, + minibatch_size, + obs_shape, + obs_dtype, + atn_shape, + cpu_offload=False, + device="cuda", + lstm=None, + lstm_total_agents=0, + ): + if minibatch_size is None: + minibatch_size = batch_size + + obs_dtype = pufferlib.pytorch.numpy_to_torch_dtype_dict[obs_dtype] + pin = device == "cuda" and cpu_offload + self.obs = torch.zeros( + batch_size, + *obs_shape, + dtype=obs_dtype, + pin_memory=pin, + device=device if not pin else "cpu", + ) + self.actions = torch.zeros( + batch_size, *atn_shape, dtype=int, pin_memory=pin + ) + self.logprobs = torch.zeros(batch_size, pin_memory=pin) + self.rewards = torch.zeros(batch_size, pin_memory=pin) + self.dones = torch.zeros(batch_size, pin_memory=pin) + self.truncateds = torch.zeros(batch_size, pin_memory=pin) + self.values = torch.zeros(batch_size, pin_memory=pin) + + # self.obs_np = np.asarray(self.obs) + self.actions_np = np.asarray(self.actions) + self.logprobs_np = np.asarray(self.logprobs) + self.rewards_np = np.asarray(self.rewards) + self.dones_np = np.asarray(self.dones) + self.truncateds_np = np.asarray(self.truncateds) + self.values_np = np.asarray(self.values) + + self.lstm_h = self.lstm_c = None + if lstm is not None: + assert lstm_total_agents > 0 + shape = (lstm.num_layers, lstm_total_agents, lstm.hidden_size) + self.lstm_h = torch.zeros(shape).to(device) + self.lstm_c = torch.zeros(shape).to(device) + + num_minibatches = batch_size / minibatch_size + self.num_minibatches = int(num_minibatches) + if self.num_minibatches != num_minibatches: + raise ValueError("batch_size must be divisible by minibatch_size") + + minibatch_rows = minibatch_size / bptt_horizon + self.minibatch_rows = int(minibatch_rows) + if self.minibatch_rows != minibatch_rows: + raise ValueError( + "minibatch_size must be divisible by bptt_horizon" + ) + + self.batch_size = batch_size + self.bptt_horizon = bptt_horizon + self.minibatch_size = minibatch_size + self.device = device + self.sort_keys = [] + self.ptr = 0 + self.step = 0 + + @property + def full(self): + return self.ptr >= self.batch_size + + def store(self, obs, value, action, logprob, reward, done, env_id, mask): + # Mask learner and Ensure indices do not exceed batch size + ptr = self.ptr + indices = torch.where(mask)[0].cpu().numpy()[: self.batch_size - ptr] + end = ptr + len(indices) + + self.obs[ptr:end] = obs.to(self.obs.device)[indices] + self.values_np[ptr:end] = value.cpu().numpy()[indices] + self.actions_np[ptr:end] = action.cpu().numpy()[indices] + self.logprobs_np[ptr:end] = logprob.cpu().numpy()[indices] + self.rewards_np[ptr:end] = reward.cpu().numpy()[indices] + self.dones_np[ptr:end] = done.cpu().numpy()[indices] + self.sort_keys.extend([(env_id[i], self.step) for i in indices]) + self.ptr = end + self.step += 1 + + def sort_training_data(self): + idxs = np.asarray( + sorted(range(len(self.sort_keys)), key=self.sort_keys.__getitem__) + ) + self.b_idxs_obs = ( + torch.as_tensor( + idxs.reshape( + self.minibatch_rows, + self.num_minibatches, + self.bptt_horizon, + ).transpose(1, 0, -1) + ) + .to(self.obs.device) + .long() + ) + self.b_idxs = self.b_idxs_obs.to(self.device) + self.b_idxs_flat = self.b_idxs.reshape( + self.num_minibatches, self.minibatch_size + ) + self.sort_keys = [] + self.ptr = 0 + self.step = 0 + return idxs + + def flatten_batch(self, advantages_np): + advantages = torch.from_numpy(advantages_np).to(self.device) + b_idxs, b_flat = self.b_idxs, self.b_idxs_flat + self.b_actions = self.actions.to(self.device, non_blocking=True) + self.b_logprobs = self.logprobs.to(self.device, non_blocking=True) + self.b_dones = self.dones.to(self.device, non_blocking=True) + self.b_values = self.values.to(self.device, non_blocking=True) + self.b_advantages = ( + advantages.reshape( + self.minibatch_rows, self.num_minibatches, self.bptt_horizon + ) + .transpose(0, 1) + .reshape(self.num_minibatches, self.minibatch_size) + ) + self.returns_np = advantages_np + self.values_np + self.b_obs = self.obs[self.b_idxs_obs] + self.b_actions = self.b_actions[b_idxs].contiguous() + self.b_logprobs = self.b_logprobs[b_idxs] + self.b_dones = self.b_dones[b_idxs] + self.b_values = self.b_values[b_flat] + self.b_returns = self.b_advantages + self.b_values + + +class Utilization(Thread): + def __init__(self, delay=1, maxlen=20): + super().__init__() + self.cpu_mem = deque(maxlen=maxlen) + self.cpu_util = deque(maxlen=maxlen) + self.gpu_util = deque(maxlen=maxlen) + self.gpu_mem = deque(maxlen=maxlen) + + self.delay = delay + self.stopped = False + self.start() + + def run(self): + while not self.stopped: + self.cpu_util.append(psutil.cpu_percent()) + mem = psutil.virtual_memory() + self.cpu_mem.append(mem.active / mem.total) + self.gpu_util.append(torch.cuda.utilization()) + free, total = torch.cuda.mem_get_info() + self.gpu_mem.append(free / total) + time.sleep(self.delay) + + def stop(self): + self.stopped = True + + +def save_checkpoint(data, save_checkpoint_to_wandb=True): + + config = data.config + path = os.path.join(config.checkpoint_path, config.exp_id) + + if not os.path.exists(path): + os.makedirs(path) + + model_name = f"model_{config.exp_id}_{data.epoch:06d}.pt" + model_path = os.path.join(path, model_name) + + # Save training state + state = { + "parameters": data.uncompiled_policy.state_dict(), + "optimizer_state_dict": data.optimizer.state_dict(), + "global_step": data.global_step, + "agent_step": data.global_step, + "update": data.epoch, + "model_name": model_name, + "model_class": data.uncompiled_policy.__class__.__name__, + "model_arch": config.network, + "action_dim": data.uncompiled_policy.action_dim, + "exp_id": config.exp_id, + "num_params": config.network["num_parameters"], + } + + torch.save(state, model_path) + if save_checkpoint_to_wandb and data.wandb is not None: + + data.wandb.save(model_path) + + data.wandb.config.update( + { + "network_class": data.uncompiled_policy.__class__.__name__, + "network_arch": config.network, + "exp_id": config.exp_id, + } + ) + + # Optionally log the optimizer state path + data.wandb.save(model_path) + + return model_path + + +def count_params(policy): + return sum(p.numel() for p in policy.parameters() if p.requires_grad) + + +def seed_everything(seed, torch_deterministic): + random.seed(seed) + np.random.seed(seed) + if seed is not None: + torch.manual_seed(seed) + torch.backends.cudnn.deterministic = torch_deterministic diff --git a/gpudrive-main/gpudrive/integrations/puffer/utils.py b/gpudrive-main/gpudrive/integrations/puffer/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..1b76feeb142bec272ee2b053558974f92b4a230b --- /dev/null +++ b/gpudrive-main/gpudrive/integrations/puffer/utils.py @@ -0,0 +1,144 @@ +from torch import nn +import torch +import torch.nn.functional as F +from pdb import set_trace as T +import pufferlib.models +import numpy as np +from pygpudrive.env import constants + +def unpack_obs(obs_flat, env): + """ + Unpack the flattened observation into the ego state and visible state. + Args: + obs_flat (torch.Tensor): flattened observation tensor of shape (batch_size, obs_dim) + Return: + ego_state, road_objects, stop_signs, road_graph (torch.Tensor). + """ + top_k_road_points = env.env.config.roadgraph_top_k + + # Unpack ego and visible state + ego_state = obs_flat[:, : constants.EGO_FEAT_DIM] + vis_state = obs_flat[:, constants.EGO_FEAT_DIM :] + + # Visible state object order: road_objects, road_points + # Find the ends of each section + ro_end_idx = constants.PARTNER_FEAT_DIM * constants.ROAD_GRAPH_FEAT_DIM + rg_end_idx = ro_end_idx + ( + constants.ROAD_GRAPH_FEAT_DIM * top_k_road_points + ) + + # Unflatten and reshape to (batch_size, num_objects, object_dim) + road_objects = (vis_state[:, :ro_end_idx]).reshape( + -1, constants.ROAD_GRAPH_FEAT_DIM, constants.PARTNER_FEAT_DIM + ) + road_graph = (vis_state[:, ro_end_idx:rg_end_idx]).reshape( + -1, + top_k_road_points, + constants.ROAD_GRAPH_FEAT_DIM, + ) + return ego_state, road_objects, road_graph + + +class Policy(nn.Module): + def __init__( + self, env, input_size=64, hidden_size=128, act_func="tanh", **kwargs + ): + super().__init__() + self.env = env + self.act_func = ( + torch.nn.Tanh() if act_func == "tanh" else torch.nn.ReLU() + ) + + self.ego_embed = nn.Sequential( + pufferlib.pytorch.layer_init( + nn.Linear(constants.EGO_FEAT_DIM, input_size) + ), + nn.LayerNorm(input_size), + self.act_func, + pufferlib.pytorch.layer_init(nn.Linear(input_size, input_size)), + ) + + self.partner_embed = nn.Sequential( + pufferlib.pytorch.layer_init( + nn.Linear(constants.PARTNER_FEAT_DIM, input_size) + ), + self.act_func, + pufferlib.pytorch.layer_init(nn.Linear(input_size, input_size)), + ) + + self.road_map_embed = nn.Sequential( + pufferlib.pytorch.layer_init( + nn.Linear(constants.ROAD_GRAPH_FEAT_DIM, input_size) + ), + nn.LayerNorm(input_size), + self.act_func, + pufferlib.pytorch.layer_init(nn.Linear(input_size, input_size)), + ) + + self.shared_embed = pufferlib.pytorch.layer_init( + nn.Linear(3 * input_size, hidden_size) + ) + + self.actor = pufferlib.pytorch.layer_init( + nn.Linear(hidden_size, env.single_action_space.n), std=0.01 + ) + self.value_fn = pufferlib.pytorch.layer_init( + nn.Linear(hidden_size, 1), std=1 + ) + + def forward(self, observations): + hidden, lookup = self.encode_observations(observations) + actions, value = self.decode_actions(hidden, lookup) + return actions, value + + def encode_observations(self, observations): + ego_state, road_objects, road_graph = unpack_obs( + observations, self.env + ) + ego_embed = self.ego_embed(ego_state) + + partner_embed, _ = self.partner_embed(road_objects).max(dim=1) + road_map_embed, _ = self.road_map_embed(road_graph).max(dim=1) + embed = torch.cat([ego_embed, partner_embed, road_map_embed], dim=1) + + return self.shared_embed(embed), None + + def decode_actions(self, flat_hidden, lookup, concat=None): + action = self.actor(flat_hidden) + value = self.value_fn(flat_hidden) + return action, value + + +class LiDARPolicy(nn.Module): + def __init__( + self, env, input_size=600, hidden_size=128, act_func="tanh", **kwargs + ): + super().__init__() + self.env = env + self.act_func = ( + torch.nn.Tanh() if act_func == "tanh" else torch.nn.ReLU() + ) + + self.embed = nn.Sequential( + pufferlib.pytorch.layer_init(nn.Linear(input_size, hidden_size)), + nn.LayerNorm(hidden_size), + self.act_func, + pufferlib.pytorch.layer_init(nn.Linear(hidden_size, hidden_size)), + ) + + self.actor = pufferlib.pytorch.layer_init( + nn.Linear(hidden_size, env.single_action_space.n), std=0.01 + ) + self.value_fn = pufferlib.pytorch.layer_init( + nn.Linear(hidden_size, 1), std=1 + ) + + def forward(self, observations): + hidden = self.embed(observations) + actions, value = self.decode_actions(hidden) + return actions, value + + def decode_actions(self, flat_hidden, concat=None): + action = self.actor(flat_hidden) + value = self.value_fn(flat_hidden) + return action, value diff --git a/gpudrive-main/gpudrive/integrations/sb3/__init__.py b/gpudrive-main/gpudrive/integrations/sb3/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/gpudrive-main/gpudrive/integrations/sb3/callbacks.py b/gpudrive-main/gpudrive/integrations/sb3/callbacks.py new file mode 100644 index 0000000000000000000000000000000000000000..c4b86b94a87c34c577b7b412c9e7abef5a5699ba --- /dev/null +++ b/gpudrive-main/gpudrive/integrations/sb3/callbacks.py @@ -0,0 +1,165 @@ +from collections import deque +import os +import numpy as np +import torch +import wandb +from stable_baselines3.common.callbacks import BaseCallback +from time import perf_counter +from gpudrive.visualize.utils import img_from_fig + + +class MultiAgentCallback(BaseCallback): + """Stable Baselines3 callback for multi-agent gpudrive env.""" + + def __init__(self, config, wandb_run=None, **kwargs) -> None: + super().__init__(**kwargs) + self.config = config + self.wandb_run = wandb_run + self.num_rollouts = 0 + self.step_counter = 0 + self.policy_base_path = os.path.join(wandb.run.dir, "policies") + os.makedirs(self.policy_base_path, exist_ok=True) + self.perc_goal_achieved = deque( + maxlen=config.logging_collection_window + ) + self.perc_off_road = deque(maxlen=config.logging_collection_window) + self.perc_veh_collisions = deque( + maxlen=config.logging_collection_window + ) + self.perc_non_veh_collision = deque( + maxlen=config.logging_collection_window + ) + self.num_agent_rollouts = deque( + maxlen=config.logging_collection_window + ) + self.perc_truncated = deque(maxlen=config.logging_collection_window) + self.max_obs = deque(maxlen=config.logging_collection_window) + self.min_obs = deque(maxlen=config.logging_collection_window) + + self._define_wandb_metrics() + + def _define_wandb_metrics(self): + """Automatically set correct x-axis for metrics.""" + wandb.define_metric("global_step") + metrics = [ + "metrics/mean_episode_reward_per_agent", + "metrics/perc_goal_achieved", + "metrics/perc_off_road", + "metrics/perc_veh_collisions", + "metrics/perc_non_veh_collision", + "charts/obs_max", + "charts/obs_min", + ] + for metric in metrics: + wandb.define_metric(metric, step_metric="global_step") + + def _on_training_start(self) -> None: + """This method is called before the first rollout starts.""" + self.start_training = perf_counter() + self.log_first_to_95 = True + + def _on_training_end(self) -> None: + """This method is called at the end of training.""" + if self.config.save_policy: + self._save_policy_checkpoint() + + def _on_rollout_start(self) -> None: + """Triggered before collecting new samples.""" + pass + + def _on_step(self) -> bool: + """Will be called by the model after each call to `env.step()`.""" + self.step_counter += 1 + env_info = self.locals["env"].info_dict + + if env_info: + self.num_agent_rollouts.append(env_info["num_controlled_agents"]) + self.perc_off_road.append(env_info["off_road"]) + self.perc_veh_collisions.append(env_info["veh_collisions"]) + self.perc_non_veh_collision.append(env_info["non_veh_collision"]) + self.perc_goal_achieved.append(env_info["goal_achieved"]) + self.perc_truncated.append(env_info["truncated"]) + self.max_obs.append(self.locals["env"].obs_alive.max().item()) + self.min_obs.append(self.locals["env"].obs_alive.min().item()) + + if self.step_counter % self.config.log_freq == 0: + self._log_metrics() + self._log_obs_stats() + + if self.config.track_time_to_solve: + self._log_time_to_solve() + + def _log_metrics(self): + """Log performance metrics to wandb.""" + total_agents = sum(self.num_agent_rollouts) + metrics = { + "global_step": self.num_timesteps, + "metrics/wallclock_time (s)": perf_counter() - self.start_training, + "metrics/perc_off_road": sum(self.perc_off_road) / total_agents, + "metrics/perc_veh_collisions": sum(self.perc_veh_collisions) + / total_agents, + "metrics/perc_non_veh_collision": sum(self.perc_non_veh_collision) + / total_agents, + "metrics/perc_goal_achieved": sum(self.perc_goal_achieved) + / total_agents, + "metrics/perc_truncated": sum(self.perc_truncated) / total_agents, + } + wandb.log(metrics) + + def _log_obs_stats(self): + """Log observation statistics to wandb.""" + wandb.log( + { + "charts/obs_max": np.array(self.max_obs).max(), + "charts/obs_min": np.array(self.min_obs).min(), + } + ) + + def _log_time_to_solve(self): + """Log the time and steps taken to achieve 95% goal achievement.""" + if ( + sum(self.perc_goal_achieved) / sum(self.num_agent_rollouts) >= 0.95 + and self.log_first_to_95 + ): + wandb.log( + { + "charts/time_to_95": perf_counter() - self.start_training, + "charts/steps_to_95": self.num_timesteps, + } + ) + self.log_first_to_95 = False + + def _on_rollout_end(self) -> None: + """Triggered before updating the policy.""" + rewards = ( + torch.nan_to_num(self.locals["rollout_buffer"].rewards, nan=0) + .sum() + .item() + ) + completions = torch.nan_to_num( + self.locals["rollout_buffer"].episode_starts + ).sum() + + if ( + self.config.save_policy + and self.num_rollouts % self.config.save_policy_freq == 0 + ): + self._save_policy_checkpoint() + + self.num_rollouts += 1 + wandb.log( + { + "global_step": self.num_timesteps, + "metrics/mean_episode_reward_per_agent": rewards / completions, + } + ) + + def _save_policy_checkpoint(self) -> None: + """Save the policy locally and to wandb.""" + path = os.path.join( + self.policy_base_path, f"policy_{self.num_timesteps}.zip" + ) + self.model.save(path) + if self.wandb_run is not None: + wandb.save(path, base_path=self.policy_base_path) + print(f"Saved policy on step {self.num_timesteps:,} at: {path}") diff --git a/gpudrive-main/gpudrive/integrations/sb3/ppo.py b/gpudrive-main/gpudrive/integrations/sb3/ppo.py new file mode 100644 index 0000000000000000000000000000000000000000..c95aa9e5c0a78b6104b1ed7a703d9024e5a21e64 --- /dev/null +++ b/gpudrive-main/gpudrive/integrations/sb3/ppo.py @@ -0,0 +1,528 @@ +import logging +from time import perf_counter +import wandb +import torch +from torch.nn import functional as F +import numpy as np +from gymnasium import spaces +from stable_baselines3 import PPO +from stable_baselines3.common.callbacks import BaseCallback +from stable_baselines3.common.utils import get_schedule_fn +from stable_baselines3.common.vec_env import VecEnv +from torch import nn + +# Import masked rollout buffer class +from gpudrive.integrations.sb3.rollout_buffer import MaskedRolloutBuffer +from gpudrive.networks.perm_eq_late_fusion import LateFusionNet + +# From stable baselines +def explained_variance( + y_pred: torch.tensor, y_true: torch.tensor +) -> torch.tensor: + """ + Computes fraction of variance that ypred explains about y. + Returns 1 - Var[y-ypred] / Var[y] + + interpretation: + ev=0 => might as well have predicted zero + ev=1 => perfect prediction + ev<0 => worse than just predicting zero + + :param y_pred: the prediction + :param y_true: the expected value + :return: explained variance of ypred and y + """ + assert y_true.ndim == 1 and y_pred.ndim == 1 + var_y = torch.var(y_true) + return torch.nan if var_y == 0 else 1 - torch.var(y_true - y_pred) / var_y + + +class IPPO(PPO): + """Adapted Proximal Policy Optimization algorithm (PPO) that is compatible with multi-agent environments.""" + + def __init__( + self, + *args, + env_config=None, + exp_config=None, + mlp_class: nn.Module = LateFusionNet, + mlp_config=None, + **kwargs, + ): + self.env_config = env_config + self.exp_config = exp_config + self.mlp_class = mlp_class + self.mlp_config = mlp_config + self.resample_counter = 0 + self.start = perf_counter() + self.uptime = 0 + self.prev_steps = 0 + self.prev_steps_pad = 0 + self.prev_env_elapsed = 0 + self.global_step_pad = 0 + super().__init__(*args, **kwargs) + + def collect_rollouts( + self, + env: VecEnv, + callback: BaseCallback, + rollout_buffer: MaskedRolloutBuffer, + n_rollout_steps: int, + ) -> bool: + """Adapted collect_rollouts function.""" + + assert ( + self._last_obs is not None + ), "No previous observation was provided" + + # Check resampling criterion and resample batch of scenarios if needed + if self.env.exp_config.resample_scenes: + if self.env.exp_config.resample_criterion == "global_step": + if self.resample_counter >= self.env.exp_config.resample_freq: + print( + f"Resampling {self.env.num_worlds} scenarios at global_step {self.num_timesteps:,}..." + ) + # Re-initialize the scenes and controlled agents mask + self.env.resample_scenario_batch() + self.resample_counter = 0 + # Get new initial observation + self._last_obs = self.env.reset() + # Update storage shapes + self.n_envs = env.num_valid_controlled_agents_across_worlds + rollout_buffer.n_envs = self.n_envs + self._last_episode_starts = ( + self.env._env.get_dones().clone()[ + ~self.env.dead_agent_mask + ] + ) + + else: + raise NotImplementedError( + f"Resampling criterion {self.env.exp_config.resample_criterion} not implemented" + ) + + # Switch to eval mode (this affects batch norm / dropout) + self.policy.set_training_mode(False) + + n_steps = 0 + rollout_buffer.reset() + # Sample new weights for the state dependent exploration + if self.use_sde: + self.policy.reset_noise(env.num_envs) + + callback.on_rollout_start() + + time_rollout = perf_counter() + + while n_steps < n_rollout_steps: + if ( + self.use_sde + and self.sde_sample_freq > 0 + and n_steps % self.sde_sample_freq == 0 + ): + # Sample a new noise matrix + self.policy.reset_noise(env.num_envs) + + with torch.no_grad(): + obs_tensor = self._last_obs + + # EDIT_1: Mask out invalid observations (NaN axes and/or dead agents) + # Create dummy actions, values and log_probs (NaN) + actions = torch.full( + fill_value=float("nan"), size=(self.n_envs,) + ).to(self.device) + log_probs = torch.full( + fill_value=float("nan"), + size=(self.n_envs,), + dtype=torch.float32, + ).to(self.device) + values = ( + torch.full( + fill_value=float("nan"), + size=(self.n_envs,), + dtype=torch.float32, + ) + .unsqueeze(dim=1) + .to(self.device) + ) + + # Get indices of alive agent ids + # Convert env_dead_agent_mask to boolean tensor with the same shape as obs_tensor + alive_agent_mask = ~( + env.dead_agent_mask[env.controlled_agent_mask].reshape( + env.num_envs, 1 + ) + ) + + # Use boolean indexing to select elements in obs_tensor + obs_tensor_alive = obs_tensor[ + alive_agent_mask.expand_as(obs_tensor) + ].reshape(-1, obs_tensor.shape[-1]) + + # Predict actions, vals and log_probs given obs + time_actions = perf_counter() + actions_tmp, values_tmp, log_prob_tmp = self.policy( + obs_tensor_alive + ) + nn_fps = actions_tmp.shape[0] / (perf_counter() - time_actions) + # self.logger.record("performance/network_AFPS", nn_fps) + + # Predict actions, vals and log_probs given obs + ( + actions[alive_agent_mask.squeeze(dim=1)], + values[alive_agent_mask.squeeze(dim=1)], + log_probs[alive_agent_mask.squeeze(dim=1)], + ) = ( + actions_tmp.float(), + values_tmp.float(), + log_prob_tmp.float(), + ) + + # Rescale and perform action + clipped_actions = actions + + if isinstance(self.action_space, spaces.Box): + if self.policy.squash_output: + # Unscale the actions to match env bounds + # if they were previously squashed (scaled in [-1, 1]) + clipped_actions = self.policy.unscale_action( + clipped_actions + ) + else: + # Otherwise, clip the actions to avoid out of bound error + # as we are sampling from an unbounded Gaussian distribution + clipped_actions = torch.clamp( + actions, self.action_space.low, self.action_space.high + ) + + new_obs, rewards, dones, infos = env.step(clipped_actions) + + # EDIT_2: Increment the global step by the number of valid samples + # (i.e., samples that are from controlled and alive agents + self.num_timesteps += int( + alive_agent_mask.sum().item() + ) # self.env.controlled_agent_mask.sum().item() + self.global_step_pad += ( + self.env.num_worlds * self.env.max_agent_count + ) + + self.resample_counter += int( + (~rewards.isnan()).float().sum().item() + ) + # Give access to local variables + callback.update_locals(locals()) + if callback.on_step() is False: + return False + n_steps += 1 + + if isinstance(self.action_space, spaces.Discrete): + # Reshape in case of discrete action + actions = actions.reshape(-1, 1) + + rollout_buffer.add( + self._last_obs, # type: ignore[arg-type] + actions, + rewards, + torch.Tensor(self._last_episode_starts), # type: ignore[arg-type] + values, + log_probs, + ) + self._last_obs = new_obs # type: ignore[assignment] + self._last_episode_starts = dones + + # # # # # END LOOP # # # # # + total_steps = self.n_envs * n_rollout_steps + elapsed_time = perf_counter() - time_rollout + fps = total_steps / elapsed_time + self.logger.record("performance/controlled_agent_sps_rollout", fps) + + with torch.no_grad(): + # Compute value for the last timestep + values = self.policy.predict_values(new_obs) # type: ignore[arg-type] + + rollout_buffer.compute_returns_and_advantage( + last_values=values, dones=dones + ) + + callback.update_locals(locals()) + callback.on_rollout_end() + + return True + + def _setup_model(self) -> None: + self._setup_lr_schedule() + self.set_random_seed(self.seed) + + # Change buffer to our own masked version + buffer_cls = MaskedRolloutBuffer + + self.rollout_buffer = buffer_cls( + self.n_steps, + self.observation_space, # type: ignore[arg-type] + self.action_space, + device=self.device, + gamma=self.gamma, + gae_lambda=self.gae_lambda, + n_envs=self.n_envs, + ) + + if self.mlp_class == LateFusionNet: + self.policy = self.policy_class( + observation_space=self.observation_space, + env_config=self.env_config, + exp_config=self.exp_config, + action_space=self.action_space, + lr_schedule=self.lr_schedule, + use_sde=self.use_sde, + mlp_class=self.mlp_class, + mlp_config=self.mlp_config, + **self.policy_kwargs, + ) + else: + self.policy = self.policy_class( + observation_space=self.observation_space, + action_space=self.action_space, + lr_schedule=self.lr_schedule, + use_sde=self.use_sde, + mlp_class=self.mlp_class, + **self.policy_kwargs, + ) + + self.policy = self.policy.to(self.device) + + # Initialize schedules for policy/value clipping + self.clip_range = get_schedule_fn(self.clip_range) + if self.clip_range_vf is not None: + if isinstance(self.clip_range_vf, (float, int)): + assert self.clip_range_vf > 0, ( + "`clip_range_vf` must be positive, " + "pass `None` to deactivate vf clipping" + ) + + self.clip_range_vf = get_schedule_fn(self.clip_range_vf) + + def train(self) -> None: + """ + Update policy using the currently gathered rollout buffer. + """ + # Switch to train mode (this affects batch norm / dropout) + self.policy.set_training_mode(True) + # Update optimizer learning rate + self._update_learning_rate(self.policy.optimizer) + # Compute current clip range + clip_range = self.clip_range(self._current_progress_remaining) # type: ignore[operator] + # Optional: clip range for the value function + if self.clip_range_vf is not None: + clip_range_vf = self.clip_range_vf(self._current_progress_remaining) # type: ignore[operator] + + entropy_losses = [] + pg_losses, value_losses = [], [] + clip_fractions = [] + + continue_training = True + # train for n_epochs epochs + for epoch in range(self.n_epochs): + approx_kl_divs = [] + # Do a complete pass on the rollout buffer + for rollout_data in self.rollout_buffer.get(self.batch_size): + actions = rollout_data.actions + if isinstance(self.action_space, spaces.Discrete): + # Convert discrete action from float to long + actions = rollout_data.actions.long().flatten() + + # Re-sample the noise matrix because the log_std has changed + if self.use_sde: + self.policy.reset_noise(self.batch_size) + + values, log_prob, entropy = self.policy.evaluate_actions( + rollout_data.observations, actions + ) + values = values.flatten() + # Normalize advantage + advantages = rollout_data.advantages + # Normalization does not make sense if mini batchsize == 1, see GH issue #325 + if self.normalize_advantage and len(advantages) > 1: + advantages = (advantages - advantages.mean()) / ( + advantages.std() + 1e-8 + ) + + # ratio between old and new policy, should be one at the first iteration + ratio = torch.exp(log_prob - rollout_data.old_log_prob) + + # clipped surrogate loss + policy_loss_1 = advantages * ratio + policy_loss_2 = advantages * torch.clamp( + ratio, 1 - clip_range, 1 + clip_range + ) + policy_loss = -torch.min(policy_loss_1, policy_loss_2).mean() + + # Logging + pg_losses.append(policy_loss.item()) + clip_fraction = torch.mean( + (torch.abs(ratio - 1) > clip_range).float() + ).item() + clip_fractions.append(clip_fraction) + + if self.clip_range_vf is None: + # No clipping + values_pred = values + else: + # Clip the difference between old and new value + # NOTE: this depends on the reward scaling + values_pred = rollout_data.old_values + torch.clamp( + values - rollout_data.old_values, + -clip_range_vf, + clip_range_vf, + ) + # Value loss using the TD(gae_lambda) target + value_loss = F.mse_loss(rollout_data.returns, values_pred) + value_losses.append(value_loss.item()) + + # Entropy loss favor exploration + if entropy is None: + # Approximate entropy when no analytical form + entropy_loss = -torch.mean(-log_prob) + else: + entropy_loss = -torch.mean(entropy) + + entropy_losses.append(entropy_loss.item()) + + loss = ( + policy_loss + + self.ent_coef * entropy_loss + + self.vf_coef * value_loss + ) + + # Calculate approximate form of reverse KL Divergence for early stopping + # see issue #417: https://github.com/DLR-RM/stable-baselines3/issues/417 + # and discussion in PR #419: https://github.com/DLR-RM/stable-baselines3/pull/419 + # and Schulman blog: http://joschu.net/blog/kl-approx.html + with torch.no_grad(): + log_ratio = log_prob - rollout_data.old_log_prob + approx_kl_div = torch.mean( + (torch.exp(log_ratio) - 1) - log_ratio + ).cpu() + approx_kl_divs.append(approx_kl_div) + + if ( + self.target_kl is not None + and approx_kl_div > 1.5 * self.target_kl + ): + continue_training = False + if self.verbose >= 1: + print( + f"Early stopping at step {epoch} due to reaching max kl: {approx_kl_div:.2f}" + ) + break + + # Optimization step + self.policy.optimizer.zero_grad() + loss.backward() + # Clip grad norm + torch.nn.utils.clip_grad_norm_( + self.policy.parameters(), self.max_grad_norm + ) + self.policy.optimizer.step() + + self._n_updates += 1 + if not continue_training: + break + + explained_var = explained_variance( + self.rollout_buffer.values.flatten(), + self.rollout_buffer.returns.flatten(), + ) + + # Logs + self.logger.record("train/explained_variance", explained_var.item()) + self.logger.record("train/entropy", np.mean(entropy_losses)) + self.logger.record("train/advantages", advantages.mean().item()) + self.logger.record("train/policy_loss", np.mean(pg_losses)) + self.logger.record("train/value_loss", np.mean(value_losses)) + self.logger.record("train/approx_kl", np.mean(approx_kl_divs)) + self.logger.record("train/clipfrac", np.mean(clip_fractions)) + self.logger.record("train/loss", loss.item()) + if hasattr(self.policy, "log_std"): + self.logger.record( + "train/std", torch.exp(self.policy.log_std).mean().item() + ) + self.logger.record( + "train/n_updates", self._n_updates, exclude="tensorboard" + ) + self.logger.record("train/clip_range", clip_range) + if self.clip_range_vf is not None: + self.logger.record("train/clip_range_vf", clip_range_vf) + + def learn( + self, + total_timesteps, + callback=None, + log_interval=1, + tb_log_name="PPO", + reset_num_timesteps=True, + progress_bar=False, + ): + iteration = 0 + + total_timesteps, callback = self._setup_learn( + total_timesteps, + callback, + reset_num_timesteps, + tb_log_name, + progress_bar, + ) + + callback.on_training_start(locals(), globals()) + + assert self.env is not None + + while self.num_timesteps < total_timesteps: + continue_training = self.collect_rollouts( + self.env, + callback, + self.rollout_buffer, + n_rollout_steps=self.n_steps, + ) + + if not continue_training: + break + + iteration += 1 + self._update_current_progress_remaining( + self.num_timesteps, total_timesteps + ) + + # Display training infos + if log_interval is not None and iteration % log_interval == 0: + assert self.ep_info_buffer is not None + self._dump_logs(iteration) + + self.train() + + # Profile the training loop + global_step = self.num_timesteps + global_step_pad = self.global_step_pad + + uptime = perf_counter() - self.start + controlled_agent_sps = (global_step - self.prev_steps) / ( + uptime - self.uptime + ) + pad_agent_sps = (global_step_pad - self.prev_steps_pad) / ( + uptime - self.uptime + ) + + # Log + self.logger.record( + "performance/controlled_agent_sps", controlled_agent_sps + ) + self.logger.record("performance/pad_agent_sps", pad_agent_sps) + self.logger.record("performance/uptime", uptime) + + # Update + self.uptime = uptime + self.prev_steps = global_step + self.prev_steps_pad = global_step_pad + + callback.on_training_end() + + return self diff --git a/gpudrive-main/gpudrive/integrations/sb3/rollout_buffer.py b/gpudrive-main/gpudrive/integrations/sb3/rollout_buffer.py new file mode 100644 index 0000000000000000000000000000000000000000..5d4b8101a7cd0b369d7c2dc327d6fb019723216a --- /dev/null +++ b/gpudrive-main/gpudrive/integrations/sb3/rollout_buffer.py @@ -0,0 +1,249 @@ +"""Module containing regularized PPO algorithm.""" +import logging +from typing import Generator, Optional +import gymnasium as gym +import numpy as np +import torch +from typing import Union, NamedTuple +from stable_baselines3.common.vec_env import VecNormalize +from stable_baselines3.common.buffers import BaseBuffer + +logging.getLogger(__name__) + + +class RolloutBufferSamples(NamedTuple): + observations: torch.Tensor + actions: torch.Tensor + old_values: torch.Tensor + old_log_prob: torch.Tensor + advantages: torch.Tensor + returns: torch.Tensor + + +class MaskedRolloutBuffer(BaseBuffer): + """Custom SB3 RolloutBuffer class that filters out invalid samples.""" + + def __init__( + self, + buffer_size: int, + observation_space: gym.spaces.Space, + action_space: gym.spaces.Space, + device: Union[torch.device, str] = "auto", + storage_device: Union[torch.device, str] = "cpu", + gae_lambda: float = 1, + gamma: float = 0.99, + n_envs: int = 1, + ): + super().__init__( + buffer_size, observation_space, action_space, device, n_envs=n_envs + ) + self.gae_lambda = gae_lambda + self.gamma = gamma + self.generator_ready = False + self.storage_device = storage_device + self.reset() + + def reset(self) -> None: + """Reset the buffer.""" + self.observations = torch.zeros( + (self.buffer_size, self.n_envs, *self.obs_shape), + device=self.storage_device, + dtype=torch.float32, + ) + self.actions = torch.zeros( + (self.buffer_size, self.n_envs, self.action_dim), + device=self.storage_device, + dtype=torch.float32, + ) + self.rewards = torch.zeros( + (self.buffer_size, self.n_envs), + device=self.storage_device, + dtype=torch.float32, + ) + self.returns = torch.zeros( + (self.buffer_size, self.n_envs), + device=self.storage_device, + dtype=torch.float32, + ) + self.episode_starts = torch.zeros( + (self.buffer_size, self.n_envs), + device=self.storage_device, + dtype=torch.float32, + ) + self.values = torch.zeros( + (self.buffer_size, self.n_envs), + device=self.storage_device, + dtype=torch.float32, + ) + self.log_probs = torch.zeros( + (self.buffer_size, self.n_envs), + device=self.storage_device, + dtype=torch.float32, + ) + self.advantages = torch.zeros( + (self.buffer_size, self.n_envs), + device=self.storage_device, + dtype=torch.float32, + ) + self.generator_ready = False + super().reset() + + def add( + self, + obs: torch.Tensor, + action: torch.Tensor, + reward: torch.Tensor, + episode_start: torch.Tensor, + value: torch.Tensor, + log_prob: torch.Tensor, + ) -> None: + """ + EDIT: We do rollouts on the GPU --> convert torch arrays to torch tensors + """ + if len(log_prob.shape) == 0: + # Reshape 0-d tensor to avoid error + log_prob = log_prob.reshape(-1, 1) + + # Reshape needed when using multiple envs with discrete observations + # as numpy cannot broadcast (n_discrete,) to (n_discrete, 1) + if isinstance(self.observation_space, gym.spaces.Discrete): + obs = obs.reshape((self.n_envs, *self.obs_shape)) + + # Reshape to handle multi-dim and discrete action spaces, see GH #970 #1392 + action = action.reshape((self.n_envs, self.action_dim)) + + self.observations[self.pos] = obs.to(self.storage_device) + self.actions[self.pos] = action.to(self.storage_device) + self.rewards[self.pos] = reward.to(self.storage_device) + self.episode_starts[self.pos] = episode_start.to(self.storage_device) + self.values[self.pos] = value.flatten().to(self.storage_device) + self.log_probs[self.pos] = log_prob.clone().to(self.storage_device) + self.pos += 1 + if self.pos == self.buffer_size: + self.full = True + + def compute_returns_and_advantage( + self, last_values: torch.Tensor, dones: torch.Tensor + ) -> None: + """GAE (General Advantage Estimation) to compute advantages and returns.""" + # Convert to numpy + last_values = last_values.clone().flatten().to(self.storage_device) + dones = dones.clone().flatten().to(self.storage_device) + + last_gae_lam = 0 + for step in reversed(range(self.buffer_size)): + if step == self.buffer_size - 1: + # EDIT_1: Map NaNs to 1 + dones = torch.nan_to_num(dones, nan=1.0) + + next_non_terminal = 1.0 - dones + next_values = last_values + + else: + # EDIT_1: Map NaNs to 1 + episode_starts = torch.nan_to_num( + self.episode_starts[step + 1], nan=1.0 + ) + + next_non_terminal = 1.0 - episode_starts + next_values = self.values[step + 1] + + delta = ( + torch.nan_to_num( + self.rewards[step], nan=0 + ) # EDIT_2: Set invalid rewards to zero + + torch.nan_to_num( + self.gamma * next_values * next_non_terminal, nan=0 + ) # EDIT_3: Set invalid rewards to zero + - torch.nan_to_num( + self.values[step], nan=0 + ) # EDIT_4: Set invalid values to zero + ) + + last_gae_lam = ( + delta + + self.gamma + * self.gae_lambda + * next_non_terminal + * last_gae_lam + ) + self.advantages[step] = last_gae_lam + # TD(lambda) estimator, see Github PR #375 or "Telescoping in TD(lambda)" + # in David Silver Lecture 4: https://www.youtube.com/watch?v=PnHCvfgC_ZA + self.returns = self.advantages + self.values + + assert not torch.isnan( + self.advantages + ).any(), "Advantages arr contains NaN values: Check GAE computation" + + def get( + self, batch_size: Optional[int] = None + ) -> Generator[RolloutBufferSamples, None, None]: + assert self.full, "" + + # Prepare the data + if not self.generator_ready: + _tensor_names = [ + "observations", + "actions", + "values", + "log_probs", + "advantages", + "returns", + "rewards", + ] + # Create mask + self.valid_samples_mask = ~torch.isnan( + self.swap_and_flatten(self.__dict__["rewards"]) + ) + + # Flatten data + # EDIT_5: And mask out invalid samples + for tensor in _tensor_names: + if tensor == "observations": + self.__dict__[tensor] = self.swap_and_flatten( + self.__dict__[tensor] + )[self.valid_samples_mask.flatten(), :] + else: + self.__dict__[tensor] = self.swap_and_flatten( + self.__dict__[tensor] + )[self.valid_samples_mask] + + assert not torch.isnan( + self.__dict__[tensor] + ).any(), f"{tensor} tensor contains NaN values; something went wrong" + + self.generator_ready = True + + # EDIT_6: Compute total number of samples and create indices + total_num_samples = self.valid_samples_mask.sum() + indices = torch.randperm(total_num_samples) + + # if self.__dict__["observations"].max() > 1 or self.__dict__["observations"].min() < -1: + # print("Observations are out of range") + + # Return everything, don't create minibatches + if batch_size is None: + batch_size = total_num_samples + + start_idx = 0 + while start_idx < total_num_samples: + yield self._get_samples( + indices[start_idx : start_idx + batch_size] + ) + start_idx += batch_size + + def _get_samples( + self, + batch_inds: np.ndarray, + env: Optional[VecNormalize] = None, + ) -> RolloutBufferSamples: # type: ignore[signature-mismatch] + data = ( + self.observations[batch_inds], + self.actions[batch_inds], + self.values[batch_inds].flatten(), + self.log_probs[batch_inds].flatten(), + self.advantages[batch_inds].flatten(), + self.returns[batch_inds].flatten(), + ) + return RolloutBufferSamples(*tuple(map(self.to_torch, data))) diff --git a/gpudrive-main/gpudrive/integrations/sb3/utils.py b/gpudrive-main/gpudrive/integrations/sb3/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..60e86d0d58588be7de23bed7aca3d64f7713389d --- /dev/null +++ b/gpudrive-main/gpudrive/integrations/sb3/utils.py @@ -0,0 +1,22 @@ +import torch + +# From stable baselines, adapted np to torch +def explained_variance( + y_pred: torch.tensor, y_true: torch.tensor +) -> torch.tensor: + """ + Computes fraction of variance that ypred explains about y. + Returns 1 - Var[y-ypred] / Var[y] + + interpretation: + ev=0 => might as well have predicted zero + ev=1 => perfect prediction + ev<0 => worse than just predicting zero + + :param y_pred: the prediction + :param y_true: the expected value + :return: explained variance of ypred and y + """ + assert y_true.ndim == 1 and y_pred.ndim == 1 + var_y = torch.var(y_true) + return torch.nan if var_y == 0 else 1 - torch.var(y_true - y_pred) / var_y diff --git a/gpudrive-main/gpudrive/integrations/vbd/__init__.py b/gpudrive-main/gpudrive/integrations/vbd/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/gpudrive-main/gpudrive/integrations/vbd/data_utils.py b/gpudrive-main/gpudrive/integrations/vbd/data_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..54916875a16a1b100d49dd55d59ea368450a3679 --- /dev/null +++ b/gpudrive-main/gpudrive/integrations/vbd/data_utils.py @@ -0,0 +1,409 @@ +"""Functions used for processing roadgraph data and other features for VBD.""" +import torch +import numpy as np +import madrona_gpudrive +from gpudrive.datatypes.roadgraph import GlobalRoadGraphPoints + +def wrap_to_pi(angle): + """ + Wrap an angle to the range [-pi, pi]. + + Args: + angle (float): The input angle. + + Returns: + float: The wrapped angle. + """ + return (angle + np.pi) % (2 * np.pi) - np.pi + +def filter_topk_roadgraph_points(global_road_graph, reference_points, topk): + """ + Returns the topk closest roadgraph points to a reference point. + + If `topk` is larger than the number of points, an exception will be raised. + + Args: + roadgraph: Roadgraph information to filter, GlobalRoadGraphPoints. + reference_points: A tensor of shape (..., 2) - the reference point used to measure distance. + topk: Number of points to keep. + + Returns: + GlobalRoadGraphPoints data structure that has been filtered to only contain the `topk` closest points to a reference point. + """ + if topk > global_road_graph.num_points: + raise NotImplementedError("Not enough points in roadgraph.") + + elif topk < global_road_graph.num_points: + roadgraph_xy = np.asarray(global_road_graph.xy[0]) + distances = np.linalg.norm( + reference_points[..., None, :] - roadgraph_xy, axis=-1 + ) + valid_distances = np.where(global_road_graph.id > 0, distances, float("inf")) + top_idx = np.argpartition(valid_distances, topk, axis=-1)[..., :topk] + + # Gather the topk points by slicing along the indices + filtered_xy = global_road_graph.xy[0][top_idx] + filtered_length = global_road_graph.segment_length[0][top_idx] + filtered_width = global_road_graph.segment_width[0][top_idx] + filtered_height = global_road_graph.segment_height[0][top_idx] + filtered_orientation = global_road_graph.orientation[0][top_idx] + filtered_type = global_road_graph.vbd_type[0][top_idx] + filtered_id = global_road_graph.id[0][top_idx] + + # Stack the filtered attributes to form a new roadgraph tensor + filtered_tensor = torch.stack( + [ + filtered_xy[..., 0], + filtered_xy[..., 1], + filtered_length, + filtered_width, + filtered_height, + filtered_orientation, + torch.zeros_like(filtered_length), + filtered_id, + filtered_type + ], + dim=-1 + ) + + return GlobalRoadGraphPoints(filtered_tensor.clone()) + else: + return global_road_graph + + +def calculate_relations(agents, polylines, traffic_lights): + """ + Calculate the relations between agents, polylines, and traffic lights. + + Args: + agents (numpy.ndarray): Array of agent positions and orientations. + polylines (numpy.ndarray): Array of polyline positions. + traffic_lights (numpy.ndarray): Array of traffic light positions. + + Returns: + numpy.ndarray: Array of relations between the elements. + """ + n_agents = agents.shape[0] + n_polylines = polylines.shape[0] + n_traffic_lights = traffic_lights.shape[0] + n = n_agents + n_polylines + n_traffic_lights + + # Prepare a single array to hold all elements + all_elements = np.concatenate( + [ + agents[:, -1, :3], + polylines[:, 0, :3], + np.concatenate( + [traffic_lights[:, :2], np.zeros((n_traffic_lights, 1))], + axis=1, + ), + ], + axis=0, + ) + + # Compute pairwise differences using broadcasting + pos_diff = ( + all_elements[:, :2][:, None, :] - all_elements[:, :2][None, :, :] + ) + + # Compute local positions and angle differences + cos_theta = np.cos(all_elements[:, 2])[:, None] + sin_theta = np.sin(all_elements[:, 2])[:, None] + local_pos_x = pos_diff[..., 0] * cos_theta + pos_diff[..., 1] * sin_theta + local_pos_y = -pos_diff[..., 0] * sin_theta + pos_diff[..., 1] * cos_theta + theta_diff = wrap_to_pi( + all_elements[:, 2][:, None] - all_elements[:, 2][None, :] + ) + + # Set theta_diff to zero for traffic lights + start_idx = n_agents + n_polylines + theta_diff = np.where( + (np.arange(n) >= start_idx)[:, None] + | (np.arange(n) >= start_idx)[None, :], + 0, + theta_diff, + ) + + # Set the diagonal of the differences to a very small value + diag_mask = np.eye(n, dtype=bool) + epsilon = 0.01 + local_pos_x = np.where(diag_mask, epsilon, local_pos_x) + local_pos_y = np.where(diag_mask, epsilon, local_pos_y) + theta_diff = np.where(diag_mask, epsilon, theta_diff) + + # Conditions for zero coordinates + zero_mask = np.logical_or( + all_elements[:, 0][:, None] == 0, all_elements[:, 0][None, :] == 0 + ) + + # Initialize relations array + relations = np.stack([local_pos_x, local_pos_y, theta_diff], axis=-1) + + # Apply zero mask + relations = np.where(zero_mask[..., None], 0.0, relations) + + return relations + + +def process_agents_vectorized(num_worlds, max_cont_agents, init_steps, global_agent_obs, log_trajectory, metadata, raw_agent_types, controlled_agent_mask): + """ + Vectorized function to process agent data across multiple worlds. + Using controlled_agent_mask instead of SDC proximity. + """ + # Initialize output arrays with batch dimension + agents_history = np.zeros((num_worlds, max_cont_agents, init_steps + 1, 8), dtype=np.float32) + agents_type = np.zeros((num_worlds, max_cont_agents), dtype=np.int32) + agents_interested = np.zeros((num_worlds, max_cont_agents), dtype=np.int32) + agents_future = np.zeros( + (num_worlds, max_cont_agents, log_trajectory.pos_xy.shape[2] - init_steps, 5), + dtype=np.float32 + ) + agents_id = np.zeros((num_worlds, max_cont_agents), dtype=np.int32) + + # Process each world using controlled_agent_mask + for w in range(num_worlds): + # Get indices of controlled agents + controlled_indices = np.where(controlled_agent_mask[w])[0] + + # Sort by agent ID for consistency + sorted_agent_indices = np.sort(controlled_indices) + + # Handle case where we have fewer controlled agents than max_cont_agents + if len(sorted_agent_indices) < max_cont_agents: + # Pad with -1 i.e. invalid agent index + padded_indices = np.full(max_cont_agents, -1, dtype=np.int32) + padded_indices[:len(sorted_agent_indices)] = sorted_agent_indices + sorted_agent_indices = padded_indices + + # Store agent indices + agents_id[w] = sorted_agent_indices + + # Process each agent for this world + for i, a in enumerate(sorted_agent_indices): + if a == -1: + break + agent_type = raw_agent_types[w][a] if isinstance(raw_agent_types, list) else raw_agent_types[w, a] + valid = log_trajectory.valids[w, a, init_steps] + + if valid.item() != 1: + agents_interested[w, i] = 0 + continue + + if metadata.isModeled[w, a] or metadata.isOfInterest[w, a]: + agents_interested[w, i] = 10 + else: + agents_interested[w, i] = 1 + + agents_type[w, i] = agent_type + agents_history[w, i] = torch.column_stack( + [ + log_trajectory.pos_xy[w, a, :init_steps+1, 0], + log_trajectory.pos_xy[w, a, :init_steps+1, 1], + log_trajectory.yaw[w, a, :init_steps+1, 0], + log_trajectory.vel_xy[w, a, :init_steps+1, 0], + log_trajectory.vel_xy[w, a, :init_steps+1, 1], + global_agent_obs.vehicle_length[w, a].repeat(init_steps + 1), + global_agent_obs.vehicle_width[w, a].repeat(init_steps + 1), + global_agent_obs.vehicle_height[w, a].repeat(init_steps + 1), + ], + ).numpy() + + mask = log_trajectory.valids[w, a, :init_steps+1].numpy() + agents_history[w, i] *= mask + + agents_future[w, i] = torch.column_stack( + [ + log_trajectory.pos_xy[w, a, init_steps:, 0], + log_trajectory.pos_xy[w, a, init_steps:, 1], + log_trajectory.yaw[w, a, init_steps:, 0], + log_trajectory.vel_xy[w, a, init_steps:, 0], + log_trajectory.vel_xy[w, a, init_steps:, 1], + ], + ).numpy() + + mask = log_trajectory.valids[w, a, init_steps:].numpy() + agents_future[w, i] *= mask + + # Map agent types for all worlds at once (this is vectorized) + mapped_agents_type = np.zeros_like(agents_type) + mapped_agents_type[agents_type == int(madrona_gpudrive.EntityType.Vehicle)] = 1 + mapped_agents_type[agents_type == int(madrona_gpudrive.EntityType.Pedestrian)] = 2 + mapped_agents_type[agents_type == int(madrona_gpudrive.EntityType.Cyclist)] = 3 + + return agents_history, agents_future, agents_interested, mapped_agents_type, agents_id + +def process_world_roadgraph(global_road_graph, world_idx, agents_history, agents_interested, max_polylines, num_points_polyline): + """ + Process the roadgraph for a single world. + """ + # Extract the world's roadgraph data + world_road_graph = extract_world_data(global_road_graph, world_idx) + + # Get map IDs based on agent positions + map_ids = [] + current_valid = agents_interested > 0 + + for agent_idx in range(agents_history.shape[0]): + if not current_valid[agent_idx]: + continue + agent_position = agents_history[agent_idx, -1, :2] + nearby_roadgraph_points = filter_topk_roadgraph_points( + world_road_graph, agent_position, 3000 + ) + map_ids.append(nearby_roadgraph_points.id[0].tolist()) + + # Sort map IDs + sorted_map_ids = [] + if map_ids and len(map_ids[0]) > 0: + for i in range(len(map_ids[0])): + for j in range(len(map_ids)): + if i < len(map_ids[j]) and map_ids[j][i] > 0 and map_ids[j][i] not in sorted_map_ids: + sorted_map_ids.append(map_ids[j][i]) + + # Extract roadgraph properties + roadgraph_points_x = np.asarray(global_road_graph.x[world_idx]) + roadgraph_points_y = np.asarray(global_road_graph.y[world_idx]) + roadgraph_points_heading = np.asarray(global_road_graph.orientation[world_idx]) + roadgraph_points_types = np.asarray(global_road_graph.vbd_type[world_idx]) + road_graph_points_ids = np.asarray(global_road_graph.id[world_idx]) + + # Build polylines + polylines = [] + for id in sorted_map_ids: + id_mask = road_graph_points_ids == id + p_x = roadgraph_points_x[id_mask] + p_y = roadgraph_points_y[id_mask] + heading = roadgraph_points_heading[id_mask] + lane_type = roadgraph_points_types[id_mask] + traffic_light_state = np.zeros_like(lane_type) + + polyline = np.stack([p_x, p_y, heading, traffic_light_state, lane_type], axis=1) + polyline_len = polyline.shape[0] + + # Sample points evenly + sampled_points = np.linspace(0, polyline_len - 1, num_points_polyline, dtype=np.int32) + cur_polyline = np.take(polyline, sampled_points, axis=0) + polylines.append(cur_polyline) + + # Post-processing polylines + if len(polylines) > 0: + polylines = np.stack(polylines, axis=0) + polylines_valid = np.ones((polylines.shape[0],), dtype=np.int32) + else: + polylines = np.zeros((1, num_points_polyline, 5), dtype=np.float32) + polylines_valid = np.zeros((1,), dtype=np.int32) + + # Ensure polylines fit max_polylines limit + if polylines.shape[0] >= max_polylines: + polylines = polylines[:max_polylines] + polylines_valid = polylines_valid[:max_polylines] + else: + polylines = np.pad( + polylines, + ((0, max_polylines - polylines.shape[0]), (0, 0), (0, 0)) + ) + polylines_valid = np.pad( + polylines_valid, (0, max_polylines - polylines_valid.shape[0]) + ) + + return polylines, polylines_valid + +def extract_world_data(data, world_idx): + """ + Extract data for a specific world from batched inputs. + """ + # For simple tensor attributes + if isinstance(data, torch.Tensor): + return data[world_idx:world_idx+1] # Keep dimension for compatibility + + # For custom objects with tensor attributes + if hasattr(data, 'x') and hasattr(data, 'y'): # GlobalRoadGraphPoints-like object + world_data = type(data).__new__(type(data)) + # Copy tensor attributes with slicing for world_idx + for attr_name in dir(data): + if attr_name.startswith('__'): + continue + attr = getattr(data, attr_name) + if isinstance(attr, torch.Tensor) and attr.dim() > 0: + setattr(world_data, attr_name, attr[world_idx:world_idx+1]) + else: + setattr(world_data, attr_name, attr) + return world_data + + # For other types, return as is + return data + +def process_scenario_data( + max_controlled_agents, + controlled_agent_mask, + global_agent_obs, + global_road_graph, + log_trajectory, + init_steps, + episode_len, + raw_agent_types, + metadata, + max_polylines=256, + num_points_polyline=30, +): + """ + Process scenario data for multiple worlds in parallel where possible. + First dim of all inputs and outputs is num_worlds. + """ + num_worlds = global_agent_obs.vehicle_length.shape[0] + + # Process all agents across all worlds in a vectorized way + agents_history, agents_future, agents_interested, agents_type, agents_id = process_agents_vectorized( + num_worlds, max_controlled_agents, init_steps, global_agent_obs, + log_trajectory, metadata, raw_agent_types, controlled_agent_mask + ) + + # Initialize output tensors with batch dimension + all_polylines = np.zeros((num_worlds, max_polylines, num_points_polyline, 5), dtype=np.float32) + all_polylines_valid = np.zeros((num_worlds, max_polylines), dtype=np.int32) + all_traffic_light_points = np.zeros((num_worlds, 16, 3), dtype=np.float32) + all_relations = np.zeros((num_worlds, agents_history.shape[1] + max_polylines + 16, + agents_history.shape[1] + max_polylines + 16, 3), dtype=np.float32) + + # Process roadgraph data for each world (parallel processing isn't efficient here due to variable data dependencies) + for w in range(num_worlds): + world_polylines, world_polylines_valid = process_world_roadgraph( + global_road_graph, w, agents_history[w], agents_interested[w], + max_polylines, num_points_polyline + ) + + all_polylines[w] = world_polylines + all_polylines_valid[w] = world_polylines_valid + + # Calculate relations for this world + all_relations[w] = calculate_relations( + agents_history[w], + all_polylines[w], + all_traffic_light_points[w] + ) + + # Prepare the output dictionary with batch dimensions + data_dict = { + "agents_history": np.float32(agents_history), + "agents_interested": np.int32(agents_interested), + "agents_type": np.int32(agents_type), + "agents_future": np.float32(agents_future), + "traffic_light_points": np.float32(all_traffic_light_points), + "polylines": np.float32(all_polylines), + "polylines_valid": np.int32(all_polylines_valid), + "relations": np.float32(all_relations), + "agents_id": np.int32(agents_id), + } + + # Convert to PyTorch tensors + torch_dict = { + key: torch.from_numpy(value) + for key, value in data_dict.items() + } + torch_dict["anchors"] = torch.zeros(num_worlds, 32, 64, 2) # Batch-sized placeholder + + return torch_dict + +def sample_to_action(): + """Todo: Implement this function.""" + pass diff --git a/gpudrive-main/gpudrive/integrations/vbd/model/VBD.py b/gpudrive-main/gpudrive/integrations/vbd/model/VBD.py new file mode 100644 index 0000000000000000000000000000000000000000..5e2fc3da1b469ca63929b3e9f00d74119705eb9d --- /dev/null +++ b/gpudrive-main/gpudrive/integrations/vbd/model/VBD.py @@ -0,0 +1,694 @@ +import torch + +# import lightning.pytorch as pl +import pytorch_lightning as pl + +from .modules import Encoder, Denoiser, GoalPredictor +from .utils import DDPM_Sampler +from .model_utils import ( + inverse_kinematics, + roll_out, + batch_transform_trajs_to_global_frame, +) +from torch.nn.functional import smooth_l1_loss, cross_entropy + + +class VBD(pl.LightningModule): + """ + Versertile prior-guided diffusion model. + """ + + def __init__( + self, + cfg: dict, + ): + """ + Initialize the VPD model. + + Args: + cfg (dict): Configuration parameters for the model. + """ + super().__init__() + self.save_hyperparameters() + + self.cfg = cfg + self._future_len = cfg["future_len"] + self._agents_len = cfg["agents_len"] + self._action_len = cfg["action_len"] + self._diffusion_steps = cfg["diffusion_steps"] + self._encoder_layers = cfg["encoder_layers"] + self._action_mean = cfg["action_mean"] + self._action_std = cfg["action_std"] + + self._train_encoder = cfg.get("train_encoder", True) + self._train_denoiser = cfg.get("train_denoiser", True) + self._train_predictor = cfg.get("train_predictor", True) + self._with_predictor = cfg.get("with_predictor", True) + + self.encoder = Encoder(self._encoder_layers) + + self.denoiser = Denoiser( + future_len=self._future_len, + action_len=self._action_len, + agents_len=self._agents_len, + steps=self._diffusion_steps, + ) + if self._with_predictor: + self.predictor = GoalPredictor( + future_len=self._future_len, + agents_len=self._agents_len, + action_len=self._action_len, + ) + else: + self.predictor = None + self._train_predictor = False + + self.noise_scheduler = DDPM_Sampler(steps=self._diffusion_steps) + + self.register_buffer("action_mean", torch.tensor(self._action_mean)) + self.register_buffer("action_std", torch.tensor(self._action_std)) + + ################### Training Setup ################### + def configure_optimizers(self): + """ + This function is called by Lightning to create the optimizer and learning rate scheduler. + """ + if not self._train_encoder: + for param in self.encoder.parameters(): + param.requires_grad = False + if not self._train_denoiser: + for param in self.denoiser.parameters(): + param.requires_grad = False + if self._with_predictor and (not self._train_predictor): + for param in self.predictor.parameters(): + param.requires_grad = False + + params_to_update = [] + for param in self.parameters(): + if param.requires_grad == True: + params_to_update.append(param) + + assert len(params_to_update) > 0, "No parameters to update" + + optimizer = torch.optim.AdamW( + params_to_update, + lr=self.cfg["lr"], + weight_decay=self.cfg["weight_decay"], + ) + + lr_warmpup_step = self.cfg["lr_warmup_step"] + lr_step_freq = self.cfg["lr_step_freq"] + lr_step_gamma = self.cfg["lr_step_gamma"] + + def lr_update(step, warmup_step, step_size, gamma): + if step < warmup_step: + # warm up lr + lr_scale = 1 - (warmup_step - step) / warmup_step * 0.95 + else: + n = (step - warmup_step) // step_size + lr_scale = gamma**n + + if lr_scale < 1e-2: + lr_scale = 1e-2 + elif lr_scale > 1: + lr_scale = 1 + + return lr_scale + + scheduler = torch.optim.lr_scheduler.LambdaLR( + optimizer, + lr_lambda=lambda step: lr_update( + step, + lr_warmpup_step, + lr_step_freq, + lr_step_gamma, + ), + ) + + return [optimizer], [{"scheduler": scheduler, "interval": "step"}] + + def forward(self, inputs, noised_actions_normalized, diffusion_step): + """ + Forward pass of the VPD model. + + Args: + inputs: Input data. + noised_actions: noised actions. + diffusion_step: Diffusion step. + + Returns: + output_dict: Dictionary containing the model outputs. + """ + # Encode scene + output_dict = {} + encoder_outputs = self.encoder(inputs) + + if self._train_denoiser: + denoiser_outputs = self.forward_denoiser( + encoder_outputs, noised_actions_normalized, diffusion_step + ) + output_dict.update(denoiser_outputs) + + if self._train_predictor: + predictor_outputs = self.forward_predictor(encoder_outputs) + output_dict.update(predictor_outputs) + + return output_dict + + def forward_denoiser( + self, + encoder_outputs, + noised_actions_normalized, + diffusion_step, + global_frame=True, + ): + """ + Forward pass of the denoiser module. + + Args: + encoder_outputs: Outputs from the encoder module. + noised_actions: noised actions. + diffusion_step: Diffusion step. + + Returns: + denoiser_outputs: Dictionary containing the denoiser outputs. + """ + noised_actions = self.unnormalize_actions(noised_actions_normalized) + denoised_actions_normalized = self.denoiser( + encoder_outputs, noised_actions, diffusion_step + ) + current_states = encoder_outputs["agents"][:, : self._agents_len, -1] + assert ( + encoder_outputs["agents"].shape[1] >= self._agents_len + ), "Too many agents to consider" + + # Roll out + denoised_actions = self.unnormalize_actions( + denoised_actions_normalized + ) + denoised_trajs = roll_out( + current_states, + denoised_actions, + action_len=self.denoiser._action_len, + global_frame=global_frame, + ) + + return { + "denoised_actions_normalized": denoised_actions_normalized, + "denoised_actions": denoised_actions, + "denoised_trajs": denoised_trajs, + } + + def forward_predictor(self, encoder_outputs): + """ + Forward pass of the predictor module. + + Args: + encoder_outputs: Outputs from the encoder module. + + Returns: + predictor_outputs: Dictionary containing the predictor outputs. + """ + # Predict goal + goal_actions_normalized, goal_scores = self.predictor(encoder_outputs) + + current_states = encoder_outputs["agents"][:, : self._agents_len, -1] + assert ( + encoder_outputs["agents"].shape[1] >= self._agents_len + ), "Too many agents to consider" + + # Roll out + goal_actions = self.unnormalize_actions(goal_actions_normalized) + goal_trajs = roll_out( + current_states[:, :, None, :], + goal_actions, + action_len=self.predictor._action_len, + global_frame=True, + ) + + return { + "goal_actions_normalized": goal_actions_normalized, + "goal_actions": goal_actions, + "goal_scores": goal_scores, + "goal_trajs": goal_trajs, + } + + def forward_and_get_loss(self, batch, prefix="", debug=False): + """ + Forward pass of the model and compute the loss. + + Args: + batch: Input batch. + prefix: Prefix for the loss keys. + debug: Flag to enable debug mode. + + Returns: + total_loss: Total loss. + log_dict: Dictionary containing the loss values. + debug_outputs: Dictionary containing debug outputs. + """ + # data inputs + agents_future = batch["agents_future"][:, : self._agents_len] + + # TODO: Investigate why this to NAN + # agents_future_valid = batch['agents_future_valid'][:, :self._agents_len] + agents_future_valid = torch.ne(agents_future.sum(-1), 0) + agents_interested = batch["agents_interested"][:, : self._agents_len] + anchors = batch["anchors"][:, : self._agents_len] + + # get actions from trajectory + gt_actions, gt_actions_valid = inverse_kinematics( + agents_future, + agents_future_valid, + dt=0.1, + action_len=self._action_len, + ) + + gt_actions_normalized = self.normalize_actions(gt_actions) + B, A, T, D = gt_actions_normalized.shape + + log_dict = {} + debug_outputs = {} + total_loss = 0 + + ############## Run Encoder ############## + encoder_outputs = self.encoder(batch) + + ############### Denoise ################# + if self._train_denoiser: + # sample noise + noise = torch.randn(B * A, T, D).type_as(agents_future) + + diffusion_steps = ( + torch.randint( + 0, + self.noise_scheduler.num_steps, + (B,), + device=agents_future.device, + ) + .long() + .unsqueeze(-1) + .repeat(1, A) + ) + + # noise the input + noised_action_normalized = self.noise_scheduler.add_noise( + gt_actions_normalized.reshape(B * A, T, D), + noise, + diffusion_steps.reshape(B * A), + ).reshape(B, A, T, D) + + denoise_outputs = self.forward_denoiser( + encoder_outputs, noised_action_normalized, diffusion_steps + ) + debug_outputs.update(denoise_outputs) + + # Get Loss + denoised_trajs = denoise_outputs["denoised_trajs"] + + state_loss_mean, yaw_loss_mean = self.denoise_loss( + denoised_trajs, + agents_future, + agents_future_valid, + agents_interested, + ) + + denoise_ade, denoise_fde = self.calculate_metrics_denoise( + denoised_trajs, + agents_future, + agents_future_valid, + agents_interested, + 8, + ) + + denoise_loss = state_loss_mean + yaw_loss_mean + total_loss += denoise_loss + + log_dict.update( + { + prefix + "state_loss": state_loss_mean.item(), + prefix + "yaw_loss": yaw_loss_mean.item(), + prefix + "denoise_ADE": denoise_ade, + prefix + "denoise_FDE": denoise_fde, + } + ) + + ############### Behavior Prior Prediction ################# + if self._train_predictor: + goal_outputs = self.forward_predictor(encoder_outputs) + debug_outputs.update(goal_outputs) + + # get loss + goal_scores = goal_outputs["goal_scores"] + goal_trajs = goal_outputs["goal_trajs"] + + goal_loss_mean, score_loss_mean = self.goal_loss( + goal_trajs, + goal_scores, + agents_future, + agents_future_valid, + anchors, + agents_interested, + ) + + pred_loss = goal_loss_mean + 0.05 * score_loss_mean + total_loss += 50 * pred_loss #!ZZX: 0.5 is the original weight + + pred_ade, pred_fde = self.calculate_metrics_predict( + goal_trajs, + agents_future, + agents_future_valid, + agents_interested, + 8, + ) + + log_dict.update( + { + prefix + "goal_loss": goal_loss_mean.item(), + prefix + "score_loss": score_loss_mean.item(), + prefix + "pred_ADE": pred_ade, + prefix + "pred_FDE": pred_fde, + } + ) + + log_dict[prefix + "loss"] = total_loss.item() + + if debug: + return total_loss, log_dict, debug_outputs + else: + return total_loss, log_dict + + def training_step(self, batch, batch_idx): + """ + Training step of the model. + + Args: + batch: Input batch. + batch_idx: Batch index. + + Returns: + loss: Loss value. + """ + # print("******************* training_step") + + # Add random mask to the history for dropout + history_original = batch["agents_history"] + B, A, T, C = history_original.shape + history_mask = torch.rand(B, A) < 0.5 + history_mask = history_mask[:, :, None, None].repeat(1, 1, T, C) + history_mask[:, :, -1, :] = 1 + history_mask = history_mask.type_as(history_original) + batch["agents_history"] = history_original * history_mask + + loss, log_dict = self.forward_and_get_loss(batch, prefix="train/") + self.log_dict( + log_dict, + on_step=True, + on_epoch=False, + sync_dist=True, + prog_bar=True, + ) + + return loss + + def validation_step(self, batch, batch_idx): + """ + Validation step of the model. + + Args: + batch: Input batch. + batch_idx: Batch index. + """ + loss, log_dict = self.forward_and_get_loss(batch, prefix="val/") + self.log_dict( + log_dict, + on_step=False, + on_epoch=True, + sync_dist=True, + prog_bar=True, + ) + + return loss + + ################### Loss function ################### + def denoise_loss( + self, + denoised_trajs, + agents_future, + agents_future_valid, + agents_interested, + ): + """ + Calculates the denoise loss for the denoised actions and trajectories. + + Args: + denoised_actions_normalized (torch.Tensor): Normalized denoised actions tensor of shape [B, A, T, C]. + denoised_trajs (torch.Tensor): Denoised trajectories tensor of shape [B, A, T, C]. + agents_future (torch.Tensor): Future agent positions tensor of shape [B, A, T, 3]. + agents_future_valid (torch.Tensor): Future agent validity tensor of shape [B, A, T]. + gt_actions_normalized (torch.Tensor): Normalized ground truth actions tensor of shape [B, A, T, C]. + gt_actions_valid (torch.Tensor): Ground truth actions validity tensor of shape [B, A, T]. + agents_interested (torch.Tensor): Interested agents tensor of shape [B, A]. + + Returns: + state_loss_mean (torch.Tensor): Mean state loss. + yaw_loss_mean (torch.Tensor): Mean yaw loss. + action_loss_mean (torch.Tensor): Mean action loss. + """ + + agents_future = agents_future[..., 1:, :3] + future_mask = agents_future_valid[..., 1:] * ( + agents_interested[..., None] > 0 + ) + + # Calculate State Loss + # [B, A, T] + state_loss = smooth_l1_loss( + denoised_trajs[..., :2], agents_future[..., :2], reduction="none" + ).sum(-1) + yaw_error = denoised_trajs[..., 2] - agents_future[..., 2] + yaw_error = torch.atan2(torch.sin(yaw_error), torch.cos(yaw_error)) + yaw_loss = torch.abs(yaw_error) + + # Filter out the invalid state + state_loss = state_loss * future_mask + yaw_loss = yaw_loss * future_mask + + # Calculate the mean loss + state_loss_mean = state_loss.sum() / future_mask.sum() + yaw_loss_mean = yaw_loss.sum() / future_mask.sum() + + return state_loss_mean, yaw_loss_mean + + def goal_loss( + self, + trajs, + scores, + agents_future, + agents_future_valid, + anchors, + agents_interested, + ): + """ + Calculates the loss for trajectory prediction. + + Args: + trajs (torch.Tensor): Tensor of shape [B*A, Q, T, 3] representing predicted trajectories. + scores (torch.Tensor): Tensor of shape [B*A, Q] representing predicted scores. + agents_future (torch.Tensor): Tensor of shape [B, A, T, 3] representing future agent states. + agents_future_valid (torch.Tensor): Tensor of shape [B, A, T] representing validity of future agent states. + anchors (torch.Tensor): Tensor of shape [B, A, Q, 2] representing anchor points. + agents_interested (torch.Tensor): Tensor of shape [B, A] representing interest in agents. + + Returns: + traj_loss_mean (torch.Tensor): Mean trajectory loss. + score_loss_mean (torch.Tensor): Mean score loss. + """ + # Convert Anchor to Global Frame + current_states = agents_future[:, :, 0, :3] + anchors_global = batch_transform_trajs_to_global_frame( + anchors, current_states + ) + num_batch, num_agents, num_query, _ = anchors_global.shape + + # Get Mask + traj_mask = agents_future_valid[..., 1:] * ( + agents_interested[..., None] > 0 + ) # [B, A, T] + + # Flatten batch and agents + goal_gt = agents_future[:, :, -1:, :2].flatten(0, 1) # [B*A, 1, 2] + trajs_gt = agents_future[:, :, 1:, :3].flatten(0, 1) # [B*A, T, 3] + trajs = trajs.flatten(0, 1)[..., :3] # [B*A, Q, T, 3] + anchors_global = anchors_global.flatten(0, 1) # [B*A, Q, 2] + + # Find the closest anchor + idx_anchor = torch.argmin( + torch.norm(anchors_global - goal_gt, dim=-1), dim=-1 + ) # [B*A,] + + # For agents that do not have valid end point, use the minADE + dist = torch.norm( + trajs[:, :, :, :2] - trajs_gt[:, None, :, :2], dim=-1 + ) # [B*A, Q, T] + dist = dist * traj_mask.flatten(0, 1)[:, None, :] # [B*A, Q, T] + idx = torch.argmin(dist.mean(-1), dim=-1) # [B*A,] + + # Select trajectory + idx = torch.where( + agents_future_valid[..., -1].flatten(0, 1), idx_anchor, idx + ) + trajs_select = trajs[ + torch.arange(num_batch * num_agents), idx + ] # [B*A, T, 3] + + # Calculate the trajectory loss + traj_loss = smooth_l1_loss( + trajs_select, trajs_gt, reduction="none" + ).sum( + -1 + ) # [B*A, T] + traj_loss = traj_loss * traj_mask.flatten(0, 1) # [B*A, T] + + # Calculate the score loss + scores = scores.flatten(0, 1) # [B*A, Q] + score_loss = cross_entropy(scores, idx, reduction="none") # [B*A] + score_loss = score_loss * ( + agents_interested.flatten(0, 1) > 0 + ) # [B*A] + + # Calculate the mean loss + traj_loss_mean = traj_loss.sum() / traj_mask.sum() + score_loss_mean = score_loss.sum() / (agents_interested > 0).sum() + + return traj_loss_mean, score_loss_mean + + @torch.no_grad() + def calculate_metrics_denoise( + self, + denoised_trajs, + agents_future, + agents_future_valid, + agents_interested, + top_k=None, + ): + """ + Calculates the denoising metrics for the predicted trajectories. + + Args: + denoised_trajs (torch.Tensor): Denoised trajectories of shape [B, A, T, 2]. + agents_future (torch.Tensor): Ground truth future trajectories of agents of shape [B, A, T, 2]. + agents_future_valid (torch.Tensor): Validity mask for future trajectories of agents of shape [B, A, T]. + agents_interested (torch.Tensor): Interest mask for agents of shape [B, A]. + top_k (int, optional): Number of top agents to consider. Defaults to None. + + Returns: + Tuple[float, float]: A tuple containing the denoising ADE (Average Displacement Error) and FDE (Final Displacement Error). + """ + + if not top_k: + top_k = self._agents_len + + pred_traj = denoised_trajs[:, :top_k, :, :2] # [B, A, T, 2] + gt = agents_future[:, :top_k, 1:, :2] # [B, A, T, 2] + gt_mask = ( + agents_future_valid[:, :top_k, 1:] + & (agents_interested[:, :top_k, None] > 0) + ).bool() # [B, A, T] + + denoise_mse = torch.norm(pred_traj - gt, dim=-1) + denoise_ADE = denoise_mse[gt_mask].mean() + denoise_FDE = denoise_mse[..., -1][gt_mask[..., -1]].mean() + + return denoise_ADE.item(), denoise_FDE.item() + + @torch.no_grad() + def calculate_metrics_predict( + self, + goal_trajs, + agents_future, + agents_future_valid, + agents_interested, + top_k=None, + ): + """ + Calculates the metrics for predicting goal trajectories. + + Args: + goal_trajs (torch.Tensor): Tensor of shape [B, A, Q, T, 2] representing the goal trajectories. + agents_future (torch.Tensor): Tensor of shape [B, A, T, 2] representing the future trajectories of agents. + agents_future_valid (torch.Tensor): Tensor of shape [B, A, T] representing the validity of future trajectories. + agents_interested (torch.Tensor): Tensor of shape [B, A] representing the interest level of agents. + top_k (int, optional): The number of top agents to consider. Defaults to None. + + Returns: + tuple: A tuple containing the goal Average Displacement Error (ADE) and goal Final Displacement Error (FDE). + """ + + if not top_k: + top_k = self._agents_len + goal_trajs = goal_trajs[:, :top_k, :, :, :2] # [B, A, Q, T, 2] + gt = agents_future[:, :top_k, 1:, :2] # [B, A, T, 2] + gt_mask = ( + agents_future_valid[:, :top_k, 1:] + & (agents_interested[:, :top_k, None] > 0) + ).bool() # [B, A, T] + + goal_mse = torch.norm( + goal_trajs - gt[:, :, None, :, :], dim=-1 + ) # [B, A, Q, T] + goal_mse = goal_mse * gt_mask[..., None, :] # [B, A, Q, T] + best_idx = torch.argmin(goal_mse.sum(-1), dim=-1) + + best_goal_mse = goal_mse[ + torch.arange(goal_mse.shape[0])[:, None], + torch.arange(goal_mse.shape[1])[None, :], + best_idx, + ] + + goal_ADE = best_goal_mse.sum() / gt_mask.sum() + goal_FDE = best_goal_mse[..., -1].sum() / gt_mask[..., -1].sum() + + return goal_ADE.item(), goal_FDE.item() + + ################### Helper Functions ############## + def batch_to_device(self, input_dict: dict, device: torch.device): + """ + Move the tensors in the input dictionary to the specified device. + + Args: + input_dict (dict): A dictionary containing tensors to be moved. + device (torch.device): The target device to move the tensors to. + + Returns: + dict: The input dictionary with tensors moved to the specified device. + """ + for key, value in input_dict.items(): + if isinstance(value, torch.Tensor): + input_dict[key] = value.to(device) + + return input_dict + + def normalize_actions(self, actions: torch.Tensor): + """ + Normalize the given actions using the mean and standard deviation. + + Args: + actions : The actions to be normalized. + + Returns: + The normalized actions. + """ + return (actions - self.action_mean) / self.action_std + + def unnormalize_actions(self, actions: torch.Tensor): + """ + Unnormalize the given actions using the stored action standard deviation and mean. + + Args: + actions: The normalized actions to be unnormalized. + + Returns: + The unnormalized actions. + """ + return actions * self.action_std + self.action_mean diff --git a/gpudrive-main/gpudrive/integrations/vbd/model/__init__.py b/gpudrive-main/gpudrive/integrations/vbd/model/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/gpudrive-main/gpudrive/integrations/vbd/model/model_utils.py b/gpudrive-main/gpudrive/integrations/vbd/model/model_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..a8fd3af89772ab4a0aa024b25f845e30c9d9818a --- /dev/null +++ b/gpudrive-main/gpudrive/integrations/vbd/model/model_utils.py @@ -0,0 +1,248 @@ +import torch +import numpy as np + + +def batch_transform_trajs_to_local_frame(trajs, ref_idx=-1): + """ + Batch transform trajectories to the local frame of reference. + + Args: + trajs (torch.Tensor): Trajectories tensor of shape [B, N, T, x]. + ref_idx (int): Reference index for the local frame. Default is -1. + + Returns: + torch.Tensor: Transformed trajectories in the local frame. + + """ + x = trajs[..., 0] + y = trajs[..., 1] + theta = trajs[..., 2] + v_x = trajs[..., 3] + v_y = trajs[..., 4] + + local_x = (x - x[:, :, ref_idx, None]) * torch.cos( + theta[:, :, ref_idx, None] + ) + (y - y[:, :, ref_idx, None]) * torch.sin(theta[:, :, ref_idx, None]) + local_y = -(x - x[:, :, ref_idx, None]) * torch.sin( + theta[:, :, ref_idx, None] + ) + (y - y[:, :, ref_idx, None]) * torch.cos(theta[:, :, ref_idx, None]) + + local_theta = theta - theta[:, :, ref_idx, None] + local_theta = wrap_angle(local_theta) + + local_v_x = v_x * torch.cos(theta[:, :, ref_idx, None]) + v_y * torch.sin( + theta[:, :, ref_idx, None] + ) + local_v_y = -v_x * torch.sin(theta[:, :, ref_idx, None]) + v_y * torch.cos( + theta[:, :, ref_idx, None] + ) + + local_trajs = torch.stack( + [local_x, local_y, local_theta, local_v_x, local_v_y], dim=-1 + ) + local_trajs[trajs[..., :5] == 0] = 0 + + if trajs.shape[-1] > 5: + trajs = torch.cat([local_trajs, trajs[..., 5:]], dim=-1) + else: + trajs = local_trajs + + return trajs + + +def batch_transform_polylines_to_local_frame(polylines): + """ + Batch transform polylines to the local frame of reference. + + Args: + polylines (torch.Tensor): Polylines tensor of shape [B, M, W, 5]. + + Returns: + torch.Tensor: Transformed polylines in the local frame. + + """ + x = polylines[..., 0] + y = polylines[..., 1] + theta = polylines[..., 2] + + local_x = (x - x[:, :, 0, None]) * torch.cos(theta[:, :, 0, None]) + ( + y - y[:, :, 0, None] + ) * torch.sin(theta[:, :, 0, None]) + local_y = -(x - x[:, :, 0, None]) * torch.sin(theta[:, :, 0, None]) + ( + y - y[:, :, 0, None] + ) * torch.cos(theta[:, :, 0, None]) + + local_theta = theta - theta[:, :, 0, None] + local_theta = wrap_angle(local_theta) + + local_polylines = torch.stack([local_x, local_y, local_theta], dim=-1) + local_polylines[polylines[..., :3] == 0] = 0 + polylines = torch.cat([local_polylines, polylines[..., 3:]], dim=-1) + + return polylines + + +def batch_transform_trajs_to_global_frame(trajs, current_states): + """ + Batch transform trajectories to the global frame of reference. + + Args: + trajs (torch.Tensor): Trajectories tensor of shape [B, N, x, 2 or 3]. + current_states (torch.Tensor): Current states tensor of shape [B, N, 5]. + + Returns: + torch.Tensor: Transformed trajectories in the global frame. [B, N, x, 3] + + """ + x, y, theta = ( + current_states[:, :, 0], + current_states[:, :, 1], + current_states[:, :, 2], + ) + g_x = trajs[..., 0] * torch.cos(theta[:, :, None]) - trajs[ + ..., 1 + ] * torch.sin(theta[:, :, None]) + g_y = trajs[..., 0] * torch.sin(theta[:, :, None]) + trajs[ + ..., 1 + ] * torch.cos(theta[:, :, None]) + x = g_x + x[:, :, None] + y = g_y + y[:, :, None] + + if trajs.shape[-1] == 2: + trajs = torch.stack([x, y], dim=-1) + else: + theta = trajs[..., 2] + theta[:, :, None] + theta = wrap_angle(theta) + trajs = torch.stack([x, y, theta], dim=-1) + + return trajs + + +def wrap_angle(angle): + """ + Wrap the angle to [-pi, pi]. + + Args: + angle (torch.Tensor): Angle tensor. + + Returns: + torch.Tensor: Wrapped angle. + + """ + # return torch.atan2(torch.sin(angle), torch.cos(angle)) + return (angle + torch.pi) % (2 * torch.pi) - torch.pi + + +def inverse_kinematics( + agents_future: torch.Tensor, + agents_future_valid: torch.Tensor, + dt: float = 0.1, + action_len: int = 5, +): + """ + Perform inverse kinematics to compute actions. + + Args: + agents_future (torch.Tensor): Future agent positions tensor. + [B, A, T, 8] # x, y, yaw, velx, vely, length, width, height + agents_future_valid (torch.Tensor): Future agent validity tensor. [B, A, T] + dt (float): Time interval. Default is 0.1. + action_len (int): Length of each action. Default is 5. + + Returns: + torch.Tensor: Predicted actions. + + """ + # Inverse kinematics implementation goes here + batch_size, num_agents, num_timesteps, _ = agents_future.shape + assert ( + num_timesteps - 1 + ) % action_len == 0, "future_len must be divisible by action_len" + num_actions = (num_timesteps - 1) // action_len + + yaw = agents_future[..., 2] + speed = torch.norm(agents_future[..., 3:5], dim=-1) + + yaw_rate = wrap_angle(torch.diff(yaw, dim=-1)) / dt + accel = torch.diff(speed, dim=-1) / dt + action_valid = agents_future_valid[..., :1] & agents_future_valid[..., 1:] + + # filter out invalid actions + yaw_rate = torch.where(action_valid, yaw_rate, 0.0) + accel = torch.where(action_valid, accel, 0.0) + + # Reshape for mean pooling + yaw_rate = yaw_rate.reshape(batch_size, num_agents, num_actions, -1) + accel = accel.reshape(batch_size, num_agents, num_actions, -1) + action_valid = action_valid.reshape( + batch_size, num_agents, num_actions, -1 + ) + + yaw_rate_sample = yaw_rate.sum(dim=-1) / torch.clamp( + action_valid.sum(dim=-1), min=1.0 + ) + accel_sample = accel.sum(dim=-1) / torch.clamp( + action_valid.sum(dim=-1), min=1.0 + ) + action = torch.stack([accel_sample, yaw_rate_sample], dim=-1) + action_valid = action_valid.any(dim=-1) + + # Filter again + action = torch.where(action_valid[..., None], action, 0.0) + + return action, action_valid + + +def roll_out( + current_states: torch.Tensor, + actions: torch.Tensor, + dt: float = 0.1, + action_len: int = 5, + global_frame: float = True, +): + """ + Forward pass of the dynamics model. + + Args: + current_states (torch.Tensor): Current states tensor of shape [B, N, x, 5]. [x, y, theta, v_x, v_y] + actions (torch.Tensor): Inputs tensor of shape [B, N, x, T_f//T_a, 2]. [Accel, yaw_rate] + global_frame (bool): Flag indicating whether to use the global frame of reference. Default is False. + + Returns: + torch.Tensor: Predicted trajectories. + + """ + x = current_states[..., 0] + y = current_states[..., 1] + theta = current_states[..., 2] + v_x = current_states[..., 3] + v_y = current_states[..., 4] + v = torch.sqrt(v_x**2 + v_y**2) + + a = actions[..., 0].repeat_interleave(action_len, dim=-1) + v = v.unsqueeze(-1) + torch.cumsum(a * dt, dim=-1) + v += torch.randn_like(v) * 0.1 + v = torch.clamp(v, min=0) + + yaw_rate = actions[..., 1].repeat_interleave(action_len, dim=-1) + yaw_rate += torch.randn_like(yaw_rate) * 0.01 + + if global_frame: + theta = theta.unsqueeze(-1) + torch.cumsum(yaw_rate * dt, dim=-1) + else: + theta = torch.cumsum(yaw_rate * dt, dim=2) + + # theta = torch.fmod(theta + torch.pi, 2*torch.pi) - torch.pi + # theta = wrap_angle(theta) + + v_x = v * torch.cos(theta) + v_y = v * torch.sin(theta) + + if global_frame: + x = x.unsqueeze(-1) + torch.cumsum(v_x * dt, dim=-1) + y = y.unsqueeze(-1) + torch.cumsum(v_y * dt, dim=-1) + else: + x = torch.cumsum(v_x * dt, dim=-1) + y = torch.cumsum(v_y * dt, dim=-1) + + return torch.stack([x, y, theta, v_x, v_y], dim=-1) diff --git a/gpudrive-main/gpudrive/integrations/vbd/model/modules.py b/gpudrive-main/gpudrive/integrations/vbd/model/modules.py new file mode 100644 index 0000000000000000000000000000000000000000..fe0fa00203e8bb5421ae535003474829b75f19cb --- /dev/null +++ b/gpudrive-main/gpudrive/integrations/vbd/model/modules.py @@ -0,0 +1,617 @@ +import math +import torch +import numpy as np +import torch.nn as nn +import torch.nn.functional as F +from torch.nn.init import constant_, xavier_uniform_ +from .model_utils import ( + batch_transform_trajs_to_local_frame, + batch_transform_polylines_to_local_frame, + batch_transform_trajs_to_global_frame, + roll_out, +) + + +class Encoder(nn.Module): + def __init__(self, layers=6): + super().__init__() + self.agent_encoder = AgentEncoder() + self.map_encoder = MapEncoder() + self.traffic_light_encoder = TrafficLightEncoder() + self.relation_encoder = FourierEmbedding(input_dim=3) + self.transformer_encoder = TransformerEncoder(layers=layers) + + def forward(self, inputs): + # agent encoding + agents = inputs["agents_history"] + agents_type = inputs["agents_type"] + agents_interested = inputs["agents_interested"] + agents_local = batch_transform_trajs_to_local_frame(agents) + + encoded_agents = torch.stack( + [ + self.agent_encoder(agents_local[:, i], agents_type[:, i]) + for i in range(agents.shape[1]) + ], + dim=1, + ) + agents_mask = torch.eq(agents_interested, 0) + + # map and traffic light encoding + map_polylines = inputs["polylines"] + map_polylines_local = batch_transform_polylines_to_local_frame( + map_polylines + ) + encoded_map_lanes = self.map_encoder(map_polylines_local) + maps_mask = inputs["polylines_valid"].logical_not() + + traffic_lights = inputs["traffic_light_points"] + encoded_traffic_lights = self.traffic_light_encoder(traffic_lights) + traffic_lights_mask = torch.eq(traffic_lights.sum(-1), 0) + + # relation encoding + relations = inputs["relations"] + relations = self.relation_encoder(relations) + + # transformer encoding + encoder_outputs = {} + encoder_outputs["agents"] = agents + encoder_outputs["anchors"] = inputs["anchors"] + encoder_outputs["agents_type"] = agents_type + encoder_outputs["agents_mask"] = agents_mask + encoder_outputs["maps_mask"] = maps_mask + encoder_outputs["traffic_lights_mask"] = traffic_lights_mask + encoder_outputs["relation_encodings"] = relations + + encodings = self.transformer_encoder( + relations, + encoded_agents, + encoded_map_lanes, + encoded_traffic_lights, + agents_mask, + maps_mask, + traffic_lights_mask, + ) + encoder_outputs["encodings"] = encodings + + return encoder_outputs + + +class GoalPredictor(nn.Module): + def __init__(self, future_len=80, action_len=5, agents_len=32): + super().__init__() + self._agents_len = agents_len + self._future_len = future_len + self._action_len = action_len + + self.attention_layers = nn.ModuleList( + [CrossTransformer() for _ in range(4)] + ) + self.anchor_encoder = nn.Sequential( + nn.Linear(2, 128), nn.ReLU(), nn.Linear(128, 256) + ) + self.act_decoder = nn.Sequential( + nn.Linear(256, 256), + nn.ELU(), + nn.Dropout(0.1), + nn.Linear(256, (self._future_len // self._action_len) * 2), + ) + self.score_decoder = nn.Sequential( + nn.Linear(256, 128), nn.ELU(), nn.Dropout(0.1), nn.Linear(128, 1) + ) + + def forward(self, inputs): + anchors_points = inputs["anchors"][:, : self._agents_len] + anchors = self.anchor_encoder(anchors_points) + encodings = inputs["encodings"] + query = encodings[:, : self._agents_len, None] + anchors + + num_batch, num_agents, num_queries, _ = query.shape + + mask = torch.cat( + [ + inputs["agents_mask"], + inputs["maps_mask"], + inputs["traffic_lights_mask"], + ], + dim=-1, + ) + relations = inputs["relation_encodings"] + + actions = [] + scores = [] + for i in range(self._agents_len): + query_content = self.attention_layers[0]( + query[:, i], encodings, relations[:, i], key_mask=mask + ) + query_content = self.attention_layers[1]( + query_content, encodings, relations[:, i], key_mask=mask + ) + query_content = query_content + query[:, i] + query_content = self.attention_layers[2]( + query_content, encodings, relations[:, i], key_mask=mask + ) + query_content = self.attention_layers[3]( + query_content, encodings, relations[:, i], key_mask=mask + ) + actions.append( + self.act_decoder(query_content).reshape( + num_batch, + num_queries, + self._future_len // self._action_len, + 2, + ) + ) + scores.append(self.score_decoder(query_content).squeeze(-1)) + + actions = torch.stack(actions, dim=1) + scores = torch.stack(scores, dim=1) + + return actions, scores + + def reset_agent_length(self, agents_len): + self._agents_len = agents_len + + +class Denoiser(nn.Module): + def __init__(self, future_len=80, action_len=5, agents_len=32, steps=100): + super().__init__() + self._agents_len = agents_len + self._action_len = action_len + self.noise_level_embedding = nn.Embedding(steps, 256) + self.decoder = TransformerDecoder( + future_len, agents_len, self._action_len + ) + + def forward(self, encoder_inputs, noisy_actions, diffusion_step): + """ + Args: + noisy_actions: [B, A, T_r, 2], [acc, yaw_rate] Unnormalized actions + diffusion_step: [B, A] + Output: + denoised_states: [B, A, T, 3], [x, y, theta] + """ + noisy_actions = noisy_actions[:, : self._agents_len] + + if type(diffusion_step) == int: + diffusion_step = torch.full( + noisy_actions.shape[:-2], + diffusion_step, + dtype=torch.long, + device=noisy_actions.device, + ) + else: + diffusion_step = diffusion_step[:, : self._agents_len] + + current_states = encoder_inputs["agents"][:, : self._agents_len, -1] + + encodings = encoder_inputs["encodings"] + relations = encoder_inputs["relation_encodings"] + + agents_mask = encoder_inputs["agents_mask"] + maps_mask = encoder_inputs["maps_mask"] + traffic_lights_mask = encoder_inputs["traffic_lights_mask"] + mask = torch.cat([agents_mask, maps_mask, traffic_lights_mask], dim=-1) + + # denoise step + noise_level = self.noise_level_embedding(diffusion_step) + noisy_states_local = roll_out( + current_states, + noisy_actions, + action_len=self._action_len, + global_frame=False, + ) + + denoised_actions_normalized = self.decoder( + noisy_states_local, noise_level, encodings, relations, mask + ) + + return denoised_actions_normalized + + def reset_agent_length(self, agents_len): + self._agents_len = agents_len + self.decoder.reset_agent_length(agents_len) + + +class AgentEncoder(nn.Module): + def __init__(self): + super().__init__() + self.motion = nn.GRU(8, 256, 2, batch_first=True) + self.type_embed = nn.Embedding(4, 256, padding_idx=0) + + def forward(self, history, type): + traj, _ = self.motion(history) + output = traj[:, -1] # current frame + type_embed = self.type_embed(type) + output = output + type_embed + + return output + + +class MapEncoder(nn.Module): + def __init__(self): + super().__init__() + self.point = nn.Sequential( + nn.Linear(3, 128), nn.ReLU(), nn.Linear(128, 256) + ) + self.traffic_light_embed = nn.Embedding(8, 256) + self.type_embed = nn.Embedding(21, 256, padding_idx=0) + + def forward(self, inputs): + # inputs [B, M, W, 5] + output = self.point(inputs[..., :3]) + output = torch.max(output, dim=-2).values # max pooling on W + + traffic_light_type = inputs[:, :, 0, 3].long().clamp(0, 7) + traffic_light_embed = self.traffic_light_embed(traffic_light_type) + polyline_type = inputs[:, :, 0, 4].long().clamp(0, 20) + type_embed = self.type_embed(polyline_type) + output = output + traffic_light_embed + type_embed + + return output + + +class TrafficLightEncoder(nn.Module): + def __init__(self): + super().__init__() + self.type_embed = nn.Embedding(8, 256) + + def forward(self, inputs): + # inputs [B, TL, 3] + traffic_light_type = inputs[:, :, 2].long().clamp(0, 7) + type_embed = self.type_embed(traffic_light_type) + output = type_embed + + return output + + +class QCMHA(nn.Module): + """ + Quadratic Complexity Multi-Head Attention module. + + Args: + embed_dim (int): The dimension of the input embeddings. + num_heads (int): The number of attention heads. + dropout (float, optional): The dropout probability. Default is 0.1. + """ + + def __init__(self, embed_dim, num_heads, dropout=0.1): + super().__init__() + self.embed_dim = embed_dim + self.num_heads = num_heads + self.dropout = dropout + self.head_dim = embed_dim // num_heads + assert ( + self.head_dim * num_heads == self.embed_dim + ), "embed_dim must be divisible by num_heads" + + self.in_proj = nn.Linear(embed_dim, 3 * embed_dim, bias=True) + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=True) + + self.dropout = nn.Dropout(dropout) + + self._reset_parameters() + + def _reset_parameters(self): + xavier_uniform_(self.in_proj.weight) + xavier_uniform_(self.out_proj.weight) + constant_(self.in_proj.bias, 0.0) + constant_(self.out_proj.bias, 0.0) + + def forward(self, query, rel_pos, attn_mask=None): + """ + Forward pass of the QCMHA module. + + Args: + query (torch.Tensor): The input query tensor of shape [batch_size, query_length, embed_dim]. + rel_pos (torch.Tensor): The relative position tensor of shape [batch_size, query_length, key_length, embed_dim]. + attn_mask (torch.Tensor, optional): The attention mask tensor of shape [batch_size, query_length, key_length]. + + Returns: + torch.Tensor: The output tensor of shape [batch_size, query_length, embed_dim]. + """ + query = self.in_proj(query) + b, t, d = query.shape + query = query.reshape(b, t, self.num_heads, self.head_dim * 3) + + res = torch.split(query, self.head_dim, dim=-1) + q, k, v = res + + rel_pos_q = rel_pos_v = rel_pos + + q = q.permute(0, 2, 1, 3) + k = k.permute(0, 2, 3, 1) + v = v.permute(0, 2, 1, 3) + + dot_score = torch.matmul(q, k) + + if rel_pos is not None: + rel_pos_q = rel_pos_q.reshape( + b, t, t, self.num_heads, self.head_dim + ) + rel_pos_q = rel_pos_q.permute(0, 3, 1, 4, 2) # [b, h, q, d, k] + # [b, h, q, 1, d] * [b, h, q, d, k] -> [b, h, q, 1, k] + dot_score_rel = torch.matmul(q.unsqueeze(-2), rel_pos_q).squeeze( + -2 + ) + dot_score += dot_score_rel + + dot_score = dot_score / np.sqrt(self.head_dim) + + if attn_mask is not None: + dot_score = dot_score - attn_mask.float() * 1e9 + + dot_score = F.softmax(dot_score, dim=-1) + dot_score = self.dropout(dot_score) + + value = torch.matmul(dot_score, v) + + if rel_pos is not None: + rel_pos_v = rel_pos_v.reshape( + b, t, t, self.num_heads, self.head_dim + ) + rel_pos_v = rel_pos_v.permute(0, 3, 1, 2, 4) # [b, h, q, k, d] + # [b, h, q, 1, k] * [b, h, q, k, d] -> [b, h, q, d] + value_rel = torch.matmul( + dot_score.unsqueeze(-2), rel_pos_v + ).squeeze(-2) + value += value_rel + + value = value.permute(0, 2, 1, 3) # [b, t, h, d//h] + value = value.reshape(b, t, self.embed_dim) + value = self.out_proj(value) + + return value + + +class SelfTransformer(nn.Module): + def __init__(self): + super().__init__() + heads, dim, dropout = 8, 256, 0.1 + self.qc_attention = QCMHA(dim, heads, dropout) + self.norm_1 = nn.LayerNorm(dim) + self.norm_2 = nn.LayerNorm(dim) + self.ffn = nn.Sequential( + nn.Linear(dim, dim * 4), + nn.GELU(), + nn.Dropout(dropout), + nn.Linear(dim * 4, dim), + nn.Dropout(dropout), + ) + + def forward(self, inputs, relations, mask=None): + attention_output = self.qc_attention(inputs, relations, mask) + attention_output = self.norm_1(attention_output + inputs) + output = self.norm_2(self.ffn(attention_output) + attention_output) + + return output + + +class FourierEmbedding(nn.Module): + def __init__(self, input_dim, hidden_dim=256, num_freq_bands=64): + super().__init__() + self.input_dim = input_dim + self.hidden_dim = hidden_dim + + self.freqs = ( + nn.Embedding(input_dim, num_freq_bands) if input_dim != 0 else None + ) + + self.mlps = nn.ModuleList( + [ + nn.Sequential( + nn.Linear(num_freq_bands * 2 + 1, hidden_dim), + nn.LayerNorm(hidden_dim), + nn.ReLU(inplace=True), + nn.Linear(hidden_dim, hidden_dim), + ) + for _ in range(input_dim) + ] + ) + + self.to_out = nn.Sequential( + nn.LayerNorm(hidden_dim), + nn.ReLU(inplace=True), + nn.Linear(hidden_dim, hidden_dim), + ) + + def forward(self, continuous_inputs): + x = continuous_inputs.unsqueeze(-1) * self.freqs.weight * 2 * math.pi + x = torch.cat( + [x.cos(), x.sin(), continuous_inputs.unsqueeze(-1)], dim=-1 + ) + x = torch.stack( + [self.mlps[i](x[:, :, :, i]) for i in range(self.input_dim)] + ).sum(dim=0) + + return self.to_out(x) + + +class TransformerEncoder(nn.Module): + def __init__(self, layers=6): + super().__init__() + self.layers = nn.ModuleList([SelfTransformer() for _ in range(layers)]) + + def forward( + self, + encoded_relations, + encoded_trajs, + encoded_polylines, + encoded_traffic_lights, + trajs_mask, + polylines_mask, + traffic_lights_mask, + ): + # relations: [B, N+M+TL, N+M+TL, 256] + # encoded_trajs: [B, N, 256] + # encoded_polylines: [B, M, 256] + # encoded_traffic_lights: [B, TL, 256] + + encodings = torch.cat( + [encoded_trajs, encoded_polylines, encoded_traffic_lights], dim=1 + ) + encodings_mask = torch.cat( + [trajs_mask, polylines_mask, traffic_lights_mask], dim=-1 + ) + attention_mask = encodings_mask.unsqueeze(-1).repeat( + 1, 1, encodings_mask.shape[1] + ) + attention_mask = attention_mask.unsqueeze(1) + + for layer in self.layers: + encodings = layer(encodings, encoded_relations, attention_mask) + + return encodings + + +class CrossTransformer(nn.Module): + def __init__(self): + super().__init__() + heads, dim, dropout = 8, 256, 0.1 + self.cross_attention = nn.MultiheadAttention( + dim, heads, dropout, batch_first=True + ) + self.norm_1 = nn.LayerNorm(dim) + self.norm_2 = nn.LayerNorm(dim) + self.ffn = nn.Sequential( + nn.Linear(dim, dim * 4), + nn.GELU(), + nn.Dropout(dropout), + nn.Linear(dim * 4, dim), + nn.Dropout(dropout), + ) + + def forward(self, query, key, relations, attn_mask=None, key_mask=None): + # add relations to key and value + key = key + relations + value = key + + if key_mask is not None: + attention_output, _ = self.cross_attention( + query, key, value, key_padding_mask=key_mask + ) + elif attn_mask is not None: + attention_output, _ = self.cross_attention( + query, key, value, attn_mask=attn_mask + ) + else: + attention_output, _ = self.cross_attention(query, key, value) + + attention_output = self.norm_1(attention_output) + output = self.norm_2(self.ffn(attention_output) + attention_output) + + return output + + +class TransformerDecoder(nn.Module): + def __init__(self, future_len, agents_len, action_len): + super().__init__() + self._future_len = future_len + self._action_len = action_len + self._agents_len = agents_len + self._seq_len = future_len // action_len + + self.time_embedding = nn.Embedding(self._seq_len, 256) + self.attention_layers = nn.ModuleList( + [CrossTransformer() for _ in range(4)] + ) + self.encoder = nn.Sequential( + nn.Linear(5, 128), nn.ReLU(), nn.Linear(128, 256) + ) + self.decoder = nn.Sequential( + nn.Linear(256, 128), nn.ELU(), nn.Dropout(0.1), nn.Linear(128, 2) + ) + + self.register_buffer("casual_mask", self.generate_casual_mask()) + self.register_buffer("time", torch.arange(self._seq_len).unsqueeze(0)) + + def generate_casual_mask(self): + # Initialize a zero mask + mask = torch.zeros( + self._agents_len, self._seq_len, self._agents_len * self._seq_len + ) + + # An agent can attend to all of its own actions + for i in range(self._agents_len): + mask[i, :, i * self._seq_len : (i + 1) * self._seq_len] = 1.0 + + # An agent can attend to other agents from all previous timesteps but not future timesteps + for i in range(self._agents_len): + for j in range(self._agents_len): + if i != j: + for t in range(self._seq_len): + mask[ + i, t, j * self._seq_len : j * self._seq_len + t + 1 + ] = 1.0 + + # Convert to boolean mask + mask = mask.bool().logical_not() + + return mask + + def forward( + self, noisy_trajectories, noise_level, encodings, relations, mask + ): + """ + noisy_trajectories: [B, Na, T_f, 5] + """ + # get query + noisy_trajectories = torch.reshape( + noisy_trajectories, + (-1, self._agents_len, self._seq_len, self._action_len, 5), + ) + future_states = self.encoder(noisy_trajectories) + future_states = future_states.max(dim=3).values # [B, Na, T, 256] + time_embedding = self.time_embedding(self.time) # [1, T, 256] + query = future_states + time_embedding[:, None] # [B, Na, T, 256] + query = query + noise_level[:, :, None, :] + + # decode denoised actions + query_content_list = [] + for i in range(self._agents_len): + query_content = self.attention_layers[0]( + query[:, i], + query.reshape(-1, self._agents_len * self._seq_len, 256), + relations[:, i, : self._agents_len].repeat_interleave( + self._seq_len, dim=1 + ), + attn_mask=self.casual_mask[i], + ) # [B, T, 256] + query_content = self.attention_layers[1]( + query_content, encodings, relations[:, i], key_mask=mask + ) # [B, T, 256] + query_content_list.append(query_content) + + query_content_stack = torch.stack( + query_content_list, dim=1 + ) # [B, Na, T, 256] + query_content_stack = query_content_stack + query + + query_content_list = [] + for i in range(self._agents_len): + query_content = self.attention_layers[2]( + query_content_stack[:, i], + query_content_stack.reshape( + -1, self._agents_len * self._seq_len, 256 + ), + relations[:, i, : self._agents_len].repeat_interleave( + self._seq_len, dim=1 + ), + attn_mask=self.casual_mask[i], + ) # [B, T, 256] + query_content = self.attention_layers[3]( + query_content, encodings, relations[:, i], key_mask=mask + ) # [B, T, 256] + query_content_list.append(query_content) + + query_content_stack = torch.stack( + query_content_list, dim=1 + ) # [B, Na, T, 256] + actions = self.decoder(query_content_stack) + + return actions + + def reset_agent_length(self, agents_len): + self._agents_len = agents_len + new_mask = self.generate_casual_mask().type_as(self.casual_mask) + self.casual_mask = new_mask diff --git a/gpudrive-main/gpudrive/integrations/vbd/model/utils.py b/gpudrive-main/gpudrive/integrations/vbd/model/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..fb420759642ebd0f24066299be21706bb22a6c15 --- /dev/null +++ b/gpudrive-main/gpudrive/integrations/vbd/model/utils.py @@ -0,0 +1,280 @@ +import torch +import logging +import pickle +import glob +import random +import numpy as np +from .model_utils import * +from torch.utils.data import Dataset +from torch.nn import functional as F + + +def set_seed(CUR_SEED): + random.seed(CUR_SEED) + np.random.seed(CUR_SEED) + torch.manual_seed(CUR_SEED) + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + + +def get_beta_schedule(variant, timesteps): + if variant == "cosine": + return betas_for_alpha_bar(timesteps) + elif variant == "linear": + return linear_beta_schedule(timesteps) + else: + raise NotImplemented + + +def linear_beta_schedule(timesteps): + beta_start = 0.0001 + beta_end = 0.02 + + return torch.linspace(beta_start, beta_end, timesteps) + + +def betas_for_alpha_bar(num_diffusion_timesteps, max_beta=0.999): + """ + Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of + (1-beta) over time from t = [0,1]. + + Contains a function alpha_bar that takes an argument t and transforms it to the cumulative product of (1-beta) up + to that part of the diffusion process. + """ + + def alpha_bar(time_step): + # ! Hard code to shift the schedule + # return np.cos((time_step + 0.008) / 1.008 * np.pi / 2) ** 2 + return ( + np.cos((time_step + 0.008) / 1.008 * np.pi / 2) ** 2 + ) * 0.98 + 0.02 + + betas = [] + for i in range(num_diffusion_timesteps): + t1 = i / num_diffusion_timesteps + t2 = (i + 1) / num_diffusion_timesteps + betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta)) + + return torch.tensor(betas, dtype=torch.float32) + + +class DDPM_Sampler(torch.nn.Module): + def __init__(self, steps=100, schedule="cosine", clamp_val: float = 5.0): + super().__init__() + self.num_steps = steps + self.schedule = schedule + self.clamp_val = clamp_val + + self.register_buffer( + "betas", get_beta_schedule(self.schedule, self.num_steps) + ) + self.register_buffer("betas_sqrt", self.betas.sqrt()) + self.register_buffer("alphas", 1 - self.betas) + self.register_buffer("alphas_cumprod", torch.cumprod(self.alphas, 0)) + + @torch.no_grad() + def add_noise( + self, + original_samples: torch.FloatTensor, + noise: torch.FloatTensor, + timesteps: torch.IntTensor, + ): + + assert (timesteps < self.num_steps).all() + + # Make sure alphas_cumprod and timestep have same device and dtype as original_samples + alphas_cumprod = self.alphas_cumprod.to( + device=original_samples.device, dtype=original_samples.dtype + ) + timesteps = timesteps.to(original_samples.device) + + sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5 + sqrt_alpha_prod = sqrt_alpha_prod.flatten() + + while len(sqrt_alpha_prod.shape) < len(original_samples.shape): + sqrt_alpha_prod = sqrt_alpha_prod.unsqueeze(-1) + + sqrt_one_minus_alpha_prod = (1 - alphas_cumprod[timesteps]) ** 0.5 + sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten() + + while len(sqrt_one_minus_alpha_prod.shape) < len( + original_samples.shape + ): + sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1) + + noised_samples = ( + sqrt_alpha_prod * original_samples + + sqrt_one_minus_alpha_prod * noise + ) + + return noised_samples + + def set_timesteps(self, num_inference_steps=None, device=None): + + timesteps = ( + np.linspace(0, self.num_steps - 1, num_inference_steps) + .round()[::-1] + .copy() + .astype(np.int64) + ) + + self.timesteps = torch.from_numpy(timesteps).to(device) + + def step( + self, + model_output: torch.FloatTensor, + timestep: int, + sample: torch.FloatTensor, + prediction_type: str = "sample", + ): + """ + Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion + process from the learned model outputs (most often the predicted noise). + + Args: + model_output (`torch.FloatTensor`): + The direct output from learned diffusion model. + timestep (`float`): + The current discrete timestep in the diffusion chain. + sample (`torch.FloatTensor`): + A current instance of a sample created by the diffusion process. + """ + # Compute predicted previous sample µ_t-1 + pred_prev_sample_mean = self.q_mean( + model_output, timestep, sample, prediction_type=prediction_type + ) + # 6. Add noise + device = model_output.device + variance_noise = torch.randn( + model_output.shape, device=device, dtype=model_output.dtype + ) + + variance = (self.q_variance(timestep) ** 0.5) * variance_noise + + pred_prev_sample = pred_prev_sample_mean + variance + + return pred_prev_sample + + def q_mean( + self, + model_output: torch.FloatTensor, + timestep: int, + sample: torch.FloatTensor, + prediction_type: str = "sample", + ): + """ + Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion + process from the learned model outputs (most often the predicted noise). + + Args: + model_output (`torch.FloatTensor`): + The direct output from learned diffusion model. + timestep (`float`): + The current discrete timestep in the diffusion chain. + sample (`torch.FloatTensor`): + A current instance of a sample created by the diffusion process. + """ + if type(timestep) == int: + t = timestep + else: + t = timestep[0][0] + prev_t = t - 1 + + # 1. Compute alphas, betas + alpha_prod_t = self.alphas_cumprod[t] + alpha_prod_t_prev = ( + self.alphas_cumprod[prev_t] if prev_t >= 0 else torch.tensor(1.0) + ) + beta_prod_t = 1 - alpha_prod_t + beta_prod_t_prev = 1 - alpha_prod_t_prev + current_alpha_t = alpha_prod_t / alpha_prod_t_prev + current_beta_t = 1 - current_alpha_t + + # 2. Compute predicted original sample from predicted noise also called "predicted x_0" + if prediction_type == "sample": + pred_original_sample = model_output + elif prediction_type == "error": + pred_original_sample = ( + sample - beta_prod_t ** (0.5) * model_output + ) / alpha_prod_t ** (0.5) + elif prediction_type == "v": + pred_original_sample = (alpha_prod_t**0.5) * sample - ( + beta_prod_t**0.5 + ) * model_output + else: + raise NotImplementedError + + # 3. Clip or threshold "predicted x_0" + pred_original_sample = pred_original_sample.clamp( + -self.clamp_val, self.clamp_val + ) + # samxple = sample.clamp(-self.clamp_val, self.clamp_val) + + # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t + pred_original_sample_coeff = ( + alpha_prod_t_prev**0.5 * current_beta_t + ) / beta_prod_t + current_sample_coeff = ( + current_alpha_t**0.5 * beta_prod_t_prev / beta_prod_t + ) + + # 5. Compute predicted previous sample µ_t + pred_prev_sample_mean = ( + pred_original_sample_coeff * pred_original_sample + + current_sample_coeff * sample + ) + return pred_prev_sample_mean + + def q_x0( + self, + model_output: torch.FloatTensor, + timestep: int, + sample: torch.FloatTensor, + prediction_type: str = "sample", + ): + """ + Predict the denoised x0 from the previous timestep by reversing the SDE. This function propagates the diffusion + process from the learned model outputs (most often the predicted noise). + + Args: + model_output (`torch.FloatTensor`): + The direct output from learned diffusion model. + timestep (`float`): + The current discrete timestep in the diffusion chain. + sample (`torch.FloatTensor`): + A current instance of a sample created by the diffusion process. + """ + + # 2. Compute predicted original sample from predicted noise also called "predicted x_0" + if prediction_type == "sample": + pred_original_sample = model_output + elif prediction_type == "error": + alpha_prod_t = self.alphas_cumprod[timestep] + for _ in range(len(sample.shape) - len(alpha_prod_t.shape)): + alpha_prod_t = alpha_prod_t[..., None] + beta_prod_t = 1 - alpha_prod_t + + pred_original_sample = ( + sample - beta_prod_t ** (0.5) * model_output + ) / alpha_prod_t ** (0.5) + # elif prediction_type == "v": + # pred_original_sample = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output + else: + raise NotImplementedError + + return pred_original_sample + + def q_variance(self, t): + if t == 0: + return 0 + prev_t = t - 1 + alpha_prod_t = self.alphas_cumprod[t] + alpha_prod_t_prev = self.alphas_cumprod[prev_t] + beta_prod_t = 1 - alpha_prod_t + beta_prod_t_prev = 1 - alpha_prod_t_prev + current_alpha_t = alpha_prod_t / alpha_prod_t_prev + current_beta_t = 1 - current_alpha_t + + variance = beta_prod_t_prev / beta_prod_t * current_beta_t + variance = torch.clamp(variance, min=1e-20) + return variance diff --git a/gpudrive-main/gpudrive/integrations/vbd/notebooks/00_align_simulators_vbd.ipynb b/gpudrive-main/gpudrive/integrations/vbd/notebooks/00_align_simulators_vbd.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..94e0c00b2c281f34eb672ee3aa47bcdbd0537e6e --- /dev/null +++ b/gpudrive-main/gpudrive/integrations/vbd/notebooks/00_align_simulators_vbd.ipynb @@ -0,0 +1,960 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Objective: Same scenario debugging\n", + "\n", + "The purpose of this notebook is to align `gpudrive` with `waymax` so that we can reliably use a pre-trained VBD model.\n", + "\n", + "**Notebook sections**\n", + "\n", + "- Configurations\n", + "- Load VDB model\n", + "- **Waymax** video and distributions\n", + " - Initialize waymax env with debugging scene\n", + " \n", + " - Make video with log-playback trajectories\n", + " - Make video with VBD trajs\n", + " - Feature distributions\n", + "- **GPUDrive** video and distributions\n", + " - Initialize GPUDrive env with debugging scene\n", + " - Make video with log-playback trajectories\n", + " - Make video with VBD trajs\n", + " - Feature distributions\n", + "\n", + "----\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Dependencies" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-01-03 16:33:35.594142: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", + "E0000 00:00:1735902215.612686 127346 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "E0000 00:00:1735902215.618205 127346 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "2025-01-03 16:33:35.636618: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" + ] + } + ], + "source": [ + "%%capture\n", + "import waymax\n", + "import numpy as np\n", + "import math\n", + "import mediapy\n", + "from tqdm import tqdm\n", + "import dataclasses\n", + "import os\n", + "from pathlib import Path\n", + "import pickle\n", + "import pandas as pd\n", + "import tensorflow as tf\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import torch\n", + "from waymax import config as _config\n", + "from waymax import dataloader, datatypes, visualization, dynamics\n", + "from waymax.datatypes.simulator_state import SimulatorState\n", + "from waymax.config import EnvironmentConfig, ObjectType\n", + "\n", + "# Set working directory to the base directory 'gpudrive'\n", + "working_dir = Path.cwd()\n", + "while working_dir.name != 'gpudrive':\n", + " working_dir = working_dir.parent\n", + " if working_dir == Path.home():\n", + " raise FileNotFoundError(\"Base directory 'gpudrive' not found\")\n", + "os.chdir(working_dir)\n", + "\n", + "# VBD dependencies\n", + "from integrations.models.vbd.sim_agent.waymax_env import WaymaxEnvironment\n", + "from integrations.models.vbd.data.dataset import WaymaxTestDataset\n", + "from integrations.models.vbd.waymax_visualization.plotting import plot_state\n", + "from integrations.models.vbd.sim_agent.sim_actor import VBDTest, sample_to_action\n", + "from integrations.models.vbd.model.utils import set_seed\n", + "\n", + "# GPUDrive dependencies\n", + "from pygpudrive.env.config import EnvConfig, RenderConfig, SceneConfig, SelectionDiscipline\n", + "from pygpudrive.env.env_torch import GPUDriveTorchEnv\n", + "\n", + "# Plotting\n", + "sns.set(\"notebook\")\n", + "sns.set_style(\"ticks\", rc={\"figure.facecolor\": \"none\", \"axes.facecolor\": \"none\"})\n", + "#%config InlineBackend.figure_format = 'svg'\n", + "\n", + "# Ignore all warnings\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Configurations" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "DATA_DIR = 'data/processed' # Base data path\n", + "CKPT_DIR = 'data/checkpoints' # Base checkpoint path\n", + "\n", + "SCENARIO_ID = 'b065dcb23730589b' # Debugging scenario we use \n", + "CKPT_PATH = 'integrations/models/vbd/weights/epoch=18.ckpt'\n", + "\n", + "FPS = 20\n", + "INIT_STEPS = 11 # Warmup period\n", + "MAX_CONTROLLED_OBJECTS = 32" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load pre-trained VBD model" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Load model\n", + "model = VBDTest.load_from_checkpoint(CKPT_PATH, torch.device('cpu'))\n", + "_ = model.cpu()\n", + "_ = model.eval();\n", + "\n", + "# Model settings\n", + "replan_freq=80 # Roll out every X steps 80 means openloop\n", + "model.early_stop=0 # Stop Diffusion Early From 100 to X\n", + "model.skip = 1 # Skip Alpha \n", + "model.reward_func = None\n", + "\n", + "# Ensure reproducability\n", + "#set_seed(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### **Waymax** video and distributions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Initialize waymax env with debugging scene" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 55 scenarios\n" + ] + } + ], + "source": [ + "env_config = EnvironmentConfig(\n", + " controlled_object=ObjectType.VALID,\n", + " allow_new_objects_after_warmup=False,\n", + " init_steps=INIT_STEPS+1,\n", + " max_num_objects=MAX_CONTROLLED_OBJECTS,\n", + ")\n", + "\n", + "waymax_env = WaymaxEnvironment(\n", + " dynamics_model=dynamics.StateDynamics(),\n", + " config=env_config,\n", + " log_replay = True,\n", + ")\n", + "\n", + "scenario_path = os.path.join(DATA_DIR, SCENARIO_ID + '.pkl')\n", + "with open(f'{DATA_DIR}/debug/waymax/waymax_scenario_{SCENARIO_ID}.pkl', 'rb') as f:\n", + " scenario = pickle.load(f)\n", + " \n", + "# Create \"dataset\" (need for utils)\n", + "dataset = WaymaxTestDataset(\n", + " data_dir = 'data/processed/debug/waymax', \n", + " anchor_path = 'integrations/models/vbd/data/cluster_64_center_dict.pkl',\n", + " max_object=MAX_CONTROLLED_OBJECTS,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Make video with log-playback trajectories\n", + "\n", + "- As a sanity check, we first look at the replayed human trajectories " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "scenario.sim_trajectory = scenario.log_trajectory\n", + "\n", + "log_playback_imgs = []\n", + "for t in range(scenario.remaining_timesteps):\n", + " scenario.timestep = t\n", + " log_playback_imgs.append(plot_state(scenario))\n", + "\n", + "mediapy.show_video(log_playback_imgs, codec='gif', fps=FPS)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "mediapy.write_video(f\"logged_waymax_trajs_{SCENARIO_ID}.gif\", log_playback_imgs, codec='gif', fps=FPS)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Make video with VBD trajectories (unguided)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "controlling agents: [0 1 2 3 4 5 6]\n", + "Replan at 11\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Diffusion: 100%|██████████| 50/50 [00:20<00:00, 2.41it/s]\n" + ] + } + ], + "source": [ + "# Reset \n", + "init_state = waymax_env.reset(scenario)\n", + "\n", + "# Process the scenario\n", + "current_state = init_state\n", + "sample = dataset.process_scenario(\n", + " init_state,\n", + " current_index=init_state.timestep,\n", + " use_log=False\n", + ")\n", + "is_controlled = sample['agents_interested'] > 0\n", + "selected_agents = sample['agents_id'][is_controlled]\n", + "\n", + "print(f'controlling agents: {selected_agents}')\n", + "\n", + "state_logs = [current_state]\n", + "\n", + "for i in range(current_state.remaining_timesteps):\n", + " t = i % replan_freq\n", + " if t == 0:\n", + " print(\"Replan at \", current_state.timestep)\n", + " sample = dataset.process_scenario(\n", + " current_state, \n", + " current_index = current_state.timestep,\n", + " use_log=False,\n", + " # selected_agents=selected_agents, # override the agent selection by distance to the ego\n", + " )\n", + " batch = dataset.__collate_fn__([sample])\n", + " pred = model.sample_denoiser(batch)\n", + " traj_pred = pred['denoised_trajs'].cpu().numpy()[0]\n", + "\n", + " # Get action\n", + " action_sample = traj_pred[:, t, :]\n", + " action = sample_to_action(\n", + " action_sample, \n", + " is_controlled, \n", + " agents_id=selected_agents, \n", + " max_num_objects=MAX_CONTROLLED_OBJECTS\n", + " )\n", + " # Step the simulator\n", + " current_state = waymax_env.step_sim_agent(current_state, [action])\n", + " state_logs.append(current_state)\n", + "\n", + "vbd_waymax_imgs = [plot_state(state) for state in state_logs]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "> ### Observations from watching a couple of VBD-generated traj videos:\n", + "\n", + "- **Pro**: Generated trajectories are smooth and human-like\n", + "- **Pro**: Vehicles do not collide with road edges\n", + "- **Limitation**: Trajectories often \"drive through\" other vehicles\n", + "- **Limitation**: Vehicles sometimes drive through road edges*\n", + "\n", + "---" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mediapy.show_video(vbd_waymax_imgs, codec='gif', fps=FPS)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "mediapy.write_video(f\"vbd_waymax_trajs_{SCENARIO_ID}.gif\", vbd_waymax_imgs, codec='gif', fps=FPS)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Save waymax input + output features\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Steps remaining: 79\n" + ] + } + ], + "source": [ + "init_state = waymax_env.reset(scenario)\n", + "\n", + "print(f'Steps remaining: {init_state.remaining_timesteps}')\n", + "\n", + "waymax_vbd_sample = dataset.process_scenario(\n", + " init_state,\n", + " current_index=init_state.timestep,\n", + " use_log=False\n", + ")\n", + "\n", + "# Save predicted trajectories\n", + "waymax_vbd_sample['pred_denoised_trajs'] = pred['denoised_trajs']\n", + "\n", + "# Save dictionary for further inspection\n", + "with open(f'waymax_vbd_sample_{SCENARIO_ID}.pkl', 'wb') as f:\n", + " pickle.dump(waymax_vbd_sample, f)\n", + " \n", + "#plot_batch_distributions(waymax_vbd_sample, title='Waymax ', timestep=int(init_state.timestep), dist_type='box_plot')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### **GPUDrive** video and distributions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Initialize GPUDrive env with debugging scene" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from pygpudrive.visualize.utils import img_from_fig\n", + "\n", + "env_config = EnvConfig(\n", + " init_steps=INIT_STEPS, # Warmup period\n", + " remove_non_vehicles=False, # Control vehicles, pedestrians, and cyclists\n", + " return_vbd_data=True, # Use VBD\n", + " dynamics_model=\"state\", # Use state-based dynamics model\n", + " dist_to_goal_threshold=1e-5, # Trick to make sure the agents don't disappear when they reach the goal\n", + " collision_behavior=\"ignore\", # Ignore collisions\n", + " init_all_objects = True\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Ratio unique scenes / number of worls = 1 / 1 ---\n", + "\n" + ] + } + ], + "source": [ + "# Make env\n", + "gpudrive_env = GPUDriveTorchEnv(\n", + " config=env_config,\n", + " scene_config=SceneConfig(path=\"data/processed/temp\", num_scenes=1),\n", + " render_config=RenderConfig(draw_obj_idx=True, render_init=True, resolution=(650, 650)),\n", + " max_cont_agents=MAX_CONTROLLED_OBJECTS, # Maximum number of agents to control per scene\n", + " device=\"cpu\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Make video with log-playback trajectories\n", + "\n", + "- As a sanity check, we first look at the replayed human trajectories \n", + "- Note that only 3 agents are actually moving, the rest of the vehicles are static" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "init_state = gpudrive_env.reset()\n", + "\n", + "log_playback_traj = gpudrive_env.get_expert_actions()\n", + "\n", + "gpudrive_log_playback_frames = []\n", + "\n", + "for t in range(env_config.episode_len-(INIT_STEPS+1)):\n", + " \n", + " # Step\n", + " gpudrive_env.step_dynamics(log_playback_traj[0][:, :, t, :]) \n", + "\n", + " fig = gpudrive_env.vis.plot_simulator_state(\n", + " env_indices=[0],\n", + " time_steps=[t],\n", + " zoom_radius=100,\n", + " )[0]\n", + " \n", + " gpudrive_log_playback_frames.append(img_from_fig(fig));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "> **Note: Passes sanity check. ✅** This is exactly the same as the one in Waymax \n", + "\n", + "---" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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GPUDrive with log-playback trajectories
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mediapy.show_video(gpudrive_log_playback_frames, codec='gif', title='GPUDrive with log-playback trajectories', width=500)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "mediapy.write_video(f\"logged_gpudrive_trajs_{SCENARIO_ID}_rot90.gif\", gpudrive_log_playback_frames, codec='gif', fps=FPS)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "agents_history: torch.Size([1, 32, 12, 8])\n", + "agents_interested: torch.Size([1, 32])\n", + "agents_type: torch.Size([1, 32])\n", + "agents_future: torch.Size([1, 32, 80, 5])\n", + "traffic_light_points: torch.Size([1, 16, 3])\n", + "polylines: torch.Size([1, 256, 30, 5])\n", + "polylines_valid: torch.Size([1, 256])\n", + "relations: torch.Size([1, 304, 304, 3])\n", + "agents_id: torch.Size([1, 32])\n", + "anchors: torch.Size([1, 32, 64, 2])\n" + ] + } + ], + "source": [ + "init_state = gpudrive_env.reset()\n", + "gpudrive_sample_batch = gpudrive_env.sample_batch\n", + "for key in gpudrive_sample_batch:\n", + " print(f\"{key}: {gpudrive_sample_batch[key].shape}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "#TODO: Check shape of each tensor input and align\n", + "#TODO: Figure out how to add the mean to the predicted trajectories\n", + "#TODO: Add the last point to the roads?\n", + "#TODO: Align object ids in agent history?\n", + "#TODO: Understand relations feature" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Make video with VBD trajectories (unguided)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Controlling 26 agents: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 43, 48]\n", + "agents_interested: tensor([ 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 10, 1, 1, 10, 10, 1, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", + " dtype=torch.int32)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Diffusion: 100%|██████████| 50/50 [00:19<00:00, 2.57it/s]\n" + ] + } + ], + "source": [ + "# Reset env with warmup period \n", + "init_state = gpudrive_env.reset()\n", + "\n", + "selected_agents = torch.nonzero(gpudrive_env.cont_agent_mask[0, :]).flatten().tolist()\n", + "\n", + "print(f\"Controlling {len(selected_agents)} agents: {selected_agents}\")\n", + "\n", + "# Obtain all info for diffusion model (warmup)\n", + "gpudrive_sample_batch = gpudrive_env.sample_batch\n", + "is_controlled = gpudrive_sample_batch['agents_interested'][0]\n", + "print(f\"agents_interested: {is_controlled}\")\n", + "\n", + "\n", + "# Obtain predicted trajectories\n", + "pred = model.sample_denoiser(gpudrive_sample_batch)#, x_t=x_t)\n", + "vbd_traj_pred = pred['denoised_trajs'].cpu().numpy()[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Rolling out from t = 12 to t = 91\n" + ] + } + ], + "source": [ + "# Our sim expects a 10D action with the state dynamics model\n", + "# (x, y, z, yaw, vel x, vel y, vel z, ang_vel_x, ang_vel_y, ang_vel_z)\n", + "# (0, 1, 2, 3, 4, 5, 6, 7, 8, 9)\n", + "agent_indices = gpudrive_sample_batch['agents_id'][0]\n", + "pred_trajs = torch.zeros((gpudrive_env.max_agent_count, env_config.episode_len-INIT_STEPS, 10))\n", + "pred_trajs[agent_indices, :, :2] = torch.Tensor(vbd_traj_pred[:, :, 0:2]) # pos x, y\n", + "pred_trajs[agent_indices, :, :2] -= gpudrive_env.sim.world_means_tensor().to_torch()[0, :2] #re-mean the predicted trajectory positions\n", + "pred_trajs[agent_indices, :, 3] = torch.Tensor(vbd_traj_pred[:, :, 2]) # yaw \n", + "pred_trajs[agent_indices, :, 4:6] = torch.Tensor(vbd_traj_pred[:, :, 3:5]) # vel x, y\n", + "pred_trajs = pred_trajs.unsqueeze(0)\n", + "\n", + "print(f'Rolling out from t = {INIT_STEPS+1} to t = {env_config.episode_len}')\n", + "\n", + "frames = []\n", + "\n", + "# Step\n", + "for t in range(env_config.episode_len-(INIT_STEPS+1)):\n", + " \n", + " gpudrive_env.step_dynamics(pred_trajs[:, :, t, :])\n", + " \n", + " fig = gpudrive_env.vis.plot_simulator_state(\n", + " env_indices=[0],\n", + " time_steps=[t],\n", + " zoom_radius=100,\n", + " )[0]\n", + " \n", + " frames.append(img_from_fig(fig));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "> ### Observations:\n", + "- Good: Trajectories look smooth/human-like similar to those in Waymax\n", + "- Problem: Vehicles drive through road edges (why?)\n", + "---" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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GPUDrive with VBD-trajs
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mediapy.show_video(frames, codec='gif', title='GPUDrive with VBD-trajs', width=600, fps=FPS)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "mediapy.write_video(f\"vbd_gpudrive_trajs_{SCENARIO_ID}_after_yaw_fix_1109.gif\", frames, codec='gif', fps=FPS)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Save GPUDrive input + output features" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "def to_numpy(d):return {k: v.cpu().numpy() for k, v in d.items()}\n", + "\n", + "gpudrive_sample_batch_np = to_numpy(gpudrive_sample_batch)\n", + "\n", + "# Save VBD predicted trajectories\n", + "gpudrive_sample_batch_np['pred_denoised_trajs'] = pred['denoised_trajs']\n", + "\n", + "# Save as pickle \n", + "with open(f'gpudrive_vbd_sample_{SCENARIO_ID}.pkl', 'wb') as f:\n", + " pickle.dump(gpudrive_sample_batch_np, f)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### RoadGraph Feature Comparison" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 2 scenarios\n" + ] + } + ], + "source": [ + "env_config = EnvironmentConfig(\n", + " controlled_object=ObjectType.VALID,\n", + " allow_new_objects_after_warmup=False,\n", + " init_steps=INIT_STEPS+1,\n", + " max_num_objects=MAX_CONTROLLED_OBJECTS,\n", + ")\n", + "scenario_path = os.path.join(DATA_DIR, SCENARIO_ID + '.pkl')\n", + "with open(f'{DATA_DIR}/debug/waymax/waymax_scenario_{SCENARIO_ID}_max_{MAX_CONTROLLED_OBJECTS}.pkl', 'rb') as f:\n", + " scenario = pickle.load(f)\n", + "waymax_env = WaymaxEnvironment(\n", + " dynamics_model=dynamics.StateDynamics(),\n", + " config=env_config,\n", + " log_replay = True,\n", + ")\n", + "dataset = WaymaxTestDataset(\n", + " data_dir = 'data/processed/debug/waymax', \n", + " anchor_path = 'integrations/models/vbd/data/cluster_64_center_dict.pkl',\n", + " max_object=MAX_CONTROLLED_OBJECTS,\n", + ")\n", + "# Reset \n", + "init_state = waymax_env.reset(scenario)\n", + "\n", + "# Process the scenario\n", + "current_state = init_state\n", + "sample = dataset.process_scenario(\n", + " init_state,\n", + " current_index=init_state.timestep,\n", + " use_log=False\n", + ")\n", + "is_controlled = sample['agents_interested'] > 0\n", + "selected_agents = sample['agents_id'][is_controlled]\n", + "sample = dataset.process_scenario(\n", + " current_state, \n", + " current_index = current_state.timestep,\n", + " use_log=False,\n", + " selected_agents=selected_agents, # override the agent selection by distance to the ego\n", + ")\n", + "batch = dataset.__collate_fn__([sample])\n", + "waymax_polylines = batch['polylines']\n", + "waymax_polylines_valid = batch['polylines_valid']" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Ratio unique scenes / number of worls = 1 / 1 ---\n", + "\n" + ] + } + ], + "source": [ + "env_config = EnvConfig(\n", + " init_steps=INIT_STEPS, # Warmup period\n", + " remove_non_vehicles=False, # Control vehicles, pedestrians, and cyclists\n", + " return_vbd_data=True, # Use VBD\n", + " dynamics_model=\"state\", # Use state-based dynamics model\n", + " dist_to_goal_threshold=1e-5, # Trick to make sure the agents don't disappear when they reach the goal\n", + " collision_behavior=\"ignore\", # Ignore collisions|\n", + ")\n", + "\n", + "# Make env\n", + "gpudrive_env = GPUDriveTorchEnv(\n", + " config=env_config,\n", + " scene_config=SceneConfig(path=\"data/processed/debug/gpudrive\", num_scenes=1),\n", + " render_config=RenderConfig(draw_obj_idx=True, render_init=True, resolution=(650, 650)),\n", + " max_cont_agents=MAX_CONTROLLED_OBJECTS, # Maximum number of agents to control per scene\n", + " device=\"cpu\",\n", + ")\n", + "\n", + "init_state = gpudrive_env.reset()\n", + "gpudrive_sample_batch = gpudrive_env.sample_batch\n", + "gpudrive_polylines = gpudrive_sample_batch['polylines']\n", + "gpudrive_polylines_valid = gpudrive_sample_batch['polylines_valid']" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Extract x and y coordinates from the tensors\n", + "x1, y1 = waymax_polylines[0, :, :, 0], waymax_polylines[0, :, :, 1]\n", + "x2, y2 = gpudrive_polylines[0, :, :, 0], gpudrive_polylines[0, :, :, 1]\n", + "\n", + "x_min = min(x1[x1 != 0].min(), x2[x2 != 0].min())\n", + "x_max = max(x1[x1 != 0].max(), x2[x2 != 0].max())\n", + "y_min = min(y1[y1 != 0].min(), y2[y2 != 0].min())\n", + "y_max = max(y1[y1 != 0].max(), y2[y2 != 0].max())\n", + "\n", + "# Plot the data\n", + "fig, axes = plt.subplots(1, 2, figsize=(12, 6))\n", + "# Plot tensor1 x and y coordinates\n", + "for polyline in range(x1.size(0)):\n", + " axes[0].plot(x1[polyline].numpy(), y1[polyline].numpy(), alpha=0.5)\n", + "\n", + "axes[0].set_title(\"Waymax Polylines\")\n", + "axes[0].set_xlabel(\"x\")\n", + "axes[0].set_ylabel(\"y\")\n", + "axes[0].grid(True)\n", + "axes[0].set_xlim(x_min, x_max)\n", + "axes[0].set_ylim(y_min, y_max)\n", + "\n", + "# Plot tensor2 x and y coordinates\n", + "for polyline in range(x2.size(0)):\n", + " axes[1].plot(x2[polyline].numpy(), y2[polyline].numpy(), alpha=0.5)\n", + "\n", + "axes[1].set_title(\"GPUDrive Polylines\")\n", + "axes[1].set_xlabel(\"x\")\n", + "axes[1].set_ylabel(\"y\")\n", + "axes[1].grid(True)\n", + "axes[1].set_xlim(x_min, x_max)\n", + "axes[1].set_ylim(y_min, y_max)\n", + "\n", + "# Show plots side by side\n", + "plt.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "gpudrive", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/gpudrive-main/gpudrive/integrations/vbd/notebooks/01_features_deepdive.ipynb b/gpudrive-main/gpudrive/integrations/vbd/notebooks/01_features_deepdive.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..bca191c12e73ead8bc6537257d7be0720b555921 --- /dev/null +++ b/gpudrive-main/gpudrive/integrations/vbd/notebooks/01_features_deepdive.ipynb @@ -0,0 +1,1491 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import pickle\n", + "import warnings\n", + "from pathlib import Path\n", + "import numpy as np\n", + "import math\n", + "\n", + "# GPUDrive dependencies\n", + "working_dir = Path.cwd()\n", + "while working_dir.name != 'gpudrive':\n", + " working_dir = working_dir.parent\n", + " if working_dir == Path.home():\n", + " raise FileNotFoundError(\"Base directory 'gpudrive' not found\")\n", + "os.chdir(working_dir)\n", + "\n", + "sns.set(\"notebook\", font_scale=1.05, rc={\"figure.figsize\": (7, 2)})\n", + "sns.set_style(\"ticks\", rc={\"figure.facecolor\": \"none\", \"axes.facecolor\": \"none\"})\n", + "%config InlineBackend.figure_format = 'png'\n", + "plt.rcParams['figure.dpi'] = 120\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Configurations" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "BASE_DIR = \"./\"\n", + "SCENARIO_ID = \"6d2a107f2e8390a\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load debug scenario and saved samples \n", + "\n", + "We saved the following inputs from the same scenario:\n", + "- Processed VBD inputs (used in waymax)\n", + "- Processed VBD inputs (used in gpudrive)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "with open(f\"{BASE_DIR}waymax_vbd_sample_{SCENARIO_ID}.pkl\", \"rb\") as f:\n", + " waymax_vbd_data = pickle.load(f)\n", + "\n", + "with open(f\"{BASE_DIR}gpudrive_vbd_sample_{SCENARIO_ID}.pkl\", \"rb\") as f:\n", + " gpudrive_vbd_data = pickle.load(f)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Helper functions" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_batch_distributions(batch, timestep, title, dist_type='hist'):\n", + "\n", + " num_keys = len(batch.keys())\n", + " num_cols = 4\n", + " num_rows = math.ceil(num_keys / num_cols)\n", + "\n", + " fig, axes = plt.subplots(num_rows, num_cols, figsize=(11, num_rows * 2))\n", + " fig.suptitle(f\"{title} | Scenario id: {SCENARIO_ID} | t = {timestep}\", y=1.02)\n", + " axes = axes.flatten()\n", + "\n", + " for i, key in enumerate(batch.keys()):\n", + " data = batch[key].flatten()\n", + " \n", + " if dist_type == 'hist':\n", + " axes[i].hist(batch[key].flatten(), bins=30)\n", + " elif dist_type == 'box_plot':\n", + " \n", + " sns.boxplot(data=data, ax=axes[i], width=0.3)\n", + " # Add a strip plot to visualize individual data points\n", + " sns.stripplot(data=data, ax=axes[i], color='k', size=3, jitter=True)\n", + " \n", + " max_value = data.max()\n", + " min_value = data.min()\n", + " axes[i].set_title(f\"{key}: Min={min_value:.2f}, Max={max_value:.2f}\", fontsize=8)\n", + "\n", + " # Hide any unused subplots\n", + " for j in range(i + 1, len(axes)):\n", + " axes[j].axis('off')\n", + "\n", + " # Adjust layout for better readability\n", + " plt.tight_layout()\n", + " plt.show()\n", + " \n", + " # Save as pdf\n", + " fig.savefig(f'{title}_{SCENARIO_ID}_t{timestep}_{dist_type}.png', format='png', bbox_inches='tight')\n", + " \n", + "def make_heatmaps(waymax_vbd_data, gpudrive_vbd_data, array_name, index, feature_name, \n", + " share_axes=True, share_color_scale=True, x_label=\"Time step\", y_label=\"Agent index\"):\n", + " \n", + " # Determine vmin, vmax, cmap, and center based on sharing options\n", + " if share_color_scale:\n", + " gpudrive_mask = gpudrive_vbd_data[array_name][:, :, index] != -1e4\n", + " # Create masks for the error value (-1e4)\n", + " vmin = min(waymax_vbd_data[array_name][:, :, index].min(),\n", + " gpudrive_vbd_data[array_name][:, :, index][gpudrive_mask].min())\n", + " vmax = max(waymax_vbd_data[array_name][:, :, index].max(),\n", + " gpudrive_vbd_data[array_name][:, :, index].max())\n", + " else:\n", + " vmin = None \n", + " vmax = None \n", + "\n", + " if vmin is not None and vmin >= 0:\n", + " cmap = 'Reds'\n", + " center = None\n", + " else:\n", + " cmap = 'bwr'\n", + " center = 0\n", + "\n", + " # Set up subplots with optional shared axes\n", + " fig, axs = plt.subplots(1, 2, figsize=(12, 4), sharex=share_axes, sharey=share_axes)\n", + "\n", + " # Waymax VBD data heatmap\n", + " sns.heatmap(\n", + " waymax_vbd_data[array_name][:, :, index], cmap=cmap, center=center, linewidth=.5,\n", + " vmin=vmin, vmax=vmax, cbar=False, annot=False, ax=axs[0]\n", + " )\n", + " axs[0].set_title(\"Waymax VBD data\")\n", + " axs[0].set_xlabel(x_label)\n", + " axs[0].set_ylabel(y_label)\n", + "\n", + " # GPUDrive VBD data heatmap\n", + " sns.heatmap(\n", + " gpudrive_vbd_data[array_name][:, :, index], cmap=cmap, center=center, linewidth=.5,\n", + " vmin=vmin, vmax=vmax, cbar=False, annot=False, ax=axs[1]\n", + " )\n", + " gpudrive_mask = gpudrive_vbd_data[array_name][:, :, index] != -1e4\n", + " gpudrive_masked_data = gpudrive_vbd_data[array_name][:, :, index][gpudrive_mask]\n", + " axs[1].set_title(f\"GPUDrive VBD data [min: {gpudrive_masked_data.min():.2f}, max: {gpudrive_masked_data.max():.2f}]\")\n", + " axs[1].set_xlabel(x_label)\n", + "\n", + " # Add separate color bars if axes and color scales are not shared\n", + " if not share_color_scale:\n", + " # Color bar for the first heatmap\n", + " cbar1 = fig.colorbar(axs[0].collections[0], ax=axs[0], orientation=\"vertical\", pad=0.04)\n", + " cbar1.set_label(f\"{feature_name} (Waymax)\")\n", + "\n", + " # Color bar for the second heatmap\n", + " cbar2 = fig.colorbar(axs[1].collections[0], ax=axs[1], orientation=\"vertical\", pad=0.04)\n", + " cbar2.set_label(f\"{feature_name} (GPUDrive)\")\n", + " else:\n", + " cbar = fig.colorbar(axs[0].collections[0], ax=axs, orientation=\"vertical\", pad=0.04)\n", + " cbar.set_label(f\"{feature_name}\")\n", + "\n", + " plt.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### **Overview**" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['agents_history', 'agents_interested', 'agents_type', 'agents_future', 'traffic_light_points', 'polylines', 'polylines_valid', 'relations', 'agents_id', 'anchors', 'pred_denoised_trajs'])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "waymax_vbd_data.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['agents_history', 'agents_interested', 'agents_type', 'agents_future', 'traffic_light_points', 'polylines', 'polylines_valid', 'relations', 'agents_id', 'anchors'])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gpudrive_vbd_data.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "agents_history: (32, 12, 8), \n", + "agents_interested: (32,), \n", + "agents_type: (32,), \n", + "agents_future: (32, 80, 5), \n", + "traffic_light_points: (16, 3), \n", + "polylines: (256, 30, 5), \n", + "polylines_valid: (256,), \n", + "relations: (304, 304, 3), \n", + "agents_id: (32,), \n", + "anchors: (1, 32, 64, 2), \n" + ] + } + ], + "source": [ + "for key in gpudrive_vbd_data:\n", + " print(f\"{key}: {gpudrive_vbd_data[key].shape}, {type(gpudrive_vbd_data[key])}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "agents_history: (32, 12, 8), \n", + "agents_interested: (32,), \n", + "agents_type: (32,), \n", + "agents_future: (32, 80, 5), \n", + "traffic_light_points: (16, 3), \n", + "polylines: (256, 30, 5), \n", + "polylines_valid: (256,), \n", + "relations: (304, 304, 3), \n", + "agents_id: (32,), \n", + "anchors: (32, 64, 2), \n", + "pred_denoised_trajs: torch.Size([1, 32, 80, 5]), \n" + ] + } + ], + "source": [ + "for key in waymax_vbd_data:\n", + " print(f\"{key}: {waymax_vbd_data[key].shape}, {type(waymax_vbd_data[key])}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## **Inputs**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### ❌ 1. Agents history [todo: needs deep dive]\n", + "\n", + "- What:\n", + " - First $t$ time steps of the log playback trajectories. Used to predict trajectories\n", + "- Shape: Number of agents (32), time steps (12), features (8)\n", + "- **Summary** of feature comparison:\n", + " - `(x, y)`: ❌*\n", + " - Note:\n", + " - `yaw`: ❌\n", + " - Notes: Why is the GPUDrive yaw always positive? Waymax yaw can be negative. Negative yaw angle usually indicates rotation to the left, so this seems important.\n", + " - `vel_x, vel_y`: ❌*\n", + " - Kind of surprising that there is so much deceleration (negative velocities)\n", + " - Patterns look very different compared to waymax\n", + " - `veh_len, veh_width, veh_height`: ❌\n", + "\n", + "- Checklist:\n", + " - ✔️: Is the order or features aligned? -> yes\n", + " - ✔️: Ensure that the `yaw` is mapped from [0,2pi] -> [-pi, pi]\n", + " - _todo_: Find out where diffs come from\n", + "\n", + "---\n", + "$*$ Not the same, but these values cannot be compared because we substract the mean in GPUDrive to center these at 0, which is not done in Waymax." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((32, 12, 8), (32, 12, 8))" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "waymax_vbd_data['agents_history'].shape, gpudrive_vbd_data['agents_history'].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "time_step = 0" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "feature_names = {\n", + " 0: \"x\",\n", + " 1: \"y\",\n", + " 2: \"yaw\",\n", + " 3: \"velocity_x\",\n", + " 4: \"velocity_y\",\n", + " 5: \"vehicle_length\",\n", + " 6: \"vehicle_width\",\n", + " 7: \"vehicle_height\"\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Yaw " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "#waymax_vbd_data['agents_history'][:, :, 2]" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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"gpudrive_vbd_data['agents_history'][1, :, 4]" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Wx1L+Fj4TEd/sOP/cl/Les0tEfD8zj42ymMcnKRdRd6mx129mnhoRn6Ccw18cET+iFHE3pBRUv0SZpLjdOs3thHN1td0/FxPYa57zqqoWo4nmpWn9++yO24m2uXM+msw8t7NA09z/fcqJxgaU7sKt+y+izLHy8CgT07V7AaVHyjc6u4A2zmkVaJp9jbKsMPJw4PXtw5SyTHT3PUoj/9j2HWXm9zrHYWfm7c2H/8spVzM6f6YfAQdSrrR+MSI2pZxI/QvYrf0ksgtfoVyde0ZTRGjXTbf49zVXVUaB2yhdePehfLjYPDP/M8Vzp9P6EHHPKbfiziUedwfuAF7bKtAAZFn69eCJnpeZV2fmDzt/Z01B8O3Nt8+ZSejMvCUzl3aeFDa/izfOZp/SEN2ruZ3wQ31E/G9EHNnx9YyJtu143prNVfrWsJxD+pS3a83/+9YExxO9z3xvBgUaKB/gRpl41bwXUs71vtp6v2l6tryc8l6/R3t703xoexml6PvqGWRovV+/aIIeON28px/R9p5+C3BB87yDgSf22JtqJu/p61GGOP0HeHP7VfHM/C6lqDVOlvkvfjLR/ZSLHstTCl9dm4t2okufbBVomuNdy7LevQ+gtHXtH6K/SOlJ9fhJeoZN5lLgre2vbWYeT+mR8cAYv+rNPyhDq+ZiaOLGlLZ8F0px5GZKj6IZXfCJiC0oPY8Pbf8ddmntJsNLKD3PVqP0xn3bDM+vpnMHpfh450TamXkK5QLVWsAPWgWa5rGbKROeQ0cP48z8dWeBprn/55T/u6vTMZ9OZv6X8nu+GTg4yrQBX6H0RHlrZk7Ua+4yymvf1bxjcyUz3wK8iVLMewql1/xDKMO3zm5+tnZLmtvJJs9uFa/uOsnjWsTsSaPFaKJ5abYArmi7YnJO2zbHN4WER1IKC2NWJGo+qO9AWS7xXixbeaNVnNmAMryn5fOUq3ovpyzh1/Ly5naioU6wrKtlu1beWym9WSZ7fK3OB6LMsfMMytXFu1FOImny3ysi7jnBFeb3Ua7sPYtyJWQF4BWd45Snk5l/i4hTKSciz6dcsWxdsXsG5ff8lcn3wK8pJxQta1KuvO8E/DciXj1BY9mt1vLd3Yz135hyNfynk1xt+yrwtsme3BTqtqGc+K7SHLvVWG/QZd7OfW5IOXlYl3KSN8Kyn2lW+5Qq9CKWFXJaLqbMKdapc86PlqMohedhmOp9prN34ZQy89KIOBvYMsavmjduqBPlPWdFyoex9ivcrf39PSIuogxfWiUzb+oiw3kRcSFl2MQ2NL0ImyFYj6UsOTvRa9NyDsuGbo5QerI8ltKj84aIeEcPH1Rn8p7e6jF74iQXS77KsuG4YzTFqW0pH/LvR+mRMNL8G2b/nt73dmIaU51r/CIzr2l/IMt8bpdQ2sOZzO90ek48R1BSLjytRSnktI7zWeZohaPMfDvw9mYY4IMpBZpDgWdGxPO66dnR9Dg5jNID6u3TbD5RhhOBkShzRa1DOTd6D/C0iNg+My+Z6T4ncWlm5gT3/5FyHjXV6z/RueRKlHOOTSlz0qzcPLRBx+2dMvM3EfE2SvHn55Rz0O9m5uc6t222322i+wepGer1ZcpFzAMoPb2uobxPfYbSzjw8M/cZWkgtKBZptBj9hNL74hERcffm34+mjKUFIDP/EhF/YVkh5/GUq5E/z8zrW9tFmVzta5SuqZPprJB/m3IS84KIeGNm/iciHt4c47dTXH0ZVwjJzOujTEj390muNLayrtx+Z0S8gXIleaVpco8p0mTmbc0Y499TGtVjmitfs3EMpUizK02RhlJkWRk4Odtmz5/AREvcrkTppv4ySpFnrwme1401mttuhkC0TljGrczQuGSiO5ui3/GUgtdkZnRlpbmCeThTrJo1031KQ9T6ILjGRA9m5p33x7LJ1SfTXgD4L+X/6w8y81cd23U7CetMPvSP03yYv0fz7UTvM7NZDefLlPbqxTQXEpoCyWMo7cqv27Zdt7ndKyKme5+8J6XHTTeOAT7IsiG4sOz96BuTfCBvmWgJ7tbQq30pk9dOO4H0JAbxnr42pbj2mCn2PdP39L63E12a6KLL9VM81v74ypM8PpHJJuZv9fCYyb7Gaf7+JyqWHJiTrJDWFCR/A7wiIu6g9Ip5Hd3NLfNuSq+KF3X2VJ6JpvfWn4CDIuIySmHwf1k2RLNX072GU73+neeSD6f83a83xfEm+xv9DKUH+eMp7/d7TrJdLd5JmZvn05nZ3t6cHhE7UM6L3xwRX2r7+2r93ladZJ+rNbfXTfK4FjGHO2nRaa4c/h/l7//xlPG5yzN2rhkoQ54e2Zwotoo1d27T9KA5jlKgOYDSc+auwHJZVt5oLZ04QpumAf4i5c25tWTpdL1ooHRRnc1jY0RZ3vpTlA8re1Imll0lM0ea3K0i0cjEe+AFbY9t2BRHZuMESgO2dUTcv7lvtiuA0FzpehPlg9NLo0y6NxutpRB/P8vnd+MjlBPvsyhdgdcEVmx+/61lICb7/U/mzZQPRL+jTCh9X2ClZp+tE6uZ7lMallZRYaoPvd36Ymbu0Xy9KjM/PEGBBpZ1SV9tgsfatR4f1wulSw+nFMhvZOIP/bPpBXgcZfjAC9qGnLR60XTOZ9E69/sVYyesnehrJpMqH0N5/31eRNylmTdt17bHZqQZjtGa3+bNM30+3Dl3W2tSzrl8T/8i5W91KeWc4p7ACs3771OabWb6/jsX7UQ3+nKu0eNx+uG+lOFDnV+dCyNMprVwwbO63L7VC/hVUSa6vfOr7ZjHNfc9tct9HkeZp+WpPZxrdZru997V69L83zqOUqA5jPL3vzqwfPM32loIY7K/0WDZ+dbdqb+nb+v9dNyFySzLzJ9HeW/dqu2hVsH9/p3P6bh/sqKwFjF70mixOovSNXNLli1t2Dn53jmULvWPZ1kX6B+1Pb4V5QrdNzOzc+UGKF1mJ3MYpSr/8igrFbyE0hD3MuFtt1pj2N+VmRMNAZg0d1PgeQ9lbPjPKJP8HkCZD2ZGsiyPegJl+dddoizzvSXlg8+3pnzy5Pu8LiL+QTmZfXCTsWvN2OhHU05SzuziKa1u3ZNN+jbZ/c+lmZNngi71U/3dTOW5ze0LJxgjPtt9SsPyfcpk4DtFxL49zknSrdYV5DUi4m5TzG21Xsf2M9UaLnNWdr/M9ZQy818R8X3K+/tTKKu77UIpmnQWaVq5z87M1/Xj+E2GSyPiLErb+EzKKkUPpMwZds5Uz53Cn5vbe0TEmpl59Qyf/yRKO/0fyrCK6cz4Pb25YLM9ZZjL8yf4W+3lPb3f7cSikZln0FsRq/W3tuYMnrM8U68M+bjm9shudtYMJfsXpYfXPSh/Y7V4CGW4/C8y8xUTPD7VueRKlPelVSjnvS+hrC61UftcOZVpFVQmmxendX/73FfTXWx4TMd20p3sSaPFqn1emi0pXQ073yRbJ5XbUQo6o4wt5LR6aozrshsRazBFF+XM/AvlQ8hjKKsD3ZOy1Oy/Z/JDzNJUuZ/EJCckEbGE0qV+BcpQol0p3eDfEhFPnOg5XWhdqdqVcpViBDhhonkSutF0D291bb9+qm0neO7ylPHuI5TXopsu/q0l3TduJpzs9MJJnncP4LpJ5jx40STPaRUTJyuuT/q6TrFPqVbfp/R8eABlGMGcy8wrWLbS0o4TbdNMZvooSiF3xqvqNEMw3th8+6kZh5xaa96ZFzfzmKwH/Khpb9qdTvnwv+MMJ3rtRvt7eqsXzZdz2QqHM9V6Xx1l8sk3JxRlxcRPNN8e0s28Iix7TZ86waT2MPF7+uqU8+krJikmTtYOdPOePtN2Qv3TKrZ0tbpTZm7U6pHc+cWynhIPaO47spt9NheO1qIUGaddGW3ApjqXXIllF44m8kHK+e+3mvlmPk/5vz4ncw71SWthjsd2PtCcP7Z6BV3S9tAPKG3Fls2E7e3PWZllQ9immq9Li5RFGi1WZ1NO+jYBNgN+MsHJ1fmUhvFllAkA/5Bjlw9tTbz2vChLYgJ3XlX7IqX75lRaS5u+qbmdaqhTP7Vyv7y9+2xErMvUK538L2WZwUMz89vNpMK7N48dHRHTrpwxgdMpV3U3ZNnv4ejJN59c87N8glJk+RNldZBun/tIymR52wN/a8sypWZ+omMoV8/+t5l0sLXPjRm/HOOdTwXuHhG7tN8ZZZnziVZoockFy7q5T7RPKL0P2ve5HWVZT2neaD7U70oZ+rNfRHxioiGMTfH4EZ339+BTze1HIuKhHce6B2WyyOWA42cyYXpErBARL6b04lwNOKKZKLSfvkeZoPeZLBtqMG7p3qYAfThlbpqvRsS44R8R8eCIeN4sMhxPec2eyrIeQ7PqIdoMNW6twnXGTIr3UVbZOYfStvyGLuezaSaAP5ky59rHmw9frX0+nTJvWqcrKb/3R0TElm3bj0TEOxi7SEG7bt7TZ9pOLFgR8dqIuCAiPjz91l3tb92I2L2ZELbzsaewbFLxwzse27TJcWqfcrw5IsZdHGvef1oLKBzVeY7aDJsajYg9+pFjFi6mFCCe2BSfW7lWpLyPrj/Rk5q5HN9K+ftvDfV/C+WcbbeIeMEEzzm6+Z3P+QqVUxyr1cP7A1GWDG9tvzzlYuu6lAu+J7Uey7Li6pcpw1s/F2VS6JaDKBdFT8vM/+v7D6J5z+FOWpQy858R8XuWzQ0w7opoZt4RET+hfHCHjjlrMvMXEXESpWv5hc2449soXb3voHRn3WOKGCdSKu7rAr/LzNl2B5+pIyjj+3cALoqI8yhz6WxD6Q5+NWWI150i4vmUnyVpK2Bk5qkR8QlKA/sFJj6BnVTzOz6WsgLSGpSeOadN/SwAnt0UlVrWoFzFWIsyXOqlk1y5XaMZXgalsLI65W+gdbX2x8CuzRX1br2DcsXt6cAfI+JHlCtM21IKb3tP8JwDKJMBHhsRe1Ousj2UsrLCQSybh6HdT4CrgOc3f2t/ovydHd6s5vIRygejD0XETpTX6oGU+RE+whSrTEk1ysxfRsS2lA/+bwL2joifUQq7K1H+vz+S0mX+L3Q3RHE6h1D+z+wKnB8R5zbHuwdl2OtdKb0uXzPpHsa+P92FciL+WMoH/9spH/7e24esY2TmzRFxHOWDz+6U+WSOm2TzN1DanudRVo/5JWX+hFUp74kPpkxy/80ZZrg2Ir5NKdCsSLkActE0T4MyifE2zb9HKHO9bULpZXoVE7+PAjyk7T19Bcrr9CjKcsZQrlC/rH3C/y68mjL58l7Ats3f3FqUYsvnOrM0Q1I+QpmH7vTm/flqSk+B9SmTzk5UKD+ZUtB6U0Q8gvKhdRT4aLP6zmzaiYVsDUpB637Tbdilu1PO0z4bEb+g/P6XNMdofQj/WGae0PG8VZttxhV3Zum9lKLwrynt+gjl/+ZjKAXh0ynD4zu1LrTf2qccM5KZV0XEYZSC8K8i4jRKkWJzysp7n6XjQlVT6D6G8jPu0VoprBn+/mLKec7nI+LcjiL4OpTf+eozyRgR32LZ30vrPWGvaJsTKDMf1/G0yY71AUoP+YexrG34J+Xccz2a+Ygy818dz3sTZZjb84ALIuLnlPfYR1AKvLVPmKwhsSeNFrP2ostk3dbbCyc/muDxZ1PeuK+gFGs2p1zNfCzTTASWZTnRM5pvD5s2bZ80PWA2ofRYGaFcdW2dSD6ZjgY/yqoVX2juf3FmdnY5fyflQ8vzI+Kls4jUfpX1K9ndMquPYuxEgE+iDG86BHhkZk70WkG5gt16zgsoDec1zfO2zczHZ+afZhK++X0+odnHCOVvYh1K8WbC+R4y82uUgso5lMZ6B8rJzbOYpDdTliXFdwBOoZyk70Hp5fU/zeNnU4prpzTHfwblQ9KeWZYXleadzPwJZULJ11LeL9endKPfnvKh7f9RehX8T2Z2Tv4+m+ONZuZLKAXnUygfilsrkPyOcgX48dmxDHGH9venHZr8P6bM57VuZr4jM+fqg1X7Utvfm2wIbbOKzdOajD+h/JzPp7QNV1NWy5ptEeCYSf49lSew7He2G6Ug8jdKMWLDzPzDJM+7T9vznkdZBvpS4JPAYzPzmTOdx6ZpAzaj9GK4O+U9/W7AS4GPTfKcAymFvV9R2pWnUObT2YYyP9BEz/kb5T3/J5QC4J6U9/T7NY/PuJ3QjPyZcv5yDmUBhedQzoFWpLz2W+dgllN+C2UhhbtRXu9nUeY/OZnyf2G7ziJjlBXiNgT+xSR/XwOyN6XgeyHlb31bylyAm1KGg3f6POVn+1RmntL+QNOb5D2UQuvRzc/Yq0dT/i9vxrI5ZdZuu2+zbnfUnOttRimq/YHyMz6LsjDD14HNM/MrkzxvU0pP7xUof2f3pPS4f3RmOmmwJjQyOjrbYcKSetF0sb2cclVmrQmq75IkST2LiFHg0sxcd9hZ1JuI2AT4KfDuzDxg2Hk0N5rehadThrvtMdw0GjSHO0nD82pKNf1LFmgkSdIcax/ye0hmnjfMMJq1J1J6AX9m2EHUfxHxRkqP6W6Xi9cCZJFGGqCIuBdlfpD7ULqa30gZdy5JkjSXWkN+ocyLZ5FmHsrMj1DOJbUwbUcZ3qhFzOFO0gA1k0n+mbL05m+BfTOzLysESJIkSZLmN4s0kiRJkiRJFXB1J0mSJEmSpApYpJEkSZIkSarAgp04uFme7v3A5sBKwO+AT020hr0kSZIkSdKwLcg5aZp15U+iTM76NeBa4LnAg4B3ZeaHhpdOkiRJkqThiYgRYNU+7/bGzFx4BYYBW3BFmohYAbgAuD+weWb+srl/CfATIICHZeZFw0spSZIkSdJwRMRqwPV93u2SzLyhz/tcdBbicKcnAusDR7QKNACZeX1EfAD4OvBS4J19ONbCqnBJkmo0MuwAC4RttiRprtlmq2cLsUizTXN78gSPndjcbt23o73rXX3b1awdcAB89KPDTgH77AMnnTTsFMVTngK/+92wU8DDHw6nnDLsFPDkJ8OZZw47BWy9NVxUSSe2DTaAP/952CngQQ+Ca68ddgpYfXW4/PJhp4C114af/WzYKYpNNoHbbx92Clh++WEnWDhq6D08MlJHDqgniznqzAH1ZDGHOaZTS5aR+VmjOXfHHVllhdmVBm667TYe/93v9jnR4rYQizQbNLfjPglm5n8i4sq2bSRJkiRJWrRWWWEFVp1lkUb9txBfidWb28kuTV8LrDuYKJIkSZIkVWy55crXbJ+rvlqIRZqeNZMoTSsz5zqKJEmagm22JEk9skhTlYVYpGn1oFl9ksdXZ/JeNi39nuVakiTNDdtsSZK0YCzEsldrLppx885ExN2A+zDBfDWSJEmSJC06rZ40s/1SXy3E32hrCZvtJ3jsqR3bTGZJl1+SJGm4bLMlSdKCsRCHO50K/AnYJSI+k5m/AoiIJcB7gduAI6baQWbeMNchJUlS72yzJUnSQrLgijSZeVtE7AWcBJwVEV8F/gM8F3gQ8O7MdLiTJEmSJElOHFyVBfkbzczTgS2As4EXAK8BrgF2zcwDhplNkiRJkqRqjIzMfj6akZFhp19wRkZHR4edYT7zlydJmmue/fSHbbYkaa7NmzY7IlajWSHxly9+MauuuOKs9nPjrbfy6GOPbX27xGHIvVtww50GrYbC4eioOTqNjsLyyw87Bdx+ez05Zvm+21e33gorrzzsFMXNN8Mqqww7Bdx0E6y22rBTwA031JPjrncddoriuutg9dWHnQKuvXbYCRaQGi5MjYzUkQPqyWKOOnNAPVnMYY7p1JKllg9DmtcW5HAnSZIkSZKk+caeNJIkSZIkLVZOHFwVf6OSJEmSJEkVsCeNJEmSJEmLlT1pqmKRRpIkSZKkxcoiTVX8jUqSJEmSJFXAIo0kSZIkSVIFFtxwp4hYG9gJeDrwEOC+wD+Bc4CDMvO8IcaTJEmSJKkeDneqykL8jb4O+CSwHnAy8HHgbOBZwLkRsfMQs0mSJEmSJE1owfWkAX4KbJOZZ7bfGRFbAqcCh0TE0sy8eSjpJEmSJEmqhT1pqrLgijSZecIk958VEacD2wMbAj/vx/FGR/uxl96ZY7zbbx92gqKWHLfeOuwExc0VlUdvumnYCYobbhh2gqKWHNddN+wEy1x77bATqK9GRoadoKglB9STxRxj1ZID6slijrHMMV5NWeYbizRVWXBFmmm0Pqbe1rc91lCVGBkxR6daspijzhxQTxZz1JkD6sniSWf/1PJ61pAD6slijjpzQD1ZzGGO6dSSZb622RZpqrJofqMRsQ6wHXAF8Jshx5EkSZIkSRpjURRpImJF4BhgZeBtmVnJABRJkiRJkqRiwQ93iojlgCOBrYBDM/OYLp6zWjf7zszewkmSpJ7YZkuS1COHO1VlQRdpmgLN4cAuwFHAq7t86vVzFkqSJPWTbbYkSVowFmyRpinQHAHsBhwL7JmZdww3lSRJkiRJFbEnTVUWZJGmo0DzNWD3GRZolnS5nVfvJEkaLttsSZJ6MTIy+2LLfF3RqmILrkjTNsRpN+A4YNeZThScmTfMRTZJktRfttmSJGkhWXBFGuC9wO6UK2YXAu+JiM5tjszMSwacS5IkSZIkaVILsUizbnO7BHjXJNucAVwygCySJEmSJNXLOWmqsuCKNJm5B7DHkGNIkiRJkiTNyIIr0gxcLRMlmWO8WrKYY6xackA9WcwxVi05oK4s6l0tr2ctOaCeLOYYq5YcUE8Wc4xljvFqyjLf2JOmKhZpejU6OuwE5Q3JHGPVksUcdeaAerKYo84cUE8WTzr7512TjYIeoAMOgI9+dNgpin32gZNOGnYKeMpT4He/G3YKePjD4ZRThp0CnvxkOPPMYacott4aLrpo2Clggw3gz38edgp40IPg2muHnQJWXx0uv3zYKWDtteFnPxt2imKTTeD2Ga0VMzeWX37YCWbHIk1V/I1KkiRJkiRVwCKNJEmSJElSBRzuJEmSJEnSYuVwp6pYpJEkSZIkabGySFMVf6OSJEmSJEkVsCeNJEmSJEmLlT1pqrJoijQRcTDwmubbNTPzH8PMI0mSJEmS1G5RlL0iYlvg1cANw84iSZIkSZI0kQXfkyYiVgO+BCwF7glsPdRAkiRJkiTVwuFOVRkZHR0ddoY5FRGfBV4MPAz4KqVI06/hTgv7lydJqsHIsAMsELbZkqS5Nm/a7KYzw/UAv3zve1l1pZVmtZ8bb7mFR3/gA61vl2Smo1d6tKB70kTE1pR5aPbKzCsiov8HqaHINTJijk61ZDFHnTmgnizmqDMH1JNlZN6c71Wvhl/l6GgdOaD5867lb9wc9eWAerKYwxzTqSVLLW/wmtcWbJEmIlalDHM6NTMPH3YeSZIkSZKq43CnqizYIg1wIHBf4MnDDiJJkiRJUpUs0lRlQRZpImJL4LXAmzPzz7N4/mrdbJeZM921JEnqI9tsSZIWjojYFdgK2Bh4BLASsFNmHj/UYAO04Io0EbECcDhwHvCZWe7m+v4lkiRJc8g2W5KkhWN/4IHA1cCVwAOGG2fwFlyRBlgCPLj5un2SyYKvbu5/UGZeMrhokiRJkiRVpK7hTnsBF2bmZRGxH/C+fh+gdguxSHMzZcLgiexAmafmy812102y3ZIuj+XVO0mShss2W5KkXoyMzL7Y0ucVrTLzh33d4Ty04Io0mXkTpfo2TkScQSnSvCkz/zHFPlzbXZKkecA2W5Kkaqw6yUiWO9luT2/BFWkkSZIkSVKX+jfc6aountHfrjcLkOtlSZIkSZIkVWBR9aTJzG2GnUGSJEmSpAXo3sCNww4x3y2qIs2c6PNESbNmjvFqyWKOsWrJAfVkMcdYteSAurKoZ6Ojw05Q1JIDqOdv3Bxj1ZID6slijrHMMV5NWeab/g13utE5Z3pnkaZXNZxpjYyYo1MtWcxRZw6oJ4s56swB9WTxpLNvavhVjo7WkQOaP+9a/sbNUV8OqCeLOcwxnVqy1PIGP1N1LcG96PkblSRJkiRJqoA9aSRJkiRJWqzsSVMVizSSJEmSJGnoImIvYIvm242a270jYsfm30szc+mgcw2SRRpJkiRJkharunrSbAHs3nHfNm3/vgRY2u+D1sQijSRJkiRJGrrM3APYY8gxhsoijSRJkiRJi1VdPWkWPYs0kiRJkiQtVhZpqrKgizQR8ULglZQJh1YG/gqcC7wuM68bYjRJkiRJkqQxFmSRJiKWA44AdgMuBL4M3Ag8AHgasDpgkUaSJEmSJFVjQRZpgLdQCjSfBt6cmXe0HmgKOP0zMtLX3c2aOcarJYs5xqolB9STxRxj1ZID6sqino2ODjtBUUsOoJ6/cXOMVUsOqCeLOcYyx3g1ZZlvHO5UlQVXpImIVYF3AhcDb2kv0AB0ft+zGs60RkbM0amWLOaoMwfUk8UcdeaAerJ40tk3NfwqR0fryAH1ZDFHnTmgnizmGJ+jmvaphhxQT5Ya/kA07y24Ig2wPXB34EvAihHxXODBwD+AkzLzsiFmkyRJkiSpHvakqcpCLNJs3NzeAZwPbND22K0R8a7M/OjgY0mSJEmSVBmLNFVZiEWaNZvbNwM/A3amDH16PPBF4KCI+ENmfneyHUTEat0cKDN7jCpJknphmy1JkhaShVikaZXybgaek5l/b74/KSL2Ak4E3gRMWqQBrp/DfJIkqX9ssyVJ6oU9aaqyEH+j1za3P28r0LScQinebIwkSZIkSVJFFmJPmgub22s7H8jMOyLiOuBu0+xjSZfH8uqdJEnDZZstSVIvRkZm3yPGFa36biEWaU5vbh/a+UBErAGsAVw01Q4y84Y5yCVJkvrMNluSJC0kC264U2ZeDJwKPDgi9mjdHxEjwP7Nt8cPIZokSZIkSXVpzUkz2y/11ULsSQPwauDHwOER8WyWre60OfAb4MPDiyZJkiRJUiWcOLgqC/I3mpkXAY8FvkIpzLweuC/wMWCLzLxuiPEkSZIkSZLGWag9acjMS4Bd5/xAtUyUZI7xaslijrFqyQH1ZDHHWLXkgLqyqGejo8NOUNSSA+rJYo6xaskB9WQxR4da2qdackBdWaQeLNgizcDU8E49MmKOTrVkMUedOaCeLOaoMwfUk8WTzv6p5fWsIQfUk8UcdeaAerKYwxzTqSXLfG2zHe5UFX+jkiRJkiRJFbAnjSRJkiRJi5U9aapikUaSJEmSpMXKIk1V/I1KkiRJkiRVwCKNJEmSJElSBRzuJEmSJEnSYuVwp6pYpJEkSZIkabGySFOVBVmkiYgVgJcCewHrAysBlwLHA5/OzH8PL50kSZIkSdJ4C7XsdRxwKLA6cCzwReBWYD/g3IhYbXjRJEmSJEmqRKsnzWy/1FcLridNRGwKPBv4MbBVZt7W9tjxwPOA5wNH9eWAIyN92U3PzDFeLVnMMVYtOaCeLOYYq5YcUFcW9a6W17OWHFBPFnOMVUsOqCeLOcYyx3g1ZZF6sOCKNMB6ze0p7QWaxvcoRZo1+3a00dG+7WrWRkbM0amWLOaoMwfUk8UcdeaAerJ40tk/tbyeNeSAerKYo84cUE8Wc5hjOrVksc1WHyzEvkm/b26f3MxN024HYBQ4Y6CJJEmSJEmqkcOdqrLgetJk5vkRcTCwN/DbiDgRuB3YFtgA2Dszfz7MjJIkSZIkVcHVnaqy4Io0AJn52oi4BPgw8Ia2h74C/GC653c7sXBmziqfJEnqD9tsSZK0kCy4Ik1ELAd8AdgZeBXw/4BbgCcBnwV+GhGbZuYlU+zm+rnOKUmS+sI2W5KkXoyMzL5HjPPw9N2CK9IAewJ7Aa/PzC+13f/NiLgF+A7wdkoBR5IkSZKkxcvhTlVZiEWapzW3Z0zwWOu+jabZx5Iuj+XVO0mShss2W5IkLRgLsUizcnO7xgSPtZbevnmqHWTmDX1NJEmS5oRttiRJWkgWYt+kc5vbd0TESq07m7lq9mu+PWPAmSRJkiRJqo9LcFdlIfakORjYHXgy8PuIOAm4FXgisCFwEfCpoaWTJEmSJKkWzklTlQX3G83Ma4HHAR8FbgNeRpkkeGXg48Bmmfmv4SWUJEmSJEkabyH2pKEpwuzbfM2tWpYcM8d4tWQxx1i15IB6sphjrFpyQF1Z1LtaXs9ackA9WcwxVi05oJ4s5hjLHOPVlGW+sSdNVRZkkWagRkeHnaC8IZljrFqymKPOHFBPFnPUmQPqyeJJZ//U8nrWkAPqyWKOOnNAPVnMYY7p1JLFNlt9YNlLkiRJkiSpAvakkSRJkiRpsXK4U1Us0kiSJEmStFhZpKmKv1FJkiRJkqQK2JNGkiRJkqTFyp40VfE3KkmSJEmSVAGLNJIkSZIkSRWYF8OdImJXYCtgY+ARwErATpl5/CTb3xfYH9gBuAdwGfBl4MDMvGUgoSVJkiRJqp3DnaoyL4o0lILLA4GrgSuBB0y2YVOgOa/ZZilwIbAF8H7g8RHx9My8Y64DS5IkSZJUPYs0VZkvRZq9gAsz87KI2A943xTbfgRYB3hNZh4CEBEjwBHA7s3XEX1LNjLSt131xBzj1ZLFHGPVkgPqyWKOsWrJAXVlUe9qeT1ryQH1ZDHHWLXkgHqymGMsc4xXU5b5xiJNVeZFkSYzf9jNdhFxV2Bn4E/A59uePxoR7wB2BV5OP4s0o6N929WsjYyYo1MtWcxRZw6oJ4s56swB9WTxpLN/ank9a8gB9WQxR505oJ4s5jDHdGrJYputPlhoZa/NgZWBUzJzzP/SzLwC+DWwaUTcZRjhJEmSJEmSJrPQijQbNLcXTfL4RcDywHqDiSNJkiRJUsVaw51m+6W+mhfDnWZg9eb22kkev7ZjuwlFxGrdHCwzu4wlSZLmgm22JElaSBZakaZfrh92AEmS1BXbbEmSejEyMvseMXMwD09EbEJZnXlzYCXgd8CnMvMrfT9YhRZa36TpespM19NGkiRJkqTFo6LhThGxDXA2sCVwPHAIsAZwbES8s68Hq9RC60nTmotmg0ke3wC4g7L601SWdHk8r95JkjRcttmSJC0AEbEC8EVgFNgqM3/Z3L8f8BPg/RFxXGZONgftgjCwIk1ErJyZN8/xYX4C3AI8OSJG2ld4ioj7AY8CzsvM/061k8y8YW5jSpKkfrDNliSpR730iOlvT5onAusDR7QKNACZeX1EfAD4OvBSYEH3qOnpNxoR34iIKSfhbbbbCPi/Xo7Vjcz8D/A1yupNr+p4+MOUlZ0Om+sckiRJkiRpRrZpbk+e4LETm9utBxNleHrtSfN8YNOI2D0zz5xog4h4K/BByoQ/sxIRewFbNN9u1NzuHRE7Nv9emplLm3+/HdgWODgitgMupIxnewJwEnDUbHNIkiRJkqQJrRoRU24wTQ/Y1rQl44YzZeZ/IuJKJp/aZMHotUjzaeD1wKkR8VHgPZl5G0BErAUcTSmYXAvs3sNxtpjg+du0/fsSYClAZl4REZsB+wM7ADsClwHvAz6SmXf0kEOSJEmSpIWjf8OdruriGVMtBzXdQj/XAut2cYx5raciTWa+KSJOBI4E9gW2i4gXA48EPg/cEzgD2C0z/9rDcfYA9pjB9lcAL5vt8WZkDpYcmxVzjFdLFnOMVUsOqCeLOcaqJQfUlUW9q+X1rCUH1JPFHGPVkgPqyWKOscwxXk1Z5plRRhidsnYy9XPVXz1PHJyZJ0XEI4DDgWcA5wMrArdRhh59tH0C3wVntIIfbWTEHJ1qyWKOOnNAPVnMUWcOqCeLJ539U8vrWUMOqCeLOerMAfVkMYc5plNLlnnaZt9xR/ma7XPb3Bu4sYcorR40k817uzqT97JZMPqyulNmXhMR+1Mm8bkbZcmsbwIfW9AFGkmSJEmSBHBjj6sutuai2QD4RfsDEXE34D7AuT3sf17oy3pZEfEO4GxKgearlLFoOwNnR8R6/TiGJEmSJElasFqLEW0/wWNP7dhmwep1Ce77R8QZlEl6rwWenZkvBjYEvg88DvhVROzRY05JkiRJktRnreFOs/3qo1OBPwG7RMRGrTsjYgnwXsqUKkf09YgV6rUnzW+ArYAfAo/MzO8AZOY/MvMZwN7A8sCXIuK4Ho8lSZIkSZIWoGal6L0odYqzIuLQiPgYZd7bhwP7Zea45bkXml6LNKsAb87Mp2Tm3zsfzMxDgI2BXwPP7fFYkiRJkiSpjyrqSUNmng5sQZlO5QXAa4BrgF0z84D+Hq1OvU4cvGlmnj/VBpl5QURsBnywx2NJkiRJkqQ+Gh2dfbFlLhbVysyfAk/r/57nh56KNNMVaNq2u5WyHLckSZIkSdK8EBG/BL4AHJuZ18318fqyBHdE3AvYFdgUWAM4NTMPah57OLA+8MPMnNWa6RGxK2Xum42BRwArATtl5vEd260GPAd4JrAR8ADgZspwq89n5ldnc3xJkiRJkrQoPQo4GPhoRHwdOCwzz5urg/VcpImInYAvAkuAEWAUuLxtk7WBbwG7A1+e5WH2Bx4IXA1cSSm+TGRL4BjKmLVTgW8C96bMh/OViHhCZr52lhkkSZIkSVpQeplbpt9z0lTqUcCrgF2APYGXRsRvgEOBL2fmf/p5sF6X4N4c+AplKay3UHrSjHRsdiplee5eJg7eC3hgZt4bOHyK7f4OvARYKzN3zsx3ZObLgIcAlwJ7R8SmPeSQJEmSJEmLRGb+JjP3BtaiFGl+AjwS+F/gbxFxeFMb6Ytee9K8E7gDeHJm/h9ARIzZIDNvj4j/owxTmpXM/GGX2/0K+NUE918ZEV8APkQZNvXT2WYZZ6SzJjUk5hivlizmGKuWHFBPFnOMVUsOqCuLelfL61lLDqgniznGqiUH1JPFHGOZY7yasswz9qTpTmbeBBwJHBkRDwNeQekksgewe0T8jtK75pjMvHa2x+m1SPN44MetAs0U/g5s1uOxenVrc3tbX/c6F9NZz9TIiDk61ZLFHHXmgHqymKPOHFBPFk86+6eW17OGHFBPFnPUmQPqyWIOc0ynlizztM22SDNzmfl74I0R8TZKZ5A3AQ8HPg18JCK+DHwoMy+d6b57Gu4ErEqZJ2Y69+zxOD2JiOWB3Sjz5XTVK0eSJEmSJKlTRCyJiFcAZwNvbO6+FDgKuAl4OfDbiNhmpvvutSfN5ZRq0aQiYjnKUKc/93isXnwQ2BA4PDN/O93GzSpR08rMXnNJkqQe2GZLkqRBiYjNKAWYFwCrUaZ/+S7weeDEzByNiJWB1wAHNV8zmhe31yLNicCrI2KXzPzKJNvsBdwfOLbHY81KRLwKeAfwf8Abunza9XOXSJIk9ZFttiRJPXC409Qi4u6UuWdeTumkMgJcAXySshz3X9u3z8ybgU9GxLbAk2d6vF6LNAdSlqE6KiIeDZzQ3L9a8/1zgH0pQ6I+2eOxZiwi9gI+B/yaMrmxJ3KSJEmSJDUs0kzrb8DKzb9PpfSa+XZm3j7N865qe17XeirSZOZfI2IH4JuUJbjfTJn35fnN10gT7FmZeVUvx5qpiHg58AXgt8B2mfnPGTx9SZfbWfSRJGm4bLMlSdJcuhE4GPhCZl48g+ftS5l6ZUZ67UlDZv44yrrbL6N05VmXMiHxX4FTKD/IrJefmo22As3vgSdl5j9m8vzMvGFOgkmSpL6yzZYkqTf2pJnWWpl5y0yflJn/jIgtImL3zPxAt8/ruUjTHPw64FPN11A1Q5y+AFxAKdB0s/qUJEmSJEmLjkWaqc2mQNPmOZSVpgdbpJlrTeFli+bbjZrbvSNix+bfSzNzaUQ8ETiUMszqR5RJjTt3d0ZmnjG3iSVJkiRJkmZmRkWaiFinl4Nl5mWzfOoWwO4d923T9u9LgKXAOpQCDcArp9jfGbPMIUmSJEmSNCdm2pPmEsrEwLMxOovjAZCZewB7dLHdkcCRsznGrI2MTL/NIJhjvFqymGOsWnJAPVnMMVYtOaCuLOpdLa9nLTmgnizmGKuWHFBPFnOMZY7xasoyzzjcqS4zLZr8iPFFmpWBxzX//hdwafPvdYB7NtufB9w8y4x1G51tzaqPRkbM0amWLOaoMwfUk8UcdeaAerJ40tk/tbyeNeSAerKYo84cUE8Wc5hjOrVkmadttkWausyoSJOZ27R/HxF3BU6jLHO9T2ae1PH49sBBwIrAU3pKKkmSJEmStIAt1+PzPwisD2zbWaAByMyTge2abQ7o8ViSJEmSJKmPWj1pZvul/uq1SPNc4LTMvGayDTLzH5TeNs/p8ViSJEmSJEkLVq9FmjXpbsjUCsAaPR5LkiRJkiRpweq1SHMJ8KSIeMBkGzSPPanZVpIkSZIkVcLhTnXptUjzJWA14MyI2C0i7tJ6ICJWjoiXAGcCqzbbSpIkSZKkSoyOzr5AU8OiWnMtInoZFfRFYM+ZPGGmS3B3+gSwMbAzcARwRERc3Ty2ZnM7AhzXbCtJkiRJkjRf/DUilgKHZuZpM3liZp4DnDOT5/RUpMnMO4AXNYFfA2wG3Lt5+BbgPOCQzPxaL8eJiF2BrSgFoUcAKwE7ZebxXTx3J+AbzbddPUeSJEmSpMWgl2FLi2S407XAC4CdIuKPlN4xR2bmVXNxsF570gCQmV8Hvh4RKwD3au6+JjNv68f+gf2BBwJXA1cCk86B0y4i1gQOBm6gDMuSJEmSJEnq1tqU1apfTplv98PAByPi25TeNT/s58H6UqRpaYoyV/Zzn429gAsz87KI2A94X5fPOxi4CTgaeMsc5JIkSZIkSQtUU+c4DjguItalFGv2AJ4PPC8iLgEOA47IzJ7rIX0t0syV2VSmIuJ5wE7A04DH9T1Uy8jInO16RswxXi1ZzDFWLTmgnizmGKuWHFBXFvWultezlhxQTxZzjFVLDqgniznGMsd4NWWZZxzu1L3MvAR4V0S8F3gG8Apge+AA4P0R8f+AL2TmKbM9Rs9Fmoh4CLAvsDWwFmW+mImMZuZAikLN7MufA47JzBMjYu6KNDVMZz0yYo5OtWQxR505oJ4s5qgzB9STxZPO/qnl9awhB9STxRx15oB6spjDHNOpJcs8bbMt0sxcZt4OLI2Is4B3A28AVgSeCzwnIi4E3p2Z35zpvnsqmkTEpsBpwCqUVZz+Cfy9l332yWeb2zcOM4QkSZIkSTWzSDNzEbEtZdjTcygdVf5LWbDoNMrq108FvhERr8vMz81k3732bPkQsCqlKPL+zLymx/31LCKeQ/ml7JyZ/5zlPrqaZDgzZ7N7SZLUJ7bZkiRpECLi3sBLKXPmrkfpqHIh8AXKak//ajY9OiI2Bn5ImRt3oEWazYDfZObre9xPX0TEvYBDgG9n5jem234K1/cpkiRJmlu22ZIkac5ExFMpvWZ2pAxpuhX4JnBIZp4+0XMy8xcR8X3K0t0z0muR5nbgdz3uo58+DtwFeM2wg0iSJEmSVDuHO03r+83tZcChwJe6XMXpL8BfZ3qwXos0Pwce3OM++mkjYHXg8oiY6PHjmvtfmplHTrGfJV0ez6t3kiQNl222JEmaS9+njNj5fmZ2PUN1Zr4dePtMD9ZrkWY/4PSI2Ckzj+txX/1wAqVw1OkxwKOBU4FLKOPGJpWZN/Q9mSRJ6jvbbEmSemNPmqll5o6DPF6vRZrlgE8BX42IZwMnUrr0TPhSZeaPejzelDLzAxPdHxH7UYo0n8/M4+cygyRJkiRJ84VFmqlFxO2UiYFfNs12h1FG7fRUZ+m1SHMGMEqZ1fhFwAun2X752RwkIvYCtmi+3ai53TsiWhWtpZm5dDb7liRJkiRJmsRI89Xttj3ptUhzNKVIM9e2AHbvuG+btn9fAiwdQA5JkiRJkhYMe9L0zerAzb3upKciTWbu0WuAGRxn1sfKzP0o8+f030jPhbL+MMd4tWQxx1i15IB6sphjrFpyQF1Z1LtaXs9ackA9WcwxVi05oJ4s5hjLHOPVlEXzXkSs03HXkgnua1kBCGB74I+9HrvXnjQaHURHommMjJijUy1ZzFFnDqgniznqzAH1ZPGks39qeT1ryAH1ZDFHnTmgnizmMMd0aslim72QXMLYUUPPa76mMgIc1uuBLdJIkiRJkrRIOdxpQj9iWZFma+Aq4IJJtr0F+Bvwncz8Vq8HnlGRJiLeSwl6cGb+s/m+W6OZ+cEZpZMkSZIkSXPGIs14mblN698RcQfwg8zccxDHnmlPmv0oRZqvA/9s+76bfl2jgEUaSZIkSZIqMTo6+2JLDaPMBuBBwPWDOthMizQvbW6v6PhekiRJkiRpQcnMSwd5vBkVaTLzqKm+lyRJkiRJmq8iYqvmnz/NzP+2fd+VzPxRL8d34mBJkiRJkhYp56QZ5wzKdC0PBS5s+75by/dycIs0kiRJkiRJxdGUosy1Hd8PxLwo0kTErsBWwMbAI4CVgJ0y8/gpnrMJsC+wJXAP4Grg58D7MvPXcx5akiRJkqTK2ZNmrMzcY6rv59q8KNIA+wMPpBRargQeMNXGEfEK4BDKClT/D/g7cB/gCcCGgEUaSZIkSdKiZ5GmLvOlSLMXcGFmXhYR+wHvm2zDiNgM+BzwY2CHzLy24/H58jNLkiRJkqQhiojlgCXATZl56yTbrAisAlyfmT2VruZFwSIzfziDzT8IjAC7dRZomn3d1rdgACMjfd3drJljvFqymGOsWnJAPVnMMVYtOaCuLOpdLa9nLTmgnizmGKuWHFBPFnOMZY7xasqiheZNwEHAU4DJahNbAycBbwY+3cvB5kWRplsRcU9gO+D/MvNPEbEtsAlwM3B2Zv6i7wcdHdj8QZMbGTFHp1qymKPOHFBPFnPUmQPqyeJJZ//U8nrWkAPqyWKOOnNAPVnMYY7p1JJlnrbZDnea1nOAv0zVeSQzfxgRfwWexzCLNBHxXuBXmfmdabZ7BvDozPxAL8frwqMpvWj+GhHfBXboyPENSg+bm+c4hyRJkiRJmv82oCxCNJ3fAo/p9WC99qTZDzgSmLJIAzwT2BOY6yLNms3tjsBVze2PgAcBnwVeAFwG7DPVTiJitW4OlpmzDipJknpnmy1JUm/sSTOt1Vm2HPdUrqWsLN2TQQ13Wh4YxMu3XNvxXpWZ32u+Pz8ingv8CXhNRLx7mt40189lSEmS1De22ZIk9cAizbSuAB7VxXaPonQW6cly02/SFw8H/jWA47SqW7cDP2h/IDP/AfwEWBV46ACySJIkSZKk+e004CERsctkG0TEiyh1htN7PdiMe9JExOEdd20xwX3t+w/gscDSmR5rFi5sbm+cZGmsVhFnlWn2s6TL43n1TpKk4bLNliRJrblwtwc2BjaifO7fJzM/1uOuPwrsAhwRERsCR7Gs9rABsDtlVadbmm17MpvhTnu0/XsUeHDzNZXzmWYemD65GPgrcP+IWDszL+94vNWD5tKpdpKZN8xFOEmS1F+22ZIk9WYBDXd6C2Up7GspQ5TW68dOM/OCiNiNUpzZt/lqNwL8F3hpZv621+PNZrjTts3XE5swJ7bd1/n1BOBBmblRZv6p17DTycxR4AvNtx+MiDvXQGu6Hz0c+HFm/m2us0iSJEmSVLtWkWa2XxV5N6Vnyz2AD/Zzx5l5HPBISr3hYuDm5uti4BDgUZn59X4ca8Y9aTLzzNa/I+Io4Kz2++ZCROwFbNF8u1Fzu3dE7Nj8e2lmLm3+/XHKqk4vBR4WEWdTVnd6NvAf4FVzmVWSJEmSJA1WZp7d+ndEzMX+LwZe0/cdd+hpdafMfGm/gkxjC8o4r3bbtP37Epo5bzLzpoh4EqWKtjPweuDfwNeB/TLzQiRJkiRJ0kIa7rQgDGoJ7p5k5h6MnQtnuu1vAN7RfM2tkZHptxkEc4xXSxZzjFVLDqgniznGqiUH1JVFvavl9awlB9STxRxj1ZID6slijrHMMV5NWeaZPhZpVp2uB8t8nksuIu4C7ARsCazd3H05cBZwXGb+tx/H6blIExEPoUycszWwFrDSJJuOZua8KArNyOjosBOUNyRzjFVLFnPUmQPqyWKOOnNAPVk86eybGn6Vo6N15IB6soyOwvLLDzsF3H57PTlWXHHYKYpbb4WVVx52Crj5ZlhlurVZB+Cmm2C11YadAm64oZ4cd73rsFMU110Hq68+7BRw7bXTb7PAXdXFNhW0PDMXEVsBXwHux/if4WXAhyJil8w8q9dj9VQ0iYhNKWuGr0IJ+k/g772GkiRJkiRJi0tEHAgsmcFT3puZ/5yrPABRugd9D1gN+DlwNPDn5uF1gd2ATYDvR8RjMzN7OV6vPVs+BKwKfBZ4f2Ze0+P+JEmSJEnSgPRxuNO9gRt7jLMXcK8ZbP8xSmeRufRuSoHmTZn56QkePzgiXg98CngXpWgza70WaTYDfpOZr+9xP5IkSZIkaf66sdc5ZzJzjX6F6aMnAr+cpEADQGZ+JiJ2B57U68F6LdLcDvyu1xCSJEmSJGnwRkdn35Omhun7BmAN4IwutrsAeESvB+u1SPNz4MG9hpAkSZIkSYPnEtzT+gcw9bJVRTTb9qTXIs1+wOkRsVNmHtdrGEmSJEmSpJmKiGcDz26+bXUm2TkiWr1bzs7ML85i16cDL4qIV2Xm5yc59quBxwDHzmL/Y/RapFmOMjnOV5tfyInAX4AJ62mZ+aMejydJkiRJktRpI2D3jvse23y1zKZIsz+l+HNwRLyYshT3Jc1j6wIvBjanTJp8wCz2P0avRZozgFHK8tsvAl44zfbLz+YgEbErsBWwMWWM10rATpl5/CTbbwfsAzwcuCdwOXAmcGBmXjybDJIkSZIkLTQLZbhTZu5HGe3T7/1eEBHPovSSeQLw+I5NRoArgRdn5gW9Hq/XIs3RlCLNXNsfeCBwNeWHf8BkG0bEG4FPUpbhOqG53RDYk9LV6QmZef5cB5YkSZIkqXYLpUgzlzLz1IhYD3gBsCWwVvPQ34CzgG9kZq/LjwM9Fmkyc49+hOjCXsCFmXlZROwHvG+ijSJiReD9wL+BR2bm5W2PvQ74DPAm4KVzHViSJEmSJC0MTRHmyOZrzvTak2YgMvOHXW56L+BuwI/aCzSN71KKNGv2MxsjI33d3ayZY7xasphjrFpyQD1ZzDFWLTmgrizqWS3LhNaSA+rJcvvtw05Q1JLj1luHnWCZm28edoLippuGnaC44YZhJyhqyXHddcNOsMy11w47wfxlT5q69K1IExH3pMwZswZwaWae2699z8CVwDXAIyNi7Y5CzY7N7Wl9PWINZzcjI+boVEsWc9SZA+rJYo46c0A9WSwU9U8tr2cNOaCeLOaoMwfUk8Uc5phOLVlssxeEiFinl+dn5mW9PL/nIk1ErAl8Gng+yyYGPgo4t3l8L+Ag4JmZeXavx5tKZo42w5qOBs6PiG+ybE6a7YHDgP+dywySJEmSJGneuoTZz707So91lp6e3PSeORdYH/gVcA6wd8dmJwCHUIo4c1qkAcjMr0bE1ZRlsV7e9tB5wJczs6IOpJIkSZIkDY/Dncb5EYNZIGlCvfakeRelQPOBZrkrImJMkSYz/xkR5wNb93isrkTEy4DPUXr3HAz8A3gk8Cng1Ih4XmZ+Z5p9rNbNsTKzt7CSJKknttmSJPXGIs1YmbnNMI/fa5Hm2ZRVl/abZrs/Atv0eKxpRcRDgM8D38nMfdse+nFEPBP4M2Xo1ZRFGuD6OYooSZL6yzZbkiQtGMv1+Py1gV93sd0oZdWlufZkSuHpjM4HMvNK4A/A/0TEKgPIIkmSJElS1Vo9aWb7tdhExMoRcb9m+pe+67UnzX+A+3Wx3frA1T0eqxsrN7drTPL4msAdwHTz0izp8nhevZMkabhssyVJ0pyLiFcAr6YsTDRCWTBpz+ax5wK7Avtm5sW9HKfXIs3PgCdGxHqZ+aeJNoiIjYCNgON7PFY3Wst+vyIiPp+ZV7Tl2At4APCjzLxtqp1k5g1zmFGSJPWJbbYkSb1xTpqpRcTylHrGMykdPv4APLxjs1832/wCOKCX4/VapPlf4GnA0ojYJTN/2/5gRDyYshz2CPDZ2R6kKbBs0Xy7UXO7d0Ts2Px7aWYuzcxzI+LrwM7AHyLiW5QePBtRhkLdBOwz2xySJEmSJGlReS3wLOD7wMsy88qIGFOeysw/RsTFlPrI8Io0mXliRBwE7Av8ugk1CjwlIn4NPAxYHjggM3tZfnsLYPeO+7Zp+/clwNLm3y8GzgJ2oyz7fRfgKuBY4EOZ+fseckiSJEmStGDYk2ZaewBXAjtP04P3d8Bjez1Yrz1pyMy3R8QvKMtxP7K5+37N1wXABzPzqz0eYw/KL6abbW+nLL19cC/H7NrIyEAOMy1zjFdLFnOMVUsOqCeLOcaqJQfUlUW9q+X1rCUH1JPFHGPVkgPqyWKOscwxXk1Z5hmLNNMK4OQuhljfSJkHtyc9F2kAMvM44LiIWBNYl7Jq1F8z8/J+7L9qo6PDTlDekMwxVi1ZzFFnDqgniznqzAH1ZPGks39qeT1ryAH1ZDFHnTmgnizmMMd0aslim71Q3UoZoTOddYDrej1YX4o0LZl5NYNZxUmSJEmSJPXInjTT+h3w2Ii4a2ZOWISJiPsAjwZ+3OvBlut1B5IkSZIkSQvUMcA9gcMiYlyPmmb1p88Cq1CW5e5JTz1pIuK0Lje9BbgG+BXwtcz8Sy/HlSRJkiRJGoBDgZ2AFwCbR8SJzf2PiohPAzsCDwJOpixY1JNehztt09yOUpbZnkj7Yy8C9o+It2Xmp3o8tiRJkiRJ6sHo6OyHLdUwFdBcy8zbI+LpwMeBvYCXNw89uvm6HTgMeENm9vwb6bVI8yDgjcBrgG8AXwcuax57ALBz8/X55rGtgHcAH4+I32fmyT0eX5IkSZIkzZJz0kwvM/8L7B0R+1E6q6xLs2AScHpm/q1fx+q1SPM44HXA0zLzlI7Hzge+FxHHAN8HfpKZB0bEecCpzfMs0kiSJEmSpCpFxBeBYzLzzGaxpOPm8ni9Thz8VuCsCQo0d2oeOxt4S/P96cCvgU17PLYkSZIkSepBqyfNbL8WgT2B0yLisog4MCI2nMuD9dqT5qHAt7vY7gpgk7bvLwIe1s0BImJtyiQ9TwceAtwX+CdwDnBQZp43wXM2AA4AtgWWNMc7FPhcZi6OPyNJkiRJktSrlwEvpgxz2hfYJyJ+C3wZ+EpmXt7Pg/Xak+ZGYJOImGzSYCJiOeCxzbYtdwH+0+UxXgd8EliPMjzq45SeOc8Czo2InTuO9zDgZ8CzgZOAzzQP/S9lbhxJkiRJkoQ9aaaTmUdk5naUeXffSlm1ekPgQODSiDg1IvaMiLv143i9Fml+CKwPfC4ilnQ+GBGrUdYLX5+x888E0O0y3D8FtsnMB2fmXpn5jsx8PqWXzO3AIRGxctv2hwCrA8/OzF0z823AxpR5cF4eEdvM6CeUJEmSJEmLWmZekZmfyMyNKaOKPgRcSqlNHAb8PSK+0etxeh3u9A5gO+AVwM7NeuGt1Z3WAZ4C3B24GngXQEQ8HNgA+Gg3B8jMEya5/6yIOB3YnlLF+nlE/A9lBanTM/P7bdveGhHvBM5rsp4xo59yKiOTdiIaLHOMV0sWc4xVSw6oJ4s5xqolB9SVRb2r5fWsJQfUk8UcY9WSA+rJYo6xzDFeTVnmGVd3mrnMTOA9wHsi4nHAK4Hdgef1uu+eijSZeWlEbA58AXgi8MIJNjsVeHVmXtp8fxGwJnBdL8du3Nrc3tbcbtPcTrRq1M+Aa4Ct+3DcZWpYGH5kxBydaslijjpzQD1ZzFFnDqgniyed/VPL61lDDqgniznqzAH1ZDGHOaZTS5Z52mZbpJmdiFie0jHlxcAz+7XfXnvSkJl/BLaLiPWBJwD3ax66Ajg3My/u2P4WSrGkJxGxDqUXzxXAb5q7N2huL5og52hEXAxsFhGrZuaNndtIkiRJkiRNpuk582LgBcAawAjwb8qQpy/3uv+eizQtTbHmj5M93s/CSESsCBwDrAy8LTNvbx5avbm9dpKnXtu2nUUaSZIkSZI0pWZqlRc3Xw+iFGZuBpZSCjPfazqk9KxvRZqJNKs+PQl4CWW1pdWnfEJ3+1wOOJIy98yhmXlMr/uc4BirdbNdGYYmSZKGxTZbkqTeONxpahHxM+AxlMLMKGW16S8Dx2Xmv/t9vDkp0kTEI4DdgF0ow5/6MjivKdAc3uz3KODVHZu095SZSOv+6Zb/vn5WASVJ0qDZZkuSpLm0MfAHSmHm2My8bJrte9K3Ik1E3IfS9eclwCObu0eA3wNfab562f9ywBGU4s+xwJ6Z2Vm3a81Fs0HH/a1ePQ8G/paZN/SSRZIkSZKkhcCeNNPaODN/OaiD9VSkiYhVgOdQCjNPApZnWa+ZUWCjzDy/p4SMK9B8Ddh9ggINwJnN7fbAgR2PbQLcC/hqF4dc0mU0r95JkjRcttmSJPXAIs3UBlmggVkWaSLiiZSCyXOB1SiFmRuAEygT+r4feFwfCzSHN8c7Dti1baLgMTIzI+JHwLYR8fTM/H6zjxWBA5rNDpvumPa0kSRpfrDNliRJC8mMijQRcSBlPpi1KYWZO4AfAkcD32qt3hQR7+tjxvcCu1OugF0IvCciOrc5MjMvaf79auBc4FsR8Q3gb8BTKUOwvpiZp/cxmyRJkiRJUl/MtCfNvpRhTFcAnwC+mplX9D3VWOs2t0uAd02yzRnAJQCZ+fuI2JTSc+ZpzfMuBl4PHDyHOSVJkiRJmlcc7lSX2Qx3GqGs2PRC4NaI+FpmXt3fWMtk5h7AHjN8zoXATnORZ5yRvixc1TtzjFdLFnOMVUsOqCeLOcaqJQfUlUW9q+X1rCUH1JPFHGPVkgPqyWKOscwxXk1Z5hmLNHWZaZGmfWntx1KWovp4RJxCWY5qaWbe1N+IlRsdHXaC8oZkjrFqyWKOOnNAPVnMUWcOqCeLJ539U8vrWUMOqCeLOerMAfVkMYc5plNLFtts9cFyM9k4M3+fmW8HHghsRynM/JcyrOjLwJURcRRw334HlSRJkiRJ/TU6uqw3zUy/aqiNLTQzKtK0ZOZoZp6WmbsD96H0rvkhsAplOe71ACLiUxGxWb/CSpIkSZKk/pltgaaXYVKa3KyKNO0y86bM/HJmPgV4AGVy4d9Q5q55PXBuRFwcER/o9ViSJEmSJEkLVc9FmnaZ+ffM/FhmbgRsBHwSuJLSs2aylZkkSZIkSZIWvb4Wadpl5vmZ+Rbg/sDTga/O1bEkSZIkSdLMOdypLrNZgntGMvMO4MTmS5IkSZIkSROY8yKNJEmSJEmqUy89YuxJ03/VF2kiYm1gJ8qQqYdQlvf+J3AOcFBmnte27b2A5wE7Ao8A1gauA34GfCozTxpsekmSJEmS6mWRpi5zNidNH72OMgHxesDJwMeBs4FnUVaO2rlt252ALwAbN9t8AvgBsDVwYkTsM8DckiRJkiRJXau+Jw3wU2CbzDyz/c6I2BI4FTgkIpZm5s3AhcAzge81c+G0tt0fOA84ICKOzcy/DS6+JEmSJEnS9Kov0mTmCZPcf1ZEnA5sD2wI/DwzT5tk24yIrwOvAB4PHN+3gCMjfdtVT8wxXi1ZzDFWLTmgnizmGKuWHFBXFvWultezlhxQTxZzjFVLDqgniznGMsd4NWWZZxzuVJfqizTTuLW5va3P23ZvdLSvu5uVkRFzdKoliznqzAH1ZDFHnTmgniyedPZPLa9nDTmgnizmqDMH1JPFHOaYTi1Z5mmbbZGmLvNhTpoJRcQ6wHbAFcBvptn2bsDzgf8CZ819OkmSJEmSpJmZlz1pImJF4BhgZeBtmXn7NE/5PHAf4L2Zec1c55MkSZIkaT6wJ01d5l2RJiKWA44EtgIOzcxjptn+w8CLgO8DH+ryGKt1s11mdrOZJEmaI7bZkiRpIZlXRZqmQHM4sAtwFPDqabb/IPB24BTgeV30uGm5vpeckiRpYGyzJUnSgjFvijRNgeYIYDfgWGDP9mW2J9j+g8C7gdOAZ2XmfwcSVJIkSZKkecLhTnWZF0WajgLN14DduyzQnAk8IzNvmuEhl3S5nVfvJEkaLttsSZJ6YJGmLtUXadqGOO0GHAfsOtWwpYj4AKVAcxawQ2beONNjZuYNs4wrSZIGyDZbkiRFxL2A5wE7Ao8A1gauA34GfCozTxpivBmpvkgDvBfYnXIF7ELgPRHRuc2RmXlJROwBvAe4DfgpsM8E2y7NzF/NZWBJkiRJkuaDBdKTZifgEOBvwKnA5cD9KYWbp0bEvpn50SHm69p8KNKs29wuAd41yTZnAJe0bbsC8JZJtr0E+FUfckmSJEmSpOG7EHgm8L32qVEiYn/gPOCAiDg2M/82rIDdGhkdHR12hvlslBp+fyMjmKNDLVnMUWcOqCeLOerMAfVkGRkZGXaEBcI2u1MtWcxRZw6oJ4s5zDGdWrLMozY7Ilajma9tu+1+yQorrDqr/dx224388IePbn27pMZhyBHxBeAVwE6Zefyw80xnPvSkqVst/w/NMV4tWcwxVi05oJ4s5hirlhxQVxb1rpbXs5YcUE8Wc4xVSw6oJ4s5xjLHeDVlmWf6ONxp1QmmGxljSEWcW5vb24Zw7BmzSNOrOiq25uhUSxZz1JkD6slijjpzQD1ZPOnsn1pezxpyQD1ZzFFnDqgniznMMZ1asszTNnt0dPZFmo5f+1VdPGWgv6SIuBvwfOC/lMWFqmeRRpIkSZKkReqOO2ZfX6po4uDJfB64D/DezLxm2GG6YZFGkiRJkiT16t7Ajb3sICIOpCwa1K33ZuY/J9nXh4EXAd8HPtRLrkGySCNJkiRJ0iLVx540N/Zhzpm9gHvNYPuPAeOKNBHxQeDtwCnA8zLz9h5zDYxFGkmSJEmSNHSZuUav+2gKNO8GTgOelZn/7TnYAC037ACSJEmSJEm9aivQnAk8IzNvGnKkGbMnjSRJkiRJi9RCmTg4Ij5AKdCcBeyQmT3NjzMs1RdpImJtYCfg6cBDgPtSxpydAxyUmedN8/x9gIOabzfJzJ/PYVxJkiRJkuaNhVCkiYg9gPcAtwE/BfaJiM7NlmbmrwabbOaqL9IArwPeBvwROBm4GtgAeDbw7IjYJTO/PtETI+IhwAeAG4DVBpJWkiRJkiQN0rrN7QrAWybZ5hLgVwPI0pP5UKT5KbBNZp7ZfmdEbAmcChwSEUsz8+aOx5cDDgd+AySw64DySpIkSZI0LyyEnjSZuR+w35Bj9EX1RZrMPGGS+8+KiNOB7YENgc5hTG8GHgtszOSVtN7N9q+538wxXi1ZzDFWLTmgnizmGKuWHFBXFvWultezlhxQTxZzjFVLDqgniznGMsd4NWWRelB9kWYatza3t7XfGRH/A3wQODAzfzPBWLT+GR2du313a2TEHJ1qyWKOOnNAPVnMUWcOqCeLJ539U8vrWUMOqCeLOerMAfVkMYc5plNLFtts9cG8LdJExDrAdsAVlCFNrfuXA44A/gTsP5x0kiRJkiTVbyEMd1pI5mWRJiJWBI4BVgbelpm3tz38RuBxwBMy85YhxJMkSZIkaV6wSFOXeVekaXrKHAlsBRyamce0PbYBpffM/2bmT3o4RlcrQWXmbA8hSZL6wDZbkqTeWKSpy7wq0rSt2LQLcBTw6o5NDgOuBN7V46Gu7/H5kiRpMGyzJUnSgjFvijRtc83sBhwL7JmZnXW7jYDVgesnmSz4Z83922bmGXMWVpIkSZIkaYbmRZGmo0DzNWD3CQo0AEcDq05w/1bABsC3gX9QJhueypIuo3n1TpKk4bLNliSpBw53qkv1RZq2IU67AccBu3ZMFHynzHz9JPs4klKk2T8zfz7dMTPzhlkHliRJA2ObLUmSFpLqizTAe4HdKVfALgTeM8FQpiMz85IB55IkSZIkaV6zJ01d5kORZt3mdgmTTwh8BnDJALJIkiRJkrRgjI7OvtgyOtrfLJoHRZrM3APYY9j7kCRJkiRJmkvVF2mqN9t+Yf1mjvFqyWKOsWrJAfVkMcdYteSAurKod7W8nrXkgHqymGOsWnJAPVnMMZY5xqspi9QDizS9qqF/18iIOTrVksUcdeaAerKYo84cUE8WTzr7p5bXs4YcUE8Wc9SZA+rJYg5zTKeWLPO0ze5lXhnnpOm/5YYdQJIkSZIkSfakkSRJkiRp0bInTV0s0kiSJEmStEhZpKmLRRpJkiRJkhYpizR1cU4aSZIkSZKkClikkSRJkiRJqoDDnSRJkiRJWqQc7lSX6os0EbE2sBPwdOAhwH2BfwLnAAdl5nmTPG974A3AZsAS4ArgPGCfzPzLAKJLkiRJkiR1bT4Md3od8ElgPeBk4OPA2cCzgHMjYufOJ0TE/sBJwCOB45vn/wjYHHjgYGJLkiRJklS3O+7o7Uv9VX1PGuCnwDaZeWb7nRGxJXAqcEhELM3Mm5v7dwLeBXwTeHHr/rbn9fdnHhnp6+5mzRzj1ZLFHGPVkgPqyWKOsWrJAXVlUe9qeT1ryQH1ZDHHWLXkgHqymGMsc4xXU5Z5xuFOdam+SJOZJ0xy/1kRcTqwPbAh8POIGAH2B/4D7NlZoGmed1tfA46O9nV3szIyYo5OtWQxR505oJ4s5qgzB9STxZPO/qnl9awhB9STxRx15oB6spjDHNOpJYtttvqg+iLNNG5tbluFl0cB/0PpRXNDROxAKeD8BzgtMy8YfERJkiRJkqTpzdsiTUSsA2xHmRD4N83dGze3/wTOBTZte8poRHwGeFNmVlBmlSRJkiRpuBzuVJd5WaSJiBWBY4CVgbdl5u3NQ2s2t3sCFwNbAb+k9KY5jLLa00XAwdPsf7VucmTmjLNLkqT+sc2WJEkLybwr0kTEcsCRlALMoZl5TNvDy7XdvjAzf9V8/+OIeAGlx82bmaZIA1zft8CSJGku2WZLktQDe9LUZT4swX2npkBzOLALcBTw6o5Nrm1u/9JWoAEgM38P/BFYLyLuPrdJJUmSJEmqn0tw12Xe9KRpCjRHALsBx1JWb+r8k7iwub2WibXuXwX49xSHW9JlLK/eSZI0XLbZkiRpwZgXRZqOAs3XgN0nKNAA/Bj4L6W3zMrtS3A389isD9wIXD3V8TLzhn5llyRJc8c2W5IkLSTVD3dqG+K0G3AcsGvbRMFjZOb1lF42qwHv6Hj4rcA9gG9n5m2dz5UkSZIkabFxuFNd5kNPmvcCu1O6KV8IvCciOrc5MjMvaf79DmAb4H0RsQXwK8rqTtsDlwP7zHliSZIkSZLmgdHR2RdbRkf7m0Xzo0izbnO7BHjXJNucAVwCkJlXR8TjgA8Az6SsAnUVcCiwX2ZeMYdZJUmSJEmSZmVk1NJXL/zlSZLm2siwAywQttmSpLk2b9rsiFiNZlL9e93rl4yMrDqr/YyO3sg11zy69e0S54rr3XzoSVO3GopcIyPm6FRLFnPUmQPqyWKOOnNAPVlG5s35Xv1qeT1ryAH1ZDFHnTmgnizmMMd0aslim60+qH7iYEmSJEmSpMXAnjSSJEmSJC1Sd9wx+05ANXRgWmgs0kiSJEmStEhZpKmLw50kSZIkSZIqYE8aSZIkSZIWKXvS1MUijSRJkiRJi5RFmro43EmSJEmSJKkC1fekiYi1gZ2ApwMPAe4L/BM4BzgoM8/r2H4EeD7wOuB/gCXAX4AfAB/NzCsGl16SJEmSJKk786EnzeuATwLrAScDHwfOBp4FnBsRO3ds/0ngG8323wQOAa4C3gT8X0Tcb0C5JUmSJEmq2h139Pal/qq+Jw3wU2CbzDyz/c6I2BI4FTgkIpZm5s0RcV/g9cCfgI0y87q27T8OvBl4BfD+vqWb7eC9fjPHeLVkMcdYteSAerKYY6xackBdWdS7Wl7PWnJAPVnMMVYtOaCeLOYYyxzj1ZRlnnFOmrpUX6TJzBMmuf+siDgd2B7YEPg5sC4wApzdXqBpfJdSpFmzrwFr+KscGTFHp1qymKPOHFBPFnPUmQPqyeJJZ//U8nrWkAPqyWKOOnNAPVnMYY7p1JLFNlt9MB+GO03l1ub2tub2IuAWYIuIuGvHtjs2t6cNIpgkSZIkSbVzuFNdqu9JM5mIWAfYDrgC+A1AZl4TEe8GDgL+EBHfBm4ANmu+9p+sZ44kSZIkSdIwzcsiTUSsCBwDrAy8LTNvbz2WmR+NiL8BXwBe0/a0k4Djutz/at1sl5ldZ5YkSf1nmy1JkhaSeTfcKSKWA44EtgIOzcxjOh5/H3AEZXLg+wN3o8xb8yDKalCbdHGY67v8kiRJw2WbLUlSDxzuVJd51ZOmKdAcDuwCHAW8uuPx7YD9gE9k5kfbHjolIp4PnA98EHjqQAJLkiRJklQxV3eqy7wp0jQFmiOA3YBjgT0zs7Nu97Tm9ozO52fmbyLin8BGXRxuSZexvDInSdJw2WZLkqQFY14UaToKNF8Ddp+gQANljhqANSbYx8qUoU9/m+54mXnD7NNKkqRBsc2WJKk3o6P2iKlJ9UWatiFOu1Em/t21faLgDucCewNvjojjM/O6tsfeQ/l5z5jDuJIkSZIkacAi4oPAE4D/oXTc+A9wMfAl4OjMvHWI8bpWfZEGeC+wO6Wb8oXAeyKic5sjM/MS4BuUFZ2eAFwYEf8PuA7YvPm6mjJnjSRJkiRJi16vk/8uV89yRHsDCZxI+ey/OmU+2i8CO0XE0zKz+j5D86FIs25zuwR41yTbnAFckpm3RcSTgTcDLwB2pfyMlwOHAh/MzL/OaVpJkiRJkjRoa2Xmf9vviIgVgJOApzRfJw4j2EyMjDr4rBf+8iRJc22W6y2og222JGmuzZs2OyJWo5lU/7bbfgmsOss93cgKKzy69c2SGueKi4jXA58GXpuZBw87z3TmQ0+autVQ5BoZMUenWrKYo84cUE8Wc9SZA+rJMts1MTVeLa9nDTmgnizmqDMH1JPFHOaYTi1Z5mmb3etwp5o1c9w+tfn2t8PM0i2LNJIkSZIkqVerTjB/7BiD6GkTEe8EVgLuBTwJeCjwxcw8c66P3Q8WaSRJkiRJUq+u6mKbQXQ3eiewWvPvUeDjwNsHcNy+sEgjSZIkSdIiVdNwp4g4kLJoULfem5n/bL8jM5dExAiwFrADcCCwaUTskJnX9S/t3LBII0mSJEmSenVv4MYe97EXZZhStz4G/LPzzmap7cuBQyPiGuB4Sm+ayVaMroZFGkmSJEmSFqk+9qS5sdc5ZzJzjX6FaXNKc7vNHOy77yzSSJIkSZK0SNU03GmOrNXc3jbUFF1abtgBJEmSJEmSZisi1ouIe0xw/yqUIVEAJw421exU35MmIu4CfBjYBFgfuCdwDXABcDBwQjPerP059wX2p0wSdA/gMuDLwIGZecvg0kuSJEmSVK8F0pNmK+CQiDgL+DPwL0oPmqcCawLnAJ8aWroZmA89aZYArwRuBf4fZfms7wEPo0z+84X2jZsCzXnAnsCPKS/EVcD7ge9ExHz4mSVJkiRJUnfOAQ6jTF78fOCtlE4bfwBeA2ybmTcNL173qu9JQ5mp+e6dPWAiYgmlGPPyiPhEZl7QPPQRYB3gNZl5SLPtCHAEsHvzdUTf0o0MYpn3LphjvFqymGOsWnJAPVnMMVYtOaCuLOpdLa9nLTmgnizmGKuWHFBPFnOMZY7xasoyzyyEnjSZeRHw+mHn6IfqizSZeQcwbohSZl4fESdRetSsD1wQEXcFdgb+BHy+bdvRiHgHsCvwcvpZpBkdnX6buTYyYo5OtWQxR505oJ4s5qgzB9STxZPO/qnl9awhB9STxRx15oB6spjDHNOpJYtttvpg3g79aeaqeSJwB/D75u7NgZWBUzrnqcnMK4BfA5s2z5UkSZIkaVG7447evtRf1fekaYmIVYF9gRHKOLOnU4Y1vScz/9xstkFze9Eku7kIeAywHssKO5IkSZIkLUoWWuoyb4o0wKrA+9q+vxXYhzKRcMvqze21k+zj2o7tJhQRq3UTKDO72UySJM0R22xJkrSQzJsiTWb+AxiJiOWB+1PmntkfeFxE7JyZt/fxcNf3cV+SJGnu2GZLkqQFY94UaVqaYsylwEERcTvwMcpy24cxfU+Z6XraSJIkSZK0aDjcqS7zrkjT4ZTmdhtKkaY1F80GE25d7r+DsvrTVJZ0eXyv3kmSNFy22ZIkacGY70WatZrb25rbn1CW635yRIy0r/AUEfcDHgWcl5n/nWqnmXnDXISVJEn9ZZstSVJvRkdnv4K5q473X/VLcEfEQ5uVnTrvvwdwQPPtiQCZ+R/ga5TVm17V8ZQPA8tTetxIkiRJkrTouQR3XeZDT5qdgTdFxNnAJZTuyusAOwB3Bb5JKcy0vB3YFjg4IrYDLgS2BJ4AnAQcNbDkkiRJkiRJXZoPRZrvAmsDj6cUWlYD/gWcCxwNfLV9WFNmXhERm1FWftoB2BG4jLJ890cy01qfJEmSJEmqzsjobAefCcBfniRprjnauz9ssyVJc23etNkRsRrNpPqXX/5LRkfHzTDSlZGRG1l77Ue3vl3iXHG9mw89aepWQ5FrZMQcnWrJYo46c0A9WcxRZw6oJ4sz8vVPLa9nDTmgnizmqDMH1JPFHOaYTi1Z5mmbfccdThxck+onDpYkSZIkSVoM7EkjSZIkSdIiZU+autiTRpIkSZIkqQIWaSRJkiRJkirgcCdJkiRJkhYphzvVxSKNJEmSJEmLlEWaujjcSZIkSZIkqQL2pJEkSZIkaZGyJ01dLNJIkiRJkrRILbfcTdxxx+yfq/6ySCNJkiRJ0iK1/vqPH3YEtbFI05uR2fbviojVgOubb5dk5g29JZnfOWrKYo46c9SUxRx15qgpS99zqB9ss2vOYo46c9SUxRwLKkdNWWyzVRuLNJIkSZIkLS43AkvmYJ/qkUUaSZIkSZIWkcwcBew1VCGX4JYkSZIkSaqARRpJkiRJkqQKWKSRJEmSJEmqgEUaSZIkSZKkClikkSRJkiRJqoBFGkmSJEmSpApYpJEkSZIkSarAyOjo6LAzSJIkSZIkLXr2pJEkSZIkSaqARRpJkiRJkqQKWKSRJEmSJEmqgEUaSZIkSZKkClikkSRJkiRJqsAKww6wGEXEJsD7gc2BlYDfAZ/KzK8MMMOuwFbAxsAjmhw7Zebxg8rQ5Fgb2Al4OvAQ4L7AP4FzgIMy87wB5bgL8GFgE2B94J7ANcAFwMHACZk5tKXQIuJg4DXNt2tm5j8GdNypfuZ9MvNjg8jREhEvBF4JbASsDPwVOBd4XWZeN8fH3gM4YprNDs/Ml81ljibLCsBLgb0of68rAZcCxwOfzsx/z3WGJsdylL/LPSn/f28DfgV8LDO/MwfHm9H7VkTcF9gf2AG4B3AZ8GXgwMy8ZVBZImIr4FnNto8B7gocnJmv7SWDBsM2e0wO2+zu8tlmY5vdlsU22zZbmhGLNAMWEdsAJwG3AF8DrgWeCxwbEetm5ocGFGV/4IHA1cCVwAMGdNxOrwPeBvwROLnJswHwbODZEbFLZn59ADmWUE4kzgP+H/APYE3gGZRG9DDgFQPIMU5EbAu8GrgBWG0IES4Fjpzg/nMHFaA5sTgC2A24kNJo30j5u30asDowpyd8lBOZ90/y2B6U/08nz3GGluMo/0cSOBa4HdgG2A/YOSI2ycwb5jJARIwA3wCeR/n/+yXKSfizgG9HxOsy87N9PmzX71vNyd55zTZLKX83W1Bew8dHxNMz845BZKGcEO9O+Zv9CxA9HFcDZJs9jm32NGyzbbMnYJttmy3NiEWaAWoq6V8ERoGtMvOXzf37AT8B3h8Rx2XmRQOIsxdwYWZe1hz/fQM45kR+CmyTmWe23xkRWwKnAodExNLMvHmOc/wTuHtnpT4illAajZdHxCcy84I5zjFGRKxGaUiXUq4Ubj3I4zcuycz9hnDcdm+hnOx9Gnhze0PdnAzOucz8FeWkb4yIuAfwdsrf0NK5zhERm1JO9n5MeR+5re2x4yknYM8HjprjKM9rvs4BnpyZNzUZ3gX8DPhYRHw3My/p4zFn8r71EWAd4DWZeUiTbYTywWH35mu6q6z9yvJZ4KOUq/xbAqf3cFwNiG32hGyzp2CbfSfb7GXHs822zZZmzDlpBuuJlG6OX2md7AFk5vXAByhFs5cOIkhm/jAzLxvEsabJcULnyV5z/1mUN8V7ABsOIMcdE3WlbF6bk5pv15/rHBP4COV3sPcQjl2FiFgVeCdwMfCWzispzWvXy9WVXr2YcjXqKwP4YAKwXnN7SvvJXuN7ze2aA8jxrOb2Q62TPYDMvBr4JOV30tf3s27ftyLirsDOwJ+Az7c9fxR4B+Uq5ssHkaXZ9ueZ+bvMvL2XY2rgbLPH57DNnppttm12J9vsadhmS+NZpBmsbZrbibpXntjcDuOqS61ubW47G7WBaca9PxG4A/j9gI+9NWXs8Fsy84pBHrvDPSLilRHxzoh4eURsMODjbw/cHfg2sGJE7BQR72iyrDPgLBPZs7k9fEDHa/0dPrm50t9uB8pV/zMGkOO+ze2fJ3isdd8TB5BjIptTTjhP6ZyXovm/9Gtg0+b/tzSZbZpb2+zu2GbbZoNtdifb7OnZZksdHO40WK2GclzX6Mz8T0Rc2bbNotY05NsBVwC/GeBxVwX2BUaAe1MmR1wHeE9mTtSwzWWOLwGnZuagTiQm80jarmwAoxFxLPDKzLxxAMffuLm9Azifsf9Hbo2Id2XmRweQY5yIeBTwaOCX7Vfa51Jmnt9MSrk38NuIOJFylWlbyu9m78z8+QCitCbCfBDwh47HHtTc/s8Ackxk0vfatvsfQ7nCOdAPcppXbLO7ZJttm93GNruNbXZXbLOlDvakGazVm9trJ3n82rZtFq2IWBE4hlJVf9uAuxuuShmn+l7gVcD9gH2AAwaYAeBAylWPoUx82OZjwGaUsfX3oFxlOQ/YlTJXwyC0ugG/mbJ6x2OAuwFPpUz+dlBE7DigLJ1aq0IM9KS8WWFgH0p3/jdQfjePBr4D/GBAMVrHeXv71a2IuBfwxubbuw8oS6du3mvbt5MmYpvdBdtswDa7nW12B9vsadlmSx3sSaOqNBPKHUlZJu/QzDxmkMfPskzmSEQsD9yfMkZ2f+BxEbHzIE4+mwkYX0uZbG9gVwInkpn7dNx1ekQ8idL19EUR8YEBTMzYKibfDDwnM//efH9SROxFGXbwJuC7c5xjjIhYCdilyXXsAI+7HPAFyt/mqygrm9wCPIky2d1PI2LTPk/+N5GvUFbI2Bb4TUT8gGUrRVzZbDPMeQckzTHbbNvsCdhmjz2ubbakGbMnzWBNVwlencmryAte05AdTmlEj6IsYTkUmXl7Zl6amQcB76LMiL/nNE/rWTNe+XDKla/PzPXxZqPpLv3V5tvHD+CQrf8TP2872Ws5hXLCtTGD92zgXsDSzPzXAI+7J2WVgndl5pcy86rM/HdmfpNyFXdNysoVc6qZAPFplCVE76Ash/scyjwEz282u2quc0yim/fa9u2kidhmT8E22zZ7ErbZY9lmT882W+pgT5rBao213AD4RfsDEXE34D7AuYMOVYPmZO8IypKNxwJ7Dnn2/3anNLfbAIfN8bGWAA9uvm6PiIm2ubq5/0EDuPIymdbY5lUHcKwLm9txjXNm3hER11G6Ug9a6wPAlwZ83Kc1t2dM8Fjrvo0GEaRZGeP9zdedImKb5p+DGGc/kfb32olsQDlJ/dNg4miess2ehG32nWyzx7PNHss2e3q22VIHizSDdSZlKbntga91PPbUtm0WlY6Tva8Bu1d0sgewVnM7iBUrbmbyE4gdKGPev9xsd90A8kxm0+b2kgEc6/Tm9qGdD0TEGsAaTD7Z3JyIiPsDTwYuA04d5LEp3ZOh/NydWnMBDGJZ0am8uLntfJ8blJ9QupM/OSJG2leLiIj7AY8CzsvM/w4pn+YH2+wJ2GaPYZs9nm32WLbZ07PNljo43GmwTqVUgXeJiI1ad0bEEsqkd7dRTnwWjbbu0rsBxwG7DnjSwVaOhzarM3Tefw+WTUB4Yufj/ZaZN2XmXhN9Adls9qbmvmvmMktEPLwZw915/3Mp3duvYtnJ2JzJzIsp/3ceHBF7tOUYocw9AHD8XOfosAfl/fPIIXw4aV25f0f769P8X9qv+faMQQRpehN03vd8yhXLnwEnDCJHp8z8D+Vkcz3KHADtPgwsz9xfYdf8Z5vdwTZ7LNvs8Wyzx7HNnoZttjTeyOjo6PRbqW8iYlvgJErV/KvAf4DnUpa/e3dmDmRFgmbyti2abzeiVKnPAC5t7luamUsHkGM/ysoM1wOfZuIrX0fOdRfhJsebgLMpV5qupyzjuQNwV+CbwE7t1f1Bi4gzgK2BNZvJEuf6eJ8CXgL8iHL16Q7KagRbA/8FnpuZA1mVICI2AH5MWbHiO8DFlLH1m1OWe31CZg7kKmVzonkR5WRivUF3X4+I1YGfUpbK/CPl/eRWyioeGzbZNhvEmPuI+APwF8pynv+lXK3dhvLB9omZeenkz57V8bp+32quvp1HmUz0W5Qu+FsCT6D8zp7ey8n6DLNsQZmTAMqV9acAFzT5AC7IzANnm0VzxzZ7XI79sM3uim22bXZzfNvsYiNss6WuOdxpwDLz9OY///uBFwArAb8D3pOZA5ttnvJGtXvHfdu0/fsSYOkAcqzb3C6hTPY3kTOY+y663wXWppxEPAFYDfgX5QrI0cBXh3myNyQnAg+gnOQ9GVgR+BvlyvFHM/MPgwqSmRdFxGMpV+GeDDwd+CtludEPDupkr7EVZRnN04Yxv0BmXhsRj6MMw3gmy5YUvRT4OHDAACdF/DrlA+vjKH8ff6a8Rh9troz1W9fvW5l5RURs1uTZAdiR8sHlfcBH+nA1dSbvoQ+eYNuHNF9Qhsx4wlch2+xx1m1ubbPrY5s9MdvsZWyzu8iCbbYqYE8aSZIkSZKkCjgnjSRJkiRJUgUs0kiSJEmSJFXAIo0kSZIkSVIFLNJIkiRJkiRVwCKNJEmSJElSBSzSSJIkSZIkVcAijSRJkiRJUgUs0kiSJEmSJFXAIo0kSZIkSVIFLNJIkiRJkiRVYIVhB5A0tYgYnelzMnMkIo4Edge2zcwz+p1LkiSNZZstSeqVRRqpfkdNcN8WwPrAr4FfDTRNRSLiDGBr4EGZeclw00iSZJs9GdtsSeqORRqpcpm5R+d9zRW39YGlmbnfJE99B3AgcNlcZZMkScvYZkuSemWRRlqgMvMK4Iph55AkSVOzzZYktVikkRaoyca3R8QlwAObMfCvAF4LPBj4B3AM8L7MvC0i1gM+ADwZuBvwS2DfzDx7kuNtBryV0q37XsBVwInABzKz6yuDEbEG8Cbg2cA6wGizr18Ah2XmDyNiXeDPbU/7c0Tc+U1mjrTtbwR4IfBy4NHAKs1zvw4clJk3dhz/DJru2M3P8ibgocANwA+Ad2bmX7v9eSRJmo5t9p37s82WtOi5upO0SEXEx4HPAJcDJwOrAe8EvhARGwDnAZsBpwO/BTYHTomIh02wr9cA5wLPpXTVXgpcA7wM+HlEPLTLTEuAnzQ5Vgd+SDlpvBp4JuXEDeB6yrj/K5vvv9l83/pq7W854FjgK8AmlLkAvt/8rO8DTo+IVSaJ81bg6OZY3wauA14CnBcR63Tz80iS1A+22bbZkhYPe9JIi9cuwCMz80KAiFibcuXtpZSTu6MoV+HuaB7/MPB2YF9gj9ZOIuJxlBPHK4FnZebP2h57GfBF4AjgcV1kej5l3P7/A56Tmbe37evuwHoAmfkPYI/mCtp9gLdOMgnhW4AXAT8CXth0JyciVgI+RzkhfV/zc3V6JbBjZn6/ec4Kzc+ye/PcHbv4eSRJ6gfbbNtsSYuEPWmkxes9rZM9gMy8nHIFawS4C6WL8B1t23+kud26Yz9vB5YHXtV+stfs80vAd4DNIuLRXWRas7k9tf1kr9nXvzPz/7rYB3DnCdq+wI20new1+7oFeB3wd+AVzdW7Tt9onew1z7mN0o36euDpEfGgbrNIktQj22zbbEmLhEUaafE6eYL7/tjcntGcFN0pM/9N6Q69Vuu+5kTpSZSTqh9McpyzmttNu8j0i+Z234h4QdOVerYeA6wBnNt+steSmTc1x7sHsMEEz//aBM/5F6Ur9whl7LskSYNgm22bLWmRcLiTtHhNNJHe9VM81nr8Xm3frwG0TspuaZ8IcAJrTBcoM0+LiI9Sujx/HbgtIs4HTgWOzMzfT7ePNus2t9tFxOg0264BZMd9l06y7SXN7VqTPC5JUr/ZZo/NZpstacGySCMtUh3dojtN9Vi7Vm+86ykTAU7ld93sMDP3jYhDgWdRrvg9gXKF7S0RsXdmfn6G2S4Gzplm22u63KckSQNnmz2GbbakBc0ijaRe/AP4L+UE8aWZOd3Vr65k5sXAx4GPR8SKlBUaDgM+FRHHZuZ1XeymdWXxgszcYxYxHgicP8n9AH+bxT4lSRoW22xJmgeck0bSrDUT850B3I1yBW0ujnFrZh4O/BpYGXhw28OtMfgTFZx/BlwLbB0R95zFoV/QeUezWsVTm2+nu9InSVI1bLMlaX6wSCOpVwdQrsodERHbdj4YEUsiYs+IWGW6HUXEsyNi8wnu34AyUeAdjL0a1vr3uIH1mXkzcBBwV+BbEbH+BPtdOyJeMkmcnSPiKW3brgB8otnfDzLzT9P9PJIkVcY2W5Iq53AnST3JzLMjYm/gs8BpEfE74ELKFbN1gY0oV9NOAG6aZnfbAG+IiCuA/wP+DdwH2ApYCfh4Zl7Ztv13gN2Br0TEyZSrcGTmXs3jBwIPoXS9/kNE/BL4M7Ai5STxYZTu0cdMkOVQ4AcR8SPgCspKF+tRlgB9zTQ/hyRJ1bHNlqT62ZNGUs+aiQEfCxxFWTliB+Apzb+PBXakORmbxpHAx4DLmv3tRDkxOx14Vma+teO4JwBvooxlfwbwsuar9fgdmbkbZULDkyknoM+lLMX5X+CjwJ6TZPlY89jqwLOb2//fzh3bMAzDABCkR0mlEVxm2uyRNdRlE2eFFCkext0Gqig8CL5m5tx7f354CwDkmNkAbcd1/eVmGMAtrLXeM/OcmYePHQB0mdnAHdmkAQAAAAgQaQAAAAACRBoAAACAADdpAAAAAAJs0gAAAAAEiDQAAAAAASINAAAAQIBIAwAAABAg0gAAAAAEiDQAAAAAASINAAAAQIBIAwAAABAg0gAAAAAEiDQAAAAAASINAAAAQMAXQnW+UJgUw/IAAAAASUVORK5CYII=", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "make_heatmaps(\n", + " waymax_vbd_data, \n", + " gpudrive_vbd_data,\n", + " array_name='agents_history', \n", + " index=4, \n", + " feature_name=feature_names[4]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.13916016, 6.6064453)" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gpudrive_vbd_data['agents_history'].squeeze(0)[:, :, 3].max(), gpudrive_vbd_data['agents_history'].squeeze(0)[:, :, 4].max()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Vehicle length" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "make_heatmaps(\n", + " waymax_vbd_data, \n", + " gpudrive_vbd_data, \n", + " array_name='agents_history',\n", + " index=7, \n", + " feature_name=feature_names[7]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### ✅ 2. Agents interested \n", + "\n", + "\n", + "- What:\n", + " - Encodes what we control. `10` means we control the agent; `1` means we don't control the agent (10 is an arbitrary value adopted from VBD code)\n", + "- Notes:\n", + " - In GPUDrive we control 12 agents whereas in waymax its 5 (this doesn't have to be a problem though)\n", + " - Order is different but that shouldn't matter for the model performance\n", + "- Checklist:\n", + " - ✔️: Values aligned? (1, 10) -> yes\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((32,), (32,))" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "waymax_vbd_data['agents_interested'].shape, gpudrive_vbd_data['agents_interested'].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([10, 10, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", + " dtype=int32),\n", + " 2)" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "waymax_vbd_data['agents_interested'], (waymax_vbd_data['agents_interested'] == 10).sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([ 0, 1, 1, 10, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 10, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),\n", + " 2)" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gpudrive_vbd_data['agents_interested'].astype(int), (gpudrive_vbd_data['agents_interested'] == 10).sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([False, True, True, True, True, False, False, True, False,\n", + " False, True, True, False, False, False, True, True, True,\n", + " True, False, True, True, True, True, True, True, True,\n", + " True, True, True, True, True]),\n", + " 23)" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.equal(gpudrive_vbd_data['agents_interested'].astype(int).squeeze(0), waymax_vbd_data['agents_interested']), np.equal(gpudrive_vbd_data['agents_interested'].astype(int).squeeze(0), waymax_vbd_data['agents_interested']).sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### ✅ 3. Agents type\n", + "\n", + "- What:\n", + " - Encodes object type: `metadata.object_types`, can be `(Unset=0, Vehicle=1, Pedestrian=2, Cyclist=3, Other=4)`\n", + "- Notes:\n", + " - I don't understand why none of the VBD elements are 0, I mean, some of these must be padding agents.\n", + " - Other than that, things look good\n", + "- Checklist:\n", + " - ✔️ Check types (we see a 1 and a 2 in both arrays)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((32,), (32,))" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "waymax_vbd_data['agents_type'].shape, gpudrive_vbd_data['agents_type'].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int32)" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "waymax_vbd_data['agents_type']" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int32)" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gpudrive_vbd_data['agents_type']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### ❌ 4. Agents future\n", + "\n", + "- Shape: `(32, 80, 5) ; (max_controlled_agents, remaining_timesteps, features)`\n", + "- What:\n", + " - The _log playback_ futures starting at $t=11$\n", + " - This tensor only contains info in the 0th index, the rest is fille with zeros.\n", + " - _todo_: Check how this information is used in the VBD model\n", + "- Notes:\n", + " - _todo_: Ask why VBD's futures are zero (only the 0th time index has info)\n", + "- Checklist:\n", + " - ✔️ Is the order of the features the same? -> yes\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((32, 80, 5), (32, 80, 5))" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "waymax_vbd_data['agents_future'].shape, gpudrive_vbd_data['agents_future'].squeeze(0).shape" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "agents_future_feat_names = {\n", + " 0: \"x\",\n", + " 1: \"y\",\n", + " 2: \"yaw\",\n", + " 3: \"vel_x\",\n", + " 4: \"vel_y\",\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ -8.935547 , 0. , 0. , ..., 0. ,\n", + " 0. , 0. ],\n", + " [ -4.3359375 , 0. , 0. , ..., 0. ,\n", + " 0. , 0. ],\n", + " [ -3.7988281 , 0. , 0. , ..., 0. ,\n", + " 0. , 0. ],\n", + " ...,\n", + " [ 0.10742188, 0. , 0. , ..., 0. ,\n", + " 0. , 0. ],\n", + " [ 8.691406 , 0. , 0. , ..., 0. ,\n", + " 0. , 0. ],\n", + " [-13.203125 , 0. , 0. , ..., 0. ,\n", + " 0. , 0. ]], dtype=float32)" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "time_step = 1\n", + "\n", + "waymax_vbd_data['agents_future'][:, :, 3]" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0. , 10.645508 , 11.585693 , 11.684326 , 41.816162 ,\n", + " 6.36792 , 48.346436 , 0. , 6.0566406 , 6.0324707 ,\n", + " 0. , 0. , -0.13598633, 68.92383 , -0.6652832 ,\n", + " 0. , 0. , 0. , 0. , 36.339355 ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. , 0. , 0. , 0. ,\n", + " 0. , 0. ], dtype=float32)" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gpudrive_vbd_data['agents_future'].squeeze(0)[:, time_step, 0]" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "make_heatmaps(\n", + " waymax_vbd_data, \n", + " gpudrive_vbd_data, \n", + " array_name='agents_future',\n", + " index=2, \n", + " feature_name=agents_future_feat_names[2]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "make_heatmaps(\n", + " waymax_vbd_data, \n", + " gpudrive_vbd_data, \n", + " array_name='agents_future',\n", + " index=3, \n", + " feature_name=agents_future_feat_names[3]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Qd18UFR7eBHZP+JkeAM4lfEJ9lZltTngT+AFwUOEb4DL8mfCpza4Jn2yVM5Th9OjTnRbCJ0OvEiaOvZHwJr+WbsL5f4BWaK+hmfUhDClZDPwkXxwCiN4kXpa0n7vPdvd/Fv/OomLkL6K7e1SStLt/6u4TiofsRb+Ln1VzTJEarBjdJhYUzOxSM7u26GvXpLZF+60c9U7ID4UanVK+ZYv+bvOTaSe9TtxZQXEIYDyhh9H+Cdt+QHivdkP+9SL65PgIwmv1wYXXi6gH7GGEf6RGVpBD/vV234SeR+W8Jo8peE3+FHgx2u8y4Ns19iKr5DV5HcKwsg+B4wqH2bn730keipOfG6rV8tMeFqo4i7Dq5XcrSboer/NluqjwH97o+ZHvlbw64VpVONz4KkJvly1L9Igr5TXg5x6fr+cW4HlgTQvDzwu9Sxg61CHDQdMSvUfJr/B3kbsX5/8LwvPjR4XFIQB3v5rwIdggCwtppKlvdNvWJOL5DwaXbaNNR0jlOVnD+5xzCO/9d4jaHUso3j5KKBwVm0N4rlY6NPBvhF5kaxJ6O21I6LHfHTjHwjQG5Vo+uj2cUIgeQbiurksozPYF/hx9GCySKvUgkq4oaR6irYCZvmTVsocL2twSvUH4OqGoEVt5KyoSDAe+QXjxzl/M8oWhAcQ/ufkT4dPQIwgXp7wjotuk4WUQhhMUy+e7kNCLp9T2Vt1bo+61uxI+lf0C4QJGlP+KZrZCwifzpxMuqrsRPr1eCvhhie7NJbn7W2Y2kXCx3ovozVf0qdKuhN/zn0sfIbYMNcDKhB4LewOfWFjatdqJF3PRbTlzQ2xK6EXwuLde8Q7CJ1onldo5KhIOJbxB6hWdO/9GbkCZ+RYfcyNC9+u1CMXBHEt+pqqOKVIH+7KkiJT3MmEOuGLFc7zkjSUUrTtDW68Txb0i2+Tur5nZQ8AQa726Y6vhZYTXjB7AXUU9Q/PHe9vMXiIMGevl7h+XkcNUM5tOGGo9lKj3YzTs7ZvA/0h+bPIKl7nPEXrwfJPwz808Mzu5wg8RClXympz/lP3uEh+03MCSIcwxUWFsGKGnZj/CcuG56Huo/jU59df5drT1XuEpd3+vcIOH+fdmEK5nlcznNcmT54RywodWXyIUkfLn+QPhH9vMiHoojyc8TlMJUwoUbu9GWDVtPmHoVJIHCcXFzYnmTjOzlUjudb1edHu4hRXKYrzjVrBLW6rPyUrf50THO5Dw3vGcKPwRcEBS8drdbyMMM62Iu/+0KPQ8oRf/i4T39mea2VVlvj/NF+p7AId6WCQB4H3gaAtzFO1G6PFZatoKkaqoQCRd0WOEXicbmtkXo+8HUnAxcPc3zOwNlhSRtiS8WD/pYUwz8PkSozcSljwupfiTm9sJF7vvm9nP3P1DM9sgOsf/tdHVtVURxt3nmhnA2yU+oc3n2rMwaGbHED6BX7qdvGMFInf/zMwOIkyy+QVgXPSJYTXGEQpEB7Dk07m9o1zvdfe32tg3aRnqpQlDCw4jFJgOT9ivHCtFt+UMW8kX3l4rsX1GUjAqON5CKLaVUtEnftGnbNfQxupwlR5TpAb5N/wrJW1098/jtmQi/lIKiw+fEP7e7nL3p4valTvhbyUFh1aifwrzn+4mvU5UMyHt9YTrzf5EH0JExZlvEK4LzxS0XSu6PdzM2nudW4HQ06gc4wifpueHLcOS15O/lCgG5CUtc58f7nYi4Z+xdicrL6EjXpP7Ewp732jj2JW+Jqf+Ol+mpA9s5raxrXB7zxLbk5RaxOGjKo7VSvT8/0XCpnO941YCHE2Y6/EFYLi3XuhhJZb05Pk0ej9WSuFrYV9Cj5BSBkdfxQ4u+D7/mPVu4zh9otuP2mjTEVJ5TtbyPicqxJ9ImOoB4MdJIwDq5GrC698qhKGGk8vYJ//zf0LyB6ZXEApEQ2tPTyROQ8yky4k+cf0X4fm/JbAFoffMg0VNHwK+Hr3JzReKPm8T9Ry6mVAcOpvQY2hZoJuHFWryn1LkKBB1eb+KcOHOLyvcXu8hCMOYqtkWE42Xv5hw0TmUMLlfL3fPRXnnC1S55CPw/YJtG0WFmWr8lXAB3MbMvhzFql0ph+iN27GEf/oOsTB5dDXy3cD/XeX+5TiP8E/Dg4Qx+CsDPaLff/4dZqnffynHEd40PU94Q7sasHR0zPwbrEqPKVKtfEGjrX+4y3WVux8cff3I3c9JKA7BkqEWfRK2Fcpvb9X7pkwbEIrr80kuOFTTe/FmwrxG3y8YUpHvPTS+qG3+vdvTxCdHTvqqZALvcYTXzz3NbJlonrsDCrZVJBrulp/P6LhK94fP59rbOLpbz9fkqwjP1QmE9wQrAEtFr587Rm0qff2sx+t8OVJ5r1DjedKwGqGIUvxVvIhGXZjZuYT3Zq8BOxT3conk/xbn0v7f4vP5ndx9Rv49V+EXYRVaCKuTJm0vlC9Ef5kE0XvUL0Z3SxVMO0paz8mq3+dEryXfLwh9M6ldPUS9J/M9pvq11bbAjOj29RK9L1+Nbtv6gFqkKupBJF3Vg4TuvkMI8yVA68kvHyYMg9iSJd3WHyjYvjXhE6Fb3b14hRNY0lU4yZWECTyPsLCix4GE1dVqmVy5XPnx2ae4e9KwjZJ5R8WlUwlzCTxBmFD6bML8PxXxsITxXwlLNO9nZjcRHo95VNG1NzrmR2b2LuGN+HpRjmUzswGEAtFiwsSJ7cl3ey6ea4F24t8jmoMpYRhEW8+btnwvuv2Bu/9fSscUqdY/CBPH721mJ9Y4B0258p9Er2RmX/DSc5GtU9S+UvkhSg96+UvJt8ndPzCzfxBen3ckrM6zH6FgU1wgyuf9kLsfncb5oxxeM7MHCde27xJWa1qT8I/Iw23t24b8PzHLm9nK7j67wv23JVxnPySsmNWeil+To3+kdyBMMrxXwnO1ltfktF/nuwx3n0wnfagR9TQ5ibBS1vZtDKN/l1AQXkwo6lTVK7FKLxIKwCubWX93L+4pmC/Oz2jjtTBranmfczzh9eQhoD8w0szudPc7U86xlPyHluV+MDEtui0191p+iPbcEttFqqYeRNJVFc5DNITQ/faZojb5N8TbEYpJLcSLSPkX+1bdrKPx5SW7lbv7G4R/oL5BWAVrBcJy0P+r5IeoUlt5b0sorrRiZn0JwyCWIgzfOoAwdOF4M/t2lbnkl7E/gPBpeQ74a9K8GuWIuvTnu3FXdNGM5hr4Q5TDzQlvtpI8RSjsbRpNjlrsByX2Wx74qMQcGfuW2CdfyCxV2C/5uLZxTJF6+Qehx8fqhJVo6i6aPDa/otguSW2iiXM3JvxDV/HqUdGwl59Fdy+uOMm25eeY2D+at2Yd4IHoelFoEqHwsEuFkwqXo/A1Od976Poa/vHNvy620PZkuq1YWNnzd9Hd0QnDe5LkH9PvJCyAAMmvycsR3g/PLFHILPU6Xs5rcqWv89LJzOwIQu+v/xF6DhWvUvi5qEA8mTDkftuOyK/g3J8QFj2BMDy/2F7RbVtzh2VNVe9zzGxjwoeZcwjvNQ8kvIZeY2FVwbqKppHI9xosd2XPRwgFyJXMbMOE7fn33fVaKVS6MBWIpKt6iPCGdTNgEPBYwhvDZwmfWh5GmKzyBY8v8evR7Z6FF5jo08irWNK1t5T88sPHRrdtDS9LUz7vIwqHh5nZWrS9ItClhNUTrnD326MJrPNj6K8zs3ZXmEkwifBp+EYs+T1cV7p5adHP8jtCgec/hE/Xyt3364RJFHcA3irIpU3RfFTjCEMULzWzXgXH3BQotWKFA180s/0Kg2aWL5Qlyc/JVGqSg/zj+uOiY25H+ORMpMNEBYUDCJ+ujzKz3yUN+4wKz0lvfqt1cXR7npmtX3Su5QnLTXcDbqlkcn0zW8rM9if0Pu0DjHH3WpZuT3In4Z/S7wJHRrHi3kNExetrCHMR3WBmrYbcmNl6ZrZnFTncQnjMvsOSnlJV9WyNhmfnV5ubXEnh38Ky4g8Trg3PUeb8RdFiAfcS/mG/MCr854+5M8n/SM8i/N43NLMhBe1zZnYy8QUtCpXzmlzp63zTMrOfmNmLZnZO+63rnsuL0Vf/ovhehPdmc4Gdi+b+KuVsQsF5jJkNSzhXXzM7tPD9QYryE13/KuoBnT/nFoTXkIWkVMg2s4MtrFQ4OY3jVani9znR7/3PhGHBI939dXd/mPDh7CosmQOzcJ89oudH2e9HzWznpA9Lo/eXNxPem95efN0p9XcR/U+Sf3z/WHj9tLCKcH7Yboev5CnNT0PMpEty9/fN7N8smUui1SfJ7r7YzB4jFA2gaI4id3/KzO4hDAeYHl00PyN0z18MXEt8QsFidxPGGK8FPB9dsDrCGMKFZTjwkplNJcydNJTQhX82YVjd56I3TQcTLs6fF0/cfaKZ/Y5wYb6c5DffJUW/4/GErtwrEXok3d/2XgDsHhW08lYiDA37EqH7bqmu3itFQ/ogFHWWIzwH8p9yP0pY1aKSZXhPBrYBdgZeMbMHCJ9yDSMU/Y5K2Odswmo6483sKML8AOsTVmI7nyXzdhR6jNDdfa/oufYfwvPsGg+rHp1H+KfuN2a2N+GxWpMwn8Z5tLGamkg9uPu06B+mWwivG0eZ2ROEovDShL/XrxNWdnqD8oZ1tmc04Tl/APCsmT0SnW95wlDhZQm9RX9c8gjx15dlCL0qv0koOiwirJx2WvKu1XP3BWZ2M2HekxGE4SM3l2h+DOHasSewk5lNI8xJ0pvwmrYeYUGEWyvMYY6Z3U4oDvUgfHhSsgdFgcJVl3KEeTE2I/SOfYfk10GArxa8Ji9FeJw2JgwBgdD74bDCxSHKMJLw6fvhwLDoOfclQqHnj8W5RCscnUeYN3BS9Po6m9DDd13gQpL/+byXUEw7NvqE/y3CB08XuLtT3et8M1uJUEwrdw6WdllYjbVwSHq+KPxHM8sPq/qXuxf/veeLep/3wDOzVQgF2W6EoZFHmtmRtPaQu1+Vv+PuD0WP7x+A+83seWA6oYfZWoTHuydh3sV2VxSshLtPMrPfAj8Hnjaz+wivrdsT/p5GJk3EbGZ/ZMkQtHyv6+HRe968PYreC+U7FcSWmO9g1bzPuQD4GqEn5A0F8V8T3t/vbGZHuftlBduWIzxH3q4gt82B083sNcI1Zj7hveU3CI/FsyQvntLW38WFhPfm3yH8n/Eo4Tq0JeG5O9rdK3qNFymHehBJV1ZY8Ck11KCwaPNAwvbdCReZmYRC0RaET4G/STuTAkaTzk2O7l7ZbrYpiXr+bEboqZMjfFqdfxO8PUUX/+gTtsuj+P7uXjxM4JeEi+FeZnYIlSv8dPrPJSbjK7Yx8UkrtyV84jca+Lq7Jz1WED75z+/zfcJqEu9F+w1z9y0rXdUi+n0Ojo6RIzwn1iAUjhLnB3H3GwkX/IcJ/8wNJwxz3I0SnwZF3cmHA/cR3nAeTOjd9pVo+0OENw33RefflSXLoyatBCNSd+7+GGHZ4Z8QXu/WJcwjsQPhjfEdhN4UX3H34oUCqjlfi7sfSChW30f4h/H7hL+N5wn/SG1ZYsLZvMLXl+FR/o8S5l9by91Pdvd6/ZNUuJz9naWGHXtYun6nKMfHCD/nXoTX9tmEVeGqLUCMK/F9Wwaz5Hd2EKEY8xahELKRu79QYr9VC/bbk7Cs9WvARcA33f27lc5bFL2GDyL0Gvgi4TX5C4QJgJOWFsfdzyUUFZ8mXBd2JBQJhhLmg0ra5y3Ca/ZjhOLjoYTX5H7R9opf56ViPQmPdf4rP6xw/YLY18o8Vm+WrOy6EckTZI9gyZyUn3P3PxHe940lrFA2nPAc6ksoOu1CGN6UOnc/gfDcfY4wJcJgwvvb70R5JfkaS34/60axlYj/LotXodskuq2ql3caKn2fY2Y7EQrCM2hdGP6McO2ZC1xgZuU+T0q5h9AbaU6U416E92ePEYYlDyoahdCuKMddCB+wvEl4fDcFHid8mNnWBx0iVcu1tHTkfGoikmdmyxBe8HsDX3L3Dzo5JREREWlAZtYCvObua3V2LtL1RD2jugMbdNCiA1KDqBfsq8AUdx/audlI1miImUjnGUnofn+1ikMiIiLSjsJh0qPdfWpnJiNdQzTP5teA/VQcamzRHGfbEXqviVRFBSKRDmRmKxLGSa9KGB4wnzBPgYiIiEhb8sOkIcxjqAKR1J27zyIMoZfG9y2WvEaIVEVDzEQ6UEGXz0+B/wNOdPeJbe4kIiIiIiIiUmcqEImIiIiIiIiIdHFaxUxEREREREREpItTgUhEREREREREpItr2kmqzWwz4AxgC2Bp4HngYnf/c6cmJiIiIiIiIiLSYJpyDiIzGwrcQ5gI+EZgDvA9YG3gFHf/TedlJyIiIiIiItJ5zCwH9E75sPPdvaYCg5kdAGwNbApsSOjssbe731Ki/WrAWcBwYHngdeB64Fx3/7SC83YDjgKOAAYAc4FJhPrBS1X/QBnTdAUiM1sKeBH4MrCFu0+L4n2BxwADvtaVHmQRERERERGRPDPrQyiCpKmvu8+r5QBmNgNYE5gNfAKsTokCUVQcmhq1mQBMB7YCBhM6jOzs7ovLPO8VhOLQv4E7gVWBfaIctnT3f9fyc2VFMw4x+zawLjAmXxwCcPe5ZvZr4CbgEOCXKZyruaprIiLSiHKdnUCT0DVbRETqTdfs2h0OTHf3181sFHB6G23PA9YAfuzuo+HznlFjgBHR15j2TmhmwwjFoQeB7d19QRS/DrgPGA1sU+0PlCXNWCAaGt3em7Dt7ug2vQf3kkvi9485huJOWbkcZcUqadtZMeWTrXyykKPyyX6Oyqf+OUo6mvl5pnyyn6PyyX6Oyif7OaZxzCx65LPP6FXlvh8DWy6VXlnB3f9ZTjszW5bQw+c/wJ8K9m8xs5OBAwhFnzFlHO6I6PZX+eJQdKyJZnY3sJOZfcXdp5f5Y2RWMxaIBkS3rYaQufuHZjaroI2IiIiIiIhIl9WL1CYj6m1mbTaodQhagS2AnsB9xfMeuftMM3sG2NzMlnH3T9o51lBgHvBwwra7gJ0InUyavkDUjMvcLxfdzimxfU5BGxERERERERGp3TuEeY3a+kpLyY4hBfHuwDptHSSai6kf8Kq7LypxnMLzNbVm7EFUs+hJ0i53r3cqIiIi0gZds0VERGrULZP9RsrpGFLYrt7HaQrNWCBq7wFcjtIPfl7as7mLiIhIfeiaLSIi0hhWAeZ3dhJSvWYsEBV2AXuqcIOZfYGwXN0jHZ2UiIiIiIiISMNJrwfR/BTnGGpPOR1DCtvV+zhNIZN9ydoxJbrdIWHbd4ralNK3zC8RERHpXLpmi4iI1CKXC0Wiar46b+m29uYGGgAsJqxyVlJU0JoJrG1m3Uscp/B8Ta0ZexBNJDwJ9jOz37v70wBm1hc4DfiMdpa668Cqp4iIiNRA12wREZEu6THgU2B7M8sVrmRmZv2AjYGpZaxgBqEDyQ+AwcADRdt2KmjT9JquB5G7fwYcTvjZHjSzK8zst8CzwAbAKHfvEtU/ERERERERkTZV23so/9UJ3P1D4EbCKmU/Ktp8DmEFsysLg2a2kpl91cxWKmp/RXR7lpktXdB+W8IopAfcvemXuIfm7EGEu08ys62AM4DvA0sDzwOnuvv4Tk1OREREREREpFHUMlSspQUWJa0OXx0zOxzYKrq7SXR7lJntEn0/wd0nRN//AhgGXGZm2wHTgSGEnkD3AGOLDv8T4HRCnWBUPhjVD64idDSZZmZ3EuYu3gf4EBiZ0o/X8JqyQATg7o+zpDtY/RxzTKtQ0t9WubFa9++ImPLJVj5ZyFH5VB5TPtnKp9YcJR3N/jxTPpXHlE+28slCjsqn8liz5ZNJtRaI0rUVMKIoNrTg+xnABAB3n2lmg4CzgOHALsDrhCLQee6+uILzHkkYdXQk8FPCKql3AKd0ld5DALmW9B/QrqSFtdeOR159tdXfSC7X+u8mKVZJ286KKZ9s5ZOFHJVP9nNUPnXPsVnefna2lmnT4oGBA+Gpp+KxTTeFqVPjsUGD4OGHWx9w8GB48MF4bMiQ5Fjx/oMHw6OPxmNbbJHcLilWnGM+z1rOrb9D5dPI+WQhR+WT/RxTOGZmrtlm1odQBGFaz570zlWX+vyWFgYuWJC/21dzA2Zb0/YgEhEREREREZF2NFYPIulEKhCJiIiIiIiIdFUqEEmk6VYxExERERERERGRyqgHkYiIiIiIiEhXpR5EElGBSERERERERKSrUoFIIhpiJiIiIiIiIiLSxakHkYiIiIiIiEhXpR5EEmm6ApGZ9Qf2BnYGvgqsBrwPPAyc7+5TOzE9ERERERERkcahApFEmnGI2dHARcA6wL3AhcBDwG7AI2a2TyfmJiIiIiIiIiLScJquBxHwODDU3acUBs1sCDARGG1mE9x9QadkJyIiIiIiItIo1INIIrmWLvSAmtk9wA7AZu7+ZAqH7Dq/PBER6SxVvmOTIrpmi4hIvWXmmm1mfYC5ANNWWYXe3aobXDR/8WIGvvNO/m5fd5+XTobSGZqxB1FbFka3n6V2xG22id+fMqVVETWXa11YTYpV0razYsonW/lkIUflk/0clU/9c5R0JP5uE4JZfJ7l44sWxWPdu5d/zIUL47EePZKPlxRbkNAvu2fP5GN+/HE81qtXcmzOnHhsueXgo4/isWWXTW733nvx2Iorto7l47NmxWOrrgqzZ8djK68Mb74Zj/XvD2+8EY+tvnpy7LXX4rE114RXX22dz9prwyuvxGPrrpscc4/HzJJjL7wQj62/Pjz/fDy2wQbw3HOt89loI3jmmXhs441h2rR4bODA5NhTT8Vjm24Kjz4aj22xBUwtmiF00CB4+OF4bPBgePDB1jkOGVJe2yFDkmNTpsRj22yTHJs0KR4bNqx1rFR82DCYODEe23bb9GOl8rnvvnhs++3hnnvisR13LL9dLbE77mid4667lr//XXfFYzvtBLffHo/tthvcdls8tsceye1uuql1Pvvs0zq+zz5www3x2L77JscyqVu38CVdXpd5FpjZGsB2wEwg4RIoIiIiIiIiItI1dYkeRGbWAxgH9AROcvdF7ewiIiIiIiIi0vzUg0giTV8gMrNuwLXA1sAV7j6ujH36lHNsL+7HKyIiIh1K12wRERGRdDR1gSgqDl0D7AeMBUaWuevcuiUlIiIiadI1W0RERCQFTVsgiopDY4CDgPHAoe6+uHOzEhEREREREWkgGmImkaYsEBUVh24ERlRYHOpbZjt9aikiItK5dM0WERGphQpEEmm6AlHBsLKDgJuBAyqdlNrd59UjNxEREUmXrtkiIiIi6Wi6AhFwGjCC8EnhdOBUMytuc627z+jgvEREREREREQai3oQSaQZC0RrRbd9gVNKtJkMzOiAXEREREREREQalwpEEmm6ApG7Hwwc3MlpiIiIiIiIiIhkRq6lpaWzc8gy/fJERKTecp2dQJPQNVtEROotM9dsM+tDtIDDtPXWo3eVPYjmL17MwJdfzt/tq7kBs63pehB1uAUL4vd79qS45pbLUVaskradFVM+2conCzkqn+znqHzqn6OkozMfw0VFy2V0715+bOHCeKxHj9axUvEePRLfqvDxx/FYr17w0Ufx2LLLwgcfxGPLLw/vvRePrbgizJ7dOp+VV4aZM+Oxfv3gzTfjsf794Y034rHVV4fXXovH1lwzOfbqq/HY2msnx155pXWO664L7vGYWeu2pdolxZ5/Ph7bYAN44YV4bP31W8fy8Weeicc23rj82LRp8djAgcmxJ56IxzbbrHW7ttpOnRqPDRpUfuzRR+OxLbaAhx+OxwYPTm734IOtcxwyJHn/4rZDhiTHpkyJx7bZJjk2aVI8NmxY63ZttZ04MR7bdtvk8yS1SzrefffFY9tv3zqWjycd85574rEdd4S77orHdtoJ7rwzHhs+PDl2++3x2G67wW23xWN77NG6Xb7tTTfFY/vsAzfcEI/tu29ybPz4eGz//WHcuHjswANhzJh47JBDWsfy8csvj8eOPBIuuyweO+oouOiieOzYY1sfLxM0xEwiKhCJiIiIiIiIdFUqEElEzwIRERERERERkS5OPYhEREREREREuir1IJKICkQiIiIiIiIiXVUDFYjM7GAgYXaomGvc/bB2jnMtMKLE5ufdfcPKs2t+KhCJiIiIiIiISCN4GjijxLaDgTWBeys43iXA/4pi71SaVFehApGIiIiIiIhIV5XLVd+DKGmZzxq4+9OEIlGMmS0P/AJ4H5hQwSEvdvcZKaTWJXSZApGZXQb8OLq7sru/25n5iIiIiIiIiHS6WoaYpVwgasP+QE/gSndf0FEn7Wq6RIHIzIYBI4F5QJ9OTkdERERERESk2fQ2szYbuPu8Ko99aHR7TYX77WJmywKfAM8AU9x9UZU5NL2mLxCZWR/gakI3tBWAbTo1IREREREREZFGkV4PonLm9slVegoz2xgYCExz92kV7n5p0f3pZravu/+r0jy6glxLx3UJ6xRm9gdCd7SvATcQCkRpDTFr7l+eiIg0gorfSEkiXbNFRKTeMnPNjjpSzAWYtumm9O7evarjzF+0iIFPPVV2e3evpkD0e+Bo4Gh3/0OZ+xwCzAEeB94F1gJ+CPyUMGn1Ru4+s9Jcml1T9yAys20I8w4d7u4z2+vuVo3i+louV32s1v07IqZ8spVPFnJUPtnPUfnUP0dJR6c+hgnBrD7vK0moUf7mKv2d1fvxqtdjWEveJRNaVDQSo3v38hNK2rfc4y1cGI/16NE6lo8vKJqOpGfP2mIffxyP9eqVHJuXMFKmTx/46KN4bNll4YMP4rHlly8/NmtWPLbqqjB7djy28sowM+F/3X79Wsf79YM334zH+veHN96Ix1ZfHV57LR5bc83kWLX7tnXMpNirr8Zja69d/r615pN07pdeiscGDGh9vK5lFWB+mgc0s6WB/YAFwPhy93P3MUWhF4HjzOxj4JeEQtHJaeXZLJq2QGRmvQlDyya6e6XjFEVERERERESaX3pDzObXMMdQKbsDKwI3ufsH7bQtx9WEAtHgFI7VdJq2QAScC6wGbN/ZiYiIiIiIiIg0pMZexSw/OfXVKR0vP9VM75SO11SaskBkZkOAnwDHufur7bVP2L+slc7cvdJDi4iISIp0zRYREalRgxaIzOzLhA4frwMTUzrs5tHtjJSO11SarkBkZksRlr6bCvy+ysPMTS8jERERqSNds0VERJrTwUA34Fp3X5zUwMzWIPQGet3d50exFYBl3f21orZfAi6J7t5Yr6SzrOkKREBfYL3oa1GJialnR/G13X1Gx6UmIiIiIiIiIm0xsxyhQNQCFE84Xeg6wkrlw4DJUWwN4EkzewSYThhWtiawC6FecC1wax3SzrxmLBAtoPT4xOGEeYmuj9p9VKJd3zLPpU8tRUREOpeu2SIiIrVozCFmWwPrAvdX0anjLeAqYBCwB/AF4EPCKKOr3F29h0pougKRu38MHJ60zcwmEwpEx7r7u0ltomOkPfO6iIiI1IGu2SIiIs3H3acAuTLaDU2IvQP8qA5pNb2mKxCJiIiIiIiISJkasweRdAIViERERERERES6KhWIJNKlCkRJ3c9ERERERERERLq6XIsqfrXQL09EROqt3fH3UhZds0VEpN4yc802sz5ECzhMGzaM3t27V3Wc+YsWMXDSpPzdvpobMNu6VA+ieiiur+Vy1cdq3b8jYsonW/lkIUflk/0clU/9c5R01PIYlnoQazrmokXxYPfuHfecSghm9e8w4ddYVrt826T9k2IffxyP9eoF84r+DerTBz4qWiN32WXhvffisRVXhNmzW+ez8srw5pvxWP/+8Npr8diaa8Krr8Zja68N7vGYWXLsuefisY02gieeaJ3PZpvB1Knx2KBB8Oij8dgWW8DDD8djgwfDlCnx2DbbwMSJ8di228J998Vj22+fHLvzztY5Dh8Od9wRj+26K9x+ezy2225wa9Gi1nvuCTffHI/tvTeMHx+P7b8/3HBDPLbvvjB2bOt8RoxoHR8xAsYULdJ9yCHJsSuvjMeOOAKuLlqf+bDDkmPF++b3L/eYo0fHYyNHJu9bbruk2OWXt87xyCOT2yb9fsr9XYwbF48deGB5sXw86fEuN5ZJuVz1Q8wWL043F+lUVT4LRERERERERESkWagHkYiIiIiIiEhXVcsk1dXuJw1JBSIRERERERGRrkoFIomoQCQiIiIiIiLSValAJBE9miIiIiIiIiIiXZx6EImIiIiIiIh0VepBJBEViERERERERES6KhWIJNLUBSIz+wFwJLAJ0BP4L/AIcLS7f9SJqYmIiIiIiIiINIymLBCZWTdgDHAQMB24HpgPrA7sBCwHqEAkIiIiIiIiXZt6EEkk19LS0tk5pM7MTgDOBy4BjnP3xQXbugEUxmrQfL88ERFpNLnOTqBJ6JotIiL1lplrtpn1AeYCTNt9d3ovVV3fkfmffcbACRPyd/u6+7xUEpRO0XQ9iMysN/BL4GXg+OJCUEqFoc8tWhS/3707FNfccrnyYpW07ayY8slWPlnIUflkP0flU/8cJR2Jv9uEYGK74gs+QPfu5T+GZZ6n1udU1T9PBT9LWT9fW+eu8+8ijb/Dsn+PScEqf7efH7Pej025P0sbbTsiVs7vodL9k963p/pY5w+a1HbhwnisRw9YsCAe69kz+dwffxwP9uqVfLziWD6e9IOXmU9Z5+nRIznHpFjxOfLn+ahocMeyy8KcOfHYcsslt5tXVI/o0yc5lnS84lg+Pnt2PLbyyvDee/HYiivCrFnx2Kqrtj6eSIY0XYEI2AH4InA10MPMvgesB7wL3OPur3dibiIiIiIiIiKNQ0PMJNKMBaJNo9vFwLPAgIJtC83sFHe/oOPTEhEREREREWkwKhBJpBkLRCtHt8cBTwD7EIabbQlcBZxvZi+4+99LHSAaj9kud68xVREREamFrtkiIiIi6WjGAlG+hLkA2MPd347u32NmhwN3A8cCJQtERJN1iYiISMPTNVtERKQW6kEkkWYsEOVnGnuyoDiUdx+hcLQpIiIiIiIiIl2dCkQSacYC0fTottWU9O6+2Mw+Ar7QzjH6lnkufWopIiLSuXTNFhEREUlBMxaIJkW36xdvMLOVgJWAl9o6gLvPa2u7iIiINAZds0VERETS0XT9wdz9ZWAisJ6ZHZyPm1kOOCu6e0snpCYiIiIiIiLSWHK5JcPMKv3K5To7e0lRM/YgAhgJPApcY2a7s2QVsy2A54BzOi81ERERERERkQahOYgk0pSPpru/BHwT+DOhKPRTYDXgt8BW7v5RJ6YnIiIiIiIiItJQci0tLZ2dQ5bplyciIvWmvtvp0DVbRETqLTPXbDPrQ7SAw7QDD6R3jx5VHWf+woUMHDcuf7ev5gbMtmYdYtZhiutruVz1sVr374iY8slWPlnIUflkP0flU/8cJR3N/DxTPtnPUflkP0flk/0c0zhmJjXQEDMzS3hUPneCu/+2zOOsRpiDeDiwPPA6cD1wrrt/WnOiTUoFIhERERERERFpFK8B1ybEHyln56g4NBVYHZgATAe2As4AtjSznd19cSqZNhkViERERERERES6qgbqQRSZ4e6jatj/PGAN4MfuPho+X9V8DDAi+hpTa5LNqCknqRYRERERERGRMlS7xH0thaU6MbNlgX2A/wB/ysfdvQU4GVgEHNE52TU+9SASERERERERkVr1NrM2G5Q5ifXyZnYksCIwG5gcrVReji2AnsB9UVGo8NwzzewZYHMzW8bdPynzmF2GCkQiIiIiIiIiXVV6Q8zeKWOPcqby/joFvX+AFjMbDxzp7vPb2XdAdFuqoPQS8A1gHeDfZeTSpTRWfzARERERERER6TiNNcTst8AgYAXC6mPfJkw4fQBwVRn7LxfdzimxfU5ROymgHkQiIiIiIiIiXVV6PYhWAdrr4dMmdz+hKDTJzLYFngH2NbNfu/uLtZxDSmvKApGZLQUcAhwOrAssTVgq7xbgEnf/X+dlJyIiIiIiItJ05pc5x1BF3H2+md0AnApsCbRVIGqvh1B7PYy6tGYdYnYzcAXhwR9P6Iq2EBgFPGJmfTovNREREREREZEG0VhDzEp5N7rt3U67/NxDA0psHwAsJqxyJkWargeRmW0O7A48Cmzt7p8VbLsF2BPYCxibxvlyCVNs1RKrxzGbPUflU3lM+WQrnyzkqHwqj1XaVmrX7M8z5VN5TPlkK58s5Kh8Ko81Wz6ZlN4Qs3raPLqd0U67x4BPge3NLFe4kpmZ9QM2BqZqBbNkTVcgIsxGDmFZu8+Ktt1JKBCtnNbJTj89fv+MM6ClJR7L5cqLVdK2s2LKJ1v5ZCFH5ZP9HJVP/XOUdCxYEL/fsyd8/HE81qsXzCvqHN+nD8yZ0/p4yy0HH3wQjy2/PLz3Xjy24oowa1Y8tuqq8Oab8Vj//vDqq/HY2msnx155pXU+664LLxWt2TJgALzwQjy2/vrJseefj8c22ACeey4e22gjmDYtHhs4EJ55pnU+G28MTz0Vj226KTzxRDy22Wa1xaZOjccGDYJHH43HttgCHnywdY5DhrSODxkCU6bEY9tskxybNCkeGzYsOXbXXfHYTjvBnXe2zmf48Nbx4cPhttvisT32SI7dfHM8tvfeMH58PLb//jBuXDx24IEwdmzrfEaMaB0vFbvyynjsiCPgssvisaOOSm530UXx2LHHwiWXxGPHHAOXXto6x6OPhgsvjMeOPx7OOSceO/lkOOmkeOy88+CCC+KxE05Ifn+fFDv55Nb5nHNOyKnQpZcmn3vkyHhs9Ohw/kIXXABHHhmPXX55eMwKjRsXHodiY8eG33GhK68Mz4NC48e33n/s2PAcKnTzzcnt9t03HrvhhvCcLHTbbbDrrq1zvOMO2GefeOymm+CQQ+KxMWOSfz/HHhuPXXQRHHZYPHb11eXF8vGknzEplvQ4SPXMbAPgJXf/tCj+PWA/wippkwri6wI9gFfcfSGAu39oZjcCBwE/AkYXHOocoDtQ9Eokec1YIMovVbe9mZ1ZVCQaDrQAkzs8KxEREREREZFGk8tV3xMo3U+yjgAONLMHgNcJQ8EGAtsAnwAHF81xNBFYE1ibeM+iXwDDgMvMbDtgOjAEGAzcQ0qjiZpR0xWI3P1ZM7sMOAr4PzO7G1hEeIIMAI5y9yc7M0cRERERERGRhtA4Q8zuBlYnFIW2J/QOegsYA1zg7i+0se/n3H2mmQ0CziJ0EtmFUHA6HTjP3RenmXQzaboCEYC7/8TMZhC6kB1TsOnPwF2JOxUodxJrd68qPxEREUmHrtkiIiLNwd3vJhSJym2/VhvbZgIJgwilLU1XIDKzbsDlwD6EMYd3ECap2hb4A/C4mW3u7jPaOMzceucpIiIiqdA1W0REpBaN04NIOlnTFYiAQ4HDgZ+6+9UF8VvN7FPgb4QxiT/qjOREREREREREGoYKRBJpxgLRTtHt5IRt+dgm7Ryjb5nn0qeWIiIinUvXbBEREZEUNGOBqGd0u1LCtvzy9gsStn2uaGZ0ERERaVC6ZouIiNRIPYgk0oyP5iPR7clmtnQ+GM1NNCq6O7mDcxIRERERERERaVjN2IPoMmAEYVm8f5vZPcBC4NvARsBLwMWdlp2IiIiIiIhIo1APIok03aPp7nOAbwEXAJ8Rlrb7EWHo2YXAIHf/oPMyFBERERERERFpLLmWlpbOziHL9MsTEZF6y3V2Ak1C12wREam3zFyzzawP0QIO0375S3ovvXQ7eySb/+mnDPzNb/J3+2puwGxrxiFmHaq4vpbLVR+rdf+OiCmfbOWThRyVT/ZzVD71z1HSsWhR/H737rBwYTzWo0d5sXx8QdGyFz17wscfx2O9eiXHPvooHlt22eTYB0X9npdfHubMaZ3PcsvBe+/FYyuumBybNSseW3VVmDkzHuvXD958Mx7r3x9eey0eW3NNePXV1vmsvTa88ko8tu664B6PmcELL8Rj668Pzz0Xj220UXLsmWfisY03hqeeisc23RSeeKJ1jpttBlOnxmODBsGDD8ZjQ4Ykxx5+OB4bPBgmTYrHhg1Ljk2c2DqfbbeFu+6Kx3baCe65Jx7bcUe48854bPjw5HZ33BGP7bor3HZbPLbHHq2Plz9mUtubborH9tkHbr01HttzT7j99nhst91g/Ph4bP/9k4+XtO/VV7fO8bDD4IYb4rF9900+5pgx8dghhyTHRo+Ox0aOLK9dvu3ZZ8djp5wCF1wQj51wApx+ejx2xhlw6aXx2NFH63qYtXwySUPMJKJHU0RERERERESki1MPIhEREREREZGuSj2IJKICkYiIiIiIiEhXpQKRRPRoioiIiIiIiIh0cepBJCIiIiIiItJVqQeRRFQgEhEREREREemqVCCSiB5NEREREREREZEuLhM9iMzsAGBrYFNgQ2BpYG93v6VE+9WAs4DhwPLA68D1wLnu/mmHJC0iIiIiIiLS6HK56nsC5XLp5iKdKhMFIkKxZ01gNjALWL1Uw6g4NDVqMwGYDmwFnAFsaWY7u/vieicsIiIiIiIi0vA0xEwiuZaWls7OoV1mth0w3d1fN7NRwOmU6EFkZmOBg4Afu/voKJYDxgAjgEPdfUxKqTX+L09ERLJOH82lQ9dsERGpt8xcs82sDzAXYNp559G7Z8+qjjN/wQIGnnRS/m5fd5+XTobSGTLRg8jd/1lOOzNbFtgH+A/wp4L9W8zsZOAA4AhCsSgVxfW1XK76WK37d0RM+WQrnyzkqHyyn6PyqX+Oko5mfp4pn+znqHyyn6PyyX6OaRxTJMuarT/YFkBP4D53j/25uvtM4BlgczNbpjOSExEREREREWko+SFm1X5J02i2R3NAdPtSie0vAd2BdTomHREREREREZEGpgKRRDIxxKwCy0W3c0psn1PULlE0HrNd7l5mWiIiIlIPumaLiIiIpKPZCkRpmdvZCYiIiEhZdM0WERGphVYxk0izFYja6yHUXg8jERERERERka5DBSKJNFuBKD/30IAS2wcAiwmrnLWlb5nn06eWIiIinUvXbBEREZEUdFiByMx6uvuCOp/mMeBTYHszyxWuZGZm/YCNganu/klbB3H3efVNU0RERNKga7aIiEiN1INIIjUViMzsL8AR7t7mkC0z2wQYD2xQy/na4+4fmtmNwEHAj4DRBZvPIaxgdmU9cxARERERERGRyphZf2BvYGfgq8BqwPvAw8D57j61zONMBrYpsflOd9+l9mybU609iPYCNjezEe4+JamBmf0cOBNYutqTmNnhwFbR3U2i26PMLP/ATnD3CdH3vwCGAZeZ2XbAdGAIMBi4BxhbbR4iIiIiIiIiUhdHAycBrwD3ArMJ08TsDuxuZvu5+00VHO+MhNj0WpNsZrUWiC4BfgpMNLMLgFPd/TMAM/sScB2hWDMHGFHDebZK2H9owfczgAkA7j7TzAYBZwHDgV2A14HTgfPcfXENeYiIiIiIiIg0j8YZYvY4MLS484mZDQEmAqPNbEK5U9e4+6g0k+sKci0tLe23aoOZ7QhcC6wC/AvYH/g68CdgBWAycJC7/7emEzWm2n55IiIi7ct1dgJNQtdsERGpt8xcs82sD9ECDtNGj6Z3z55VHWf+ggUMHDkyf7dvveYGNLN7gB2Azdz9yXbaTga2cffMPB6NouZJqt39HjPbELgG2BV4FugBfEYY7nVB4WTRTae4wJbLJYXKilXStrNiyidb+WQhR+WT/RyVT/1zlHQ08/NM+WQ/R+WT/RyVT/ZzTOOYmZReD6LeZtZm8xoKSAuj28/K3cHM9gfWJBTCnnD3R6s8d5eRyipm7v6emZ1FmAjqC0ALcCvw26YuDomIiIiIiIgIwDtltKm4jGZmawDbATOB5yrY9fqi4zwB7OPur1aaQ1eRyoBBMzsZeIhQHLqB8MTYB3jIzNZJ4xwiIiIiIiIikrJ8D6Jqv+rIzHoA44CewEnuvqiM3SYAOwH9gD6Eha7GAZsB95lZr7ok2wRqXeb+y4Sq3BDgPeBwd/+bma0EjCFMEv20mf3U3a+tNVkRERERERERSVEuV32hJz6ubhVgfgoZAWBm3QjzHW8NXOHu48rZz90vLgo9AxxkZt2B/YCDgMvTyrOZ1DrE7DlgOeA+YIS7vw3g7u8Cu5rZSOC3wNVmNtzd967xfCIiIiIiIiLSeOanNUl1VBy6hlDQGQuMbHuPslwdHW8wKhAlqrU/WC/gOHffMV8cKuTuo4FNCRW779V4LhERERERERFJU4MNMYuKQ2OAEcB44FB3X5zCod+NbnuncKymVOujuXlC960Yd38RGARcUOO5RERERERERCRNDVQgKigOHQTcSBiplEZxCGDz6HZGSsdrOjUNMXP3Z8tst5Cw5L2IiIiIiIiISEzBsLKDgJuBA9qalNrM1gV6AK9ENYf8PMmfRNPeFLY14Kzo7k11SL8ppLLMvZmtCBxAqMitBEx09/OjbRsA6wL/dPeqJqwyswMIE1NtCmwILA3s7e63FLXrA+wBfJcwU/nqwALCELc/ufsN1ZxfREREREREpCnV0hMo3R5EpxGGlc0FpgOnhrpOzLXuPiP6fiKwJrA2S3oFfRO40cweBF4B5gADCAtoLQ382t2fSDPpZlJzgcjM9gauAvoCOaAFeLOgSX/gNsIDfX2VpzmL8MDPBmYRCj9JhhCWr3uP8GS5lTCT+veAP5vZYHf/SZU5iIiIiIiIiDSXxikQrRXd9gVOKdFmMm0PEXseuIGwpP03o2O9D9wDXObu96SQZ9OqdZn7LYA/Ax8CxwMPAY8XNZtIqNp9j+oLRIcD0939dTMbBZxeot3bwIHAX9z904I8fwlMBY4ys+vcvThHEREREREREekk7n4wcHAF7ddKiL0EHJJaUl1MrT2IfgksBrZ3938BFHcBc/dFZvYvwtCwqrj7P8ts9zTwdEJ8lpldDvyGMFQtvQJRLldOqOxYrft3REz5ZCufLOSofCqPKZ9s5VNrjpKOZn+eKZ/KY8onW/lkIUflU3ms2fLJpMbpQSSdrNYC0ZbAo/niUBveJqxk1pkWRrefpXnQBx+M3x8yBFpa4rFcrrxYJW07K6Z8spVPFnJUPtnPUfnUP0dJx6xZ8furrlp+7I03Wh9v9dVh5sx4rF+/1m1XXx1efTUeW3vt5Jh7PGaWHHv++db5bLBB6/gGG8Azz8RjG28M06bFYwMHwhNFMzJsthk8/HA8NnhwcmzSpNb5DBuW/D5p4sR4bNttk2N33hmPDR8O990Xj22/Pdx1Vzy2005wxx3x2K67wm23tc5xjz1ax/fYA8aNi8cOPBDGj4/H9t8fxoyJxw45BK6+Oh477LDkdsWxfPyyy+Kxo46CCy+Mx44/Pjk2enQ8NnJkcrvjj4/HLryw9b75/c87Lx476SQ4vagv/xlnhK9Cp58OJ5wQj11wQdi/0HnnwbHHxmMXXZR8vOJ2+bZJ+x99dDx26aXJ+SS1O+aYeOySS5J/Z0cd1Tqfyy6DI4+Mxy6/PPk8ScccOTIeGz0aTj45HjvnnOR2xefIn+eII+KxK69MjiX9Hot/xssuC8/pQldfnXy8ESPisbFjW++b3z+p7SGHxGNjxiS3O/DAeGzcONhnn3jsppuSr68DBrTO56WXWsdfeqn19bilJfl1KpNUIJJIrQWi3oR5gdqzQo3nqYmZdSfMhN4ClNUbSURERERERKTpqUAkkVoLRG8CG7TVIFqqbkPg1bba1dmZwEbANe7+f+01jlZDa5cXf6QnIiIiHUrXbBEREZF01FruuxswM9uvjTaHA18G7myjTd2Y2Y+Ak4F/Ace00zxvbplfIiIi0rl0zRYREalFvgdRtV/SNGrtQXQusB8w1swGAn+N4n2i+3sAJxKGoV1U47kqZmaHA38EniFMpK03iCIiIiIiIiJ5GmImkZoKRO7+XzMbDtxKWOb+OMI8P3tFXzngHWA3d3+nxlwrYmZHAJcD/wds5+7vV7B73zLbqeAkIiLSuXTNFhEREUlBrT2IcPdHLaxtfxiwPbAWYejaf4H7gMvdfU6t56lEQXHo38C27v5uJfu7+7y6JCYiIiKp0jVbRESkRupBJJGaC0QA7v4RcHH01amiYWWXAy8SikPlrLImIiIiIiIi0vXkctUXenK5dHORTpVKgajeoqLPVtHdTaLbo8xsl+j7Ce4+wcy+DVxBGNr2ADAydG6Kmezuk+ubsYiIiIiIiIhIdlRUIDKzNWo5mbu/XuWuWwEjimJDC76fAUwA1iAUhwCObON4k6vMQ0RERERERESk6eRaWlrKbmxmiwmTUFejxd0z0WOpAtX+LkRERMqlvtvp0DVbRETqLTPXbDPrQ7SAw7Q77qB3r15VHWf+xx8zcNdd83f7am7AbKu0YPMArd9g9QS+FX3/AfBa9P0awApR+6nAgipzbGjF9bVcrvpYrft3REz5ZCufLOSofLKfo/Kpf46SkrPPjt8/5RQ455x47OST4aKL4rFjj4VLL219vKOPhssui8eOOgouuSQeO+YYGD06Hhs5Eq6+Oh477DAYOzYeGzECrrwyHjviCBg3rnU+Bx4IY8bEY4ccAjfdFI/tsw/ccEM8tu++cOut8diee8Ltt8dju+0Gd94Zjw0fDvfd1zqf7bdvHd9+e5gyJR7bZhuYNCkeGzYsuV1SbOrUeGzQIHj44Xhs8GB46qnWOW66KTzxRDy22WbJx5w2LR4bOBCefz4e22CD5NhLL8VjAwbAq6+2zmftteGVV+Kxdddt3bZUuzfeiMdWX7382MyZrfPp1w/efDMe69+/ddt+/ZJjs2bFY6uuCrOLpgddeeXkdh98EI8tv3zrffP7v/dePLbiisn7J8U++igeW3ZZmFO0vs5yy8G8ov95+/RpvW9+/6RjfvxxPNarV/IxFy6Mx3r0SG6XdLwFCf9u9ezZOt6zZ/Ixqz1Pz57V/8z58yTtnxRLyjHp8UqKJT3+xe0q3T8plkWapFoiFRWI3H1o4X0zWxa4n7CU/Anufk/R9h2A84EewI41ZSoiIiIiIiIiInVRa7nvTGBdYFhxcQjA3e8FtovanF28XUREREREREQ6Ub4HUbVf0jRqfTS/B9zv7u+VauDu7xJ6Ge1R47lEREREREREJE0qEEmk1kdzZcobprYUsFKN5xIRERERERERkTqotUA0A9jWzFYv1SDatm3UVkREREREREQahXoQSaTWR/NqoA8wxcwOMrNl8hvMrKeZHQhMAXpHbUVERERERESkUahAJJFKl7kv9jtgU2AfYAwwxszy60+uHN3mgJujtiIiIiIiIiIi0mBqKhC5+2JgXzObAPwYGASsEm3+FJgKjHb3G2s5j5kdAGxNKEZtCCwN7O3ut5Sx797AX6K7Ze0jIiIiIiIi0iXU0hNIPYiaSq09iABw95uAm8xsKWDFKPyeu3+WxvGBs4A1gdnALKDknEeFzGxl4DJgHmEonIiIiIiIiIjkqUDUkMxspWhV+A6T6qPp7p+5+6zoK63iEMDhwJruvgpwTQX7XQZ8DPwpxVxEREREREREROrpv2Z2o5l9u6NOmGtpaemoc6XCzEYBp9POcDEz2xO4BdgJ+FY5+1QhW788ERHJolxnJ9AkdM0WEZF6y8w128z6AHMBpj34IL179arqOPM//piBQ4bk7/Z193npZChmNoswt3ML8ApwFXCtu79Tr3PWPMTMzL4KnAhsA3yJMD9QkhZ3T2VIWxk5rQT8ERjn7neb2bfqda7i+louV32s1v07IqZ8spVPFnJUPtnPUfnUP0dJxwcfxO8vvzy89148tuKK5cXy8TffjMf690+OvfpqPLb22uXHXnopHhswAF55pXU+664LL7wQj62/Pjz/fDy2wQbw3HPx2EYbwbRp8djAgfDUU/HYppsmx6ZObZ3PoEGt44MGwcMPx2ODB8Ojj8ZjW2wBDz4Yjw0ZkhybNCkeGzYsOTZxYusct922dXzbbeGee+KxHXeEO++Mx4YPLz92883x2N57w223tc5njz2S244bF48deGBybMyYeOyQQ+DKK+OxI46A0aPjsZEj4bLLWudz1FFw3nnx2EknwYUXxmPHHw/nnBOPnXwynHlmPHbqqXD66fHYGWeUHzv55NY5nnMOHHtsPHbRRa3bnnMOHHlkPHb55cmxY46Jxy65JJvXn0bLJws5pnHMTNIQs0bVH9gDOALYFjgHONPMbgeucPd/pn3Cmgo2ZrY5cD/Qi1AtfR94O4W8avWH6PZnnZmEiIiIiIiISENTgaghRdP23AzcbGZrEQpFBwN7AXua2QzgSmCMu89K45y19uj5DdCbUJA5w90TPl/rWGa2B7APsI+7v1/lMcqa0Nrdqzm8iIiIpETXbBEREWl27j4DOMXMTgN2BX4I7ACcDZxhZncAl7v7fbWcp9YC0SDgOXf/aY3HSYWZrQiMBm5397+0174Nc1NKSUREROpL12wREZFa5HLV9wSqw7g6M9sMOAPYgjCFzfPAxe7+5wqO8QVgFLAnsBphpNOtwCh3/zDtnDuKuy8CJpjZg8CvgGOAHsD3gD3MbDrwK3e/tZrj19ofbBHhwWoUFwLLAD/u7EREREREREREGl5+iFm1Xykys6HAQ8AQwqJTo4GVgPFm9ssyj9EHmAIcCzhwEfDv6P6UcnsfNyIzG2Zmfwb+C/wU+AS4jjD07G7gK8BfzKyqmkitPYieBNar8Rhp2gRYDnjTzJK23xzFD3H3a9s4Tt8yz6dPLUVERDqXrtkiIiJNwMyWIqzU1QJs7e7Tovgo4DHCUKqb3f2l0kcBwiJamwDnu/tJBcc/Azgt2n568q6Nx8xWAQ4BDgfWIcz/PB24nLCqWX4ZjuvMbFPgn8DxhIW7KlJrgWgUMMnM9nb3m9tr3AH+SihaFfsGMBCYCMwg/DJL0tJ8IiIi2aBrtoiISI0aZ5LqbwPrEiZd/nxtTXefa2a/Bm4iFEpK9iQysxyhkDIX+HXR5nOAo4HDzGyUuyesY9c4zOw7hImpdyEMI1tIGCY32t0nJe3j7k+Z2T+A71dzzloLRN2Ai4EbzGx3QpemN4DFSY3d/YEaz9cmdy9+AgCfVxwHAn9y91vqmYOIiIiIiIhIZqRXIOpdYiTP59r5YGdodHtvwra7o9tt2sloAPAl4J7ic7n7J2Y2iTBfz3pAez2ROts/otvXgSuAq8tcrewNwhC0itVaIJpM6P6VA/YFftBO++7VnMTMDge2iu5uEt0eZWa7RN9PcPcJ1RxbRERERERERGr2Thlt2prVekB026pw4+4fmtmsgjYVH6MoPqCNNo3iTuBPwD8q6e3k7r8AflHNCWstEF1HKBDV21bAiKLY0ILvZwATOiAPERERERERkabRQo6WNus2be+bouWi2zklts8B1krhGIXtGpa771rtvmZ2ErCju3+7kv1yLS0NPeyu0emXJyIi9Zb++rFdk67ZIiJSb5m5Zkcrec0F+Ne/ptG7d++qjjN//ny+8Y2B+burAPPbat/WEDMzuxfYHhjg7i8nbHdgLXfv2cYx9gPGA2e7+68Stp8M/AbYz91vaCvXLDOzMcBB7l7RKK5aexB1eSuvHL8/ezYU19xyufJilbTtrJjyyVY+WchR+WQ/R+VT/xwlHc38PFM+2c9R+WQ/R+WT/RzTOGYXN7/GxSPmRLfLldi+HKV7BlVyjMJ2UiDVKcdFREREREREJDsWL67tK0WF8wPFmNkXgFVpf96gkscoijf6/EOdoqIeRGZ2GtACXObu70f3y9Xi7mdWlJ2IiIiIiIiI1E0thZ6UC0RTgJOBHYAbi7Z9p6BNW14C3gIGm1mfwh5NZrYMYS7jt4BWQ9ik8iFmowgFopuA9wvul9OZrgVQgUhERERERESkQTRQgWgi8B9gPzP7vbs/DWBmfYHTgM+AMfnGZrYG0Bt43d3nA7h7i5ldFbU/DTip4PgnAysAv65kVbCupNIC0SHR7cyi+yIiIiIiIiIiVXH3z8zscOAe4EEzuwH4EPgesDbwK3cvHBp2HbANMAyYXBA/H/gucKKZDQSeAjYGdgKejrZLgooKRO4+tq37IiIiIiIiIpIdLS3V9wRKmui7Fu4+ycy2As4Avg8sDTwPnOru48s8xjwzGwqcDuxFGFb2NnARcEaNE2k3Na1iJiIiIiIiItJFNdAQMwDc/XFCb5/22g1tY9sc4LjoS8qkVcxERERERERERLq4TPQgMrMDgK2BTYENCd3M9nb3W9rYZzPgRGAIsDwwG3gSON3dn6l70iIiIiIiIiINrtF6EEkqcpS3mFhMJgpEwFnAmoQizyxg9bYam9kPgdGEldbuIIw3XBUYDGwEqEAkIiIiIiIiXZ4KRE3peMIcTBXJSoHocGC6u79uZqNo4wc1s0HAH4FHgeHR2MPC7Vn5mUVERERERESkCzCzNWrZ391fL/j+PeC9So+RiWKJu/+zguZnErpSHVRcHIqO9VlqiQGzZ7eO5RI6cpUbq3X/jogpn2zlk4UclU/lMeWTrXxqzVHS0ezPM+VTeUz5ZCufLOSofCqPNVs+WaQeRA1jBlDtunAtpFDfyUSBqFxmtgKwHfAvd/+PmQ0DNgMWAA+5+1Npn7N4Wb9crvpYrft3REz5ZCufLOSofLKfo/Kpf46SjmZ+nimf7OeofLKfo/LJfo5pHDOLVCBqGA9A1QWiVNRUIDKz04Cn3f1v7bTbFRjo7r+u5XxlGAjkgP+a2d+B4UV5/IXQs2hBnfMQERERERERESmLuw/t7Bxq7UE0CrgWaLNABHwXOBSod4Fo5eh2F+Cd6PYBYG3gD8D3gdeBE9o6iJn1Kedk7l51oiIiIlI7XbNFRERqox5EktdRQ8y6Ax3x1OlWcL4fufud0f1nzex7wH+AH5vZr9rpRTS3nkmKiIhIanTNFhERqYEKRNlgZj2BFYAF7v5+Pc7Rrf0mqdgA+KADzjMnul0E3FW4wd3fBR4DegPrd0AuIiIiIiIiIg0tXyCq9kvqy8x+aGbTgHnAf4HfFmz7npn91czWS+NcFfcgMrNrikJbJcQKj2/AN4EJlZ6rCtOj2/nuvjBhe76A1Kud4/Qt83z61FJERKRz6ZotIiIiTcfMugO3EKbsWQi8QOh8U+iZqM1TwNm1nrOaIWYHF3zfAqwXfbXlWdqZ9yclLxMqal82s/7u/mbR9nzPodfaOoi7z6tHciIiIpIuXbNFRERqoyFmDesnwG7AP4DD3H2WmcV+4+7+ipm9DOxEJxWIhkW3OeB+4G7gvBJtPwXecvc2CzJpcfcWM7scOBM408wOc/cWADPbl1Bte9Td3+qIfEREREREREQamQpEDetgYBawTzsfiD1PGLVVs4oLRO4+Jf+9mY0FHiyM1YOZHQ5sFd3dJLo9ysx2ib6f4O4Tou8vJKxedgjwNTN7iLCK2e7Ah8CP6pmriIiIiIiIiEiNDLi3jN7S81myontNalrFzN0PSSOJMmwFjCiKDS34fgbRHEfu/rGZbQv8CtgH+CnwP+AmYJS7T0dERERERERE1IOocS0Elimj3RrAR2mcsKOWua+Jux9MfO6j9trPA06Ovuoql0s3Vo9jNnuOyqfymPLJVj5ZyFH5VB6rtK3UrtmfZ8qn8pjyyVY+WchR+VQea7Z8sqilpfpCT0tLurlIzPPAN81sWXdPLACZ2arAQODRNE5Yc4HIzL4KnAhsA3wJWLpE0xZ3z0RBqhLHHx+/f+GFrf9IcrnyYpW07ayY8slWPlnIUflkP0flU/8cJR3N/DxTPtnPUflkP0flk/0c0zimSIrGAZcBV5rZwe7+SeHGaJWzPxBWaR+bxglrKtiY2eaEiap7ATngfeDtFPISEREREREREemqrgD2Br4PbGFmd0fxjc3sEsLcy2sD9wLj0zhhrT16fgP0JlStznD392pPSUREREREREQ6guYgakzuvsjMdiYsxHU4cES0aWD0tQi4Ejgmv3p7rWotEA0CnnP3n6aRjIiIiIiIiIiIQDSs7CgzG0VYqGstoBvwX2CSu7+V5vlqLRAtIkycJCIiIiIiIiIZox5EjcnMrgLGufsUd58N3Fzvc9ZaIHoSWC+NRERERERERESkY6lA1LAOBQ4xszeBPwPj3f25ep6wW437jwIGmtneKeQiIiIiIiIiIh0oXyCq9kvq5jBgEmG1+BOBp83sGTM7wcz61+OEtfYg6gZcDNxgZrsDdwNvAIlPE3d/oMbziYiIiIiIiIg0NXcfA4wxs37AvsD+hMmpzwXOMbMphNXLbnH3D9M4Z60FoslAC2GJ+32BH7TTvns1JzGzA4CtgU2BDYGlgb3d/ZYS7bcDTgA2AFYA3gSmAOe6+8vV5CAiIiIiIiLSbDTErLG5+0zgd8DvzMyAA4D9gGGEiav/YGZ/d/fv13quWgtE1xEKRPV2FrAmMBuYBaxeqqGZ/Qy4CHgf+Gt0uxFh/N4+ZjbY3Z+td8IiIiIiIiIijU4FouxwdwdOBU41s28BRwIjgD3TOH5NBSJ3PziNJMpwODDd3V+Plnc7PamRmfUAzgD+B3zd3d8s2HY08HvgWOCQeicsIiIiIiIiIpImM+sO7EgYcvbdNI9daw+iDuHu/yyz6YrAF4AHCotDkb8TCkQrp5nbhRe2juVy1cdq3b8jYsonW/lkIUflU3lM+WQrn1pzlHQ0+/NM+VQeUz7ZyicLOSqfymPNlk8WNUMPIjNbkdCLZhfCtDT9gY+AJ4CL3f2eCo51LaFXTpLn3X3D2rKtTtRjaH/g+8BKQI7QOeZK4Po0zpFagcjMViDMEbQS8Jq7P5LWsSswC3gP+LqZ9S8qEu0S3d6f5glbigbY5XLVx2rdvyNiyidb+WQhR+WT/RyVT/1zlHQ08/NM+WQ/R+WT/RyVT/ZzTOOYWdQMBSJgb2A08BYwkTAP8ZcJRaPvmNmJ7n5Bhce8hFCAKfROjXlWxMy+QigK7Q+sDeSABcAEQlHoTnf/NK3z1VwgMrOVCb+4vVgyCfVY4JFo++HA+cB33f2hWs/XFndviYaSXQc8a2a3smQOoh0IlbVL65mDiIiIiIiIiHSo6YThVne6++dlKzM7C5gKnG1m4939rQqOebG7z0g3zfKZ2RPANwhFoRbgIUJR6GZ3/189zllTgSjqNfQIsC7wNPAwcFRRs78SKnl7EX6gunL3G8xsNvBn4IiCTVOB6919Yb1zEBEREREREcmCZuhB5O6JI4Xc3c3sJuCHwJZA4kroDWpT4AVCUWi8u79e7xPW2oPoFEJx6NfuPgrAzGIFInd/38yeBbap8VxlMbPDgD8SejVdBrwLfB24GJhoZnu6+9/aOUafcs4VJhAXERGRzqJrtoiISG1SLBD1Dquwl+bu86o7U03ynUQ+q3C/XcxsWeAT4BlgirsvSjWztm3q7tM68Hw1F4h2J6wuNqqddq8AQ2s8V7vM7KvAn4C/ufuJBZseNbPvAq8Shru1WSAC5tYpRREREUmXrtkiIiKNoZz5eTp0piYz+wJhNNMnwIMV7l48Pc10M9vX3f+VSnLt6OjiEEC3GvfvT6iktaeFsLpYvW1PKHpNLt7g7rMI3bO+Yma9OiAXERERERERkYbW0rKkF1GlX0kTfTeYPwGrAr9x9/fK3GcKYXLr1YFewPrARYTRU/eaWb96JNoIau1B9CFQzi9nXWB2jecqR8/odqUS21cGFrOki1kpfcs8nz61FBER6Vy6ZouIiNQgxSFmqwDza8nFzM6l/Gs7wGnu/n6JY50D7Av8A/hNuQd09zFFoReB48zsY+CXwE+BkyvIMTNqLRA9AXzbzNZx9/8kNTCzTYBN6JjJoB6Jbn9oZn9y95kFeRxOqAA+4O5tjj3spHGRIiIiUiFds0VERGqTYoFofgrX5cOBFSto/1vCyuUxZnYm8AvgPmDPlOYOuppQIBqcwrEaUq0FokuBnYAJZrafu/9f4UYzW4+w5HwO+EO1J4mKO1tFdzeJbo8ys12i7ye4+wR3fySaoXwf4AUzu43Qc2kTwvCzj4ETqs1DREREREREROrD3UuNBipbVBz6FXA/sJu7f1JzYsG70W3vlI7XcGoqELn73WZ2PnAi8IyZvUyYb2hHM3sG+BrQHTjb3WtZ4n4rYERRbGjB9zOACdH3+xMmnzqIMBnVMoTJssYTxh3+u4Y8RERERERERJpGMyxzn1dQHJoC7OruH6d4+M2j2xkpHrOh5FpSmFXKzPYmLHn/9aJNLwJnuvsNNZ+kMTX+lFwiIpJ1HbraRxPTNVtEROotM9dsM+tDND/fpZdOo2fP6jrFLFgwn6OPHpi/27czh36b2a+BUwkdRnZqLxczW4PQG+h1d58fxVYAlnX314rafokwXO1rwN7u3hFT6HS4WoeYAeDuNwM3m9nKwFqE1dH+6+5vpnH8RlZcX8vlqo/Vun9HxJRPtvLJQo7KJ/s5Kp/65yjpaObnmfLJfo5ttU0MLiqaTqN79+TYwqK1WXr0SI4tWNA6oZ49W8d79oSPiz6Q79UL5hX9H9anD3z0UTy27LLJsTlz4rHlloMPPojHll8e3ktYgGjFFWF20Vo4K6+cHJs1Kx5bdVWYOTMe69cvOfZm0b81/fu3juXjb7wRj62+Orz2Wjy25prpx159tXU+a6/dOl4q9sor8di669Y/1lbbcvNOilX7e2yrbdJzIOm5Ip3CzA4mFIc+Ax4HTjCz4mYT3P3pgvvXAdsAw1iyEvoawJNm9ggwnTCsbE1gF8Lk2dcCt9bhR2gIqRSI8tx9Nh2zWpmIiIiIiIiI1KhJhpitFd0uBRxfos0M4Ol2jvMWcBUwCNgD+AJh9fapwFXufmONeTa0VAtEIiIiIiIiIiIdyd1HAaMq3GdoQuwd4EepJJVBNRWIzOz+Mpt+CrxHqNbd6O5vtN1cREREREREREQ6Sq09iIZGty2UnpCrcNu+wFlmdpK7X1zjuUVERERERESkBk0yxExS0K3G/dcGLiFMBDUe+C6wSfS1K3B9tO1SwlL1vwQ+AS40sx1qPLeIiIiIiIiI1CBfIKr2S5pHrT2IvgUcTVhC7r6ibc8Cd5rZOOAfwGPufq6ZTQUmRvvdW+P5RURERERERESkRrX2IPo58GBCcehz0baHiGYSd/dJwDPA5jWeW0RERERERERqoB5EkldrD6L1gdvLaDcT2Kzg/kvA18o5gZn1B/YGdga+CqwGvA88DJzv7lMT9hkAnA0MA/pG57sC+KO76yksIiIiIiIiguYgkiVq7UE0H9jMzEpNUI2ZdQO+GbXNWwb4sMxzHA1cBKxDGJJ2IaFH0m7AI2a2T9H5vgY8AewO3AP8Ptp0KfCnMs8pIiIiIiIi0vTUg0jyai0Q/RNYF/ijmfUt3mhmfYA/RG0K5xsyoNyl7h8Hhrr7eu5+uLuf7O57EXoHLQJGm1nPgvajgeWA3d39AHc/CdiUMO/REWY2tKKfUERERERERESkyeVaWlqq3tnM1iT01lkRmAPcDbwebV4D2BH4IjAbGOTur5nZBsBzwAVR8aaW898D7ABs5u5PmtlXAAcmufu3i9puDkwFbnD3/Wo5b4Hqf3kiIiLlKdlLVyqia7aIiNRbZq7ZUWeOuQBnnz2Nnj17V3WcBQvmc8opA/N3+7r7vHQylM5Q0xxEUcFnC+By4NvADxKaTQRGuvtr0f2XgJWBj2o5d2RhdPtZdDs0uk1aHe0J4D1gmxTOu8TMmfH7/fpRXHPL5SgrVknbzoopn2zlk4UclU/2c1Q+9c9R0tHMzzPlk/0clU/2c1Q+2c8xjWNmUUtL9UPFkn6Hkl21TlKNu78CbGdm6wKDgX7RppnAI+7+clH7TwmFmpqY2RrAdtF5novCA6LblxLybDGzl4FBZtbb3ecXtxERERERERER6YpqLhDlRYWiV0ptT7MoY2Y9gHFAT+Akd18UbVouup1TYtc5Be1UIBIREREREZEuTauYSV5qBaIk0epm2wIHElYVW67NHco7ZjfgWmBr4Ap3H1frMRPO0aecdu6e9qlFRESkArpmi4iI1EYFIsmrS4HIzDYEDgL2Iww5S2U0ZlQcuiY67lhgZFGTwh5CSfLxD9s51dyqEhQREZGOpmu2iIiISApSKxCZ2arA/oTeQl+Pwjng38Cfo69ajt8NGEMoPI0HDnX34nplfu6hAUXxfG+m9YC3NLO6iIiIiIiIiHoQyRI1FYjMrBewB6EotC3QnSW9hVqATdz92ZoypFVx6EZgREJxCGBKdLsDcG7Rts2AFYEbyjhl3zJT06eWIiIinUvXbBERkRqoQCR5VRWIzOzbhGLN94A+hKLQPOCvhMmjzwC+lWJx6JrofDcDBxRMSh3j7m5mDwDDzGxnd/9HdIwewNlRsyvbO6d6GImIiGSDrtkiIiIi6aioQGRm5xLm/+lPKAotBv4JXAfcll+lzMxOTzHH04ARhE/+pgOnmllxm2vdfUb0/UjgEeA2M/sL8BbwHcKwt6vcfVKKuYmIiIiIiIhklnoQSV6lPYhOJAwdmwn8DrjB3WemnlXcWtFtX+CUEm0mAzMA3P3fZrY5ocfQTtF+LwM/BS6rY54iIiIiIiIimaICkeRVM8QsR1iZ7AfAQjO70d1np5vWEu5+MHBwhftMB/auRz6t9OvXKpRLWLOt3Fit+3dETPlkK58s5Kh8Ko8pn2zlU2uOko5mf54pn8pjyidb+WQhR+VTeazZ8skiFYgkr9ICUeHy9d8ENgUuNLP7gOuBCe7+cbopNriWlvj9XC4pVFaskradFVM+2conCzkqn+znqHzqn6Oko5mfZ8on+zkqn+znqHyyn2MaxxTJsm6VNHb3f7v7L4A1ge0IRaFPCEO5rgdmmdlYYLW0ExURERERERGRdOV7EFX7Jc2jogJRnru3uPv97j4CWJXQq+ifQC/CkvfrAJjZxWY2KK1kRURERERERCQ9KhBJXlUFokLu/rG7X+/uOwKrEyayfg7IESaGfsTMXjazX9d6LhERERERERERSV/NBaJC7v62u//W3TcBNgEuAmYRehSVWoFMREREREREREQ6UaoFokLu/qy7Hw98GdgZuKFe5xIRERERERGRymmImeRVs8x9Rdx9MXB39CUiIiIiIiIiIg2m7gUiEREREREREWlMtfQEaqQeRGY2g7DiepLL3P0nFRzrC8AoYE/CKu1vA7cCo9z9w9oybVwNXyAys/7A3oRhal8lPDjvAw8D57v71IK2KxIewF2ADYH+wEfAE8DF7n5Px2YvIiIiIiIi0rhaWqov9LS0pJtLCuYAFyfEHy/3AGbWB5hCmFf5PsJ0ORsDxwLDzGwrd59Xc6YNqOELRMDRwEnAK8C9wGxgALA7sLuZ7efuN0Vt9wZGA28BE4E3CXMg7Ql8x8xOdPcLOjZ9EREREREREekA/3P3UTUe40RCceh8dz8pHzSzM4DTou2n13iOhpSFAtHjwFB3n1IYNLMhhCLQaDOb4O4LgOnAd4E7o7mP8m3PAqYCZ5vZeHd/q+PSFxEREREREWlMzTLELA1mlgMOB+YCvy7afA6hA8thZjbK3Ruv/1SNGr5A5O5/LRF/0MwmATsAGwFPuvv9Jdq6md0E/BDYErgltQRzuXJCZcdq3b8jYsonW/lkIUflU3lM+WQrn1pzlHQ0+/NM+VQeUz7ZyicLOSqfymPNlk8WpVgg6m1mbbbvgKFZy5jZIcCXCFPTPOLuz1Sw/4Bo33uKc3X3T6IaxPeA9YCXUsq5YTR8gagdC6Pbz1JuW7biMZe5XPWxWvfviJjyyVY+WchR+WQ/R+VT/xwlHc38PFM+2c9R+WQ/R+WT/RzTOGYWpVggeqeMXer9W1oVuKYwYGZ3Awe6+7tl7D8gui1V/HmpoF3TFYi6dXYC1TKzNYDtgJnAc+20/QKwF/AJ8GD9sxMRERERERGRDnQNMBRYGVgW+BbwD+A7wO3R8LH2LBfdzimxfU5Ru6aSyR5EZtYDGAf0BE5y90Xt7PInQiXxNHd/r975iYiIiIiIiGRBij2IVgHm15KLmZ0L9K1gl9Pc/X0Ady+eM2iqme1KWJFsK8L0NFrZvA2ZKxCZWTfgWmBr4Ap3H9dO+3OAfQmVw9+UeY4+5bRz93KaiYiISJ3omi0iIlKbFAtE81OYY+hwYMUK2v+WMNdQIndfbGZjCAWiwbRfIGqvh1B7PYwyLVMFoqg4dA2wHzAWGNlO+zOBXwD3AXuW0dMob24teYqIiEiH0TVbRESkSbj7SnU4bH7uod5ltC2cYyhJe3MUZVpm5iCKikNjgBHAeODQwqXsE9qfCfwKuB/Yzd0/6ZBERURERERERDIi34Oo2q8M2Dy6nVFG25eAt4DBxb2UzWwZwhxHbwEvp5hfw8hEgaigOHQQcCMwoszi0BRgV3f/uMJT9i3zS0RERDqXrtkiIiI1aIYCkZmtZ2atrvdmtgVwHLAAmFC0bQ0z+6qZfd6zyN1bgKsI7x1OKzrcycAKwFVRu6bT8EPMCoaVHQTcDBzQ1lAxM/s1oTj0IDDc3SueJCuFcZMiIiLSAXTNFhEREWAX4Ewzm0LoKfQJ8DXCCmaLgSPd/b9F+1wHbAMMAyYXxM8HvgucaGYDgaeAjYGdgKej7U2p4QtEhKrdCMIcA9OBU82suM217j7DzA4GTgU+Ax4HTkhoO8Hdn65nwiIiIiIiIiJZkOIk1Z3pEeAOYFPCglbLAO8AfwEucvep5R7I3eeZ2VDgdGAvwrCyt4GLgDOa+cOpLBSI1opu+wKnlGgzmVAlzLddCji+RNsZhKqfiIiIiIiISJfWDAUid3+csJhVJfsMbWPbHMLQtONqyyxbGr5A5O4HAweX2XYUMKp+2YiIiIiIiIiINJ+GLxA1ulwu3Vg9jtnsOSqfymPKJ1v5ZCFH5VN5rNK2Urtmf54pn8pjyidb+WQhR+VTeazZ8smiZuhBJOlQgahGLUVzl+dy1cdq3b8jYsonW/lkIUflk/0clU/9c5R0NPPzTPlkP0flk/0clU/2c0zjmFnU0lJ9oSfpdyjZpQKRiIiIiIiISBelHkSS162zExARERERERERkc6lHkQiIiIiIiIiXZR6EEmeehCJiIiIiIiIiHRxKhCJiIiIiIiIiHRxGmImIiIiIiIi0kVpiJnkNXyByMz6A3sDOwNfBVYD3gceBs5396nt7H8CcH50dzN3f7KO6YqIiIiIiIhkhgpEkpeFIWZHAxcB6wD3AhcCDwG7AY+Y2T6ldjSzrwK/BuZ1QJ4iIiIiIiIiIpnU8D2IgMeBoe4+pTBoZkOAicBoM5vg7guKtncDrgGeAxw4oIPyFREREREREckE9SCSvIYvELn7X0vEHzSzScAOwEZA8dCx44BvApsCx9crv1wu3Vg9jtnsOSqfymPKJ1v5ZCFH5VN5rNK2Urtmf54pn8pjyidb+WQhR+VTeazZ8skiFYgkr+ELRO1YGN1+Vhg0s68AZwLnuvtzZla3BFpa4vdzuepjte7fETHlk618spCj8sl+jsqn/jlKOpr5eaZ8sp+j8sl+json+zmmcUyRLMtsgcjM1gC2A2YShpHl492AMcB/gLM6JzsRERERERGRxqceRJKXyQKRmfUAxgE9gZPcfVHB5p8B3wIGu/unnZCeiIiIiIiISCaoQCR5mSsQRT2ErgW2Bq5w93EF2wYQeg1d6u6P1XCOPuW0c/dqTyEiIiIp0DVbRESkNioQSV6mCkQFK5PtB4wFRhY1uRKYBZxS46nm1ri/iIiIdAxds0VERERSkJkCUcHcQgcB44FD3b24XrkJsBwwt8TE1E9E8WHuPrluyYqIiIiIiIhkgHoQSV4mCkRFxaEbgREJxSGA64DeCfGtgQHA7cC7hImt29K3zNT0qaWIiEjn0jVbRESkBioQSV7DF4gKhpUdBNwMHFA0KfXn3P2nJY5xLaFAdJa7P9neOd19XtUJi4iISIfRNVtEREQkHQ1fIAJOA0YQPvmbDpyaMHzsWnef0cF5iYiIiIiIiGRaS0v1PYFaWtLNRTpXFgpEa0W3fSk9+fRkYEYH5CIiIiIiIiLSNBYvhlyu+n2leTR8gcjdDwYO7uxjiIiIiIiIiIg0q4YvEDW6pEprLbF6HLPZc1Q+lceUT7byyUKOyqfyWKVtpXbN/jxTPpXHlE+28slCjsqn8liz5ZNF6kEkeSoQ1ah4zGUuV32s1v07IqZ8spVPFnJUPtnPUfnUP0dJRzM/z5RP9nNUPtnPUflkP8c0jplFKhBJXrfOTkBERERERERERDqXehCJiIiIiIiIdFHN0IPIzIYCk9ppdr+7b1vGsUYBp5fYPM/d+1aWXXaoQCQiIiIiIiLSRTVDgYiwqvkZJbbtDmwM3FvhMcfSerX0Tys8RqaoQCQiIiIiIiLSRTVDgcjdZwCjiuNm1h34IbAIuK7Cw17r7pNrzS1LNAeRiIiIiIiIiDSjnYB+wF3uPrOzk2l06kEkIiIiIiIiIrXqbWZtNnD3eR2US96h0e01Vey7jZl9C1gIvAj8090XpJZZA1KBSERERERERKSLSnGI2Ttl7FLlmSpnZisDuxDy+nsVhxhVdH+mmY1w9/tqza1RNXyByMz6A3sDOwNfBVYD3gceBs5396kl9tsBOAYYBPQFZgJTgRPc/Y0OSF1EREREREREOseBQA9gnLsvrGC/p4ERwBRgFvBl4AfAL4G/mdkgd3825VwbQsMXiICjgZOAVwizjs8GBhBmIt/dzPZz95sKdzCzs4BTgP8CtwAfAF8ChgJrAioQiYiIiIiISJeXYg+iVYD5teRiZucSOniU6zR3f7/EtkOi24qGl7n7hKLQy8BZZjYLuAI4Gdi3kmNmRRYKRI8DQ919SmHQzIYAE4HRZjYhPxbQzPYmFIduBfYvHiNoZqn+zEl/SLXE6nHMZs9R+VQeUz7ZyicLOSqfymOVtpXaNfvzTPlUHlM+2conCzkqn8pjzZZPFqVYIJqfwhxDhwMrVtD+t4QRRjFmtjmwITDV3f9dY055Y4E/AoNTOl7DafgCkbv/tUT8QTObBOwAbAQ8aWY54CzgQ+DQpAmk3P2zNPNraYnfz+Wqj9W6f0fElE+28slCjson+zkqn/rnKOlo5ueZ8sl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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "make_heatmaps(\n", + " waymax_vbd_data, \n", + " gpudrive_vbd_data, \n", + " array_name='agents_future',\n", + " index=4, \n", + " feature_name=agents_future_feat_names[4]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### ✅ 5. Traffic light points\n", + "\n", + "- Not supported in GPUDrive, but fairly confident that this doesn't cause any problems." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((16, 3), (16, 3))" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "waymax_vbd_data['traffic_light_points'].shape, gpudrive_vbd_data['traffic_light_points'].squeeze(0).shape" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([[8.4860176e+03, 1.4842620e+03, 4.0000000e+00],\n", + " [8.4883740e+03, 1.4843839e+03, 4.0000000e+00],\n", + " [8.4918564e+03, 1.4845614e+03, 4.0000000e+00],\n", + " [8.5254482e+03, 1.4642095e+03, 4.0000000e+00],\n", + " [8.4950791e+03, 1.4848386e+03, 4.0000000e+00],\n", + " [8.5254482e+03, 1.4677252e+03, 4.0000000e+00],\n", + " [8.5249268e+03, 1.4710636e+03, 4.0000000e+00],\n", + " [8.5027246e+03, 1.4853600e+03, 3.0000000e+00],\n", + " [8.5027246e+03, 1.4853600e+03, 3.0000000e+00],\n", + " [8.4991396e+03, 1.4852823e+03, 3.0000000e+00],\n", + " [8.4836152e+03, 1.4840068e+03, 4.0000000e+00],\n", + " [8.5245918e+03, 1.4773523e+03, 4.0000000e+00],\n", + " [0.0000000e+00, 0.0000000e+00, 0.0000000e+00],\n", + " [0.0000000e+00, 0.0000000e+00, 0.0000000e+00],\n", + " [0.0000000e+00, 0.0000000e+00, 0.0000000e+00],\n", + " [0.0000000e+00, 0.0000000e+00, 0.0000000e+00]], dtype=float32),\n", + " array([[[0., 0., 0.],\n", + " [0., 0., 0.],\n", + " [0., 0., 0.],\n", + " [0., 0., 0.],\n", + " [0., 0., 0.],\n", + " [0., 0., 0.],\n", + " [0., 0., 0.],\n", + " [0., 0., 0.],\n", + " [0., 0., 0.],\n", + " [0., 0., 0.],\n", + " [0., 0., 0.],\n", + " [0., 0., 0.],\n", + " [0., 0., 0.],\n", + " [0., 0., 0.],\n", + " [0., 0., 0.],\n", + " [0., 0., 0.]]], dtype=float32))" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "waymax_vbd_data['traffic_light_points'], gpudrive_vbd_data['traffic_light_points']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 6. Polylines\n", + "\n", + "- Shape: `(max_polylines, num_points_polyline, features)`\n", + " - Features: `(p_x, p_y, heading, traffic_light_state, lane_type)`\n", + "- What: Encodes the road graph \n", + "- Notes:\n", + " - Again, since we are centering the coordinates at zero, these cannot be compared directly\n", + " - _todo_: Find out why there are more polyline points in waymax.\n", + "- Checklist:\n", + " - ✔️ Is the feature order the same?\n", + " - ✔️ Do we process the road points in the same way?\n", + " - ✔️ Are the road object types aligned?\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((256, 30, 5), (256, 30, 5))" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "waymax_vbd_data['polylines'].shape, gpudrive_vbd_data['polylines'].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "make_heatmaps(\n", + " waymax_vbd_data, \n", + " gpudrive_vbd_data, \n", + " array_name='polylines',\n", + " index=0, \n", + " feature_name=\"x\",\n", + " share_axes=False,\n", + " share_color_scale=False,\n", + " x_label='Point index',\n", + " y_label='Polyline index',\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "make_heatmaps(\n", + " waymax_vbd_data, \n", + " gpudrive_vbd_data, \n", + " array_name='polylines',\n", + " index=1, \n", + " feature_name='y',\n", + " share_axes=False,\n", + " share_color_scale=False,\n", + " x_label='Point index',\n", + " y_label='Polyline index',\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "make_heatmaps(\n", + " waymax_vbd_data, \n", + " gpudrive_vbd_data, \n", + " array_name='polylines',\n", + " index=2, \n", + " feature_name='heading',\n", + " x_label='Point index',\n", + " y_label='Polyline index',\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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jdPVXiMFp1keo9vsNDbfNxrzfP8jdb2oMBmXbLyV8iFmOkGJd2/4Q4erMx83sUw2ynQmZNn/05o0Eb6wFg7Jj9TMtCPNx4Gv1E7S7/5uQjjwH0zJnavv+5g39N9z9vSzQ8BSwbZO/6TrgaMIV41PNbE3Ch7RXgD3qP6C2wbmEq4yfyQIW9bRTSnBo7eoR4WrQo4TGrOcTPoR3vGxsHbUvKPMNNtDM5iCUdEwlfEB6v1mmh+WNT2imc/cX3P2qxscsCz4elN3drhPT7v6Ou49vUjP/GmGFnY6PKUSFmD+7bRpAMLNfm9m4hp/PNBvboFswyz6olTadGMlv22TvA7Xm183ed/7WQTAI4BxCBlGz1Rx3JXxWPK/ui+FChKDTU8CY+vkn+zK6DyHg/OUOPNTev3drklnUznv86XXv8e8AD2S6E4CNC2aJdfIevwyhTOw14Fv1XyTd/a80LyWp9Xa6udl2wgWXkYQgW9t0Y95ok1/49P1TXmVa5sjihLmvvhz5VEKG2NotMt5a8Rjwnfrn1t0vBO4DlrQZVwB8kVCe1kl555zZbavG1rUv/nO12B/jeO1qOvExGI8Syr/qXzvHZbcfA37odWW2HprBn03Iep6uX5e7X90YDMq2n0oWKGz8XOfuTvgcMgdwjoV2CycQ/rd3d/c3m3j27KdZFlUzaqtKzk94Px9HyPyfj/C+9wohMHZ4m8cb7HmCYs9vrOdWiBlQhpAQg9Osj9C6wDM+bVWxG+vGXJhNbp8gBDGmWxkrCwpsBaxKmIhqH4BqgaDlCCVSNU4iXD3Zj5BuW6OWStssRRdCOn8jNb/vErJ0Wu1ftHGHhZ5InyFcJZ2baVdSZgbmN7P5mlwpP5RwhfKzhKvJMwFf8BYrybTC3Z82s6sJZQM7Eq681q48fobwOJ/b+gjcTcjkqbEgIYNgJ+AtM/uyu7/Viac6akvWt9OLYTXCVf1bfcYV6SBk5nyvlTgLCm5I+FA9e3bu2oeE5dr023jMFQllY0sRPnz1Me1vynVMIRJgN6YFjWr8l9ATrpHGHi01ziAEvctgoPedxizKAXH3x8zsBmA9m3E1xxnKxQjvQTMDf292JdzdnzWzhwglYLO3+PLWqLnFzB4kfCHbkCxbMitjWx34H82fmxo3Mq0cto+QobM6IXP1DTP7focXIerp5D2+lhl8WYsLNecxrcR5OrJA2EaEzM5FCNkLfdnvkP89Pvq8MQgDffa4w91fqt/hoR/fRML82Ek/rmu9eU8nJ1z0WpQQNKqd5zeEjCIxOBMagna1zO+XCO+bAz3HzT4/zkv4rLYiIRBT+/65MOH1+GEasjnd/XdZ9vy2hNLAuYHvuvtdzQy7e6uS11bUAs8zAVe7e/0qXn8ws7cJpZtfN7OjImVeCVFJFBASYnBuJmSVrJDVCE8hNOr8U22Auz9hZk8wLWi0NmGyud3dJ9XGmdmGhKyUDw5wvsarAH8mfEDa2cy+4e6vmdnHs3PcW38lroEZgi7uPsnMAJ5tccW05nXW+o1m9nXCFZRZBvE9XUDI3aeY2R6EJpZzA2dlV/DycBYhIDSaLCBECOjMClzh7k8PoG22jPMshNT+fQgBpU6Wja1ngey2nbKR2gelx1rsn9hsYxZgvJAQXGtFR1ePsiuxpzHA6m2dHlOIClH70rlAs53u/v52m9aIvxX1wYa3CP+/f2/yxaTdBr2dBBhmIAsc1K5uN3vfybMq09mE+etzZBcxsmDMqoR55u66sUtlt/tmZR8DMR8hk6gdzgJ+zLSyZpj2/vTHFl/+azRbdr5WvnYgobHxoM3FWzAU7/GLEQJ5qw5w7E7f46PPG23S7ILPpAH21e+ftcX+ZrRaxKH2xb2TY7Wi5mtUi/1zNJyzG8drV9OJj8EY6Hmav8X+Vp8fdyZkgQ30Wmu1b1/CBdG5CRdnfzbAMTplUt3vv2/c6e7jzewFwgXENRl8BdvBnico9vwqICW6hkrGhBiE7Arovwj/L2sDnyJkx1zfMPQG4BPZh9BaYOj9MVlm0AWEYNBPCBlBcwEjPKz4clQ2tI86spTzUwmTQm1Z3sGygyCUJeXZNx0WlnQ/nvBFaG9Cs8HZ3b0v810LSPU1PwI71+1bMQvE5OFiwsS5QVavDvlXniG7+vVNwpeyvbIrWHlYJbv9T059OxxD+FBfW21iQWDm7PGvNSds9fi34luEL1v3EZqNLwzMkh2z9oGu02MKURVqAYyBvmC3y6nuPib7+ZK7H9XiKnUt5X+OJvvqqe1v1WdiMD5OCM5PpnmAIU+24wWEvkQ715Xt1LKDzmkYW/vseBfTNzNu9tNJw+2zCO/HO5jZbFnfu9F1+zoiu6Jf60f0rU718H7vvVoz126+x59KeK2OJ3zGmA+YKXs//r9sTKfvx92YN9ohymePgueJRS24+qEW+2vbWwUAYxyvXc0rEbNYBnxs2822M7MlCD0gZyP8Dxrh/a/2ufe8bGir1+FWTCupWpZpgfAY1D/GE1uMqfVCGugibo3a8zRv9nm/GUWe33ZfY0J0jDKEhGiP6wlXCNYj1DDDjM0lbySUIazNtLTx6+r2r0+40niRuzeuGAIhZbYVvyM089vPwgoZnyes0lCkGXK71HoM/MDdm5VNtPSdBZN+SKjdv43QAPonhP49HeFhCeCLCTXdu1tY2n49wpeqPw0obn3M183sRcIH5Q9nHtsmq2tfhfDh6R9tSGqp8K2aA7bavj1ZD6UmZQgDvW4Gorac667ufm+kYwpRFS4lNI7fycwOLNhDpl1qV80XMLO5B+hNtkzD+E6plRxd7+0v7T4g7v6KmV1KeL//P8JKQLsTAjSNAaGa7xvc/asxzp95eMzCEsvrE/rlPEt4T3yUaWXZnVL7QjevmS3o7i90qN+EMG+/RihbGYyO3+OzL4+bE5rb7tjktVrkPT72vDHcuJtQ8t4qsLxq3bhuHa/2+wpmNnNj378cHoaSrQgXmH7m7r9osn+gz49LE0r83iKUi+5EWGhlpxjG3P1VM3uE8H7cqj9Yrax4Uov99cf7n5k9TmgSvQoN3xHMbHHCe8lbhLLGGrFfY0J0jDKEhGiP+j5C6xFSNxvfnGsfWDclBI/6mX5CqF3ZmCHN2cwWYIC0bg/LVV5KmBiOJExeF3hDo+cuMZDvTQjBlBkwszkJZQgzEdJ+RxNKB75tZhvn9FJbVn404ep1H3Bxsz4W7ZCl1NfKAQad8Bu0IwkfVvoIz0U7ZRF3EAJ5q2XNRxvZtYVuXuD1Fj0pdmuhqQUuWwX+Wz6vAxxTiFS4lJDRsThh9aKu4+7PMG3Fr62bjcka3a5ECCJ3vLpTVsb1jezu8R2bHJhan6DPZX1nlgGuy+afeq4lBBq27rAJcDvUv8fXsoPO9mkrb3ZK7X22n4Gbvc6AhZU8f57dPbGxp0oLas/pFk0WQIDm7/HzED6PP9MicNlqXmjnPb7TeUNMz1+z28+YWWMp1AcJnwffI7zfdOV4Hlbs+g+h/9NWTY65Y3Y7UI+tshjo8+PyTMuwbtw3kvB+NBehr+JowmfuHbOm/rGo9VvbsImHpZlWHjvDMvItqD2/OzbZV9t2ZUP5a+zXmBAdo4CQEO1xA+ED5RrAWsDNTT643UO4irgPIT32fp9+idzaFYEdsjd54P2rg6cCHxjEQ2353m9mtwOVi8Wk5nu/+nIvM1uKgVfY+TUhxfcUd/9z1nB6z2zfmWY26IotTbiWcHV6RaY9Dme2Ht6a7G/5OSGg8whhVZp2tZ8gNFXcHHi6zsuAZP2kziKUHP7azGavO+ZqwFdaSYEPmNnu9RstLMPabGUgMl8wrTSg2TEhZFHUH3NT4izpKkRpZAGE0YSrsWPN7OfNykKzwPUKjdsLcHx2e4yZfbThXPMSlsIeAVzYSXN9M5vJwlLM1xNKLk539yJLqTfjb4TmzdsAX8y2NWYHkQW/TyN8WTrPzBZu4vfDZrZDDg8XEp6zLZiWCZUrEzYr366tBjehkwsHZrYu4SLPioRFHtrqP5QtFnAFoefJz7IvtrVjfprm2Q3PER73FcxsvbrxfWb2faZf0KKedt7jO503ehYz+4qZPWBmRw0+OuBh9c/rgIUIJXi1Y81E6EE4M3BGY+aZmR2VnesrMY7HtBVij7Wwyl9Ntz3h//VlmvTBsWzlvezzWhnUPmfskb3XAmBm8xPeC1sFMw8hZNtfDvw6C8buTnhv+JWZLdsoyB7vB7J+XO1yPKGsdf+G/725CNlII4G/NgbFBzjX8YQ+o1/KMuRr45cDfpDdPa5eUOA1IUQ0VDImRBu4+8tm9h+m9W6Y4cquu081s5sJQQJo6DHk7neY2eWEdPwHzWwCYeJYn3C1eBwwZgAblxHqnJcC7nP3vCn0nXI6ofZ7K+AhM7uFcNVmQ0IK/QuEift9zGxHwt/i1AVL3P1qM/s5IeDQcepv9hifQ7hitAAh4+iagVUAbNvwgWgBwpWpRQklZ3u1uAK9QFaiB+GDwTyE10DtqvM/gdFZZkC7fB/YAPg08LCZXUe4irYRIch3QBPNTwi19ueY2QGEWvKPElZKO5ZpfTLquRl4nnBFbQIh6DUVOC1bRegYwpeuI81sJ8JztSShf8UxDLDamRAp4O53mtlGhCDDN4EDzOw2QlB5FsL//ycIV96foL2yz8E4kfA/NBq4x8xuys43L6GUeC7Cle79Wx5h+ver2QhZmKsTggzvEVY2+1EEr9Ph7m+b2QWEHnV7Er4oXdBi+NcJc9EOwJZmdiehF8YownvkhwkLIlzUoYdXzezPhGDQzISLLw8NIoPQ4HrD7Pc+Qs+PNQjZtM/T/H0VYPm69/iZCM/TSkDti95fgH3qF4dogy8TGnPvC2yUveYWJQR2ftvoJVtl6xhCH8Frs/frFwgZwcsSGuk2C9JfQfiC/E0zW4EQIOoHjnN3J9+80cssQAieLTLYwAb2Isz1X8+ym/9DeG0tQ7iQ1Oy5WSQ7V7Om9nmON45Qcr8T8ICFVVcXIHyWmALs2ZgJZqH5fI12l2KPzSWEgOqqhM87NxD+zzYilFeOJ6wi9j5ZIOUQQquBMbXPZu7+HzP7LuFi49lmtm7DhdlaYLTtrMWsTHU/wuN7TfYZ/iXgk4T3kIeBLzSRNj2Xuz9kZt/MPF5vZlcSMvk2J8wzR7t7Y/9RyPeaECIayhASon3q38RbpfrXB2mua7J/W+BwwkT4f4QvDn8jfNgfsGFc1sRvQnb3d4O6jUSW2bMGIROnj3A1qvYhdTMaPmhkV0xOzrZ/zt0b0/QPpljqb/3V4nPbbG64EuELTu1nE0KJ2InAJ9y92XMF4Up8TbMz4UPCS5luI3df290f6cR89niukx2jj/CaWIIQKGraj8PdzycEb24kfNnailC2+FlaZGm5+1vZuCsJXwDGELLXPpLtv4EQyLsyO/9nCB9u9nb3gzr5m4SoKu5+M2Fp7a8Q3j+XJfRW2ZzwheovhGyJj7T4oN7p+frd/fOEL25XEr6A70z4X7sP+A6wtjcsvd1A/fvVVpn/fxL6sS3l7t9v0kckFvXLy/+tVVmyh6Xkt8w83kz4O3ckzBUvEFZtyxtwOKvF7wOxDtMesz0IwZenCYGPFd39/ha6D9bpdiAsff4Y8AtgdXffptMr89mcsBZwLiHzd1tCMG8vpmV6NGqOJgQR7yLMM/9H6H+0IdNKSho1TxPmgJsJwca9Ce/xi2T7O543xIxkz+cqhEzu+Ql9tkYSMozX6rR0P8/xsqDIrsDXCMHrrQnvE5cC67h7s9fIytntNW2WtEcne59aH/gV4TPXVgTfZxBe541BrOlaDbj7sw3H+w3w90wbJSju7mdlHi8DPkZ4X3udkMmzZocX/GoeP034nrAuoY3EPYTPw99voYn6GhOiU/r6+/OWZQshhhIzm42QETMKWNTdXynZkhBCCCGGAWbWDzzm7kuV7UUMTpZNcyyw7hBmlIshxszGEDL5D3P3seW6EamikjEh0uHLhPT33ysYJIQQQoghpr6M+kR3v6VMM2JANgauUDCoNzGzIwhL0mvFQFEYBYTqMLM1gMMIZTyzENK7j3f3c0s1JoYtWeO9Ywhp7VsSVkn5SammhBBCCDEcqZVRQyixUUCoorj7lmV7EF1lW0IpqBCFUclYRtaM8HJC86/zCXWt2wNLAz9w9yPLcyeGK1lj0UcJr8t7gQPd/epSTQkhhBBCCCGESB4FhHh/ab8HCKl3n3L3O7PtcxKa9RnwsTZXuhBCCCGEEEIIIYSoNFplLLAxYeWRc2vBIIBsmdHDCaV1eVZDEkIIIYQQQgghhKgcCggFNsxur2iy77LsdoOhsSKEEEIIIYQQQgjRXRQQCiyX3c5QEuburwHP1Y0RQgghhBBCCCGESBqtMhaYJ7t9tcX+V4GlhsaKEEIIIYQQQghRHcysDxgV+bCT3V1NjUtEAaEImNkc7Yxz9ze67UUIIYQQrdGcLYQQQuRiFDAp8jHnBDTflogCQoFaZtA8LfbPQ+vsIYj/jyGEEELkpa9sAxVHc7YQQoiqoDlblIoCQoFa76DlgDvqd5jZ3MAHgZuG2pQQQgghhBBCCFEl1nv4OUZOzVfp9d6IPq5f9oORHYm8KCAU+AfwfWBz4PyGfVvUjWnFnG2eZ9J7J3y3Q2sw8oDjGCrdUJ5rqHXyGEeXgse8OnmMo5PHcnUjDziu4/MMM9qes3npqc6PPv9iVF4nj3F08liuTh7j6FLwmFeXiscEGTm1n5H9OVv/TI3rRRSjrz/vE9lDmNlMgAOLAZ9097uy7XMCNwMGfMzdZ1iFrEP0YAshhOg2Sj+Pg+ZsIYQQ3SaZOTvrwTcJYOP/Ppc7IPReXx/XfPj9DKE51bOvXJQhBLj7FDPbF7gcuN7MzgNeA7YHlgYOiRAMEkIIIYQQQgghkqYPGJFTqwShaqGAUIa7X2tm6wKHATsDswD3AT9093Ninaf/Ke9Y07eYDZluKM811Dp5jKNLwWNenTzG0cljubq+xazj84gWVL3UIK9OHuPo5LFcnTzG0aXgMa8uFY9ClIhKxoYWPdhCCCG6TTLp5xVHc7YQQohuk8ycXV8yttnDzzFTzjjClL4+rlxWJWNVQRlCQgghhBBCCCGEaIsR5C8Zy6sT3UEBoSHm2oU6Twvc6Pmnhkw3lOcaap08xtGl4DGvTh7j6OSxXN1Gz+dIcxdNqXp5YF6dPMbRyWO5OnmMo0vBY15dKh6FKBOVjA0terCFEEJ0m2TSzyuO5mwhhBDdJpk5u75kbMtHnmOmnLPklD74+zIqGasKyhASQgghhBBCCCFEW6hkrHdQQEgIIYQQQgghhBBtMaKvQEAomZyo4UFPBoTMbDSwPrAasAJhCfmd3P3CNrQ7AX/M7ral6YT3Tvhux5qRBxw3ZLqhPNdQ6+Qxji4Fj3l18hhHJ4/l6kYecFzH5xEtqPpyxXl18hhHJ4/l6uQxji4Fj3l1qXgUokR6soeQmU0ElgReAN4CFqeN4I6ZLQjcB4wC5mhH0yG992ALIYSoGrr2FgfN2UIIIbpNMnN2fQ+hzz5arIfQn5dWD6Gq0KslfPsCS7r7QsBpHehOAN4ETuqKKyGEEEIIIYQQImH6+voK/Yjq0JMlY+5+VacaM9sB2AnYEvhkdFMZKSxXXHWPeXXyGEeXgse8OnmMo5PHcnVadj4iVS81yKuTxzg6eSxXJ49xdCl4zKtLxaMQJdKTJWP1mNlY4FAGKP8yswUIpWKXu/se7Why0tsPthBCiCqgS29x0JwthBCi2yQzZ9eXjG0/8flCJWMXL7VQ7a5KxkqmJzOEcvCb7PYbZZoQQgghhBBCCCGqjFYZ6x2GfUDIzLYDdgF2cfeXu32+FEoNqu4xr04e4+hS8JhXJ49xdPJYrk4lYxGpeqlBXp08xtHJY7k6eYyjS8FjXl0qHoUokWFdMmZm8xNKxW52923b0bQ4xxzteHH3SW0bF0IIIfKha28DoDlbCCFEhUhmzq4vGdv5sWIlY39cUiVjVWG4Zwj9DJgN2L/gcfShUQghhEgDzdlCCCFEAUb09alkrEcY7gGhlYF5gKfMrNn+C7Lte7n7uBgn/FLf3B1rTup/bch0Q3muodbJYxxdCh7z6uQxjk4ey9Wd1P9ax+cRzel/yjvW9C1mldfJYxydPJark8c4uhQ85tWl4lGIMhnuJWM/ApZoIlsVWAW4GpgInObuNw1wDqWfCyGEqAq69jYAmrOFEEJUiGTm7PqSsd0ef4GZc4YR3u2D85ZYsHZXJWMlM6wzhNz98GbbsyDSKsBJ7fQQ0otYCCGESAPN2UIIIUQxqrTKmJmNBtYHVgNWAGaheTLIUsCjgxzuEXdfto1zjgFOH2DIgu7+4mDHqQI9GRAys32BdbO7K2e3B5jZ1tnv4919/FD7EkIIIYQQQgghUmYEBQJCMY0EjgCWBF4AngMWbzHuf8BhLfZtCGwAXNHhuf8M3NVk++QOj1MaPRkQIgSD9mzYtmHd7xOB8UPkZTpS6D1RdY95dfIYR5eCx7w6eYyjk8dydeohFJGqL1ecVyePcXTyWK5OHuPoUvCYV5eKR1GUfYEH3f3xunYxM+Du/wPGNttnZrdmv57W4bnHx+o1XBY930OoYujBFkII0W2S6UdQcTRnCyGE6DbJzNn1PYT2fOLFQj2Ezlh8gdrdqD2EBuofPIDm48C9wL3uvmKbmjGEkrFoi0+VRa9mCAkhhBBCCCGEECIyEXsIjWqx2vf7DEHvv32y206zgwBWNbMFCA/Hf4Er3f31aM6GAAWEhpipD/yzY82I5T81ZLqhPNdQ6+Qxji4Fj3l18hhHJ4/l6kYs/6mOzyNaUPVSg7w6eYyjk8dydfIYR5eCx7y6VDwOb55vY0zXsqjMbGZgNPAOcFaOQ3y14f6rZvYVdz+7sLkhQiVjQ4sebCGEEN0mmfTziqM5WwghRLdJZs6uLxnb58liJWO//9ACgw/McPe2H6NOS8bMbHvgIuAid9+xg/OsD3yc0IT6aWAhYCtCg+sPAJ9298vaPV6ZKENICCGEEEIIIYQQbRGxZGwhyl2Ra+/stqNyMXe/DriubtNjwG/N7L/A5YSglAJCYkb6n/KONX2L2ZDphvJcQ62Txzi6FDzm1cljHJ08lqvrW2zgWnzRPlV/rvPq5DGOTh7L1cljHF0KHvPqUvE4zJk8BD2CmmJmiwBbAE8RgjiFcfcrzOwJYE0zm8ndp8Q4bjdRydjQogdbCCFEt0km/bziaM4WQgjRbZKZs+tLxr741EuFSsZOXmz+2t3SVhkzs4OAo4Aj3f0HET38C1gFmMvdJ8U6brfoyQwhM1sM2An4NLA8sDDwMnAjcKy739Iwvg/YkdAU6iPAnMATwN+B49z9maFzL4QQQgghhBBCVJOIJWNlsld2e3qsA5rZXIT4w0spBIOgRwNChMDO94CHCY2eXgCWA7YFtjWz3d39D3XjfwF8nZAudhGhjnFN4JvAbma2aqygUAppi1X3mFcnj3F0KXjMq5PHODp5LFen9POIVH11mrw6eYyjk8dydfIYR5eCx7y6VDyKIcfM1iUkglzn7v8dYNwCwALAi+7+Yt32Vdz9zoaxswGnALOTbwn7UujJkrGsW/hL7v6Phu3rAVcTUt0Wcfe3zWxhQmfwR4GV3f31uvE/A74FjHX3wyJY670HWwghRNWozrW3tNGcLYQQotskM2fXl4wd8HSxkrETFo1XMmZm+wLrZndXBlYCJhAaPQOMd/fxDZrTCBlCY9z9jAGOPZZQgnaYu4+t2/4K8CRwFyGpZEFgU2AJ4G5gQ3f/X4E/a8joyQwhd7+4xfbrzexaYHNgReB2YCnCP+IN9cGgjL8SAkILds+tEEIIIYQQQgiRBhUrGVsX2LNh24Z1v08ExtfuZIGtnYDXgQtynvPX2Tk2A+YD3gYcOBH4pbu/mfO4Q05PBoQG4d3sttbx+yHgHWBdM5urISi0dXZ7zVCZE0IIIYQQQgghqkoffbkDQrHjQe4+BhjTwfg3gLnaHDsWGNtk+4/aPV/VGVYBITNbgpDK9QzwbwB3f8nMDgGOBe43sz8DbwBrZT9HtMo4ysO1C3VeJ7rR808NmW4ozzXUOnmMo0vBY16dPMbRyWO5uo2ez9H3QDSn6r0n8urkMY5OHsvVyWMcXQoe8+pS8ShEifRkD6FmmNnMwFXA+sAe7n5Ww/7PAScDc9Rtvhw40N3vGeTYcwy0v0YqncaFEEIkTTL9CMpAc7YQQogKkcycXd9D6BvPvswsOcMI7/TB8QvPV7sbddl50Tl5M72SwsxGAOMIwaBTmgSDDiUsN3cY8CFgbkKfoaWBm8xsjUFOManNHyGEEEKUi+ZsIYQQogB9BX9Edej5krEsGHQasDtwBvDlhv2bEuoCf+7ux9XtutLMdgTuAX4MbBHDTwqlBlX3mFcnj3F0KXjMq5PHODp5LFenkrGIVL3UIK9OHuPo5LFcnTzG0aXgMa8uFY9ClEhPl4xlwaDTgT2AcwilYlMbxtSWlt/G3f/S5BgvAe+6+8IDnEfp50IIIaqCLr4NgOZsIYQQFSKZObu+ZOzbzxUrGfvZB1UyVhV6NkOoIRh0PrBnYzAoY9bsdoEmx5iVUD729EDn0otYCCGESAPN2UIIIUQxRhRYZWxY9KxJiJ4MCNWVie0BXACMdvf3Wgy/CTgA+JaZXdiw7PwPCY/RhFjeXl57hY41891075DphvJcQ62Txzi6FDzm1cljHJ08lqub76Z7Oz6PaEHVSw3y6uQxjk4ey9XJYxxdCh7z6lLxKESJ9GTJmJmNBQ4lpLT9EpjSZNg4d59oZrWAzzrAs8BfgNeBT2U/LwBrufujEaz13oMthBCiaiSTfl5xNGcLIYToNsnM2fUlY9977hVmyXmcd4BjPjhv7a5KxkqmJzOEgKWy2zmBH7QYMwGY6O5TzGwzQh+hnYHRhMflKeAU4Mfu/mRX3QohhBBCCCGEEAkwgvylXyoZqxY9mSFUYfr7n/KORX2LGUOlG8pzDbVOHuPoUvCYVyePcXTyWK6ubzFL5mpjxemvfKlBXp08xtHJY7k6eYyjS8FjXl0aHpOZs+szhL5fMEPoKGUIVQYFhIYWPdhCCCG6TTIfLiuO5mwhhBDdJpk5uz4gdHDBgNCRCghVhl4tGRNCCCGEEEIIIURkRvRplbFeQQEhIYQQQgghhBBCtEUf+VObkkmJGiYoIDTEJNB7ovIe8+rkMY4uBY95dfIYRyeP5er6FrOOzyNaUPXeE3l18hhHJ4/l6uQxji4Fj3l1qXgUokTUQ2ho0YMthBCi2+jiWxw0ZwshhOg2yczZ9T2EfvT8/wr1EDp8oQ/U7qqHUMkMmwwhMzsB2D+7u6C7v5htnwPYDtgGWBlYHHgbuBs4yd3PG3q3QgghhBBCCCFE9VDJWO8wLAJCZrYR8GXgDWCOht3rAWcBLwFXAxcBCwHbA+ea2Tru/pVYXlIoNai6x7w6eYyjS8FjXp08xtHJY7k6lYzFo+rPdV6dPMbRyWO5OnmMo0vBY15dKh6FKJOeLxnLMoD+DdwFzAdswPQZQisDKwB/dPd36nQfBG4BlgTWcvdbI9jp7QdbCCFEFdDFtzhozhZCCNFtkpmz60vGxr7waqGSsbELzlO7q5KxkhkOq74dA8wLHNBsp7vf5e5n1weDsu3PASdnd9fvrkUhhBBCCCGEEKL69BX8EdWhp0vGzGwDQt+gfd39GbOOU/LezW6nxPI09YF/dqwZsfynhkw3lOcaap08xtGl4DGvTh7j6OSxXN2I5T/V8XlEc6peapBXJ49xdPJYrk4e4+hS8JhXl4pHIcqkZ0vGzGwUcA/wqLtvlm2bQEPJ2AD6kcCdhHKyT7j7vRFs9eaDLYQQokro4lscNGcLIYToNsnM2fUlYz8uWDL2Q5WMVYZezhA6GlgY2Cyn/sfAisBpgwWDsn+OQXHvPNIshBBCiHhozhZCCCGK0denVcZ6hZ4MCJnZesBXgG+5+6M59F8Cvg/8C/h6G5JJ7R77d3Mt2Kkd9nv9hSHTDeW5hlonj3F0KXjMq5PHODp5LFe33+svdHyeYUbbczYvPdX50edfrPo6eYyjk8dydfIYR5eCx7y6VDwKUSI9VzJmZjMB9wMvAuu4+9S6fRMYpGTMzPYFTiGUm23s7i+3cc62HkRdbRRCCDEE6OLbAGjOFkIIUSGSmbPrS8Z+8uJrhUrGfrDA3LW7KhkrmV7MEJoT+HD2816LRtIvZNuXdveJtY1mth9hZbF7gU3bCQbVnbMd2r8qKYQQQohuoDlbCCGEKEgykSwxIL0YEHob+H2LfVsR+gqdnY17vbajLhj0H2CTwZpO16OophBCCJEGmrOFEEKIYhRZPl6BpGrRcwEhd38T2LfZvqxkbGHgm/UBn6xM7GTgAUIwqGsNGFJYrrjqHvPq5DGOLgWPeXXyGEcnj+XqtOx8RKreeyKvTh7j6OSxXJ08xtGl4DGvLhWPQpRIz/UQGohmPYTMbGPgKkKw8mTg2SbSCe4+IYKF4fNgCyGEKAtdfIuD5mwhhBDdJpk5u76H0NEvvcasOY/zNnDQ/OohVBV6LkMoB0sw7R/xiwOMm9B9K0IIIYQQQgghRHXpo08lYz3CsAoIufuGTbaNA8YNlYf+pzpftaRvMRsy3VCea6h18hhHl4LHvDp5jKOTx3J1fYs1XUxB5KHqpQZ5dfIYRyeP5erkMY4uBY95dal4FKJEhlXJWAXQgy2EEKLb6OJbHDRnCyGE6DbJzNn1JWPHvvR6oZKxA+efq3ZXJWMlM6wyhIQQQgghhBBCCJGfvj6tMtYrKCA0xKRQalB1j3l18hhHl4LHvDp5jKOTx3J1KhmLSNVLDfLq5DGOTh7L1cljHF0KHvPqUvEoRImoZGxo0YMthBCi2+jiWxw0ZwshhOg2yczZ9SVjP325WMnYd+aLVzJmZqOB9YHVgBWAWYCd3P3CJmMnEFYdb8bf3H3rDs47K/A9YDRhoapXgL8Bh7h7s5XLK4kyhIQQQgghhBBCCNEWI+hjRG5tdI4AlgReAJ4DFm9Dc1iTbQ+2e0IzGwH8Gfg/4BbgYmBZYC9gMzNbK5WgUM8GhMxsoCt733X3nzbRbA58HVgLmBN4hvAEf9fdn4jh6+W1V+hYM99N9w6ZbijPNdQ6eYyjS8FjXp08xtHJY7m6+W66t+PziBZUvdQgr04e4+jksVydPMbRpeAxry4Vj6Io+wIPuvvjZjYWOHQwgbuPLXjOPQnBoPOB3d29H8DM9gJOA47JxlSeni0ZywJCj9F8Sfkr3P2mhvFHAD8AniSker0CLApsCHzO3W+IYKs3H2whhBBVIpn084qjOVsIIUS3SWbOri8Z+8XLkwqVjH1zvjlrd6OuMlYXEBqwZMzdCz3uZnYT8ClgKXd/rGHffYRsoQXd/fUi5xkKejZDKGNiO9E/M9uJEAy6iBD8ebthf68/TkIIIYQQQgghxKCkvsqYmX2OUGY2CbjN3f/ZgXY2QkWRNwaDMi4DvgV8Ergygt2uMuwDHWbWR6g7fA3YuzEYBODuU4bcmBBCCCGEEEIIUTH6iBYQGmU28OqoMTOI6ji7/o6Z3Qbs4u6PtqFdltAK6aEW+2vbl0MBodKZ18y+CMxPaDI1wd0bn7iVgI8QsoPeMLOtgBUJAaJr3P2BmIZSWK646h7z6uQxji4Fj3l18hhHJ4/l6rTsfDyq/lzn1cljHJ08lquTxzi6FDzm1aXicZjzfBtjYiYVjQeOBu4ifN9fDvg28HngSjNb0d3fHOQY82S3r7bY/2rDuErT6z2EGukHzgG+6O6Ts3H7AKcCvyMEh9ZsGP8r4Ju1RlEF6c0HWwghRJWoQjZ2L6A5WwghRLdJZs6u7yH0q1feKNRD6GvzztH2+E76/QzWQ2gA3TnA7sCX3P3kQcauDdwInOPuo5vs3w04FzjY3Y9q10NZ9HKG0E+BCwgpW/3AKsCRwGhgJOEJB1gwu90b+C+wPnAnIUvod4RVxx4CTmh1ouyfY1DcO480CyGEECIemrOFEEKIYozoy798fINuIWByUT8R+D0hPrAOMGBAiMEzgAbLIKoUPRsQcvfvNmy61sw2Ae4GdjOzw7NysNprcgSwq7vfld3/p5ntDPyb0BSqZUCILFLaDtcu1PnSghs9/9SQ6YbyXEOtk8c4uhQ85tXJYxydPJar2+j5HEvlDi/anrOrXmqQVyePcXTyWK5OHuPoUvCYV5eKx2HO5C71COqUF7PbUW2MfQSYSig3a0Zte6seQ5WiZ0vGWmFmhwM/BPZx99PM7ADgN8Dj7r5kk/EPEp7Ued39fy2O2daDqKuNQgghhoBk0s/LQHO2EEKICpHMnF1fMnbC/4qVjB3wgfeTdYd02fkBdPsSqoN+5u7faWP8PwmriA207PxC7v5aB/ZLoWczhAagMfr3YHY7WFOo2YH/tRgzZ5vnbvuqpBBCCCG6guZsIYQQogARVxkbMszsQ8Bb7v5iw3YjrDoO8IeGfYsQSsCecff6eMEphIDQ0Wa2e63fsJntBXwMODOFYBAMz4BQrWn0xOz2n8BbwDJmNmv9svNmNjMhujeZsEpZUzqJaqZQalB1j3l18hhHl4LHvDp5jKOTx3J1KhkbmI6uRL6U47Gcf7Hq6+Qxjk4ey9XJYxxdCh7z6lLxKAqRZfesm91dObs9wMy2zn4f7+7jgdWB883seuBhQuLHcsBWwCzA4e5+W8PhjwL2BPYCxtVtPwPYBdgVWNrMJgDLADsATwDfi/PXdZ+eLBkzs48DD7n7Ow3btwcuJAR3lql9KDSzU4F9gMPcfWzd+O8TGlGf5+67U5zee7CFEEJUjWTSzyuO5mwhhBDdJpk5u75k7MT/TS5UMvblD7zfqqdwyZiZjSMEbVpxmLuPNbPlgIOBNYDFCBnDLwO3ACe4++UDHHsvdx/XsG9WQuDn88ASwCvA34BD3P2ZIn/TUNKrAaHjCU/MdcDjhKZPqwAbELKBtnf3v9eNX5CQKbQscDVwF2GVsc2Bp4C13D3GJdfee7CFEEJUjWQ+XFYczdlCCCG6TTJzdn1A6KRXiwWEvjRPvICQKEavloxdBixOCAJtBswMPA2cDhzn7vfXD3b3F8zsk8DhwDaEpeefJ9QGjo0Z4Uuh033VPebVyWMcXQoe8+rkMY5OHsvVacWSeEx94J8da0Ys/6nK6+Qxjk4ey9XJYxxdCh7z6lLxKESZ9GSGUIXRgy2EEKLbJHO1seJozhZCCNFtkpmz6zOETimYIfQFZQhVhl7NEBJCCCGEEEIIIURkUlxlTDRHASEhhBBCCCGEEEK0R1+fAjs9ggJCQ0wKyxVX3WNenTzG0aXgMa9OHuPo5LFcnZadj0fVe0/k1cljHJ08lquTxzi6FDzm1aXiUYgyUQ+hoUUPthBCiG6ji3Zx0JwthBCi2yQzZ9f3EDr1tTeZLedx3gL2nXv22l31ECoZZQgJIYQQQgghhBCiLdRDqHfoyYCQmY0hLDE/EKe5+z51moWBI4CtgHmBx4GzgaPd/Z1Y3lIoNai6x7w6eYyjS8FjXp08xtHJY7k6lYzFo+qlBnl18hhHJ4/l6uQxji4Fj3l1qXgUokx6smTMzFYGtm2xewywJLCru/8hG78wcAuwODAeeBBYF1gHuBz4tLtPjWCt9x5sIYQQVUMX3+KgOVsIIUS3SWbOri8ZO+31twqVjO091/tqlYyVTE9mCLn7XcBdjdvNbF7gIOBlQuCnxjHAEsD+7n5iNraPkGW0Z/YzWMaREEIIIYQQQgjR04zogxF5tVGdiKL0ZEBoAD4HzAr8zt3fBjCzuYBdgEeAk2oD3b3fzL4PjAb2I1JAKIVSg6p7zKuTxzi6FDzm1cljHJ08lqtTyVg8ql5qkFcnj3F08liuTh7j6FLwmFeXikchyqQnS8ZaYWb/AlYBVnX3O7NtmxPKwk529y810dwBrERIZ3uroIXh82ALIYQoi2TSzyuO5mwhhBDdJpk5u75k7IzJbxcqGdtz1Ky1uyoZK5lhk7FlZisRgkF31oJBGctltw+1kD4EjASW6aI9IYQQQgghhBCi8vT1FfsR1WE4lYzVVhQ7rWH7PNntqy10rzaMK8SX+ubuWHNS/2tDphvKcw21Th7j6FLwmFcnj3F08liu7qT+1zo+j2hO1UsN8urkMY5OHsvVyWMcXQoe8+pS8ShEmQyLkjEzmwV4GpgTWMTdX6nbdzDwE2A/dz+1ifZk4AvA2u7e9D88S58bFHeflMO+EEII0Qm69jYAmrOFEEJUiGTm7PqSsbPeKlYy9vnZVDJWFYZLhtC2wPzAH+qDQRmDZQANlkEE2T+GEEIIISqP5mwhhBCiAH19fbkjWclEwIYJwyUgtHd2+/sm+2q9g5Zrsq+2fSphFTIhhBBCCCGEEGLY0teXP7CjgFC16PmAkJl9CNgMeBy4usmQm4F3gM3MrM/d++u0ixBWGLtlkBXG5mzTzqQUliuuuse8OnmMo0vBY16dPMbRyWO5Oi07Pyhtz9lV7z2RVyePcXTyWK5OHuPoUvCYV5eKRyHKpOd7CJnZIcCPgcPd/dAWY84A9gD2d/cT67aPA/YE9nb30yPY6e0HWwghRBXQxbc4aM4WQgjRbZKZs+t7CJ377ruFegjtPvPMtbvqIVQyPZ0hZGZ9wBjCh7qBAjoHARsBJ5jZpsCDwHrAOsDlwBnddSqEEEIIIYQQQlQflYz1Dj0dEALWB5YFrnH3ia0GufszZrYWcASwFbA1ocTsUOAYd58azdHkgXpTt2DUPEOnG8pzDbVOHuPoUvCYVyePcXTyWK5uVKs1EkSnVL3UIK9OHuPo5LFcnTzG0aXgMa8uFY9ClEnPl4xVDD3YQgghuo0uvsVBc7YQQohuk8ycXV8y9of3phQqGdtl5Pt5KSoZK5lezxASQgghhBBCCCFEJFQy1jsoIDTUpFBqUHWPeXXyGEeXgse8OnmMo5PHcnUqGYtG1UsN8urkMY5OHsvVyWMcXQoe8+pS8ShEmahkbGjRgy2EEKLb6OJbHDRnCyGE6DbJzNn1JWMX9L9XqGRsp76RtbsqGSsZZQgJIYQQQgghhBCiLfr6wk8urS63VAoFhIaaFEoNqu4xr04e4+hS8JhXJ49xdPJYrk4lY9GoeqlBXp08xtHJY7k6eYyjS8FjXl0qHoUok54sGTOzmYC9gH0Jy87PAjwGXAj80t3/N4D2u8Cx2d013P32iNZ678EWQghRNZJJP684mrOFEEJ0m2Tm7PqSsYv6pjJbTudv9cMO/SNqd1UyVjIjBh/SGjPbv81xc5jZ6UXO1SEXAKcA8wDnAKcC7wJjgZuyF/MMmNnywOGAXpRCCCGEEEIIIUQDtZKxvD+iOhQtGfuNmW0B7O3uLzYbYGafBM4CliFk7XQVM1sT2Bb4J7C+u0+p23chsAOwI3BGg24EcBrwb8CB0d32KoQQQgghhBBCpERfX1/+HkJxrYiCFA0I3Q5sDdxjZnu7+2W1HWbWB/wI+EF2npMKnqtdlslur6wPBmX8jRAQWrCJ7lvA6sBqwLe75i6F3hNV95hXJ49xdCl4zKuTxzg6eSxXpx5C0ah674m8OnmMo5PHcnXyGEeXgse8ulQ8ClEmhXoIZb16DgcOJAT7fpP9vihwNvBJ4EVgH3f/a2G37Xn6BHA3rTOEtgfWrO8NZGYfyTTHufuPzGwcsCfqISSEECI9dPEtDpqzhRBCdJtk5uz6HkLjZ+ov1ENo2ynvi9VDqGQKZQhlwZaDzewyQlnYV4DNCQGhuYC/A3u5+/NFjXbg6R4zOwE4ALg38/YesBGwHHBAQzBoBHA68AhwxFD5FEIIIYQQQgghUqOvr48RKhnrCaIsO+/u15nZesA9wEeyzWe5+54xjp/Dz1fMbCJwFPD1ul3nEoJU9XyDkMm0jru/03VzKZQaVN1jXp08xtGl4DGvTh7j6OSxXJ1KxqJR9VKDvDp5jKOTx3J18hhHl4LHvLpUPApRJlGWnTezdQgZQksBE4Elsl2/AA5293cLn6R9LyOAk4FdgG8CfwHeATYhlLSNJJSMTTSz5QilYqe4+zfqjjGODkrGWq1a1oi7T+rojxFCCCE6RxffBkBzthBCiAqRzJxdXzJ2ySwwe07nb/bDNtPSMAqXjJnZaGB9Qi/gFYBZgJ3c/cKGcXMA2wHbACsDiwNvE+IBJ7n7eR2ccyxwaIvdb7j7nJ39FeVRKEMoC74cBhxEWML++Oz3tYEzCY2aNzGzz7n7/cWsts3ewL7A19z993XbLzKzd4BLMo9fAn4HPEdofF0EfWgUQggh0kBzthBCCFGAsHx8vohQX/wWfUcASwIvEL7bL95i3HqEJJaXgKuBi4CFCD2GzzWzddz9Kx2e+wxCQkw93a86ikjRkrGbgDUID/wYd78i2z4ha+58MrATcLuZHejuJxQ8XztsWfPQZF9t28p1t/MAk8ys2bFuy7Zv5O7Njtc5KZQaVN1jXp08xtGl4DGvTh7j6OSxXJ1KxqJR9VKDvDp5jKOTx3J18hhHl4LHvLpUPIrC7As86O6PD5K58yzweeCP9a1izOxg4BbgADM7091v7eDc46LFCUqi6CpjUwkZN/u4+0stxuwJ/BqYw91H5j5Z+57+CmwFbOzu1zbsWwZ4GLjO3Tcws18Bo5ocZn1CA+o/E1ZJO87dfYBzKv1cCCFEVUgm/bwMNGcLIYSoEMnM2fUlY3+dDWbPmSH0Zn8/W7/1/t2oq4zVBYRmKBkbRPd94Ejgu+7+0w7OEy9xpCSKZgjt7+4nDTTA3c8ws+sI6VlDwU2EgND3zezGWvQvK28bm42ZkHn7WrMDZD2ElgOOaKeHkJbKE0IIIdJAc7YQQghRjFAyllM7/d1RLSp13meI5u1az+MpHeo2MLNPZvoHgKvc/e2ozrpM0WXnBwwG1Y171MzWL3KuDjiB0BB6M+A/ZnY54QnaGFgReIjQ66gcUig1qLrHvDp5jKNLwWNenTzG0cljuTqVjEWj6qUGeXXyGEcnj+Xq5DGOLgWPeXWpeBzmPN/GmK5mUZnZSGAPoB+4qkP52Ib7z5jZnu5+ZQxvQ0GUVcYAzGwlYE1gAeA+d78k2z4rMKu7vxblRO15mRf4PqGD+FLZ5scIK479xN1fGUQ/jg5WGeuA6B20hBBCiAaSST+vOJqzhRBCdJtk5uz6krFL5+grVDL26Tfan2Ldve0T5SkZM7MjCbGD09x9nzY12wJzA/8g9FP+ELArcDDhOV3L3e9p13eZFC0Zw0KO1+nAWnWbzyD0FgLYHTjVzLZy98uKnq8dsoDPgdlPHv0YYExES0IIIYQQQgghRPJELBlbCJhc2FBOzOxLhGDQv4Cvt6tz9/ENm/4LHGFmzwGnZMfcLZLNrlJ02fnFgeuABQkBoOuB4xqG/RH4LbADMCQBISGEEEIIIYQQQsRnRF8fI3JGhEYAdUm4k8vq7Wdm+xLiFHcDm0VaTOKM7JjrRDjWkFA0Q+hHhBKxfd39NAAzmy4g5O5vmNldTJ9BNHxJofdE1T3m1cljHF0KHvPq5DGOTh7L1amHUDSq3nsir04e4+jksVydPMbRpeAxry4Vj2LoMbP9gJOBe4FN3f3lGMd193fM7HWar2ReSYouO/8E8KK7r1K3bSowzt33rtt2HrC5u89fxGwPoH4EQgghuk0y/QgqjuZsIYQQ3SaZObu+h9AVc48o1ENo89em1u4O+bLzdcGg/xCWjX8h4vk/TFjE6g53Xz3WcbtJ0QyhhYAb2xg3CwlFyYQQQgghhBBCCDEjfX199OUMCJUZAcvKxE4mLBG/yWDBIDNbBJgHeMbdX822zQIs5+73NYz9APC77O75ka13jaIBoZeAJdsY9xHgmYLn6g1SKDWouse8OnmMo0vBY16dPMbRyWO5OpWMRaPqpQZ5dfIYRyeP5erkMY4uBY95dal4FMXIgjzrZndXzm4PMLOts9/Hu/t4M9uY0PC5j9AH+cthfazpmODuE+ruH0VYfXwvYFy2bRTwbzO7HbiPsMrYosAWhN7KVwK/ivG3DQVFS8YuALYlLKv2r2zbdCVjZrYhcA0NZWTDFKWfCyGE6DbJpJ9XHM3ZQgghuk0yc3Z9ydhVHxhZqGRs0/+9V7tbuGTMzMYRgjatOMzdx5rZGMLq6ANxmLuPbXLsvdx9XLZtVuBY4JPA0sC8wBuEfkRnA79z9/dIhKIBobUIJWPPAF8irCL2LlnwJ4vCjQMWBlZ393sKO27P1whgf2BvYHlgCnAX8FN3v6TJ+NWBg4FVgQ8S/p7bgKPd/c6I1vThUgghRLdJ5sNlxdGcLYQQotskM2fXB4SunnemQgGhTV6ZUrsbtYeQ6JxCASEAM9sf+CVhBbnJhBSqScBUYG7CB6r93f2UYlbb9tMHXEBY5v5h4O/ArMBnCT2Pvuruv6kbv302/i3gIuBp4MPZeICt3P2KSPb6kyg1qLrHvDp5jKNLwWNenTzG0cljubpR8yTz4bLi9Fe91CCvTh7j6OSxXJ08xtGl4DGvLhGPyczZCgj1JoUDQgBm9kngIGBjYM5s81vABOAn7t5O4+komNmOhADPjcBm7v5mtn1BQtbPwsDy7j4x2/4fQo+jVeszmMzsM8AlhDrCjSLZ09VGIYQQ3SaZD5cVR3O2EEKIbpPMnF0fELpmvmIBoY1fVkCoKoyIcRB3v9ndtyV04P4gsAgwl7t/eiiDQRm1zJ4ja8GgzOMLwC8I2UJ71Y1fBniqSTnb3wlZTgt20asQQgghhBBCCJEMtVXG8v6I6lB0lbHpcPd+YMCl24aAhbPbR5vsq23bGDg0+/0+YCUz+0RDUGhLQsDsmqjuql9qUH2PeXXyGEeXgse8OnmMo5PHcnVaZSwaVS81yKuTxzg6eSxXJ49xdCl4zKtLxaMQrTCzjwH7AWsCCwB/dvcDs31rA6sDZ7v7y3nPEaVkrEqY2XnAroTeP5c27Ps6cDzwvLt/MNu2IfAXQrrexcBTTOsh9DdgT3d/LZK93nqwhRBCVBFdeouD5mwhhBDdJpk5u75kbMKCMxcqGdvwhXdrd1Uy1gIz+xZwNNOSePqBM+pWc18buJ7Qr/nkvOfpKEPIzB7JeyKg392XLaBvl78TAkIHmdk17v4WgJnND3wjG/OB2mB3n2Bm6wMXAp+vO44TVksbNBiU/XMMiru3M0wIIYQQXUJzthBCCFGMIqVfyUTASsTMtgJ+Sqhw+jZwA/B8/Rh3v8nMXiAksgxNQAhYKsc5+hna5/1cYAywEfBvM6tfZey5bMzU2uDswT4P+BOhTOwJQpPpo4DxZvYVdz9hkHNOivkHCCGEEKJraM4WQgghitDXByNyfsWfOvgQwbeANwiLZD0CYGbNxt0JNN3RLh0FhNx9hibUZvYrQgDmBEJgZWK2a0lgN+AAQmrT14oY7cDjFDPbkrDq2e7AF4FXCQGfnwIPkkXXsqyhcwjZQGOyHkgAd5vZDsBDwJFmdqq7vx3FYAq9J6ruMa9OHuPoUvCYVyePcXTyWK5OPYSiUfXeE3l18hhHJ4/l6uQxji4Fj3l1qXgUogmrATfXgkED8CKwfpETFeohZGZfA44D1nb3O1qMWQ24Cfieux+f+2QRyPoFXQtc7O471C0t/3N3/3aT8RcB2wMfdfcHBjhuu+nnuiophBCi2ygbewA0ZwshhKgQyczZ9T2E/rHwbMyeM0Pozan9bPDsW7W76iHUBDObDFzm7tvXbZtKaGmzd922K4C13D331cCiq4x9Ebi2VTAIwN3vMLNrCd2xjy94vqJ8Lrs9P7udNbtdoMX42pLzA2YH6UUshBBCpIHmbCGEEKIYhXoIJRMCK5VHgZXNrK+uimk6zGwW4BOECqjcFA0ILQPcM+goeIWCqUydYGZzNzaDNrMdgb2B2wiriQHcArwH7Ghmx7r7fXXjNwfWBR5z92ZL2OcjhVKDqnvMq5PHOLoUPObVyWMcnTyWq1PJWDSqXmqQVyePcXTyWK5OHuPoUvCYV5eKRyGacAlwIPAdQkVWMw4kJLD8ssiJipaMPZX9umxtNa8mY2YDHgZw98Vyn6wzX/cTmkPfD7wFrAlsCDwCbOzuj9WNPRr4XjbuT0xrKr0NoSH2Du7+50jWtIStEEKIbqNrb3HQnC2EEKLbJDNn15eMXbfYqEIlY+s/Nbl2VyVjTTCzeYG7gcUIySwXE3of/x04FdiOUP30KLCKu7+e91wzNInukD8BiwAXmtlSjTuzbRcAC2djh4o/ZOfcC/ga8EHgCMKD9Vj9QHc/iPBg3gJsRVjWbW1CVG69iMEgIYQQQgghhBAibfr6iv2IAXH3V4BNgfuAHYCzs11bABcCownJL1sUCQZB8QyheYEbgeUJpVe3A7WAy5KE7tgzAQ8A62R/2HCmP4lSg6p7zKuTxzi6FDzm1cljHJ08lqsbNY8+acWhv+qlBnl18hhHJ4/l6uQxji4Fj3l1iXhMZs6eLkPoQ3MUyxB68v2kIGUIDYCZjQA+A2wOLEVI6HkSuBK4yN3fK3qOQgEhADP7AHAUsAcwe8PuN4GzgO8rGAQo/VwIIUT3SebDZcXRnC2EEKLbJDNn1weErl9izkIBofUef38hTwWESqZoU2nc/X/Al83s24SMoEWzXc8Ad+gJFkIIIYQQQggheoQipV/JhMCGB4UDQjXcfTJwfazjCSGEEEIIIYQQolr0jeijL2eGkOJB7WNmqwEHAOsxLfHmaULc5SR3v7XoOaIFhESbVL/3RPU95tXJYxxdCh7z6uQxjk4ey9Vp2floVL33RF6dPMbRyWO5OnmMo0vBY15dKh6FaIaZHQ4czIwLgS2b/exhZke7+yFFzhOjh9AswK7ABoSo1awthva7+yaFTpY+6kcghBCi2+jiWxw0ZwshhOg2yczZ9T2EblhmnkI9hNZ95P2LXeoh1AQz2xM4HXgd+BVwHjAx270ksBvwVWBuYG93PyPvuQplCJnZIsDVgDH4izn3ByszGw2sT+hRtAIwC7CTu1/YZOz6wGezsasCcwEnuPtXmoztIyzdtg2wDuHBnRl4iLB0/c/d/a28voUQQgghhBBCiJ5iRF/4Ed3i68A7wIbufmfDvvuBH5nZn4Cbs7HlBISAYwhLzt8C/BRwQhQrNkcQgjUvAM8Biw8wdm9gT2Ay8AQhWNWKWYFLgbeBCcDlwGzA/wE/AbY1sw2z/khxSKHUoOoe8+rkMY4uBY95dfIYRyeP5epUMhaNqpca5NXJYxydPJark8c4uhQ85tWl4lGIJiwPXNskGPQ+7n6nmV1DqNTKTaGSMTN7gbC0/PJRgyYznmdT4EF3f9zMxgKH0jpDaPXM0wOE5kvX0jpDaGbgQOC37v5Kw/aLgM8AB7r7cZH+FKWfCyGE6Da6ZBcHzdlCCCG6TTJzdn3J2I3LzVuoZGydh97/6q2SsSaY2TPABHffbZBx5wMbuPsiec9VNENoDkLkqmvBIAB3v6qDsbfXfjcbKDkI3P1dQibQDNvN7ChCQGgDIFZASAghhBBCCCGESBeVjHWby4HNzWx2d3+z2QAzm53QVueKIicqGhBy4AMFj1FV3s1up0Q9agqlBlX3mFcnj3F0KXjMq5PHODp5LFenkrFoVL3UIK9OHuPo5LFcnTzG0aXgMa8uFY9CNOEgYEPgYjP7qrv/t36nmS0L/JrQZ+h7RU5UtGRsb+BEYGV3v7+IkQ7OOZYBSsYaxm7IACVjg2h/C3wZOMDdf5vP7Qwo/VwIIUS30SW7OGjOFkII0W2SmbOnKxlbfv5iJWMPvFS7q5KxJpjZacB8hMWv3gPuAh7Ldi8JrExYjv6vwEsN8n5336fdcxXKEHL308zsI8C1ZvZD4DJ3f6LIMauAmW0JfJHQwfv3bYyfo53juntBZ0IIIYQoguZsIYQQohh9I8JPLm1cK73KmLrfRxJWUF+tybjPNNnWDwxNQMjM3st+7QNOyra1Gt7v7kVL1LqOma1BWHL+f4QspLfbkE1q+wQplBpU3WNenTzG0aXgMa9OHuPo5LFcnUrGBqPtObvqpQZ5dfIYRyeP5erkMY4uBY95dal4FKIJGw3ViYqWjE2kg5Rqd18698mmnXMsXSoZy1YouzK7u6m739Gmp7YeA11tFEIIMQTo4tsAaM4WQghRIZKZs+tLxm76+AKFSsbWvu/F2l2VjJVM0ZKxpSL5KJ26YNAIOggGZczZ5rj2M4mEEEII0Q00ZwshhBAF6Ovroy9nQKhPHfoGxcwWcPcXBx9ZnMqXcA0FdcGgkcDm7n5bJ3pFNYUQQog00JwthBBCFKSvL/zk0sa10qM8aWbjgVPc/ZpunmjYB4TMbDVCMGgm4P/c/eaunjCF3hNV95hXJ49xdCl4zKuTxzg6eSxXpx5C0ah674m8OnmMo5PHcnXyGEeXgse8ulQ8CtGEV4GdgZ3M7GHgVGCcuz8f+0Qd9RAys/WzX29197fq7reFu1/Xyfi68+4LrJvdXRlYCZjAtKXXxrv7+GzsusC+2faFgf8DHgBuybY94O5HZ2PnA/4LzAtcVjemnonuPi6P7yYoQU4IIUS30bW3OGjOFkII0W2SmbPrewj9c6WFmH1kvmXG3nxvKp+6+/24hnoINcHMZgK2A/YDNsk2TwH+TMgauirWuTrNEJpA+ID0UeDBuvvtMrLD89VYF9izYduGdb9PBMZnv3+4ydjlsx+AfwBHZ7/PTQgGAWyR/TTyD2BcZ3aFEEIIIYQQQogepK+PvtwlY8nEwErD3acAFwAXmNlShMDQGGBHYIdsca/fAae7+3NFztVphtA4QgDoIHd/ru5+W7j7Xp0a7DH6kyg1qLrHvDp5jKNLwWNenTzG0cljubpR8+iTVhz6q15qkFcnj3F08liuTh7j6FLwmFeXiMdk5uzpMoRW/iCjcmYITX5vKp+66/0YhjKE2sTMRgKfAb4AbE7ILpsC/AU42d2vHEDekkLLzouO0YMthBCi2yTz4bLiaM4WQgjRbZKZs6cLCK26cLGA0L+erd0tHBAys9HA+sBqwArALMBO7n5hi/ELA0cAWxGqhR4HzgaOdvd3OjjvCOAAQvbOcoTH5lrgB+7+UO4/aPDzzg8cAny9bnM/oYLrEHe/qJPj5XsWhRBCCCGEEEIIMfyorTKW9ycuRxCCMosDA5ZPZcGgW4C9gX8CxwPPA4cBl2RBnnY5CfgVoS3Or4FLgW2A28zsY539CYNjZhuZ2bnAk8DXgLeAMwmlZJcBHwH+aGb7d3LcYb/K2JBT/VKD6nvMq5PHOLoUPObVyWMcnTyWq9MqY9GoeqlBXp08xtHJY7k6eYyjS8FjXl0qHkVh9gUedPfHzWwscOgAY48BlgD2d/cTAcysDzid0Id4z+z3ATGzjQhBqOuBzdz97Wz7mYQVzE8ENsj7B9WdZyFgL8LfuAwho+xB4GTCqmOvZEPPzFZPvwr4NvDbds+hkrGhRQ+2EEKIbpNM+nnF0ZwthBCi2yQzZ9eXjN28+qKFSsY+efvTtbtRewjVBYRmKBkzs7mAF4CngA+7e3/dvkWAJwirqa/dxnnOBXYDNmhcSd3MLgW2BMzdH8z5d2xBCDhtDcwMvEtYYexEd792AN05wM7uPnO751KGkBBCCCGEEEIIIdpjRF/4yUP/dLpRZjbg8IgBo08BswJX1geDsnM8Y2Z3A2ua2Wzu/tYgx9oQeAO4scm+vxMCQhsQsnnycGl2+zhwCvD7NlcTe4JQUtY2SQSE2m0UlUUttyPU7q1MqCN8G7gbOMndzxvgHB8BDgY2ARYCXs50xwwUheuYFEoNqu4xr04e4+hS8JhXJ49xdPJYrk4lY9GoeqlBXp08xtHJY7k6eYyjS8FjXl0qHoc5z7cxJlYW1XLZbauGzw8BqxJKs/7T6iBZzGER4F53f6/FcerPl4dLCWVnlzYGrwbC3Q8CDurkREmUjJnZRGBJQorXW4RAT7OA0BaEiNxLwNXAI4TgzvbAB4AT3P0rTY6/NXAhIRXrEuAxYH5gdeBid/9JpD+l+g+2EEKI1Ekm/bziaM4WQgjRbZKZs+tLxm5Z60OFSsbWuqX9JBZ3b/sxGqRk7GDgJ8B+7n5qE+3JhCXd13b3lpE9M1uUUHZ2o7uv22T/p4CbgFPc/Yvtei+LJDKEaL9R1LPA54E/1i8Zlz35twAHmNmZ7n5r3b6lgPOAicAm7v5U/QHNLJXHSAghhBBCCCGE6C7xSsYWAiZHcNRTmNl7hKbR+wwy7nfAXu6eO2aRRLDD3a9qc9xdwF1Ntj+XRfyOJJSe3Vq3+2BgTuCLjcGgTDslh2UhhBBCCCGEEKL3KLJ8/PS6yTGbSg9CrQa/VW39PA3jun2cgeij/eyxQllmUQJCWRbNVsCawALALe5+WrZv0Wzbf0oOrryb3b7vIVtibifgRXf/h5mtQQgY9QO3N3YMj0IKvSeq7jGvTh7j6FLwmFcnj3F08liuTj2EolH13hN5dfIYRyeP5erkMY4uBY95dal4FEPGYL19lgOmEtrOtMTd3zCzZ4ClzWxkkz5Cg/Uqisk8hJ7JuSncQ8jM1gXOJvT16SMEU85w972z/TsAfyTU8V1c6GQMXBc4gGYkcCehIfUn3P3ebPuywH+B2wiZRfs1SCcA27v7K0V9Z6gfgRBCiG6TTD+CiqM5WwghRLdJZs6u7yF067pLFuohtOYNj9XuDuWy83MTehI/SfFl588DdiXisvNmtkTd3YmEHsffaTF8JsAIrW+ecPcV2z1PI/mexQwz+xhwGaHL9q+BnZnxRf0XQl3gDkXOVZAfAysCp9eCQRkLZrerEp7Q0YTm0x8BLiYsJ3fKkLkUQgghhBBCCCGqTK1kLO9PCbj7a8D5hFXEvtSw+yhgJPC7+o1mtoCZLW9mCzSMr8UIjjCzWerGbwJsAVzXSTAoYyLwaPYDIX7yaIufh4C/AnM3eu6UoiVjPwRmAz7t7lcAmNl0A9z9HTP7F7BKwXPlwsy+BHwf+Bfw9YbdtYDYSOAQdz8nu/+qme0GPAjsYGZLuPvjA5xjjna8uHsapQZV95hXJ49xdCl4zKuTxzg6eSxXp5KxAelkzq56qUFenTzG0cljuTp5jKNLwWNeXSoeRTHMbF+gttrXytntAdlK4gDj3X189vtBwEbACWa2KeH7/nrAOsDlwBkNh/8KIePoMGBsbaO7X2tmpxIWv7rTzP4GfBDYBXgN+HKOP+U6pmUnbwA8DzzQYuw7wNPAJe7+pxznep9CJWNm9izwSH1alZlNJXTE3rtu2znAVu7+gQJea8caS5slY9mL4xTgHmBjd3+5Yf/HgVrG0DLu/mjD/lMIZWTb1b2Imp2nrQfR3dsZJoQQQhQhmfTzMtCcLYQQokIkM2dPVzK2wdLFSsb+8f7X7sIlY2Y2DthzgCGHufvYuvGLAEcQeiDPCzwOnAUc4+7T9eOpiz1Md4xs3wjgAOCLwIcJj821wA9yZAc1/k0zxFS6RdEMoQ8Qau0GY05g5oLn6ggz2w84mRDw2bQxGJTxMPAeIUOo2SXY2rbZu2JSCCGEEEIIIYRIiL6+Pvpyln7l1bXC3ccAYzoY/www4HLudWPHUpcZ1LBvKqFtzq/bPXcHLE0WfOsUM/sssJK7H97O+KIBoedp3aW7no/SXuAoCnXBoP8Am7j7i83GuftbZnYzIUXso8CNDUM+mt0+xsDM2aa1SUmUGlTdY16dPMbRpeAxr04e4+jksVydSsYGo+05u+qlBnl18hhHJ4/l6uQxji4Fj3l1qXgUohF3Hyz+MBDbAnsAbQWEipaMjQM+D2zu7ldn26ZLbzKz7Qkdsk9w96/mPtm0c45lgJKxujKxB4CN3P25QY43mpAidiWwtbu/k21fh1DH9ySwrLtPaX2UttGKJUIIIbpNMunnFUdzthBCiG6TzJxdXzJ228bLFioZW+Oah2t3o64yJsDMTgf2cPeR7YwvmiF0NKFx0p/M7PuElblqRuYFtgN+CrwB/DzvSdptFGVmGxOCQX2EYM6XG5tcAxPcfULd/XOAHYHPAv8ysysJq4/tCEwB9osUDBJCCCGEEEIIIRKnyGphycTAhgWFAkLu/kC2GtdZwK+yn35CU6daY6e3gN0aGzZ3yLrM2Chqw7rfJwLjgSWY9gr74gDHm1D7xd37zWwn4JvAXsD+hADW5cCP3f32/LabkEKpQdU95tXJYxxdCh7z6uQxjk4ey9WpZCwaVS81yKuTxzg6eSxXJ49xdCl4zKtLxaMQZVKoZKyGmS1JCKhsBixFWM79SUIZ1s/c/eHW6mGF0s+FEEJ0G116i4PmbCGEEN0mmTl7upKxTZZj1Ew5S8amTGWNqx+q3VXJWGSGumQMeL/p0TdiHEsIIYQQQgghhBAVpa9AyVjkVcZEMaIEhIQQQgghhBBCCDEMGDEi/OTSxrUiihEtIGRmI4EFgFlbjXH3x2OdL1lS6D1RdY95dfIYR5eCx7w6eYyjk8dydeohFI2q957Iq5PHODp5LFcnj3F0KXjMq0vFoxBlUriHkJltBPwQWBuYeYCh/e4+3DOS1I9ACCFEt1Eudhw0ZwshhOg2yczZ0/UQ+r+PFushdPn9tbvqIRSZIe0hZGZbAH8BRgKvAI8Crxc5phBCCCGEEEIIISqKegj1DEUzdg4lVAF+F/ilu08pbmlGzGw0sD6wGrACMAuwk7tf2IZ2J+CP2d2mGjObFfgeMJqwdP0rwN+AQ9z92Sh/RI0USg2q7jGvTh7j6FLwmFcnj3F08liuTiVj0ah6qUFenTzG0cljuTp5jKNLwWNeXSoehYjMqcCEdgcXKhkzszeAe9y9q69kM5sILAm8ALwFLE4bASEzWxC4DxgFzNFMY2YjgEuB/wNuITx4ywLbA08Ca0UMCin9XAghRLfRpbc4aM4WQgjRbZKZs6crGdvy48VKxv5+X+2uSsYGwMzmJyStrEno13y1ux+b7fs4IW5xlbtPznuOoj2+3yKUiXWbfYEl3X0h4LQOdCcAbwInDTBmT0Iw6HzgU+5+kLvvlJ1zCeCYfJaFEEIIIYQQQogeY0TftJXGOv5JJgZWKlml0yPAz4HdgE2B5euGLAb8iZDIkpuiJWPXA58oeIxBcferOtWY2Q7ATsCWwCcHGLpfdnuQu79/NdDdTzez7wC7mNlX3D1Ob6QUSg2q7jGvTh7j6FLwmFcnj3F08liuTiVj0ah6qUFenTzG0cljuTp5jKNLwWNeXSoehWjEzD4FnAu8BnwbuAG4tWHY1cCrhIDQ2XnPVbRk7OOEMqtD3f1nuQ/U2TnHEnoXtSwZM7MFCKVil7v7Hq00ZjYb8AbwkLsv3+Q4PwO+BWzu7ldGsK/0cyGEEN1Gl97ioDlbCCFEt0lmzp6uZGzrFRk1U1uLWM3A5CnvscZf/127q5KxJpjZX4DNCRVM/8q2TQXGufvedeOuApZw94/kPVfRDKHVCCVcx5rZdsDlwBPA1GaD3f3Mgudrl99kt98YZNyyhLK5h1rsr21fDogREBJCCCGEEEIIIdJFq4x1m7WBf9aCQQPwLLBWkRMVDQiNI1xB6yOYXpvmV9T6su1dDwhlgaldgF3c/eVBhtfy6lvl47/aMK44KZQaVN1jXp08xtGl4DGvTh7j6OSxXJ1KxqLx3mW/71gzcot9Kq+Txzg6eSxXJ49xdCl4zKtLxaMQTRhFWFBrMOYreqKiJWNj6SCl2t0Py32y6c/ZtGQs68J9H3Czu287mMbM1gZuBM5x99FNzrUboXbvYHc/agBPc7Tj3d0ntTNOCCGEKIAuvQ2A5mwhhBAVIpk5e7qSsW1WKlYydsndtbsqGWuCmf0XeMfdP1a3bbqSsWy19InA/9w9d1/nQhlC7j62iL4L/AyYDdi/zfGDZQANlkFUQx8ahRBCiDTQnC2EEEIUobZiWC6tWvS1wWXAl81sd3c/t8WYfYEPAecUOVHRkrGqsTIhiPOUmTXbf0G2fS93H0dYxm0qoUdQM2rbW/UYEkIIIYQQQgghhg/qIdRtjgZ2B84ws1WAi7Ptc2T3twMOJJSV/aLIiXotIHQxcHuT7asCqxCWZpsIPAjg7m+a2a3AJ81sSXd/rEG3BfA2YSW1gZizTX+Tkug9UXWPeXXyGEeXgse8OnmMo5PHcnXqITQYbc/ZVe89kVcnj3F08liuTh7j6FLwmFeXikchGnH3J81sK+AiwrLz3yK06tkx++kDngc+6+7PFzlXRz2EzOy0zMjB7v5cdr9d+t298Cu+nWXnO9GY2V6EldLOB3Z39/6G7We6+55FfWcoP04IIUS30aW3OGjOFkII0W2SmbOn6yG0/WrFeghdfEftrnoIDYCZzQXsA2wGLEVYIf1JwgroJ7t7jquN09NphtAYwgekY4Dnsvvt0k/4YzrGzPYF1s3urpzdHmBmW2e/j3f38XmODZxBWJVsV2BpM5sALAPsADwBfC/ncYUQQgghhBBCiN5CJWNDgru/Dhyf/XSFTgNCG2W3jzfc7zbrAo1ZOhvW/T4RGJ/nwO4+1cw+Swj8fB74JvAKMA44xN2fzXPclqRQalB1j3l18hhHl4LHvDp5jKOTx3J1KhmLRtVLDfLq5DGOTh7L1cljHF0KHvPqUvEoRJkUWnZedIwebCGEEN1Gl97ioDlbCCFEt0lmzq4vGbt9pzULlYytfsGttbsqGRsEM1scWBSYtdUYd78u7/F7ram0EEIIIYQQQgghuoVKxrqOme1J6IO8ZBvD80XnUEBo6Emh1KDqHvPq5DGOLgWPeXXyGEcnj+XqVDIWjaqXGuTVyWMcnTyWq5PHOLoUPObVpeJRiEbM7PPA6dnd+wAHXu/GuTpdZeyaAufqd/dNCuh7AaWfCyGE6Da69BYHzdlCCCG6TTJz9nQlY7t8sljJ2B9urt1VyVgTzOxu4GPALu5+cTfP1WmG0IYFzqUPVkIIIYQQQgghRMqoZKzbfAS4vtvBIOg8ILR0V1wMJ1IoNai6x7w6eYyjS8FjXp08xtHJY7k6lYxF473zju1YM3K3Ayuvk8c4OnksVyePcXQpeMyrS8WjEE14DYi72nkLtMrY0KIHWwghRLfRpbc4aM4WQgjRbZKZs6crGdttHUbNnLNk7N33WP28G2t3VTLWBDM7C1gb+LC7d/XzSKGm0ma2gLu/GMvMAOcZDawPrAasAMwC7OTuFzYZOwHYoMWh/ubuWw9yrk8CNwIjgO+6+08LWBdCCCGEEEIIIXoHlYx1m+8DtwO/MLPvuvu73TpR0VXGnjSz8cAp7l6k4fRgHEFYbu0F4Dlg8TY0hzXZ9uBAAjObjdDN+01gjg49CiGEEEIIIYQQvY0CQt1mb+BS4KvANmZ2LfAEMLXJ2H53/3HeExUNCL0K7AzsZGYPA6cC49z9+YLHbWRf4EF3f9zMxgKHDiZw97E5znM4sChwNJD7QR2QFHpPVN1jXp08xtGl4DGvTh7j6OSxXJ16CEWj6r0n8urkMY5OHsvVyWMcXQoe8+pS8SiKYWZjmLZEeytOc/d9BjnOOGDPFrvvc/cVOneXm7GE0vU+YClgrwHG9lMgdlGoh5CZzQRsB+wH1JaUnwL8mZA1dFXug7c+51hCQGjAkjF37yj0aGZrEUrFvgK8RXhRxS4ZUz8CIYQQ3UaX3uKgOVsIIUS3SWbOnq6H0Oj1i/UQOvu62t3CPYTMbGVg2xa7xxAqjXZ19z8McpxxhIDQL4H/Nex+3t1/W8BmR5hZq8BUU9z9jLznKpQh5O5TgAuAC8xsKUJgaAywI7CDmU0Efgec7u7PFTlXp5jZ5whP/iTgNnf/5wBjZyUEgG4ATqZ1ZFAIIYQQQgghhBi+jBgRfnJp415vcfe7gLsat5vZvMBBwMvA+A4Oeby7T4xgLTdFAjydUrRk7H2yB+0HZvYj4DPAF4DNgZ8Ah5nZX4CT3f3KWOcchLPr75jZbcAu7v5ok7GHEVKxPuvu/WbWPVcplBpU3WNenTzG0aXgMa9OHuPo5LFcnUrG4lH15zqvTh7j6OSxXJ08xtGl4DGvLhWPolt8DpgV+J27v122maoSLSBUw93fA8ab2fXAIcDXgZmB7YHtzOxB4BB3vyj2uTPGE3oA3QW8BiwHfBv4PHClma3o7m/WBpvZGsB3gIPd/aE8J8zS5wbF3fP/0w+lLgWPeXXyGEeXgse8OnmMo5PH8nWiKT03Z+fVyWMcnTyWq5PHOLoUPObVpeAxRdJoKr13dntah7qtzWwuQhuZu4F/ZDGOniR6QMjMNiKUjm1HWB7+LeCPwDXALsAWwB/N7KvdqMNz9+MbNt0N7GFmI4HdgT0IZWGY2SyEUrG7gJ8VOO2kAlohhBBCDB2as4UQQogixAsIjRqsOidPjyEzWwlYBbjT3e/sUP7rhvsPmtlu7v6vTn0Uxcx2J8RVlgPmonnPqX53XzbvOaIEhMxsIULn632BZQhGHyQEXsa5+yvZ0DPNbDXgKkLWzpA1ZgJ+TwgIrZP5AjgYMGD1IYv6pZC2WHWPeXXyGEeXgse8OnmMo5PHcnXD5erkUFD15zqvTh7j6OSxXJ08xtGl4DGvLhWPw5t2VifPE3mqrSjWSXbQP4BLgFuBFwktZb4AfA24Iqs0eiaHl44xs5mBvwCb0frv7x9gX9sUCgiZ2RaEbKCtCWVh7wIXASe6+7XNNO5+h5ldSliufih5MbsdVbdtZcJjcFeLyORxZnYccNggy9jP2aaHSUmkLabgMa9OHuPoUvCYVyePcXTyWL5OtKK35uy8OnmMo5PHcnXyGEeXgse8uhQ8psiIvgJNpafG9dJAVgW0O/A2cE67Onc/vWHTA8C3zOxNQiLJ14Dvx/I5CN8g9GP+G/BN4IfAaGA24MOEv+9bwC/d/eAiJyqaIXRpdvs4cArw+zZXE3sCeLLguTtlzex2Yt22K5kWKKpnOWB9QnTw38CA6WFFl8oTQgghxNCgOVsIIYQoSLySsYWAyREc1bMtMD/wh7pKpSL8nhAQWifCsdplV+AVYDd3n2RmUwHc/V3gfuCHZvYP4HIze8Ddz8x7oqIBob8BJwGXunt/uyJ3P4iwBFxUzOxDwFvu/mLDdgOOyO7+oc7HCS2OM4YQELrA3X8a1WQKaYtV95hXJ49xdCl4zKuTxzg6eSxXN1yuTg4FVX+u8+rkMY5OHsvVyWMcXQoe8+pS8Ti8mdyFCzW1ZtK/j3S8ZpVG3eYjwA3uXut72A9gZiNrrW7c/Sozuxk4ACgnIOTunymibxcz2xdYN7u7cnZ7gJltnf0+3t3HA6sD52crnD0MvErI9tmK0OD6cHe/bSg8tySFtMUUPObVyWMcXQoe8+rkMY5OHsvXieKk8lz36utfHuPoUvCYVyePcXQpeMyrS8FjilR0lbEsQWQzQgXT1ZEO26zSaCioz26qBc3mA16o2/4YoX1PbqKvMtYl1gX2bNi2Yd3vEwnLzd8HnAesQQgOzQm8DFwOnODul3fZpxBCCCGEEEII0btUNCAEjAFGEBa2atqsyMyWIGT7PO7uk7Nt8wFzuftjDWMXBX6Z3T2/W6ab8BSwWN39idntasBldduNsKp7bjoKCJnZjwqcq9/df5xH6O5jCE/uYOMeIqx2Vgh3HweMK3ocIYQQQgghhBCipxgxokBT6Zy6QTCzPkLMoB9obBBdz5nABsBGwIRs2xLA7WZ2E2G19BeBJQnZN3MSYgMXdcF2K+4ANq8rEbscOA44xsweI/Rj3h9YBbiiyIk6zRAaS/7lzfqBXAGhniKFOtaqe8yrk8c4uhQ85tXJYxydPJarGy7p6kNB1Z/rvDp5jKOTx3J18hhHl4LHvLpUPIpYrA8sC1zj7hM71D4NnAqsBWwHzA28BtwCnOruQ5kdBPBXYDdC65tL3P1eMzsH+Bxwb924d4EfFDlRX39/f9uDzayxbKsj3P2MIvoeoP0HWwghhMhHV3OxhxGas4UQQnSbZOZsM5sDmARw+5e2ZNTM+brPTH53Cquf9Pfa3Tm1+ueMZNlOo4B3spXFMLOZgG8TAlbzAg4c4+43FjlXR8+iAjpCCCGEEEIIIcQwpro9hHqCbAX3Nxq2TQGOyX6ikUpT6d4hhbTFqnvMq5PHOLoUPObVyWMcnTyWq1P6eTyq/lzn1cljHJ08lquTxzi6FDzm1aXiUYgS6ahkbCCybt3rMq0b9lPADe7+eJQT9AZKPxdCCNFtdOktDpqzhRBCdJtk5uzpSsb237pYydhv/1q7q5KxkimcIWRmHwROBLZhxhd0v5n9Gdjf3Z8rcI7RhCZRqwErALMAO7n7hQNo1gAOBNYj1Ni9ANwOHOrudzeMNUIzprUJAa0XgHuAn7r7hLy+hRBCCCGEEEKInmJEX/jJqxXTYWaPFJD3u/uyecWFAkJm9gHgOmA5YDJwGfBotnspYAtC06OPm9kn3f1/OU91BGHZtxeA54DFB/H1BUKQ6mXgL8CzwAeBdYAVgbvrxn4SuBYYCfwZuJAQFNoe2MrM9nH303L6npEU0har7jGvTh7j6FLwmFcnj3F08liuTunn8aj6c51XJ49xdPJYrk4e4+hS8JhXl4pHIULspBQKlYyZ2THAd4ELgAPc/cWG/fMDJwA7A8e6+0E5z7Mp8KC7P25mY4FDaZEhZGZrATcCNwNbufurDftnyhoy1e5fCmwJbO3uf6vbvhLwL+Bxd186j+8mKP1cCCFEt9GltzhozhZCCNFtkpmzpysZ++pni5WM/frPtbsqGSuZEQX12wJPAKMbg0EA7v4S8PlszHZ5T+LuV3XQi+jHhH+sPRqDQdmxpjRsWgaYSshuqh93N/AksGDnjoUQQgghhBBCiB6ktspY3h9RGYr2EFoC+JO7v9tqgLu/a2Y3UiAg1C5mNh+wKfAvd3/EzDYC1gDeJjS4vqOJ7D7ACOVtjRlCH6rfFoUU0har7jGvTh7j6FLwmFcnj3F08liuTunn8aj6c51XJ49xdPJYrk4e4+hS8JhXl4pHIUqkaEBoMu1l0CyQje02qxCyg540s78CW9XvNLM/EjKH3q7b/EPC6mh/MrPxwMOEHkI7EErPvhTVYd5/+qHUpeAxr04e4+hS8JhXJ49xdPJYvk4UJ5Xnuldf//IYR5eCx7w6eYyjS8FjXl0KHlOkbwSMyFls1Fe0SEnEpGhA6A5gQzNbw91vazbAzNYENiQ0bu42teDU1sDz2e11wNLAbwi9jB4n9D0CwN3/kzWWvgjYqe5YTwOnufvTg500q6ccFHdvZ5gQQgghuoTmbCGEEKIgRUq/VDJWKYoGhH5BKNG6ysx+BZwLTMz2LQV8DvgqYQWvXxQ8VzvUwo0jgS/VNYm+x8y2Bx4B9jezQ2pZQlnA6s/AncDKwIOEVcx+AJxuZh9z9wMHOe+kuH+GEEIIIbqE5mwhhBCiCH0j8mf6KEOoUhQKCLn7383sB4RGzgdnP/X0ERo2/8DdL2vUd4Fa0eZ7wN/rd7j7i2Z2M7AZ8FHgLjObGTgfmALs4O5vZsMfNLMxwMeAb5nZr9z9ySgOU6hjrbrHvDp5jKNLwWNenTzG0cljubrhkq4+FFT9uc6rk8c4OnksVyePcXQpeMyrS8WjECVSNEMIdz/KzK4CvkLoxbNotutp4HrghFblZF3gwex2cotG17X/0Nmz2+UJ5WQX1wWDAHD3fjO7DlgdWIGw4lgr5mzT36Qk6lhT8JhXJ49xdCl4zKuTxzg6eSxfJ1rRW3N2Xp08xtHJY7k6eYyjS8FjXl0KHlNEJWM9Q+GAEEAW8NkzxrEK8l9C4OZDZraYuz/VsP+j2e1j2e2s2e0CLY5X60n0dov9ALj7G50aFUIIIcTQozlbCCGEKMiIAk2l8+pEV+g4IGRmMwH7AdsSlp1/G7gbONndb4rqrkOyrJ6TCSVsPzazfdy9H8DMdgM+DvyzrlH0vcBrwLpmtom7X107lpl9AtgReAO4PZrJFNIWq+4xr04e4+hS8JhXJ49xdPJYrm64XJ0cCqr+XOfVyWMcnTyWq5PHOLoUPObVpeJRiBLp6+/vb3twFgy6DNiI0B+onqnA/u5+Sjx77593X0I5GoTGzysBE5iW6TPe3cdnY2cnrGi2FnALcAOhLGxbQiPJ9dz9nrpjfxn4LaHv0CXAQ4Sm0tsTMoi+6u6/ifSntP9gCyGEEPlQLnYcNGcLIYToNsnM2dkqnZMAbv/uroyaZeZcx5n8zrusftz5tbtzKnO3XDrN1zoA2JiQVXMosA1hJbE/ZMf6pZktHNVhYF1CSdqehGAQhKXsa9tWrg3MegFtAhwNLAR8DVgv87hGfTAoG38i8GngSmAD4NvAFsA/gK0iBoOEEEIIIYQQQoi0qa0ylvdHVIZOS8Z2A94BNmgIrJxnZo8B3wW2A06M5A8Adx8DjOlg/BvA97Ofdsb/nYZVybpGCmmLVfeYVyePcXQpeMyrk8c4OnksV6f083hU/bnOq5PHODp5LFcnj3F0KXjMq0vFoxAl0mnJ2P+AO919oyb7lgAmAr9296/HMthjKP1cCCFEt0km/bziaM4WQgjRbZKZs6crGTvoc8VKxo4+p3ZXJWMl02m+1tyEoM8MuPvj2a/tLucqhBBCCCGEEEKIlFDJWM+QZ9n5wa6YJRPlLIUU0har7jGvTh7j6FLwmFcnj3F08liuTunn8aj6c51XJ49xdPJYrk4e4+hS8JhXl4pHIUqk05KxqcCFwHdaDJk40P66LKLhitLPhRBCdBtdmImD5mwhhBDdJpk5e7qSsYP3KFYyduSZtbsqGSuZPBlCO2Q/zegfYH9/zvMJIYQQQgghhBCiChQp/VLJWKXIE6DJG8XMHf00s9HA+sBqwArALMBO7n5hi/GbElY8+zgwH/AUYRn5o939v3XjZga2yX7WBBYnBK7+A4wDTnH39/L6FkIIIYQQQggheooRfeEnr1ZUho4CQu5eVjjvCGBJ4AXgOULgpilm9g3gF8DLwMXZ7YrA3sAuZraOu9+TDV+WUOI2CbgauASYB/gM8Fvg02a2jbvHSxtPoY616h7z6uQxji4Fj3l18hhHJ4/l6tSPIB5Vf67z6uQxjk4ey9XJYxxdCh7z6lLxKESJdNRDqCyyjJ8H3f1xMxsLHEqTDKEs4+dFYCqwgrs/Vbfvq8CvgHHuvle2bTHgs8AZ9bWLWX3kBGB1YGd3vyDSn1L9B1sIIUTq6NJbHDRnCyGE6DbJzNnT9RD60d7FeggdflrtrnoIlUwSPX3c/ao2h84PzA1cVx8MyvgrISC0YN1xnyJkAjWe7w0z+zlwLrABECsgJIQQQgghhBBCpIt6CPUMSQSEOuA54CXgE2a2WENQaOvs9po2j/VudjslljkgjbTFqnvMq5PHOLoUPObVyWMcnTyWq1P6eTyq/lzn1cljHJ08lquTxzi6FDzm1aXiUYgS6amAkLv3Z6VhZwL3mNlFTOshtDnwO+DXbR5u7+z2iqgm8/7TD6UuBY95dfIYR5eCx7w6eYyjk8fydaI4qTzXvfr6l8c4uhQ85tXJYxxdCh7z6lLwmCJqKt0z9FRACMDdzzOzFwjlXvvV7boFONvd322unIaZfQHYErjG3S9tY/wcbXprZ5gQQgghuoTmbCGEEKIgKhnrGXouIGRm+xD6Av0SOIHQZPoTwPHA1Wa2g7tfMoB+a+A3wERgdJunndS2wRTSFqvuMa9OHuPoUvCYVyePcXTyWK5uuFydzE/vzNl5dfIYRyeP5erkMY4uBY95dal4FKJEeiogZGbLAycBl7j7gXW7/mlm2wCPAscSlpdvpv80YRn6Z4CN3P2Z6CZTSFtMwWNenTzG0aXgMa9OHuPo5LF8nShOKs91r77+5TGOLgWPeXXyGEeXgse8uhQ8pkhfX/jJqxWVodfytTYjBLkmNO5w9+eA+4GPmNnsjfvNbCvgYkJG0cbuPrGD887Z5o8QQgghykVzthBCCFGEWslY3h9RGXoqQwiYNbtdoMX+BYGpTFtBDHg/GHQRYYWyjdz94U5O6u5vtD04hbTFqnvMq5PHOLoUPObVyWMcnTyWqxsuVydz0lNzdl6dPMbRyWO5OnmMo0vBY15dKh6FKJFeCwjdlN1+wcxOqi/5MrN9gcWB69x9St32LQnBoFcImUEPddVhCmmLKXjMq5PHOLoUPObVyWMcnTyWrxPFSeW57tXXvzzG0aXgMa9OHuPoUvCYV5eCxxTpK7DKWBdKxsysf4Dd33X3n7Z5nIWBI4CtgHmBx4GzgaPd/Z3CRitIEgGhLJizbnZ35ez2gKwBNMB4dx/v7jeZ2R+AXYD7zexPwAuZZjPgTeC7dcddHvgTIbNoArCbmTWe/i53Hx/5TxJCCCGEEEIIIdKjmquMPQaMa7L9pibbZiALBt1CSCIZDzxIiEEcBqxtZp9296lRnFaIJAJChCdiz4ZtG9b9PpHwpAF8Drge2APYEZgNeB44BzjS3f9Tp1uYaWVmu7Y49xl1xxZCCCGEEEIIIYYv1WwqPdHdxxbQHwMsAezv7icCmFkfcDohFrFn9ntPkURAyN3HAGPaHPseYbn5E9oYOwEY2jbnKdSxVt1jXp08xtGl4DGvTh7j6OSxXN1wSVcfCqr+XOfVyWMcnTyWq5PHOLoUPObVpeJRlI6ZzUWoMnqEsGo5AO7eb2bfB0YD+9GDAaG+/v6Byu1EZPRgCyGE6DZDe6Gjd9GcLYQQotskM2eb2RzAJIDbj/k6o2adJddxJr/9Dqt/75e1uwsBkwca385iEFkPoXuA3wLzE9rGTGi3P7CZbQ5cDpzs7l9qsv8OYCVgTnd/q51jpkISGUJCCCGEEEIIIYSoAPFKxp5vR9HmkT9BXXYP0G9m5wBfdPcBg07ActltqwDSQ8CqwDLAf1qMSRIFhIaaFNIWq+4xr04e4+hS8JhXJ49xdPJYrk7p5/Go+nOdVyePcXTyWK5OHuPoUvCYV5eKRxGDnwIXEAI3/cAqwJGEUq+RwO6D6GtPRKsn8NWGcT2DAkJDTQpLH6bgMa9OHuPoUvCYVyePcXTyWL5OFCeV57pXX//yGEeXgse8OnmMo0vBY15dCh5TJN4qY4OWjLWDu3+3YdO1ZrYJcDdhJfHD3f2BoufpRRQQEkIIIYQQQgghRHvEKxmb3E6PoDy4+2QzOw/4IbA2MFBAaLAMoMEyiJIliYCQmY0G1gdWA1YAZgF2cvcLW4zfAvgOsDphWfn/EjqC/zJbhayZ5gPA94DtgCUJkcqHgDNqy85FIYW0xap7zKuTxzi6FDzm1cljHJ08lqsbLlcnh4KqP9d5dfIYRyeP5erkMY4uBY95dal4FN3kxex21CDjar2DlmuxfzlgKmEVsp4iiYAQcAQhSPMC8ByweKuBZvY14JeE6N3FwGvApsDPgE8BOzXRLANckx33cmA84UXzUeAzQLyAUAppiyl4zKuTxzi6FDzm1cljHJ08lq8TxUnlue7V1788xtGl4DGvTh7j6FLwmFeXgscUGTEi/OTVDh1rZrcTBxl3M/AOsJmZ9bl7f22HmS1CWGHsll5bYQzSCQjtCzzo7o+b2Vjg0GaDzGxR4FjgFWAVd38s2z4zocnUjma2q7ufX6eZGbgIWBDYyN2vazhmKo+REEIIIYQQQgjRXeKVjBXGzD4OPOTu7zRs357QTPp54Nq67csCMwMPu/u7AO7+mpmdD+wBfInpE0KOIjSm/l1U4xUhiWCHu1/V5tAtCSViv6oFgzL9u2Z2KPBZ4MvA+XWanYCVgbGNwaBMOyWv76akkLZYdY95dfIYR5eCx7w6eYyjk8dydcPl6uRQUPXnOq9OHuPo5LFcnTzG0aXgMa8uFY+iKPsBnzez64DHCaVdqwAbAG8BYxr6FF1NqD5amukzhw4CNgJOMLNNgQeB9YB1CFVEZ3T3zyiHJAJCHbBwdvtok321bWub2azu/nZ2f+fs9iIzWwLYGpgLcOCy6GlhKaQtpuAxr04e4+hS8JhXJ49xdPJYvk4UJ5Xnuldf//IYR5eCx7w6eYyjS8FjXl0KHlMk3ipjMbiM0PplFWAzQvbP04Qewse5+/3tHMTdnzGztQjtarYixAUeJ1QnHePuU2MbrwK9FhCqNY1ausm+2raZgGWA2gtjtex2A+DnhIbVNR4zs8+6+92xjQohhBBCCCGEEMlRoZIxd7+MEBRqd/xSA+x7Btgngq1kGNKOTkPAFcB7wD5m9n7j6awP0KF14z5Q9/uC2e3xhP5DiwGLAD8ClgD+Ymazd8+yEEIIIYQQQgiRCLUMobw/ojL0VIaQuz9qZkcCPwTuMbPaKmObEII7j2e39eletVfkX9z9h3Xbf2xmHwN2BXYEzmp1XjObo01/adSxVt1jXp08xtGl4DGvTh7j6OSxXN1wSVfPSU/N2Xl18hhHJ4/l6uQxji4Fj3l1qXgUokR6KiAE4O4/MrMHga8CuwFTgBuy32vNpJ+vk7wKLAD8pcnh/kIICK3GAAEhYFLbBlOoY03BY16dPMbRpeAxr04e4+jksXydaEVvzdl5dfIYRyeP5erkMY4uBY95dSl4TJERfeEnr1ZUhp4LCAG4+9nA2fXbzGxWYDngJXd/tG7Xg4SAULNwbm2bSsaEEEIIIYQQQohqNZUWBejJgFALdiQsSf/7hu3XAmsDH22iqW17bJBjz9mmh0lJpC1W3WNenTzG0aXgMa9OHuPo5LFc3XC5Opmf3pmz8+rkMY5OHsvVyWMcXQoe8+pS8ShEifT19/eX7aEjzGwsoUH0Tu5+YZP9c7v7aw3bPkoI/MwOrODuT9Ttq6049jKwatZZHDNbCLid0GT64+7+QAT7aT3YQgghUkS52HHQnC2EEKLbJDNnZz34JgHc/ttDGTXrLIMomjP57XdYff/DanfndPc34jgUeUgiQ8jM9gXWze6unN0eYGZbZ7+Pd/fx2e8/M7NVgdsIQZ7lgM8QGklvWx8MAnD3R8zsIMKS83eZ2fjaWGBhYGykYJAQQgghhBBCCJE2KhnrGZIICBGCQXs2bNuw7veJwPjs9yuA5YGdgLmAZ4FzgCPd/eFmB3f3X5jZY8C3gc8RIrX3AN909/ObaXKTQtpi1T3m1cljHF0KHvPq5DGOTh7L1Sn9PB5Vf67z6uQxjk4ey9XJYxxdCh7z6lLxKESJJFcyljh6sIUQQnSbZNLPK47mbCGEEN0mmTm7vmTsjpMOL1QyttqXflS7q5KxkkklQ0gIIYQQQgghhBBlo5KxnkEBoaEmhbTFqnvMq5PHOLoUPObVyWMcnTyWq1P6eTyq/lzn1cljHJ08lquTxzi6FDzm1aXiUYgSUcnY0KIHWwghRLdJJv284mjOFkII0W2SmbOnKxk75SeMmnXWXMeZ/PbbrPaFH9TuqmSsZJQhJIQQQgghhBBCiPbo64MROWNZfcnEwIYFCggJIYQQQgghhBCiPdRDqGeofEDIzBYjLCH/acJy8gsDLwM3Ase6+y0N4z8DbA6sBqwMzA58191/OsA55gbGAjtkx38WuAgY6+6vRf2DUqhjrbrHvDp5jKNLwWNenTzG0cljuTr1I4hH1Z/rvDp5jKOTx3J18hhHl4LHvLpUPApRIpXvIWRmRwPfAx4GJgAvAMsB2xJqLnd39z/UjZ8AbAC8CrwELMMAAaGsFvIGQvDoSuBfwErAFsBdwLoR6xqr/WALIYToBZSLHQfN2UIIIbpNMnP2dD2Efn8Mo2bL2UPorbdZbZ/v1e6qh1DJVD5DCLgV2NDd/1G/0czWA64GTjSz8e7+drbrEEKGz8PAnsDpgxz/QEIw6Fh3f/+VaWaHAT/K9h8a4e8QQgghhBBCCCHSRiVjPUPlA0LufnGL7deb2bWE8rAVgduz7TfUxpjZgMc2sz5gX0Kk8/CG3UcBXwX2MbOx7h7nSmEKaYtV95hXJ49xdCl4zKuTxzg6eSxXp/TzeFT9uc6rk8c4OnksVyePcXQpeMyrS8WjECVS+YDQILyb3U7JqV8OWBS4vDFVzd3fygJO2wMfBh7K7bKevP/0Q6lLwWNenTzG0aXgMa9OHuPo5LF8nShOKs91r77+5TGOLgWPeXXyGEeXgse8uhQ8pkhfX/7VwrTKWKVINiBkZksAmwLPAP/OeZjlsttWwZ6H6sbFCQgJIYQQQgghhBCpopKxniHJgJCZzQycBcwKfM/d38t5qFoIt1Vu36sN44qTQtpi1T3m1cljHF0KHvPq5DGOTh7L1Q2Xq5NDQdWf67w6eYyjk8dydfIYR5eCx7y6VDwKUSLJBYTMbAQwDlgfOMXdzyrX0fsd1wfF3dNIW0zBY16dPMbRpeAxr04e4+jksXydaErPzdl5dfIYRyeP5erkMY4uBY95dSl4TJERfeEnr1ZUhqQCQlkw6DRgd+AM4MsFDzlYBtBgGUQ1JhX0IYQQQoihQXO2EEIIUQSVjPUMyQSEsmDQ6cAewDnA3u4+teBh63sENWOwHkOdk0LaYtU95tXJYxxdCh7z6uQxjk4ey9UNl6uTQ0HVn+u8OnmMo5PHcnXyGEeXgse8ulQ8ClEiSQSEGoJB5wN7RggGQQj0PA2sY2Zz1K80ZmazARtm+/87yHHmbPN8k5JIW0zBY16dPMbRpeAxr04e4+jksXydaEVvzdl5dfIYRyeP5erkMY4uBY95dSl4TBGtMtYzVD4gVFcmtgdwATC6QBPp6XD3fjM7FfhR9vO9ut3fB+YDDnf3/kGO88ZA+4UQQghRDTRnCyGEEAVRyVjPUPmAECFQsyeh5v9B4Idm1jhmnLtPBDCzbYFts+0fzm53MbMVst9vcPdT67THAtsAB5rZKsAdwErAlsBd2X4hhBBCCCGEEEIoQ6hnSCEgtFR2OyfwgxZjJgATs99XJgSQ6lk9+6nxfkDI3d8wsw2BQ4EdCWVizwK/AA6LfiUxhTrWqnvMq5PHOLoUPObVyWMcnTyWqxsu6epDQdWf67w6eYyjk8dydfIYR5eCx7y6VDwKUSJ9/f0DVkOJuOjBFkII0W106S0OmrOFEEJ0m2TmbDObg2ylzjvOO5FRs82a6ziT33qb1XZ7f7HwOVXKXS4pZAgJIYQQQgghhBCiCowYEX7yakVlUEBoqEkhbbHqHvPq5DGOLgWPeXXyGEcnj+XqlH4ej6o/13l18hhHJ4/l6uQxji4Fj3l1qXgUokRUMja06MEWQgjRbZJJP684mrOFEEJ0m2Tm7PqSsX/98ZRCJWOr7vyF2l2VjJWMMoSEEEIIIYQQQgjRHn19BZadTyYGNixQQGioSSFtseoe8+rkMY4uBY95dfIYRyeP5eqUfh6Pqj/XeXXyGEcnj+Xq5DGOLgWPeXWpeBSFMLPFgJ2ATwPLAwsDLwM3Ase6+y1tHmcCsEGL3X9z962Lu60eKhkbWvRgCyGE6Da69BYHzdlCCCG6TTJz9nQlYxeeyqjZZst1nMlvvcWqO+5bu1u4ZMzMjga+BzwMTABeAJYDtiU8vru7+x/aOM4EQkDosCa7H3T3c4v4rCqVzxDqJOJnZvMDOwBbAysAiwGvA7cBx7v75W2cbyfgj9ndndz9wnh/jRBCCCGEEEIIkTB9IwqUjEVfZexWYEN3/0f9RjNbD7gaONHMxrv72+0czN3HxjZYZSofEAK+yrSI3xVMH/Hb1szqI347AScCTxOe/KeADxGCRFuY2YHuflyrE5nZgsAJwBvAHF35a1JIW6y6x7w6eYyjS8FjXp08xtHJY7k6pZ/Ho+rPdV6dPMbRyWO5OnmMo0vBY15dKh5FIdz94hbbrzeza4HNgRWB24fUWCJUvmTMzLYHXhog4jcJWMTd3zazjQmBnL+5+9S6sQbcAowClnL3p1uc64/AWsAFwLeJnyFU7QdbCCFEL5BM+nnF0ZwthBCi2yQzZ09XMnbx6cVKxrbfq3a3q6uMmdlfga2AVdz9rkHGTiCUjI0GliT8rbe5+z+75a8KVD5DqJOIn7tf02Ksm9kfgC8AawMzBHnMbAdChtGWwCcj2RdCCCGEEEIIIXqHESPCT17tNEaF3I3W5A0YmdkSwKbAM8C/O5Ce3XCc24Bd3P3RPD6qTvQCviHm3ex2SpGxZrYA8FvgLHe/LJI3IYQQQgghhBCit+jrK/YzjecJmTgD/XSMmc0MnAXMCnzP3d9rQzaekByyCKHqaOXsGGsAV5rZ7Hm8VJ3KZwi1opOIn5nNDewIvAVc32TIb7Lbb0S02JwU6lir7jGvTh7j6FLwmFcnj3F08liuTv0I4lH15zqvTh7j6OSxXJ08xtGl4DGvLhWPIipmNgIYB6wPnOLuZ7Wjc/fjGzbdDexhZiOB3YE9gJPjOa0GSQaEckT8TgI+CPzI3V9qONZ2wC6ENLCXc/ppqwG1u+f/px9KXQoe8+rkMY4uBY95dfIYRyeP5etEU3puzs6rk8c4OnksVyePcXQpeMyrS8FjkhRYZWz6IqWFgMnF/QSyYNBphADOGcCXIxz299nx1kEBofLpNOJnZkcBuwGXAkc27JufsCrZn939j03k7ZIrlU0IIYQQQ47mbCGEEKIIM5Z+daadxuRYTaWzOMHphEyec4C96xeaKsCL2e2oCMeqHEkFhDqN+JnZj4GDgCuBHZpkEv0MmA3YP77bFqSQtlh1j3l18hhHl4LHvDp5jKOTx3J1w+bq5BBQ9ec6r04e4+jksVydPMbRpeAxry4Vj6IwDcGg84E9IwWDANbMbidGOl6lqPyy8zWaRPz2GOhJzoJBhwDXAFu7+5tNxtwFrNTG6fdy93EDnKvd9HNdlRRCCNFtcl6yGx5ozhZCCFEhkpmzp1t2/pJzGTV7zmXn33yLVbfZvXa38LLzdUkjewIXALsN1FLGzJYFZgYedvd3s20fAt5y9xcbxhrwD0L7mTXd/bYiXqtIEhlCnUb86oJB/wA+0ywYlHExcHuT7asCqwBXEyKBDw7kL1aamxBCCCG6i+ZsIcT/t3ffcXMV9R7HPw89EEEFriAXDCX+sFCliFKSQDAQRKQqYoIUQYoKKEiTQGiiiOjlUgwQaugGFEiIgQeCQGKhCFx/FIk0xdCJECAk94+ZJZtld5/ds7PP7tn9vl+vvDa7e34zv7NtzjNnZo6INCjdlLEUfkzoDJpN+Lv9+DKXsh/v7jPj/6cCnwBWZ8Gon42Aq8xsGvAk8BowGBgJLAGc1ImdQZCDDqGiHr9RhB6/vfro8TuJ0Bk0DRjp7hUXqXL3kyqUMYbQIXSeu1+XPfsy8jBssd1zzBqnHNPE5SHHrHHKMU2ccmxtnIafp9Pu73XWOOWYJk45tjZOOaaJy0OOWePykqM0alC8HQgcW2GbXqpP+XoEmEC4xPxGsayXgcnAOe4+OUGebantp4zFzpkTCD1+ZwNzy2w23t1nmtnehJFEc+O25YZ7T3T3B2qsc7fEHULt/WKLiEgnyM3w8zanNltERJotN232QlPGfndVY1PGdvha4W7DU8akMW0/Qoj6evwK2y4GHFFh25nAAwnyEhEREREREekyPWTvy8pNH1hXaPsRQh1mfi6GLbZ7jlnjlGOauDzkmDVOOaaJU46tjVt6OR1ppdH+bXbWOOWYJk45tjZOOaaJy0OOWePykWNu2uyFRwhd3eAIoT0KdzVCqMXUIdS/9GKLiEiz5ebgss2pzRYRkWbLTZu9UIfQzdc01iE0cvfCXXUItVgepoyJiIiIiIiISDvQjLGOoQ4hEREREREREamReoQ6hTqE+lv7z2Nt/xyzxinHNHF5yDFrnHJME6ccWxunS9im0+7vddY45ZgmTjm2Nk45ponLQ45Z4/KSo0gLaQ2h/qUXW0REmk2n3tJQmy0iIs2WmzZ7oTWEbr2usTWEttu1cFdrCLVY248QMrNVgN2A7YG1gZWAl4E/AGe4+/SS7ccCXwQ+CawAvA48AVwIXOru75apYyPgGGBD4GPAP4E/Aqe7+/3N2TMRERERERGRnOnpCf+yxkrbaPsOIeBQ4CjgSeA2YBYwGNgJ2MnM9nT3q4u2PxhwYFLcdjlgBDAO2M3MtnP398/6mdnOwLXAHOB64HlgLWBnYGczG+nutyXbmzwMW2z3HLPGKcc0cXnIMWucckwTpxxbG6fh5+m0+3udNU45polTjq2NU45p4vKQY9a4vOQo0kJtP2Usdti85O53ljy+BTCVMGxtZXd/Oz6+lLvPKdl2MWAyMAzYzt0nFT33KGE00Ybu/lDR418GbgJ63X1oot1p7xdbREQ6gU69paE2W0REmi03bfZCU8Ym3dDYlLEROxfuaspYiy3S6gT64u43lHYGxcenAXcAHwHWKXp8Tplt5wI3xrtrljy9BvBccWdQdCswD1gxe/YiIiIiIiIiHaQwZSzrP2kbeZgyVk1hPaC51TYys0UI08YAHi55+hFgPTNbt6RTaDtCh9ntKRJ9Xx6GLbZ7jlnjlGOauDzkmDVOOaaJU46tjdPw83Ta/b3OGqcc08Qpx9bGKcc0cXnIMWtcXnIUaaG2nzJWiZmtBjxGWGB6VXd/r+T5Y4AlgOWBrYFPAePcff+S7YYAvyUM17sBeI6whtBXgJuB0e7+eqK08/lii4hInujUWxpqs0VEpNly02YvNGVs8sTGpox9aafCXU0Za7FcjhAys8WBy4AlgaNKO4OiY4Bl4v/nA2cCPyrdyN17zWxL4Drgm8VPAeNr6QyKX44+uXstm4mIiEiTqM0WERFpkK4y1jFy1yEUp3+NB7YELnD3y8pt5+4DzawH+DgwEjgd2CReNeyNovJGAhOA3xCmiT1DWGT6NGCimR3i7uf0kdbsmncgD8MW2z3HrHHKMU1cHnLMGqcc08Qpx9bGafh5Xzqnzc4apxzTxCnH1sYpxzRxecgxa1xechRpoVxNGYudQRcBo4FLgH3cfV6NsbsQRgGd6u7HxseWJ1zO/nFgk5LL0Q+Ij38I+K/CVcwqlF3Ti6izjSIi0g906q0KtdkiItJGctNmLzRlbMqNLD1gQKZy3nzrLTYc/pXCXU0Za7G2v8pYQewMupjQGXQFdXQGRVPi7ZCix74ALAfcVdwZBODubwHTgWWB1fsoe2CN/0RERKS11GaLiIg0pKfBf9IucjFlrKgzaBRwFWGh53o6gyBMHYOFr0i2ZLxdoUJM4ZLzFUcHAahXU0REJB/UZouIiDSmp6eHnoxrAWWNk+Zo+w6homlio4Brgb0qLCKNma0BvOLur5Q8PgD4Wbw7qeip6cB7wK5mdoa7P1IUsy2wOfAPd38q1f7kYh5ru+eYNU45ponLQ45Z45Rjmjjl2No4rUeQTru/11njlGOaOOXY2jjlmCYuDzlmjctLjiIt1PZrCJnZGOAEwnzFs1l4hE/BeHefaWZ7A+cC04CngFcII4NGEEb7/AEYHqeDFco/HTgKmENYWLqwqPSOhKuT7eLuNybanfZ+sUVEpBPo1FsaarNFRKTZctNmF68hdP/U3zW0htAGW+9QuKs1hFqs7UcIAYPi7UDg2Arb9AIzCR0+vyZcgexzhPWBXgMeJnQqjXP3d4sD3f1HZvYQ8G3C1ciWAV4CbgLOcPd70+2KiIiIiIiISJ41shZQbvrAukLbjxDqMPNzMWyx3XPMGqcc08TlIcesccoxTZxybG3c0svpSCuN9m+zs8YpxzRxyrG1ccoxTVwecswal48cc9NmLzxC6OYGRwiNLNzVCKEWU4dQ/9KLLSIizZabg8s2pzZbRESaLTdt9kIdQrff0liH0LDtC3fVIdRieZgyJiIiIiIiIiLtoKcn/MsaK21DHUL9rf2HLbZ/jlnjlGOauDzkmDVOOaaJU46tjdMVS9Jp9/c6a5xyTBOnHFsbpxzTxOUhx6xxeclRpIU0Zax/6cUWEZFm06m3NNRmi4hIs+WmzV5oytgdkxqbMjZ0ROGupoy1mEYIiYiIiIiIiEhtNGWsY+SiQ8jMVgF2A7YH1gZWAl4mXGb+DHefXrL9TOATFYo7x90PKdp2GeCrwI7A+sCqwNvAg8B57j4h5b7kYthiu+eYNU45ponLQ45Z45Rjmjjl2No4DT9Pp93f66xxyjFNnHJsbZxyTBOXhxyzxuUlR5EWysWUMTM7HTgKeBLoBWYBg4GdCMPs9nT3q4u2nwl8GPhFmeJmuPstRduOAG4FXgKmAn8H/gvYOZaxUAdSg9r/xRYRkbzTqbc01GaLiEiz5abNXmjK2J2TG5syttWXCnc1ZazFcjFCCJgBDHH3O4sfNLMtCJ0455rZRHd/u+jpV919TA1l/wv4JnCNu79TVPYxwHTgYDO71N1nNLoTIiIiIiIiIvnWQ/a+rNz0gXWFRVqdQC3c/YbSzqD4+DTgDuAjwDoZy37A3S8v7gyKj78AnB/vbpmlbBEREREREZGOUlhDKOs/aRt5GSFUzbvxdm7J40uZ2beAjxPWG7rH3R9MVHZ2eZjH2u45Zo1Tjmni8pBj1jjlmCZOObY2TusRpNPu73XWOOWYJk45tjZOOaaJy0OOWePykqNIC+ViDaFKzGw14DFCh8+q7v5efHwm5ReVngR8091frKHsRYH7gc8C67r7wwlSzu+LLSIieaFTb2mozRYRkWbLTZu90BpC037f2BpCW2xTuKs1hFostyOEzGxx4DJgSeCoQmdQdBFwJ/AIMAf4DPBjwlXKbjSzzd29rwO9sYRpaBf11RkUvxx9cvdaNhMREZEmUZstIiLSKK0h1Cly2SFkZosA4wlr+1zg7pcVP+/uJ5WETDezLxM6iTYHtgUmVyn/QOBo4C/A92pIaXbNyedh2GK755g1TjmmictDjlnjlGOaOOXY2jgNP+9L57TZWeOUY5o45djaOOWYJi4POWaNy0uOkoSZbQycCGwGLEEYHPILd7+yjjKWBcYAuwArES5AdT0wxt1fT51zO8jdlLHYGXQRMBq4BNjH3efVGLsPcCEw1t1/XGGb/YALgIeAYe7+cg3l1vQi6myjiIj0A516q0JttoiItJHctNkLTRm7+/bGpoxtPqxwN8mUMTMbQhjw8Q5wFfAasDOwOnCsu59aQxnLAHcD6wNTCIND1gNGAA8Am3fi9LZcjRCKnUEXA6OAK6ijMygqrB20dIXy9ydcWexhYJtaOoOigTVuV/tZSREREWkGtdkiIiKNaORqYYmvMmZmiwHjCGv/benu98fHxwD3ASea2bXu/ngfRR1J6Aw6w92PKir/RMLyM0cCJyRNvg3kpkOopDPoKmB0nZ1BAJvE25llyi90Bj0KbF3LwtMFdfUU5mHYYrvnmDVOOaaJy0OOWeOUY5o45djaOA0/r6qj2uysccoxTZxybG2cckwTl4ccs8blJUdp1DBgTeDiQmcQgLvPNrOTgKuBbwHHVCrAzHqA/Qgng0qXnzkNOBTY18zG1LAWca7kokOoaJrYKOBaYK+SRaSLt10L+Je7zy55fDPgcOBtYGLJc/sROoP+RugMmpV6H96X9Uvfn3F5yDFrnHJME5eHHLPGKcc0ccqx9XHSuLy81536+VeOaeLykGPWOOWYJi4POWaNy0OOudRDGy0qPSTe3lbmuUnxdqs+yhgMfByYXHriyN3nmNkdhCloawF9jTTKlVx0CBGGaI0m9Ng9BhxvZqXbjHf3mcAOwFgzu5MwEmgO8GnC3L95wAHu/mwhyMyGEdYM6gHuAr5Tpuxed+9NukciIiIiIiIiOfPWnDmZp369NWdO8d2ly/ztvZAaRvYOjrcf6Khx99fN7IWibeouo+TxwVW2yaW8dAgNircDgWMrbNNL6AC6B/gt8DnCVciWAv4NXAOc5e7TS+JWY0E35QFVcuitL+UK8jBssd1zzBqnHNPE5SHHrHHKMU2ccmxtXNecnewH7f5eZ41TjmnilGNr45Rjmrg85Jg1Li855tAXth6Rqqh/17BNXz1PhRex0ov/Ggv6Exopo3i7jpG7q4x1muLV2km0ynq71NepdfV3fdq3/NXV3/Vp3/JXVyvqk8Z18mekU+vq7/q0b/mrq7/r077lr65W1NeOSl6DfuPuVTuEzOw2YDgw2N2fKPO8A4PcfckqZexJuGjVKe5+XJnnjwZOBfZ09wl17kJby8sIIRERERERERFpjTep/UqdtVg6ltmovkbvLEflkT/1lFG8XcdQh5CIiIiIiIiIVBSvrpVyZFSqsorX9/lz8RNmtizwMcKyMrWWUU5fawzl1iKtTkBEREREREREJIM74+22ZZ4bUbJNJY8DzwNfjFPj3mdmSxGuZPY88IEpaXmnDiERERERERERyaOpwN+BPc1s/cKDZjaQcLXyucDFRY+vZmZrm9nShcfi6KdxhClxPy4p/2jgo8C4uF1H0ZQxEREREREREckdd59rZvsBk4FpZjYBeB3YGVgdOM7di6d6XQpsBQxl4SuJnwHsCBxpZhsQpp+tB2wHPBCf7zgaISQiIiIiIiIiueTudwCbA3cDuwMHAS8Be7n7KTWW8R/C1LCzgLWBI4DPxvtDOvXKchohJCIiIiIiIiK55e4zCKN5+tpuSJXnXgMOj/+6gkYIiYiIiIiIiIh0GXUIiYiIiIiIiIh0mZ758ztuoWwREREREREREalCI4RERERERERERLqMOoRERERERERERLqMOoRERERERERERLqMOoRERERERERERLqMOoRERERERERERLrMYq1OoJuZ2cbAicBmwBLAI8Av3P3KxPWsAuwGbA+sDawEvAz8ATjD3aenrK9CDucAB8W7K7r7i02o42vAAcD6wJLAs8A9wKHu/kaiOhYDvgXsB6xJeN/+AVwHnO3ur2Yocy9gS+BzwGdjmbu5+3UVtl8JOBkYCXwEeBq4HDjd3d9JVZ+ZLQN8FdiR8JquCrwNPAic5+4TUu9bSexuwDXxbp8xWeqK38EjgS0Ir+Us4E/ACe7+YMr6zGwb4IfAZ4CPAs8BdxLetyeq1FP399fMBgOnAEOBgcDjwAXA/7r7vD72q+b6zGx5YBdgh/garAK8AfyR8Fs2OVVdFeJ/CJwR727s7n9qRn1mti3wPWBTwuv5T2A68EN3fyZFXWbWA+wKHAp8MtbzDHAr8FN3/2e1fZPmU5udvA612Wqz1WajNjt1fWqzReqjEUItYmZDgLsJjdp1wLnACsAVZnZM4uoOBc4C1gBuA86MdX8FuMfM9khc30LMbCjwHeA/TSp/ETO7BJgAfJxwoPUrwkHCdsByCau7ltBILwdcAYwD3gXGEF7LZTKUeTKwP+Hg7YVqG8YDy+nAPsC9wC+AfxP+SLnJzGr5Ttda3xbAZcAw4P5Y1/XAusCVZvY/CetaiJmtCJxDfZ+Zuuoys28D9wFDgFsI34vJhAOBdVLWZ2bfB6YAGxEOFn5FOODbB7jfzNatEl7X99fMPk04uNsp7s8v41O/As6rYb/qqW834HzCAfbdwM/j/m0FTIoHf6nqWoiZrQ2cRH2fkbrrM7OTCa/juoTf6rOAuwidAp9IWNdZhD+m1iB8z84lfLcPA/5iZivXsZ+SmNrspOWrzVabXXddarOT1Kc2O11darOlY2iEUAvEM1bjgPnAlu5+f3x8DKGxO9HMrnX3xxNVOQMY4u53luSxBTAVONfMJrr724nqK65jGeBCYCLhDMtWqesAjgBGAWcDhxefTanxYKsmZrYJocG+l/C+zS167jrCWZddgUvqLHo/4DF3fzp+Bk6osu1PgNWAg9z93Fh3D3AxMDr+uzhRff8CvglcU3wWM/7xMx042MwudfcZifat2DnAW8ClhPe3FjXXZWabAv9LeC9HuvtrJc/X8ttYU31mtjjh4P9VYF13f67ouUMJB3+HEc5il1Pv9/dcwh8/I939lrjtcYSDvv3N7Ep3762yX/XU9xjhbPTNJd+7kwmfkVPM7Ap3fz7RvhWeXwS4CPgr4MBeVfYnc33xjPexhIO9b5TJo9rnpOa64h+N3wX+DqxfPDrCzM4EDge+TfgcST9Tm52c2my12XXVpTZbbXYt9anNFslGI4RaYxhh6PKVhQNLAHefTeg9LwxxTsLdbyj9gYuPTwPuIAy7reXsShY/ieUf3IzCzWxp4BjgCeCI0qG17j6vr+G2dVgj3k4pPrCMbo63K9ZbqLv/3t2f7ms7M/sQsAehAXr/rJG7zweOBt4jnP1KUp+7P+Dul5cOaXf3FwhnmCAMv264rmJmtgvhLNYBwOxa4+qsayzQA4wqPbCMZZW+v43UtzywLPBQ8YFl9Lt4W/FzU8/318w+SXhP7igcWMZt3yV8TyAcpFRUT33ufru7/7bM986Bq4HFgS+kqKvE4YQzt/sSPvc1qfO17CGcUX4d2KfcH9/VPid17tsgwufxbv/gVJk+PyPSdGqzE1GbrTY7Y11qsxPUpzZbbbZIOeoQao0h8fa2Ms9NirfNOCtXzrvxts/GtF5mthVhDYIjvHlzabcFPgzcCCxuZruZ2dFmtr+ZrZa4rkfj7fAyZxlGEs4e9yaus9hmhHUWpsQDyvfF1/dBYBMzW6qJORQ05XNjZisQzgJe5u6T+to+Yx0fBbYB7nf3v5vZUDM70sy+Z2afa0KVLwAvAetamKNebId4e3vGskvfhyHxttxvyx9jHo38ttTzvjf6GSkbHw+gxxLWcfhrxrJrqW89wroAU4D/mNlIM/uRmR0Uh76nrOtx4B1g8/hHZLFGPyPSuCHxVm1249Rmoza7zjrUZmenNltttkifNGWsNQbH2w8ML3f3183shaJtmiYefG1DWGwt5Y904SzghcBUd78oZdklCgcD84CHWPh1e9fMjnX3n6aoyN0fsrDQ5sHAw2Y2iXCmY2is92DvY4G8BlX83BQ9viHhrOijFbZpmJktShjuPx/4feLiC2scfD9xucU2IJzZedbMfkf4w+B9ZnYN4SxkkukY7j4/DjO/FHjIzK4nLFS4DuGPo18T1gqoS4Xvb7Xflvlm9gSwqZkt7e5vJqiv0rbLEqZizAGm1VNPtbrisPOLCWfcT6633DrrK/y2vExY6HaTopD5ZvZL4LDSP/Sy1OXuL8UpAmcA/2dmNxLWWdg0/jvZ3W/Ism+ShNrsdNRmL6A2uzZqs9Vm11Kf2myRjDRCqDUKCyZ+YNhr0eMpF1X8gDhH+jLC2auj3L3mIZw1Op2wQn/Voa4JFIZkHk44k7IhYajvCMJZnjPMbIcKsXVz90MIV51Yk3AFg8MJBys3EeZ7N1Mtn5vi7ZplLOHA6GJ3fzhVoWb2VcLw+kPd/eVU5ZZR+MzsQPi87ED4zKxHOBDanYQHLgAeru6yHQumCBxFuJLFn4HL4/DwmlX5/jblM5Lh9+I84GPAqe7+UsK6vg98Hti3dFpEVlXqK3xO9iG8XlsCHyIMp3+U8P0/iDpU27f4R/BehNETBxF+Z7YkjGC4tv49k4TUZqejNnsBtdm1UZutNruW+tRmi2SkDqEuFHvsxxN+uC5w98sSl78FcAhwnLs/lbLsMgqf4beBr7r7/e7+hodLZ+4XnzssRUUWrozya+DHwIGExvMjhLMqw4AZZjYoRV3tyswOJKx98BdC45qq3OUJCyve6O7X9LV9gwqfmUWBA9395viZeQjYmXAJ1oPMbMlUFZrZvoQ1K8YT5p4PJByk9ABTzWzHOspq6ve30frM7DTg64SrwJyaqi4Ll+U9GfiVu99XT7lZ6mPB52QR4GvuPs3dZ7v7vYQ/QOYR/rhMURdmdgLhTOqJwH8T/uDZFlidcIWTjeveQekIarOzUZutNjsrtdmN16U2W2225IemjLVGXz3+y1H5bEFDbMFK/3sSrqzxncTlLxbLn86CS2c2U+F1+pO7/6vkuSmEg85Uc8z3IRywftfdLyx6/Hoze4dwxvFHhAPPZqjlc1O8XVJmth9hrYAHgeEeFlRN5UxgKeo8e5NR4fV5j5IzxO7+opndBwwHPgU80Ghlce76ecBN7n5k0VP3xoPKpwjDjm+qoay+vr+1fkZerzH3un4vzGws4TswBdilnlEMNdT1a8IIgmNrLbPB+gqv5TPu/kDxE+7+qJk9CQw2sw+7+6uN1GVm2xAug/3zkukyU8xsV8LUmrGEURTS/9Rmp6M2ewG12bVRm602W222SBOpQ6g1CnOFBxOGn74vzuP9GGH+a1JFc3lHAVcQVuFPdTWPgoHAWvHfe2ZWbptZ8fHV3X1mg/U9Fm/LXXVinpm9Qei1T2G7eNtb5rnCY+snqquc4s9NOYMJZ0D+nrpiM9ufcJWSh4FtmjA8fH3Cgc9zFT4z18bHv+Xu4xusq/CZebPCsO/CZ2lAg/UUDCf81vaWPuHuL5jZ/wEbmNkAd3+rUiE1fn8rfkYsXIFjLeB5d/9PX0nX+3sRDyyPIyyk+BV3n9NXHXXWtT7hMzK7wmfkj/HxoV79Er211lfxt6Xk8QGEyxM3UlfF3xZ3/6uZvUxzf1ukOrXZarOzUJutNlttttpskbanDqHWuJMwhHdb4KqS50YUbZNMyQ/cVcDoJhxYQji7d2GF50YS1ii4PG5XeqnGLO6It58qfcLC1S9WoPKCjvUqDEdeocxzhbnLSRY1rOA+wlUNhptZT/HCeGa2MmE+/fR6GvVaFB1YPgps7e4vpiw/ugEot7jnhoT1HqYCM1nQ4DfiCeBZ4L/NbBX/4GVlC5+lfySoC6p/biB8duax4CoWH1DH97fwu7EtYU2QYhsTLqc7oa+E6/29KDqwvBP4crWD5AbquhRYuszjWxIOpm8EXiQs/JiivnsJC2yuYWZLetGCpXFdgTWBN4FZCeqq+BmJ0yCWBZ6vtl/SVGqz1WZnoTZbbbba7A9Smy3SZtQh1BpTCWeE9jSzXxaGNprZQMJc97mEH6QkioY+jiIsdLZXPcNC6xEblf3KPWdmvYSDy8NSHaC4+xNmNhXY2sz2LpyJimdWCosMXpeiLsIZ4JHA0Wb2h8ICefH1HRO36U1U1wd4uJrNVYT38UDC/P2C0wjz63+dss445Px84G+EA8uKDWkj3P2kCvWPIRxcnufuSd5HD1fuOJ8wnHesme1bOFA3s68DnwHudfdUjXlh5MC3zew8L7qcc3x9VwXucveyl3qt5/vr7m5mdwFDzWx7d78llrE4cErcrOpnpN7fCzM7iXBgOQ0Y6XVcCaXOfftuhTLGEw4uT/Y+rhhUZ32zzewKYF9CZ8CYoqd/QFiLZEKK943wGTkYONzMrnP34j+8j6fC2WrpN2qz1WbXTW222mzUZpcrYzxqs0XaSs/8+XVdfU8SMbOhwGTC2akJhPnBOxMWIzvO3U+pEl5vXWOAE4DZwNmEg9dS4xMMBe8rj15gK2DFlGesLCxcdy/wUcKc7icIi/9tRrhE5BdLfqyz1rMcMAP4JPAk4f17l7A45TqEs5qbuvsrdZa7H7B5vLs+4axhLwvOdk1094lx25UJaz38N/Abwtm3LYAvxny27+sscq31mdkwwiVqewgHmKXrPQD0VhvqW8++VYgfQ/js7tbXwWWdr+MAwpnqTQmv592E795OhO/JFh4WrExV31WEq7G8RnjfZsWY4cBbwBB3n1GhnjHU8f01s08TDlYGANcQzlKNANYFxrn7/n3sV831mdnehD+E58Zty61RMdFL5vNn3bcKZYwHRgMb13BwWVd9ZrYi4bdlTUKnwAMsuPTwc4Tve+nZ6rrrsrCOSy/he/wv4LeE0RibxX+zYl3NXvBXKlCbrTa7qFy12ZXjx6A2ewxqs2dWyXc8arNF2opGCLWIu99hZpsTVqffHVgCeAQ43t2vSFzdoHg7kMqLu/UShvfmjrs/bmYbEc4uDidcGvRZ4GfA2BQHlrGe18zs84QzDzsSzkJAOJg4Ezil3gPLaHNC41hsSNH/ZwITYw7/NLNNCfs6knD51acJjdhP+jqwrLO+1QgHlgAHVCmvN0FdKdTzOr5lZlsTzpLtAXyXMKf8amCMu9cyzL2effsG4WzcKMIVbpYC/k2Yn36quz9apZ5B8bam76+HxRM3IZxd3C7GPUHYx3Oq1JOlvsK2iwFHVNh2JpUX+qynrhTqqs/dZ8Xv/EmE7/yWhPftAsLnpNpQ95rrcve5ZjaccAWU3QmXsl2McAB7AeF37NmqeyZNpTY7HbXZarPrrUttdlX11FfYVm12A3WpzZZOoxFCIiIiIiIiIiJdZpFWJyAiIiIiIiIiIv1LHUIiIiIiIiIiIl1GHUIiIiIiIiIiIl1GHUIiIiIiIiIiIl1GHUIiIiIiIiIiIl1GHUIiIiIiIiIiIl1GHUIiIiIiIiIiIl1GHUIiIiIiIiIiIl1GHUIiIiIiIiIiIl1GHUIiIiIiIiIiIl1msVYnICKNMbP5ZR5+HfgbMAE4x93fbaD8vYGLgRPdfUzWchrVn3mYWS+wFbC6u89sZl0iItI91GY3pa5e1GaLiGSiDiGRznE9MBvoAQYBmwGbADua2Qh3f6eFuQFgZkOAO4BL3H3v1mYjIiLSMmqzRUSk5dQhJNI5flB8ZszM1gd6gaHA/sA5Gcv9DXAf8GJj6TWsXfIQERFplNpsERFpOXUIiXQod3/AzH4OnAjsRMaDS3d/DXgtYWqZtEseIiIiqanNFhGRVlCHkEhnuz/erlb8oJltBxwGbAQsDTwNTAROc/dXSrbdmzLrAJjZeGA04WzmXGAMsDGwKDAdOMbdp5fZHmC0mRX+T2nZ5aTKoyhu0fga7EcYrj8LuAE4vo88VgWOArYDVgHeBO4BTnX3e4q2Ww+YAbwFrOvuT5eUcxbwfeB6d9+1Wp0iItIV1GarzRYR6Ve6yphIZ/tQvH278ICZHQ3cAgwjHHxOBAYAPwRmmNnKddaxA2GY+4eBW4GnYtl3mNmni7a7G5gc//8kcEnRvwfqrLORPAouB34KrArcBvyRcJB6O7BkuQrMbDPgQeBg4F3gZuBh4EvAXWa2R2Fbd38QOBpYDrjczBYpKudLwPeAZ4FvZ9xfERHpLGqz1WaLiPQrjRAS6WxfjrcPAZjZxsDJhIUsh7v7ffHxJYHLgN2AcwnD1Wt1OLCnu18Vy+oBfgkcAhwJ7A3g7uPM7AnCgdjdTVigsqY84nN7AF8jnGXdqrCOg5mtSDi4/Fxp4Wa2LGER0OWAUe5+WdFzGxEOUMeZ2e3uPis+dRYwAhhOONA8xcxWAMbH50e7+8sJ9l1ERPJPbbbabBGRfqURQiIdxsx6zOwTZnY64QBqPnB+fPoQwvf+V4UDSwB3f5twBu1NwhVOBtVR5TWFA7pY1nzCGggQLgPbX+rJ46B4O6Z4Uc94UPjDCuXvA6wMnF18YBnj/gSMBQYCe5XkMJqwqOYYM9sEuBBYCfiZu99ezw6KiEhnUZtdUx5qs0VEmkQdQiKd4ykzmw/MA2YS5sy/Axzs7tPiNlvE2ytKg+OB1STCJXA3r6PeW8uU9SLwMvDxOsppVE15mNniwOfj3avLxEwCXil9HNg23l5fof7Ca7xJSXn/JKx5sBgwBdiRMOz/uArliIhI51ObXUMearNFRJpLU8ZEOsf1hGHl8+Pt34DfuPvzRdsUDrJmVijjqXi7Sh31PlPh8TeAj9ZRTqNqzWN5YAlglru/WSHmH8BHSh4bFG/vNrNqeaxQ+oC732hmE4CvE9Yx2NPd36lWiIiIdDS12bXloTZbRKSJ1CEk0jl+UDyUuh/Na0Gd5TQ7j8KIyuuA/1TZ7m+lD8R1DobFu4sD65bbTkREuoba7OZSmy0iUgN1CIl0l+eB1Qlnzh4p8/ygePtcP+XTCi8RhuWvaGYD3P2tMtusVuaxZwEDTnf3P9dZ50XAxwjD3XcGzjOze9z92TrLERGR7qE2W222iEhTaQ0hke5SmDP/jdIn4pU0RhCGr9/dpPoLQ65b1hnt7u8C0+Pd3UufN7NtKT9sfkq8/Wo99ZnZdwiX151BWLzyeMLQ9kuLL2srIiJSQm222mwRkabSD5tIdzmHMEz7UDPbtPCgmS0B/A+wDHBTE4exF9ZGqDqhvx+cG29PNLP3zyzGA+yfVog5H/g3cKSZHWBmixY/aWaLmdmXzOyzRY+tDZxJGK6+l7vPjeX3AkOBHyTaHxER6TxqswO12SIiTaIOIZEu4u4zCFfKGEhYaHFKXDjxCWCPePudJtY/E3gI2MjMZpjZxWY2zsx2bFadFfKYAFwLfAJ41MxuNLPrgceBucB9ZWJeBb4CvAacB8w0s1vN7AozmwoUrviyFrx/wH4lMAD4nrs/HsuZB4wCXgXGmtkGTdxVERHJKbXZ7+ehNltEpEnUISTSZdz9NGAk4YzXRoT58W8DPwM2jZdcbaZdgInAGoSDrH2BDZtcZzl7Ei7z+xxh2P3nCQeDwwivxwe4+33AOsAZhIPMLYGdCAepdwJ7A7+Pm48FNiBcNebCknKeAQ4kXDnlSjMbkG63RESkU6jNfp/abBGRJuiZP39+q3MQEREREREREZF+pBFCIiIiIiIiIiJdRh1CIiIiIiIiIiJdRh1CIiIiIiIiIiJdRh1CIiIiIiIiIiJdRh1CIiIiIiIiIiJdRh1CIiIiIiIiIiJdRh1CIiIiIiIiIiJdRh1CIiIiIiIiIiJdRh1CIiIiIiIiIiJdRh1CIiIiIiIiIiJdRh1CIiIiIiIiIiJdRh1CIiIiIiIiIiJdRh1CIiIiIiIiIiJdRh1CIiIiIiIiIiJdRh1CIiIiIiIiIiJd5v8BfQqVEcf4deYAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "make_heatmaps(\n", + " waymax_vbd_data, \n", + " gpudrive_vbd_data, \n", + " array_name='polylines',\n", + " index=4, \n", + " feature_name='lane_type',\n", + " x_label='Point index',\n", + " y_label='Polyline index',\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 277, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([[8478.251 , 8477.762 , 8477.271 , ..., 8462.099 , 8461.609 ,\n", + " 8460.631 ],\n", + " [8501.151 , 8501.0625, 8500.936 , ..., 8484.239 , 8483.419 ,\n", + " 8482.19 ],\n", + " [8478.13 , 8479.614 , 8481.1 , ..., 8521.192 , 8522.677 ,\n", + " 8524.657 ],\n", + " ...,\n", + " [ 0. , 0. , 0. , ..., 0. , 0. ,\n", + " 0. ],\n", + " [ 0. , 0. , 0. , ..., 0. , 0. ,\n", + " 0. ],\n", + " [ 0. , 0. , 0. , ..., 0. , 0. ,\n", + " 0. ]], dtype=float32),\n", + " array([[-80.34973, -80.34973, -80.34973, ..., -46.41858, -46.41858,\n", + " -46.68335],\n", + " [174. , 174. , 174. , ..., 167.73218, 167.73218,\n", + " 166.2826 ],\n", + " [166.37476, 166.37476, 166.37476, ..., 166.37476, 166.37476,\n", + " 166.37476],\n", + " ...,\n", + " [ 0. , 0. , 0. , ..., 0. , 0. ,\n", + " 0. ],\n", + " [ 0. , 0. , 0. , ..., 0. , 0. ,\n", + " 0. ],\n", + " [ 0. , 0. , 0. , ..., 0. , 0. ,\n", + " 0. ]], dtype=float32))" + ] + }, + "execution_count": 277, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Can't be compared directly\n", + "waymax_vbd_data['polylines'][:, :, 0], gpudrive_vbd_data['polylines'].squeeze(0)[:, :, 0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## **Outputs** (predicted trajectories)\n", + "\n", + "- What: The predicted actions for the future 80 time steps\n", + " - Features:\n", + " - `x`: x positions\n", + " - How can `x` be in a local coordinate frame?\n", + " - `y`: y positions\n", + " - How can `y` be in a local coordinate frame?\n", + " - `theta`: What is theta? Is this the yaw?\n", + " - `v_x`: Velocity x (is this used by the dynamics model?)\n", + " - `v_y`: Velocity y (is this used?)\n", + "- Notes:\n", + " - Tried setting `global_frame=False`, but that doesn't help\n", + " - ..." + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "metadata": {}, + "outputs": [], + "source": [ + "waymax_vbd_data['pred_denoised_trajs'] = waymax_vbd_data['pred_denoised_trajs'].squeeze(0).cpu().numpy()\n", + "gpudrive_vbd_data['pred_denoised_trajs'] = gpudrive_vbd_data['pred_denoised_trajs'].cpu().numpy()" + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(32, 80, 5)" + ] + }, + "execution_count": 137, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "waymax_vbd_data['pred_denoised_trajs'].shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### $x$" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "make_heatmaps(\n", + " waymax_vbd_data, \n", + " gpudrive_vbd_data, \n", + " array_name='pred_denoised_trajs',\n", + " index=0, \n", + " feature_name='x',\n", + " x_label='Time index',\n", + " y_label='Agent index',\n", + " share_axes=False,\n", + " share_color_scale=False \n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### $y$" + ] + }, + { + "cell_type": "code", + "execution_count": 116, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "make_heatmaps(\n", + " waymax_vbd_data, \n", + " gpudrive_vbd_data, \n", + " array_name='pred_denoised_trajs',\n", + " index=1, \n", + " feature_name='y',\n", + " x_label='Time index',\n", + " y_label='Agent index',\n", + " share_axes=False,\n", + " share_color_scale=False \n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### $\\theta$" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "make_heatmaps(\n", + " waymax_vbd_data, \n", + " gpudrive_vbd_data, \n", + " array_name='pred_denoised_trajs',\n", + " index=2, \n", + " feature_name='theta',\n", + " x_label='Time index',\n", + " y_label='Agent index',\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((32, 80, 5), (1, 32, 80, 5))" + ] + }, + "execution_count": 130, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "waymax_vbd_data['pred_denoised_trajs'].shape, gpudrive_vbd_data['pred_denoised_trajs'].shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "gpudrive", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/gpudrive-main/gpudrive/integrations/vbd/notebooks/02_raw_data_comparison_waymax_gpudrive_json.ipynb b/gpudrive-main/gpudrive/integrations/vbd/notebooks/02_raw_data_comparison_waymax_gpudrive_json.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..1d52f0e7ef550ba79e5f09e97571c2655ae4e02a --- /dev/null +++ b/gpudrive-main/gpudrive/integrations/vbd/notebooks/02_raw_data_comparison_waymax_gpudrive_json.ipynb @@ -0,0 +1,1563 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Goal: align `gpudrive` with `waymax`\n", + "\n", + "- Using the same scenario (`2bd577a009790706`)\n", + "\n", + "### The following features need to be aligned:\n", + "\n", + "**Agents** [in progress]\n", + "- (maybe) `x` \n", + "- (maybe) `y` \n", + "- `yaw` (@Kevin, @Aarav)\n", + " - json file: ✅\n", + " - simulator: ✅\n", + "- `vel_x` (@Kevin, @Aarav)\n", + " - json file: ✅\n", + " - simulator: ✅\n", + "- `vel_y` (@Kevin, @Aarav)\n", + " - json file: ✅\n", + " - simulator: ✅\n", + "- `vehicle_width` (@Kevin)\n", + " - json file: ✅\n", + " - simulator: ✅\n", + "- `vehicle_height` (@Kevin)\n", + " - json file: ❓\n", + " - simulator: ❓\n", + "- `vehicle_length` (@Kevin)\n", + " - json file: ✅\n", + " - simulator: ✅\n", + "\n", + "**Road graph** [todo @Daphne]\n", + "- `x`\n", + " - json file: ❓❔\n", + " - simulator: ❓❔\n", + "- `y`\n", + " - json file: ❓❔\n", + " - simulator: ❓❔\n", + "- `id`\n", + " - json file: ✅ ❔\n", + " - simulator: ✅\n", + " - note: misaligned by one because waymax has an extra \"final\" point at the end of every road graph\n", + "- `dir_x`\n", + " - json file: ✅ ❔ ❔\n", + " - simulator: ✅❔\n", + "- `dir_y`\n", + " - json file: ✅\n", + " - simulator: ✅\n", + "- `lane_type`\n", + " - json file: ❓❔\n", + " - simulator: ❓❔\n", + "\n", + "---\n", + "\n", + "> * `roadgraph_samples/dir` [link]()\n", + "- A unit direction vector for each map feature sample point. [num_points, 3] float\n", + "Unit vectors\n", + "roadgraph_samples/type\n", + "A unique integer for each combination of map feature type and properties. See the table in the Value Range column.\n", + "[num_points, 1]\n", + "int64" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import json\n", + "import pickle\n", + "from pathlib import Path\n", + "import numpy as np\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.lines import Line2D\n", + "\n", + "# Set working directory to the base directory 'gpudrive'\n", + "working_dir = Path.cwd()\n", + "while working_dir.name != 'gpudrive':\n", + " working_dir = working_dir.parent\n", + " if working_dir == Path.home():\n", + " raise FileNotFoundError(\"Base directory 'gpudrive' not found\")\n", + "os.chdir(working_dir)\n", + "\n", + "import gpudrive\n", + "from pygpudrive.env.config import SceneConfig\n", + "from pygpudrive.env.scene_selector import select_scenes\n", + "from data_utils.datatypes import MapElementIds\n", + "\n", + "sns.set(\"notebook\", font_scale=1.05, rc={\"figure.figsize\": (7, 2)})\n", + "sns.set_style(\"ticks\", rc={\"figure.facecolor\": \"none\", \"axes.facecolor\": \"none\"})\n", + "%config InlineBackend.figure_format = 'png'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Configurations" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "DATA_DIR = 'data/processed' # Base data path\n", + "CKPT_DIR = 'data/checkpoints' # Base checkpoint path\n", + "\n", + "SCENARIO_ID = '2bd577a009790706' # Debugging scenario we use \n", + "CKPT_PATH = 'integrations/models/vbd/weights/epoch=18.ckpt'\n", + "\n", + "TRAJ_LEN = 91 # Length of the trajectory\n", + "CMAP = 'bwr'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load scenarios\n", + "\n", + "- We load an idential scenario both in waymax and gpudrive so that we can do a like for like comparison" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Ratio unique scenes / number of worls = 1 / 1 ---\n", + "\n" + ] + } + ], + "source": [ + "# same scenario in gpudrive\n", + "sim = gpudrive.SimManager( \n", + " exec_mode=gpudrive.madrona.ExecMode.CPU, \n", + " scenes=select_scenes(SceneConfig(\"data/processed/debug/gpudrive\", 1)),\n", + " gpu_id=0,\n", + " params=gpudrive.Parameters(),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-11-13 08:31:19.364128: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "2024-11-13 08:31:19.371604: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "2024-11-13 08:31:19.373865: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", + "2024-11-13 08:31:19.937403: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n", + "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n" + ] + } + ], + "source": [ + "# scenario in waymax\n", + "with open(f'{DATA_DIR}/debug/waymax/waymax_scenario_{SCENARIO_ID}_max_70.pkl', 'rb') as f:\n", + " waymax_scenario = pickle.load(f)\n", + " \n", + "# raw json scenario\n", + "with open(f'data/processed/debug/gpudrive/tfrecord-00005-of-00150_{SCENARIO_ID}.json') as file:\n", + " json_scenario = json.load(file)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "65" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "TOTAL_AGENTS = len(json_scenario['objects'])\n", + "\n", + "TOTAL_AGENTS" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(torch.Size([70, 91, 2]), torch.Size([70, 91, 2]), torch.Size([70, 91]))" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gpudrive_log_trajectory = sim.expert_trajectory_tensor().to_torch()\n", + "\n", + "sim_pos_xy = gpudrive_log_trajectory[:, :, : 2 * TRAJ_LEN].reshape(1, 70, TRAJ_LEN, -1).squeeze(0)\n", + "sim_vel_xy = gpudrive_log_trajectory[:, :, 2 * TRAJ_LEN : 4 * TRAJ_LEN].view(1, 70, TRAJ_LEN, -1).squeeze(0)\n", + "sim_yaw_xy = gpudrive_log_trajectory[:, :, 4 * TRAJ_LEN : 5 * TRAJ_LEN].view(1, 70, TRAJ_LEN, -1).squeeze(0, -1)\n", + "\n", + "sim_pos_xy.shape, sim_vel_xy.shape, sim_yaw_xy.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Agent ordering \n", + "\n", + "- How are the ids assigned in `waymax`?\n", + " - The ids attribute assigns a unique ID to each object.\n", + " - These are taken directly from `state/id` https://waymo.com/open/data/motion/tfexample " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Array([3843, 3842, 3841, 3839, 3775, 3774, 3766, 3750, 998, 997, 996,\n", + " 995, 994, 993, 992, 990, 989, 988, 987, 986, 985, 983,\n", + " 980, 979, 978, 977, 976, 973, 971, 968, 966, 964, 962,\n", + " 961, 960, 959, 957, 953, 951, 944, 943, 942, 940, 939,\n", + " 937, 927, 926, 918, 916, 915, 914, 912, 909, 907, 906,\n", + " 905, 904, 903, 902, 901, 900, 899, 898, 897, 896, -1,\n", + " -1, -1, -1, -1], dtype=int32)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "json_agent_ids = []\n", + "\n", + "for obj in json_scenario['objects']:\n", + " if 'id' in obj:\n", + " json_agent_ids.append(obj['id'])\n", + " \n", + "json_agent_ids = np.array(json_agent_ids)\n", + "\n", + "# Get the sorted agent indices\n", + "waymax_sorted_agent_idx = np.argsort(waymax_scenario.object_metadata.ids)[::-1]\n", + "gpudrive_sorted_agent_idx = np.argsort(json_agent_ids)[::-1]\n", + "\n", + "waymax_scenario.object_metadata.ids[waymax_sorted_agent_idx]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([3843, 3842, 3841, 3839, 3775, 3774, 3766, 3750, 998, 997, 996,\n", + " 995, 994, 993, 992, 990, 989, 988, 987, 986, 985, 983,\n", + " 980, 979, 978, 977, 976, 973, 971, 968, 966, 964, 962,\n", + " 961, 960, 959, 957, 953, 951, 944, 943, 942, 940, 939,\n", + " 937, 927, 926, 918, 916, 915, 914, 912, 909, 907, 906,\n", + " 905, 904, 903, 902, 901, 900, 899, 898, 897, 896])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "json_agent_ids[gpudrive_sorted_agent_idx]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Yaw \n", + "\n", + "\n", + "- Waymax:\n", + " - Link: [in waymax](https://github.com/waymo-research/waymax/blob/720f9214a9bf79b3da7926497f0cd0468ca3e630/waymax/dataloader/womd_factories.py#L234C3-L234C15) they take the yaw directly from `bbox_yaw` [[tfexample data](https://waymo.com/open/data/motion/tfexample)]\n", + "- Raw json scenario\n", + " - Processed from protobuf in `data_utils/process_waymo_files.py`" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((70, 91), Array(-3.1391213, dtype=float32), Array(3.140661, dtype=float32))" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "waymax_yaw = waymax_scenario.log_trajectory.yaw\n", + "\n", + "# Note that the waymax yaw is in radians from -pi to pi\n", + "waymax_yaw.shape, waymax_yaw.min(), waymax_yaw.max()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(65, 91)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "json_yaws = []\n", + "for obj in json_scenario['objects']:\n", + " if 'heading' in obj:\n", + " json_yaws.append(obj['heading'])\n", + "json_yaws = np.array(json_yaws)\n", + "json_yaws.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(1, 3, figsize=(14, 4), sharex=False, sharey=False)\n", + "\n", + "# GPUDrive \n", + "sns.heatmap(sim_yaw_xy[gpudrive_sorted_agent_idx.copy()][:TOTAL_AGENTS], cmap=CMAP, center=0, linewidth=.5, cbar=True, annot=False, ax=axs[0])\n", + "axs[0].set_title('GPUDrive', y=1.05)\n", + "axs[0].set_xlabel('Time step')\n", + "axs[0].set_ylabel('Agent index')\n", + "\n", + "# Raw json data \n", + "sns.heatmap(json_yaws[gpudrive_sorted_agent_idx], cmap=CMAP, linewidth=.5, cbar=True, annot=False, ax=axs[1])\n", + "axs[1].set_title('Raw json data', y=1.05)\n", + "axs[1].set_xlabel('Time step')\n", + "axs[1].set_ylabel('Agent index')\n", + "\n", + "# Waymax VBD data \n", + "sns.heatmap(waymax_scenario.log_trajectory.yaw[waymax_sorted_agent_idx][:TOTAL_AGENTS], cmap=CMAP, center=0, linewidth=.5, cbar=True, annot=False, ax=axs[2])\n", + "axs[2].set_title('Waymax data', y=1.05)\n", + "axs[2].set_ylabel('Agent index')\n", + "axs[2].set_xlabel('Time step');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Velocities `x` and `y`\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "json_vel_x = []\n", + "json_vel_y = []\n", + "\n", + "for obj in json_scenario['objects']:\n", + " if 'velocity' in obj:\n", + " json_vel_x.append([obj['velocity'][time_step]['x'] for time_step in range(TRAJ_LEN)])\n", + " json_vel_y.append([obj['velocity'][time_step]['y'] for time_step in range(TRAJ_LEN)])\n", + " \n", + "json_vel_y = np.array(json_vel_y)\n", + "json_vel_x = np.array(json_vel_x)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(torch.Size([70, 91]), (65, 91))" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sim_vel_xy[:, :, 0].shape, json_vel_x.shape, " + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(1, 3, figsize=(15, 5), sharex=False, sharey=False)\n", + "fig.suptitle('Velocity x', y=1.05)\n", + "\n", + "# GPUDrive (vel_x)\n", + "sns.heatmap(sim_vel_xy[:, :, 0][gpudrive_sorted_agent_idx.copy()], cmap=CMAP, center=0, linewidth=.5, cbar=True, annot=False, ax=axs[0])\n", + "axs[0].set_title('GPUDrive', y=1.05)\n", + "axs[0].set_xlabel('Time step')\n", + "axs[0].set_ylabel('Agent index')\n", + "\n", + "# Raw json data \n", + "sns.heatmap(json_vel_x[gpudrive_sorted_agent_idx], center=0, cmap=CMAP, linewidth=.5, cbar=True, annot=False, ax=axs[1])\n", + "axs[1].set_title('Raw json data ', y=1.05)\n", + "axs[1].set_xlabel('Time step')\n", + "axs[1].set_ylabel('Agent index')\n", + "\n", + "# Waymax VBD data \n", + "sns.heatmap(waymax_scenario.log_trajectory.vel_x[waymax_sorted_agent_idx], center=0, cmap=CMAP, linewidth=.5, cbar=True, annot=False, ax=axs[2])\n", + "axs[2].set_title('Waymax data', y=1.05)\n", + "axs[2].set_ylabel('Agent index')\n", + "axs[2].set_xlabel('Time step')\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(1, 3, figsize=(15, 5), sharex=True, sharey=True)\n", + "\n", + "fig.suptitle('Velocity y', y=1.05)\n", + "\n", + "# GPUDrive (vel_x)\n", + "sns.heatmap(sim_vel_xy[:, :, 1][:65, :][gpudrive_sorted_agent_idx.copy()], cmap=CMAP, center=0, linewidth=.5, cbar=True, annot=False, ax=axs[0])\n", + "axs[0].set_title('GPUDrive', y=1.05)\n", + "axs[0].set_xlabel('Time step')\n", + "axs[0].set_ylabel('Agent index')\n", + "\n", + "# Raw json data \n", + "sns.heatmap(json_vel_y[gpudrive_sorted_agent_idx], cmap=CMAP, linewidth=.5, center=0, cbar=True, annot=False, ax=axs[1])\n", + "axs[1].set_title('Raw json data ', y=1.05)\n", + "axs[1].set_xlabel('Time step')\n", + "axs[1].set_ylabel('Agent index')\n", + "\n", + "# Waymax VBD data \n", + "sns.heatmap(waymax_scenario.log_trajectory.vel_y[waymax_sorted_agent_idx], cmap=CMAP, center=0, linewidth=.5, cbar=True, annot=False, ax=axs[2])\n", + "axs[2].set_title('Waymax data', y=1.05)\n", + "axs[2].set_ylabel('Agent index')\n", + "axs[2].set_xlabel('Time step')\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Vehicle length, width, height" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "json_lens: (65, 91), json_widths: (65, 91), json_heights: (65, 1)\n", + "waymax_lens: (70, 91), waymax_widths: (70, 91), waymax_heights: (70, 91)\n", + "sim_lens: (70, 91), sim_widths: (70, 91)\n" + ] + } + ], + "source": [ + "# JSON data\n", + "json_lens = []\n", + "json_widths = []\n", + "json_heights = []\n", + "\n", + "for obj in json_scenario['objects']:\n", + " json_lens.append(obj['length'])\n", + " json_widths.append(obj['width'])\n", + " json_heights.append(obj['height'])\n", + "\n", + "json_lens = np.array(json_lens)\n", + "json_widths = np.array(json_widths)\n", + "json_heights = np.array(json_heights)\n", + "\n", + "json_lens = np.repeat(np.expand_dims(json_lens, 1), repeats=91, axis=1)\n", + "json_widths = np.repeat(np.expand_dims(json_widths, 1), repeats=91, axis=1)\n", + "json_heights = np.expand_dims(json_heights, axis=1)\n", + "print(f'json_lens: {json_lens.shape}, json_widths: {json_widths.shape}, json_heights: {json_heights.shape}')\n", + "\n", + "# Waymax data\n", + "waymax_lens = waymax_scenario.log_trajectory.length\n", + "waymax_widths = waymax_scenario.log_trajectory.width\n", + "waymax_heights = waymax_scenario.log_trajectory.height\n", + "print(f'waymax_lens: {waymax_lens.shape}, waymax_widths: {waymax_widths.shape}, waymax_heights: {waymax_heights.shape}')\n", + "\n", + "\n", + "# Sim data\n", + "abs_self_obs = sim.self_observation_tensor().to_torch().numpy()\n", + "\n", + "sim_lens = np.repeat(abs_self_obs[:, :, 1], 91, axis=0).swapaxes(1, 0)\n", + "sim_widths = np.repeat(abs_self_obs[:, :, 2], 91, axis=0).swapaxes(1, 0)\n", + "# TODO: Where does the height come from?\n", + "print(f'sim_lens: {sim_lens.shape}, sim_widths: {sim_widths.shape}')" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "CMAP = 'Reds'\n", + "\n", + "fig, axs = plt.subplots(1, 3, figsize=(15, 5), sharex=False, sharey=True)\n", + "\n", + "fig.suptitle('veh_len', y=1.05)\n", + "\n", + "# GPUDrive \n", + "sns.heatmap(sim_lens[gpudrive_sorted_agent_idx], cmap=CMAP, cbar=True, annot=False, ax=axs[0])\n", + "axs[0].set_title('GPUDrive', y=1.05)\n", + "axs[0].set_xlabel('Time step')\n", + "axs[0].set_ylabel('Agent index')\n", + "\n", + "# Raw json data \n", + "sns.heatmap(json_lens[gpudrive_sorted_agent_idx], cmap=CMAP, cbar=True, annot=False, ax=axs[1])\n", + "axs[1].set_title('Raw json data', y=1.05)\n", + "axs[1].set_xlabel('Time step')\n", + "axs[1].set_ylabel('Agent index')\n", + "\n", + "# Waymax VBD data \n", + "sns.heatmap(waymax_lens[waymax_sorted_agent_idx], cmap=CMAP, cbar=True, annot=False, ax=axs[2])\n", + "axs[2].set_title('Waymax data', y=1.05)\n", + "axs[2].set_ylabel('Agent index')\n", + "axs[2].set_xlabel('Time step');" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "CMAP = 'Reds'\n", + "\n", + "fig, axs = plt.subplots(1, 3, figsize=(15, 5), sharex=True, sharey=True)\n", + "\n", + "fig.suptitle('veh_width', y=1.05)\n", + "\n", + "# GPUDrive \n", + "sns.heatmap(sim_widths[gpudrive_sorted_agent_idx], cmap=CMAP, cbar=True, vmin=-1, vmax=3, annot=False, ax=axs[0])\n", + "axs[0].set_title('GPUDrive', y=1.05)\n", + "axs[0].set_xlabel('Time step')\n", + "axs[0].set_ylabel('Agent index')\n", + "\n", + "# Raw json data \n", + "sns.heatmap(json_widths[gpudrive_sorted_agent_idx], cmap=CMAP, cbar=True, vmin=-1, vmax=3, annot=False, ax=axs[1])\n", + "axs[1].set_title('Raw json data', y=1.05)\n", + "axs[1].set_xlabel('Time step')\n", + "axs[1].set_ylabel('Agent index')\n", + "\n", + "# Waymax VBD data \n", + "sns.heatmap(waymax_widths[waymax_sorted_agent_idx], cmap=CMAP, cbar=True, vmin=-1, vmax=3, annot=False, ax=axs[2])\n", + "axs[2].set_title('Waymax data', y=1.05)\n", + "axs[2].set_ylabel('Agent index')\n", + "axs[2].set_xlabel('Time step');" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "CMAP = 'Reds'\n", + "\n", + "fig, axs = plt.subplots(1, 2, figsize=(15, 5), sharex=False, sharey=True)\n", + "\n", + "fig.suptitle('veh_heights', y=1.05)\n", + "\n", + "# GPUDrive \n", + "# sns.heatmap(sim_heights[gpudrive_sorted_agent_idx], cmap=CMAP, cbar=True, annot=False, ax=axs[0])\n", + "# axs[0].set_title('GPUDrive', y=1.05)\n", + "# axs[0].set_xlabel('Time step')\n", + "# axs[0].set_ylabel('Agent index')\n", + "\n", + "# Raw json data \n", + "sns.heatmap(json_heights[gpudrive_sorted_agent_idx], cmap=CMAP, cbar=True, annot=False, ax=axs[0])\n", + "axs[0].set_title('Raw json data', y=1.05)\n", + "axs[0].set_xlabel('Time step')\n", + "axs[0].set_ylabel('Agent index')\n", + "\n", + "# Waymax VBD data \n", + "sns.heatmap(waymax_heights[waymax_sorted_agent_idx], cmap=CMAP, cbar=True, annot=False, ax=axs[1])\n", + "axs[1].set_title('Waymax data', y=1.05)\n", + "axs[1].set_ylabel('Agent index')\n", + "axs[1].set_xlabel('Time step');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Road points \n", + "\n", + "\n", + "**Information**\n", + "\n", + "- _Done_: Compare `x, y`\n", + "- _Todo_: Understand `dir_x`, `dir_y`" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "MAX_RPS = 10_000\n", + "MS = 0.5\n", + "\n", + "# Road edges \n", + "color_map = {\n", + " int(MapElementIds.ROAD_EDGE_BOUNDARY): 'red',\n", + " int(MapElementIds.ROAD_EDGE_MEDIAN): 'darkred',\n", + " int(MapElementIds.DRIVEWAY): 'green',\n", + "}\n", + "\n", + "marker_size_map = {\n", + " int(MapElementIds.ROAD_EDGE_BOUNDARY): MS,\n", + " int(MapElementIds.ROAD_EDGE_MEDIAN): MS,\n", + " int(MapElementIds.DRIVEWAY): 3 * MS,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "'''Waymax road graph information'''\n", + "way_roadgraph_points_x = np.asarray(waymax_scenario.roadgraph_points.x)\n", + "way_roadgraph_points_y = np.asarray(waymax_scenario.roadgraph_points.y)\n", + "way_roadgraph_points_types = np.asarray(waymax_scenario.roadgraph_points.types)\n", + "way_valid_roadgraph_points = np.asarray(waymax_scenario.roadgraph_points.valid)" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(13207,)" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "'''json scenario road graph information'''\n", + "json_road_ids = []\n", + "json_road_points_x = []\n", + "json_road_points_y = []\n", + "json_road_points_dir_x = []\n", + "json_road_points_dir_y = []\n", + "json_road_points_types = []\n", + "json_road_points_valid = []\n", + "\n", + "for road_idx, road in enumerate(json_scenario['roads']):\n", + " for road_point_idx in range(len(json_scenario['roads'][road_idx]['geometry'])):\n", + " json_road_ids.append(road['id']) # Append road id for each road point\n", + " json_road_points_x.append(road['geometry'][road_point_idx]['x'])\n", + " json_road_points_y.append(road['geometry'][road_point_idx]['y'])\n", + " json_road_points_types.append(road['map_element_id']) # lane type\n", + " #json_road_points_valid.append(road['geometry'][road_point_idx]['valid']) # NOTE: We don't store the valid flag, but I don't think we need it\n", + "\n", + "# Convert to numpy arrays\n", + "json_road_ids = np.array(json_road_ids)\n", + "json_road_points_x = np.array(json_road_points_x)\n", + "json_road_points_y = np.array(json_road_points_y)\n", + "json_road_points_types = np.array(json_road_points_types)\n", + "\n", + "json_road_points_x.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [], + "source": [ + "def restore_mean(x, y, mean_x, mean_y):\n", + " \"\"\"Add the previously subtracted mean back to restore the original coordinates.\"\"\"\n", + " return x + mean_x, y + mean_y" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [], + "source": [ + "'''GPUDrive road graph information'''\n", + "\n", + "# Road graph points\n", + "gpudrive_road_points_means = sim.world_means_tensor().to_torch()[0, :].numpy()\n", + "\n", + "gpudrive_road_points_x = sim.map_observation_tensor().to_torch()[0, :, 0].numpy() \n", + "gpudrive_road_points_y = sim.map_observation_tensor().to_torch()[0, :, 1].numpy()\n", + "gpudrive_road_points_types = sim.map_observation_tensor().to_torch()[0, :, 8].numpy()" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [], + "source": [ + "gpudrive_road_points_x, gpudrive_road_points_y = restore_mean(\n", + " gpudrive_road_points_x, \n", + " gpudrive_road_points_y, \n", + " gpudrive_road_points_means[0], \n", + " gpudrive_road_points_means[1]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [], + "source": [ + "mask_road_edge_points = gpudrive_road_points_types == int(MapElementIds.ROAD_EDGE_BOUNDARY)\n", + "mask_road_median_points = gpudrive_road_points_types == int(MapElementIds.ROAD_EDGE_MEDIAN)\n", + "mask_road_driveway_points = gpudrive_road_points_types == int(MapElementIds.DRIVEWAY)" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(1, 3, figsize=(14, 5), dpi=120, sharex=False, sharey=False)\n", + "\n", + "# Plot waymax roadgraph points\n", + "axs[0].set_title('Waymax road graph points')\n", + "for idx in range(way_roadgraph_points_x.shape[0]):\n", + " if way_valid_roadgraph_points[idx]:\n", + " color = color_map[way_roadgraph_points_types[idx]] if way_roadgraph_points_types[idx] in color_map.keys() else 'lightgray'\n", + " ms = marker_size_map[way_roadgraph_points_types[idx]] if way_roadgraph_points_types[idx] in marker_size_map.keys() else MS\n", + " axs[0].plot(way_roadgraph_points_x[idx], way_roadgraph_points_y[idx], \".\", color=color, markersize=ms)\n", + "\n", + "# Plot JSON road points\n", + "axs[1].set_title('JSON road points')\n", + "for idx in range(json_road_points_x.shape[0]):\n", + " color = color_map[json_road_points_types[idx]] if json_road_points_types[idx] in color_map.keys() else 'lightgray'\n", + " ms = marker_size_map[json_road_points_types[idx]] if json_road_points_types[idx] in marker_size_map.keys() else MS\n", + " axs[1].plot(json_road_points_x[idx], json_road_points_y[idx], \".\", color=color, markersize=ms)\n", + "\n", + "# Plot GPUDrive road points\n", + "axs[2].plot(gpudrive_road_points_x[mask_road_edge_points], gpudrive_road_points_y[mask_road_edge_points], \".\", color=color_map[int(MapElementIds.ROAD_EDGE_BOUNDARY)], markersize=MS)\n", + "axs[2].plot(gpudrive_road_points_x[mask_road_median_points], gpudrive_road_points_y[mask_road_median_points], \".\", color=color_map[int(MapElementIds.ROAD_EDGE_MEDIAN)], markersize=MS)\n", + "axs[2].plot(gpudrive_road_points_x[~mask_road_edge_points & ~mask_road_median_points], gpudrive_road_points_y[~mask_road_edge_points & ~mask_road_median_points], \".\", color='lightgray', markersize=MS)\n", + "axs[2].plot(gpudrive_road_points_x[mask_road_driveway_points], gpudrive_road_points_y[mask_road_driveway_points], \".\", color=color_map[int(MapElementIds.DRIVEWAY)], markersize=3*MS)\n", + "\n", + "axs[2].set_title('GPUDrive road points')\n", + "# Define a shared legend for the figure\n", + "legend_elements = [\n", + " Line2D([0], [0], marker='o', color='w', markerfacecolor=color_map[int(MapElementIds.ROAD_EDGE_BOUNDARY)], markersize=8, label='Impassable road edge [boundary]'),\n", + " Line2D([0], [0], marker='o', color='w', markerfacecolor=color_map[int(MapElementIds.ROAD_EDGE_MEDIAN)], markersize=8, label='Impassable road edge [median]'),\n", + " Line2D([0], [0], marker='o', color='w', markerfacecolor='lightgray', markersize=8, label='Passable Road Point'),\n", + " Line2D([0], [0], marker='o', color='w', markerfacecolor='green', markersize=8, label='Driveway [entry point]')\n", + "]\n", + "\n", + "fig.legend(handles=legend_elements, loc='upper center', ncol=4, title='Road point type', bbox_to_anchor=(0.5, 1.13))\n", + "\n", + "plt.tight_layout();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### `dir`" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [], + "source": [ + "way_roadgraph_points_dir_x = np.asarray(waymax_scenario.roadgraph_points.dir_x)\n", + "way_roadgraph_points_dir_y = np.asarray(waymax_scenario.roadgraph_points.dir_y)\n", + "\n", + "# They using this\n", + "heading = np.arctan2(way_roadgraph_points_dir_x, way_roadgraph_points_dir_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [], + "source": [ + "gpudrive_road_point_scale_len = sim.map_observation_tensor().to_torch()[:, :, 2].squeeze(0).numpy()\n", + "gpudrive_road_point_heading = sim.map_observation_tensor().to_torch()[:, :, 5].squeeze(0).numpy()" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(heading[way_valid_roadgraph_points], '.', label='Waymax');\n", + "plt.plot(gpudrive_road_point_heading, '.', label='GPUDrive')\n", + "plt.legend();" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.82588035, 0.8258952 , 0.82591254, ..., -2.3561945 ,\n", + " -2.3561945 , -2.3561945 ], dtype=float32)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "heading" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [], + "source": [ + "#waymax_scenario.roadgraph_points.ids[way_valid_roadgraph_points].shape\n", + "#waymax_scenario.roadgraph_points.ids[way_valid_roadgraph_points]" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(13207,)" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "json_road_ids = np.array(json_road_ids)\n", + "\n", + "json_road_ids.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(json_road_ids, '.' )\n", + "plt.plot(waymax_scenario.roadgraph_points.ids[way_valid_roadgraph_points], '.');" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 2, 2, 2, ..., 223, 223, 223])" + ] + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "json_road_ids" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "way_roadgraph_points_dir_x = np.asarray(waymax_scenario.roadgraph_points.dir_x)\n", + "way_roadgraph_points_dir_y = np.asarray(waymax_scenario.roadgraph_points.dir_y)\n", + "\n", + "# They using this\n", + "heading = np.arctan2(way_roadgraph_points_dir_x, way_roadgraph_points_dir_y)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(heading, '.')" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.7351265, 0.7351365, 0.7351483, ..., -1. , -1. ,\n", + " -1. ], dtype=float32)" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "way_roadgraph_points_dir_x" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [], + "source": [ + "def rescale_to_range(data, min_val=0, max_val=10):\n", + " data_min = np.min(data)\n", + " data_max = np.max(data)\n", + " # Rescale to [0, 1] then scale to [min_val, max_val]\n", + " return min_val + (data - data_min) * (max_val - min_val) / (data_max - data_min)\n", + "\n", + "# Apply rescaling\n", + "way_roadgraph_points_x = rescale_to_range(way_roadgraph_points_x)\n", + "way_roadgraph_points_y = rescale_to_range(way_roadgraph_points_y)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [], + "source": [ + "NUM_POINTS = 5" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plot\n", + "plt.figure(figsize=(10, 5))\n", + "# Plot red dots for x, y points\n", + "plt.scatter(\n", + " way_roadgraph_points_x[way_valid_roadgraph_points][:NUM_POINTS],\n", + " way_roadgraph_points_y[way_valid_roadgraph_points][:NUM_POINTS],\n", + " color='red', label='Points'\n", + ")\n", + "\n", + "# Plot blue arrows for direction vectors\n", + "plt.quiver(\n", + " way_roadgraph_points_x[way_valid_roadgraph_points][:NUM_POINTS], \n", + " way_roadgraph_points_y[way_valid_roadgraph_points][:NUM_POINTS], \n", + " way_roadgraph_points_dir_x[way_valid_roadgraph_points][:NUM_POINTS], \n", + " way_roadgraph_points_dir_y[way_valid_roadgraph_points][:NUM_POINTS], \n", + " color='blue', label='Direction Vectors'\n", + ")\n", + "plt.axis('equal')\n", + "plt.xlabel('Point Index')\n", + "plt.ylabel('Direction')\n", + "plt.title('Unit Direction Vectors for Map Feature Sample Points')\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Process the road data\n", + "- Align heading and scale to get feature vector x, y, tl, yaw, type\n", + "- sort the road ids by distance to reference vehicles" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(20000, 9)" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "abs_map_obs = sim.map_observation_tensor().to_torch().numpy().squeeze(0)\n", + "abs_map_obs.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([2., 2., 2., 2., 2., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4., 4.,\n", + " 4., 4., 4., 4., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.,\n", + " 5., 5., 5., 5., 5., 6., 6., 6., 6., 6., 6., 6., 6., 6., 6., 6., 6.,\n", + " 6., 6., 6., 6., 6., 6., 6., 6., 7., 7., 7., 7., 7., 7., 7., 7., 7.,\n", + " 7., 7., 7., 7., 7., 7., 7., 7., 7., 7., 8., 8., 8., 8., 8., 8., 8.,\n", + " 8., 8., 8., 8., 8., 8., 8., 8., 8., 8., 9., 9., 9., 9., 9.],\n", + " dtype=float32)" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x = abs_map_obs[:5,0]\n", + "road_ids = abs_map_obs[:,7]\n", + "road_ids[0:100]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# drill down on id = 2\n", + "#GPUDrive features\n", + "x = abs_map_obs[5:21, 0]\n", + "y = abs_map_obs[5:21, 1]\n", + "heading = abs_map_obs[5:21, 5]\n", + "road_type = abs_map_obs[5:21, 6]\n", + "\n", + "#raw JSON features\n", + "json_x = []\n", + "json_y = []\n", + "json_heading = []\n", + "json_road_type = []\n", + "for road in json_scenario['roads']:\n", + " if road['id'] == 4:\n", + " for point in road['geometry']:\n", + " json_x.append(point['x'])\n", + " json_y.append(point['y'])\n", + " json_road_type.append(road['type'])\n", + "mid_x = []\n", + "mid_y = []\n", + "for i in range(len(json_x) - 1):\n", + " x1, y1, x2, y2 = json_x[i], json_y[i], json_x[i + 1], json_y[i + 1]\n", + " mid_x.append((x1 + x2)/2)\n", + " mid_y.append((y1 + y2)/2)\n", + "scale = abs_map_obs[5:21, 2]\n", + "x_pred = []\n", + "y_pred = []\n", + "for x_i, y_i, head, sc in zip(mid_x, mid_y, heading, scale):\n", + " x_start = x_i - sc * np.cos(head)\n", + " y_start = y_i - sc * np.sin(head)\n", + " x_pred.append(x_start)\n", + " y_pred.append(y_start)\n", + "\n", + "#waymax features\n", + "waymax_x = waymax_scenario['roadgraph_points']['x'][6:23]\n", + "waymax_y = waymax_scenario['roadgraph_points']['y'][6:23]\n", + "waymax_dir_x = waymax_scenario['roadgraph_points']['dir_x'][6:23]\n", + "waymax_dir_y = waymax_scenario['roadgraph_points']['dir_y'][6:23]\n", + "waymax_heading = np.arctan2(waymax_dir_y, waymax_dir_x)\n", + "waymax_road_type = waymax_scenario['roadgraph_points']['types'][6:23]" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[3105.094 3104.6785 3104.2632 3103.8484 3103.4343 3103.0205 3102.6077\n", + " 3102.195 3101.7834 3101.3723 3100.9622 3100.5525 3100.1956 3099.8389\n", + " 3099.483 3099.1277 3098.773 ]\n", + "[3105.094027023975, 3104.6784527866475, 3104.2632311339903, 3103.8483967362913, 3103.4343507473895, 3103.020518039617, 3102.607666615696, 3102.195100009796, 3101.7834648341923, 3101.372309493049, 3100.962159367432, 3100.5524783915807, 3100.195546919637, 3099.8389653586246, 3099.4829577325113, 3099.127625790085]\n" + ] + } + ], + "source": [ + "# they align!!!!\n", + "print(waymax_x)\n", + "print(x_pred)" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['x', 'y', 'z', 'dir_x', 'dir_y', 'dir_z', 'types', 'ids', 'valid'])" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "waymax_scenario['roadgraph_points'].keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(20000,)" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "waymax_scenario['roadgraph_points']['ids'].shape" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Array([2, 2, 2, 2, 2, 2, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n", + " 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6,\n", + " 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7,\n", + " 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8,\n", + " 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8], dtype=int32)" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# why is it longer?\n", + "waymax_scenario['roadgraph_points']['ids'][:100]" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GPUDrive: [95.33887 94.92346 94.50842 94.093994 93.68005 93.26672 92.854004\n", + " 92.441895 92.03052 91.61987 91.20996 90.82666 90.46985 90.113525\n", + " 89.757935 89.402954]\n", + "Raw JSON: [3105.0941037471634, 3104.6784228974316, 3104.263199442936, 3103.84846790317, 3103.4342630694127, 3103.0206200361895, 3102.6075742163675, 3102.195161358774, 3101.783417567068, 3101.372379318619, 3100.9620834770835, 3100.552567318833, 3100.195455870578, 3099.8389484857685, 3099.483025170429, 3099.1276656852187, 3098.772849566826]\n", + "Waymax: [3105.094 3104.6785 3104.2632 3103.8484 3103.4343 3103.0205 3102.6077\n", + " 3102.195 3101.7834 3101.3723 3100.9622 3100.5525 3100.1956 3099.8389\n", + " 3099.483 3099.1277 3098.773 ]\n" + ] + } + ], + "source": [ + "print(f\"GPUDrive: {x}\")\n", + "print(f\"Raw JSON: {json_x}\")\n", + "print(f\"Waymax: {waymax_x}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GPUDrive: [2.5705228 2.5688007 2.5670195 2.5649688 2.56325 2.5605135 2.5585907\n", + " 2.555852 2.553453 2.5505056 2.548109 2.5449588 2.5427704 2.5404124\n", + " 2.5375865 2.5357797]\n", + "Waymax: [2.57055 2.5688396 2.567005 2.5650463 2.562964 2.5607572 2.5584266\n", + " 2.555972 2.5533934 2.5506907 2.547864 2.5451355 2.5426488 2.5402527\n", + " 2.5379477 2.5357335 0. ]\n" + ] + } + ], + "source": [ + "print(f\"GPUDrive: {heading}\")\n", + "print(f\"Waymax: {waymax_heading}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- The reason waymax road objects have an extra map point is because they represent the road graph in (start_point, heading angle), meaning there would have to be an extra point at the end for each id with heading angle 0." + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# heading (again we observe the discrepency due to the extra point)\n", + "# also, the graph makes it seem like heading value for invalid roads in gpudrive is 0, which means something else in waymax(the last point for each road id)\n", + "\n", + "#GPUDrive features\n", + "heading = abs_map_obs[:, 5]\n", + "\n", + "#waymax features\n", + "waymax_dir_x = waymax_scenario['roadgraph_points']['dir_x']\n", + "waymax_dir_y = waymax_scenario['roadgraph_points']['dir_y']\n", + "\n", + "waymax_heading = np.arctan2(waymax_dir_y, waymax_dir_x)\n", + "\n", + "plt.figure(figsize=(15, 7))\n", + "plt.plot(heading, label=\"GPUDrive\")\n", + "plt.plot(waymax_heading, label=\"Waymax\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# x and y \n", + "#GPUDrive features\n", + "heading = abs_map_obs[:, 5]\n", + "road_ids = abs_map_obs[:,7]\n", + "\n", + "#raw JSON features\n", + "json_x = []\n", + "json_y = []\n", + "for road in json_scenario['roads']:\n", + " for point in road['geometry']:\n", + " json_x.append(point['x'])\n", + " json_y.append(point['y'])\n", + "mid_x = []\n", + "mid_y = []\n", + "for i in range(len(json_x) - 1):\n", + " x1, y1, x2, y2 = json_x[i], json_y[i], json_x[i + 1], json_y[i + 1]\n", + " mid_x.append((x1 + x2)/2)\n", + " mid_y.append((y1 + y2)/2)\n", + "scale = abs_map_obs[5:21, 2]\n", + "x_pred = []\n", + "y_pred = []\n", + "for x_i, y_i, head, sc in zip(mid_x, mid_y, heading, scale):\n", + " x_start = x_i - sc * np.cos(head)\n", + " y_start = y_i - sc * np.sin(head)\n", + " x_pred.append(x_start)\n", + " y_pred.append(y_start)\n", + "\n", + "#waymax features\n", + "waymax_x = waymax_scenario['roadgraph_points']['x'][6:23]\n", + "waymax_y = waymax_scenario['roadgraph_points']['y'][6:23]\n", + "waymax_dir_x = waymax_scenario['roadgraph_points']['dir_x'][6:23]\n", + "waymax_dir_y = waymax_scenario['roadgraph_points']['dir_y'][6:23]\n", + "waymax_heading = np.arctan2(waymax_dir_y, waymax_dir_x)\n", + "waymax_road_type = waymax_scenario['roadgraph_points']['types'][6:23]" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "gpudrive", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/gpudrive-main/gpudrive/integrations/vbd/notebooks/03_vbd_output_comparison.ipynb b/gpudrive-main/gpudrive/integrations/vbd/notebooks/03_vbd_output_comparison.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..34af878eee54b807c2fb2ffb9c476872b3e9509a --- /dev/null +++ b/gpudrive-main/gpudrive/integrations/vbd/notebooks/03_vbd_output_comparison.ipynb @@ -0,0 +1,317 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Comparing VBD Outputs: Waymax vs GPUDrive" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-03-25 15:24:52.394752: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", + "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", + "E0000 00:00:1742930692.413969 56624 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", + "E0000 00:00:1742930692.419632 56624 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n" + ] + } + ], + "source": [ + "%matplotlib inline\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "import numpy as np\n", + "import mediapy\n", + "import os\n", + "from pathlib import Path\n", + "from PIL import Image\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import torch\n", + "# Set working directory to the base directory 'gpudrive'\n", + "working_dir = Path.cwd()\n", + "while working_dir.name != 'gpudrive':\n", + " working_dir = working_dir.parent.parent\n", + " if working_dir == Path.home():\n", + " raise FileNotFoundError(\"Base directory 'gpudrive' not found\")\n", + "os.chdir(working_dir)\n", + "\n", + "# GPUDrive dependencies\n", + "import gpudrive\n", + "from gpudrive.env.config import EnvConfig, RenderConfig, SceneConfig\n", + "from gpudrive.env.env_torch import GPUDriveTorchEnv\n", + "from gpudrive.env.dataset import SceneDataLoader\n", + "from gpudrive.visualize.utils import img_from_fig\n", + "\n", + "# Plotting\n", + "sns.set(\"notebook\")\n", + "sns.set_style(\"ticks\", rc={\"figure.facecolor\": \"none\", \"axes.facecolor\": \"none\"})\n", + "#%config InlineBackend.figure_format = 'svg'\n", + "\n", + "# Ignore all warnings\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Configurations" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "DATA_DIR = 'data/processed' # Base data path\n", + "CKPT_PATH = 'gpudrive/integrations/vbd/weights/epoch=18.ckpt'\n", + "\n", + "SCENARIO_ID = 'efc5cbe01b4a526f'\n", + "\n", + "FPS = 20\n", + "INIT_STEPS = 11 # Warmup period\n", + "MAX_CONTROLLED_OBJECTS = 32" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Make Videos" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Diffusion: 100%|██████████| 50/50 [00:17<00:00, 2.94it/s]\n" + ] + } + ], + "source": [ + "#Init GPUDrive env\n", + "env_config = EnvConfig(\n", + " init_steps=INIT_STEPS, # Warmup period\n", + " dynamics_model=\"state\", # Use state-based dynamics model\n", + " dist_to_goal_threshold=1e-5, # Trick to make sure the agents don't disappear when they reach the goal\n", + " init_mode = 'all_non_trivial',\n", + " use_vbd=True,\n", + " max_controlled_agents=32,\n", + " vbd_model_path=CKPT_PATH,\n", + ")\n", + " \n", + "scene_config = SceneConfig(batch_size=1, dataset_size=1, path=\"data/processed/training\", num_scenes=1)\n", + "# Make env\n", + "gpudrive_env = GPUDriveTorchEnv(\n", + " config=env_config,\n", + " data_loader = SceneDataLoader(\n", + " root=\"data/processed/training\",\n", + " batch_size=scene_config.batch_size,\n", + " dataset_size=scene_config.dataset_size,\n", + " ),\n", + " render_config=RenderConfig(resolution=(400, 400)),\n", + " max_cont_agents=MAX_CONTROLLED_OBJECTS, # Maximum number of agents to control per scene\n", + " device=\"cpu\",\n", + ")\n", + "gpudrive_sample_batch = gpudrive_env._generate_sample_batch()\n", + "\n", + "# Reset predictions tensor\n", + "pred_trajs = torch.zeros((gpudrive_env.num_worlds, gpudrive_env.max_agent_count, env_config.episode_len-INIT_STEPS, 10))\n", + "\n", + "# Fill pred_trajs correctly for each world\n", + "for i in range(gpudrive_env.num_worlds):\n", + " world_agent_indices = gpudrive_sample_batch['agents_id'][i]\n", + " \n", + " # Filter out negative indices (padding values)\n", + " valid_mask = world_agent_indices >= 0 # Boolean mask of valid indices\n", + " valid_agent_indices = world_agent_indices[valid_mask] # Filtered tensor\n", + "\n", + " # Use tensor indexing with valid agent indices\n", + " pred_trajs[i, valid_agent_indices, :, :2] = gpudrive_env.vbd_trajectories[i, valid_agent_indices, :, :2] # pos x, y\n", + " pred_trajs[i, valid_agent_indices, :, 3] = gpudrive_env.vbd_trajectories[i, valid_agent_indices, :, 2] # yaw\n", + " pred_trajs[i, valid_agent_indices, :, 4:6] = gpudrive_env.vbd_trajectories[i, valid_agent_indices, :, 3:5] # vel x, y\n", + "\n", + "# Now step through the simulation\n", + "gpudrive_frames = []\n", + "for t in range(env_config.episode_len-INIT_STEPS):\n", + " gpudrive_env.step_dynamics(pred_trajs[:, :, t, :])\n", + " fig = gpudrive_env.vis.plot_simulator_state(\n", + " time_steps=[t],\n", + " env_indices=[0],\n", + " zoom_radius=70,\n", + " )[0]\n", + " gpudrive_frames.append(img_from_fig(fig))" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GIF saved at gpudrive/integrations/vbd/viz/train.gif\n" + ] + } + ], + "source": [ + "mediapy.write_video('gpudrive/integrations/vbd/viz/train.gif', gpudrive_frames, fps=FPS, codec=\"gif\")\n", + "print(\"GIF saved at gpudrive/integrations/vbd/viz/train.gif\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([1, 64, 80, 10]) torch.Size([1, 64, 80, 5])\n" + ] + } + ], + "source": [ + "print(pred_trajs.shape, gpudrive_env.vbd_trajectories.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([32, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n", + " -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1],\n", + " dtype=torch.int32)\n", + "tensor([32], dtype=torch.int32)\n" + ] + } + ], + "source": [ + "world_agent_indices = gpudrive_sample_batch['agents_id'][0]\n", + "valid_mask = world_agent_indices >= 0 # Boolean mask of valid indices\n", + "valid_agent_indices = world_agent_indices[valid_mask] # Filtered tensor\n", + "\n", + "print(world_agent_indices)\n", + "print(valid_agent_indices)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plotting VBD trajectory as part of agent obs" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def transform_to_ego_frame(trajectory: torch.Tensor, ego_pos: torch.Tensor, ego_yaw: torch.Tensor) -> torch.Tensor:\n", + " \"\"\"\n", + " Transform trajectory from global coordinates to ego-centric frame.\n", + " Args:\n", + " trajectory: Shape (time_steps, 2) containing x,y coordinates in global frame\n", + " ego_pos: Shape (2,) containing ego x,y position\n", + " ego_yaw: Shape (1,) containing ego yaw angle in radians\n", + " Returns:\n", + " transformed_trajectory: Shape (time_steps, 2) in ego-centric frame\n", + " \"\"\"\n", + " # Step 1: Translate trajectory to be relative to ego position\n", + " translated = trajectory - ego_pos\n", + " \n", + " # Step 2: Rotate trajectory to align with ego orientation\n", + " # Create rotation matrix\n", + " cos_yaw = torch.cos(ego_yaw)\n", + " sin_yaw = torch.sin(ego_yaw)\n", + " rotation_matrix = torch.tensor([\n", + " [cos_yaw, sin_yaw],\n", + " [-sin_yaw, cos_yaw]\n", + " ])\n", + " \n", + " # Apply rotation matrix to the translated trajectory\n", + " # We need to transpose rotation_matrix for batch matrix multiplication\n", + " transformed_trajectory = torch.matmul(translated, rotation_matrix.T)\n", + " \n", + " return transformed_trajectory" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# plotting vbd trajectory as part of observation\n", + "from gpudrive.datatypes.observation import GlobalEgoState\n", + "init_state = gpudrive_env.reset()\n", + "# Get global agent observations\n", + "global_agent_obs = GlobalEgoState.from_tensor(\n", + " abs_self_obs_tensor=gpudrive_env.sim.absolute_self_observation_tensor(),\n", + " backend=gpudrive_env.backend,\n", + " device=gpudrive_env.device,\n", + ")\n", + "\n", + "for agent_index in valid_agent_indices:\n", + " vbd_trajectory = gpudrive_env.vbd_trajectories[0, agent_index, :, :2]\n", + " pos_xy = torch.tensor([global_agent_obs.pos_x[0, agent_index], global_agent_obs.pos_y[0, agent_index]])\n", + " yaw = torch.tensor(global_agent_obs.rotation_angle[0, agent_index])\n", + "\n", + " transformed_trajectory = transform_to_ego_frame(vbd_trajectory, pos_xy, yaw)\n", + "\n", + " fig = gpudrive_env.vis.plot_agent_observation(\n", + " agent_idx=agent_index,\n", + " env_idx=0,\n", + " trajectory=transformed_trajectory,\n", + " figsize = (4, 4)\n", + " )\n", + " fig.savefig(f'gpudrive/integrations/vbd/viz/{agent_index}_vbd_trajectory.png', facecolor='white', transparent=False)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "gpudrive", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.8" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/gpudrive-main/gpudrive/integrations/vbd/sim_agent/__init__.py b/gpudrive-main/gpudrive/integrations/vbd/sim_agent/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/gpudrive-main/gpudrive/integrations/vbd/sim_agent/guidance_metrics/__init__.py b/gpudrive-main/gpudrive/integrations/vbd/sim_agent/guidance_metrics/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5fc3afded280a6e33635c3c3225d57a35a82046c --- /dev/null +++ b/gpudrive-main/gpudrive/integrations/vbd/sim_agent/guidance_metrics/__init__.py @@ -0,0 +1,4 @@ +from .tracking_metric import AnchorReward, TrackingReward, GoalReward +from .overlap_metric import OverlapReward, OverlapRewardSimple +from .onroad_metric import OnroadReward +from .control_metric import ControlReward diff --git a/gpudrive-main/gpudrive/integrations/vbd/sim_agent/guidance_metrics/control_metric.py b/gpudrive-main/gpudrive/integrations/vbd/sim_agent/guidance_metrics/control_metric.py new file mode 100644 index 0000000000000000000000000000000000000000..2de66e0d0c4d26b515acf6d73206e4068e22a02a --- /dev/null +++ b/gpudrive-main/gpudrive/integrations/vbd/sim_agent/guidance_metrics/control_metric.py @@ -0,0 +1,28 @@ +import torch +from torch import nn + + +class ControlReward(nn.Module): + def __init__(self, weight_a=1.0, weight_yaw=1.0) -> None: + super().__init__() + self.weight_a = weight_a + self.weight_yaw = weight_yaw + + def forward( + self, action_pred: torch.Tensor, c: dict, aoi: list = None, **kwargs + ): + """ + action_normalized: [B, A, T, 2] + c: dict + """ + mask = ~c["agents_mask"] # [B, A] + + # B, A, T, 2 -> B, A, T + cost = ( + action_pred[..., 0] ** 2 * self.weight_a + + action_pred[..., 1] ** 2 * self.weight_yaw + ) + + cost = cost * mask[..., None] + + return -cost diff --git a/gpudrive-main/gpudrive/integrations/vbd/sim_agent/guidance_metrics/ilqgame.py b/gpudrive-main/gpudrive/integrations/vbd/sim_agent/guidance_metrics/ilqgame.py new file mode 100644 index 0000000000000000000000000000000000000000..7ab68c1cc2ca5ae16dd2749e8ce961048e2ccc3d --- /dev/null +++ b/gpudrive-main/gpudrive/integrations/vbd/sim_agent/guidance_metrics/ilqgame.py @@ -0,0 +1,64 @@ +import jax +import jax.numpy as jnp +from jaxlib.xla_extension import ArrayImpl +from functools import partial + + +@partial(jax.jit, static_argnums=(2, 3)) +def dynamics( + current_states: ArrayImpl, + actions: ArrayImpl, + dt: float = 0.1, + action_len: int = 2, +): + """ + Forward pass of the dynamics model. + + Args: + current_states (torch.Tensor): Current states tensor of shape [..., 5]. [x, y, theta, v_x, v_y] + actions (torch.Tensor): Inputs tensor of shape [..., 2]. [Accel, yaw_rate] + + Returns: + torch.Tensor: Predicted next state. + """ + + # Dim： [..., 1] + x = current_states[..., 0:1] + y = current_states[..., 1:2] + theta = current_states[..., 2:3] + v_x = current_states[..., 3:4] + v_y = current_states[..., 4:5] + v = jnp.sqrt(v_x**2 + v_y**2) # [..., 1] + + actions_full = jnp.expand_dims(actions, axis=-2).repeat( + action_len, axis=-2 + ) # Dim: [..., action_len, 2] + + accel = actions_full[..., 0] + v = v + jnp.cumsum(accel * dt, axis=-1) # Dim: [..., action_len] + v = jnp.clip(v, a_min=0.0, a_max=None) + + yaw_rate = actions_full[..., 1] + yaw_rate = jnp.where(v > 0.1, yaw_rate, 0.0) + theta = jnp.cumsum(yaw_rate * dt, axis=-1) + theta + theta = wrap_angle(theta) + v_x = v * jnp.cos(theta) + v_y = v * jnp.sin(theta) + + x = jnp.cumsum(v_x * dt, axis=-1) + x + y = jnp.cumsum(v_y * dt, axis=-1) + y + next_states = jnp.stack([x, y, theta, v_x, v_y], axis=-1) + + return next_states[..., -1, :] + + +@jax.jit +def get_A_and_B(state_start, pred_action): + jac_par = jax.jacfwd(dynamics, argnums=(0, 1)) + for _ in state_start.shape[:-1]: + jac_par = jax.vmap(jac_par) + return jac_par(state_start, pred_action) + + +def wrap_angle(angle): + return (angle + jnp.pi) % (2 * jnp.pi) - jnp.pi diff --git a/gpudrive-main/gpudrive/integrations/vbd/sim_agent/guidance_metrics/onroad_metric.py b/gpudrive-main/gpudrive/integrations/vbd/sim_agent/guidance_metrics/onroad_metric.py new file mode 100644 index 0000000000000000000000000000000000000000..3d552225de5fe07de6cc63bdb8dd7d8e4b4215b1 --- /dev/null +++ b/gpudrive-main/gpudrive/integrations/vbd/sim_agent/guidance_metrics/onroad_metric.py @@ -0,0 +1,248 @@ +"""Metrics to calculate the signed distance of road map.""" +import jax +from jax import numpy as jnp +from waymax import datatypes +import numpy as np +import torch +from torch import nn +from torch.autograd import Function + + +class OnroadReward(nn.Module): + def __init__(self): + super().__init__() + + def forward( + self, + traj_pred: torch.Tensor, + c: dict, + roadgraph_points: datatypes.RoadgraphPoints, + weight: torch.Tensor = 0.1, + aoi: list = None, + **kwargs + ): + """ + traj_pred: [B, A, T, D] + c: dict + weight: [B, A, T] + """ + T = traj_pred.shape[-2] + traj_pred_xy = traj_pred[..., :2] + traj_pred_yaw = traj_pred[..., 2:3] + length = c["agents"][..., -1, 5:6].repeat(1, 1, T).unsqueeze(-1) + width = c["agents"][..., -1, 6:7].repeat(1, 1, T).unsqueeze(-1) + + traj_5dof = torch.concatenate( + [traj_pred_xy, length, width, traj_pred_yaw], dim=-1 + ) + + mask = (~c["agents_mask"]).unsqueeze(-1).repeat(1, 1, T) # [B, A, T] + + if aoi is not None: + traj_5dof = traj_5dof[:, aoi] + mask = mask[:, aoi] + + # negative means on road + signed_distance = distance_offroad( + traj_5dof, roadgraph_points + ) # [B, A, T] + + # filter out already offroad + signed_distance = signed_distance * (signed_distance[:, :, 0:1] < 0) + + # compute cost + cost = torch.functional.F.relu(signed_distance) + cost = cost * mask * weight + + return -cost + + +def distance_offroad( + pose_5dof: torch.Tensor, + roadgraph_points: datatypes.RoadgraphPoints, +) -> torch.Tensor: + """Checks if the given trajectory is offroad. + + This determines the signed distance between each bounding box corner and the + closest road edge (median or boundary). If the distance is negative, then the + trajectory is onroad else offroad. + + Args: + pose_5dof: Agent trajectories to test to see if they are on or off road of + shape (B, A, T, D). The bounding boxes derived from center and shape + of the trajectory will be used to determine if any point in the box is + offroad. + roadgraph_points: All of the roadgraph points in the run segment of shape + (num_map_points). Roadgraph points of type `ROAD_EDGE_BOUNDARY` and + `ROAD_EDGE_MEDIAN` are used to do the check. + + Returns: + min_distances: a float array with the shape (..., num_objects). The object is offroad + if the value is positive. + """ + # ! We assume that all batches are on the same roadgraph + # ! TODO: batched roadgraph + + # Shape: (num_batch, num_agents, num_steps, num_corners=4, 2). + bbox_corners = corners_from_bboxes(pose_5dof) + + # ! Ignore z for now + # Add in the Z dimension from the current center. This assumption will help + # disambiguate between different levels of the roadgraph (i.e. under and over + # passes). + # Shape: (..., num_objects, 1, 1). + # z = jnp.ones_like(bbox_corners[..., 0:1]) * trajectory.z[..., jnp.newaxis, :] + # Shape: (..., num_objects, num_corners=4, 3). + # bbox_corners = jnp.concatenate((bbox_corners, z), axis=-1) + + # [B, A] + num_corners, dim = bbox_corners.shape[-2:] + pre_dims = bbox_corners.shape[:-2] + + # Shape: (...*num_corners=4, 2). + bbox_corners = torch.reshape(bbox_corners, [-1, dim]) + + # Shape: (...*num_corners=4). + distances, sign = compute_signed_distance_to_nearest_road_edge_point( + bbox_corners, roadgraph_points + ) + # Shape: (num_batches, num_agents, num_steps, num_map_points=4). + distances = torch.reshape(distances, [*pre_dims, num_corners]) + signs = torch.reshape(sign, [*pre_dims, num_corners]) + + signed_distance = distances * signs # [B,A,T] + max_distances, _ = torch.max(signed_distance, dim=-1) + + # Shape: (B, A, T). + return max_distances + + +def compute_signed_distance_to_nearest_road_edge_point( + query_points: torch.Tensor, + roadgraph_points: datatypes.RoadgraphPoints, + # z_stretch: float = 2.0, +) -> torch.Tensor: + """Computes the signed distance from a set of queries to roadgraph points. + + Args: + query_points: A set of query points for the metric of shape + (num_query_points, 2). + roadgraph_points: A set of roadgraph points of shape (num_map_points). + z_stretch: Tolerance in the z dimension which determines how close to + associate points in the roadgraph. This is used to fix problems with + overpasses. + + Returns: + Signed distances of the query points with the closest road edge points of + shape (num_query_points). If the value is negative, it means that the + actor is on the correct side of the road, if it is positive, it is + considered `offroad`. + """ + # Shape: (..., num_map_points, 2). + # extract information from roadgraph_points + # Do not consider invalid points. + # Shape: (num_map_points). + is_road_edge = datatypes.is_road_edge(roadgraph_points.types) + is_valid = roadgraph_points.valid & is_road_edge + + sampled_points = np.asarray(roadgraph_points.xy) + dir_xy = np.asarray(roadgraph_points.dir_xy) + id = np.asarray(roadgraph_points.ids) + + # Filter out the invalid points + sampled_points = sampled_points[is_valid, :] + dir_xy = dir_xy[is_valid, :] + id = id[is_valid] + + # Make them all in torch + sampled_points_torch = torch.from_numpy(sampled_points).type_as( + query_points + ) + dir_xy_torch = torch.from_numpy(dir_xy).type_as(query_points) + id_torch = torch.from_numpy(id).to(query_points.device) + + # Shape: (num_query_points, num_map_points, 2). + differences = sampled_points_torch - query_points[:, None, :] + + # !Ignore the over/underpasses + # Stretch difference in altitude to avoid over/underpasses. + # z_stretched_differences = differences * jnp.array([[[1.0, 1.0, z_stretch]]]) + # square_distances = jnp.sum(z_stretched_differences**2, axis=-1) + + # Shape: (num_query_points, num_map_points). + square_distances = torch.sum(differences**2, dim=-1) + + # Shape: (num_query_points). + nearest_indices = torch.argmin(square_distances, dim=-1) + prior_indices = torch.clip(nearest_indices - 1, min=0, max=None) + + nearest_xys = sampled_points_torch[nearest_indices, :] + + # Direction of the road edge at the nearest points. Should be normed and + # tangent to the road edge. + # Shape: (num_map_points, 2). + nearest_vector_xys = dir_xy_torch[nearest_indices, :] + + # Direction of the road edge at the points that precede the nearest points. + # Shape: (num_map_points, 2). + prior_vector_xys = dir_xy_torch[prior_indices, :] + + # Shape: (num_query_points, 2). + points_to_edge = query_points - nearest_xys + + # Get the signed distance to the half-plane boundary with a cross product. + cross_product = cross_2d(points_to_edge, nearest_vector_xys) + cross_product_prior = cross_2d(points_to_edge, prior_vector_xys) + + # If the prior point is contiguous, consider both half-plane distances. + # Shape: (num_map_points). + prior_point_in_same_curve = ( + id_torch[nearest_indices] == id_torch[prior_indices] + ) + + offroad_sign = torch.sign( + torch.where( + torch.logical_and( + prior_point_in_same_curve, cross_product_prior < cross_product + ), + cross_product_prior, + cross_product, + ) + ) + + # ! Hard code make all zeros to be positive + + offroad_sign = torch.where( + offroad_sign == 0, torch.ones_like(offroad_sign), offroad_sign + ) + # Shape: (num_query_points). + return torch.norm(nearest_xys - query_points, dim=-1), offroad_sign + + +def corners_from_bboxes(bbox: torch.Tensor) -> torch.Tensor: + """ + Computes corners for one 5 dof bbox. + Args: + bbox: [..., 5] + Returns: + points: [..., 4, 2] + """ + # bbox: [..., 5] + c, s = torch.cos(bbox[..., 4]), torch.sin(bbox[..., 4]) + lc = bbox[..., 2] / 2 * c + ls = bbox[..., 2] / 2 * s + wc = bbox[..., 3] / 2 * c + ws = bbox[..., 3] / 2 * s + + dx = torch.stack([lc + ws, lc - ws, -lc - ws, -lc + ws], dim=-1) + dy = torch.stack([ls - wc, ls + wc, -ls + wc, -ls - wc], dim=-1) + # [..., 2] + points = torch.stack([dx, dy], dim=-1) + + points += bbox[..., None, :2] + + return points + + +def cross_2d(a, b): + return a[..., 0] * b[..., 1] - a[..., 1] * b[..., 0] diff --git a/gpudrive-main/gpudrive/integrations/vbd/sim_agent/guidance_metrics/overlap_metric.py b/gpudrive-main/gpudrive/integrations/vbd/sim_agent/guidance_metrics/overlap_metric.py new file mode 100644 index 0000000000000000000000000000000000000000..f25db1f011c821390a0bfb6e82ca84761bcb4b16 --- /dev/null +++ b/gpudrive-main/gpudrive/integrations/vbd/sim_agent/guidance_metrics/overlap_metric.py @@ -0,0 +1,504 @@ +"""Metrics to calculate the signed distance between objects.""" + +import jax +from jax import numpy as jnp + +from waymax.utils import geometry +import numpy as np +from typing import Tuple +import torch +from torch import nn +from torch.autograd import Function + + +class OverlapReward(nn.Module): + def __init__( + self, + clip=5.0, + weight=1.0, + ): + super().__init__() + self.clip = clip + self.weight = weight + + def forward( + self, traj_pred: torch.Tensor, c: dict, aoi: list = None, **kwargs + ): + """ + traj_pred: [B, A, T, D] + c: dict + """ + T = traj_pred.shape[2] + traj_pred_xy = traj_pred[..., :2] + traj_pred_yaw = traj_pred[..., 2:3] + length = c["agents"][..., -1, 5:6].repeat(1, 1, T).unsqueeze(-1) + width = c["agents"][..., -1, 6:7].repeat(1, 1, T).unsqueeze(-1) + + traj_5dof = torch.concatenate( + [traj_pred_xy, length, width, traj_pred_yaw], dim=-1 + ) + + mask = (~c["agents_mask"]).unsqueeze(-1).repeat(1, 1, T) # [B, A, T] + + if aoi is not None: + traj_5dof = traj_5dof[:, aoi] + mask = mask[:, aoi] + + A = traj_5dof.shape[1] + + # negative means collision + signed_distance = ComputeOverlap.apply(traj_5dof, mask) # [B, A, T, A] + + # valid mask + valid = mask.unsqueeze(-1).repeat(1, 1, 1, A) # [B, A, T, A] + valid = valid * valid.transpose(1, 3) # [B, A, T, A] + signed_distance = torch.where( + valid, signed_distance, self.clip + ) # [B, A, T, A] + + # Ignore all distance larger than clip + reward = signed_distance * (signed_distance < self.clip) * self.weight + + return reward + + +class OverlapRewardSimple(nn.Module): + def __init__(self, clip=5.0, weight=1.0): + super().__init__() + + self.clip = clip + self.weight = weight + + def forward(self, traj_pred: torch.Tensor, c: dict, aoi=None, **kwargs): + """ + traj_pred: [B, A, T, D] + weight: [B, A, T] + """ + mask = ~c["agents_mask"] # [B, A] + + if aoi is not None: + traj_pred = traj_pred[:, aoi] + mask = mask[:, aoi] + + B, A, T, _ = traj_pred.shape + + valid = mask.unsqueeze(-1).repeat(1, 1, A) # [B, A, A] + valid = valid * valid.transpose(1, 2) # [B, A, A] + valid = valid.unsqueeze(-2).repeat(1, 1, T, 1) # [B, A, T, A] + + traj_pred_xy = traj_pred[..., :2] # [B, A, T, 2] + + traj_all = traj_pred_xy.unsqueeze(3).repeat( + 1, 1, 1, A, 1 + ) # [B, A, T, A, 2] + traj_all_transpose = traj_all.detach().transpose( + 1, 3 + ) # [B, A, T, A, 2] + + distance = torch.norm( + traj_all - traj_all_transpose, dim=-1 + ) # [B, A, T, A] + + self_interaction = ( + torch.eye(A, dtype=torch.bool) + .unsqueeze(0) + .unsqueeze(-2) + .repeat(B, 1, T, 1) + ) # [B, A, T, A] + self_interaction = self_interaction.type_as(mask) + + distance = torch.where( + self_interaction, self.clip, distance + ) # remove self interaction + distance = torch.where( + valid, distance, self.clip + ) # remove invalid objects + + # Ignore all distance larger than clip + reward = distance * (distance < self.clip) * self.weight + + return reward + + +# Wrapper to pytorch function +class ComputeOverlap(Function): + @staticmethod + def forward(ctx, traj, mask): + # Convert input tensor to JAX array + traj_jax = jnp.array(traj.detach().cpu().numpy()) + mask_jax = jnp.array(mask.detach().cpu().numpy()) + + # Call the JAX function + signed_distance_jax = jax.vmap( + jax.vmap( + compute_overlap, + in_axes=(1, 1), + out_axes=1, + ), + in_axes=(0, 0), + )(traj_jax, mask_jax) + + # signed_distance_jax[b, x1, t, x2] -> J(x1, x2| b, t) + signed_distance = torch.from_numpy( + np.array(signed_distance_jax) + ).type_as(traj) + + # Save the JAX input and output for backward pass + ctx.save_for_backward(traj, mask) + + return signed_distance # [B, A, T, A] + + @staticmethod + def backward(ctx, grad_output): + # grad_output is the gradient of the loss with respect to the output of forward (signed_distance) + # [B, A, T, A, 1, 1], grad_output(b, x1, t, x2, 0, 0) -> dL/dJ(x1, x2|b, t) + grad_output = grad_output[..., None, None] # [B, A, T, A, 1] + + # Retrieve the saved JAX input and output + (traj, mask) = ctx.saved_tensors + + # Convert input and grad_output tensors to JAX arrays + traj_jax = jnp.array(traj.detach().cpu().numpy()) + mask_jax = jnp.array(mask.detach().cpu().numpy()) + + # Compute the gradient using JAX + # grad_traj_jax[b, x1, t, x2, x3, d] -> dJ(x1, x2|b, t)/d(x3=x1,d|b,t) + (grad_traj_jax,) = jax.vmap( + jax.vmap( + jax.jacfwd(compute_overlap, argnums=(0,)), + in_axes=(1, 1), + out_axes=1, + ), + in_axes=(0, 0), + )(traj_jax, mask_jax) + + # Convert grad_input JAX array to PyTorch tensor + grad_traj = torch.from_numpy(np.array(grad_traj_jax)).type_as( + grad_output + ) + + # [B, A, T, A, A, D], + # grad_traj(b, x1, t, x2, x3, d) -> [dL/dJ(x1, x2|b, t)]*[dJ(x1, x2|b, t)/d(x3=x2,d|b,t)] + grad_prod = grad_traj * grad_output + + # # [B, A, T, D], sum over x1 and x2 + # grad_output[b, x1, t, d] -> dL/dx1(d|b, t) + grad = grad_prod.sum(dim=(1, 3)).transpose(1, 2) + + return grad, None # grad_mask is None + + +"""' +Adapt from https://github.com/waymo-research/waymo-open-dataset/blob/master/src/waymo_open_dataset/utils/geometry_utils.py +""" + + +@jax.jit +def minkowski_sum_of_box_and_box_points( + box1_points: jax.Array, box2_points: jax.Array +) -> jax.Array: + """Batched Minkowski sum of two boxes (counter-clockwise corners in xy). + + The last dimensions of the input and return store the x and y coordinates of + the points. Both box1_points and box2_points needs to be stored in + counter-clockwise order. Otherwise the function will return incorrect results + silently. + + Args: + box1_points: Tensor of vertices for box 1, with shape: + (num_boxes, num_points_per_box, 2). + box2_points: Tensor of vertices for box 2, with shape: + (num_boxes, num_points_per_box, 2). + + Returns: + The Minkowski sum of the two boxes, of size (num_boxes, + num_points_per_box * 2, 2). The points will be stored in counter-clockwise + order. + """ + # NUM_BOX_1 = box1_points.shape[0] + # NUM_BOX_2 = box2_points.shape[0] + NUM_VERTICES_IN_BOX = box1_points.shape[1] + assert NUM_VERTICES_IN_BOX == 4, "Only support boxes" + # Hard coded order to pick points from the two boxes. This is a simplification + # of the generic convex polygons case. For boxes, the adjacent edges are + # always 90 degrees apart from each other, so the index of vertices can be + # hard coded. + point_order_1 = jnp.array([0, 0, 1, 1, 2, 2, 3, 3]) + point_order_2 = jnp.array([0, 1, 1, 2, 2, 3, 3, 0]) + + box1_start_idx, downmost_box1_edge_direction = _get_downmost_edge_in_box( + box1_points + ) + box2_start_idx, downmost_box2_edge_direction = _get_downmost_edge_in_box( + box2_points + ) + + # The cross-product of the unit vectors indicates whether the downmost edge + # in box2 is pointing to the left side (the inward side of the resulting + # Minkowski sum) of the downmost edge in box1. If this is the case, pick + # points from box1 in the order `point_order_2`, and pick points from box2 in + # the order of `point_order_1`. Otherwise, we switch the order to pick points + # from the two boxes, pick points from box1 in the order of `point_order_1`, + # and pick points from box2 in the order of `point_order_2`. + # Shape: (num_boxes, 1) + condition = ( + jnp.cross(downmost_box1_edge_direction, downmost_box2_edge_direction) + >= 0.0 + ) + # box1_point_order of size [num_boxes, num_points_per_box * 2 = 8, 1]. + box1_point_order = jnp.where(condition, point_order_2, point_order_1) + box1_point_order = jnp.expand_dims(box1_point_order, axis=-1) + + # Shift box1_point_order by box1_start_idx, so that the first index in + # box1_point_order is the downmost vertex in the box. + box1_point_order = jnp.mod( + box1_point_order + box1_start_idx, NUM_VERTICES_IN_BOX + ) + # Gather points from box1 in order. + # ordered_box1_points is of size [num_boxes, num_points_per_box * 2, 2]. + ordered_box1_points = jnp.take_along_axis( + box1_points, box1_point_order, axis=-2 + ) + + # Gather points from box2 as well. + box2_point_order = jnp.where(condition, point_order_1, point_order_2) + box2_point_order = jnp.expand_dims(box2_point_order, axis=-1) + box2_point_order = jnp.mod( + box2_point_order + box2_start_idx, NUM_VERTICES_IN_BOX + ) + ordered_box2_points = jnp.take_along_axis( + box2_points, box2_point_order, axis=-2 + ) + + minkowski_sum = ordered_box1_points + ordered_box2_points + return minkowski_sum + + +@jax.jit +def _get_downmost_edge_in_box(box: jax.Array) -> Tuple[jax.Array, jax.Array]: + """Finds the downmost (lowest y-coordinate) edge in the box. + + Note: We assume box edges are given in a counter-clockwise order, so that + the edge which starts with the downmost vertex (i.e. the downmost edge) is + uniquely identified. + + Args: + box: (num_boxes, num_points_per_box, 2). The last dimension contains the x-y + coordinates of corners in boxes. + + Returns: + A tuple of two tensors: + downmost_vertex_idx: The index of the downmost vertex, which is also the + index of the downmost edge. Shape: (num_boxes, 1, 1). + downmost_edge_direction: The tangent unit vector of the downmost edge, + pointing in the counter-clockwise direction of the box. + Shape: (num_boxes, 1, 2). + """ + # The downmost vertex is the lowest in the y dimension. + # Shape: (num_boxes, 1). + + NUM_BOX, NUM_VERTICES_IN_BOX, _ = box.shape + assert NUM_VERTICES_IN_BOX == 4, "Only support boxes" + downmost_vertex_idx = jnp.argmin(box[..., 1], axis=-1)[..., None, None] + + # Find the counter-clockwise point edge from the downmost vertex. + edge_start_vertex = jnp.take_along_axis(box, downmost_vertex_idx, axis=1) + # edge_start_vertex = box[np.arange(NUM_BOX), downmost_vertex_idx, :] + edge_end_idx = jnp.mod(downmost_vertex_idx + 1, NUM_VERTICES_IN_BOX) + edge_end_vertex = jnp.take_along_axis(box, edge_end_idx, axis=1) + # edge_end_vertex = box[np.arange(NUM_BOX), edge_end_idx, :] + + # Compute the direction of this downmost edge. + downmost_edge = edge_end_vertex - edge_start_vertex + downmost_edge_length = jnp.linalg.norm( + downmost_edge, axis=-1, keepdims=True + ) + downmost_edge_direction = downmost_edge / downmost_edge_length + return downmost_vertex_idx, downmost_edge_direction + + +@jax.jit +def _get_edge_info( + polygon_points: jax.Array, +) -> Tuple[jax.Array, jax.Array, jax.Array]: + """Computes properties about the edges of a polygon. + + Args: + polygon_points: Tensor containing the vertices of each polygon, with + shape (num_polygons, num_points_per_polygon, 2). Each polygon is assumed + to have an equal number of vertices. + + Returns: + tangent_unit_vectors: A unit vector in (x,y) with the same direction as + the tangent to the edge. Shape: (num_polygons, num_points_per_polygon, 2). + normal_unit_vectors: A unit vector in (x,y) with the same direction as + the normal to the edge. + Shape: (num_polygons, num_points_per_polygon, 2). + edge_lengths: Lengths of the edges. + Shape (num_polygons, num_points_per_polygon). + """ + # Shift the polygon points by 1 position to get the edges. + # Shape: (num_polygons, 1, 2). + first_point_in_polygon = polygon_points[:, 0:1, :] + # Shape: (num_polygons, num_points_per_polygon, 2). + shifted_polygon_points = jnp.concatenate( + [polygon_points[:, 1:, :], first_point_in_polygon], axis=-2 + ) + # Shape: (num_polygons, num_points_per_polygon, 2). + edge_vectors = shifted_polygon_points - polygon_points + + # Shape: (num_polygons, num_points_per_polygon). + edge_lengths = jnp.linalg.norm(edge_vectors, axis=-1) + # Shape: (num_polygons, num_points_per_polygon, 2). + tangent_unit_vectors = edge_vectors / jnp.expand_dims( + edge_lengths, axis=-1 + ) + # Shape: (num_polygons, num_points_per_polygon, 2). + normal_unit_vectors = jnp.stack( + [-tangent_unit_vectors[..., 1], tangent_unit_vectors[..., 0]], axis=-1 + ) + return tangent_unit_vectors, normal_unit_vectors, edge_lengths + + +@jax.jit +def signed_distance_from_point_to_convex_polygon( + query_points: jax.Array, polygon_points: jax.Array +) -> jax.Array: + """Finds the signed distances from query points to convex polygons. + + Each polygon is represented by a 2d tensor storing the coordinates of its + vertices. The vertices must be ordered in counter-clockwise order. An + arbitrary number of pairs (point, polygon) can be batched on the 1st + dimension. + + Note: Each polygon is associated to a single query point. + + Args: + query_points: (2). The last dimension is the x and y + coordinates of points. + polygon_points: (batch_size, num_points_per_polygon, 2). The last + dimension is the x and y coordinates of vertices. + + Returns: + A tensor containing the signed distances of the query points to the + polygons. Shape: (batch_size,). + """ + tangent_unit_vectors, normal_unit_vectors, edge_lengths = _get_edge_info( + polygon_points + ) + + # Expand the shape of `query_points` to (num_polygons, 1, 2), so that + # it matches the dimension of `polygons_points` for broadcasting. + # query_points = query_points[None, None, :] + query_points = jnp.expand_dims(query_points, axis=(0, 1)) + + # Compute query points to polygon points distances. + # Shape (num_polygons, num_points_per_polygon, 2). + vertices_to_query_vectors = query_points - polygon_points + + # Shape (num_polygons, num_points_per_polygon). + vertices_distances = jnp.linalg.norm(vertices_to_query_vectors, axis=-1) + + # Query point to edge distances are measured as the perpendicular distance + # of the point from the edge. If the projection of this point on to the edge + # falls outside the edge itself, this distance is not considered (as there) + # will be a lower distance with the vertices of this specific edge. + + # Make distances negative if the query point is in the inward side of the + # edge. Shape: (num_polygons, num_points_per_polygon). + edge_signed_perp_distances = jnp.sum( + -normal_unit_vectors * vertices_to_query_vectors, axis=-1 + ) + + # If `edge_signed_perp_distances` are all less than 0 for a + # polygon-query_point pair, then the query point is inside the convex polygon. + is_inside = jnp.all(edge_signed_perp_distances <= 0, axis=-1) + + # Project the distances over the tangents of the edge, and verify where the + # projections fall on the edge. + # Shape: (num_polygons, num_edges_per_polygon). + projection_along_tangent = jnp.sum( + tangent_unit_vectors * vertices_to_query_vectors, axis=-1 + ) + projection_along_tangent_proportion = ( + projection_along_tangent / edge_lengths + ) + + # Shape: (num_polygons, num_edges_per_polygon). + is_projection_on_edge = jnp.logical_and( + projection_along_tangent_proportion >= 0.0, + projection_along_tangent_proportion <= 1.0, + ) + + # If the point projection doesn't lay on the edge, set the distance to inf. + edge_perp_distances = jnp.abs(edge_signed_perp_distances) + edge_distances = jnp.where( + is_projection_on_edge, edge_perp_distances, np.inf + ) + + # Aggregate vertex and edge distances. + # Shape: (num_polyons, 2 * num_edges_per_polygon). + edge_and_vertex_distance = jnp.concatenate( + [edge_distances, vertices_distances], axis=-1 + ) + # Aggregate distances per polygon and change the sign if the point lays inside + # the polygon. Shape: (num_polygons,). + min_distance = jnp.min(edge_and_vertex_distance, axis=-1) + signed_distances = jnp.where(is_inside, -min_distance, min_distance) + + return signed_distances + + +@jax.jit +def compute_overlap( + pose_5dof: jax.Array, + mask: jax.Array, +) -> jax.Array: + """Computes the sigend distance between objects, negative means collision. + + Args: + pose_5dof: The pose of the objects at the current time step. + Shape: (num_objects, 5) + Must be in the format (x, y, length, width, yaw) + mask: The mask of the objects. Shape: (num_batch, num_objects, num_steps) + Returns: + The signed distance between objects. Shape: (num_batch, num_objects, num_steps, num_objects) + """ + A, _ = pose_5dof.shape + + # Shape: (A, 4, 2) + corners = geometry.corners_from_bboxes(pose_5dof) + + corners = jnp.expand_dims(corners, axis=1) # Shape: (A, 1, 4, 2) + # corners = current_traj.bbox_corners + corners_all = corners.repeat(A, axis=1) # Shape: (0:A, 1:A, 2:4, 3:2) + corners_all_transpose = corners_all.transpose( + (1, 0, 2, 3) + ) # Shape: (A, A, 4, 2) + corners_all_transpose = jax.lax.stop_gradient(corners_all_transpose) + + corners_all = corners_all.reshape(-1, 4, 2) + corners_all_transpose = corners_all_transpose.reshape(-1, 4, 2) + + minkowski_diff = minkowski_sum_of_box_and_box_points( + corners_all, -corners_all_transpose + ) + # (A*A,) + signed_distance = signed_distance_from_point_to_convex_polygon( + np.array([0, 0]), minkowski_diff + ) + + signed_distance = signed_distance.reshape(A, A) + + # Remove self-interaction + self_interaction = jnp.eye( + A, dtype=jnp.bool_ + ) # Shape: (num_objects, num_objects) + signed_distance = jnp.where(self_interaction, 1e3, signed_distance) + + # Remove Invalid objects + valid = jnp.outer(mask, mask) + valid = valid * ~self_interaction # Shape: (num_objects, num_objects) + signed_distance = jnp.where(valid, signed_distance, 1e3) + + return signed_distance # Shape: (A, A) diff --git a/gpudrive-main/gpudrive/integrations/vbd/sim_agent/guidance_metrics/tracking_metric.py b/gpudrive-main/gpudrive/integrations/vbd/sim_agent/guidance_metrics/tracking_metric.py new file mode 100644 index 0000000000000000000000000000000000000000..20a3022a21071f0779bb8cb60e47424e8d1eb437 --- /dev/null +++ b/gpudrive-main/gpudrive/integrations/vbd/sim_agent/guidance_metrics/tracking_metric.py @@ -0,0 +1,168 @@ +import torch +import torch.nn as nn +from torch.nn.functional import mse_loss, l1_loss, smooth_l1_loss + + +class TrackingReward(nn.Module): + def __init__(self, loss_fn=smooth_l1_loss): + super().__init__() + self.loss_fn = loss_fn + + def forward( + self, + traj_pred: torch.Tensor, + traj_ref: torch.Tensor, + weight: torch.Tensor = None, + **kwargs, + ): + """ + Forward pass of the metrics module. + + Args: + traj_pred (torch.Tensor): The input tensor. + traj_ref (torch.Tensor): The traj_reference tensor. + weight (torch.Tensor): The weight tensor. + + Returns: + torch.Tensor: The computed loss tensor. + + Raises: + AssertionError: If traj_pred and traj_ref do not have the same shape. + ValueError: If the weight shape is not compatible with traj_ref. + + """ + if weight is None: + weight = torch.ones_like(traj_ref) + + assert ( + traj_pred.shape[:-1] == traj_ref.shape[:-1] + ), f"traj_pred {traj_pred.shape} and traj_ref {traj_ref.shape} must have the same shape" + d = traj_ref.shape[-1] + + if len(weight.shape) == (len(traj_ref.shape) - 1): + weight = weight.unsqueeze(-1) + elif len(weight.shape) == len(traj_ref.shape): + assert ( + weight.shape[-1] == traj_ref.shape[-1] + ), "weight shape must be either (batch, seq) or same as traj_ref" + else: + raise ValueError( + "weight shape must be either (B, A, T) or same as traj_ref" + ) + + rewards = ( + -self.loss_fn( + input=traj_pred[..., :d], + target=traj_ref[..., :d], + reduction="none", + ) + * weight + ) + + return rewards + + +class GoalReward(nn.Module): + def __init__(self, loss_fn=smooth_l1_loss): + self.loss_fn = loss_fn + + super().__init__() + + def forward( + self, + traj_pred: torch.Tensor, + goal: torch.Tensor, + goal_mask: torch.Tensor = None, + **kwargs, + ): + """ + Forward pass of the metrics module. + + Args: + traj_pred (torch.Tensor): The input tensor. + traj_ref (torch.Tensor): The traj_reference tensor. + weight (torch.Tensor): The weight tensor. + + Returns: + torch.Tensor: The computed loss tensor. + + Raises: + AssertionError: If traj_pred and traj_ref do not have the same shape. + ValueError: If the weight shape is not compatible with traj_ref. + + """ + if goal_mask is None: + goal_mask = torch.ones_like(goal) + + d = goal.shape[-1] + look_ahead = kwargs.get("look_ahead", -1) + rewards = ( + -self.loss_fn( + input=traj_pred[..., look_ahead, :d], + target=goal, + reduction="none", + ) + * goal_mask + ) + + return rewards + + +class AnchorReward(nn.Module): # Does not work well + def __init__(self, loss_fn=smooth_l1_loss): + self.loss_fn = loss_fn + super().__init__() + + def forward( + self, + traj_pred: torch.Tensor, + traj_ref: torch.Tensor, + weight: torch.Tensor = None, + **kwargs, + ): + """ + Forward pass of the metrics module. + + Args: + traj_pred (torch.Tensor): The input tensor. [B, A, T, D] + traj_ref (torch.Tensor): The traj_reference tensor. [B, A, D] + weight (torch.Tensor): The weight tensor. [B, A] or [B, A, D] + + Returns: + torch.Tensor: The computed loss tensor. + + Raises: + AssertionError: If traj_pred and traj_ref do not have the same shape. + ValueError: If the weight shape is not compatible with traj_ref. + + """ + if weight is None: + weight = torch.ones_like(traj_ref) + + d = traj_ref.shape[-1] + if len(weight.shape) == (len(traj_ref.shape) - 1): + weight = weight.unsqueeze(-1) + elif len(weight.shape) == len(traj_ref.shape): + assert ( + weight.shape[-1] == traj_ref.shape[-1] + ), "weight shape must be either (batch, seq) or same as traj_ref" + else: + raise ValueError( + "weight shape must be either (B, A, T) or same as traj_ref" + ) + + traj_ref = traj_ref.unsqueeze(-2).repeat(1, 1, traj_pred.shape[-2], 1) + weight = weight.unsqueeze(-2).repeat(1, 1, traj_pred.shape[-2], 1) + + rewards = ( + -self.loss_fn( + input=traj_pred[..., :d], + target=traj_ref[..., :d], + reduction="none", + ) + * weight + ) + + rewards, _ = torch.min(torch.sum(rewards, dim=-1), dim=-1) + + return rewards diff --git a/gpudrive-main/gpudrive/integrations/vbd/sim_agent/sim_actor.py b/gpudrive-main/gpudrive/integrations/vbd/sim_agent/sim_actor.py new file mode 100644 index 0000000000000000000000000000000000000000..67ab81cd9ca0f3869173196774a25a18877d9f87 --- /dev/null +++ b/gpudrive-main/gpudrive/integrations/vbd/sim_agent/sim_actor.py @@ -0,0 +1,654 @@ +import torch +from torch import nn +from typing import Dict, Tuple +import numpy as np +from tqdm import tqdm +from gpudrive.integrations.vbd.model.VBD import VBD +from gpudrive.integrations.vbd.sim_agent.utils import * +from gpudrive.integrations.vbd.model.model_utils import roll_out +from gpudrive.integrations.vbd.sim_agent.guidance_metrics import * + + +class VBDTest(VBD): + def __init__( + self, + cfg: dict, + early_stop: int = 0, + skip: int = 1, + reward_func: nn.Module = None, + guidance_iter: int = 5, + guidance_end: int = 1, + guidance_start: int = 99, + gradient_scale: float = 1.0, + scale_grad_by_std: bool = True, + guide_mode: str = "waymo", + ): + """ + Initializes the SimActor object. + Args: + cfg (dict): Configuration dictionary. + early_stop (int, optional): Early stop parameter. Defaults to 0. + skip (int, optional): Skip diffusion step. Defaults to 1. + reward_func (nn.Module, optional): Reward function module. Defaults to None. + guidance_iter (int, optional): Guidance iteration parameter. Defaults to 5. + guidance_end (int, optional): Guidance end parameter. Defaults to 1. + guidance_start (int, optional): Guidance start parameter. Defaults to 99. + gradient_scale (float, optional): Gradient scale parameter. Defaults to 1.0. + scale_grad_by_std (bool, optional): Flag to scale gradient by standard deviation. Defaults to True. + guide_mode (str, optional): Guidance mode. Defaults to 'waymo'. + """ + super().__init__(cfg) + + # Parameters for the denoiser sampling + self.early_stop = early_stop + self.skip = skip + + # Parameters for the guidance function + self.reward_func = reward_func + self.guidance_iter = guidance_iter + self.guidance_start = guidance_start + self.guidance_end = guidance_end + self.gradient_scale = gradient_scale + self.scale_grad_by_std = scale_grad_by_std + + if guide_mode == "waymo": + self.guidance_func = self.waymo_guidance + elif guide_mode == "ctg": + self.guidance_func = self.ctg_guidance + + def reset_agent_length(self, _agents_len) -> None: + """ + Resets the number of the agent. + + Args: + _agents_len (int): The new length of the agent. + + Returns: + None + """ + + self._agents_len = _agents_len + if self.predictor is not None: + self.predictor.reset_agent_length(_agents_len) + + if self.denoiser is not None: + self.denoiser.reset_agent_length(_agents_len) + + ################### Testing Setup ################### + def inference_predictor(self, batch) -> Dict[str, torch.Tensor]: + """ + Perform inference using the predictor model. + + Args: + batch: The input batch for inference. + + Returns: + The output of the predictor model. + """ + if self.predictor is None: + raise RuntimeError("Predictor is not defined") + + batch = self.batch_to_device(batch, self.device) + encoder_outputs = self.encoder(batch) + goal_outputs = self.forward_predictor(encoder_outputs) + + return goal_outputs + + ################### Guidance ################### + def ctg_guidance( + self, x_t: torch.Tensor, c: dict, t: int, **kwargs + ) -> Tuple[Dict[str, torch.Tensor], torch.Tensor, Dict[str, np.ndarray]]: + """ + Performs guidance for the simulation actor using the method proposed by Controllable Traffic Generation. + Args: + x_t (torch.Tensor): The input tensor representing the normalized actions. + c (dict): The dictionary containing the encoder outputs. + t (int): The current timestep. + **kwargs: Additional keyword arguments. + Returns: + Tuple[Dict[str, torch.Tensor], torch.Tensor, Dict[str, np.ndarray]]: A tuple containing the denoiser output, the previous actions, and the guidance history. + Raises: + None + """ + + mu_guide = [] + traj_guide = [] + reward_guide = [] + grad_guide = [] + + # Denoise and step + denoiser_output = self.forward_denoiser( + encoder_outputs=c, + noised_actions_normalized=x_t, + diffusion_step=t, + ) + + x_0 = denoiser_output["denoised_actions_normalized"] + + mu = self.noise_scheduler.q_mean( + model_output=x_0, + timestep=t, + sample=x_t, + ) + mu_guide.append(mu.detach().cpu().numpy()) + std = self.noise_scheduler.q_variance(t) ** 0.5 + mu = mu.detach() + + with torch.enable_grad(): + mu.requires_grad_() + + if self.scale_grad_by_std: + lr = std * self.gradient_scale + else: + lr = self.gradient_scale + + optimizer = torch.optim.Adam([mu], lr=lr) + + for _ in range(self.guidance_iter): + optimizer.zero_grad() + noised_actions = self.unnormalize_actions(mu) + currnet_states = c["agents"][:, : self._agents_len, -1] + noised_trajs = roll_out( + currnet_states, + noised_actions, + action_len=self.denoiser._action_len, + global_frame=True, + ) + + total_rewards = 0 + for reward_func in self.reward_func: + reward = reward_func( + traj_pred=noised_trajs, + action_pred=noised_actions, + c=c, + **kwargs + ) + total_rewards += reward.sum() + + cost = -1.0 * total_rewards + cost.backward() + grad = mu.grad + optimizer.step() + + mu_guide.append(mu.detach().cpu().numpy()) + traj_guide.append(noised_trajs.detach().cpu().numpy()) + reward_guide.append(-cost.detach().cpu().numpy()) + grad_guide.append(grad.detach().cpu().numpy()) + + mu = mu.detach() + noise = torch.randn(mu.shape).type_as(mu) + x_t_prev = mu + noise * std + + guide_history = { + "t_guide": np.array(t), + "mu_guide": np.stack(mu_guide, axis=0), + "traj_guide": np.stack(traj_guide, axis=0), + "reward_guide": np.stack(reward_guide, axis=0), + "grad_guide": np.stack(grad_guide, axis=0), + } + + return denoiser_output, x_t_prev, guide_history + + def waymo_guidance( + self, x_t: torch.Tensor, c: dict, t: int, **kwargs + ) -> Tuple[Dict[str, torch.Tensor], torch.Tensor, Dict[str, np.ndarray]]: + """ + Perform guidance for the given input based on Motion Diffuser paper. + Args: + x_t (torch.Tensor): The input tensor. + c (dict): The dictionary of encoder outputs. + t (int): The diffusion step. + **kwargs: Additional keyword arguments. + Returns: + Tuple[Dict[str, torch.Tensor], torch.Tensor, Dict[str, np.ndarray]]: A tuple containing: + - denoiser_output (Dict[str, torch.Tensor]): The output of the denoiser. + - x_t_prev (torch.Tensor): The previous input tensor. + - guide_history (Dict[str, np.ndarray]): The history of guidance. + Raises: + None + """ + + # Save History + mu_guide = [] + x_0_guide = [] + traj_guide = [] + reward_guide = [] + grad_guide = [] + + # Denoise and step + denoiser_output = self.forward_denoiser( + encoder_outputs=c, + noised_actions_normalized=x_t, + diffusion_step=t, + ) + + x_0 = denoiser_output["denoised_actions_normalized"] + + mu = self.noise_scheduler.q_mean( + model_output=x_0, + timestep=t, + sample=x_t, + ).detach() + + mu_guide.append(mu.detach().cpu().numpy()) + std = self.noise_scheduler.q_variance(t) ** 0.5 + + for _ in range(self.guidance_iter): + with torch.enable_grad(): + mu.requires_grad_() + + guidance_denoiser_output = self.forward_denoiser( + encoder_outputs=c, + noised_actions_normalized=mu, + diffusion_step=t - 1, + ) + + traj_pred = guidance_denoiser_output["denoised_trajs"] + action_pred = guidance_denoiser_output["denoised_actions"] + action_pred_normalized = guidance_denoiser_output[ + "denoised_actions_normalized" + ] + + total_rewards = 0 + for reward_func in self.reward_func: + reward = reward_func( + traj_pred=traj_pred, + action_pred=action_pred, + action_normalized=action_pred_normalized, + c=c, + **kwargs + ) + total_rewards += reward.sum() + print("round: ", _, "reward: ", total_rewards.item()) + grad = torch.autograd.grad([total_rewards], [mu])[0] + + if self.scale_grad_by_std: + grad = grad * (std) + + mu = mu.detach() + grad.detach() * self.gradient_scale + + mu_guide.append(mu.detach().cpu().numpy()) + x_0_guide.append(action_pred_normalized.detach().cpu().numpy()) + traj_guide.append(traj_pred.detach().cpu().numpy()) + reward_guide.append(reward.detach().cpu().numpy()) + grad_guide.append(grad.detach().cpu().numpy()) + + noise = torch.randn(mu.shape).type_as(mu) + x_t_prev = mu + noise * std + + guide_history = { + "t_guide": np.array(t), + "mu_guide": np.stack(mu_guide, axis=0), + "x_0_guide": np.stack(x_0_guide, axis=0), + "traj_guide": np.stack(traj_guide, axis=0), + "reward_guide": np.stack(reward_guide, axis=0), + "grad_guide": np.stack(grad_guide, axis=0), + } + + return denoiser_output, x_t_prev, guide_history + + def ibr_guidance( + self, + x_t: torch.Tensor, + c: dict, + t: int, + ego_idx: int, + adv_idx: int, + other_idx: list = None, + ego_iter: int = 5, + adv_iter=5, + t_react: int = 81, + roadgraph_points=None, + adv_use_ctg: bool = False, + ego_use_ctg: bool = False, + **kwargs + ): + + """ + Perform Iterative Best Response (IBR) guidance for a simulated actor. + Args: + x_t (torch.Tensor): The input tensor representing the current state. + c (dict): A dictionary containing encoder outputs. + t (int): The current time step. + ego_idx (int): The index of the ego agent. + adv_idx (int): The index of the adversary agent. + other_idx (list, optional): A list of indices of other agents. Defaults to None. + ego_iter (int, optional): The number of iterations for the ego agent. Defaults to 5. + adv_iter (int, optional): The number of iterations for the adversary agent. Defaults to 5. + t_react (int, optional): The time step at which the agent starts reacting. Defaults to 81. + roadgraph_points (None, optional): Points representing the road graph. Defaults to None. + adv_use_ctg (bool, optional): Whether to use the cost-to-go (CTG) method for the adversary agent. Defaults to False. + ego_use_ctg (bool, optional): Whether to use the cost-to-go (CTG) method for the ego agent. Defaults to False. + **kwargs: Additional keyword arguments. + Returns: + Tuple: A tuple containing the denoiser output, the previous state, and the guidance history. + + vgd.guidance_iter = 5 + vgd.guidance_start = 99 + vgd.guidance_end = 1 + vgd.gradient_scale = 0.1 + vgd.scale_grad_by_std = True + """ + # Save History + mu_guide = [] + x_0_guide = [] + traj_guide = [] + reward_guide = [] + grad_guide = [] + + # Denoise and step + denoiser_output = self.forward_denoiser( + encoder_outputs=c, + noised_actions_normalized=x_t, + diffusion_step=t, + ) + + x_0 = denoiser_output["denoised_actions_normalized"] + + mu = self.noise_scheduler.q_mean( + model_output=x_0, + timestep=t, + sample=x_t, + ) + + mu_guide.append(mu.detach().cpu().numpy()) + std = self.noise_scheduler.q_variance(t) ** 0.5 + + for iter_round in range(self.guidance_iter): + for iter in range(ego_iter + adv_iter): + with torch.enable_grad(): + mu = mu.detach() + mu.requires_grad_() + if (iter < adv_iter and adv_use_ctg) or ( + iter >= adv_iter and ego_use_ctg + ): + method = "ctg" + noised_actions = self.unnormalize_actions(mu) + currnet_states = c["agents"][:, : self._agents_len, -1] + traj_pred = roll_out( + currnet_states, + noised_actions, + action_len=self.denoiser._action_len, + global_frame=True, + ) + gradient_scale = 0.1 * self.gradient_scale + else: + method = "waymo" + guidance_denoiser_output = self.forward_denoiser( + encoder_outputs=c, + noised_actions_normalized=mu, + diffusion_step=t - 1, + ) + traj_pred = guidance_denoiser_output["denoised_trajs"] + gradient_scale = self.gradient_scale + + if iter < adv_iter: + pursue_reward = -OverlapReward( + saturate=False, + ).forward( + traj_pred=traj_pred, + c=c, + aoi=[adv_idx, ego_idx], + **kwargs + ) # [B, A, T, A] + pursue_reward = torch.max( + pursue_reward[:, 0, :, 1], dim=-1 + )[ + 0 + ] # [B, T] + + # Positive reward for stay on road + onroad_reward = OnroadReward(weight=2).forward( + traj_pred=traj_pred, + c=c, + aoi=[adv_idx], + roadgraph_points=roadgraph_points, + ) # [B, 1, T] + + onroad_reward = torch.mean( + onroad_reward, dim=-1 + ) # [B] + + # Only care about getting close to adv + grad_mask = torch.zeros_like(mu) # [B, A, T, D] + grad_mask[:, adv_idx, :t_react, :] = 1 + + # Do a softmin over time + reward = pursue_reward + onroad_reward # [B] + print( + method, + " Adv round: ", + iter_round, + ", step: ", + iter, + "pursue reward: ", + round(pursue_reward.mean().item(), 3), + "onroad_reward: ", + round(onroad_reward.mean().item(), 3), + ) + else: + if other_idx is None: + aoi = None + ego_i = ego_idx + adv_i = adv_idx + else: + aoi = [adv_idx, ego_idx] + other_idx + ego_i = 1 + adv_i = 0 + + offset = 0.5 + # Ego Plays second + evasion_reward = OverlapReward( + offset=offset, + weight=1.0, + saturate=True, + ).forward( + traj_pred=traj_pred, c=c, aoi=aoi, **kwargs + ) # [B, A, T, A] + + evasion_reward[ + :, adv_i, :t_react, : + ] = 100 # adv Ignore collision before t_react + evasion_reward = evasion_reward.reshape( + *evasion_reward.shape[:2], -1 + ) # [B, A, T*A] + evasion_reward_min = torch.min(evasion_reward, dim=-1)[ + 0 + ] # [B, A] + onroad_reward = OnroadReward().forward( + traj_pred=traj_pred, + c=c, + aoi=aoi, + roadgraph_points=roadgraph_points, + ) # [B, A, T] + onroad_reward = torch.mean( + onroad_reward, dim=-1 + ) # [B, A] + # onroad_reward = torch.min(onroad_reward, dim=-1)[0] + + grad_mask = torch.ones_like(mu) # [B, A, T, D] + grad_mask[:, adv_idx, :t_react, :] = 0 + + # Do a softmin over time and other agents + reward = onroad_reward + evasion_reward_min * 15 + + print( + method, + " ego round: ", + iter_round, + ", step: ", + iter - adv_iter, + "ego evasion reward: ", + round( + evasion_reward_min[0, ego_i].item() + offset, 3 + ), + "adv evasion reward: ", + round( + evasion_reward_min[0, adv_i].item() + offset, 3 + ), + "onroad reward: ", + round(onroad_reward[0, ego_i].item(), 3), + ) + + grad = torch.autograd.grad([reward.sum()], [mu])[0] + grad = grad * grad_mask + if self.scale_grad_by_std: + grad = grad * (std) + mu = mu.detach() + grad.detach() * gradient_scale + # Clip the mu + mu = torch.clamp(mu, -self._action_max, self._action_max) + + mu_guide.append(mu.detach().cpu().numpy()) + # x_0_guide.append(guidance_denoiser_output['denoised_actions_normalized'].detach().cpu().numpy()) + traj_guide.append(traj_pred.detach().cpu().numpy()) + # reward_guide.append(reward.detach().cpu().numpy()) + grad_guide.append(grad.detach().cpu().numpy()) + + noise = torch.randn(mu.shape).type_as(mu) + x_t_prev = mu + noise * std + + guide_history = { + "t_guide": np.array(t), + "mu_guide": np.stack(mu_guide, axis=0), + # 'x_0_guide': np.stack(x_0_guide, axis=0), + "traj_guide": np.stack(traj_guide, axis=0), + # 'reward_guide': np.stack(reward_guide, axis=0), + "grad_guide": np.stack(grad_guide, axis=0), + } + + return denoiser_output, x_t_prev, guide_history + + ################### Denoising ################### + def step_denoiser( + self, x_t: torch.Tensor, c: dict, t: int, global_frame: bool = True + ): + """ + Perform a denoising step to sample x_{t-1} ~ P[x_{t-1} | x_t, D(x_t, c, t)]. + + Args: + x_t (torch.Tensor): The input tensor representing the current state. Shape: (num_batch, num_agent, num_action, action_dim) + c (dict): The conditional variable dictionary. + t (int): The number of diffusion steps. + + Returns: + denoiser_output (dict): The denoiser outputs. + x_t_prev (torch.Tensor): The tensor representing the previous noised action. Shape: (num_batch, num_agent, num_action, action_dim) + """ + + if self.denoiser is None: + raise RuntimeError("Denoiser is not defined") + + # Denoise to reconstruct x_0 ~ D(x_t, c, t) + denoiser_output = self.forward_denoiser( + encoder_outputs=c, + noised_actions_normalized=x_t, + diffusion_step=t, + global_frame=global_frame, + ) + + x_0 = denoiser_output["denoised_actions_normalized"] + + # Step to sample from P(x_t-1 | x_t, x_0) + x_t_prev = self.noise_scheduler.step( + model_output=x_0, + timestep=t, + sample=x_t, + ) + + return denoiser_output, x_t_prev + + @torch.no_grad() + def sample_denoiser( + self, + batch, + num_samples=1, + x_t=None, + use_tqdm=True, + global_frame=True, + **kwargs + ): + """ + Perform denoising inference on the given batch of data. + + Args: + batch (dict): The input batch of data. + guidance_func (callable, optional): A callable function that provides guidance for denoising. Defaults to None. + early_stop (int, optional): The index of the step at which denoising should stop. Defaults to 0. + skip (int, optional): The number of steps to skip between denoising iterations. Defaults to 1. + **kwargs: Additional keyword arguments for guidance. + Returns: + dict: The denoising outputs, including the history of noised action normalization. + + """ + # Encode the scene + batch = self.batch_to_device(batch, self.device) + encoder_outputs = self.encoder(batch) + + if num_samples > 1: + encoder_outputs = duplicate_batch(encoder_outputs, num_samples) + + agents_history = encoder_outputs["agents"] + num_batch, num_agent = agents_history.shape[:2] + num_step = self._future_len // self._action_len + action_dim = 2 + + diffusion_steps = list( + reversed( + range( + self.early_stop, self.noise_scheduler.num_steps, self.skip + ) + ) + ) + + # History + x_t_history = [] + denoiser_output_history = [] + guide_history = [] + + # Inital X_T + if x_t is None: + x_t = torch.randn( + num_batch, num_agent, num_step, action_dim, device=self.device + ) + else: + x_t = x_t.to(self.device) + + if use_tqdm: + diffusion_steps = tqdm( + diffusion_steps, total=len(diffusion_steps), desc="Diffusion" + ) + + for t in diffusion_steps: + x_t_history.append(x_t.detach().cpu().numpy()) + + if ( + t <= self.guidance_start + and t >= self.guidance_end + and self.guidance_func is not None + and self.reward_func is not None + ): + + denoiser_output, x_t, guide = self.guidance_func( + x_t=x_t, c=encoder_outputs, t=t, **kwargs + ) + guide_history.append(guide) + else: + denoiser_output, x_t = self.step_denoiser( + x_t=x_t, + c=encoder_outputs, + t=t, + global_frame=global_frame, + ) + guide = None + + denoiser_output_history.append( + torch_dict_to_numpy(denoiser_output) + ) + + denoiser_output["history"] = { + "x_t_history": np.stack(x_t_history, axis=0), + "denoiser_output_history": stack_dict(denoiser_output_history), + "guide_history": stack_dict(guide_history), + } + + return denoiser_output diff --git a/gpudrive-main/gpudrive/integrations/vbd/sim_agent/utils.py b/gpudrive-main/gpudrive/integrations/vbd/sim_agent/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..eb76c6a1856a4e3e747f55d962a6ff7ab0d0fc08 --- /dev/null +++ b/gpudrive-main/gpudrive/integrations/vbd/sim_agent/utils.py @@ -0,0 +1,49 @@ +import torch +import numpy as np + +# Parameters +MAX_NUM_OBJECTS = 32 +MAX_MAP_POINTS = 3000 +MAX_POLYLINES = 256 +MAX_TRAFFIC_LIGHTS = 16 +num_points_polyline = 30 + + +def duplicate_batch(batch: dict, num_samples: int): + """Duplicates the batch for the given number of samples.""" + for key, value in batch.items(): + if isinstance(value, torch.Tensor): + assert value.shape[0] == 1, "Only support batch size of 1" + batch[key] = torch.cat([value] * num_samples, dim=0) + + return batch + + +def torch_dict_to_numpy(input: dict): + output = {} + for key, value in input.items(): + if isinstance(value, torch.Tensor): + output[key] = value.detach().cpu().numpy() + else: + output[key] = value + return output + + +def stack_dict(input: list): + list_len = len(input) + if list_len == 0: + return {} + key_to_list = {} + for key in input[0].keys(): + key_to_list[key] = [input[i][key] for i in range(list_len)] + + output = {} + for key, value in key_to_list.items(): + if isinstance(value[0], np.ndarray): + output[key] = np.stack(value, axis=0) + elif isinstance(value[0], dict): + output[key] = stack_dict(value) + else: + output[key] = value + + return output diff --git a/gpudrive-main/gpudrive/integrations/vbd/viz/32_vbd_trajectory.png b/gpudrive-main/gpudrive/integrations/vbd/viz/32_vbd_trajectory.png new file mode 100644 index 0000000000000000000000000000000000000000..86c2910f03b7d6d232a26dd8fe1bbf1c53b05567 --- /dev/null +++ b/gpudrive-main/gpudrive/integrations/vbd/viz/32_vbd_trajectory.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a8bd8ad9ee99fe98a7d981257ead63f180a91d7a04fb635924d57a9981a2314a +size 32275 diff --git a/gpudrive-main/gpudrive/integrations/vbd/viz/train.gif b/gpudrive-main/gpudrive/integrations/vbd/viz/train.gif new file mode 100644 index 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sha256:f75c9e5a6d171920a141f258ae93dacd39b7038b50cf0184cf2a5876b2f3418c +size 50918822 diff --git a/gpudrive-main/gpudrive/networks/.gitkeep b/gpudrive-main/gpudrive/networks/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/gpudrive-main/gpudrive/networks/__init__.py b/gpudrive-main/gpudrive/networks/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/gpudrive-main/gpudrive/networks/basic_ffn.py b/gpudrive-main/gpudrive/networks/basic_ffn.py new file mode 100644 index 0000000000000000000000000000000000000000..5d2162c38f7de6dd7e69a97dcf3f019de50759cc --- /dev/null +++ b/gpudrive-main/gpudrive/networks/basic_ffn.py @@ -0,0 +1,112 @@ +import copy +from typing import Callable, Dict, List, Optional, Tuple, Type, Union + +import torch +from gymnasium import spaces +from stable_baselines3.common.policies import ActorCriticPolicy +from torch import nn + + +class FFN(nn.Module): + """Custom feedforward neural network.""" + + def __init__( + self, + feature_dim: int, + layers: List[int] = [128], + act_func: str = "tanh", + dropout: float = 0.0, + last_layer_dim_pi: int = 64, + last_layer_dim_vf: int = 64, + ): + super().__init__() + self.dropout = dropout + self.act_func = nn.Tanh() if act_func == "tanh" else nn.ReLU() + + # DON'T CHANGE: Save output dimensions, used to create the distributions + self.latent_dim_pi = last_layer_dim_pi + self.latent_dim_vf = last_layer_dim_vf + + # Actor network + self.actor_net = self._build_network( + input_dim=feature_dim, + net_arch=layers + [last_layer_dim_pi], + ) + + # Value network + self.critic_net = self._build_network( + input_dim=feature_dim, + net_arch=layers + [last_layer_dim_vf], + ) + + def _build_network( + self, + input_dim: int, + net_arch: List[int], + ) -> nn.Module: + """Build a network with the specified architecture.""" + layers = [] + last_dim = input_dim + for layer_dim in net_arch: + layers.append(nn.Linear(last_dim, layer_dim)) + layers.append(nn.Dropout(self.dropout)) + layers.append(nn.LayerNorm(layer_dim)) + layers.append(self.act_func) + last_dim = layer_dim + return nn.Sequential(*layers) + + def train(self, mode): + """Turn on updates to mean and standard deviation.""" + self.track_running_states = True + + def forward( + self, features: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Args: + features (torch.Tensor): input tensor of shape (batch_size, feature_dim) + Return: + (torch.Tensor, torch.Tensor) latent_policy, latent_value of the specified network. + If all layers are shared, then ``latent_policy == latent_value`` + """ + return self.forward_actor(features), self.forward_critic(features) + + def forward_actor(self, features: torch.Tensor) -> torch.Tensor: + """Forward step for the actor network.""" + return self.actor_net(features) + + def forward_critic(self, features: torch.Tensor) -> torch.Tensor: + """Forward step for the value network.""" + return self.critic_net(features) + + def update_running_mean_std(self, features: torch.Tensor) -> None: + """Update the mean and standard deviation.""" + self.mean = features.mean(dim=0) + self.std = features.std(dim=0) + + +class FeedForwardPolicy(ActorCriticPolicy): + def __init__( + self, + observation_space: spaces.Space, + action_space: spaces.Space, + lr_schedule: Callable[[float], float], + mlp_class: Type[FFN] = FFN, + *args, + **kwargs, + ): + # Disable orthogonal initialization + kwargs["ortho_init"] = False + self.mlp_class = mlp_class + super().__init__( + observation_space, + action_space, + lr_schedule, + # Pass remaining arguments to base class + *args, + **kwargs, + ) + + def _build_mlp_extractor(self) -> None: + # Build the network architecture + self.mlp_extractor = self.mlp_class(self.features_dim) diff --git a/gpudrive-main/gpudrive/networks/late_fusion.py b/gpudrive-main/gpudrive/networks/late_fusion.py new file mode 100644 index 0000000000000000000000000000000000000000..aa14b3e369761dd3d4207bd69b3759d4618f943a --- /dev/null +++ b/gpudrive-main/gpudrive/networks/late_fusion.py @@ -0,0 +1,248 @@ +import copy +from typing import List, Union +import torch +from torch import nn +from torch.distributions.utils import logits_to_probs +import pufferlib.models +from gpudrive.env import constants +from huggingface_hub import PyTorchModelHubMixin +from box import Box + +import madrona_gpudrive + +TOP_K_ROAD_POINTS = madrona_gpudrive.kMaxAgentMapObservationsCount + + +def log_prob(logits, value): + value = value.long().unsqueeze(-1) + value, log_pmf = torch.broadcast_tensors(value, logits) + value = value[..., :1] + return log_pmf.gather(-1, value).squeeze(-1) + + +def entropy(logits): + min_real = torch.finfo(logits.dtype).min + logits = torch.clamp(logits, min=min_real) + p_log_p = logits * logits_to_probs(logits) + return -p_log_p.sum(-1) + + +def sample_logits( + logits: Union[torch.Tensor, List[torch.Tensor]], + action=None, + deterministic=False, +): + """Sample logits: Supports deterministic sampling.""" + + normalized_logits = [logits - logits.logsumexp(dim=-1, keepdim=True)] + logits = [logits] + + if action is None: + if deterministic: + # Select the action with the maximum probability + action = torch.stack([l.argmax(dim=-1) for l in logits]) + else: + # Sample actions stochastically from the logits + action = torch.stack( + [ + torch.multinomial(logits_to_probs(l), 1).squeeze() + for l in logits + ] + ) + else: + batch = logits[0].shape[0] + action = action.view(batch, -1).T + + assert len(logits) == len(action) + + logprob = torch.stack( + [log_prob(l, a) for l, a in zip(normalized_logits, action)] + ).T.sum(1) + + logits_entropy = torch.stack( + [entropy(l) for l in normalized_logits] + ).T.sum(1) + + return action.squeeze(0), logprob.squeeze(0), logits_entropy.squeeze(0) + + +class NeuralNet( + nn.Module, + PyTorchModelHubMixin, + repo_url="https://github.com/Emerge-Lab/gpudrive", + docs_url="https://arxiv.org/abs/2502.14706", + tags=["ffn"], +): + def __init__( + self, + action_dim=91, # Default: 7 * 13 + input_dim=64, + hidden_dim=128, + dropout=0.00, + act_func="tanh", + max_controlled_agents=64, + obs_dim=2984, # Size of the flattened observation vector (hardcoded) + config=None, # Optional config + ): + super().__init__() + self.input_dim = input_dim + self.hidden_dim = hidden_dim + self.action_dim = action_dim + self.max_controlled_agents = max_controlled_agents + self.max_observable_agents = max_controlled_agents - 1 + self.obs_dim = obs_dim + self.num_modes = 3 # Ego, partner, road graph + self.dropout = dropout + self.act_func = nn.Tanh() if act_func == "tanh" else nn.GELU() + + # Indices for unpacking the observation + self.ego_state_idx = constants.EGO_FEAT_DIM + self.partner_obs_idx = ( + constants.PARTNER_FEAT_DIM * self.max_controlled_agents + ) + if config is not None: + self.config = Box(config) + if "reward_type" in self.config: + if self.config.reward_type == "reward_conditioned": + # Agents know their "type", consisting of three weights + # that determine the reward (collision, goal, off-road) + self.ego_state_idx += 3 + self.partner_obs_idx += 3 + + self.vbd_in_obs = self.config.vbd_in_obs + + # Calculate the VBD predictions size: 91 timesteps * 5 features = 455 + self.vbd_size = 91 * 5 + + self.ego_embed = nn.Sequential( + pufferlib.pytorch.layer_init( + nn.Linear(self.ego_state_idx, input_dim) + ), + nn.LayerNorm(input_dim), + self.act_func, + nn.Dropout(self.dropout), + pufferlib.pytorch.layer_init(nn.Linear(input_dim, input_dim)), + ) + + self.partner_embed = nn.Sequential( + pufferlib.pytorch.layer_init( + nn.Linear(constants.PARTNER_FEAT_DIM, input_dim) + ), + nn.LayerNorm(input_dim), + self.act_func, + nn.Dropout(self.dropout), + pufferlib.pytorch.layer_init(nn.Linear(input_dim, input_dim)), + ) + + self.road_map_embed = nn.Sequential( + pufferlib.pytorch.layer_init( + nn.Linear(constants.ROAD_GRAPH_FEAT_DIM, input_dim) + ), + nn.LayerNorm(input_dim), + self.act_func, + nn.Dropout(self.dropout), + pufferlib.pytorch.layer_init(nn.Linear(input_dim, input_dim)), + ) + + if self.vbd_in_obs: + self.vbd_embed = nn.Sequential( + pufferlib.pytorch.layer_init( + nn.Linear(self.vbd_size, input_dim) + ), + nn.LayerNorm(input_dim), + self.act_func, + nn.Dropout(self.dropout), + pufferlib.pytorch.layer_init(nn.Linear(input_dim, input_dim)), + ) + + self.shared_embed = nn.Sequential( + nn.Linear(self.input_dim * self.num_modes, self.hidden_dim), + nn.Dropout(self.dropout), + ) + + self.actor = pufferlib.pytorch.layer_init( + nn.Linear(hidden_dim, action_dim), std=0.01 + ) + self.critic = pufferlib.pytorch.layer_init( + nn.Linear(hidden_dim, 1), std=1 + ) + + def encode_observations(self, observation): + + if self.vbd_in_obs: + ( + ego_state, + road_objects, + road_graph, + vbd_predictions, + ) = self.unpack_obs(observation) + else: + ego_state, road_objects, road_graph = self.unpack_obs(observation) + + # Embed the ego state + ego_embed = self.ego_embed(ego_state) + + if self.vbd_in_obs: + vbd_embed = self.vbd_embed(vbd_predictions) + # Concatenate the VBD predictions with the ego state embedding + ego_embed = torch.cat([ego_embed, vbd_embed], dim=1) + + # Max pool + partner_embed, _ = self.partner_embed(road_objects).max(dim=1) + road_map_embed, _ = self.road_map_embed(road_graph).max(dim=1) + + # Concatenate the embeddings + embed = torch.cat([ego_embed, partner_embed, road_map_embed], dim=1) + + return self.shared_embed(embed) + + def forward(self, obs, action=None, deterministic=False): + + # Encode the observations + hidden = self.encode_observations(obs) + + # Decode the actions + value = self.critic(hidden) + logits = self.actor(hidden) + + action, logprob, entropy = sample_logits(logits, action, deterministic) + + return action, logprob, entropy, value + + def unpack_obs(self, obs_flat): + """ + Unpack the flattened observation into the ego state, visible simulator state. + + Args: + obs_flat (torch.Tensor): Flattened observation tensor of shape (batch_size, obs_dim). + + Returns: + tuple: If vbd_in_obs is True, returns (ego_state, road_objects, road_graph, vbd_predictions). + Otherwise, returns (ego_state, road_objects, road_graph). + """ + + # Unpack modalities + ego_state = obs_flat[:, : self.ego_state_idx] + partner_obs = obs_flat[:, self.ego_state_idx : self.partner_obs_idx] + + if self.vbd_in_obs: + # Extract the VBD predictions (last 455 elements) + vbd_predictions = obs_flat[:, -self.vbd_size :] + + # The rest (excluding ego_state and partner_obs) is the road graph + roadgraph_obs = obs_flat[:, self.partner_obs_idx : -self.vbd_size] + else: + # Without VBD, all remaining elements are road graph observations + roadgraph_obs = obs_flat[:, self.partner_obs_idx :] + + road_objects = partner_obs.view( + -1, self.max_observable_agents, constants.PARTNER_FEAT_DIM + ) + road_graph = roadgraph_obs.view( + -1, TOP_K_ROAD_POINTS, constants.ROAD_GRAPH_FEAT_DIM + ) + + if self.vbd_in_obs: + return ego_state, road_objects, road_graph, vbd_predictions + else: + return ego_state, road_objects, road_graph diff --git a/gpudrive-main/gpudrive/networks/perm_eq_late_fusion.py b/gpudrive-main/gpudrive/networks/perm_eq_late_fusion.py new file mode 100644 index 0000000000000000000000000000000000000000..266e99d94a80be6c52a6edcb1c4b093b581cc5c8 --- /dev/null +++ b/gpudrive-main/gpudrive/networks/perm_eq_late_fusion.py @@ -0,0 +1,259 @@ +import copy +from typing import Callable, Dict, List, Optional, Tuple, Type, Union +import os + +import torch +import torch.nn.functional as F +from box import Box +from gymnasium import spaces +from stable_baselines3.common.policies import ActorCriticPolicy +from torch import nn +import wandb + +# Import env wrapper that makes gym env compatible with stable-baselines3 +from gpudrive.env.wrappers.sb3_wrapper import SB3MultiAgentEnv +from gpudrive.env.config import EnvConfig +from gpudrive.env import constants + + +class LateFusionNet(nn.Module): + """Processes the env observation using a late fusion architecture.""" + + def __init__( + self, + observation_space: spaces.Box, + env_config: EnvConfig, + exp_config, + ): + super().__init__() + + self.config = env_config + self.net_config = exp_config + + # Unpack feature dimensions + self.ego_input_dim = constants.EGO_FEAT_DIM if self.config.ego_state else 0 + self.ro_input_dim = constants.PARTNER_FEAT_DIM if self.config.partner_obs else 0 + self.rg_input_dim = constants.ROAD_GRAPH_FEAT_DIM if self.config.road_map_obs else 0 + + self.ro_max = self.config.max_num_agents_in_scene-1 + self.rg_max = self.config.roadgraph_top_k + + # Network architectures + self.arch_ego_state = self.net_config.ego_state_layers + self.arch_road_objects = self.net_config.road_object_layers + self.arch_road_graph = self.net_config.road_graph_layers + self.arch_shared_net = self.net_config.shared_layers + self.act_func = ( + nn.Tanh() if self.net_config.act_func == "tanh" else nn.ReLU() + ) + self.dropout = self.net_config.dropout + + # Save output dimensions, used to create the action distribution & value + self.latent_dim_pi = self.net_config.last_layer_dim_pi + self.latent_dim_vf = self.net_config.last_layer_dim_vf + + # If using max pool across object dim + self.shared_net_input_dim = ( + self.net_config.ego_state_layers[-1] + + self.net_config.road_object_layers[-1] + + self.net_config.road_graph_layers[-1] + ) + + # Build the networks + # Actor network + self.actor_ego_state_net = self._build_network( + input_dim=self.ego_input_dim, + net_arch=self.arch_ego_state, + ) + self.actor_ro_net = self._build_network( + input_dim=self.ro_input_dim, + net_arch=self.arch_road_objects, + ) + self.actor_rg_net = self._build_network( + input_dim=self.rg_input_dim, + net_arch=self.arch_road_graph, + ) + self.actor_out_net = self._build_out_network( + input_dim=self.shared_net_input_dim, + output_dim=self.latent_dim_pi, + net_arch=self.arch_shared_net, + ) + + # Value network + self.val_ego_state_net = copy.deepcopy(self.actor_ego_state_net) + self.val_ro_net = copy.deepcopy(self.actor_ro_net) + self.val_rg_net = copy.deepcopy(self.actor_rg_net) + self.val_out_net = self._build_out_network( + input_dim=self.shared_net_input_dim, + output_dim=self.latent_dim_vf, + net_arch=self.arch_shared_net, + ) + + def _build_network( + self, + input_dim: int, + net_arch: List[int], + ) -> nn.Module: + """Build a network with the specified architecture.""" + layers = [] + last_dim = input_dim + for layer_dim in net_arch: + layers.append(nn.Linear(last_dim, layer_dim)) + layers.append(nn.Dropout(self.dropout)) + layers.append(nn.LayerNorm(layer_dim)) + layers.append(self.act_func) + last_dim = layer_dim + return nn.Sequential(*layers) + + def _build_out_network( + self, input_dim: int, output_dim: int, net_arch: List[int] + ): + """Create the output network architecture.""" + layers = [] + prev_dim = input_dim + for layer_dim in net_arch: + layers.append(nn.Linear(prev_dim, layer_dim)) + layers.append(nn.LayerNorm(layer_dim)) + layers.append(self.act_func) + layers.append(nn.Dropout(self.dropout)) + prev_dim = layer_dim + + # Add final layer + layers.append(nn.Linear(prev_dim, output_dim)) + layers.append(nn.LayerNorm(output_dim)) + + return nn.Sequential(*layers) + + def forward( + self, features: torch.Tensor + ) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Args: + features (torch.Tensor): input tensor of shape (batch_size, feature_dim) + Return: + (torch.Tensor, torch.Tensor) latent_policy, latent_value of the specified network. + If all layers are shared, then ``latent_policy == latent_value`` + """ + return self.forward_actor(features), self.forward_critic(features) + + def forward_actor(self, features: torch.Tensor) -> torch.Tensor: + """Forward step for the actor network.""" + + # Unpack observation + ego_state, road_objects, road_graph = self._unpack_obs(features) + + # Embed features + ego_state = self.actor_ego_state_net(ego_state) + road_objects = self.actor_ro_net(road_objects) + road_graph = self.actor_rg_net(road_graph) + + # Max pooling across the object dimension + # (M, E) -> (1, E) (max pool across features) + road_objects = F.max_pool1d( + road_objects.permute(0, 2, 1), kernel_size=self.ro_max + ).squeeze(-1) + road_graph = F.max_pool1d( + road_graph.permute(0, 2, 1), kernel_size=self.rg_max + ).squeeze(-1) + + # Concatenate processed ego state and observation and pass through the output layer + out = self.actor_out_net( + torch.cat((ego_state, road_objects, road_graph), dim=1) + ) + + return out + + def forward_critic(self, features: torch.Tensor) -> torch.Tensor: + """Forward step for the value network.""" + + ego_state, road_objects, road_graph = self._unpack_obs(features) + + # Embed features + ego_state = self.val_ego_state_net(ego_state) + road_objects = self.val_ro_net(road_objects) + road_graph = self.val_rg_net(road_graph) + + # Max pooling across the object dimension + # (M, E) -> (1, E) (max pool across features) + road_objects = F.max_pool1d( + road_objects.permute(0, 2, 1), kernel_size=self.ro_max + ).squeeze(-1) + road_graph = F.max_pool1d( + road_graph.permute(0, 2, 1), kernel_size=self.rg_max + ).squeeze(-1) + + # Concatenate processed ego state and observation and pass through the output layer + out = self.val_out_net( + torch.cat((ego_state, road_objects, road_graph), dim=1) + ) + + return out + + def _unpack_obs(self, obs_flat): + """ + Unpack the flattened observation into the ego state and visible state. + Args: + obs_flat (torch.Tensor): flattened observation tensor of shape (batch_size, obs_dim) + Return: + ego_state, road_objects, stop_signs, road_graph (torch.Tensor). + """ + + # Unpack ego and visible state + ego_state = obs_flat[:, : self.ego_input_dim] + vis_state = obs_flat[:, self.ego_input_dim :] + + # Visible state object order: road_objects, road_points + # Find the ends of each section + ro_end_idx = self.ro_input_dim * self.ro_max + rg_end_idx = ro_end_idx + (self.rg_input_dim * self.rg_max) + + # Unflatten and reshape to (batch_size, num_objects, object_dim) + road_objects = (vis_state[:, :ro_end_idx]).reshape( + -1, self.ro_max, self.ro_input_dim + ) + road_graph = (vis_state[:, ro_end_idx:rg_end_idx]).reshape( + -1, + self.rg_max, + self.rg_input_dim, + ) + + return ego_state, road_objects, road_graph + + +class LateFusionPolicy(ActorCriticPolicy): + def __init__( + self, + observation_space: spaces.Space, + env_config: Box, + exp_config: Box, + action_space: spaces.Space, + lr_schedule: Callable[[float], float], + mlp_class: Type[LateFusionNet] = LateFusionNet, + mlp_config: Optional[Box] = None, + *args, + **kwargs, + ): + # Disable orthogonal initialization + kwargs["ortho_init"] = False + self.observation_space = observation_space + self.env_config = env_config + self.exp_config = exp_config + self.mlp_class = mlp_class + self.mlp_config = mlp_config if mlp_config is not None else Box({}) + super().__init__( + observation_space, + action_space, + lr_schedule, + # Pass remaining arguments to base class + *args, + **kwargs, + ) + + def _build_mlp_extractor(self) -> None: + # Build the network architecture + self.mlp_extractor = self.mlp_class( + self.observation_space, + self.env_config, + self.exp_config, + **self.mlp_config, + ) diff --git a/gpudrive-main/gpudrive/utils/__init__.py b/gpudrive-main/gpudrive/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/gpudrive-main/gpudrive/utils/config.py b/gpudrive-main/gpudrive/utils/config.py new file mode 100644 index 0000000000000000000000000000000000000000..daef9c747587ffee820d0177f41bca665d2177e0 --- /dev/null +++ b/gpudrive-main/gpudrive/utils/config.py @@ -0,0 +1,15 @@ +from box import Box +import yaml + +def load_config(cfg: str) -> Box: + """Load configurations as a Box object. + Args: + cfg (str): Name of config file. + + Returns: + Box: Box representation of configurations. + """ + with open(f"{cfg}.yaml", "r") as stream: + config = Box(yaml.safe_load(stream)) + return config + diff --git a/gpudrive-main/gpudrive/utils/generate_sbatch.py b/gpudrive-main/gpudrive/utils/generate_sbatch.py new file mode 100644 index 0000000000000000000000000000000000000000..95850cca3d4039652abaae7e3cfd95e80b38d32f --- /dev/null +++ b/gpudrive-main/gpudrive/utils/generate_sbatch.py @@ -0,0 +1,304 @@ +""" +A script for generating sbatch array submission scripts. +Based on https://github.com/TysonRayJones/PythonTools/tree/master +""" +import numpy as np +import os +import re +from typing import Dict +from dotenv import load_dotenv + +# Load environment variables from .env file +load_dotenv() + +# Constants (from .env) +LOG_FOLDER = os.getenv("LOG_FOLDER") +HPC_ACCOUNT = os.getenv("NYU_HPC_ACCOUNT") +USERNAME = os.getenv("USERNAME") +SINGULARITY_IMAGE = os.getenv("SINGULARITY_IMAGE") +OVERLAY_FILE = os.getenv("OVERLAY_FILE") + +# Set to python file to run +RUN_FILE = "baselines/ppo/ppo_pufferlib.py" + +# Default SLURM fields +DEFAULT_SLURM_FIELDS = { + "num_nodes": 1, + "num_cpus": 1, + "num_gpus": 1, + "gpu_type": None, # --gres=gpu:1:rtx8000; logic: if gpu_type in supported list, add to end. If not supported list, throw exception, and if not provided, don't add GPU type + "memory": 10, + "memory_unit": "GB", + "time_d": 0, + "time_h": 0, + "time_m": 0, + "time_s": 0, + "max_sim_jobs": None, + "output": f"{LOG_FOLDER}output_%A_%a.txt", + "error": f"{LOG_FOLDER}error_%A_%a.txt", + "account": HPC_ACCOUNT, + "username": USERNAME, + "singularity_image": SINGULARITY_IMAGE, + "overlay_file": OVERLAY_FILE, + "run_file": RUN_FILE, +} + +# a template for the submit script +# (bash braces must be escaped by doubling: $var = ${{var}}) +# num_jobs, param_arr_init, param_val_assign and param_list are special fields + +TEMPLATE_SBATCH = """ +#!/bin/bash + +#SBATCH --array=0-{num_jobs}%{max_sim_jobs} +#SBATCH --job-name={job_name} +#SBATCH --output={output} +#SBATCH --error={error} +#SBATCH --mem={memory}{memory_unit} +#SBATCH --time={time_d}-{time_h}:{time_m}:{time_s} +#SBATCH --nodes={num_nodes} +#SBATCH --cpus-per-task={num_cpus} +#SBATCH --gres=gpu:{num_gpus} +#SBATCH --account={account} + +SINGULARITY_IMAGE={singularity_image} +OVERLAY_FILE=/scratch/{username}/{overlay_file} + +singularity exec --nv --overlay "${{OVERLAY_FILE}}:ro" \ + "${{SINGULARITY_IMAGE}}" \ + /bin/bash + +echo "Successfully launched image." + +{param_arr_init} + +trial=${{SLURM_ARRAY_TASK_ID}} +{param_val_assign} + +# Source the Conda setup script to make the `conda` command available +source /share/apps/anaconda3/2020.07/etc/profile.d/conda.sh + +# Activate conda environment +conda activate /scratch/{username}/.conda/gpudrive + +# Set up SSL certificates for wandb logging +export SSL_CERT_FILE=$(python -m certifi) +export REQUESTS_CA_BUNDLE=$(python -m certifi) + +# Run PPO +python {run_file} {param_cli_list} +""".strip() + + +def _mth(exp): + return "$(( %s ))" % exp + + +def _len(arr): + return "${{#%s[@]}}" % arr + + +def _get(arr, elem): + return "${{%s[%s]}}" % (arr, elem) + + +def _eq(var, val): + return "%s=%s" % (var, val) + + +def _op(a, op, b): + return _mth("%s %s %s" % (a, op, b)) + + +def _arr(arr): + return "( %s )" % " ".join(map(str, arr)) + + +def _seq(a, b, step): + return "($( seq %d %d %d ))" % (a, step, b) + + +def _var(var): + return "${%s}" % var + + +def _cli_var(var): + tmp = f"--{var}".replace("_", "-") + return f"{tmp}=${{{var}}}" + + +# Templates for param array construction and element access +PARAM_ARR = "{param}_values" +PARAM_EXPRS = { + "param_arr_init": _eq(PARAM_ARR, "{values}"), + "param_val_assign": { + "assign": _eq( + "{param}", _get(PARAM_ARR, _op("trial", "%", _len(PARAM_ARR))) + ), + "increment": _eq("trial", _op("trial", "/", _len(PARAM_ARR))), + }, +} + + +def _to_bash(obj): + if isinstance(obj, range): + return _seq(obj.start, obj.stop - 1, obj.step) + if isinstance(obj, list) or isinstance(obj, tuple): + return _arr(obj) + raise ValueError("Unknown object type %s" % type(obj).__name__) + + +def _get_params_bash(params, values): + # Get lines of bash code for creating/accessing param arrays + init_lines = [] + assign_lines = [] + init_temp = PARAM_EXPRS["param_arr_init"] + assign_temps = PARAM_EXPRS["param_val_assign"] + + for param, vals in zip(params, values): + init_lines.append(init_temp.format(param=param, values=_to_bash(vals))) + assign_lines.append(assign_temps["assign"].format(param=param)) + assign_lines.append(assign_temps["increment"].format(param=param)) + + # Remove superfluous final trial reassign + assign_lines.pop() + + return init_lines, assign_lines + + +def get_script( + fields: Dict = DEFAULT_SLURM_FIELDS, params: Dict = {}, param_order=None +): + """ + returns a string of a SLURM submission script using the passed fields + and which creates an array of jobs which sweep the given params + + fields: dict of SLURM field names to their values. type is ignored + params: a dict of (param names, param value list) pairs. + The param name is the name of the bash variable created in + the submission script which will contain the param's current + value (for that SLURM job instance). param value list is + a list (or range instance) of the values the param should take, + to be run once against every other possible configuration of all params. + param_order: a list containing all param names which indicates the ordering + of the params in the sweep. The last param changes every + job number. If not supplied, uses an arbitrary order + """ + + assert isinstance(fields, dict) + assert isinstance(params, dict) + assert ( + isinstance(param_order, list) + or isinstance(param_order, tuple) + or param_order == None + ) + if param_order == None: + param_order = list(params.keys()) + + # Check each field appears in the template + for field in fields: + if ("{%s}" % field) not in TEMPLATE_SBATCH: + raise ValueError("passed field %s unused in template" % field) + + # Calculate total number of jobs (minus 1; SLURM is inclusive) + num_jobs = 1 + for vals in params.values(): + num_jobs *= len(vals) + num_jobs -= 1 + + # Get bash code for param sweeping + init_lines, assign_lines = _get_params_bash( + param_order, [params[key] for key in param_order] + ) + + # Build template substitutions (overriding defaults) + subs = { + "param_arr_init": "\n".join(init_lines), + "param_val_assign": "\n".join(assign_lines), + "param_cli_list": " ".join(map(_cli_var, param_order)), + "num_jobs": num_jobs, + } + + for key, val in DEFAULT_SLURM_FIELDS.items(): + subs[key] = val + for key, val in fields.items(): + subs[key] = val + if "job_name" not in subs: + subs["job_name"] = "my_job" + + return TEMPLATE_SBATCH.format(**subs) + + +def save_script(filename, file_path, fields, params, param_order=None): + """Generate and save sbatch (.sh) submission script.""" + + sbatch_script = get_script(fields, params, param_order) + + if not file_path: + os.makedirs(os.path.dirname(file_path), exist_ok=True) + + with open(file_path + filename, "w") as file: + file.write(sbatch_script) + + +if __name__ == "__main__": + + group = "02_24_S10_000" + + fields = { + "time_h": 47, # Max time per job (job will finish if run is done before) + "num_gpus": 1, # GPUs per job + "max_sim_jobs": 30, # Max jobs at the same time + "memory": 70, + "job_name": group, + } + + hyperparams = { + "group": [group], # Group name + "num_worlds": [800], + "resample_scenes": [1], # Yes + "k_unique_scenes": [800], + "resample_interval": [5_000_000], + "total_timesteps": [4_000_000_000], + "resample_dataset_size": [10_000], + "batch_size": [524288], + "minibatch_size": [16384], + "update_epochs": [4], + "ent_coef": [0.001, 0.003, 0.0001], + "render": [0], + #"seed": [42, 3], + } + + save_script( + file_path="examples/experimental/sbatch_scripts/", + filename=f"sbatch_{group}.sh", + fields=fields, + params=hyperparams, + ) + + # hyperparams = { + # "group": [group], # Group name + # "num_worlds": [800], + # "resample_scenes": [1], # Yes + # "k_unique_scenes": [1000], # Sample in batches of 500 + # "resample_interval": [2_000_000], + # "total_timesteps": [3_000_000_000], + # "resample_dataset_size": [1000], + # "batch_size": [262_144, 524_288], + # "minibatch_size": [16_384], + # "update_epochs": [2, 4, 5], + # "ent_coef": [0.0001, 0.001, 0.003], + # "learning_rate": [1e-4, 3e-4], + # "gamma": [0.99], + # "render": [0], + # } + + # save_script( + # file_path="examples/experimental/sbatch_scripts/", + # filename=f"sbatch_{group}.sh", + # fields=fields, + # params=hyperparams, + # ) + + + diff --git a/gpudrive-main/gpudrive/utils/geometry.py b/gpudrive-main/gpudrive/utils/geometry.py new file mode 100644 index 0000000000000000000000000000000000000000..8201b0906a1dbe32aad40809e3a8cc5f140eaa6f --- /dev/null +++ b/gpudrive-main/gpudrive/utils/geometry.py @@ -0,0 +1,36 @@ +def restore_mean(x, y, mean_x, mean_y): + """ + In GPUDrive, everything is centered at zero by subtracting the mean. + This function reapplies the mean to go back to the original coordinates. + The mean (xyz) is exported per world as world_means_tensor. + Args: + x (torch.Tensor): x coordinates + y (torch.Tensor): y coordinates + mean_x (torch.Tensor): mean of x coordinates. Shape: (num_worlds, 1) + mean_y (torch.Tensor): mean of y coordinates. Shape: (num_worlds, 1) + """ + return x + mean_x, y + mean_y + + +def normalize_min_max(tensor, min_val, max_val): + """Normalizes an array of values to the range [-1, 1]. + + Args: + x (np.array): Array of values to normalize. + min_val (float): Minimum value for normalization. + max_val (float): Maximum value for normalization. + + Returns: + np.array: Normalized array of values. + """ + return 2 * ((tensor - min_val) / (max_val - min_val)) - 1 + + +def normalize_min_max_inplace(tensor, min_val, max_val): + """Normalizes an array of values to the range [-1, 1]. + Args: + x (np.array): Array of values to normalize. + min_val (float): Minimum value for normalization. + max_val (float): Maximum value for normalization. + """ + tensor.sub_(min_val).div_(max_val - min_val).mul_(2).sub_(1) \ No newline at end of file diff --git a/gpudrive-main/gpudrive/utils/multi_policy_rollout.py b/gpudrive-main/gpudrive/utils/multi_policy_rollout.py new file mode 100644 index 0000000000000000000000000000000000000000..20985eaadd86f02acd840215311c9aac726c2034 --- /dev/null +++ b/gpudrive-main/gpudrive/utils/multi_policy_rollout.py @@ -0,0 +1,195 @@ +import torch +import pandas as pd +from gpudrive.visualize.utils import img_from_fig +from gpudrive.datatypes.observation import GlobalEgoState + +def multi_policy_rollout( + env, + policies, + device, + deterministic: bool = False, + render_sim_state: bool = False, + render_every_n_steps: int = 1, + zoom_radius: int = 100, + return_agent_positions: bool = False, + center_on_ego: bool = False, +): + """ + Perform a rollout of multiple policies in the environment. + + Args: + env: The simulation environment. + policies (dict): Dictionary of policies {policy_name: (policy_function,mask)}. + device: The device to execute computations on (CPU/GPU). + policy_masks (dict): Dictionary of policy masks {policy_name: mask_tensor}. + deterministic (bool): Whether to use deterministic policy actions. + return_agent_positions (bool): Whether to return agent positions. + + Returns: + policy_metrics: Dictionary of metrics corresponding to policies {policy_name: metrics(dict)} + metrics: { + 'goal_achieved', 'collided', 'off_road', 'off_road_count', 'collided_count', 'goal_achieved_count', 'frac_off_road', 'frac_collided', 'frac_goal_achieved' + } + + """ + + # Initialize storage + num_worlds = env.num_worlds + max_agent_count = env.max_agent_count + episode_len = env.config.episode_len + sim_state_frames = {env_id: [] for env_id in range(num_worlds)} + agent_positions = torch.zeros((num_worlds, max_agent_count, episode_len, 2)) + + # Reset environment + next_obs = env.reset() + policy_metrics = { + policy_name: { + "goal_achieved": torch.zeros((num_worlds, max_agent_count), device=device), + "collided": torch.zeros((num_worlds, max_agent_count), device=device), + "off_road": torch.zeros((num_worlds, max_agent_count), device=device), + } + for policy_name in policies + } + episode_lengths = torch.zeros(num_worlds) + + active_worlds = list(range(num_worlds)) + control_mask = env.cont_agent_mask + live_agent_mask = control_mask.clone() + + for time_step in range(episode_len): + print(f't: {time_step}') + + policy_live_masks = {name: mask & live_agent_mask for name, (policy_fn,mask) in policies.items()} + + + actions = {} + for policy_name, (policy_fn,policy_mask) in policies.items(): + live_mask = policy_live_masks[policy_name] + if live_mask.any(): + actions[policy_name], _, _, _ = policy_fn( + next_obs[live_mask], deterministic=deterministic + ) + + + combined_mask = torch.zeros_like(live_agent_mask, dtype=torch.bool) + for live_mask in policy_live_masks.values(): + combined_mask |= live_mask + assert torch.all(live_agent_mask == combined_mask), "Live agent mask mismatch!" + + action_template = torch.zeros((num_worlds, max_agent_count), dtype=torch.int64, device=device) + + # Assign actions based on policy masks + for policy_name, action in actions.items(): + live_mask = policy_live_masks[policy_name] + if action.numel() > 0: + action_template[live_mask] = action.to(dtype=action_template.dtype, device=device) + + # Step environment + env.step_dynamics(action_template) + + if render_sim_state and len(active_worlds) > 0: + + has_live_agent = torch.where( + live_agent_mask[active_worlds, :].sum(axis=1) > 0 + )[0].tolist() + + if time_step % render_every_n_steps == 0: + if center_on_ego: + agent_indices = torch.argmax(control_mask.to(torch.uint8), dim=1).tolist() + else: + agent_indices = None + + sim_state_figures = env.vis.plot_simulator_state( + env_indices=has_live_agent, + time_steps=[time_step] * len(has_live_agent), + zoom_radius=zoom_radius, + center_agent_indices=agent_indices, + policy_masks=policies + ) + for idx, env_id in enumerate(has_live_agent): + sim_state_frames[env_id].append( + img_from_fig(sim_state_figures[idx]) + ) + + + # Update observations and agent statuses + next_obs = env.get_obs() + dones = env.get_dones().bool() + infos = env.get_infos() + + for policy_name, live_mask in policy_live_masks.items(): + policy_metrics[policy_name]["off_road"][live_mask] += infos.off_road[live_mask] + policy_metrics[policy_name]["collided"][live_mask] += infos.collided[live_mask] + policy_metrics[policy_name]["goal_achieved"][live_mask] += infos.goal_achieved[live_mask] + + live_agent_mask[dones] = False + + # Process completed worlds + num_dones_per_world = (dones & control_mask).sum(dim=1) + total_controlled_agents = control_mask.sum(dim=1) + done_worlds = (num_dones_per_world == total_controlled_agents).nonzero(as_tuple=True)[0] + + for world in done_worlds: + if world in active_worlds: + active_worlds.remove(world) + episode_lengths[world] = time_step + + if return_agent_positions: + global_agent_states = GlobalEgoState.from_tensor(env.sim.absolute_self_observation_tensor()) + agent_positions[:, :, time_step, 0] = global_agent_states.pos_x + agent_positions[:, :, time_step, 1] = global_agent_states.pos_y + + if not active_worlds: + break + + controlled_per_scene = sum(mask.sum(dim=1).float() for policy_name,(policy_fn,mask) in policies.items()) + + + + metrics =compute_metrics(policy_metrics,policy_live_masks,controlled_per_scene) + + if render_sim_state: + return metrics, sim_state_frames + + return metrics + +def compute_metrics(policy_metrics,policy_live_masks,controlled_per_scene): + + for policy_name, live_mask in policy_live_masks.items(): + + policy_metrics[policy_name]['off_road_count'] = ( policy_metrics[policy_name]["off_road"] > 0).float().sum(axis=1) + policy_metrics[policy_name]['collided_count'] = (policy_metrics[policy_name]["collided"] > 0).float().sum(axis=1) + policy_metrics[policy_name]['goal_achieved_count'] = (policy_metrics[policy_name]['goal_achieved'] > 0).float().sum(axis=1) + + policy_metrics[policy_name]['frac_off_road'] = policy_metrics[policy_name]['off_road_count'] / controlled_per_scene + policy_metrics[policy_name]['frac_collided'] = policy_metrics[policy_name]['collided_count'] / controlled_per_scene + policy_metrics[policy_name]['frac_goal_achieved'] = policy_metrics[policy_name]['goal_achieved_count'] / controlled_per_scene + + + return policy_metrics + + +def create_data_table(data): + # Extract unique policies + policies = sorted(set(policy for pair in data.keys() for policy in pair)) + + # Create empty DataFrames + collisions_table = pd.DataFrame(index=policies, columns=policies) + off_roads_table = pd.DataFrame(index=policies, columns=policies) + goal_achieved_table = pd.DataFrame(index=policies, columns=policies) + + # Populate DataFrames + for (p1, p2), metrics in data.items(): + collisions_table.loc[p1, p2] = metrics['frac_collided'].item() + off_roads_table.loc[p1, p2] = metrics['frac_off_road'].item() + goal_achieved_table.loc[p1, p2] = metrics['frac_goal_achieved'].item() + + # Print Tables + print("Average Collisions Table:") + print(collisions_table, "\n") + + print("Average Off Roads Table:") + print(off_roads_table, "\n") + + print("Average Goal Achieved Table:") + print(goal_achieved_table, "\n") diff --git a/gpudrive-main/gpudrive/utils/push_checkpoint_to_huggingface.py b/gpudrive-main/gpudrive/utils/push_checkpoint_to_huggingface.py new file mode 100644 index 0000000000000000000000000000000000000000..7f40c88a5946cdd481529463a83ad5276d011790 --- /dev/null +++ b/gpudrive-main/gpudrive/utils/push_checkpoint_to_huggingface.py @@ -0,0 +1,34 @@ +import torch +from huggingface_hub import login +from gpudrive.utils.config import load_config +from gpudrive.networks.late_fusion import NeuralNet + +login() + +config = load_config("examples/experimental/config/reliable_agents_params") +checkpoint_path = "examples/experimental/models/model_PPO____R_10000__02_27_09_19_10_626_003200.pt" + +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +saved_cpt = torch.load( + checkpoint_path, map_location=device, weights_only=False +) + +# Reconstruct the model +policy = NeuralNet( + input_dim=saved_cpt["model_arch"]["input_dim"], + action_dim=saved_cpt["action_dim"], + hidden_dim=saved_cpt["model_arch"]["hidden_dim"], + config=config, +).to(device) + +# Load the model parameters +policy.load_state_dict(saved_cpt["parameters"]) + +# Set model to eval mode +policy.eval() + +# policy.save_pretrained("policy_test") # Saves both the model weights and config.json + +# Push to Hugging Face Hub +print("Pushing model to Hugging Face...") +policy.push_to_hub("daphne-cornelisse/policy_S10_000_02_27") diff --git a/gpudrive-main/gpudrive/visualize/color.py b/gpudrive-main/gpudrive/visualize/color.py new file mode 100644 index 0000000000000000000000000000000000000000..e249dadf82b5eb736b7dc714ff9bcac949f7a5cc --- /dev/null +++ b/gpudrive-main/gpudrive/visualize/color.py @@ -0,0 +1,38 @@ +import madrona_gpudrive +import numpy as np + +dark_red = np.array([139, 0, 0]) / 255.0 +dark_grey = "#000000" # np.array([80, 80, 80]) / 255.0 + +ROAD_GRAPH_COLORS = { + 1: dark_grey, # 'RoadEdgeBoundary' (Gray) + 2: np.array([120, 120, 120]) + / 255.0, # 'RoadLine-BrokenSingleYellow' (Light Purple) + 3: np.array([230, 230, 230]) / 255.0, # 'LaneCenter-Freeway' (Light Gray) + 4: np.array([200, 200, 200]) / 255.0, # 'Crosswalk' (Light Gray) + 5: np.array([0.85, 0.65, 0.13]), # 'SpeedBump' (Dark yellow) + 6: np.array([255, 0, 0]) / 255.0, # 'StopSign' (Red) +} + +ROAD_GRAPH_TYPE_NAMES = { # 1-6 ; 0 is None (padding) + int(madrona_gpudrive.EntityType.RoadEdge): "Road edge", + int(madrona_gpudrive.EntityType.RoadLine): "Road line", + int(madrona_gpudrive.EntityType.RoadLane): "Lane center", + int(madrona_gpudrive.EntityType.CrossWalk): "Crosswalk", + int(madrona_gpudrive.EntityType.SpeedBump): "Speed bump", + int(madrona_gpudrive.EntityType.StopSign): "Stop sign", +} + +AGENT_COLOR_BY_STATE = { + "ok": "#4B77BE", # Controlled and doing fine + "collided": "r", # Controlled and collided + "off_road": "orange", # Controlled and off-road + "log_replay": "#c7c7c7", # Agents marked as expert controlled or static +} + +REL_OBS_OBJ_COLORS = { + "ego": "#0066ff", + "ego_goal": "#0099cc", + "other_agents": "#ff884d", +} +AGENT_COLOR_BY_POLICY= ['g','b','p'] \ No newline at end of file diff --git a/gpudrive-main/gpudrive/visualize/core.py b/gpudrive-main/gpudrive/visualize/core.py new file mode 100644 index 0000000000000000000000000000000000000000..25fd506d36d55b7ac1a12d750b35a72e9f9d0052 --- /dev/null +++ b/gpudrive-main/gpudrive/visualize/core.py @@ -0,0 +1,1613 @@ +import torch +import matplotlib + +matplotlib.use("Agg") +from typing import Tuple, Optional, List, Dict, Any, Union +import seaborn as sns +import matplotlib.pyplot as plt +from matplotlib.patches import Circle +from matplotlib.collections import LineCollection +from mpl_toolkits.mplot3d import Axes3D +from mpl_toolkits.mplot3d.art3d import Poly3DCollection, Line3DCollection +from matplotlib.colors import ListedColormap +import numpy as np +import madrona_gpudrive +from gpudrive.visualize import utils +from gpudrive.datatypes.roadgraph import ( + LocalRoadGraphPoints, + GlobalRoadGraphPoints, +) +from gpudrive.datatypes.observation import ( + LocalEgoState, + GlobalEgoState, + PartnerObs, +) +from gpudrive.datatypes.trajectory import LogTrajectory +from gpudrive.datatypes.control import ResponseType +from gpudrive.visualize.color import ( + ROAD_GRAPH_COLORS, + ROAD_GRAPH_TYPE_NAMES, + REL_OBS_OBJ_COLORS, + AGENT_COLOR_BY_STATE, + AGENT_COLOR_BY_POLICY, +) + +OUT_OF_BOUNDS = 1000 + +try: + import jax + + JAX_ARRAY_TYPES = (jax.Array,) +except (ImportError, AttributeError): + try: + from jaxlib.xla_extension import ArrayImpl + + JAX_ARRAY_TYPES = (ArrayImpl,) + except ImportError: + JAX_ARRAY_TYPES = () + + +class MatplotlibVisualizer: + def __init__( + self, + sim_object, + controlled_agent_mask, + goal_radius, + backend: str, + num_worlds: int, + render_config: Dict[str, Any], + env_config: Dict[str, Any], + ): + self.sim_object = sim_object + self.backend = "torch" # Always use torch or np for plotting + self.device = "cpu" + self.goal_radius = goal_radius + self.num_worlds = num_worlds + self.render_config = render_config + self.figsize = (15, 15) + self.env_config = env_config + self.render_3d = render_config.render_3d + self.vehicle_height = ( + render_config.vehicle_height + ) # Default vehicle height + self.initialize_static_scenario_data(controlled_agent_mask) + + def initialize_static_scenario_data(self, controlled_agent_mask): + """ + Initialize key information for visualization based on the + current batch of scenarios. + """ + self.response_type = ResponseType.from_tensor( + tensor=self.sim_object.response_type_tensor(), + backend=self.backend, + device=self.device, + ) + self.global_roadgraph = GlobalRoadGraphPoints.from_tensor( + roadgraph_tensor=self.sim_object.map_observation_tensor(), + backend=self.backend, + device=self.device, + ) + self.controlled_agent_mask = controlled_agent_mask + + if JAX_ARRAY_TYPES and isinstance(controlled_agent_mask, JAX_ARRAY_TYPES): + self.controlled_agent_mask = torch.from_numpy( + np.array(controlled_agent_mask) + ) + + self.controlled_agent_mask = self.controlled_agent_mask.to(self.device) + + self.log_trajectory = LogTrajectory.from_tensor( + self.sim_object.expert_trajectory_tensor(), + self.num_worlds, + self.controlled_agent_mask.shape[1], + backend=self.backend, + ) + + def plot_simulator_state( + self, + env_indices: List[int], + time_steps: Optional[List[int]] = None, + center_agent_indices: Optional[List[int]] = None, + zoom_radius: int = 100, + plot_log_replay_trajectory: bool = False, + agent_positions: Optional[torch.Tensor] = None, + backward_goals: bool = False, + policy_masks: Optional[Dict[int,Dict[str,torch.Tensor]]] = None, + ): + """ + Plot simulator states for one or multiple environments. + + Args: + env_indices: List of environment indices to plot. + time_steps: Optional list of time steps corresponding to each environment. + center_agent_indices: Optional list of center agent indices for zooming. + figsize: Tuple for figure size of each subplot. + zoom_radius: Radius for zooming in around the center agent. + plot_log_replay_trajectory: If True, plots the log replay trajectory. + agent_positions: Optional tensor to plot rolled out agent positions. + backward_goals: If True, plots backward goals for controlled agents. + policy_mask: dict + A dictionary that maps policies to world and specifies which agents are assigned to each policy. + For now maximum number of policies is 3 as there are only 3 colors in COLOR_AGENT_BY_POLICY + The structure follows the format: {Policy Name: (Policy Function,mask) }, where: + - Policy (str): The policy assigned to agents within the world. + + - Policy Function (Neural Network): The identifier for the simulation environment. + + - Mask (torch.Tensor): A boolean or index-based mask indicating which agents follow the given policy, for all worlds. + """ + if not isinstance(env_indices, list): + env_indices = [env_indices] # Ensure env_indices is a list + + if time_steps is None: + time_steps = [None] * len(env_indices) # Default to None for all + if center_agent_indices is None: + center_agent_indices = [None] * len( + env_indices + ) # Default to None for all + + # Changes at every time step + global_agent_states = GlobalEgoState.from_tensor( + self.sim_object.absolute_self_observation_tensor(), + backend=self.backend, + device=self.device, + ) + + if backward_goals: + + # Get world means for coordinate transformation + means_xy = ( + self.sim_object.world_means_tensor() + .to_torch()[:, :2] + .to(self.device) + ) + + # Create extended goals dictionary + extended_goals = { + "x": torch.zeros_like(global_agent_states.goal_x), + "y": torch.zeros_like(global_agent_states.goal_y), + } + # Generate reverse offsets for controlled agents + for env_idx in env_indices: + controlled_mask = self.controlled_agent_mask[env_idx] + + # Calculate direction vectors for each agent (from initial position to original goal) + direction_x = ( + global_agent_states.goal_x[env_idx] + - global_agent_states.pos_x[env_idx] + ) + direction_y = ( + global_agent_states.goal_y[env_idx] + - global_agent_states.pos_y[env_idx] + ) + + # Store extended goals - place them in opposite direction from current position + # For controlled agents, the new goal will be behind them relative to their original goal + extended_goals["x"][env_idx] = ( + global_agent_states.pos_x[env_idx] - direction_x + ) + extended_goals["y"][env_idx] = ( + global_agent_states.pos_y[env_idx] - direction_y + ) + + # Only modify goals for controlled agents + uncontrolled_mask = ~controlled_mask + extended_goals["x"][ + env_idx, uncontrolled_mask + ] = global_agent_states.goal_x[env_idx, uncontrolled_mask] + extended_goals["y"][ + env_idx, uncontrolled_mask + ] = global_agent_states.goal_y[env_idx, uncontrolled_mask] + + # Print information for controlled agents + for agent_idx in torch.where(controlled_mask)[0]: + # Get original goal in world coordinates + orig_goal_x = ( + global_agent_states.goal_x[env_idx, agent_idx] + + means_xy[env_idx, 0] + ) + orig_goal_y = ( + global_agent_states.goal_y[env_idx, agent_idx] + + means_xy[env_idx, 1] + ) + + # Get extended goal in world coordinates + ext_goal_x = ( + extended_goals["x"][env_idx, agent_idx] + + means_xy[env_idx, 0] + ) + ext_goal_y = ( + extended_goals["y"][env_idx, agent_idx] + + means_xy[env_idx, 1] + ) + + print( + f"Agent ID: {global_agent_states.id[env_idx, agent_idx].item()}" + ) + print( + f"Original goal (world coords): ({orig_goal_x.item():.6f}, {orig_goal_y.item():.6f})" + ) + print( + f"Extended goal (world coords): ({ext_goal_x.item():.6f}, {ext_goal_y.item():.6f})" + ) + print( + f"World mean: ({means_xy[env_idx, 0].item():.6f}, {means_xy[env_idx, 1].item():.6f})\n" + ) + + else: + extended_goals = None + + agent_infos = ( + self.sim_object.info_tensor().to_torch().clone().to(self.device) + ) + + figs = [] + + # Calculate scale factors based on figure size + marker_scale = max(self.figsize) / 15 + line_width_scale = max(self.figsize) / 15 + + + if policy_masks: + + world_based_policy_mask = {} + + for policy_name, (fn,mask) in policy_masks.items(): + for world in range(mask.shape[0]): + if world not in world_based_policy_mask: + world_based_policy_mask[world] = {} + world_based_policy_mask[world][policy_name] = mask[world] + + else: + world_based_policy_mask = None + + # Iterate over each environment index + for idx, (env_idx, time_step, center_agent_idx) in enumerate( + zip(env_indices, time_steps, center_agent_indices) + ): + + # Initialize figure and axes from cached road graph + fig, ax = plt.subplots( + figsize=self.figsize, + subplot_kw={"projection": "3d"} if self.render_3d else {}, + ) + if self.render_3d: + ax.view_init(elev=30, azim=45) # Set default 3D view angle + fig.subplots_adjust(left=0, right=1, bottom=0, top=1) + ax.clear() # Clear any existing content + ax.set_aspect("equal", adjustable="box") + figs.append(fig) # Add the new figure + plt.close(fig) # Close the figure to prevent carryover + + # Get control mask and omit out-of-bound agents (dead agents) + controlled = self.controlled_agent_mask[env_idx, :] + controlled_live = controlled & ( + torch.abs(global_agent_states.pos_x[env_idx, :]) < 1_000 + ) + + is_offroad = (agent_infos[env_idx, :, 0] == 1) & controlled_live + is_collided = ( + agent_infos[env_idx, :, 1:3].sum(axis=1) == 1 + ) & controlled_live + is_ok = ~is_offroad & ~is_collided & controlled_live + + # Draw the road graph + self._plot_roadgraph( + road_graph=self.global_roadgraph, + env_idx=env_idx, + ax=ax, + line_width_scale=line_width_scale, + marker_size_scale=marker_scale, + ) + + if plot_log_replay_trajectory: + self._plot_log_replay_trajectory( + ax=ax, + control_mask=controlled_live, + env_idx=env_idx, + log_trajectory=self.log_trajectory, + line_width_scale=line_width_scale, + ) + + # Draw the agents + self._plot_filtered_agent_bounding_boxes( + ax=ax, + env_idx=env_idx, + agent_states=global_agent_states, + is_ok_mask=is_ok, + is_offroad_mask=is_offroad, + is_collided_mask=is_collided, + response_type=self.response_type, + alpha=1.0, + line_width_scale=line_width_scale, + marker_size_scale=marker_scale, + extended_goals=extended_goals, + world_based_policy_mask=world_based_policy_mask, + ) + + if agent_positions is not None: + # First calculate the maximum valid trajectory length across all agents for this env_idx + max_valid_length = 0 + for agent_idx in range(agent_positions.shape[1]): + if controlled_live[agent_idx]: + trajectory = agent_positions[ + env_idx, agent_idx, :time_step, : + ] + valid_mask = ( + (trajectory[:, 0] != 0) + & (trajectory[:, 1] != 0) + & (torch.abs(trajectory[:, 0]) < OUT_OF_BOUNDS) + & (torch.abs(trajectory[:, 1]) < OUT_OF_BOUNDS) + ) + max_valid_length = max( + max_valid_length, valid_mask.sum().item() + ) + + # Create color palette + palette = sns.light_palette(AGENT_COLOR_BY_STATE["ok"]) + cmap = ListedColormap(palette) + norm = plt.Normalize(vmin=0, vmax=max_valid_length) + sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm) + + for agent_idx in range(agent_positions.shape[1]): + if controlled_live[agent_idx]: + trajectory = agent_positions[ + env_idx, agent_idx, :time_step, : + ] + valid_mask = ( + (trajectory[:, 0] != 0) + & (trajectory[:, 1] != 0) + & (torch.abs(trajectory[:, 0]) < OUT_OF_BOUNDS) + & (torch.abs(trajectory[:, 1]) < OUT_OF_BOUNDS) + ) + # Get valid trajectory points + valid_trajectory = trajectory[valid_mask] + + if len(valid_trajectory) > 1: + points = valid_trajectory.cpu().numpy() + + if self.render_3d: + trajectory_height = 0.05 + segments_3d = [] + for i in range(len(points) - 1): + segment = np.array( + [ + [ + points[i][0], + points[i][1], + trajectory_height, + ], + [ + points[i + 1][0], + points[i + 1][1], + trajectory_height, + ], + ] + ) + segments_3d.append(segment) + + # Adjust color mapping to use actual position in the valid trajectory + t = np.linspace( + 0, len(segments_3d), len(segments_3d) + ) + colors = cmap(norm(t)) + colors[:, 3] = np.linspace( + 0.3, 0.9, len(segments_3d) + ) + + lc = Line3DCollection( + segments_3d, + colors=colors, + linewidth=5, + zorder=1, + ) + ax.add_collection3d(lc) + else: + segments = [] + for i in range(len(points) - 1): + segment = np.array( + [ + [points[i][0], points[i][1]], + [ + points[i + 1][0], + points[i + 1][1], + ], + ] + ) + segments.append(segment) + + # Adjust color mapping to use actual position in the valid trajectory + t = np.linspace( + 0, len(segments), len(segments) + ) + colors = cmap(norm(t)) + colors[:, 3] = np.linspace( + 0.3, 0.9, len(segments) + ) + + lc = LineCollection( + segments, + colors=colors, + linewidth=5, + zorder=1, + ) + ax.add_collection(lc) + + # Add the colorbar + try: + fig = ax.get_figure() + cbar_ax = fig.add_axes([0.92, 0.09, 0.02, 0.8]) + cbar = fig.colorbar(sm, cax=cbar_ax) + cbar.set_label("Timestep", fontsize=15 * marker_scale) + cbar.ax.tick_params(labelsize=12 * marker_scale) + except Exception as e: + print(f"Warning: Could not add colorbar: {e}") + + # Determine center point for zooming + if center_agent_idx is not None: + center_x = global_agent_states.pos_x[ + env_idx, center_agent_idx + ].item() + center_y = global_agent_states.pos_y[ + env_idx, center_agent_idx + ].item() + else: + center_x = 0 # Default center x-coordinate + center_y = 0 # Default center y-coordinate + + # Set zoom window around the center + ax.set_xlim(center_x - zoom_radius, center_x + zoom_radius) + ax.set_ylim(center_y - zoom_radius, center_y + zoom_radius) + + # Remove ticks + ax.set_xticks([]) + ax.set_yticks([]) + + # 3d plot settings + if self.render_3d: + ax.set_zlim(0, zoom_radius * 0.05) + ax.set_zticks([]) + ax.xaxis.pane.fill = False + ax.yaxis.pane.fill = False + ax.zaxis.pane.fill = False + + ax.set_axis_off() + + for fig in figs: + fig.tight_layout(pad=2, rect=[0.00, 0.00, 0.9, 1]) + + return figs + + def _plot_log_replay_trajectory( + self, + ax: matplotlib.axes.Axes, + env_idx: int, + control_mask: torch.Tensor, + log_trajectory: LogTrajectory, + line_width_scale: int = 1.0, + ): + """Plot the log replay trajectory for controlled agents in either 2D or 3D.""" + if self.render_3d: + # Get trajectory points + trajectory_points = log_trajectory.pos_xy[ + env_idx, control_mask, :, : + ].numpy() + + # Set a fixed height for trajectory visualization + trajectory_height = 0.05 # Small height above ground + + # Plot trajectories for each controlled agent + for agent_trajectory in trajectory_points: + # Filter out invalid points (zeros or out of bounds) + valid_mask = ( + (agent_trajectory[:, 0] != 0) + & (agent_trajectory[:, 1] != 0) + & (np.abs(agent_trajectory[:, 0]) < OUT_OF_BOUNDS) + & (np.abs(agent_trajectory[:, 1]) < OUT_OF_BOUNDS) + ) + valid_points = agent_trajectory[valid_mask] + + if len(valid_points) > 1: + # Create segments for the trajectory + segments = [] + for i in range(len(valid_points) - 1): + segment = np.array( + [ + [ + valid_points[i, 0], + valid_points[i, 1], + trajectory_height, + ], + [ + valid_points[i + 1, 0], + valid_points[i + 1, 1], + trajectory_height, + ], + ] + ) + segments.append(segment) + + # Create line collection with fade effect + colors = np.zeros((len(segments), 4)) + colors[:, 1] = 0.9 # Green component + colors[:, 3] = np.linspace( + 0.2, 0.6, len(segments) + ) # Alpha gradient + + lc = Line3DCollection( + segments, colors=colors, linewidth=2 * line_width_scale + ) + ax.add_collection3d(lc) + + # Add points at trajectory positions + ax.scatter3D( + valid_points[:, 0], + valid_points[:, 1], + np.full_like(valid_points[:, 0], trajectory_height), + color="lightgreen", + s=10, + alpha=0.5, + zorder=0, + ) + else: + # Original 2D plotting + ax.scatter( + log_trajectory.pos_xy[env_idx, control_mask, :, 0].numpy(), + log_trajectory.pos_xy[env_idx, control_mask, :, 1].numpy(), + color="lightgreen", + linewidth=0.35 * line_width_scale, + alpha=0.35, + zorder=0, + ) + + def _get_endpoints(self, x, y, length, yaw): + """Compute the start and end points of a road segment.""" + center = np.array([x, y]) + start = center - np.array([length * np.cos(yaw), length * np.sin(yaw)]) + end = center + np.array([length * np.cos(yaw), length * np.sin(yaw)]) + return start, end + + def _get_corners_polygon(self, x, y, length, width, orientation): + """Calculate the four corners of a speed bump (can be any) polygon.""" + # Compute the direction vectors based on orientation + # print(length) + c = np.cos(orientation) + s = np.sin(orientation) + u = np.array((c, s)) # Unit vector along the orientation + ut = np.array((-s, c)) # Unit vector perpendicular to the orientation + + # Center point of the speed bump + pt = np.array([x, y]) + + # corners + tl = pt + (length / 2) * u - (width / 2) * ut + tr = pt + (length / 2) * u + (width / 2) * ut + br = pt - (length / 2) * u + (width / 2) * ut + bl = pt - (length / 2) * u - (width / 2) * ut + + return [tl.tolist(), tr.tolist(), br.tolist(), bl.tolist()] + + def _plot_3d_road_segment( + self, ax, start, end, width, height, color, line_width=1.0 + ): + """Helper method to plot 3D road segment with width and height.""" + # Calculate direction vector + direction = np.array([end[0] - start[0], end[1] - start[1]]) + length = np.linalg.norm(direction) + if length == 0: + return + + direction = direction / length + perpendicular = np.array([-direction[1], direction[0]]) + + # Create vertices for 3D box + vertices = [] + for z in [0, height]: # Bottom and top faces + vertices.extend( + [ + [ + start[0] - perpendicular[0] * width / 2, + start[1] - perpendicular[1] * width / 2, + z, + ], + [ + start[0] + perpendicular[0] * width / 2, + start[1] + perpendicular[1] * width / 2, + z, + ], + [ + end[0] + perpendicular[0] * width / 2, + end[1] + perpendicular[1] * width / 2, + z, + ], + [ + end[0] - perpendicular[0] * width / 2, + end[1] - perpendicular[1] * width / 2, + z, + ], + ] + ) + + # Create faces + faces = [ + [vertices[0], vertices[1], vertices[2], vertices[3]], # Bottom + [vertices[4], vertices[5], vertices[6], vertices[7]], # Top + [vertices[0], vertices[1], vertices[5], vertices[4]], # Side 1 + [vertices[1], vertices[2], vertices[6], vertices[5]], # Side 2 + [vertices[2], vertices[3], vertices[7], vertices[6]], # Side 3 + [vertices[3], vertices[0], vertices[4], vertices[7]], # Side 4 + ] + + # Create 3D collection and add to axis + poly3d = Poly3DCollection(faces, alpha=0.7) + poly3d.set_facecolor(color) + ax.add_collection3d(poly3d) + + def _plot_3d_polygon( + self, ax, points, height, facecolor, edgecolor=None, alpha=1.0 + ): + """Helper method to plot 3D polygon with height.""" + points = np.array(points) + vertices = [] + + # Create bottom and top faces + for z in [0, height]: + for point in points: + vertices.append([point[0], point[1], z]) + + vertices = np.array(vertices) + n_points = len(points) + + # Create faces + faces = [] + # Bottom face + faces.append(vertices[:n_points]) + # Top face + faces.append(vertices[n_points:]) + # Side faces + for i in range(n_points): + next_i = (i + 1) % n_points + faces.append( + [ + vertices[i], + vertices[next_i], + vertices[next_i + n_points], + vertices[i + n_points], + ] + ) + + # Create 3D collection and add to axis + poly3d = Poly3DCollection(faces, alpha=alpha, zorder=1) + poly3d.set_facecolor(facecolor) + if edgecolor: + poly3d.set_edgecolor(edgecolor) + ax.add_collection3d(poly3d) + + def _plot_3d_stop_sign( + self, ax, x, y, radius, height, facecolor, alpha=1.0 + ): + """Helper method to plot 3D stop sign.""" + # Create octagon points + n_sides = 8 + angles = np.linspace(0, 2 * np.pi, n_sides, endpoint=False) + points = [ + [x + radius * np.cos(angle), y + radius * np.sin(angle)] + for angle in angles + ] + + # Plot as 3D polygon + self._plot_3d_polygon(ax, points, height, facecolor, alpha=alpha) + + # Add pole + pole_radius = radius * 0.1 + pole_points = [ + [x - pole_radius, y - pole_radius], + [x + pole_radius, y - pole_radius], + [x + pole_radius, y + pole_radius], + [x - pole_radius, y + pole_radius], + ] + self._plot_3d_polygon( + ax, pole_points, height * 0.8, facecolor="#808080", alpha=alpha + ) + + def _plot_roadgraph( + self, + env_idx: int, + road_graph: GlobalRoadGraphPoints, + ax: matplotlib.axes.Axes, + marker_size_scale: int = 1.0, + line_width_scale: int = 1.0, + ): + """Plot the road graph.""" + + for road_point_type in road_graph.type.unique().tolist(): + if road_point_type == int(madrona_gpudrive.EntityType._None): + continue + + road_mask = road_graph.type[env_idx, :] == road_point_type + + # Get coordinates and metadata for the current road type + x_coords = road_graph.x[env_idx, road_mask].tolist() + y_coords = road_graph.y[env_idx, road_mask].tolist() + segment_lengths = road_graph.segment_length[ + env_idx, road_mask + ].tolist() + segment_widths = road_graph.segment_width[ + env_idx, road_mask + ].tolist() + segment_orientations = road_graph.orientation[ + env_idx, road_mask + ].tolist() + + if road_point_type in [ + int(madrona_gpudrive.EntityType.RoadEdge), + int(madrona_gpudrive.EntityType.RoadLine), + int(madrona_gpudrive.EntityType.RoadLane), + ]: + # Handle road edges, lines, and lanes + if self.render_3d: + for x, y, length, width, orientation in zip( + x_coords, + y_coords, + segment_lengths, + segment_widths, + segment_orientations, + ): + start, end = self._get_endpoints( + x, y, length, orientation + ) + + # Create 3D road segment + if road_point_type == int( + madrona_gpudrive.EntityType.RoadEdge + ): + # For road edges, create raised borders + height = 0.01 # Small height for road edges + self._plot_3d_road_segment( + ax, + start, + end, + width, + height, + ROAD_GRAPH_COLORS[road_point_type], + line_width=1.1 * line_width_scale, + ) + else: + # For lanes and lines, plot at ground level + ax.plot3D( + [start[0], end[0]], + [start[1], end[1]], + [0, 0], # Ground level + color=ROAD_GRAPH_COLORS[road_point_type], + linewidth=1.25 * line_width_scale, + ) + else: + # Original 2D plotting + for x, y, length, orientation in zip( + x_coords, + y_coords, + segment_lengths, + segment_orientations, + ): + start, end = self._get_endpoints( + x, y, length, orientation + ) + line_width = ( + 1.1 * line_width_scale + if road_point_type + == int(madrona_gpudrive.EntityType.RoadEdge) + else 0.75 * line_width_scale + ) + + ax.plot( + [start[0], end[0]], + [start[1], end[1]], + color=ROAD_GRAPH_COLORS[road_point_type], + linewidth=line_width, + ) + + elif road_point_type == int(madrona_gpudrive.EntityType.SpeedBump): + if self.render_3d: + for x, y, length, width, orientation in zip( + x_coords, + y_coords, + segment_lengths, + segment_widths, + segment_orientations, + ): + # Create 3D speed bump with height + points = self._get_corners_polygon( + x, y, length, width, orientation + ) + height = 0.0 # Height of speed bump + self._plot_3d_polygon( + ax, + points, + height, + facecolor=ROAD_GRAPH_COLORS[road_point_type], + alpha=0.6, + ) + else: + utils.plot_speed_bumps( + x_coords, + y_coords, + segment_lengths, + segment_widths, + segment_orientations, + ax, + ) + + elif road_point_type == int(madrona_gpudrive.EntityType.StopSign): + if self.render_3d: + for x, y in zip(x_coords, y_coords): + # Create 3D stop sign + height = 0.1 # Standard stop sign height + radius = 0.3 + self._plot_3d_stop_sign( + ax, + x, + y, + radius, + height, + facecolor="#c04000", + alpha=0.9, + ) + else: + for x, y in zip(x_coords, y_coords): + utils.plot_stop_sign( + point=np.array([x, y]), + ax=ax, + radius=1.5, + facecolor="#c04000", + edgecolor="none", + linewidth=3.0, + alpha=0.9, + ) + + elif road_point_type == int(madrona_gpudrive.EntityType.CrossWalk): + if self.render_3d: + for x, y, length, width, orientation in zip( + x_coords, + y_coords, + segment_lengths, + segment_widths, + segment_orientations, + ): + points = self._get_corners_polygon( + x, y, length, width, orientation + ) + height = 0.0 # Slight elevation for crosswalk + self._plot_3d_polygon( + ax, + points, + height, + facecolor="white", + edgecolor="xkcd:bluish grey", + alpha=0.4, + ) + else: + for x, y, length, width, orientation in zip( + x_coords, + y_coords, + segment_lengths, + segment_widths, + segment_orientations, + ): + points = self._get_corners_polygon( + x, y, length, width, orientation + ) + utils.plot_crosswalk( + points=points, + ax=ax, + facecolor="none", + edgecolor="xkcd:bluish grey", + alpha=0.4, + ) + else: + # Handle other road point types as markers + if self.render_3d: + ax.scatter3D( + road_graph.x[env_idx, road_mask], + road_graph.y[env_idx, road_mask], + np.zeros_like( + road_graph.x[env_idx, road_mask] + ), # Ground level + s=5 * marker_size_scale, + label=road_point_type, + color=ROAD_GRAPH_COLORS[int(road_point_type)], + ) + else: + ax.scatter( + road_graph.x[env_idx, road_mask], + road_graph.y[env_idx, road_mask], + s=5 * marker_size_scale, + label=road_point_type, + color=ROAD_GRAPH_COLORS[int(road_point_type)], + ) + + def _create_3d_vehicle_box(self, x, y, length, width, orientation): + """Create simple 3D cuboid vertices and faces for vehicle representation.""" + # Rotation matrix + c, s = np.cos(orientation), np.sin(orientation) + R = np.array([[c, -s], [s, c]]) + + # Define base points for cuboid + base_points = np.array( + [ + [-length / 2, -width / 2], # Back left + [length / 2, -width / 2], # Front left + [length / 2, width / 2], # Front right + [-length / 2, width / 2], # Back right + ] + ) + + # Rotate and translate points + transformed_points = base_points @ R.T + np.array([x, y]) + + # Create 3D points + bottom = np.column_stack( + [transformed_points, np.zeros_like(transformed_points[:, 0])] + ) + top = np.column_stack( + [ + transformed_points, + np.full_like(transformed_points[:, 0], self.vehicle_height), + ] + ) + + # Define faces (6 faces for cuboid) + faces = [ + bottom, # Bottom face + top, # Top face + np.array([bottom[0], bottom[1], top[1], top[0]]), # Left side + np.array([bottom[1], bottom[2], top[2], top[1]]), # Front + np.array([bottom[2], bottom[3], top[3], top[2]]), # Right side + np.array([bottom[3], bottom[0], top[0], top[3]]), # Back + ] + + return faces + + def _plot_filtered_agent_bounding_boxes( + self, + env_idx: int, + ax: matplotlib.axes.Axes, + agent_states: GlobalEgoState, + is_ok_mask: torch.Tensor, + is_offroad_mask: torch.Tensor, + is_collided_mask: torch.Tensor, + response_type: Any, + alpha: Optional[float] = 1.0, + as_center_pts: bool = False, + label: Optional[str] = None, + plot_goal_points: bool = True, + line_width_scale: int = 1.0, + marker_size_scale: int = 1.0, + extended_goals: Optional[Dict[str, torch.Tensor]] = None, + world_based_policy_mask : Optional[Dict[int,Dict[str,torch.Tensor]]] = None, + ) -> None: + """Plots bounding boxes for agents filtered by environment index and mask. + + Args: + env_idx: Environment indices to select specific environments. + ax: Matplotlib axis for plotting. + agent_state: The global state of agents from `GlobalEgoState`. + is_ok_mask: Mask for agents that are in a valid state. + is_offroad_mask: Mask for agents that are off-road. + is_collided_mask: Mask for agents that have collided. + response_type: Mask to filter static agents. + alpha: Alpha value for drawing, i.e., 0 means fully transparent. + as_center_pts: If True, only plot center points instead of full boxes. + label: Label for the plotted elements. + plot_goal_points: If True, plot goal points for agents. + line_width_scale: Scale factor for line width. + marker_size_scale: Scale factor for marker size. + extended_goals: Optional dictionary of backward goals for controlled agents. + """ + + def plot_agent_group_3d( + bboxes, color, alpha=1.0, line_width_scale=1.5 + ): + """Helper function to plot a group of agents in 3D""" + for x, y, length, width, angle in bboxes: + # Create 3D vehicle box + faces = self._create_3d_vehicle_box(x, y, length, width, angle) + + # Plot the cuboid (vehicle box) + poly3d = Poly3DCollection( + faces, alpha=alpha, zsort="max", zorder=6 + ) + poly3d.set_facecolor(color) + poly3d.set_edgecolor("black") + poly3d.set_linewidth(0.5 * line_width_scale) + ax.add_collection3d(poly3d) + + # Heading arrow (use a small 3D line to indicate the orientation) + c = np.cos(angle) + s = np.sin(angle) + arrow_length = 4.5 + + # Coordinates of the arrow's base (center of the box) and the tip + arrow_base = np.array( + [x, y, 0] + ) # Starting point (at the top of the box) + arrow_tip = arrow_base + np.array( + [arrow_length * c, arrow_length * s, 0] + ) # Pointing in the direction of the angle + + # Plot the heading arrow + ax.plot( + [arrow_base[0], arrow_tip[0]], + [arrow_base[1], arrow_tip[1]], + [arrow_base[2], arrow_tip[2]], + color="black", + linewidth=2, + alpha=alpha, + zorder=5, + ) + + # Add arrowhead (tip) + tip_angle = np.pi / 1.5 # Angle of the arrowhead + arrowhead_length = arrow_length / 8 # Length of the arrowhead + + # Calculate the left and right arrowhead points + arrowhead_left = arrow_tip + np.array( + [ + arrowhead_length * (np.cos(angle + tip_angle) - c), + arrowhead_length * (np.sin(angle + tip_angle) - s), + 0, + ] + ) + arrowhead_right = arrow_tip + np.array( + [ + arrowhead_length * (np.cos(angle - tip_angle) - c), + arrowhead_length * (np.sin(angle - tip_angle) - s), + 0, + ] + ) + + # Plot the left and right arrowhead lines + ax.plot( + [arrow_tip[0], arrowhead_left[0]], + [arrow_tip[1], arrowhead_left[1]], + [arrow_tip[2], arrowhead_left[2]], + color="black", + linewidth=1.5, + alpha=alpha, + zorder=5, + ) + ax.plot( + [arrow_tip[0], arrowhead_right[0]], + [arrow_tip[1], arrowhead_right[1]], + [arrow_tip[2], arrowhead_right[2]], + color="black", + linewidth=1.5, + alpha=alpha, + zorder=5, + ) + + + def plot_agent_group_2d(bboxes, color,by_policy = False): + """Helper function to plot a group of agents in 2D""" + if not by_policy: + utils.plot_numpy_bounding_boxes( + ax=ax, + bboxes=bboxes, + color=color, + alpha=alpha, + line_width_scale=line_width_scale, + as_center_pts=as_center_pts, + label=label, + ) + else: + num_policies = len(bboxes) + utils.plot_numpy_bounding_boxes_multiple_policy( + ax=ax, + bboxes_s=bboxes, + colors=color[:num_policies], + alpha=alpha, + line_width_scale=line_width_scale, + as_center_pts=as_center_pts, + label=label, + ) + # Off-road agents + bboxes_controlled_offroad = np.stack( + ( + agent_states.pos_x[env_idx, is_offroad_mask].numpy(), + agent_states.pos_y[env_idx, is_offroad_mask].numpy(), + agent_states.vehicle_length[env_idx, is_offroad_mask].numpy(), + agent_states.vehicle_width[env_idx, is_offroad_mask].numpy(), + agent_states.rotation_angle[env_idx, is_offroad_mask].numpy(), + ), + axis=1, + ) + + if self.render_3d: + plot_agent_group_3d( + bboxes_controlled_offroad, AGENT_COLOR_BY_STATE["off_road"] + ) + else: + plot_agent_group_2d( + bboxes_controlled_offroad, AGENT_COLOR_BY_STATE["off_road"] + ) + + # Plot goals + if plot_goal_points: + for mask, color in [ + (is_ok_mask, AGENT_COLOR_BY_STATE["ok"]), + (is_offroad_mask, AGENT_COLOR_BY_STATE["off_road"]), + (is_collided_mask, AGENT_COLOR_BY_STATE["collided"]), + ]: + if not mask.any(): + continue + + goal_x = agent_states.goal_x[env_idx, mask].numpy() + goal_y = agent_states.goal_y[env_idx, mask].numpy() + + if self.render_3d: + # Plot goals as vertical lines in 3D + for x, y in zip(goal_x, goal_y): + ax.plot3D( + [x, x], + [y, y], + [0, self.vehicle_height], + color=color, + linestyle="--", + linewidth=2 * line_width_scale, + ) + # Add goal circle on the ground + circle_points = np.linspace(0, 2 * np.pi, 32) + circle_x = x + self.goal_radius * np.cos(circle_points) + circle_y = y + self.goal_radius * np.sin(circle_points) + circle_z = np.zeros_like(circle_points) + ax.plot3D( + circle_x, + circle_y, + circle_z, + color=color, + linestyle="--", + linewidth=2 * line_width_scale, + ) + else: + # Original 2D goal plotting + ax.scatter( + goal_x, + goal_y, + s=5 * marker_size_scale, + linewidth=1.5 * line_width_scale, + c=color, + marker="o", + ) + for x, y in zip(goal_x, goal_y): + circle = Circle( + (x, y), + radius=self.goal_radius, + color=color, + fill=False, + linestyle="--", + ) + ax.add_patch(circle) + + # Collided agents + bboxes_controlled_collided = np.stack( + ( + agent_states.pos_x[env_idx, is_collided_mask].numpy(), + agent_states.pos_y[env_idx, is_collided_mask].numpy(), + agent_states.vehicle_length[env_idx, is_collided_mask].numpy(), + agent_states.vehicle_width[env_idx, is_collided_mask].numpy(), + agent_states.rotation_angle[env_idx, is_collided_mask].numpy(), + ), + axis=1, + ) + + if self.render_3d: + plot_agent_group_3d( + bboxes_controlled_collided, AGENT_COLOR_BY_STATE["collided"] + ) + else: + plot_agent_group_2d( + bboxes_controlled_collided, AGENT_COLOR_BY_STATE["collided"] + ) + + # Living agents + bboxes_controlled_ok = np.stack( + ( + agent_states.pos_x[env_idx, is_ok_mask].numpy(), + agent_states.pos_y[env_idx, is_ok_mask].numpy(), + agent_states.vehicle_length[env_idx, is_ok_mask].numpy(), + agent_states.vehicle_width[env_idx, is_ok_mask].numpy(), + agent_states.rotation_angle[env_idx, is_ok_mask].numpy(), + ), + axis=1, + ) + + if not world_based_policy_mask: ## controlled by the same policy + # Living agents + bboxes_controlled_ok = np.stack( + ( + agent_states.pos_x[env_idx, is_ok_mask].numpy(), + agent_states.pos_y[env_idx, is_ok_mask].numpy(), + agent_states.vehicle_length[env_idx, is_ok_mask].numpy(), + agent_states.vehicle_width[env_idx, is_ok_mask].numpy(), + agent_states.rotation_angle[env_idx, is_ok_mask].numpy(), + ), + axis=1, + ) + if self.render_3d: + plot_agent_group_3d( + bboxes_controlled_ok, AGENT_COLOR_BY_STATE["ok"] + ) + else: + plot_agent_group_2d( + bboxes_controlled_ok, AGENT_COLOR_BY_STATE["ok"] + ) + + else: + policy_mask = world_based_policy_mask[env_idx] + if self.render_3d: + for policy_idx, (policy_name, mask) in enumerate(policy_mask.items()): + mask = mask & is_ok_mask + if not mask.any(): + continue + bboxes = np.stack( + ( + agent_states.pos_x[env_idx, mask].numpy(), + agent_states.pos_y[env_idx, mask].numpy(), + agent_states.vehicle_length[env_idx, mask].numpy(), + agent_states.vehicle_width[env_idx, mask].numpy(), + agent_states.rotation_angle[env_idx, mask].numpy(), + ), + axis=1, + ) + color = AGENT_COLOR_BY_POLICY[ + policy_idx % len(AGENT_COLOR_BY_POLICY) + ] + plot_agent_group_3d(bboxes, color) + else: + bboxes_controlled_ok = [] + for policy_name, mask in policy_mask.items(): + mask = mask & is_ok_mask + if not mask.any(): + continue + bboxes = np.stack( + ( + agent_states.pos_x[env_idx, mask].numpy(), + agent_states.pos_y[env_idx, mask].numpy(), + agent_states.vehicle_length[env_idx, mask].numpy(), + agent_states.vehicle_width[env_idx, mask].numpy(), + agent_states.rotation_angle[env_idx, mask].numpy(), + ), + axis=1, + ) + bboxes_controlled_ok.append(bboxes) + + if bboxes_controlled_ok: + plot_agent_group_2d( + bboxes_controlled_ok, + AGENT_COLOR_BY_POLICY, + by_policy=True, + ) + + # Plot log replay agents + log_replay = ( + response_type.static[env_idx, :] | response_type.moving[env_idx, :] + ) & ~self.controlled_agent_mask[env_idx, :] + + pos_x = agent_states.pos_x[env_idx, log_replay] + pos_y = agent_states.pos_y[env_idx, log_replay] + rotation_angle = agent_states.rotation_angle[env_idx, log_replay] + vehicle_length = agent_states.vehicle_length[env_idx, log_replay] + vehicle_width = agent_states.vehicle_width[env_idx, log_replay] + + valid_mask = ( + (torch.abs(pos_x) < OUT_OF_BOUNDS) + & (torch.abs(pos_y) < OUT_OF_BOUNDS) + & ( + (vehicle_length > 0.5) + & (vehicle_length < 15) + & (vehicle_width > 0.5) + & (vehicle_width < 15) + ) + ) + + bboxes_static = np.stack( + ( + pos_x[valid_mask].numpy(), + pos_y[valid_mask].numpy(), + vehicle_length[valid_mask].numpy(), + vehicle_width[valid_mask].numpy(), + rotation_angle[valid_mask].numpy(), + ), + axis=1, + ) + + if self.render_3d: + plot_agent_group_3d( + bboxes_static, AGENT_COLOR_BY_STATE["log_replay"] + ) + else: + plot_agent_group_2d( + bboxes_static, AGENT_COLOR_BY_STATE["log_replay"] + ) + + def _plot_expert_trajectories( + self, + ax: matplotlib.axes.Axes, + env_idx: int, + expert_trajectories: torch.Tensor, + response_type: Any, + ) -> None: + """Plot expert trajectories. + Args: + ax: Matplotlib axis for plotting. + env_idx: Environment index to select specific environment agents. + expert_trajectories: The global state of expert from `LogTrajectory`. + """ + if self.vis_config.draw_expert_trajectories: + controlled_mask = self.controlled_agents[env_idx, :] + non_controlled_mask = ( + ~response_type.static[env_idx, :] + & response_type.moving[env_idx, :] + & ~controlled_mask + ) + mask = ( + controlled_mask + if self.vis_config.draw_only_controllable_veh + else controlled_mask | non_controlled_mask + ) + agent_indices = torch.where(mask)[0] + trajectories = expert_trajectories[env_idx][mask] + for idx, trajectory in zip(agent_indices, trajectories): + color = ( + AGENT_COLOR_BY_STATE["ok"] + if controlled_mask[idx] + else AGENT_COLOR_BY_STATE["log_replay"] + ) + for step in trajectory: + x, y = step[:2].numpy() + if x < OUT_OF_BOUNDS and y < OUT_OF_BOUNDS: + ax.add_patch( + Circle( + (x, y), + radius=0.3, + color=color, + fill=True, + alpha=0.5, + ) + ) + + def plot_agent_observation( + self, + agent_idx: int, + env_idx: int, + figsize: Tuple[int, int] = (10, 10), + trajectory: Optional[np.ndarray] = None, + ): + """ + Plot observation from agent POV to inspect the information available + to the agent. + Args: + agent_idx (int): Index of the agent whose observation is to be plotted. + env_idx (int): Index of the environment in the batch. + trajectory (Optional[np.ndarray], optional): Array of trajectory points to plot. + Should be of shape (N, 2) where N is the number of points and each point + is an (x, y) coordinate. Defaults to None. + """ + observation_ego = LocalEgoState.from_tensor( + self_obs_tensor=self.sim_object.self_observation_tensor(), + backend=self.backend, + device="cpu", + ) + + observation_roadgraph = LocalRoadGraphPoints.from_tensor( + local_roadgraph_tensor=self.sim_object.agent_roadmap_tensor(), + backend=self.backend, + device="cpu", + ) + + observation_partner = PartnerObs.from_tensor( + partner_obs_tensor=self.sim_object.partner_observations_tensor(), + backend=self.backend, + device="cpu", + ) + + # Check if agent index is valid, otherwise return None + if observation_ego.id[env_idx, agent_idx] == -1: + return None, None + + fig, ax = plt.subplots(figsize=figsize) + ax.clear() # Clear any previous plots + ax.set_aspect("equal", adjustable="box") + + # Plot roadgraph if provided + if observation_roadgraph is not None: + for road_type, type_name in ROAD_GRAPH_TYPE_NAMES.items(): + mask = ( + observation_roadgraph.type[env_idx, agent_idx, :] + == road_type + ) + + # Extract relevant roadgraph data for plotting + x_points = observation_roadgraph.x[env_idx, agent_idx, mask] + y_points = observation_roadgraph.y[env_idx, agent_idx, mask] + orientations = observation_roadgraph.orientation[ + env_idx, agent_idx, mask + ] + segment_lengths = observation_roadgraph.segment_length[ + env_idx, agent_idx, mask + ] + widths = observation_roadgraph.segment_width[ + env_idx, agent_idx, mask + ] + + # Scatter plot for the points + ax.scatter( + x_points, + y_points, + c=[ROAD_GRAPH_COLORS[road_type]], + s=8, + label=type_name, + ) + + # Plot lines for road edges + for x, y, orientation, segment_length, width in zip( + x_points, y_points, orientations, segment_lengths, widths + ): + dx = segment_length * 0.5 * np.cos(orientation) + dy = segment_length * 0.5 * np.sin(orientation) + + # Calculate line endpoints for the road edge + x_start = x - dx + y_start = y - dy + x_end = x + dx + y_end = y + dy + + # Add width as a perpendicular offset + width_dx = width * 0.5 * np.sin(orientation) + width_dy = -width * 0.5 * np.cos(orientation) + + # Draw the road edge as a polygon (line with width) + ax.plot( + [x_start - width_dx, x_end - width_dx], + [y_start - width_dy, y_end - width_dy], + color=ROAD_GRAPH_COLORS[road_type], + alpha=0.5, + linewidth=1.0, + ) + ax.plot( + [x_start + width_dx, x_end + width_dx], + [y_start + width_dy, y_end + width_dy], + color=ROAD_GRAPH_COLORS[road_type], + alpha=0.5, + linewidth=1.0, + ) + ax.plot( + [x_start - width_dx, x_start + width_dx], + [y_start - width_dy, y_start + width_dy], + color=ROAD_GRAPH_COLORS[road_type], + alpha=0.5, + linewidth=1.0, + ) + ax.plot( + [x_end - width_dx, x_end + width_dx], + [y_end - width_dy, y_end + width_dy], + color=ROAD_GRAPH_COLORS[road_type], + alpha=0.5, + linewidth=1.0, + ) + + # Plot partner agents if provided + if observation_partner is not None: + partner_positions = torch.stack( + ( + observation_partner.rel_pos_x[env_idx, agent_idx, :, :] + .squeeze() + .cpu(), + observation_partner.rel_pos_y[env_idx, agent_idx, :, :] + .squeeze() + .cpu(), + ), + dim=1, + ) # Shape: (num_partners, 2) + + utils.plot_bounding_box( + ax=ax, + center=partner_positions, + vehicle_length=observation_partner.vehicle_length[ + env_idx, agent_idx, :, : + ].squeeze(), + vehicle_width=observation_partner.vehicle_width[ + env_idx, agent_idx, :, : + ].squeeze(), + orientation=observation_partner.orientation[ + env_idx, agent_idx, :, : + ].squeeze(), + color=REL_OBS_OBJ_COLORS["other_agents"], + alpha=1.0, + ) + + if observation_ego is not None: + ego_agent_color = ( + "darkred" + if observation_ego.is_collided[env_idx, agent_idx] + else REL_OBS_OBJ_COLORS["ego"] + ) + utils.plot_bounding_box( + ax=ax, + center=(0, 0), + vehicle_length=observation_ego.vehicle_length[ + env_idx, agent_idx + ].item(), + vehicle_width=observation_ego.vehicle_width[ + env_idx, agent_idx + ].item(), + orientation=0.0, + color=ego_agent_color, + alpha=1.0, + label="Ego agent", + ) + + # Add an arrow for speed + speed = observation_ego.speed[env_idx, agent_idx].item() + ax.arrow( + 0, + 0, # Start at the ego vehicle's position + speed, + 0, # Arrow points to the right, proportional to speed + head_width=1.0, + head_length=1.1, + fc=REL_OBS_OBJ_COLORS["ego"], + ec=REL_OBS_OBJ_COLORS["ego"], + ) + + ax.scatter( + observation_ego.rel_goal_x[env_idx, agent_idx], + observation_ego.rel_goal_y[env_idx, agent_idx], + s=5, + linewidth=1.5, + c=ego_agent_color, + marker="x", + ) + + circle = Circle( + ( + observation_ego.rel_goal_x[env_idx, agent_idx], + observation_ego.rel_goal_y[env_idx, agent_idx], + ), + radius=self.goal_radius, + color=ego_agent_color, + fill=False, + linestyle="--", + ) + ax.add_patch(circle) + + observation_radius = Circle( + (0, 0), + radius=self.env_config.obs_radius, + color="#000000", + linewidth=0.8, + fill=False, + linestyle="-", + ) + ax.add_patch(observation_radius) + plt.axis("off") + + if trajectory is not None and len(trajectory) > 0: + # Plot the trajectory as a line + ax.plot( + trajectory[:, 0], # x coordinates + trajectory[:, 1], # y coordinates + color='blue', # trajectory color + linestyle='-', # solid line + linewidth=1.0, # line width + marker='o', # circular markers at each point + markersize=1, # size of markers + alpha=0.7, # slight transparency + label='Trajectory' # label for legend + ) + + ax.set_xlim((-self.env_config.obs_radius, self.env_config.obs_radius)) + ax.set_ylim((-self.env_config.obs_radius, self.env_config.obs_radius)) + ax.set_xticks([]) + ax.set_yticks([]) + + return fig diff --git a/gpudrive-main/gpudrive/visualize/utils.py b/gpudrive-main/gpudrive/visualize/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..094c1d03015e68abd0af736ed4a4e03024e985b4 --- /dev/null +++ b/gpudrive-main/gpudrive/visualize/utils.py @@ -0,0 +1,518 @@ +import os +from typing import Optional + +import matplotlib +import matplotlib.pylab as plt +import numpy as np +from PIL import Image + +import os +import torch +import matplotlib +from typing import Tuple, Optional, List, Dict, Any, Union +from matplotlib.patches import Circle, Polygon, RegularPolygon + +from gpudrive.visualize.color import ROAD_GRAPH_COLORS, ROAD_GRAPH_TYPE_NAMES + +def img_from_fig(fig: matplotlib.figure.Figure) -> np.ndarray: + """Returns a [H, W, 3] uint8 np image from fig.canvas.tostring_rgb().""" + # Adjusted margins to better accommodate 3D plots + fig.subplots_adjust( + left=0.0, # Reduce left margin + bottom=0.0, # Reduce bottom margin + right=1.0, # Extend to right edge + top=1.0, # Extend to top edge + wspace=0.0, + hspace=0.0 + ) + + # Force render + fig.canvas.draw() + + # Convert to numpy array + data = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8) + img = data.reshape(fig.canvas.get_width_height()[::-1] + (3,)) + + plt.close(fig) + return img + + +def save_img_as_png(img: np.ndarray, filename: str = "/tmp/img.png"): + """Saves np image to disk.""" + outdir = os.path.dirname(filename) + os.makedirs(outdir, exist_ok=True) + Image.fromarray(img).save(filename) + + +def plot_roadgraph_points(ax, observation_roadgraph, env_idx, agent_idx): + """Plots the road graph points by their type, using names instead of type numbers.""" + + # Extract road graph types and positions + roadgraph_types = observation_roadgraph.type[env_idx, agent_idx, :] + roadgraph_x = observation_roadgraph.x[env_idx, agent_idx, :] + roadgraph_y = observation_roadgraph.y[env_idx, agent_idx, :] + + # Plot points by type, mapping types to names + for road_type, color in ROAD_GRAPH_COLORS.items(): + # Filter points by road type + idx = roadgraph_types == road_type + if idx.sum() > 0: + ax.plot( + roadgraph_x[idx], + roadgraph_y[idx], + ".", # Plot as dots + color=color, + label=ROAD_GRAPH_TYPE_NAMES.get( + road_type, f"Type {road_type}" + ), + ) + + +def plot_numpy_bounding_boxes( + ax: matplotlib.axes.Axes, + bboxes: np.ndarray, + color: np.ndarray, + alpha: Optional[float] = 1.0, + line_width_scale: float = 1.5, + as_center_pts: bool = False, + label: Optional[str] = None, +) -> None: + """Plots multiple bounding boxes. + + Args: + ax: Fig handles. + bboxes: Shape (num_bbox, 5), with last dimension as (x, y, length, width, + yaw). + color: Shape (3,), represents RGB color for drawing. + alpha: Alpha value for drawing, i.e. 0 means fully transparent. + as_center_pts: If set to True, bboxes will be drawn as center points, + instead of full bboxes. + label: String, represents the meaning of the color for different boxes. + """ + if bboxes.ndim != 2 or bboxes.shape[1] != 5: + raise ValueError( + ( + "Expect bboxes rank 2, last dimension of bbox 5" + " got{}, {}, {} respectively" + ).format(bboxes.ndim, bboxes.shape[1], color.shape) + ) + + if as_center_pts: + ax.plot( + bboxes[:, 0], + bboxes[:, 1], + "o", + color=color, + ms=2, + alpha=alpha, + linewidth=1.7 * line_width_scale, + label=label, + ) + else: + c = np.cos(bboxes[:, 4]) + s = np.sin(bboxes[:, 4]) + pt = np.array((bboxes[:, 0], bboxes[:, 1])) # (2, N) + length, width = bboxes[:, 2], bboxes[:, 3] + u = np.array((c, s)) + ut = np.array((s, -c)) + + # Compute box corner coordinates. + tl = pt + length / 2 * u - width / 2 * ut + tr = pt + length / 2 * u + width / 2 * ut + br = pt - length / 2 * u + width / 2 * ut + bl = pt - length / 2 * u - width / 2 * ut + + # Compute heading arrow using center left/right/front. + cl = pt - width / 2 * ut + cr = pt + width / 2 * ut + cf = pt + length / 2 * u + + # Draw bboxes. + ax.plot( + [tl[0, :], tr[0, :], br[0, :], bl[0, :], tl[0, :]], + [tl[1, :], tr[1, :], br[1, :], bl[1, :], tl[1, :]], + color=color, + zorder=4, + linewidth=1.7 * line_width_scale, + alpha=alpha, + label=label, + ) + + # Draw heading arrow. + ax.plot( + [cl[0, :], cr[0, :], cf[0, :], cl[0, :]], + [cl[1, :], cr[1, :], cf[1, :], cl[1, :]], + color=color, + zorder=6, + alpha=alpha, + linewidth=1.5 * line_width_scale, + label=label, + ) + + +def plot_bounding_box( + ax: matplotlib.axes.Axes, + center: Optional[Union[Tuple[float, float], torch.Tensor]], + vehicle_length: Union[float, torch.Tensor], + vehicle_width: Union[float, torch.Tensor], + orientation: Union[float, torch.Tensor], + color: str, + alpha: Optional[float] = 1.0, + label: Optional[str] = None, +) -> None: + """Plots bounding boxes, supporting both single and multiple agents. + + Args: + ax: Matplotlib Axes handle. + center: Tuple (x, y) specifying a single bounding box center or + a tensor of shape (num_agents, 2) with x, y positions for multiple agents. + vehicle_length: Length of the bounding box (float or tensor of shape (num_agents,)). + vehicle_width: Width of the bounding box (float or tensor of shape (num_agents,)). + orientation: Orientation of the bounding box (float or tensor of shape (num_agents,)). + color: Color for the bounding boxes. + alpha: Transparency of the bounding boxes (0.0 to 1.0). + label: Optional label for the bounding boxes (only used for single-agent plots). + """ + if isinstance(center, torch.Tensor): + # Multiple bounding boxes + if center.shape[-1] != 2: + raise ValueError( + "Center tensor must have shape (num_agents, 2) for multiple bounding boxes." + ) + + num_agents = center.shape[0] + for i in range(num_agents): + cx, cy = center[i] + length = vehicle_length[i].item() + width = vehicle_width[i].item() + angle = orientation[i].item() + + # Compute bounding box corners + corners_x = [ + cx - length / 2, + cx + length / 2, + cx + length / 2, + cx - length / 2, + cx - length / 2, + ] + corners_y = [ + cy - width / 2, + cy - width / 2, + cy + width / 2, + cy + width / 2, + cy - width / 2, + ] + + # Apply rotation + rotated_corners = [ + ( + (x - cx) * np.cos(angle) - (y - cy) * np.sin(angle) + cx, + (x - cx) * np.sin(angle) + (y - cy) * np.cos(angle) + cy, + ) + for x, y in zip(corners_x, corners_y) + ] + + rotated_corners_x, rotated_corners_y = zip(*rotated_corners) + ax.plot( + np.concatenate( + [rotated_corners_x] + ), # Use np.concatenate to fix the addition + np.concatenate( + [rotated_corners_y] + ), # Use np.concatenate to fix the addition + color=color, + alpha=alpha, + linestyle="-", + linewidth=2, + label=label if i == 0 else None, + ) + else: + # Single bounding box + cx, cy = center + corners_x = [ + cx - vehicle_length / 2, + cx + vehicle_length / 2, + cx + vehicle_length / 2, + cx - vehicle_length / 2, + cx - vehicle_length / 2, + ] + corners_y = [ + cy - vehicle_width / 2, + cy - vehicle_width / 2, + cy + vehicle_width / 2, + cy + vehicle_width / 2, + cy - vehicle_width / 2, + ] + + # Apply rotation for single bounding box + rotated_corners = [ + ( + (x - cx) * np.cos(orientation) + - (y - cy) * np.sin(orientation) + + cx, + (x - cx) * np.sin(orientation) + + (y - cy) * np.cos(orientation) + + cy, + ) + for x, y in zip(corners_x, corners_y) + ] + + rotated_corners_x, rotated_corners_y = zip(*rotated_corners) + ax.plot( + np.concatenate([rotated_corners_x]), + np.concatenate([rotated_corners_y]), + color=color, + alpha=alpha, + linestyle="-", + label=label, + linewidth=2, + ) + + +def get_corners_polygon(x, y, length, width, orientation): + """Calculate the four corners of a speed bump (can be any) polygon.""" + # Compute the direction vectors based on orientation + # print(length) + c = np.cos(orientation) + s = np.sin(orientation) + u = np.array((c, s)) # Unit vector along the orientation + ut = np.array((-s, c)) # Unit vector perpendicular to the orientation + + # Center point of the speed bump + pt = np.array([x, y]) + + # corners + tl = pt + (length / 2) * u - (width / 2) * ut + tr = pt + (length / 2) * u + (width / 2) * ut + br = pt - (length / 2) * u + (width / 2) * ut + bl = pt - (length / 2) * u - (width / 2) * ut + + return [tl.tolist(), tr.tolist(), br.tolist(), bl.tolist()] + + +def get_stripe_polygon( + x: float, + y: float, + length: float, + width: float, + orientation: float, + index: int, + num_stripes: int, +) -> np.ndarray: + + """Calculate the corners of a stripe within the speed bump polygon.""" + + # Compute the direction vectors + c = np.cos(orientation) + s = np.sin(orientation) + u = np.array([c, s]) # Unit vector along the orientation (lengthwise) + ut = np.array([-s, c]) # Perpendicular unit vector (widthwise) + + # Total stripe height along the width + stripe_width = length / num_stripes + half_length = length / 2 + half_width = width / 2 + + # Offset for the current stripe + offset_start = -half_length + index * stripe_width + offset_end = offset_start + stripe_width + + # Center of the speed bump + center = np.array([x, y]) + + # Calculate stripe corners + stripe_corners = [ + center + u * offset_start + ut * half_width, # Top-left + center + u * offset_start - ut * half_width, # Bottom-left + center + u * offset_end - ut * half_width, # Bottom-right + center + u * offset_end + ut * half_width, # Top-right + ] + + return np.array(stripe_corners) + + +def plot_speed_bumps( + x_coords: Union[float, np.ndarray], + y_coords: Union[float, np.ndarray], + segment_lengths: Union[float, torch.Tensor], + segment_widths: Union[float, torch.Tensor], + segment_orientations: Union[float, torch.Tensor], + ax: matplotlib.axes.Axes, + facecolor: str = None, + edgecolor: str = None, + alpha: float = None, +) -> None: + facecolor = "xkcd:goldenrod" + edgecolor = "xkcd:black" + alpha = 0.5 + for x, y, length, width, orientation in zip( + x_coords, + y_coords, + segment_lengths, + segment_widths, + segment_orientations, + ): + # method1: from waymax using hatch as diagonals + points = get_corners_polygon(x, y, length, width, orientation) + + p = Polygon( + points, + facecolor=facecolor, + edgecolor=edgecolor, + linewidth=0, + alpha=alpha, + hatch=r"//", + zorder=2, + ) + + ax.add_patch(p) + + pass + + +def plot_stop_sign( + point: np.ndarray, + ax: matplotlib.axes.Axes, + radius: float = None, + facecolor: str = None, + edgecolor: str = None, + linewidth: float = None, + alpha: float = None, +) -> None: + # Default configurations for the stop sign + facecolor = "#c04000" if facecolor is None else facecolor + edgecolor = "white" if edgecolor is None else edgecolor + linewidth = 1.5 if linewidth is None else linewidth + radius = 1.0 if radius is None else radius + alpha = 1.0 if alpha is None else alpha + + point = np.array(point).reshape(-1) + + p = RegularPolygon( + point, + numVertices=6, # For hexagonal stop sign + radius=radius, + facecolor=facecolor, + edgecolor=edgecolor, + linewidth=linewidth, + alpha=alpha, + zorder=2, + ) + ax.add_patch(p) + + +def plot_crosswalk( + points, + ax: plt.Axes = None, + facecolor: str = None, + edgecolor: str = None, + alpha: float = None, +): + if ax is None: + ax = plt.gca() + # override default config + facecolor = ( + crosswalk_config["facecolor"] if facecolor is None else facecolor + ) + edgecolor = ( + crosswalk_config["edgecolor"] if edgecolor is None else edgecolor + ) + alpha = crosswalk_config["alpha"] if alpha is None else alpha + + p = Polygon( + points, + facecolor=facecolor, + edgecolor=edgecolor, + linewidth=2, + alpha=alpha, + hatch=r"//", + zorder=1, + ) + + ax.add_patch(p) + + +def plot_numpy_bounding_boxes_multiple_policy( + ax: matplotlib.axes.Axes, + bboxes_s: List[np.ndarray], + colors: List[np.ndarray], + alpha: Optional[float] = 1.0, + line_width_scale: float = 1.5, + as_center_pts: bool = False, + label: Optional[str] = None, +) -> None: + """Plots multiple bounding boxes. + + Args: + ax: Fig handles. + bboxes_s: Shape (num_policies,bboxes) + bboxes: Shape (num_bbox, 5), with last dimension as (x, y, length, width, + yaw). + colors: (num_policies,color) + color: Shape (3,), represents RGB color for drawing. + alpha: Alpha value for drawing, i.e. 0 means fully transparent. + as_center_pts: If set to True, bboxes will be drawn as center points, + instead of full bboxes. + label: String, represents the meaning of the color for different boxes. + """ + + for bboxes,color in zip(bboxes_s,colors): + if bboxes.ndim != 2 or bboxes.shape[1] != 5: + raise ValueError( + ( + "Expect bboxes rank 2, last dimension of bbox 5" + " got{}, {}, {} respectively" + ).format(bboxes.ndim, bboxes.shape[1], color.shape) + ) + + if as_center_pts: + ax.plot( + bboxes[:, 0], + bboxes[:, 1], + "o", + color=color, + ms=2, + alpha=alpha, + linewidth=1.7 * line_width_scale, + label=label, + ) + else: + c = np.cos(bboxes[:, 4]) + s = np.sin(bboxes[:, 4]) + pt = np.array((bboxes[:, 0], bboxes[:, 1])) # (2, N) + length, width = bboxes[:, 2], bboxes[:, 3] + u = np.array((c, s)) + ut = np.array((s, -c)) + + # Compute box corner coordinates. + tl = pt + length / 2 * u - width / 2 * ut + tr = pt + length / 2 * u + width / 2 * ut + br = pt - length / 2 * u + width / 2 * ut + bl = pt - length / 2 * u - width / 2 * ut + + # Compute heading arrow using center left/right/front. + cl = pt - width / 2 * ut + cr = pt + width / 2 * ut + cf = pt + length / 2 * u + + # Draw bboxes. + ax.plot( + [tl[0, :], tr[0, :], br[0, :], bl[0, :], tl[0, :]], + [tl[1, :], tr[1, :], br[1, :], bl[1, :], tl[1, :]], + color=color, + zorder=4, + linewidth=1.7 * line_width_scale, + alpha=alpha, + label=label, + ) + + # Draw heading arrow. + ax.plot( + [cl[0, :], cr[0, :], cf[0, :], cl[0, :]], + [cl[1, :], cr[1, :], cf[1, :], cl[1, :]], + color=color, + zorder=4, + alpha=alpha, + linewidth=1.5 * line_width_scale, + label=label, + ) \ No newline at end of file diff --git a/gpudrive-main/outputs/shard_000/events.jsonl b/gpudrive-main/outputs/shard_000/events.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..090210bc766c2b07bdf1b80c8a8e72206b305610 --- /dev/null +++ b/gpudrive-main/outputs/shard_000/events.jsonl @@ -0,0 +1,1416 @@ +{"shard_id": 0, "batch_idx": 0, "world_idx": 15, "scenario_file": "tfrecord-00475-of-01000_425.json", "controlled_agents": 5, "risk_agents": [1, 4, 38], "collision": true, "near_miss": true, "offroad": true, "first_collision_step": 61, "first_offroad_step": 38, "min_distance_m": 1.3015, "min_distance_step": 63, 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true, + "offroad": true, + "first_collision_step": 57, + "first_offroad_step": 32, + "min_distance_m": 0.3953, + "min_distance_step": 59, + "min_distance_risk_agent": 2, + "min_distance_partner_agent": 3, + "collided_agents": [ + 2, + 3, + 4, + 9 + ], + "offroad_agents": [ + 2, + 4, + 9 + ], + "goal_agents": 22 + }, + { + "shard_id": 3, + "batch_idx": 1, + "world_idx": 418, + "scenario_file": "tfrecord-00005-of-01000_117.json", + "controlled_agents": 2, + "risk_agents": [ + 0, + 1 + ], + "collision": true, + "near_miss": true, + "offroad": true, + "first_collision_step": 16, + "first_offroad_step": 27, + "min_distance_m": 0.3966, + "min_distance_step": 24, + "min_distance_risk_agent": 1, + "min_distance_partner_agent": 0, + "collided_agents": [ + 0, + 1 + ], + "offroad_agents": [ + 0 + ], + "goal_agents": 2 + }, + { + "shard_id": 0, + "batch_idx": 9, + "world_idx": 607, + "scenario_file": "tfrecord-00004-of-01000_282.json", + "controlled_agents": 5, + "risk_agents": [ + 2, + 9, + 17 + ], + "collision": true, + "near_miss": true, + "offroad": false, + "first_collision_step": 16, + "first_offroad_step": -1, + "min_distance_m": 0.3968, + "min_distance_step": 22, + "min_distance_risk_agent": 17, + "min_distance_partner_agent": 19, + "collided_agents": [ + 17 + ], + "offroad_agents": [], + "goal_agents": 5 + } +] \ No newline at end of file diff --git "a/gpudrive-main/papers/LLM\350\257\204\344\274\260\345\234\272\346\231\257.pdf" "b/gpudrive-main/papers/LLM\350\257\204\344\274\260\345\234\272\346\231\257.pdf" new file mode 100644 index 0000000000000000000000000000000000000000..c0fcbaf1f56de75bd96c991ab426f4f953631c8b --- /dev/null +++ "b/gpudrive-main/papers/LLM\350\257\204\344\274\260\345\234\272\346\231\257.pdf" @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b5c112b786853382a453872cfb2df6ee1f92a9d3f9295360cbc325d6c436a6d2 +size 3214178 diff --git a/gpudrive-main/pyproject.toml b/gpudrive-main/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..b77056294c5403306dd8ee8962aacd3559ac5e6b --- /dev/null +++ b/gpudrive-main/pyproject.toml @@ -0,0 +1,93 @@ +[build-system] +requires = [ + "setuptools>=42", + "scikit-build>=0.13", + "cmake>=3.18", + "ninja", +] +build-backend = "madrona_py_build" +backend-path = ["external/madrona/py"] +wheel-directory = "build" + +[project] +name = "gpudrive" +version = "0.4.0" +authors = [ + {name = "Saman Kazemkheni", email = "skazemkhani@gmail.com"}, + {name = "Aarav Pandya", email = "pandya.aarav.97@gmail.com"}, + {name = "Daphne Cornelisse", email = "cor.daphne@gmail.com"}, + {name = "Eugene Vinitsky", email ="vinitsky.eugene@gmail.com"}, +] +readme = "README.md" +description = "A GPU-accelerated, multi-agent driving simulator" +requires-python = ">=3.11" +dependencies = [ + "numpy>=1.26.4,<2", + "gymnasium", + "pygame", + "matplotlib==3.9", + "pandas", + "python-box==7.2.0", + "typer", + "pyyaml", + "mediapy", + "wandb", + "torch>=2.2.1", + "seaborn", + "safetensors", + "python-box", + "tqdm", + "jax", + "huggingface_hub", + "cmake==4.0.0", +] + +[dependency-groups] +pufferlib = [ + "pufferlib>=2.0.6,<3", +] +sb3 = [ + "stable-baselines3==2.3.2", +] +test = [ + "pytest>=8.2.1", +] +vbd = [ + "lightning", + "jaxlib==0.5.3", # see https://github.com/Emerge-Lab/gpudrive/issues/464 + "waymo-waymax @ git+https://github.com/waymo-research/waymax.git@main", +] + +[tool.uv] +default-groups = "all" +cache-dir = "./.uv_cache" + +# Use the CUDA index for torch on Linux +[[tool.uv.index]] +name = "pytorch-cuda" +url = "https://download.pytorch.org/whl/cu124" +explicit = true + +[[tool.uv.index]] +name = "pytorch-cpu" +url = "https://download.pytorch.org/whl/cpu" +explicit = true + +[tool.uv.sources] +torch = [ + { index = "pytorch-cuda", marker = "platform_system == 'Linux'" }, + { index = "pytorch-cpu", marker = "platform_system != 'Linux'" } +] + +[tool.madrona.packages.madrona_gpudrive] +ext-only = true +ext-out-dir = "build" + +[tool.madrona.packages.gpudrive] +path = "gpudrive" + +[project.packages] +include = [ + "baselines", + "baselines/*", +] diff --git a/gpudrive-main/pytest.ini b/gpudrive-main/pytest.ini new file mode 100644 index 0000000000000000000000000000000000000000..923843c028dcc487583af1f7d117b0b4fa7c26c5 --- /dev/null +++ b/gpudrive-main/pytest.ini @@ -0,0 +1,2 @@ +[pytest] +addopts = --ignore=external/madrona/external/SPIRV-Reflect --ignore=external/madrona/external/nanobind --ignore=external/madrona/external/tinyusdz --ignore=external/madrona/external/googletest diff --git a/gpudrive-main/rules/longtail_event_evaluation.md b/gpudrive-main/rules/longtail_event_evaluation.md new file mode 100644 index 0000000000000000000000000000000000000000..86e8a22e9bba7ff14fdc2b4606b88d264f5a7706 --- /dev/null +++ b/gpudrive-main/rules/longtail_event_evaluation.md @@ -0,0 +1,600 @@ +# Long-tail Event Evaluation Rules + +This document defines a first-pass rule set for evaluating GPUDrive long-tail +search events from `events.jsonl`. The goal is to automatically filter invalid +or low-value events, assign a value score, and provide feedback for future +reward-condition search. + +## 1. Input Format + +Each input event is one JSON object with fields such as: + +```json +{ + "shard_id": 0, + "batch_idx": 0, + "world_idx": 15, + "scenario_file": "tfrecord-00475-of-01000_425.json", + "controlled_agents": 5, + "risk_agents": [1, 4, 38], + "collision": true, + "near_miss": true, + "offroad": true, + "first_collision_step": 61, + "first_offroad_step": 38, + "min_distance_m": 1.3015, + "min_distance_step": 63, + "min_distance_risk_agent": 4, + "min_distance_partner_agent": 21, + "collided_agents": [4], + "offroad_agents": [4], + "goal_agents": 5 +} +``` + +The current JSON is an event summary, not a full trajectory trace. Therefore, +these rules can reliably judge coarse event quality, but cannot fully determine +fine-grained semantics such as cut-in, hard braking, rear-end causality, or +intersection right-of-way violations. + +## 2. Evaluation Objective + +A valuable generated event should satisfy most of the following: + +- It contains a collision or a close near-miss. +- It involves at least one risk-conditioned agent. +- The event is not merely an off-road failure. +- The event has evidence of interaction with another dynamic participant. +- The event occurs after a non-trivial rollout duration, not immediately at initialization. +- The scene has enough controlled agents to support multi-agent interaction. +- The event can be traced back to a specific scenario file, world, and agent IDs. + +## 3. Hard Rejection Rules + +Reject the event if any rule below is true: + +### R1. No Long-tail Signal + +Reject if: + +```text +collision == false +and near_miss == false +and offroad == false +``` + +Reason: + +```text +no_longtail_signal +``` + +### R2. No Risk Agent + +Reject if: + +```text +risk_agents is empty +``` + +Reason: + +```text +no_risk_agent +``` + +### R3. Pure Off-road Failure + +Reject if: + +```text +collision == false +and near_miss == false +and offroad == true +``` + +Reason: + +```text +pure_offroad_failure +``` + +Pure off-road events may be useful for debugging, but they are lower priority +for long-tail interaction generation. + +### R4. Too Few Controlled Agents + +Reject or mark low value if: + +```text +controlled_agents < 2 +``` + +Reason: + +```text +insufficient_interaction_agents +``` + +### R5. Likely Initialization Artifact + +Reject or mark suspicious if: + +```text +first_collision_step >= 0 +and first_collision_step <= 2 +``` + +Reason: + +```text +possible_initial_overlap +``` + +Current JSON cannot fully verify initialization artifacts. These events should +be deprioritized unless a future trajectory trace proves the collision emerged +from interaction. + +## 4. Value Scoring + +Assign each event a score from 0 to 10. + +### Base Score + +Start from: + +```text +score = 0 +``` + +### Collision Reward + +```text +if collision == true: + score += 4 +``` + +Collision events are the highest-priority long-tail candidates. + +### Near-miss Reward + +```text +if near_miss == true: + score += 2 +``` + +If no collision occurs but `min_distance_m <= 2.0`, the event may still be +valuable as a safety-critical near-miss. + +### Close-distance Bonus + +```text +if min_distance_m <= 0.5: + score += 2 +elif min_distance_m <= 1.0: + score += 1.5 +elif min_distance_m <= 2.0: + score += 1 +``` + +Do not apply this bonus if `min_distance_m` is missing or invalid. + +### Risk-agent Involvement Bonus + +```text +if min_distance_risk_agent in risk_agents: + score += 1 +``` + +This suggests that a risk-conditioned agent was involved in the most critical +close interaction. + +### Risk-agent Collision Bonus + +```text +if any(agent in collided_agents for agent in risk_agents): + score += 1.5 +``` + +This is strong evidence that the risk-conditioned agent directly contributed to +the long-tail event. + +### Multi-agent Interaction Bonus + +```text +if controlled_agents >= 5: + score += 1 +elif controlled_agents >= 3: + score += 0.5 +``` + +Scenes with more controlled agents are generally more useful for multi-agent +long-tail generation. + +### Non-trivial Rollout Bonus + +```text +if first_collision_step >= 20: + score += 1 +elif first_collision_step >= 5: + score += 0.5 +``` + +Later collisions are more likely to result from interaction rather than initial +overlap. + +### Off-road Penalty + +```text +if offroad == true and first_offroad_step >= 0: + score -= 1 +``` + +If off-road happens before collision, apply an additional penalty: + +```text +if offroad == true +and collision == true +and first_offroad_step >= 0 +and first_offroad_step < first_collision_step: + score -= 1 +``` + +Reason: the collision may be caused by leaving the road rather than meaningful +traffic interaction. + +### Goal Completion Bonus + +```text +if controlled_agents > 0: + goal_ratio = goal_agents / controlled_agents + if goal_ratio >= 0.8: + score += 0.5 +``` + +A high goal completion ratio suggests that the scenario is not simply chaotic +or broken. + +### Clamp + +Finally: + +```text +score = min(max(score, 0), 10) +``` + +## 5. Priority Labels + +Map scores to priority labels: + +```text +score >= 8.0: high_value +score >= 5.0: medium_value +score >= 3.0: low_value +score < 3.0: reject_or_debug_only +``` + +## 6. Event Type Classification + +Use the current JSON fields to assign coarse event types. + +### Collision Interaction + +```text +collision == true +and len(collided_agents) > 0 +``` + +Label: + +```text +collision_interaction +``` + +### Risk-agent Collision + +```text +collision == true +and any(agent in collided_agents for agent in risk_agents) +``` + +Label: + +```text +risk_agent_collision +``` + +### Near-miss Only + +```text +collision == false +and near_miss == true +``` + +Label: + +```text +near_miss_only +``` + +### Off-road Before Collision + +```text +collision == true +and offroad == true +and first_offroad_step >= 0 +and first_offroad_step < first_collision_step +``` + +Label: + +```text +offroad_before_collision +``` + +This label should be treated cautiously. It may still be useful, but should not +be considered a clean traffic-interaction event without additional trace data. + +## 7. LLM Evaluation Prompt Contract + +When using an LLM to evaluate an event, provide: + +1. This rule document. +2. The event JSON object. +3. Optional aggregate search summary. + +The LLM must output valid JSON only: + +```json +{ + "valid": true, + "priority": "high_value", + "score": 8.0, + "event_type": ["collision_interaction", "risk_agent_collision"], + "reason": "Risk-conditioned agent is involved in a collision after a non-trivial rollout duration.", + "reject_reason": null, + "needs_trace_review": false, + "reward_search_feedback": { + "increase_collision_tolerance": false, + "increase_goal_pressure": false, + "increase_offroad_penalty": true + } +} +``` + +If the event is invalid, use: + +```json +{ + "valid": false, + "priority": "reject_or_debug_only", + "score": 0.0, + "event_type": [], + "reason": "The event is a pure off-road failure without collision or near-miss.", + "reject_reason": "pure_offroad_failure", + "needs_trace_review": false, + "reward_search_feedback": { + "increase_collision_tolerance": false, + "increase_goal_pressure": false, + "increase_offroad_penalty": true + } +} +``` + +## 8. Reward Search Feedback + +Use accepted and rejected events to guide future reward-condition search. + +The reward condition is: + +```text +[collision_weight, goal_achieved_weight, off_road_weight] +``` + +Within the trained range: + +```text +collision_weight: [-1.0, 0.0] +goal_weight: [ 1.0, 2.0] +offroad_weight: [-1.0, 0.0] +``` + +### If Too Many Pure Off-road Events + +Recommendation: + +```text +make offroad_weight more negative +``` + +Example: + +```text +from -0.4 to -0.6 +``` + +### If Too Few Collision or Near-miss Events + +Recommendation: + +```text +make collision_weight less negative +or increase goal_weight +``` + +Examples: + +```text +collision_weight: -0.4 -> -0.2 +goal_weight: 1.8 -> 2.0 +``` + +### If Collisions Happen Too Early + +Recommendation: + +```text +increase collision penalty slightly +or reduce goal pressure +``` + +Examples: + +```text +collision_weight: -0.2 -> -0.4 +goal_weight: 2.0 -> 1.7 +``` + +### If Events Are High-value and Mostly On-road + +Recommendation: + +```text +keep current reward condition +sample nearby parameters in the next round +``` + +This is suitable for CEM, random search, or Bayesian optimization over reward +conditions. + +## 9. Example Evaluations + +### Example A + +Input: + +```json +{ + "world_idx": 15, + "controlled_agents": 5, + "risk_agents": [1, 4, 38], + "collision": true, + "near_miss": true, + "offroad": true, + "first_collision_step": 61, + "first_offroad_step": 38, + "min_distance_m": 1.3015, + "min_distance_risk_agent": 4, + "collided_agents": [4], + "offroad_agents": [4], + "goal_agents": 5 +} +``` + +Assessment: + +```json +{ + "valid": true, + "priority": "high_value", + "event_type": ["collision_interaction", "risk_agent_collision", "offroad_before_collision"], + "needs_trace_review": true, + "reason": "The risk-conditioned agent collided after a non-trivial rollout, but off-road happened before collision, so trace review is needed." +} +``` + +### Example B + +Input: + +```json +{ + "world_idx": 27, + "controlled_agents": 4, + "risk_agents": [0, 3, 10], + "collision": true, + "near_miss": false, + "offroad": false, + "first_collision_step": 15, + "min_distance_m": 3.5441, + "min_distance_risk_agent": 3, + "collided_agents": [1], + "goal_agents": 4 +} +``` + +Assessment: + +```json +{ + "valid": true, + "priority": "medium_value", + "event_type": ["collision_interaction"], + "needs_trace_review": true, + "reason": "The event has a clean on-road collision, but the collided agent is not listed as a risk agent and the closest distance is not very small in the summary." +} +``` + +### Example C + +Input: + +```json +{ + "world_idx": 34, + "controlled_agents": 9, + "risk_agents": [3, 5, 37], + "collision": true, + "near_miss": true, + "offroad": true, + "first_collision_step": 21, + "first_offroad_step": 15, + "min_distance_m": 1.2763, + "min_distance_risk_agent": 5, + "collided_agents": [2, 3, 4, 5], + "offroad_agents": [4], + "goal_agents": 8 +} +``` + +Assessment: + +```json +{ + "valid": true, + "priority": "high_value", + "event_type": ["collision_interaction", "risk_agent_collision", "offroad_before_collision"], + "needs_trace_review": true, + "reason": "The event includes multiple collided agents and risk-agent involvement, but off-road occurs before collision." +} +``` + +## 10. Missing Fields for Future Trace-level Evaluation + +The current event summary is useful for first-pass filtering. For higher-quality +automatic verification, future search outputs should also save: + +- Per-step position `(x, y)` for risk agents and closest partner agents. +- Per-step velocity and acceleration. +- Per-step yaw or heading. +- Per-step action index or continuous action. +- Per-step pairwise distance between risk agent and partner. +- TTC or approximate TTC before collision. +- Agent live/done mask. +- Collision partner identity if available. +- Whether the partner is controlled or log-replay. +- Whether the event occurs in lane, intersection, merge, or off-road region. +- A short trace window around the event, e.g. `[event_step - 30, event_step + 5]`. + +With these fields, future rules can classify: + +- cut-in collision +- rear-end hard-brake +- intersection conflict +- merge conflict +- side-swipe +- invalid artifact + +## 11. Recommended First-pass Pipeline + +1. Parse `events.jsonl`. +2. Apply hard rejection rules. +3. Compute rule-based score. +4. Ask an LLM to produce a structured judgment using this document. +5. Save accepted events to `accepted_events.jsonl`. +6. Save rejected events to `rejected_events.jsonl`. +7. Aggregate reward-search feedback. +8. Use the feedback to sample the next round of reward conditions. + +Videos should be used only for human inspection and presentation, not as the +primary source of automatic verification. diff --git a/gpudrive-main/rules/longtail_llm_case_evaluation.md b/gpudrive-main/rules/longtail_llm_case_evaluation.md new file mode 100644 index 0000000000000000000000000000000000000000..4673299ca46fa88f8db336744409a44684487efe --- /dev/null +++ b/gpudrive-main/rules/longtail_llm_case_evaluation.md @@ -0,0 +1,111 @@ +# Long-Tail LLM Case Evaluation Rules + +This document defines how to evaluate compact GPUDrive long-tail scenario +cases generated from `event_traces.jsonl`. + +The evaluator receives structured evidence only. Do not infer hidden intent, +traffic-law context, or map semantics not present in the case. + +## Evaluation Dimensions + +### 1. Safety Criticality + +High criticality cases show at least one of: + +- collision involving the primary pair +- near miss with very small clean distance +- low TTC before the event +- sustained TTC exposure below 1s or 2s +- meaningful closing speed or relative speed at the event + +Low criticality cases include: + +- no collision or weak near miss +- large clean distance +- no decreasing distance trend +- no meaningful relative motion + +### 2. Motion Naturalness + +Natural scenarios should have plausible speeds, accelerations, yaw changes, and +continuous trajectories after clean masking. + +Artifact warnings should be taken seriously but not all are automatic rejects: + +- `high_yaw_rate`: warning; reject only if it makes the scenario visibly + implausible or combines with other artifacts. +- `high_acceleration` / `hard_deceleration`: warning; reject if extreme or + unsupported by the interaction. +- `extreme_speed` / `large_frame_displacement`: strong artifact signal. +- `short_clean_track`: lower confidence, especially if the primary pair has too + few clean frames. +- `offroad_before_collision`: lower value unless the offroad behavior itself is + a meaningful road departure conflict. + +### 3. Interaction Rationality + +High-value cases should have a clear interaction story: + +- risk agent participates in the primary pair +- primary pair includes a collision or near-miss participant +- distance mostly decreases before the event +- TTC and closing speed evolve consistently +- timeline shows a plausible lead-up rather than an instantaneous overlap + +Reject or downgrade: + +- risk agent is unrelated to the collision +- primary pair is selected only by fallback and has weak evidence +- event is pure offroad without interaction +- collision appears immediately with no lead-up + +### 4. Scenario Value + +High-value scenarios are natural, safety-critical, and useful for long-tail +testing. Prefer diverse interaction types: + +- cut-in or side conflict +- rear-end or following conflict +- crossing or intersection conflict +- near-miss without collision if physically meaningful + +## Output Contract + +Return JSON only: + +```json +{ + "evaluations": [ + { + "case_id": "case id from input", + "accepted": true, + "danger_level": 4, + "naturalness_score": 80, + "interaction_score": 85, + "scenario_value_score": 88, + "scenario_type": "cut_in_or_side_conflict", + "artifact_flags": [], + "rejection_reasons": [], + "optimization_feedback": { + "increase_collision_tolerance": false, + "decrease_collision_tolerance": false, + "increase_goal_pressure": false, + "decrease_goal_pressure": false, + "increase_offroad_penalty": false, + "relax_offroad_penalty": false, + "increase_risk_agents_per_world": false, + "decrease_risk_agents_per_world": false, + "target_scenario_types": ["crossing_or_intersection_conflict"] + }, + "reason": "short reason grounded in the provided evidence", + "evidence": [ + "clean TTC below 1s before collision", + "risk agent directly involved" + ] + } + ] +} +``` + +Scores must be integers from 0 to 100. `danger_level` must be an integer from 1 +to 5. diff --git a/gpudrive-main/scripts/__pycache__/search_longtail_reward_conditioned.cpython-312.pyc b/gpudrive-main/scripts/__pycache__/search_longtail_reward_conditioned.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..47c0eaf8d69d278a93b0a399570f34ac29b19ff0 Binary files /dev/null and b/gpudrive-main/scripts/__pycache__/search_longtail_reward_conditioned.cpython-312.pyc differ diff --git a/gpudrive-main/scripts/benchmark_nuscenes_env.py b/gpudrive-main/scripts/benchmark_nuscenes_env.py new file mode 100644 index 0000000000000000000000000000000000000000..5b68c74d25a7972e19651f93498a2c1173051f1f --- /dev/null +++ b/gpudrive-main/scripts/benchmark_nuscenes_env.py @@ -0,0 +1,156 @@ +#!/usr/bin/env python +"""Benchmark GPUDrive NuScenes environment throughput without PPO training.""" + +from __future__ import annotations + +import argparse +import os +import time +from pathlib import Path + +import torch + +from gpudrive.env.config import EnvConfig, RenderConfig +from gpudrive.env.dataset import SceneDataLoader +from gpudrive.env.env_torch import GPUDriveTorchEnv + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser() + parser.add_argument("--data-dir", default="data/processed/nuscenes") + parser.add_argument("--num-worlds", type=int, default=75) + parser.add_argument("--dataset-size", type=int, default=10000) + parser.add_argument("--steps", type=int, default=200) + parser.add_argument("--warmup-steps", type=int, default=20) + parser.add_argument("--seed", type=int, default=42) + parser.add_argument("--device", default="cuda") + parser.add_argument("--obs-radius", type=float, default=50.0) + parser.add_argument("--polyline-reduction-threshold", type=float, default=0.1) + parser.add_argument("--max-controlled-agents", type=int, default=64) + parser.add_argument("--remove-non-vehicles", type=int, default=1) + parser.add_argument("--init-mode", default="all_valid") + parser.add_argument("--action-mode", choices=["zero", "random"], default="zero") + return parser.parse_args() + + +def sync_if_cuda(device: str) -> None: + if device == "cuda": + torch.cuda.synchronize() + + +def main() -> None: + args = parse_args() + torch.manual_seed(args.seed) + + project_root = Path(__file__).resolve().parents[1] + data_dir = (project_root / args.data_dir).resolve() + os.chdir(project_root / "gpudrive") + + device = args.device + if device == "cuda" and not torch.cuda.is_available(): + device = "cpu" + + data_loader = SceneDataLoader( + root=str(data_dir), + batch_size=args.num_worlds, + dataset_size=args.dataset_size, + sample_with_replacement=True, + shuffle=True, + seed=args.seed, + ) + + env_config = EnvConfig( + ego_state=True, + road_map_obs=True, + partner_obs=True, + norm_obs=True, + reward_type="weighted_combination", + dynamics_model="classic", + collision_behavior="ignore", + remove_non_vehicles=bool(args.remove_non_vehicles), + obs_radius=args.obs_radius, + polyline_reduction_threshold=args.polyline_reduction_threshold, + init_mode=args.init_mode, + ) + env = GPUDriveTorchEnv( + config=env_config, + render_config=RenderConfig(render_3d=False), + data_loader=data_loader, + max_cont_agents=args.max_controlled_agents, + device=device, + ) + control_mask = env.cont_agent_mask.clone() + num_controlled = int(control_mask.sum().item()) + total_slots = env.num_worlds * env.max_agent_count + + print( + "[bench] env ready:", + f"device={device}", + f"num_worlds={env.num_worlds}", + f"max_agent_count={env.max_agent_count}", + f"controlled_agents={num_controlled}", + f"obs_radius={args.obs_radius}", + f"polyline_reduction_threshold={args.polyline_reduction_threshold}", + flush=True, + ) + + generator = torch.Generator(device=device) + generator.manual_seed(args.seed) + + def make_actions() -> torch.Tensor: + if args.action_mode == "random": + return torch.randint( + low=0, + high=env.action_space.n, + size=(env.num_worlds, env.max_agent_count), + generator=generator, + device=device, + ) + return torch.zeros( + (env.num_worlds, env.max_agent_count), + dtype=torch.int64, + device=device, + ) + + for _ in range(args.warmup_steps): + env.step_dynamics(make_actions()) + _ = env.get_obs(control_mask) + _ = env.get_rewards() + _ = env.get_infos() + if bool(env.get_dones().all().item()): + env.reset() + + sync_if_cuda(device) + start = time.perf_counter() + + measured_steps = 0 + for _ in range(args.steps): + env.step_dynamics(make_actions()) + _ = env.get_obs(control_mask) + _ = env.get_rewards() + _ = env.get_infos() + measured_steps += 1 + if bool(env.get_dones().all().item()): + env.reset() + + sync_if_cuda(device) + elapsed = time.perf_counter() - start + sim_steps_per_sec = measured_steps / elapsed + controlled_sps = measured_steps * num_controlled / elapsed + padded_sps = measured_steps * total_slots / elapsed + + print( + "[bench] result:", + f"elapsed_sec={elapsed:.3f}", + f"sim_steps={measured_steps}", + f"sim_steps_per_sec={sim_steps_per_sec:.2f}", + f"controlled_agent_sps={controlled_sps:.2f}", + f"padded_agent_sps={padded_sps:.2f}", + flush=True, + ) + + env.close() + + +if __name__ == "__main__": + main() diff --git a/gpudrive-main/scripts/benchmark_nuscenes_env.sbatch b/gpudrive-main/scripts/benchmark_nuscenes_env.sbatch new file mode 100644 index 0000000000000000000000000000000000000000..27c49139d4f77efd22f55db7135b454b98a1753a --- /dev/null +++ b/gpudrive-main/scripts/benchmark_nuscenes_env.sbatch @@ -0,0 +1,66 @@ +#!/bin/bash +#SBATCH --partition=gre +#SBATCH --nodes=1 +#SBATCH --ntasks-per-node=1 +#SBATCH --gres=gpu:1 +#SBATCH --time=01:00:00 +#SBATCH --job-name=gpudrive_nusc_bench +#SBATCH --output=%x-%j.out +#SBATCH --mem=64G +#SBATCH --cpus-per-task=8 + +set -euo pipefail + +PROJECT_ROOT="${PROJECT_ROOT:-/mingli01/project/zihanliu/gpudrive-main}" +CONDA_ENV_NAME="${CONDA_ENV_NAME:-gpudrive}" +DATA_DIR="${DATA_DIR:-data/processed/nuscenes}" + +NUM_WORLDS="${NUM_WORLDS:-75}" +DATASET_SIZE="${DATASET_SIZE:-10000}" +STEPS="${STEPS:-200}" +WARMUP_STEPS="${WARMUP_STEPS:-20}" +OBS_RADIUS="${OBS_RADIUS:-50.0}" +POLYLINE_REDUCTION_THRESHOLD="${POLYLINE_REDUCTION_THRESHOLD:-0.1}" +MAX_CONTROLLED_AGENTS="${MAX_CONTROLLED_AGENTS:-64}" +SEED="${SEED:-42}" +ACTION_MODE="${ACTION_MODE:-zero}" + +module load conda/3 +source "$(conda info --base)/etc/profile.d/conda.sh" +conda activate "${CONDA_ENV_NAME}" + +export PYTHONNOUSERSITE=1 +export PYTHONUNBUFFERED=1 +export OMP_NUM_THREADS="${OMP_NUM_THREADS:-1}" +export MKL_NUM_THREADS="${MKL_NUM_THREADS:-1}" +export MADRONA_MWGPU_KERNEL_CACHE="${MADRONA_MWGPU_KERNEL_CACHE:-${PROJECT_ROOT}/gpudrive_cache}" + +cd "${PROJECT_ROOT}" +export PYTHONPATH="${PROJECT_ROOT}:${PYTHONPATH:-}" + +echo "PROJECT_ROOT=${PROJECT_ROOT}" +echo "DATA_DIR=${DATA_DIR}" +echo "NUM_WORLDS=${NUM_WORLDS}" +echo "DATASET_SIZE=${DATASET_SIZE}" +echo "STEPS=${STEPS}" +echo "OBS_RADIUS=${OBS_RADIUS}" +echo "POLYLINE_REDUCTION_THRESHOLD=${POLYLINE_REDUCTION_THRESHOLD}" +echo "MAX_CONTROLLED_AGENTS=${MAX_CONTROLLED_AGENTS}" +echo "ACTION_MODE=${ACTION_MODE}" +echo "CUDA_VISIBLE_DEVICES=${CUDA_VISIBLE_DEVICES:-unset}" + +if command -v nvidia-smi >/dev/null 2>&1; then + nvidia-smi -L || true +fi + +python -u scripts/benchmark_nuscenes_env.py \ + --data-dir "${DATA_DIR}" \ + --num-worlds "${NUM_WORLDS}" \ + --dataset-size "${DATASET_SIZE}" \ + --steps "${STEPS}" \ + --warmup-steps "${WARMUP_STEPS}" \ + --obs-radius "${OBS_RADIUS}" \ + --polyline-reduction-threshold "${POLYLINE_REDUCTION_THRESHOLD}" \ + --max-controlled-agents "${MAX_CONTROLLED_AGENTS}" \ + --seed "${SEED}" \ + --action-mode "${ACTION_MODE}" diff --git a/gpudrive-main/scripts/convert_event_traces_to_llm_cases.sbatch b/gpudrive-main/scripts/convert_event_traces_to_llm_cases.sbatch new file mode 100644 index 0000000000000000000000000000000000000000..571e09fb608297e6c70dad9e5f7254132c15a346 --- /dev/null +++ b/gpudrive-main/scripts/convert_event_traces_to_llm_cases.sbatch @@ -0,0 +1,88 @@ +#!/bin/bash +#SBATCH --partition=gre +#SBATCH --nodes=1 +#SBATCH --ntasks=1 +#SBATCH --time=02:00:00 +#SBATCH --job-name=gpudrive_trace2llm +#SBATCH --output=%x-%A_%a.out +#SBATCH --mem=64G +#SBATCH --cpus-per-task=8 + +set -euo pipefail + +# Single shard: +# sbatch scripts/convert_event_traces_to_llm_cases.sbatch +# +# Multiple shards: +# sbatch --array=0-3 scripts/convert_event_traces_to_llm_cases.sbatch + +PROJECT_ROOT="${PROJECT_ROOT:-/mingli01/project/zihanliu/gpudrive-main}" +CONDA_ENV_NAME="${CONDA_ENV_NAME:-gpudrive}" + +SEARCH_DIR="${SEARCH_DIR:-longtail_outputs/reward_conditioned_search}" +OUTPUT_BASE="${OUTPUT_BASE:-search_outputs/llm_cases}" + +SHARD_ID="${SLURM_ARRAY_TASK_ID:-${SHARD_ID:-0}}" +SHARD_PADDED="$(printf "%03d" "${SHARD_ID}")" + +INPUT="${INPUT:-${SEARCH_DIR}/shard_${SHARD_PADDED}/event_traces.jsonl}" +OUTPUT_DIR="${OUTPUT_DIR:-${OUTPUT_BASE}/shard_${SHARD_PADDED}}" + +START_INDEX="${START_INDEX:-0}" +MAX_EVENTS="${MAX_EVENTS:-0}" +MAX_CARDS="${MAX_CARDS:-100}" +WRITE_CARDS="${WRITE_CARDS:-1}" +PRETTY_JSON="${PRETTY_JSON:-0}" +MAX_CONTEXT_AGENTS="${MAX_CONTEXT_AGENTS:-8}" +TIMELINE_OFFSETS_SEC="${TIMELINE_OFFSETS_SEC:--5,-3,-2,-1,-0.5,0,0.5,1}" +DT="${DT:-0.0}" + +module load conda/3 +source "$(conda info --base)/etc/profile.d/conda.sh" +conda activate "${CONDA_ENV_NAME}" + +export PYTHONNOUSERSITE=1 +export PYTHONUNBUFFERED=1 +export OMP_NUM_THREADS="${OMP_NUM_THREADS:-1}" +export MKL_NUM_THREADS="${MKL_NUM_THREADS:-1}" + +cd "${PROJECT_ROOT}" +export PYTHONPATH="${PROJECT_ROOT}:${PYTHONPATH:-}" + +if [[ ! -f "${INPUT}" ]]; then + echo "ERROR: INPUT not found: ${INPUT}" + exit 1 +fi + +mkdir -p "${OUTPUT_DIR}" + +echo "SLURM_JOB_ID=${SLURM_JOB_ID:-unknown}" +echo "SLURM_ARRAY_TASK_ID=${SLURM_ARRAY_TASK_ID:-none}" +echo "PROJECT_ROOT=${PROJECT_ROOT}" +echo "CONDA_ENV_NAME=${CONDA_ENV_NAME}" +echo "SHARD_ID=${SHARD_ID}" +echo "INPUT=${INPUT}" +echo "OUTPUT_DIR=${OUTPUT_DIR}" +echo "START_INDEX=${START_INDEX}" +echo "MAX_EVENTS=${MAX_EVENTS}" +echo "MAX_CARDS=${MAX_CARDS}" +echo "WRITE_CARDS=${WRITE_CARDS}" +echo "MAX_CONTEXT_AGENTS=${MAX_CONTEXT_AGENTS}" +echo "TIMELINE_OFFSETS_SEC=${TIMELINE_OFFSETS_SEC}" +echo "DT=${DT}" + +python -u search/convert_event_traces_to_llm_cases.py \ + --input "${INPUT}" \ + --output-dir "${OUTPUT_DIR}" \ + --start-index "${START_INDEX}" \ + --max-events "${MAX_EVENTS}" \ + --max-cards "${MAX_CARDS}" \ + --write-cards "${WRITE_CARDS}" \ + --pretty-json "${PRETTY_JSON}" \ + --max-context-agents "${MAX_CONTEXT_AGENTS}" \ + --timeline-offsets-sec="${TIMELINE_OFFSETS_SEC}" \ + --dt "${DT}" + +echo "Conversion finished." +echo "Outputs:" +find "${OUTPUT_DIR}" -maxdepth 2 -type f | sort | head -50 diff --git a/gpudrive-main/scripts/demo_nuscenes.sbatch b/gpudrive-main/scripts/demo_nuscenes.sbatch new file mode 100644 index 0000000000000000000000000000000000000000..aaa22f8eaaaee76d3ad7f256451fbc2aa81c7af7 --- /dev/null +++ b/gpudrive-main/scripts/demo_nuscenes.sbatch @@ -0,0 +1,140 @@ +#!/bin/bash +#SBATCH --partition=gre +#SBATCH --nodes=1 +#SBATCH --ntasks-per-node=1 +#SBATCH --gres=gpu:1 +#SBATCH --time=01:00:00 +#SBATCH --job-name=gpudrive_nusc_demo +#SBATCH --output=%x-%j.out +#SBATCH --mem=64G +#SBATCH --cpus-per-task=8 + +set -euo pipefail + +PROJECT_ROOT="${PROJECT_ROOT:-/mingli01/project/zihanliu/gpudrive-main}" +CONDA_ENV_NAME="${CONDA_ENV_NAME:-gpudrive}" +DATA_DIR="${DATA_DIR:-data/processed/nuscenes}" +OUTPUT_DIR="${OUTPUT_DIR:-demo_outputs/nuscenes_min}" + +MODE="${MODE:-single_cutin}" +NUM_WORLDS="${NUM_WORLDS:-4}" +DATASET_SIZE="${DATASET_SIZE:-32}" +STEPS="${STEPS:-91}" +RENDER_WORLDS="${RENDER_WORLDS:-4}" +RENDER_EVERY="${RENDER_EVERY:-2}" +FPS="${FPS:-10}" +SEED="${SEED:-42}" +INIT_MODE="${INIT_MODE:-all_valid}" +ZOOM_RADIUS="${ZOOM_RADIUS:-70.0}" +PERTURB_WORLD="${PERTURB_WORLD:--1}" +PERTURB_AGENT_RANK="${PERTURB_AGENT_RANK:--1}" +PERTURB_START_STEP="${PERTURB_START_STEP:-15}" +PERTURB_END_STEP="${PERTURB_END_STEP:-40}" +PERTURB_ACCEL="${PERTURB_ACCEL:-1.0}" +PERTURB_STEER="${PERTURB_STEER:-0.12}" +PERTURB_BRAKE_ACCEL="${PERTURB_BRAKE_ACCEL:--4.0}" +TARGET_AGENT_RANK="${TARGET_AGENT_RANK:--1}" +TARGET_SELECTION_STEP="${TARGET_SELECTION_STEP:-25}" +TARGET_MIN_DIST="${TARGET_MIN_DIST:-2.0}" +TARGET_MAX_DIST="${TARGET_MAX_DIST:-30.0}" +TARGET_BLEND="${TARGET_BLEND:-0.85}" +TARGET_OFFSET_M="${TARGET_OFFSET_M:-0.0}" +CUTIN_MIN_LATERAL_M="${CUTIN_MIN_LATERAL_M:-2.0}" +CUTIN_MAX_LATERAL_M="${CUTIN_MAX_LATERAL_M:-8.0}" +CUTIN_MAX_LONGITUDINAL_M="${CUTIN_MAX_LONGITUDINAL_M:-3.0}" +CUTIN_MIN_HEADING_COS="${CUTIN_MIN_HEADING_COS:-0.65}" +CUTIN_FINAL_LONGITUDINAL_M="${CUTIN_FINAL_LONGITUDINAL_M:-0.0}" +CUTIN_FINAL_LATERAL_M="${CUTIN_FINAL_LATERAL_M:-0.0}" + +module load conda/3 +source "$(conda info --base)/etc/profile.d/conda.sh" +conda activate "${CONDA_ENV_NAME}" + +export PYTHONNOUSERSITE=1 +export PYTHONUNBUFFERED=1 +export OMP_NUM_THREADS="${OMP_NUM_THREADS:-1}" +export MKL_NUM_THREADS="${MKL_NUM_THREADS:-1}" +export MADRONA_MWGPU_KERNEL_CACHE="${MADRONA_MWGPU_KERNEL_CACHE:-${PROJECT_ROOT}/gpudrive_cache}" + +cd "${PROJECT_ROOT}" +export PYTHONPATH="${PROJECT_ROOT}:${PYTHONPATH:-}" + +if [[ ! -d "${DATA_DIR}" ]]; then + echo "ERROR: DATA_DIR does not exist: ${DATA_DIR}" + exit 1 +fi + +if ! compgen -G "${DATA_DIR}/tfrecord*.json" >/dev/null; then + echo "ERROR: no tfrecord*.json files found under DATA_DIR=${DATA_DIR}" + exit 1 +fi + +echo "SLURM_JOB_ID=${SLURM_JOB_ID:-unknown}" +echo "PROJECT_ROOT=${PROJECT_ROOT}" +echo "DATA_DIR=${DATA_DIR}" +echo "OUTPUT_DIR=${OUTPUT_DIR}" +echo "MODE=${MODE}" +echo "NUM_WORLDS=${NUM_WORLDS}" +echo "DATASET_SIZE=${DATASET_SIZE}" +echo "STEPS=${STEPS}" +echo "RENDER_WORLDS=${RENDER_WORLDS}" +echo "RENDER_EVERY=${RENDER_EVERY}" +echo "SEED=${SEED}" +echo "INIT_MODE=${INIT_MODE}" +echo "PERTURB_WORLD=${PERTURB_WORLD}" +echo "PERTURB_AGENT_RANK=${PERTURB_AGENT_RANK}" +echo "PERTURB_START_STEP=${PERTURB_START_STEP}" +echo "PERTURB_END_STEP=${PERTURB_END_STEP}" +echo "PERTURB_ACCEL=${PERTURB_ACCEL}" +echo "PERTURB_STEER=${PERTURB_STEER}" +echo "PERTURB_BRAKE_ACCEL=${PERTURB_BRAKE_ACCEL}" +echo "TARGET_AGENT_RANK=${TARGET_AGENT_RANK}" +echo "TARGET_SELECTION_STEP=${TARGET_SELECTION_STEP}" +echo "TARGET_MIN_DIST=${TARGET_MIN_DIST}" +echo "TARGET_MAX_DIST=${TARGET_MAX_DIST}" +echo "TARGET_BLEND=${TARGET_BLEND}" +echo "TARGET_OFFSET_M=${TARGET_OFFSET_M}" +echo "CUTIN_MIN_LATERAL_M=${CUTIN_MIN_LATERAL_M}" +echo "CUTIN_MAX_LATERAL_M=${CUTIN_MAX_LATERAL_M}" +echo "CUTIN_MAX_LONGITUDINAL_M=${CUTIN_MAX_LONGITUDINAL_M}" +echo "CUTIN_MIN_HEADING_COS=${CUTIN_MIN_HEADING_COS}" +echo "CUTIN_FINAL_LONGITUDINAL_M=${CUTIN_FINAL_LONGITUDINAL_M}" +echo "CUTIN_FINAL_LATERAL_M=${CUTIN_FINAL_LATERAL_M}" +echo "CUDA_VISIBLE_DEVICES=${CUDA_VISIBLE_DEVICES:-unset}" + +if command -v nvidia-smi >/dev/null 2>&1; then + nvidia-smi -L || true +fi + +python -u scripts/nuscenes_min_demo.py \ + --data-dir "${DATA_DIR}" \ + --output-dir "${OUTPUT_DIR}" \ + --mode "${MODE}" \ + --num-worlds "${NUM_WORLDS}" \ + --dataset-size "${DATASET_SIZE}" \ + --steps "${STEPS}" \ + --render-worlds "${RENDER_WORLDS}" \ + --render-every "${RENDER_EVERY}" \ + --fps "${FPS}" \ + --seed "${SEED}" \ + --init-mode "${INIT_MODE}" \ + --zoom-radius "${ZOOM_RADIUS}" \ + --perturb-world "${PERTURB_WORLD}" \ + --perturb-agent-rank "${PERTURB_AGENT_RANK}" \ + --perturb-start-step "${PERTURB_START_STEP}" \ + --perturb-end-step "${PERTURB_END_STEP}" \ + --perturb-accel "${PERTURB_ACCEL}" \ + --perturb-steer "${PERTURB_STEER}" \ + --perturb-brake-accel "${PERTURB_BRAKE_ACCEL}" \ + --target-agent-rank "${TARGET_AGENT_RANK}" \ + --target-selection-step "${TARGET_SELECTION_STEP}" \ + --target-min-dist "${TARGET_MIN_DIST}" \ + --target-max-dist "${TARGET_MAX_DIST}" \ + --target-blend "${TARGET_BLEND}" \ + --target-offset-m "${TARGET_OFFSET_M}" \ + --cutin-min-lateral-m "${CUTIN_MIN_LATERAL_M}" \ + --cutin-max-lateral-m "${CUTIN_MAX_LATERAL_M}" \ + --cutin-max-longitudinal-m "${CUTIN_MAX_LONGITUDINAL_M}" \ + --cutin-min-heading-cos "${CUTIN_MIN_HEADING_COS}" \ + --cutin-final-longitudinal-m "${CUTIN_FINAL_LONGITUDINAL_M}" \ + --cutin-final-lateral-m "${CUTIN_FINAL_LATERAL_M}" diff --git a/gpudrive-main/scripts/demo_official_policy.py b/gpudrive-main/scripts/demo_official_policy.py new file mode 100644 index 0000000000000000000000000000000000000000..9455226c1ba8d0c3e3a8dab78dd34f8a5fe7a4c5 --- /dev/null +++ b/gpudrive-main/scripts/demo_official_policy.py @@ -0,0 +1,222 @@ +#!/usr/bin/env python +"""Visualize the official GPUDrive pretrained policy. + +All controlled agents share the same official policy checkpoint. Each agent +receives its own observation and independently samples/actions from the shared +network. Non-controlled agents remain log-replay/static background actors. +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +import mediapy as media +import torch + +from gpudrive.env.config import EnvConfig, RenderConfig +from gpudrive.env.dataset import SceneDataLoader +from gpudrive.env.env_torch import GPUDriveTorchEnv +from gpudrive.networks.late_fusion import NeuralNet +from gpudrive.visualize.utils import img_from_fig + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser() + parser.add_argument( + "--model", + default="daphne-cornelisse/policy_S10_000_02_27", + help="HF repo id or local directory containing the official policy.", + ) + parser.add_argument("--data-dir", default="/mingli01/data/GPUDrive_mini/training") + parser.add_argument("--output-dir", default="demo_outputs/official_policy") + parser.add_argument("--num-worlds", type=int, default=4) + parser.add_argument("--dataset-size", type=int, default=64) + parser.add_argument("--steps", type=int, default=91) + parser.add_argument("--render-worlds", type=int, default=4) + parser.add_argument("--render-every", type=int, default=2) + parser.add_argument("--fps", type=int, default=10) + parser.add_argument("--seed", type=int, default=42) + parser.add_argument("--device", default="cuda") + parser.add_argument("--deterministic", type=int, default=1) + parser.add_argument("--zoom-radius", type=float, default=70.0) + parser.add_argument( + "--center-on-first-agent", + type=int, + default=0, + help="If 1, center each rendered world on its first controlled agent.", + ) + return parser.parse_args() + + +def make_official_env_config(num_worlds: int) -> EnvConfig: + return EnvConfig( + num_worlds=num_worlds, + max_controlled_agents=64, + ego_state=True, + road_map_obs=True, + partner_obs=True, + norm_obs=True, + remove_non_vehicles=True, + lidar_obs=False, + reward_type="weighted_combination", + collision_weight=-0.75, + off_road_weight=-0.75, + goal_achieved_weight=1.0, + dynamics_model="classic", + collision_behavior="ignore", + dist_to_goal_threshold=2.0, + polyline_reduction_threshold=0.1, + obs_radius=50.0, + steer_actions=torch.round(torch.linspace(-torch.pi, torch.pi, 13), decimals=3), + accel_actions=torch.round(torch.linspace(-4.0, 4.0, 7), decimals=3), + init_steps=11, + vbd_in_obs=False, + use_vbd=False, + ) + + +def render_worlds( + env: GPUDriveTorchEnv, + frames: dict[int, list], + worlds: list[int], + time_step: int, + zoom_radius: float, + center_on_first_agent: bool, +) -> None: + center_agents = None + if center_on_first_agent: + center_agents = [] + for world in worlds: + agent_ids = torch.where(env.cont_agent_mask[world])[0] + center_agents.append(int(agent_ids[0].item()) if len(agent_ids) else None) + + figures = env.vis.plot_simulator_state( + env_indices=worlds, + time_steps=[time_step] * len(worlds), + center_agent_indices=center_agents, + zoom_radius=zoom_radius, + ) + for world, fig in zip(worlds, figures): + frames[world].append(img_from_fig(fig)) + + +def main() -> None: + args = parse_args() + device = args.device if torch.cuda.is_available() or args.device == "cpu" else "cpu" + output_dir = Path(args.output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + + env_config = make_official_env_config(args.num_worlds) + data_loader = SceneDataLoader( + root=args.data_dir, + batch_size=args.num_worlds, + dataset_size=args.dataset_size, + sample_with_replacement=True, + shuffle=True, + seed=args.seed, + ) + env = GPUDriveTorchEnv( + config=env_config, + data_loader=data_loader, + max_cont_agents=env_config.max_controlled_agents, + device=device, + render_config=RenderConfig(render_3d=False), + ) + + print(f"[official-demo] loading model={args.model}", flush=True) + policy = NeuralNet.from_pretrained(args.model).to(device) + policy.eval() + + obs = env.reset() + control_mask = env.cont_agent_mask.clone() + live_agent_mask = control_mask.clone() + rendered_worlds = list(range(min(args.render_worlds, env.num_worlds))) + frames = {world: [] for world in rendered_worlds} + + collided = torch.zeros_like(control_mask, dtype=torch.bool, device=device) + off_road = torch.zeros_like(control_mask, dtype=torch.bool, device=device) + goal = torch.zeros_like(control_mask, dtype=torch.bool, device=device) + + print( + "[official-demo] env ready:", + f"data_dir={data_loader.root}", + f"dataset_files={len(data_loader.dataset)}", + f"num_worlds={env.num_worlds}", + f"controlled_agents={int(control_mask.sum().item())}", + f"controlled_per_world={control_mask.sum(dim=1).detach().cpu().tolist()}", + f"deterministic={bool(args.deterministic)}", + flush=True, + ) + + if rendered_worlds: + render_worlds( + env, + frames, + rendered_worlds, + time_step=0, + zoom_radius=args.zoom_radius, + center_on_first_agent=bool(args.center_on_first_agent), + ) + + for step in range(args.steps): + active = live_agent_mask & control_mask + actions = torch.zeros((env.num_worlds, env.max_agent_count), dtype=torch.int64, device=device) + if bool(active.any().item()): + with torch.no_grad(): + action, _, _, _ = policy(obs[active], deterministic=bool(args.deterministic)) + actions[active] = action.to(dtype=torch.int64, device=device) + + env.step_dynamics(actions) + infos = env.get_infos() + collided |= (infos.collided > 0).bool() & active + off_road |= (infos.off_road > 0).bool() & active + goal |= (infos.goal_achieved > 0).bool() & active + live_agent_mask &= ~env.get_dones().bool() + obs = env.get_obs() + + if rendered_worlds and (step + 1) % args.render_every == 0: + render_worlds( + env, + frames, + rendered_worlds, + time_step=step + 1, + zoom_radius=args.zoom_radius, + center_on_first_agent=bool(args.center_on_first_agent), + ) + + if not bool(live_agent_mask.any().item()): + print(f"[official-demo] all controlled agents done at step={step + 1}", flush=True) + break + + summary = { + "model": args.model, + "data_dir": str(data_loader.root), + "num_worlds": env.num_worlds, + "controlled_agents": int(control_mask.sum().item()), + "collided_agents": int((collided & control_mask).sum().item()), + "off_road_agents": int((off_road & control_mask).sum().item()), + "goal_achieved_agents": int((goal & control_mask).sum().item()), + "rendered_worlds": rendered_worlds, + "color_map": { + "official_policy_controlled": "blue", + "log_replay_or_static": "gray", + "collided": "red", + "off_road": "orange", + }, + } + print("[official-demo] summary:", summary, flush=True) + + with open(output_dir / "summary.json", "w", encoding="utf-8") as f: + json.dump(summary, f, indent=2) + + for world, world_frames in frames.items(): + if world_frames: + video_path = output_dir / f"world_{world:03d}_official_policy.mp4" + media.write_video(video_path, world_frames, fps=args.fps) + print(f"[official-demo] wrote {video_path}", flush=True) + + +if __name__ == "__main__": + main() diff --git a/gpudrive-main/scripts/demo_official_policy.sbatch b/gpudrive-main/scripts/demo_official_policy.sbatch new file mode 100644 index 0000000000000000000000000000000000000000..d1fecd930778939c21723d400a9a207832654d98 --- /dev/null +++ b/gpudrive-main/scripts/demo_official_policy.sbatch @@ -0,0 +1,87 @@ +#!/bin/bash +#SBATCH --partition=gre +#SBATCH --nodes=1 +#SBATCH --ntasks-per-node=1 +#SBATCH --gres=gpu:1 +#SBATCH --time=01:00:00 +#SBATCH --job-name=gpudrive_official_demo +#SBATCH --output=%x-%j.out +#SBATCH --mem=64G +#SBATCH --cpus-per-task=8 + +set -euo pipefail + +PROJECT_ROOT="${PROJECT_ROOT:-/mingli01/project/zihanliu/gpudrive-main}" +CONDA_ENV_NAME="${CONDA_ENV_NAME:-gpudrive}" +OFFICIAL_MODEL="${OFFICIAL_MODEL:-daphne-cornelisse/policy_S10_000_02_27}" +DATA_DIR="${DATA_DIR:-/mingli01/data/GPUDrive_mini/training}" +OUTPUT_DIR="${OUTPUT_DIR:-demo_outputs/official_policy}" + +NUM_WORLDS="${NUM_WORLDS:-4}" +DATASET_SIZE="${DATASET_SIZE:-64}" +STEPS="${STEPS:-91}" +RENDER_WORLDS="${RENDER_WORLDS:-4}" +RENDER_EVERY="${RENDER_EVERY:-2}" +FPS="${FPS:-10}" +SEED="${SEED:-42}" +DETERMINISTIC="${DETERMINISTIC:-1}" +ZOOM_RADIUS="${ZOOM_RADIUS:-70.0}" +CENTER_ON_FIRST_AGENT="${CENTER_ON_FIRST_AGENT:-0}" + +module load conda/3 +source "$(conda info --base)/etc/profile.d/conda.sh" +conda activate "${CONDA_ENV_NAME}" + +export PYTHONNOUSERSITE=1 +export PYTHONUNBUFFERED=1 +export OMP_NUM_THREADS="${OMP_NUM_THREADS:-1}" +export MKL_NUM_THREADS="${MKL_NUM_THREADS:-1}" +export MADRONA_MWGPU_KERNEL_CACHE="${MADRONA_MWGPU_KERNEL_CACHE:-${PROJECT_ROOT}/gpudrive_cache}" +export HF_HOME="${HF_HOME:-/mingli01/data/.hf_cache}" +export HF_ENDPOINT="${HF_ENDPOINT:-https://hf-mirror.com}" +export HF_HUB_ENABLE_HF_TRANSFER="${HF_HUB_ENABLE_HF_TRANSFER:-0}" + +cd "${PROJECT_ROOT}" +export PYTHONPATH="${PROJECT_ROOT}:${PYTHONPATH:-}" + +if [[ ! -d "${DATA_DIR}" ]]; then + echo "ERROR: DATA_DIR does not exist: ${DATA_DIR}" + exit 1 +fi + +if ! compgen -G "${DATA_DIR}/tfrecord*.json" >/dev/null; then + echo "ERROR: no tfrecord*.json files found under DATA_DIR=${DATA_DIR}" + exit 1 +fi + +echo "SLURM_JOB_ID=${SLURM_JOB_ID:-unknown}" +echo "PROJECT_ROOT=${PROJECT_ROOT}" +echo "OFFICIAL_MODEL=${OFFICIAL_MODEL}" +echo "DATA_DIR=${DATA_DIR}" +echo "OUTPUT_DIR=${OUTPUT_DIR}" +echo "HF_HOME=${HF_HOME}" +echo "HF_ENDPOINT=${HF_ENDPOINT}" +echo "NUM_WORLDS=${NUM_WORLDS}" +echo "DATASET_SIZE=${DATASET_SIZE}" +echo "RENDER_WORLDS=${RENDER_WORLDS}" +echo "DETERMINISTIC=${DETERMINISTIC}" +echo "CUDA_VISIBLE_DEVICES=${CUDA_VISIBLE_DEVICES:-unset}" + +if command -v nvidia-smi >/dev/null 2>&1; then + nvidia-smi -L || true +fi + +python -u scripts/demo_official_policy.py \ + --model "${OFFICIAL_MODEL}" \ + --data-dir "${DATA_DIR}" \ + --output-dir "${OUTPUT_DIR}" \ + --num-worlds "${NUM_WORLDS}" \ + --dataset-size "${DATASET_SIZE}" \ + --steps "${STEPS}" \ + --render-worlds "${RENDER_WORLDS}" \ + --render-every "${RENDER_EVERY}" \ + --fps "${FPS}" \ + --seed "${SEED}" \ + --deterministic "${DETERMINISTIC}" \ + --zoom-radius "${ZOOM_RADIUS}" \ + --center-on-first-agent "${CENTER_ON_FIRST_AGENT}" diff --git a/gpudrive-main/scripts/demo_reward_conditioned_policy.py b/gpudrive-main/scripts/demo_reward_conditioned_policy.py new file mode 100644 index 0000000000000000000000000000000000000000..6adbc1a83e73907dd48ca916962f99bbd7f52d56 --- /dev/null +++ b/gpudrive-main/scripts/demo_reward_conditioned_policy.py @@ -0,0 +1,374 @@ +#!/usr/bin/env python +"""Visualize a reward-conditioned GPUDrive policy. + +The script loads a Puffer PPO checkpoint, assigns one controlled agent a +``risk_taker`` reward condition, assigns the remaining controlled agents a +normal/balanced condition, rolls out the shared policy, and writes videos. +""" + +from __future__ import annotations + +import argparse +import json +from dataclasses import fields +from pathlib import Path +from typing import Any + +import mediapy as media +import torch +import yaml + +from gpudrive.env.config import EnvConfig, RenderConfig +from gpudrive.env.dataset import SceneDataLoader +from gpudrive.env.env_torch import GPUDriveTorchEnv +from gpudrive.networks.late_fusion import NeuralNet +from gpudrive.visualize.utils import img_from_fig + + +POLICY_COLOR_MAP = { + "risk_style": "green", + "normal_style": "blue", + "log_replay": "gray", + "collided": "red", + "off_road": "orange", +} + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser() + parser.add_argument("--config", default="baselines/ppo/config/ppo_mini_reward_conditioned.yaml") + parser.add_argument("--checkpoint", default="") + parser.add_argument("--normal-checkpoint", default="") + parser.add_argument("--normal-config", default="baselines/ppo/config/ppo_mini_official_sanity.yaml") + parser.add_argument("--normal-mode", choices=["expert", "policy"], default="expert") + parser.add_argument("--data-dir", default="/mingli01/data/GPUDrive_mini/training") + parser.add_argument("--output-dir", default="demo_outputs/mini_reward_conditioned_policy") + parser.add_argument("--num-worlds", type=int, default=8) + parser.add_argument("--dataset-size", type=int, default=64) + parser.add_argument("--steps", type=int, default=91) + parser.add_argument("--render-worlds", type=int, default=4) + parser.add_argument("--render-every", type=int, default=2) + parser.add_argument("--fps", type=int, default=10) + parser.add_argument("--seed", type=int, default=42) + parser.add_argument("--device", default="cuda") + parser.add_argument("--deterministic", type=int, default=1) + parser.add_argument("--risk-world", type=int, default=-1) + parser.add_argument("--risk-agent-rank", type=int, default=0) + parser.add_argument("--risk-style", default="risk_taker") + parser.add_argument("--normal-style", default="balanced") + parser.add_argument("--risk-collision-weight", type=float, default=None) + parser.add_argument("--risk-goal-weight", type=float, default=None) + parser.add_argument("--risk-offroad-weight", type=float, default=None) + parser.add_argument("--zoom-radius", type=float, default=70.0) + parser.add_argument("--obs-radius", type=float, default=None) + parser.add_argument("--polyline-reduction-threshold", type=float, default=None) + return parser.parse_args() + + +def load_config(path: str) -> dict[str, Any]: + with open(path, "r", encoding="utf-8") as f: + return yaml.safe_load(f) + + +def find_latest_checkpoint(pattern_prefix: str = "PPO_MINI_RC") -> Path: + patterns = [ + f"runs/{pattern_prefix}*/model_*.pt", + f"gpudrive/runs/{pattern_prefix}*/model_*.pt", + "runs/*MINI*RC*/model_*.pt", + "gpudrive/runs/*MINI*RC*/model_*.pt", + "runs/*/model_*.pt", + "gpudrive/runs/*/model_*.pt", + ] + for pattern in patterns: + candidates = list(Path(".").glob(pattern)) + if candidates: + return max(candidates, key=lambda p: p.stat().st_mtime) + raise FileNotFoundError( + "No checkpoint found under runs/. Pass --checkpoint /path/to/model.pt." + ) + + +def reward_preset(config: EnvConfig, style: str, device: str) -> torch.Tensor: + presets = { + "cautious": [ + config.collision_weight_lb * 0.9, + config.goal_achieved_weight_ub * 0.7, + config.off_road_weight_lb * 0.9, + ], + "aggressive": [ + config.collision_weight_lb * 0.5, + config.goal_achieved_weight_ub * 0.9, + config.off_road_weight_lb * 0.6, + ], + "balanced": [ + (config.collision_weight_lb + config.collision_weight_ub) / 2, + (config.goal_achieved_weight_lb + config.goal_achieved_weight_ub) / 2, + (config.off_road_weight_lb + config.off_road_weight_ub) / 2, + ], + "risk_taker": [ + config.collision_weight_lb * 0.3, + config.goal_achieved_weight_ub, + config.off_road_weight_lb * 0.4, + ], + } + if style not in presets: + raise ValueError(f"Unknown style={style}. Available: {sorted(presets)}") + return torch.tensor(presets[style], dtype=torch.float32, device=device) + + +def apply_weight_overrides( + weights: torch.Tensor, + collision_weight: float | None, + goal_weight: float | None, + offroad_weight: float | None, +) -> torch.Tensor: + weights = weights.clone() + if collision_weight is not None: + weights[0] = collision_weight + if goal_weight is not None: + weights[1] = goal_weight + if offroad_weight is not None: + weights[2] = offroad_weight + return weights + + +def make_env_config(raw_env: dict[str, Any], args: argparse.Namespace) -> EnvConfig: + env_fields = {field.name for field in fields(EnvConfig)} + cfg = {k: v for k, v in raw_env.items() if k in env_fields} + cfg["num_worlds"] = args.num_worlds + cfg["reward_type"] = "reward_conditioned" + cfg["condition_mode"] = "random" + if args.obs_radius is not None: + cfg["obs_radius"] = args.obs_radius + if args.polyline_reduction_threshold is not None: + cfg["polyline_reduction_threshold"] = args.polyline_reduction_threshold + + steer_disc = int(raw_env.get("action_space_steer_disc", 13)) + accel_disc = int(raw_env.get("action_space_accel_disc", 7)) + cfg["steer_actions"] = torch.round(torch.linspace(-torch.pi, torch.pi, steer_disc), decimals=3) + cfg["accel_actions"] = torch.round(torch.linspace(-4.0, 4.0, accel_disc), decimals=3) + return EnvConfig(**cfg) + + +def load_policy(checkpoint_path: Path, model_config: dict[str, Any], device: str) -> NeuralNet: + saved = torch.load(checkpoint_path, map_location=device, weights_only=False) + arch = saved["model_arch"] + arch_get = arch.get if hasattr(arch, "get") else lambda key, default=None: getattr(arch, key, default) + policy = NeuralNet( + input_dim=arch_get("input_dim"), + action_dim=saved["action_dim"], + hidden_dim=arch_get("hidden_dim"), + dropout=arch_get("dropout", 0.0), + config=model_config["environment"], + ).to(device) + policy.load_state_dict(saved["parameters"]) + policy.eval() + return policy + + +def choose_risk_agent(control_mask: torch.Tensor, requested_world: int, rank: int) -> tuple[int, int]: + controlled_per_world = control_mask.sum(dim=1) + if requested_world >= 0: + world = requested_world + else: + world = int(torch.argmax(controlled_per_world).item()) + if world < 0 or world >= control_mask.shape[0]: + raise ValueError(f"risk_world={world} is outside [0, {control_mask.shape[0]})") + + agents = torch.where(control_mask[world])[0] + if len(agents) == 0: + raise RuntimeError(f"No controlled agents in world {world}.") + rank = min(max(rank, 0), len(agents) - 1) + return world, int(agents[rank].item()) + + +def render(env: GPUDriveTorchEnv, frames: dict[int, list], worlds: list[int], time_step: int, + risk_world: int, risk_agent: int, policy_masks: dict, zoom_radius: float) -> None: + center_agents = [risk_agent if world == risk_world else None for world in worlds] + render_policy_masks = { + name: (policy_fn, mask.detach().cpu()) + for name, (policy_fn, mask) in policy_masks.items() + } + figures = env.vis.plot_simulator_state( + env_indices=worlds, + time_steps=[time_step] * len(worlds), + center_agent_indices=center_agents, + zoom_radius=zoom_radius, + policy_masks=render_policy_masks, + ) + for world, fig in zip(worlds, figures): + frames[world].append(img_from_fig(fig)) + + +def main() -> None: + args = parse_args() + device = args.device if torch.cuda.is_available() or args.device == "cpu" else "cpu" + config = load_config(args.config) + checkpoint = Path(args.checkpoint) if args.checkpoint else find_latest_checkpoint() + normal_checkpoint = Path(args.normal_checkpoint) if args.normal_checkpoint else None + normal_config = load_config(args.normal_config) if normal_checkpoint else config + output_dir = Path(args.output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + + env_config = make_env_config(config["environment"], args) + if env_config.reward_type != "reward_conditioned": + raise RuntimeError("This demo requires a reward_conditioned config/policy.") + + data_loader = SceneDataLoader( + root=args.data_dir, + batch_size=args.num_worlds, + dataset_size=args.dataset_size, + sample_with_replacement=True, + shuffle=True, + seed=args.seed, + ) + env = GPUDriveTorchEnv( + config=env_config, + data_loader=data_loader, + max_cont_agents=env_config.max_controlled_agents, + device=device, + render_config=RenderConfig(render_3d=False), + ) + env.reset() + + control_mask = env.cont_agent_mask.clone() + risk_world, risk_agent = choose_risk_agent(control_mask, args.risk_world, args.risk_agent_rank) + risk_mask = torch.zeros_like(control_mask, dtype=torch.bool, device=device) + risk_mask[risk_world, risk_agent] = True + normal_mask = control_mask & ~risk_mask + + normal_weights = reward_preset(env_config, args.normal_style, device) + risk_weights = apply_weight_overrides( + reward_preset(env_config, args.risk_style, device), + args.risk_collision_weight, + args.risk_goal_weight, + args.risk_offroad_weight, + ) + env.reward_weights_tensor[:] = normal_weights + env.reward_weights_tensor[risk_world, risk_agent] = risk_weights + + risk_policy = load_policy(checkpoint, config, device) + normal_policy = ( + load_policy(normal_checkpoint, normal_config, device) + if args.normal_mode == "policy" and normal_checkpoint is not None + else risk_policy + ) + policy_masks = { + args.risk_style: (risk_policy, risk_mask), + args.normal_style: (normal_policy, normal_mask), + } + + rendered_worlds = [risk_world] + for world in range(env.num_worlds): + if len(rendered_worlds) >= args.render_worlds: + break + if world != risk_world: + rendered_worlds.append(world) + frames = {world: [] for world in rendered_worlds} + + collided = torch.zeros_like(control_mask, dtype=torch.bool, device=device) + off_road = torch.zeros_like(control_mask, dtype=torch.bool, device=device) + goal = torch.zeros_like(control_mask, dtype=torch.bool, device=device) + live_mask = control_mask.clone() + + print( + "[policy-demo] start:", + f"risk_checkpoint={checkpoint}", + f"normal_mode={args.normal_mode}", + f"normal_checkpoint={normal_checkpoint if normal_checkpoint is not None else checkpoint}", + f"data_dir={data_loader.root}", + f"dataset_files={len(data_loader.dataset)}", + f"num_worlds={env.num_worlds}", + f"controlled_agents={int(control_mask.sum().item())}", + f"risk_world={risk_world}", + f"risk_agent={risk_agent}", + f"risk_color={POLICY_COLOR_MAP['risk_style']}", + f"normal_color={POLICY_COLOR_MAP['normal_style']}", + f"risk_weights={risk_weights.detach().cpu().tolist()}", + f"normal_weights={normal_weights.detach().cpu().tolist()}", + flush=True, + ) + + if args.render_worlds > 0: + render(env, frames, rendered_worlds, 0, risk_world, risk_agent, policy_masks, args.zoom_radius) + + expert_actions = None + if args.normal_mode == "expert": + expert_actions, _, _, _ = env.get_expert_actions() + + for step in range(args.steps): + active = live_mask & control_mask + if args.normal_mode == "expert": + action_step = min(step, expert_actions.shape[2] - 1) + actions = expert_actions[:, :, action_step, :].clone() + else: + actions = torch.zeros((env.num_worlds, env.max_agent_count), dtype=torch.int64, device=device) + + active_risk = active & risk_mask + active_normal = active & normal_mask + if bool(active_risk.any().item()): + obs = env.get_obs(active_risk) + with torch.no_grad(): + action, _, _, _ = risk_policy(obs, deterministic=bool(args.deterministic)) + if args.normal_mode == "expert": + actions[active_risk] = env.action_keys_tensor[action.to(dtype=torch.long, device=device)] + else: + actions[active_risk] = action.to(dtype=torch.int64, device=device) + if args.normal_mode == "policy" and bool(active_normal.any().item()): + obs = env.get_obs(active_normal) + with torch.no_grad(): + action, _, _, _ = normal_policy(obs, deterministic=bool(args.deterministic)) + actions[active_normal] = action.to(dtype=torch.int64, device=device) + + env.step_dynamics(actions) + infos = env.get_infos() + collided |= (infos.collided > 0).bool() & active + off_road |= (infos.off_road > 0).bool() & active + goal |= (infos.goal_achieved > 0).bool() & active + live_mask &= ~env.get_dones().bool() + + if args.render_worlds > 0 and (step + 1) % args.render_every == 0: + render(env, frames, rendered_worlds, step + 1, risk_world, risk_agent, policy_masks, args.zoom_radius) + + if not bool(live_mask.any().item()): + print(f"[policy-demo] all controlled agents done at step={step + 1}", flush=True) + break + + summary = { + "risk_checkpoint": str(checkpoint), + "normal_checkpoint": str(normal_checkpoint) if normal_checkpoint is not None else str(checkpoint), + "risk_world": risk_world, + "risk_agent": risk_agent, + "risk_style": args.risk_style, + "normal_style": args.normal_style, + "normal_mode": args.normal_mode, + "color_map": { + args.risk_style: POLICY_COLOR_MAP["risk_style"], + args.normal_style: POLICY_COLOR_MAP["normal_style"], + "log_replay": POLICY_COLOR_MAP["log_replay"], + "collided": POLICY_COLOR_MAP["collided"], + "off_road": POLICY_COLOR_MAP["off_road"], + }, + "risk_collided": bool(collided[risk_world, risk_agent].item()), + "risk_off_road": bool(off_road[risk_world, risk_agent].item()), + "risk_goal_achieved": bool(goal[risk_world, risk_agent].item()), + "controlled_agents": int(control_mask.sum().item()), + "collided_agents": int((collided & control_mask).sum().item()), + "off_road_agents": int((off_road & control_mask).sum().item()), + "goal_achieved_agents": int((goal & control_mask).sum().item()), + "rendered_worlds": rendered_worlds, + } + print("[policy-demo] summary:", summary, flush=True) + + with open(output_dir / "summary.json", "w", encoding="utf-8") as f: + json.dump(summary, f, indent=2) + + for world, world_frames in frames.items(): + if world_frames: + video_path = output_dir / f"world_{world:03d}_{args.risk_style}_policy.mp4" + media.write_video(video_path, world_frames, fps=args.fps) + print(f"[policy-demo] wrote {video_path}", flush=True) + + +if __name__ == "__main__": + main() diff --git a/gpudrive-main/scripts/demo_reward_conditioned_policy.sbatch b/gpudrive-main/scripts/demo_reward_conditioned_policy.sbatch new file mode 100644 index 0000000000000000000000000000000000000000..7754a611be2dd67a41fb23bc1d9539dd24c61100 --- /dev/null +++ b/gpudrive-main/scripts/demo_reward_conditioned_policy.sbatch @@ -0,0 +1,140 @@ +#!/bin/bash +#SBATCH --partition=gre +#SBATCH --nodes=1 +#SBATCH --ntasks-per-node=1 +#SBATCH --gres=gpu:1 +#SBATCH --time=01:00:00 +#SBATCH --job-name=gpudrive_rc_policy_demo +#SBATCH --output=%x-%j.out +#SBATCH --mem=64G +#SBATCH --cpus-per-task=8 + +set -euo pipefail + +PROJECT_ROOT="${PROJECT_ROOT:-/mingli01/project/zihanliu/gpudrive-main}" +CONDA_ENV_NAME="${CONDA_ENV_NAME:-gpudrive}" +CONFIG_PATH="${CONFIG_PATH:-baselines/ppo/config/ppo_mini_reward_conditioned.yaml}" +CHECKPOINT="${CHECKPOINT:-}" +NORMAL_CHECKPOINT="${NORMAL_CHECKPOINT:-}" +NORMAL_CONFIG_PATH="${NORMAL_CONFIG_PATH:-baselines/ppo/config/ppo_mini_official_sanity.yaml}" +NORMAL_MODE="${NORMAL_MODE:-expert}" +DATA_DIR="${DATA_DIR:-/mingli01/data/GPUDrive_mini/training}" +OUTPUT_DIR="${OUTPUT_DIR:-demo_outputs/mini_reward_conditioned_policy}" + +NUM_WORLDS="${NUM_WORLDS:-8}" +DATASET_SIZE="${DATASET_SIZE:-64}" +STEPS="${STEPS:-91}" +RENDER_WORLDS="${RENDER_WORLDS:-1}" +RENDER_EVERY="${RENDER_EVERY:-2}" +FPS="${FPS:-10}" +SEED="${SEED:-42}" +DETERMINISTIC="${DETERMINISTIC:-1}" +RISK_WORLD="${RISK_WORLD:--1}" +RISK_AGENT_RANK="${RISK_AGENT_RANK:-0}" +RISK_STYLE="${RISK_STYLE:-risk_taker}" +NORMAL_STYLE="${NORMAL_STYLE:-balanced}" +RISK_COLLISION_WEIGHT="${RISK_COLLISION_WEIGHT:-}" +RISK_GOAL_WEIGHT="${RISK_GOAL_WEIGHT:-}" +RISK_OFFROAD_WEIGHT="${RISK_OFFROAD_WEIGHT:-}" +ZOOM_RADIUS="${ZOOM_RADIUS:-70.0}" + +module load conda/3 +source "$(conda info --base)/etc/profile.d/conda.sh" +conda activate "${CONDA_ENV_NAME}" + +export PYTHONNOUSERSITE=1 +export PYTHONUNBUFFERED=1 +export OMP_NUM_THREADS="${OMP_NUM_THREADS:-1}" +export MKL_NUM_THREADS="${MKL_NUM_THREADS:-1}" +export MADRONA_MWGPU_KERNEL_CACHE="${MADRONA_MWGPU_KERNEL_CACHE:-${PROJECT_ROOT}/gpudrive_cache}" + +cd "${PROJECT_ROOT}" +export PYTHONPATH="${PROJECT_ROOT}:${PYTHONPATH:-}" + +if [[ ! -f "${CONFIG_PATH}" ]]; then + echo "ERROR: config not found: ${CONFIG_PATH}" + exit 1 +fi + +if [[ ! -d "${DATA_DIR}" ]]; then + echo "ERROR: DATA_DIR does not exist: ${DATA_DIR}" + exit 1 +fi + +if ! compgen -G "${DATA_DIR}/tfrecord*.json" >/dev/null; then + echo "ERROR: no tfrecord*.json files found under DATA_DIR=${DATA_DIR}" + exit 1 +fi + +echo "SLURM_JOB_ID=${SLURM_JOB_ID:-unknown}" +echo "PROJECT_ROOT=${PROJECT_ROOT}" +echo "CONFIG_PATH=${CONFIG_PATH}" +echo "CHECKPOINT=${CHECKPOINT:-auto-latest}" +echo "NORMAL_CHECKPOINT=${NORMAL_CHECKPOINT:-same-as-risk}" +echo "NORMAL_CONFIG_PATH=${NORMAL_CONFIG_PATH}" +echo "NORMAL_MODE=${NORMAL_MODE}" +echo "DATA_DIR=${DATA_DIR}" +echo "OUTPUT_DIR=${OUTPUT_DIR}" +echo "NUM_WORLDS=${NUM_WORLDS}" +echo "DATASET_SIZE=${DATASET_SIZE}" +echo "RISK_WORLD=${RISK_WORLD}" +echo "RISK_AGENT_RANK=${RISK_AGENT_RANK}" +echo "RISK_STYLE=${RISK_STYLE}" +echo "NORMAL_STYLE=${NORMAL_STYLE}" +echo "RISK_COLLISION_WEIGHT=${RISK_COLLISION_WEIGHT:-preset}" +echo "RISK_GOAL_WEIGHT=${RISK_GOAL_WEIGHT:-preset}" +echo "RISK_OFFROAD_WEIGHT=${RISK_OFFROAD_WEIGHT:-preset}" +echo "CUDA_VISIBLE_DEVICES=${CUDA_VISIBLE_DEVICES:-unset}" + +if command -v nvidia-smi >/dev/null 2>&1; then + nvidia-smi -L || true +fi + +CMD=( + python -u scripts/demo_reward_conditioned_policy.py + --config "${CONFIG_PATH}" + --normal-config "${NORMAL_CONFIG_PATH}" + --normal-mode "${NORMAL_MODE}" + --data-dir "${DATA_DIR}" + --output-dir "${OUTPUT_DIR}" + --num-worlds "${NUM_WORLDS}" + --dataset-size "${DATASET_SIZE}" + --steps "${STEPS}" + --render-worlds "${RENDER_WORLDS}" + --render-every "${RENDER_EVERY}" + --fps "${FPS}" + --seed "${SEED}" + --deterministic "${DETERMINISTIC}" + --risk-world "${RISK_WORLD}" + --risk-agent-rank "${RISK_AGENT_RANK}" + --risk-style "${RISK_STYLE}" + --normal-style "${NORMAL_STYLE}" + --zoom-radius "${ZOOM_RADIUS}" +) + +if [[ -n "${CHECKPOINT}" ]]; then + CMD+=(--checkpoint "${CHECKPOINT}") +fi + +if [[ -n "${NORMAL_CHECKPOINT}" ]]; then + CMD+=(--normal-checkpoint "${NORMAL_CHECKPOINT}") +fi + +if [[ -n "${RISK_COLLISION_WEIGHT}" ]]; then + CMD+=(--risk-collision-weight "${RISK_COLLISION_WEIGHT}") +fi + +if [[ -n "${RISK_GOAL_WEIGHT}" ]]; then + CMD+=(--risk-goal-weight "${RISK_GOAL_WEIGHT}") +fi + +if [[ -n "${RISK_OFFROAD_WEIGHT}" ]]; then + CMD+=(--risk-offroad-weight "${RISK_OFFROAD_WEIGHT}") +fi + +echo "COMMAND_BEGIN" +printf '%q ' "${CMD[@]}" +echo +echo "COMMAND_END" + +"${CMD[@]}" diff --git a/gpudrive-main/scripts/evaluate_llm_cases_deepseek.sbatch b/gpudrive-main/scripts/evaluate_llm_cases_deepseek.sbatch new file mode 100644 index 0000000000000000000000000000000000000000..3a0cba60aff6b0fb2d81e41ef51136eff4b4d542 --- /dev/null +++ b/gpudrive-main/scripts/evaluate_llm_cases_deepseek.sbatch @@ -0,0 +1,143 @@ +#!/bin/bash +#SBATCH --partition=gre +#SBATCH --nodes=1 +#SBATCH --ntasks=1 +#SBATCH --time=24:00:00 +#SBATCH --job-name=gpudrive_eval_agent +#SBATCH --output=%x-%A_%a.out +#SBATCH --mem=64G +#SBATCH --cpus-per-task=8 + +set -euo pipefail + +# Single shard: +# sbatch scripts/evaluate_llm_cases_deepseek.sbatch +# +# Multiple shards: +# sbatch --array=0-3 scripts/evaluate_llm_cases_deepseek.sbatch + +PROJECT_ROOT="${PROJECT_ROOT:-/mingli01/project/zihanliu/gpudrive-main}" +CONDA_ENV_NAME="${CONDA_ENV_NAME:-gpudrive}" + +CASES_BASE="${CASES_BASE:-search_outputs/llm_cases}" +OUTPUT_BASE="${OUTPUT_BASE:-search_outputs/evaluator_agent}" +RULES_PATH="${RULES_PATH:-rules/longtail_llm_case_evaluation.md}" + +SHARD_ID="${SLURM_ARRAY_TASK_ID:-${SHARD_ID:-0}}" +SHARD_PADDED="$(printf "%03d" "${SHARD_ID}")" + +CASES="${CASES:-${CASES_BASE}/shard_${SHARD_PADDED}/llm_cases.jsonl}" +OUTPUT_DIR="${OUTPUT_DIR:-${OUTPUT_BASE}/shard_${SHARD_PADDED}}" + +START_INDEX="${START_INDEX:-0}" +MAX_CASES="${MAX_CASES:-0}" +BATCH_SIZE="${BATCH_SIZE:-1}" +ACCEPT_THRESHOLD="${ACCEPT_THRESHOLD:-70.0}" +HIGH_VALUE_THRESHOLD="${HIGH_VALUE_THRESHOLD:-82.0}" +DRY_RUN="${DRY_RUN:-0}" +RESUME="${RESUME:-1}" +WRITE_PARTIAL_SUMMARY="${WRITE_PARTIAL_SUMMARY:-1}" +SAVE_PROMPTS="${SAVE_PROMPTS:-0}" +SAVE_RAW_RESPONSES="${SAVE_RAW_RESPONSES:-1}" +FALLBACK_ON_PARSE_ERROR="${FALLBACK_ON_PARSE_ERROR:-1}" +RETRY_SINGLE_ON_PARSE_ERROR="${RETRY_SINGLE_ON_PARSE_ERROR:-1}" + +DEEPSEEK_BASE_URL="${DEEPSEEK_BASE_URL:-https://api.deepseek.com}" +DEEPSEEK_MODEL="${DEEPSEEK_MODEL:-deepseek-v4-flash}" +TEMPERATURE="${TEMPERATURE:-0.0}" +MAX_TOKENS="${MAX_TOKENS:-8192}" +TIMEOUT_SEC="${TIMEOUT_SEC:-120}" +RETRIES="${RETRIES:-3}" +SLEEP_SEC="${SLEEP_SEC:-0.5}" + +CURRENT_COLLISION_WEIGHT="${CURRENT_COLLISION_WEIGHT:-0.0}" +CURRENT_GOAL_WEIGHT="${CURRENT_GOAL_WEIGHT:-2.0}" +CURRENT_OFFROAD_WEIGHT="${CURRENT_OFFROAD_WEIGHT:--0.4}" +CURRENT_RISK_AGENTS_PER_WORLD="${CURRENT_RISK_AGENTS_PER_WORLD:-3}" +CURRENT_NORMAL_MODE="${CURRENT_NORMAL_MODE:-policy}" +CURRENT_DETERMINISTIC="${CURRENT_DETERMINISTIC:-0}" +CURRENT_NORMAL_STYLE="${CURRENT_NORMAL_STYLE:-balanced}" +CURRENT_RISK_STYLE="${CURRENT_RISK_STYLE:-risk_taker}" + +module load conda/3 +source "$(conda info --base)/etc/profile.d/conda.sh" +conda activate "${CONDA_ENV_NAME}" + +export PYTHONNOUSERSITE=1 +export PYTHONUNBUFFERED=1 +export OMP_NUM_THREADS="${OMP_NUM_THREADS:-1}" +export MKL_NUM_THREADS="${MKL_NUM_THREADS:-1}" +export DEEPSEEK_BASE_URL +export DEEPSEEK_MODEL + +cd "${PROJECT_ROOT}" +export PYTHONPATH="${PROJECT_ROOT}:${PYTHONPATH:-}" + +if [[ ! -f "${CASES}" ]]; then + echo "ERROR: CASES not found: ${CASES}" + exit 1 +fi +if [[ ! -f "${RULES_PATH}" ]]; then + echo "ERROR: RULES_PATH not found: ${RULES_PATH}" + exit 1 +fi +if [[ "${DRY_RUN}" != "1" && -z "${DEEPSEEK_API_KEY:-}" ]]; then + echo "ERROR: DEEPSEEK_API_KEY is not set. Use DRY_RUN=1 for rule-only evaluation." + exit 1 +fi + +mkdir -p "${OUTPUT_DIR}" + +echo "SLURM_JOB_ID=${SLURM_JOB_ID:-unknown}" +echo "SLURM_ARRAY_TASK_ID=${SLURM_ARRAY_TASK_ID:-none}" +echo "PROJECT_ROOT=${PROJECT_ROOT}" +echo "SHARD_ID=${SHARD_ID}" +echo "CASES=${CASES}" +echo "RULES_PATH=${RULES_PATH}" +echo "OUTPUT_DIR=${OUTPUT_DIR}" +echo "START_INDEX=${START_INDEX}" +echo "MAX_CASES=${MAX_CASES}" +echo "BATCH_SIZE=${BATCH_SIZE}" +echo "DRY_RUN=${DRY_RUN}" +echo "RESUME=${RESUME}" +echo "WRITE_PARTIAL_SUMMARY=${WRITE_PARTIAL_SUMMARY}" +echo "SAVE_RAW_RESPONSES=${SAVE_RAW_RESPONSES}" +echo "FALLBACK_ON_PARSE_ERROR=${FALLBACK_ON_PARSE_ERROR}" +echo "RETRY_SINGLE_ON_PARSE_ERROR=${RETRY_SINGLE_ON_PARSE_ERROR}" +echo "DEEPSEEK_MODEL=${DEEPSEEK_MODEL}" + +python -u search/evaluate_llm_cases_deepseek.py \ + --cases "${CASES}" \ + --rules "${RULES_PATH}" \ + --output-dir "${OUTPUT_DIR}" \ + --start-index "${START_INDEX}" \ + --max-cases "${MAX_CASES}" \ + --batch-size "${BATCH_SIZE}" \ + --accept-threshold "${ACCEPT_THRESHOLD}" \ + --high-value-threshold "${HIGH_VALUE_THRESHOLD}" \ + --dry-run "${DRY_RUN}" \ + --resume "${RESUME}" \ + --write-partial-summary "${WRITE_PARTIAL_SUMMARY}" \ + --save-prompts "${SAVE_PROMPTS}" \ + --save-raw-responses "${SAVE_RAW_RESPONSES}" \ + --fallback-on-parse-error "${FALLBACK_ON_PARSE_ERROR}" \ + --retry-single-on-parse-error "${RETRY_SINGLE_ON_PARSE_ERROR}" \ + --base-url "${DEEPSEEK_BASE_URL}" \ + --model "${DEEPSEEK_MODEL}" \ + --temperature "${TEMPERATURE}" \ + --max-tokens "${MAX_TOKENS}" \ + --timeout-sec "${TIMEOUT_SEC}" \ + --retries "${RETRIES}" \ + --sleep-sec "${SLEEP_SEC}" \ + --current-collision-weight "${CURRENT_COLLISION_WEIGHT}" \ + --current-goal-weight "${CURRENT_GOAL_WEIGHT}" \ + --current-offroad-weight "${CURRENT_OFFROAD_WEIGHT}" \ + --current-risk-agents-per-world "${CURRENT_RISK_AGENTS_PER_WORLD}" \ + --current-normal-mode "${CURRENT_NORMAL_MODE}" \ + --current-deterministic "${CURRENT_DETERMINISTIC}" \ + --current-normal-style "${CURRENT_NORMAL_STYLE}" \ + --current-risk-style "${CURRENT_RISK_STYLE}" + +echo "Evaluator Agent finished." +echo "Outputs:" +find "${OUTPUT_DIR}" -maxdepth 1 -type f | sort diff --git a/gpudrive-main/scripts/nuscenes_min_demo.py b/gpudrive-main/scripts/nuscenes_min_demo.py new file mode 100644 index 0000000000000000000000000000000000000000..0de66be21a5b7755efac3f72ee24890464188daa --- /dev/null +++ b/gpudrive-main/scripts/nuscenes_min_demo.py @@ -0,0 +1,837 @@ +#!/usr/bin/env python +"""Minimal NuScenes GPUDrive rollout demo. + +This script intentionally avoids training. It loads converted GPUDrive JSON +scenes, rolls out either expert log playback or simple hand-coded actions, saves +short videos, and prints collision/off-road/goal statistics. +""" + +from __future__ import annotations + +import argparse +import json +import os +from pathlib import Path + +import mediapy as media +import numpy as np +import torch + +from gpudrive.env.config import EnvConfig, RenderConfig +from gpudrive.env.dataset import SceneDataLoader +from gpudrive.env.env_torch import GPUDriveTorchEnv +from gpudrive.visualize.utils import img_from_fig + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser() + parser.add_argument("--data-dir", default="data/processed/nuscenes") + parser.add_argument("--output-dir", default="demo_outputs/nuscenes_min") + parser.add_argument( + "--mode", + choices=[ + "expert", + "single_aggressive", + "single_swerve", + "single_brake", + "single_cutin", + "single_target_collision", + "aggressive", + "all_aggressive", + "straight", + "random", + ], + default="single_cutin", + ) + parser.add_argument("--num-worlds", type=int, default=4) + parser.add_argument("--dataset-size", type=int, default=32) + parser.add_argument("--steps", type=int, default=91) + parser.add_argument("--render-worlds", type=int, default=4) + parser.add_argument("--render-every", type=int, default=2) + parser.add_argument("--fps", type=int, default=10) + parser.add_argument("--seed", type=int, default=42) + parser.add_argument("--device", default="cuda") + parser.add_argument("--zoom-radius", type=float, default=70.0) + parser.add_argument("--max-controlled-agents", type=int, default=64) + parser.add_argument( + "--init-mode", + choices=[ + "all_non_trivial", + "all_valid", + "all_objects", + "womd_tracks_to_predict", + ], + default="all_valid", + ) + parser.add_argument("--remove-non-vehicles", type=int, default=1) + parser.add_argument("--obs-radius", type=float, default=50.0) + parser.add_argument("--polyline-reduction-threshold", type=float, default=0.1) + parser.add_argument("--perturb-world", type=int, default=-1) + parser.add_argument("--perturb-agent-rank", type=int, default=-1) + parser.add_argument("--perturb-start-step", type=int, default=15) + parser.add_argument("--perturb-end-step", type=int, default=40) + parser.add_argument("--perturb-accel", type=float, default=1.0) + parser.add_argument("--perturb-steer", type=float, default=0.12) + parser.add_argument("--perturb-brake-accel", type=float, default=-4.0) + parser.add_argument("--target-agent-rank", type=int, default=-1) + parser.add_argument("--target-selection-step", type=int, default=25) + parser.add_argument("--target-min-dist", type=float, default=2.0) + parser.add_argument("--target-max-dist", type=float, default=30.0) + parser.add_argument("--target-blend", type=float, default=0.85) + parser.add_argument("--target-offset-m", type=float, default=0.0) + parser.add_argument("--cutin-min-lateral-m", type=float, default=2.0) + parser.add_argument("--cutin-max-lateral-m", type=float, default=8.0) + parser.add_argument("--cutin-max-longitudinal-m", type=float, default=3.0) + parser.add_argument("--cutin-min-heading-cos", type=float, default=0.65) + parser.add_argument("--cutin-final-lateral-m", type=float, default=0.0) + parser.add_argument("--cutin-final-longitudinal-m", type=float, default=0.0) + return parser.parse_args() + + +def closest_action_index(env: GPUDriveTorchEnv, accel: float, steer: float) -> int: + actions = env.action_keys_tensor[:, :2] + target = torch.tensor([accel, steer], device=actions.device) + return int(torch.argmin(((actions - target) ** 2).sum(dim=1)).item()) + + +def make_discrete_actions( + env: GPUDriveTorchEnv, + mode: str, + live_agent_mask: torch.Tensor, + generator: torch.Generator, +) -> torch.Tensor: + actions = torch.zeros( + (env.num_worlds, env.max_agent_count), + dtype=torch.int64, + device=env.device, + ) + + if not bool(live_agent_mask.any().item()): + return actions + + if mode == "random": + actions[live_agent_mask] = torch.randint( + low=0, + high=env.action_space.n, + size=(int(live_agent_mask.sum().item()),), + generator=generator, + device=env.device, + ) + return actions + + straight = closest_action_index(env, accel=4.0, steer=0.0) + if mode == "straight": + actions[live_agent_mask] = straight + return actions + + left = closest_action_index(env, accel=4.0, steer=0.5) + right = closest_action_index(env, accel=4.0, steer=-0.5) + choices = torch.tensor([straight, straight, straight, left, right], device=env.device) + sampled = choices[ + torch.randint( + low=0, + high=len(choices), + size=(int(live_agent_mask.sum().item()),), + generator=generator, + device=env.device, + ) + ] + actions[live_agent_mask] = sampled + return actions + + +def is_single_perturb_mode(mode: str) -> bool: + return mode in {"single_aggressive", "single_swerve", "single_brake", "aggressive"} + + +def is_target_collision_mode(mode: str) -> bool: + return mode == "single_target_collision" + + +def is_cutin_mode(mode: str) -> bool: + return mode == "single_cutin" + + +def is_expert_based_mode(mode: str) -> bool: + return is_single_perturb_mode(mode) or is_target_collision_mode(mode) or is_cutin_mode(mode) + + +def smoothstep(x: float) -> float: + x = min(max(x, 0.0), 1.0) + return x * x * (3.0 - 2.0 * x) + + +def select_perturb_agent( + control_mask: torch.Tensor, + requested_world: int, + requested_rank: int, +) -> tuple[int, int]: + num_worlds = control_mask.shape[0] + world_order = [requested_world] + [ + world_idx for world_idx in range(num_worlds) if world_idx != requested_world + ] + + for world_idx in world_order: + if world_idx < 0 or world_idx >= num_worlds: + continue + agent_ids = torch.where(control_mask[world_idx])[0] + if len(agent_ids) == 0: + continue + rank = min(max(requested_rank, 0), len(agent_ids) - 1) + return world_idx, int(agent_ids[rank].item()) + + raise RuntimeError("No controlled agent is available for perturbation.") + + +def valid_state_rows(state_actions: torch.Tensor) -> torch.Tensor: + pos = state_actions[:, :2] + return torch.isfinite(pos).all(dim=1) & (pos.abs() < 9999.0).all(dim=1) + + +def select_target_pair( + expert_actions: torch.Tensor, + control_mask: torch.Tensor, + requested_world: int, + requested_agent_rank: int, + requested_target_rank: int, + selection_step: int, + min_dist: float, + max_dist: float, +) -> tuple[int, int, int, float]: + step = min(max(selection_step, 0), expert_actions.shape[2] - 1) + candidate_worlds = ( + [requested_world] + if 0 <= requested_world < control_mask.shape[0] + else list(range(control_mask.shape[0])) + ) + + best: tuple[float, int, int, int] | None = None + fallback: tuple[float, int, int, int] | None = None + + for world_idx in candidate_worlds: + states = expert_actions[world_idx, :, step, :] + valid = valid_state_rows(states) + controlled_agents = torch.where(control_mask[world_idx] & valid)[0] + if len(controlled_agents) == 0: + continue + + if requested_agent_rank >= 0: + rank = min(requested_agent_rank, len(controlled_agents) - 1) + perturb_candidates = controlled_agents[rank : rank + 1] + else: + perturb_candidates = controlled_agents + + for perturb_agent_tensor in perturb_candidates: + perturb_agent = int(perturb_agent_tensor.item()) + target_candidates = torch.where(valid)[0] + target_candidates = target_candidates[target_candidates != perturb_agent] + if len(target_candidates) == 0: + continue + + if requested_target_rank >= 0: + target_rank = min(requested_target_rank, len(target_candidates) - 1) + target_candidates = target_candidates[target_rank : target_rank + 1] + + distances = torch.linalg.norm( + states[target_candidates, :2] - states[perturb_agent, :2], + dim=1, + ) + nearest_idx = int(torch.argmin(distances).item()) + distance = float(distances[nearest_idx].item()) + target_agent = int(target_candidates[nearest_idx].item()) + candidate = (distance, world_idx, perturb_agent, target_agent) + + if fallback is None or distance < fallback[0]: + fallback = candidate + if min_dist <= distance <= max_dist and (best is None or distance < best[0]): + best = candidate + + selected = best if best is not None else fallback + if selected is None: + raise RuntimeError("Could not find a valid target pair for collision demo.") + + distance, world_idx, perturb_agent, target_agent = selected + return world_idx, perturb_agent, target_agent, distance + + +def select_cutin_pair( + expert_actions: torch.Tensor, + control_mask: torch.Tensor, + requested_world: int, + requested_agent_rank: int, + requested_target_rank: int, + start_step: int, + end_step: int, + min_lateral_m: float, + max_lateral_m: float, + max_longitudinal_m: float, + min_heading_cos: float, +) -> tuple[int, int, int, int, float, float, float]: + first_step = min(max(start_step, 0), expert_actions.shape[2] - 1) + last_step = min(max(end_step, first_step + 1), expert_actions.shape[2]) + candidate_worlds = ( + [requested_world] + if 0 <= requested_world < control_mask.shape[0] + else list(range(control_mask.shape[0])) + ) + + best: tuple[float, int, int, int, int, float, float, float] | None = None + fallback: tuple[float, int, int, int, int, float, float, float] | None = None + + for step in range(first_step, last_step): + for world_idx in candidate_worlds: + states = expert_actions[world_idx, :, step, :] + valid = valid_state_rows(states) + controlled_agents = torch.where(control_mask[world_idx] & valid)[0] + if len(controlled_agents) == 0: + continue + + if requested_agent_rank >= 0: + rank = min(requested_agent_rank, len(controlled_agents) - 1) + perturb_candidates = controlled_agents[rank : rank + 1] + else: + perturb_candidates = controlled_agents + + valid_targets = torch.where(valid)[0] + for perturb_agent_tensor in perturb_candidates: + perturb_agent = int(perturb_agent_tensor.item()) + source = states[perturb_agent] + source_yaw = source[3] + + target_candidates = valid_targets[valid_targets != perturb_agent] + if len(target_candidates) == 0: + continue + + if requested_target_rank >= 0: + rank = min(requested_target_rank, len(target_candidates) - 1) + target_candidates = target_candidates[rank : rank + 1] + + for target_tensor in target_candidates: + target_agent = int(target_tensor.item()) + target = states[target_agent] + heading_cos = float(torch.cos(source_yaw - target[3]).item()) + if heading_cos < min_heading_cos: + continue + + forward = torch.stack([torch.cos(target[3]), torch.sin(target[3])]) + lateral = torch.stack([-torch.sin(target[3]), torch.cos(target[3])]) + rel = source[:2] - target[:2] + longitudinal = float(torch.dot(rel, forward).item()) + lateral_dist = float(torch.dot(rel, lateral).item()) + abs_lateral = abs(lateral_dist) + abs_longitudinal = abs(longitudinal) + + lane_like = min_lateral_m <= abs_lateral <= max_lateral_m + close_longitudinal = abs_longitudinal <= max_longitudinal_m + score = abs_longitudinal + 0.25 * abs(abs_lateral - 3.5) + candidate = ( + score, + world_idx, + perturb_agent, + target_agent, + step, + longitudinal, + lateral_dist, + heading_cos, + ) + + if fallback is None or score < fallback[0]: + fallback = candidate + if lane_like and close_longitudinal and ( + best is None or score < best[0] + ): + best = candidate + + selected = best if best is not None else fallback + if selected is None: + raise RuntimeError("Could not find a valid cut-in pair.") + + ( + _score, + world_idx, + perturb_agent, + target_agent, + step, + longitudinal, + lateral_dist, + heading_cos, + ) = selected + return ( + world_idx, + perturb_agent, + target_agent, + step, + longitudinal, + lateral_dist, + heading_cos, + ) + + +def blend_yaw(source_yaw: torch.Tensor, target_yaw: torch.Tensor, alpha: float) -> torch.Tensor: + delta = torch.atan2(torch.sin(target_yaw - source_yaw), torch.cos(target_yaw - source_yaw)) + return source_yaw + alpha * delta + + +def apply_single_perturbation( + actions: torch.Tensor, + mode: str, + world_idx: int, + agent_idx: int, + accel_delta: float, + steer_delta: float, + brake_accel: float, +) -> None: + if mode == "aggressive": + mode = "single_aggressive" + + if mode == "single_brake": + actions[world_idx, agent_idx, 0] = brake_accel + return + + if mode == "single_swerve": + actions[world_idx, agent_idx, 1] = torch.clamp( + actions[world_idx, agent_idx, 1] + steer_delta, + min=-0.3, + max=0.3, + ) + return + + actions[world_idx, agent_idx, 0] = torch.clamp( + actions[world_idx, agent_idx, 0] + accel_delta, + min=-4.0, + max=4.0, + ) + actions[world_idx, agent_idx, 1] = torch.clamp( + actions[world_idx, agent_idx, 1] + steer_delta, + min=-0.3, + max=0.3, + ) + + +def apply_target_collision( + actions: torch.Tensor, + world_idx: int, + agent_idx: int, + target_agent_idx: int, + alpha: float, + target_offset_m: float, +) -> None: + source = actions[world_idx, agent_idx].clone() + target = actions[world_idx, target_agent_idx].clone() + heading = target[3] + offset = torch.stack([torch.cos(heading), torch.sin(heading)]) * target_offset_m + target_xy = target[:2] - offset + + actions[world_idx, agent_idx, :2] = (1.0 - alpha) * source[:2] + alpha * target_xy + actions[world_idx, agent_idx, 3] = blend_yaw(source[3], target[3], alpha) + actions[world_idx, agent_idx, 4:6] = (1.0 - alpha) * source[4:6] + alpha * target[4:6] + + +def apply_cutin( + actions: torch.Tensor, + world_idx: int, + agent_idx: int, + target_agent_idx: int, + alpha: float, + final_longitudinal_m: float, + final_lateral_m: float, +) -> None: + source = actions[world_idx, agent_idx].clone() + target = actions[world_idx, target_agent_idx].clone() + forward = torch.stack([torch.cos(target[3]), torch.sin(target[3])]) + lateral = torch.stack([-torch.sin(target[3]), torch.cos(target[3])]) + desired_xy = ( + target[:2] + + final_longitudinal_m * forward + + final_lateral_m * lateral + ) + + actions[world_idx, agent_idx, :2] = (1.0 - alpha) * source[:2] + alpha * desired_xy + actions[world_idx, agent_idx, 3] = blend_yaw(source[3], target[3], 0.5 * alpha) + actions[world_idx, agent_idx, 4:6] = source[4:6] + alpha * (target[4:6] - source[4:6]) + + +def render_worlds( + env: GPUDriveTorchEnv, + frames: dict[int, list[np.ndarray]], + world_indices: list[int], + highlight_agents: dict[int, int], + time_step: int, + zoom_radius: float, +) -> None: + if not world_indices: + return + + center_agent_indices = [highlight_agents.get(world_idx, 0) for world_idx in world_indices] + figures = env.vis.plot_simulator_state( + env_indices=world_indices, + time_steps=[time_step] * len(world_indices), + zoom_radius=zoom_radius, + center_agent_indices=center_agent_indices, + ) + + for idx, world_idx in enumerate(world_indices): + frames[world_idx].append(img_from_fig(figures[idx])) + + +def main() -> None: + args = parse_args() + torch.manual_seed(args.seed) + np.random.seed(args.seed) + + project_root = Path(__file__).resolve().parents[1] + data_dir = (project_root / args.data_dir).resolve() + output_dir = (project_root / args.output_dir).resolve() + output_dir.mkdir(parents=True, exist_ok=True) + + # Madrona's runtime assets are loaded relative to the gpudrive package dir. + os.chdir(project_root / "gpudrive") + + device = args.device + if device == "cuda" and not torch.cuda.is_available(): + device = "cpu" + + if args.mode == "aggressive": + print( + "[demo] MODE=aggressive is now treated as a single-agent perturbation. " + "Use MODE=all_aggressive for the old all-agent behavior.", + flush=True, + ) + + dynamics_model = ( + "state" + if args.mode == "expert" + or is_target_collision_mode(args.mode) + or is_cutin_mode(args.mode) + else "classic" + ) + env_config = EnvConfig( + ego_state=True, + road_map_obs=True, + partner_obs=True, + norm_obs=True, + reward_type="weighted_combination", + collision_weight=-0.75, + off_road_weight=-0.75, + goal_achieved_weight=1.0, + dynamics_model=dynamics_model, + collision_behavior="ignore", + dist_to_goal_threshold=2.0, + polyline_reduction_threshold=args.polyline_reduction_threshold, + remove_non_vehicles=bool(args.remove_non_vehicles), + obs_radius=args.obs_radius, + init_mode=args.init_mode, + ) + render_config = RenderConfig(render_3d=False) + + data_loader = SceneDataLoader( + root=str(data_dir), + batch_size=args.num_worlds, + dataset_size=args.dataset_size, + sample_with_replacement=True, + shuffle=True, + seed=args.seed, + ) + print( + "[demo] data_loader:", + f"root={data_loader.root}", + f"dataset_files={len(data_loader.dataset)}", + f"batch_size={data_loader.batch_size}", + flush=True, + ) + + env = GPUDriveTorchEnv( + config=env_config, + data_loader=data_loader, + max_cont_agents=args.max_controlled_agents, + device=device, + render_config=render_config, + ) + env.reset() + + control_mask = env.cont_agent_mask.clone() + controlled_per_world = control_mask.sum(dim=1).detach().cpu() + total_controlled = int(control_mask.sum().item()) + print( + "[demo] env ready:", + f"device={device}", + f"mode={args.mode}", + f"dynamics_model={dynamics_model}", + f"num_worlds={env.num_worlds}", + f"controlled_agents={total_controlled}", + f"controlled_per_world={controlled_per_world.tolist()}", + flush=True, + ) + + if total_controlled == 0: + raise RuntimeError( + "No controlled agents were found. Try --init-mode all_objects or inspect the converted JSON." + ) + + expert_actions = None + if args.mode == "expert" or is_expert_based_mode(args.mode): + expert_actions, _, _, _ = env.get_expert_actions() + + render_count = min(args.render_worlds, env.num_worlds) + rendered_worlds = list(range(render_count)) + perturb_world = None + perturb_agent = None + target_agent = None + pair_min_action_distance = None + if is_cutin_mode(args.mode): + ( + perturb_world, + perturb_agent, + target_agent, + cutin_step, + cutin_longitudinal, + cutin_lateral, + cutin_heading_cos, + ) = select_cutin_pair( + expert_actions, + control_mask, + args.perturb_world, + args.perturb_agent_rank, + args.target_agent_rank, + args.perturb_start_step, + args.perturb_end_step, + args.cutin_min_lateral_m, + args.cutin_max_lateral_m, + args.cutin_max_longitudinal_m, + args.cutin_min_heading_cos, + ) + pair_min_action_distance = float("inf") + if perturb_world not in rendered_worlds: + if rendered_worlds: + rendered_worlds[-1] = perturb_world + else: + rendered_worlds = [perturb_world] + rendered_worlds = sorted(set(rendered_worlds)) + print( + "[demo] cut-in pair:", + f"world={perturb_world}", + f"perturb_agent={perturb_agent}", + f"target_agent={target_agent}", + f"selection_step={cutin_step}", + f"longitudinal_m={cutin_longitudinal:.2f}", + f"lateral_m={cutin_lateral:.2f}", + f"heading_cos={cutin_heading_cos:.3f}", + f"start_step={args.perturb_start_step}", + f"end_step={args.perturb_end_step}", + f"final_longitudinal_m={args.cutin_final_longitudinal_m}", + f"final_lateral_m={args.cutin_final_lateral_m}", + flush=True, + ) + elif is_target_collision_mode(args.mode): + ( + perturb_world, + perturb_agent, + target_agent, + target_distance, + ) = select_target_pair( + expert_actions, + control_mask, + args.perturb_world, + args.perturb_agent_rank, + args.target_agent_rank, + args.target_selection_step, + args.target_min_dist, + args.target_max_dist, + ) + if perturb_world not in rendered_worlds: + if rendered_worlds: + rendered_worlds[-1] = perturb_world + else: + rendered_worlds = [perturb_world] + rendered_worlds = sorted(set(rendered_worlds)) + print( + "[demo] target collision pair:", + f"world={perturb_world}", + f"perturb_agent={perturb_agent}", + f"target_agent={target_agent}", + f"selection_step={args.target_selection_step}", + f"distance_m={target_distance:.2f}", + f"start_step={args.perturb_start_step}", + f"end_step={args.perturb_end_step}", + f"target_blend={args.target_blend}", + f"target_offset_m={args.target_offset_m}", + flush=True, + ) + elif is_single_perturb_mode(args.mode): + perturb_world, perturb_agent = select_perturb_agent( + control_mask, + args.perturb_world, + args.perturb_agent_rank, + ) + if perturb_world not in rendered_worlds: + if rendered_worlds: + rendered_worlds[-1] = perturb_world + else: + rendered_worlds = [perturb_world] + rendered_worlds = sorted(set(rendered_worlds)) + print( + "[demo] perturbation target:", + f"world={perturb_world}", + f"agent={perturb_agent}", + f"start_step={args.perturb_start_step}", + f"end_step={args.perturb_end_step}", + f"accel_delta={args.perturb_accel}", + f"steer_delta={args.perturb_steer}", + flush=True, + ) + + highlight_agents: dict[int, int] = {} + for world_idx in rendered_worlds: + agent_ids = torch.where(control_mask[world_idx])[0] + if perturb_world == world_idx and perturb_agent is not None: + highlight_agents[world_idx] = perturb_agent + else: + highlight_agents[world_idx] = int(agent_ids[0].item()) if len(agent_ids) else 0 + + frames: dict[int, list[np.ndarray]] = {world_idx: [] for world_idx in rendered_worlds} + collided = torch.zeros_like(control_mask, dtype=torch.bool, device=env.device) + off_road = torch.zeros_like(control_mask, dtype=torch.bool, device=env.device) + goal_achieved = torch.zeros_like(control_mask, dtype=torch.bool, device=env.device) + live_agent_mask = control_mask.clone() + + generator = torch.Generator(device=env.device) + generator.manual_seed(args.seed) + + render_worlds(env, frames, rendered_worlds, highlight_agents, 0, args.zoom_radius) + + for step in range(args.steps): + if args.mode == "expert": + action_step = min(step, expert_actions.shape[2] - 1) + actions = expert_actions[:, :, action_step, :] + elif is_cutin_mode(args.mode): + action_step = min(step, expert_actions.shape[2] - 1) + actions = expert_actions[:, :, action_step, :].clone() + should_perturb = ( + args.perturb_start_step <= step < args.perturb_end_step + and bool(live_agent_mask[perturb_world, perturb_agent].item()) + ) + if should_perturb: + duration = max(args.perturb_end_step - args.perturb_start_step - 1, 1) + progress = (step - args.perturb_start_step) / duration + apply_cutin( + actions, + perturb_world, + perturb_agent, + target_agent, + smoothstep(progress), + args.cutin_final_longitudinal_m, + args.cutin_final_lateral_m, + ) + pair_distance = torch.linalg.norm( + actions[perturb_world, perturb_agent, :2] + - actions[perturb_world, target_agent, :2] + ).item() + pair_min_action_distance = min(pair_min_action_distance, float(pair_distance)) + elif is_target_collision_mode(args.mode): + action_step = min(step, expert_actions.shape[2] - 1) + actions = expert_actions[:, :, action_step, :].clone() + should_perturb = ( + args.perturb_start_step <= step < args.perturb_end_step + and bool(live_agent_mask[perturb_world, perturb_agent].item()) + ) + if should_perturb: + duration = max(args.perturb_end_step - args.perturb_start_step - 1, 1) + progress = (step - args.perturb_start_step) / duration + alpha = min(max(progress * args.target_blend, 0.0), args.target_blend) + apply_target_collision( + actions, + perturb_world, + perturb_agent, + target_agent, + alpha, + args.target_offset_m, + ) + elif is_single_perturb_mode(args.mode): + action_step = min(step, expert_actions.shape[2] - 1) + actions = expert_actions[:, :, action_step, :].clone() + should_perturb = ( + args.perturb_start_step <= step < args.perturb_end_step + and bool(live_agent_mask[perturb_world, perturb_agent].item()) + ) + if should_perturb: + apply_single_perturbation( + actions, + args.mode, + perturb_world, + perturb_agent, + args.perturb_accel, + args.perturb_steer, + args.perturb_brake_accel, + ) + else: + discrete_mode = "aggressive" if args.mode == "all_aggressive" else args.mode + actions = make_discrete_actions(env, discrete_mode, live_agent_mask, generator) + + env.step_dynamics(actions) + infos = env.get_infos() + active = live_agent_mask & control_mask + collided |= (infos.collided > 0).bool() & active + off_road |= (infos.off_road > 0).bool() & active + goal_achieved |= (infos.goal_achieved > 0).bool() & active + live_agent_mask &= ~env.get_dones().bool() + + if (step + 1) % args.render_every == 0: + render_worlds( + env, + frames, + rendered_worlds, + highlight_agents, + step + 1, + args.zoom_radius, + ) + + if not bool(live_agent_mask.any().item()): + print(f"[demo] all controlled agents are done at step={step + 1}", flush=True) + break + + summary = [] + for world_idx in range(env.num_worlds): + controlled = int(control_mask[world_idx].sum().item()) + row = { + "world": world_idx, + "controlled_agents": controlled, + "perturbed_agent": int(perturb_agent) + if perturb_world == world_idx and perturb_agent is not None + else None, + "target_agent": int(target_agent) + if perturb_world == world_idx and target_agent is not None + else None, + "pair_min_action_distance_m": round(pair_min_action_distance, 3) + if perturb_world == world_idx and pair_min_action_distance is not None + else None, + "perturbed_agent_collided": int(collided[world_idx, perturb_agent].item()) + if perturb_world == world_idx and perturb_agent is not None + else None, + "target_agent_collided": int(collided[world_idx, target_agent].item()) + if perturb_world == world_idx and target_agent is not None + else None, + "collided_agents": int((collided[world_idx] & control_mask[world_idx]).sum().item()), + "off_road_agents": int((off_road[world_idx] & control_mask[world_idx]).sum().item()), + "goal_achieved_agents": int( + (goal_achieved[world_idx] & control_mask[world_idx]).sum().item() + ), + } + summary.append(row) + print("[demo] world_summary:", row, flush=True) + + for world_idx, world_frames in frames.items(): + if not world_frames: + continue + video_path = output_dir / f"nuscenes_{args.mode}_world{world_idx}.gif" + media.write_video(str(video_path), np.asarray(world_frames), fps=args.fps, codec="gif") + print(f"[demo] wrote {video_path}", flush=True) + + summary_path = output_dir / f"nuscenes_{args.mode}_summary.json" + summary_path.write_text(json.dumps(summary, indent=2), encoding="utf-8") + print(f"[demo] wrote {summary_path}", flush=True) + + env.close() + + +if __name__ == "__main__": + main() diff --git a/gpudrive-main/scripts/optimizer_agent_mlp.sbatch b/gpudrive-main/scripts/optimizer_agent_mlp.sbatch new file mode 100644 index 0000000000000000000000000000000000000000..8f124b65a3ec7b3da3fe40a071332e3caaaf6c55 --- /dev/null +++ b/gpudrive-main/scripts/optimizer_agent_mlp.sbatch @@ -0,0 +1,77 @@ +#!/bin/bash +#SBATCH --partition=gre +#SBATCH --nodes=1 +#SBATCH --ntasks=1 +#SBATCH --time=01:00:00 +#SBATCH --job-name=gpudrive_mlp_opt +#SBATCH --output=%x-%j.out +#SBATCH --mem=32G +#SBATCH --cpus-per-task=4 + +set -euo pipefail + +PROJECT_ROOT="${PROJECT_ROOT:-/mingli01/project/zihanliu/gpudrive-main}" +CONDA_ENV_NAME="${CONDA_ENV_NAME:-gpudrive}" + +METRICS_GLOB="${METRICS_GLOB:-search_outputs/evaluator_agent/**/optimizer_metrics.json}" +OUTPUT_DIR="${OUTPUT_DIR:-search_outputs/optimizer_agent/mlp_next}" + +TOP_K="${TOP_K:-8}" +EXPLORE_K="${EXPLORE_K:-4}" +ALLOW_REPEAT="${ALLOW_REPEAT:-0}" +SEED="${SEED:-42}" +HIDDEN_DIM="${HIDDEN_DIM:-64}" +EPOCHS="${EPOCHS:-1200}" +LR="${LR:-0.001}" +WEIGHT_DECAY="${WEIGHT_DECAY:-0.0001}" +MIN_TRAIN_SAMPLES="${MIN_TRAIN_SAMPLES:-6}" +EXPLORATION_WEIGHT="${EXPLORATION_WEIGHT:-0.12}" +CANDIDATE_GRID_JSON="${CANDIDATE_GRID_JSON:-}" + +module load conda/3 +source "$(conda info --base)/etc/profile.d/conda.sh" +conda activate "${CONDA_ENV_NAME}" + +export PYTHONNOUSERSITE=1 +export PYTHONUNBUFFERED=1 +export OMP_NUM_THREADS="${OMP_NUM_THREADS:-1}" +export MKL_NUM_THREADS="${MKL_NUM_THREADS:-1}" + +cd "${PROJECT_ROOT}" +export PYTHONPATH="${PROJECT_ROOT}:${PYTHONPATH:-}" + +mkdir -p "${OUTPUT_DIR}" + +echo "SLURM_JOB_ID=${SLURM_JOB_ID:-unknown}" +echo "PROJECT_ROOT=${PROJECT_ROOT}" +echo "METRICS_GLOB=${METRICS_GLOB}" +echo "OUTPUT_DIR=${OUTPUT_DIR}" +echo "TOP_K=${TOP_K}" +echo "EXPLORE_K=${EXPLORE_K}" +echo "MIN_TRAIN_SAMPLES=${MIN_TRAIN_SAMPLES}" + +CMD=( + python -u search/optimizer_agent_mlp.py + --metrics-glob "${METRICS_GLOB}" + --output-dir "${OUTPUT_DIR}" + --top-k "${TOP_K}" + --explore-k "${EXPLORE_K}" + --allow-repeat "${ALLOW_REPEAT}" + --seed "${SEED}" + --hidden-dim "${HIDDEN_DIM}" + --epochs "${EPOCHS}" + --lr "${LR}" + --weight-decay "${WEIGHT_DECAY}" + --min-train-samples "${MIN_TRAIN_SAMPLES}" + --exploration-weight "${EXPLORATION_WEIGHT}" +) + +if [[ -n "${CANDIDATE_GRID_JSON}" ]]; then + CMD+=(--candidate-grid-json "${CANDIDATE_GRID_JSON}") +fi + +"${CMD[@]}" + +echo "Optimizer Agent finished." +echo "Outputs:" +find "${OUTPUT_DIR}" -maxdepth 1 -type f | sort diff --git a/gpudrive-main/scripts/search_longtail_reward_conditioned.py b/gpudrive-main/scripts/search_longtail_reward_conditioned.py new file mode 100644 index 0000000000000000000000000000000000000000..7a2b7add7e5d1e93e6595e463efdf76f7d5832a5 --- /dev/null +++ b/gpudrive-main/scripts/search_longtail_reward_conditioned.py @@ -0,0 +1,1211 @@ +#!/usr/bin/env python +"""Search long-tail events with a reward-conditioned GPUDrive policy. + +This script is optimized for high-throughput scanning rather than rendering. +It loads one reward-conditioned checkpoint, assigns multiple risk-taking agents +per world, rolls out many batches, and writes collision / near-miss records. +""" + +from __future__ import annotations + +import argparse +import json +import math +import time +from dataclasses import fields +from pathlib import Path +from typing import Any + +import torch +import yaml + +from gpudrive.datatypes.observation import AGENT_SCALE +from gpudrive.env.config import EnvConfig, RenderConfig +from gpudrive.env.dataset import SceneDataLoader +from gpudrive.env.env_torch import GPUDriveTorchEnv +from gpudrive.networks.late_fusion import NeuralNet + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser() + parser.add_argument("--config", default="baselines/ppo/config/ppo_mini_reward_conditioned.yaml") + parser.add_argument("--checkpoint", default="") + parser.add_argument("--data-dir", default="/mingli01/data/GPUDrive_mini/training") + parser.add_argument("--output-dir", default="longtail_outputs/reward_conditioned_search") + parser.add_argument("--num-worlds", type=int, default=1000) + parser.add_argument("--dataset-size", type=int, default=864) + parser.add_argument("--num-batches", type=int, default=10) + parser.add_argument("--steps", type=int, default=91) + parser.add_argument("--seed", type=int, default=42) + parser.add_argument("--shard-id", type=int, default=0) + parser.add_argument("--num-shards", type=int, default=1) + parser.add_argument("--device", default="cuda") + parser.add_argument("--deterministic", type=int, default=0) + parser.add_argument("--risk-agents-per-world", type=int, default=3) + parser.add_argument("--normal-mode", choices=["policy", "expert"], default="policy") + parser.add_argument("--normal-style", default="balanced") + parser.add_argument("--risk-style", default="risk_taker") + parser.add_argument("--risk-collision-weight", type=float, default=0.0) + parser.add_argument("--risk-goal-weight", type=float, default=2.0) + parser.add_argument("--risk-offroad-weight", type=float, default=-0.4) + parser.add_argument("--near-miss-threshold-m", type=float, default=2.0) + parser.add_argument("--save-offroad", type=int, default=0) + parser.add_argument("--obs-radius", type=float, default=None) + parser.add_argument("--polyline-reduction-threshold", type=float, default=None) + parser.add_argument("--save-event-traces", type=int, default=1) + parser.add_argument("--trace-pre-steps", type=int, default=50) + parser.add_argument("--trace-post-steps", type=int, default=10) + parser.add_argument("--trace-include-all-agents", type=int, default=1) + parser.add_argument("--trace-top-k-agents", type=int, default=16) + parser.add_argument("--trace-map-radius-m", type=float, default=80.0) + parser.add_argument("--trace-map-top-k", type=int, default=256) + parser.add_argument("--trace-dt", type=float, default=0.1) + return parser.parse_args() + + +def load_config(path: str) -> dict[str, Any]: + with open(path, "r", encoding="utf-8") as f: + return yaml.safe_load(f) + + +def find_latest_checkpoint() -> Path: + patterns = [ + "runs/PPO_MINI_RC*/model_*.pt", + "gpudrive/runs/PPO_MINI_RC*/model_*.pt", + "runs/*MINI*RC*/model_*.pt", + "gpudrive/runs/*MINI*RC*/model_*.pt", + ] + for pattern in patterns: + candidates = list(Path(".").glob(pattern)) + if candidates: + return max(candidates, key=lambda p: p.stat().st_mtime) + raise FileNotFoundError("No reward-conditioned checkpoint found. Pass --checkpoint.") + + +def reward_preset(config: EnvConfig, style: str, device: str) -> torch.Tensor: + presets = { + "cautious": [ + config.collision_weight_lb * 0.9, + config.goal_achieved_weight_ub * 0.7, + config.off_road_weight_lb * 0.9, + ], + "aggressive": [ + config.collision_weight_lb * 0.5, + config.goal_achieved_weight_ub * 0.9, + config.off_road_weight_lb * 0.6, + ], + "balanced": [ + (config.collision_weight_lb + config.collision_weight_ub) / 2, + (config.goal_achieved_weight_lb + config.goal_achieved_weight_ub) / 2, + (config.off_road_weight_lb + config.off_road_weight_ub) / 2, + ], + "risk_taker": [ + config.collision_weight_lb * 0.3, + config.goal_achieved_weight_ub, + config.off_road_weight_lb * 0.4, + ], + } + if style not in presets: + raise ValueError(f"Unknown style={style}. Available: {sorted(presets)}") + return torch.tensor(presets[style], dtype=torch.float32, device=device) + + +def make_env_config(raw_env: dict[str, Any], args: argparse.Namespace) -> EnvConfig: + env_fields = {field.name for field in fields(EnvConfig)} + cfg = {k: v for k, v in raw_env.items() if k in env_fields} + cfg["num_worlds"] = args.num_worlds + cfg["reward_type"] = "reward_conditioned" + cfg["condition_mode"] = "random" + if args.obs_radius is not None: + cfg["obs_radius"] = args.obs_radius + if args.polyline_reduction_threshold is not None: + cfg["polyline_reduction_threshold"] = args.polyline_reduction_threshold + + steer_disc = int(raw_env.get("action_space_steer_disc", 13)) + accel_disc = int(raw_env.get("action_space_accel_disc", 7)) + cfg["steer_actions"] = torch.round(torch.linspace(-torch.pi, torch.pi, steer_disc), decimals=3) + cfg["accel_actions"] = torch.round(torch.linspace(-4.0, 4.0, accel_disc), decimals=3) + return EnvConfig(**cfg) + + +def load_policy(checkpoint_path: Path, config: dict[str, Any], device: str) -> NeuralNet: + saved = torch.load(checkpoint_path, map_location=device, weights_only=False) + arch = saved["model_arch"] + arch_get = arch.get if hasattr(arch, "get") else lambda key, default=None: getattr(arch, key, default) + policy = NeuralNet( + input_dim=arch_get("input_dim"), + action_dim=saved["action_dim"], + hidden_dim=arch_get("hidden_dim"), + dropout=arch_get("dropout", 0.0), + config=config["environment"], + ).to(device) + policy.load_state_dict(saved["parameters"]) + policy.eval() + return policy + + +def choose_risk_agents( + control_mask: torch.Tensor, + risk_agents_per_world: int, + generator: torch.Generator, +) -> torch.Tensor: + risk_mask = torch.zeros_like(control_mask, dtype=torch.bool) + for world_idx in range(control_mask.shape[0]): + agents = torch.where(control_mask[world_idx])[0] + if len(agents) == 0: + continue + num_risk = min(risk_agents_per_world, len(agents)) + perm = torch.randperm(len(agents), generator=generator, device=control_mask.device) + risk_mask[world_idx, agents[perm[:num_risk]]] = True + return risk_mask + + +def finite_min_scatter( + values: torch.Tensor, + world_ids: torch.Tensor, + num_worlds: int, + device: str, +) -> torch.Tensor: + out = torch.full((num_worlds,), float("inf"), dtype=torch.float32, device=device) + if values.numel() == 0: + return out + if hasattr(out, "scatter_reduce_"): + out.scatter_reduce_(0, world_ids, values, reduce="amin", include_self=True) + else: + for world_id, value in zip(world_ids.tolist(), values.tolist()): + out[world_id] = min(out[world_id], value) + return out + + +def compute_near_miss( + env: GPUDriveTorchEnv, + risk_mask: torch.Tensor, +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + abs_obs = env.sim.absolute_self_observation_tensor().to_torch().to(env.device) + pos = abs_obs[:, :, :2] + lengths = abs_obs[:, :, 10] * AGENT_SCALE + widths = abs_obs[:, :, 11] * AGENT_SCALE + valid = ( + (torch.abs(pos[:, :, 0]) < 1000) + & (torch.abs(pos[:, :, 1]) < 1000) + & (lengths > 0.5) + & (widths > 0.5) + ) + + risk_world_ids, risk_agent_ids = torch.where(risk_mask) + if risk_world_ids.numel() == 0: + inf = torch.full((env.num_worlds,), float("inf"), device=env.device) + minus = torch.full((env.num_worlds,), -1, dtype=torch.long, device=env.device) + return inf, minus, minus + + risk_pos = pos[risk_world_ids, risk_agent_ids] + other_pos = pos[risk_world_ids] + dists = torch.linalg.norm(other_pos - risk_pos[:, None, :], dim=-1) + + agent_ids = torch.arange(env.max_agent_count, device=env.device).view(1, -1) + other_valid = valid[risk_world_ids] & (agent_ids != risk_agent_ids[:, None]) + dists = dists.masked_fill(~other_valid, float("inf")) + + min_dist_per_risk, partner_ids = dists.min(dim=1) + world_min = finite_min_scatter(min_dist_per_risk, risk_world_ids, env.num_worlds, env.device) + + best_risk = torch.full((env.num_worlds,), -1, dtype=torch.long, device=env.device) + best_partner = torch.full((env.num_worlds,), -1, dtype=torch.long, device=env.device) + for idx in torch.where(torch.isfinite(min_dist_per_risk))[0].tolist(): + world = int(risk_world_ids[idx].item()) + if min_dist_per_risk[idx] <= world_min[world]: + best_risk[world] = risk_agent_ids[idx] + best_partner[world] = partner_ids[idx] + + return world_min, best_risk, best_partner + + +def tensor_agent_lists(mask: torch.Tensor) -> list[list[int]]: + return [torch.where(mask[w])[0].detach().cpu().tolist() for w in range(mask.shape[0])] + + +def as_jsonable(value: Any, digits: int = 4) -> Any: + if isinstance(value, torch.Tensor): + return as_jsonable(value.detach().cpu().tolist(), digits=digits) + if isinstance(value, dict): + return {str(k): as_jsonable(v, digits=digits) for k, v in value.items()} + if isinstance(value, (list, tuple)): + return [as_jsonable(v, digits=digits) for v in value] + if isinstance(value, bool) or value is None: + return value + if isinstance(value, int): + return value + if isinstance(value, float): + if not math.isfinite(value): + return None + return round(value, digits) + return value + + +def tensor_series(tensor: torch.Tensor, digits: int = 4) -> list[Any]: + return as_jsonable(tensor, digits=digits) + + +def capture_state(env: GPUDriveTorchEnv) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + states = env.sim.absolute_self_observation_tensor().to_torch().detach().cpu().clone() + infos = env.sim.info_tensor().to_torch().detach().cpu().clone() + dones = env.get_dones().detach().cpu().clone().bool() + return states, infos, dones + + +def capture_actions( + env: GPUDriveTorchEnv, + actions: torch.Tensor, +) -> tuple[torch.Tensor, torch.Tensor]: + if actions.dtype in (torch.int8, torch.int16, torch.int32, torch.int64, torch.uint8): + action_index = actions.detach().cpu().clone().long() + action_values = env.action_keys_tensor[actions.to(dtype=torch.long, device=env.device)] + else: + action_index = torch.full( + actions.shape[:2], + -1, + dtype=torch.long, + device=actions.device, + ) + action_values = actions + return action_index, action_values.detach().cpu().clone().float() + + +def valid_agent_mask(states: torch.Tensor) -> torch.Tensor: + pos = states[:, :, :2] + lengths = states[:, :, 10] * AGENT_SCALE + widths = states[:, :, 11] * AGENT_SCALE + return ( + torch.isfinite(pos).all(dim=-1) + & (pos[:, :, 0].abs() < 1000) + & (pos[:, :, 1].abs() < 1000) + & (lengths > 0.5) + & (widths > 0.5) + ) + + +def wrap_angle(angle: torch.Tensor) -> torch.Tensor: + return torch.atan2(torch.sin(angle), torch.cos(angle)) + + +def safe_step(step: int, num_steps: int) -> int: + if num_steps <= 0: + return 0 + return max(0, min(int(step), num_steps - 1)) + + +def event_step_from_record(record: dict[str, Any], num_steps: int) -> int: + if record["first_collision_step"] >= 0: + return safe_step(record["first_collision_step"], num_steps) + if record["min_distance_step"] >= 0: + return safe_step(record["min_distance_step"], num_steps) + if record["first_offroad_step"] >= 0: + return safe_step(record["first_offroad_step"], num_steps) + return num_steps - 1 + + +def pick_trace_window( + event_step: int, + num_states: int, + pre_steps: int, + post_steps: int, +) -> tuple[int, int]: + start = max(0, event_step - max(0, pre_steps)) + end = min(num_states, event_step + max(0, post_steps) + 1) + return start, end + + +def compute_kinematics(states: torch.Tensor, dt: float) -> dict[str, torch.Tensor]: + dt = max(float(dt), 1e-6) + pos = states[:, :, :2] + yaw = states[:, :, 7] + vel = torch.zeros_like(pos) + vel[1:] = (pos[1:] - pos[:-1]) / dt + speed = torch.linalg.norm(vel, dim=-1) + accel = torch.zeros_like(speed) + accel[1:] = (speed[1:] - speed[:-1]) / dt + jerk = torch.zeros_like(speed) + jerk[1:] = (accel[1:] - accel[:-1]) / dt + yaw_rate = torch.zeros_like(yaw) + yaw_rate[1:] = wrap_angle(yaw[1:] - yaw[:-1]) / dt + pos_jump = torch.zeros_like(speed) + pos_jump[1:] = torch.linalg.norm(pos[1:] - pos[:-1], dim=-1) + return { + "velocity": vel, + "speed": speed, + "accel": accel, + "jerk": jerk, + "yaw_rate": yaw_rate, + "pos_jump": pos_jump, + } + + +def finite_stat(values: torch.Tensor, mode: str, default: float = 0.0) -> float: + values = values[torch.isfinite(values)] + if values.numel() == 0: + return default + if mode == "max": + return float(values.max().item()) + if mode == "min": + return float(values.min().item()) + if mode == "mean": + return float(values.mean().item()) + raise ValueError(mode) + + +def select_trace_agents( + record: dict[str, Any], + states: torch.Tensor, + control_mask: torch.Tensor, + risk_mask: torch.Tensor, + include_all_agents: bool, + top_k_agents: int, + local_event_step: int, +) -> list[int]: + valid_any = valid_agent_mask(states).any(dim=0) + if not bool(valid_any.any()): + return [] + if include_all_agents: + return torch.where(valid_any)[0].tolist() + + selected = set(record["risk_agents"]) + selected.update(record["collided_agents"]) + selected.update(record["offroad_agents"]) + if record["min_distance_risk_agent"] >= 0: + selected.add(record["min_distance_risk_agent"]) + if record["min_distance_partner_agent"] >= 0: + selected.add(record["min_distance_partner_agent"]) + + event_step = safe_step(local_event_step, states.shape[0]) + key_agents = [a for a in selected if 0 <= a < states.shape[1] and bool(valid_any[a])] + if not key_agents: + key_agents = torch.where((control_mask | risk_mask) & valid_any)[0].tolist() + + if key_agents: + center = states[event_step, key_agents, :2].mean(dim=0) + else: + center = states[event_step, valid_any, :2].mean(dim=0) + + distances = torch.linalg.norm(states[event_step, :, :2] - center[None, :], dim=-1) + distances = distances.masked_fill(~valid_any, float("inf")) + k = min(max(top_k_agents, len(key_agents)), int(valid_any.sum().item())) + if k > 0: + nearest = torch.topk(-distances, k=k).indices.tolist() + selected.update(nearest) + + return sorted(a for a in selected if 0 <= a < states.shape[1] and bool(valid_any[a])) + + +def reward_weights_for_agent( + agent_idx: int, + control_mask: torch.Tensor, + risk_mask: torch.Tensor, + normal_weights: torch.Tensor, + risk_weights: torch.Tensor, +) -> list[float] | None: + if not bool(control_mask[agent_idx]): + return None + weights = risk_weights if bool(risk_mask[agent_idx]) else normal_weights + return [float(v) for v in weights.tolist()] + + +def build_agent_metadata( + selected_agents: list[int], + states: torch.Tensor, + infos: torch.Tensor, + response_type: torch.Tensor, + control_mask: torch.Tensor, + risk_mask: torch.Tensor, + normal_weights: torch.Tensor, + risk_weights: torch.Tensor, +) -> list[dict[str, Any]]: + metadata = [] + valid_any = valid_agent_mask(states).any(dim=0) + first_valid = valid_agent_mask(states).float().argmax(dim=0) + for agent_idx in selected_agents: + first_step = int(first_valid[agent_idx].item()) if bool(valid_any[agent_idx]) else 0 + state = states[first_step, agent_idx] + raw_type = int(infos[first_step, agent_idx, 4].item()) if infos.shape[-1] > 4 else None + response = int(response_type[agent_idx].item()) if agent_idx < response_type.shape[0] else None + metadata.append( + { + "agent_index": agent_idx, + "sim_agent_id": int(state[13].item()), + "is_controlled": bool(control_mask[agent_idx]), + "is_risk_agent": bool(risk_mask[agent_idx]), + "response_type": response, + "raw_agent_type": raw_type, + "reward_weights": reward_weights_for_agent( + agent_idx, control_mask, risk_mask, normal_weights, risk_weights + ), + "length_m": float(state[10].item() * AGENT_SCALE), + "width_m": float(state[11].item() * AGENT_SCALE), + "height_m": float(state[12].item()), + "goal_xy": [float(state[8].item()), float(state[9].item())], + } + ) + return metadata + + +def pair_candidates(record: dict[str, Any]) -> list[tuple[int, int]]: + pairs: list[tuple[int, int]] = [] + risk = int(record["min_distance_risk_agent"]) + partner = int(record["min_distance_partner_agent"]) + if risk >= 0 and partner >= 0 and risk != partner: + pairs.append((risk, partner)) + risk_agents = [int(a) for a in record.get("risk_agents", [])] + for risk_agent in risk_agents: + for other_agent in record.get("collided_agents", []): + other_agent = int(other_agent) + if risk_agent != other_agent: + pairs.append((risk_agent, other_agent)) + seen = set() + unique_pairs = [] + for a, b in pairs: + key = (a, b) + if key not in seen: + seen.add(key) + unique_pairs.append(key) + return unique_pairs[:12] + + +def compute_pair_trace( + states: torch.Tensor, + kin: dict[str, torch.Tensor], + agent_a: int, + agent_b: int, + dt: float, +) -> dict[str, Any]: + pos_a = states[:, agent_a, :2] + pos_b = states[:, agent_b, :2] + yaw_a = states[:, agent_a, 7] + yaw_b = states[:, agent_b, 7] + rel = pos_b - pos_a + distance = torch.linalg.norm(rel, dim=-1) + vel_rel = kin["velocity"][:, agent_b] - kin["velocity"][:, agent_a] + denom = distance.clamp_min(1e-6) + closing_speed = -torch.sum(rel * vel_rel, dim=-1) / denom + ttc = torch.full_like(distance, float("inf")) + approaching = closing_speed > 0.1 + ttc[approaching] = distance[approaching] / closing_speed[approaching] + longitudinal = rel[:, 0] * torch.cos(yaw_a) + rel[:, 1] * torch.sin(yaw_a) + lateral = -rel[:, 0] * torch.sin(yaw_a) + rel[:, 1] * torch.cos(yaw_a) + rel_heading = wrap_angle(yaw_b - yaw_a) + finite_distance = distance.masked_fill(~torch.isfinite(distance), float("inf")) + min_idx = int(torch.argmin(finite_distance).item()) + ttc_valid = ttc[torch.isfinite(ttc)] + return { + "agent_a": agent_a, + "agent_b": agent_b, + "distance_m": tensor_series(distance), + "closing_speed_mps": tensor_series(closing_speed), + "ttc_s": tensor_series(ttc), + "relative_heading_rad": tensor_series(rel_heading), + "longitudinal_gap_m": tensor_series(longitudinal), + "lateral_offset_m": tensor_series(lateral), + "min_distance_m": float(finite_distance[min_idx].item()), + "min_distance_step": min_idx, + "min_ttc_s": float(ttc_valid.min().item()) if ttc_valid.numel() > 0 else None, + "time_ttc_below_1s": float(((ttc < 1.0) & torch.isfinite(ttc)).sum().item() * dt), + "time_ttc_below_2s": float(((ttc < 2.0) & torch.isfinite(ttc)).sum().item() * dt), + "relative_speed_at_min_mps": float(torch.linalg.norm(vel_rel[min_idx]).item()), + "closing_speed_at_min_mps": float(closing_speed[min_idx].item()), + "lateral_offset_at_min_m": float(lateral[min_idx].item()), + "longitudinal_gap_at_min_m": float(longitudinal[min_idx].item()), + "relative_heading_at_min_rad": float(rel_heading[min_idx].item()), + } + + +def build_pairwise_trace( + record: dict[str, Any], + states: torch.Tensor, + kin: dict[str, torch.Tensor], + selected_agents: list[int], + dt: float, +) -> list[dict[str, Any]]: + selected = set(selected_agents) + traces = [] + for agent_a, agent_b in pair_candidates(record): + if agent_a in selected and agent_b in selected: + traces.append(compute_pair_trace(states, kin, agent_a, agent_b, dt)) + return traces + + +def score_naturalness( + states: torch.Tensor, + infos: torch.Tensor, + kin: dict[str, torch.Tensor], + selected_agents: list[int], + record: dict[str, Any], +) -> dict[str, Any]: + if not selected_agents: + return {"score": 0.0, "reasons": ["no_valid_agents"]} + selected = torch.tensor(selected_agents, dtype=torch.long) + valid = valid_agent_mask(states)[:, selected] + max_speed = finite_stat(kin["speed"][:, selected][valid], "max") + max_accel = finite_stat(kin["accel"][:, selected][valid], "max") + max_decel = -finite_stat(kin["accel"][:, selected][valid], "min") + max_yaw_rate = finite_stat(kin["yaw_rate"][:, selected].abs()[valid], "max") + max_jerk = finite_stat(kin["jerk"][:, selected].abs()[valid], "max") + max_jump = finite_stat(kin["pos_jump"][:, selected][valid], "max") + offroad_before_collision = ( + record["first_offroad_step"] >= 0 + and record["first_collision_step"] >= 0 + and record["first_offroad_step"] < record["first_collision_step"] + ) + + penalties = [] + score = 100.0 + if max_speed > 45: + penalties.append("extreme_speed") + score -= min(30.0, (max_speed - 45.0) * 1.5) + if max_accel > 8: + penalties.append("high_acceleration") + score -= min(20.0, (max_accel - 8.0) * 2.0) + if max_decel > 10: + penalties.append("hard_deceleration") + score -= min(20.0, (max_decel - 10.0) * 1.5) + if max_yaw_rate > 1.6: + penalties.append("high_yaw_rate") + score -= min(20.0, (max_yaw_rate - 1.6) * 8.0) + if max_jerk > 40: + penalties.append("high_jerk") + score -= min(20.0, (max_jerk - 40.0) * 0.5) + if max_jump > 8: + penalties.append("position_jump") + score -= 35.0 + if offroad_before_collision: + penalties.append("offroad_before_collision") + score -= 25.0 + + return { + "score": max(0.0, min(100.0, score)), + "max_speed_mps": max_speed, + "max_accel_mps2": max_accel, + "max_decel_mps2": max_decel, + "max_abs_yaw_rate_radps": max_yaw_rate, + "max_abs_jerk_mps3": max_jerk, + "max_position_jump_m": max_jump, + "offroad_before_collision": offroad_before_collision, + "penalties": penalties, + } + + +def score_interaction( + record: dict[str, Any], + pair_traces: list[dict[str, Any]], + risk_mask: torch.Tensor, + response_type: torch.Tensor, +) -> dict[str, Any]: + risk_involved = any(int(a) in set(record.get("risk_agents", [])) for a in record.get("collided_agents", [])) + primary_partner = int(record.get("min_distance_partner_agent", -1)) + partner_dynamic = ( + primary_partner >= 0 + and primary_partner < response_type.shape[0] + and int(response_type[primary_partner].item()) != 2 + ) + best_pair = pair_traces[0] if pair_traces else None + min_ttc = best_pair.get("min_ttc_s") if best_pair else None + rel_speed = best_pair.get("relative_speed_at_min_mps") if best_pair else 0.0 + score = 40.0 + reasons = [] + if risk_involved: + score += 20.0 + else: + reasons.append("risk_agent_not_directly_collided") + if partner_dynamic: + score += 15.0 + else: + reasons.append("partner_may_be_static_or_padding") + if min_ttc is not None and min_ttc <= 3.0: + score += 15.0 + if rel_speed >= 1.0: + score += 10.0 + else: + reasons.append("low_relative_speed") + return { + "score": max(0.0, min(100.0, score)), + "risk_involved_in_collision": risk_involved, + "primary_partner_dynamic": partner_dynamic, + "primary_min_ttc_s": min_ttc, + "primary_relative_speed_at_min_mps": rel_speed, + "notes": reasons, + } + + +def score_criticality( + record: dict[str, Any], + pair_traces: list[dict[str, Any]], +) -> dict[str, Any]: + score = 0.0 + if record["collision"]: + score += 55.0 + if record["near_miss"]: + score += 20.0 + min_dist = record.get("min_distance_m") + if min_dist is not None: + score += max(0.0, min(15.0, (3.0 - float(min_dist)) * 7.5)) + impact_speed = 0.0 + ttc_exposure_2s = 0.0 + if pair_traces: + impact_speed = float(pair_traces[0].get("relative_speed_at_min_mps") or 0.0) + ttc_exposure_2s = float(pair_traces[0].get("time_ttc_below_2s") or 0.0) + if impact_speed >= 2.0: + score += min(10.0, impact_speed) + return { + "score": max(0.0, min(100.0, score)), + "has_collision": bool(record["collision"]), + "has_near_miss": bool(record["near_miss"]), + "min_distance_m": min_dist, + "impact_or_min_relative_speed_mps": impact_speed, + "time_ttc_below_2s": ttc_exposure_2s, + } + + +def semantic_candidates(pair_traces: list[dict[str, Any]]) -> list[dict[str, Any]]: + candidates = [] + for pair in pair_traces[:3]: + lat = float(pair.get("lateral_offset_at_min_m") or 0.0) + lon = float(pair.get("longitudinal_gap_at_min_m") or 0.0) + heading = abs(float(pair.get("relative_heading_at_min_rad") or 0.0)) + rel_speed = float(pair.get("relative_speed_at_min_mps") or 0.0) + if abs(lat) < 1.5 and lon > 0 and heading < 0.6: + label = "rear_end_or_following_conflict" + elif abs(lat) < 3.5 and heading < 0.8: + label = "same_direction_side_conflict_or_cut_in" + elif heading > 0.8: + label = "crossing_or_intersection_conflict" + else: + label = "generic_close_interaction" + candidates.append( + { + "pair": [pair["agent_a"], pair["agent_b"]], + "label": label, + "relative_speed_mps": rel_speed, + "lateral_offset_m": lat, + "longitudinal_gap_m": lon, + "relative_heading_rad": heading, + } + ) + return candidates + + +def build_map_context( + map_obs: torch.Tensor, + states: torch.Tensor, + selected_agents: list[int], + event_step: int, + radius_m: float, + top_k: int, +) -> dict[str, Any]: + if selected_agents: + selected = torch.tensor(selected_agents, dtype=torch.long) + selected_valid = valid_agent_mask(states)[event_step, selected] + selected = selected[selected_valid] + center = states[event_step, selected, :2].mean(dim=0) if selected.numel() else None + else: + center = None + if center is None: + valid = valid_agent_mask(states)[event_step] + center = states[event_step, valid, :2].mean(dim=0) if bool(valid.any()) else torch.zeros(2) + xy = map_obs[:, :2] + valid = torch.isfinite(xy).all(dim=-1) & (xy[:, 0].abs() < 1000) & (xy[:, 1].abs() < 1000) + dist = torch.linalg.norm(xy - center[None, :], dim=-1) + keep = valid & (dist <= radius_m) + candidate_ids = torch.where(keep)[0] + if candidate_ids.numel() > top_k: + nearest = torch.topk(-dist[candidate_ids], k=top_k).indices + candidate_ids = candidate_ids[nearest] + points = [] + type_counts: dict[str, int] = {} + for idx in candidate_ids.tolist(): + point = map_obs[idx] + road_type = int(point[6].item()) + type_counts[str(road_type)] = type_counts.get(str(road_type), 0) + 1 + points.append( + { + "x": float(point[0].item()), + "y": float(point[1].item()), + "segment_length": float(point[2].item()), + "segment_width": float(point[3].item()), + "orientation": float(point[5].item()), + "type": road_type, + "id": int(point[7].item()), + "vbd_type": int(point[8].item()), + } + ) + return { + "center_xy": [float(center[0].item()), float(center[1].item())], + "radius_m": radius_m, + "num_points": len(points), + "type_counts": type_counts, + "points": points, + } + + +def build_event_trace_record( + record: dict[str, Any], + state_history: list[torch.Tensor], + info_history: list[torch.Tensor], + done_history: list[torch.Tensor], + action_index_history: list[torch.Tensor], + action_value_history: list[torch.Tensor], + response_type_batch: torch.Tensor, + map_obs_batch: torch.Tensor, + control_mask_batch: torch.Tensor, + risk_mask_batch: torch.Tensor, + normal_weights: torch.Tensor, + risk_weights: torch.Tensor, + args: argparse.Namespace, +) -> dict[str, Any]: + world_idx = int(record["world_idx"]) + all_states = torch.stack(state_history, dim=0)[:, world_idx] + all_infos = torch.stack(info_history, dim=0)[:, world_idx] + all_dones = torch.stack(done_history, dim=0)[:, world_idx] + actions_index = torch.stack(action_index_history, dim=0)[:, world_idx] if action_index_history else None + actions_value = torch.stack(action_value_history, dim=0)[:, world_idx] if action_value_history else None + response_type = response_type_batch[world_idx] + if response_type.ndim > 1: + response_type = response_type.squeeze(-1) + control_mask = control_mask_batch[world_idx] + risk_mask = risk_mask_batch[world_idx] + + event_step = event_step_from_record(record, all_states.shape[0]) + start, end = pick_trace_window( + event_step, + all_states.shape[0], + args.trace_pre_steps, + args.trace_post_steps, + ) + states = all_states[start:end] + infos = all_infos[start:end] + dones = all_dones[start:end] + kin = compute_kinematics(states, args.trace_dt) + + selected_agents = select_trace_agents( + record, + states, + control_mask, + risk_mask, + bool(args.trace_include_all_agents), + args.trace_top_k_agents, + event_step - start, + ) + agent_metadata = build_agent_metadata( + selected_agents, + states, + infos, + response_type, + control_mask, + risk_mask, + normal_weights.detach().cpu(), + risk_weights.detach().cpu(), + ) + + agent_traces = {} + for agent_idx in selected_agents: + agent_slice = states[:, agent_idx] + info_slice = infos[:, agent_idx] + trace: dict[str, Any] = { + "pos_x_m": tensor_series(agent_slice[:, 0]), + "pos_y_m": tensor_series(agent_slice[:, 1]), + "yaw_rad": tensor_series(agent_slice[:, 7]), + "speed_mps": tensor_series(kin["speed"][:, agent_idx]), + "accel_mps2": tensor_series(kin["accel"][:, agent_idx]), + "yaw_rate_radps": tensor_series(kin["yaw_rate"][:, agent_idx]), + "valid": tensor_series(valid_agent_mask(states)[:, agent_idx]), + "done": tensor_series(dones[:, agent_idx]), + "off_road": tensor_series(info_slice[:, 0]), + "collided": tensor_series(info_slice[:, 1:3].sum(dim=-1)), + "goal_achieved": tensor_series(info_slice[:, 3]), + } + if actions_index is not None and actions_value is not None: + action_start = max(0, start) + action_end = max(action_start, min(end - 1, actions_index.shape[0])) + trace["action_index"] = tensor_series(actions_index[action_start:action_end, agent_idx]) + trace["action_value"] = tensor_series(actions_value[action_start:action_end, agent_idx]) + agent_traces[str(agent_idx)] = trace + + pair_traces = build_pairwise_trace(record, states, kin, selected_agents, args.trace_dt) + naturalness = score_naturalness(states, infos, kin, selected_agents, record) + interaction = score_interaction(record, pair_traces, risk_mask, response_type) + criticality = score_criticality(record, pair_traces) + validity_gate = { + "has_collision_or_near_miss": bool(record["collision"] or record["near_miss"]), + "enough_controlled_agents": int(record["controlled_agents"]) >= 2, + "motion_natural_enough": float(naturalness["score"]) >= 50.0, + "interaction_meaningful_enough": float(interaction["score"]) >= 45.0, + } + final_score = ( + 0.35 * float(criticality["score"]) + + 0.30 * float(naturalness["score"]) + + 0.35 * float(interaction["score"]) + ) + + return { + "schema_version": "longtail_event_trace_v1", + "event": record, + "generation_context": { + "checkpoint": str(args.checkpoint), + "config": str(args.config), + "data_dir": str(args.data_dir), + "seed": int(args.seed), + "shard_id": int(args.shard_id), + "num_shards": int(args.num_shards), + "num_worlds": int(args.num_worlds), + "normal_mode": args.normal_mode, + "normal_style": args.normal_style, + "risk_style": args.risk_style, + "risk_agents_per_world": int(args.risk_agents_per_world), + "dt": float(args.trace_dt), + }, + "trace_window": { + "start_step": int(start), + "end_step_exclusive": int(end), + "event_step": int(event_step), + "num_frames": int(end - start), + "selected_agents": selected_agents, + "include_all_agents": bool(args.trace_include_all_agents), + }, + "agent_metadata": agent_metadata, + "agent_traces": agent_traces, + "pairwise_trace": pair_traces, + "map_context": build_map_context( + map_obs_batch[world_idx], + states, + selected_agents, + safe_step(event_step - start, states.shape[0]), + args.trace_map_radius_m, + args.trace_map_top_k, + ), + "naturalness_metrics": naturalness, + "interaction_metrics": interaction, + "criticality_metrics": criticality, + "semantic_candidates": semantic_candidates(pair_traces), + "quality_scores": { + "validity_gate": validity_gate, + "final_score": max(0.0, min(100.0, final_score)), + }, + "replay_context": { + "note": "This stores observed rollout states and actions for evaluation. Recreating the exact rollout still requires the same checkpoint, seed, data file, and simulator version.", + "action_space": "discrete_index_plus_action_value" if actions_index is not None else "unknown", + }, + "visualization_hints": { + "center_agent": int(record["min_distance_risk_agent"]) + if int(record["min_distance_risk_agent"]) >= 0 + else (selected_agents[0] if selected_agents else -1), + "partner_agent": int(record["min_distance_partner_agent"]), + "zoom_radius_m": max(25.0, min(120.0, float(args.trace_map_radius_m))), + "event_frame_index": int(event_step - start), + }, + } + + +def main() -> None: + args = parse_args() + device = args.device if torch.cuda.is_available() or args.device == "cpu" else "cpu" + config = load_config(args.config) + checkpoint = Path(args.checkpoint) if args.checkpoint else find_latest_checkpoint() + args.checkpoint = str(checkpoint) + output_dir = Path(args.output_dir) / f"shard_{args.shard_id:03d}" + output_dir.mkdir(parents=True, exist_ok=True) + + seed = args.seed + args.shard_id * 100_000 + torch.manual_seed(seed) + generator = torch.Generator(device=device) + generator.manual_seed(seed) + + env_config = make_env_config(config["environment"], args) + data_loader = SceneDataLoader( + root=args.data_dir, + batch_size=args.num_worlds, + dataset_size=args.dataset_size, + sample_with_replacement=True, + shuffle=True, + seed=seed, + ) + env = GPUDriveTorchEnv( + config=env_config, + data_loader=data_loader, + max_cont_agents=env_config.max_controlled_agents, + device=device, + render_config=RenderConfig(render_3d=False), + ) + policy = load_policy(checkpoint, config, device) + + normal_weights = reward_preset(env_config, args.normal_style, device) + risk_weights = torch.tensor( + [ + args.risk_collision_weight, + args.risk_goal_weight, + args.risk_offroad_weight, + ], + dtype=torch.float32, + device=device, + ) + + events_path = output_dir / "events.jsonl" + traces_path = output_dir / "event_traces.jsonl" + summary: dict[str, Any] = { + "checkpoint": str(checkpoint), + "data_dir": str(data_loader.root), + "shard_id": args.shard_id, + "num_shards": args.num_shards, + "num_worlds": args.num_worlds, + "num_batches": args.num_batches, + "steps": args.steps, + "risk_agents_per_world": args.risk_agents_per_world, + "normal_mode": args.normal_mode, + "normal_weights": normal_weights.detach().cpu().tolist(), + "risk_weights": risk_weights.detach().cpu().tolist(), + "near_miss_threshold_m": args.near_miss_threshold_m, + "save_event_traces": bool(args.save_event_traces), + "event_traces_path": str(traces_path) if args.save_event_traces else None, + "trace_pre_steps": args.trace_pre_steps, + "trace_post_steps": args.trace_post_steps, + "trace_include_all_agents": bool(args.trace_include_all_agents), + "trace_map_radius_m": args.trace_map_radius_m, + "total_worlds": 0, + "event_worlds": 0, + "collision_worlds": 0, + "near_miss_worlds": 0, + "offroad_worlds": 0, + "best_min_distance_m": float("inf"), + "start_time": time.time(), + } + + print( + "[search] start:", + f"checkpoint={checkpoint}", + f"data_dir={data_loader.root}", + f"dataset_files={len(data_loader.dataset)}", + f"device={device}", + f"shard={args.shard_id}/{args.num_shards}", + f"num_worlds={args.num_worlds}", + f"num_batches={args.num_batches}", + f"risk_agents_per_world={args.risk_agents_per_world}", + f"risk_weights={summary['risk_weights']}", + f"save_event_traces={bool(args.save_event_traces)}", + flush=True, + ) + + events_f = open(events_path, "w", encoding="utf-8") + trace_f = open(traces_path, "w", encoding="utf-8") if args.save_event_traces else None + try: + scan_start = time.time() + for batch_idx in range(args.num_batches): + batch_start = time.time() + if batch_idx > 0: + env.swap_data_batch() + env.reset() + + control_mask = env.cont_agent_mask.clone() + risk_mask = choose_risk_agents(control_mask, args.risk_agents_per_world, generator) + normal_mask = control_mask & ~risk_mask + + env.reward_weights_tensor[:] = normal_weights + env.reward_weights_tensor[risk_mask] = risk_weights + response_type_batch = None + map_obs_batch = None + control_mask_batch = None + risk_mask_batch = None + state_history: list[torch.Tensor] = [] + info_history: list[torch.Tensor] = [] + done_history: list[torch.Tensor] = [] + action_index_history: list[torch.Tensor] = [] + action_value_history: list[torch.Tensor] = [] + if args.save_event_traces: + response_type_batch = ( + env.sim.response_type_tensor().to_torch().detach().cpu().clone() + ) + map_obs_batch = env.sim.map_observation_tensor().to_torch().detach().cpu().clone() + control_mask_batch = control_mask.detach().cpu().clone() + risk_mask_batch = risk_mask.detach().cpu().clone() + state, info, done = capture_state(env) + state_history.append(state) + info_history.append(info) + done_history.append(done) + + live_mask = control_mask.clone() + collided = torch.zeros_like(control_mask, dtype=torch.bool, device=device) + offroad = torch.zeros_like(control_mask, dtype=torch.bool, device=device) + goal = torch.zeros_like(control_mask, dtype=torch.bool, device=device) + first_collision_step = torch.full((env.num_worlds,), -1, dtype=torch.long, device=device) + first_offroad_step = torch.full((env.num_worlds,), -1, dtype=torch.long, device=device) + best_min_dist = torch.full((env.num_worlds,), float("inf"), dtype=torch.float32, device=device) + best_min_step = torch.full((env.num_worlds,), -1, dtype=torch.long, device=device) + best_risk_agent = torch.full((env.num_worlds,), -1, dtype=torch.long, device=device) + best_partner_agent = torch.full((env.num_worlds,), -1, dtype=torch.long, device=device) + + expert_actions = None + if args.normal_mode == "expert": + expert_actions, _, _, _ = env.get_expert_actions() + + for step in range(args.steps): + active = live_mask & control_mask + if args.normal_mode == "expert": + action_step = min(step, expert_actions.shape[2] - 1) + actions = expert_actions[:, :, action_step, :].clone() + active_risk = active & risk_mask + if bool(active_risk.any().item()): + risk_obs = env.get_obs(active_risk) + with torch.no_grad(): + risk_action, _, _, _ = policy( + risk_obs, deterministic=bool(args.deterministic) + ) + actions[active_risk] = env.action_keys_tensor[ + risk_action.to(dtype=torch.long, device=device) + ] + else: + actions = torch.zeros( + (env.num_worlds, env.max_agent_count), + dtype=torch.int64, + device=device, + ) + if bool(active.any().item()): + active_obs = env.get_obs(active) + with torch.no_grad(): + action, _, _, _ = policy( + active_obs, deterministic=bool(args.deterministic) + ) + actions[active] = action.to(dtype=torch.int64, device=device) + + if args.save_event_traces: + action_index, action_value = capture_actions(env, actions) + action_index_history.append(action_index) + action_value_history.append(action_value) + + env.step_dynamics(actions) + infos = env.get_infos() + if args.save_event_traces: + state, info, done = capture_state(env) + state_history.append(state) + info_history.append(info) + done_history.append(done) + + new_collision = (infos.collided > 0).bool() & control_mask & ~collided + new_offroad = (infos.off_road > 0).bool() & control_mask & ~offroad + new_collision_world = new_collision.any(dim=1) & (first_collision_step < 0) + new_offroad_world = new_offroad.any(dim=1) & (first_offroad_step < 0) + first_collision_step[new_collision_world] = step + 1 + first_offroad_step[new_offroad_world] = step + 1 + + collided |= (infos.collided > 0).bool() & control_mask + offroad |= (infos.off_road > 0).bool() & control_mask + goal |= (infos.goal_achieved > 0).bool() & control_mask + + min_dist, min_risk, min_partner = compute_near_miss(env, risk_mask & live_mask) + improved = min_dist < best_min_dist + best_min_dist[improved] = min_dist[improved] + best_min_step[improved] = step + 1 + best_risk_agent[improved] = min_risk[improved] + best_partner_agent[improved] = min_partner[improved] + + live_mask &= ~env.get_dones().bool() + if not bool(live_mask.any().item()): + break + + collision_world = (collided & control_mask).any(dim=1) + near_miss_world = best_min_dist <= args.near_miss_threshold_m + offroad_world = (offroad & control_mask).any(dim=1) + event_world = collision_world | near_miss_world + if args.save_offroad: + event_world |= offroad_world + + risk_agent_lists = tensor_agent_lists(risk_mask) + controlled_counts = control_mask.sum(dim=1).detach().cpu().tolist() + files = [str(Path(path).name) for path in env.data_batch] + + for world_idx in torch.where(event_world)[0].detach().cpu().tolist(): + record = { + "shard_id": args.shard_id, + "batch_idx": batch_idx, + "world_idx": world_idx, + "scenario_file": files[world_idx], + "controlled_agents": int(controlled_counts[world_idx]), + "risk_agents": risk_agent_lists[world_idx], + "collision": bool(collision_world[world_idx].item()), + "near_miss": bool(near_miss_world[world_idx].item()), + "offroad": bool(offroad_world[world_idx].item()), + "first_collision_step": int(first_collision_step[world_idx].item()), + "first_offroad_step": int(first_offroad_step[world_idx].item()), + "min_distance_m": round(float(best_min_dist[world_idx].item()), 4), + "min_distance_step": int(best_min_step[world_idx].item()), + "min_distance_risk_agent": int(best_risk_agent[world_idx].item()), + "min_distance_partner_agent": int(best_partner_agent[world_idx].item()), + "collided_agents": torch.where(collided[world_idx] & control_mask[world_idx])[0].detach().cpu().tolist(), + "offroad_agents": torch.where(offroad[world_idx] & control_mask[world_idx])[0].detach().cpu().tolist(), + "goal_agents": int((goal[world_idx] & control_mask[world_idx]).sum().item()), + } + events_f.write(json.dumps(record) + "\n") + if trace_f is not None: + trace_record = build_event_trace_record( + record=record, + state_history=state_history, + info_history=info_history, + done_history=done_history, + action_index_history=action_index_history, + action_value_history=action_value_history, + response_type_batch=response_type_batch, + map_obs_batch=map_obs_batch, + control_mask_batch=control_mask_batch, + risk_mask_batch=risk_mask_batch, + normal_weights=normal_weights, + risk_weights=risk_weights, + args=args, + ) + trace_f.write(json.dumps(as_jsonable(trace_record), ensure_ascii=False) + "\n") + + events_f.flush() + if trace_f is not None: + trace_f.flush() + batch_min = float(best_min_dist.min().item()) + summary["total_worlds"] += args.num_worlds + summary["event_worlds"] += int(event_world.sum().item()) + summary["collision_worlds"] += int(collision_world.sum().item()) + summary["near_miss_worlds"] += int(near_miss_world.sum().item()) + summary["offroad_worlds"] += int(offroad_world.sum().item()) + summary["best_min_distance_m"] = min(summary["best_min_distance_m"], batch_min) + + completed_batches = batch_idx + 1 + elapsed = time.time() - scan_start + batch_elapsed = time.time() - batch_start + batches_left = args.num_batches - completed_batches + eta_sec = (elapsed / completed_batches) * batches_left if completed_batches else 0.0 + scanned_worlds = completed_batches * args.num_worlds + event_rate = summary["event_worlds"] / scanned_worlds if scanned_worlds else 0.0 + + print( + "[search] progress:", + f"shard={args.shard_id}", + f"batch={batch_idx + 1}/{args.num_batches}", + f"progress={100.0 * completed_batches / args.num_batches:.1f}%", + f"batch_sec={batch_elapsed:.1f}", + f"elapsed={elapsed / 60.0:.1f}m", + f"eta={eta_sec / 60.0:.1f}m", + f"scanned_worlds={scanned_worlds}", + f"cum_events={summary['event_worlds']}", + f"event_rate={event_rate:.4f}", + f"events={int(event_world.sum().item())}", + f"collisions={int(collision_world.sum().item())}", + f"near_miss={int(near_miss_world.sum().item())}", + f"offroad={int(offroad_world.sum().item())}", + f"best_min_dist={batch_min:.3f}", + flush=True, + ) + finally: + events_f.close() + if trace_f is not None: + trace_f.close() + + summary["elapsed_sec"] = round(time.time() - summary["start_time"], 3) + if math.isinf(summary["best_min_distance_m"]): + summary["best_min_distance_m"] = None + with open(output_dir / "summary.json", "w", encoding="utf-8") as f: + json.dump(summary, f, indent=2) + + print("[search] done:", summary, flush=True) + + +if __name__ == "__main__": + main() diff --git a/gpudrive-main/scripts/search_longtail_reward_conditioned.sbatch b/gpudrive-main/scripts/search_longtail_reward_conditioned.sbatch new file mode 100644 index 0000000000000000000000000000000000000000..915348775fa628a374aeeb4af0ab40b50bfc1446 --- /dev/null +++ b/gpudrive-main/scripts/search_longtail_reward_conditioned.sbatch @@ -0,0 +1,169 @@ +#!/bin/bash +#SBATCH --partition=gre +#SBATCH --nodes=1 +#SBATCH --ntasks=1 +#SBATCH --gres=gpu:4 +#SBATCH --time=48:00:00 +#SBATCH --job-name=lzh_longtail_search +#SBATCH --output=%x-%j.out +#SBATCH --mem=256G +#SBATCH --cpus-per-task=32 + +set -euo pipefail + +PROJECT_ROOT="${PROJECT_ROOT:-/mingli01/project/zihanliu/gpudrive-main}" +CONDA_ENV_NAME="${CONDA_ENV_NAME:-gpudrive}" +CONFIG_PATH="${CONFIG_PATH:-baselines/ppo/config/ppo_mini_reward_conditioned.yaml}" +CHECKPOINT="${CHECKPOINT:-}" +DATA_DIR="${DATA_DIR:-/mingli01/data/GPUDrive_mini/training}" +OUTPUT_DIR="${OUTPUT_DIR:-longtail_outputs/reward_conditioned_search}" + +NUM_GPUS="${NUM_GPUS:-4}" +NUM_WORLDS="${NUM_WORLDS:-1000}" +DATASET_SIZE="${DATASET_SIZE:-864}" +NUM_BATCHES="${NUM_BATCHES:-10}" +STEPS="${STEPS:-91}" +SEED="${SEED:-42}" +DETERMINISTIC="${DETERMINISTIC:-0}" +RISK_AGENTS_PER_WORLD="${RISK_AGENTS_PER_WORLD:-3}" +NORMAL_MODE="${NORMAL_MODE:-policy}" +NORMAL_STYLE="${NORMAL_STYLE:-balanced}" +RISK_STYLE="${RISK_STYLE:-risk_taker}" +RISK_COLLISION_WEIGHT="${RISK_COLLISION_WEIGHT:-0.0}" +RISK_GOAL_WEIGHT="${RISK_GOAL_WEIGHT:-2.0}" +RISK_OFFROAD_WEIGHT="${RISK_OFFROAD_WEIGHT:--0.4}" +NEAR_MISS_THRESHOLD_M="${NEAR_MISS_THRESHOLD_M:-2.0}" +SAVE_OFFROAD="${SAVE_OFFROAD:-0}" +OBS_RADIUS="${OBS_RADIUS:-}" +POLYLINE_REDUCTION_THRESHOLD="${POLYLINE_REDUCTION_THRESHOLD:-}" +SUMMARY_TOP_K="${SUMMARY_TOP_K:-100}" +SAVE_EVENT_TRACES="${SAVE_EVENT_TRACES:-1}" +TRACE_PRE_STEPS="${TRACE_PRE_STEPS:-50}" +TRACE_POST_STEPS="${TRACE_POST_STEPS:-10}" +TRACE_INCLUDE_ALL_AGENTS="${TRACE_INCLUDE_ALL_AGENTS:-1}" +TRACE_TOP_K_AGENTS="${TRACE_TOP_K_AGENTS:-16}" +TRACE_MAP_RADIUS_M="${TRACE_MAP_RADIUS_M:-80.0}" +TRACE_MAP_TOP_K="${TRACE_MAP_TOP_K:-256}" +TRACE_DT="${TRACE_DT:-0.1}" + +module load conda/3 +source "$(conda info --base)/etc/profile.d/conda.sh" +conda activate "${CONDA_ENV_NAME}" + +export PYTHONNOUSERSITE=1 +export PYTHONUNBUFFERED=1 +export OMP_NUM_THREADS="${OMP_NUM_THREADS:-1}" +export MKL_NUM_THREADS="${MKL_NUM_THREADS:-1}" +export MADRONA_MWGPU_KERNEL_CACHE="${MADRONA_MWGPU_KERNEL_CACHE:-${PROJECT_ROOT}/gpudrive_cache}" + +cd "${PROJECT_ROOT}" +export PYTHONPATH="${PROJECT_ROOT}:${PYTHONPATH:-}" + +if [[ ! -f "${CONFIG_PATH}" ]]; then + echo "ERROR: config not found: ${CONFIG_PATH}" + exit 1 +fi + +if [[ ! -d "${DATA_DIR}" ]]; then + echo "ERROR: DATA_DIR does not exist: ${DATA_DIR}" + exit 1 +fi + +if ! compgen -G "${DATA_DIR}/tfrecord*.json" >/dev/null; then + echo "ERROR: no tfrecord*.json files found under DATA_DIR=${DATA_DIR}" + exit 1 +fi + +mkdir -p "${OUTPUT_DIR}/logs" + +echo "SLURM_JOB_ID=${SLURM_JOB_ID:-unknown}" +echo "PROJECT_ROOT=${PROJECT_ROOT}" +echo "CONFIG_PATH=${CONFIG_PATH}" +echo "CHECKPOINT=${CHECKPOINT:-auto-latest}" +echo "DATA_DIR=${DATA_DIR}" +echo "OUTPUT_DIR=${OUTPUT_DIR}" +echo "NUM_GPUS=${NUM_GPUS}" +echo "NUM_WORLDS=${NUM_WORLDS}" +echo "DATASET_SIZE=${DATASET_SIZE}" +echo "NUM_BATCHES=${NUM_BATCHES}" +echo "STEPS=${STEPS}" +echo "RISK_AGENTS_PER_WORLD=${RISK_AGENTS_PER_WORLD}" +echo "NORMAL_MODE=${NORMAL_MODE}" +echo "RISK_COLLISION_WEIGHT=${RISK_COLLISION_WEIGHT}" +echo "RISK_GOAL_WEIGHT=${RISK_GOAL_WEIGHT}" +echo "RISK_OFFROAD_WEIGHT=${RISK_OFFROAD_WEIGHT}" +echo "NEAR_MISS_THRESHOLD_M=${NEAR_MISS_THRESHOLD_M}" +echo "SAVE_EVENT_TRACES=${SAVE_EVENT_TRACES}" +echo "TRACE_PRE_STEPS=${TRACE_PRE_STEPS}" +echo "TRACE_POST_STEPS=${TRACE_POST_STEPS}" +echo "TRACE_INCLUDE_ALL_AGENTS=${TRACE_INCLUDE_ALL_AGENTS}" +echo "TRACE_MAP_RADIUS_M=${TRACE_MAP_RADIUS_M}" +echo "CUDA_VISIBLE_DEVICES=${CUDA_VISIBLE_DEVICES:-unset}" + +if command -v nvidia-smi >/dev/null 2>&1; then + nvidia-smi -L || true +fi + +IFS=',' read -r -a GPU_IDS <<< "${CUDA_VISIBLE_DEVICES:-0,1,2,3}" + +for SHARD_ID in $(seq 0 $((NUM_GPUS - 1))); do + GPU_ID="${GPU_IDS[$SHARD_ID]:-${SHARD_ID}}" + CMD=( + python -u scripts/search_longtail_reward_conditioned.py + --config "${CONFIG_PATH}" + --data-dir "${DATA_DIR}" + --output-dir "${OUTPUT_DIR}" + --num-worlds "${NUM_WORLDS}" + --dataset-size "${DATASET_SIZE}" + --num-batches "${NUM_BATCHES}" + --steps "${STEPS}" + --seed "${SEED}" + --shard-id "${SHARD_ID}" + --num-shards "${NUM_GPUS}" + --deterministic "${DETERMINISTIC}" + --risk-agents-per-world "${RISK_AGENTS_PER_WORLD}" + --normal-mode "${NORMAL_MODE}" + --normal-style "${NORMAL_STYLE}" + --risk-style "${RISK_STYLE}" + --risk-collision-weight "${RISK_COLLISION_WEIGHT}" + --risk-goal-weight "${RISK_GOAL_WEIGHT}" + --risk-offroad-weight "${RISK_OFFROAD_WEIGHT}" + --near-miss-threshold-m "${NEAR_MISS_THRESHOLD_M}" + --save-offroad "${SAVE_OFFROAD}" + --save-event-traces "${SAVE_EVENT_TRACES}" + --trace-pre-steps "${TRACE_PRE_STEPS}" + --trace-post-steps "${TRACE_POST_STEPS}" + --trace-include-all-agents "${TRACE_INCLUDE_ALL_AGENTS}" + --trace-top-k-agents "${TRACE_TOP_K_AGENTS}" + --trace-map-radius-m "${TRACE_MAP_RADIUS_M}" + --trace-map-top-k "${TRACE_MAP_TOP_K}" + --trace-dt "${TRACE_DT}" + ) + + if [[ -n "${CHECKPOINT}" ]]; then + CMD+=(--checkpoint "${CHECKPOINT}") + fi + if [[ -n "${OBS_RADIUS}" ]]; then + CMD+=(--obs-radius "${OBS_RADIUS}") + fi + if [[ -n "${POLYLINE_REDUCTION_THRESHOLD}" ]]; then + CMD+=(--polyline-reduction-threshold "${POLYLINE_REDUCTION_THRESHOLD}") + fi + + echo "Launching shard ${SHARD_ID} on CUDA_VISIBLE_DEVICES=${GPU_ID}: ${CMD[*]}" + CUDA_VISIBLE_DEVICES="${GPU_ID}" "${CMD[@]}" \ + > "${OUTPUT_DIR}/logs/shard_${SHARD_ID}.out" \ + 2> "${OUTPUT_DIR}/logs/shard_${SHARD_ID}.err" & +done + +wait + +echo "All shards finished. Summaries:" +find "${OUTPUT_DIR}" -path "*/summary.json" -print + +echo "Shard progress logs:" +find "${OUTPUT_DIR}/logs" -name "shard_*.out" -print + +python -u scripts/summarize_longtail_search.py \ + --search-dir "${OUTPUT_DIR}" \ + --top-k "${SUMMARY_TOP_K}" diff --git a/gpudrive-main/scripts/submit.sbatch b/gpudrive-main/scripts/submit.sbatch new file mode 100644 index 0000000000000000000000000000000000000000..d6923fc8adea4aaecbe3c779226d57d6c03510ed --- /dev/null +++ b/gpudrive-main/scripts/submit.sbatch @@ -0,0 +1,143 @@ +#!/bin/bash +#SBATCH --partition=gre +#SBATCH --nodes=1 +#SBATCH --ntasks-per-node=1 +# NuScenes conversion is CPU-bound. If this partition requires GPU allocation, +# uncomment the next line. +##SBATCH --gres=gpu:1 +#SBATCH --time=24:00:00 +#SBATCH --job-name=lzh_gpu +#SBATCH --output=%x-%j.out +#SBATCH --mem=128G +#SBATCH --cpus-per-task=16 + +set -euo pipefail + +PROJECT_ROOT="${PROJECT_ROOT:-/mingli01/project/zihanliu/gpudrive-main}" +NUSCENES_ROOT="${NUSCENES_ROOT:-/mingli01/data/nuscenes}" +OUTPUT_DIR="${OUTPUT_DIR:-${PROJECT_ROOT}/data/processed/nuscenes}" +CONDA_ENV_NAME="${CONDA_ENV_NAME:-gpudrive}" + +NUSCENES_VERSION="${NUSCENES_VERSION:-v1.0-trainval}" +NUSCENES_SPLIT="${NUSCENES_SPLIT-train}" +SCENE_NAMES="${SCENE_NAMES:-}" +MAX_SCENES="${MAX_SCENES:-}" +TARGET_HZ="${TARGET_HZ:-10.0}" +NUM_STEPS="${NUM_STEPS:-91}" +MAX_ANNOTATION_GAP_SEC="${MAX_ANNOTATION_GAP_SEC:-1.1}" +WINDOW_STRIDE_SEC="${WINDOW_STRIDE_SEC:-}" +FILE_PREFIX="${FILE_PREFIX:-tfrecord-nuscenes}" +MAP_RADIUS_M="${MAP_RADIUS_M:-120.0}" +MAP_DISCRETIZATION_M="${MAP_DISCRETIZATION_M:-1.0}" +MAX_ROAD_ELEMENTS="${MAX_ROAD_ELEMENTS:-900}" +MAX_GEOMETRY_POINTS="${MAX_GEOMETRY_POINTS:-512}" +ALLOW_PARTIAL_WINDOW="${ALLOW_PARTIAL_WINDOW:-false}" +NO_EGO="${NO_EGO:-false}" +NO_MAP="${NO_MAP:-false}" +FAIL_ON_MISSING_MAP="${FAIL_ON_MISSING_MAP:-false}" +QUIET="${QUIET:-true}" + +module load conda/3 +source "$(conda info --base)/etc/profile.d/conda.sh" +conda activate "${CONDA_ENV_NAME}" + +export PYTHONNOUSERSITE=1 +export OMP_NUM_THREADS="${OMP_NUM_THREADS:-${SLURM_CPUS_PER_TASK:-1}}" +export MKL_NUM_THREADS="${MKL_NUM_THREADS:-${SLURM_CPUS_PER_TASK:-1}}" + +cd "${PROJECT_ROOT}" +export PYTHONPATH="${PROJECT_ROOT}:${PYTHONPATH:-}" + +if [[ ! -d "${PROJECT_ROOT}" ]]; then + echo "ERROR: PROJECT_ROOT does not exist: ${PROJECT_ROOT}" + exit 1 +fi + +if [[ ! -f "${PROJECT_ROOT}/data_utils/process_nuscenes_files.py" ]]; then + echo "ERROR: NuScenes converter not found under PROJECT_ROOT=${PROJECT_ROOT}" + exit 1 +fi + +if [[ ! -d "${NUSCENES_ROOT}" ]]; then + echo "ERROR: NUSCENES_ROOT does not exist: ${NUSCENES_ROOT}" + exit 1 +fi + +mkdir -p "${OUTPUT_DIR}" + +echo "SLURM_JOB_ID=${SLURM_JOB_ID:-unknown}" +echo "PROJECT_ROOT=${PROJECT_ROOT}" +echo "NUSCENES_ROOT=${NUSCENES_ROOT}" +echo "OUTPUT_DIR=${OUTPUT_DIR}" +echo "CONDA_ENV_NAME=${CONDA_ENV_NAME}" +echo "NUSCENES_VERSION=${NUSCENES_VERSION}" +echo "NUSCENES_SPLIT=${NUSCENES_SPLIT:-}" +echo "SCENE_NAMES=${SCENE_NAMES:-}" +echo "MAX_SCENES=${MAX_SCENES:-}" +echo "TARGET_HZ=${TARGET_HZ}" +echo "NUM_STEPS=${NUM_STEPS}" +echo "WINDOW_STRIDE_SEC=${WINDOW_STRIDE_SEC:-}" +echo "NO_MAP=${NO_MAP}" +echo "HOSTNAME=$(hostname)" +echo "PWD=$(pwd)" +echo "SLURM_CPUS_PER_TASK=${SLURM_CPUS_PER_TASK:-unset}" +echo "OMP_NUM_THREADS=${OMP_NUM_THREADS}" +echo "MKL_NUM_THREADS=${MKL_NUM_THREADS}" + +CMD=( + python data_utils/process_nuscenes_files.py + --dataroot "${NUSCENES_ROOT}" + --version "${NUSCENES_VERSION}" + --output-dir "${OUTPUT_DIR}" + --file-prefix "${FILE_PREFIX}" + --target-hz "${TARGET_HZ}" + --num-steps "${NUM_STEPS}" + --max-annotation-gap-sec "${MAX_ANNOTATION_GAP_SEC}" + --map-radius-m "${MAP_RADIUS_M}" + --map-discretization-m "${MAP_DISCRETIZATION_M}" + --max-road-elements "${MAX_ROAD_ELEMENTS}" + --max-geometry-points "${MAX_GEOMETRY_POINTS}" +) + +if [[ -n "${NUSCENES_SPLIT}" ]]; then + CMD+=(--split "${NUSCENES_SPLIT}") +fi + +if [[ -n "${SCENE_NAMES}" ]]; then + CMD+=(--scene-names "${SCENE_NAMES}") +fi + +if [[ -n "${MAX_SCENES}" ]]; then + CMD+=(--max-scenes "${MAX_SCENES}") +fi + +if [[ -n "${WINDOW_STRIDE_SEC}" ]]; then + CMD+=(--window-stride-sec "${WINDOW_STRIDE_SEC}") +fi + +if [[ "${ALLOW_PARTIAL_WINDOW}" == "true" ]]; then + CMD+=(--allow-partial-window) +fi + +if [[ "${NO_EGO}" == "true" ]]; then + CMD+=(--no-ego) +fi + +if [[ "${NO_MAP}" == "true" ]]; then + CMD+=(--no-map) +fi + +if [[ "${FAIL_ON_MISSING_MAP}" == "true" ]]; then + CMD+=(--fail-on-missing-map) +fi + +if [[ "${QUIET}" == "true" ]]; then + CMD+=(--quiet) +fi + +echo "COMMAND_BEGIN" +printf '%q ' "${CMD[@]}" +echo +echo "COMMAND_END" + +"${CMD[@]}" diff --git a/gpudrive-main/scripts/summarize_longtail_search.py b/gpudrive-main/scripts/summarize_longtail_search.py new file mode 100644 index 0000000000000000000000000000000000000000..85682da87f1e104b9734083a67e57fe69e1592fb --- /dev/null +++ b/gpudrive-main/scripts/summarize_longtail_search.py @@ -0,0 +1,91 @@ +#!/usr/bin/env python +"""Summarize reward-conditioned long-tail search outputs.""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser() + parser.add_argument("--search-dir", default="longtail_outputs/reward_conditioned_search") + parser.add_argument("--top-k", type=int, default=100) + return parser.parse_args() + + +def main() -> None: + args = parse_args() + search_dir = Path(args.search_dir) + records = [] + for path in sorted(search_dir.glob("shard_*/events.jsonl")): + with open(path, "r", encoding="utf-8") as f: + for line in f: + if line.strip(): + records.append(json.loads(line)) + + records.sort( + key=lambda r: ( + 0 if r.get("collision") else 1, + float(r.get("min_distance_m", 1e9)), + ) + ) + top = records[: args.top_k] + + collision_records = [r for r in records if r.get("collision")] + near_miss_records = [r for r in records if r.get("near_miss")] + offroad_records = [r for r in records if r.get("offroad")] + unique_scenarios = sorted({r.get("scenario_file") for r in records if r.get("scenario_file")}) + + shard_summaries = [] + total_scanned_worlds = 0 + total_event_worlds = 0 + total_collision_worlds = 0 + total_near_miss_worlds = 0 + total_offroad_worlds = 0 + for path in sorted(search_dir.glob("shard_*/summary.json")): + with open(path, "r", encoding="utf-8") as f: + shard_summary = json.load(f) + shard_summaries.append(shard_summary) + total_scanned_worlds += int(shard_summary.get("total_worlds", 0)) + total_event_worlds += int(shard_summary.get("event_worlds", 0)) + total_collision_worlds += int(shard_summary.get("collision_worlds", 0)) + total_near_miss_worlds += int(shard_summary.get("near_miss_worlds", 0)) + total_offroad_worlds += int(shard_summary.get("offroad_worlds", 0)) + + found_data_count = len(records) + summary = { + "search_dir": str(search_dir), + "found_data_count": found_data_count, + "total_scanned_worlds": total_scanned_worlds, + "event_rate": found_data_count / total_scanned_worlds if total_scanned_worlds else None, + "event_records": len(records), + "collision_records": len(collision_records), + "near_miss_records": len(near_miss_records), + "offroad_records": len(offroad_records), + "unique_event_scenarios": len(unique_scenarios), + "shard_reported_event_worlds": total_event_worlds, + "shard_reported_collision_worlds": total_collision_worlds, + "shard_reported_near_miss_worlds": total_near_miss_worlds, + "shard_reported_offroad_worlds": total_offroad_worlds, + "best_min_distance_m": min( + [float(r["min_distance_m"]) for r in records], + default=None, + ), + "shards": len(shard_summaries), + "top_k": args.top_k, + } + + with open(search_dir / "summary_all.json", "w", encoding="utf-8") as f: + json.dump(summary, f, indent=2) + with open(search_dir / f"top_{args.top_k}.json", "w", encoding="utf-8") as f: + json.dump(top, f, indent=2) + + print(json.dumps(summary, indent=2)) + print(f"Wrote {search_dir / 'summary_all.json'}") + print(f"Wrote {search_dir / f'top_{args.top_k}.json'}") + + +if __name__ == "__main__": + main() diff --git a/gpudrive-main/scripts/train_official_mini_sanity.sbatch b/gpudrive-main/scripts/train_official_mini_sanity.sbatch new file mode 100644 index 0000000000000000000000000000000000000000..0b4769264aaa7818c6364f3b42f6897f1486676f --- /dev/null +++ b/gpudrive-main/scripts/train_official_mini_sanity.sbatch @@ -0,0 +1,42 @@ +#!/bin/bash +#SBATCH --partition=gre +#SBATCH --nodes=1 +#SBATCH --ntasks-per-node=1 +#SBATCH --gres=gpu:1 +#SBATCH --time=02:00:00 +#SBATCH --job-name=gpudrive_official_sanity +#SBATCH --output=%x-%j.out +#SBATCH --mem=128G +#SBATCH --cpus-per-task=16 + +set -euo pipefail + +PROJECT_ROOT="${PROJECT_ROOT:-/mingli01/project/zihanliu/gpudrive-main}" +CONDA_ENV_NAME="${CONDA_ENV_NAME:-gpudrive}" +CONFIG_PATH="${CONFIG_PATH:-baselines/ppo/config/ppo_mini_official_sanity.yaml}" + +module load conda/3 +source "$(conda info --base)/etc/profile.d/conda.sh" +conda activate "${CONDA_ENV_NAME}" + +export PYTHONNOUSERSITE=1 +export PYTHONUNBUFFERED=1 +export OMP_NUM_THREADS="${OMP_NUM_THREADS:-1}" +export MKL_NUM_THREADS="${MKL_NUM_THREADS:-1}" +export MADRONA_MWGPU_KERNEL_CACHE="${MADRONA_MWGPU_KERNEL_CACHE:-${PROJECT_ROOT}/gpudrive_cache}" +export WANDB_MODE="${WANDB_MODE:-offline}" + +cd "${PROJECT_ROOT}" +export PYTHONPATH="${PROJECT_ROOT}:${PYTHONPATH:-}" + +echo "SLURM_JOB_ID=${SLURM_JOB_ID:-unknown}" +echo "PROJECT_ROOT=${PROJECT_ROOT}" +echo "CONFIG_PATH=${CONFIG_PATH}" +echo "HOSTNAME=$(hostname)" +echo "CUDA_VISIBLE_DEVICES=${CUDA_VISIBLE_DEVICES:-unset}" + +if command -v nvidia-smi >/dev/null 2>&1; then + nvidia-smi -L || true +fi + +python -u baselines/ppo/ppo_pufferlib.py "${CONFIG_PATH}" diff --git a/gpudrive-main/scripts/train_reward_conditioned.sbatch b/gpudrive-main/scripts/train_reward_conditioned.sbatch new file mode 100644 index 0000000000000000000000000000000000000000..f9d90d97f7ce2054011c884b9380e36cc9167ab2 --- /dev/null +++ b/gpudrive-main/scripts/train_reward_conditioned.sbatch @@ -0,0 +1,111 @@ +#!/bin/bash +#SBATCH --partition=gre +#SBATCH --nodes=1 +#SBATCH --ntasks-per-node=1 +#SBATCH --gres=gpu:1 +#SBATCH --time=24:00:00 +#SBATCH --job-name=lzh_gpudrive_rc_train +#SBATCH --output=%x-%j.out +#SBATCH --mem=128G +#SBATCH --cpus-per-task=16 + +set -euo pipefail + +PROJECT_ROOT="${PROJECT_ROOT:-/mingli01/project/zihanliu/gpudrive-main}" +CONDA_ENV_NAME="${CONDA_ENV_NAME:-gpudrive}" +CONFIG_PATH="${CONFIG_PATH:-baselines/ppo/config/ppo_mini_reward_conditioned.yaml}" +DATA_DIR="${DATA_DIR:-/mingli01/data/GPUDrive_mini/training}" + +TOTAL_TIMESTEPS="${TOTAL_TIMESTEPS:-1000000000}" +SEED="${SEED:-42}" +NUM_WORLDS="${NUM_WORLDS:-1000}" +K_UNIQUE_SCENES="${K_UNIQUE_SCENES:-10000}" +RESAMPLE_DATASET_SIZE="${RESAMPLE_DATASET_SIZE:-864}" +RESAMPLE_INTERVAL="${RESAMPLE_INTERVAL:-2000000}" +BATCH_SIZE="${BATCH_SIZE:-524288}" +MINIBATCH_SIZE="${MINIBATCH_SIZE:-8192}" +OBS_RADIUS="${OBS_RADIUS:-50.0}" +POLYLINE_REDUCTION_THRESHOLD="${POLYLINE_REDUCTION_THRESHOLD:-0.1}" +WANDB_MODE="${WANDB_MODE:-offline}" +WANDB_PROJECT="${WANDB_PROJECT:-gpudrive}" +WANDB_GROUP="${WANDB_GROUP:-mini_reward_conditioned}" + +module load conda/3 +source "$(conda info --base)/etc/profile.d/conda.sh" +conda activate "${CONDA_ENV_NAME}" + +export PYTHONNOUSERSITE=1 +export PYTHONUNBUFFERED=1 +export OMP_NUM_THREADS="${OMP_NUM_THREADS:-1}" +export MKL_NUM_THREADS="${MKL_NUM_THREADS:-1}" +export MADRONA_MWGPU_KERNEL_CACHE="${MADRONA_MWGPU_KERNEL_CACHE:-${PROJECT_ROOT}/gpudrive_cache}" +export WANDB_MODE="${WANDB_MODE}" + +cd "${PROJECT_ROOT}" +export PYTHONPATH="${PROJECT_ROOT}:${PYTHONPATH:-}" + +if [[ ! -f "${CONFIG_PATH}" ]]; then + echo "ERROR: training config not found: ${CONFIG_PATH}" + exit 1 +fi + +if [[ ! -d "${DATA_DIR}" ]]; then + echo "ERROR: DATA_DIR does not exist: ${DATA_DIR}" + exit 1 +fi + +if ! compgen -G "${DATA_DIR}/tfrecord*.json" >/dev/null; then + echo "ERROR: no tfrecord*.json files found under DATA_DIR=${DATA_DIR}" + exit 1 +fi + +echo "SLURM_JOB_ID=${SLURM_JOB_ID:-unknown}" +echo "PROJECT_ROOT=${PROJECT_ROOT}" +echo "CONFIG_PATH=${CONFIG_PATH}" +echo "DATA_DIR=${DATA_DIR}" +echo "TOTAL_TIMESTEPS=${TOTAL_TIMESTEPS}" +echo "SEED=${SEED}" +echo "NUM_WORLDS=${NUM_WORLDS}" +echo "K_UNIQUE_SCENES=${K_UNIQUE_SCENES}" +echo "RESAMPLE_DATASET_SIZE=${RESAMPLE_DATASET_SIZE}" +echo "RESAMPLE_INTERVAL=${RESAMPLE_INTERVAL}" +echo "BATCH_SIZE=${BATCH_SIZE}" +echo "MINIBATCH_SIZE=${MINIBATCH_SIZE}" +echo "OBS_RADIUS=${OBS_RADIUS}" +echo "POLYLINE_REDUCTION_THRESHOLD=${POLYLINE_REDUCTION_THRESHOLD}" +echo "WANDB_MODE=${WANDB_MODE}" +echo "HOSTNAME=$(hostname)" +echo "PWD=$(pwd)" +echo "CUDA_VISIBLE_DEVICES=${CUDA_VISIBLE_DEVICES:-unset}" +echo "SLURM_GPUS_ON_NODE=${SLURM_GPUS_ON_NODE:-unset}" + +if command -v nvidia-smi >/dev/null 2>&1; then + nvidia-smi -L || true +fi + +CMD=( + python -u baselines/ppo/ppo_pufferlib.py + "${CONFIG_PATH}" + --data-dir "${DATA_DIR}" + --num-worlds "${NUM_WORLDS}" + --k-unique-scenes "${K_UNIQUE_SCENES}" + --resample-scenes 1 + --resample-dataset-size "${RESAMPLE_DATASET_SIZE}" + --resample-interval "${RESAMPLE_INTERVAL}" + --total-timesteps "${TOTAL_TIMESTEPS}" + --seed "${SEED}" + --batch-size "${BATCH_SIZE}" + --minibatch-size "${MINIBATCH_SIZE}" + --obs-radius "${OBS_RADIUS}" + --polyline-reduction-threshold "${POLYLINE_REDUCTION_THRESHOLD}" + --project "${WANDB_PROJECT}" + --group "${WANDB_GROUP}" + --render 0 +) + +echo "COMMAND_BEGIN" +printf '%q ' "${CMD[@]}" +echo +echo "COMMAND_END" + +"${CMD[@]}" diff --git a/gpudrive-main/scripts/visualize_longtail_search.py b/gpudrive-main/scripts/visualize_longtail_search.py new file mode 100644 index 0000000000000000000000000000000000000000..ed5a16889811be1432170274a61ea6bba1d1241b --- /dev/null +++ b/gpudrive-main/scripts/visualize_longtail_search.py @@ -0,0 +1,758 @@ +#!/usr/bin/env python +"""Render MP4 videos for reward-conditioned long-tail search records.""" + +from __future__ import annotations + +import argparse +import json +import time +from collections import defaultdict +from dataclasses import fields +from pathlib import Path +from typing import Any + +import mediapy as media +import torch +import yaml + +from gpudrive.env.config import EnvConfig, RenderConfig +from gpudrive.env.dataset import SceneDataLoader +from gpudrive.env.env_torch import GPUDriveTorchEnv +from gpudrive.networks.late_fusion import NeuralNet +from gpudrive.visualize.utils import img_from_fig + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser() + parser.add_argument("--config", default="baselines/ppo/config/ppo_mini_reward_conditioned.yaml") + parser.add_argument("--checkpoint", default="") + parser.add_argument("--search-dir", default="longtail_outputs/reward_conditioned_search") + parser.add_argument("--events-file", default="") + parser.add_argument("--data-dir", default="") + parser.add_argument("--output-dir", default="longtail_outputs/reward_conditioned_search/videos") + parser.add_argument("--replay-mode", choices=["exact", "fast"], default="exact") + parser.add_argument("--event-type", choices=["any", "collision", "near_miss", "offroad"], default="any") + parser.add_argument("--max-videos", type=int, default=4) + parser.add_argument("--num-worlds", type=int, default=0) + parser.add_argument("--dataset-size", type=int, default=864) + parser.add_argument("--steps", type=int, default=0) + parser.add_argument("--seed", type=int, default=42) + parser.add_argument("--device", default="cuda") + parser.add_argument("--deterministic", type=int, default=0) + parser.add_argument("--normal-mode", choices=["policy", "expert", ""], default="") + parser.add_argument("--normal-style", default="balanced") + parser.add_argument("--risk-style", default="risk_taker") + parser.add_argument("--risk-agents-per-world", type=int, default=0) + parser.add_argument("--risk-collision-weight", type=float, default=None) + parser.add_argument("--risk-goal-weight", type=float, default=None) + parser.add_argument("--risk-offroad-weight", type=float, default=None) + parser.add_argument("--render-every", type=int, default=1) + parser.add_argument("--fps", type=int, default=5) + parser.add_argument("--stop-on-collision", type=int, default=1) + parser.add_argument("--render-3d", type=int, default=0) + parser.add_argument("--zoom-radius", type=float, default=70.0) + parser.add_argument("--obs-radius", type=float, default=None) + parser.add_argument("--polyline-reduction-threshold", type=float, default=None) + return parser.parse_args() + + +def load_config(path: str) -> dict[str, Any]: + with open(path, "r", encoding="utf-8") as f: + return yaml.safe_load(f) + + +def normalize_event_record(record: dict[str, Any]) -> dict[str, Any]: + """Accept raw search events or LLM evaluation wrapper records.""" + if isinstance(record.get("event"), dict): + event = dict(record["event"]) + for key in ("event_key", "final_valid", "final_score", "final_priority"): + if key in record: + event[f"llm_{key}"] = record[key] + if isinstance(record.get("llm_eval"), dict): + event["llm_eval"] = record["llm_eval"] + if isinstance(record.get("rule_eval"), dict): + event["rule_eval"] = record["rule_eval"] + return event + return record + + +def load_json_records(path: Path) -> list[dict[str, Any]]: + if path.suffix == ".jsonl": + records = [] + with open(path, "r", encoding="utf-8") as f: + for line in f: + if line.strip(): + records.append(normalize_event_record(json.loads(line))) + return records + + with open(path, "r", encoding="utf-8") as f: + data = json.load(f) + if isinstance(data, list): + return [normalize_event_record(record) for record in data] + raise ValueError(f"Expected a JSON list or JSONL records: {path}") + + +def load_records(search_dir: Path, events_file: str) -> list[dict[str, Any]]: + if events_file: + return load_json_records(Path(events_file)) + + top_files = sorted(search_dir.glob("top_*.json")) + if top_files: + return load_json_records(max(top_files, key=lambda p: p.stat().st_mtime)) + + records = [] + for path in sorted(search_dir.glob("shard_*/events.jsonl")): + records.extend(load_json_records(path)) + return records + + +def load_shard_summaries(search_dir: Path) -> dict[int, dict[str, Any]]: + summaries: dict[int, dict[str, Any]] = {} + for path in sorted(search_dir.glob("shard_*/summary.json")): + with open(path, "r", encoding="utf-8") as f: + summary = json.load(f) + summaries[int(summary.get("shard_id", len(summaries)))] = summary + return summaries + + +def find_latest_checkpoint() -> Path: + patterns = [ + "runs/PPO_MINI_RC*/model_*.pt", + "gpudrive/runs/PPO_MINI_RC*/model_*.pt", + "runs/*MINI*RC*/model_*.pt", + "gpudrive/runs/*MINI*RC*/model_*.pt", + ] + for pattern in patterns: + candidates = list(Path(".").glob(pattern)) + if candidates: + return max(candidates, key=lambda p: p.stat().st_mtime) + raise FileNotFoundError("No reward-conditioned checkpoint found. Pass --checkpoint.") + + +def resolve_checkpoint(args_checkpoint: str, first_summary: dict[str, Any] | None) -> Path: + if args_checkpoint: + return Path(args_checkpoint) + if first_summary and first_summary.get("checkpoint"): + return Path(first_summary["checkpoint"]) + return find_latest_checkpoint() + + +def reward_preset(config: EnvConfig, style: str, device: str) -> torch.Tensor: + presets = { + "cautious": [ + config.collision_weight_lb * 0.9, + config.goal_achieved_weight_ub * 0.7, + config.off_road_weight_lb * 0.9, + ], + "aggressive": [ + config.collision_weight_lb * 0.5, + config.goal_achieved_weight_ub * 0.9, + config.off_road_weight_lb * 0.6, + ], + "balanced": [ + (config.collision_weight_lb + config.collision_weight_ub) / 2, + (config.goal_achieved_weight_lb + config.goal_achieved_weight_ub) / 2, + (config.off_road_weight_lb + config.off_road_weight_ub) / 2, + ], + "risk_taker": [ + config.collision_weight_lb * 0.3, + config.goal_achieved_weight_ub, + config.off_road_weight_lb * 0.4, + ], + } + if style not in presets: + raise ValueError(f"Unknown style={style}. Available: {sorted(presets)}") + return torch.tensor(presets[style], dtype=torch.float32, device=device) + + +def tensor_from_summary_or_preset( + summary: dict[str, Any] | None, + key: str, + fallback: torch.Tensor, + device: str, +) -> torch.Tensor: + if summary and key in summary and summary[key] is not None: + return torch.tensor(summary[key], dtype=torch.float32, device=device) + return fallback + + +def apply_weight_overrides(weights: torch.Tensor, args: argparse.Namespace) -> torch.Tensor: + weights = weights.clone() + if args.risk_collision_weight is not None: + weights[0] = args.risk_collision_weight + if args.risk_goal_weight is not None: + weights[1] = args.risk_goal_weight + if args.risk_offroad_weight is not None: + weights[2] = args.risk_offroad_weight + return weights + + +def make_env_config(raw_env: dict[str, Any], num_worlds: int, args: argparse.Namespace) -> EnvConfig: + env_fields = {field.name for field in fields(EnvConfig)} + cfg = {k: v for k, v in raw_env.items() if k in env_fields} + cfg["num_worlds"] = num_worlds + cfg["reward_type"] = "reward_conditioned" + cfg["condition_mode"] = "random" + if args.obs_radius is not None: + cfg["obs_radius"] = args.obs_radius + if args.polyline_reduction_threshold is not None: + cfg["polyline_reduction_threshold"] = args.polyline_reduction_threshold + + steer_disc = int(raw_env.get("action_space_steer_disc", 13)) + accel_disc = int(raw_env.get("action_space_accel_disc", 7)) + cfg["steer_actions"] = torch.round(torch.linspace(-torch.pi, torch.pi, steer_disc), decimals=3) + cfg["accel_actions"] = torch.round(torch.linspace(-4.0, 4.0, accel_disc), decimals=3) + return EnvConfig(**cfg) + + +def load_policy(checkpoint_path: Path, model_config: dict[str, Any], device: str) -> NeuralNet: + saved = torch.load(checkpoint_path, map_location=device, weights_only=False) + arch = saved["model_arch"] + arch_get = arch.get if hasattr(arch, "get") else lambda key, default=None: getattr(arch, key, default) + policy = NeuralNet( + input_dim=arch_get("input_dim"), + action_dim=saved["action_dim"], + hidden_dim=arch_get("hidden_dim"), + dropout=arch_get("dropout", 0.0), + config=model_config["environment"], + ).to(device) + policy.load_state_dict(saved["parameters"]) + policy.eval() + return policy + + +def choose_risk_agents( + control_mask: torch.Tensor, + risk_agents_per_world: int, + generator: torch.Generator, +) -> torch.Tensor: + risk_mask = torch.zeros_like(control_mask, dtype=torch.bool) + for world_idx in range(control_mask.shape[0]): + agents = torch.where(control_mask[world_idx])[0] + if len(agents) == 0: + continue + num_risk = min(risk_agents_per_world, len(agents)) + perm = torch.randperm(len(agents), generator=generator, device=control_mask.device) + risk_mask[world_idx, agents[perm[:num_risk]]] = True + return risk_mask + + +def risk_mask_from_records(env: GPUDriveTorchEnv, records: list[dict[str, Any]]) -> torch.Tensor: + mask = torch.zeros_like(env.cont_agent_mask, dtype=torch.bool, device=env.device) + for world_idx, record in enumerate(records): + for agent_idx in record.get("risk_agents", []): + if 0 <= int(agent_idx) < mask.shape[1]: + mask[world_idx, int(agent_idx)] = True + return mask & env.cont_agent_mask + + +def center_agent_for_record(record: dict[str, Any], control_mask: torch.Tensor, world_idx: int) -> int | None: + for key in ("min_distance_risk_agent",): + agent_idx = int(record.get(key, -1)) + if 0 <= agent_idx < control_mask.shape[1] and bool(control_mask[world_idx, agent_idx].item()): + return agent_idx + for agent_idx in record.get("risk_agents", []): + agent_idx = int(agent_idx) + if 0 <= agent_idx < control_mask.shape[1] and bool(control_mask[world_idx, agent_idx].item()): + return agent_idx + agents = torch.where(control_mask[world_idx])[0] + return int(agents[0].item()) if len(agents) else None + + +def render( + env: GPUDriveTorchEnv, + frames: dict[int, list], + worlds: list[int], + center_agents: dict[int, int | None], + time_step: int, + policy_masks: dict[str, tuple[NeuralNet, torch.Tensor]], + zoom_radius: float, + live_mask: torch.Tensor | None = None, +) -> None: + render_policy_masks = {} + for name, (policy_fn, mask) in policy_masks.items(): + draw_mask = mask + if live_mask is not None: + draw_mask = draw_mask & live_mask + render_policy_masks[name] = (policy_fn, draw_mask.detach().cpu()) + + figures = env.vis.plot_simulator_state( + env_indices=worlds, + time_steps=[time_step] * len(worlds), + center_agent_indices=[center_agents.get(world) for world in worlds], + zoom_radius=zoom_radius, + policy_masks=render_policy_masks, + ) + for world, fig in zip(worlds, figures): + frames[world].append(img_from_fig(fig)) + + +def rollout( + env: GPUDriveTorchEnv, + policy: NeuralNet, + steps: int, + normal_mode: str, + deterministic: bool, + risk_mask: torch.Tensor, + normal_weights: torch.Tensor, + risk_weights: torch.Tensor, + render_worlds: list[int] | None, + center_agents: dict[int, int | None], + frames: dict[int, list], + render_every: int, + stop_on_collision: bool, + zoom_radius: float, +) -> dict[str, torch.Tensor]: + control_mask = env.cont_agent_mask.clone() + normal_mask = control_mask & ~risk_mask + env.reward_weights_tensor[:] = normal_weights + env.reward_weights_tensor[risk_mask] = risk_weights + + policy_masks = { + "risk_taker": (policy, risk_mask), + "balanced": (policy, normal_mask), + } + live_mask = control_mask.clone() + collided = torch.zeros_like(control_mask, dtype=torch.bool, device=env.device) + offroad = torch.zeros_like(control_mask, dtype=torch.bool, device=env.device) + goal = torch.zeros_like(control_mask, dtype=torch.bool, device=env.device) + stopped_render_worlds = torch.zeros(env.num_worlds, dtype=torch.bool, device=env.device) + + if render_worlds: + render( + env, + frames, + render_worlds, + center_agents, + 0, + policy_masks, + zoom_radius, + live_mask=live_mask, + ) + + expert_actions = None + if normal_mode == "expert": + expert_actions, _, _, _ = env.get_expert_actions() + + for step in range(steps): + active = live_mask & control_mask + if normal_mode == "expert": + action_step = min(step, expert_actions.shape[2] - 1) + actions = expert_actions[:, :, action_step, :].clone() + active_risk = active & risk_mask + if bool(active_risk.any().item()): + obs = env.get_obs(active_risk) + with torch.no_grad(): + action, _, _, _ = policy(obs, deterministic=deterministic) + actions[active_risk] = env.action_keys_tensor[action.to(dtype=torch.long, device=env.device)] + else: + actions = torch.zeros((env.num_worlds, env.max_agent_count), dtype=torch.int64, device=env.device) + if bool(active.any().item()): + obs = env.get_obs(active) + with torch.no_grad(): + action, _, _, _ = policy(obs, deterministic=deterministic) + actions[active] = action.to(dtype=torch.int64, device=env.device) + + env.step_dynamics(actions) + infos = env.get_infos() + collided |= (infos.collided > 0).bool() & active + offroad |= (infos.off_road > 0).bool() & active + goal |= (infos.goal_achieved > 0).bool() & active + live_mask &= ~env.get_dones().bool() + + render_worlds_alive: list[int] = [] + render_collision_worlds = torch.zeros(env.num_worlds, dtype=torch.bool, device=env.device) + if render_worlds: + render_collision_worlds = (collided & control_mask).any(dim=1) + render_worlds_alive = [ + world for world in render_worlds + if not bool(stopped_render_worlds[world].item()) + ] + + should_render = bool(render_worlds_alive) and ( + (step + 1) % render_every == 0 + or ( + stop_on_collision + and any(bool(render_collision_worlds[world].item()) for world in render_worlds_alive) + ) + ) + if should_render: + render( + env, + frames, + render_worlds_alive, + center_agents, + step + 1, + policy_masks, + zoom_radius, + live_mask=live_mask, + ) + + if stop_on_collision and render_worlds: + for world in render_worlds: + if bool(render_collision_worlds[world].item()): + stopped_render_worlds[world] = True + if all(bool(stopped_render_worlds[world].item()) for world in render_worlds): + print(f"[visualize] all rendered worlds stopped on collision at step={step + 1}", flush=True) + break + + if not bool(live_mask.any().item()): + break + + return {"collided": collided, "offroad": offroad, "goal": goal} + + +def event_sort_key(record: dict[str, Any]) -> tuple[int, float]: + min_dist = record.get("min_distance_m", 1e9) + try: + min_dist_f = float(min_dist) + except (TypeError, ValueError): + min_dist_f = 1e9 + return (0 if record.get("collision") else 1, min_dist_f) + + +def select_records(records: list[dict[str, Any]], event_type: str, max_videos: int) -> list[dict[str, Any]]: + if event_type != "any": + records = [r for r in records if bool(r.get(event_type))] + records = sorted(records, key=event_sort_key) + return records[:max_videos] + + +def scenario_path(data_dir: Path, record: dict[str, Any]) -> Path: + scenario_file = Path(str(record["scenario_file"])) + return scenario_file if scenario_file.is_absolute() else data_dir / scenario_file.name + + +def write_videos( + output_dir: Path, + frames: dict[int, list], + records_by_world: dict[int, dict[str, Any]], + fps: int, + prefix: str, +) -> dict[tuple[int, int, int], str]: + written = {} + for world_idx, world_frames in frames.items(): + if not world_frames: + continue + record = records_by_world[world_idx] + shard_id = int(record.get("shard_id", 0)) + batch_idx = int(record.get("batch_idx", 0)) + original_world_idx = int(record.get("world_idx", world_idx)) + tag = "collision" if record.get("collision") else "near_miss" if record.get("near_miss") else "event" + eval_tag = "" + if record.get("llm_final_priority"): + eval_tag = f"_{record['llm_final_priority']}" + path = output_dir / ( + f"{prefix}_shard{shard_id:03d}" + f"_batch{batch_idx:03d}" + f"_world{original_world_idx:04d}_{tag}{eval_tag}.mp4" + ) + media.write_video(path, world_frames, fps=fps) + written[(shard_id, batch_idx, original_world_idx)] = str(path) + print(f"[visualize] wrote {path}", flush=True) + return written + + +def run_fast( + args: argparse.Namespace, + records: list[dict[str, Any]], + config: dict[str, Any], + policy: NeuralNet, + env_config: EnvConfig, + data_dir: Path, + normal_mode: str, + normal_weights: torch.Tensor, + risk_weights: torch.Tensor, +) -> list[dict[str, Any]]: + data_batch = [str(scenario_path(data_dir, record)) for record in records] + loader = SceneDataLoader( + root=str(data_dir), + batch_size=len(records), + dataset_size=max(args.dataset_size, len(records)), + sample_with_replacement=False, + shuffle=False, + seed=args.seed, + ) + env = GPUDriveTorchEnv( + config=env_config, + data_loader=loader, + max_cont_agents=env_config.max_controlled_agents, + device=args.device, + render_config=RenderConfig(render_3d=bool(args.render_3d)), + ) + env.swap_data_batch(data_batch) + env.reset() + + risk_mask = risk_mask_from_records(env, records) + worlds = list(range(len(records))) + centers = {world: center_agent_for_record(records[world], env.cont_agent_mask, world) for world in worlds} + frames = {world: [] for world in worlds} + stats = rollout( + env=env, + policy=policy, + steps=args.steps or env_config.episode_len, + normal_mode=normal_mode, + deterministic=bool(args.deterministic), + risk_mask=risk_mask, + normal_weights=normal_weights, + risk_weights=risk_weights, + render_worlds=worlds, + center_agents=centers, + frames=frames, + render_every=args.render_every, + stop_on_collision=bool(args.stop_on_collision), + zoom_radius=args.zoom_radius, + ) + + records_by_world = {world: records[world] for world in worlds} + videos = write_videos(Path(args.output_dir), frames, records_by_world, args.fps, "fast") + return build_render_summaries(records_by_world, stats, videos, world_index_mode="local") + + +def build_render_summaries( + records_by_world: dict[int, dict[str, Any]], + stats: dict[str, torch.Tensor], + videos: dict[tuple[int, int, int], str], + world_index_mode: str, +) -> list[dict[str, Any]]: + summaries = [] + for world, record in records_by_world.items(): + if world_index_mode == "original": + stats_world = int(record["world_idx"]) + else: + stats_world = world + risk_agents = [int(a) for a in record.get("risk_agents", [])] + risk_collided = any( + 0 <= agent < stats["collided"].shape[1] and bool(stats["collided"][stats_world, agent].item()) + for agent in risk_agents + ) + video_key = ( + int(record.get("shard_id", 0)), + int(record.get("batch_idx", 0)), + int(record.get("world_idx", world)), + ) + summaries.append( + { + "record": record, + "video_path": videos.get(video_key), + "risk_agents": risk_agents, + "rerun_collision": bool(stats["collided"][stats_world].any().item()), + "rerun_risk_collision": risk_collided, + "rerun_offroad": bool(stats["offroad"][stats_world].any().item()), + "rerun_goal_agents": int(stats["goal"][stats_world].sum().item()), + } + ) + return summaries + + +def run_exact_group( + args: argparse.Namespace, + records: list[dict[str, Any]], + summary: dict[str, Any], + config: dict[str, Any], + policy: NeuralNet, + data_dir: Path, + normal_mode: str, + normal_weights: torch.Tensor, + risk_weights: torch.Tensor, +) -> list[dict[str, Any]]: + shard_id = int(records[0].get("shard_id", summary.get("shard_id", 0))) + batch_idx = int(records[0].get("batch_idx", 0)) + seed = args.seed + shard_id * 100_000 + num_worlds = args.num_worlds or int(summary.get("num_worlds", 1000)) + dataset_size = args.dataset_size + steps = args.steps or int(summary.get("steps", 91)) + risk_agents_per_world = args.risk_agents_per_world or int(summary.get("risk_agents_per_world", 3)) + env_config = make_env_config(config["environment"], num_worlds, args) + + torch.manual_seed(seed) + generator = torch.Generator(device=args.device) + generator.manual_seed(seed) + + loader = SceneDataLoader( + root=str(data_dir), + batch_size=num_worlds, + dataset_size=dataset_size, + sample_with_replacement=True, + shuffle=True, + seed=seed, + ) + env = GPUDriveTorchEnv( + config=env_config, + data_loader=loader, + max_cont_agents=env_config.max_controlled_agents, + device=args.device, + render_config=RenderConfig(render_3d=bool(args.render_3d)), + ) + + target_records_by_world = {int(record["world_idx"]): record for record in records} + frames = {world: [] for world in target_records_by_world} + final_stats = None + + for current_batch in range(batch_idx + 1): + if current_batch > 0: + env.swap_data_batch() + env.reset() + risk_mask = choose_risk_agents(env.cont_agent_mask.clone(), risk_agents_per_world, generator) + + render_worlds = None + centers: dict[int, int | None] = {} + if current_batch == batch_idx: + for world, record in target_records_by_world.items(): + actual = Path(env.data_batch[world]).name + expected = str(record.get("scenario_file", "")) + if expected and Path(expected).name != actual: + print( + "[visualize] warning:", + f"record scenario_file={expected} but replay batch has {actual}", + flush=True, + ) + expected_risk = sorted(int(a) for a in record.get("risk_agents", [])) + actual_risk = sorted(torch.where(risk_mask[world])[0].detach().cpu().tolist()) + if expected_risk and expected_risk != actual_risk: + print( + "[visualize] warning:", + f"record risk_agents={expected_risk} but replay risk_agents={actual_risk}", + flush=True, + ) + centers[world] = center_agent_for_record(record, env.cont_agent_mask, world) + render_worlds = list(target_records_by_world) + + stats = rollout( + env=env, + policy=policy, + steps=steps, + normal_mode=normal_mode, + deterministic=bool(args.deterministic), + risk_mask=risk_mask, + normal_weights=normal_weights, + risk_weights=risk_weights, + render_worlds=render_worlds, + center_agents=centers, + frames=frames, + render_every=args.render_every, + stop_on_collision=bool(args.stop_on_collision), + zoom_radius=args.zoom_radius, + ) + if current_batch == batch_idx: + final_stats = stats + + videos = write_videos(Path(args.output_dir), frames, target_records_by_world, args.fps, "exact") + return build_render_summaries(target_records_by_world, final_stats, videos, world_index_mode="original") + + +def main() -> None: + args = parse_args() + args.device = args.device if torch.cuda.is_available() or args.device == "cpu" else "cpu" + search_dir = Path(args.search_dir) + output_dir = Path(args.output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + + records = load_records(search_dir, args.events_file) + if not records: + raise RuntimeError(f"No event records found under {search_dir}") + records = select_records(records, args.event_type, args.max_videos) + if not records: + raise RuntimeError(f"No records matched event_type={args.event_type}") + + shard_summaries = load_shard_summaries(search_dir) + first_summary = shard_summaries.get(int(records[0].get("shard_id", 0)), None) + config = load_config(args.config) + data_dir = Path(args.data_dir or (first_summary or {}).get("data_dir", "/mingli01/data/GPUDrive_mini/training")) + checkpoint = resolve_checkpoint(args.checkpoint, first_summary) + + num_worlds_for_config = len(records) if args.replay_mode == "fast" else int((first_summary or {}).get("num_worlds", args.num_worlds or 1000)) + env_config = make_env_config(config["environment"], num_worlds_for_config, args) + normal_mode = args.normal_mode or str((first_summary or {}).get("normal_mode", "policy")) + + normal_weights = tensor_from_summary_or_preset( + first_summary, + "normal_weights", + reward_preset(env_config, args.normal_style, args.device), + args.device, + ) + risk_weights = tensor_from_summary_or_preset( + first_summary, + "risk_weights", + reward_preset(env_config, args.risk_style, args.device), + args.device, + ) + risk_weights = apply_weight_overrides(risk_weights, args) + + policy = load_policy(checkpoint, config, args.device) + print( + "[visualize] start:", + f"mode={args.replay_mode}", + f"records={len(records)}", + f"checkpoint={checkpoint}", + f"data_dir={data_dir}", + f"normal_mode={normal_mode}", + f"normal_weights={normal_weights.detach().cpu().tolist()}", + f"risk_weights={risk_weights.detach().cpu().tolist()}", + flush=True, + ) + + start = time.time() + if args.replay_mode == "fast": + summaries = run_fast( + args=args, + records=records, + config=config, + policy=policy, + env_config=make_env_config(config["environment"], len(records), args), + data_dir=data_dir, + normal_mode=normal_mode, + normal_weights=normal_weights, + risk_weights=risk_weights, + ) + else: + summaries = [] + groups: dict[tuple[int, int], list[dict[str, Any]]] = defaultdict(list) + for record in records: + groups[(int(record.get("shard_id", 0)), int(record.get("batch_idx", 0)))].append(record) + for (shard_id, batch_idx), group_records in sorted(groups.items()): + summary = shard_summaries.get(shard_id, first_summary or {}) + print( + "[visualize] exact group:", + f"shard={shard_id}", + f"batch={batch_idx}", + f"worlds={[int(r['world_idx']) for r in group_records]}", + flush=True, + ) + summaries.extend( + run_exact_group( + args=args, + records=group_records, + summary=summary, + config=config, + policy=policy, + data_dir=data_dir, + normal_mode=normal_mode, + normal_weights=normal_weights, + risk_weights=risk_weights, + ) + ) + + output_summary = { + "search_dir": str(search_dir), + "output_dir": str(output_dir), + "replay_mode": args.replay_mode, + "checkpoint": str(checkpoint), + "data_dir": str(data_dir), + "normal_mode": normal_mode, + "normal_weights": normal_weights.detach().cpu().tolist(), + "risk_weights": risk_weights.detach().cpu().tolist(), + "stop_on_collision": bool(args.stop_on_collision), + "render_3d": bool(args.render_3d), + "rendered_records": len(summaries), + "videos": [s.get("video_path") for s in summaries if s.get("video_path")], + "elapsed_sec": round(time.time() - start, 3), + "records": summaries, + } + with open(output_dir / "render_summary.json", "w", encoding="utf-8") as f: + json.dump(output_summary, f, indent=2) + print(json.dumps({k: v for k, v in output_summary.items() if k != "records"}, indent=2), flush=True) + + +if __name__ == "__main__": + main() diff --git a/gpudrive-main/scripts/visualize_longtail_search.sbatch b/gpudrive-main/scripts/visualize_longtail_search.sbatch new file mode 100644 index 0000000000000000000000000000000000000000..dafabd5a6bd17ae7f03cfe9a404b406598c7365e --- /dev/null +++ b/gpudrive-main/scripts/visualize_longtail_search.sbatch @@ -0,0 +1,143 @@ +#!/bin/bash +#SBATCH --partition=gre +#SBATCH --nodes=1 +#SBATCH --ntasks=1 +#SBATCH --gres=gpu:1 +#SBATCH --time=04:00:00 +#SBATCH --job-name=gpudrive_longtail_vis +#SBATCH --output=%x-%j.out +#SBATCH --mem=128G +#SBATCH --cpus-per-task=8 + +set -euo pipefail + +PROJECT_ROOT="${PROJECT_ROOT:-/mingli01/project/zihanliu/gpudrive-main}" +CONDA_ENV_NAME="${CONDA_ENV_NAME:-gpudrive}" +CONFIG_PATH="${CONFIG_PATH:-baselines/ppo/config/ppo_mini_reward_conditioned.yaml}" +CHECKPOINT="${CHECKPOINT:-}" +SEARCH_DIR="${SEARCH_DIR:-longtail_outputs/reward_conditioned_search}" +EVENTS_FILE="${EVENTS_FILE:-}" +DATA_DIR="${DATA_DIR:-}" +OUTPUT_DIR="${OUTPUT_DIR:-${SEARCH_DIR}/videos}" + +REPLAY_MODE="${REPLAY_MODE:-fast}" +EVENT_TYPE="${EVENT_TYPE:-any}" +MAX_VIDEOS="${MAX_VIDEOS:-4}" +NUM_WORLDS="${NUM_WORLDS:-}" +DATASET_SIZE="${DATASET_SIZE:-864}" +STEPS="${STEPS:-}" +SEED="${SEED:-42}" +DETERMINISTIC="${DETERMINISTIC:-0}" +NORMAL_MODE="${NORMAL_MODE:-}" +NORMAL_STYLE="${NORMAL_STYLE:-balanced}" +RISK_STYLE="${RISK_STYLE:-risk_taker}" +RISK_AGENTS_PER_WORLD="${RISK_AGENTS_PER_WORLD:-}" +RISK_COLLISION_WEIGHT="${RISK_COLLISION_WEIGHT:-}" +RISK_GOAL_WEIGHT="${RISK_GOAL_WEIGHT:-}" +RISK_OFFROAD_WEIGHT="${RISK_OFFROAD_WEIGHT:-}" +RENDER_EVERY="${RENDER_EVERY:-1}" +FPS="${FPS:-5}" +STOP_ON_COLLISION="${STOP_ON_COLLISION:-1}" +RENDER_3D="${RENDER_3D:-0}" +ZOOM_RADIUS="${ZOOM_RADIUS:-70.0}" +OBS_RADIUS="${OBS_RADIUS:-}" +POLYLINE_REDUCTION_THRESHOLD="${POLYLINE_REDUCTION_THRESHOLD:-}" + +module load conda/3 +source "$(conda info --base)/etc/profile.d/conda.sh" +conda activate "${CONDA_ENV_NAME}" + +export PYTHONNOUSERSITE=1 +export PYTHONUNBUFFERED=1 +export OMP_NUM_THREADS="${OMP_NUM_THREADS:-1}" +export MKL_NUM_THREADS="${MKL_NUM_THREADS:-1}" +export MADRONA_MWGPU_KERNEL_CACHE="${MADRONA_MWGPU_KERNEL_CACHE:-${PROJECT_ROOT}/gpudrive_cache}" + +cd "${PROJECT_ROOT}" +export PYTHONPATH="${PROJECT_ROOT}:${PYTHONPATH:-}" + +if [[ ! -f "${CONFIG_PATH}" ]]; then + echo "ERROR: config not found: ${CONFIG_PATH}" + exit 1 +fi + +if [[ ! -d "${SEARCH_DIR}" ]]; then + echo "ERROR: SEARCH_DIR does not exist: ${SEARCH_DIR}" + exit 1 +fi + +mkdir -p "${OUTPUT_DIR}" + +CMD=( + python -u scripts/visualize_longtail_search.py + --config "${CONFIG_PATH}" + --search-dir "${SEARCH_DIR}" + --output-dir "${OUTPUT_DIR}" + --replay-mode "${REPLAY_MODE}" + --event-type "${EVENT_TYPE}" + --max-videos "${MAX_VIDEOS}" + --dataset-size "${DATASET_SIZE}" + --seed "${SEED}" + --deterministic "${DETERMINISTIC}" + --normal-style "${NORMAL_STYLE}" + --risk-style "${RISK_STYLE}" + --render-every "${RENDER_EVERY}" + --fps "${FPS}" + --stop-on-collision "${STOP_ON_COLLISION}" + --render-3d "${RENDER_3D}" + --zoom-radius "${ZOOM_RADIUS}" +) + +if [[ -n "${CHECKPOINT}" ]]; then + CMD+=(--checkpoint "${CHECKPOINT}") +fi +if [[ -n "${EVENTS_FILE}" ]]; then + CMD+=(--events-file "${EVENTS_FILE}") +fi +if [[ -n "${DATA_DIR}" ]]; then + CMD+=(--data-dir "${DATA_DIR}") +fi +if [[ -n "${NUM_WORLDS}" ]]; then + CMD+=(--num-worlds "${NUM_WORLDS}") +fi +if [[ -n "${STEPS}" ]]; then + CMD+=(--steps "${STEPS}") +fi +if [[ -n "${NORMAL_MODE}" ]]; then + CMD+=(--normal-mode "${NORMAL_MODE}") +fi +if [[ -n "${RISK_AGENTS_PER_WORLD}" ]]; then + CMD+=(--risk-agents-per-world "${RISK_AGENTS_PER_WORLD}") +fi +if [[ -n "${RISK_COLLISION_WEIGHT}" ]]; then + CMD+=(--risk-collision-weight "${RISK_COLLISION_WEIGHT}") +fi +if [[ -n "${RISK_GOAL_WEIGHT}" ]]; then + CMD+=(--risk-goal-weight "${RISK_GOAL_WEIGHT}") +fi +if [[ -n "${RISK_OFFROAD_WEIGHT}" ]]; then + CMD+=(--risk-offroad-weight "${RISK_OFFROAD_WEIGHT}") +fi +if [[ -n "${OBS_RADIUS}" ]]; then + CMD+=(--obs-radius "${OBS_RADIUS}") +fi +if [[ -n "${POLYLINE_REDUCTION_THRESHOLD}" ]]; then + CMD+=(--polyline-reduction-threshold "${POLYLINE_REDUCTION_THRESHOLD}") +fi + +echo "SLURM_JOB_ID=${SLURM_JOB_ID:-unknown}" +echo "PROJECT_ROOT=${PROJECT_ROOT}" +echo "SEARCH_DIR=${SEARCH_DIR}" +echo "OUTPUT_DIR=${OUTPUT_DIR}" +echo "REPLAY_MODE=${REPLAY_MODE}" +echo "EVENT_TYPE=${EVENT_TYPE}" +echo "MAX_VIDEOS=${MAX_VIDEOS}" +echo "STOP_ON_COLLISION=${STOP_ON_COLLISION}" +echo "RENDER_3D=${RENDER_3D}" +echo "COMMAND: ${CMD[*]}" + +if command -v nvidia-smi >/dev/null 2>&1; then + nvidia-smi -L || true +fi + +"${CMD[@]}" diff --git a/gpudrive-main/search/README.md b/gpudrive-main/search/README.md new file mode 100644 index 0000000000000000000000000000000000000000..41f789ee5121295b8ac47fcb55cc5df77a1612a3 --- /dev/null +++ b/gpudrive-main/search/README.md @@ -0,0 +1,484 @@ +# Search Module + +This folder contains the first version of the LLM-assisted long-tail event +evaluation and reward-condition feedback loop. + +The current coarse event pipeline is: + +```text +events.jsonl +-> local rule-based pre-score +-> DeepSeek structured evaluation using rules/*.md +-> accepted/rejected JSONL files +-> aggregate feedback for the next reward-condition search round +``` + +## Setup + +Set your DeepSeek API key: + +```bash +export DEEPSEEK_API_KEY="your_api_key_here" +``` + +The script uses DeepSeek's OpenAI-compatible Chat Completions endpoint by +default: + +```bash +DEEPSEEK_BASE_URL=https://api.deepseek.com +DEEPSEEK_MODEL=deepseek-v4-flash +``` + +Both can be overridden: + +```bash +export DEEPSEEK_BASE_URL=https://api.deepseek.com +export DEEPSEEK_MODEL=deepseek-v4-flash +``` + +## Complete Closed-Loop Commands + +This is the full search-to-optimization loop: + +```text +Generator/Search Agent + -> event_traces.jsonl +Converter Agent + -> llm_cases.jsonl +Evaluator Agent + -> optimizer_metrics.json +MLP Optimizer Agent + -> next search parameters +Generator/Search Agent + -> next round +``` + +### 0. Common Variables + +Set these once before a round: + +```bash +export PROJECT_ROOT=/mingli01/project/zihanliu/gpudrive-main +export CONDA_ENV_NAME=gpudrive +export CHECKPOINT=/mingli01/project/zihanliu/gpudrive-main/runs/PPO_MINI_RC____R_864__06_05_02_10_44_732/model_PPO_MINI_RC____R_864__06_05_02_10_44_732_001800.pt +export DATA_DIR=/mingli01/data/GPUDrive/training +export DATASET_SIZE=1000 +export NUM_GPUS=4 +export NUM_WORLDS=1000 +export NUM_BATCHES=10 +export STEPS=91 +``` + +For DeepSeek evaluation: + +```bash +export DEEPSEEK_API_KEY="sk-048d8c86b772404fa47f4880d5c3f1c5" +``` + +### 1. Round 001: Search And Save Event Traces + +Run the generator/search agent with explicit search parameters: + +```bash +export ROUND_ID=round_001 +export SEARCH_DIR=search_outputs/${ROUND_ID}/search + +CHECKPOINT="${CHECKPOINT}" \ +DATA_DIR="${DATA_DIR}" \ +OUTPUT_DIR="${SEARCH_DIR}" \ +DATASET_SIZE="${DATASET_SIZE}" \ +NUM_GPUS="${NUM_GPUS}" \ +NUM_WORLDS="${NUM_WORLDS}" \ +NUM_BATCHES="${NUM_BATCHES}" \ +STEPS="${STEPS}" \ +SAVE_EVENT_TRACES=1 \ +RISK_COLLISION_WEIGHT=0.0 \ +RISK_GOAL_WEIGHT=2.0 \ +RISK_OFFROAD_WEIGHT=-0.4 \ +RISK_AGENTS_PER_WORLD=3 \ +NORMAL_MODE=policy \ +DETERMINISTIC=0 \ +sbatch scripts/search_longtail_reward_conditioned.sbatch +``` + +Expected outputs: + +```text +search_outputs/round_001/search/shard_000/events.jsonl +search_outputs/round_001/search/shard_000/event_traces.jsonl +search_outputs/round_001/search/shard_000/summary.json +... +``` + +### 2. Convert Event Traces To LLM Cases + +Convert all four shards: + +```bash +SEARCH_DIR=search_outputs/round_001/search \ +OUTPUT_BASE=search_outputs/round_001/llm_cases \ +MAX_CARDS=200 \ +sbatch --array=0-3 scripts/convert_event_traces_to_llm_cases.sbatch +``` + +Expected outputs: + +```text +search_outputs/round_001/llm_cases/shard_000/llm_cases.jsonl +search_outputs/round_001/llm_cases/shard_000/case_cards/ +search_outputs/round_001/llm_cases/shard_000/summary.json +... +``` + +### 3. Evaluate LLM Cases + +Rule-only dry run: + +```bash +CASES_BASE=search_outputs/round_001/llm_cases \ +OUTPUT_BASE=search_outputs/round_001/evaluator_agent \ +DRY_RUN=1 \ +RESUME=1 \ +CURRENT_COLLISION_WEIGHT=0.0 \ +CURRENT_GOAL_WEIGHT=2.0 \ +CURRENT_OFFROAD_WEIGHT=-0.4 \ +CURRENT_RISK_AGENTS_PER_WORLD=3 \ +CURRENT_NORMAL_MODE=policy \ +CURRENT_DETERMINISTIC=0 \ +sbatch --array=0-3 scripts/evaluate_llm_cases_deepseek.sbatch +``` + +DeepSeek evaluation: + +```bash +CASES_BASE=search_outputs/round_001/llm_cases \ +OUTPUT_BASE=search_outputs/round_001/evaluator_agent \ +DRY_RUN=0 \ +BATCH_SIZE=1 \ +MAX_TOKENS=8192 \ +SAVE_RAW_RESPONSES=1 \ +RESUME=1 \ +CURRENT_COLLISION_WEIGHT=0.0 \ +CURRENT_GOAL_WEIGHT=2.0 \ +CURRENT_OFFROAD_WEIGHT=-0.4 \ +CURRENT_RISK_AGENTS_PER_WORLD=3 \ +CURRENT_NORMAL_MODE=policy \ +CURRENT_DETERMINISTIC=0 \ +sbatch --array=0-3 scripts/evaluate_llm_cases_deepseek.sbatch +``` + +Expected outputs: + +```text +search_outputs/round_001/evaluator_agent/shard_000/all_evaluations.jsonl +search_outputs/round_001/evaluator_agent/shard_000/accepted_cases.jsonl +search_outputs/round_001/evaluator_agent/shard_000/rejected_cases.jsonl +search_outputs/round_001/evaluator_agent/shard_000/optimizer_metrics.json +... +``` + +Evaluator jobs are resumable by default. The script uses +`all_evaluations.jsonl` as the completed-case ledger and skips any case id that +already exists there. It also rewrites `accepted_cases.jsonl` and +`rejected_cases.jsonl` from the ledger before continuing, so a timed-out job can +be resumed safely: + +```bash +RESUME=1 \ +CASES_BASE=search_outputs/round_001/llm_cases \ +OUTPUT_BASE=search_outputs/round_001/evaluator_agent \ +DRY_RUN=0 \ +BATCH_SIZE=1 \ +MAX_TOKENS=8192 \ +sbatch --array=0-3 scripts/evaluate_llm_cases_deepseek.sbatch +``` + +### 4. Run MLP Optimizer Agent + +Use all evaluator outputs from the round: + +```bash +METRICS_GLOB="search_outputs/round_001/evaluator_agent/**/optimizer_metrics.json" \ +OUTPUT_DIR=search_outputs/round_001/optimizer_agent/mlp_next \ +TOP_K=8 \ +EXPLORE_K=4 \ +sbatch scripts/optimizer_agent_mlp.sbatch +``` + +Expected outputs: + +```text +search_outputs/round_001/optimizer_agent/mlp_next/optimizer_plan.json +search_outputs/round_001/optimizer_agent/mlp_next/candidate_predictions.jsonl +search_outputs/round_001/optimizer_agent/mlp_next/best_recommendation.env +search_outputs/round_001/optimizer_agent/mlp_next/recommended_search_commands.sh +``` + +### 5. Launch The Next Search Round + +Inspect: + +```bash +cat search_outputs/round_001/optimizer_agent/mlp_next/best_recommendation.env +cat search_outputs/round_001/optimizer_agent/mlp_next/recommended_search_commands.sh +``` + +Then launch round 002 using the best recommendation: + +```bash +export ROUND_ID=round_002 +export SEARCH_DIR=search_outputs/${ROUND_ID}/search +source search_outputs/round_001/optimizer_agent/mlp_next/best_recommendation.env + +CHECKPOINT="${CHECKPOINT}" \ +DATA_DIR="${DATA_DIR}" \ +OUTPUT_DIR="${SEARCH_DIR}" \ +DATASET_SIZE="${DATASET_SIZE}" \ +NUM_GPUS="${NUM_GPUS}" \ +NUM_WORLDS="${NUM_WORLDS}" \ +NUM_BATCHES="${NUM_BATCHES}" \ +STEPS="${STEPS}" \ +SAVE_EVENT_TRACES=1 \ +RISK_COLLISION_WEIGHT="${RISK_COLLISION_WEIGHT}" \ +RISK_GOAL_WEIGHT="${RISK_GOAL_WEIGHT}" \ +RISK_OFFROAD_WEIGHT="${RISK_OFFROAD_WEIGHT}" \ +RISK_AGENTS_PER_WORLD="${RISK_AGENTS_PER_WORLD}" \ +NORMAL_MODE="${NORMAL_MODE}" \ +DETERMINISTIC="${DETERMINISTIC}" \ +sbatch scripts/search_longtail_reward_conditioned.sbatch +``` + +Then repeat steps 2-5 for `round_002`, `round_003`, and so on. + +### 6. Full Parameter Sweep Option + +The default optimization grid has 216 combinations: + +```json +{ + "risk_collision_weight": [-0.2, -0.1, 0.0], + "risk_goal_weight": [1.6, 1.8, 2.0], + "risk_offroad_weight": [-0.8, -0.6, -0.4], + "risk_agents_per_world": [1, 2, 3, 4], + "normal_mode": ["policy", "expert"], + "deterministic": [0] +} +``` + +In practice, do not run all 216 at once unless compute is plentiful. A good +loop is: + +```text +round 001: hand-picked baseline +round 002: top recommendation + 2-4 exploration configs +round 003+: MLP top-k + exploration configs +``` + +## Run On Local Downloaded Outputs + +```bash +python search/evaluate_events_deepseek.py \ + --events outputs/shard_000/events.jsonl \ + --rules rules/longtail_event_evaluation.md \ + --output-dir search_outputs/deepseek_eval_shard_000 \ + --max-events 20 +``` + +## Dry Run Without API + +Use this to test parsing and local rule scoring: + +```bash +python search/evaluate_events_deepseek.py \ + --events outputs/shard_000/events.jsonl \ + --rules rules/longtail_event_evaluation.md \ + --output-dir search_outputs/dry_run_shard_000 \ + --max-events 20 \ + --dry-run 1 +``` + +## Outputs + +The script writes: + +```text +all_evaluations.jsonl +accepted_events.jsonl +rejected_events.jsonl +summary.json +``` + +Each output record contains: + +```text +event +rule_eval +llm_eval +final_valid +final_score +final_priority +``` + +## Notes + +The current `events.jsonl` files are event summaries, not full trajectory +traces. The evaluator can filter and score coarse event quality, but cannot +reliably classify fine-grained maneuvers such as cut-in, hard braking, or +intersection right-of-way violations until the search code saves trace windows. + +## Convert Event Traces For LLM Review + +After `scripts/search_longtail_reward_conditioned.py` writes +`event_traces.jsonl`, convert the raw trace windows into compact LLM-readable +cases: + +```bash +python search/convert_event_traces_to_llm_cases.py \ + --input longtail_outputs/reward_conditioned_search/shard_000/event_traces.jsonl \ + --output-dir search_outputs/llm_cases_shard_000 \ + --max-cards 100 +``` + +The converter writes: + +```text +llm_cases.jsonl +case_cards/*.md +summary.json +``` + +The clean mask is intentionally conservative: + +```text +agent_frame_clean = valid == true and frame <= first_done_frame +pair_frame_clean = agent_a_clean and agent_b_clean +``` + +This removes simulator states after an agent is done, while keeping the +terminal frame where collision/offroad flags often appear. It then recomputes +primary-pair distance, TTC, closing speed, relative pose, timeline rows, and +naturalness warning flags from the cleaned trace. + +For a quick local sanity check: + +```bash +python search/convert_event_traces_to_llm_cases.py \ + --input server_resource/event_traces.jsonl \ + --output-dir search_outputs/llm_cases_test \ + --max-events 5 \ + --max-cards 5 +``` + +On Slurm, use: + +```bash +sbatch scripts/convert_event_traces_to_llm_cases.sbatch +``` + +For four shards: + +```bash +sbatch --array=0-3 scripts/convert_event_traces_to_llm_cases.sbatch +``` + +Useful overrides: + +```bash +SEARCH_DIR=longtail_outputs/reward_conditioned_search \ +OUTPUT_BASE=search_outputs/llm_cases_full \ +MAX_CARDS=200 \ +sbatch --array=0-3 scripts/convert_event_traces_to_llm_cases.sbatch +``` + +## Evaluator Agent + +After conversion, run the Evaluator Agent on `llm_cases.jsonl`. + +Rule-only dry run: + +```bash +python search/evaluate_llm_cases_deepseek.py \ + --cases search_outputs/llm_cases/shard_000/llm_cases.jsonl \ + --rules rules/longtail_llm_case_evaluation.md \ + --output-dir search_outputs/evaluator_agent/shard_000_dry \ + --dry-run 1 +``` + +DeepSeek evaluation: + +```bash +export DEEPSEEK_API_KEY="your_api_key_here" +python search/evaluate_llm_cases_deepseek.py \ + --cases search_outputs/llm_cases/shard_000/llm_cases.jsonl \ + --rules rules/longtail_llm_case_evaluation.md \ + --output-dir search_outputs/evaluator_agent/shard_000 \ + --batch-size 1 \ + --max-tokens 8192 \ + --resume 1 +``` + +Slurm: + +```bash +DRY_RUN=1 sbatch scripts/evaluate_llm_cases_deepseek.sbatch +sbatch --array=0-3 scripts/evaluate_llm_cases_deepseek.sbatch +``` + +The Evaluator Agent writes: + +```text +all_evaluations.jsonl +accepted_cases.jsonl +rejected_cases.jsonl +summary.json +optimizer_metrics.json +``` + +`optimizer_metrics.json` is the handoff file for the Optimizer Agent. It +contains accepted/high-value rates, risk-agent involvement, artifact rates, +scenario-type diversity, score distributions, rejection reasons, feedback +counts, top cases, and suggested next search parameter sets. + +## MLP Optimizer Agent + +The MLP Optimizer Agent reads one or more `optimizer_metrics.json` files, +learns a surrogate model over the search parameter grid, and recommends the +next search configurations. + +```bash +python search/optimizer_agent_mlp.py \ + --metrics-glob "search_outputs/evaluator_agent/**/optimizer_metrics.json" \ + --output-dir search_outputs/optimizer_agent/mlp_next +``` + +Slurm: + +```bash +sbatch scripts/optimizer_agent_mlp.sbatch +``` + +The default optimized parameter grid is: + +```json +{ + "risk_collision_weight": [-0.2, -0.1, 0.0], + "risk_goal_weight": [1.6, 1.8, 2.0], + "risk_offroad_weight": [-0.8, -0.6, -0.4], + "risk_agents_per_world": [1, 2, 3, 4], + "normal_mode": ["policy", "expert"], + "deterministic": [0] +} +``` + +Outputs: + +```text +optimizer_plan.json +candidate_predictions.jsonl +best_recommendation.env +recommended_search_commands.sh +``` + +Early rounds may have too few distinct parameter samples for meaningful MLP +training. In that case the agent writes an exploration/fallback plan and +automatically switches to MLP training once enough history is available. diff --git a/gpudrive-main/search/__pycache__/convert_event_traces_to_llm_cases.cpython-312.pyc b/gpudrive-main/search/__pycache__/convert_event_traces_to_llm_cases.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7b7880eb94b0cf9cf90fe3958564e2affab1b735 Binary files /dev/null and b/gpudrive-main/search/__pycache__/convert_event_traces_to_llm_cases.cpython-312.pyc differ diff --git a/gpudrive-main/search/__pycache__/evaluate_llm_cases_deepseek.cpython-312.pyc b/gpudrive-main/search/__pycache__/evaluate_llm_cases_deepseek.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ecb6552975b1f438d03e56c2cf7e27399647250a Binary files /dev/null and b/gpudrive-main/search/__pycache__/evaluate_llm_cases_deepseek.cpython-312.pyc differ diff --git a/gpudrive-main/search/__pycache__/optimizer_agent_mlp.cpython-312.pyc b/gpudrive-main/search/__pycache__/optimizer_agent_mlp.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..f53ae43f80943027b0e6b2e81d6c4355e9fa17b2 Binary files /dev/null and b/gpudrive-main/search/__pycache__/optimizer_agent_mlp.cpython-312.pyc differ diff --git a/gpudrive-main/search/convert_event_traces_to_llm_cases.py b/gpudrive-main/search/convert_event_traces_to_llm_cases.py new file mode 100644 index 0000000000000000000000000000000000000000..2a5f1c7449ad2e7c41114f1cb389b318e495cd8f --- /dev/null +++ b/gpudrive-main/search/convert_event_traces_to_llm_cases.py @@ -0,0 +1,1020 @@ +#!/usr/bin/env python +"""Convert GPUDrive event traces into compact LLM-readable scenario cases. + +The converter intentionally does not send anything to an API. It performs a +small amount of deterministic cleanup and feature recomputation, then writes: + + - llm_cases.jsonl: machine-readable compact case records + - case_cards/*.md: human-readable cards for inspection and prompt design + - summary.json: aggregate conversion statistics + +Clean mask policy: + agent_frame_clean = trace.valid == true and frame <= first_done_frame + +This keeps the terminal frame, where collision/offroad flags often appear, but +removes frames after an agent is done so default simulator positions do not +pollute speed, TTC, or distance metrics. +""" + +from __future__ import annotations + +import argparse +import json +import math +from collections import Counter +from pathlib import Path +from typing import Any + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser() + parser.add_argument("--input", default="server_resource/event_traces.jsonl") + parser.add_argument("--output-dir", default="search_outputs/llm_cases") + parser.add_argument("--start-index", type=int, default=0) + parser.add_argument("--max-events", type=int, default=0) + parser.add_argument("--dt", type=float, default=0.0) + parser.add_argument("--timeline-offsets-sec", default="-5,-3,-2,-1,-0.5,0,0.5,1") + parser.add_argument("--max-context-agents", type=int, default=8) + parser.add_argument("--write-cards", type=int, default=1) + parser.add_argument("--max-cards", type=int, default=100) + parser.add_argument("--pretty-json", type=int, default=0) + return parser.parse_args() + + +def load_jsonl(path: Path, start_index: int, max_events: int) -> list[dict[str, Any]]: + records = [] + with path.open("r", encoding="utf-8") as f: + for line_idx, line in enumerate(f): + if line_idx < start_index: + continue + if max_events and len(records) >= max_events: + break + line = line.strip() + if not line: + continue + record = json.loads(line) + record["_source_line_index"] = line_idx + records.append(record) + return records + + +def write_json(path: Path, data: Any) -> None: + with path.open("w", encoding="utf-8") as f: + json.dump(data, f, ensure_ascii=False, indent=2) + + +def append_jsonl(path: Path, data: dict[str, Any], pretty: bool = False) -> None: + with path.open("a", encoding="utf-8") as f: + if pretty: + f.write(json.dumps(data, ensure_ascii=False, indent=2) + "\n") + else: + f.write(json.dumps(data, ensure_ascii=False) + "\n") + + +def as_float(value: Any, default: float | None = None) -> float | None: + try: + out = float(value) + except (TypeError, ValueError): + return default + if not math.isfinite(out): + return default + return out + + +def as_int(value: Any, default: int = -1) -> int: + try: + return int(value) + except (TypeError, ValueError): + return default + + +def round_or_none(value: Any, digits: int = 4) -> float | None: + value = as_float(value, None) + if value is None: + return None + return round(value, digits) + + +def parse_offsets(raw: str) -> list[float]: + offsets = [] + for item in raw.split(","): + item = item.strip() + if item: + offsets.append(float(item)) + if 0.0 not in offsets: + offsets.append(0.0) + return sorted(offsets) + + +def case_id(event: dict[str, Any], source_line_index: int) -> str: + return ( + f"shard{as_int(event.get('shard_id'), 0):03d}_" + f"batch{as_int(event.get('batch_idx'), 0):04d}_" + f"world{as_int(event.get('world_idx'), 0):04d}_" + f"line{source_line_index:06d}" + ) + + +def get_dt(record: dict[str, Any], override_dt: float) -> float: + if override_dt > 0: + return override_dt + dt = as_float(record.get("generation_context", {}).get("dt"), None) + return dt if dt and dt > 0 else 0.1 + + +def trace_len(trace: dict[str, Any]) -> int: + lengths = [] + for key in ("pos_x_m", "pos_y_m", "yaw_rad", "valid", "done"): + value = trace.get(key) + if isinstance(value, list): + lengths.append(len(value)) + return min(lengths) if lengths else 0 + + +def clean_mask(trace: dict[str, Any]) -> list[bool]: + n = trace_len(trace) + valid = trace.get("valid", [])[:n] + done = trace.get("done", [])[:n] + pos_x = trace.get("pos_x_m", [])[:n] + pos_y = trace.get("pos_y_m", [])[:n] + + first_done_idx = None + for idx, is_done in enumerate(done): + if bool(is_done): + first_done_idx = idx + break + + mask = [] + for idx in range(n): + before_or_terminal = first_done_idx is None or idx <= first_done_idx + x = as_float(pos_x[idx], None) + y = as_float(pos_y[idx], None) + finite_pos = x is not None and y is not None + mask.append(bool(valid[idx]) and before_or_terminal and finite_pos) + return mask + + +def get_agent_trace(record: dict[str, Any], agent_id: int) -> dict[str, Any] | None: + return record.get("agent_traces", {}).get(str(agent_id)) + + +def metadata_by_agent(record: dict[str, Any]) -> dict[int, dict[str, Any]]: + out = {} + for meta in record.get("agent_metadata", []): + out[as_int(meta.get("agent_index"))] = meta + return out + + +def get_series_value(trace: dict[str, Any], key: str, idx: int) -> float | None: + series = trace.get(key, []) + if idx < 0 or idx >= len(series): + return None + return as_float(series[idx], None) + + +def get_position(trace: dict[str, Any], idx: int) -> tuple[float, float] | None: + x = get_series_value(trace, "pos_x_m", idx) + y = get_series_value(trace, "pos_y_m", idx) + if x is None or y is None: + return None + return x, y + + +def get_yaw(trace: dict[str, Any], idx: int) -> float | None: + return get_series_value(trace, "yaw_rad", idx) + + +def angle_wrap(angle: float) -> float: + return math.atan2(math.sin(angle), math.cos(angle)) + + +def clean_agent_kinematics(trace: dict[str, Any], mask: list[bool], dt: float) -> dict[str, list[float | None]]: + n = len(mask) + speed: list[float | None] = [None] * n + accel: list[float | None] = [None] * n + yaw_rate: list[float | None] = [None] * n + displacement: list[float | None] = [None] * n + + for idx in range(1, n): + if not (mask[idx] and mask[idx - 1]): + continue + p0 = get_position(trace, idx - 1) + p1 = get_position(trace, idx) + if p0 is None or p1 is None: + continue + dx = p1[0] - p0[0] + dy = p1[1] - p0[1] + disp = math.hypot(dx, dy) + displacement[idx] = disp + speed[idx] = disp / dt + yaw0 = get_yaw(trace, idx - 1) + yaw1 = get_yaw(trace, idx) + if yaw0 is not None and yaw1 is not None: + yaw_rate[idx] = angle_wrap(yaw1 - yaw0) / dt + + for idx in range(2, n): + if speed[idx] is None or speed[idx - 1] is None: + continue + accel[idx] = (speed[idx] - speed[idx - 1]) / dt + + return { + "speed_mps": speed, + "accel_mps2": accel, + "yaw_rate_radps": yaw_rate, + "displacement_m": displacement, + } + + +def pair_frame_metrics( + trace_a: dict[str, Any], + trace_b: dict[str, Any], + kin_a: dict[str, list[float | None]], + kin_b: dict[str, list[float | None]], + idx: int, + dt: float, +) -> dict[str, Any]: + pos_a = get_position(trace_a, idx) + pos_b = get_position(trace_b, idx) + if pos_a is None or pos_b is None: + return {"pair_valid": False} + dx = pos_b[0] - pos_a[0] + dy = pos_b[1] - pos_a[1] + distance = math.hypot(dx, dy) + + yaw_a = get_yaw(trace_a, idx) + yaw_b = get_yaw(trace_b, idx) + if yaw_a is not None: + longitudinal = dx * math.cos(yaw_a) + dy * math.sin(yaw_a) + lateral = -dx * math.sin(yaw_a) + dy * math.cos(yaw_a) + else: + longitudinal = None + lateral = None + relative_heading = angle_wrap((yaw_b or 0.0) - (yaw_a or 0.0)) if yaw_a is not None and yaw_b is not None else None + + closing_speed = None + relative_speed = None + if idx > 0: + p0_a = get_position(trace_a, idx - 1) + p0_b = get_position(trace_b, idx - 1) + if p0_a is not None and p0_b is not None and distance > 1e-6: + vel_a = ((pos_a[0] - p0_a[0]) / dt, (pos_a[1] - p0_a[1]) / dt) + vel_b = ((pos_b[0] - p0_b[0]) / dt, (pos_b[1] - p0_b[1]) / dt) + rel_vel = (vel_b[0] - vel_a[0], vel_b[1] - vel_a[1]) + relative_speed = math.hypot(rel_vel[0], rel_vel[1]) + closing_speed = -((dx * rel_vel[0]) + (dy * rel_vel[1])) / distance + + ttc = None + if closing_speed is not None and closing_speed > 0.1: + ttc = distance / closing_speed + + speed_a = kin_a["speed_mps"][idx] if idx < len(kin_a["speed_mps"]) else None + speed_b = kin_b["speed_mps"][idx] if idx < len(kin_b["speed_mps"]) else None + return { + "pair_valid": True, + "distance_m": round_or_none(distance), + "ttc_s": round_or_none(ttc), + "closing_speed_mps": round_or_none(closing_speed), + "relative_speed_mps": round_or_none(relative_speed), + "longitudinal_gap_m": round_or_none(longitudinal), + "lateral_offset_m": round_or_none(lateral), + "relative_heading_rad": round_or_none(relative_heading), + "agent_a_speed_mps": round_or_none(speed_a), + "agent_b_speed_mps": round_or_none(speed_b), + } + + +def compute_pair_summary( + record: dict[str, Any], + agent_a: int, + agent_b: int, + dt: float, +) -> dict[str, Any] | None: + trace_a = get_agent_trace(record, agent_a) + trace_b = get_agent_trace(record, agent_b) + if trace_a is None or trace_b is None: + return None + + mask_a = clean_mask(trace_a) + mask_b = clean_mask(trace_b) + n = min(len(mask_a), len(mask_b)) + if n == 0: + return None + pair_mask = [mask_a[i] and mask_b[i] for i in range(n)] + valid_indices = [idx for idx, ok in enumerate(pair_mask) if ok] + if not valid_indices: + return None + + kin_a = clean_agent_kinematics(trace_a, mask_a, dt) + kin_b = clean_agent_kinematics(trace_b, mask_b, dt) + frame_metrics = [ + pair_frame_metrics(trace_a, trace_b, kin_a, kin_b, idx, dt) + if pair_mask[idx] + else {"pair_valid": False} + for idx in range(n) + ] + + distances = [ + (idx, metric["distance_m"]) + for idx, metric in enumerate(frame_metrics) + if metric.get("pair_valid") and metric.get("distance_m") is not None + ] + if not distances: + return None + min_idx, min_distance = min(distances, key=lambda item: item[1]) + + ttc_values = [ + metric["ttc_s"] + for metric in frame_metrics + if metric.get("pair_valid") and metric.get("ttc_s") is not None + ] + min_ttc = min(ttc_values) if ttc_values else None + ttc_below_1s = sum(1 for value in ttc_values if value < 1.0) * dt + ttc_below_2s = sum(1 for value in ttc_values if value < 2.0) * dt + + return { + "agent_a": agent_a, + "agent_b": agent_b, + "num_clean_pair_frames": len(valid_indices), + "clean_frame_indices": valid_indices, + "frame_metrics": frame_metrics, + "min_clean_distance_m": round_or_none(min_distance), + "min_clean_distance_frame": min_idx, + "min_ttc_s": round_or_none(min_ttc), + "time_ttc_below_1s": round_or_none(ttc_below_1s), + "time_ttc_below_2s": round_or_none(ttc_below_2s), + "metrics_at_min_distance": frame_metrics[min_idx], + "kinematics_a": kin_a, + "kinematics_b": kin_b, + "mask_a": mask_a, + "mask_b": mask_b, + "pair_mask": pair_mask, + } + + +def candidate_pairs(record: dict[str, Any]) -> list[dict[str, Any]]: + event = record.get("event", {}) + selected_agents = set(as_int(a) for a in record.get("trace_window", {}).get("selected_agents", [])) + risk_agents = [as_int(a) for a in event.get("risk_agents", [])] + collided_agents = [as_int(a) for a in event.get("collided_agents", [])] + + candidates: list[dict[str, Any]] = [] + seen: set[tuple[int, int, str]] = set() + + def add(a: int, b: int, reason: str, priority: int, confidence: str) -> None: + if a < 0 or b < 0 or a == b: + return + if a not in selected_agents or b not in selected_agents: + return + key = (a, b, reason) + if key in seen: + return + seen.add(key) + candidates.append( + { + "agent_a": a, + "agent_b": b, + "selection_reason": reason, + "priority": priority, + "confidence": confidence, + } + ) + + for risk_agent in risk_agents: + if risk_agent in collided_agents: + for other in collided_agents: + add(risk_agent, other, "risk_agent_directly_collided_with_partner", 100, "high") + + add( + as_int(event.get("min_distance_risk_agent")), + as_int(event.get("min_distance_partner_agent")), + "event_min_distance_risk_partner", + 80, + "medium", + ) + + for i, a in enumerate(collided_agents): + for b in collided_agents[i + 1 :]: + add(a, b, "collided_agents_pair", 70, "medium") + + non_risk_agents = [agent for agent in selected_agents if agent not in set(risk_agents)] + for risk_agent in risk_agents: + for other in non_risk_agents: + add(risk_agent, other, "risk_partner_low_ttc_or_distance_fallback", 20, "low") + + return candidates + + +def choose_primary_pair(record: dict[str, Any], dt: float) -> tuple[dict[str, Any] | None, dict[str, Any] | None]: + best_candidate = None + best_summary = None + best_key = None + for candidate in candidate_pairs(record): + summary = compute_pair_summary(record, candidate["agent_a"], candidate["agent_b"], dt) + if summary is None: + continue + min_distance = summary.get("min_clean_distance_m") + min_ttc = summary.get("min_ttc_s") + # Lower distance and TTC break ties, but priority keeps the intended + # narrative rules dominant. + key = ( + candidate["priority"], + -(min_distance if min_distance is not None else 1e6), + -(min_ttc if min_ttc is not None else 1e6), + ) + if best_key is None or key > best_key: + best_key = key + best_candidate = candidate + best_summary = summary + + if best_candidate is None or best_summary is None: + return None, None + + event = record.get("event", {}) + risk_agents = set(as_int(a) for a in event.get("risk_agents", [])) + collided_agents = set(as_int(a) for a in event.get("collided_agents", [])) + best_candidate = dict(best_candidate) + best_candidate["risk_directly_involved"] = ( + best_candidate["agent_a"] in risk_agents and best_candidate["agent_a"] in collided_agents + ) or ( + best_candidate["agent_b"] in risk_agents and best_candidate["agent_b"] in collided_agents + ) + best_candidate["agent_a_role"] = agent_role(best_candidate["agent_a"], event) + best_candidate["agent_b_role"] = agent_role(best_candidate["agent_b"], event) + return best_candidate, best_summary + + +def agent_role(agent_id: int, event: dict[str, Any]) -> list[str]: + roles = [] + if agent_id in set(as_int(a) for a in event.get("risk_agents", [])): + roles.append("risk_agent") + if agent_id in set(as_int(a) for a in event.get("collided_agents", [])): + roles.append("collided_agent") + if agent_id in set(as_int(a) for a in event.get("offroad_agents", [])): + roles.append("offroad_agent") + if not roles: + roles.append("context_agent") + return roles + + +def local_event_frame(record: dict[str, Any]) -> int: + event_step = as_int(record.get("trace_window", {}).get("event_step"), 0) + start_step = as_int(record.get("trace_window", {}).get("start_step"), 0) + n = as_int(record.get("trace_window", {}).get("num_frames"), 1) + return max(0, min(n - 1, event_step - start_step)) + + +def nearest_valid_frame(pair_mask: list[bool], target_idx: int, max_delta: int = 3) -> int | None: + if not pair_mask: + return None + target_idx = max(0, min(len(pair_mask) - 1, target_idx)) + if pair_mask[target_idx]: + return target_idx + for delta in range(1, max_delta + 1): + left = target_idx - delta + right = target_idx + delta + if left >= 0 and pair_mask[left]: + return left + if right < len(pair_mask) and pair_mask[right]: + return right + return None + + +def build_timeline( + record: dict[str, Any], + primary_pair: dict[str, Any], + pair_summary: dict[str, Any], + offsets: list[float], + dt: float, +) -> list[dict[str, Any]]: + local_event = local_event_frame(record) + trace_a = get_agent_trace(record, primary_pair["agent_a"]) + trace_b = get_agent_trace(record, primary_pair["agent_b"]) + if trace_a is None or trace_b is None: + return [] + + rows = [] + used_indices = set() + for offset in offsets: + target = int(round(local_event + offset / dt)) + frame_idx = nearest_valid_frame(pair_summary["pair_mask"], target) + if frame_idx is None or frame_idx in used_indices: + continue + used_indices.add(frame_idx) + metric = pair_summary["frame_metrics"][frame_idx] + row = { + "t_to_event_s": round((frame_idx - local_event) * dt, 3), + "frame_index": frame_idx, + "target_offset_s": offset, + "distance_m": metric.get("distance_m"), + "ttc_s": metric.get("ttc_s"), + "closing_speed_mps": metric.get("closing_speed_mps"), + "relative_speed_mps": metric.get("relative_speed_mps"), + "longitudinal_gap_m": metric.get("longitudinal_gap_m"), + "lateral_offset_m": metric.get("lateral_offset_m"), + "relative_heading_rad": metric.get("relative_heading_rad"), + "agent_a_speed_mps": metric.get("agent_a_speed_mps"), + "agent_b_speed_mps": metric.get("agent_b_speed_mps"), + "agent_a_collision": bool_value_at(trace_a.get("collided", []), frame_idx), + "agent_b_collision": bool_value_at(trace_b.get("collided", []), frame_idx), + "agent_a_offroad": bool_value_at(trace_a.get("off_road", []), frame_idx), + "agent_b_offroad": bool_value_at(trace_b.get("off_road", []), frame_idx), + } + rows.append(row) + return sorted(rows, key=lambda item: item["frame_index"]) + + +def bool_value_at(series: list[Any], idx: int) -> bool: + if idx < 0 or idx >= len(series): + return False + value = series[idx] + if isinstance(value, bool): + return value + return as_float(value, 0.0) > 0 + + +def distance_trend(pair_summary: dict[str, Any], local_event: int, dt: float) -> dict[str, Any]: + start = max(0, local_event - int(round(3.0 / dt))) + end = local_event + distances = [] + for idx in range(start, end + 1): + if idx >= len(pair_summary["frame_metrics"]): + continue + metric = pair_summary["frame_metrics"][idx] + if metric.get("pair_valid") and metric.get("distance_m") is not None: + distances.append(metric["distance_m"]) + if len(distances) < 3: + return {"label": "insufficient_clean_frames", "decreasing_ratio": None} + decreases = sum(1 for prev, cur in zip(distances, distances[1:]) if cur < prev) + ratio = decreases / max(1, len(distances) - 1) + if ratio >= 0.75: + label = "mostly_decreasing" + elif ratio >= 0.45: + label = "mixed" + else: + label = "not_decreasing" + return { + "label": label, + "decreasing_ratio": round(ratio, 4), + "distance_start_m": round_or_none(distances[0]), + "distance_end_m": round_or_none(distances[-1]), + } + + +def finite_values(values: list[float | None]) -> list[float]: + return [float(v) for v in values if v is not None and math.isfinite(float(v))] + + +def max_or_none(values: list[float | None], absolute: bool = False) -> float | None: + vals = finite_values(values) + if absolute: + vals = [abs(v) for v in vals] + return round_or_none(max(vals), 4) if vals else None + + +def min_or_none(values: list[float | None]) -> float | None: + vals = finite_values(values) + return round_or_none(min(vals), 4) if vals else None + + +def naturalness_checks(record: dict[str, Any], agent_ids: list[int], dt: float) -> dict[str, Any]: + max_speed = None + max_accel = None + max_decel = None + max_yaw_rate = None + max_displacement = None + clean_frame_counts: dict[str, int] = {} + + for agent_id in agent_ids: + trace = get_agent_trace(record, agent_id) + if trace is None: + continue + mask = clean_mask(trace) + clean_frame_counts[str(agent_id)] = sum(1 for ok in mask if ok) + kin = clean_agent_kinematics(trace, mask, dt) + agent_max_speed = max_or_none(kin["speed_mps"]) + agent_max_accel = max_or_none(kin["accel_mps2"]) + agent_max_decel_raw = min_or_none(kin["accel_mps2"]) + agent_max_decel = round_or_none(-agent_max_decel_raw) if agent_max_decel_raw is not None else None + agent_max_yaw_rate = max_or_none(kin["yaw_rate_radps"], absolute=True) + agent_max_displacement = max_or_none(kin["displacement_m"]) + max_speed = choose_max(max_speed, agent_max_speed) + max_accel = choose_max(max_accel, agent_max_accel) + max_decel = choose_max(max_decel, agent_max_decel) + max_yaw_rate = choose_max(max_yaw_rate, agent_max_yaw_rate) + max_displacement = choose_max(max_displacement, agent_max_displacement) + + warnings = [] + if max_speed is not None and max_speed > 45: + warnings.append("extreme_speed") + if max_accel is not None and max_accel > 12: + warnings.append("high_acceleration") + if max_decel is not None and max_decel > 12: + warnings.append("hard_deceleration") + if max_yaw_rate is not None and max_yaw_rate > 2.5: + warnings.append("high_yaw_rate") + if max_displacement is not None and max_displacement > 10: + warnings.append("large_frame_displacement") + if any(count < 3 for count in clean_frame_counts.values()): + warnings.append("short_clean_track") + + event = record.get("event", {}) + offroad_before_collision = ( + as_int(event.get("first_offroad_step")) >= 0 + and as_int(event.get("first_collision_step")) >= 0 + and as_int(event.get("first_offroad_step")) < as_int(event.get("first_collision_step")) + ) + if offroad_before_collision: + warnings.append("offroad_before_collision") + + return { + "max_speed_mps": max_speed, + "max_accel_mps2": max_accel, + "max_decel_mps2": max_decel, + "max_yaw_rate_radps": max_yaw_rate, + "max_frame_displacement_m": max_displacement, + "offroad_before_collision": offroad_before_collision, + "clean_frame_counts": clean_frame_counts, + "artifact_warnings": sorted(set(warnings)), + } + + +def choose_max(current: float | None, new_value: float | None) -> float | None: + if new_value is None: + return current + if current is None: + return new_value + return max(current, new_value) + + +def select_context_agents( + record: dict[str, Any], + primary_pair: dict[str, Any], + pair_summary: dict[str, Any], + max_context_agents: int, +) -> list[int]: + event = record.get("event", {}) + selected = set() + selected.add(primary_pair["agent_a"]) + selected.add(primary_pair["agent_b"]) + selected.update(as_int(a) for a in event.get("risk_agents", [])) + selected.update(as_int(a) for a in event.get("collided_agents", [])) + selected.update(as_int(a) for a in event.get("offroad_agents", [])) + + center_idx = nearest_valid_frame(pair_summary["pair_mask"], local_event_frame(record)) + if center_idx is not None: + trace_a = get_agent_trace(record, primary_pair["agent_a"]) + trace_b = get_agent_trace(record, primary_pair["agent_b"]) + center = None + if trace_a and trace_b: + pa = get_position(trace_a, center_idx) + pb = get_position(trace_b, center_idx) + if pa and pb: + center = ((pa[0] + pb[0]) / 2.0, (pa[1] + pb[1]) / 2.0) + if center is not None: + distances = [] + for agent_str, trace in record.get("agent_traces", {}).items(): + agent_id = as_int(agent_str) + mask = clean_mask(trace) + if center_idx >= len(mask) or not mask[center_idx]: + continue + pos = get_position(trace, center_idx) + if pos is None: + continue + distances.append((math.hypot(pos[0] - center[0], pos[1] - center[1]), agent_id)) + for _, agent_id in sorted(distances)[: max_context_agents * 2]: + selected.add(agent_id) + if len(selected) >= max_context_agents: + break + + selected = [agent for agent in selected if get_agent_trace(record, agent) is not None] + selected.sort(key=lambda agent: (agent not in (primary_pair["agent_a"], primary_pair["agent_b"]), agent)) + return selected[:max_context_agents] + + +def context_agent_summaries( + record: dict[str, Any], + context_agents: list[int], + primary_pair: dict[str, Any], + pair_summary: dict[str, Any], + metadata: dict[int, dict[str, Any]], + dt: float, +) -> list[dict[str, Any]]: + frame_idx = nearest_valid_frame(pair_summary["pair_mask"], local_event_frame(record)) + summaries = [] + for agent_id in context_agents: + trace = get_agent_trace(record, agent_id) + if trace is None: + continue + mask = clean_mask(trace) + kin = clean_agent_kinematics(trace, mask, dt) + speed = kin["speed_mps"][frame_idx] if frame_idx is not None and frame_idx < len(kin["speed_mps"]) else None + meta = metadata.get(agent_id, {}) + summaries.append( + { + "agent_id": agent_id, + "roles": agent_role(agent_id, record.get("event", {})), + "is_controlled": bool(meta.get("is_controlled", False)), + "response_type": meta.get("response_type"), + "raw_agent_type": meta.get("raw_agent_type"), + "length_m": round_or_none(meta.get("length_m")), + "width_m": round_or_none(meta.get("width_m")), + "clean_frames": sum(1 for ok in mask if ok), + "speed_at_event_mps": round_or_none(speed), + } + ) + return summaries + + +def semantic_label(primary_pair: dict[str, Any], pair_summary: dict[str, Any]) -> str: + metric = pair_summary.get("metrics_at_min_distance", {}) + lateral = abs(as_float(metric.get("lateral_offset_m"), 0.0) or 0.0) + longitudinal = as_float(metric.get("longitudinal_gap_m"), 0.0) or 0.0 + heading = abs(as_float(metric.get("relative_heading_rad"), 0.0) or 0.0) + if lateral < 1.5 and longitudinal > 0 and heading < 0.6: + return "rear_end_or_following_conflict" + if lateral < 3.5 and heading < 0.8: + return "same_direction_side_conflict_or_cut_in" + if heading >= 0.8: + return "crossing_or_intersection_conflict" + return "generic_close_interaction" + + +def map_summary(record: dict[str, Any]) -> dict[str, Any]: + map_context = record.get("map_context", {}) + return { + "center_xy": map_context.get("center_xy"), + "radius_m": map_context.get("radius_m"), + "num_points": map_context.get("num_points"), + "type_counts": map_context.get("type_counts", {}), + } + + +def build_llm_case(record: dict[str, Any], args: argparse.Namespace, offsets: list[float]) -> dict[str, Any] | None: + dt = get_dt(record, args.dt) + event = record.get("event", {}) + primary_pair, pair_summary = choose_primary_pair(record, dt) + if primary_pair is None or pair_summary is None: + return None + + local_event = local_event_frame(record) + event_pair_idx = nearest_valid_frame(pair_summary["pair_mask"], local_event) + event_metric = ( + pair_summary["frame_metrics"][event_pair_idx] + if event_pair_idx is not None + else pair_summary.get("metrics_at_min_distance", {}) + ) + trend = distance_trend(pair_summary, local_event, dt) + context_agents = select_context_agents(record, primary_pair, pair_summary, args.max_context_agents) + metadata = metadata_by_agent(record) + naturalness = naturalness_checks(record, context_agents, dt) + timeline = build_timeline(record, primary_pair, pair_summary, offsets, dt) + + source_line_index = as_int(record.get("_source_line_index"), 0) + cid = case_id(event, source_line_index) + first_collision_step = as_int(event.get("first_collision_step")) + first_offroad_step = as_int(event.get("first_offroad_step")) + min_distance_step = as_int(event.get("min_distance_step")) + + return { + "case_id": cid, + "schema_version": "llm_longtail_case_v1", + "source": { + "event_trace_line_index": source_line_index, + "scenario_file": event.get("scenario_file"), + "shard_id": event.get("shard_id"), + "batch_idx": event.get("batch_idx"), + "world_idx": event.get("world_idx"), + }, + "outcome": { + "collision": bool(event.get("collision")), + "near_miss": bool(event.get("near_miss")), + "offroad": bool(event.get("offroad")), + "first_collision_time_s": round_or_none(first_collision_step * dt) if first_collision_step >= 0 else None, + "first_offroad_time_s": round_or_none(first_offroad_step * dt) if first_offroad_step >= 0 else None, + "min_distance_time_s": round_or_none(min_distance_step * dt) if min_distance_step >= 0 else None, + "event_time_s": round_or_none(as_int(record.get("trace_window", {}).get("event_step"), 0) * dt), + "raw_min_distance_m": round_or_none(event.get("min_distance_m")), + "clean_min_distance_m": pair_summary.get("min_clean_distance_m"), + }, + "primary_pair": { + "agent_a": primary_pair["agent_a"], + "agent_b": primary_pair["agent_b"], + "agent_a_role": primary_pair["agent_a_role"], + "agent_b_role": primary_pair["agent_b_role"], + "selection_reason": primary_pair["selection_reason"], + "confidence": primary_pair["confidence"], + "risk_directly_involved": bool(primary_pair["risk_directly_involved"]), + "semantic_candidate": semantic_label(primary_pair, pair_summary), + }, + "scene_summary": { + "controlled_agents": event.get("controlled_agents"), + "risk_agents": event.get("risk_agents", []), + "collided_agents": event.get("collided_agents", []), + "offroad_agents": event.get("offroad_agents", []), + "goal_agents": event.get("goal_agents"), + "context_agents": context_agents, + }, + "primary_interaction_metrics": { + "num_clean_pair_frames": pair_summary.get("num_clean_pair_frames"), + "min_clean_distance_m": pair_summary.get("min_clean_distance_m"), + "min_clean_distance_time_to_event_s": round_or_none( + (pair_summary.get("min_clean_distance_frame", local_event) - local_event) * dt + ), + "min_ttc_s": pair_summary.get("min_ttc_s"), + "time_ttc_below_1s": pair_summary.get("time_ttc_below_1s"), + "time_ttc_below_2s": pair_summary.get("time_ttc_below_2s"), + "distance_trend_before_event": trend, + "metrics_at_event_or_nearest_clean_frame": event_metric, + "metrics_at_min_clean_distance": pair_summary.get("metrics_at_min_distance"), + }, + "naturalness_checks": naturalness, + "interaction_checks": { + "risk_agent_in_primary_pair": "risk_agent" in primary_pair["agent_a_role"] + or "risk_agent" in primary_pair["agent_b_role"], + "primary_pair_has_collision_agent": "collided_agent" in primary_pair["agent_a_role"] + or "collided_agent" in primary_pair["agent_b_role"], + "primary_pair_has_offroad_agent": "offroad_agent" in primary_pair["agent_a_role"] + or "offroad_agent" in primary_pair["agent_b_role"], + "distance_trend_label": trend.get("label"), + }, + "context_agent_summaries": context_agent_summaries( + record, context_agents, primary_pair, pair_summary, metadata, dt + ), + "map_summary": map_summary(record), + "timeline": timeline, + "llm_task": { + "question": "Judge whether this is a natural and safety-critical long-tail driving scenario.", + "return_format": "strict_json", + "expected_keys": [ + "accepted", + "danger_level", + "naturalness_score", + "interaction_score", + "scenario_value_score", + "scenario_type", + "artifact_flags", + "reason", + ], + }, + } + + +def markdown_table(headers: list[str], rows: list[list[Any]]) -> str: + out = ["| " + " | ".join(headers) + " |"] + out.append("| " + " | ".join(["---"] * len(headers)) + " |") + for row in rows: + out.append("| " + " | ".join("" if value is None else str(value) for value in row) + " |") + return "\n".join(out) + + +def case_to_markdown(case: dict[str, Any]) -> str: + outcome = case["outcome"] + pair = case["primary_pair"] + metrics = case["primary_interaction_metrics"] + nat = case["naturalness_checks"] + timeline_rows = [ + [ + row.get("t_to_event_s"), + row.get("distance_m"), + row.get("ttc_s"), + row.get("closing_speed_mps"), + row.get("relative_speed_mps"), + row.get("lateral_offset_m"), + row.get("longitudinal_gap_m"), + row.get("agent_a_speed_mps"), + row.get("agent_b_speed_mps"), + ] + for row in case.get("timeline", []) + ] + agent_rows = [ + [ + item.get("agent_id"), + ",".join(item.get("roles", [])), + item.get("is_controlled"), + item.get("clean_frames"), + item.get("speed_at_event_mps"), + item.get("length_m"), + item.get("width_m"), + ] + for item in case.get("context_agent_summaries", []) + ] + + return "\n".join( + [ + f"# {case['case_id']}", + "", + "## Outcome", + f"- collision: {outcome.get('collision')}", + f"- near_miss: {outcome.get('near_miss')}", + f"- offroad: {outcome.get('offroad')}", + f"- event_time_s: {outcome.get('event_time_s')}", + f"- raw_min_distance_m: {outcome.get('raw_min_distance_m')}", + f"- clean_min_distance_m: {outcome.get('clean_min_distance_m')}", + "", + "## Primary Pair", + f"- agent_a: {pair.get('agent_a')} ({','.join(pair.get('agent_a_role', []))})", + f"- agent_b: {pair.get('agent_b')} ({','.join(pair.get('agent_b_role', []))})", + f"- selection_reason: {pair.get('selection_reason')}", + f"- confidence: {pair.get('confidence')}", + f"- risk_directly_involved: {pair.get('risk_directly_involved')}", + f"- semantic_candidate: {pair.get('semantic_candidate')}", + "", + "## Primary Interaction Metrics", + f"- num_clean_pair_frames: {metrics.get('num_clean_pair_frames')}", + f"- min_ttc_s: {metrics.get('min_ttc_s')}", + f"- time_ttc_below_1s: {metrics.get('time_ttc_below_1s')}", + f"- time_ttc_below_2s: {metrics.get('time_ttc_below_2s')}", + f"- distance_trend_before_event: {metrics.get('distance_trend_before_event')}", + "", + "## Naturalness Checks", + f"- max_speed_mps: {nat.get('max_speed_mps')}", + f"- max_accel_mps2: {nat.get('max_accel_mps2')}", + f"- max_decel_mps2: {nat.get('max_decel_mps2')}", + f"- max_yaw_rate_radps: {nat.get('max_yaw_rate_radps')}", + f"- max_frame_displacement_m: {nat.get('max_frame_displacement_m')}", + f"- artifact_warnings: {nat.get('artifact_warnings')}", + "", + "## Context Agents", + markdown_table( + ["agent", "roles", "controlled", "clean_frames", "speed_event", "length", "width"], + agent_rows, + ), + "", + "## Timeline", + markdown_table( + [ + "t_to_event", + "distance", + "ttc", + "closing", + "rel_speed", + "lat_offset", + "long_gap", + "a_speed", + "b_speed", + ], + timeline_rows, + ), + "", + "## LLM Task", + "Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON.", + "", + ] + ) + + +def convert(args: argparse.Namespace) -> None: + input_path = Path(args.input) + output_dir = Path(args.output_dir) + cards_dir = output_dir / "case_cards" + output_dir.mkdir(parents=True, exist_ok=True) + if args.write_cards: + cards_dir.mkdir(parents=True, exist_ok=True) + + records = load_jsonl(input_path, args.start_index, args.max_events) + offsets = parse_offsets(args.timeline_offsets_sec) + cases_path = output_dir / "llm_cases.jsonl" + if cases_path.exists(): + cases_path.unlink() + + summary: dict[str, Any] = { + "input": str(input_path), + "output_dir": str(output_dir), + "records_read": len(records), + "cases_written": 0, + "cases_skipped_no_primary_pair": 0, + "semantic_counts": Counter(), + "primary_pair_reason_counts": Counter(), + "artifact_warning_counts": Counter(), + } + + for idx, record in enumerate(records): + case = build_llm_case(record, args, offsets) + if case is None: + summary["cases_skipped_no_primary_pair"] += 1 + continue + append_jsonl(cases_path, case, pretty=bool(args.pretty_json)) + summary["cases_written"] += 1 + summary["semantic_counts"][case["primary_pair"]["semantic_candidate"]] += 1 + summary["primary_pair_reason_counts"][case["primary_pair"]["selection_reason"]] += 1 + for warning in case["naturalness_checks"].get("artifact_warnings", []): + summary["artifact_warning_counts"][warning] += 1 + + card_limit = args.max_cards + if args.write_cards and (card_limit == 0 or idx < card_limit): + card_path = cards_dir / f"{case['case_id']}.md" + card_path.write_text(case_to_markdown(case), encoding="utf-8") + + for key in ("semantic_counts", "primary_pair_reason_counts", "artifact_warning_counts"): + summary[key] = dict(summary[key]) + + write_json(output_dir / "summary.json", summary) + print("[convert] done:", json.dumps(summary, ensure_ascii=False), flush=True) + + +def main() -> None: + convert(parse_args()) + + +if __name__ == "__main__": + main() diff --git a/gpudrive-main/search/evaluate_events_deepseek.py b/gpudrive-main/search/evaluate_events_deepseek.py new file mode 100644 index 0000000000000000000000000000000000000000..79fe80cca359b7b41e3b52ab3e201b07a57a5229 --- /dev/null +++ b/gpudrive-main/search/evaluate_events_deepseek.py @@ -0,0 +1,600 @@ +#!/usr/bin/env python +"""Evaluate GPUDrive long-tail events with rules and DeepSeek. + +The script reads event summaries from JSONL, computes a local rule-based score, +asks DeepSeek for a structured judgment, and writes accepted/rejected events. +""" + +from __future__ import annotations + +import argparse +import json +import os +import time +import urllib.error +import urllib.request +from pathlib import Path +from typing import Any + + +DEFAULT_BASE_URL = "https://api.deepseek.com" +DEFAULT_MODEL = "deepseek-v4-flash" + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser() + parser.add_argument("--events", default="outputs/shard_000/events.jsonl") + parser.add_argument("--rules", default="rules/longtail_event_evaluation.md") + parser.add_argument("--summary", default="") + parser.add_argument("--output-dir", default="search_outputs/deepseek_event_eval") + parser.add_argument("--max-events", type=int, default=0) + parser.add_argument("--start-index", type=int, default=0) + parser.add_argument("--batch-size", type=int, default=5) + parser.add_argument("--accept-threshold", type=float, default=5.0) + parser.add_argument("--dry-run", type=int, default=0) + parser.add_argument("--save-prompts", type=int, default=0) + parser.add_argument("--base-url", default=os.environ.get("DEEPSEEK_BASE_URL", DEFAULT_BASE_URL)) + parser.add_argument("--model", default=os.environ.get("DEEPSEEK_MODEL", DEFAULT_MODEL)) + parser.add_argument("--api-key-env", default="DEEPSEEK_API_KEY") + parser.add_argument("--temperature", type=float, default=0.0) + parser.add_argument("--max-tokens", type=int, default=4096) + parser.add_argument("--timeout-sec", type=float, default=120.0) + parser.add_argument("--retries", type=int, default=3) + parser.add_argument("--sleep-sec", type=float, default=0.5) + parser.add_argument("--json-mode", type=int, default=1) + parser.add_argument("--current-collision-weight", type=float, default=-0.3) + parser.add_argument("--current-goal-weight", type=float, default=2.0) + parser.add_argument("--current-offroad-weight", type=float, default=-0.4) + return parser.parse_args() + + +def read_text(path: str | Path) -> str: + with open(path, "r", encoding="utf-8") as f: + return f.read() + + +def read_json(path: str | Path) -> dict[str, Any]: + with open(path, "r", encoding="utf-8") as f: + return json.load(f) + + +def read_jsonl(path: str | Path) -> list[dict[str, Any]]: + records = [] + with open(path, "r", encoding="utf-8") as f: + for line_no, line in enumerate(f, start=1): + line = line.strip() + if not line: + continue + try: + record = json.loads(line) + except json.JSONDecodeError as exc: + raise ValueError(f"Invalid JSONL at line {line_no}: {exc}") from exc + record["_line_no"] = line_no + records.append(record) + return records + + +def write_json(path: str | Path, data: Any) -> None: + with open(path, "w", encoding="utf-8") as f: + json.dump(data, f, indent=2) + + +def append_jsonl(path: str | Path, record: dict[str, Any]) -> None: + with open(path, "a", encoding="utf-8") as f: + f.write(json.dumps(record, ensure_ascii=False) + "\n") + + +def as_float(value: Any, default: float = 0.0) -> float: + try: + return float(value) + except (TypeError, ValueError): + return default + + +def as_int(value: Any, default: int = 0) -> int: + try: + return int(value) + except (TypeError, ValueError): + return default + + +def rule_based_eval(event: dict[str, Any]) -> dict[str, Any]: + collision = bool(event.get("collision", False)) + near_miss = bool(event.get("near_miss", False)) + offroad = bool(event.get("offroad", False)) + risk_agents = [int(a) for a in event.get("risk_agents", [])] + collided_agents = [int(a) for a in event.get("collided_agents", [])] + controlled_agents = as_int(event.get("controlled_agents"), 0) + first_collision_step = as_int(event.get("first_collision_step"), -1) + first_offroad_step = as_int(event.get("first_offroad_step"), -1) + min_distance_m = as_float(event.get("min_distance_m"), float("inf")) + min_distance_risk_agent = as_int(event.get("min_distance_risk_agent"), -1) + goal_agents = as_int(event.get("goal_agents"), 0) + + reject_reasons = [] + warnings = [] + labels = [] + + if not collision and not near_miss and not offroad: + reject_reasons.append("no_longtail_signal") + if not risk_agents: + reject_reasons.append("no_risk_agent") + if not collision and not near_miss and offroad: + reject_reasons.append("pure_offroad_failure") + if controlled_agents < 2: + reject_reasons.append("insufficient_interaction_agents") + if 0 <= first_collision_step <= 2: + warnings.append("possible_initial_overlap") + + score = 0.0 + if collision: + score += 4.0 + labels.append("collision_interaction") + if near_miss: + score += 2.0 + if min_distance_m <= 0.5: + score += 2.0 + elif min_distance_m <= 1.0: + score += 1.5 + elif min_distance_m <= 2.0: + score += 1.0 + + if min_distance_risk_agent in risk_agents: + score += 1.0 + if any(agent in collided_agents for agent in risk_agents): + score += 1.5 + labels.append("risk_agent_collision") + + if controlled_agents >= 5: + score += 1.0 + elif controlled_agents >= 3: + score += 0.5 + + if first_collision_step >= 20: + score += 1.0 + elif first_collision_step >= 5: + score += 0.5 + + if offroad and first_offroad_step >= 0: + score -= 1.0 + if offroad and collision and first_offroad_step >= 0 and first_offroad_step < first_collision_step: + score -= 1.0 + labels.append("offroad_before_collision") + warnings.append("offroad_before_collision") + + if controlled_agents > 0 and goal_agents / controlled_agents >= 0.8: + score += 0.5 + + if not collision and near_miss: + labels.append("near_miss_only") + + score = min(max(score, 0.0), 10.0) + if score >= 8.0: + priority = "high_value" + elif score >= 5.0: + priority = "medium_value" + elif score >= 3.0: + priority = "low_value" + else: + priority = "reject_or_debug_only" + + valid = not reject_reasons and score >= 3.0 + return { + "valid": valid, + "score": round(score, 3), + "priority": priority, + "event_type": sorted(set(labels)), + "reject_reasons": reject_reasons, + "warnings": sorted(set(warnings)), + "needs_trace_review": bool(warnings), + "reward_search_feedback": rule_feedback(event, reject_reasons, warnings), + } + + +def rule_feedback( + event: dict[str, Any], + reject_reasons: list[str], + warnings: list[str], +) -> dict[str, bool]: + collision = bool(event.get("collision", False)) + near_miss = bool(event.get("near_miss", False)) + offroad = bool(event.get("offroad", False)) + return { + "increase_collision_tolerance": (not collision and not near_miss), + "increase_goal_pressure": (not collision and not near_miss), + "increase_offroad_penalty": offroad or "pure_offroad_failure" in reject_reasons, + "reduce_goal_pressure": "possible_initial_overlap" in warnings, + } + + +def build_prompt( + rules_md: str, + events: list[dict[str, Any]], + rule_evals: list[dict[str, Any]], + summary: dict[str, Any] | None, +) -> list[dict[str, str]]: + compact_items = [] + for local_idx, (event, rule_eval) in enumerate(zip(events, rule_evals)): + compact_items.append( + { + "local_index": local_idx, + "event_key": event_key(event), + "event": strip_internal_fields(event), + "rule_eval": rule_eval, + } + ) + + user_payload = { + "task": "Evaluate GPUDrive long-tail event summaries using the provided markdown rules.", + "rules_markdown": rules_md, + "search_summary": summary, + "events": compact_items, + "output_contract": { + "evaluations": [ + { + "local_index": 0, + "event_key": "shard0_batch0_world0_line1", + "valid": True, + "priority": "high_value|medium_value|low_value|reject_or_debug_only", + "score": 8.0, + "event_type": ["collision_interaction"], + "reason": "short reason grounded in JSON fields", + "reject_reason": None, + "needs_trace_review": False, + "reward_search_feedback": { + "increase_collision_tolerance": False, + "increase_goal_pressure": False, + "increase_offroad_penalty": False, + "reduce_goal_pressure": False, + }, + } + ] + }, + } + + system = ( + "You are a strict evaluator for autonomous-driving simulation events. " + "Use only the provided JSON fields and markdown rules. " + "Do not infer fine-grained maneuvers that are not supported by the event summary. " + "Return valid JSON only." + ) + user = json.dumps(user_payload, ensure_ascii=False) + return [ + {"role": "system", "content": system}, + {"role": "user", "content": user}, + ] + + +def event_key(event: dict[str, Any]) -> str: + return ( + f"shard{event.get('shard_id', 'x')}_" + f"batch{event.get('batch_idx', 'x')}_" + f"world{event.get('world_idx', 'x')}_" + f"line{event.get('_line_no', 'x')}" + ) + + +def strip_internal_fields(event: dict[str, Any]) -> dict[str, Any]: + return {k: v for k, v in event.items() if not k.startswith("_")} + + +def call_deepseek( + messages: list[dict[str, str]], + args: argparse.Namespace, + api_key: str, +) -> dict[str, Any]: + url = args.base_url.rstrip("/") + "/chat/completions" + payload: dict[str, Any] = { + "model": args.model, + "messages": messages, + "temperature": args.temperature, + "max_tokens": args.max_tokens, + } + if args.json_mode: + payload["response_format"] = {"type": "json_object"} + + body = json.dumps(payload).encode("utf-8") + request = urllib.request.Request( + url, + data=body, + headers={ + "Authorization": f"Bearer {api_key}", + "Content-Type": "application/json", + }, + method="POST", + ) + + last_error: Exception | None = None + for attempt in range(args.retries + 1): + try: + with urllib.request.urlopen(request, timeout=args.timeout_sec) as response: + return json.loads(response.read().decode("utf-8")) + except urllib.error.HTTPError as exc: + content = exc.read().decode("utf-8", errors="replace") + last_error = RuntimeError(f"HTTP {exc.code}: {content}") + except Exception as exc: # noqa: BLE001 - keep retry handling simple. + last_error = exc + if attempt < args.retries: + sleep_sec = args.sleep_sec * (2 ** attempt) + print(f"[deepseek] retry {attempt + 1}/{args.retries} after error: {last_error}", flush=True) + time.sleep(sleep_sec) + + raise RuntimeError(f"DeepSeek request failed: {last_error}") + + +def parse_llm_content(response: dict[str, Any]) -> dict[str, Any]: + try: + content = response["choices"][0]["message"]["content"] + except (KeyError, IndexError, TypeError) as exc: + raise ValueError(f"Unexpected API response shape: {response}") from exc + + content = content.strip() + if content.startswith("```"): + lines = content.splitlines() + if lines and lines[0].startswith("```"): + lines = lines[1:] + if lines and lines[-1].startswith("```"): + lines = lines[:-1] + content = "\n".join(lines).strip() + + data = json.loads(content) + if isinstance(data, list): + return {"evaluations": data} + if "evaluations" not in data: + return {"evaluations": [data]} + return data + + +def normalize_llm_eval( + event: dict[str, Any], + rule_eval: dict[str, Any], + llm_eval: dict[str, Any] | None, +) -> dict[str, Any]: + if llm_eval is None: + return { + "valid": rule_eval["valid"], + "priority": rule_eval["priority"], + "score": rule_eval["score"], + "event_type": rule_eval["event_type"], + "reason": "dry_run_rule_based_eval", + "reject_reason": ";".join(rule_eval["reject_reasons"]) or None, + "needs_trace_review": rule_eval["needs_trace_review"], + "reward_search_feedback": rule_eval["reward_search_feedback"], + } + + feedback = llm_eval.get("reward_search_feedback") or {} + return { + "valid": bool(llm_eval.get("valid", rule_eval["valid"])), + "priority": str(llm_eval.get("priority", rule_eval["priority"])), + "score": as_float(llm_eval.get("score"), rule_eval["score"]), + "event_type": normalize_event_types(llm_eval.get("event_type", rule_eval["event_type"])), + "reason": str(llm_eval.get("reason", "")), + "reject_reason": llm_eval.get("reject_reason"), + "needs_trace_review": bool(llm_eval.get("needs_trace_review", rule_eval["needs_trace_review"])), + "reward_search_feedback": { + "increase_collision_tolerance": bool(feedback.get("increase_collision_tolerance", False)), + "increase_goal_pressure": bool(feedback.get("increase_goal_pressure", False)), + "increase_offroad_penalty": bool(feedback.get("increase_offroad_penalty", False)), + "reduce_goal_pressure": bool(feedback.get("reduce_goal_pressure", False)), + }, + } + + +def chunked(records: list[Any], size: int) -> list[list[Any]]: + return [records[i : i + size] for i in range(0, len(records), size)] + + +def normalize_event_types(value: Any) -> list[str]: + if value is None: + return [] + if isinstance(value, str): + return [value] + if isinstance(value, list): + return [str(item) for item in value] + return [str(value)] + + +def final_validity(final_eval: dict[str, Any], accept_threshold: float) -> bool: + if not bool(final_eval["valid"]): + return False + if final_eval["priority"] == "reject_or_debug_only": + return False + return as_float(final_eval["score"]) >= accept_threshold + + +def summarize_results( + records: list[dict[str, Any]], + args: argparse.Namespace, +) -> dict[str, Any]: + accepted = [r for r in records if r["final_valid"]] + rejected = [r for r in records if not r["final_valid"]] + feedback_counts = { + "increase_collision_tolerance": 0, + "increase_goal_pressure": 0, + "increase_offroad_penalty": 0, + "reduce_goal_pressure": 0, + } + priority_counts: dict[str, int] = {} + event_type_counts: dict[str, int] = {} + + for record in records: + final_eval = record["llm_eval"] + priority = str(final_eval.get("priority", "unknown")) + priority_counts[priority] = priority_counts.get(priority, 0) + 1 + for event_type in final_eval.get("event_type", []) or []: + event_type_counts[event_type] = event_type_counts.get(event_type, 0) + 1 + for key in feedback_counts: + if final_eval.get("reward_search_feedback", {}).get(key): + feedback_counts[key] += 1 + + suggestions = suggest_reward_conditions(feedback_counts, len(records), args) + return { + "total_evaluated": len(records), + "accepted_count": len(accepted), + "rejected_count": len(rejected), + "accept_rate": len(accepted) / len(records) if records else 0.0, + "priority_counts": priority_counts, + "event_type_counts": event_type_counts, + "feedback_counts": feedback_counts, + "current_reward_condition": [ + args.current_collision_weight, + args.current_goal_weight, + args.current_offroad_weight, + ], + "suggested_reward_conditions": suggestions, + } + + +def clamp(value: float, lower: float, upper: float) -> float: + return min(max(value, lower), upper) + + +def suggest_reward_conditions( + feedback_counts: dict[str, int], + total: int, + args: argparse.Namespace, +) -> list[dict[str, Any]]: + if total <= 0: + return [] + + collision = args.current_collision_weight + goal = args.current_goal_weight + offroad = args.current_offroad_weight + offroad_rate = feedback_counts["increase_offroad_penalty"] / total + collision_rate = feedback_counts["increase_collision_tolerance"] / total + goal_rate = feedback_counts["increase_goal_pressure"] / total + reduce_goal_rate = feedback_counts["reduce_goal_pressure"] / total + + suggestions = [] + if offroad_rate >= 0.3: + suggestions.append( + { + "name": "more_on_road_risk_taker", + "reward_condition": [ + collision, + goal, + round(clamp(offroad - 0.2, -1.0, 0.0), 3), + ], + "reason": "Many events need stronger off-road penalty.", + } + ) + if collision_rate >= 0.3 or goal_rate >= 0.3: + suggestions.append( + { + "name": "higher_interaction_pressure", + "reward_condition": [ + round(clamp(collision + 0.15, -1.0, 0.0), 3), + round(clamp(goal + 0.1, 1.0, 2.0), 3), + offroad, + ], + "reason": "More collision tolerance or goal pressure may increase interactions.", + } + ) + if reduce_goal_rate >= 0.2: + suggestions.append( + { + "name": "less_early_collision_pressure", + "reward_condition": [ + round(clamp(collision - 0.15, -1.0, 0.0), 3), + round(clamp(goal - 0.2, 1.0, 2.0), 3), + offroad, + ], + "reason": "Some events look too early or artifact-like.", + } + ) + if not suggestions: + suggestions.append( + { + "name": "local_exploitation", + "reward_condition": [collision, goal, offroad], + "reason": "Keep current condition and sample nearby perturbations.", + } + ) + return suggestions + + +def main() -> None: + args = parse_args() + output_dir = Path(args.output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + + events = read_jsonl(args.events) + events = events[args.start_index :] + if args.max_events > 0: + events = events[: args.max_events] + rules_md = read_text(args.rules) + summary = read_json(args.summary) if args.summary else None + api_key = os.environ.get(args.api_key_env, "") + if not args.dry_run and not api_key: + raise RuntimeError(f"Missing API key. Set {args.api_key_env}.") + + all_path = output_dir / "all_evaluations.jsonl" + accepted_path = output_dir / "accepted_events.jsonl" + rejected_path = output_dir / "rejected_events.jsonl" + for path in [all_path, accepted_path, rejected_path]: + if path.exists(): + path.unlink() + + final_records: list[dict[str, Any]] = [] + total_batches = len(chunked(events, max(args.batch_size, 1))) + for batch_idx, batch in enumerate(chunked(events, max(args.batch_size, 1)), start=1): + rule_evals = [rule_based_eval(event) for event in batch] + llm_evals: list[dict[str, Any] | None] + + if args.dry_run: + llm_evals = [None for _ in batch] + else: + messages = build_prompt(rules_md, batch, rule_evals, summary) + if args.save_prompts: + write_json(output_dir / f"prompt_batch_{batch_idx:04d}.json", messages) + response = call_deepseek(messages, args, api_key) + parsed = parse_llm_content(response) + raw_evals = parsed.get("evaluations", []) + eval_by_index = { + as_int(item.get("local_index"), idx): item + for idx, item in enumerate(raw_evals) + if isinstance(item, dict) + } + llm_evals = [eval_by_index.get(i) for i in range(len(batch))] + + for event, rule_eval, llm_eval in zip(batch, rule_evals, llm_evals): + final_eval = normalize_llm_eval(event, rule_eval, llm_eval) + out_record = { + "event_key": event_key(event), + "event": strip_internal_fields(event), + "rule_eval": rule_eval, + "llm_eval": final_eval, + "final_valid": final_validity(final_eval, args.accept_threshold), + "final_score": final_eval["score"], + "final_priority": final_eval["priority"], + } + append_jsonl(all_path, out_record) + append_jsonl(accepted_path if out_record["final_valid"] else rejected_path, out_record) + final_records.append(out_record) + + print( + "[eval]", + f"batch={batch_idx}/{total_batches}", + f"events={len(batch)}", + f"accepted_so_far={sum(1 for r in final_records if r['final_valid'])}", + flush=True, + ) + if not args.dry_run and args.sleep_sec > 0: + time.sleep(args.sleep_sec) + + summary_out = summarize_results(final_records, args) + summary_out.update( + { + "events_path": str(Path(args.events)), + "rules_path": str(Path(args.rules)), + "model": args.model if not args.dry_run else "dry_run_rule_based", + "base_url": args.base_url if not args.dry_run else None, + "output_dir": str(output_dir), + } + ) + write_json(output_dir / "summary.json", summary_out) + print(json.dumps(summary_out, indent=2), flush=True) + + +if __name__ == "__main__": + main() diff --git a/gpudrive-main/search/evaluate_llm_cases_deepseek.py b/gpudrive-main/search/evaluate_llm_cases_deepseek.py new file mode 100644 index 0000000000000000000000000000000000000000..51c1dd57d71662cc87ce7b91349b3808366e9514 --- /dev/null +++ b/gpudrive-main/search/evaluate_llm_cases_deepseek.py @@ -0,0 +1,1128 @@ +#!/usr/bin/env python +"""Evaluator Agent for LLM-readable GPUDrive long-tail cases. + +Inputs: + llm_cases.jsonl from search/convert_event_traces_to_llm_cases.py + +Outputs: + all_evaluations.jsonl + accepted_cases.jsonl + rejected_cases.jsonl + optimizer_metrics.json + summary.json + +The evaluator combines deterministic evidence-based scoring with optional +DeepSeek semantic review. The aggregate optimizer metrics are designed to feed +the next closed-loop Optimizer Agent. +""" + +from __future__ import annotations + +import argparse +import json +import math +import os +import time +import urllib.error +import urllib.request +from collections import Counter, defaultdict +from pathlib import Path +from statistics import mean +from typing import Any + + +DEFAULT_BASE_URL = "https://api.deepseek.com" +DEFAULT_MODEL = "deepseek-v4-flash" + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser() + parser.add_argument("--cases", default="search_outputs/llm_cases_shard_000/llm_cases.jsonl") + parser.add_argument("--rules", default="rules/longtail_llm_case_evaluation.md") + parser.add_argument("--output-dir", default="search_outputs/evaluator_agent/shard_000") + parser.add_argument("--max-cases", type=int, default=0) + parser.add_argument("--start-index", type=int, default=0) + parser.add_argument("--batch-size", type=int, default=3) + parser.add_argument("--accept-threshold", type=float, default=70.0) + parser.add_argument("--high-value-threshold", type=float, default=82.0) + parser.add_argument("--dry-run", type=int, default=0) + parser.add_argument("--resume", type=int, default=1) + parser.add_argument("--write-partial-summary", type=int, default=1) + parser.add_argument("--save-prompts", type=int, default=0) + parser.add_argument("--save-raw-responses", type=int, default=1) + parser.add_argument("--fallback-on-parse-error", type=int, default=1) + parser.add_argument("--retry-single-on-parse-error", type=int, default=1) + parser.add_argument("--base-url", default=os.environ.get("DEEPSEEK_BASE_URL", DEFAULT_BASE_URL)) + parser.add_argument("--model", default=os.environ.get("DEEPSEEK_MODEL", DEFAULT_MODEL)) + parser.add_argument("--api-key-env", default="DEEPSEEK_API_KEY") + parser.add_argument("--temperature", type=float, default=0.0) + parser.add_argument("--max-tokens", type=int, default=8192) + parser.add_argument("--timeout-sec", type=float, default=120.0) + parser.add_argument("--retries", type=int, default=3) + parser.add_argument("--sleep-sec", type=float, default=0.5) + parser.add_argument("--json-mode", type=int, default=1) + parser.add_argument("--current-collision-weight", type=float, default=0.0) + parser.add_argument("--current-goal-weight", type=float, default=2.0) + parser.add_argument("--current-offroad-weight", type=float, default=-0.4) + parser.add_argument("--current-risk-agents-per-world", type=int, default=3) + parser.add_argument("--current-normal-mode", default="policy") + parser.add_argument("--current-deterministic", type=int, default=0) + parser.add_argument("--current-normal-style", default="balanced") + parser.add_argument("--current-risk-style", default="risk_taker") + return parser.parse_args() + + +def read_text(path: str | Path) -> str: + with open(path, "r", encoding="utf-8") as f: + return f.read() + + +def read_jsonl(path: str | Path, start_index: int = 0, max_records: int = 0) -> list[dict[str, Any]]: + records = [] + with open(path, "r", encoding="utf-8") as f: + for line_idx, line in enumerate(f): + if line_idx < start_index: + continue + if max_records and len(records) >= max_records: + break + line = line.strip() + if not line: + continue + record = json.loads(line) + record["_line_index"] = line_idx + records.append(record) + return records + + +def write_json(path: str | Path, data: Any) -> None: + with open(path, "w", encoding="utf-8") as f: + json.dump(data, f, ensure_ascii=False, indent=2) + + +def append_jsonl(path: str | Path, record: dict[str, Any]) -> None: + with open(path, "a", encoding="utf-8") as f: + f.write(json.dumps(record, ensure_ascii=False) + "\n") + + +def as_float(value: Any, default: float = 0.0) -> float: + try: + out = float(value) + except (TypeError, ValueError): + return default + return out if math.isfinite(out) else default + + +def as_int(value: Any, default: int = 0) -> int: + try: + return int(value) + except (TypeError, ValueError): + return default + + +def clamp(value: float, lower: float, upper: float) -> float: + return min(max(value, lower), upper) + + +def score_from_thresholds(value: float | None, thresholds: list[tuple[float, float]], default: float = 0.0) -> float: + if value is None: + return default + for threshold, score in thresholds: + if value <= threshold: + return score + return default + + +def get_nested(data: dict[str, Any], path: list[str], default: Any = None) -> Any: + cur: Any = data + for key in path: + if not isinstance(cur, dict) or key not in cur: + return default + cur = cur[key] + return cur + + +def normalize_scenario_type(value: Any) -> str: + text = str(value or "unknown").lower() + if "cut" in text or "side" in text: + return "cut_in_or_side_conflict" + if "rear" in text or "following" in text: + return "rear_end_or_following_conflict" + if "cross" in text or "intersection" in text: + return "crossing_or_intersection_conflict" + if "near" in text: + return "near_miss" + if "artifact" in text: + return "artifact_or_debug" + if "generic" in text: + return "generic_close_interaction" + return text + + +def danger_level(criticality_score: float) -> int: + if criticality_score >= 85: + return 5 + if criticality_score >= 70: + return 4 + if criticality_score >= 50: + return 3 + if criticality_score >= 25: + return 2 + return 1 + + +def local_case_eval(case: dict[str, Any]) -> dict[str, Any]: + outcome = case.get("outcome", {}) + primary = case.get("primary_pair", {}) + interaction = case.get("primary_interaction_metrics", {}) + natural = case.get("naturalness_checks", {}) + checks = case.get("interaction_checks", {}) + + collision = bool(outcome.get("collision")) + near_miss = bool(outcome.get("near_miss")) + offroad = bool(outcome.get("offroad")) + clean_distance = maybe_float(interaction.get("min_clean_distance_m")) + min_ttc = maybe_float(interaction.get("min_ttc_s")) + ttc_1s = maybe_float(interaction.get("time_ttc_below_1s")) or 0.0 + event_metric = interaction.get("metrics_at_event_or_nearest_clean_frame", {}) or {} + rel_speed = maybe_float(event_metric.get("relative_speed_mps")) + closing_speed = maybe_float(event_metric.get("closing_speed_mps")) + distance_trend = get_nested(interaction, ["distance_trend_before_event", "label"], "unknown") + artifact_warnings = [str(item) for item in natural.get("artifact_warnings", []) or []] + semantic = normalize_scenario_type(primary.get("semantic_candidate")) + + criticality = 0.0 + if collision: + criticality += 35.0 + if near_miss: + criticality += 12.0 + criticality += score_from_thresholds( + clean_distance, + [(0.75, 18.0), (1.5, 15.0), (2.5, 11.0), (4.0, 6.0)], + 0.0, + ) + criticality += score_from_thresholds( + min_ttc, + [(0.5, 18.0), (1.0, 15.0), (2.0, 10.0), (3.0, 5.0)], + 0.0, + ) + if ttc_1s >= 0.5: + criticality += min(8.0, ttc_1s * 6.0) + if rel_speed is not None: + criticality += min(7.0, max(0.0, rel_speed)) + if closing_speed is not None and closing_speed > 0: + criticality += min(5.0, closing_speed) + criticality = clamp(criticality, 0.0, 100.0) + + naturalness = 100.0 + penalty_map = { + "large_frame_displacement": 45.0, + "extreme_speed": 35.0, + "offroad_before_collision": 20.0, + "short_clean_track": 12.0, + "high_acceleration": 10.0, + "hard_deceleration": 10.0, + "high_yaw_rate": 8.0, + } + for flag in artifact_warnings: + naturalness -= penalty_map.get(flag, 6.0) + if offroad and not collision and not near_miss: + naturalness -= 20.0 + naturalness = clamp(naturalness, 0.0, 100.0) + + interaction_score = 15.0 + if checks.get("risk_agent_in_primary_pair"): + interaction_score += 25.0 + if primary.get("risk_directly_involved"): + interaction_score += 15.0 + if checks.get("primary_pair_has_collision_agent"): + interaction_score += 15.0 + if distance_trend == "mostly_decreasing": + interaction_score += 15.0 + elif distance_trend == "mixed": + interaction_score += 7.0 + if min_ttc is not None and min_ttc <= 3.0: + interaction_score += 8.0 + if interaction.get("num_clean_pair_frames", 0) >= 10: + interaction_score += 7.0 + interaction_score = clamp(interaction_score, 0.0, 100.0) + + scenario_value = clamp(0.42 * criticality + 0.28 * naturalness + 0.30 * interaction_score, 0.0, 100.0) + + hard_rejects = [] + soft_reasons = [] + if clean_distance is None: + hard_rejects.append("missing_clean_distance") + if "large_frame_displacement" in artifact_warnings: + hard_rejects.append("large_frame_displacement_artifact") + if "extreme_speed" in artifact_warnings and naturalness < 55: + hard_rejects.append("extreme_speed_artifact") + if not checks.get("risk_agent_in_primary_pair"): + soft_reasons.append("risk_agent_not_in_primary_pair") + if not checks.get("primary_pair_has_collision_agent") and collision: + soft_reasons.append("primary_pair_not_collision_pair") + if distance_trend == "not_decreasing": + soft_reasons.append("distance_not_decreasing") + if offroad and "offroad_before_collision" in artifact_warnings: + soft_reasons.append("offroad_before_collision") + if interaction.get("num_clean_pair_frames", 0) < 5: + soft_reasons.append("too_few_clean_pair_frames") + + accepted = scenario_value >= 70.0 and not hard_rejects + if scenario_value < 55.0: + priority = "reject_or_debug_only" + elif scenario_value < 70.0: + priority = "low_value" + elif scenario_value < 82.0: + priority = "medium_value" + else: + priority = "high_value" + + return { + "accepted": accepted, + "priority": priority, + "danger_level": danger_level(criticality), + "criticality_score": round(criticality, 3), + "naturalness_score": round(naturalness, 3), + "interaction_score": round(interaction_score, 3), + "scenario_value_score": round(scenario_value, 3), + "scenario_type": semantic, + "artifact_flags": artifact_warnings, + "rejection_reasons": hard_rejects + soft_reasons, + "optimization_feedback": local_optimization_feedback(case, scenario_value, hard_rejects, soft_reasons), + "reason": local_reason(case, criticality, naturalness, interaction_score, scenario_value, hard_rejects, soft_reasons), + "evidence": local_evidence(case), + } + + +def maybe_float(value: Any) -> float | None: + if value is None: + return None + try: + out = float(value) + except (TypeError, ValueError): + return None + return out if math.isfinite(out) else None + + +def local_optimization_feedback( + case: dict[str, Any], + scenario_value: float, + hard_rejects: list[str], + soft_reasons: list[str], +) -> dict[str, Any]: + outcome = case.get("outcome", {}) + natural = case.get("naturalness_checks", {}) + primary = case.get("primary_pair", {}) + interaction = case.get("primary_interaction_metrics", {}) + warnings = set(natural.get("artifact_warnings", []) or []) + distance_trend = get_nested(interaction, ["distance_trend_before_event", "label"], "unknown") + scenario_type = normalize_scenario_type(primary.get("semantic_candidate")) + + return { + "increase_collision_tolerance": scenario_value < 70 and not outcome.get("collision") and not hard_rejects, + "decrease_collision_tolerance": "extreme_speed_artifact" in hard_rejects or "large_frame_displacement_artifact" in hard_rejects, + "increase_goal_pressure": scenario_value < 70 and distance_trend in {"not_decreasing", "insufficient_clean_frames"}, + "decrease_goal_pressure": "offroad_before_collision" in warnings or "too_few_clean_pair_frames" in soft_reasons, + "increase_offroad_penalty": "offroad_before_collision" in warnings or (outcome.get("offroad") and not outcome.get("collision")), + "relax_offroad_penalty": False, + "increase_risk_agents_per_world": not bool(primary.get("risk_directly_involved")) and scenario_value < 70, + "decrease_risk_agents_per_world": "large_frame_displacement_artifact" in hard_rejects, + "target_scenario_types": [scenario_type] if scenario_value >= 70 else [], + } + + +def local_reason( + case: dict[str, Any], + criticality: float, + naturalness: float, + interaction_score: float, + scenario_value: float, + hard_rejects: list[str], + soft_reasons: list[str], +) -> str: + primary = case.get("primary_pair", {}) + if hard_rejects: + return f"Rejected by deterministic artifact checks: {', '.join(hard_rejects)}." + return ( + f"Primary pair {primary.get('agent_a')}->{primary.get('agent_b')} scored " + f"criticality={criticality:.1f}, naturalness={naturalness:.1f}, " + f"interaction={interaction_score:.1f}, value={scenario_value:.1f}; " + f"notes={', '.join(soft_reasons) if soft_reasons else 'none'}." + ) + + +def local_evidence(case: dict[str, Any]) -> list[str]: + outcome = case.get("outcome", {}) + primary = case.get("primary_pair", {}) + interaction = case.get("primary_interaction_metrics", {}) + metric = interaction.get("metrics_at_event_or_nearest_clean_frame", {}) or {} + evidence = [ + f"collision={outcome.get('collision')} near_miss={outcome.get('near_miss')} offroad={outcome.get('offroad')}", + f"primary_pair={primary.get('agent_a')}->{primary.get('agent_b')} reason={primary.get('selection_reason')}", + f"clean_min_distance={interaction.get('min_clean_distance_m')} min_ttc={interaction.get('min_ttc_s')}", + f"event_relative_speed={metric.get('relative_speed_mps')} closing_speed={metric.get('closing_speed_mps')}", + f"distance_trend={get_nested(interaction, ['distance_trend_before_event', 'label'], 'unknown')}", + ] + return evidence + + +def compact_case_for_llm(case: dict[str, Any], local_eval: dict[str, Any]) -> dict[str, Any]: + return { + "case_id": case.get("case_id"), + "source": case.get("source"), + "outcome": case.get("outcome"), + "primary_pair": case.get("primary_pair"), + "scene_summary": case.get("scene_summary"), + "primary_interaction_metrics": case.get("primary_interaction_metrics"), + "naturalness_checks": case.get("naturalness_checks"), + "interaction_checks": case.get("interaction_checks"), + "context_agent_summaries": case.get("context_agent_summaries"), + "timeline": case.get("timeline"), + "local_eval": local_eval, + } + + +def build_prompt( + rules_md: str, + cases: list[dict[str, Any]], + local_evals: list[dict[str, Any]], +) -> list[dict[str, str]]: + compact_cases = [ + compact_case_for_llm(case, local_eval) + for case, local_eval in zip(cases, local_evals) + ] + payload = { + "task": "Evaluate compact GPUDrive long-tail scenario cases for a closed-loop generator optimizer.", + "rules_markdown": rules_md, + "cases": compact_cases, + "output_contract": { + "evaluations": [ + { + "case_id": "copy from input", + "accepted": True, + "danger_level": 4, + "naturalness_score": 80, + "interaction_score": 85, + "scenario_value_score": 88, + "scenario_type": "cut_in_or_side_conflict", + "artifact_flags": [], + "rejection_reasons": [], + "optimization_feedback": { + "increase_collision_tolerance": False, + "decrease_collision_tolerance": False, + "increase_goal_pressure": False, + "decrease_goal_pressure": False, + "increase_offroad_penalty": False, + "relax_offroad_penalty": False, + "increase_risk_agents_per_world": False, + "decrease_risk_agents_per_world": False, + "target_scenario_types": ["crossing_or_intersection_conflict"], + }, + "reason": "short evidence-grounded reason", + "evidence": ["specific field-based evidence"], + } + ] + }, + } + system = ( + "You are the Evaluator Agent in a closed-loop autonomous-driving " + "scenario generation system. Be strict, evidence-grounded, and JSON-only. " + "Use deterministic local metrics as evidence, but correct their judgment " + "when the timeline supports a different semantic interpretation." + ) + return [ + {"role": "system", "content": system}, + {"role": "user", "content": json.dumps(payload, ensure_ascii=False)}, + ] + + +def call_deepseek( + messages: list[dict[str, str]], + args: argparse.Namespace, + api_key: str, +) -> dict[str, Any]: + url = args.base_url.rstrip("/") + "/chat/completions" + payload: dict[str, Any] = { + "model": args.model, + "messages": messages, + "temperature": args.temperature, + "max_tokens": args.max_tokens, + } + if args.json_mode: + payload["response_format"] = {"type": "json_object"} + + body = json.dumps(payload).encode("utf-8") + request = urllib.request.Request( + url, + data=body, + headers={ + "Authorization": f"Bearer {api_key}", + "Content-Type": "application/json", + }, + method="POST", + ) + + last_error: Exception | None = None + for attempt in range(args.retries + 1): + try: + with urllib.request.urlopen(request, timeout=args.timeout_sec) as response: + return json.loads(response.read().decode("utf-8")) + except urllib.error.HTTPError as exc: + content = exc.read().decode("utf-8", errors="replace") + last_error = RuntimeError(f"HTTP {exc.code}: {content}") + except Exception as exc: # noqa: BLE001 - retry any transport issue. + last_error = exc + if attempt < args.retries: + sleep_sec = args.sleep_sec * (2 ** attempt) + print(f"[deepseek] retry {attempt + 1}/{args.retries}: {last_error}", flush=True) + time.sleep(sleep_sec) + raise RuntimeError(f"DeepSeek request failed: {last_error}") + + +def parse_llm_content(response: dict[str, Any]) -> dict[str, Any]: + try: + content = response["choices"][0]["message"]["content"] + except (KeyError, IndexError, TypeError) as exc: + raise ValueError(f"Unexpected API response shape: {response}") from exc + content = str(content).strip() + if content.startswith("```"): + lines = content.splitlines() + if lines and lines[0].startswith("```"): + lines = lines[1:] + if lines and lines[-1].startswith("```"): + lines = lines[:-1] + content = "\n".join(lines).strip() + data = json.loads(content) + if isinstance(data, list): + return {"evaluations": data} + if "evaluations" not in data: + return {"evaluations": [data]} + return data + + +def llm_content(response: dict[str, Any]) -> str: + try: + return str(response["choices"][0]["message"]["content"]) + except (KeyError, IndexError, TypeError): + return json.dumps(response, ensure_ascii=False) + + +def save_raw_response(output_dir: Path, batch_idx: int, suffix: str, response: dict[str, Any], error: Exception | None = None) -> None: + raw_dir = output_dir / "raw_responses" + raw_dir.mkdir(parents=True, exist_ok=True) + payload = { + "batch_idx": batch_idx, + "suffix": suffix, + "error": str(error) if error else None, + "content": llm_content(response), + "response": response, + } + write_json(raw_dir / f"batch_{batch_idx:04d}_{suffix}.json", payload) + + +def get_llm_evals_for_batch( + rules_md: str, + batch: list[dict[str, Any]], + local_evals: list[dict[str, Any]], + batch_idx: int, + output_dir: Path, + args: argparse.Namespace, + api_key: str, +) -> list[dict[str, Any] | None]: + messages = build_prompt(rules_md, batch, local_evals) + if args.save_prompts: + write_json(output_dir / f"prompt_batch_{batch_idx:04d}.json", messages) + + response = call_deepseek(messages, args, api_key) + if args.save_raw_responses: + save_raw_response(output_dir, batch_idx, "ok_raw", response) + try: + parsed = parse_llm_content(response) + except Exception as exc: # noqa: BLE001 - recover from malformed model JSON. + print( + "[evaluator] malformed JSON response:", + f"batch={batch_idx}", + f"cases={len(batch)}", + f"error={exc}", + flush=True, + ) + save_raw_response(output_dir, batch_idx, "parse_error", response, exc) + if args.retry_single_on_parse_error and len(batch) > 1: + return retry_batch_as_singletons(rules_md, batch, local_evals, batch_idx, output_dir, args, api_key) + if args.fallback_on_parse_error: + return [None for _ in batch] + raise + + raw_evals = parsed.get("evaluations", []) + eval_by_case_id = { + str(item.get("case_id")): item + for item in raw_evals + if isinstance(item, dict) + } + return [eval_by_case_id.get(str(case.get("case_id"))) for case in batch] + + +def retry_batch_as_singletons( + rules_md: str, + batch: list[dict[str, Any]], + local_evals: list[dict[str, Any]], + batch_idx: int, + output_dir: Path, + args: argparse.Namespace, + api_key: str, +) -> list[dict[str, Any] | None]: + out: list[dict[str, Any] | None] = [] + for local_idx, (case, local_eval) in enumerate(zip(batch, local_evals)): + messages = build_prompt(rules_md, [case], [local_eval]) + if args.save_prompts: + write_json(output_dir / f"prompt_batch_{batch_idx:04d}_case_{local_idx:02d}.json", messages) + response = call_deepseek(messages, args, api_key) + if args.save_raw_responses: + save_raw_response(output_dir, batch_idx, f"case_{local_idx:02d}_raw", response) + try: + parsed = parse_llm_content(response) + raw_evals = parsed.get("evaluations", []) + eval_by_case_id = { + str(item.get("case_id")): item + for item in raw_evals + if isinstance(item, dict) + } + out.append(eval_by_case_id.get(str(case.get("case_id")))) + except Exception as exc: # noqa: BLE001 - keep shard alive. + print( + "[evaluator] malformed singleton JSON response:", + f"batch={batch_idx}", + f"case_idx={local_idx}", + f"case_id={case.get('case_id')}", + f"error={exc}", + flush=True, + ) + save_raw_response(output_dir, batch_idx, f"case_{local_idx:02d}_parse_error", response, exc) + if args.fallback_on_parse_error: + out.append(None) + else: + raise + return out + + +def normalize_feedback(feedback: Any) -> dict[str, Any]: + feedback = feedback if isinstance(feedback, dict) else {} + return { + "increase_collision_tolerance": bool(feedback.get("increase_collision_tolerance", False)), + "decrease_collision_tolerance": bool(feedback.get("decrease_collision_tolerance", False)), + "increase_goal_pressure": bool(feedback.get("increase_goal_pressure", False)), + "decrease_goal_pressure": bool(feedback.get("decrease_goal_pressure", False)), + "increase_offroad_penalty": bool(feedback.get("increase_offroad_penalty", False)), + "relax_offroad_penalty": bool(feedback.get("relax_offroad_penalty", False)), + "increase_risk_agents_per_world": bool(feedback.get("increase_risk_agents_per_world", False)), + "decrease_risk_agents_per_world": bool(feedback.get("decrease_risk_agents_per_world", False)), + "target_scenario_types": normalize_list(feedback.get("target_scenario_types", [])), + } + + +def normalize_list(value: Any) -> list[str]: + if value is None: + return [] + if isinstance(value, str): + return [value] + if isinstance(value, list): + return [str(item) for item in value] + return [str(value)] + + +def normalize_llm_eval( + case: dict[str, Any], + local_eval: dict[str, Any], + raw_eval: dict[str, Any] | None, +) -> dict[str, Any]: + if raw_eval is None: + return dict(local_eval) + return { + "accepted": bool(raw_eval.get("accepted", local_eval["accepted"])), + "priority": priority_from_value(as_float(raw_eval.get("scenario_value_score"), local_eval["scenario_value_score"])), + "danger_level": as_int(raw_eval.get("danger_level"), local_eval["danger_level"]), + "criticality_score": local_eval.get("criticality_score"), + "naturalness_score": clamp(as_float(raw_eval.get("naturalness_score"), local_eval["naturalness_score"]), 0, 100), + "interaction_score": clamp(as_float(raw_eval.get("interaction_score"), local_eval["interaction_score"]), 0, 100), + "scenario_value_score": clamp(as_float(raw_eval.get("scenario_value_score"), local_eval["scenario_value_score"]), 0, 100), + "scenario_type": normalize_scenario_type(raw_eval.get("scenario_type", local_eval["scenario_type"])), + "artifact_flags": normalize_list(raw_eval.get("artifact_flags", local_eval["artifact_flags"])), + "rejection_reasons": normalize_list(raw_eval.get("rejection_reasons", local_eval["rejection_reasons"])), + "optimization_feedback": normalize_feedback(raw_eval.get("optimization_feedback", local_eval["optimization_feedback"])), + "reason": str(raw_eval.get("reason", local_eval["reason"])), + "evidence": normalize_list(raw_eval.get("evidence", local_eval["evidence"])), + } + + +def priority_from_value(value: float) -> str: + if value >= 82: + return "high_value" + if value >= 70: + return "medium_value" + if value >= 55: + return "low_value" + return "reject_or_debug_only" + + +def final_eval_from_local_and_llm( + local_eval: dict[str, Any], + llm_eval: dict[str, Any], + args: argparse.Namespace, +) -> dict[str, Any]: + if args.dry_run: + final = dict(local_eval) + else: + final = dict(llm_eval) + # Keep local hard artifacts visible even when the LLM is optimistic. + local_hard = { + reason + for reason in local_eval.get("rejection_reasons", []) + if "artifact" in reason or reason == "missing_clean_distance" + } + if local_hard: + final["accepted"] = False + final["rejection_reasons"] = sorted(set(final.get("rejection_reasons", []) + list(local_hard))) + final["scenario_value_score"] = min(as_float(final.get("scenario_value_score"), 0), 69.0) + final["priority"] = priority_from_value(final["scenario_value_score"]) + + final["accepted"] = bool(final.get("accepted")) and as_float(final.get("scenario_value_score"), 0) >= args.accept_threshold + final["high_value"] = bool(final["accepted"]) and as_float(final.get("scenario_value_score"), 0) >= args.high_value_threshold + final["priority"] = priority_from_value(as_float(final.get("scenario_value_score"), 0)) + return final + + +def chunked(records: list[Any], size: int) -> list[list[Any]]: + return [records[i : i + size] for i in range(0, len(records), size)] + + +def stats(values: list[float]) -> dict[str, Any]: + values = [float(v) for v in values if math.isfinite(float(v))] + if not values: + return {"count": 0} + values_sorted = sorted(values) + n = len(values_sorted) + return { + "count": n, + "min": round(values_sorted[0], 4), + "p25": round(values_sorted[n // 4], 4), + "median": round(values_sorted[n // 2], 4), + "p75": round(values_sorted[(3 * n) // 4], 4), + "max": round(values_sorted[-1], 4), + "mean": round(mean(values_sorted), 4), + } + + +def entropy(counter: Counter[str]) -> float: + total = sum(counter.values()) + if total <= 0: + return 0.0 + value = 0.0 + for count in counter.values(): + p = count / total + value -= p * math.log(p + 1e-12) + max_entropy = math.log(max(len(counter), 1)) + return round(value / max_entropy, 4) if max_entropy > 0 else 0.0 + + +def summarize_for_optimizer(records: list[dict[str, Any]], args: argparse.Namespace) -> dict[str, Any]: + total = len(records) + accepted = [r for r in records if r["final_eval"]["accepted"]] + high_value = [r for r in records if r["final_eval"].get("high_value")] + rejected = [r for r in records if not r["final_eval"]["accepted"]] + + counters = { + "scenario_type": Counter(), + "scenario_type_accepted": Counter(), + "semantic_candidate": Counter(), + "primary_pair_reason": Counter(), + "artifact_flags": Counter(), + "rejection_reasons": Counter(), + "optimization_feedback": Counter(), + "outcomes": Counter(), + } + + score_values = defaultdict(list) + metric_values = defaultdict(list) + risk_direct_total = 0 + risk_direct_accepted = 0 + natural_critical = 0 + warning_record_count = 0 + hard_artifact_record_count = 0 + hard_artifact_flags = { + "large_frame_displacement", + "extreme_speed", + "offroad_before_collision", + } + + for record in records: + case = record["case"] + final = record["final_eval"] + outcome = case.get("outcome", {}) + primary = case.get("primary_pair", {}) + interaction = case.get("primary_interaction_metrics", {}) + natural = case.get("naturalness_checks", {}) + + scenario_type = final.get("scenario_type", "unknown") + counters["scenario_type"][scenario_type] += 1 + if final["accepted"]: + counters["scenario_type_accepted"][scenario_type] += 1 + counters["semantic_candidate"][primary.get("semantic_candidate", "unknown")] += 1 + counters["primary_pair_reason"][primary.get("selection_reason", "unknown")] += 1 + for flag in final.get("artifact_flags", []) or []: + counters["artifact_flags"][flag] += 1 + artifact_flags = set(final.get("artifact_flags", []) or []) + if artifact_flags: + warning_record_count += 1 + if artifact_flags & hard_artifact_flags: + hard_artifact_record_count += 1 + for reason in final.get("rejection_reasons", []) or []: + counters["rejection_reasons"][reason] += 1 + for key, value in final.get("optimization_feedback", {}).items(): + if isinstance(value, bool) and value: + counters["optimization_feedback"][key] += 1 + for name in ("collision", "near_miss", "offroad"): + if outcome.get(name): + counters["outcomes"][name] += 1 + + for key in ("criticality_score", "naturalness_score", "interaction_score", "scenario_value_score"): + if final.get(key) is not None: + score_values[key].append(as_float(final[key])) + metric_values["clean_min_distance_m"].append(as_float(interaction.get("min_clean_distance_m"), float("nan"))) + metric_values["min_ttc_s"].append(as_float(interaction.get("min_ttc_s"), float("nan"))) + metric_values["time_ttc_below_1s"].append(as_float(interaction.get("time_ttc_below_1s"), float("nan"))) + event_metric = interaction.get("metrics_at_event_or_nearest_clean_frame", {}) or {} + metric_values["event_relative_speed_mps"].append(as_float(event_metric.get("relative_speed_mps"), float("nan"))) + metric_values["event_closing_speed_mps"].append(as_float(event_metric.get("closing_speed_mps"), float("nan"))) + metric_values["max_speed_mps"].append(as_float(natural.get("max_speed_mps"), float("nan"))) + metric_values["max_accel_mps2"].append(as_float(natural.get("max_accel_mps2"), float("nan"))) + metric_values["max_yaw_rate_radps"].append(as_float(natural.get("max_yaw_rate_radps"), float("nan"))) + + if primary.get("risk_directly_involved"): + risk_direct_total += 1 + if final["accepted"]: + risk_direct_accepted += 1 + if ( + final["accepted"] + and as_float(final.get("criticality_score"), 0) >= 70 + and as_float(final.get("naturalness_score"), 0) >= 65 + ): + natural_critical += 1 + + accepted_rate = len(accepted) / total if total else 0.0 + warning_record_rate = warning_record_count / total if total else 0.0 + hard_artifact_record_rate = hard_artifact_record_count / total if total else 0.0 + artifact_flags_per_case = sum(counters["artifact_flags"].values()) / total if total else 0.0 + risk_direct_rate = risk_direct_total / total if total else 0.0 + risk_direct_accept_rate = risk_direct_accepted / len(accepted) if accepted else 0.0 + semantic_entropy = entropy(counters["scenario_type_accepted"] or counters["scenario_type"]) + composite = ( + 0.35 * accepted_rate + + 0.25 * (len(high_value) / total if total else 0.0) + + 0.20 * risk_direct_accept_rate + + 0.20 * semantic_entropy + - 0.15 * hard_artifact_record_rate + ) + + optimizer = { + "schema_version": "optimizer_feedback_v1", + "source_cases": str(Path(args.cases)), + "evaluator": { + "model": args.model if not args.dry_run else "dry_run_rule_based", + "dry_run": bool(args.dry_run), + "accept_threshold": args.accept_threshold, + "high_value_threshold": args.high_value_threshold, + }, + "current_search_parameters": { + "risk_collision_weight": args.current_collision_weight, + "risk_goal_weight": args.current_goal_weight, + "risk_offroad_weight": args.current_offroad_weight, + "risk_agents_per_world": args.current_risk_agents_per_world, + "normal_mode": args.current_normal_mode, + "deterministic": args.current_deterministic, + "normal_style": args.current_normal_style, + "risk_style": args.current_risk_style, + }, + "objective_metrics": { + "total_evaluated": total, + "accepted_count": len(accepted), + "rejected_count": len(rejected), + "high_value_count": len(high_value), + "accepted_rate": round(accepted_rate, 4), + "high_value_rate": round(len(high_value) / total, 4) if total else 0.0, + "natural_critical_count": natural_critical, + "natural_critical_rate": round(natural_critical / total, 4) if total else 0.0, + "risk_direct_involved_rate": round(risk_direct_rate, 4), + "risk_direct_accept_rate": round(risk_direct_accept_rate, 4), + "warning_record_count": warning_record_count, + "warning_record_rate": round(warning_record_rate, 4), + "hard_artifact_record_count": hard_artifact_record_count, + "hard_artifact_record_rate": round(hard_artifact_record_rate, 4), + "artifact_flags_per_case": round(artifact_flags_per_case, 4), + "semantic_diversity_entropy": semantic_entropy, + "composite_objective": round(clamp(composite, -1.0, 1.0), 4), + }, + "score_stats": {key: stats(values) for key, values in score_values.items()}, + "metric_stats": {key: stats(values) for key, values in metric_values.items()}, + "counts": {name: dict(counter) for name, counter in counters.items()}, + } + optimizer["suggested_next_search_parameters"] = suggest_next_parameters(optimizer) + optimizer["optimizer_ready_records"] = [ + compact_optimizer_record(record) + for record in sorted(records, key=lambda r: as_float(r["final_eval"].get("scenario_value_score"), 0), reverse=True)[:200] + ] + return optimizer + + +def compact_optimizer_record(record: dict[str, Any]) -> dict[str, Any]: + case = record["case"] + final = record["final_eval"] + return { + "case_id": case.get("case_id"), + "scenario_file": get_nested(case, ["source", "scenario_file"]), + "accepted": final.get("accepted"), + "high_value": final.get("high_value"), + "scenario_value_score": final.get("scenario_value_score"), + "danger_level": final.get("danger_level"), + "scenario_type": final.get("scenario_type"), + "artifact_flags": final.get("artifact_flags"), + "rejection_reasons": final.get("rejection_reasons"), + "risk_directly_involved": get_nested(case, ["primary_pair", "risk_directly_involved"]), + "primary_pair": { + "agent_a": get_nested(case, ["primary_pair", "agent_a"]), + "agent_b": get_nested(case, ["primary_pair", "agent_b"]), + "selection_reason": get_nested(case, ["primary_pair", "selection_reason"]), + }, + "optimizer_feedback": final.get("optimization_feedback"), + } + + +def suggest_next_parameters(optimizer: dict[str, Any]) -> list[dict[str, Any]]: + current = optimizer["current_search_parameters"] + metrics = optimizer["objective_metrics"] + counts = optimizer["counts"] + total = max(1, metrics["total_evaluated"]) + + offroad_artifact_rate = counts["artifact_flags"].get("offroad_before_collision", 0) / total + extreme_artifact_rate = ( + counts["artifact_flags"].get("large_frame_displacement", 0) + + counts["artifact_flags"].get("extreme_speed", 0) + ) / total + risk_direct_accept_rate = metrics["risk_direct_accept_rate"] + accepted_rate = metrics["accepted_rate"] + + base_collision = float(current["risk_collision_weight"]) + base_goal = float(current["risk_goal_weight"]) + base_offroad = float(current["risk_offroad_weight"]) + base_risk_agents = int(current["risk_agents_per_world"]) + + suggestions = [] + + def add(name: str, dc: float, dg: float, doff: float, dra: int, reason: str) -> None: + suggestions.append( + { + "name": name, + "risk_collision_weight": round(clamp(base_collision + dc, -1.0, 0.0), 3), + "risk_goal_weight": round(clamp(base_goal + dg, 0.5, 2.0), 3), + "risk_offroad_weight": round(clamp(base_offroad + doff, -1.2, 0.0), 3), + "risk_agents_per_world": max(1, min(8, base_risk_agents + dra)), + "normal_style": current["normal_style"], + "risk_style": current["risk_style"], + "reason": reason, + } + ) + + if offroad_artifact_rate >= 0.15: + add("more_on_road_risk", 0.0, -0.05, -0.2, 0, "Many events have offroad-before-collision artifacts.") + if extreme_artifact_rate >= 0.03: + add("less_extreme_motion", -0.1, -0.1, -0.1, -1, "Extreme-speed or displacement artifacts are present.") + if risk_direct_accept_rate < 0.45: + add("stronger_risk_involvement", 0.1, 0.05, 0.0, 1, "Accepted cases do not involve risk agents often enough.") + if accepted_rate < 0.25: + add("broader_search_pressure", 0.05, 0.0, 0.0, 1, "Accepted rate is low; increase interaction search breadth.") + if metrics["semantic_diversity_entropy"] < 0.65: + add("diversity_exploration", 0.0, -0.05, 0.0, 1, "Accepted scenario types are not diverse enough.") + + add("local_exploitation", 0.0, 0.0, 0.0, 0, "Keep the current setting as a local exploitation baseline.") + + # De-duplicate while preserving order. + seen = set() + unique = [] + for item in suggestions: + key = ( + item["risk_collision_weight"], + item["risk_goal_weight"], + item["risk_offroad_weight"], + item["risk_agents_per_world"], + ) + if key not in seen: + seen.add(key) + unique.append(item) + return unique + + +def load_existing_evaluations(path: Path) -> list[dict[str, Any]]: + if not path.exists(): + return [] + by_case_id: dict[str, dict[str, Any]] = {} + malformed = 0 + with path.open("r", encoding="utf-8") as f: + for line_no, line in enumerate(f, start=1): + line = line.strip() + if not line: + continue + try: + record = json.loads(line) + except json.JSONDecodeError: + malformed += 1 + continue + case_id = str(record.get("case_id") or get_nested(record, ["case", "case_id"], "")) + if not case_id: + malformed += 1 + continue + by_case_id[case_id] = record + if malformed: + print( + "[evaluator] resume skipped malformed existing lines:", + f"path={path}", + f"malformed={malformed}", + flush=True, + ) + return list(by_case_id.values()) + + +def rebuild_split_files( + records: list[dict[str, Any]], + accepted_path: Path, + rejected_path: Path, +) -> None: + for path in (accepted_path, rejected_path): + if path.exists(): + path.unlink() + for record in records: + append_jsonl(accepted_path if record.get("final_valid") else rejected_path, record) + + +def write_evaluator_summaries( + final_records: list[dict[str, Any]], + output_dir: Path, + args: argparse.Namespace, + partial: bool, +) -> dict[str, Any]: + optimizer_metrics = summarize_for_optimizer(final_records, args) + optimizer_metrics["partial"] = partial + write_json(output_dir / "optimizer_metrics.json", optimizer_metrics) + summary = { + "cases_path": str(Path(args.cases)), + "rules_path": str(Path(args.rules)), + "output_dir": str(output_dir), + "model": args.model if not args.dry_run else "dry_run_rule_based", + "dry_run": bool(args.dry_run), + "partial": partial, + **optimizer_metrics["objective_metrics"], + } + write_json(output_dir / "summary.json", summary) + return summary + + +def main() -> None: + args = parse_args() + output_dir = Path(args.output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + + cases = read_jsonl(args.cases, args.start_index, args.max_cases) + rules_md = read_text(args.rules) + api_key = os.environ.get(args.api_key_env, "") + if not args.dry_run and not api_key: + raise RuntimeError(f"Missing API key. Set {args.api_key_env}.") + + all_path = output_dir / "all_evaluations.jsonl" + accepted_path = output_dir / "accepted_cases.jsonl" + rejected_path = output_dir / "rejected_cases.jsonl" + if args.resume: + final_records = load_existing_evaluations(all_path) + completed_case_ids = { + str(record.get("case_id") or get_nested(record, ["case", "case_id"], "")) + for record in final_records + } + completed_case_ids.discard("") + rebuild_split_files(final_records, accepted_path, rejected_path) + print( + "[evaluator] resume enabled:", + f"existing={len(final_records)}", + f"remaining={sum(1 for case in cases if str(case.get('case_id')) not in completed_case_ids)}", + flush=True, + ) + else: + for path in (all_path, accepted_path, rejected_path): + if path.exists(): + path.unlink() + final_records = [] + completed_case_ids = set() + + cases_to_eval = [case for case in cases if str(case.get("case_id")) not in completed_case_ids] + batches = chunked(cases_to_eval, max(1, args.batch_size)) + if not batches and final_records: + write_evaluator_summaries(final_records, output_dir, args, partial=False) + print( + "[evaluator] nothing to do; all cases already evaluated:", + f"total={len(final_records)}", + flush=True, + ) + return + + for batch_idx, batch in enumerate(batches, start=1): + api_batch_idx = len(final_records) + 1 + local_evals = [local_case_eval(case) for case in batch] + if args.dry_run: + llm_evals = [None for _ in batch] + else: + llm_evals = get_llm_evals_for_batch( + rules_md=rules_md, + batch=batch, + local_evals=local_evals, + batch_idx=api_batch_idx, + output_dir=output_dir, + args=args, + api_key=api_key, + ) + + for case, local_eval, raw_llm_eval in zip(batch, local_evals, llm_evals): + llm_eval = normalize_llm_eval(case, local_eval, raw_llm_eval) + final_eval = final_eval_from_local_and_llm(local_eval, llm_eval, args) + out = { + "case_id": case.get("case_id"), + "case": strip_internal(case), + "local_eval": local_eval, + "llm_eval": llm_eval, + "final_eval": final_eval, + "final_valid": final_eval["accepted"], + "final_score": final_eval["scenario_value_score"], + "final_priority": final_eval["priority"], + } + append_jsonl(all_path, out) + append_jsonl(accepted_path if out["final_valid"] else rejected_path, out) + final_records.append(out) + + print( + "[evaluator]", + f"batch={batch_idx}/{len(batches)}", + f"cases={len(batch)}", + f"completed={len(final_records)}/{len(cases)}", + f"accepted_so_far={sum(1 for item in final_records if item['final_valid'])}", + flush=True, + ) + if args.write_partial_summary: + write_evaluator_summaries(final_records, output_dir, args, partial=True) + if not args.dry_run and args.sleep_sec > 0: + time.sleep(args.sleep_sec) + + summary = write_evaluator_summaries(final_records, output_dir, args, partial=False) + print("[evaluator] done:", json.dumps(summary, ensure_ascii=False), flush=True) + + +def strip_internal(case: dict[str, Any]) -> dict[str, Any]: + return {k: v for k, v in case.items() if not k.startswith("_")} + + +if __name__ == "__main__": + main() diff --git a/gpudrive-main/search/optimizer_agent_mlp.py b/gpudrive-main/search/optimizer_agent_mlp.py new file mode 100644 index 0000000000000000000000000000000000000000..f2bd2a02f16a185ccf1f3d1594c0872800d09578 --- /dev/null +++ b/gpudrive-main/search/optimizer_agent_mlp.py @@ -0,0 +1,521 @@ +#!/usr/bin/env python +"""MLP surrogate Optimizer Agent for GPUDrive long-tail search. + +The optimizer consumes Evaluator Agent `optimizer_metrics.json` files, learns a +small surrogate model from search parameters to quality metrics, scores a +discrete candidate grid, and writes next-round search recommendations. + +When history is still too small for a meaningful MLP, the script falls back to a +deterministic exploration plan while still recording the MLP-ready dataset. +""" + +from __future__ import annotations + +import argparse +import glob +import itertools +import json +import math +import random +from collections import defaultdict +from pathlib import Path +from typing import Any + +import torch +from torch import nn + + +DEFAULT_GRID = { + "risk_collision_weight": [-0.2, -0.1, 0.0], + "risk_goal_weight": [1.6, 1.8, 2.0], + "risk_offroad_weight": [-0.8, -0.6, -0.4], + "risk_agents_per_world": [1, 2, 3, 4], + "normal_mode": ["policy", "expert"], + "deterministic": [0], +} + +TARGET_KEYS = [ + "composite_objective", + "accepted_rate", + "high_value_rate", + "natural_critical_rate", + "risk_direct_accept_rate", + "semantic_diversity_entropy", + "hard_artifact_record_rate", +] + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser() + parser.add_argument("--metrics-glob", action="append", default=[]) + parser.add_argument("--metrics-file", action="append", default=[]) + parser.add_argument("--output-dir", default="search_outputs/optimizer_agent/mlp_next") + parser.add_argument("--candidate-grid-json", default="") + parser.add_argument("--top-k", type=int, default=8) + parser.add_argument("--explore-k", type=int, default=4) + parser.add_argument("--allow-repeat", type=int, default=0) + parser.add_argument("--seed", type=int, default=42) + parser.add_argument("--hidden-dim", type=int, default=64) + parser.add_argument("--epochs", type=int, default=1200) + parser.add_argument("--lr", type=float, default=1e-3) + parser.add_argument("--weight-decay", type=float, default=1e-4) + parser.add_argument("--min-train-samples", type=int, default=6) + parser.add_argument("--exploration-weight", type=float, default=0.12) + parser.add_argument("--default-normal-mode", default="policy") + parser.add_argument("--default-deterministic", type=int, default=0) + parser.add_argument("--default-normal-style", default="balanced") + parser.add_argument("--default-risk-style", default="risk_taker") + return parser.parse_args() + + +def read_json(path: str | Path) -> dict[str, Any]: + with open(path, "r", encoding="utf-8") as f: + return json.load(f) + + +def write_json(path: str | Path, data: Any) -> None: + with open(path, "w", encoding="utf-8") as f: + json.dump(data, f, ensure_ascii=False, indent=2) + + +def append_jsonl(path: str | Path, record: dict[str, Any]) -> None: + with open(path, "a", encoding="utf-8") as f: + f.write(json.dumps(record, ensure_ascii=False) + "\n") + + +def as_float(value: Any, default: float = 0.0) -> float: + try: + out = float(value) + except (TypeError, ValueError): + return default + return out if math.isfinite(out) else default + + +def as_int(value: Any, default: int = 0) -> int: + try: + return int(value) + except (TypeError, ValueError): + return default + + +def clamp(value: float, lower: float, upper: float) -> float: + return min(max(value, lower), upper) + + +def discover_metrics(args: argparse.Namespace) -> list[Path]: + paths: list[Path] = [] + for pattern in args.metrics_glob: + paths.extend(Path(p) for p in glob.glob(pattern, recursive=True)) + paths.extend(Path(p) for p in args.metrics_file) + if not paths: + paths.extend(Path(p) for p in glob.glob("search_outputs/evaluator_agent/**/optimizer_metrics.json", recursive=True)) + unique = [] + seen = set() + for path in paths: + path = path.resolve() + if path.exists() and path not in seen: + seen.add(path) + unique.append(path) + return sorted(unique) + + +def load_candidate_grid(raw: str) -> dict[str, list[Any]]: + if not raw: + return dict(DEFAULT_GRID) + loaded = json.loads(raw) + grid = dict(DEFAULT_GRID) + grid.update(loaded) + return grid + + +def params_from_metrics(metrics: dict[str, Any], args: argparse.Namespace) -> dict[str, Any]: + params = metrics.get("current_search_parameters", {}) or {} + return { + "risk_collision_weight": as_float(params.get("risk_collision_weight"), 0.0), + "risk_goal_weight": as_float(params.get("risk_goal_weight"), 2.0), + "risk_offroad_weight": as_float(params.get("risk_offroad_weight"), -0.4), + "risk_agents_per_world": as_int(params.get("risk_agents_per_world"), 3), + "normal_mode": str(params.get("normal_mode", args.default_normal_mode)), + "deterministic": as_int(params.get("deterministic"), args.default_deterministic), + "normal_style": str(params.get("normal_style", args.default_normal_style)), + "risk_style": str(params.get("risk_style", args.default_risk_style)), + } + + +def param_key(params: dict[str, Any]) -> tuple[Any, ...]: + return ( + round(as_float(params["risk_collision_weight"]), 5), + round(as_float(params["risk_goal_weight"]), 5), + round(as_float(params["risk_offroad_weight"]), 5), + as_int(params["risk_agents_per_world"]), + str(params["normal_mode"]), + as_int(params["deterministic"]), + ) + + +def targets_from_metrics(metrics: dict[str, Any]) -> dict[str, float]: + obj = metrics.get("objective_metrics", {}) or {} + return {key: as_float(obj.get(key), 0.0) for key in TARGET_KEYS} + + +def aggregate_history(paths: list[Path], args: argparse.Namespace) -> tuple[list[dict[str, Any]], list[dict[str, Any]]]: + raw_samples = [] + grouped: dict[tuple[Any, ...], list[dict[str, Any]]] = defaultdict(list) + for path in paths: + metrics = read_json(path) + params = params_from_metrics(metrics, args) + targets = targets_from_metrics(metrics) + sample = { + "metrics_path": str(path), + "params": params, + "targets": targets, + "objective_metrics": metrics.get("objective_metrics", {}), + "counts": metrics.get("counts", {}), + "suggested_next_search_parameters": metrics.get("suggested_next_search_parameters", []), + } + raw_samples.append(sample) + grouped[param_key(params)].append(sample) + + aggregated = [] + for key, samples in grouped.items(): + params = dict(samples[0]["params"]) + target_mean = {} + target_std = {} + for target_key in TARGET_KEYS: + values = [sample["targets"][target_key] for sample in samples] + target_mean[target_key] = sum(values) / len(values) + target_std[target_key] = std(values) + aggregated.append( + { + "params": params, + "targets": target_mean, + "target_std": target_std, + "replicates": len(samples), + "metrics_paths": [sample["metrics_path"] for sample in samples], + } + ) + return raw_samples, aggregated + + +def std(values: list[float]) -> float: + if len(values) <= 1: + return 0.0 + mu = sum(values) / len(values) + return math.sqrt(sum((value - mu) ** 2 for value in values) / (len(values) - 1)) + + +def generate_candidates(grid: dict[str, list[Any]], args: argparse.Namespace) -> list[dict[str, Any]]: + keys = [ + "risk_collision_weight", + "risk_goal_weight", + "risk_offroad_weight", + "risk_agents_per_world", + "normal_mode", + "deterministic", + ] + candidates = [] + for values in itertools.product(*(grid[key] for key in keys)): + params = dict(zip(keys, values)) + params["normal_style"] = args.default_normal_style + params["risk_style"] = args.default_risk_style + candidates.append(params) + return candidates + + +def feature_vector(params: dict[str, Any]) -> list[float]: + normal_mode = str(params.get("normal_mode", "policy")) + return [ + as_float(params.get("risk_collision_weight"), 0.0), + as_float(params.get("risk_goal_weight"), 2.0), + as_float(params.get("risk_offroad_weight"), -0.4), + as_float(params.get("risk_agents_per_world"), 3), + 1.0 if normal_mode == "policy" else 0.0, + 1.0 if normal_mode == "expert" else 0.0, + as_float(params.get("deterministic"), 0.0), + ] + + +def target_vector(targets: dict[str, float]) -> list[float]: + return [as_float(targets.get(key), 0.0) for key in TARGET_KEYS] + + +class SurrogateMLP(nn.Module): + def __init__(self, input_dim: int, output_dim: int, hidden_dim: int) -> None: + super().__init__() + self.net = nn.Sequential( + nn.Linear(input_dim, hidden_dim), + nn.ReLU(), + nn.Linear(hidden_dim, hidden_dim), + nn.ReLU(), + nn.Linear(hidden_dim, output_dim), + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return self.net(x) + + +def normalize_matrix(values: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + mean = values.mean(dim=0, keepdim=True) + std_value = values.std(dim=0, keepdim=True) + std_value = torch.where(std_value < 1e-6, torch.ones_like(std_value), std_value) + return (values - mean) / std_value, mean, std_value + + +def train_surrogate(samples: list[dict[str, Any]], args: argparse.Namespace) -> dict[str, Any]: + x = torch.tensor([feature_vector(sample["params"]) for sample in samples], dtype=torch.float32) + y = torch.tensor([target_vector(sample["targets"]) for sample in samples], dtype=torch.float32) + x_norm, x_mean, x_std = normalize_matrix(x) + y_norm, y_mean, y_std = normalize_matrix(y) + + torch.manual_seed(args.seed) + model = SurrogateMLP(x.shape[1], y.shape[1], args.hidden_dim) + optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=args.weight_decay) + loss_fn = nn.MSELoss() + losses = [] + for _ in range(args.epochs): + pred = model(x_norm) + loss = loss_fn(pred, y_norm) + optimizer.zero_grad() + loss.backward() + optimizer.step() + losses.append(float(loss.item())) + + with torch.no_grad(): + train_pred = model(x_norm) * y_std + y_mean + mae = (train_pred - y).abs().mean(dim=0) + + return { + "trained": True, + "model": model, + "x_mean": x_mean, + "x_std": x_std, + "y_mean": y_mean, + "y_std": y_std, + "train_loss_final": losses[-1] if losses else None, + "train_loss_initial": losses[0] if losses else None, + "train_mae": {key: round(float(value), 5) for key, value in zip(TARGET_KEYS, mae.tolist())}, + } + + +def predict(surrogate: dict[str, Any], params: dict[str, Any]) -> dict[str, float]: + x = torch.tensor([feature_vector(params)], dtype=torch.float32) + x_norm = (x - surrogate["x_mean"]) / surrogate["x_std"] + with torch.no_grad(): + y = surrogate["model"](x_norm) * surrogate["y_std"] + surrogate["y_mean"] + return { + key: round(float(value), 5) + for key, value in zip(TARGET_KEYS, y.squeeze(0).tolist()) + } + + +def heuristic_prediction(params: dict[str, Any], samples: list[dict[str, Any]]) -> dict[str, float]: + if not samples: + return {key: 0.0 for key in TARGET_KEYS} + # Use inverse-distance weighting from observed samples so early rounds still + # produce smooth candidate scores. + weights = [] + for sample in samples: + dist = param_distance(params, sample["params"]) + weights.append(1.0 / (0.05 + dist)) + total = sum(weights) + pred = {} + for key in TARGET_KEYS: + pred[key] = sum(weight * sample["targets"][key] for weight, sample in zip(weights, samples)) / total + return {key: round(value, 5) for key, value in pred.items()} + + +def param_distance(a: dict[str, Any], b: dict[str, Any]) -> float: + fa = feature_vector(a) + fb = feature_vector(b) + scales = [0.2, 0.4, 0.4, 3.0, 1.0, 1.0, 1.0] + sq = 0.0 + for va, vb, scale in zip(fa, fb, scales): + sq += ((va - vb) / max(scale, 1e-6)) ** 2 + return math.sqrt(sq / len(fa)) + + +def nearest_history_distance(params: dict[str, Any], samples: list[dict[str, Any]]) -> float: + if not samples: + return 1.0 + return min(param_distance(params, sample["params"]) for sample in samples) + + +def utility(pred: dict[str, float]) -> float: + return ( + 0.45 * pred["composite_objective"] + + 0.16 * pred["accepted_rate"] + + 0.16 * pred["high_value_rate"] + + 0.12 * pred["risk_direct_accept_rate"] + + 0.11 * pred["semantic_diversity_entropy"] + - 0.18 * pred["hard_artifact_record_rate"] + ) + + +def score_candidates( + candidates: list[dict[str, Any]], + samples: list[dict[str, Any]], + surrogate: dict[str, Any] | None, + args: argparse.Namespace, +) -> list[dict[str, Any]]: + evaluated = {param_key(sample["params"]) for sample in samples} + scored = [] + for params in candidates: + already = param_key(params) in evaluated + if already and not args.allow_repeat: + continue + pred = predict(surrogate, params) if surrogate else heuristic_prediction(params, samples) + base_utility = utility(pred) + distance = nearest_history_distance(params, samples) + exploration_bonus = args.exploration_weight * clamp(distance, 0.0, 2.0) + acquisition = base_utility + exploration_bonus + scored.append( + { + "params": params, + "predicted_metrics": pred, + "utility": round(base_utility, 5), + "exploration_score": round(distance, 5), + "acquisition": round(acquisition, 5), + "already_evaluated": already, + } + ) + return sorted(scored, key=lambda item: item["acquisition"], reverse=True) + + +def choose_recommendations(scored: list[dict[str, Any]], args: argparse.Namespace) -> list[dict[str, Any]]: + exploit = scored[: max(args.top_k, 0)] + remaining = scored[max(args.top_k, 0) :] + explore = sorted(remaining, key=lambda item: item["exploration_score"], reverse=True)[: max(args.explore_k, 0)] + combined = [] + seen = set() + for source, items in (("mlp_top", exploit), ("exploration", explore)): + for item in items: + key = param_key(item["params"]) + if key in seen: + continue + seen.add(key) + out = dict(item) + out["source"] = source + out["rank"] = len(combined) + 1 + combined.append(out) + return combined + + +def env_block(params: dict[str, Any]) -> str: + return "\n".join( + [ + f"RISK_COLLISION_WEIGHT={params['risk_collision_weight']}", + f"RISK_GOAL_WEIGHT={params['risk_goal_weight']}", + f"RISK_OFFROAD_WEIGHT={params['risk_offroad_weight']}", + f"RISK_AGENTS_PER_WORLD={params['risk_agents_per_world']}", + f"NORMAL_MODE={params['normal_mode']}", + f"DETERMINISTIC={params['deterministic']}", + ] + ) + + +def command_line(params: dict[str, Any]) -> str: + prefix = " ".join( + [ + f"RISK_COLLISION_WEIGHT={params['risk_collision_weight']}", + f"RISK_GOAL_WEIGHT={params['risk_goal_weight']}", + f"RISK_OFFROAD_WEIGHT={params['risk_offroad_weight']}", + f"RISK_AGENTS_PER_WORLD={params['risk_agents_per_world']}", + f"NORMAL_MODE={params['normal_mode']}", + f"DETERMINISTIC={params['deterministic']}", + ] + ) + return f"{prefix} sbatch scripts/search_longtail_reward_conditioned.sbatch" + + +def main() -> None: + args = parse_args() + random.seed(args.seed) + torch.manual_seed(args.seed) + output_dir = Path(args.output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + + metric_paths = discover_metrics(args) + raw_samples, samples = aggregate_history(metric_paths, args) + grid = load_candidate_grid(args.candidate_grid_json) + candidates = generate_candidates(grid, args) + + surrogate = None + train_report: dict[str, Any] = { + "trained": False, + "reason": f"Need at least {args.min_train_samples} aggregated samples.", + } + if len(samples) >= args.min_train_samples: + surrogate = train_surrogate(samples, args) + train_report = { + "trained": True, + "num_samples": len(samples), + "train_loss_initial": surrogate["train_loss_initial"], + "train_loss_final": surrogate["train_loss_final"], + "train_mae": surrogate["train_mae"], + } + + scored = score_candidates(candidates, samples, surrogate, args) + recommendations = choose_recommendations(scored, args) + + candidate_path = output_dir / "candidate_predictions.jsonl" + if candidate_path.exists(): + candidate_path.unlink() + for item in scored: + append_jsonl(candidate_path, item) + + for item in recommendations: + item["env_block"] = env_block(item["params"]) + item["search_command"] = command_line(item["params"]) + + plan = { + "schema_version": "mlp_optimizer_plan_v1", + "metrics_paths": [str(path) for path in metric_paths], + "num_raw_metric_files": len(raw_samples), + "num_aggregated_parameter_samples": len(samples), + "candidate_grid": grid, + "num_candidates": len(candidates), + "num_scored_candidates": len(scored), + "feature_schema": [ + "risk_collision_weight", + "risk_goal_weight", + "risk_offroad_weight", + "risk_agents_per_world", + "normal_mode_is_policy", + "normal_mode_is_expert", + "deterministic", + ], + "target_schema": TARGET_KEYS, + "training": train_report, + "history_samples": samples, + "recommendations": recommendations, + } + write_json(output_dir / "optimizer_plan.json", plan) + + if recommendations: + (output_dir / "best_recommendation.env").write_text( + recommendations[0]["env_block"] + "\n", + encoding="utf-8", + ) + (output_dir / "recommended_search_commands.sh").write_text( + "\n".join(item["search_command"] for item in recommendations) + "\n", + encoding="utf-8", + ) + + print( + "[optimizer] done:", + json.dumps( + { + "metrics": len(raw_samples), + "aggregated_samples": len(samples), + "trained": train_report["trained"], + "recommendations": len(recommendations), + "output_dir": str(output_dir), + }, + ensure_ascii=False, + ), + flush=True, + ) + + +if __name__ == "__main__": + main() diff --git a/gpudrive-main/search_outputs/evaluator_agent_test/accepted_cases.jsonl b/gpudrive-main/search_outputs/evaluator_agent_test/accepted_cases.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..2f914f1ad715eefef1974acc59f459effe1ece23 --- /dev/null +++ b/gpudrive-main/search_outputs/evaluator_agent_test/accepted_cases.jsonl @@ -0,0 +1,8 @@ +{"case_id": "shard000_batch0000_world0002_line000000", "case": {"case_id": "shard000_batch0000_world0002_line000000", "schema_version": "llm_longtail_case_v1", "source": {"event_trace_line_index": 0, "scenario_file": "tfrecord-00005-of-01000_436.json", "shard_id": 0, "batch_idx": 0, "world_idx": 2}, "outcome": {"collision": true, "near_miss": true, "offroad": true, "first_collision_time_s": 2.9, "first_offroad_time_s": 3.0, "min_distance_time_s": 2.9, "event_time_s": 2.9, "raw_min_distance_m": 1.9574, "clean_min_distance_m": 1.9575}, "primary_pair": {"agent_a": 5, "agent_b": 7, "agent_a_role": ["risk_agent", "collided_agent"], "agent_b_role": ["collided_agent", "offroad_agent"], "selection_reason": "risk_agent_directly_collided_with_partner", "confidence": "high", "risk_directly_involved": true, "semantic_candidate": "same_direction_side_conflict_or_cut_in"}, "scene_summary": {"controlled_agents": 13, "risk_agents": [0, 4, 5], "collided_agents": [5, 7], "offroad_agents": [0, 7], "goal_agents": 13, "context_agents": [5, 7, 0, 2, 3, 4, 6, 17]}, "primary_interaction_metrics": {"num_clean_pair_frames": 34, "min_clean_distance_m": 1.9575, "min_clean_distance_time_to_event_s": 0.0, "min_ttc_s": 0.4335, "time_ttc_below_1s": 1.2, "time_ttc_below_2s": 2.3, "distance_trend_before_event": {"label": "mostly_decreasing", "decreasing_ratio": 0.9655, "distance_start_m": 29.5168, "distance_end_m": 1.9575}, "metrics_at_event_or_nearest_clean_frame": {"pair_valid": true, "distance_m": 1.9575, "ttc_s": 0.4496, "closing_speed_mps": 4.3538, "relative_speed_mps": 7.2043, "longitudinal_gap_m": 1.2124, "lateral_offset_m": -1.5368, "relative_heading_rad": 0.4045, "agent_a_speed_mps": 10.645, "agent_b_speed_mps": 14.2738}, "metrics_at_min_clean_distance": {"pair_valid": true, "distance_m": 1.9575, "ttc_s": 0.4496, "closing_speed_mps": 4.3538, "relative_speed_mps": 7.2043, "longitudinal_gap_m": 1.2124, "lateral_offset_m": -1.5368, "relative_heading_rad": 0.4045, "agent_a_speed_mps": 10.645, "agent_b_speed_mps": 14.2738}}, "naturalness_checks": {"max_speed_mps": 23.926, "max_accel_mps2": 4.0166, "max_decel_mps2": 4.0081, "max_yaw_rate_radps": 6.665, "max_frame_displacement_m": 2.3926, "offroad_before_collision": false, "clean_frame_counts": {"5": 34, "7": 39, "0": 40, "2": 40, "3": 40, "4": 23, "6": 40, "17": 32}, "artifact_warnings": ["high_yaw_rate"]}, "interaction_checks": {"risk_agent_in_primary_pair": true, "primary_pair_has_collision_agent": true, "primary_pair_has_offroad_agent": true, "distance_trend_label": "mostly_decreasing"}, "context_agent_summaries": [{"agent_id": 5, "roles": ["risk_agent", "collided_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.0827, "width_m": 1.391, "clean_frames": 34, "speed_at_event_mps": 10.645}, {"agent_id": 7, "roles": ["collided_agent", "offroad_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.1654, "width_m": 1.4256, "clean_frames": 39, "speed_at_event_mps": 14.2738}, {"agent_id": 0, "roles": ["risk_agent", "offroad_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.7002, "width_m": 1.6324, "clean_frames": 40, "speed_at_event_mps": 17.2471}, {"agent_id": 2, "roles": ["context_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.2432, "width_m": 1.5205, "clean_frames": 40, "speed_at_event_mps": 19.003}, {"agent_id": 3, "roles": ["context_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.2124, "width_m": 1.4202, "clean_frames": 40, "speed_at_event_mps": 16.8588}, {"agent_id": 4, "roles": ["risk_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.5435, "width_m": 1.5256, "clean_frames": 23, "speed_at_event_mps": null}, {"agent_id": 6, "roles": ["context_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.2835, "width_m": 1.4368, "clean_frames": 40, "speed_at_event_mps": 13.7644}, {"agent_id": 17, "roles": ["context_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 2.8417, "width_m": 1.3018, "clean_frames": 32, "speed_at_event_mps": 23.6592}], "map_summary": {"center_xy": [20.7116, -14.1158], "radius_m": 80.0, "num_points": 256, "type_counts": {"1": 31, "3": 70, "2": 27, "4": 1, "0": 127}}, "timeline": [{"t_to_event_s": -2.9, "frame_index": 0, "target_offset_s": -5.0, "distance_m": 29.5168, "ttc_s": null, "closing_speed_mps": null, "relative_speed_mps": null, "longitudinal_gap_m": 29.4564, "lateral_offset_m": 1.8873, "relative_heading_rad": 2.6233, "agent_a_speed_mps": null, "agent_b_speed_mps": null, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -2.0, "frame_index": 9, "target_offset_s": -2.0, "distance_m": 20.5942, "ttc_s": 1.6912, "closing_speed_mps": 12.177, "relative_speed_mps": 12.8039, "longitudinal_gap_m": 19.5775, "lateral_offset_m": -6.3909, "relative_heading_rad": 2.1197, "agent_a_speed_mps": 5.179, "agent_b_speed_mps": 10.1396, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -1.0, "frame_index": 19, "target_offset_s": -1.0, "distance_m": 7.7682, "ttc_s": 0.6847, "closing_speed_mps": 11.3453, "relative_speed_mps": 11.6026, "longitudinal_gap_m": 4.1909, "lateral_offset_m": -6.5408, "relative_heading_rad": 1.1884, "agent_a_speed_mps": 9.179, "agent_b_speed_mps": 13.3404, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -0.5, "frame_index": 24, "target_offset_s": -0.5, "distance_m": 3.6239, "ttc_s": 0.4436, "closing_speed_mps": 8.1688, "relative_speed_mps": 8.9406, "longitudinal_gap_m": 1.0071, "lateral_offset_m": -3.4811, "relative_heading_rad": 0.6411, "agent_a_speed_mps": 10.6456, "agent_b_speed_mps": 14.8069, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": 0.0, "frame_index": 29, "target_offset_s": 0.0, "distance_m": 1.9575, "ttc_s": 0.4496, "closing_speed_mps": 4.3538, "relative_speed_mps": 7.2043, "longitudinal_gap_m": 1.2124, "lateral_offset_m": -1.5368, "relative_heading_rad": 0.4045, "agent_a_speed_mps": 10.645, "agent_b_speed_mps": 14.2738, "agent_a_collision": true, "agent_b_collision": true, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": 0.4, "frame_index": 33, "target_offset_s": 0.5, "distance_m": 3.1975, "ttc_s": 12.4782, "closing_speed_mps": 0.2562, "relative_speed_mps": 2.2195, "longitudinal_gap_m": 0.4287, "lateral_offset_m": -3.1686, "relative_heading_rad": 0.0381, "agent_a_speed_mps": 12.2457, "agent_b_speed_mps": 14.4066, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}], "llm_task": {"question": "Judge whether this is a natural and safety-critical long-tail driving scenario.", "return_format": "strict_json", "expected_keys": ["accepted", "danger_level", "naturalness_score", "interaction_score", "scenario_value_score", "scenario_type", "artifact_flags", "reason"]}}, "local_eval": {"accepted": true, "priority": "high_value", "danger_level": 5, "criticality_score": 94.554, "naturalness_score": 92.0, "interaction_score": 100.0, "scenario_value_score": 95.473, "scenario_type": "cut_in_or_side_conflict", "artifact_flags": ["high_yaw_rate"], "rejection_reasons": [], "optimization_feedback": {"increase_collision_tolerance": false, "decrease_collision_tolerance": false, "increase_goal_pressure": false, "decrease_goal_pressure": false, "increase_offroad_penalty": false, "relax_offroad_penalty": false, "increase_risk_agents_per_world": false, "decrease_risk_agents_per_world": false, "target_scenario_types": ["cut_in_or_side_conflict"]}, "reason": "Primary pair 5->7 scored criticality=94.6, naturalness=92.0, interaction=100.0, value=95.5; notes=none.", "evidence": ["collision=True near_miss=True offroad=True", "primary_pair=5->7 reason=risk_agent_directly_collided_with_partner", "clean_min_distance=1.9575 min_ttc=0.4335", "event_relative_speed=7.2043 closing_speed=4.3538", "distance_trend=mostly_decreasing"]}, "llm_eval": {"accepted": true, "priority": "high_value", "danger_level": 5, "criticality_score": 94.554, "naturalness_score": 92.0, "interaction_score": 100.0, "scenario_value_score": 95.473, "scenario_type": "cut_in_or_side_conflict", "artifact_flags": ["high_yaw_rate"], "rejection_reasons": [], "optimization_feedback": {"increase_collision_tolerance": false, "decrease_collision_tolerance": false, "increase_goal_pressure": false, "decrease_goal_pressure": false, "increase_offroad_penalty": false, "relax_offroad_penalty": false, "increase_risk_agents_per_world": false, "decrease_risk_agents_per_world": false, "target_scenario_types": ["cut_in_or_side_conflict"]}, "reason": "Primary pair 5->7 scored criticality=94.6, naturalness=92.0, interaction=100.0, value=95.5; notes=none.", "evidence": ["collision=True near_miss=True offroad=True", "primary_pair=5->7 reason=risk_agent_directly_collided_with_partner", "clean_min_distance=1.9575 min_ttc=0.4335", "event_relative_speed=7.2043 closing_speed=4.3538", "distance_trend=mostly_decreasing"]}, "final_eval": {"accepted": true, "priority": "high_value", "danger_level": 5, "criticality_score": 94.554, "naturalness_score": 92.0, "interaction_score": 100.0, "scenario_value_score": 95.473, "scenario_type": "cut_in_or_side_conflict", "artifact_flags": ["high_yaw_rate"], "rejection_reasons": [], "optimization_feedback": {"increase_collision_tolerance": false, "decrease_collision_tolerance": false, "increase_goal_pressure": false, "decrease_goal_pressure": false, "increase_offroad_penalty": false, "relax_offroad_penalty": false, "increase_risk_agents_per_world": false, "decrease_risk_agents_per_world": false, "target_scenario_types": ["cut_in_or_side_conflict"]}, "reason": "Primary pair 5->7 scored criticality=94.6, naturalness=92.0, interaction=100.0, value=95.5; notes=none.", "evidence": ["collision=True near_miss=True offroad=True", "primary_pair=5->7 reason=risk_agent_directly_collided_with_partner", "clean_min_distance=1.9575 min_ttc=0.4335", "event_relative_speed=7.2043 closing_speed=4.3538", "distance_trend=mostly_decreasing"], "high_value": true}, "final_valid": true, "final_score": 95.473, "final_priority": "high_value"} +{"case_id": "shard000_batch0000_world0020_line000004", "case": {"case_id": "shard000_batch0000_world0020_line000004", "schema_version": "llm_longtail_case_v1", "source": {"event_trace_line_index": 4, "scenario_file": "tfrecord-00000-of-01000_190.json", "shard_id": 0, "batch_idx": 0, "world_idx": 20}, "outcome": {"collision": true, "near_miss": false, "offroad": false, "first_collision_time_s": 4.9, "first_offroad_time_s": null, "min_distance_time_s": 4.9, "event_time_s": 4.9, "raw_min_distance_m": 2.1322, "clean_min_distance_m": 2.1322}, "primary_pair": {"agent_a": 10, "agent_b": 0, "agent_a_role": ["risk_agent", "collided_agent"], "agent_b_role": ["context_agent"], "selection_reason": "event_min_distance_risk_partner", "confidence": "medium", "risk_directly_involved": true, "semantic_candidate": "same_direction_side_conflict_or_cut_in"}, "scene_summary": {"controlled_agents": 9, "risk_agents": [2, 7, 10], "collided_agents": [10], "offroad_agents": [], "goal_agents": 9, "context_agents": [0, 10, 2, 3, 5, 6, 7]}, "primary_interaction_metrics": {"num_clean_pair_frames": 60, "min_clean_distance_m": 2.1322, "min_clean_distance_time_to_event_s": 0.0, "min_ttc_s": 0.0973, "time_ttc_below_1s": 1.2, "time_ttc_below_2s": 1.9, "distance_trend_before_event": {"label": "mostly_decreasing", "decreasing_ratio": 0.9333, "distance_start_m": 42.7184, "distance_end_m": 2.1322}, "metrics_at_event_or_nearest_clean_frame": {"pair_valid": true, "distance_m": 2.1322, "ttc_s": 0.0973, "closing_speed_mps": 21.9152, "relative_speed_mps": 24.68, "longitudinal_gap_m": 0.9973, "lateral_offset_m": -1.8846, "relative_heading_rad": 0.5289, "agent_a_speed_mps": 28.041, "agent_b_speed_mps": 6.4672}, "metrics_at_min_clean_distance": {"pair_valid": true, "distance_m": 2.1322, "ttc_s": 0.0973, "closing_speed_mps": 21.9152, "relative_speed_mps": 24.68, "longitudinal_gap_m": 0.9973, "lateral_offset_m": -1.8846, "relative_heading_rad": 0.5289, "agent_a_speed_mps": 28.041, "agent_b_speed_mps": 6.4672}}, "naturalness_checks": {"max_speed_mps": 31.1746, "max_accel_mps2": 130.8809, "max_decel_mps2": 5.9354, "max_yaw_rate_radps": 12.724, "max_frame_displacement_m": 3.1175, "offroad_before_collision": false, "clean_frame_counts": {"0": 60, "10": 60, "2": 52, "3": 60, "5": 60, "6": 60, "7": 36}, "artifact_warnings": ["high_acceleration", "high_yaw_rate"]}, "interaction_checks": {"risk_agent_in_primary_pair": true, "primary_pair_has_collision_agent": true, "primary_pair_has_offroad_agent": false, "distance_trend_label": "mostly_decreasing"}, "context_agent_summaries": [{"agent_id": 0, "roles": ["context_agent"], "is_controlled": false, "response_type": 0, "raw_agent_type": 7, "length_m": 3.7002, "width_m": 1.6324, "clean_frames": 60, "speed_at_event_mps": 6.4672}, {"agent_id": 10, "roles": ["risk_agent", "collided_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.3421, "width_m": 1.4774, "clean_frames": 60, "speed_at_event_mps": 28.041}, {"agent_id": 2, "roles": ["risk_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.2188, "width_m": 1.4623, "clean_frames": 52, "speed_at_event_mps": 25.8953}, {"agent_id": 3, "roles": ["context_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 4.1701, "width_m": 1.7598, "clean_frames": 60, "speed_at_event_mps": 26.3787}, {"agent_id": 5, "roles": ["context_agent"], "is_controlled": false, "response_type": 0, "raw_agent_type": 7, "length_m": 3.4134, "width_m": 1.5414, "clean_frames": 60, "speed_at_event_mps": 8.3336}, {"agent_id": 6, "roles": ["context_agent"], "is_controlled": false, "response_type": 0, "raw_agent_type": 7, "length_m": 3.2981, "width_m": 1.4325, "clean_frames": 60, "speed_at_event_mps": 13.1443}, {"agent_id": 7, "roles": ["risk_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.1299, "width_m": 1.3812, "clean_frames": 36, "speed_at_event_mps": null}], "map_summary": {"center_xy": [3.971, -11.1785], "radius_m": 80.0, "num_points": 256, "type_counts": {"1": 10, "3": 48, "2": 3, "5": 1, "0": 194}}, "timeline": [{"t_to_event_s": -4.9, "frame_index": 0, "target_offset_s": -5.0, "distance_m": 55.212, "ttc_s": null, "closing_speed_mps": null, "relative_speed_mps": null, "longitudinal_gap_m": 55.1196, "lateral_offset_m": 3.1922, "relative_heading_rad": -0.0028, "agent_a_speed_mps": null, "agent_b_speed_mps": null, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -3.0, "frame_index": 19, "target_offset_s": -3.0, "distance_m": 42.7184, "ttc_s": 4.4631, "closing_speed_mps": 9.5715, "relative_speed_mps": 9.6031, "longitudinal_gap_m": 42.5891, "lateral_offset_m": 3.3206, "relative_heading_rad": 0.0039, "agent_a_speed_mps": 20.375, "agent_b_speed_mps": 10.772, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -2.0, "frame_index": 29, "target_offset_s": -2.0, "distance_m": 30.5419, "ttc_s": 2.1306, "closing_speed_mps": 14.3347, "relative_speed_mps": 14.4364, "longitudinal_gap_m": 30.3458, "lateral_offset_m": 3.455, "relative_heading_rad": 0.0091, "agent_a_speed_mps": 24.2421, "agent_b_speed_mps": 9.8062, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -1.0, "frame_index": 39, "target_offset_s": -1.0, "distance_m": 18.8455, "ttc_s": 1.0754, "closing_speed_mps": 17.5236, "relative_speed_mps": 17.6025, "longitudinal_gap_m": 18.7069, "lateral_offset_m": -2.2811, "relative_heading_rad": 0.1155, "agent_a_speed_mps": 25.7746, "agent_b_speed_mps": 8.2282, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -0.5, "frame_index": 44, "target_offset_s": -0.5, "distance_m": 9.264, "ttc_s": 0.4591, "closing_speed_mps": 20.1769, "relative_speed_mps": 20.423, "longitudinal_gap_m": 9.0834, "lateral_offset_m": -1.8203, "relative_heading_rad": 0.1175, "agent_a_speed_mps": 27.7078, "agent_b_speed_mps": 7.3543, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": 0.0, "frame_index": 49, "target_offset_s": 0.0, "distance_m": 2.1322, "ttc_s": 0.0973, "closing_speed_mps": 21.9152, "relative_speed_mps": 24.68, "longitudinal_gap_m": 0.9973, "lateral_offset_m": -1.8846, "relative_heading_rad": 0.5289, "agent_a_speed_mps": 28.041, 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{"question": "Judge whether this is a natural and safety-critical long-tail driving scenario.", "return_format": "strict_json", "expected_keys": ["accepted", "danger_level", "naturalness_score", "interaction_score", "scenario_value_score", "scenario_type", "artifact_flags", "reason"]}}, "local_eval": {"accepted": true, "priority": "high_value", "danger_level": 4, "criticality_score": 83.2, "naturalness_score": 82.0, "interaction_score": 100.0, "scenario_value_score": 87.904, "scenario_type": "cut_in_or_side_conflict", "artifact_flags": ["high_acceleration", "high_yaw_rate"], "rejection_reasons": [], "optimization_feedback": {"increase_collision_tolerance": false, "decrease_collision_tolerance": false, "increase_goal_pressure": false, "decrease_goal_pressure": false, "increase_offroad_penalty": false, "relax_offroad_penalty": false, "increase_risk_agents_per_world": false, "decrease_risk_agents_per_world": false, "target_scenario_types": ["cut_in_or_side_conflict"]}, "reason": "Primary 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"raw_agent_type": 7, "length_m": 3.3351, "width_m": 1.4516, "clean_frames": 27, "speed_at_event_mps": 8.4573}, {"agent_id": 10, "roles": ["context_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.2178, "width_m": 1.4285, "clean_frames": 17, "speed_at_event_mps": 8.4417}], "map_summary": {"center_xy": [16.3534, 2.2163], "radius_m": 80.0, "num_points": 256, "type_counts": {"3": 87, "1": 67, "6": 1, "4": 1, "0": 100}}, "timeline": [{"t_to_event_s": -1.6, "frame_index": 0, "target_offset_s": -5.0, "distance_m": 8.463, "ttc_s": null, "closing_speed_mps": null, "relative_speed_mps": null, "longitudinal_gap_m": 8.4629, "lateral_offset_m": -0.0425, "relative_heading_rad": 0.0327, "agent_a_speed_mps": null, "agent_b_speed_mps": null, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -1.0, "frame_index": 6, "target_offset_s": -1.0, "distance_m": 7.29, "ttc_s": 3.4432, 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false}], "llm_task": {"question": "Judge whether this is a natural and safety-critical long-tail driving scenario.", "return_format": "strict_json", "expected_keys": ["accepted", "danger_level", "naturalness_score", "interaction_score", "scenario_value_score", "scenario_type", "artifact_flags", "reason"]}}, "local_eval": {"accepted": true, "priority": "high_value", "danger_level": 5, "criticality_score": 98.38, "naturalness_score": 92.0, "interaction_score": 100.0, "scenario_value_score": 97.08, "scenario_type": "rear_end_or_following_conflict", "artifact_flags": ["high_yaw_rate"], "rejection_reasons": [], "optimization_feedback": {"increase_collision_tolerance": false, "decrease_collision_tolerance": false, "increase_goal_pressure": false, "decrease_goal_pressure": false, "increase_offroad_penalty": false, "relax_offroad_penalty": false, "increase_risk_agents_per_world": false, "decrease_risk_agents_per_world": false, "target_scenario_types": ["rear_end_or_following_conflict"]}, 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"first_collision_time_s": 2.0, "first_offroad_time_s": 4.2, "min_distance_time_s": 2.0, "event_time_s": 2.0, "raw_min_distance_m": 2.5916, "clean_min_distance_m": 2.5917}, "primary_pair": {"agent_a": 0, "agent_b": 2, "agent_a_role": ["risk_agent", "collided_agent"], "agent_b_role": ["risk_agent", "collided_agent"], "selection_reason": "risk_agent_directly_collided_with_partner", "confidence": "high", "risk_directly_involved": true, "semantic_candidate": "crossing_or_intersection_conflict"}, "scene_summary": {"controlled_agents": 3, "risk_agents": [0, 1, 2], "collided_agents": [0, 2], "offroad_agents": [1], "goal_agents": 3, "context_agents": [0, 2, 1]}, "primary_interaction_metrics": {"num_clean_pair_frames": 31, "min_clean_distance_m": 2.5917, "min_clean_distance_time_to_event_s": 0.0, "min_ttc_s": 0.2654, "time_ttc_below_1s": 0.9, "time_ttc_below_2s": 1.5, "distance_trend_before_event": {"label": "mostly_decreasing", "decreasing_ratio": 1.0, "distance_start_m": 23.0461, 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"criticality_score": 76.4, "naturalness_score": 92.0, "interaction_score": 100.0, "scenario_value_score": 87.848, "scenario_type": "crossing_or_intersection_conflict", "artifact_flags": ["high_yaw_rate"], "rejection_reasons": [], "optimization_feedback": {"increase_collision_tolerance": false, "decrease_collision_tolerance": false, "increase_goal_pressure": false, "decrease_goal_pressure": false, "increase_offroad_penalty": false, "relax_offroad_penalty": false, "increase_risk_agents_per_world": false, "decrease_risk_agents_per_world": false, "target_scenario_types": ["crossing_or_intersection_conflict"]}, "reason": "Primary pair 0->2 scored criticality=76.4, naturalness=92.0, interaction=100.0, value=87.8; notes=none.", "evidence": ["collision=True near_miss=False offroad=True", "primary_pair=0->2 reason=risk_agent_directly_collided_with_partner", "clean_min_distance=2.5917 min_ttc=0.2654", "event_relative_speed=11.4348 closing_speed=9.5365", "distance_trend=mostly_decreasing"]}, 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"semantic_candidate": "crossing_or_intersection_conflict"}, "scene_summary": {"controlled_agents": 5, "risk_agents": [1, 2, 3], "collided_agents": [2, 3], "offroad_agents": [], "goal_agents": 4, "context_agents": [2, 3, 0, 1, 4]}, "primary_interaction_metrics": {"num_clean_pair_frames": 34, "min_clean_distance_m": 1.4903, "min_clean_distance_time_to_event_s": 0.0, "min_ttc_s": 0.047, "time_ttc_below_1s": 1.0, "time_ttc_below_2s": 1.7, "distance_trend_before_event": {"label": "mostly_decreasing", "decreasing_ratio": 1.0, "distance_start_m": 63.1041, "distance_end_m": 1.4903}, "metrics_at_event_or_nearest_clean_frame": {"pair_valid": true, "distance_m": 1.4903, "ttc_s": 0.047, "closing_speed_mps": 31.6875, "relative_speed_mps": 31.6907, "longitudinal_gap_m": 1.3458, "lateral_offset_m": -0.6404, "relative_heading_rad": -2.9936, "agent_a_speed_mps": 18.9078, "agent_b_speed_mps": 15.5137}, "metrics_at_min_clean_distance": {"pair_valid": true, "distance_m": 1.4903, "ttc_s": 0.047, 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"collided_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 4.0613, "width_m": 1.6962, "clean_frames": 34, "speed_at_event_mps": 15.5137}, {"agent_id": 0, "roles": ["context_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.7002, "width_m": 1.6324, "clean_frames": 34, "speed_at_event_mps": 7.6926}, {"agent_id": 1, "roles": ["risk_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.1814, "width_m": 1.4456, "clean_frames": 34, "speed_at_event_mps": 11.6465}, {"agent_id": 4, "roles": ["context_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.1687, "width_m": 1.3982, "clean_frames": 34, "speed_at_event_mps": 0.1782}], "map_summary": {"center_xy": [10.8455, 13.0416], "radius_m": 80.0, "num_points": 256, "type_counts": {"1": 13, "6": 1, "3": 47, "2": 16, "0": 179}}, "timeline": [{"t_to_event_s": -2.3, "frame_index": 0, "target_offset_s": -5.0, 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"relative_heading_rad": -2.9294, "agent_a_speed_mps": 16.5081, "agent_b_speed_mps": 12.2477, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -0.5, "frame_index": 18, "target_offset_s": -0.5, "distance_m": 15.0135, "ttc_s": 0.4699, "closing_speed_mps": 31.9524, "relative_speed_mps": 32.0428, "longitudinal_gap_m": 15.0108, "lateral_offset_m": 0.2842, "relative_heading_rad": -2.9287, "agent_a_speed_mps": 17.9753, "agent_b_speed_mps": 14.2476, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": 0.0, "frame_index": 23, "target_offset_s": 0.0, "distance_m": 1.4903, "ttc_s": 0.047, "closing_speed_mps": 31.6875, "relative_speed_mps": 31.6907, "longitudinal_gap_m": 1.3458, "lateral_offset_m": -0.6404, "relative_heading_rad": -2.9936, "agent_a_speed_mps": 18.9078, "agent_b_speed_mps": 15.5137, "agent_a_collision": true, "agent_b_collision": true, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": 0.5, "frame_index": 28, "target_offset_s": 0.5, "distance_m": 15.1832, "ttc_s": null, "closing_speed_mps": -35.1463, "relative_speed_mps": 35.9996, "longitudinal_gap_m": -14.9212, "lateral_offset_m": -2.8085, "relative_heading_rad": 3.0702, "agent_a_speed_mps": 19.8413, "agent_b_speed_mps": 16.1812, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": 1.0, "frame_index": 33, "target_offset_s": 1.0, "distance_m": 33.7562, "ttc_s": null, "closing_speed_mps": -38.2159, "relative_speed_mps": 38.399, "longitudinal_gap_m": -33.6825, "lateral_offset_m": -2.2302, "relative_heading_rad": 3.071, "agent_a_speed_mps": 21.2423, "agent_b_speed_mps": 17.1803, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}], "llm_task": {"question": "Judge whether this is a natural and safety-critical long-tail 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"primary_pair_has_collision_agent": true, "primary_pair_has_offroad_agent": false, "distance_trend_label": "mostly_decreasing"}, "context_agent_summaries": [{"agent_id": 1, "roles": ["collided_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.1414, "width_m": 1.4302, "clean_frames": 32, "speed_at_event_mps": 1.5775}, {"agent_id": 17, "roles": ["risk_agent", "collided_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.2597, "width_m": 1.4496, "clean_frames": 22, "speed_at_event_mps": 7.8759}, {"agent_id": 2, "roles": ["context_agent"], "is_controlled": false, "response_type": 0, "raw_agent_type": 7, "length_m": 3.3076, "width_m": 1.5035, "clean_frames": 32, "speed_at_event_mps": 14.9878}, {"agent_id": 4, "roles": ["context_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.3246, "width_m": 1.5186, "clean_frames": 32, "speed_at_event_mps": 20.8681}, {"agent_id": 5, "roles": ["context_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.2225, "width_m": 1.4071, "clean_frames": 32, "speed_at_event_mps": 20.3311}, {"agent_id": 7, "roles": ["offroad_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.2615, "width_m": 1.4287, "clean_frames": 32, "speed_at_event_mps": 8.0306}, {"agent_id": 24, "roles": ["risk_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.5002, "width_m": 1.5828, "clean_frames": 32, "speed_at_event_mps": 19.8472}, {"agent_id": 27, "roles": ["risk_agent", "offroad_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 4.2011, "width_m": 1.7979, "clean_frames": 32, "speed_at_event_mps": 21.0522}], "map_summary": {"center_xy": [63.6659, 7.3346], "radius_m": 80.0, "num_points": 256, "type_counts": {"3": 142, "2": 38, "1": 76}}, "timeline": [{"t_to_event_s": -2.1, "frame_index": 0, "target_offset_s": -5.0, 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"relative_heading_rad": -0.2666, "agent_a_speed_mps": 4.0089, "agent_b_speed_mps": 1.5778, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -0.5, "frame_index": 16, "target_offset_s": -0.5, "distance_m": 4.1575, "ttc_s": 1.2793, "closing_speed_mps": 3.2497, "relative_speed_mps": 4.4997, "longitudinal_gap_m": 3.2448, "lateral_offset_m": -2.5992, "relative_heading_rad": -0.2548, "agent_a_speed_mps": 6.0087, "agent_b_speed_mps": 1.5776, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": 0.0, "frame_index": 21, "target_offset_s": 0.0, "distance_m": 2.4349, "ttc_s": 0.3886, "closing_speed_mps": 6.2656, "relative_speed_mps": 6.3061, "longitudinal_gap_m": 2.0321, "lateral_offset_m": -1.3414, "relative_heading_rad": 0.0861, "agent_a_speed_mps": 7.8759, "agent_b_speed_mps": 1.5775, "agent_a_collision": true, "agent_b_collision": true, "agent_a_offroad": false, "agent_b_offroad": false}], "llm_task": {"question": "Judge whether this is a natural and safety-critical long-tail driving scenario.", "return_format": "strict_json", "expected_keys": ["accepted", "danger_level", "naturalness_score", "interaction_score", "scenario_value_score", "scenario_type", "artifact_flags", "reason"]}}, "local_eval": {"accepted": true, "priority": "high_value", "danger_level": 4, "criticality_score": 75.306, "naturalness_score": 82.0, "interaction_score": 100.0, "scenario_value_score": 84.589, "scenario_type": "rear_end_or_following_conflict", "artifact_flags": ["high_acceleration", "high_yaw_rate"], "rejection_reasons": [], "optimization_feedback": {"increase_collision_tolerance": false, "decrease_collision_tolerance": false, "increase_goal_pressure": false, "decrease_goal_pressure": false, "increase_offroad_penalty": false, "relax_offroad_penalty": false, "increase_risk_agents_per_world": false, "decrease_risk_agents_per_world": false, 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"decrease_goal_pressure": false, "increase_offroad_penalty": false, "relax_offroad_penalty": false, "increase_risk_agents_per_world": false, "decrease_risk_agents_per_world": false, "target_scenario_types": ["rear_end_or_following_conflict"]}, "reason": "Primary pair 17->1 scored criticality=75.3, naturalness=82.0, interaction=100.0, value=84.6; notes=none.", "evidence": ["collision=True near_miss=False offroad=True", "primary_pair=17->1 reason=risk_agent_directly_collided_with_partner", "clean_min_distance=2.4349 min_ttc=0.3886", "event_relative_speed=6.3061 closing_speed=6.2656", "distance_trend=mostly_decreasing"], "high_value": true}, "final_valid": true, "final_score": 84.589, "final_priority": "high_value"} diff --git a/gpudrive-main/search_outputs/evaluator_agent_test/all_evaluations.jsonl b/gpudrive-main/search_outputs/evaluator_agent_test/all_evaluations.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..6f65b178ec60762b5a245830d46bfa7a73689ec4 --- /dev/null +++ b/gpudrive-main/search_outputs/evaluator_agent_test/all_evaluations.jsonl @@ -0,0 +1,20 @@ +{"case_id": "shard000_batch0000_world0002_line000000", "case": {"case_id": "shard000_batch0000_world0002_line000000", "schema_version": "llm_longtail_case_v1", "source": {"event_trace_line_index": 0, "scenario_file": "tfrecord-00005-of-01000_436.json", "shard_id": 0, "batch_idx": 0, "world_idx": 2}, "outcome": {"collision": true, "near_miss": true, "offroad": true, "first_collision_time_s": 2.9, "first_offroad_time_s": 3.0, "min_distance_time_s": 2.9, "event_time_s": 2.9, "raw_min_distance_m": 1.9574, "clean_min_distance_m": 1.9575}, "primary_pair": {"agent_a": 5, "agent_b": 7, "agent_a_role": ["risk_agent", "collided_agent"], "agent_b_role": ["collided_agent", "offroad_agent"], "selection_reason": "risk_agent_directly_collided_with_partner", "confidence": "high", "risk_directly_involved": true, "semantic_candidate": "same_direction_side_conflict_or_cut_in"}, "scene_summary": 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20.9832, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -0.1, "frame_index": 49, "target_offset_s": 0.0, "distance_m": 3.4719, "ttc_s": null, "closing_speed_mps": -1.1008, "relative_speed_mps": 2.5098, "longitudinal_gap_m": -1.9722, "lateral_offset_m": -2.8573, "relative_heading_rad": 0.0176, "agent_a_speed_mps": 23.9944, "agent_b_speed_mps": 21.5161, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}], "llm_task": {"question": "Judge whether this is a natural and safety-critical long-tail driving scenario.", "return_format": "strict_json", "expected_keys": ["accepted", "danger_level", "naturalness_score", "interaction_score", "scenario_value_score", "scenario_type", "artifact_flags", "reason"]}}, "local_eval": {"accepted": false, "priority": "low_value", "danger_level": 2, "criticality_score": 48.51, "naturalness_score": 72.0, "interaction_score": 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"roles": ["context_agent"], "is_controlled": false, "response_type": 0, "raw_agent_type": 7, "length_m": 3.1209, "width_m": 1.4512, "clean_frames": 42, "speed_at_event_mps": 5.3768}, {"agent_id": 3, "roles": ["risk_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 2.9235, "width_m": 1.3487, "clean_frames": 44, "speed_at_event_mps": 20.9943}, {"agent_id": 6, "roles": ["context_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.1435, "width_m": 1.4294, "clean_frames": 43, "speed_at_event_mps": 20.0458}], "map_summary": {"center_xy": [-18.793, -23.1618], "radius_m": 80.0, "num_points": 256, "type_counts": {"2": 13, "1": 5, "3": 17, "0": 221}}, "timeline": [{"t_to_event_s": -3.8, "frame_index": 0, "target_offset_s": -5.0, "distance_m": 20.0383, "ttc_s": null, "closing_speed_mps": null, "relative_speed_mps": null, "longitudinal_gap_m": -19.922, "lateral_offset_m": -2.1553, "relative_heading_rad": 0.018, 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"agent_b_offroad": false}, {"t_to_event_s": -1.0, "frame_index": 28, "target_offset_s": -1.0, "distance_m": 7.8288, "ttc_s": 1.697, "closing_speed_mps": 4.6132, "relative_speed_mps": 4.62, "longitudinal_gap_m": -7.7441, "lateral_offset_m": -1.1481, "relative_heading_rad": 0.0177, "agent_a_speed_mps": 19.6161, "agent_b_speed_mps": 24.22, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -0.5, "frame_index": 33, "target_offset_s": -0.5, "distance_m": 5.5233, "ttc_s": 1.1982, "closing_speed_mps": 4.6096, "relative_speed_mps": 4.6211, "longitudinal_gap_m": -5.4457, "lateral_offset_m": -0.9224, "relative_heading_rad": 0.0181, "agent_a_speed_mps": 20.2829, "agent_b_speed_mps": 24.8864, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": 0.0, "frame_index": 38, "target_offset_s": 0.0, "distance_m": 3.3008, "ttc_s": 0.8114, "closing_speed_mps": 4.0682, "relative_speed_mps": 4.0894, "longitudinal_gap_m": -3.2274, "lateral_offset_m": -0.6924, "relative_heading_rad": 0.0179, "agent_a_speed_mps": 20.9505, "agent_b_speed_mps": 25.0195, "agent_a_collision": true, "agent_b_collision": true, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": 0.2, "frame_index": 40, "target_offset_s": 0.5, "distance_m": 6.3712, "ttc_s": 2.5986, "closing_speed_mps": 2.4517, "relative_speed_mps": 4.3242, "longitudinal_gap_m": -6.1834, "lateral_offset_m": -1.5354, "relative_heading_rad": -0.5623, "agent_a_speed_mps": 21.4824, "agent_b_speed_mps": 24.8857, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}], "llm_task": {"question": "Judge whether this is a natural and safety-critical long-tail driving scenario.", "return_format": "strict_json", "expected_keys": ["accepted", "danger_level", "naturalness_score", "interaction_score", "scenario_value_score", "scenario_type", "artifact_flags", "reason"]}}, "local_eval": {"accepted": false, "priority": "low_value", "danger_level": 3, "criticality_score": 64.158, "naturalness_score": 0.0, "interaction_score": 100.0, "scenario_value_score": 56.946, "scenario_type": "cut_in_or_side_conflict", "artifact_flags": ["extreme_speed", "hard_deceleration", "high_acceleration", "high_yaw_rate", "large_frame_displacement"], "rejection_reasons": ["large_frame_displacement_artifact", "extreme_speed_artifact"], "optimization_feedback": {"increase_collision_tolerance": false, "decrease_collision_tolerance": true, "increase_goal_pressure": false, "decrease_goal_pressure": false, "increase_offroad_penalty": false, "relax_offroad_penalty": false, "increase_risk_agents_per_world": false, "decrease_risk_agents_per_world": true, "target_scenario_types": []}, "reason": "Rejected by deterministic artifact checks: large_frame_displacement_artifact, extreme_speed_artifact.", "evidence": ["collision=True near_miss=False offroad=False", "primary_pair=4->7 reason=risk_agent_directly_collided_with_partner", "clean_min_distance=3.3008 min_ttc=0.8114", "event_relative_speed=4.0894 closing_speed=4.0682", "distance_trend=mostly_decreasing"]}, "llm_eval": {"accepted": false, "priority": "low_value", "danger_level": 3, "criticality_score": 64.158, "naturalness_score": 0.0, "interaction_score": 100.0, "scenario_value_score": 56.946, "scenario_type": "cut_in_or_side_conflict", "artifact_flags": ["extreme_speed", "hard_deceleration", "high_acceleration", "high_yaw_rate", "large_frame_displacement"], "rejection_reasons": ["large_frame_displacement_artifact", "extreme_speed_artifact"], "optimization_feedback": {"increase_collision_tolerance": false, "decrease_collision_tolerance": true, "increase_goal_pressure": false, "decrease_goal_pressure": false, "increase_offroad_penalty": false, "relax_offroad_penalty": false, "increase_risk_agents_per_world": false, "decrease_risk_agents_per_world": true, "target_scenario_types": []}, "reason": "Rejected by deterministic artifact checks: large_frame_displacement_artifact, extreme_speed_artifact.", "evidence": ["collision=True near_miss=False offroad=False", "primary_pair=4->7 reason=risk_agent_directly_collided_with_partner", "clean_min_distance=3.3008 min_ttc=0.8114", "event_relative_speed=4.0894 closing_speed=4.0682", "distance_trend=mostly_decreasing"]}, "final_eval": {"accepted": false, "priority": "low_value", "danger_level": 3, "criticality_score": 64.158, "naturalness_score": 0.0, "interaction_score": 100.0, "scenario_value_score": 56.946, "scenario_type": "cut_in_or_side_conflict", "artifact_flags": ["extreme_speed", "hard_deceleration", "high_acceleration", "high_yaw_rate", "large_frame_displacement"], "rejection_reasons": ["large_frame_displacement_artifact", "extreme_speed_artifact"], "optimization_feedback": {"increase_collision_tolerance": false, "decrease_collision_tolerance": true, "increase_goal_pressure": false, "decrease_goal_pressure": false, 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"near_miss": false, "offroad": false, "first_collision_time_s": 3.2, "first_offroad_time_s": null, "min_distance_time_s": 0.1, "event_time_s": 3.2, "raw_min_distance_m": 4.0969, "clean_min_distance_m": 4.0743}, "primary_pair": {"agent_a": 1, "agent_b": 0, "agent_a_role": ["risk_agent"], "agent_b_role": ["context_agent"], "selection_reason": "event_min_distance_risk_partner", "confidence": "medium", "risk_directly_involved": false, "semantic_candidate": "generic_close_interaction"}, "scene_summary": {"controlled_agents": 11, "risk_agents": [1, 6, 9], "collided_agents": [15], "offroad_agents": [], "goal_agents": 11, "context_agents": [0, 1, 6, 9, 15]}, "primary_interaction_metrics": {"num_clean_pair_frames": 9, "min_clean_distance_m": 4.0743, "min_clean_distance_time_to_event_s": -3.2, "min_ttc_s": null, "time_ttc_below_1s": 0.0, "time_ttc_below_2s": 0.0, "distance_trend_before_event": {"label": "not_decreasing", "decreasing_ratio": 0.0, "distance_start_m": 4.131, "distance_end_m": 4.5567}, "metrics_at_event_or_nearest_clean_frame": {"pair_valid": true, "distance_m": 4.0743, "ttc_s": null, "closing_speed_mps": null, "relative_speed_mps": null, "longitudinal_gap_m": 0.0218, "lateral_offset_m": 4.0742, "relative_heading_rad": 0.0277, "agent_a_speed_mps": null, "agent_b_speed_mps": null}, "metrics_at_min_clean_distance": {"pair_valid": true, "distance_m": 4.0743, "ttc_s": null, "closing_speed_mps": null, "relative_speed_mps": null, "longitudinal_gap_m": 0.0218, "lateral_offset_m": 4.0742, "relative_heading_rad": 0.0277, "agent_a_speed_mps": null, "agent_b_speed_mps": null}}, "naturalness_checks": {"max_speed_mps": 22.3423, "max_accel_mps2": 4.0106, "max_decel_mps2": -0.6604, "max_yaw_rate_radps": 4.5679, "max_frame_displacement_m": 2.2342, "offroad_before_collision": false, "clean_frame_counts": {"0": 41, "1": 9, "6": 37, "9": 2, "15": 33}, "artifact_warnings": ["high_yaw_rate", "short_clean_track"]}, "interaction_checks": {"risk_agent_in_primary_pair": true, "primary_pair_has_collision_agent": false, "primary_pair_has_offroad_agent": false, "distance_trend_label": "not_decreasing"}, "context_agent_summaries": [{"agent_id": 0, "roles": ["context_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.7002, "width_m": 1.6324, "clean_frames": 41, "speed_at_event_mps": null}, {"agent_id": 1, "roles": ["risk_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.4315, "width_m": 1.5318, "clean_frames": 9, "speed_at_event_mps": null}, {"agent_id": 6, "roles": ["risk_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.01, "width_m": 1.4046, "clean_frames": 37, "speed_at_event_mps": null}, {"agent_id": 9, "roles": ["risk_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.3504, "width_m": 1.4788, "clean_frames": 2, "speed_at_event_mps": null}, {"agent_id": 15, "roles": ["collided_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.3941, "width_m": 1.5125, "clean_frames": 33, "speed_at_event_mps": null}], "map_summary": {"center_xy": [34.1139, 4.452], "radius_m": 80.0, "num_points": 256, "type_counts": {"3": 58, "1": 25, "5": 1, "2": 11, "6": 1, "0": 160}}, "timeline": [{"t_to_event_s": -3.2, "frame_index": 0, "target_offset_s": -5.0, "distance_m": 4.0743, "ttc_s": null, "closing_speed_mps": null, "relative_speed_mps": null, "longitudinal_gap_m": 0.0218, "lateral_offset_m": 4.0742, "relative_heading_rad": 0.0277, "agent_a_speed_mps": null, "agent_b_speed_mps": null, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -3.0, "frame_index": 2, "target_offset_s": -3.0, "distance_m": 4.131, "ttc_s": null, "closing_speed_mps": -0.3919, "relative_speed_mps": 2.0784, "longitudinal_gap_m": 0.4362, "lateral_offset_m": 4.1079, "relative_heading_rad": 0.0277, "agent_a_speed_mps": 4.1743, "agent_b_speed_mps": 6.2478, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}], "llm_task": {"question": "Judge whether this is a natural and safety-critical long-tail driving scenario.", "return_format": "strict_json", "expected_keys": ["accepted", "danger_level", "naturalness_score", "interaction_score", "scenario_value_score", "scenario_type", "artifact_flags", "reason"]}}, "local_eval": {"accepted": false, "priority": "reject_or_debug_only", "danger_level": 2, "criticality_score": 35.0, "naturalness_score": 80.0, "interaction_score": 40.0, "scenario_value_score": 49.1, "scenario_type": "generic_close_interaction", "artifact_flags": ["high_yaw_rate", "short_clean_track"], "rejection_reasons": ["primary_pair_not_collision_pair", "distance_not_decreasing"], "optimization_feedback": {"increase_collision_tolerance": false, "decrease_collision_tolerance": false, "increase_goal_pressure": true, "decrease_goal_pressure": false, "increase_offroad_penalty": false, "relax_offroad_penalty": false, "increase_risk_agents_per_world": true, "decrease_risk_agents_per_world": false, "target_scenario_types": []}, "reason": "Primary pair 1->0 scored criticality=35.0, naturalness=80.0, interaction=40.0, value=49.1; notes=primary_pair_not_collision_pair, distance_not_decreasing.", "evidence": ["collision=True near_miss=False offroad=False", "primary_pair=1->0 reason=event_min_distance_risk_partner", "clean_min_distance=4.0743 min_ttc=None", "event_relative_speed=None closing_speed=None", "distance_trend=not_decreasing"]}, "llm_eval": {"accepted": false, "priority": "reject_or_debug_only", "danger_level": 2, "criticality_score": 35.0, "naturalness_score": 80.0, "interaction_score": 40.0, "scenario_value_score": 49.1, "scenario_type": "generic_close_interaction", "artifact_flags": ["high_yaw_rate", "short_clean_track"], "rejection_reasons": ["primary_pair_not_collision_pair", "distance_not_decreasing"], "optimization_feedback": {"increase_collision_tolerance": false, "decrease_collision_tolerance": false, "increase_goal_pressure": true, "decrease_goal_pressure": false, "increase_offroad_penalty": false, "relax_offroad_penalty": false, "increase_risk_agents_per_world": true, "decrease_risk_agents_per_world": false, "target_scenario_types": []}, "reason": "Primary pair 1->0 scored criticality=35.0, naturalness=80.0, interaction=40.0, value=49.1; notes=primary_pair_not_collision_pair, distance_not_decreasing.", "evidence": ["collision=True near_miss=False offroad=False", "primary_pair=1->0 reason=event_min_distance_risk_partner", "clean_min_distance=4.0743 min_ttc=None", "event_relative_speed=None closing_speed=None", "distance_trend=not_decreasing"]}, "final_eval": {"accepted": false, "priority": "reject_or_debug_only", "danger_level": 2, "criticality_score": 35.0, "naturalness_score": 80.0, "interaction_score": 40.0, "scenario_value_score": 49.1, "scenario_type": "generic_close_interaction", "artifact_flags": ["high_yaw_rate", "short_clean_track"], "rejection_reasons": ["primary_pair_not_collision_pair", "distance_not_decreasing"], "optimization_feedback": {"increase_collision_tolerance": false, "decrease_collision_tolerance": false, "increase_goal_pressure": true, "decrease_goal_pressure": false, "increase_offroad_penalty": false, "relax_offroad_penalty": false, "increase_risk_agents_per_world": true, "decrease_risk_agents_per_world": false, "target_scenario_types": []}, "reason": "Primary pair 1->0 scored criticality=35.0, naturalness=80.0, interaction=40.0, value=49.1; notes=primary_pair_not_collision_pair, distance_not_decreasing.", "evidence": ["collision=True near_miss=False offroad=False", "primary_pair=1->0 reason=event_min_distance_risk_partner", "clean_min_distance=4.0743 min_ttc=None", "event_relative_speed=None closing_speed=None", "distance_trend=not_decreasing"], "high_value": false}, "final_valid": false, "final_score": 49.1, "final_priority": "reject_or_debug_only"} +{"case_id": "shard000_batch0000_world0020_line000004", "case": {"case_id": "shard000_batch0000_world0020_line000004", "schema_version": "llm_longtail_case_v1", "source": {"event_trace_line_index": 4, "scenario_file": "tfrecord-00000-of-01000_190.json", "shard_id": 0, "batch_idx": 0, "world_idx": 20}, "outcome": {"collision": true, "near_miss": false, "offroad": false, "first_collision_time_s": 4.9, "first_offroad_time_s": null, "min_distance_time_s": 4.9, "event_time_s": 4.9, "raw_min_distance_m": 2.1322, "clean_min_distance_m": 2.1322}, "primary_pair": {"agent_a": 10, "agent_b": 0, "agent_a_role": ["risk_agent", "collided_agent"], "agent_b_role": ["context_agent"], "selection_reason": "event_min_distance_risk_partner", "confidence": "medium", "risk_directly_involved": true, "semantic_candidate": "same_direction_side_conflict_or_cut_in"}, "scene_summary": {"controlled_agents": 9, "risk_agents": [2, 7, 10], "collided_agents": [10], "offroad_agents": [], "goal_agents": 9, "context_agents": [0, 10, 2, 3, 5, 6, 7]}, "primary_interaction_metrics": {"num_clean_pair_frames": 60, "min_clean_distance_m": 2.1322, "min_clean_distance_time_to_event_s": 0.0, "min_ttc_s": 0.0973, "time_ttc_below_1s": 1.2, "time_ttc_below_2s": 1.9, "distance_trend_before_event": {"label": "mostly_decreasing", "decreasing_ratio": 0.9333, "distance_start_m": 42.7184, "distance_end_m": 2.1322}, "metrics_at_event_or_nearest_clean_frame": {"pair_valid": true, "distance_m": 2.1322, "ttc_s": 0.0973, "closing_speed_mps": 21.9152, "relative_speed_mps": 24.68, "longitudinal_gap_m": 0.9973, "lateral_offset_m": -1.8846, "relative_heading_rad": 0.5289, "agent_a_speed_mps": 28.041, "agent_b_speed_mps": 6.4672}, "metrics_at_min_clean_distance": {"pair_valid": true, "distance_m": 2.1322, "ttc_s": 0.0973, "closing_speed_mps": 21.9152, "relative_speed_mps": 24.68, "longitudinal_gap_m": 0.9973, "lateral_offset_m": -1.8846, "relative_heading_rad": 0.5289, 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{"t_to_event_s": 0.8, "frame_index": 24, "target_offset_s": 1.0, "distance_m": 0.3061, "ttc_s": null, "closing_speed_mps": -1.7131, "relative_speed_mps": 11.5657, "longitudinal_gap_m": 0.1629, "lateral_offset_m": -0.2592, "relative_heading_rad": -0.3165, "agent_a_speed_mps": 9.1166, "agent_b_speed_mps": 3.6332, "agent_a_collision": true, "agent_b_collision": true, "agent_a_offroad": false, "agent_b_offroad": false}], "llm_task": {"question": "Judge whether this is a natural and safety-critical long-tail driving scenario.", "return_format": "strict_json", "expected_keys": ["accepted", "danger_level", "naturalness_score", "interaction_score", "scenario_value_score", "scenario_type", "artifact_flags", "reason"]}}, "local_eval": {"accepted": true, "priority": "high_value", "danger_level": 5, "criticality_score": 98.38, "naturalness_score": 92.0, "interaction_score": 100.0, "scenario_value_score": 97.08, "scenario_type": "rear_end_or_following_conflict", "artifact_flags": ["high_yaw_rate"], 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"length_m": 3.7002, "width_m": 1.6324, "clean_frames": 2, "speed_at_event_mps": null}, {"agent_id": 5, "roles": ["collided_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 2.9782, "width_m": 1.3462, "clean_frames": 24, "speed_at_event_mps": null}, {"agent_id": 7, "roles": ["risk_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.2675, "width_m": 1.4398, "clean_frames": 2, "speed_at_event_mps": null}], "map_summary": {"center_xy": [5.8498, -15.4747], "radius_m": 80.0, "num_points": 256, "type_counts": {"0": 256}}, "timeline": [{"t_to_event_s": -1.8, "frame_index": 0, "target_offset_s": -5.0, "distance_m": 19.2038, "ttc_s": null, "closing_speed_mps": null, "relative_speed_mps": null, "longitudinal_gap_m": 18.8756, "lateral_offset_m": -3.5352, "relative_heading_rad": 0.0122, "agent_a_speed_mps": null, "agent_b_speed_mps": null, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}], "llm_task": {"question": "Judge whether this is a natural and safety-critical long-tail driving scenario.", "return_format": "strict_json", "expected_keys": ["accepted", "danger_level", "naturalness_score", "interaction_score", "scenario_value_score", "scenario_type", "artifact_flags", "reason"]}}, "local_eval": {"accepted": false, "priority": "reject_or_debug_only", "danger_level": 2, "criticality_score": 43.02, "naturalness_score": 80.0, "interaction_score": 40.0, "scenario_value_score": 52.468, "scenario_type": "generic_close_interaction", "artifact_flags": ["high_yaw_rate", "short_clean_track"], "rejection_reasons": ["primary_pair_not_collision_pair", "too_few_clean_pair_frames"], "optimization_feedback": {"increase_collision_tolerance": false, "decrease_collision_tolerance": false, "increase_goal_pressure": true, "decrease_goal_pressure": true, "increase_offroad_penalty": false, "relax_offroad_penalty": false, "increase_risk_agents_per_world": true, 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{"increase_collision_tolerance": false, "decrease_collision_tolerance": false, "increase_goal_pressure": true, "decrease_goal_pressure": false, "increase_offroad_penalty": false, "relax_offroad_penalty": false, "increase_risk_agents_per_world": true, "decrease_risk_agents_per_world": false, "target_scenario_types": []}, "reason": "Primary pair 5->3 scored criticality=48.8, naturalness=92.0, interaction=55.0, value=62.8; notes=primary_pair_not_collision_pair, distance_not_decreasing.", "evidence": ["collision=True near_miss=False offroad=False", "primary_pair=5->3 reason=event_min_distance_risk_partner", "clean_min_distance=5.9556 min_ttc=1.0781", "event_relative_speed=3.7861 closing_speed=-3.7143", "distance_trend=not_decreasing"]}, "llm_eval": {"accepted": false, "priority": "low_value", "danger_level": 2, "criticality_score": 48.786, "naturalness_score": 92.0, "interaction_score": 55.0, "scenario_value_score": 62.75, "scenario_type": "generic_close_interaction", "artifact_flags": 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"high_value": false}, "final_valid": false, "final_score": 62.75, "final_priority": "low_value"} +{"case_id": "shard000_batch0000_world0088_line000017", "case": {"case_id": "shard000_batch0000_world0088_line000017", "schema_version": "llm_longtail_case_v1", "source": {"event_trace_line_index": 17, "scenario_file": "tfrecord-00000-of-01000_326.json", "shard_id": 0, "batch_idx": 0, "world_idx": 88}, "outcome": {"collision": true, "near_miss": false, "offroad": true, "first_collision_time_s": 2.1, "first_offroad_time_s": 3.3, "min_distance_time_s": 2.1, "event_time_s": 2.1, "raw_min_distance_m": 2.435, "clean_min_distance_m": 2.4349}, "primary_pair": {"agent_a": 17, "agent_b": 1, "agent_a_role": ["risk_agent", "collided_agent"], "agent_b_role": ["collided_agent"], "selection_reason": "risk_agent_directly_collided_with_partner", "confidence": "high", "risk_directly_involved": true, "semantic_candidate": "rear_end_or_following_conflict"}, "scene_summary": {"controlled_agents": 14, 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"raw_agent_type": 7, "length_m": 3.5002, "width_m": 1.5828, "clean_frames": 32, "speed_at_event_mps": 19.8472}, {"agent_id": 27, "roles": ["risk_agent", "offroad_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 4.2011, "width_m": 1.7979, "clean_frames": 32, "speed_at_event_mps": 21.0522}], "map_summary": {"center_xy": [63.6659, 7.3346], "radius_m": 80.0, "num_points": 256, "type_counts": {"3": 142, "2": 38, "1": 76}}, "timeline": [{"t_to_event_s": -2.1, "frame_index": 0, "target_offset_s": -5.0, "distance_m": 7.2844, "ttc_s": null, "closing_speed_mps": null, "relative_speed_mps": null, "longitudinal_gap_m": 7.2763, "lateral_offset_m": -0.3451, "relative_heading_rad": 0.0311, "agent_a_speed_mps": null, "agent_b_speed_mps": null, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -2.0, "frame_index": 1, "target_offset_s": -2.0, "distance_m": 7.2567, "ttc_s": 26.1496, 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"agent_a_speed_mps": 6.0087, "agent_b_speed_mps": 1.5776, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": 0.0, "frame_index": 21, "target_offset_s": 0.0, "distance_m": 2.4349, "ttc_s": 0.3886, "closing_speed_mps": 6.2656, "relative_speed_mps": 6.3061, "longitudinal_gap_m": 2.0321, "lateral_offset_m": -1.3414, "relative_heading_rad": 0.0861, "agent_a_speed_mps": 7.8759, "agent_b_speed_mps": 1.5775, "agent_a_collision": true, "agent_b_collision": true, "agent_a_offroad": false, "agent_b_offroad": false}], "llm_task": {"question": "Judge whether this is a natural and safety-critical long-tail driving scenario.", "return_format": "strict_json", "expected_keys": ["accepted", "danger_level", "naturalness_score", "interaction_score", "scenario_value_score", "scenario_type", "artifact_flags", "reason"]}}, "local_eval": {"accepted": true, "priority": "high_value", "danger_level": 4, "criticality_score": 75.306, "naturalness_score": 82.0, "interaction_score": 100.0, "scenario_value_score": 84.589, "scenario_type": "rear_end_or_following_conflict", "artifact_flags": ["high_acceleration", "high_yaw_rate"], "rejection_reasons": [], "optimization_feedback": {"increase_collision_tolerance": false, "decrease_collision_tolerance": false, "increase_goal_pressure": false, "decrease_goal_pressure": false, "increase_offroad_penalty": false, "relax_offroad_penalty": false, "increase_risk_agents_per_world": false, "decrease_risk_agents_per_world": false, "target_scenario_types": ["rear_end_or_following_conflict"]}, "reason": "Primary pair 17->1 scored criticality=75.3, naturalness=82.0, interaction=100.0, value=84.6; notes=none.", "evidence": ["collision=True near_miss=False offroad=True", "primary_pair=17->1 reason=risk_agent_directly_collided_with_partner", "clean_min_distance=2.4349 min_ttc=0.3886", "event_relative_speed=6.3061 closing_speed=6.2656", "distance_trend=mostly_decreasing"]}, "llm_eval": 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"increase_risk_agents_per_world": true, "decrease_risk_agents_per_world": false, "target_scenario_types": []}, "reason": "Primary pair 15->4 scored criticality=48.9, naturalness=72.0, interaction=55.0, value=57.2; notes=primary_pair_not_collision_pair, distance_not_decreasing, offroad_before_collision.", "evidence": ["collision=True near_miss=False offroad=True", "primary_pair=15->4 reason=event_min_distance_risk_partner", "clean_min_distance=3.293 min_ttc=2.6387", "event_relative_speed=2.8559 closing_speed=-2.608", "distance_trend=not_decreasing"], "high_value": false}, "final_valid": false, "final_score": 57.179, "final_priority": "low_value"} diff --git a/gpudrive-main/search_outputs/evaluator_agent_test/optimizer_metrics.json b/gpudrive-main/search_outputs/evaluator_agent_test/optimizer_metrics.json new file mode 100644 index 0000000000000000000000000000000000000000..7c5625c0b245585dadee39174535ff8855d84435 --- /dev/null +++ b/gpudrive-main/search_outputs/evaluator_agent_test/optimizer_metrics.json @@ -0,0 +1,893 @@ +{ + "schema_version": "optimizer_feedback_v1", + "source_cases": "search_outputs\\llm_cases_shard_000\\llm_cases.jsonl", + "evaluator": { + "model": "dry_run_rule_based", + "dry_run": true, + "accept_threshold": 70.0, + "high_value_threshold": 82.0 + }, + "current_search_parameters": { + "risk_collision_weight": 0.0, + "risk_goal_weight": 2.0, + "risk_offroad_weight": -0.4, + "risk_agents_per_world": 3, + "normal_style": "balanced", + "risk_style": "risk_taker" + }, + "objective_metrics": { + "total_evaluated": 20, + "accepted_count": 8, + "rejected_count": 12, + "high_value_count": 7, + "accepted_rate": 0.4, + "high_value_rate": 0.35, + "natural_critical_count": 8, + "natural_critical_rate": 0.4, + "risk_direct_involved_rate": 0.4, + "risk_direct_accept_rate": 0.875, + "warning_record_count": 20, + "warning_record_rate": 1.0, + "hard_artifact_record_count": 3, + "hard_artifact_record_rate": 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"increase_risk_agents_per_world": true, + "decrease_risk_agents_per_world": false, + "target_scenario_types": [] + } + }, + { + "case_id": "shard000_batch0000_world0045_line000009", + "scenario_file": "tfrecord-00002-of-01000_482.json", + "accepted": false, + "high_value": false, + "scenario_value_score": 44.461, + "danger_level": 1, + "scenario_type": "crossing_or_intersection_conflict", + "artifact_flags": [ + "high_yaw_rate", + "short_clean_track" + ], + "rejection_reasons": [ + "too_few_clean_pair_frames" + ], + "risk_directly_involved": false, + "primary_pair": { + "agent_a": 41, + "agent_b": 7, + "selection_reason": "event_min_distance_risk_partner" + }, + "optimizer_feedback": { + "increase_collision_tolerance": true, + "decrease_collision_tolerance": false, + "increase_goal_pressure": true, + "decrease_goal_pressure": true, + "increase_offroad_penalty": false, + "relax_offroad_penalty": false, + "increase_risk_agents_per_world": true, + "decrease_risk_agents_per_world": false, + "target_scenario_types": [] + } + }, + { + "case_id": "shard000_batch0000_world0034_line000007", + "scenario_file": "tfrecord-00000-of-01000_464.json", + "accepted": false, + "high_value": false, + "scenario_value_score": 44.138, + "danger_level": 1, + "scenario_type": "cut_in_or_side_conflict", + "artifact_flags": [ + "short_clean_track" + ], + "rejection_reasons": [ + "too_few_clean_pair_frames" + ], + "risk_directly_involved": false, + "primary_pair": { + "agent_a": 1, + "agent_b": 7, + "selection_reason": "event_min_distance_risk_partner" + }, + "optimizer_feedback": { + "increase_collision_tolerance": true, + "decrease_collision_tolerance": false, + "increase_goal_pressure": true, + "decrease_goal_pressure": true, + "increase_offroad_penalty": false, + "relax_offroad_penalty": false, + "increase_risk_agents_per_world": true, + "decrease_risk_agents_per_world": false, + "target_scenario_types": [] + } + }, + { + "case_id": "shard000_batch0000_world0093_line000018", + "scenario_file": "tfrecord-00002-of-01000_482.json", + "accepted": false, + "high_value": false, + "scenario_value_score": 44.06, + "danger_level": 1, + "scenario_type": "crossing_or_intersection_conflict", + "artifact_flags": [ + "high_yaw_rate", + "short_clean_track" + ], + "rejection_reasons": [ + "too_few_clean_pair_frames" + ], + "risk_directly_involved": false, + "primary_pair": { + "agent_a": 41, + "agent_b": 7, + "selection_reason": "event_min_distance_risk_partner" + }, + "optimizer_feedback": { + "increase_collision_tolerance": true, + "decrease_collision_tolerance": false, + "increase_goal_pressure": true, + "decrease_goal_pressure": true, + "increase_offroad_penalty": false, + "relax_offroad_penalty": false, + "increase_risk_agents_per_world": true, + "decrease_risk_agents_per_world": false, + "target_scenario_types": [] + } + } + ] +} \ No newline at end of file diff --git a/gpudrive-main/search_outputs/evaluator_agent_test/rejected_cases.jsonl b/gpudrive-main/search_outputs/evaluator_agent_test/rejected_cases.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..93c0dd5a35a0ebea2d8b9f452a38a7dc23f6129a --- /dev/null +++ b/gpudrive-main/search_outputs/evaluator_agent_test/rejected_cases.jsonl @@ -0,0 +1,12 @@ +{"case_id": "shard000_batch0000_world0009_line000001", "case": {"case_id": "shard000_batch0000_world0009_line000001", "schema_version": "llm_longtail_case_v1", "source": {"event_trace_line_index": 1, "scenario_file": "tfrecord-00007-of-01000_251.json", "shard_id": 0, "batch_idx": 0, "world_idx": 9}, "outcome": {"collision": true, "near_miss": false, "offroad": true, "first_collision_time_s": 5.4, "first_offroad_time_s": 4.0, "min_distance_time_s": 4.7, "event_time_s": 5.4, "raw_min_distance_m": 3.1167, "clean_min_distance_m": 3.1166}, "primary_pair": {"agent_a": 4, "agent_b": 8, "agent_a_role": ["risk_agent"], "agent_b_role": ["context_agent"], "selection_reason": "event_min_distance_risk_partner", "confidence": "medium", "risk_directly_involved": false, "semantic_candidate": "same_direction_side_conflict_or_cut_in"}, "scene_summary": {"controlled_agents": 18, "risk_agents": [3, 4, 6], "collided_agents": [5, 11], "offroad_agents": [18], "goal_agents": 18, "context_agents": [4, 8, 0, 3, 5, 6, 11, 18]}, "primary_interaction_metrics": {"num_clean_pair_frames": 50, "min_clean_distance_m": 3.1166, "min_clean_distance_time_to_event_s": -0.7, "min_ttc_s": 2.5086, "time_ttc_below_1s": 0.0, "time_ttc_below_2s": 0.0, "distance_trend_before_event": {"label": "mostly_decreasing", "decreasing_ratio": 0.7931, "distance_start_m": 6.4593, "distance_end_m": 3.4719}, "metrics_at_event_or_nearest_clean_frame": {"pair_valid": true, "distance_m": 3.4719, "ttc_s": null, "closing_speed_mps": -1.1008, "relative_speed_mps": 2.5098, "longitudinal_gap_m": -1.9722, "lateral_offset_m": -2.8573, "relative_heading_rad": 0.0176, "agent_a_speed_mps": 23.9944, "agent_b_speed_mps": 21.5161}, "metrics_at_min_clean_distance": {"pair_valid": true, "distance_m": 3.1166, "ttc_s": null, "closing_speed_mps": -0.0278, "relative_speed_mps": 2.5084, "longitudinal_gap_m": -0.4802, "lateral_offset_m": -3.0794, "relative_heading_rad": 0.0173, "agent_a_speed_mps": 23.1957, "agent_b_speed_mps": 20.7162}}, "naturalness_checks": {"max_speed_mps": 33.7012, "max_accel_mps2": 4.0153, "max_decel_mps2": 3.9965, "max_yaw_rate_radps": 14.554, "max_frame_displacement_m": 3.3701, "offroad_before_collision": true, "clean_frame_counts": {"4": 55, "8": 50, "0": 58, "3": 55, "5": 61, "6": 30, "11": 51, "18": 43}, "artifact_warnings": ["high_yaw_rate", "offroad_before_collision"]}, "interaction_checks": {"risk_agent_in_primary_pair": true, "primary_pair_has_collision_agent": false, "primary_pair_has_offroad_agent": false, "distance_trend_label": "mostly_decreasing"}, "context_agent_summaries": [{"agent_id": 4, "roles": ["risk_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 4.3251, "width_m": 1.9082, "clean_frames": 55, "speed_at_event_mps": 23.9944}, {"agent_id": 8, "roles": ["context_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.219, "width_m": 1.473, "clean_frames": 50, "speed_at_event_mps": 21.5161}, {"agent_id": 0, "roles": ["context_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.7002, "width_m": 1.6324, "clean_frames": 58, "speed_at_event_mps": 21.8984}, {"agent_id": 3, "roles": ["risk_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.1886, "width_m": 1.4024, "clean_frames": 55, "speed_at_event_mps": 20.7969}, {"agent_id": 5, "roles": ["collided_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.2417, "width_m": 1.3969, "clean_frames": 61, "speed_at_event_mps": 33.3017}, {"agent_id": 6, "roles": ["risk_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.1494, "width_m": 1.4529, "clean_frames": 30, "speed_at_event_mps": null}, {"agent_id": 11, "roles": ["collided_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.0509, "width_m": 1.4043, "clean_frames": 51, "speed_at_event_mps": 23.837}, {"agent_id": 18, "roles": ["offroad_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.2048, "width_m": 1.388, "clean_frames": 43, "speed_at_event_mps": null}], "map_summary": {"center_xy": [11.7025, 81.9044], "radius_m": 80.0, "num_points": 256, "type_counts": {"3": 108, "2": 68, "1": 78, "6": 1, "4": 1}}, "timeline": [{"t_to_event_s": -5.0, "frame_index": 0, "target_offset_s": -5.0, "distance_m": 12.2163, "ttc_s": null, "closing_speed_mps": null, "relative_speed_mps": null, "longitudinal_gap_m": 11.4296, "lateral_offset_m": -4.3129, "relative_heading_rad": 0.0176, "agent_a_speed_mps": null, "agent_b_speed_mps": null, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -3.0, "frame_index": 20, "target_offset_s": -3.0, "distance_m": 6.4593, "ttc_s": 3.31, "closing_speed_mps": 1.9514, "relative_speed_mps": 2.2334, "longitudinal_gap_m": 5.2038, "lateral_offset_m": -3.8266, "relative_heading_rad": 0.0161, "agent_a_speed_mps": 19.7301, "agent_b_speed_mps": 17.5167, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -2.0, "frame_index": 30, "target_offset_s": -2.0, "distance_m": 4.4688, "ttc_s": 2.5086, "closing_speed_mps": 1.7814, "relative_speed_mps": 2.503, "longitudinal_gap_m": 2.7526, "lateral_offset_m": -3.5204, "relative_heading_rad": 0.017, "agent_a_speed_mps": 21.4632, "agent_b_speed_mps": 18.9835, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -1.0, "frame_index": 40, "target_offset_s": -1.0, "distance_m": 3.1972, "ttc_s": 5.7704, "closing_speed_mps": 0.5541, "relative_speed_mps": 2.5069, "longitudinal_gap_m": 0.2657, "lateral_offset_m": -3.1861, "relative_heading_rad": 0.0172, "agent_a_speed_mps": 22.7958, "agent_b_speed_mps": 20.3163, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -0.5, "frame_index": 45, "target_offset_s": -0.5, "distance_m": 3.1616, "ttc_s": null, "closing_speed_mps": -0.4216, "relative_speed_mps": 2.5091, "longitudinal_gap_m": -0.9775, "lateral_offset_m": -3.0067, "relative_heading_rad": 0.0174, "agent_a_speed_mps": 23.4626, "agent_b_speed_mps": 20.9832, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -0.1, "frame_index": 49, "target_offset_s": 0.0, "distance_m": 3.4719, "ttc_s": null, "closing_speed_mps": -1.1008, "relative_speed_mps": 2.5098, "longitudinal_gap_m": -1.9722, "lateral_offset_m": -2.8573, "relative_heading_rad": 0.0176, "agent_a_speed_mps": 23.9944, "agent_b_speed_mps": 21.5161, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}], "llm_task": {"question": "Judge whether this is a natural and safety-critical long-tail driving scenario.", "return_format": "strict_json", "expected_keys": ["accepted", "danger_level", "naturalness_score", "interaction_score", "scenario_value_score", "scenario_type", "artifact_flags", "reason"]}}, "local_eval": {"accepted": false, "priority": "low_value", "danger_level": 2, "criticality_score": 48.51, "naturalness_score": 72.0, "interaction_score": 70.0, "scenario_value_score": 61.534, "scenario_type": "cut_in_or_side_conflict", "artifact_flags": ["high_yaw_rate", "offroad_before_collision"], "rejection_reasons": ["primary_pair_not_collision_pair", "offroad_before_collision"], "optimization_feedback": {"increase_collision_tolerance": false, "decrease_collision_tolerance": false, "increase_goal_pressure": false, "decrease_goal_pressure": true, "increase_offroad_penalty": true, "relax_offroad_penalty": false, "increase_risk_agents_per_world": true, "decrease_risk_agents_per_world": false, "target_scenario_types": []}, "reason": "Primary pair 4->8 scored criticality=48.5, naturalness=72.0, interaction=70.0, value=61.5; notes=primary_pair_not_collision_pair, offroad_before_collision.", "evidence": ["collision=True near_miss=False offroad=True", "primary_pair=4->8 reason=event_min_distance_risk_partner", "clean_min_distance=3.1166 min_ttc=2.5086", "event_relative_speed=2.5098 closing_speed=-1.1008", "distance_trend=mostly_decreasing"]}, "llm_eval": {"accepted": false, "priority": "low_value", "danger_level": 2, "criticality_score": 48.51, "naturalness_score": 72.0, "interaction_score": 70.0, "scenario_value_score": 61.534, "scenario_type": "cut_in_or_side_conflict", "artifact_flags": ["high_yaw_rate", "offroad_before_collision"], "rejection_reasons": ["primary_pair_not_collision_pair", "offroad_before_collision"], "optimization_feedback": {"increase_collision_tolerance": false, "decrease_collision_tolerance": false, "increase_goal_pressure": false, "decrease_goal_pressure": true, "increase_offroad_penalty": true, "relax_offroad_penalty": false, "increase_risk_agents_per_world": true, "decrease_risk_agents_per_world": false, "target_scenario_types": []}, "reason": "Primary pair 4->8 scored criticality=48.5, naturalness=72.0, interaction=70.0, value=61.5; notes=primary_pair_not_collision_pair, offroad_before_collision.", "evidence": ["collision=True near_miss=False offroad=True", "primary_pair=4->8 reason=event_min_distance_risk_partner", "clean_min_distance=3.1166 min_ttc=2.5086", "event_relative_speed=2.5098 closing_speed=-1.1008", "distance_trend=mostly_decreasing"]}, "final_eval": {"accepted": false, "priority": "low_value", "danger_level": 2, "criticality_score": 48.51, "naturalness_score": 72.0, "interaction_score": 70.0, "scenario_value_score": 61.534, "scenario_type": "cut_in_or_side_conflict", "artifact_flags": ["high_yaw_rate", "offroad_before_collision"], "rejection_reasons": ["primary_pair_not_collision_pair", "offroad_before_collision"], "optimization_feedback": {"increase_collision_tolerance": false, "decrease_collision_tolerance": false, "increase_goal_pressure": false, "decrease_goal_pressure": true, "increase_offroad_penalty": true, "relax_offroad_penalty": false, "increase_risk_agents_per_world": true, "decrease_risk_agents_per_world": false, "target_scenario_types": []}, "reason": "Primary pair 4->8 scored criticality=48.5, naturalness=72.0, interaction=70.0, value=61.5; notes=primary_pair_not_collision_pair, offroad_before_collision.", "evidence": ["collision=True near_miss=False offroad=True", "primary_pair=4->8 reason=event_min_distance_risk_partner", "clean_min_distance=3.1166 min_ttc=2.5086", "event_relative_speed=2.5098 closing_speed=-1.1008", "distance_trend=mostly_decreasing"], "high_value": false}, "final_valid": false, "final_score": 61.534, "final_priority": "low_value"} +{"case_id": "shard000_batch0000_world0012_line000002", "case": {"case_id": "shard000_batch0000_world0012_line000002", "schema_version": "llm_longtail_case_v1", "source": {"event_trace_line_index": 2, "scenario_file": "tfrecord-00001-of-01000_19.json", "shard_id": 0, "batch_idx": 0, "world_idx": 12}, "outcome": {"collision": true, "near_miss": false, "offroad": false, "first_collision_time_s": 3.8, "first_offroad_time_s": null, "min_distance_time_s": 3.8, "event_time_s": 3.8, "raw_min_distance_m": 3.3008, "clean_min_distance_m": 3.3008}, "primary_pair": {"agent_a": 4, "agent_b": 7, "agent_a_role": ["risk_agent", "collided_agent"], "agent_b_role": ["risk_agent", "collided_agent"], "selection_reason": "risk_agent_directly_collided_with_partner", "confidence": "high", "risk_directly_involved": true, "semantic_candidate": "same_direction_side_conflict_or_cut_in"}, "scene_summary": {"controlled_agents": 6, "risk_agents": [3, 4, 7], "collided_agents": [4, 7], "offroad_agents": [], "goal_agents": 6, "context_agents": [4, 7, 0, 1, 2, 3, 6]}, "primary_interaction_metrics": {"num_clean_pair_frames": 41, "min_clean_distance_m": 3.3008, "min_clean_distance_time_to_event_s": 0.0, "min_ttc_s": 0.8114, "time_ttc_below_1s": 0.4, "time_ttc_below_2s": 1.4, "distance_trend_before_event": {"label": "mostly_decreasing", "decreasing_ratio": 1.0, "distance_start_m": 16.6732, "distance_end_m": 3.3008}, "metrics_at_event_or_nearest_clean_frame": {"pair_valid": true, "distance_m": 3.3008, "ttc_s": 0.8114, "closing_speed_mps": 4.0682, "relative_speed_mps": 4.0894, "longitudinal_gap_m": -3.2274, "lateral_offset_m": -0.6924, "relative_heading_rad": 0.0179, "agent_a_speed_mps": 20.9505, "agent_b_speed_mps": 25.0195}, "metrics_at_min_clean_distance": {"pair_valid": true, "distance_m": 3.3008, "ttc_s": 0.8114, "closing_speed_mps": 4.0682, "relative_speed_mps": 4.0894, "longitudinal_gap_m": -3.2274, "lateral_offset_m": -0.6924, "relative_heading_rad": 0.0179, "agent_a_speed_mps": 20.9505, "agent_b_speed_mps": 25.0195}}, "naturalness_checks": {"max_speed_mps": 654.3072, "max_accel_mps2": 6488.7513, "max_decel_mps2": 18.4696, "max_yaw_rate_radps": 30.336, "max_frame_displacement_m": 65.4307, "offroad_before_collision": false, "clean_frame_counts": {"4": 43, "7": 41, "0": 49, "1": 44, "2": 42, "3": 44, "6": 43}, "artifact_warnings": ["extreme_speed", "hard_deceleration", "high_acceleration", "high_yaw_rate", "large_frame_displacement"]}, "interaction_checks": {"risk_agent_in_primary_pair": true, "primary_pair_has_collision_agent": true, "primary_pair_has_offroad_agent": false, "distance_trend_label": "mostly_decreasing"}, "context_agent_summaries": [{"agent_id": 4, "roles": ["risk_agent", "collided_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.3869, "width_m": 1.5994, "clean_frames": 43, "speed_at_event_mps": 20.9505}, {"agent_id": 7, "roles": ["risk_agent", "collided_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.101, "width_m": 1.4958, "clean_frames": 41, "speed_at_event_mps": 25.0195}, {"agent_id": 0, "roles": ["context_agent"], "is_controlled": false, "response_type": 0, "raw_agent_type": 7, "length_m": 3.7002, "width_m": 1.6324, "clean_frames": 49, "speed_at_event_mps": 5.4707}, {"agent_id": 1, "roles": ["context_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 2.9684, "width_m": 1.3518, "clean_frames": 44, "speed_at_event_mps": 21.0514}, {"agent_id": 2, "roles": ["context_agent"], "is_controlled": false, "response_type": 0, "raw_agent_type": 7, "length_m": 3.1209, "width_m": 1.4512, "clean_frames": 42, "speed_at_event_mps": 5.3768}, {"agent_id": 3, "roles": ["risk_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 2.9235, "width_m": 1.3487, "clean_frames": 44, "speed_at_event_mps": 20.9943}, {"agent_id": 6, "roles": ["context_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.1435, "width_m": 1.4294, "clean_frames": 43, "speed_at_event_mps": 20.0458}], "map_summary": {"center_xy": [-18.793, -23.1618], "radius_m": 80.0, "num_points": 256, "type_counts": {"2": 13, "1": 5, "3": 17, "0": 221}}, "timeline": [{"t_to_event_s": -3.8, "frame_index": 0, "target_offset_s": -5.0, "distance_m": 20.0383, "ttc_s": null, "closing_speed_mps": null, "relative_speed_mps": null, "longitudinal_gap_m": -19.922, "lateral_offset_m": -2.1553, "relative_heading_rad": 0.018, "agent_a_speed_mps": null, "agent_b_speed_mps": null, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -3.0, "frame_index": 8, "target_offset_s": -3.0, "distance_m": 16.6732, "ttc_s": 3.9614, "closing_speed_mps": 4.2089, "relative_speed_mps": 4.212, "longitudinal_gap_m": -16.5609, "lateral_offset_m": -1.9318, "relative_heading_rad": 0.0187, 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"agent_b_offroad": false}, {"t_to_event_s": -0.5, "frame_index": 33, "target_offset_s": -0.5, "distance_m": 5.5233, "ttc_s": 1.1982, "closing_speed_mps": 4.6096, "relative_speed_mps": 4.6211, "longitudinal_gap_m": -5.4457, "lateral_offset_m": -0.9224, "relative_heading_rad": 0.0181, "agent_a_speed_mps": 20.2829, "agent_b_speed_mps": 24.8864, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": 0.0, "frame_index": 38, "target_offset_s": 0.0, "distance_m": 3.3008, "ttc_s": 0.8114, "closing_speed_mps": 4.0682, "relative_speed_mps": 4.0894, "longitudinal_gap_m": -3.2274, "lateral_offset_m": -0.6924, "relative_heading_rad": 0.0179, "agent_a_speed_mps": 20.9505, "agent_b_speed_mps": 25.0195, "agent_a_collision": true, "agent_b_collision": true, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": 0.2, "frame_index": 40, "target_offset_s": 0.5, "distance_m": 6.3712, "ttc_s": 2.5986, "closing_speed_mps": 2.4517, "relative_speed_mps": 4.3242, "longitudinal_gap_m": -6.1834, "lateral_offset_m": -1.5354, "relative_heading_rad": -0.5623, "agent_a_speed_mps": 21.4824, "agent_b_speed_mps": 24.8857, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}], "llm_task": {"question": "Judge whether this is a natural and safety-critical long-tail driving scenario.", "return_format": "strict_json", "expected_keys": ["accepted", "danger_level", "naturalness_score", "interaction_score", "scenario_value_score", "scenario_type", "artifact_flags", "reason"]}}, "local_eval": {"accepted": false, "priority": "low_value", "danger_level": 3, "criticality_score": 64.158, "naturalness_score": 0.0, "interaction_score": 100.0, "scenario_value_score": 56.946, "scenario_type": "cut_in_or_side_conflict", "artifact_flags": ["extreme_speed", "hard_deceleration", "high_acceleration", "high_yaw_rate", "large_frame_displacement"], "rejection_reasons": ["large_frame_displacement_artifact", "extreme_speed_artifact"], "optimization_feedback": {"increase_collision_tolerance": false, "decrease_collision_tolerance": true, "increase_goal_pressure": false, "decrease_goal_pressure": false, "increase_offroad_penalty": false, "relax_offroad_penalty": false, "increase_risk_agents_per_world": false, "decrease_risk_agents_per_world": true, "target_scenario_types": []}, "reason": "Rejected by deterministic artifact checks: large_frame_displacement_artifact, extreme_speed_artifact.", "evidence": ["collision=True near_miss=False offroad=False", "primary_pair=4->7 reason=risk_agent_directly_collided_with_partner", "clean_min_distance=3.3008 min_ttc=0.8114", "event_relative_speed=4.0894 closing_speed=4.0682", "distance_trend=mostly_decreasing"]}, "llm_eval": {"accepted": false, "priority": "low_value", "danger_level": 3, "criticality_score": 64.158, "naturalness_score": 0.0, "interaction_score": 100.0, "scenario_value_score": 56.946, "scenario_type": "cut_in_or_side_conflict", "artifact_flags": ["extreme_speed", "hard_deceleration", "high_acceleration", "high_yaw_rate", "large_frame_displacement"], "rejection_reasons": ["large_frame_displacement_artifact", "extreme_speed_artifact"], "optimization_feedback": {"increase_collision_tolerance": false, "decrease_collision_tolerance": true, "increase_goal_pressure": false, "decrease_goal_pressure": false, "increase_offroad_penalty": false, "relax_offroad_penalty": false, "increase_risk_agents_per_world": false, "decrease_risk_agents_per_world": true, "target_scenario_types": []}, "reason": "Rejected by deterministic artifact checks: large_frame_displacement_artifact, extreme_speed_artifact.", "evidence": ["collision=True near_miss=False offroad=False", "primary_pair=4->7 reason=risk_agent_directly_collided_with_partner", "clean_min_distance=3.3008 min_ttc=0.8114", "event_relative_speed=4.0894 closing_speed=4.0682", "distance_trend=mostly_decreasing"]}, "final_eval": {"accepted": false, 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"confidence": "medium", "risk_directly_involved": false, "semantic_candidate": "generic_close_interaction"}, "scene_summary": {"controlled_agents": 11, "risk_agents": [1, 6, 9], "collided_agents": [15], "offroad_agents": [], "goal_agents": 11, "context_agents": [0, 1, 6, 9, 15]}, "primary_interaction_metrics": {"num_clean_pair_frames": 9, "min_clean_distance_m": 4.0743, "min_clean_distance_time_to_event_s": -3.2, "min_ttc_s": null, "time_ttc_below_1s": 0.0, "time_ttc_below_2s": 0.0, "distance_trend_before_event": {"label": "not_decreasing", "decreasing_ratio": 0.0, "distance_start_m": 4.131, "distance_end_m": 4.5567}, "metrics_at_event_or_nearest_clean_frame": {"pair_valid": true, "distance_m": 4.0743, "ttc_s": null, "closing_speed_mps": null, "relative_speed_mps": null, "longitudinal_gap_m": 0.0218, "lateral_offset_m": 4.0742, "relative_heading_rad": 0.0277, "agent_a_speed_mps": null, "agent_b_speed_mps": null}, "metrics_at_min_clean_distance": {"pair_valid": true, "distance_m": 4.0743, "ttc_s": null, "closing_speed_mps": null, "relative_speed_mps": null, "longitudinal_gap_m": 0.0218, "lateral_offset_m": 4.0742, "relative_heading_rad": 0.0277, "agent_a_speed_mps": null, "agent_b_speed_mps": null}}, "naturalness_checks": {"max_speed_mps": 22.3423, "max_accel_mps2": 4.0106, "max_decel_mps2": -0.6604, "max_yaw_rate_radps": 4.5679, "max_frame_displacement_m": 2.2342, "offroad_before_collision": false, "clean_frame_counts": {"0": 41, "1": 9, "6": 37, "9": 2, "15": 33}, "artifact_warnings": ["high_yaw_rate", "short_clean_track"]}, "interaction_checks": {"risk_agent_in_primary_pair": true, "primary_pair_has_collision_agent": false, "primary_pair_has_offroad_agent": false, "distance_trend_label": "not_decreasing"}, "context_agent_summaries": [{"agent_id": 0, "roles": ["context_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.7002, "width_m": 1.6324, "clean_frames": 41, "speed_at_event_mps": null}, {"agent_id": 1, "roles": ["risk_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.4315, "width_m": 1.5318, "clean_frames": 9, "speed_at_event_mps": null}, {"agent_id": 6, "roles": ["risk_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.01, "width_m": 1.4046, "clean_frames": 37, "speed_at_event_mps": null}, {"agent_id": 9, "roles": ["risk_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.3504, "width_m": 1.4788, "clean_frames": 2, "speed_at_event_mps": null}, {"agent_id": 15, "roles": ["collided_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.3941, "width_m": 1.5125, "clean_frames": 33, "speed_at_event_mps": null}], "map_summary": {"center_xy": [34.1139, 4.452], "radius_m": 80.0, "num_points": 256, "type_counts": {"3": 58, "1": 25, "5": 1, "2": 11, "6": 1, "0": 160}}, "timeline": [{"t_to_event_s": -3.2, "frame_index": 0, "target_offset_s": -5.0, "distance_m": 4.0743, "ttc_s": null, "closing_speed_mps": null, "relative_speed_mps": null, "longitudinal_gap_m": 0.0218, "lateral_offset_m": 4.0742, "relative_heading_rad": 0.0277, "agent_a_speed_mps": null, "agent_b_speed_mps": null, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -3.0, "frame_index": 2, "target_offset_s": -3.0, "distance_m": 4.131, "ttc_s": null, "closing_speed_mps": -0.3919, "relative_speed_mps": 2.0784, "longitudinal_gap_m": 0.4362, "lateral_offset_m": 4.1079, "relative_heading_rad": 0.0277, "agent_a_speed_mps": 4.1743, "agent_b_speed_mps": 6.2478, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}], "llm_task": {"question": "Judge whether this is a natural and safety-critical long-tail driving scenario.", "return_format": "strict_json", "expected_keys": ["accepted", "danger_level", "naturalness_score", "interaction_score", "scenario_value_score", "scenario_type", "artifact_flags", "reason"]}}, "local_eval": {"accepted": false, "priority": "reject_or_debug_only", "danger_level": 2, "criticality_score": 35.0, "naturalness_score": 80.0, "interaction_score": 40.0, "scenario_value_score": 49.1, "scenario_type": "generic_close_interaction", "artifact_flags": ["high_yaw_rate", "short_clean_track"], "rejection_reasons": ["primary_pair_not_collision_pair", "distance_not_decreasing"], "optimization_feedback": {"increase_collision_tolerance": false, "decrease_collision_tolerance": false, "increase_goal_pressure": true, "decrease_goal_pressure": false, "increase_offroad_penalty": false, "relax_offroad_penalty": false, "increase_risk_agents_per_world": true, "decrease_risk_agents_per_world": false, "target_scenario_types": []}, "reason": "Primary pair 1->0 scored criticality=35.0, naturalness=80.0, interaction=40.0, value=49.1; notes=primary_pair_not_collision_pair, distance_not_decreasing.", "evidence": ["collision=True near_miss=False offroad=False", "primary_pair=1->0 reason=event_min_distance_risk_partner", "clean_min_distance=4.0743 min_ttc=None", "event_relative_speed=None closing_speed=None", "distance_trend=not_decreasing"]}, "llm_eval": {"accepted": false, "priority": "reject_or_debug_only", "danger_level": 2, "criticality_score": 35.0, "naturalness_score": 80.0, "interaction_score": 40.0, "scenario_value_score": 49.1, "scenario_type": "generic_close_interaction", "artifact_flags": ["high_yaw_rate", "short_clean_track"], "rejection_reasons": ["primary_pair_not_collision_pair", "distance_not_decreasing"], "optimization_feedback": {"increase_collision_tolerance": false, "decrease_collision_tolerance": false, "increase_goal_pressure": true, "decrease_goal_pressure": false, "increase_offroad_penalty": false, "relax_offroad_penalty": false, "increase_risk_agents_per_world": true, "decrease_risk_agents_per_world": false, "target_scenario_types": []}, "reason": "Primary pair 1->0 scored criticality=35.0, naturalness=80.0, interaction=40.0, value=49.1; notes=primary_pair_not_collision_pair, distance_not_decreasing.", "evidence": ["collision=True near_miss=False offroad=False", "primary_pair=1->0 reason=event_min_distance_risk_partner", "clean_min_distance=4.0743 min_ttc=None", "event_relative_speed=None closing_speed=None", "distance_trend=not_decreasing"]}, "final_eval": {"accepted": false, "priority": "reject_or_debug_only", "danger_level": 2, "criticality_score": 35.0, "naturalness_score": 80.0, "interaction_score": 40.0, "scenario_value_score": 49.1, "scenario_type": "generic_close_interaction", "artifact_flags": ["high_yaw_rate", "short_clean_track"], "rejection_reasons": ["primary_pair_not_collision_pair", "distance_not_decreasing"], "optimization_feedback": {"increase_collision_tolerance": false, "decrease_collision_tolerance": false, "increase_goal_pressure": true, "decrease_goal_pressure": false, "increase_offroad_penalty": false, "relax_offroad_penalty": false, "increase_risk_agents_per_world": true, "decrease_risk_agents_per_world": false, "target_scenario_types": []}, "reason": "Primary pair 1->0 scored criticality=35.0, naturalness=80.0, interaction=40.0, value=49.1; notes=primary_pair_not_collision_pair, distance_not_decreasing.", "evidence": ["collision=True near_miss=False offroad=False", "primary_pair=1->0 reason=event_min_distance_risk_partner", "clean_min_distance=4.0743 min_ttc=None", "event_relative_speed=None closing_speed=None", "distance_trend=not_decreasing"], "high_value": false}, "final_valid": false, "final_score": 49.1, "final_priority": "reject_or_debug_only"} +{"case_id": "shard000_batch0000_world0034_line000007", "case": {"case_id": "shard000_batch0000_world0034_line000007", "schema_version": "llm_longtail_case_v1", "source": {"event_trace_line_index": 7, "scenario_file": "tfrecord-00000-of-01000_464.json", "shard_id": 0, "batch_idx": 0, "world_idx": 34}, "outcome": {"collision": false, "near_miss": true, "offroad": false, "first_collision_time_s": null, "first_offroad_time_s": null, "min_distance_time_s": 2.7, "event_time_s": 2.7, "raw_min_distance_m": 1.7209, "clean_min_distance_m": 21.3232}, "primary_pair": {"agent_a": 1, "agent_b": 7, "agent_a_role": ["risk_agent"], "agent_b_role": ["context_agent"], "selection_reason": "event_min_distance_risk_partner", "confidence": "medium", "risk_directly_involved": false, "semantic_candidate": "same_direction_side_conflict_or_cut_in"}, "scene_summary": {"controlled_agents": 5, "risk_agents": [1, 2, 32], "collided_agents": [], "offroad_agents": [], "goal_agents": 5, "context_agents": [1, 7, 2, 32]}, "primary_interaction_metrics": {"num_clean_pair_frames": 2, "min_clean_distance_m": 21.3232, "min_clean_distance_time_to_event_s": -2.6, "min_ttc_s": 7.2993, "time_ttc_below_1s": 0.0, "time_ttc_below_2s": 0.0, "distance_trend_before_event": {"label": "insufficient_clean_frames", "decreasing_ratio": null}, "metrics_at_event_or_nearest_clean_frame": {"pair_valid": true, "distance_m": 21.3232, "ttc_s": 7.2993, "closing_speed_mps": 2.9213, "relative_speed_mps": 2.9305, "longitudinal_gap_m": 21.2556, "lateral_offset_m": -1.6968, "relative_heading_rad": 0.0268, "agent_a_speed_mps": 2.9305, "agent_b_speed_mps": 0.0}, "metrics_at_min_clean_distance": {"pair_valid": true, "distance_m": 21.3232, "ttc_s": 7.2993, "closing_speed_mps": 2.9213, "relative_speed_mps": 2.9305, "longitudinal_gap_m": 21.2556, "lateral_offset_m": -1.6968, "relative_heading_rad": 0.0268, "agent_a_speed_mps": 2.9305, "agent_b_speed_mps": 0.0}}, "naturalness_checks": {"max_speed_mps": 20.3078, "max_accel_mps2": 4.0166, "max_decel_mps2": 0.0075, "max_yaw_rate_radps": 1.684, "max_frame_displacement_m": 2.0308, "offroad_before_collision": false, "clean_frame_counts": {"1": 38, "7": 2, "2": 38, "32": 30}, "artifact_warnings": ["short_clean_track"]}, "interaction_checks": {"risk_agent_in_primary_pair": true, 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"primary_pair_has_collision_agent": false, "primary_pair_has_offroad_agent": false, "distance_trend_label": "insufficient_clean_frames"}, "context_agent_summaries": [{"agent_id": 7, "roles": ["context_agent"], "is_controlled": false, "response_type": 2, "raw_agent_type": 7, "length_m": 3.0489, "width_m": 1.3928, "clean_frames": 2, "speed_at_event_mps": null}, {"agent_id": 41, "roles": ["risk_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.8889, "width_m": 1.6192, "clean_frames": 57, "speed_at_event_mps": null}, {"agent_id": 0, "roles": ["risk_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.7002, "width_m": 1.6324, "clean_frames": 26, "speed_at_event_mps": null}, {"agent_id": 1, "roles": ["risk_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 2.9842, "width_m": 1.3869, "clean_frames": 44, "speed_at_event_mps": null}], "map_summary": {"center_xy": [-7.4162, -27.7608], "radius_m": 80.0, "num_points": 256, "type_counts": {"1": 36, "3": 38, "2": 18, "4": 3, "0": 161}}, "timeline": [{"t_to_event_s": -4.6, "frame_index": 0, "target_offset_s": -5.0, "distance_m": 2.4991, "ttc_s": null, "closing_speed_mps": null, "relative_speed_mps": null, "longitudinal_gap_m": -0.0558, "lateral_offset_m": -2.4985, "relative_heading_rad": 3.0741, "agent_a_speed_mps": null, "agent_b_speed_mps": null, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}], "llm_task": {"question": "Judge whether this is a natural and safety-critical long-tail driving scenario.", "return_format": "strict_json", "expected_keys": ["accepted", "danger_level", "naturalness_score", "interaction_score", "scenario_value_score", "scenario_type", "artifact_flags", "reason"]}}, "local_eval": {"accepted": false, "priority": "reject_or_debug_only", "danger_level": 1, "criticality_score": 23.0, "naturalness_score": 80.0, "interaction_score": 40.0, "scenario_value_score": 44.06, "scenario_type": "crossing_or_intersection_conflict", "artifact_flags": ["high_yaw_rate", "short_clean_track"], "rejection_reasons": ["too_few_clean_pair_frames"], "optimization_feedback": {"increase_collision_tolerance": true, "decrease_collision_tolerance": false, "increase_goal_pressure": true, "decrease_goal_pressure": true, "increase_offroad_penalty": false, "relax_offroad_penalty": false, "increase_risk_agents_per_world": true, "decrease_risk_agents_per_world": false, "target_scenario_types": []}, "reason": "Primary pair 41->7 scored criticality=23.0, naturalness=80.0, interaction=40.0, value=44.1; notes=too_few_clean_pair_frames.", "evidence": ["collision=False near_miss=True offroad=False", "primary_pair=41->7 reason=event_min_distance_risk_partner", "clean_min_distance=2.4991 min_ttc=None", "event_relative_speed=None closing_speed=None", "distance_trend=insufficient_clean_frames"]}, "llm_eval": {"accepted": false, "priority": "reject_or_debug_only", "danger_level": 1, "criticality_score": 23.0, "naturalness_score": 80.0, "interaction_score": 40.0, "scenario_value_score": 44.06, "scenario_type": "crossing_or_intersection_conflict", "artifact_flags": ["high_yaw_rate", "short_clean_track"], "rejection_reasons": ["too_few_clean_pair_frames"], "optimization_feedback": {"increase_collision_tolerance": true, "decrease_collision_tolerance": false, "increase_goal_pressure": true, "decrease_goal_pressure": true, "increase_offroad_penalty": false, "relax_offroad_penalty": false, "increase_risk_agents_per_world": true, "decrease_risk_agents_per_world": false, "target_scenario_types": []}, "reason": "Primary pair 41->7 scored criticality=23.0, naturalness=80.0, interaction=40.0, value=44.1; notes=too_few_clean_pair_frames.", "evidence": ["collision=False near_miss=True offroad=False", "primary_pair=41->7 reason=event_min_distance_risk_partner", "clean_min_distance=2.4991 min_ttc=None", "event_relative_speed=None closing_speed=None", "distance_trend=insufficient_clean_frames"]}, "final_eval": {"accepted": false, "priority": "reject_or_debug_only", "danger_level": 1, "criticality_score": 23.0, "naturalness_score": 80.0, "interaction_score": 40.0, "scenario_value_score": 44.06, "scenario_type": "crossing_or_intersection_conflict", "artifact_flags": ["high_yaw_rate", "short_clean_track"], "rejection_reasons": ["too_few_clean_pair_frames"], "optimization_feedback": {"increase_collision_tolerance": true, "decrease_collision_tolerance": false, "increase_goal_pressure": true, "decrease_goal_pressure": true, "increase_offroad_penalty": false, "relax_offroad_penalty": false, "increase_risk_agents_per_world": true, "decrease_risk_agents_per_world": false, "target_scenario_types": []}, "reason": "Primary pair 41->7 scored criticality=23.0, naturalness=80.0, interaction=40.0, value=44.1; notes=too_few_clean_pair_frames.", "evidence": ["collision=False near_miss=True offroad=False", "primary_pair=41->7 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null, "closing_speed_mps": -1.5827, "relative_speed_mps": 1.9277, "longitudinal_gap_m": -4.3065, "lateral_offset_m": -3.557, "relative_heading_rad": -0.0099, "agent_a_speed_mps": 17.9233, "agent_b_speed_mps": 16.0029, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -0.5, "frame_index": 36, "target_offset_s": -0.5, "distance_m": 6.3531, "ttc_s": null, "closing_speed_mps": -3.0229, "relative_speed_mps": 3.0262, "longitudinal_gap_m": -5.4227, "lateral_offset_m": -3.31, "relative_heading_rad": -0.006, "agent_a_speed_mps": 18.5899, "agent_b_speed_mps": 16.0023, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": 0.0, "frame_index": 41, "target_offset_s": 0.0, "distance_m": 7.6295, "ttc_s": null, "closing_speed_mps": -2.608, "relative_speed_mps": 2.8559, "longitudinal_gap_m": -6.8527, "lateral_offset_m": -3.354, "relative_heading_rad": 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"agent_b_offroad": false}], "llm_task": {"question": "Judge whether this is a natural and safety-critical long-tail driving scenario.", "return_format": "strict_json", "expected_keys": ["accepted", "danger_level", "naturalness_score", "interaction_score", "scenario_value_score", "scenario_type", "artifact_flags", "reason"]}}, "local_eval": {"accepted": false, "priority": "low_value", "danger_level": 2, "criticality_score": 48.856, "naturalness_score": 72.0, "interaction_score": 55.0, "scenario_value_score": 57.179, "scenario_type": "cut_in_or_side_conflict", "artifact_flags": ["high_yaw_rate", "offroad_before_collision"], "rejection_reasons": ["primary_pair_not_collision_pair", "distance_not_decreasing", "offroad_before_collision"], "optimization_feedback": {"increase_collision_tolerance": false, "decrease_collision_tolerance": false, "increase_goal_pressure": true, "decrease_goal_pressure": true, "increase_offroad_penalty": true, "relax_offroad_penalty": false, 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{"increase_collision_tolerance": false, "decrease_collision_tolerance": false, "increase_goal_pressure": true, "decrease_goal_pressure": true, "increase_offroad_penalty": true, "relax_offroad_penalty": false, "increase_risk_agents_per_world": true, "decrease_risk_agents_per_world": false, "target_scenario_types": []}, "reason": "Primary pair 15->4 scored criticality=48.9, naturalness=72.0, interaction=55.0, value=57.2; notes=primary_pair_not_collision_pair, distance_not_decreasing, offroad_before_collision.", "evidence": ["collision=True near_miss=False offroad=True", "primary_pair=15->4 reason=event_min_distance_risk_partner", "clean_min_distance=3.293 min_ttc=2.6387", "event_relative_speed=2.8559 closing_speed=-2.608", "distance_trend=not_decreasing"]}, "final_eval": {"accepted": false, "priority": "low_value", "danger_level": 2, "criticality_score": 48.856, "naturalness_score": 72.0, "interaction_score": 55.0, "scenario_value_score": 57.179, "scenario_type": "cut_in_or_side_conflict", "artifact_flags": ["high_yaw_rate", "offroad_before_collision"], "rejection_reasons": ["primary_pair_not_collision_pair", "distance_not_decreasing", "offroad_before_collision"], "optimization_feedback": {"increase_collision_tolerance": false, "decrease_collision_tolerance": false, "increase_goal_pressure": true, "decrease_goal_pressure": true, "increase_offroad_penalty": true, "relax_offroad_penalty": false, "increase_risk_agents_per_world": true, "decrease_risk_agents_per_world": false, "target_scenario_types": []}, "reason": "Primary pair 15->4 scored criticality=48.9, naturalness=72.0, interaction=55.0, value=57.2; notes=primary_pair_not_collision_pair, distance_not_decreasing, offroad_before_collision.", "evidence": ["collision=True near_miss=False offroad=True", "primary_pair=15->4 reason=event_min_distance_risk_partner", "clean_min_distance=3.293 min_ttc=2.6387", "event_relative_speed=2.8559 closing_speed=-2.608", "distance_trend=not_decreasing"], "high_value": false}, "final_valid": false, "final_score": 57.179, "final_priority": "low_value"} diff --git a/gpudrive-main/search_outputs/evaluator_agent_test/summary.json b/gpudrive-main/search_outputs/evaluator_agent_test/summary.json new file mode 100644 index 0000000000000000000000000000000000000000..f20d7989a1fafe42e8d26ba40f0541e050369e4f --- /dev/null +++ b/gpudrive-main/search_outputs/evaluator_agent_test/summary.json @@ -0,0 +1,24 @@ +{ + "cases_path": "search_outputs\\llm_cases_shard_000\\llm_cases.jsonl", + "rules_path": "rules\\longtail_llm_case_evaluation.md", + "output_dir": "search_outputs\\evaluator_agent_test", + "model": "dry_run_rule_based", + "dry_run": true, + "total_evaluated": 20, + "accepted_count": 8, + "rejected_count": 12, + "high_value_count": 7, + "accepted_rate": 0.4, + "high_value_rate": 0.35, + "natural_critical_count": 8, + "natural_critical_rate": 0.4, + "risk_direct_involved_rate": 0.4, + "risk_direct_accept_rate": 0.875, + "warning_record_count": 20, + "warning_record_rate": 1.0, + "hard_artifact_record_count": 3, + "hard_artifact_record_rate": 0.15, + "artifact_flags_per_case": 1.8, + "semantic_diversity_entropy": 0.9851, + "composite_objective": 0.577 +} \ No newline at end of file diff --git a/gpudrive-main/search_outputs/evaluator_resume_test/accepted_cases.jsonl b/gpudrive-main/search_outputs/evaluator_resume_test/accepted_cases.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..515564d637c882a0ef1056d0eef184a69ab3b63c --- /dev/null +++ b/gpudrive-main/search_outputs/evaluator_resume_test/accepted_cases.jsonl @@ -0,0 +1 @@ +{"case_id": "shard000_batch0000_world0002_line000000", "case": {"case_id": "shard000_batch0000_world0002_line000000", "schema_version": "llm_longtail_case_v1", "source": {"event_trace_line_index": 0, "scenario_file": "tfrecord-00005-of-01000_436.json", "shard_id": 0, "batch_idx": 0, "world_idx": 2}, "outcome": {"collision": true, "near_miss": true, "offroad": true, "first_collision_time_s": 2.9, "first_offroad_time_s": 3.0, "min_distance_time_s": 2.9, "event_time_s": 2.9, "raw_min_distance_m": 1.9574, "clean_min_distance_m": 1.9575}, "primary_pair": {"agent_a": 5, "agent_b": 7, "agent_a_role": ["risk_agent", "collided_agent"], "agent_b_role": ["collided_agent", "offroad_agent"], "selection_reason": "risk_agent_directly_collided_with_partner", "confidence": "high", "risk_directly_involved": true, "semantic_candidate": "same_direction_side_conflict_or_cut_in"}, "scene_summary": {"controlled_agents": 13, "risk_agents": [0, 4, 5], "collided_agents": [5, 7], "offroad_agents": [0, 7], "goal_agents": 13, "context_agents": [5, 7, 0, 2, 3, 4, 6, 17]}, "primary_interaction_metrics": {"num_clean_pair_frames": 34, "min_clean_distance_m": 1.9575, "min_clean_distance_time_to_event_s": 0.0, "min_ttc_s": 0.4335, "time_ttc_below_1s": 1.2, "time_ttc_below_2s": 2.3, "distance_trend_before_event": {"label": "mostly_decreasing", "decreasing_ratio": 0.9655, "distance_start_m": 29.5168, "distance_end_m": 1.9575}, "metrics_at_event_or_nearest_clean_frame": {"pair_valid": true, "distance_m": 1.9575, "ttc_s": 0.4496, "closing_speed_mps": 4.3538, "relative_speed_mps": 7.2043, "longitudinal_gap_m": 1.2124, "lateral_offset_m": -1.5368, "relative_heading_rad": 0.4045, "agent_a_speed_mps": 10.645, "agent_b_speed_mps": 14.2738}, "metrics_at_min_clean_distance": {"pair_valid": true, "distance_m": 1.9575, "ttc_s": 0.4496, "closing_speed_mps": 4.3538, "relative_speed_mps": 7.2043, "longitudinal_gap_m": 1.2124, "lateral_offset_m": -1.5368, "relative_heading_rad": 0.4045, "agent_a_speed_mps": 10.645, "agent_b_speed_mps": 14.2738}}, "naturalness_checks": {"max_speed_mps": 23.926, "max_accel_mps2": 4.0166, "max_decel_mps2": 4.0081, "max_yaw_rate_radps": 6.665, "max_frame_displacement_m": 2.3926, "offroad_before_collision": false, "clean_frame_counts": {"5": 34, "7": 39, "0": 40, "2": 40, "3": 40, "4": 23, "6": 40, "17": 32}, "artifact_warnings": ["high_yaw_rate"]}, "interaction_checks": {"risk_agent_in_primary_pair": true, "primary_pair_has_collision_agent": true, "primary_pair_has_offroad_agent": true, "distance_trend_label": "mostly_decreasing"}, "context_agent_summaries": [{"agent_id": 5, "roles": ["risk_agent", "collided_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.0827, "width_m": 1.391, "clean_frames": 34, "speed_at_event_mps": 10.645}, {"agent_id": 7, "roles": ["collided_agent", "offroad_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.1654, "width_m": 1.4256, "clean_frames": 39, "speed_at_event_mps": 14.2738}, {"agent_id": 0, "roles": ["risk_agent", "offroad_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.7002, "width_m": 1.6324, "clean_frames": 40, "speed_at_event_mps": 17.2471}, {"agent_id": 2, "roles": ["context_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.2432, "width_m": 1.5205, "clean_frames": 40, "speed_at_event_mps": 19.003}, {"agent_id": 3, "roles": ["context_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.2124, "width_m": 1.4202, "clean_frames": 40, "speed_at_event_mps": 16.8588}, {"agent_id": 4, "roles": ["risk_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.5435, "width_m": 1.5256, "clean_frames": 23, "speed_at_event_mps": null}, {"agent_id": 6, "roles": ["context_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.2835, "width_m": 1.4368, "clean_frames": 40, "speed_at_event_mps": 13.7644}, {"agent_id": 17, "roles": ["context_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 2.8417, "width_m": 1.3018, "clean_frames": 32, "speed_at_event_mps": 23.6592}], "map_summary": {"center_xy": [20.7116, -14.1158], "radius_m": 80.0, "num_points": 256, "type_counts": {"1": 31, "3": 70, "2": 27, "4": 1, "0": 127}}, "timeline": [{"t_to_event_s": -2.9, "frame_index": 0, "target_offset_s": -5.0, "distance_m": 29.5168, "ttc_s": null, "closing_speed_mps": null, "relative_speed_mps": null, "longitudinal_gap_m": 29.4564, "lateral_offset_m": 1.8873, "relative_heading_rad": 2.6233, "agent_a_speed_mps": null, "agent_b_speed_mps": null, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -2.0, "frame_index": 9, "target_offset_s": -2.0, "distance_m": 20.5942, "ttc_s": 1.6912, "closing_speed_mps": 12.177, "relative_speed_mps": 12.8039, "longitudinal_gap_m": 19.5775, "lateral_offset_m": -6.3909, "relative_heading_rad": 2.1197, "agent_a_speed_mps": 5.179, "agent_b_speed_mps": 10.1396, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -1.0, "frame_index": 19, "target_offset_s": -1.0, "distance_m": 7.7682, "ttc_s": 0.6847, "closing_speed_mps": 11.3453, "relative_speed_mps": 11.6026, "longitudinal_gap_m": 4.1909, "lateral_offset_m": -6.5408, "relative_heading_rad": 1.1884, "agent_a_speed_mps": 9.179, "agent_b_speed_mps": 13.3404, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -0.5, "frame_index": 24, "target_offset_s": -0.5, "distance_m": 3.6239, "ttc_s": 0.4436, "closing_speed_mps": 8.1688, "relative_speed_mps": 8.9406, "longitudinal_gap_m": 1.0071, "lateral_offset_m": -3.4811, "relative_heading_rad": 0.6411, "agent_a_speed_mps": 10.6456, "agent_b_speed_mps": 14.8069, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": 0.0, "frame_index": 29, "target_offset_s": 0.0, "distance_m": 1.9575, "ttc_s": 0.4496, "closing_speed_mps": 4.3538, "relative_speed_mps": 7.2043, "longitudinal_gap_m": 1.2124, "lateral_offset_m": -1.5368, "relative_heading_rad": 0.4045, "agent_a_speed_mps": 10.645, "agent_b_speed_mps": 14.2738, "agent_a_collision": true, "agent_b_collision": true, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": 0.4, "frame_index": 33, "target_offset_s": 0.5, "distance_m": 3.1975, "ttc_s": 12.4782, "closing_speed_mps": 0.2562, "relative_speed_mps": 2.2195, "longitudinal_gap_m": 0.4287, "lateral_offset_m": -3.1686, "relative_heading_rad": 0.0381, "agent_a_speed_mps": 12.2457, "agent_b_speed_mps": 14.4066, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}], "llm_task": {"question": "Judge whether this is a natural and safety-critical long-tail driving scenario.", "return_format": "strict_json", "expected_keys": ["accepted", "danger_level", "naturalness_score", "interaction_score", "scenario_value_score", "scenario_type", "artifact_flags", "reason"]}}, "local_eval": {"accepted": true, "priority": "high_value", "danger_level": 5, "criticality_score": 94.554, "naturalness_score": 92.0, "interaction_score": 100.0, "scenario_value_score": 95.473, "scenario_type": "cut_in_or_side_conflict", "artifact_flags": ["high_yaw_rate"], "rejection_reasons": [], "optimization_feedback": {"increase_collision_tolerance": false, "decrease_collision_tolerance": false, "increase_goal_pressure": false, "decrease_goal_pressure": false, "increase_offroad_penalty": false, "relax_offroad_penalty": false, "increase_risk_agents_per_world": false, "decrease_risk_agents_per_world": false, "target_scenario_types": ["cut_in_or_side_conflict"]}, "reason": "Primary pair 5->7 scored criticality=94.6, naturalness=92.0, interaction=100.0, value=95.5; notes=none.", "evidence": ["collision=True near_miss=True offroad=True", "primary_pair=5->7 reason=risk_agent_directly_collided_with_partner", "clean_min_distance=1.9575 min_ttc=0.4335", "event_relative_speed=7.2043 closing_speed=4.3538", "distance_trend=mostly_decreasing"]}, "llm_eval": {"accepted": true, "priority": "high_value", "danger_level": 5, "criticality_score": 94.554, "naturalness_score": 92.0, "interaction_score": 100.0, "scenario_value_score": 95.473, "scenario_type": "cut_in_or_side_conflict", "artifact_flags": ["high_yaw_rate"], "rejection_reasons": [], "optimization_feedback": {"increase_collision_tolerance": false, "decrease_collision_tolerance": false, "increase_goal_pressure": false, "decrease_goal_pressure": false, "increase_offroad_penalty": false, "relax_offroad_penalty": false, "increase_risk_agents_per_world": false, "decrease_risk_agents_per_world": false, "target_scenario_types": ["cut_in_or_side_conflict"]}, "reason": "Primary pair 5->7 scored criticality=94.6, naturalness=92.0, interaction=100.0, value=95.5; notes=none.", "evidence": ["collision=True near_miss=True offroad=True", "primary_pair=5->7 reason=risk_agent_directly_collided_with_partner", "clean_min_distance=1.9575 min_ttc=0.4335", "event_relative_speed=7.2043 closing_speed=4.3538", "distance_trend=mostly_decreasing"]}, "final_eval": {"accepted": true, "priority": "high_value", "danger_level": 5, "criticality_score": 94.554, "naturalness_score": 92.0, "interaction_score": 100.0, "scenario_value_score": 95.473, "scenario_type": "cut_in_or_side_conflict", "artifact_flags": ["high_yaw_rate"], "rejection_reasons": [], "optimization_feedback": {"increase_collision_tolerance": false, "decrease_collision_tolerance": false, "increase_goal_pressure": false, "decrease_goal_pressure": false, "increase_offroad_penalty": false, "relax_offroad_penalty": false, "increase_risk_agents_per_world": false, "decrease_risk_agents_per_world": false, "target_scenario_types": ["cut_in_or_side_conflict"]}, "reason": "Primary pair 5->7 scored criticality=94.6, naturalness=92.0, interaction=100.0, value=95.5; notes=none.", "evidence": ["collision=True near_miss=True offroad=True", "primary_pair=5->7 reason=risk_agent_directly_collided_with_partner", "clean_min_distance=1.9575 min_ttc=0.4335", "event_relative_speed=7.2043 closing_speed=4.3538", "distance_trend=mostly_decreasing"], "high_value": true}, "final_valid": true, "final_score": 95.473, "final_priority": "high_value"} diff --git a/gpudrive-main/search_outputs/evaluator_resume_test/all_evaluations.jsonl b/gpudrive-main/search_outputs/evaluator_resume_test/all_evaluations.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..ccb6ff32a8d332c7aba1e5514a07023dba41b660 --- /dev/null +++ b/gpudrive-main/search_outputs/evaluator_resume_test/all_evaluations.jsonl @@ -0,0 +1,3 @@ +{"case_id": "shard000_batch0000_world0002_line000000", "case": {"case_id": "shard000_batch0000_world0002_line000000", "schema_version": "llm_longtail_case_v1", "source": {"event_trace_line_index": 0, "scenario_file": "tfrecord-00005-of-01000_436.json", "shard_id": 0, "batch_idx": 0, "world_idx": 2}, "outcome": {"collision": true, "near_miss": true, "offroad": true, "first_collision_time_s": 2.9, "first_offroad_time_s": 3.0, "min_distance_time_s": 2.9, "event_time_s": 2.9, "raw_min_distance_m": 1.9574, "clean_min_distance_m": 1.9575}, "primary_pair": {"agent_a": 5, "agent_b": 7, "agent_a_role": ["risk_agent", "collided_agent"], "agent_b_role": ["collided_agent", "offroad_agent"], "selection_reason": "risk_agent_directly_collided_with_partner", "confidence": "high", "risk_directly_involved": true, "semantic_candidate": "same_direction_side_conflict_or_cut_in"}, "scene_summary": {"controlled_agents": 13, "risk_agents": [0, 4, 5], "collided_agents": [5, 7], "offroad_agents": [0, 7], "goal_agents": 13, "context_agents": [5, 7, 0, 2, 3, 4, 6, 17]}, "primary_interaction_metrics": {"num_clean_pair_frames": 34, "min_clean_distance_m": 1.9575, "min_clean_distance_time_to_event_s": 0.0, "min_ttc_s": 0.4335, "time_ttc_below_1s": 1.2, "time_ttc_below_2s": 2.3, "distance_trend_before_event": {"label": "mostly_decreasing", "decreasing_ratio": 0.9655, "distance_start_m": 29.5168, "distance_end_m": 1.9575}, "metrics_at_event_or_nearest_clean_frame": {"pair_valid": true, "distance_m": 1.9575, "ttc_s": 0.4496, "closing_speed_mps": 4.3538, "relative_speed_mps": 7.2043, "longitudinal_gap_m": 1.2124, "lateral_offset_m": -1.5368, "relative_heading_rad": 0.4045, "agent_a_speed_mps": 10.645, "agent_b_speed_mps": 14.2738}, "metrics_at_min_clean_distance": {"pair_valid": true, "distance_m": 1.9575, "ttc_s": 0.4496, "closing_speed_mps": 4.3538, "relative_speed_mps": 7.2043, "longitudinal_gap_m": 1.2124, "lateral_offset_m": -1.5368, "relative_heading_rad": 0.4045, "agent_a_speed_mps": 10.645, "agent_b_speed_mps": 14.2738}}, "naturalness_checks": {"max_speed_mps": 23.926, "max_accel_mps2": 4.0166, "max_decel_mps2": 4.0081, "max_yaw_rate_radps": 6.665, "max_frame_displacement_m": 2.3926, "offroad_before_collision": false, "clean_frame_counts": {"5": 34, "7": 39, "0": 40, "2": 40, "3": 40, "4": 23, "6": 40, "17": 32}, "artifact_warnings": ["high_yaw_rate"]}, "interaction_checks": {"risk_agent_in_primary_pair": true, "primary_pair_has_collision_agent": true, "primary_pair_has_offroad_agent": true, "distance_trend_label": "mostly_decreasing"}, "context_agent_summaries": [{"agent_id": 5, "roles": ["risk_agent", "collided_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.0827, "width_m": 1.391, "clean_frames": 34, "speed_at_event_mps": 10.645}, {"agent_id": 7, "roles": ["collided_agent", "offroad_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.1654, "width_m": 1.4256, "clean_frames": 39, "speed_at_event_mps": 14.2738}, {"agent_id": 0, "roles": ["risk_agent", "offroad_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.7002, "width_m": 1.6324, "clean_frames": 40, "speed_at_event_mps": 17.2471}, {"agent_id": 2, "roles": ["context_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.2432, "width_m": 1.5205, "clean_frames": 40, "speed_at_event_mps": 19.003}, {"agent_id": 3, "roles": ["context_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.2124, "width_m": 1.4202, "clean_frames": 40, "speed_at_event_mps": 16.8588}, {"agent_id": 4, "roles": ["risk_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.5435, "width_m": 1.5256, "clean_frames": 23, "speed_at_event_mps": null}, {"agent_id": 6, "roles": ["context_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.2835, "width_m": 1.4368, "clean_frames": 40, "speed_at_event_mps": 13.7644}, {"agent_id": 17, "roles": ["context_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 2.8417, "width_m": 1.3018, "clean_frames": 32, "speed_at_event_mps": 23.6592}], "map_summary": {"center_xy": [20.7116, -14.1158], "radius_m": 80.0, "num_points": 256, "type_counts": {"1": 31, "3": 70, "2": 27, "4": 1, "0": 127}}, "timeline": [{"t_to_event_s": -2.9, "frame_index": 0, 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"agent_b_collision": true, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": 0.4, "frame_index": 33, "target_offset_s": 0.5, "distance_m": 3.1975, "ttc_s": 12.4782, "closing_speed_mps": 0.2562, "relative_speed_mps": 2.2195, "longitudinal_gap_m": 0.4287, "lateral_offset_m": -3.1686, "relative_heading_rad": 0.0381, "agent_a_speed_mps": 12.2457, "agent_b_speed_mps": 14.4066, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}], "llm_task": {"question": "Judge whether this is a natural and safety-critical long-tail driving scenario.", "return_format": "strict_json", "expected_keys": ["accepted", "danger_level", "naturalness_score", "interaction_score", "scenario_value_score", "scenario_type", "artifact_flags", "reason"]}}, "local_eval": {"accepted": true, "priority": "high_value", "danger_level": 5, "criticality_score": 94.554, "naturalness_score": 92.0, "interaction_score": 100.0, "scenario_value_score": 95.473, "scenario_type": "cut_in_or_side_conflict", "artifact_flags": ["high_yaw_rate"], "rejection_reasons": [], "optimization_feedback": {"increase_collision_tolerance": false, "decrease_collision_tolerance": false, "increase_goal_pressure": false, "decrease_goal_pressure": false, "increase_offroad_penalty": false, "relax_offroad_penalty": false, "increase_risk_agents_per_world": false, "decrease_risk_agents_per_world": false, "target_scenario_types": ["cut_in_or_side_conflict"]}, "reason": "Primary pair 5->7 scored criticality=94.6, naturalness=92.0, interaction=100.0, value=95.5; notes=none.", "evidence": ["collision=True near_miss=True offroad=True", "primary_pair=5->7 reason=risk_agent_directly_collided_with_partner", "clean_min_distance=1.9575 min_ttc=0.4335", "event_relative_speed=7.2043 closing_speed=4.3538", "distance_trend=mostly_decreasing"]}, "llm_eval": {"accepted": true, "priority": "high_value", "danger_level": 5, "criticality_score": 94.554, "naturalness_score": 92.0, 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"final_valid": true, "final_score": 95.473, "final_priority": "high_value"} +{"case_id": "shard000_batch0000_world0009_line000001", "case": {"case_id": "shard000_batch0000_world0009_line000001", "schema_version": "llm_longtail_case_v1", "source": {"event_trace_line_index": 1, "scenario_file": "tfrecord-00007-of-01000_251.json", "shard_id": 0, "batch_idx": 0, "world_idx": 9}, "outcome": {"collision": true, "near_miss": false, "offroad": true, "first_collision_time_s": 5.4, "first_offroad_time_s": 4.0, "min_distance_time_s": 4.7, "event_time_s": 5.4, "raw_min_distance_m": 3.1167, "clean_min_distance_m": 3.1166}, "primary_pair": {"agent_a": 4, "agent_b": 8, "agent_a_role": ["risk_agent"], "agent_b_role": ["context_agent"], "selection_reason": "event_min_distance_risk_partner", "confidence": "medium", "risk_directly_involved": false, "semantic_candidate": "same_direction_side_conflict_or_cut_in"}, "scene_summary": {"controlled_agents": 18, "risk_agents": [3, 4, 6], "collided_agents": [5, 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"clean_frames": 51, "speed_at_event_mps": 23.837}, {"agent_id": 18, "roles": ["offroad_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.2048, "width_m": 1.388, "clean_frames": 43, "speed_at_event_mps": null}], "map_summary": {"center_xy": [11.7025, 81.9044], "radius_m": 80.0, "num_points": 256, "type_counts": {"3": 108, "2": 68, "1": 78, "6": 1, "4": 1}}, "timeline": [{"t_to_event_s": -5.0, "frame_index": 0, "target_offset_s": -5.0, "distance_m": 12.2163, "ttc_s": null, "closing_speed_mps": null, "relative_speed_mps": null, "longitudinal_gap_m": 11.4296, "lateral_offset_m": -4.3129, "relative_heading_rad": 0.0176, "agent_a_speed_mps": null, "agent_b_speed_mps": null, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -3.0, "frame_index": 20, "target_offset_s": -3.0, "distance_m": 6.4593, "ttc_s": 3.31, "closing_speed_mps": 1.9514, "relative_speed_mps": 2.2334, 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"agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -0.5, "frame_index": 45, "target_offset_s": -0.5, "distance_m": 3.1616, "ttc_s": null, "closing_speed_mps": -0.4216, "relative_speed_mps": 2.5091, "longitudinal_gap_m": -0.9775, "lateral_offset_m": -3.0067, "relative_heading_rad": 0.0174, "agent_a_speed_mps": 23.4626, "agent_b_speed_mps": 20.9832, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -0.1, "frame_index": 49, "target_offset_s": 0.0, "distance_m": 3.4719, "ttc_s": null, "closing_speed_mps": -1.1008, "relative_speed_mps": 2.5098, "longitudinal_gap_m": -1.9722, "lateral_offset_m": -2.8573, "relative_heading_rad": 0.0176, "agent_a_speed_mps": 23.9944, "agent_b_speed_mps": 21.5161, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}], "llm_task": {"question": "Judge whether this is a natural and safety-critical long-tail driving scenario.", "return_format": "strict_json", "expected_keys": ["accepted", "danger_level", "naturalness_score", "interaction_score", "scenario_value_score", "scenario_type", "artifact_flags", "reason"]}}, "local_eval": {"accepted": false, "priority": "low_value", "danger_level": 2, "criticality_score": 48.51, "naturalness_score": 72.0, "interaction_score": 70.0, "scenario_value_score": 61.534, "scenario_type": "cut_in_or_side_conflict", "artifact_flags": ["high_yaw_rate", "offroad_before_collision"], "rejection_reasons": ["primary_pair_not_collision_pair", "offroad_before_collision"], "optimization_feedback": {"increase_collision_tolerance": false, "decrease_collision_tolerance": false, "increase_goal_pressure": false, "decrease_goal_pressure": true, "increase_offroad_penalty": true, "relax_offroad_penalty": false, "increase_risk_agents_per_world": true, "decrease_risk_agents_per_world": false, "target_scenario_types": []}, 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"min_clean_distance_m": 3.3008, "min_clean_distance_time_to_event_s": 0.0, "min_ttc_s": 0.8114, "time_ttc_below_1s": 0.4, "time_ttc_below_2s": 1.4, "distance_trend_before_event": {"label": "mostly_decreasing", "decreasing_ratio": 1.0, "distance_start_m": 16.6732, "distance_end_m": 3.3008}, "metrics_at_event_or_nearest_clean_frame": {"pair_valid": true, "distance_m": 3.3008, "ttc_s": 0.8114, "closing_speed_mps": 4.0682, "relative_speed_mps": 4.0894, "longitudinal_gap_m": -3.2274, "lateral_offset_m": -0.6924, "relative_heading_rad": 0.0179, "agent_a_speed_mps": 20.9505, "agent_b_speed_mps": 25.0195}, "metrics_at_min_clean_distance": {"pair_valid": true, "distance_m": 3.3008, "ttc_s": 0.8114, "closing_speed_mps": 4.0682, "relative_speed_mps": 4.0894, "longitudinal_gap_m": -3.2274, "lateral_offset_m": -0.6924, "relative_heading_rad": 0.0179, "agent_a_speed_mps": 20.9505, "agent_b_speed_mps": 25.0195}}, "naturalness_checks": {"max_speed_mps": 654.3072, "max_accel_mps2": 6488.7513, 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0, "roles": ["context_agent"], "is_controlled": false, "response_type": 0, "raw_agent_type": 7, "length_m": 3.7002, "width_m": 1.6324, "clean_frames": 49, "speed_at_event_mps": 5.4707}, {"agent_id": 1, "roles": ["context_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 2.9684, "width_m": 1.3518, "clean_frames": 44, "speed_at_event_mps": 21.0514}, {"agent_id": 2, "roles": ["context_agent"], "is_controlled": false, "response_type": 0, "raw_agent_type": 7, "length_m": 3.1209, "width_m": 1.4512, "clean_frames": 42, "speed_at_event_mps": 5.3768}, {"agent_id": 3, "roles": ["risk_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 2.9235, "width_m": 1.3487, "clean_frames": 44, "speed_at_event_mps": 20.9943}, {"agent_id": 6, "roles": ["context_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.1435, "width_m": 1.4294, "clean_frames": 43, "speed_at_event_mps": 20.0458}], "map_summary": {"center_xy": [-18.793, -23.1618], "radius_m": 80.0, "num_points": 256, "type_counts": {"2": 13, "1": 5, "3": 17, "0": 221}}, "timeline": [{"t_to_event_s": -3.8, "frame_index": 0, "target_offset_s": -5.0, "distance_m": 20.0383, "ttc_s": null, "closing_speed_mps": null, "relative_speed_mps": null, "longitudinal_gap_m": -19.922, "lateral_offset_m": -2.1553, "relative_heading_rad": 0.018, "agent_a_speed_mps": null, "agent_b_speed_mps": null, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -3.0, "frame_index": 8, "target_offset_s": -3.0, "distance_m": 16.6732, "ttc_s": 3.9614, "closing_speed_mps": 4.2089, "relative_speed_mps": 4.212, "longitudinal_gap_m": -16.5609, "lateral_offset_m": -1.9318, "relative_heading_rad": 0.0187, "agent_a_speed_mps": 13.2175, "agent_b_speed_mps": 17.4198, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -2.0, 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"agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}], "llm_task": {"question": "Judge whether this is a natural and safety-critical long-tail driving scenario.", "return_format": "strict_json", "expected_keys": ["accepted", "danger_level", "naturalness_score", "interaction_score", "scenario_value_score", "scenario_type", "artifact_flags", "reason"]}}, "local_eval": {"accepted": false, "priority": "low_value", "danger_level": 3, "criticality_score": 64.158, "naturalness_score": 0.0, "interaction_score": 100.0, "scenario_value_score": 56.946, "scenario_type": "cut_in_or_side_conflict", "artifact_flags": ["extreme_speed", "hard_deceleration", "high_acceleration", "high_yaw_rate", "large_frame_displacement"], "rejection_reasons": ["large_frame_displacement_artifact", "extreme_speed_artifact"], "optimization_feedback": {"increase_collision_tolerance": false, "decrease_collision_tolerance": true, "increase_goal_pressure": false, "decrease_goal_pressure": false, "increase_offroad_penalty": false, "relax_offroad_penalty": false, "increase_risk_agents_per_world": false, "decrease_risk_agents_per_world": true, "target_scenario_types": []}, "reason": "Rejected by deterministic artifact checks: large_frame_displacement_artifact, extreme_speed_artifact.", "evidence": ["collision=True near_miss=False offroad=False", "primary_pair=4->7 reason=risk_agent_directly_collided_with_partner", "clean_min_distance=3.3008 min_ttc=0.8114", "event_relative_speed=4.0894 closing_speed=4.0682", "distance_trend=mostly_decreasing"]}, "llm_eval": {"accepted": false, "priority": "low_value", "danger_level": 3, "criticality_score": 64.158, "naturalness_score": 0.0, "interaction_score": 100.0, "scenario_value_score": 56.946, "scenario_type": "cut_in_or_side_conflict", "artifact_flags": ["extreme_speed", "hard_deceleration", "high_acceleration", "high_yaw_rate", "large_frame_displacement"], "rejection_reasons": ["large_frame_displacement_artifact", "extreme_speed_artifact"], "optimization_feedback": {"increase_collision_tolerance": false, "decrease_collision_tolerance": true, "increase_goal_pressure": false, "decrease_goal_pressure": false, "increase_offroad_penalty": false, "relax_offroad_penalty": false, "increase_risk_agents_per_world": false, "decrease_risk_agents_per_world": true, "target_scenario_types": []}, "reason": "Rejected by deterministic artifact checks: large_frame_displacement_artifact, extreme_speed_artifact.", "evidence": ["collision=True near_miss=False offroad=False", "primary_pair=4->7 reason=risk_agent_directly_collided_with_partner", "clean_min_distance=3.3008 min_ttc=0.8114", "event_relative_speed=4.0894 closing_speed=4.0682", "distance_trend=mostly_decreasing"]}, "final_eval": {"accepted": false, "priority": "low_value", "danger_level": 3, "criticality_score": 64.158, "naturalness_score": 0.0, "interaction_score": 100.0, "scenario_value_score": 56.946, "scenario_type": "cut_in_or_side_conflict", "artifact_flags": ["extreme_speed", "hard_deceleration", "high_acceleration", "high_yaw_rate", "large_frame_displacement"], "rejection_reasons": ["large_frame_displacement_artifact", "extreme_speed_artifact"], "optimization_feedback": {"increase_collision_tolerance": false, "decrease_collision_tolerance": true, "increase_goal_pressure": false, "decrease_goal_pressure": false, "increase_offroad_penalty": false, "relax_offroad_penalty": false, "increase_risk_agents_per_world": false, "decrease_risk_agents_per_world": true, "target_scenario_types": []}, "reason": "Rejected by deterministic artifact checks: large_frame_displacement_artifact, extreme_speed_artifact.", "evidence": ["collision=True near_miss=False offroad=False", "primary_pair=4->7 reason=risk_agent_directly_collided_with_partner", "clean_min_distance=3.3008 min_ttc=0.8114", "event_relative_speed=4.0894 closing_speed=4.0682", "distance_trend=mostly_decreasing"], "high_value": false}, "final_valid": false, "final_score": 56.946, "final_priority": "low_value"} diff --git a/gpudrive-main/search_outputs/evaluator_resume_test/optimizer_metrics.json b/gpudrive-main/search_outputs/evaluator_resume_test/optimizer_metrics.json new file mode 100644 index 0000000000000000000000000000000000000000..4b34a3c5d878ac494cbdf629f44f0d7f4d8e43a7 --- /dev/null +++ b/gpudrive-main/search_outputs/evaluator_resume_test/optimizer_metrics.json @@ -0,0 +1,340 @@ +{ + "schema_version": "optimizer_feedback_v1", + "source_cases": "search_outputs\\llm_cases_shard_000\\llm_cases.jsonl", + "evaluator": { + "model": "dry_run_rule_based", + "dry_run": true, + "accept_threshold": 70.0, + "high_value_threshold": 82.0 + }, + "current_search_parameters": { + "risk_collision_weight": 0.0, + "risk_goal_weight": 2.0, + "risk_offroad_weight": -0.4, + "risk_agents_per_world": 3, + "normal_mode": "policy", + "deterministic": 0, + "normal_style": "balanced", + "risk_style": "risk_taker" + }, + "objective_metrics": { + "total_evaluated": 3, + "accepted_count": 1, + "rejected_count": 2, + "high_value_count": 1, + "accepted_rate": 0.3333, + "high_value_rate": 0.3333, + "natural_critical_count": 1, + "natural_critical_rate": 0.3333, + "risk_direct_involved_rate": 0.6667, + "risk_direct_accept_rate": 1.0, + "warning_record_count": 3, + "warning_record_rate": 1.0, + "hard_artifact_record_count": 2, + "hard_artifact_record_rate": 0.6667, + "artifact_flags_per_case": 2.6667, + "semantic_diversity_entropy": 0.0, + "composite_objective": 0.3 + }, + "score_stats": { + "criticality_score": { + "count": 3, + "min": 48.51, + "p25": 48.51, + "median": 64.158, + "p75": 94.554, + "max": 94.554, + "mean": 69.074 + }, + "naturalness_score": { + "count": 3, + "min": 0.0, + "p25": 0.0, + "median": 72.0, + "p75": 92.0, + "max": 92.0, + "mean": 54.6667 + }, + "interaction_score": { + "count": 3, + "min": 70.0, + "p25": 70.0, + "median": 100.0, + "p75": 100.0, + "max": 100.0, + "mean": 90.0 + }, + "scenario_value_score": { + "count": 3, + "min": 56.946, + "p25": 56.946, + "median": 61.534, + "p75": 95.473, + "max": 95.473, + "mean": 71.3177 + } + }, + "metric_stats": { + "clean_min_distance_m": { + "count": 3, + "min": 1.9575, + "p25": 1.9575, + "median": 3.1166, + "p75": 3.3008, + "max": 3.3008, + "mean": 2.7916 + }, + "min_ttc_s": { + "count": 3, + "min": 0.4335, + "p25": 0.4335, + "median": 0.8114, + "p75": 2.5086, + "max": 2.5086, + "mean": 1.2512 + }, + "time_ttc_below_1s": { + "count": 3, + "min": 0.0, + "p25": 0.0, + "median": 0.4, + "p75": 1.2, + "max": 1.2, + "mean": 0.5333 + }, + "event_relative_speed_mps": { + "count": 3, + "min": 2.5098, + "p25": 2.5098, + "median": 4.0894, + "p75": 7.2043, + "max": 7.2043, + "mean": 4.6012 + }, + "event_closing_speed_mps": { + "count": 3, + "min": -1.1008, + "p25": -1.1008, + "median": 4.0682, + "p75": 4.3538, + "max": 4.3538, + "mean": 2.4404 + }, + "max_speed_mps": { + "count": 3, + "min": 23.926, + "p25": 23.926, + "median": 33.7012, + "p75": 654.3072, + "max": 654.3072, + "mean": 237.3115 + }, + "max_accel_mps2": { + "count": 3, + "min": 4.0153, + "p25": 4.0153, + "median": 4.0166, + "p75": 6488.7513, + "max": 6488.7513, + "mean": 2165.5944 + }, + "max_yaw_rate_radps": { + "count": 3, + "min": 6.665, + "p25": 6.665, + "median": 14.554, + "p75": 30.336, + "max": 30.336, + "mean": 17.185 + } + }, + "counts": { + "scenario_type": { + "cut_in_or_side_conflict": 3 + }, + "scenario_type_accepted": { + "cut_in_or_side_conflict": 1 + }, + "semantic_candidate": { + "same_direction_side_conflict_or_cut_in": 3 + }, + "primary_pair_reason": { + "risk_agent_directly_collided_with_partner": 2, + "event_min_distance_risk_partner": 1 + }, + "artifact_flags": { + "high_yaw_rate": 3, + "offroad_before_collision": 1, + "extreme_speed": 1, + "hard_deceleration": 1, + "high_acceleration": 1, + "large_frame_displacement": 1 + }, + "rejection_reasons": { + "primary_pair_not_collision_pair": 1, + "offroad_before_collision": 1, + "large_frame_displacement_artifact": 1, + "extreme_speed_artifact": 1 + }, + "optimization_feedback": { + "decrease_goal_pressure": 1, + "increase_offroad_penalty": 1, + "increase_risk_agents_per_world": 1, + "decrease_collision_tolerance": 1, + "decrease_risk_agents_per_world": 1 + }, + "outcomes": { + "collision": 3, + "near_miss": 1, + "offroad": 2 + } + }, + "suggested_next_search_parameters": [ + { + "name": "more_on_road_risk", + "risk_collision_weight": 0.0, + "risk_goal_weight": 1.95, + "risk_offroad_weight": -0.6, + "risk_agents_per_world": 3, + "normal_style": "balanced", + "risk_style": "risk_taker", + "reason": "Many events have offroad-before-collision artifacts." + }, + { + "name": "less_extreme_motion", + "risk_collision_weight": -0.1, + "risk_goal_weight": 1.9, + "risk_offroad_weight": -0.5, + "risk_agents_per_world": 2, + "normal_style": "balanced", + "risk_style": "risk_taker", + "reason": "Extreme-speed or displacement artifacts are present." + }, + { + "name": "diversity_exploration", + "risk_collision_weight": 0.0, + "risk_goal_weight": 1.95, + "risk_offroad_weight": -0.4, + "risk_agents_per_world": 4, + "normal_style": "balanced", + "risk_style": "risk_taker", + "reason": "Accepted scenario types are not diverse enough." + }, + { + "name": "local_exploitation", + "risk_collision_weight": 0.0, + "risk_goal_weight": 2.0, + "risk_offroad_weight": -0.4, + "risk_agents_per_world": 3, + "normal_style": "balanced", + "risk_style": "risk_taker", + "reason": "Keep the current setting as a local exploitation baseline." + } + ], + "optimizer_ready_records": [ + { + "case_id": "shard000_batch0000_world0002_line000000", + "scenario_file": "tfrecord-00005-of-01000_436.json", + "accepted": true, + "high_value": true, + "scenario_value_score": 95.473, + "danger_level": 5, + "scenario_type": "cut_in_or_side_conflict", + "artifact_flags": [ + "high_yaw_rate" + ], + "rejection_reasons": [], + "risk_directly_involved": true, + "primary_pair": { + "agent_a": 5, + "agent_b": 7, + "selection_reason": "risk_agent_directly_collided_with_partner" + }, + "optimizer_feedback": { + "increase_collision_tolerance": false, + "decrease_collision_tolerance": false, + "increase_goal_pressure": false, + "decrease_goal_pressure": false, + "increase_offroad_penalty": false, + "relax_offroad_penalty": false, + "increase_risk_agents_per_world": false, + "decrease_risk_agents_per_world": false, + "target_scenario_types": [ + "cut_in_or_side_conflict" + ] + } + }, + { + "case_id": "shard000_batch0000_world0009_line000001", + "scenario_file": "tfrecord-00007-of-01000_251.json", + "accepted": false, + "high_value": false, + "scenario_value_score": 61.534, + "danger_level": 2, + "scenario_type": "cut_in_or_side_conflict", + "artifact_flags": [ + "high_yaw_rate", + "offroad_before_collision" + ], + "rejection_reasons": [ + "primary_pair_not_collision_pair", + "offroad_before_collision" + ], + "risk_directly_involved": false, + "primary_pair": { + "agent_a": 4, + "agent_b": 8, + "selection_reason": "event_min_distance_risk_partner" + }, + "optimizer_feedback": { + "increase_collision_tolerance": false, + "decrease_collision_tolerance": false, + "increase_goal_pressure": false, + "decrease_goal_pressure": true, + "increase_offroad_penalty": true, + "relax_offroad_penalty": false, + "increase_risk_agents_per_world": true, + "decrease_risk_agents_per_world": false, + "target_scenario_types": [] + } + }, + { + "case_id": "shard000_batch0000_world0012_line000002", + "scenario_file": "tfrecord-00001-of-01000_19.json", + "accepted": false, + "high_value": false, + "scenario_value_score": 56.946, + "danger_level": 3, + "scenario_type": "cut_in_or_side_conflict", + "artifact_flags": [ + "extreme_speed", + "hard_deceleration", + "high_acceleration", + "high_yaw_rate", + "large_frame_displacement" + ], + "rejection_reasons": [ + "large_frame_displacement_artifact", + "extreme_speed_artifact" + ], + "risk_directly_involved": true, + "primary_pair": { + "agent_a": 4, + "agent_b": 7, + "selection_reason": "risk_agent_directly_collided_with_partner" + }, + "optimizer_feedback": { + "increase_collision_tolerance": false, + "decrease_collision_tolerance": true, + "increase_goal_pressure": false, + "decrease_goal_pressure": false, + "increase_offroad_penalty": false, + "relax_offroad_penalty": false, + "increase_risk_agents_per_world": false, + "decrease_risk_agents_per_world": true, + "target_scenario_types": [] + } + } + ], + "partial": false +} \ No newline at end of file diff --git a/gpudrive-main/search_outputs/evaluator_resume_test/rejected_cases.jsonl b/gpudrive-main/search_outputs/evaluator_resume_test/rejected_cases.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..70795b89ee938506ce92fd4d6d4c3c9f6984a318 --- /dev/null +++ b/gpudrive-main/search_outputs/evaluator_resume_test/rejected_cases.jsonl @@ -0,0 +1,2 @@ +{"case_id": "shard000_batch0000_world0009_line000001", "case": {"case_id": "shard000_batch0000_world0009_line000001", "schema_version": "llm_longtail_case_v1", "source": {"event_trace_line_index": 1, "scenario_file": "tfrecord-00007-of-01000_251.json", "shard_id": 0, "batch_idx": 0, "world_idx": 9}, "outcome": {"collision": true, "near_miss": false, "offroad": true, "first_collision_time_s": 5.4, "first_offroad_time_s": 4.0, "min_distance_time_s": 4.7, "event_time_s": 5.4, "raw_min_distance_m": 3.1167, "clean_min_distance_m": 3.1166}, "primary_pair": {"agent_a": 4, "agent_b": 8, "agent_a_role": ["risk_agent"], "agent_b_role": ["context_agent"], "selection_reason": "event_min_distance_risk_partner", "confidence": "medium", "risk_directly_involved": false, "semantic_candidate": "same_direction_side_conflict_or_cut_in"}, "scene_summary": {"controlled_agents": 18, "risk_agents": [3, 4, 6], "collided_agents": [5, 11], "offroad_agents": [18], "goal_agents": 18, "context_agents": [4, 8, 0, 3, 5, 6, 11, 18]}, "primary_interaction_metrics": {"num_clean_pair_frames": 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"max_decel_mps2": 3.9965, "max_yaw_rate_radps": 14.554, "max_frame_displacement_m": 3.3701, "offroad_before_collision": true, "clean_frame_counts": {"4": 55, "8": 50, "0": 58, "3": 55, "5": 61, "6": 30, "11": 51, "18": 43}, "artifact_warnings": ["high_yaw_rate", "offroad_before_collision"]}, "interaction_checks": {"risk_agent_in_primary_pair": true, "primary_pair_has_collision_agent": false, "primary_pair_has_offroad_agent": false, "distance_trend_label": "mostly_decreasing"}, "context_agent_summaries": [{"agent_id": 4, "roles": ["risk_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 4.3251, "width_m": 1.9082, "clean_frames": 55, "speed_at_event_mps": 23.9944}, {"agent_id": 8, "roles": ["context_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.219, "width_m": 1.473, "clean_frames": 50, "speed_at_event_mps": 21.5161}, {"agent_id": 0, "roles": ["context_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.7002, "width_m": 1.6324, "clean_frames": 58, "speed_at_event_mps": 21.8984}, {"agent_id": 3, "roles": ["risk_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.1886, "width_m": 1.4024, "clean_frames": 55, "speed_at_event_mps": 20.7969}, {"agent_id": 5, "roles": ["collided_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.2417, "width_m": 1.3969, "clean_frames": 61, "speed_at_event_mps": 33.3017}, {"agent_id": 6, "roles": ["risk_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.1494, "width_m": 1.4529, "clean_frames": 30, "speed_at_event_mps": null}, {"agent_id": 11, "roles": ["collided_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.0509, "width_m": 1.4043, "clean_frames": 51, "speed_at_event_mps": 23.837}, {"agent_id": 18, "roles": ["offroad_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.2048, "width_m": 1.388, "clean_frames": 43, "speed_at_event_mps": null}], "map_summary": {"center_xy": [11.7025, 81.9044], "radius_m": 80.0, "num_points": 256, "type_counts": {"3": 108, "2": 68, "1": 78, "6": 1, "4": 1}}, "timeline": [{"t_to_event_s": -5.0, "frame_index": 0, "target_offset_s": -5.0, "distance_m": 12.2163, "ttc_s": null, "closing_speed_mps": null, "relative_speed_mps": null, "longitudinal_gap_m": 11.4296, "lateral_offset_m": -4.3129, "relative_heading_rad": 0.0176, "agent_a_speed_mps": null, "agent_b_speed_mps": null, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -3.0, "frame_index": 20, "target_offset_s": -3.0, "distance_m": 6.4593, "ttc_s": 3.31, "closing_speed_mps": 1.9514, "relative_speed_mps": 2.2334, "longitudinal_gap_m": 5.2038, "lateral_offset_m": -3.8266, "relative_heading_rad": 0.0161, "agent_a_speed_mps": 19.7301, "agent_b_speed_mps": 17.5167, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -2.0, "frame_index": 30, "target_offset_s": -2.0, "distance_m": 4.4688, "ttc_s": 2.5086, "closing_speed_mps": 1.7814, "relative_speed_mps": 2.503, "longitudinal_gap_m": 2.7526, "lateral_offset_m": -3.5204, "relative_heading_rad": 0.017, "agent_a_speed_mps": 21.4632, "agent_b_speed_mps": 18.9835, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -1.0, "frame_index": 40, "target_offset_s": -1.0, "distance_m": 3.1972, "ttc_s": 5.7704, "closing_speed_mps": 0.5541, "relative_speed_mps": 2.5069, "longitudinal_gap_m": 0.2657, "lateral_offset_m": -3.1861, "relative_heading_rad": 0.0172, "agent_a_speed_mps": 22.7958, "agent_b_speed_mps": 20.3163, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -0.5, "frame_index": 45, "target_offset_s": -0.5, "distance_m": 3.1616, "ttc_s": null, "closing_speed_mps": -0.4216, "relative_speed_mps": 2.5091, "longitudinal_gap_m": -0.9775, "lateral_offset_m": -3.0067, "relative_heading_rad": 0.0174, "agent_a_speed_mps": 23.4626, "agent_b_speed_mps": 20.9832, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -0.1, "frame_index": 49, "target_offset_s": 0.0, "distance_m": 3.4719, "ttc_s": null, "closing_speed_mps": -1.1008, "relative_speed_mps": 2.5098, "longitudinal_gap_m": -1.9722, "lateral_offset_m": -2.8573, "relative_heading_rad": 0.0176, "agent_a_speed_mps": 23.9944, "agent_b_speed_mps": 21.5161, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}], "llm_task": {"question": "Judge whether this is a natural and safety-critical long-tail driving scenario.", "return_format": "strict_json", "expected_keys": ["accepted", "danger_level", "naturalness_score", "interaction_score", "scenario_value_score", "scenario_type", "artifact_flags", "reason"]}}, "local_eval": {"accepted": false, "priority": "low_value", "danger_level": 2, "criticality_score": 48.51, "naturalness_score": 72.0, "interaction_score": 70.0, "scenario_value_score": 61.534, "scenario_type": "cut_in_or_side_conflict", "artifact_flags": ["high_yaw_rate", "offroad_before_collision"], "rejection_reasons": ["primary_pair_not_collision_pair", "offroad_before_collision"], "optimization_feedback": {"increase_collision_tolerance": false, "decrease_collision_tolerance": false, "increase_goal_pressure": false, "decrease_goal_pressure": true, "increase_offroad_penalty": true, "relax_offroad_penalty": false, "increase_risk_agents_per_world": true, "decrease_risk_agents_per_world": false, "target_scenario_types": []}, "reason": "Primary pair 4->8 scored criticality=48.5, naturalness=72.0, interaction=70.0, value=61.5; notes=primary_pair_not_collision_pair, offroad_before_collision.", "evidence": ["collision=True near_miss=False offroad=True", "primary_pair=4->8 reason=event_min_distance_risk_partner", "clean_min_distance=3.1166 min_ttc=2.5086", "event_relative_speed=2.5098 closing_speed=-1.1008", "distance_trend=mostly_decreasing"]}, "llm_eval": {"accepted": false, "priority": "low_value", "danger_level": 2, "criticality_score": 48.51, "naturalness_score": 72.0, "interaction_score": 70.0, "scenario_value_score": 61.534, "scenario_type": "cut_in_or_side_conflict", "artifact_flags": ["high_yaw_rate", "offroad_before_collision"], "rejection_reasons": ["primary_pair_not_collision_pair", "offroad_before_collision"], "optimization_feedback": {"increase_collision_tolerance": false, "decrease_collision_tolerance": false, "increase_goal_pressure": false, "decrease_goal_pressure": true, "increase_offroad_penalty": true, "relax_offroad_penalty": false, "increase_risk_agents_per_world": true, "decrease_risk_agents_per_world": false, "target_scenario_types": []}, "reason": "Primary pair 4->8 scored criticality=48.5, naturalness=72.0, interaction=70.0, value=61.5; notes=primary_pair_not_collision_pair, offroad_before_collision.", "evidence": ["collision=True near_miss=False offroad=True", "primary_pair=4->8 reason=event_min_distance_risk_partner", "clean_min_distance=3.1166 min_ttc=2.5086", "event_relative_speed=2.5098 closing_speed=-1.1008", "distance_trend=mostly_decreasing"]}, "final_eval": {"accepted": false, "priority": "low_value", "danger_level": 2, "criticality_score": 48.51, "naturalness_score": 72.0, "interaction_score": 70.0, "scenario_value_score": 61.534, "scenario_type": "cut_in_or_side_conflict", "artifact_flags": ["high_yaw_rate", "offroad_before_collision"], "rejection_reasons": ["primary_pair_not_collision_pair", "offroad_before_collision"], "optimization_feedback": {"increase_collision_tolerance": false, "decrease_collision_tolerance": false, "increase_goal_pressure": false, "decrease_goal_pressure": true, "increase_offroad_penalty": true, "relax_offroad_penalty": false, "increase_risk_agents_per_world": true, "decrease_risk_agents_per_world": false, "target_scenario_types": []}, "reason": "Primary pair 4->8 scored criticality=48.5, naturalness=72.0, interaction=70.0, value=61.5; notes=primary_pair_not_collision_pair, offroad_before_collision.", "evidence": ["collision=True near_miss=False offroad=True", "primary_pair=4->8 reason=event_min_distance_risk_partner", "clean_min_distance=3.1166 min_ttc=2.5086", "event_relative_speed=2.5098 closing_speed=-1.1008", "distance_trend=mostly_decreasing"], "high_value": false}, "final_valid": false, "final_score": 61.534, "final_priority": "low_value"} +{"case_id": "shard000_batch0000_world0012_line000002", "case": {"case_id": "shard000_batch0000_world0012_line000002", "schema_version": "llm_longtail_case_v1", "source": {"event_trace_line_index": 2, "scenario_file": "tfrecord-00001-of-01000_19.json", "shard_id": 0, "batch_idx": 0, "world_idx": 12}, "outcome": {"collision": true, "near_miss": false, "offroad": false, "first_collision_time_s": 3.8, "first_offroad_time_s": null, "min_distance_time_s": 3.8, "event_time_s": 3.8, "raw_min_distance_m": 3.3008, "clean_min_distance_m": 3.3008}, "primary_pair": {"agent_a": 4, "agent_b": 7, "agent_a_role": ["risk_agent", "collided_agent"], "agent_b_role": ["risk_agent", "collided_agent"], "selection_reason": "risk_agent_directly_collided_with_partner", "confidence": "high", "risk_directly_involved": true, "semantic_candidate": "same_direction_side_conflict_or_cut_in"}, "scene_summary": {"controlled_agents": 6, "risk_agents": [3, 4, 7], "collided_agents": [4, 7], "offroad_agents": [], "goal_agents": 6, "context_agents": [4, 7, 0, 1, 2, 3, 6]}, "primary_interaction_metrics": {"num_clean_pair_frames": 41, "min_clean_distance_m": 3.3008, "min_clean_distance_time_to_event_s": 0.0, "min_ttc_s": 0.8114, "time_ttc_below_1s": 0.4, "time_ttc_below_2s": 1.4, "distance_trend_before_event": {"label": "mostly_decreasing", "decreasing_ratio": 1.0, "distance_start_m": 16.6732, "distance_end_m": 3.3008}, "metrics_at_event_or_nearest_clean_frame": {"pair_valid": true, "distance_m": 3.3008, "ttc_s": 0.8114, "closing_speed_mps": 4.0682, "relative_speed_mps": 4.0894, "longitudinal_gap_m": -3.2274, "lateral_offset_m": -0.6924, "relative_heading_rad": 0.0179, "agent_a_speed_mps": 20.9505, "agent_b_speed_mps": 25.0195}, "metrics_at_min_clean_distance": {"pair_valid": true, "distance_m": 3.3008, "ttc_s": 0.8114, "closing_speed_mps": 4.0682, "relative_speed_mps": 4.0894, "longitudinal_gap_m": -3.2274, "lateral_offset_m": -0.6924, "relative_heading_rad": 0.0179, "agent_a_speed_mps": 20.9505, "agent_b_speed_mps": 25.0195}}, "naturalness_checks": {"max_speed_mps": 654.3072, "max_accel_mps2": 6488.7513, "max_decel_mps2": 18.4696, "max_yaw_rate_radps": 30.336, "max_frame_displacement_m": 65.4307, "offroad_before_collision": false, "clean_frame_counts": {"4": 43, "7": 41, "0": 49, "1": 44, "2": 42, "3": 44, "6": 43}, "artifact_warnings": ["extreme_speed", "hard_deceleration", "high_acceleration", "high_yaw_rate", "large_frame_displacement"]}, "interaction_checks": {"risk_agent_in_primary_pair": true, "primary_pair_has_collision_agent": true, "primary_pair_has_offroad_agent": false, "distance_trend_label": "mostly_decreasing"}, "context_agent_summaries": [{"agent_id": 4, "roles": ["risk_agent", "collided_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.3869, "width_m": 1.5994, "clean_frames": 43, "speed_at_event_mps": 20.9505}, {"agent_id": 7, "roles": ["risk_agent", "collided_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.101, "width_m": 1.4958, "clean_frames": 41, "speed_at_event_mps": 25.0195}, {"agent_id": 0, "roles": ["context_agent"], "is_controlled": false, "response_type": 0, "raw_agent_type": 7, "length_m": 3.7002, "width_m": 1.6324, "clean_frames": 49, "speed_at_event_mps": 5.4707}, {"agent_id": 1, "roles": ["context_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 2.9684, "width_m": 1.3518, "clean_frames": 44, "speed_at_event_mps": 21.0514}, {"agent_id": 2, "roles": ["context_agent"], "is_controlled": false, "response_type": 0, "raw_agent_type": 7, "length_m": 3.1209, "width_m": 1.4512, "clean_frames": 42, "speed_at_event_mps": 5.3768}, {"agent_id": 3, "roles": ["risk_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 2.9235, "width_m": 1.3487, "clean_frames": 44, "speed_at_event_mps": 20.9943}, {"agent_id": 6, "roles": ["context_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.1435, "width_m": 1.4294, "clean_frames": 43, "speed_at_event_mps": 20.0458}], "map_summary": {"center_xy": [-18.793, -23.1618], "radius_m": 80.0, "num_points": 256, "type_counts": {"2": 13, "1": 5, "3": 17, "0": 221}}, "timeline": [{"t_to_event_s": -3.8, "frame_index": 0, "target_offset_s": -5.0, "distance_m": 20.0383, "ttc_s": null, "closing_speed_mps": null, "relative_speed_mps": null, "longitudinal_gap_m": -19.922, "lateral_offset_m": -2.1553, "relative_heading_rad": 0.018, "agent_a_speed_mps": null, "agent_b_speed_mps": null, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -3.0, "frame_index": 8, "target_offset_s": -3.0, "distance_m": 16.6732, "ttc_s": 3.9614, "closing_speed_mps": 4.2089, "relative_speed_mps": 4.212, "longitudinal_gap_m": -16.5609, "lateral_offset_m": -1.9318, "relative_heading_rad": 0.0187, "agent_a_speed_mps": 13.2175, "agent_b_speed_mps": 17.4198, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -2.0, "frame_index": 18, "target_offset_s": -2.0, "distance_m": 12.4874, "ttc_s": 2.8728, "closing_speed_mps": 4.3468, "relative_speed_mps": 4.3496, "longitudinal_gap_m": -12.3885, "lateral_offset_m": -1.5683, "relative_heading_rad": 0.0185, "agent_a_speed_mps": 16.9507, "agent_b_speed_mps": 21.2861, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -1.0, "frame_index": 28, "target_offset_s": -1.0, "distance_m": 7.8288, "ttc_s": 1.697, "closing_speed_mps": 4.6132, "relative_speed_mps": 4.62, "longitudinal_gap_m": -7.7441, "lateral_offset_m": -1.1481, "relative_heading_rad": 0.0177, "agent_a_speed_mps": 19.6161, "agent_b_speed_mps": 24.22, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -0.5, "frame_index": 33, "target_offset_s": -0.5, "distance_m": 5.5233, "ttc_s": 1.1982, "closing_speed_mps": 4.6096, "relative_speed_mps": 4.6211, "longitudinal_gap_m": -5.4457, "lateral_offset_m": -0.9224, "relative_heading_rad": 0.0181, "agent_a_speed_mps": 20.2829, "agent_b_speed_mps": 24.8864, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": 0.0, "frame_index": 38, "target_offset_s": 0.0, "distance_m": 3.3008, "ttc_s": 0.8114, "closing_speed_mps": 4.0682, "relative_speed_mps": 4.0894, "longitudinal_gap_m": -3.2274, "lateral_offset_m": -0.6924, "relative_heading_rad": 0.0179, "agent_a_speed_mps": 20.9505, "agent_b_speed_mps": 25.0195, "agent_a_collision": true, "agent_b_collision": true, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": 0.2, "frame_index": 40, "target_offset_s": 0.5, "distance_m": 6.3712, "ttc_s": 2.5986, "closing_speed_mps": 2.4517, "relative_speed_mps": 4.3242, "longitudinal_gap_m": -6.1834, "lateral_offset_m": -1.5354, "relative_heading_rad": -0.5623, "agent_a_speed_mps": 21.4824, "agent_b_speed_mps": 24.8857, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}], "llm_task": {"question": "Judge whether this is a natural and safety-critical long-tail driving scenario.", "return_format": "strict_json", "expected_keys": ["accepted", "danger_level", "naturalness_score", "interaction_score", "scenario_value_score", "scenario_type", "artifact_flags", "reason"]}}, "local_eval": {"accepted": false, "priority": "low_value", "danger_level": 3, "criticality_score": 64.158, "naturalness_score": 0.0, "interaction_score": 100.0, "scenario_value_score": 56.946, "scenario_type": "cut_in_or_side_conflict", "artifact_flags": ["extreme_speed", "hard_deceleration", "high_acceleration", "high_yaw_rate", "large_frame_displacement"], "rejection_reasons": ["large_frame_displacement_artifact", "extreme_speed_artifact"], "optimization_feedback": {"increase_collision_tolerance": false, "decrease_collision_tolerance": true, "increase_goal_pressure": false, "decrease_goal_pressure": false, "increase_offroad_penalty": false, "relax_offroad_penalty": false, "increase_risk_agents_per_world": false, "decrease_risk_agents_per_world": true, "target_scenario_types": []}, "reason": "Rejected by deterministic artifact checks: large_frame_displacement_artifact, extreme_speed_artifact.", "evidence": ["collision=True near_miss=False offroad=False", "primary_pair=4->7 reason=risk_agent_directly_collided_with_partner", "clean_min_distance=3.3008 min_ttc=0.8114", "event_relative_speed=4.0894 closing_speed=4.0682", "distance_trend=mostly_decreasing"]}, "llm_eval": {"accepted": false, "priority": "low_value", "danger_level": 3, "criticality_score": 64.158, "naturalness_score": 0.0, "interaction_score": 100.0, "scenario_value_score": 56.946, "scenario_type": "cut_in_or_side_conflict", "artifact_flags": ["extreme_speed", "hard_deceleration", "high_acceleration", "high_yaw_rate", "large_frame_displacement"], "rejection_reasons": ["large_frame_displacement_artifact", "extreme_speed_artifact"], "optimization_feedback": {"increase_collision_tolerance": false, "decrease_collision_tolerance": true, "increase_goal_pressure": false, "decrease_goal_pressure": false, "increase_offroad_penalty": false, "relax_offroad_penalty": false, "increase_risk_agents_per_world": false, "decrease_risk_agents_per_world": true, "target_scenario_types": []}, "reason": "Rejected by deterministic artifact checks: large_frame_displacement_artifact, extreme_speed_artifact.", "evidence": ["collision=True near_miss=False offroad=False", "primary_pair=4->7 reason=risk_agent_directly_collided_with_partner", "clean_min_distance=3.3008 min_ttc=0.8114", "event_relative_speed=4.0894 closing_speed=4.0682", "distance_trend=mostly_decreasing"]}, "final_eval": {"accepted": false, "priority": "low_value", "danger_level": 3, "criticality_score": 64.158, "naturalness_score": 0.0, "interaction_score": 100.0, "scenario_value_score": 56.946, "scenario_type": "cut_in_or_side_conflict", "artifact_flags": ["extreme_speed", "hard_deceleration", "high_acceleration", "high_yaw_rate", "large_frame_displacement"], "rejection_reasons": ["large_frame_displacement_artifact", "extreme_speed_artifact"], "optimization_feedback": {"increase_collision_tolerance": false, "decrease_collision_tolerance": true, "increase_goal_pressure": false, "decrease_goal_pressure": false, "increase_offroad_penalty": false, "relax_offroad_penalty": false, "increase_risk_agents_per_world": false, "decrease_risk_agents_per_world": true, "target_scenario_types": []}, "reason": "Rejected by deterministic artifact checks: large_frame_displacement_artifact, extreme_speed_artifact.", "evidence": ["collision=True near_miss=False offroad=False", "primary_pair=4->7 reason=risk_agent_directly_collided_with_partner", "clean_min_distance=3.3008 min_ttc=0.8114", "event_relative_speed=4.0894 closing_speed=4.0682", "distance_trend=mostly_decreasing"], "high_value": false}, "final_valid": false, "final_score": 56.946, "final_priority": "low_value"} diff --git a/gpudrive-main/search_outputs/evaluator_resume_test/summary.json b/gpudrive-main/search_outputs/evaluator_resume_test/summary.json new file mode 100644 index 0000000000000000000000000000000000000000..3bddb1f3b1acfed66b9538ef67c28aa287a1ecbe --- /dev/null +++ b/gpudrive-main/search_outputs/evaluator_resume_test/summary.json @@ -0,0 +1,25 @@ +{ + "cases_path": "search_outputs\\llm_cases_shard_000\\llm_cases.jsonl", + "rules_path": "rules\\longtail_llm_case_evaluation.md", + "output_dir": "search_outputs\\evaluator_resume_test", + "model": "dry_run_rule_based", + "dry_run": true, + "partial": false, + "total_evaluated": 3, + "accepted_count": 1, + "rejected_count": 2, + "high_value_count": 1, + "accepted_rate": 0.3333, + "high_value_rate": 0.3333, + "natural_critical_count": 1, + "natural_critical_rate": 0.3333, + "risk_direct_involved_rate": 0.6667, + "risk_direct_accept_rate": 1.0, + "warning_record_count": 3, + "warning_record_rate": 1.0, + "hard_artifact_record_count": 2, + "hard_artifact_record_rate": 0.6667, + "artifact_flags_per_case": 2.6667, + "semantic_diversity_entropy": 0.0, + "composite_objective": 0.3 +} \ No newline at end of file diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0002_line000000.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0002_line000000.md new file mode 100644 index 0000000000000000000000000000000000000000..fec4a1cbf2fde065998b25e3d3d1e10206524319 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0002_line000000.md @@ -0,0 +1,57 @@ +# shard000_batch0000_world0002_line000000 + +## Outcome +- collision: True +- near_miss: True +- offroad: True +- event_time_s: 2.9 +- raw_min_distance_m: 1.9574 +- clean_min_distance_m: 1.9575 + +## Primary Pair +- agent_a: 5 (risk_agent,collided_agent) +- agent_b: 7 (collided_agent,offroad_agent) +- selection_reason: risk_agent_directly_collided_with_partner +- confidence: high +- risk_directly_involved: True +- semantic_candidate: same_direction_side_conflict_or_cut_in + +## Primary Interaction Metrics +- num_clean_pair_frames: 34 +- min_ttc_s: 0.4335 +- time_ttc_below_1s: 1.2 +- time_ttc_below_2s: 2.3 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 0.9655, 'distance_start_m': 29.5168, 'distance_end_m': 1.9575} + +## Naturalness Checks +- max_speed_mps: 23.926 +- max_accel_mps2: 4.0166 +- max_decel_mps2: 4.0081 +- max_yaw_rate_radps: 6.665 +- max_frame_displacement_m: 2.3926 +- artifact_warnings: ['high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 5 | risk_agent,collided_agent | True | 34 | 10.645 | 3.0827 | 1.391 | +| 7 | collided_agent,offroad_agent | True | 39 | 14.2738 | 3.1654 | 1.4256 | +| 0 | risk_agent,offroad_agent | True | 40 | 17.2471 | 3.7002 | 1.6324 | +| 2 | context_agent | True | 40 | 19.003 | 3.2432 | 1.5205 | +| 3 | context_agent | True | 40 | 16.8588 | 3.2124 | 1.4202 | +| 4 | risk_agent | True | 23 | | 3.5435 | 1.5256 | +| 6 | context_agent | True | 40 | 13.7644 | 3.2835 | 1.4368 | +| 17 | context_agent | True | 32 | 23.6592 | 2.8417 | 1.3018 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -2.9 | 29.5168 | | | | 1.8873 | 29.4564 | | | +| -2.0 | 20.5942 | 1.6912 | 12.177 | 12.8039 | -6.3909 | 19.5775 | 5.179 | 10.1396 | +| -1.0 | 7.7682 | 0.6847 | 11.3453 | 11.6026 | -6.5408 | 4.1909 | 9.179 | 13.3404 | +| -0.5 | 3.6239 | 0.4436 | 8.1688 | 8.9406 | -3.4811 | 1.0071 | 10.6456 | 14.8069 | +| 0.0 | 1.9575 | 0.4496 | 4.3538 | 7.2043 | -1.5368 | 1.2124 | 10.645 | 14.2738 | +| 0.4 | 3.1975 | 12.4782 | 0.2562 | 2.2195 | -3.1686 | 0.4287 | 12.2457 | 14.4066 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0009_line000001.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0009_line000001.md new file mode 100644 index 0000000000000000000000000000000000000000..0a38e4de331728a8e76a8811633c47877c96032d --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0009_line000001.md @@ -0,0 +1,57 @@ +# shard000_batch0000_world0009_line000001 + +## Outcome +- collision: True +- near_miss: False +- offroad: True +- event_time_s: 5.4 +- raw_min_distance_m: 3.1167 +- clean_min_distance_m: 3.1166 + +## Primary Pair +- agent_a: 4 (risk_agent) +- agent_b: 8 (context_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: False +- semantic_candidate: same_direction_side_conflict_or_cut_in + +## Primary Interaction Metrics +- num_clean_pair_frames: 50 +- min_ttc_s: 2.5086 +- time_ttc_below_1s: 0.0 +- time_ttc_below_2s: 0.0 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 0.7931, 'distance_start_m': 6.4593, 'distance_end_m': 3.4719} + +## Naturalness Checks +- max_speed_mps: 33.7012 +- max_accel_mps2: 4.0153 +- max_decel_mps2: 3.9965 +- max_yaw_rate_radps: 14.554 +- max_frame_displacement_m: 3.3701 +- artifact_warnings: ['high_yaw_rate', 'offroad_before_collision'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 4 | risk_agent | True | 55 | 23.9944 | 4.3251 | 1.9082 | +| 8 | context_agent | True | 50 | 21.5161 | 3.219 | 1.473 | +| 0 | context_agent | True | 58 | 21.8984 | 3.7002 | 1.6324 | +| 3 | risk_agent | True | 55 | 20.7969 | 3.1886 | 1.4024 | +| 5 | collided_agent | True | 61 | 33.3017 | 3.2417 | 1.3969 | +| 6 | risk_agent | True | 30 | | 3.1494 | 1.4529 | +| 11 | collided_agent | True | 51 | 23.837 | 3.0509 | 1.4043 | +| 18 | offroad_agent | True | 43 | | 3.2048 | 1.388 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -5.0 | 12.2163 | | | | -4.3129 | 11.4296 | | | +| -3.0 | 6.4593 | 3.31 | 1.9514 | 2.2334 | -3.8266 | 5.2038 | 19.7301 | 17.5167 | +| -2.0 | 4.4688 | 2.5086 | 1.7814 | 2.503 | -3.5204 | 2.7526 | 21.4632 | 18.9835 | +| -1.0 | 3.1972 | 5.7704 | 0.5541 | 2.5069 | -3.1861 | 0.2657 | 22.7958 | 20.3163 | +| -0.5 | 3.1616 | | -0.4216 | 2.5091 | -3.0067 | -0.9775 | 23.4626 | 20.9832 | +| -0.1 | 3.4719 | | -1.1008 | 2.5098 | -2.8573 | -1.9722 | 23.9944 | 21.5161 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0012_line000002.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0012_line000002.md new file mode 100644 index 0000000000000000000000000000000000000000..56047ffb03a8b749ba9ec7c2518df0dcf54948b2 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0012_line000002.md @@ -0,0 +1,57 @@ +# shard000_batch0000_world0012_line000002 + +## Outcome +- collision: True +- near_miss: False +- offroad: False +- event_time_s: 3.8 +- raw_min_distance_m: 3.3008 +- clean_min_distance_m: 3.3008 + +## Primary Pair +- agent_a: 4 (risk_agent,collided_agent) +- agent_b: 7 (risk_agent,collided_agent) +- selection_reason: risk_agent_directly_collided_with_partner +- confidence: high +- risk_directly_involved: True +- semantic_candidate: same_direction_side_conflict_or_cut_in + +## Primary Interaction Metrics +- num_clean_pair_frames: 41 +- min_ttc_s: 0.8114 +- time_ttc_below_1s: 0.4 +- time_ttc_below_2s: 1.4 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 1.0, 'distance_start_m': 16.6732, 'distance_end_m': 3.3008} + +## Naturalness Checks +- max_speed_mps: 654.3072 +- max_accel_mps2: 6488.7513 +- max_decel_mps2: 18.4696 +- max_yaw_rate_radps: 30.336 +- max_frame_displacement_m: 65.4307 +- artifact_warnings: ['extreme_speed', 'hard_deceleration', 'high_acceleration', 'high_yaw_rate', 'large_frame_displacement'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 4 | risk_agent,collided_agent | True | 43 | 20.9505 | 3.3869 | 1.5994 | +| 7 | risk_agent,collided_agent | True | 41 | 25.0195 | 3.101 | 1.4958 | +| 0 | context_agent | False | 49 | 5.4707 | 3.7002 | 1.6324 | +| 1 | context_agent | True | 44 | 21.0514 | 2.9684 | 1.3518 | +| 2 | context_agent | False | 42 | 5.3768 | 3.1209 | 1.4512 | +| 3 | risk_agent | True | 44 | 20.9943 | 2.9235 | 1.3487 | +| 6 | context_agent | True | 43 | 20.0458 | 3.1435 | 1.4294 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -3.8 | 20.0383 | | | | -2.1553 | -19.922 | | | +| -3.0 | 16.6732 | 3.9614 | 4.2089 | 4.212 | -1.9318 | -16.5609 | 13.2175 | 17.4198 | +| -2.0 | 12.4874 | 2.8728 | 4.3468 | 4.3496 | -1.5683 | -12.3885 | 16.9507 | 21.2861 | +| -1.0 | 7.8288 | 1.697 | 4.6132 | 4.62 | -1.1481 | -7.7441 | 19.6161 | 24.22 | +| -0.5 | 5.5233 | 1.1982 | 4.6096 | 4.6211 | -0.9224 | -5.4457 | 20.2829 | 24.8864 | +| 0.0 | 3.3008 | 0.8114 | 4.0682 | 4.0894 | -0.6924 | -3.2274 | 20.9505 | 25.0195 | +| 0.2 | 6.3712 | 2.5986 | 2.4517 | 4.3242 | -1.5354 | -6.1834 | 21.4824 | 24.8857 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0017_line000003.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0017_line000003.md new file mode 100644 index 0000000000000000000000000000000000000000..0e662c8d551d4670a5b3db2ed66ba8adbcfaaf47 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0017_line000003.md @@ -0,0 +1,50 @@ +# shard000_batch0000_world0017_line000003 + +## Outcome +- collision: True +- near_miss: False +- offroad: False +- event_time_s: 3.2 +- raw_min_distance_m: 4.0969 +- clean_min_distance_m: 4.0743 + +## Primary Pair +- agent_a: 1 (risk_agent) +- agent_b: 0 (context_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: False +- semantic_candidate: generic_close_interaction + +## Primary Interaction Metrics +- num_clean_pair_frames: 9 +- min_ttc_s: None +- time_ttc_below_1s: 0.0 +- time_ttc_below_2s: 0.0 +- distance_trend_before_event: {'label': 'not_decreasing', 'decreasing_ratio': 0.0, 'distance_start_m': 4.131, 'distance_end_m': 4.5567} + +## Naturalness Checks +- max_speed_mps: 22.3423 +- max_accel_mps2: 4.0106 +- max_decel_mps2: -0.6604 +- max_yaw_rate_radps: 4.5679 +- max_frame_displacement_m: 2.2342 +- artifact_warnings: ['high_yaw_rate', 'short_clean_track'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 0 | context_agent | True | 41 | | 3.7002 | 1.6324 | +| 1 | risk_agent | True | 9 | | 3.4315 | 1.5318 | +| 6 | risk_agent | True | 37 | | 3.01 | 1.4046 | +| 9 | risk_agent | True | 2 | | 3.3504 | 1.4788 | +| 15 | collided_agent | True | 33 | | 3.3941 | 1.5125 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -3.2 | 4.0743 | | | | 4.0742 | 0.0218 | | | +| -3.0 | 4.131 | | -0.3919 | 2.0784 | 4.1079 | 0.4362 | 4.1743 | 6.2478 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0020_line000004.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0020_line000004.md new file mode 100644 index 0000000000000000000000000000000000000000..7e113111f5ebe998fcbe7a2acf196fd093f73850 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0020_line000004.md @@ -0,0 +1,58 @@ +# shard000_batch0000_world0020_line000004 + +## Outcome +- collision: True +- near_miss: False +- offroad: False +- event_time_s: 4.9 +- raw_min_distance_m: 2.1322 +- clean_min_distance_m: 2.1322 + +## Primary Pair +- agent_a: 10 (risk_agent,collided_agent) +- agent_b: 0 (context_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: True +- semantic_candidate: same_direction_side_conflict_or_cut_in + +## Primary Interaction Metrics +- num_clean_pair_frames: 60 +- min_ttc_s: 0.0973 +- time_ttc_below_1s: 1.2 +- time_ttc_below_2s: 1.9 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 0.9333, 'distance_start_m': 42.7184, 'distance_end_m': 2.1322} + +## Naturalness Checks +- max_speed_mps: 31.1746 +- max_accel_mps2: 130.8809 +- max_decel_mps2: 5.9354 +- max_yaw_rate_radps: 12.724 +- max_frame_displacement_m: 3.1175 +- artifact_warnings: ['high_acceleration', 'high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 0 | context_agent | False | 60 | 6.4672 | 3.7002 | 1.6324 | +| 10 | risk_agent,collided_agent | True | 60 | 28.041 | 3.3421 | 1.4774 | +| 2 | risk_agent | True | 52 | 25.8953 | 3.2188 | 1.4623 | +| 3 | context_agent | True | 60 | 26.3787 | 4.1701 | 1.7598 | +| 5 | context_agent | False | 60 | 8.3336 | 3.4134 | 1.5414 | +| 6 | context_agent | False | 60 | 13.1443 | 3.2981 | 1.4325 | +| 7 | risk_agent | True | 36 | | 3.1299 | 1.3812 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -4.9 | 55.212 | | | | 3.1922 | 55.1196 | | | +| -3.0 | 42.7184 | 4.4631 | 9.5715 | 9.6031 | 3.3206 | 42.5891 | 20.375 | 10.772 | +| -2.0 | 30.5419 | 2.1306 | 14.3347 | 14.4364 | 3.455 | 30.3458 | 24.2421 | 9.8062 | +| -1.0 | 18.8455 | 1.0754 | 17.5236 | 17.6025 | -2.2811 | 18.7069 | 25.7746 | 8.2282 | +| -0.5 | 9.264 | 0.4591 | 20.1769 | 20.423 | -1.8203 | 9.0834 | 27.7078 | 7.3543 | +| 0.0 | 2.1322 | 0.0973 | 21.9152 | 24.68 | -1.8846 | 0.9973 | 28.041 | 6.4672 | +| 0.5 | 8.9179 | | -23.9606 | 24.1971 | -2.0314 | -8.6835 | 29.8414 | 5.6582 | +| 1.0 | 3.7519 | 2.3531 | 1.5944 | 35.6212 | 3.7299 | 0.4056 | 31.1746 | 4.9242 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0026_line000005.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0026_line000005.md new file mode 100644 index 0000000000000000000000000000000000000000..0ca935ba52e95c4f4f4bbcfe55edb4f7a1355bd0 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0026_line000005.md @@ -0,0 +1,57 @@ +# shard000_batch0000_world0026_line000005 + +## Outcome +- collision: True +- near_miss: False +- offroad: False +- event_time_s: 1.8 +- raw_min_distance_m: 2.3996 +- clean_min_distance_m: 2.3996 + +## Primary Pair +- agent_a: 3 (risk_agent) +- agent_b: 26 (context_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: False +- semantic_candidate: same_direction_side_conflict_or_cut_in + +## Primary Interaction Metrics +- num_clean_pair_frames: 29 +- min_ttc_s: 0.6679 +- time_ttc_below_1s: 0.6 +- time_ttc_below_2s: 1.2 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 0.8889, 'distance_start_m': 9.5208, 'distance_end_m': 5.2631} + +## Naturalness Checks +- max_speed_mps: 12.3503 +- max_accel_mps2: 39.2104 +- max_decel_mps2: 49.0707 +- max_yaw_rate_radps: 3.947 +- max_frame_displacement_m: 1.235 +- artifact_warnings: ['hard_deceleration', 'high_acceleration', 'high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 3 | risk_agent | True | 29 | 7.5173 | 2.9743 | 1.3512 | +| 26 | context_agent | False | 29 | 2.6578 | 3.2369 | 1.4177 | +| 1 | context_agent | False | 29 | 0.224 | 3.1033 | 1.38 | +| 2 | context_agent | True | 26 | 7.2034 | 3.0311 | 1.4425 | +| 6 | context_agent | False | 29 | 4.4622 | 3.2038 | 1.4307 | +| 7 | risk_agent | True | 29 | 9.5492 | 3.0414 | 1.3639 | +| 11 | risk_agent | True | 24 | 5.9563 | 3.2123 | 1.4985 | +| 19 | collided_agent | True | 19 | 6.1284 | 3.1159 | 1.4263 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -1.8 | 9.5208 | | | | 0.3242 | 9.5153 | | | +| -1.0 | 8.829 | 4.8892 | 1.8058 | 1.8112 | 0.149 | 8.8277 | 4.2502 | 2.4448 | +| -0.5 | 7.4258 | 2.0732 | 3.5818 | 3.601 | -0.5615 | 7.4045 | 6.0506 | 2.453 | +| 0.0 | 5.2631 | 1.0931 | 4.8149 | 4.8635 | -0.6168 | 5.2268 | 7.5173 | 2.6578 | +| 0.5 | 3.604 | 0.6679 | 5.3962 | 6.1383 | -2.1464 | 2.8952 | 8.6509 | 2.5203 | +| 1.0 | 2.481 | | -1.8899 | 7.352 | -2.443 | -0.4326 | 9.7173 | 2.471 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0029_line000006.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0029_line000006.md new file mode 100644 index 0000000000000000000000000000000000000000..5070327b7dfbeb6e76be9cadc7ab00da6d959b4e --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0029_line000006.md @@ -0,0 +1,58 @@ +# shard000_batch0000_world0029_line000006 + +## Outcome +- collision: True +- near_miss: True +- offroad: True +- event_time_s: 2.7 +- raw_min_distance_m: 1.9269 +- clean_min_distance_m: 1.9268 + +## Primary Pair +- agent_a: 0 (risk_agent,collided_agent) +- agent_b: 2 (collided_agent,offroad_agent) +- selection_reason: risk_agent_directly_collided_with_partner +- confidence: high +- risk_directly_involved: True +- semantic_candidate: crossing_or_intersection_conflict + +## Primary Interaction Metrics +- num_clean_pair_frames: 38 +- min_ttc_s: 0.0595 +- time_ttc_below_1s: 0.1 +- time_ttc_below_2s: 0.1 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 1.0, 'distance_start_m': 5.7951, 'distance_end_m': 5.3} + +## Naturalness Checks +- max_speed_mps: 27.4324 +- max_accel_mps2: 4.0132 +- max_decel_mps2: 4.0092 +- max_yaw_rate_radps: 9.033 +- max_frame_displacement_m: 2.7432 +- artifact_warnings: ['high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 0 | risk_agent,collided_agent | True | 38 | 23.7433 | 3.7002 | 1.6324 | +| 2 | collided_agent,offroad_agent | True | 38 | 24.0798 | 5.1657 | 1.891 | +| 1 | context_agent | True | 38 | 20.036 | 3.2113 | 1.504 | +| 3 | risk_agent | True | 38 | 24.3495 | 3.3242 | 1.4602 | +| 4 | context_agent | True | 38 | 26.5506 | 3.2937 | 1.4543 | +| 5 | risk_agent | True | 38 | 21.2587 | 4.1836 | 1.8205 | +| 7 | collided_agent | True | 38 | 4.3798 | 9.0058 | 2.251 | +| 8 | offroad_agent | True | 38 | 25.0315 | 3.3754 | 1.4985 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -2.7 | 5.7951 | | | | -4.2856 | -3.9009 | | | +| -2.0 | 5.7356 | | 0.0933 | 0.3875 | -4.0703 | -4.041 | 17.0772 | 16.8793 | +| -1.0 | 5.611 | 25.6297 | 0.2189 | 0.4156 | -3.698 | -4.22 | 20.7436 | 20.6795 | +| -0.5 | 5.4766 | 19.4722 | 0.2813 | 0.4434 | -3.484 | -4.2256 | 22.2764 | 22.2798 | +| 0.0 | 5.3 | 9.4982 | 0.558 | 0.5857 | -3.2508 | -4.186 | 23.7433 | 24.0798 | +| 0.5 | 6.1031 | | -3.6683 | 5.3218 | -3.5359 | -4.9745 | 23.7432 | 23.4132 | +| 1.0 | 6.5712 | | -1.6514 | 1.7987 | -3.4113 | -5.6164 | 25.3433 | 24.3461 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0034_line000007.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0034_line000007.md new file mode 100644 index 0000000000000000000000000000000000000000..89ad52ddaddc28d6b8883ac5e1a5ff91a82c0288 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0034_line000007.md @@ -0,0 +1,48 @@ +# shard000_batch0000_world0034_line000007 + +## Outcome +- collision: False +- near_miss: True +- offroad: False +- event_time_s: 2.7 +- raw_min_distance_m: 1.7209 +- clean_min_distance_m: 21.3232 + +## Primary Pair +- agent_a: 1 (risk_agent) +- agent_b: 7 (context_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: False +- semantic_candidate: same_direction_side_conflict_or_cut_in + +## Primary Interaction Metrics +- num_clean_pair_frames: 2 +- min_ttc_s: 7.2993 +- time_ttc_below_1s: 0.0 +- time_ttc_below_2s: 0.0 +- distance_trend_before_event: {'label': 'insufficient_clean_frames', 'decreasing_ratio': None} + +## Naturalness Checks +- max_speed_mps: 20.3078 +- max_accel_mps2: 4.0166 +- max_decel_mps2: 0.0075 +- max_yaw_rate_radps: 1.684 +- max_frame_displacement_m: 2.0308 +- artifact_warnings: ['short_clean_track'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 1 | risk_agent | True | 38 | | 3.2593 | 1.4714 | +| 7 | context_agent | False | 2 | | 3.227 | 1.4817 | +| 2 | risk_agent | True | 38 | | 3.1566 | 1.4277 | +| 32 | risk_agent | True | 30 | | 3.4163 | 1.5608 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -2.7 | 21.6154 | | | | -1.6969 | 21.5486 | | | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0035_line000008.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0035_line000008.md new file mode 100644 index 0000000000000000000000000000000000000000..2961ad8517a02fd7b964ab51ad01a4170640b87a --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0035_line000008.md @@ -0,0 +1,48 @@ +# shard000_batch0000_world0035_line000008 + +## Outcome +- collision: False +- near_miss: True +- offroad: False +- event_time_s: 2.6 +- raw_min_distance_m: 0.5251 +- clean_min_distance_m: 2.4621 + +## Primary Pair +- agent_a: 41 (risk_agent) +- agent_b: 7 (context_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: False +- semantic_candidate: crossing_or_intersection_conflict + +## Primary Interaction Metrics +- num_clean_pair_frames: 2 +- min_ttc_s: 6.7233 +- time_ttc_below_1s: 0.0 +- time_ttc_below_2s: 0.0 +- distance_trend_before_event: {'label': 'insufficient_clean_frames', 'decreasing_ratio': None} + +## Naturalness Checks +- max_speed_mps: 21.2656 +- max_accel_mps2: 4.0138 +- max_decel_mps2: 2.6752 +- max_yaw_rate_radps: 2.645 +- max_frame_displacement_m: 2.1266 +- artifact_warnings: ['high_yaw_rate', 'short_clean_track'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 7 | context_agent | False | 2 | | 3.0489 | 1.3928 | +| 41 | risk_agent | True | 37 | | 3.8889 | 1.6192 | +| 0 | risk_agent | True | 26 | | 3.7002 | 1.6324 | +| 2 | risk_agent | True | 37 | | 3.2812 | 1.4882 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -2.6 | 2.4991 | | | | -2.4985 | -0.0558 | | | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0045_line000009.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0045_line000009.md new file mode 100644 index 0000000000000000000000000000000000000000..b4fd962d7e71155e995412ef61d5ec0b0c3c2df7 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0045_line000009.md @@ -0,0 +1,48 @@ +# shard000_batch0000_world0045_line000009 + +## Outcome +- collision: False +- near_miss: True +- offroad: False +- event_time_s: 4.3 +- raw_min_distance_m: 0.4923 +- clean_min_distance_m: 2.4621 + +## Primary Pair +- agent_a: 41 (risk_agent) +- agent_b: 7 (context_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: False +- semantic_candidate: crossing_or_intersection_conflict + +## Primary Interaction Metrics +- num_clean_pair_frames: 2 +- min_ttc_s: 6.7233 +- time_ttc_below_1s: 0.0 +- time_ttc_below_2s: 0.0 +- distance_trend_before_event: {'label': 'insufficient_clean_frames', 'decreasing_ratio': None} + +## Naturalness Checks +- max_speed_mps: 21.5319 +- max_accel_mps2: 4.018 +- max_decel_mps2: 4.0113 +- max_yaw_rate_radps: 2.51 +- max_frame_displacement_m: 2.1532 +- artifact_warnings: ['high_yaw_rate', 'short_clean_track'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 7 | context_agent | False | 2 | | 3.0489 | 1.3928 | +| 41 | risk_agent | True | 54 | | 3.8889 | 1.6192 | +| 0 | risk_agent | True | 26 | | 3.7002 | 1.6324 | +| 2 | risk_agent | True | 40 | | 3.2812 | 1.4882 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -4.3 | 2.4991 | | | | -2.4985 | -0.0558 | | | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0057_line000010.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0057_line000010.md new file mode 100644 index 0000000000000000000000000000000000000000..dba0721dfcb1cb8a235a0c4199af802a1ada67cf --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0057_line000010.md @@ -0,0 +1,56 @@ +# shard000_batch0000_world0057_line000010 + +## Outcome +- collision: True +- near_miss: True +- offroad: True +- event_time_s: 1.6 +- raw_min_distance_m: 0.3061 +- clean_min_distance_m: 0.3061 + +## Primary Pair +- agent_a: 5 (risk_agent,collided_agent) +- agent_b: 4 (collided_agent) +- selection_reason: risk_agent_directly_collided_with_partner +- confidence: high +- risk_directly_involved: True +- semantic_candidate: rear_end_or_following_conflict + +## Primary Interaction Metrics +- num_clean_pair_frames: 25 +- min_ttc_s: 0.1871 +- time_ttc_below_1s: 0.7 +- time_ttc_below_2s: 1.3 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 1.0, 'distance_start_m': 8.463, 'distance_end_m': 3.201} + +## Naturalness Checks +- max_speed_mps: 12.4567 +- max_accel_mps2: 4.0153 +- max_decel_mps2: 3.3395 +- max_yaw_rate_radps: 3.767 +- max_frame_displacement_m: 1.2457 +- artifact_warnings: ['high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 4 | collided_agent | True | 27 | 2.434 | 3.1287 | 1.3975 | +| 5 | risk_agent,collided_agent | True | 25 | 6.7845 | 3.141 | 1.412 | +| 1 | offroad_agent | True | 27 | 1.2461 | 3.1813 | 1.451 | +| 2 | risk_agent | True | 27 | 5.9002 | 3.1529 | 1.4333 | +| 3 | context_agent | True | 20 | 6.594 | 3.3306 | 1.4454 | +| 6 | risk_agent | True | 27 | 8.4573 | 3.3351 | 1.4516 | +| 10 | context_agent | True | 17 | 8.4417 | 3.2178 | 1.4285 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -1.6 | 8.463 | | | | -0.0425 | 8.4629 | | | +| -1.0 | 7.29 | 3.4432 | 2.1172 | 2.1397 | 0.097 | 7.2894 | 3.983 | 1.9 | +| -0.5 | 5.4057 | 1.2656 | 4.2711 | 4.7146 | 0.722 | 5.3573 | 5.451 | 1.7007 | +| 0.0 | 3.201 | 0.5221 | 6.1306 | 6.18 | 0.0748 | 3.2001 | 6.7845 | 2.434 | +| 0.5 | 1.4854 | 0.2717 | 5.4678 | 10.4194 | -0.3964 | 1.4315 | 8.1839 | 3.3669 | +| 0.8 | 0.3061 | | -1.7131 | 11.5657 | -0.2592 | 0.1629 | 9.1166 | 3.6332 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0061_line000011.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0061_line000011.md new file mode 100644 index 0000000000000000000000000000000000000000..ec19cc1f6187745cbd55e4150f266ea8979dda32 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0061_line000011.md @@ -0,0 +1,49 @@ +# shard000_batch0000_world0061_line000011 + +## Outcome +- collision: True +- near_miss: False +- offroad: False +- event_time_s: 1.8 +- raw_min_distance_m: 5.286 +- clean_min_distance_m: 18.8063 + +## Primary Pair +- agent_a: 3 (risk_agent) +- agent_b: 28 (context_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: False +- semantic_candidate: generic_close_interaction + +## Primary Interaction Metrics +- num_clean_pair_frames: 2 +- min_ttc_s: 4.7321 +- time_ttc_below_1s: 0.0 +- time_ttc_below_2s: 0.0 +- distance_trend_before_event: {'label': 'insufficient_clean_frames', 'decreasing_ratio': None} + +## Naturalness Checks +- max_speed_mps: 11.6237 +- max_accel_mps2: 4.0198 +- max_decel_mps2: 0.6682 +- max_yaw_rate_radps: 5.307 +- max_frame_displacement_m: 1.1624 +- artifact_warnings: ['high_yaw_rate', 'short_clean_track'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 3 | risk_agent | True | 24 | | 3.2145 | 1.4258 | +| 28 | context_agent | False | 2 | | 3.0189 | 1.349 | +| 0 | risk_agent | True | 2 | | 3.7002 | 1.6324 | +| 5 | collided_agent | True | 24 | | 2.9782 | 1.3462 | +| 7 | risk_agent | True | 2 | | 3.2675 | 1.4398 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -1.8 | 19.2038 | | | | -3.5352 | 18.8756 | | | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0063_line000012.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0063_line000012.md new file mode 100644 index 0000000000000000000000000000000000000000..e6cc7859a96da3ff54e5b2aa5eda06797edc7a66 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0063_line000012.md @@ -0,0 +1,52 @@ +# shard000_batch0000_world0063_line000012 + +## Outcome +- collision: True +- near_miss: False +- offroad: True +- event_time_s: 2.0 +- raw_min_distance_m: 2.5916 +- clean_min_distance_m: 2.5917 + +## Primary Pair +- agent_a: 0 (risk_agent,collided_agent) +- agent_b: 2 (risk_agent,collided_agent) +- selection_reason: risk_agent_directly_collided_with_partner +- confidence: high +- risk_directly_involved: True +- semantic_candidate: crossing_or_intersection_conflict + +## Primary Interaction Metrics +- num_clean_pair_frames: 31 +- min_ttc_s: 0.2654 +- time_ttc_below_1s: 0.9 +- time_ttc_below_2s: 1.5 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 1.0, 'distance_start_m': 23.0461, 'distance_end_m': 2.5917} + +## Naturalness Checks +- max_speed_mps: 18.9326 +- max_accel_mps2: 4.0169 +- max_decel_mps2: 1.3409 +- max_yaw_rate_radps: 7.432 +- max_frame_displacement_m: 1.8933 +- artifact_warnings: ['high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 0 | risk_agent,collided_agent | True | 31 | 7.3255 | 3.7002 | 1.6324 | +| 2 | risk_agent,collided_agent | True | 31 | 15.8664 | 3.0643 | 1.403 | +| 1 | risk_agent,offroad_agent | True | 31 | 5.572 | 3.3427 | 1.4735 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -2.0 | 23.0461 | | | | 22.2835 | 5.8792 | | | +| -1.0 | 13.8491 | 1.2375 | 11.1911 | 11.3843 | 13.4949 | 3.1123 | 3.8587 | 11.8662 | +| -0.5 | 8.2047 | 0.742 | 11.0576 | 11.3012 | 8.0829 | 1.4086 | 5.6579 | 13.8663 | +| 0.0 | 2.5917 | 0.2718 | 9.5365 | 11.4348 | 2.4115 | 0.9496 | 7.3255 | 15.8664 | +| 0.5 | 5.1606 | | -3.0662 | 9.243 | 2.8956 | 4.2716 | 8.7257 | 17.0661 | +| 1.0 | 7.5759 | | -6.186 | 9.9333 | 1.2869 | 7.4658 | 10.3257 | 18.9326 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0075_line000013.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0075_line000013.md new file mode 100644 index 0000000000000000000000000000000000000000..36875ac9a99297f9a82a343a41d803f61aa05f4b --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0075_line000013.md @@ -0,0 +1,50 @@ +# shard000_batch0000_world0075_line000013 + +## Outcome +- collision: True +- near_miss: False +- offroad: False +- event_time_s: 4.6 +- raw_min_distance_m: 2.7453 +- clean_min_distance_m: 75.8727 + +## Primary Pair +- agent_a: 4 (risk_agent) +- agent_b: 45 (context_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: False +- semantic_candidate: generic_close_interaction + +## Primary Interaction Metrics +- num_clean_pair_frames: 2 +- min_ttc_s: 7.1798 +- time_ttc_below_1s: 0.0 +- time_ttc_below_2s: 0.0 +- distance_trend_before_event: {'label': 'insufficient_clean_frames', 'decreasing_ratio': None} + +## Naturalness Checks +- max_speed_mps: 29.7844 +- max_accel_mps2: 4.0126 +- max_decel_mps2: 2.0007 +- max_yaw_rate_radps: 12.289 +- max_frame_displacement_m: 2.9784 +- artifact_warnings: ['high_yaw_rate', 'short_clean_track'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 4 | risk_agent | True | 57 | | 3.1273 | 1.4352 | +| 45 | context_agent | False | 2 | | 3.7706 | 1.7075 | +| 3 | collided_agent | True | 57 | | 3.1439 | 1.4704 | +| 7 | collided_agent | True | 51 | | 2.9713 | 1.4136 | +| 33 | risk_agent | True | 2 | | 5.0875 | 1.9327 | +| 41 | risk_agent | True | 12 | | 3.2012 | 1.4626 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -4.6 | 76.93 | | | | 20.5097 | 74.1456 | | | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0078_line000014.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0078_line000014.md new file mode 100644 index 0000000000000000000000000000000000000000..e838cad1fc677246f56a2729d197ac3b73df1af3 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0078_line000014.md @@ -0,0 +1,55 @@ +# shard000_batch0000_world0078_line000014 + +## Outcome +- collision: True +- near_miss: True +- offroad: False +- event_time_s: 2.3 +- raw_min_distance_m: 1.4903 +- clean_min_distance_m: 1.4903 + +## Primary Pair +- agent_a: 2 (risk_agent,collided_agent) +- agent_b: 3 (risk_agent,collided_agent) +- selection_reason: risk_agent_directly_collided_with_partner +- confidence: high +- risk_directly_involved: True +- semantic_candidate: crossing_or_intersection_conflict + +## Primary Interaction Metrics +- num_clean_pair_frames: 34 +- min_ttc_s: 0.047 +- time_ttc_below_1s: 1.0 +- time_ttc_below_2s: 1.7 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 1.0, 'distance_start_m': 63.1041, 'distance_end_m': 1.4903} + +## Naturalness Checks +- max_speed_mps: 21.2423 +- max_accel_mps2: 4.0136 +- max_decel_mps2: 4.0076 +- max_yaw_rate_radps: 7.794 +- max_frame_displacement_m: 2.1242 +- artifact_warnings: ['high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 2 | risk_agent,collided_agent | True | 34 | 18.9078 | 3.3963 | 1.4248 | +| 3 | risk_agent,collided_agent | True | 34 | 15.5137 | 4.0613 | 1.6962 | +| 0 | context_agent | True | 34 | 7.6926 | 3.7002 | 1.6324 | +| 1 | risk_agent | True | 34 | 11.6465 | 3.1814 | 1.4456 | +| 4 | context_agent | True | 34 | 0.1782 | 3.1687 | 1.3982 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -2.3 | 63.1041 | | | | 15.281 | 61.2259 | | | +| -2.0 | 56.252 | 2.3957 | 23.4799 | 23.53 | 4.3509 | 56.0835 | 15.1752 | 8.3809 | +| -1.0 | 30.3185 | 1.0608 | 28.5805 | 28.5977 | 1.686 | 30.2716 | 16.5081 | 12.2477 | +| -0.5 | 15.0135 | 0.4699 | 31.9524 | 32.0428 | 0.2842 | 15.0108 | 17.9753 | 14.2476 | +| 0.0 | 1.4903 | 0.047 | 31.6875 | 31.6907 | -0.6404 | 1.3458 | 18.9078 | 15.5137 | +| 0.5 | 15.1832 | | -35.1463 | 35.9996 | -2.8085 | -14.9212 | 19.8413 | 16.1812 | +| 1.0 | 33.7562 | | -38.2159 | 38.399 | -2.2302 | -33.6825 | 21.2423 | 17.1803 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0081_line000015.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0081_line000015.md new file mode 100644 index 0000000000000000000000000000000000000000..c0f19b4d78bc4a53c7ed897d9571454556f6906e --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0081_line000015.md @@ -0,0 +1,58 @@ +# shard000_batch0000_world0081_line000015 + +## Outcome +- collision: True +- near_miss: False +- offroad: False +- event_time_s: 3.1 +- raw_min_distance_m: 3.0447 +- clean_min_distance_m: 4.8091 + +## Primary Pair +- agent_a: 0 (risk_agent) +- agent_b: 2 (context_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: False +- semantic_candidate: same_direction_side_conflict_or_cut_in + +## Primary Interaction Metrics +- num_clean_pair_frames: 42 +- min_ttc_s: 2.1406 +- time_ttc_below_1s: 0.0 +- time_ttc_below_2s: 0.0 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 1.0, 'distance_start_m': 16.0611, 'distance_end_m': 7.2508} + +## Naturalness Checks +- max_speed_mps: 34.6029 +- max_accel_mps2: 4.0152 +- max_decel_mps2: 0.6719 +- max_yaw_rate_radps: 13.9979 +- max_frame_displacement_m: 3.4603 +- artifact_warnings: ['high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 0 | risk_agent | True | 42 | 25.786 | 3.7002 | 1.6324 | +| 2 | context_agent | True | 42 | 28.5583 | 4.0564 | 1.6758 | +| 1 | collided_agent | True | 42 | 33.202 | 3.3208 | 1.4888 | +| 5 | collided_agent | True | 42 | 25.9475 | 3.4168 | 1.5194 | +| 6 | risk_agent | True | 42 | 25.3841 | 3.8886 | 1.6662 | +| 9 | risk_agent | True | 42 | 26.6851 | 3.92 | 1.7117 | +| 10 | collided_agent | True | 42 | 30.5459 | 3.3563 | 1.527 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -3.1 | 16.4313 | | | | -3.5647 | -16.04 | | | +| -3.0 | 16.0611 | 4.3402 | 3.7005 | 3.7737 | -3.5549 | -15.6628 | 16.3842 | 20.1569 | +| -2.0 | 12.7291 | 4.0516 | 3.1417 | 3.2405 | -3.4553 | -12.2511 | 20.1185 | 23.358 | +| -1.0 | 9.8384 | 3.7295 | 2.638 | 2.7749 | -3.3774 | -9.2406 | 23.1184 | 25.8917 | +| -0.5 | 8.5303 | 3.2914 | 2.5917 | 2.774 | -3.3347 | -7.8515 | 24.4522 | 27.2245 | +| 0.0 | 7.2508 | 2.8778 | 2.5196 | 2.7742 | -3.2886 | -6.4621 | 25.786 | 28.5583 | +| 0.5 | 5.9975 | 2.506 | 2.3932 | 2.7743 | -3.2379 | -5.0483 | 26.919 | 29.6913 | +| 1.0 | 4.8091 | 2.1406 | 2.2467 | 2.908 | -3.1824 | -3.6055 | 27.5853 | 30.4916 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0082_line000016.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0082_line000016.md new file mode 100644 index 0000000000000000000000000000000000000000..d337b7dfe29494e543d51d9f3acd0e6a54e51785 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0082_line000016.md @@ -0,0 +1,57 @@ +# shard000_batch0000_world0082_line000016 + +## Outcome +- collision: True +- near_miss: False +- offroad: False +- event_time_s: 6.6 +- raw_min_distance_m: 3.6995 +- clean_min_distance_m: 5.9556 + +## Primary Pair +- agent_a: 5 (risk_agent) +- agent_b: 3 (context_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: False +- semantic_candidate: generic_close_interaction + +## Primary Interaction Metrics +- num_clean_pair_frames: 53 +- min_ttc_s: 1.0781 +- time_ttc_below_1s: 0.0 +- time_ttc_below_2s: 0.1 +- distance_trend_before_event: {'label': 'not_decreasing', 'decreasing_ratio': 0.0, 'distance_start_m': 17.2644, 'distance_end_m': 32.8004} + +## Naturalness Checks +- max_speed_mps: 37.2855 +- max_accel_mps2: 3.3265 +- max_decel_mps2: 4.0013 +- max_yaw_rate_radps: 16.984 +- max_frame_displacement_m: 3.7285 +- artifact_warnings: ['high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 3 | context_agent | True | 61 | 35.1052 | 3.1946 | 1.4687 | +| 5 | risk_agent | True | 53 | 31.3288 | 3.334 | 1.4202 | +| 0 | collided_agent | True | 59 | 33.0344 | 3.7002 | 1.6324 | +| 2 | context_agent | True | 57 | 32.0594 | 3.3064 | 1.4572 | +| 4 | context_agent | True | 58 | 32.6618 | 3.2877 | 1.4439 | +| 6 | risk_agent | True | 52 | 29.2231 | 3.3267 | 1.4574 | +| 8 | collided_agent | True | 53 | 36.9525 | 3.0461 | 1.4391 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -5.0 | 5.9556 | | | | 3.5866 | 4.7545 | | | +| -3.0 | 17.2644 | | -4.9443 | 16.3303 | 2.7445 | 17.0448 | 27.3296 | 33.8399 | +| -2.0 | 23.4078 | | -6.0424 | 6.1781 | 4.3792 | 22.9945 | 28.6623 | 34.8392 | +| -1.0 | 28.975 | | -5.3753 | 14.2547 | 8.1146 | 27.8155 | 29.9956 | 34.5058 | +| -0.5 | 30.9461 | | -3.7015 | 3.786 | 8.6696 | 29.7068 | 30.6619 | 34.4391 | +| 0.0 | 32.8004 | | -3.7143 | 3.7861 | 8.7709 | 31.6059 | 31.3288 | 35.1052 | +| 0.2 | 33.544 | | -3.7195 | 3.7866 | 8.8101 | 32.3663 | 31.5949 | 35.3715 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0088_line000017.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0088_line000017.md new file mode 100644 index 0000000000000000000000000000000000000000..767930e939a2de744e6aecb1f647cbec1fe5d23e --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0088_line000017.md @@ -0,0 +1,56 @@ +# shard000_batch0000_world0088_line000017 + +## Outcome +- collision: True +- near_miss: False +- offroad: True +- event_time_s: 2.1 +- raw_min_distance_m: 2.435 +- clean_min_distance_m: 2.4349 + +## Primary Pair +- agent_a: 17 (risk_agent,collided_agent) +- agent_b: 1 (collided_agent) +- selection_reason: risk_agent_directly_collided_with_partner +- confidence: high +- risk_directly_involved: True +- semantic_candidate: rear_end_or_following_conflict + +## Primary Interaction Metrics +- num_clean_pair_frames: 22 +- min_ttc_s: 0.3886 +- time_ttc_below_1s: 0.2 +- time_ttc_below_2s: 0.9 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 0.9048, 'distance_start_m': 7.2844, 'distance_end_m': 2.4349} + +## Naturalness Checks +- max_speed_mps: 23.1187 +- max_accel_mps2: 147.8111 +- max_decel_mps2: 4.4998 +- max_yaw_rate_radps: 3.676 +- max_frame_displacement_m: 2.3119 +- artifact_warnings: ['high_acceleration', 'high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 1 | collided_agent | True | 32 | 1.5775 | 3.1414 | 1.4302 | +| 17 | risk_agent,collided_agent | True | 22 | 7.8759 | 3.2597 | 1.4496 | +| 2 | context_agent | False | 32 | 14.9878 | 3.3076 | 1.5035 | +| 4 | context_agent | True | 32 | 20.8681 | 3.3246 | 1.5186 | +| 5 | context_agent | True | 32 | 20.3311 | 3.2225 | 1.4071 | +| 7 | offroad_agent | True | 32 | 8.0306 | 3.2615 | 1.4287 | +| 24 | risk_agent | True | 32 | 19.8472 | 3.5002 | 1.5828 | +| 27 | risk_agent,offroad_agent | True | 32 | 21.0522 | 4.2011 | 1.7979 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -2.1 | 7.2844 | | | | -0.3451 | 7.2763 | | | +| -2.0 | 7.2567 | 26.1496 | 0.2775 | 0.3035 | -0.3908 | 7.2461 | 0.4091 | 0.11 | +| -1.0 | 5.77 | 1.7791 | 3.2431 | 4.5851 | -2.4628 | 5.218 | 4.0089 | 1.5778 | +| -0.5 | 4.1575 | 1.2793 | 3.2497 | 4.4997 | -2.5992 | 3.2448 | 6.0087 | 1.5776 | +| 0.0 | 2.4349 | 0.3886 | 6.2656 | 6.3061 | -1.3414 | 2.0321 | 7.8759 | 1.5775 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0093_line000018.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0093_line000018.md new file mode 100644 index 0000000000000000000000000000000000000000..0153f540fcf5769ceceb588c999b49690a0629fe --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0093_line000018.md @@ -0,0 +1,48 @@ +# shard000_batch0000_world0093_line000018 + +## Outcome +- collision: False +- near_miss: True +- offroad: False +- event_time_s: 4.6 +- raw_min_distance_m: 0.471 +- clean_min_distance_m: 2.4991 + +## Primary Pair +- agent_a: 41 (risk_agent) +- agent_b: 7 (context_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: False +- semantic_candidate: crossing_or_intersection_conflict + +## Primary Interaction Metrics +- num_clean_pair_frames: 2 +- min_ttc_s: None +- time_ttc_below_1s: 0.0 +- time_ttc_below_2s: 0.0 +- distance_trend_before_event: {'label': 'insufficient_clean_frames', 'decreasing_ratio': None} + +## Naturalness Checks +- max_speed_mps: 19.5335 +- max_accel_mps2: 4.0128 +- max_decel_mps2: 3.3379 +- max_yaw_rate_radps: 9.164 +- max_frame_displacement_m: 1.9533 +- artifact_warnings: ['high_yaw_rate', 'short_clean_track'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 7 | context_agent | False | 2 | | 3.0489 | 1.3928 | +| 41 | risk_agent | True | 57 | | 3.8889 | 1.6192 | +| 0 | risk_agent | True | 26 | | 3.7002 | 1.6324 | +| 1 | risk_agent | True | 44 | | 2.9842 | 1.3869 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -4.6 | 2.4991 | | | | -2.4985 | -0.0558 | | | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0108_line000019.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0108_line000019.md new file mode 100644 index 0000000000000000000000000000000000000000..9921d47de679adc9ca742561656ae192f9273735 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0108_line000019.md @@ -0,0 +1,59 @@ +# shard000_batch0000_world0108_line000019 + +## Outcome +- collision: True +- near_miss: False +- offroad: True +- event_time_s: 4.1 +- raw_min_distance_m: 3.293 +- clean_min_distance_m: 3.293 + +## Primary Pair +- agent_a: 15 (risk_agent) +- agent_b: 4 (context_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: False +- semantic_candidate: same_direction_side_conflict_or_cut_in + +## Primary Interaction Metrics +- num_clean_pair_frames: 50 +- min_ttc_s: 2.6387 +- time_ttc_below_1s: 0.0 +- time_ttc_below_2s: 0.0 +- distance_trend_before_event: {'label': 'not_decreasing', 'decreasing_ratio': 0.0333, 'distance_start_m': 3.293, 'distance_end_m': 7.6295} + +## Naturalness Checks +- max_speed_mps: 21.5023 +- max_accel_mps2: 4.0162 +- max_decel_mps2: 3.9864 +- max_yaw_rate_radps: 8.933 +- max_frame_displacement_m: 2.1502 +- artifact_warnings: ['high_yaw_rate', 'offroad_before_collision'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 4 | context_agent | True | 50 | 16.4019 | 3.1687 | 1.4487 | +| 15 | risk_agent | True | 50 | 19.2557 | 3.2184 | 1.4739 | +| 2 | collided_agent | True | 42 | 20.5463 | 3.2202 | 1.4367 | +| 5 | context_agent | True | 43 | 16.9096 | 3.2472 | 1.5059 | +| 9 | collided_agent | True | 50 | 20.0199 | 3.4209 | 1.5233 | +| 12 | risk_agent | True | 40 | | 3.3623 | 1.6157 | +| 16 | risk_agent | True | 40 | | 3.1541 | 1.4784 | +| 25 | offroad_agent | True | 23 | | 3.7795 | 1.578 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -4.1 | 4.2322 | | | | -3.2103 | 2.7578 | | | +| -3.0 | 3.293 | | -0.0798 | 2.3222 | -3.2929 | 0.0212 | 12.0557 | 9.736 | +| -2.0 | 4.1106 | | -1.4063 | 2.3248 | -3.4099 | -2.2954 | 16.0563 | 13.736 | +| -1.0 | 5.5856 | | -1.5827 | 1.9277 | -3.557 | -4.3065 | 17.9233 | 16.0029 | +| -0.5 | 6.3531 | | -3.0229 | 3.0262 | -3.31 | -5.4227 | 18.5899 | 16.0023 | +| 0.0 | 7.6295 | | -2.608 | 2.8559 | -3.354 | -6.8527 | 19.2557 | 16.4019 | +| 0.5 | 10.4703 | | -5.7447 | 10.0959 | -0.1678 | -10.4689 | 19.9226 | 16.2686 | +| 0.8 | 12.1094 | | -5.6931 | 7.264 | 1.5859 | -12.0051 | 20.3221 | 16.136 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0119_line000020.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0119_line000020.md new file mode 100644 index 0000000000000000000000000000000000000000..b7c963fd7aed017842b269ed511897d2ecc11049 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0119_line000020.md @@ -0,0 +1,53 @@ +# shard000_batch0000_world0119_line000020 + +## Outcome +- collision: True +- near_miss: False +- offroad: False +- event_time_s: 3.2 +- raw_min_distance_m: 2.5327 +- clean_min_distance_m: 2.5328 + +## Primary Pair +- agent_a: 1 (risk_agent,collided_agent) +- agent_b: 2 (risk_agent,collided_agent) +- selection_reason: risk_agent_directly_collided_with_partner +- confidence: high +- risk_directly_involved: True +- semantic_candidate: crossing_or_intersection_conflict + +## Primary Interaction Metrics +- num_clean_pair_frames: 37 +- min_ttc_s: 0.2537 +- time_ttc_below_1s: 0.6 +- time_ttc_below_2s: 1.3 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 1.0, 'distance_start_m': 21.1883, 'distance_end_m': 2.5328} + +## Naturalness Checks +- max_speed_mps: 16.6225 +- max_accel_mps2: 4.0164 +- max_decel_mps2: 1.3378 +- max_yaw_rate_radps: 5.366 +- max_frame_displacement_m: 1.6623 +- artifact_warnings: ['high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 1 | risk_agent,collided_agent | True | 37 | 12.2222 | 3.3633 | 1.4532 | +| 2 | risk_agent,collided_agent | True | 43 | 13.8222 | 3.8736 | 1.6129 | +| 0 | risk_agent | True | 23 | | 3.7002 | 1.6324 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -3.2 | 21.5413 | | | | -11.7651 | 18.0447 | | | +| -3.0 | 21.1883 | 10.7004 | 1.9801 | 2.2964 | -11.8304 | 17.5779 | 1.29 | 2.4224 | +| -2.0 | 17.3354 | 3.6126 | 4.7986 | 4.9035 | -13.9373 | 10.3086 | 5.2901 | 6.4227 | +| -1.0 | 11.5064 | 1.6395 | 7.0181 | 7.1306 | -10.5429 | 4.6092 | 8.8892 | 10.0224 | +| -0.5 | 7.6242 | 0.9288 | 8.2084 | 8.5374 | -6.6625 | 3.7066 | 10.6229 | 12.0222 | +| 0.0 | 2.5328 | 0.2537 | 9.9829 | 14.5114 | -0.9918 | 2.3305 | 12.2222 | 13.8222 | +| 0.4 | 5.4102 | | -3.1692 | 3.4375 | 2.4059 | 4.8459 | 12.7564 | 14.955 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0135_line000021.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0135_line000021.md new file mode 100644 index 0000000000000000000000000000000000000000..03efadb43eb8caf7e00800eb2f552a5afb64ad8a --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0135_line000021.md @@ -0,0 +1,55 @@ +# shard000_batch0000_world0135_line000021 + +## Outcome +- collision: True +- near_miss: False +- offroad: True +- event_time_s: 5.4 +- raw_min_distance_m: 2.6421 +- clean_min_distance_m: 2.6421 + +## Primary Pair +- agent_a: 2 (risk_agent,collided_agent,offroad_agent) +- agent_b: 4 (collided_agent) +- selection_reason: risk_agent_directly_collided_with_partner +- confidence: high +- risk_directly_involved: True +- semantic_candidate: crossing_or_intersection_conflict + +## Primary Interaction Metrics +- num_clean_pair_frames: 51 +- min_ttc_s: 0.0556 +- time_ttc_below_1s: 0.2 +- time_ttc_below_2s: 1.0 +- distance_trend_before_event: {'label': 'not_decreasing', 'decreasing_ratio': 0.1667, 'distance_start_m': 3.2669, 'distance_end_m': 2.6421} + +## Naturalness Checks +- max_speed_mps: 33.1049 +- max_accel_mps2: 3.3408 +- max_decel_mps2: 3.3296 +- max_yaw_rate_radps: 12.936 +- max_frame_displacement_m: 3.3105 +- artifact_warnings: ['high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 2 | risk_agent,collided_agent,offroad_agent | True | 61 | 26.944 | 3.0619 | 1.3909 | +| 4 | collided_agent | True | 51 | 29.2128 | 3.1616 | 1.4106 | +| 0 | context_agent | True | 61 | 27.658 | 3.7002 | 1.6324 | +| 1 | context_agent | True | 54 | 28.5752 | 3.3853 | 1.5143 | +| 3 | risk_agent,offroad_agent | True | 61 | 29.3535 | 3.924 | 1.6409 | +| 8 | risk_agent | True | 59 | 32.905 | 3.3235 | 1.4183 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -5.0 | 9.262 | | | | 3.4304 | -8.6033 | | | +| -3.0 | 3.2669 | 3.6133 | 0.9041 | 2.9389 | 3.0569 | -1.1524 | 22.3441 | 25.2801 | +| -2.0 | 3.619 | | -1.4118 | 2.7407 | 3.2086 | 1.6739 | 24.01 | 26.7462 | +| -1.0 | 5.5236 | | -2.1737 | 2.6089 | 3.3799 | 4.3688 | 25.477 | 28.0796 | +| -0.5 | 6.6143 | | -2.2103 | 2.4772 | 3.479 | 5.6254 | 26.277 | 28.7459 | +| 0.0 | 2.6421 | 0.0556 | 47.4808 | 47.9709 | 1.732 | 1.9952 | 26.944 | 29.2128 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0144_line000022.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0144_line000022.md new file mode 100644 index 0000000000000000000000000000000000000000..3a4fe79bdbda14348c479b8a7eedde0bb627522e --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0144_line000022.md @@ -0,0 +1,51 @@ +# shard000_batch0000_world0144_line000022 + +## Outcome +- collision: True +- near_miss: False +- offroad: False +- event_time_s: 3.3 +- raw_min_distance_m: 3.3664 +- clean_min_distance_m: 3.3665 + +## Primary Pair +- agent_a: 36 (risk_agent) +- agent_b: 44 (context_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: False +- semantic_candidate: same_direction_side_conflict_or_cut_in + +## Primary Interaction Metrics +- num_clean_pair_frames: 14 +- min_ttc_s: 1.2973 +- time_ttc_below_1s: 0.0 +- time_ttc_below_2s: 0.3 +- distance_trend_before_event: {'label': 'mixed', 'decreasing_ratio': 0.5, 'distance_start_m': 3.8902, 'distance_end_m': 5.0931} + +## Naturalness Checks +- max_speed_mps: 20.4243 +- max_accel_mps2: 24.5696 +- max_decel_mps2: 14.5998 +- max_yaw_rate_radps: 8.2209 +- max_frame_displacement_m: 2.0424 +- artifact_warnings: ['hard_deceleration', 'high_acceleration', 'high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 36 | risk_agent | True | 14 | | 3.1115 | 1.4105 | +| 44 | context_agent | False | 31 | | 3.0642 | 1.3886 | +| 2 | collided_agent | True | 44 | | 3.3081 | 1.4528 | +| 28 | risk_agent | True | 15 | | 3.3022 | 1.4537 | +| 42 | risk_agent | True | 8 | | 3.1537 | 1.4137 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -3.3 | 4.6394 | | | | -3.3449 | 3.215 | | | +| -3.0 | 3.8902 | 1.9017 | 2.0456 | 4.0922 | -3.3631 | 1.9554 | 6.0791 | 1.987 | +| -2.0 | 5.0931 | | -6.213 | 7.8674 | -3.3031 | -3.8768 | 10.0792 | 2.2482 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0160_line000023.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0160_line000023.md new file mode 100644 index 0000000000000000000000000000000000000000..ac4d93d09e868a9aef0cab4e5942b70a35d1c9a9 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0160_line000023.md @@ -0,0 +1,48 @@ +# shard000_batch0000_world0160_line000023 + +## Outcome +- collision: True +- near_miss: True +- offroad: False +- event_time_s: 4.0 +- raw_min_distance_m: 1.8515 +- clean_min_distance_m: 3.1402 + +## Primary Pair +- agent_a: 39 (risk_agent,collided_agent) +- agent_b: 59 (context_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: True +- semantic_candidate: same_direction_side_conflict_or_cut_in + +## Primary Interaction Metrics +- num_clean_pair_frames: 2 +- min_ttc_s: None +- time_ttc_below_1s: 0.0 +- time_ttc_below_2s: 0.0 +- distance_trend_before_event: {'label': 'insufficient_clean_frames', 'decreasing_ratio': None} + +## Naturalness Checks +- max_speed_mps: 11.1329 +- max_accel_mps2: 4.013 +- max_decel_mps2: 0.0072 +- max_yaw_rate_radps: 4.502 +- max_frame_displacement_m: 1.1133 +- artifact_warnings: ['high_yaw_rate', 'short_clean_track'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 39 | risk_agent,collided_agent | True | 45 | | 3.1506 | 1.3493 | +| 59 | context_agent | False | 2 | | 3.0456 | 1.3593 | +| 9 | risk_agent | True | 8 | | 3.0378 | 1.4198 | +| 44 | risk_agent | True | 2 | | 3.1572 | 1.4181 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -4.0 | 3.1402 | | | | -2.4552 | 1.9577 | | | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0166_line000024.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0166_line000024.md new file mode 100644 index 0000000000000000000000000000000000000000..27d779d40c42ca12708b4d3bec6eb036c9aa4107 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0166_line000024.md @@ -0,0 +1,51 @@ +# shard000_batch0000_world0166_line000024 + +## Outcome +- collision: True +- near_miss: False +- offroad: False +- event_time_s: 2.4 +- raw_min_distance_m: 2.4117 +- clean_min_distance_m: 2.4117 + +## Primary Pair +- agent_a: 0 (risk_agent,collided_agent) +- agent_b: 2 (risk_agent,collided_agent) +- selection_reason: risk_agent_directly_collided_with_partner +- confidence: high +- risk_directly_involved: True +- semantic_candidate: crossing_or_intersection_conflict + +## Primary Interaction Metrics +- num_clean_pair_frames: 25 +- min_ttc_s: 0.1452 +- time_ttc_below_1s: 0.5 +- time_ttc_below_2s: 1.5 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 1.0, 'distance_start_m': 17.8306, 'distance_end_m': 2.4117} + +## Naturalness Checks +- max_speed_mps: 17.6383 +- max_accel_mps2: 4.0148 +- max_decel_mps2: -1.3339 +- max_yaw_rate_radps: 4.83 +- max_frame_displacement_m: 1.7638 +- artifact_warnings: ['high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 0 | risk_agent,collided_agent | True | 35 | 12.6332 | 3.7002 | 1.6324 | +| 2 | risk_agent,collided_agent | True | 25 | 9.6323 | 3.3505 | 1.4904 | +| 1 | risk_agent | True | 35 | 14.3057 | 3.2501 | 1.4843 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -2.4 | 17.8306 | | | | 11.2105 | 13.8657 | | | +| -2.0 | 16.3635 | 3.7549 | 4.3579 | 4.5836 | 10.7287 | 12.3556 | 4.6332 | 1.8317 | +| -1.0 | 10.1986 | 1.4762 | 6.9086 | 6.9135 | 7.1385 | 7.2838 | 8.6337 | 5.8319 | +| -0.5 | 7.0456 | 1.2573 | 5.6036 | 5.6059 | 4.9071 | 5.0558 | 10.6335 | 7.8324 | +| 0.0 | 2.4117 | 0.1452 | 16.613 | 19.8473 | 2.0094 | 1.3337 | 12.6332 | 9.6323 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0174_line000025.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0174_line000025.md new file mode 100644 index 0000000000000000000000000000000000000000..1dde1605ce69f1b64e91f2f2ab5c326741bf13a8 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0174_line000025.md @@ -0,0 +1,55 @@ +# shard000_batch0000_world0174_line000025 + +## Outcome +- collision: True +- near_miss: False +- offroad: True +- event_time_s: 3.5 +- raw_min_distance_m: 2.4626 +- clean_min_distance_m: 2.4626 + +## Primary Pair +- agent_a: 2 (risk_agent,collided_agent,offroad_agent) +- agent_b: 1 (collided_agent) +- selection_reason: risk_agent_directly_collided_with_partner +- confidence: high +- risk_directly_involved: True +- semantic_candidate: same_direction_side_conflict_or_cut_in + +## Primary Interaction Metrics +- num_clean_pair_frames: 46 +- min_ttc_s: 0.3681 +- time_ttc_below_1s: 0.4 +- time_ttc_below_2s: 1.4 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 0.9333, 'distance_start_m': 14.5518, 'distance_end_m': 2.643} + +## Naturalness Checks +- max_speed_mps: 14.6879 +- max_accel_mps2: 4.0147 +- max_decel_mps2: 4.0077 +- max_yaw_rate_radps: 2.407 +- max_frame_displacement_m: 1.4688 +- artifact_warnings: ['offroad_before_collision'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 1 | collided_agent | True | 46 | 0.3841 | 3.5497 | 1.5819 | +| 2 | risk_agent,collided_agent,offroad_agent | True | 46 | 7.5877 | 3.8548 | 1.6712 | +| 0 | risk_agent | True | 46 | 11.9531 | 3.7002 | 1.6324 | +| 4 | risk_agent | True | 20 | | 4.0221 | 1.6611 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -3.5 | 15.6661 | | | | 1.9633 | 15.5426 | | | +| -3.0 | 14.5518 | 3.4311 | 4.2411 | 4.5592 | 0.6085 | 14.5391 | 6.5207 | 3.549 | +| -2.0 | 11.5265 | 4.3653 | 2.6405 | 4.255 | -1.9776 | 11.3556 | 5.0543 | 1.0162 | +| -1.0 | 6.794 | 1.4153 | 4.8003 | 6.2024 | -0.7786 | 6.7492 | 6.1204 | 0.0834 | +| -0.5 | 5.0672 | | -1.5432 | 6.0618 | -2.251 | 4.5397 | 5.9873 | 0.117 | +| 0.0 | 2.643 | 0.3681 | 7.1804 | 7.2097 | -1.5245 | 2.159 | 7.5877 | 0.3841 | +| 0.5 | 3.7579 | | -8.6917 | 9.0942 | -2.697 | -2.6168 | 9.1206 | 0.05 | +| 1.0 | 8.0741 | | -10.1022 | 10.1853 | -2.5476 | -7.6617 | 10.187 | 0.1491 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0175_line000026.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0175_line000026.md new file mode 100644 index 0000000000000000000000000000000000000000..1a395585ffa5ffaf34a5a1f9af027a18a10cedb7 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0175_line000026.md @@ -0,0 +1,58 @@ +# shard000_batch0000_world0175_line000026 + +## Outcome +- collision: True +- near_miss: True +- offroad: True +- event_time_s: 3.1 +- raw_min_distance_m: 0.9727 +- clean_min_distance_m: 0.9727 + +## Primary Pair +- agent_a: 4 (risk_agent,collided_agent,offroad_agent) +- agent_b: 0 (collided_agent) +- selection_reason: risk_agent_directly_collided_with_partner +- confidence: high +- risk_directly_involved: True +- semantic_candidate: crossing_or_intersection_conflict + +## Primary Interaction Metrics +- num_clean_pair_frames: 42 +- min_ttc_s: 0.04 +- time_ttc_below_1s: 0.3 +- time_ttc_below_2s: 1.1 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 0.9667, 'distance_start_m': 17.1269, 'distance_end_m': 3.4839} + +## Naturalness Checks +- max_speed_mps: 26.6375 +- max_accel_mps2: 4.01 +- max_decel_mps2: 3.9966 +- max_yaw_rate_radps: 11.1879 +- max_frame_displacement_m: 2.6638 +- artifact_warnings: ['high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 0 | collided_agent | True | 42 | 21.3436 | 3.7002 | 1.6324 | +| 4 | risk_agent,collided_agent,offroad_agent | True | 42 | 26.5715 | 3.325 | 1.4663 | +| 1 | context_agent | True | 42 | 24.8095 | 3.3262 | 1.4671 | +| 2 | risk_agent | True | 23 | | 3.1215 | 1.3958 | +| 5 | context_agent | True | 42 | 23.1323 | 3.3856 | 1.4544 | +| 6 | risk_agent | True | 16 | | 3.1088 | 1.3769 | +| 9 | context_agent | True | 42 | 23.7497 | 3.3395 | 1.5086 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -3.1 | 17.5672 | | | | 3.5714 | 17.2004 | | | +| -3.0 | 17.1269 | 3.8915 | 4.4011 | 4.4943 | 3.5721 | 16.7502 | 20.3044 | 15.8102 | +| -2.0 | 12.7608 | 2.9536 | 4.3204 | 4.4946 | 3.5745 | 12.25 | 22.9716 | 18.4771 | +| -1.0 | 8.5639 | 2.155 | 3.974 | 4.36 | 3.565 | 7.7866 | 25.3037 | 20.9438 | +| -0.5 | 6.6093 | 1.7384 | 3.8018 | 4.4932 | 3.5496 | 5.5752 | 26.1043 | 21.6112 | +| 0.0 | 3.4839 | | -6.5349 | 33.8886 | -2.058 | 2.8111 | 26.5715 | 21.3436 | +| 0.5 | 4.7011 | 6.6841 | 0.7033 | 9.6619 | 0.5616 | 4.6674 | 25.7035 | 21.0106 | +| 1.0 | 5.3825 | 2.1402 | 2.515 | 6.2606 | 2.1719 | 4.9248 | 24.0374 | 19.8767 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0186_line000027.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0186_line000027.md new file mode 100644 index 0000000000000000000000000000000000000000..8cf54a2a872a93b310dac5d6a8c642491166d1dc --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0186_line000027.md @@ -0,0 +1,54 @@ +# shard000_batch0000_world0186_line000027 + +## Outcome +- collision: True +- near_miss: False +- offroad: True +- event_time_s: 3.0 +- raw_min_distance_m: 2.1518 +- clean_min_distance_m: 2.1518 + +## Primary Pair +- agent_a: 1 (risk_agent,collided_agent) +- agent_b: 6 (collided_agent,offroad_agent) +- selection_reason: risk_agent_directly_collided_with_partner +- confidence: high +- risk_directly_involved: True +- semantic_candidate: crossing_or_intersection_conflict + +## Primary Interaction Metrics +- num_clean_pair_frames: 35 +- min_ttc_s: 0.2101 +- time_ttc_below_1s: 0.8 +- time_ttc_below_2s: 1.3 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 1.0, 'distance_start_m': 23.5763, 'distance_end_m': 2.1518} + +## Naturalness Checks +- max_speed_mps: 12.588 +- max_accel_mps2: 67.7861 +- max_decel_mps2: 9.4336 +- max_yaw_rate_radps: 3.227 +- max_frame_displacement_m: 1.2588 +- artifact_warnings: ['high_acceleration', 'high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 1 | risk_agent,collided_agent | True | 35 | 9.4367 | 5.9949 | 2.1229 | +| 6 | collided_agent,offroad_agent | True | 41 | 8.1143 | 3.5485 | 1.6043 | +| 0 | risk_agent | True | 6 | | 3.7002 | 1.6324 | +| 2 | risk_agent | True | 15 | | 3.1916 | 1.5088 | +| 3 | context_agent | False | 41 | 5.0343 | 3.7972 | 1.5883 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -3.0 | 23.5763 | | | | 15.2833 | 17.9517 | | | +| -2.0 | 18.4154 | 2.2332 | 8.2463 | 8.431 | 9.9059 | 15.5241 | 5.0361 | 4.1137 | +| -1.0 | 8.8314 | 2.4946 | 3.5402 | 4.4233 | 3.9147 | 7.9163 | 7.8364 | 7.048 | +| -0.5 | 5.3426 | 2.7301 | 1.9569 | 6.8364 | 4.051 | 3.4831 | 9.036 | 7.4479 | +| 0.0 | 2.1518 | 0.2101 | 10.2433 | 15.4738 | 2.1193 | -0.3726 | 9.4367 | 8.1143 | +| 0.4 | 5.0783 | | -5.434 | 15.8182 | 1.8965 | -4.7109 | 9.7022 | 8.7136 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0189_line000028.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0189_line000028.md new file mode 100644 index 0000000000000000000000000000000000000000..f73eed0ebda08a0bfb8050ae7010d694325950cc --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0189_line000028.md @@ -0,0 +1,54 @@ +# shard000_batch0000_world0189_line000028 + +## Outcome +- collision: True +- near_miss: False +- offroad: False +- event_time_s: 5.0 +- raw_min_distance_m: 2.6148 +- clean_min_distance_m: 2.6148 + +## Primary Pair +- agent_a: 1 (risk_agent,collided_agent) +- agent_b: 2 (risk_agent,collided_agent) +- selection_reason: risk_agent_directly_collided_with_partner +- confidence: high +- risk_directly_involved: True +- semantic_candidate: same_direction_side_conflict_or_cut_in + +## Primary Interaction Metrics +- num_clean_pair_frames: 60 +- min_ttc_s: 0.3158 +- time_ttc_below_1s: 0.7 +- time_ttc_below_2s: 1.5 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 0.8333, 'distance_start_m': 16.1018, 'distance_end_m': 2.7111} + +## Naturalness Checks +- max_speed_mps: 12.1091 +- max_accel_mps2: 4.0067 +- max_decel_mps2: 4.0133 +- max_yaw_rate_radps: 4.647 +- max_frame_displacement_m: 1.2109 +- artifact_warnings: ['high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 1 | risk_agent,collided_agent | True | 60 | 10.2422 | 3.1055 | 1.387 | +| 2 | risk_agent,collided_agent | True | 61 | 0.706 | 3.6601 | 1.54 | +| 0 | risk_agent | True | 38 | | 3.7002 | 1.6324 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -5.0 | 27.5551 | | | | 7.6979 | 26.458 | | | +| -3.0 | 16.1018 | | -0.3275 | 8.261 | 12.7495 | 9.8346 | 7.842 | 1.7734 | +| -2.0 | 12.8458 | 2.757 | 4.6593 | 7.8803 | 11.4883 | 5.7476 | 7.7746 | 0.16 | +| -1.0 | 9.2501 | 5.1348 | 1.8015 | 7.5928 | 8.3483 | 3.9838 | 7.6423 | 0.1604 | +| -0.5 | 7.0203 | 0.9295 | 7.5531 | 8.8813 | 6.0084 | 3.631 | 8.9756 | 0.2393 | +| 0.0 | 2.7111 | 0.3158 | 8.5836 | 9.7165 | 1.1563 | 2.4521 | 10.2422 | 0.706 | +| 0.5 | 3.1329 | | -4.9249 | 9.9531 | 2.6304 | -1.7018 | 11.0417 | 1.2394 | +| 0.9 | 6.6238 | | -10.3749 | 11.4446 | 2.5928 | -6.0952 | 12.1091 | 0.7729 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0193_line000029.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0193_line000029.md new file mode 100644 index 0000000000000000000000000000000000000000..5d845d97087155b2fb95ae6d13c033e282f8d50f --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0193_line000029.md @@ -0,0 +1,58 @@ +# shard000_batch0000_world0193_line000029 + +## Outcome +- collision: True +- near_miss: True +- offroad: True +- event_time_s: 3.7 +- raw_min_distance_m: 1.6492 +- clean_min_distance_m: 1.6492 + +## Primary Pair +- agent_a: 4 (risk_agent,collided_agent,offroad_agent) +- agent_b: 0 (collided_agent) +- selection_reason: risk_agent_directly_collided_with_partner +- confidence: high +- risk_directly_involved: True +- semantic_candidate: rear_end_or_following_conflict + +## Primary Interaction Metrics +- num_clean_pair_frames: 48 +- min_ttc_s: 0.0679 +- time_ttc_below_1s: 0.2 +- time_ttc_below_2s: 1.5 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 1.0, 'distance_start_m': 14.4976, 'distance_end_m': 3.0574} + +## Naturalness Checks +- max_speed_mps: 27.7031 +- max_accel_mps2: 4.0094 +- max_decel_mps2: 3.3446 +- max_yaw_rate_radps: 11.4679 +- max_frame_displacement_m: 2.7703 +- artifact_warnings: ['high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 0 | collided_agent | True | 48 | 22.81 | 3.7002 | 1.6324 | +| 4 | risk_agent,collided_agent,offroad_agent | True | 48 | 27.2374 | 3.325 | 1.4663 | +| 1 | context_agent | True | 48 | 25.61 | 3.3262 | 1.4671 | +| 3 | risk_agent | True | 19 | | 3.1834 | 1.3923 | +| 5 | context_agent | True | 48 | 24.466 | 3.3856 | 1.4544 | +| 8 | risk_agent | True | 26 | | 3.0046 | 1.3399 | +| 9 | context_agent | True | 48 | 25.0832 | 3.3395 | 1.5086 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -3.7 | 17.5672 | | | | 3.5714 | 17.2004 | | | +| -3.0 | 14.4976 | 3.3248 | 4.3605 | 4.4933 | 3.5741 | 14.0501 | 21.9036 | 17.4105 | +| -2.0 | 10.1485 | 2.3103 | 4.3926 | 4.6939 | 3.5811 | 9.4957 | 24.5707 | 19.8769 | +| -1.0 | 5.9969 | 1.6051 | 3.7362 | 4.627 | 3.557 | 4.8281 | 26.1035 | 21.4765 | +| -0.5 | 4.3452 | 1.6067 | 2.7045 | 4.6274 | 3.5409 | 2.5184 | 26.7707 | 22.1435 | +| 0.0 | 3.0574 | | -14.8635 | 35.5427 | -2.3601 | 1.9435 | 27.2374 | 22.81 | +| 0.5 | 4.53 | 2.3224 | 1.9506 | 3.8328 | 3.7863 | 2.487 | 27.0379 | 23.21 | +| 1.0 | 3.9081 | 10.095 | 0.3871 | 4.231 | 3.8737 | 0.5173 | 27.7031 | 23.4763 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0195_line000030.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0195_line000030.md new file mode 100644 index 0000000000000000000000000000000000000000..caa112d01365e4638cb541c2e2d5a9fb4fb7756e --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0195_line000030.md @@ -0,0 +1,53 @@ +# shard000_batch0000_world0195_line000030 + +## Outcome +- collision: True +- near_miss: True +- offroad: True +- event_time_s: 2.6 +- raw_min_distance_m: 1.6972 +- clean_min_distance_m: 1.6971 + +## Primary Pair +- agent_a: 2 (risk_agent,collided_agent,offroad_agent) +- agent_b: 3 (risk_agent,collided_agent) +- selection_reason: risk_agent_directly_collided_with_partner +- confidence: high +- risk_directly_involved: True +- semantic_candidate: crossing_or_intersection_conflict + +## Primary Interaction Metrics +- num_clean_pair_frames: 37 +- min_ttc_s: 0.1501 +- time_ttc_below_1s: 0.9 +- time_ttc_below_2s: 1.7 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 0.9615, 'distance_start_m': 21.1148, 'distance_end_m': 1.6971} + +## Naturalness Checks +- max_speed_mps: 11.8679 +- max_accel_mps2: 4.0121 +- max_decel_mps2: 3.3357 +- max_yaw_rate_radps: 3.358 +- max_frame_displacement_m: 1.1868 +- artifact_warnings: ['high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 2 | risk_agent,collided_agent,offroad_agent | True | 37 | 9.1982 | 4.1408 | 1.6453 | +| 3 | risk_agent,collided_agent | True | 37 | 3.2201 | 3.5938 | 1.6531 | +| 1 | risk_agent | True | 23 | | 3.5325 | 1.4651 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -2.6 | 21.1148 | | | | 3.7179 | 20.7849 | | | +| -2.0 | 17.8175 | 2.6915 | 6.6199 | 6.7508 | 3.6466 | 17.4403 | 5.1982 | 1.5535 | +| -1.0 | 8.5688 | 0.7165 | 11.9592 | 11.9634 | 0.8625 | 8.5253 | 8.9318 | 3.287 | +| -0.5 | 4.4474 | 0.4539 | 9.7992 | 10.0883 | 2.4064 | 3.7401 | 9.4648 | 3.2873 | +| 0.0 | 1.6971 | 0.1501 | 11.3096 | 11.3926 | 1.5488 | 0.694 | 9.1982 | 3.2201 | +| 0.5 | 4.6392 | | -11.2594 | 11.58 | 2.8173 | -3.6857 | 8.998 | 4.6201 | +| 1.0 | 9.4197 | | -13.0186 | 15.0184 | 0.0214 | -9.4197 | 8.5312 | 6.4874 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0204_line000031.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0204_line000031.md new file mode 100644 index 0000000000000000000000000000000000000000..a91dacebdb7a2e85acd4ba1bc3d3446858a3b219 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0204_line000031.md @@ -0,0 +1,48 @@ +# shard000_batch0000_world0204_line000031 + +## Outcome +- collision: False +- near_miss: True +- offroad: False +- event_time_s: 1.6 +- raw_min_distance_m: 1.9935 +- clean_min_distance_m: 8.086 + +## Primary Pair +- agent_a: 0 (risk_agent) +- agent_b: 2 (context_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: False +- semantic_candidate: same_direction_side_conflict_or_cut_in + +## Primary Interaction Metrics +- num_clean_pair_frames: 2 +- min_ttc_s: 4.2959 +- time_ttc_below_1s: 0.0 +- time_ttc_below_2s: 0.0 +- distance_trend_before_event: {'label': 'insufficient_clean_frames', 'decreasing_ratio': None} + +## Naturalness Checks +- max_speed_mps: 17.4496 +- max_accel_mps2: 4.0198 +- max_decel_mps2: -3.3234 +- max_yaw_rate_radps: 1.906 +- max_frame_displacement_m: 1.745 +- artifact_warnings: ['short_clean_track'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 0 | risk_agent | True | 27 | | 3.7002 | 1.6324 | +| 2 | context_agent | False | 2 | | 3.3185 | 1.4868 | +| 1 | risk_agent | True | 27 | | 3.2409 | 1.4492 | +| 5 | risk_agent | True | 19 | | 3.2448 | 1.4552 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -1.6 | 8.2744 | | | | -1.9898 | 8.0315 | | | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0215_line000032.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0215_line000032.md new file mode 100644 index 0000000000000000000000000000000000000000..d1ee251743b61bed46ebea8fdc57daa4f75f0418 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0215_line000032.md @@ -0,0 +1,50 @@ +# shard000_batch0000_world0215_line000032 + +## Outcome +- collision: True +- near_miss: False +- offroad: False +- event_time_s: 3.7 +- raw_min_distance_m: 3.0296 +- clean_min_distance_m: 4.3633 + +## Primary Pair +- agent_a: 34 (risk_agent) +- agent_b: 23 (context_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: False +- semantic_candidate: same_direction_side_conflict_or_cut_in + +## Primary Interaction Metrics +- num_clean_pair_frames: 2 +- min_ttc_s: 1.4931 +- time_ttc_below_1s: 0.0 +- time_ttc_below_2s: 0.1 +- distance_trend_before_event: {'label': 'insufficient_clean_frames', 'decreasing_ratio': None} + +## Naturalness Checks +- max_speed_mps: 20.7231 +- max_accel_mps2: 4.016 +- max_decel_mps2: 2.0004 +- max_yaw_rate_radps: 8.447 +- max_frame_displacement_m: 2.0723 +- artifact_warnings: ['high_yaw_rate', 'short_clean_track'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 23 | context_agent | False | 2 | | 4.9278 | 1.9795 | +| 34 | risk_agent | True | 45 | | 3.465 | 1.5712 | +| 2 | collided_agent | True | 48 | | 3.1889 | 1.4309 | +| 3 | collided_agent | True | 44 | | 3.0583 | 1.361 | +| 6 | risk_agent | True | 22 | | 3.2123 | 1.4313 | +| 31 | risk_agent | True | 27 | | 3.1997 | 1.3913 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -3.7 | 4.6641 | | | | -3.0267 | 3.5486 | | | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0217_line000033.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0217_line000033.md new file mode 100644 index 0000000000000000000000000000000000000000..056f223cc67de27b94124f8d9faa71c94c8677f7 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0217_line000033.md @@ -0,0 +1,48 @@ +# shard000_batch0000_world0217_line000033 + +## Outcome +- collision: True +- near_miss: False +- offroad: False +- event_time_s: 4.2 +- raw_min_distance_m: 2.0312 +- clean_min_distance_m: 3.1402 + +## Primary Pair +- agent_a: 39 (risk_agent,collided_agent) +- agent_b: 59 (context_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: True +- semantic_candidate: same_direction_side_conflict_or_cut_in + +## Primary Interaction Metrics +- num_clean_pair_frames: 2 +- min_ttc_s: None +- time_ttc_below_1s: 0.0 +- time_ttc_below_2s: 0.0 +- distance_trend_before_event: {'label': 'insufficient_clean_frames', 'decreasing_ratio': None} + +## Naturalness Checks +- max_speed_mps: 10.7997 +- max_accel_mps2: 4.017 +- max_decel_mps2: 3.3358 +- max_yaw_rate_radps: 4.799 +- max_frame_displacement_m: 1.08 +- artifact_warnings: ['high_yaw_rate', 'short_clean_track'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 39 | risk_agent,collided_agent | True | 44 | | 3.1506 | 1.3493 | +| 59 | context_agent | False | 2 | | 3.0456 | 1.3593 | +| 9 | risk_agent | True | 8 | | 3.0378 | 1.4198 | +| 44 | risk_agent | True | 2 | | 3.1572 | 1.4181 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -4.2 | 3.1402 | | | | -2.4552 | 1.9577 | | | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0220_line000034.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0220_line000034.md new file mode 100644 index 0000000000000000000000000000000000000000..415cc59b23445f9db1facc4da53b61855647cf0e --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0220_line000034.md @@ -0,0 +1,52 @@ +# shard000_batch0000_world0220_line000034 + +## Outcome +- collision: True +- near_miss: False +- offroad: False +- event_time_s: 2.5 +- raw_min_distance_m: 2.715 +- clean_min_distance_m: 2.7149 + +## Primary Pair +- agent_a: 0 (risk_agent,collided_agent) +- agent_b: 1 (risk_agent,collided_agent) +- selection_reason: risk_agent_directly_collided_with_partner +- confidence: high +- risk_directly_involved: True +- semantic_candidate: same_direction_side_conflict_or_cut_in + +## Primary Interaction Metrics +- num_clean_pair_frames: 34 +- min_ttc_s: 0.4326 +- time_ttc_below_1s: 0.8 +- time_ttc_below_2s: 1.8 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 1.0, 'distance_start_m': 23.7908, 'distance_end_m': 2.7149} + +## Naturalness Checks +- max_speed_mps: 18.8795 +- max_accel_mps2: 4.0184 +- max_decel_mps2: 1.3368 +- max_yaw_rate_radps: 6.2859 +- max_frame_displacement_m: 1.8879 +- artifact_warnings: ['high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 0 | risk_agent,collided_agent | True | 36 | 16.4797 | 3.7002 | 1.6324 | +| 1 | risk_agent,collided_agent | True | 34 | 9.8477 | 4.068 | 1.7631 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -2.5 | 23.7908 | | | | 12.7991 | 20.0546 | | | +| -2.0 | 20.4245 | 2.7344 | 7.4694 | 8.224 | 12.3056 | 16.3012 | 8.6131 | 1.8481 | +| -1.0 | 11.5256 | 1.2377 | 9.3121 | 10.0972 | 8.802 | 7.4408 | 12.6132 | 5.8476 | +| -0.5 | 6.7533 | 0.7155 | 9.4384 | 10.1224 | 5.3548 | 4.115 | 14.48 | 7.8474 | +| 0.0 | 2.7149 | 0.4485 | 6.0531 | 10.8909 | 2.714 | -0.0693 | 16.4797 | 9.8477 | +| 0.5 | 3.8015 | | -1.7581 | 6.7522 | 2.9471 | -2.4012 | 17.4133 | 11.5813 | +| 0.8 | 5.8285 | | -8.1744 | 13.6248 | 0.9511 | -5.7504 | 18.0799 | 12.5145 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0222_line000035.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0222_line000035.md new file mode 100644 index 0000000000000000000000000000000000000000..46ddf2662388bcbc5543c272f8c62fc363bb586e --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0222_line000035.md @@ -0,0 +1,56 @@ +# shard000_batch0000_world0222_line000035 + +## Outcome +- collision: False +- near_miss: True +- offroad: False +- event_time_s: 2.3 +- raw_min_distance_m: 1.7373 +- clean_min_distance_m: 1.7374 + +## Primary Pair +- agent_a: 10 (risk_agent) +- agent_b: 7 (context_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: False +- semantic_candidate: crossing_or_intersection_conflict + +## Primary Interaction Metrics +- num_clean_pair_frames: 34 +- min_ttc_s: 0.127 +- time_ttc_below_1s: 0.9 +- time_ttc_below_2s: 1.6 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 1.0, 'distance_start_m': 72.9909, 'distance_end_m': 1.7374} + +## Naturalness Checks +- max_speed_mps: 24.2166 +- max_accel_mps2: 4.0161 +- max_decel_mps2: -0.0024 +- max_yaw_rate_radps: 2.827 +- max_frame_displacement_m: 2.4217 +- artifact_warnings: ['high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 7 | context_agent | True | 34 | 19.7126 | 3.2319 | 1.4908 | +| 10 | risk_agent | True | 34 | 20.6162 | 3.1082 | 1.457 | +| 0 | context_agent | True | 29 | 15.5492 | 3.7002 | 1.6324 | +| 1 | context_agent | True | 33 | 9.5975 | 3.3897 | 1.5375 | +| 2 | risk_agent | True | 30 | 17.2002 | 3.0695 | 1.4562 | +| 14 | risk_agent | True | 15 | | 3.1625 | 1.3873 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -2.3 | 72.9909 | | | | 2.0211 | 72.9629 | | | +| -2.0 | 65.8946 | 2.6947 | 24.4538 | 24.462 | 1.9908 | 65.8645 | 12.6166 | 11.8456 | +| -1.0 | 37.0836 | 1.1483 | 32.2936 | 32.328 | 1.8674 | 37.0365 | 16.6162 | 15.7121 | +| -0.5 | 19.7541 | 0.5458 | 36.1914 | 36.3275 | 1.793 | 19.6726 | 18.6155 | 17.7124 | +| 0.0 | 1.7374 | 0.2368 | 7.3359 | 40.3284 | 1.7097 | 0.3087 | 20.6162 | 19.7126 | +| 0.5 | 21.1172 | | -44.1827 | 44.328 | 1.6176 | -21.0552 | 22.6164 | 21.712 | +| 1.0 | 44.3122 | | -47.1146 | 47.2855 | 2.191 | -44.258 | 24.2166 | 23.5126 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0230_line000036.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0230_line000036.md new file mode 100644 index 0000000000000000000000000000000000000000..235cc08b101906422f781242e62325d3ca2fd5a9 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0230_line000036.md @@ -0,0 +1,55 @@ +# shard000_batch0000_world0230_line000036 + +## Outcome +- collision: False +- near_miss: True +- offroad: False +- event_time_s: 2.3 +- raw_min_distance_m: 1.7303 +- clean_min_distance_m: 1.7303 + +## Primary Pair +- agent_a: 10 (risk_agent) +- agent_b: 7 (context_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: False +- semantic_candidate: crossing_or_intersection_conflict + +## Primary Interaction Metrics +- num_clean_pair_frames: 34 +- min_ttc_s: 0.1261 +- time_ttc_below_1s: 0.9 +- time_ttc_below_2s: 1.6 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 1.0, 'distance_start_m': 72.9909, 'distance_end_m': 1.7303} + +## Naturalness Checks +- max_speed_mps: 24.0822 +- max_accel_mps2: 4.0185 +- max_decel_mps2: 2.6709 +- max_yaw_rate_radps: 4.9 +- max_frame_displacement_m: 2.4082 +- artifact_warnings: ['high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 7 | context_agent | True | 34 | 19.7126 | 3.2319 | 1.4908 | +| 10 | risk_agent | True | 34 | 20.6162 | 3.1082 | 1.457 | +| 0 | risk_agent | True | 29 | 15.5492 | 3.7002 | 1.6324 | +| 1 | context_agent | True | 33 | 11.5974 | 3.3897 | 1.5375 | +| 2 | risk_agent | True | 31 | 17.1999 | 3.0695 | 1.4562 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -2.3 | 72.9909 | | | | 2.0211 | 72.9629 | | | +| -2.0 | 65.8946 | 2.6947 | 24.4538 | 24.462 | 1.9908 | 65.8645 | 12.6166 | 11.8456 | +| -1.0 | 37.0503 | 1.1426 | 32.4266 | 32.4611 | 1.8671 | 37.0032 | 16.6162 | 15.8452 | +| -0.5 | 19.7142 | 0.5447 | 36.1909 | 36.3275 | 1.7926 | 19.6325 | 18.6155 | 17.7124 | +| 0.0 | 1.7303 | 0.269 | 6.4328 | 40.3284 | 1.7093 | 0.2686 | 20.6162 | 19.7126 | +| 0.5 | 21.1042 | | -43.917 | 44.0613 | 1.611 | -21.0426 | 22.3497 | 21.712 | +| 1.0 | 44.2653 | | -47.7589 | 47.7945 | 1.5061 | -44.2397 | 24.0822 | 23.7127 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0232_line000037.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0232_line000037.md new file mode 100644 index 0000000000000000000000000000000000000000..6ce9e976db72a1b451c9892fbb388d58eb62ddb8 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0232_line000037.md @@ -0,0 +1,55 @@ +# shard000_batch0000_world0232_line000037 + +## Outcome +- collision: True +- near_miss: False +- offroad: False +- event_time_s: 1.8 +- raw_min_distance_m: 3.0158 +- clean_min_distance_m: 53.0476 + +## Primary Pair +- agent_a: 19 (risk_agent,collided_agent) +- agent_b: 3 (collided_agent) +- selection_reason: risk_agent_directly_collided_with_partner +- confidence: high +- risk_directly_involved: True +- semantic_candidate: crossing_or_intersection_conflict + +## Primary Interaction Metrics +- num_clean_pair_frames: 19 +- min_ttc_s: 3.9491 +- time_ttc_below_1s: 0.0 +- time_ttc_below_2s: 0.0 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 1.0, 'distance_start_m': 66.8484, 'distance_end_m': 53.0476} + +## Naturalness Checks +- max_speed_mps: 11.8159 +- max_accel_mps2: 19.7417 +- max_decel_mps2: 3.7961 +- max_yaw_rate_radps: 3.883 +- max_frame_displacement_m: 1.1816 +- artifact_warnings: ['high_acceleration', 'high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 3 | collided_agent | True | 29 | 7.0507 | 2.9743 | 1.3512 | +| 19 | risk_agent,collided_agent | True | 19 | 6.3955 | 3.1159 | 1.4263 | +| 1 | context_agent | False | 29 | 0.224 | 3.1033 | 1.38 | +| 2 | risk_agent | True | 26 | 7.2034 | 3.0311 | 1.4425 | +| 4 | context_agent | False | 29 | 1.7237 | 3.0669 | 1.3744 | +| 7 | risk_agent | True | 29 | 9.4162 | 3.0414 | 1.3639 | +| 11 | context_agent | True | 26 | 5.4224 | 3.2123 | 1.4985 | +| 26 | context_agent | False | 29 | 2.6578 | 3.2369 | 1.4177 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -1.8 | 66.8484 | | | | 1.4888 | 66.8318 | | | +| -1.0 | 63.4915 | 9.4659 | 6.7074 | 6.712 | 1.503 | 63.4737 | 2.795 | 3.9174 | +| -0.5 | 59.0986 | 5.7729 | 10.2373 | 10.2451 | 1.5596 | 59.078 | 4.6622 | 5.5836 | +| 0.0 | 53.0476 | 3.9491 | 13.433 | 13.4454 | 1.6286 | 53.0226 | 6.3955 | 7.0507 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0234_line000038.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0234_line000038.md new file mode 100644 index 0000000000000000000000000000000000000000..a513d47c3591db321ef6a1e8ebd47eb7e5b37881 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0234_line000038.md @@ -0,0 +1,52 @@ +# shard000_batch0000_world0234_line000038 + +## Outcome +- collision: True +- near_miss: False +- offroad: True +- event_time_s: 2.0 +- raw_min_distance_m: 2.2424 +- clean_min_distance_m: 2.2424 + +## Primary Pair +- agent_a: 0 (risk_agent,collided_agent) +- agent_b: 2 (risk_agent,collided_agent,offroad_agent) +- selection_reason: risk_agent_directly_collided_with_partner +- confidence: high +- risk_directly_involved: True +- semantic_candidate: crossing_or_intersection_conflict + +## Primary Interaction Metrics +- num_clean_pair_frames: 31 +- min_ttc_s: 0.2482 +- time_ttc_below_1s: 0.8 +- time_ttc_below_2s: 1.5 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 1.0, 'distance_start_m': 23.0461, 'distance_end_m': 2.2424} + +## Naturalness Checks +- max_speed_mps: 19.5998 +- max_accel_mps2: 4.0106 +- max_decel_mps2: 4.0109 +- max_yaw_rate_radps: 7.432 +- max_frame_displacement_m: 1.96 +- artifact_warnings: ['high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 0 | risk_agent,collided_agent | True | 31 | 7.0588 | 3.7002 | 1.6324 | +| 2 | risk_agent,collided_agent,offroad_agent | True | 31 | 15.8664 | 3.0643 | 1.403 | +| 1 | risk_agent,offroad_agent | True | 31 | 4.9047 | 3.3427 | 1.4735 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -2.0 | 23.0461 | | | | 22.2835 | 5.8792 | | | +| -1.0 | 13.6594 | 1.1926 | 11.4533 | 11.6197 | 13.1152 | 3.8174 | 4.0587 | 11.8662 | +| -0.5 | 7.8478 | 0.694 | 11.3084 | 11.4393 | 7.7073 | 1.4782 | 6.0583 | 13.8663 | +| 0.0 | 2.2424 | 0.2482 | 9.0337 | 11.253 | 2.1049 | 0.7732 | 7.0588 | 15.8664 | +| 0.5 | 5.918 | | -5.1614 | 9.8984 | 4.3173 | 4.0476 | 7.7925 | 17.5997 | +| 1.0 | 9.5068 | | -8.4603 | 10.0655 | 3.2431 | 8.9366 | 9.6582 | 19.5998 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0236_line000039.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0236_line000039.md new file mode 100644 index 0000000000000000000000000000000000000000..e7a61c90c27fca0d409b24b712878d44fbb73eed --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0236_line000039.md @@ -0,0 +1,59 @@ +# shard000_batch0000_world0236_line000039 + +## Outcome +- collision: True +- near_miss: True +- offroad: True +- event_time_s: 5.6 +- raw_min_distance_m: 1.4809 +- clean_min_distance_m: 1.481 + +## Primary Pair +- agent_a: 0 (risk_agent,collided_agent,offroad_agent) +- agent_b: 4 (collided_agent) +- selection_reason: risk_agent_directly_collided_with_partner +- confidence: high +- risk_directly_involved: True +- semantic_candidate: crossing_or_intersection_conflict + +## Primary Interaction Metrics +- num_clean_pair_frames: 61 +- min_ttc_s: 0.5205 +- time_ttc_below_1s: 0.3 +- time_ttc_below_2s: 1.9 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 1.0, 'distance_start_m': 14.5138, 'distance_end_m': 1.481} + +## Naturalness Checks +- max_speed_mps: 33.6026 +- max_accel_mps2: 4.0102 +- max_decel_mps2: 4.0052 +- max_yaw_rate_radps: 14.4809 +- max_frame_displacement_m: 3.3603 +- artifact_warnings: ['high_yaw_rate', 'offroad_before_collision'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 0 | risk_agent,collided_agent,offroad_agent | True | 61 | 28.3837 | 3.7002 | 1.6324 | +| 4 | collided_agent | True | 61 | 32.6964 | 3.1611 | 1.4382 | +| 1 | context_agent | True | 61 | 32.2682 | 3.3198 | 1.5149 | +| 2 | offroad_agent | True | 61 | 29.2275 | 3.3807 | 1.4919 | +| 3 | context_agent | True | 61 | 32.1832 | 3.6388 | 1.5343 | +| 6 | risk_agent | True | 35 | | 3.2967 | 1.502 | +| 7 | risk_agent | True | 61 | 29.7356 | 2.9929 | 1.3724 | +| 8 | context_agent | True | 61 | 28.0532 | 3.0795 | 1.3534 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -5.0 | 22.0383 | | | | -3.1838 | -21.8071 | | | +| -3.0 | 14.5138 | 3.9966 | 3.6316 | 3.7123 | -3.1175 | -14.1751 | 23.9854 | 27.6976 | +| -2.0 | 10.4287 | 2.3837 | 4.3749 | 4.5791 | -3.0846 | -9.9621 | 25.3177 | 29.8969 | +| -1.0 | 6.3514 | 1.6273 | 3.9031 | 4.4443 | -3.0577 | -5.567 | 26.9181 | 31.3624 | +| -0.5 | 4.6103 | 1.4563 | 3.1657 | 4.1787 | -3.0388 | -3.467 | 27.8513 | 32.0299 | +| 0.0 | 1.481 | | -29.0644 | 43.3244 | 1.2085 | 0.856 | 28.3837 | 32.6964 | +| 0.5 | 7.1719 | 20.6088 | 0.348 | 2.647 | -7.137 | -0.706 | 29.0508 | 31.6964 | +| 1.0 | 4.3414 | 0.5205 | 8.3408 | 8.3617 | -4.3362 | -0.2127 | 29.7174 | 30.6957 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0245_line000040.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0245_line000040.md new file mode 100644 index 0000000000000000000000000000000000000000..e12d160657eebff42b11bdc13d57b59acc568935 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0245_line000040.md @@ -0,0 +1,55 @@ +# shard000_batch0000_world0245_line000040 + +## Outcome +- collision: True +- near_miss: False +- offroad: False +- event_time_s: 1.4 +- raw_min_distance_m: 3.453 +- clean_min_distance_m: 3.453 + +## Primary Pair +- agent_a: 34 (risk_agent,collided_agent) +- agent_b: 16 (context_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: True +- semantic_candidate: rear_end_or_following_conflict + +## Primary Interaction Metrics +- num_clean_pair_frames: 15 +- min_ttc_s: 0.7608 +- time_ttc_below_1s: 0.3 +- time_ttc_below_2s: 0.6 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 1.0, 'distance_start_m': 7.1835, 'distance_end_m': 3.453} + +## Naturalness Checks +- max_speed_mps: 22.2439 +- max_accel_mps2: 5.7006 +- max_decel_mps2: 4.0113 +- max_yaw_rate_radps: 0.192 +- max_frame_displacement_m: 2.2244 +- artifact_warnings: [] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 16 | context_agent | False | 25 | 1.0589 | 4.0941 | 1.6523 | +| 34 | risk_agent,collided_agent | True | 15 | 5.6405 | 3.0332 | 1.3318 | +| 3 | risk_agent | True | 25 | 0.0028 | 3.1556 | 1.4402 | +| 6 | risk_agent | True | 25 | 14.13 | 3.1918 | 1.4151 | +| 15 | context_agent | True | 25 | 18.976 | 2.9948 | 1.4164 | +| 17 | context_agent | True | 25 | 15.7323 | 3.3307 | 1.5471 | +| 26 | context_agent | True | 17 | 5.6248 | 3.1693 | 1.3994 | +| 32 | context_agent | True | 23 | 5.6983 | 3.0606 | 1.3902 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -1.4 | 7.1835 | | | | 0.402 | 7.1723 | | | +| -1.0 | 6.853 | 5.5752 | 1.2292 | 1.2331 | 0.4138 | 6.8405 | 1.6397 | 0.4075 | +| -0.5 | 5.5648 | 1.7192 | 3.2369 | 3.2531 | 0.4263 | 5.5484 | 3.6406 | 0.3955 | +| 0.0 | 3.453 | 0.7608 | 4.5384 | 4.5836 | 0.4392 | 3.425 | 5.6405 | 1.0589 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0250_line000041.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0250_line000041.md new file mode 100644 index 0000000000000000000000000000000000000000..060cba4d41ea1e7c3d94cf0dc1d12682b0233cbc --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0250_line000041.md @@ -0,0 +1,55 @@ +# shard000_batch0000_world0250_line000041 + +## Outcome +- collision: True +- near_miss: False +- offroad: True +- event_time_s: 2.5 +- raw_min_distance_m: 6.5881 +- clean_min_distance_m: 6.5881 + +## Primary Pair +- agent_a: 11 (risk_agent) +- agent_b: 13 (context_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: False +- semantic_candidate: crossing_or_intersection_conflict + +## Primary Interaction Metrics +- num_clean_pair_frames: 20 +- min_ttc_s: 0.3432 +- time_ttc_below_1s: 0.9 +- time_ttc_below_2s: 1.6 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 0.8947, 'distance_start_m': 57.2002, 'distance_end_m': 11.0592} + +## Naturalness Checks +- max_speed_mps: 24.7582 +- max_accel_mps2: 4.0129 +- max_decel_mps2: 4.0004 +- max_yaw_rate_radps: 8.191 +- max_frame_displacement_m: 2.4758 +- artifact_warnings: ['high_yaw_rate', 'offroad_before_collision'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 11 | risk_agent | True | 20 | | 3.3305 | 1.4363 | +| 13 | context_agent | True | 35 | | 3.2825 | 1.4824 | +| 0 | collided_agent,offroad_agent | True | 36 | | 3.7002 | 1.6324 | +| 3 | collided_agent | True | 36 | | 3.7423 | 1.6533 | +| 4 | offroad_agent | True | 36 | | 3.494 | 1.649 | +| 10 | risk_agent | True | 13 | | 3.2834 | 1.4514 | +| 16 | risk_agent | True | 36 | | 3.2452 | 1.4435 | +| 17 | offroad_agent | True | 31 | | 3.3055 | 1.4496 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -2.5 | 57.2002 | | | | 6.4259 | 56.8381 | | | +| -2.0 | 41.537 | 1.2769 | 32.5284 | 32.8276 | 6.1974 | 41.0721 | 15.6839 | 17.1469 | +| -1.0 | 8.7404 | 0.3432 | 25.4696 | 36.5361 | 6.513 | 5.8289 | 17.0167 | 19.5466 | +| -0.6 | 11.0592 | | -30.8922 | 37.9606 | 6.299 | -9.09 | 17.5502 | 20.4136 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0252_line000042.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0252_line000042.md new file mode 100644 index 0000000000000000000000000000000000000000..bcf93f246e9014218cc42f3526f9ca36eeef2846 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0252_line000042.md @@ -0,0 +1,55 @@ +# shard000_batch0000_world0252_line000042 + +## Outcome +- collision: False +- near_miss: True +- offroad: False +- event_time_s: 2.3 +- raw_min_distance_m: 1.7262 +- clean_min_distance_m: 1.7262 + +## Primary Pair +- agent_a: 10 (risk_agent) +- agent_b: 7 (risk_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: False +- semantic_candidate: crossing_or_intersection_conflict + +## Primary Interaction Metrics +- num_clean_pair_frames: 34 +- min_ttc_s: 0.1256 +- time_ttc_below_1s: 0.9 +- time_ttc_below_2s: 1.6 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 1.0, 'distance_start_m': 72.9909, 'distance_end_m': 1.7262} + +## Naturalness Checks +- max_speed_mps: 24.3497 +- max_accel_mps2: 4.0185 +- max_decel_mps2: 2.0031 +- max_yaw_rate_radps: 4.515 +- max_frame_displacement_m: 2.435 +- artifact_warnings: ['high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 7 | risk_agent | True | 34 | 19.7117 | 3.2319 | 1.4908 | +| 10 | risk_agent | True | 34 | 20.6162 | 3.1082 | 1.457 | +| 0 | context_agent | True | 29 | 15.5487 | 3.7002 | 1.6324 | +| 1 | context_agent | True | 34 | 9.9973 | 3.3897 | 1.5375 | +| 2 | risk_agent | True | 30 | 17.4669 | 3.0695 | 1.4562 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -2.3 | 72.9909 | | | | 2.0211 | 72.9629 | | | +| -2.0 | 65.8946 | 2.6947 | 24.4538 | 24.462 | 1.9908 | 65.8645 | 12.6166 | 11.8456 | +| -1.0 | 37.0503 | 1.1426 | 32.4266 | 32.4611 | 1.8671 | 37.0032 | 16.6162 | 15.8452 | +| -0.5 | 19.6877 | 0.544 | 36.1905 | 36.3275 | 1.7924 | 19.6059 | 18.6155 | 17.7124 | +| 0.0 | 1.7262 | 0.2963 | 5.8252 | 40.3276 | 1.7092 | 0.242 | 20.6162 | 19.7117 | +| 0.5 | 21.1837 | | -44.1836 | 44.328 | 1.6171 | -21.1219 | 22.6164 | 21.712 | +| 1.0 | 44.4649 | | -48.0259 | 48.0615 | 1.5172 | -44.439 | 24.3497 | 23.7122 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0258_line000043.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0258_line000043.md new file mode 100644 index 0000000000000000000000000000000000000000..ad02f1dfb7528a664c5a52a7bbf1b91723c8e174 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0258_line000043.md @@ -0,0 +1,59 @@ +# shard000_batch0000_world0258_line000043 + +## Outcome +- collision: True +- near_miss: True +- offroad: False +- event_time_s: 5.2 +- raw_min_distance_m: 1.7484 +- clean_min_distance_m: 3.4415 + +## Primary Pair +- agent_a: 0 (risk_agent,collided_agent) +- agent_b: 3 (collided_agent) +- selection_reason: risk_agent_directly_collided_with_partner +- confidence: high +- risk_directly_involved: True +- semantic_candidate: rear_end_or_following_conflict + +## Primary Interaction Metrics +- num_clean_pair_frames: 60 +- min_ttc_s: 0.2718 +- time_ttc_below_1s: 0.3 +- time_ttc_below_2s: 1.5 +- distance_trend_before_event: {'label': 'mixed', 'decreasing_ratio': 0.7333, 'distance_start_m': 6.8153, 'distance_end_m': 3.4415} + +## Naturalness Checks +- max_speed_mps: 28.6958 +- max_accel_mps2: 4.0125 +- max_decel_mps2: 3.9974 +- max_yaw_rate_radps: 10.887 +- max_frame_displacement_m: 2.8696 +- artifact_warnings: ['high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 0 | risk_agent,collided_agent | True | 60 | 25.9525 | 3.7002 | 1.6324 | +| 3 | collided_agent | True | 61 | 24.4551 | 3.1448 | 1.4547 | +| 1 | context_agent | True | 61 | 25.8819 | 3.4177 | 1.534 | +| 2 | risk_agent | True | 55 | 27.7958 | 2.8205 | 1.3451 | +| 5 | risk_agent | True | 61 | 24.2991 | 3.3382 | 1.4961 | +| 6 | context_agent | True | 59 | 25.1546 | 3.9392 | 1.6616 | +| 7 | context_agent | True | 61 | 27.3625 | 3.2219 | 1.4175 | +| 8 | context_agent | True | 61 | 20.4075 | 3.2769 | 1.4723 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -5.0 | 3.6858 | | | | -3.351 | 1.5349 | | | +| -3.0 | 6.8153 | | -29.042 | 29.5603 | -6.7157 | 1.1608 | 20.9529 | 20.7229 | +| -2.0 | 7.5483 | 2.7323 | 2.7627 | 3.0143 | -5.6116 | 5.0485 | 22.6871 | 21.9231 | +| -1.0 | 6.0413 | 2.1795 | 2.7719 | 3.0045 | -3.4091 | 4.9876 | 24.6201 | 23.1236 | +| -0.5 | 4.6794 | 1.7717 | 2.6412 | 3.0701 | -2.1305 | 4.1663 | 25.2863 | 23.7895 | +| 0.0 | 3.4415 | 1.5441 | 2.2288 | 3.1359 | -0.814 | 3.3439 | 25.9525 | 24.4551 | +| 0.5 | 5.9108 | 0.2718 | 21.7506 | 34.465 | -2.4653 | 5.3722 | 26.4859 | 23.5226 | +| 0.9 | 4.7858 | 21.4133 | 0.2235 | 5.1334 | 0.6108 | 4.7467 | 26.0867 | 24.0562 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0289_line000044.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0289_line000044.md new file mode 100644 index 0000000000000000000000000000000000000000..b597f2b8635428bf4858d7063f6a9394e7c7d2dc --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0289_line000044.md @@ -0,0 +1,48 @@ +# shard000_batch0000_world0289_line000044 + +## Outcome +- collision: True +- near_miss: False +- offroad: False +- event_time_s: 4.5 +- raw_min_distance_m: 2.4614 +- clean_min_distance_m: 40.9843 + +## Primary Pair +- agent_a: 3 (risk_agent,collided_agent) +- agent_b: 15 (context_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: True +- semantic_candidate: rear_end_or_following_conflict + +## Primary Interaction Metrics +- num_clean_pair_frames: 2 +- min_ttc_s: 13.3256 +- time_ttc_below_1s: 0.0 +- time_ttc_below_2s: 0.0 +- distance_trend_before_event: {'label': 'insufficient_clean_frames', 'decreasing_ratio': None} + +## Naturalness Checks +- max_speed_mps: 19.2757 +- max_accel_mps2: 4.0121 +- max_decel_mps2: -0.6521 +- max_yaw_rate_radps: 9.0989 +- max_frame_displacement_m: 1.9276 +- artifact_warnings: ['high_yaw_rate', 'short_clean_track'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 3 | risk_agent,collided_agent | True | 46 | | 2.966 | 1.3395 | +| 15 | context_agent | False | 2 | | 3.2113 | 1.4431 | +| 0 | risk_agent | True | 32 | | 3.7002 | 1.6324 | +| 1 | risk_agent | True | 24 | | 3.2255 | 1.4693 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -4.5 | 41.2918 | | | | -0.9406 | 41.2811 | | | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0291_line000045.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0291_line000045.md new file mode 100644 index 0000000000000000000000000000000000000000..f09c81c449d3d3a221705e6b1308b946bfb47662 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0291_line000045.md @@ -0,0 +1,52 @@ +# shard000_batch0000_world0291_line000045 + +## Outcome +- collision: True +- near_miss: False +- offroad: False +- event_time_s: 2.6 +- raw_min_distance_m: 2.4789 +- clean_min_distance_m: 2.479 + +## Primary Pair +- agent_a: 0 (risk_agent,collided_agent) +- agent_b: 1 (risk_agent,collided_agent) +- selection_reason: risk_agent_directly_collided_with_partner +- confidence: high +- risk_directly_involved: True +- semantic_candidate: crossing_or_intersection_conflict + +## Primary Interaction Metrics +- num_clean_pair_frames: 27 +- min_ttc_s: 0.1477 +- time_ttc_below_1s: 0.9 +- time_ttc_below_2s: 1.5 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 1.0, 'distance_start_m': 47.5087, 'distance_end_m': 2.479} + +## Naturalness Checks +- max_speed_mps: 17.0863 +- max_accel_mps2: 4.0121 +- max_decel_mps2: 2.6715 +- max_yaw_rate_radps: 6.263 +- max_frame_displacement_m: 1.7086 +- artifact_warnings: ['high_yaw_rate', 'short_clean_track'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 0 | risk_agent,collided_agent | True | 37 | 16.4201 | 3.7002 | 1.6324 | +| 1 | risk_agent,collided_agent | True | 27 | 7.0379 | 3.485 | 1.5039 | +| 2 | context_agent | True | 37 | 8.4967 | 4.0598 | 1.6927 | +| 3 | risk_agent | True | 2 | | 3.1085 | 1.3729 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -2.6 | 47.5087 | | | | 9.5603 | 46.5368 | | | +| -2.0 | 39.782 | 2.6947 | 14.7632 | 15.191 | 9.5549 | 38.6175 | 12.4203 | 2.7715 | +| -1.0 | 22.3171 | 1.2165 | 18.3451 | 18.3784 | 7.2149 | 21.1187 | 16.0193 | 5.1717 | +| -0.5 | 12.9028 | 0.6429 | 20.0713 | 20.0715 | 1.3829 | 12.8285 | 16.2197 | 5.4379 | +| 0.0 | 2.479 | 0.1477 | 16.7882 | 23.2418 | 1.8659 | 1.6321 | 16.4201 | 7.0379 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0295_line000046.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0295_line000046.md new file mode 100644 index 0000000000000000000000000000000000000000..588fbac0234a9940d14bdea22530d82f0c03ef3e --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0295_line000046.md @@ -0,0 +1,53 @@ +# shard000_batch0000_world0295_line000046 + +## Outcome +- collision: True +- near_miss: True +- offroad: True +- event_time_s: 2.1 +- raw_min_distance_m: 1.8651 +- clean_min_distance_m: 1.8651 + +## Primary Pair +- agent_a: 0 (risk_agent,collided_agent) +- agent_b: 2 (risk_agent,collided_agent) +- selection_reason: risk_agent_directly_collided_with_partner +- confidence: high +- risk_directly_involved: True +- semantic_candidate: crossing_or_intersection_conflict + +## Primary Interaction Metrics +- num_clean_pair_frames: 32 +- min_ttc_s: 0.2406 +- time_ttc_below_1s: 0.9 +- time_ttc_below_2s: 1.6 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 1.0, 'distance_start_m': 23.0461, 'distance_end_m': 1.8651} + +## Naturalness Checks +- max_speed_mps: 20.133 +- max_accel_mps2: 4.0109 +- max_decel_mps2: 2.6735 +- max_yaw_rate_radps: 4.984 +- max_frame_displacement_m: 2.0133 +- artifact_warnings: ['high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 0 | risk_agent,collided_agent | True | 32 | 7.7257 | 3.7002 | 1.6324 | +| 2 | risk_agent,collided_agent | True | 32 | 16.2665 | 3.0643 | 1.403 | +| 1 | risk_agent,offroad_agent | True | 32 | 4.9057 | 3.3427 | 1.4735 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -2.1 | 23.0461 | | | | 22.2835 | 5.8792 | | | +| -2.0 | 22.2862 | 2.9397 | 7.5812 | 8.0901 | 21.5059 | 5.8458 | 0.4587 | 8.2661 | +| -1.0 | 12.6566 | 1.2004 | 10.5433 | 10.7791 | 12.2842 | 3.0475 | 4.0585 | 12.2662 | +| -0.5 | 7.3161 | 0.6969 | 10.4974 | 10.8414 | 7.2894 | 0.6244 | 6.0581 | 14.2663 | +| 0.0 | 1.8651 | 0.2704 | 6.8985 | 11.121 | 1.5294 | 1.0675 | 7.7257 | 16.2665 | +| 0.5 | 5.6734 | | -8.9476 | 9.8113 | 2.2425 | 5.2113 | 8.3921 | 18.1329 | +| 1.0 | 10.1525 | | -9.3805 | 9.9088 | 1.2986 | 10.0691 | 10.3249 | 20.133 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0305_line000047.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0305_line000047.md new file mode 100644 index 0000000000000000000000000000000000000000..95be4f4cf95ec928591d4199fa0117e0bb5731b8 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0305_line000047.md @@ -0,0 +1,56 @@ +# shard000_batch0000_world0305_line000047 + +## Outcome +- collision: True +- near_miss: True +- offroad: True +- event_time_s: 7.2 +- raw_min_distance_m: 0.6194 +- clean_min_distance_m: 0.6193 + +## Primary Pair +- agent_a: 2 (risk_agent,collided_agent,offroad_agent) +- agent_b: 5 (risk_agent,collided_agent,offroad_agent) +- selection_reason: risk_agent_directly_collided_with_partner +- confidence: high +- risk_directly_involved: True +- semantic_candidate: crossing_or_intersection_conflict + +## Primary Interaction Metrics +- num_clean_pair_frames: 61 +- min_ttc_s: 0.2127 +- time_ttc_below_1s: 1.0 +- time_ttc_below_2s: 2.1 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 0.9667, 'distance_start_m': 22.8781, 'distance_end_m': 2.8372} + +## Naturalness Checks +- max_speed_mps: 32.0038 +- max_accel_mps2: 4.0128 +- max_decel_mps2: 4.0089 +- max_yaw_rate_radps: 10.1609 +- max_frame_displacement_m: 3.2004 +- artifact_warnings: ['high_yaw_rate', 'offroad_before_collision'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 2 | risk_agent,collided_agent,offroad_agent | True | 61 | 17.0852 | 3.1811 | 1.4545 | +| 5 | risk_agent,collided_agent,offroad_agent | True | 61 | 25.3158 | 3.8317 | 1.639 | +| 0 | context_agent | True | 55 | 24.9577 | 3.7002 | 1.6324 | +| 1 | offroad_agent | True | 61 | 27.0803 | 3.9309 | 1.6467 | +| 8 | risk_agent | True | 48 | | 3.2848 | 1.4576 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -5.0 | 28.5716 | | | | -0.0463 | -28.5715 | | | +| -3.0 | 22.8781 | 4.7987 | 4.7675 | 6.2311 | 4.4757 | -22.436 | 20.0186 | 24.2504 | +| -2.0 | 16.6706 | 2.2254 | 7.4911 | 7.8759 | -1.8203 | -16.5709 | 17.6862 | 24.9165 | +| -1.0 | 8.8237 | 1.0774 | 8.1901 | 8.1904 | -1.5103 | -8.6935 | 17.8853 | 25.9837 | +| -0.5 | 4.2713 | 0.4426 | 9.6505 | 9.8884 | -1.4857 | -4.0046 | 16.8188 | 26.6502 | +| 0.0 | 2.8372 | 0.3426 | 8.2808 | 8.3164 | -0.7513 | -2.736 | 17.0852 | 25.3158 | +| 0.5 | 2.9472 | | -1.9746 | 9.2179 | -2.9226 | -0.3802 | 17.0854 | 25.783 | +| 1.0 | 2.3721 | | -18.7613 | 19.1245 | 2.0133 | 1.2544 | 17.3519 | 25.1163 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0318_line000048.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0318_line000048.md new file mode 100644 index 0000000000000000000000000000000000000000..b7db4c24ee65272c35a741980654554d511d09ee --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0318_line000048.md @@ -0,0 +1,53 @@ +# shard000_batch0000_world0318_line000048 + +## Outcome +- collision: True +- near_miss: False +- offroad: True +- event_time_s: 5.1 +- raw_min_distance_m: 2.121 +- clean_min_distance_m: 2.1209 + +## Primary Pair +- agent_a: 1 (risk_agent,collided_agent,offroad_agent) +- agent_b: 2 (risk_agent,collided_agent) +- selection_reason: risk_agent_directly_collided_with_partner +- confidence: high +- risk_directly_involved: True +- semantic_candidate: same_direction_side_conflict_or_cut_in + +## Primary Interaction Metrics +- num_clean_pair_frames: 55 +- min_ttc_s: 0.2663 +- time_ttc_below_1s: 1.1 +- time_ttc_below_2s: 2.0 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 0.8, 'distance_start_m': 14.7575, 'distance_end_m': 3.2349} + +## Naturalness Checks +- max_speed_mps: 13.175 +- max_accel_mps2: 4.016 +- max_decel_mps2: 4.0074 +- max_yaw_rate_radps: 5.94 +- max_frame_displacement_m: 1.3175 +- artifact_warnings: ['high_yaw_rate', 'offroad_before_collision'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 1 | risk_agent,collided_agent,offroad_agent | True | 55 | 11.9752 | 3.1055 | 1.387 | +| 2 | risk_agent,collided_agent | True | 61 | 1.973 | 3.6601 | 1.54 | +| 0 | risk_agent | True | 36 | | 3.7002 | 1.6324 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -5.0 | 26.7921 | | | | 5.5742 | 26.2058 | | | +| -3.0 | 14.7575 | 1.6671 | 8.8521 | 9.4616 | 7.0528 | 12.9631 | 9.2411 | 2.8392 | +| -2.0 | 11.1449 | 1.3617 | 8.1844 | 8.2017 | 5.6076 | 9.6314 | 8.5747 | 0.3733 | +| -1.0 | 3.4535 | 0.4533 | 7.6192 | 8.0581 | -1.6494 | 3.0341 | 9.3087 | 1.9066 | +| -0.5 | 2.1209 | | -1.7317 | 8.6486 | -2.0959 | -0.3251 | 10.9754 | 2.3731 | +| 0.0 | 3.2349 | 0.8617 | 3.7543 | 11.4969 | -2.1078 | -2.454 | 11.9752 | 1.973 | +| 0.4 | 5.1121 | 5.0769 | 1.0069 | 11.3499 | -2.9791 | -4.1543 | 13.175 | 1.9723 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0319_line000049.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0319_line000049.md new file mode 100644 index 0000000000000000000000000000000000000000..0ff38277b4e5e8f62b95aadef070147c7d3c37b8 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0319_line000049.md @@ -0,0 +1,50 @@ +# shard000_batch0000_world0319_line000049 + +## Outcome +- collision: True +- near_miss: False +- offroad: False +- event_time_s: 3.2 +- raw_min_distance_m: 4.0969 +- clean_min_distance_m: 4.0743 + +## Primary Pair +- agent_a: 0 (risk_agent) +- agent_b: 1 (context_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: False +- semantic_candidate: generic_close_interaction + +## Primary Interaction Metrics +- num_clean_pair_frames: 9 +- min_ttc_s: None +- time_ttc_below_1s: 0.0 +- time_ttc_below_2s: 0.0 +- distance_trend_before_event: {'label': 'not_decreasing', 'decreasing_ratio': 0.0, 'distance_start_m': 4.131, 'distance_end_m': 4.5567} + +## Naturalness Checks +- max_speed_mps: 20.5148 +- max_accel_mps2: 4.0106 +- max_decel_mps2: 4.0039 +- max_yaw_rate_radps: 4.2939 +- max_frame_displacement_m: 2.0515 +- artifact_warnings: ['high_yaw_rate', 'short_clean_track'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 0 | risk_agent | True | 41 | | 3.7002 | 1.6324 | +| 1 | context_agent | True | 9 | | 3.4315 | 1.5318 | +| 11 | risk_agent | True | 15 | | 3.1048 | 1.422 | +| 13 | risk_agent | True | 2 | | 3.2413 | 1.4622 | +| 15 | collided_agent | True | 33 | | 3.3941 | 1.5125 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -3.2 | 4.0743 | | | | -4.0721 | -0.1345 | | | +| -3.0 | 4.131 | | -0.3919 | 2.0784 | -4.0942 | -0.5497 | 6.2478 | 4.1743 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0321_line000050.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0321_line000050.md new file mode 100644 index 0000000000000000000000000000000000000000..29413e22c85a5f39b97a536d131b5fc80e2db4c7 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0321_line000050.md @@ -0,0 +1,53 @@ +# shard000_batch0000_world0321_line000050 + +## Outcome +- collision: True +- near_miss: True +- offroad: False +- event_time_s: 3.7 +- raw_min_distance_m: 1.6225 +- clean_min_distance_m: 1.6225 + +## Primary Pair +- agent_a: 3 (risk_agent,collided_agent) +- agent_b: 1 (context_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: True +- semantic_candidate: rear_end_or_following_conflict + +## Primary Interaction Metrics +- num_clean_pair_frames: 39 +- min_ttc_s: 0.1637 +- time_ttc_below_1s: 0.6 +- time_ttc_below_2s: 2.4 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 1.0, 'distance_start_m': 31.0467, 'distance_end_m': 1.6225} + +## Naturalness Checks +- max_speed_mps: 14.4722 +- max_accel_mps2: 25.2573 +- max_decel_mps2: 4.482 +- max_yaw_rate_radps: 5.349 +- max_frame_displacement_m: 1.4472 +- artifact_warnings: ['high_acceleration', 'high_yaw_rate', 'short_clean_track'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 1 | context_agent | False | 48 | 1.9774 | 2.9076 | 1.3825 | +| 3 | risk_agent,collided_agent | True | 39 | 13.3993 | 3.5141 | 1.4959 | +| 0 | risk_agent | True | 34 | | 3.7002 | 1.6324 | +| 46 | risk_agent | True | 2 | | 3.3003 | 1.467 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -3.7 | 39.3737 | | | | -1.728 | 39.3358 | | | +| -3.0 | 31.0467 | 2.4153 | 12.8541 | 13.1331 | -2.5483 | 30.942 | 10.465 | 3.0409 | +| -2.0 | 18.6089 | 1.571 | 11.8451 | 12.9595 | -4.6519 | 18.0181 | 11.3989 | 2.5408 | +| -1.0 | 8.5141 | 1.1359 | 7.4955 | 12.1367 | -5.4984 | 6.5005 | 11.3988 | 2.494 | +| -0.5 | 5.5395 | 1.5498 | 3.5744 | 12.0042 | -4.9336 | 2.5191 | 12.3319 | 2.0879 | +| 0.0 | 1.6225 | 0.1637 | 9.9138 | 12.0544 | -0.8419 | 1.387 | 13.3993 | 1.9774 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0334_line000051.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0334_line000051.md new file mode 100644 index 0000000000000000000000000000000000000000..611646f7d9cf5dcf5fe295705dd40f9a4e42e43e --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0334_line000051.md @@ -0,0 +1,52 @@ +# shard000_batch0000_world0334_line000051 + +## Outcome +- collision: True +- near_miss: False +- offroad: False +- event_time_s: 2.0 +- raw_min_distance_m: 3.1985 +- clean_min_distance_m: 3.1985 + +## Primary Pair +- agent_a: 0 (risk_agent,collided_agent) +- agent_b: 2 (risk_agent,collided_agent) +- selection_reason: risk_agent_directly_collided_with_partner +- confidence: high +- risk_directly_involved: True +- semantic_candidate: crossing_or_intersection_conflict + +## Primary Interaction Metrics +- num_clean_pair_frames: 31 +- min_ttc_s: 0.5767 +- time_ttc_below_1s: 0.8 +- time_ttc_below_2s: 1.3 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 0.95, 'distance_start_m': 19.3409, 'distance_end_m': 3.1985} + +## Naturalness Checks +- max_speed_mps: 16.7818 +- max_accel_mps2: 4.0175 +- max_decel_mps2: 0.0035 +- max_yaw_rate_radps: 5.164 +- max_frame_displacement_m: 1.6782 +- artifact_warnings: ['high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 0 | risk_agent,collided_agent | True | 31 | 13.5824 | 3.7002 | 1.6324 | +| 2 | risk_agent,collided_agent | True | 31 | 9.0502 | 3.0643 | 1.3826 | +| 1 | risk_agent | True | 31 | 10.9532 | 3.0233 | 1.3709 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -2.0 | 19.3409 | | | | 15.8249 | 11.1194 | | | +| -1.0 | 10.6835 | 1.0367 | 10.3054 | 11.3197 | 8.2943 | 6.7335 | 10.2491 | 5.05 | +| -0.5 | 5.567 | 0.6051 | 9.2001 | 11.5521 | 5.2798 | 1.765 | 12.2498 | 7.0504 | +| 0.0 | 3.1985 | 2.897 | 1.1041 | 9.9096 | 2.3272 | -2.1941 | 13.5824 | 9.0502 | +| 0.5 | 5.7421 | | -5.2648 | 10.8379 | 0.7697 | -5.6903 | 14.782 | 10.6502 | +| 1.0 | 8.1446 | | -4.2214 | 4.284 | -0.7208 | -8.1126 | 16.7818 | 12.5174 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0336_line000052.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0336_line000052.md new file mode 100644 index 0000000000000000000000000000000000000000..6ed01964591ce4e790ac1313d015168287b1114f --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0336_line000052.md @@ -0,0 +1,53 @@ +# shard000_batch0000_world0336_line000052 + +## Outcome +- collision: True +- near_miss: False +- offroad: False +- event_time_s: 3.9 +- raw_min_distance_m: 2.4279 +- clean_min_distance_m: 2.4279 + +## Primary Pair +- agent_a: 3 (risk_agent) +- agent_b: 25 (context_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: False +- semantic_candidate: same_direction_side_conflict_or_cut_in + +## Primary Interaction Metrics +- num_clean_pair_frames: 35 +- min_ttc_s: 0.7771 +- time_ttc_below_1s: 0.6 +- time_ttc_below_2s: 1.8 +- distance_trend_before_event: {'label': 'mixed', 'decreasing_ratio': 0.56, 'distance_start_m': 8.7662, 'distance_end_m': 7.5825} + +## Naturalness Checks +- max_speed_mps: 27.5591 +- max_accel_mps2: 4.017 +- max_decel_mps2: 1.9915 +- max_yaw_rate_radps: 8.605 +- max_frame_displacement_m: 2.7559 +- artifact_warnings: ['high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 3 | risk_agent | True | 35 | | 2.9727 | 1.3511 | +| 25 | context_agent | True | 45 | | 3.1573 | 1.4304 | +| 18 | risk_agent | True | 48 | | 3.103 | 1.3911 | +| 21 | risk_agent | True | 45 | | 3.3576 | 1.4899 | +| 22 | collided_agent | True | 40 | | 3.3055 | 1.4872 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -3.9 | 14.2259 | | | | 3.8389 | -13.6981 | | | +| -3.0 | 8.7662 | 1.4667 | 5.9766 | 6.2622 | 3.3879 | -8.085 | 4.7439 | 10.9943 | +| -2.0 | 3.262 | 0.7771 | 4.1977 | 6.2804 | 2.6841 | -1.8537 | 8.7435 | 14.9942 | +| -1.0 | 4.7153 | | -5.3896 | 6.3044 | 1.767 | 4.3717 | 12.7436 | 18.9932 | +| -0.5 | 7.5825 | | -5.9558 | 6.3195 | 1.2284 | 7.4823 | 14.7441 | 20.9934 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0339_line000053.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0339_line000053.md new file mode 100644 index 0000000000000000000000000000000000000000..7abcc37883f884885aca7198e224ebed9b5f4ecf --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0339_line000053.md @@ -0,0 +1,57 @@ +# shard000_batch0000_world0339_line000053 + +## Outcome +- collision: True +- near_miss: False +- offroad: False +- event_time_s: 3.9 +- raw_min_distance_m: 3.0303 +- clean_min_distance_m: 3.0256 + +## Primary Pair +- agent_a: 5 (risk_agent) +- agent_b: 22 (collided_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: False +- semantic_candidate: same_direction_side_conflict_or_cut_in + +## Primary Interaction Metrics +- num_clean_pair_frames: 37 +- min_ttc_s: 34.9623 +- time_ttc_below_1s: 0.0 +- time_ttc_below_2s: 0.0 +- distance_trend_before_event: {'label': 'not_decreasing', 'decreasing_ratio': 0.037, 'distance_start_m': 3.1532, 'distance_end_m': 5.4009} + +## Naturalness Checks +- max_speed_mps: 27.5592 +- max_accel_mps2: 48.2655 +- max_decel_mps2: 6.0132 +- max_yaw_rate_radps: 6.338 +- max_frame_displacement_m: 2.7559 +- artifact_warnings: ['high_acceleration', 'high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 5 | risk_agent | True | 37 | 15.7708 | 3.2603 | 1.4712 | +| 22 | collided_agent | True | 40 | 15.2326 | 3.3055 | 1.4872 | +| 11 | context_agent | False | 50 | 2.5277 | 3.554 | 1.5455 | +| 17 | risk_agent | True | 4 | | 2.9675 | 1.3738 | +| 18 | context_agent | True | 48 | 25.9601 | 3.103 | 1.3911 | +| 19 | risk_agent | True | 50 | 25.2902 | 3.1436 | 1.3807 | +| 23 | context_agent | True | 37 | 16.8241 | 3.4261 | 1.5811 | +| 24 | context_agent | False | 50 | 4.6181 | 3.1994 | 1.5101 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -3.9 | 3.0256 | | | | 3.0231 | 0.1224 | | | +| -3.0 | 3.1532 | | -0.2445 | 0.8618 | 3.0228 | 0.8976 | 4.9699 | 5.8317 | +| -2.0 | 3.4869 | | -0.3628 | 0.728 | 3.0223 | 1.739 | 8.9702 | 9.6981 | +| -1.0 | 3.9006 | | -0.4576 | 0.7282 | 3.0212 | 2.4672 | 12.9706 | 13.6989 | +| -0.5 | 5.1642 | | -4.8705 | 5.2617 | 5.0361 | 1.1431 | 14.9708 | 15.0317 | +| -0.3 | 5.4009 | 34.9623 | 0.1545 | 0.5406 | 5.2975 | 1.052 | 15.7708 | 15.2326 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0344_line000054.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0344_line000054.md new file mode 100644 index 0000000000000000000000000000000000000000..7c2de64d2c05056ead4a6568ff764b8bca0733fe --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0344_line000054.md @@ -0,0 +1,50 @@ +# shard000_batch0000_world0344_line000054 + +## Outcome +- collision: True +- near_miss: False +- offroad: False +- event_time_s: 4.2 +- raw_min_distance_m: 4.0982 +- clean_min_distance_m: 4.0982 + +## Primary Pair +- agent_a: 22 (risk_agent) +- agent_b: 20 (context_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: False +- semantic_candidate: generic_close_interaction + +## Primary Interaction Metrics +- num_clean_pair_frames: 7 +- min_ttc_s: 1.0072 +- time_ttc_below_1s: 0.0 +- time_ttc_below_2s: 0.6 +- distance_trend_before_event: {'label': 'insufficient_clean_frames', 'decreasing_ratio': None} + +## Naturalness Checks +- max_speed_mps: 31.4495 +- max_accel_mps2: 4.0153 +- max_decel_mps2: 4.0007 +- max_yaw_rate_radps: 10.133 +- max_frame_displacement_m: 3.145 +- artifact_warnings: ['high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 20 | context_agent | True | 7 | | 3.2226 | 1.4137 | +| 22 | risk_agent | True | 8 | | 3.1924 | 1.376 | +| 4 | collided_agent | True | 53 | | 3.1884 | 1.4022 | +| 5 | risk_agent | True | 16 | | 3.2086 | 1.4257 | +| 10 | collided_agent | True | 47 | | 3.2923 | 1.5206 | +| 18 | risk_agent | True | 16 | | 3.2201 | 1.4544 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -4.2 | 7.2072 | | | | -3.7009 | 6.1845 | | | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0361_line000055.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0361_line000055.md new file mode 100644 index 0000000000000000000000000000000000000000..dc1a01af48a308ec5fe1b63efc28e9cd8610a67b --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0361_line000055.md @@ -0,0 +1,48 @@ +# shard000_batch0000_world0361_line000055 + +## Outcome +- collision: True +- near_miss: False +- offroad: False +- event_time_s: 3.6 +- raw_min_distance_m: 2.3116 +- clean_min_distance_m: 4.3114 + +## Primary Pair +- agent_a: 15 (risk_agent,collided_agent) +- agent_b: 22 (context_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: True +- semantic_candidate: same_direction_side_conflict_or_cut_in + +## Primary Interaction Metrics +- num_clean_pair_frames: 2 +- min_ttc_s: 6.5842 +- time_ttc_below_1s: 0.0 +- time_ttc_below_2s: 0.0 +- distance_trend_before_event: {'label': 'insufficient_clean_frames', 'decreasing_ratio': None} + +## Naturalness Checks +- max_speed_mps: 9.1461 +- max_accel_mps2: 4.0127 +- max_decel_mps2: 3.3397 +- max_yaw_rate_radps: 3.643 +- max_frame_displacement_m: 0.9146 +- artifact_warnings: ['high_yaw_rate', 'short_clean_track'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 15 | risk_agent,collided_agent | True | 45 | | 3.4011 | 1.5187 | +| 22 | context_agent | False | 2 | | 3.1471 | 1.3942 | +| 1 | risk_agent | True | 32 | | 3.3485 | 1.4615 | +| 2 | risk_agent | True | 18 | | 3.255 | 1.4657 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -3.6 | 4.3771 | | | | 2.3628 | 3.6845 | | | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0381_line000056.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0381_line000056.md new file mode 100644 index 0000000000000000000000000000000000000000..9145fc8340d441d6bc05a6ebbd18cf084b2505a4 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0381_line000056.md @@ -0,0 +1,59 @@ +# shard000_batch0000_world0381_line000056 + +## Outcome +- collision: False +- near_miss: True +- offroad: True +- event_time_s: 3.4 +- raw_min_distance_m: 1.843 +- clean_min_distance_m: 1.8431 + +## Primary Pair +- agent_a: 3 (risk_agent) +- agent_b: 2 (context_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: False +- semantic_candidate: same_direction_side_conflict_or_cut_in + +## Primary Interaction Metrics +- num_clean_pair_frames: 45 +- min_ttc_s: 0.5221 +- time_ttc_below_1s: 1.0 +- time_ttc_below_2s: 1.9 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 1.0, 'distance_start_m': 20.3618, 'distance_end_m': 1.8431} + +## Naturalness Checks +- max_speed_mps: 29.6268 +- max_accel_mps2: 213.7405 +- max_decel_mps2: 12.8444 +- max_yaw_rate_radps: 3.441 +- max_frame_displacement_m: 2.9627 +- artifact_warnings: ['hard_deceleration', 'high_acceleration', 'high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 2 | context_agent | False | 45 | 20.0171 | 3.5742 | 1.5759 | +| 3 | risk_agent | True | 45 | 27.56 | 3.486 | 1.4338 | +| 0 | context_agent | True | 45 | 24.7995 | 3.7002 | 1.6324 | +| 1 | offroad_agent | True | 45 | 28.2991 | 3.2264 | 1.4438 | +| 4 | context_agent | True | 45 | 26.61 | 3.2765 | 1.4365 | +| 5 | context_agent | True | 45 | 27.0212 | 4.3421 | 1.8169 | +| 7 | risk_agent | True | 19 | | 3.1644 | 1.4099 | +| 9 | risk_agent | True | 43 | 26.8866 | 3.0713 | 1.3696 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -3.4 | 23.5046 | | | | -2.728 | 23.3457 | | | +| -3.0 | 20.3618 | 4.8775 | 4.1746 | 4.1785 | -2.521 | 20.2051 | 23.5595 | 19.4518 | +| -2.0 | 14.9602 | 2.2576 | 6.6265 | 6.6271 | -1.745 | 14.8581 | 26.3596 | 19.7793 | +| -1.0 | 7.1468 | 0.8505 | 8.4032 | 8.4117 | -0.5435 | 7.1261 | 28.1603 | 19.7955 | +| -0.5 | 4.1708 | 1.9466 | 2.1426 | 9.5871 | 2.8944 | 3.003 | 27.1598 | 19.9938 | +| 0.0 | 1.8431 | 2.1569 | 0.8545 | 7.5519 | 1.8383 | 0.1322 | 27.56 | 20.0171 | +| 0.5 | 4.324 | | -7.8239 | 8.6525 | 1.711 | -3.971 | 28.5603 | 19.9152 | +| 1.0 | 8.7842 | | -9.5396 | 9.7608 | 1.5552 | -8.6454 | 29.6268 | 19.8751 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0386_line000057.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0386_line000057.md new file mode 100644 index 0000000000000000000000000000000000000000..713c975065ca83f20aef2f74970c345abc63d6f4 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0386_line000057.md @@ -0,0 +1,57 @@ +# shard000_batch0000_world0386_line000057 + +## Outcome +- collision: True +- near_miss: True +- offroad: True +- event_time_s: 2.5 +- raw_min_distance_m: 1.8024 +- clean_min_distance_m: 1.8024 + +## Primary Pair +- agent_a: 8 (risk_agent,collided_agent) +- agent_b: 0 (collided_agent) +- selection_reason: risk_agent_directly_collided_with_partner +- confidence: high +- risk_directly_involved: True +- semantic_candidate: same_direction_side_conflict_or_cut_in + +## Primary Interaction Metrics +- num_clean_pair_frames: 28 +- min_ttc_s: 0.9524 +- time_ttc_below_1s: 0.1 +- time_ttc_below_2s: 0.8 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 1.0, 'distance_start_m': 4.7885, 'distance_end_m': 1.8024} + +## Naturalness Checks +- max_speed_mps: 14.0668 +- max_accel_mps2: 4.0166 +- max_decel_mps2: 4.0102 +- max_yaw_rate_radps: 5.938 +- max_frame_displacement_m: 1.4067 +- artifact_warnings: ['high_yaw_rate', 'short_clean_track'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 0 | collided_agent | True | 36 | 10.3338 | 3.7002 | 1.6324 | +| 8 | risk_agent,collided_agent | True | 28 | 9.2742 | 3.2562 | 1.4851 | +| 1 | collided_agent,offroad_agent | True | 36 | 6.4147 | 3.1857 | 1.4221 | +| 2 | risk_agent,collided_agent | True | 36 | 10.2679 | 3.1125 | 1.3858 | +| 6 | collided_agent | True | 36 | 10.5253 | 3.2081 | 1.5123 | +| 9 | context_agent | True | 36 | 9.8508 | 3.1948 | 1.4005 | +| 15 | context_agent | True | 30 | 9.8794 | 3.2909 | 1.4649 | +| 24 | risk_agent | True | 2 | | 3.112 | 1.4077 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -2.5 | 4.7885 | | | | 3.913 | -2.7602 | | | +| -2.0 | 4.6509 | 15.4978 | 0.3001 | 0.4036 | 3.8797 | -2.5649 | 1.9412 | 2.334 | +| -1.0 | 3.3532 | 2.6204 | 1.2796 | 1.4222 | 3.2012 | -0.998 | 5.2748 | 6.3335 | +| -0.5 | 2.6358 | 1.7274 | 1.5259 | 1.6619 | 2.5841 | -0.5194 | 7.275 | 8.3339 | +| 0.0 | 1.8024 | 1.0445 | 1.7256 | 1.9314 | 1.8015 | -0.0545 | 9.2742 | 10.3338 | +| 0.2 | 3.6842 | | -2.474 | 2.6774 | 3.6516 | -0.4891 | 10.074 | 11.0668 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0394_line000058.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0394_line000058.md new file mode 100644 index 0000000000000000000000000000000000000000..fca94391feb3fbaa55e43da49b70f78ad09cff91 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0394_line000058.md @@ -0,0 +1,48 @@ +# shard000_batch0000_world0394_line000058 + +## Outcome +- collision: True +- near_miss: False +- offroad: False +- event_time_s: 3.7 +- raw_min_distance_m: 3.4076 +- clean_min_distance_m: 3.3552 + +## Primary Pair +- agent_a: 8 (risk_agent) +- agent_b: 16 (context_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: False +- semantic_candidate: same_direction_side_conflict_or_cut_in + +## Primary Interaction Metrics +- num_clean_pair_frames: 2 +- min_ttc_s: None +- time_ttc_below_1s: 0.0 +- time_ttc_below_2s: 0.0 +- distance_trend_before_event: {'label': 'insufficient_clean_frames', 'decreasing_ratio': None} + +## Naturalness Checks +- max_speed_mps: 18.6852 +- max_accel_mps2: 4.0128 +- max_decel_mps2: 0.6734 +- max_yaw_rate_radps: 8.443 +- max_frame_displacement_m: 1.8685 +- artifact_warnings: ['high_yaw_rate', 'short_clean_track'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 8 | risk_agent | True | 2 | | 3.2487 | 1.5217 | +| 16 | context_agent | False | 2 | | 3.0393 | 1.3827 | +| 2 | risk_agent,collided_agent | True | 38 | | 3.054 | 1.3957 | +| 28 | risk_agent | True | 5 | | 3.632 | 1.603 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -3.7 | 3.3552 | | | | -3.244 | -0.8565 | | | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0417_line000059.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0417_line000059.md new file mode 100644 index 0000000000000000000000000000000000000000..23856624c79c82019fbcf2582a3b310b796674e7 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0417_line000059.md @@ -0,0 +1,57 @@ +# shard000_batch0000_world0417_line000059 + +## Outcome +- collision: True +- near_miss: True +- offroad: False +- event_time_s: 2.8 +- raw_min_distance_m: 1.6505 +- clean_min_distance_m: 1.6505 + +## Primary Pair +- agent_a: 3 (risk_agent,collided_agent) +- agent_b: 4 (context_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: True +- semantic_candidate: same_direction_side_conflict_or_cut_in + +## Primary Interaction Metrics +- num_clean_pair_frames: 39 +- min_ttc_s: 0.1255 +- time_ttc_below_1s: 0.4 +- time_ttc_below_2s: 1.1 +- distance_trend_before_event: {'label': 'mixed', 'decreasing_ratio': 0.6071, 'distance_start_m': 9.8409, 'distance_end_m': 2.1041} + +## Naturalness Checks +- max_speed_mps: 26.6983 +- max_accel_mps2: 200.2082 +- max_decel_mps2: 4.0097 +- max_yaw_rate_radps: 8.378 +- max_frame_displacement_m: 2.6698 +- artifact_warnings: ['high_acceleration', 'high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 3 | risk_agent,collided_agent | True | 39 | 24.404 | 4.1003 | 1.7341 | +| 4 | context_agent | False | 39 | 19.9986 | 3.0581 | 1.3885 | +| 0 | context_agent | True | 39 | 23.525 | 3.7002 | 1.6324 | +| 1 | context_agent | True | 39 | 24.3031 | 3.4259 | 1.5535 | +| 2 | risk_agent | True | 39 | 20.7693 | 3.0315 | 1.3958 | +| 5 | risk_agent | True | 39 | 21.4452 | 3.0787 | 1.3877 | +| 6 | context_agent | True | 29 | 26.6983 | 3.0644 | 1.3736 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -2.8 | 9.8409 | | | | -3.6246 | 9.1491 | | | +| -2.0 | 6.3146 | 1.9091 | 3.3077 | 3.3492 | -3.0131 | 5.5494 | 23.0715 | 19.945 | +| -1.0 | 4.8853 | 1.2775 | 3.8242 | 4.9894 | -4.4547 | 2.0055 | 24.1376 | 19.7652 | +| -0.5 | 5.556 | | -4.0894 | 5.6479 | -5.4362 | -1.1479 | 24.3376 | 19.7481 | +| 0.0 | 2.1041 | 0.1985 | 10.6004 | 11.1823 | -1.9737 | 0.7294 | 24.404 | 19.9986 | +| 0.5 | 3.241 | | -2.9917 | 4.2572 | -2.3383 | -2.2442 | 24.1372 | 19.8805 | +| 1.0 | 5.2326 | | -5.0843 | 5.6138 | -2.3338 | -4.6833 | 25.2039 | 19.5922 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0419_line000060.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0419_line000060.md new file mode 100644 index 0000000000000000000000000000000000000000..3625ce24c76bb37e976750fdd076a0ad02878016 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0419_line000060.md @@ -0,0 +1,56 @@ +# shard000_batch0000_world0419_line000060 + +## Outcome +- collision: False +- near_miss: True +- offroad: False +- event_time_s: 2.3 +- raw_min_distance_m: 1.9884 +- clean_min_distance_m: 1.9884 + +## Primary Pair +- agent_a: 9 (risk_agent) +- agent_b: 8 (context_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: False +- semantic_candidate: crossing_or_intersection_conflict + +## Primary Interaction Metrics +- num_clean_pair_frames: 34 +- min_ttc_s: 0.223 +- time_ttc_below_1s: 0.7 +- time_ttc_below_2s: 1.2 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 1.0, 'distance_start_m': 28.3935, 'distance_end_m': 1.9884} + +## Naturalness Checks +- max_speed_mps: 20.7236 +- max_accel_mps2: 4.0151 +- max_decel_mps2: 2.6708 +- max_yaw_rate_radps: 3.66 +- max_frame_displacement_m: 2.0724 +- artifact_warnings: ['high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 8 | context_agent | True | 34 | 10.002 | 3.2592 | 1.5072 | +| 9 | risk_agent | True | 34 | 10.0773 | 3.1155 | 1.3836 | +| 0 | risk_agent | True | 34 | 10.2154 | 3.7002 | 1.6324 | +| 1 | context_agent | True | 34 | 19.1907 | 3.1584 | 1.4562 | +| 6 | risk_agent | True | 28 | 17.437 | 3.0185 | 1.3899 | +| 7 | context_agent | True | 34 | 17.0224 | 3.1718 | 1.4535 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -2.3 | 28.3935 | | | | 3.6876 | 28.1531 | | | +| -2.0 | 26.5282 | 3.7865 | 7.006 | 7.0791 | 3.6997 | 26.2689 | 4.81 | 2.2692 | +| -1.0 | 16.6624 | 2.2146 | 7.5239 | 9.8049 | -2.6738 | 16.4464 | 7.61 | 6.2686 | +| -0.5 | 9.6358 | 0.5561 | 17.3267 | 17.4429 | -1.0617 | 9.5772 | 9.2096 | 8.2678 | +| 0.0 | 1.9884 | | -2.3113 | 20.0638 | 1.9647 | -0.3063 | 10.0773 | 10.002 | +| 0.5 | 11.4237 | | -23.1052 | 23.4603 | 1.5291 | -11.3209 | 11.4769 | 12.0012 | +| 1.0 | 23.6951 | | -25.9679 | 26.1301 | 3.4256 | -23.4462 | 13.0766 | 13.0683 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0428_line000061.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0428_line000061.md new file mode 100644 index 0000000000000000000000000000000000000000..6e61fdc6a9cc1f6e377819c3689de3cab05df9d6 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0428_line000061.md @@ -0,0 +1,58 @@ +# shard000_batch0000_world0428_line000061 + +## Outcome +- collision: True +- near_miss: True +- offroad: False +- event_time_s: 3.2 +- raw_min_distance_m: 1.0686 +- clean_min_distance_m: 1.0686 + +## Primary Pair +- agent_a: 15 (risk_agent,collided_agent) +- agent_b: 2 (collided_agent) +- selection_reason: risk_agent_directly_collided_with_partner +- confidence: high +- risk_directly_involved: True +- semantic_candidate: crossing_or_intersection_conflict + +## Primary Interaction Metrics +- num_clean_pair_frames: 42 +- min_ttc_s: 0.1051 +- time_ttc_below_1s: 0.9 +- time_ttc_below_2s: 1.6 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 1.0, 'distance_start_m': 68.759, 'distance_end_m': 2.0475} + +## Naturalness Checks +- max_speed_mps: 21.6949 +- max_accel_mps2: 69.9555 +- max_decel_mps2: 29.2382 +- max_yaw_rate_radps: 10.073 +- max_frame_displacement_m: 2.1695 +- artifact_warnings: ['hard_deceleration', 'high_acceleration', 'high_yaw_rate', 'short_clean_track'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 2 | collided_agent | True | 42 | 15.6915 | 3.1406 | 1.4324 | +| 15 | risk_agent,collided_agent | True | 43 | 18.0071 | 2.8269 | 1.366 | +| 1 | context_agent | True | 43 | 14.0515 | 3.5227 | 1.5672 | +| 3 | context_agent | True | 43 | 18.3124 | 3.1044 | 1.3862 | +| 7 | risk_agent | True | 43 | 18.2281 | 8.4922 | 2.103 | +| 17 | context_agent | False | 43 | 6.4742 | 3.1574 | 1.4031 | +| 24 | risk_agent | True | 2 | | 3.217 | 1.4091 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -3.2 | 70.3455 | | | | 7.2573 | 69.9701 | | | +| -3.0 | 68.759 | 13.914 | 4.9417 | 8.2175 | -17.5317 | 66.4864 | 7.1406 | 4.2906 | +| -2.0 | 53.7367 | 2.9032 | 18.5092 | 18.5218 | -8.5561 | 53.0511 | 10.4072 | 8.292 | +| -1.0 | 30.8813 | 1.1697 | 26.4001 | 26.4427 | -5.6765 | 30.3551 | 14.4073 | 12.292 | +| -0.5 | 16.5139 | 0.5461 | 30.2399 | 30.404 | -3.8225 | 16.0654 | 16.4081 | 14.292 | +| 0.0 | 2.0475 | 0.2135 | 9.5912 | 20.9202 | -0.6272 | 1.9491 | 18.0071 | 15.6915 | +| 0.5 | 12.2856 | | -35.142 | 35.152 | -0.4567 | -12.2771 | 19.3407 | 15.8912 | +| 0.9 | 21.5842 | | -3.7059 | 16.5151 | 21.3352 | 3.2692 | 20.1405 | 17.0915 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0459_line000062.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0459_line000062.md new file mode 100644 index 0000000000000000000000000000000000000000..dbf1844e32ce33b69dca0aca3e8ea3c9f91b4099 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0459_line000062.md @@ -0,0 +1,49 @@ +# shard000_batch0000_world0459_line000062 + +## Outcome +- collision: True +- near_miss: False +- offroad: False +- event_time_s: 3.5 +- raw_min_distance_m: 3.4507 +- clean_min_distance_m: 30.6645 + +## Primary Pair +- agent_a: 10 (risk_agent) +- agent_b: 19 (context_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: False +- semantic_candidate: same_direction_side_conflict_or_cut_in + +## Primary Interaction Metrics +- num_clean_pair_frames: 2 +- min_ttc_s: 5.5673 +- time_ttc_below_1s: 0.0 +- time_ttc_below_2s: 0.0 +- distance_trend_before_event: {'label': 'insufficient_clean_frames', 'decreasing_ratio': None} + +## Naturalness Checks +- max_speed_mps: 19.141 +- max_accel_mps2: 4.011 +- max_decel_mps2: 0.67 +- max_yaw_rate_radps: 3.967 +- max_frame_displacement_m: 1.9141 +- artifact_warnings: ['high_yaw_rate', 'short_clean_track'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 10 | risk_agent | True | 41 | | 3.0578 | 1.4333 | +| 19 | context_agent | False | 2 | | 3.5699 | 1.5819 | +| 5 | risk_agent | True | 46 | | 3.0722 | 1.3827 | +| 8 | risk_agent | True | 12 | | 3.9847 | 1.7769 | +| 11 | collided_agent | True | 36 | | 5.0816 | 2.2185 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -3.5 | 31.2153 | | | | 3.3848 | 31.0313 | | | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0478_line000063.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0478_line000063.md new file mode 100644 index 0000000000000000000000000000000000000000..c8df4ea5fe3fe23d67fd91419e053c301e0ac61b --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0478_line000063.md @@ -0,0 +1,53 @@ +# shard000_batch0000_world0478_line000063 + +## Outcome +- collision: True +- near_miss: False +- offroad: False +- event_time_s: 3.7 +- raw_min_distance_m: 2.2034 +- clean_min_distance_m: 2.2034 + +## Primary Pair +- agent_a: 43 (risk_agent,collided_agent) +- agent_b: 18 (context_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: True +- semantic_candidate: rear_end_or_following_conflict + +## Primary Interaction Metrics +- num_clean_pair_frames: 38 +- min_ttc_s: 0.2404 +- time_ttc_below_1s: 0.7 +- time_ttc_below_2s: 1.4 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 0.9333, 'distance_start_m': 14.4179, 'distance_end_m': 2.2034} + +## Naturalness Checks +- max_speed_mps: 18.8416 +- max_accel_mps2: 48.6187 +- max_decel_mps2: 6.0172 +- max_yaw_rate_radps: 7.0319 +- max_frame_displacement_m: 1.8842 +- artifact_warnings: ['high_acceleration', 'high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 18 | context_agent | False | 48 | 8.9781 | 3.2138 | 1.4378 | +| 43 | risk_agent,collided_agent | True | 38 | 17.9265 | 3.569 | 1.5729 | +| 0 | risk_agent | True | 43 | 16.3181 | 3.7002 | 1.6324 | +| 2 | risk_agent | True | 48 | 15.2422 | 3.2735 | 1.4796 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -3.7 | 15.5396 | | | | 0.0093 | 15.5396 | | | +| -3.0 | 14.4179 | 8.8577 | 1.6277 | 1.6282 | 0.0337 | 14.4179 | 7.326 | 5.6983 | +| -2.0 | 11.5734 | 3.0163 | 3.837 | 3.837 | 0.0623 | 11.5732 | 11.3267 | 7.4898 | +| -1.0 | 6.2179 | 0.9282 | 6.6987 | 6.7024 | 0.1259 | 6.2166 | 15.3264 | 8.625 | +| -0.5 | 4.8616 | 0.808 | 6.017 | 7.6046 | -2.6062 | 4.104 | 16.3938 | 8.8492 | +| 0.0 | 2.2034 | 0.2404 | 9.1668 | 17.0947 | -0.8147 | 2.0473 | 17.9265 | 8.9781 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0512_line000064.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0512_line000064.md new file mode 100644 index 0000000000000000000000000000000000000000..2647e010b4249f72a49c584077681dbea1eb8881 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0512_line000064.md @@ -0,0 +1,57 @@ +# shard000_batch0000_world0512_line000064 + +## Outcome +- collision: True +- near_miss: False +- offroad: True +- event_time_s: 5.7 +- raw_min_distance_m: 8.1025 +- clean_min_distance_m: 8.8685 + +## Primary Pair +- agent_a: 8 (risk_agent) +- agent_b: 7 (context_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: False +- semantic_candidate: generic_close_interaction + +## Primary Interaction Metrics +- num_clean_pair_frames: 52 +- min_ttc_s: None +- time_ttc_below_1s: 0.0 +- time_ttc_below_2s: 0.0 +- distance_trend_before_event: {'label': 'not_decreasing', 'decreasing_ratio': 0.3333, 'distance_start_m': 9.8642, 'distance_end_m': 10.2406} + +## Naturalness Checks +- max_speed_mps: 30.8481 +- max_accel_mps2: 4.016 +- max_decel_mps2: 1.3442 +- max_yaw_rate_radps: 13.616 +- max_frame_displacement_m: 3.0848 +- artifact_warnings: ['high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 7 | context_agent | True | 52 | 25.182 | 3.0811 | 1.4126 | +| 8 | risk_agent | True | 55 | 25.4643 | 3.3333 | 1.4955 | +| 0 | context_agent | True | 55 | 24.9652 | 3.7002 | 1.6324 | +| 1 | collided_agent | True | 51 | 26.589 | 3.2691 | 1.482 | +| 2 | risk_agent | True | 58 | 24.9915 | 3.1207 | 1.4219 | +| 3 | offroad_agent | True | 59 | 29.5817 | 3.1719 | 1.435 | +| 4 | collided_agent,offroad_agent | True | 61 | 26.399 | 4.0303 | 1.6927 | +| 39 | risk_agent | True | 20 | | 3.1546 | 1.4283 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -5.0 | 8.8685 | | | | -3.776 | -8.0244 | | | +| -3.0 | 9.8642 | | -0.1563 | 0.2523 | -3.4992 | -9.2227 | 21.1982 | 20.9822 | +| -2.0 | 9.8406 | | 0.0304 | 0.1348 | -3.353 | -9.2518 | 22.5319 | 22.5161 | +| -1.0 | 10.0208 | | -0.2181 | 0.3219 | -3.1896 | -9.4996 | 24.1306 | 23.849 | +| -0.5 | 10.1304 | | -0.2201 | 0.3257 | -3.1 | -9.6445 | 24.7975 | 24.515 | +| 0.0 | 10.2406 | | -0.2215 | 0.3282 | -3.008 | -9.7889 | 25.4643 | 25.182 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0513_line000065.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0513_line000065.md new file mode 100644 index 0000000000000000000000000000000000000000..118c04fd6bb4f7bbe640e2218505ebf95a876fc6 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0513_line000065.md @@ -0,0 +1,53 @@ +# shard000_batch0000_world0513_line000065 + +## Outcome +- collision: True +- near_miss: False +- offroad: False +- event_time_s: 3.1 +- raw_min_distance_m: 2.5341 +- clean_min_distance_m: 2.5341 + +## Primary Pair +- agent_a: 13 (risk_agent) +- agent_b: 4 (collided_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: False +- semantic_candidate: crossing_or_intersection_conflict + +## Primary Interaction Metrics +- num_clean_pair_frames: 25 +- min_ttc_s: 0.176 +- time_ttc_below_1s: 0.9 +- time_ttc_below_2s: 1.5 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 0.913, 'distance_start_m': 49.3257, 'distance_end_m': 6.3743} + +## Naturalness Checks +- max_speed_mps: 18.2502 +- max_accel_mps2: 4.0218 +- max_decel_mps2: 2.6687 +- max_yaw_rate_radps: 7.169 +- max_frame_displacement_m: 1.825 +- artifact_warnings: ['high_yaw_rate', 'short_clean_track'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 4 | collided_agent | True | 37 | | 3.3659 | 1.5513 | +| 13 | risk_agent | True | 25 | | 3.2073 | 1.4525 | +| 2 | collided_agent | True | 40 | | 3.3256 | 1.4738 | +| 7 | risk_agent | True | 2 | | 8.6779 | 2.2635 | +| 18 | risk_agent | True | 18 | | 3.1146 | 1.4287 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -3.1 | 50.7615 | | | | 2.6373 | 50.6929 | | | +| -3.0 | 49.3257 | 3.4357 | 14.3568 | 14.375 | 2.6327 | 49.2554 | 8.3401 | 6.0349 | +| -2.0 | 30.588 | 1.3714 | 22.3036 | 22.3764 | 2.5697 | 30.4799 | 12.3411 | 10.0354 | +| -1.0 | 4.456 | 0.176 | 25.313 | 30.3751 | 2.4762 | 3.7046 | 16.3403 | 14.035 | +| -0.7 | 6.3743 | | -30.2297 | 32.7753 | 2.4422 | -5.8879 | 17.541 | 15.2345 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0554_line000066.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0554_line000066.md new file mode 100644 index 0000000000000000000000000000000000000000..25e0b61e3e66450baad4c264007bf96593f6478e --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0554_line000066.md @@ -0,0 +1,53 @@ +# shard000_batch0000_world0554_line000066 + +## Outcome +- collision: True +- near_miss: True +- offroad: False +- event_time_s: 5.2 +- raw_min_distance_m: 1.6703 +- clean_min_distance_m: 1.6703 + +## Primary Pair +- agent_a: 1 (risk_agent,collided_agent) +- agent_b: 2 (risk_agent,collided_agent) +- selection_reason: risk_agent_directly_collided_with_partner +- confidence: high +- risk_directly_involved: True +- semantic_candidate: rear_end_or_following_conflict + +## Primary Interaction Metrics +- num_clean_pair_frames: 55 +- min_ttc_s: 0.1542 +- time_ttc_below_1s: 0.9 +- time_ttc_below_2s: 1.6 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 0.9, 'distance_start_m': 18.2191, 'distance_end_m': 1.6703} + +## Naturalness Checks +- max_speed_mps: 12.1085 +- max_accel_mps2: 4.0053 +- max_decel_mps2: 4.0147 +- max_yaw_rate_radps: 5.309 +- max_frame_displacement_m: 1.2108 +- artifact_warnings: ['high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 1 | risk_agent,collided_agent | True | 55 | 11.9088 | 3.1055 | 1.387 | +| 2 | risk_agent,collided_agent | True | 61 | 0.7062 | 3.6601 | 1.54 | +| 0 | risk_agent | True | 36 | | 3.7002 | 1.6324 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -5.0 | 26.0124 | | | | -0.4923 | 26.0078 | | | +| -3.0 | 18.2191 | 57.2472 | 0.3183 | 5.6463 | 13.8715 | 11.8118 | 6.8419 | 3.0392 | +| -2.0 | 15.5828 | 2.857 | 5.4542 | 7.5266 | 9.9599 | 11.9843 | 7.5087 | 1.9062 | +| -1.0 | 11.0062 | 1.2609 | 8.7289 | 8.7293 | 5.2408 | 9.6784 | 9.3081 | 0.8392 | +| -0.5 | 6.4421 | 0.6838 | 9.4216 | 10.065 | 2.089 | 6.094 | 10.775 | 0.7732 | +| 0.0 | 1.6703 | 0.1542 | 10.8287 | 11.2075 | 0.9647 | 1.3635 | 11.9088 | 0.7062 | +| 0.4 | 3.1158 | | -0.8865 | 11.0682 | 2.7585 | -1.4488 | 12.1085 | 1.1731 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0561_line000067.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0561_line000067.md new file mode 100644 index 0000000000000000000000000000000000000000..208ea1902d2966cde1dd270dbcf57c2e83bcbe21 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0561_line000067.md @@ -0,0 +1,56 @@ +# shard000_batch0000_world0561_line000067 + +## Outcome +- collision: False +- near_miss: True +- offroad: False +- event_time_s: 2.2 +- raw_min_distance_m: 1.6796 +- clean_min_distance_m: 1.6795 + +## Primary Pair +- agent_a: 1 (risk_agent) +- agent_b: 6 (context_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: False +- semantic_candidate: same_direction_side_conflict_or_cut_in + +## Primary Interaction Metrics +- num_clean_pair_frames: 23 +- min_ttc_s: 1.0142 +- time_ttc_below_1s: 0.0 +- time_ttc_below_2s: 1.7 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 1.0, 'distance_start_m': 7.4294, 'distance_end_m': 1.6795} + +## Naturalness Checks +- max_speed_mps: 23.5044 +- max_accel_mps2: 41.4004 +- max_decel_mps2: 15.2848 +- max_yaw_rate_radps: 2.97 +- max_frame_displacement_m: 2.3504 +- artifact_warnings: ['hard_deceleration', 'high_acceleration', 'high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 1 | risk_agent | True | 33 | 10.3901 | 3.5875 | 1.5496 | +| 6 | context_agent | True | 23 | 13.7229 | 3.1874 | 1.5195 | +| 0 | context_agent | True | 28 | 15.0311 | 3.7002 | 1.6324 | +| 2 | context_agent | False | 24 | 4.0201 | 3.1554 | 1.4644 | +| 5 | context_agent | True | 33 | 22.1707 | 3.0675 | 1.3739 | +| 8 | context_agent | True | 29 | 12.9114 | 3.2909 | 1.5247 | +| 9 | risk_agent | True | 29 | 15.3401 | 3.0484 | 1.4394 | +| 10 | risk_agent | True | 15 | | 3.2573 | 1.4715 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -2.2 | 7.4294 | | | | 2.8153 | -6.8754 | | | +| -2.0 | 6.7932 | 2.1427 | 3.1704 | 3.3397 | 2.7526 | -6.2105 | 2.3898 | 5.7229 | +| -1.0 | 3.6955 | 1.2579 | 2.9379 | 3.3623 | 2.3026 | -2.8904 | 6.3909 | 9.7233 | +| -0.5 | 2.3429 | 1.0142 | 2.3102 | 3.3803 | 1.9924 | -1.2327 | 8.3902 | 11.7231 | +| 0.0 | 1.6795 | | -0.0796 | 3.4013 | 1.6253 | 0.4233 | 10.3901 | 13.7229 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0584_line000068.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0584_line000068.md new file mode 100644 index 0000000000000000000000000000000000000000..f2b075defd5d9a7c2913fe837dee5861c0adda71 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0584_line000068.md @@ -0,0 +1,59 @@ +# shard000_batch0000_world0584_line000068 + +## Outcome +- collision: True +- near_miss: True +- offroad: True +- event_time_s: 5.8 +- raw_min_distance_m: 1.7959 +- clean_min_distance_m: 1.7959 + +## Primary Pair +- agent_a: 8 (risk_agent,collided_agent,offroad_agent) +- agent_b: 46 (risk_agent,collided_agent,offroad_agent) +- selection_reason: risk_agent_directly_collided_with_partner +- confidence: high +- risk_directly_involved: True +- semantic_candidate: crossing_or_intersection_conflict + +## Primary Interaction Metrics +- num_clean_pair_frames: 58 +- min_ttc_s: 0.0664 +- time_ttc_below_1s: 0.2 +- time_ttc_below_2s: 0.2 +- distance_trend_before_event: {'label': 'not_decreasing', 'decreasing_ratio': 0.4333, 'distance_start_m': 5.224, 'distance_end_m': 4.079} + +## Naturalness Checks +- max_speed_mps: 31.3296 +- max_accel_mps2: 3.9939 +- max_decel_mps2: 4.0079 +- max_yaw_rate_radps: 13.857 +- max_frame_displacement_m: 3.133 +- artifact_warnings: ['high_yaw_rate', 'offroad_before_collision'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 8 | risk_agent,collided_agent,offroad_agent | True | 60 | 30.5299 | 3.1653 | 1.392 | +| 46 | risk_agent,collided_agent,offroad_agent | True | 58 | 29.8289 | 3.3715 | 1.4876 | +| 1 | offroad_agent | True | 58 | 25.1315 | 3.1703 | 1.4175 | +| 2 | collided_agent | True | 60 | 28.9323 | 3.2624 | 1.5207 | +| 3 | offroad_agent | True | 59 | 28.742 | 3.1791 | 1.429 | +| 5 | offroad_agent | True | 58 | 29.9552 | 3.0474 | 1.4019 | +| 9 | risk_agent | True | 37 | | 3.0389 | 1.3652 | +| 44 | collided_agent | True | 57 | 28.1765 | 3.1376 | 1.3914 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -5.0 | 6.5048 | | | | 3.7527 | 5.3132 | | | +| -3.0 | 5.224 | 10.5735 | 0.4941 | 1.1952 | 4.2053 | 3.0992 | 25.3297 | 24.1618 | +| -2.0 | 4.921 | | 0.0894 | 0.7384 | 4.4549 | 2.0905 | 27.3963 | 26.6958 | +| -1.0 | 4.9627 | | -0.0886 | 0.3642 | 4.664 | 1.6958 | 28.9959 | 28.6949 | +| -0.5 | 5.0376 | | -0.179 | 0.1812 | 4.7555 | 1.6624 | 29.7958 | 29.8292 | +| 0.0 | 4.079 | 2.0555 | 1.9844 | 1.9848 | 3.8 | 1.4827 | 30.5299 | 29.8289 | +| 0.5 | 3.3281 | 0.0664 | 50.0851 | 53.9291 | -0.9131 | 3.2004 | 31.3296 | 29.6952 | +| 0.7 | 4.9048 | | -46.6331 | 57.7327 | -0.5557 | -4.8732 | 30.9957 | 29.8961 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0589_line000069.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0589_line000069.md new file mode 100644 index 0000000000000000000000000000000000000000..f1a0201480bc428fbae3f3e4f876bcf535b7b31c --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0589_line000069.md @@ -0,0 +1,57 @@ +# shard000_batch0000_world0589_line000069 + +## Outcome +- collision: True +- near_miss: False +- offroad: True +- event_time_s: 1.1 +- raw_min_distance_m: 3.4553 +- clean_min_distance_m: 3.4553 + +## Primary Pair +- agent_a: 21 (risk_agent) +- agent_b: 8 (risk_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: False +- semantic_candidate: same_direction_side_conflict_or_cut_in + +## Primary Interaction Metrics +- num_clean_pair_frames: 22 +- min_ttc_s: 1.5546 +- time_ttc_below_1s: 0.0 +- time_ttc_below_2s: 1.1 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 1.0, 'distance_start_m': 7.4423, 'distance_end_m': 3.8491} + +## Naturalness Checks +- max_speed_mps: 16.4537 +- max_accel_mps2: 4.0153 +- max_decel_mps2: 0.0068 +- max_yaw_rate_radps: 2.324 +- max_frame_displacement_m: 1.6454 +- artifact_warnings: ['short_clean_track'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 8 | risk_agent | True | 22 | 7.9696 | 3.3716 | 1.4885 | +| 21 | risk_agent | True | 22 | 12.4547 | 4.0072 | 1.6568 | +| 7 | offroad_agent | True | 22 | 7.6213 | 3.9456 | 1.613 | +| 9 | context_agent | True | 22 | 7.7764 | 3.3038 | 1.4757 | +| 10 | context_agent | True | 22 | 8.1481 | 3.1107 | 1.4276 | +| 12 | context_agent | True | 22 | 8.3447 | 3.2271 | 1.4759 | +| 30 | risk_agent | True | 2 | | 3.1594 | 1.443 | +| 33 | collided_agent | True | 22 | 5.3139 | 3.2816 | 1.5061 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -1.1 | 7.4423 | | | | -3.6976 | 6.4588 | | | +| -1.0 | 7.0515 | 1.8201 | 3.8742 | 4.4867 | -3.6873 | 6.0106 | 8.454 | 3.9694 | +| -0.5 | 5.2274 | 1.5671 | 3.3357 | 4.4895 | -3.6228 | 3.7684 | 10.4549 | 5.9696 | +| 0.0 | 3.8491 | 1.9644 | 1.9595 | 4.4917 | -3.5336 | 1.5264 | 12.4547 | 7.9696 | +| 0.5 | 3.4944 | | -0.6782 | 4.4936 | -3.4203 | -0.7156 | 14.4543 | 9.9705 | +| 1.0 | 4.4192 | | -2.7837 | 4.498 | -3.2835 | -2.9578 | 16.4537 | 11.969 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0596_line000070.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0596_line000070.md new file mode 100644 index 0000000000000000000000000000000000000000..6009a1a9cba747373a27cb7f9e2dc5473120041b --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0596_line000070.md @@ -0,0 +1,59 @@ +# shard000_batch0000_world0596_line000070 + +## Outcome +- collision: True +- near_miss: False +- offroad: True +- event_time_s: 3.7 +- raw_min_distance_m: 3.275 +- clean_min_distance_m: 3.5478 + +## Primary Pair +- agent_a: 15 (risk_agent,collided_agent,offroad_agent) +- agent_b: 1 (collided_agent) +- selection_reason: risk_agent_directly_collided_with_partner +- confidence: high +- risk_directly_involved: True +- semantic_candidate: same_direction_side_conflict_or_cut_in + +## Primary Interaction Metrics +- num_clean_pair_frames: 48 +- min_ttc_s: 0.5203 +- time_ttc_below_1s: 0.5 +- time_ttc_below_2s: 2.3 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 1.0, 'distance_start_m': 28.3111, 'distance_end_m': 4.2893} + +## Naturalness Checks +- max_speed_mps: 41.0566 +- max_accel_mps2: 4.0136 +- max_decel_mps2: 4.0074 +- max_yaw_rate_radps: 12.469 +- max_frame_displacement_m: 4.1057 +- artifact_warnings: ['high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 1 | collided_agent | True | 48 | 22.6539 | 3.3434 | 1.4989 | +| 15 | risk_agent,collided_agent,offroad_agent | True | 48 | 29.2962 | 3.3269 | 1.4827 | +| 4 | collided_agent | True | 48 | 20.4962 | 3.2732 | 1.4681 | +| 5 | collided_agent | True | 48 | 18.7679 | 3.3174 | 1.5717 | +| 9 | context_agent | True | 48 | 22.0528 | 3.29 | 1.4441 | +| 10 | risk_agent | True | 48 | 22.8311 | 3.4419 | 1.4512 | +| 13 | collided_agent | True | 44 | 28.2021 | 3.7976 | 1.6552 | +| 21 | risk_agent | True | 25 | | 3.6703 | 1.5643 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -3.7 | 35.8175 | | | | -4.7684 | 35.4986 | | | +| -3.0 | 28.3111 | 2.6445 | 10.7057 | 10.8431 | -4.6795 | 27.9217 | 24.029 | 13.1863 | +| -2.0 | 18.2781 | 1.9497 | 9.3749 | 9.6446 | -4.5277 | 17.7084 | 26.6962 | 17.0529 | +| -1.0 | 9.5472 | 1.2341 | 7.7364 | 7.8048 | -3.3413 | 8.9435 | 28.4971 | 20.8531 | +| -0.5 | 6.2529 | 1.1212 | 5.5772 | 5.8563 | -2.2503 | 5.8339 | 28.3638 | 22.5199 | +| 0.0 | 4.2893 | | -0.0261 | 9.6135 | -3.3387 | 2.6928 | 29.2962 | 22.6539 | +| 0.5 | 3.577 | 0.6873 | 5.2045 | 7.2434 | -2.4757 | 2.5819 | 29.163 | 21.9197 | +| 1.0 | 5.135 | | -2.7878 | 36.5114 | 3.3467 | 3.8947 | 29.4307 | 20.7872 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0611_line000071.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0611_line000071.md new file mode 100644 index 0000000000000000000000000000000000000000..85b674d98139c8f681ae541bb0259fb2ba471172 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0611_line000071.md @@ -0,0 +1,59 @@ +# shard000_batch0000_world0611_line000071 + +## Outcome +- collision: True +- near_miss: False +- offroad: False +- event_time_s: 4.1 +- raw_min_distance_m: 6.4296 +- clean_min_distance_m: 8.216 + +## Primary Pair +- agent_a: 4 (risk_agent) +- agent_b: 1 (context_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: False +- semantic_candidate: generic_close_interaction + +## Primary Interaction Metrics +- num_clean_pair_frames: 52 +- min_ttc_s: 1.391 +- time_ttc_below_1s: 0.0 +- time_ttc_below_2s: 0.2 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 1.0, 'distance_start_m': 17.7454, 'distance_end_m': 9.8672} + +## Naturalness Checks +- max_speed_mps: 40.4952 +- max_accel_mps2: 2.6789 +- max_decel_mps2: 4.0077 +- max_yaw_rate_radps: 18.1319 +- max_frame_displacement_m: 4.0495 +- artifact_warnings: ['high_yaw_rate', 'short_clean_track'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 1 | context_agent | True | 52 | 34.727 | 3.5914 | 1.4836 | +| 4 | risk_agent | True | 52 | 37.3132 | 3.2573 | 1.4759 | +| 3 | collided_agent | True | 52 | 40.0948 | 3.1215 | 1.4391 | +| 5 | context_agent | True | 52 | 35.5537 | 3.1599 | 1.4399 | +| 6 | context_agent | True | 52 | 37.7843 | 5.1509 | 1.9594 | +| 8 | collided_agent | True | 52 | 37.2937 | 3.3773 | 1.522 | +| 9 | risk_agent | True | 2 | | 3.3901 | 1.4538 | +| 10 | risk_agent | True | 5 | | 2.943 | 1.3064 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -4.1 | 20.4461 | | | | -7.1108 | 19.1697 | | | +| -3.0 | 17.7454 | 7.4436 | 2.384 | 2.6548 | -7.1873 | 16.2248 | 32.7139 | 30.0612 | +| -2.0 | 15.2728 | 6.5363 | 2.3366 | 2.7215 | -7.2985 | 13.4161 | 34.514 | 31.7949 | +| -1.0 | 12.9074 | 5.6427 | 2.2875 | 2.8553 | -7.4023 | 10.5739 | 36.1135 | 33.2597 | +| -0.5 | 11.7976 | 5.4925 | 2.1479 | 2.8552 | -7.4652 | 9.1354 | 36.78 | 33.9266 | +| 0.0 | 9.8672 | 1.8794 | 5.2503 | 6.3577 | -5.8715 | 7.9301 | 37.3132 | 34.727 | +| 0.5 | 8.9901 | 5.3318 | 1.6861 | 2.3219 | -5.9258 | 6.7606 | 37.7132 | 35.3932 | +| 1.0 | 8.216 | 5.8287 | 1.4096 | 2.191 | -5.9896 | 5.6238 | 38.3803 | 36.1928 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0619_line000072.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0619_line000072.md new file mode 100644 index 0000000000000000000000000000000000000000..c9ad9ac02d021f5a72744b4cbccb8f84648fe0fc --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0619_line000072.md @@ -0,0 +1,55 @@ +# shard000_batch0000_world0619_line000072 + +## Outcome +- collision: True +- near_miss: True +- offroad: True +- event_time_s: 2.8 +- raw_min_distance_m: 1.9508 +- clean_min_distance_m: 1.9508 + +## Primary Pair +- agent_a: 0 (risk_agent,collided_agent) +- agent_b: 3 (collided_agent,offroad_agent) +- selection_reason: risk_agent_directly_collided_with_partner +- confidence: high +- risk_directly_involved: True +- semantic_candidate: crossing_or_intersection_conflict + +## Primary Interaction Metrics +- num_clean_pair_frames: 38 +- min_ttc_s: 0.0808 +- time_ttc_below_1s: 0.9 +- time_ttc_below_2s: 1.5 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 1.0, 'distance_start_m': 43.6801, 'distance_end_m': 1.9508} + +## Naturalness Checks +- max_speed_mps: 18.285 +- max_accel_mps2: 4.0106 +- max_decel_mps2: 2.682 +- max_yaw_rate_radps: 6.814 +- max_frame_displacement_m: 1.8285 +- artifact_warnings: ['high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 0 | risk_agent,collided_agent | True | 38 | 8.4433 | 3.7002 | 1.6324 | +| 3 | collided_agent,offroad_agent | True | 39 | 16.2178 | 3.3454 | 1.4427 | +| 1 | context_agent | True | 39 | 12.9701 | 2.9727 | 1.3342 | +| 2 | risk_agent | True | 17 | | 3.3831 | 1.4589 | +| 15 | risk_agent | True | 25 | | 3.2308 | 1.4139 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -2.8 | 43.6801 | | | | 42.9585 | 7.9066 | | | +| -2.0 | 36.1722 | 3.1046 | 11.651 | 11.6532 | 34.6776 | 10.2902 | 3.1096 | 10.3521 | +| -1.0 | 21.5127 | 1.3583 | 15.8377 | 19.5994 | 7.1162 | 20.3017 | 6.1091 | 14.0849 | +| -0.5 | 11.4448 | 0.5413 | 21.1425 | 22.0461 | 1.1106 | 11.3908 | 7.1099 | 15.6846 | +| 0.0 | 1.9508 | 0.0808 | 24.1491 | 24.6507 | -0.8217 | 1.7692 | 8.4433 | 16.2178 | +| 0.5 | 8.4955 | | -21.3286 | 25.9536 | 3.0995 | -7.9099 | 9.6436 | 17.5509 | +| 0.9 | 19.0142 | | -26.8057 | 26.8181 | 7.5339 | -17.458 | 10.3097 | 18.0178 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0625_line000073.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0625_line000073.md new file mode 100644 index 0000000000000000000000000000000000000000..c0b57979b64d62ce73e298f9aef7e4855f553985 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0625_line000073.md @@ -0,0 +1,51 @@ +# shard000_batch0000_world0625_line000073 + +## Outcome +- collision: True +- near_miss: False +- offroad: False +- event_time_s: 1.9 +- raw_min_distance_m: 7.2792 +- clean_min_distance_m: 7.2791 + +## Primary Pair +- agent_a: 0 (risk_agent) +- agent_b: 1 (collided_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: False +- semantic_candidate: same_direction_side_conflict_or_cut_in + +## Primary Interaction Metrics +- num_clean_pair_frames: 15 +- min_ttc_s: 1.1993 +- time_ttc_below_1s: 0.0 +- time_ttc_below_2s: 0.1 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 0.8571, 'distance_start_m': 9.0138, 'distance_end_m': 7.2791} + +## Naturalness Checks +- max_speed_mps: 22.6951 +- max_accel_mps2: 4.0145 +- max_decel_mps2: 2.0058 +- max_yaw_rate_radps: 6.232 +- max_frame_displacement_m: 2.2695 +- artifact_warnings: ['high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 0 | risk_agent | True | 15 | | 3.7002 | 1.6324 | +| 1 | collided_agent | True | 30 | | 3.2337 | 1.4362 | +| 2 | collided_agent | True | 30 | | 4.1296 | 1.7107 | +| 3 | risk_agent | True | 13 | | 3.2428 | 1.4219 | +| 5 | risk_agent | True | 26 | | 3.3072 | 1.457 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -1.9 | 9.0138 | | | | 0.5424 | 8.9975 | | | +| -1.0 | 8.5812 | 21.4507 | 0.4 | 1.789 | 1.3856 | 8.4686 | 3.4017 | 3.1891 | +| -0.5 | 7.2791 | 1.1993 | 6.0694 | 9.048 | 2.5502 | 6.8178 | 5.4014 | 4.9222 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0630_line000074.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0630_line000074.md new file mode 100644 index 0000000000000000000000000000000000000000..1cbad31c64e79ec9c32b88838cc3e8f29c2670fc --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0630_line000074.md @@ -0,0 +1,49 @@ +# shard000_batch0000_world0630_line000074 + +## Outcome +- collision: True +- near_miss: False +- offroad: False +- event_time_s: 2.4 +- raw_min_distance_m: 2.4796 +- clean_min_distance_m: 16.8309 + +## Primary Pair +- agent_a: 1 (risk_agent,collided_agent) +- agent_b: 46 (context_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: True +- semantic_candidate: crossing_or_intersection_conflict + +## Primary Interaction Metrics +- num_clean_pair_frames: 2 +- min_ttc_s: 3.9422 +- time_ttc_below_1s: 0.0 +- time_ttc_below_2s: 0.0 +- distance_trend_before_event: {'label': 'insufficient_clean_frames', 'decreasing_ratio': None} + +## Naturalness Checks +- max_speed_mps: 13.9724 +- max_accel_mps2: 4.01 +- max_decel_mps2: 2.6736 +- max_yaw_rate_radps: 4.045 +- max_frame_displacement_m: 1.3972 +- artifact_warnings: ['high_yaw_rate', 'short_clean_track'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 1 | risk_agent,collided_agent | True | 30 | | 4.115 | 1.6678 | +| 46 | context_agent | False | 2 | | 3.0279 | 1.3725 | +| 2 | risk_agent | True | 35 | | 3.318 | 1.5108 | +| 37 | risk_agent | True | 2 | | 3.1603 | 1.4248 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -2.4 | 17.2578 | | | | 6.3141 | 16.0613 | | | +| -2.3 | 16.8309 | 3.9422 | 4.2694 | 4.2891 | 5.5564 | 15.8872 | 4.2891 | 0.0 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0632_line000075.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0632_line000075.md new file mode 100644 index 0000000000000000000000000000000000000000..d154c42b2a75ff04156bf745c7a64cd0c1e47064 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0632_line000075.md @@ -0,0 +1,48 @@ +# shard000_batch0000_world0632_line000075 + +## Outcome +- collision: True +- near_miss: False +- offroad: False +- event_time_s: 1.6 +- raw_min_distance_m: 3.7591 +- clean_min_distance_m: 8.3506 + +## Primary Pair +- agent_a: 9 (risk_agent,collided_agent) +- agent_b: 34 (context_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: True +- semantic_candidate: crossing_or_intersection_conflict + +## Primary Interaction Metrics +- num_clean_pair_frames: 2 +- min_ttc_s: 21.1384 +- time_ttc_below_1s: 0.0 +- time_ttc_below_2s: 0.0 +- distance_trend_before_event: {'label': 'insufficient_clean_frames', 'decreasing_ratio': None} + +## Naturalness Checks +- max_speed_mps: 14.2178 +- max_accel_mps2: 4.0136 +- max_decel_mps2: -2.6647 +- max_yaw_rate_radps: 2.821 +- max_frame_displacement_m: 1.4218 +- artifact_warnings: ['high_yaw_rate', 'short_clean_track'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 9 | risk_agent,collided_agent | True | 17 | | 3.2657 | 1.4555 | +| 34 | context_agent | False | 2 | | 3.9242 | 1.6442 | +| 0 | risk_agent | True | 24 | | 3.7002 | 1.6324 | +| 19 | risk_agent | True | 27 | | 3.3403 | 1.5002 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -1.6 | 8.3905 | | | | -3.4653 | -7.6415 | | | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0635_line000076.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0635_line000076.md new file mode 100644 index 0000000000000000000000000000000000000000..64d25b27920fb803417ea2f93a3bafcfd79021c8 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0635_line000076.md @@ -0,0 +1,55 @@ +# shard000_batch0000_world0635_line000076 + +## Outcome +- collision: True +- near_miss: False +- offroad: False +- event_time_s: 1.7 +- raw_min_distance_m: 3.0365 +- clean_min_distance_m: 3.0366 + +## Primary Pair +- agent_a: 1 (risk_agent) +- agent_b: 12 (collided_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: False +- semantic_candidate: same_direction_side_conflict_or_cut_in + +## Primary Interaction Metrics +- num_clean_pair_frames: 18 +- min_ttc_s: 0.3141 +- time_ttc_below_1s: 0.1 +- time_ttc_below_2s: 0.5 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 0.8824, 'distance_start_m': 6.2518, 'distance_end_m': 3.2724} + +## Naturalness Checks +- max_speed_mps: 18.3248 +- max_accel_mps2: 37.7154 +- max_decel_mps2: 3.0875 +- max_yaw_rate_radps: 5.4169 +- max_frame_displacement_m: 1.8325 +- artifact_warnings: ['high_acceleration', 'high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 1 | risk_agent | True | 18 | 14.8849 | 3.1545 | 1.4097 | +| 12 | collided_agent | True | 28 | 13.0655 | 11.6537 | 2.1234 | +| 0 | context_agent | True | 28 | 11.718 | 3.7002 | 1.6324 | +| 2 | collided_agent | True | 20 | 5.8137 | 3.1291 | 1.4203 | +| 5 | risk_agent | True | 14 | | 3.179 | 1.4372 | +| 6 | context_agent | True | 25 | 16.8585 | 3.5677 | 1.5295 | +| 7 | context_agent | False | 28 | 4.582 | 3.4975 | 1.5022 | +| 8 | risk_agent | True | 7 | | 3.3722 | 1.4862 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -1.7 | 6.2518 | | | | -4.4832 | 4.3572 | | | +| -1.0 | 4.558 | 0.3141 | 14.5128 | 14.6619 | -3.5155 | 2.9011 | 10.8847 | 9.8662 | +| -0.5 | 3.6333 | 1.8953 | 1.917 | 1.9477 | -2.8997 | 2.1892 | 12.8858 | 11.5323 | +| 0.0 | 3.2724 | | -1.5695 | 3.44 | -2.9631 | 1.3887 | 14.8849 | 13.0655 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0648_line000077.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0648_line000077.md new file mode 100644 index 0000000000000000000000000000000000000000..3137f39e0777387b71e2c2a35779e5feafe5b3e5 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0648_line000077.md @@ -0,0 +1,51 @@ +# shard000_batch0000_world0648_line000077 + +## Outcome +- collision: True +- near_miss: False +- offroad: True +- event_time_s: 5.6 +- raw_min_distance_m: 3.0613 +- clean_min_distance_m: 3.0613 + +## Primary Pair +- agent_a: 0 (risk_agent) +- agent_b: 7 (context_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: False +- semantic_candidate: crossing_or_intersection_conflict + +## Primary Interaction Metrics +- num_clean_pair_frames: 36 +- min_ttc_s: 0.1824 +- time_ttc_below_1s: 0.9 +- time_ttc_below_2s: 1.7 +- distance_trend_before_event: {'label': 'not_decreasing', 'decreasing_ratio': 0.1333, 'distance_start_m': 5.9043, 'distance_end_m': 38.7881} + +## Naturalness Checks +- max_speed_mps: 31.8987 +- max_accel_mps2: 4.0132 +- max_decel_mps2: 2.6741 +- max_yaw_rate_radps: 13.231 +- max_frame_displacement_m: 3.1899 +- artifact_warnings: ['high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 0 | risk_agent | True | 36 | | 3.7002 | 1.6324 | +| 7 | context_agent | True | 47 | | 9.0058 | 2.251 | +| 5 | collided_agent | True | 52 | | 4.1836 | 1.8205 | +| 8 | collided_agent,offroad_agent | True | 57 | | 3.3754 | 1.4985 | +| 10 | risk_agent | True | 5 | | 3.4248 | 1.5158 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -5.0 | 52.7738 | | | | 2.8132 | 52.6987 | | | +| -3.0 | 5.9043 | 0.2401 | 24.5947 | 27.7895 | 2.7509 | 5.2243 | 23.676 | 4.1135 | +| -2.0 | 23.7224 | | -30.2943 | 30.6851 | 2.657 | -23.5732 | 25.61 | 5.2472 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0656_line000078.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0656_line000078.md new file mode 100644 index 0000000000000000000000000000000000000000..9e20092331d00af0496d63847d53c4b98a92dcaa --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0656_line000078.md @@ -0,0 +1,47 @@ +# shard000_batch0000_world0656_line000078 + +## Outcome +- collision: False +- near_miss: True +- offroad: False +- event_time_s: 2.5 +- raw_min_distance_m: 1.9428 +- clean_min_distance_m: 23.785 + +## Primary Pair +- agent_a: 1 (risk_agent) +- agent_b: 14 (context_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: False +- semantic_candidate: rear_end_or_following_conflict + +## Primary Interaction Metrics +- num_clean_pair_frames: 2 +- min_ttc_s: 4.7504 +- time_ttc_below_1s: 0.0 +- time_ttc_below_2s: 0.0 +- distance_trend_before_event: {'label': 'insufficient_clean_frames', 'decreasing_ratio': None} + +## Naturalness Checks +- max_speed_mps: 18.4736 +- max_accel_mps2: 4.017 +- max_decel_mps2: -1.9891 +- max_yaw_rate_radps: 1.048 +- max_frame_displacement_m: 1.8474 +- artifact_warnings: ['short_clean_track'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 1 | risk_agent | True | 36 | | 3.1111 | 1.4154 | +| 14 | context_agent | False | 2 | | 3.2728 | 1.4126 | +| 0 | risk_agent | True | 20 | | 3.7002 | 1.6324 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -2.5 | 24.2857 | | | | -0.246 | 24.2845 | | | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0658_line000079.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0658_line000079.md new file mode 100644 index 0000000000000000000000000000000000000000..d34ff77dfe4491a74a68efd1f2d2d178030c7986 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0658_line000079.md @@ -0,0 +1,54 @@ +# shard000_batch0000_world0658_line000079 + +## Outcome +- collision: True +- near_miss: False +- offroad: True +- event_time_s: 2.5 +- raw_min_distance_m: 2.8824 +- clean_min_distance_m: 3.0302 + +## Primary Pair +- agent_a: 1 (risk_agent,collided_agent,offroad_agent) +- agent_b: 2 (risk_agent,collided_agent) +- selection_reason: risk_agent_directly_collided_with_partner +- confidence: high +- risk_directly_involved: True +- semantic_candidate: crossing_or_intersection_conflict + +## Primary Interaction Metrics +- num_clean_pair_frames: 36 +- min_ttc_s: 0.3216 +- time_ttc_below_1s: 0.8 +- time_ttc_below_2s: 1.5 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 0.96, 'distance_start_m': 24.39, 'distance_end_m': 3.0302} + +## Naturalness Checks +- max_speed_mps: 15.0875 +- max_accel_mps2: 4.0104 +- max_decel_mps2: 1.3368 +- max_yaw_rate_radps: 4.238 +- max_frame_displacement_m: 1.5088 +- artifact_warnings: ['high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 1 | risk_agent,collided_agent,offroad_agent | True | 36 | 8.3969 | 3.0211 | 1.3775 | +| 2 | risk_agent,collided_agent | True | 36 | 12.6213 | 4.3935 | 1.9372 | +| 0 | context_agent | True | 36 | 9.6668 | 3.7002 | 1.6324 | +| 3 | risk_agent | True | 20 | | 3.0373 | 1.392 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -2.5 | 24.39 | | | | 22.0314 | 10.4636 | | | +| -2.0 | 21.436 | 3.1311 | 6.8462 | 6.9036 | 19.4782 | 8.9499 | 2.9968 | 5.8885 | +| -1.0 | 12.2364 | 1.0702 | 11.4333 | 11.8429 | 11.5304 | 4.0963 | 6.1969 | 9.755 | +| -0.5 | 6.0516 | 0.5471 | 11.0622 | 12.4197 | 5.9986 | 0.7991 | 7.6635 | 11.6213 | +| 0.0 | 3.0302 | 0.5727 | 5.2913 | 15.6279 | 2.1866 | -2.0978 | 8.3969 | 12.6213 | +| 0.5 | 7.853 | | -18.966 | 19.6648 | -1.6619 | -7.6752 | 9.1963 | 13.4874 | +| 1.0 | 18.3306 | | -24.1348 | 24.5451 | -3.1972 | -18.0496 | 10.5966 | 15.0875 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0661_line000080.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0661_line000080.md new file mode 100644 index 0000000000000000000000000000000000000000..12c2eb025eff4d11306e3042c70837a5a7daf3a0 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0661_line000080.md @@ -0,0 +1,53 @@ +# shard000_batch0000_world0661_line000080 + +## Outcome +- collision: False +- near_miss: True +- offroad: False +- event_time_s: 5.8 +- raw_min_distance_m: 1.6206 +- clean_min_distance_m: 1.6206 + +## Primary Pair +- agent_a: 2 (risk_agent) +- agent_b: 1 (context_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: False +- semantic_candidate: same_direction_side_conflict_or_cut_in + +## Primary Interaction Metrics +- num_clean_pair_frames: 51 +- min_ttc_s: 1.3089 +- time_ttc_below_1s: 0.0 +- time_ttc_below_2s: 2.1 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 0.9667, 'distance_start_m': 7.9997, 'distance_end_m': 1.6206} + +## Naturalness Checks +- max_speed_mps: 30.5059 +- max_accel_mps2: 2.6802 +- max_decel_mps2: 3.3366 +- max_yaw_rate_radps: 11.542 +- max_frame_displacement_m: 3.0506 +- artifact_warnings: ['high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 1 | context_agent | True | 51 | 27.2089 | 3.1625 | 1.4953 | +| 2 | risk_agent | True | 56 | 25.2975 | 3.1859 | 1.4675 | +| 0 | risk_agent | True | 53 | 30.2393 | 3.7002 | 1.6324 | +| 3 | risk_agent | True | 59 | 28.938 | 3.2897 | 1.5059 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -5.0 | 12.4025 | | | | -3.5491 | -11.8839 | | | +| -3.0 | 7.9997 | 3.0452 | 2.627 | 2.6363 | -2.4219 | -7.6243 | 21.299 | 23.8768 | +| -2.0 | 5.1613 | 1.7991 | 2.8688 | 2.9007 | -1.8175 | -4.8307 | 22.6324 | 25.476 | +| -1.0 | 3.1947 | 1.7896 | 1.7852 | 1.9716 | -2.0529 | -2.4477 | 23.9653 | 25.8758 | +| -0.5 | 2.3354 | 1.4395 | 1.6223 | 1.9769 | -1.7929 | -1.4966 | 24.6314 | 26.5433 | +| 0.0 | 1.6206 | 1.4118 | 1.1479 | 1.9805 | -1.526 | -0.5453 | 25.2975 | 27.2089 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0674_line000081.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0674_line000081.md new file mode 100644 index 0000000000000000000000000000000000000000..9c8ba59c8e7e733208a98f42229f3b4ebac607ab --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0674_line000081.md @@ -0,0 +1,58 @@ +# shard000_batch0000_world0674_line000081 + +## Outcome +- collision: True +- near_miss: False +- offroad: False +- event_time_s: 3.2 +- raw_min_distance_m: 2.8826 +- clean_min_distance_m: 14.1613 + +## Primary Pair +- agent_a: 5 (risk_agent,collided_agent) +- agent_b: 10 (collided_agent) +- selection_reason: risk_agent_directly_collided_with_partner +- confidence: high +- risk_directly_involved: True +- semantic_candidate: generic_close_interaction + +## Primary Interaction Metrics +- num_clean_pair_frames: 43 +- min_ttc_s: 2.1443 +- time_ttc_below_1s: 0.0 +- time_ttc_below_2s: 0.0 +- distance_trend_before_event: {'label': 'mixed', 'decreasing_ratio': 0.7, 'distance_start_m': 25.5069, 'distance_end_m': 18.8628} + +## Naturalness Checks +- max_speed_mps: 34.546 +- max_accel_mps2: 4.0112 +- max_decel_mps2: 4.0016 +- max_yaw_rate_radps: 13.1819 +- max_frame_displacement_m: 3.4546 +- artifact_warnings: ['high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 5 | risk_agent,collided_agent | True | 43 | 25.6797 | 3.4168 | 1.5194 | +| 10 | collided_agent | True | 43 | 31.2792 | 3.3563 | 1.527 | +| 0 | risk_agent | True | 43 | 25.5851 | 3.7002 | 1.6324 | +| 1 | collided_agent | True | 43 | 31.27 | 3.3208 | 1.4888 | +| 2 | context_agent | True | 43 | 29.6247 | 4.0564 | 1.6758 | +| 6 | context_agent | True | 43 | 25.5182 | 3.8886 | 1.6662 | +| 9 | risk_agent | True | 43 | 26.9523 | 3.92 | 1.7117 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -3.2 | 25.2282 | | | | -4.0828 | -24.8957 | | | +| -3.0 | 25.5069 | | -1.4273 | 1.6669 | -3.9685 | -25.1963 | 21.4134 | 19.8794 | +| -2.0 | 26.3057 | 43.2866 | 0.6077 | 1.1563 | -3.8177 | -26.0272 | 23.2805 | 23.7456 | +| -1.0 | 23.984 | 5.819 | 4.1217 | 4.1471 | -2.4866 | -23.8548 | 23.5466 | 27.6126 | +| -0.5 | 21.5109 | 3.7773 | 5.6947 | 8.6504 | 3.1837 | -21.274 | 24.0799 | 29.6123 | +| 0.0 | 18.8628 | 4.209 | 4.4816 | 8.8623 | -0.4947 | -18.8564 | 25.6797 | 31.2792 | +| 0.5 | 17.2307 | 8.561 | 2.0127 | 8.9495 | -4.3779 | -16.6653 | 27.68 | 32.813 | +| 1.0 | 14.1613 | 2.1443 | 6.6041 | 6.8265 | -8.2184 | -11.5326 | 28.0796 | 34.546 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0678_line000082.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0678_line000082.md new file mode 100644 index 0000000000000000000000000000000000000000..51e7ed5e7fddead9e15575cb9f42f9c8d8c36a54 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0678_line000082.md @@ -0,0 +1,55 @@ +# shard000_batch0000_world0678_line000082 + +## Outcome +- collision: True +- near_miss: False +- offroad: True +- event_time_s: 2.7 +- raw_min_distance_m: 2.8715 +- clean_min_distance_m: 2.8715 + +## Primary Pair +- agent_a: 2 (risk_agent,collided_agent) +- agent_b: 1 (collided_agent,offroad_agent) +- selection_reason: risk_agent_directly_collided_with_partner +- confidence: high +- risk_directly_involved: True +- semantic_candidate: same_direction_side_conflict_or_cut_in + +## Primary Interaction Metrics +- num_clean_pair_frames: 38 +- min_ttc_s: 0.2651 +- time_ttc_below_1s: 0.5 +- time_ttc_below_2s: 1.2 +- distance_trend_before_event: {'label': 'mixed', 'decreasing_ratio': 0.6296, 'distance_start_m': 10.0908, 'distance_end_m': 3.0448} + +## Naturalness Checks +- max_speed_mps: 19.3392 +- max_accel_mps2: 4.0162 +- max_decel_mps2: 2.6613 +- max_yaw_rate_radps: 5.67 +- max_frame_displacement_m: 1.9339 +- artifact_warnings: ['high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 1 | collided_agent,offroad_agent | True | 38 | 9.1889 | 3.2936 | 1.4389 | +| 2 | risk_agent,collided_agent | True | 38 | 12.1545 | 3.0338 | 1.342 | +| 0 | context_agent | True | 38 | 10.4005 | 3.7002 | 1.6324 | +| 58 | risk_agent | True | 28 | 17.9714 | 3.1291 | 1.4333 | +| 60 | risk_agent | True | 28 | 19.3392 | 5.3512 | 2.069 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -2.7 | 10.0908 | | | | 3.5607 | 9.4417 | | | +| -2.0 | 10.9205 | 2.0336 | 5.3702 | 7.0468 | 3.136 | 10.4606 | 4.1544 | 5.9216 | +| -1.0 | 7.4145 | 0.6695 | 11.0746 | 12.6747 | 0.7466 | 7.3768 | 8.1539 | 7.5875 | +| -0.5 | 5.5148 | 3.2521 | 1.6958 | 1.705 | 0.1244 | 5.5134 | 10.1533 | 8.455 | +| 0.0 | 3.0448 | 0.3106 | 9.8039 | 13.8822 | -0.2345 | 3.0357 | 12.1545 | 9.1889 | +| 0.5 | 2.9405 | | -0.8814 | 3.4243 | 2.0821 | 2.0765 | 11.8875 | 10.5889 | +| 1.0 | 6.1555 | | -7.745 | 9.135 | 6.0421 | -1.1763 | 13.8201 | 11.0549 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0691_line000083.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0691_line000083.md new file mode 100644 index 0000000000000000000000000000000000000000..3878d8a95b2c02594cdc2eb53f51ecb7a06ab357 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0691_line000083.md @@ -0,0 +1,47 @@ +# shard000_batch0000_world0691_line000083 + +## Outcome +- collision: True +- near_miss: False +- offroad: False +- event_time_s: 7.9 +- raw_min_distance_m: 2.1737 +- clean_min_distance_m: 3.859 + +## Primary Pair +- agent_a: 6 (risk_agent,collided_agent) +- agent_b: 31 (context_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: True +- semantic_candidate: same_direction_side_conflict_or_cut_in + +## Primary Interaction Metrics +- num_clean_pair_frames: 1 +- min_ttc_s: None +- time_ttc_below_1s: 0.0 +- time_ttc_below_2s: 0.0 +- distance_trend_before_event: {'label': 'insufficient_clean_frames', 'decreasing_ratio': None} + +## Naturalness Checks +- max_speed_mps: 18.2199 +- max_accel_mps2: 4.0051 +- max_decel_mps2: 2.6653 +- max_yaw_rate_radps: 8.354 +- max_frame_displacement_m: 1.822 +- artifact_warnings: ['high_yaw_rate', 'short_clean_track'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 6 | risk_agent,collided_agent | True | 61 | | 3.1572 | 1.3974 | +| 31 | context_agent | False | 1 | | 3.2366 | 1.4329 | +| 2 | risk_agent | True | 14 | | 3.0533 | 1.3591 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -5.0 | 3.859 | | | | -3.1022 | 2.2953 | | | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0700_line000084.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0700_line000084.md new file mode 100644 index 0000000000000000000000000000000000000000..7a47dc285a5b6d445ef50c11f596e2fbdb8d27de --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0700_line000084.md @@ -0,0 +1,57 @@ +# shard000_batch0000_world0700_line000084 + +## Outcome +- collision: True +- near_miss: False +- offroad: False +- event_time_s: 2.8 +- raw_min_distance_m: 2.2094 +- clean_min_distance_m: 2.2095 + +## Primary Pair +- agent_a: 3 (risk_agent,collided_agent) +- agent_b: 17 (collided_agent) +- selection_reason: risk_agent_directly_collided_with_partner +- confidence: high +- risk_directly_involved: True +- semantic_candidate: crossing_or_intersection_conflict + +## Primary Interaction Metrics +- num_clean_pair_frames: 32 +- min_ttc_s: 0.0986 +- time_ttc_below_1s: 0.9 +- time_ttc_below_2s: 1.5 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 1.0, 'distance_start_m': 46.19, 'distance_end_m': 2.2095} + +## Naturalness Checks +- max_speed_mps: 27.8865 +- max_accel_mps2: 135.815 +- max_decel_mps2: 10.7726 +- max_yaw_rate_radps: 5.249 +- max_frame_displacement_m: 2.7887 +- artifact_warnings: ['high_acceleration', 'high_yaw_rate', 'short_clean_track'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 3 | risk_agent,collided_agent | True | 36 | 15.2117 | 4.0925 | 1.7034 | +| 17 | collided_agent | True | 32 | 8.5888 | 3.1031 | 1.3895 | +| 1 | context_agent | True | 39 | 20.2893 | 3.1858 | 1.4122 | +| 4 | context_agent | True | 39 | 25.2192 | 3.0396 | 1.3847 | +| 5 | context_agent | False | 37 | 6.4757 | 3.2751 | 1.4588 | +| 11 | risk_agent | True | 2 | | 3.3524 | 1.4385 | +| 23 | risk_agent | True | 5 | | 3.0918 | 1.3597 | +| 44 | context_agent | True | 39 | 21.876 | 3.1239 | 1.4022 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -2.8 | 46.19 | | | | 5.1869 | 45.8978 | | | +| -2.0 | 37.6391 | 2.8918 | 13.0157 | 13.0193 | 4.1961 | 37.4045 | 9.0787 | 4.0559 | +| -1.0 | 22.0571 | 1.2041 | 18.3183 | 18.3196 | 0.3666 | 22.0541 | 12.5454 | 5.7902 | +| -0.5 | 11.7724 | 0.5369 | 21.9263 | 21.9346 | 0.3254 | 11.7679 | 14.5454 | 7.3893 | +| 0.0 | 2.2095 | 0.0986 | 22.4063 | 22.6762 | 0.6489 | 2.1121 | 15.2117 | 8.5888 | +| 0.3 | 4.614 | | -17.8873 | 24.1398 | 3.6475 | -2.8258 | 15.6779 | 9.1233 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0704_line000085.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0704_line000085.md new file mode 100644 index 0000000000000000000000000000000000000000..c4a2b47dd22b11f3f97cbb83be6d0742acffa5b8 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0704_line000085.md @@ -0,0 +1,49 @@ +# shard000_batch0000_world0704_line000085 + +## Outcome +- collision: True +- near_miss: False +- offroad: True +- event_time_s: 5.7 +- raw_min_distance_m: 3.7075 +- clean_min_distance_m: 3.9416 + +## Primary Pair +- agent_a: 21 (risk_agent) +- agent_b: 10 (collided_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: False +- semantic_candidate: generic_close_interaction + +## Primary Interaction Metrics +- num_clean_pair_frames: 17 +- min_ttc_s: None +- time_ttc_below_1s: 0.0 +- time_ttc_below_2s: 0.0 +- distance_trend_before_event: {'label': 'insufficient_clean_frames', 'decreasing_ratio': None} + +## Naturalness Checks +- max_speed_mps: 28.4438 +- max_accel_mps2: 4.0115 +- max_decel_mps2: 3.3336 +- max_yaw_rate_radps: 11.4109 +- max_frame_displacement_m: 2.8444 +- artifact_warnings: ['high_yaw_rate', 'offroad_before_collision'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 10 | collided_agent | True | 51 | | 3.3421 | 1.4774 | +| 21 | risk_agent | True | 17 | | 3.3263 | 1.48 | +| 1 | offroad_agent | True | 41 | | 3.0951 | 1.3584 | +| 3 | risk_agent | True | 55 | | 4.1701 | 1.7598 | +| 8 | risk_agent | True | 21 | | 3.1431 | 1.4302 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -5.0 | 3.9416 | | | | -3.7511 | 1.2106 | | | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0705_line000086.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0705_line000086.md new file mode 100644 index 0000000000000000000000000000000000000000..576e8b66e38084cc2e1d00aaf97ae4cf35becb59 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0705_line000086.md @@ -0,0 +1,55 @@ +# shard000_batch0000_world0705_line000086 + +## Outcome +- collision: True +- near_miss: False +- offroad: True +- event_time_s: 2.0 +- raw_min_distance_m: 4.1113 +- clean_min_distance_m: 4.1113 + +## Primary Pair +- agent_a: 1 (risk_agent) +- agent_b: 2 (collided_agent,offroad_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: False +- semantic_candidate: same_direction_side_conflict_or_cut_in + +## Primary Interaction Metrics +- num_clean_pair_frames: 20 +- min_ttc_s: 3.9012 +- time_ttc_below_1s: 0.0 +- time_ttc_below_2s: 0.0 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 1.0, 'distance_start_m': 6.3372, 'distance_end_m': 4.1113} + +## Naturalness Checks +- max_speed_mps: 27.3832 +- max_accel_mps2: 36.4632 +- max_decel_mps2: 12.1585 +- max_yaw_rate_radps: 8.057 +- max_frame_displacement_m: 2.7383 +- artifact_warnings: ['hard_deceleration', 'high_acceleration', 'high_yaw_rate', 'offroad_before_collision'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 1 | risk_agent | True | 20 | 10.8605 | 3.4089 | 1.6133 | +| 2 | collided_agent,offroad_agent | True | 30 | 9.4229 | 3.1071 | 1.3957 | +| 3 | offroad_agent | True | 31 | 24.5822 | 3.0761 | 1.3745 | +| 10 | risk_agent | True | 17 | | 3.1614 | 1.4325 | +| 20 | context_agent | True | 24 | 11.0171 | 3.1683 | 1.4796 | +| 24 | risk_agent,offroad_agent | True | 23 | 12.0533 | 3.2004 | 1.4626 | +| 26 | context_agent | True | 31 | 23.5805 | 2.9748 | 1.3212 | +| 28 | context_agent | False | 31 | 2.8449 | 3.3279 | 1.4983 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -2.0 | 6.3372 | | | | 3.3083 | 5.4051 | | | +| -1.0 | 5.1227 | 4.358 | 1.1755 | 1.4418 | 3.2431 | 3.9654 | 7.2609 | 5.8232 | +| -0.5 | 4.5474 | 4.0663 | 1.1183 | 1.4438 | 3.1853 | 3.2453 | 9.2603 | 7.8236 | +| -0.1 | 4.1113 | 3.9012 | 1.0538 | 1.4475 | 3.1276 | 2.6685 | 10.8605 | 9.4229 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0715_line000087.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0715_line000087.md new file mode 100644 index 0000000000000000000000000000000000000000..c57ab16edbb527afd30ccd3d3d976cbf2eeae06e --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0715_line000087.md @@ -0,0 +1,48 @@ +# shard000_batch0000_world0715_line000087 + +## Outcome +- collision: True +- near_miss: False +- offroad: True +- event_time_s: 5.6 +- raw_min_distance_m: 3.7075 +- clean_min_distance_m: 3.8571 + +## Primary Pair +- agent_a: 21 (risk_agent) +- agent_b: 10 (collided_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: False +- semantic_candidate: generic_close_interaction + +## Primary Interaction Metrics +- num_clean_pair_frames: 17 +- min_ttc_s: None +- time_ttc_below_1s: 0.0 +- time_ttc_below_2s: 0.0 +- distance_trend_before_event: {'label': 'insufficient_clean_frames', 'decreasing_ratio': None} + +## Naturalness Checks +- max_speed_mps: 26.7082 +- max_accel_mps2: 4.013 +- max_decel_mps2: 3.9944 +- max_yaw_rate_radps: 11.1879 +- max_frame_displacement_m: 2.6708 +- artifact_warnings: ['high_yaw_rate', 'offroad_before_collision'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 10 | collided_agent | True | 53 | | 3.3421 | 1.4774 | +| 21 | risk_agent | True | 17 | | 3.3263 | 1.48 | +| 1 | risk_agent,offroad_agent | True | 41 | | 3.0951 | 1.3584 | +| 8 | risk_agent | True | 22 | | 3.1431 | 1.4302 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -5.0 | 3.8571 | | | | -3.7412 | 0.9383 | | | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0722_line000088.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0722_line000088.md new file mode 100644 index 0000000000000000000000000000000000000000..850fc122bebd52b6fd140675dfe30b58ce73b295 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0722_line000088.md @@ -0,0 +1,55 @@ +# shard000_batch0000_world0722_line000088 + +## Outcome +- collision: True +- near_miss: True +- offroad: False +- event_time_s: 2.6 +- raw_min_distance_m: 1.8224 +- clean_min_distance_m: 1.8224 + +## Primary Pair +- agent_a: 1 (risk_agent,collided_agent) +- agent_b: 3 (risk_agent,collided_agent) +- selection_reason: risk_agent_directly_collided_with_partner +- confidence: high +- risk_directly_involved: True +- semantic_candidate: crossing_or_intersection_conflict + +## Primary Interaction Metrics +- num_clean_pair_frames: 31 +- min_ttc_s: 0.226 +- time_ttc_below_1s: 0.9 +- time_ttc_below_2s: 1.7 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 1.0, 'distance_start_m': 36.5778, 'distance_end_m': 1.8224} + +## Naturalness Checks +- max_speed_mps: 15.0218 +- max_accel_mps2: 4.0124 +- max_decel_mps2: 3.3421 +- max_yaw_rate_radps: 4.812 +- max_frame_displacement_m: 1.5022 +- artifact_warnings: ['high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 1 | risk_agent,collided_agent | True | 37 | 12.2308 | 3.5775 | 1.6599 | +| 3 | risk_agent,collided_agent | True | 31 | 6.77 | 3.3318 | 1.4528 | +| 0 | context_agent | True | 31 | 10.8119 | 3.7002 | 1.6324 | +| 2 | risk_agent | True | 37 | 6.84 | 3.9211 | 1.638 | +| 4 | context_agent | True | 28 | 14.756 | 3.4472 | 1.5266 | +| 5 | context_agent | True | 37 | 8.4631 | 4.022 | 1.7005 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -2.6 | 36.5778 | | | | 8.6171 | 35.5483 | | | +| -2.0 | 30.9737 | 2.7468 | 11.2763 | 11.2787 | 4.6001 | 30.6302 | 8.3637 | 3.1702 | +| -1.0 | 17.4825 | 1.1732 | 14.9012 | 15.2138 | 1.6949 | 17.4002 | 11.3642 | 5.5028 | +| -0.5 | 9.344 | 0.5415 | 17.2545 | 17.7005 | 0.1049 | 9.3434 | 11.4308 | 6.3698 | +| 0.0 | 1.8224 | 0.3147 | 5.7899 | 18.9512 | 1.7801 | 0.3902 | 12.2308 | 6.77 | +| 0.4 | 6.2449 | | -15.6213 | 19.5046 | 3.5922 | -5.1084 | 12.2977 | 7.4368 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0731_line000089.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0731_line000089.md new file mode 100644 index 0000000000000000000000000000000000000000..6de5ef770d36d075a1f65d8104ec083734f1638c --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0731_line000089.md @@ -0,0 +1,56 @@ +# shard000_batch0000_world0731_line000089 + +## Outcome +- collision: True +- near_miss: True +- offroad: False +- event_time_s: 2.9 +- raw_min_distance_m: 1.5502 +- clean_min_distance_m: 1.5502 + +## Primary Pair +- agent_a: 49 (risk_agent,collided_agent) +- agent_b: 2 (context_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: True +- semantic_candidate: same_direction_side_conflict_or_cut_in + +## Primary Interaction Metrics +- num_clean_pair_frames: 38 +- min_ttc_s: 0.1117 +- time_ttc_below_1s: 1.0 +- time_ttc_below_2s: 1.6 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 1.0, 'distance_start_m': 23.9614, 'distance_end_m': 3.3842} + +## Naturalness Checks +- max_speed_mps: 19.0251 +- max_accel_mps2: 50.6672 +- max_decel_mps2: 7.6549 +- max_yaw_rate_radps: 6.473 +- max_frame_displacement_m: 1.9025 +- artifact_warnings: ['high_acceleration', 'high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 2 | context_agent | False | 40 | 3.6117 | 3.2313 | 1.4345 | +| 49 | risk_agent,collided_agent | True | 38 | 16.2251 | 4.1146 | 1.6678 | +| 1 | risk_agent | True | 40 | 9.5478 | 3.0229 | 1.3581 | +| 3 | context_agent | False | 40 | 3.7098 | 2.9756 | 1.3414 | +| 4 | risk_agent | True | 38 | 7.8348 | 3.2407 | 1.4824 | +| 5 | context_agent | False | 40 | 4.5489 | 3.0031 | 1.3856 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -2.9 | 23.9614 | | | | -0.4837 | 23.9565 | | | +| -2.0 | 21.3515 | 5.2448 | 4.071 | 4.071 | -0.4409 | 21.347 | 8.8924 | 4.8236 | +| -1.0 | 14.525 | 1.6304 | 8.9087 | 8.9139 | -0.4135 | 14.5191 | 12.8919 | 3.9785 | +| -0.5 | 9.4132 | 0.8597 | 10.949 | 10.9633 | -0.4205 | 9.4038 | 14.8917 | 3.9292 | +| 0.0 | 3.3842 | 0.2704 | 12.5156 | 12.6134 | -0.4238 | 3.3575 | 16.2251 | 3.6117 | +| 0.5 | 2.9373 | | -14.6175 | 15.3704 | -2.0589 | -2.0949 | 17.8256 | 3.4423 | +| 0.8 | 2.8446 | | -0.0837 | 18.6176 | -1.2923 | -2.5341 | 19.0251 | 3.252 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0734_line000090.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0734_line000090.md new file mode 100644 index 0000000000000000000000000000000000000000..64b1c5b50b3f7b563ae28ed9c343c84b8d224061 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0734_line000090.md @@ -0,0 +1,54 @@ +# shard000_batch0000_world0734_line000090 + +## Outcome +- collision: True +- near_miss: True +- offroad: False +- event_time_s: 3.5 +- raw_min_distance_m: 1.6366 +- clean_min_distance_m: 1.6367 + +## Primary Pair +- agent_a: 3 (risk_agent,collided_agent) +- agent_b: 1 (context_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: True +- semantic_candidate: rear_end_or_following_conflict + +## Primary Interaction Metrics +- num_clean_pair_frames: 41 +- min_ttc_s: 0.1731 +- time_ttc_below_1s: 0.6 +- time_ttc_below_2s: 2.5 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 1.0, 'distance_start_m': 33.3852, 'distance_end_m': 2.6374} + +## Naturalness Checks +- max_speed_mps: 14.1981 +- max_accel_mps2: 25.2573 +- max_decel_mps2: 4.482 +- max_yaw_rate_radps: 5.656 +- max_frame_displacement_m: 1.4198 +- artifact_warnings: ['high_acceleration', 'high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 1 | context_agent | False | 46 | 2.031 | 2.9076 | 1.3825 | +| 3 | risk_agent,collided_agent | True | 41 | 12.3995 | 3.5141 | 1.4959 | +| 2 | risk_agent | True | 14 | | 3.1625 | 1.44 | +| 11 | risk_agent | True | 20 | | 3.1104 | 1.4149 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -3.5 | 39.3737 | | | | -1.728 | 39.3358 | | | +| -3.0 | 33.3852 | 2.6396 | 12.6477 | 12.8438 | -2.2206 | 33.3113 | 10.3319 | 2.8056 | +| -2.0 | 20.6864 | 1.7198 | 12.0283 | 12.9499 | -4.208 | 20.2539 | 10.9995 | 2.8544 | +| -1.0 | 10.2999 | 1.1741 | 8.7729 | 11.2544 | -4.586 | 9.2226 | 10.3996 | 2.4796 | +| -0.5 | 6.7952 | 1.5896 | 4.2747 | 11.5933 | -5.7521 | 3.6177 | 11.1991 | 2.1273 | +| 0.0 | 2.6374 | 0.2715 | 9.7147 | 10.7707 | -1.2311 | 2.3324 | 12.3995 | 2.031 | +| 0.5 | 2.2455 | 0.3252 | 6.9055 | 12.4412 | -2.0013 | -1.0184 | 14.1981 | 1.8998 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0736_line000091.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0736_line000091.md new file mode 100644 index 0000000000000000000000000000000000000000..1de99ca29059af871308a2461b344d7f54cc719e --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0736_line000091.md @@ -0,0 +1,48 @@ +# shard000_batch0000_world0736_line000091 + +## Outcome +- collision: True +- near_miss: True +- offroad: True +- event_time_s: 4.6 +- raw_min_distance_m: 0.8802 +- clean_min_distance_m: 4.3556 + +## Primary Pair +- agent_a: 15 (risk_agent,collided_agent,offroad_agent) +- agent_b: 22 (context_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: True +- semantic_candidate: same_direction_side_conflict_or_cut_in + +## Primary Interaction Metrics +- num_clean_pair_frames: 2 +- min_ttc_s: 20.885 +- time_ttc_below_1s: 0.0 +- time_ttc_below_2s: 0.0 +- distance_trend_before_event: {'label': 'insufficient_clean_frames', 'decreasing_ratio': None} + +## Naturalness Checks +- max_speed_mps: 7.1045 +- max_accel_mps2: 4.0104 +- max_decel_mps2: 3.3288 +- max_yaw_rate_radps: 2.982 +- max_frame_displacement_m: 0.7104 +- artifact_warnings: ['high_yaw_rate', 'short_clean_track'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 15 | risk_agent,collided_agent,offroad_agent | True | 57 | | 3.4011 | 1.5187 | +| 22 | context_agent | False | 2 | | 3.1471 | 1.3942 | +| 0 | risk_agent | True | 17 | | 3.7002 | 1.6324 | +| 2 | risk_agent | True | 19 | | 3.255 | 1.4657 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -4.6 | 4.3771 | | | | 2.3628 | 3.6845 | | | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0737_line000092.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0737_line000092.md new file mode 100644 index 0000000000000000000000000000000000000000..62c6a1c5acfe890b752e6572c808fd205000b20e --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0737_line000092.md @@ -0,0 +1,58 @@ +# shard000_batch0000_world0737_line000092 + +## Outcome +- collision: True +- near_miss: False +- offroad: False +- event_time_s: 2.4 +- raw_min_distance_m: 3.1392 +- clean_min_distance_m: 3.1392 + +## Primary Pair +- agent_a: 17 (risk_agent) +- agent_b: 51 (collided_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: False +- semantic_candidate: crossing_or_intersection_conflict + +## Primary Interaction Metrics +- num_clean_pair_frames: 35 +- min_ttc_s: 0.3235 +- time_ttc_below_1s: 0.5 +- time_ttc_below_2s: 0.6 +- distance_trend_before_event: {'label': 'not_decreasing', 'decreasing_ratio': 0.25, 'distance_start_m': 11.9581, 'distance_end_m': 47.4059} + +## Naturalness Checks +- max_speed_mps: 20.2675 +- max_accel_mps2: 21.7274 +- max_decel_mps2: 3.9954 +- max_yaw_rate_radps: 7.1619 +- max_frame_displacement_m: 2.0267 +- artifact_warnings: ['high_acceleration', 'high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 17 | risk_agent | True | 35 | 16.4293 | 3.915 | 1.7133 | +| 51 | collided_agent | True | 35 | 16.6676 | 3.3625 | 1.5195 | +| 0 | collided_agent | True | 35 | 14.9271 | 3.7002 | 1.6324 | +| 2 | context_agent | False | 35 | 3.5459 | 3.258 | 1.5304 | +| 3 | context_agent | False | 35 | 2.7821 | 3.3165 | 1.5512 | +| 4 | context_agent | False | 35 | 1.6068 | 3.4021 | 1.5194 | +| 5 | risk_agent | True | 25 | 9.7029 | 3.4887 | 1.4979 | +| 44 | risk_agent | True | 21 | | 3.2529 | 1.4419 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -2.4 | 11.9581 | | | | 3.9136 | 11.2996 | | | +| -2.0 | 5.447 | 0.3524 | 15.4575 | 18.8028 | 3.353 | 4.2927 | 8.6951 | 10.1344 | +| -1.0 | 18.9779 | | -26.4323 | 26.7909 | 2.0324 | -18.8688 | 12.6952 | 14.1345 | +| -0.5 | 33.4551 | | -30.5887 | 30.7195 | 1.1996 | -33.4336 | 14.6292 | 16.1349 | +| 0.0 | 47.4059 | | -26.1938 | 29.3436 | 0.8134 | -47.3989 | 16.4293 | 16.6676 | +| 0.5 | 63.27 | | -35.8499 | 35.9464 | 0.2686 | -63.2695 | 17.7623 | 18.2677 | +| 1.0 | 82.3345 | | -39.608 | 39.6712 | -1.0838 | -82.3273 | 19.4964 | 20.2675 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0746_line000093.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0746_line000093.md new file mode 100644 index 0000000000000000000000000000000000000000..008ab5dacda268c4bb5f8238ee0bcc544652601f --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0746_line000093.md @@ -0,0 +1,57 @@ +# shard000_batch0000_world0746_line000093 + +## Outcome +- collision: True +- near_miss: True +- offroad: True +- event_time_s: 3.4 +- raw_min_distance_m: 1.3662 +- clean_min_distance_m: 2.9163 + +## Primary Pair +- agent_a: 1 (risk_agent,collided_agent,offroad_agent) +- agent_b: 4 (collided_agent) +- selection_reason: risk_agent_directly_collided_with_partner +- confidence: high +- risk_directly_involved: True +- semantic_candidate: rear_end_or_following_conflict + +## Primary Interaction Metrics +- num_clean_pair_frames: 45 +- min_ttc_s: 0.2328 +- time_ttc_below_1s: 0.9 +- time_ttc_below_2s: 1.9 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 1.0, 'distance_start_m': 38.2583, 'distance_end_m': 9.9981} + +## Naturalness Checks +- max_speed_mps: 15.5577 +- max_accel_mps2: 4.0121 +- max_decel_mps2: 4.0093 +- max_yaw_rate_radps: 7.609 +- max_frame_displacement_m: 1.5558 +- artifact_warnings: ['high_yaw_rate', 'offroad_before_collision'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 1 | risk_agent,collided_agent,offroad_agent | True | 45 | 12.9479 | 2.8034 | 1.2938 | +| 4 | collided_agent | True | 45 | 0.0858 | 3.907 | 1.5648 | +| 2 | risk_agent | True | 45 | 0.0247 | 3.7153 | 1.5327 | +| 3 | offroad_agent | True | 45 | 10.6746 | 3.1187 | 1.3833 | +| 5 | risk_agent,offroad_agent | True | 45 | 11.6484 | 3.1246 | 1.4035 | +| 6 | collided_agent,offroad_agent | True | 45 | 13.7563 | 2.8624 | 1.2622 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -3.4 | 38.8322 | | | | 1.3549 | 38.8086 | | | +| -3.0 | 38.2583 | 17.8786 | 2.1399 | 2.6144 | 1.2218 | 38.2388 | 6.7485 | 4.9153 | +| -2.0 | 32.555 | 3.8708 | 8.4103 | 8.9226 | -4.2364 | 32.2782 | 10.548 | 1.7811 | +| -1.0 | 22.527 | 1.8275 | 12.3266 | 12.3313 | -0.8791 | 22.5099 | 12.2148 | 0.1809 | +| -0.5 | 16.3289 | 1.3294 | 12.2829 | 12.3865 | -0.4675 | 16.3222 | 12.5486 | 0.1806 | +| 0.0 | 9.9981 | 0.7775 | 12.8596 | 12.8631 | -0.2257 | 9.9955 | 12.9479 | 0.0858 | +| 0.5 | 5.3917 | | -4.3464 | 12.8414 | -4.5448 | 2.9008 | 12.8802 | 0.1808 | +| 1.0 | 2.9163 | 0.2328 | 12.5261 | 13.6071 | -1.3745 | 2.5721 | 13.7483 | 0.1811 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0777_line000094.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0777_line000094.md new file mode 100644 index 0000000000000000000000000000000000000000..bfbaa440e7a88b7d06e19acbd2d2ac94e1b52440 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0777_line000094.md @@ -0,0 +1,52 @@ +# shard000_batch0000_world0777_line000094 + +## Outcome +- collision: True +- near_miss: False +- offroad: False +- event_time_s: 5.3 +- raw_min_distance_m: 3.5397 +- clean_min_distance_m: 3.5397 + +## Primary Pair +- agent_a: 6 (risk_agent) +- agent_b: 4 (context_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: False +- semantic_candidate: generic_close_interaction + +## Primary Interaction Metrics +- num_clean_pair_frames: 36 +- min_ttc_s: 0.0001 +- time_ttc_below_1s: 0.4 +- time_ttc_below_2s: 0.9 +- distance_trend_before_event: {'label': 'not_decreasing', 'decreasing_ratio': 0.0, 'distance_start_m': 6.0944, 'distance_end_m': 28.956} + +## Naturalness Checks +- max_speed_mps: 29.2584 +- max_accel_mps2: 112.1567 +- max_decel_mps2: 92.7575 +- max_yaw_rate_radps: 5.717 +- max_frame_displacement_m: 2.9258 +- artifact_warnings: ['hard_deceleration', 'high_acceleration', 'high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 4 | context_agent | False | 53 | | 6.9649 | 2.1591 | +| 6 | risk_agent | True | 38 | | 3.9227 | 1.663 | +| 3 | risk_agent | True | 41 | | 3.3505 | 1.4692 | +| 15 | collided_agent | True | 51 | | 4.1996 | 1.7104 | +| 30 | risk_agent | True | 26 | | 3.2955 | 1.4472 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -5.0 | 10.9149 | | | | 3.543 | 10.3239 | | | +| -3.0 | 6.0944 | | -8.529 | 10.3954 | 3.4298 | -5.0377 | 24.9929 | 14.5985 | +| -2.0 | 18.6865 | | -7.6798 | 7.9068 | 3.2409 | -18.4033 | 28.3258 | 20.4427 | +| -1.3 | 28.956 | | -14.3459 | 14.4896 | 2.9578 | -28.8046 | 29.2584 | 14.7902 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0784_line000095.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0784_line000095.md new file mode 100644 index 0000000000000000000000000000000000000000..6438a8b7d68281832449194826c3de2c4dcfc0a6 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0784_line000095.md @@ -0,0 +1,53 @@ +# shard000_batch0000_world0784_line000095 + +## Outcome +- collision: True +- near_miss: False +- offroad: False +- event_time_s: 3.9 +- raw_min_distance_m: 2.4279 +- clean_min_distance_m: 2.4279 + +## Primary Pair +- agent_a: 25 (risk_agent) +- agent_b: 3 (context_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: False +- semantic_candidate: same_direction_side_conflict_or_cut_in + +## Primary Interaction Metrics +- num_clean_pair_frames: 35 +- min_ttc_s: 0.7771 +- time_ttc_below_1s: 0.6 +- time_ttc_below_2s: 1.8 +- distance_trend_before_event: {'label': 'mixed', 'decreasing_ratio': 0.56, 'distance_start_m': 8.7662, 'distance_end_m': 7.2618} + +## Naturalness Checks +- max_speed_mps: 22.0602 +- max_accel_mps2: 4.017 +- max_decel_mps2: 0.6705 +- max_yaw_rate_radps: 6.621 +- max_frame_displacement_m: 2.206 +- artifact_warnings: ['high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 3 | context_agent | True | 35 | | 2.9727 | 1.3511 | +| 25 | risk_agent | True | 45 | | 3.1573 | 1.4304 | +| 0 | risk_agent | True | 35 | | 3.7002 | 1.6324 | +| 7 | risk_agent | True | 32 | | 3.1848 | 1.4327 | +| 22 | collided_agent | True | 40 | | 3.3055 | 1.4872 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -3.9 | 14.2259 | | | | -3.1023 | 13.8835 | | | +| -3.0 | 8.7662 | 1.4667 | 5.9766 | 6.2622 | -2.9516 | 8.2543 | 10.9943 | 4.7439 | +| -2.0 | 3.262 | 0.7771 | 4.1977 | 6.2804 | -2.5813 | 1.9943 | 14.9942 | 8.7435 | +| -1.0 | 4.7153 | | -5.3896 | 6.3044 | -1.9978 | -4.2712 | 18.9932 | 12.7436 | +| -0.5 | 7.2618 | | -5.1475 | 5.3972 | -1.1385 | -7.172 | 20.0609 | 14.7441 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0801_line000096.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0801_line000096.md new file mode 100644 index 0000000000000000000000000000000000000000..4096e2debd75c795a814faa6e9b18cb204bfdea8 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0801_line000096.md @@ -0,0 +1,57 @@ +# shard000_batch0000_world0801_line000096 + +## Outcome +- collision: True +- near_miss: True +- offroad: False +- event_time_s: 3.5 +- raw_min_distance_m: 1.7533 +- clean_min_distance_m: 1.7534 + +## Primary Pair +- agent_a: 6 (risk_agent,collided_agent) +- agent_b: 7 (collided_agent) +- selection_reason: risk_agent_directly_collided_with_partner +- confidence: high +- risk_directly_involved: True +- semantic_candidate: same_direction_side_conflict_or_cut_in + +## Primary Interaction Metrics +- num_clean_pair_frames: 40 +- min_ttc_s: 0.3018 +- time_ttc_below_1s: 0.2 +- time_ttc_below_2s: 0.2 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 0.9667, 'distance_start_m': 5.5085, 'distance_end_m': 1.7534} + +## Naturalness Checks +- max_speed_mps: 29.551 +- max_accel_mps2: 98.9884 +- max_decel_mps2: 10.6904 +- max_yaw_rate_radps: 7.909 +- max_frame_displacement_m: 2.9551 +- artifact_warnings: ['high_acceleration', 'high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 6 | risk_agent,collided_agent | True | 40 | 13.939 | 3.2788 | 1.457 | +| 7 | collided_agent | True | 46 | 17.3921 | 3.1027 | 1.3986 | +| 0 | context_agent | True | 46 | 15.042 | 3.7002 | 1.6324 | +| 1 | risk_agent | True | 31 | | 4.9893 | 1.937 | +| 2 | risk_agent | True | 46 | 13.6547 | 3.2876 | 1.4304 | +| 17 | context_agent | True | 38 | 28.817 | 3.1498 | 1.3824 | +| 19 | context_agent | False | 46 | 7.4348 | 3.3404 | 1.5478 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -3.5 | 5.8773 | | | | -3.6469 | -4.609 | | | +| -3.0 | 5.5085 | 7.5677 | 0.7279 | 0.9203 | -3.6241 | -4.1485 | 4.6061 | 5.525 | +| -2.0 | 4.7959 | 6.9374 | 0.6913 | 0.9246 | -3.5477 | -3.2272 | 8.6052 | 9.5251 | +| -1.0 | 4.1268 | 6.4493 | 0.6399 | 0.9323 | -3.4235 | -2.3045 | 12.6054 | 13.5261 | +| -0.5 | 3.8117 | 6.0742 | 0.6275 | 1.0009 | -3.3397 | -1.8373 | 14.4725 | 15.4591 | +| 0.0 | 1.7534 | 0.3018 | 5.8106 | 22.4541 | -0.9196 | 1.4929 | 13.939 | 17.3921 | +| 0.4 | 3.6926 | | -0.5293 | 2.9261 | -3.6683 | 0.423 | 14.0717 | 16.9924 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0821_line000097.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0821_line000097.md new file mode 100644 index 0000000000000000000000000000000000000000..a5aa3d6b987443dbd3c635333469e872514e7231 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0821_line000097.md @@ -0,0 +1,52 @@ +# shard000_batch0000_world0821_line000097 + +## Outcome +- collision: True +- near_miss: False +- offroad: True +- event_time_s: 1.5 +- raw_min_distance_m: 3.9322 +- clean_min_distance_m: 3.8291 + +## Primary Pair +- agent_a: 18 (risk_agent) +- agent_b: 19 (context_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: False +- semantic_candidate: same_direction_side_conflict_or_cut_in + +## Primary Interaction Metrics +- num_clean_pair_frames: 12 +- min_ttc_s: None +- time_ttc_below_1s: 0.0 +- time_ttc_below_2s: 0.0 +- distance_trend_before_event: {'label': 'not_decreasing', 'decreasing_ratio': 0.0, 'distance_start_m': 3.8291, 'distance_end_m': 5.4198} + +## Naturalness Checks +- max_speed_mps: 29.9932 +- max_accel_mps2: 4.0144 +- max_decel_mps2: 3.9999 +- max_yaw_rate_radps: 9.89 +- max_frame_displacement_m: 2.9993 +- artifact_warnings: ['high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 18 | risk_agent | True | 26 | | 3.7843 | 1.593 | +| 19 | context_agent | True | 12 | | 3.1354 | 1.3912 | +| 0 | risk_agent | True | 26 | | 3.7002 | 1.6324 | +| 3 | collided_agent,offroad_agent | True | 26 | | 3.0439 | 1.3866 | +| 6 | collided_agent | True | 26 | | 3.5927 | 1.5298 | +| 14 | risk_agent | True | 26 | | 3.247 | 1.4139 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -1.5 | 3.8291 | | | | -3.4897 | 1.5759 | | | +| -1.0 | 4.4425 | | -1.4427 | 2.3041 | -3.5049 | 2.7297 | 9.3396 | 11.6435 | +| -0.5 | 5.244 | | -1.7363 | 2.3054 | -3.5239 | 3.8835 | 11.3399 | 13.6449 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0829_line000098.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0829_line000098.md new file mode 100644 index 0000000000000000000000000000000000000000..053b3d36c1a478a31589c0aa0b89c583f2fc2e2a --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0829_line000098.md @@ -0,0 +1,58 @@ +# shard000_batch0000_world0829_line000098 + +## Outcome +- collision: True +- near_miss: False +- offroad: False +- event_time_s: 2.2 +- raw_min_distance_m: 4.4003 +- clean_min_distance_m: 4.4003 + +## Primary Pair +- agent_a: 3 (risk_agent) +- agent_b: 21 (context_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: False +- semantic_candidate: generic_close_interaction + +## Primary Interaction Metrics +- num_clean_pair_frames: 33 +- min_ttc_s: 1.1532 +- time_ttc_below_1s: 0.0 +- time_ttc_below_2s: 0.4 +- distance_trend_before_event: {'label': 'not_decreasing', 'decreasing_ratio': 0.3182, 'distance_start_m': 6.6088, 'distance_end_m': 10.4623} + +## Naturalness Checks +- max_speed_mps: 39.2239 +- max_accel_mps2: 4.0166 +- max_decel_mps2: 2.6714 +- max_yaw_rate_radps: 5.703 +- max_frame_displacement_m: 3.9224 +- artifact_warnings: ['high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 3 | risk_agent | True | 33 | 20.07 | 3.0713 | 1.4136 | +| 21 | context_agent | True | 33 | 25.0965 | 3.2689 | 1.4509 | +| 2 | collided_agent | True | 33 | 16.2167 | 3.1 | 1.3962 | +| 5 | risk_agent | True | 33 | 12.1662 | 3.2669 | 1.5058 | +| 9 | context_agent | True | 33 | 16.8472 | 3.8319 | 1.631 | +| 14 | context_agent | True | 33 | 18.1771 | 3.0418 | 1.3589 | +| 22 | collided_agent | True | 33 | 12.6736 | 3.1442 | 1.4357 | +| 24 | risk_agent | True | 21 | | 3.4509 | 1.471 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -2.2 | 6.6088 | | | | 4.3266 | -4.9957 | | | +| -2.0 | 5.5663 | 1.1985 | 4.6443 | 7.5602 | 4.3456 | -3.4784 | 12.4031 | 19.9628 | +| -1.0 | 5.7357 | | -4.2208 | 6.5601 | 4.4475 | 3.6218 | 16.0701 | 22.6296 | +| -0.5 | 8.071 | | -4.9489 | 5.8941 | 4.503 | 6.6981 | 18.07 | 23.9635 | +| 0.0 | 10.4623 | | -4.5768 | 5.0277 | 4.5649 | 9.4139 | 20.07 | 25.0965 | +| 0.5 | 12.4454 | | -3.6159 | 3.8303 | 4.6544 | 11.5423 | 21.937 | 25.7637 | +| 1.0 | 14.1068 | | -3.0456 | 3.1661 | 4.7506 | 13.2828 | 23.27 | 26.4299 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0836_line000099.md b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0836_line000099.md new file mode 100644 index 0000000000000000000000000000000000000000..1f0d459c73e0c62823136c6b80334c3138fe767d --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/case_cards/shard000_batch0000_world0836_line000099.md @@ -0,0 +1,57 @@ +# shard000_batch0000_world0836_line000099 + +## Outcome +- collision: True +- near_miss: True +- offroad: True +- event_time_s: 5.6 +- raw_min_distance_m: 1.5252 +- clean_min_distance_m: 1.5252 + +## Primary Pair +- agent_a: 4 (risk_agent,collided_agent,offroad_agent) +- agent_b: 0 (collided_agent) +- selection_reason: risk_agent_directly_collided_with_partner +- confidence: high +- risk_directly_involved: True +- semantic_candidate: rear_end_or_following_conflict + +## Primary Interaction Metrics +- num_clean_pair_frames: 58 +- min_ttc_s: 0.0701 +- time_ttc_below_1s: 0.2 +- time_ttc_below_2s: 1.7 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 0.8667, 'distance_start_m': 6.5568, 'distance_end_m': 2.8682} + +## Naturalness Checks +- max_speed_mps: 29.342 +- max_accel_mps2: 3.9948 +- max_decel_mps2: 3.3438 +- max_yaw_rate_radps: 12.08 +- max_frame_displacement_m: 2.9342 +- artifact_warnings: ['high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 0 | collided_agent | True | 58 | 25.4757 | 3.7002 | 1.6324 | +| 4 | risk_agent,collided_agent,offroad_agent | True | 61 | 27.1031 | 3.325 | 1.4663 | +| 1 | risk_agent | True | 61 | 28.008 | 3.3262 | 1.4671 | +| 3 | risk_agent | True | 14 | | 3.1834 | 1.3923 | +| 5 | context_agent | True | 61 | 26.3311 | 3.3856 | 1.4544 | +| 9 | context_agent | True | 60 | 27.5489 | 3.3395 | 1.5086 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -5.0 | 14.934 | | | | 3.5736 | 14.5002 | | | +| -3.0 | 6.5568 | 1.7778 | 3.6882 | 4.3606 | 3.5413 | 5.5183 | 25.8379 | 21.4774 | +| -2.0 | 4.6141 | | -3.6787 | 11.3201 | 1.8816 | 4.2131 | 26.5037 | 22.8103 | +| -1.0 | 3.7381 | 3.246 | 1.1516 | 1.8432 | 3.1883 | 1.9515 | 25.9708 | 24.1432 | +| -0.5 | 3.24 | 3.9752 | 0.8151 | 1.8447 | 3.0683 | 1.0408 | 26.6369 | 24.8091 | +| 0.0 | 2.8682 | | -23.1497 | 37.3695 | -2.5015 | 1.4033 | 27.1031 | 25.4757 | +| 0.5 | 5.9959 | 0.9255 | 6.4789 | 8.6786 | 2.9417 | 5.2247 | 27.1696 | 26.0093 | +| 0.7 | 5.636 | 4.1594 | 1.355 | 1.3694 | 4.1018 | 3.8651 | 27.0364 | 26.2754 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/llm_cases.jsonl b/gpudrive-main/search_outputs/llm_cases_shard_000/llm_cases.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..3b6b8abda959a2380a2e7d0507634e1a78f87937 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/llm_cases.jsonl @@ -0,0 +1,1247 @@ +{"case_id": "shard000_batch0000_world0002_line000000", "schema_version": "llm_longtail_case_v1", "source": {"event_trace_line_index": 0, "scenario_file": "tfrecord-00005-of-01000_436.json", "shard_id": 0, "batch_idx": 0, "world_idx": 2}, "outcome": {"collision": true, "near_miss": true, "offroad": true, "first_collision_time_s": 2.9, "first_offroad_time_s": 3.0, "min_distance_time_s": 2.9, "event_time_s": 2.9, "raw_min_distance_m": 1.9574, "clean_min_distance_m": 1.9575}, "primary_pair": {"agent_a": 5, "agent_b": 7, "agent_a_role": ["risk_agent", "collided_agent"], "agent_b_role": ["collided_agent", "offroad_agent"], "selection_reason": "risk_agent_directly_collided_with_partner", "confidence": "high", "risk_directly_involved": true, "semantic_candidate": "same_direction_side_conflict_or_cut_in"}, "scene_summary": {"controlled_agents": 13, "risk_agents": [0, 4, 5], "collided_agents": [5, 7], "offroad_agents": [0, 7], "goal_agents": 13, "context_agents": [5, 7, 0, 2, 3, 4, 6, 17]}, "primary_interaction_metrics": {"num_clean_pair_frames": 34, "min_clean_distance_m": 1.9575, "min_clean_distance_time_to_event_s": 0.0, "min_ttc_s": 0.4335, "time_ttc_below_1s": 1.2, "time_ttc_below_2s": 2.3, "distance_trend_before_event": {"label": "mostly_decreasing", "decreasing_ratio": 0.9655, "distance_start_m": 29.5168, "distance_end_m": 1.9575}, "metrics_at_event_or_nearest_clean_frame": {"pair_valid": true, "distance_m": 1.9575, "ttc_s": 0.4496, "closing_speed_mps": 4.3538, "relative_speed_mps": 7.2043, "longitudinal_gap_m": 1.2124, "lateral_offset_m": -1.5368, "relative_heading_rad": 0.4045, "agent_a_speed_mps": 10.645, "agent_b_speed_mps": 14.2738}, "metrics_at_min_clean_distance": {"pair_valid": true, "distance_m": 1.9575, "ttc_s": 0.4496, "closing_speed_mps": 4.3538, "relative_speed_mps": 7.2043, "longitudinal_gap_m": 1.2124, "lateral_offset_m": -1.5368, "relative_heading_rad": 0.4045, "agent_a_speed_mps": 10.645, "agent_b_speed_mps": 14.2738}}, "naturalness_checks": {"max_speed_mps": 23.926, "max_accel_mps2": 4.0166, "max_decel_mps2": 4.0081, "max_yaw_rate_radps": 6.665, "max_frame_displacement_m": 2.3926, "offroad_before_collision": false, "clean_frame_counts": {"5": 34, "7": 39, "0": 40, "2": 40, "3": 40, "4": 23, "6": 40, "17": 32}, "artifact_warnings": ["high_yaw_rate"]}, "interaction_checks": {"risk_agent_in_primary_pair": true, "primary_pair_has_collision_agent": true, "primary_pair_has_offroad_agent": true, "distance_trend_label": "mostly_decreasing"}, "context_agent_summaries": [{"agent_id": 5, "roles": ["risk_agent", "collided_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.0827, "width_m": 1.391, "clean_frames": 34, "speed_at_event_mps": 10.645}, {"agent_id": 7, "roles": ["collided_agent", "offroad_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.1654, "width_m": 1.4256, "clean_frames": 39, "speed_at_event_mps": 14.2738}, {"agent_id": 0, "roles": ["risk_agent", "offroad_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.7002, "width_m": 1.6324, "clean_frames": 40, "speed_at_event_mps": 17.2471}, {"agent_id": 2, "roles": ["context_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.2432, "width_m": 1.5205, "clean_frames": 40, "speed_at_event_mps": 19.003}, {"agent_id": 3, "roles": ["context_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.2124, "width_m": 1.4202, "clean_frames": 40, "speed_at_event_mps": 16.8588}, {"agent_id": 4, "roles": ["risk_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.5435, "width_m": 1.5256, "clean_frames": 23, "speed_at_event_mps": null}, {"agent_id": 6, "roles": ["context_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.2835, "width_m": 1.4368, "clean_frames": 40, "speed_at_event_mps": 13.7644}, {"agent_id": 17, "roles": ["context_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 2.8417, "width_m": 1.3018, "clean_frames": 32, "speed_at_event_mps": 23.6592}], "map_summary": {"center_xy": [20.7116, -14.1158], "radius_m": 80.0, "num_points": 256, "type_counts": {"1": 31, "3": 70, "2": 27, "4": 1, "0": 127}}, "timeline": [{"t_to_event_s": -2.9, "frame_index": 0, "target_offset_s": -5.0, "distance_m": 29.5168, "ttc_s": null, "closing_speed_mps": null, "relative_speed_mps": null, "longitudinal_gap_m": 29.4564, "lateral_offset_m": 1.8873, "relative_heading_rad": 2.6233, "agent_a_speed_mps": null, "agent_b_speed_mps": null, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -2.0, "frame_index": 9, "target_offset_s": -2.0, "distance_m": 20.5942, "ttc_s": 1.6912, "closing_speed_mps": 12.177, "relative_speed_mps": 12.8039, "longitudinal_gap_m": 19.5775, "lateral_offset_m": -6.3909, "relative_heading_rad": 2.1197, "agent_a_speed_mps": 5.179, "agent_b_speed_mps": 10.1396, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -1.0, "frame_index": 19, "target_offset_s": -1.0, "distance_m": 7.7682, "ttc_s": 0.6847, "closing_speed_mps": 11.3453, "relative_speed_mps": 11.6026, "longitudinal_gap_m": 4.1909, "lateral_offset_m": -6.5408, "relative_heading_rad": 1.1884, "agent_a_speed_mps": 9.179, "agent_b_speed_mps": 13.3404, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, 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"scenario_type", "artifact_flags", "reason"]}} +{"case_id": "shard000_batch0009_world0980_line001245", "schema_version": "llm_longtail_case_v1", "source": {"event_trace_line_index": 1245, "scenario_file": "tfrecord-00009-of-01000_194.json", "shard_id": 0, "batch_idx": 9, "world_idx": 980}, "outcome": {"collision": true, "near_miss": false, "offroad": false, "first_collision_time_s": 3.4, "first_offroad_time_s": null, "min_distance_time_s": 3.5, "event_time_s": 3.4, "raw_min_distance_m": 2.7475, "clean_min_distance_m": 2.7475}, "primary_pair": {"agent_a": 15, "agent_b": 24, "agent_a_role": ["risk_agent", "collided_agent"], "agent_b_role": ["collided_agent"], "selection_reason": "risk_agent_directly_collided_with_partner", "confidence": "high", "risk_directly_involved": true, "semantic_candidate": "crossing_or_intersection_conflict"}, "scene_summary": {"controlled_agents": 22, "risk_agents": [7, 14, 15], "collided_agents": [15, 24], "offroad_agents": [], "goal_agents": 22, 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"agent_b_speed_mps": 30.3995}}, "naturalness_checks": {"max_speed_mps": 30.6664, "max_accel_mps2": 106.2595, "max_decel_mps2": 4.0045, "max_yaw_rate_radps": 13.537, "max_frame_displacement_m": 3.0666, "offroad_before_collision": false, "clean_frame_counts": {"15": 45, "24": 45, "7": 44, "8": 45, "10": 44, "12": 45, "14": 7, "16": 45}, "artifact_warnings": ["high_acceleration", "high_yaw_rate"]}, "interaction_checks": {"risk_agent_in_primary_pair": true, "primary_pair_has_collision_agent": true, "primary_pair_has_offroad_agent": false, "distance_trend_label": "mostly_decreasing"}, "context_agent_summaries": [{"agent_id": 15, "roles": ["risk_agent", "collided_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 4.351, "width_m": 1.9307, "clean_frames": 45, "speed_at_event_mps": 22.4185}, {"agent_id": 24, "roles": ["collided_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.1509, "width_m": 1.3928, "clean_frames": 45, "speed_at_event_mps": 30.4006}, {"agent_id": 7, "roles": ["risk_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.1468, "width_m": 1.4226, "clean_frames": 44, "speed_at_event_mps": 21.7846}, {"agent_id": 8, "roles": ["context_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.2045, "width_m": 1.4506, "clean_frames": 45, "speed_at_event_mps": 16.8209}, {"agent_id": 10, "roles": ["context_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.1943, "width_m": 1.4618, "clean_frames": 44, "speed_at_event_mps": 14.9916}, {"agent_id": 12, "roles": ["context_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.3976, "width_m": 1.5325, "clean_frames": 45, "speed_at_event_mps": 17.2611}, {"agent_id": 14, "roles": ["risk_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.183, "width_m": 1.4293, "clean_frames": 7, "speed_at_event_mps": null}, {"agent_id": 16, "roles": ["context_agent"], "is_controlled": false, "response_type": 0, "raw_agent_type": 7, "length_m": 3.1686, "width_m": 1.4508, "clean_frames": 45, "speed_at_event_mps": 12.4599}], "map_summary": {"center_xy": [11.2979, -19.313], "radius_m": 80.0, "num_points": 256, "type_counts": {"2": 20, "3": 20, "1": 11, "0": 205}}, "timeline": [{"t_to_event_s": -3.4, "frame_index": 0, "target_offset_s": -5.0, "distance_m": 25.9456, "ttc_s": null, "closing_speed_mps": null, "relative_speed_mps": null, "longitudinal_gap_m": -25.6667, "lateral_offset_m": 3.7939, "relative_heading_rad": -0.0105, "agent_a_speed_mps": null, "agent_b_speed_mps": null, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -3.0, "frame_index": 4, "target_offset_s": -3.0, "distance_m": 22.4352, "ttc_s": 2.5593, "closing_speed_mps": 8.7662, "relative_speed_mps": 8.8504, "longitudinal_gap_m": -22.1279, "lateral_offset_m": 3.7008, "relative_heading_rad": -0.0113, "agent_a_speed_mps": 12.8837, "agent_b_speed_mps": 21.732, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -2.0, "frame_index": 14, "target_offset_s": -2.0, "distance_m": 14.0734, "ttc_s": 1.7696, "closing_speed_mps": 7.9527, "relative_speed_mps": 8.1199, "longitudinal_gap_m": -13.6546, "lateral_offset_m": 3.4076, "relative_heading_rad": -0.0134, "agent_a_speed_mps": 16.8839, "agent_b_speed_mps": 24.9991, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -1.0, "frame_index": 24, "target_offset_s": -1.0, "distance_m": 6.6846, "ttc_s": 1.013, "closing_speed_mps": 6.5986, "relative_speed_mps": 7.192, "longitudinal_gap_m": -5.9628, "lateral_offset_m": 3.0215, "relative_heading_rad": -0.0154, "agent_a_speed_mps": 20.7511, "agent_b_speed_mps": 27.9334, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -0.5, "frame_index": 29, "target_offset_s": -0.5, "distance_m": 3.989, "ttc_s": 0.6254, "closing_speed_mps": 6.3783, "relative_speed_mps": 7.9962, "longitudinal_gap_m": -2.3619, "lateral_offset_m": 3.2146, "relative_heading_rad": -0.0133, "agent_a_speed_mps": 21.6841, "agent_b_speed_mps": 29.2666, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": 0.0, "frame_index": 34, "target_offset_s": 0.0, "distance_m": 3.8863, "ttc_s": null, "closing_speed_mps": -25.2767, "relative_speed_mps": 38.0062, "longitudinal_gap_m": 3.368, "lateral_offset_m": -1.9389, "relative_heading_rad": -0.7346, "agent_a_speed_mps": 22.4185, "agent_b_speed_mps": 30.4006, "agent_a_collision": true, "agent_b_collision": true, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": 0.5, "frame_index": 39, "target_offset_s": 0.5, "distance_m": 5.8299, "ttc_s": null, "closing_speed_mps": -9.0964, "relative_speed_mps": 9.7422, "longitudinal_gap_m": 2.5953, "lateral_offset_m": 5.2204, "relative_heading_rad": 0.287, "agent_a_speed_mps": 21.6841, "agent_b_speed_mps": 30.0002, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": 1.0, "frame_index": 44, "target_offset_s": 1.0, "distance_m": 13.7321, "ttc_s": null, "closing_speed_mps": -10.9178, "relative_speed_mps": 12.8147, "longitudinal_gap_m": 13.1575, "lateral_offset_m": -3.9308, "relative_heading_rad": -0.3882, "agent_a_speed_mps": 19.9509, "agent_b_speed_mps": 30.6664, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}], "llm_task": {"question": "Judge whether this is a natural and safety-critical long-tail driving scenario.", "return_format": "strict_json", "expected_keys": ["accepted", "danger_level", "naturalness_score", "interaction_score", "scenario_value_score", "scenario_type", "artifact_flags", "reason"]}} +{"case_id": "shard000_batch0009_world0993_line001246", "schema_version": "llm_longtail_case_v1", "source": {"event_trace_line_index": 1246, "scenario_file": "tfrecord-00004-of-01000_410.json", "shard_id": 0, "batch_idx": 9, "world_idx": 993}, "outcome": {"collision": true, "near_miss": false, "offroad": false, "first_collision_time_s": 1.3, "first_offroad_time_s": null, "min_distance_time_s": 2.0, "event_time_s": 1.3, "raw_min_distance_m": 3.6078, "clean_min_distance_m": 3.6077}, "primary_pair": {"agent_a": 5, "agent_b": 1, "agent_a_role": ["risk_agent"], "agent_b_role": ["collided_agent"], "selection_reason": "event_min_distance_risk_partner", "confidence": "medium", "risk_directly_involved": false, "semantic_candidate": "crossing_or_intersection_conflict"}, "scene_summary": {"controlled_agents": 7, "risk_agents": [3, 5, 15], "collided_agents": [1, 2], "offroad_agents": [], "goal_agents": 7, "context_agents": [1, 5, 0, 2, 3, 4, 15, 18]}, "primary_interaction_metrics": {"num_clean_pair_frames": 21, "min_clean_distance_m": 3.6077, "min_clean_distance_time_to_event_s": 0.7, "min_ttc_s": 0.2709, "time_ttc_below_1s": 0.6, "time_ttc_below_2s": 1.2, "distance_trend_before_event": {"label": "mostly_decreasing", "decreasing_ratio": 1.0, "distance_start_m": 18.0927, "distance_end_m": 9.7953}, "metrics_at_event_or_nearest_clean_frame": {"pair_valid": true, "distance_m": 9.7953, "ttc_s": 0.9155, "closing_speed_mps": 10.699, "relative_speed_mps": 10.7531, "longitudinal_gap_m": 7.8116, "lateral_offset_m": 5.9097, "relative_heading_rad": -1.8041, "agent_a_speed_mps": 6.6373, "agent_b_speed_mps": 6.0534}, "metrics_at_min_clean_distance": {"pair_valid": true, "distance_m": 3.6077, "ttc_s": 0.2709, "closing_speed_mps": 13.3196, "relative_speed_mps": 13.3704, "longitudinal_gap_m": 2.7416, "lateral_offset_m": 2.3451, "relative_heading_rad": -1.4164, "agent_a_speed_mps": 9.3049, "agent_b_speed_mps": 7.7867}}, "naturalness_checks": {"max_speed_mps": 15.5461, "max_accel_mps2": 4.3002, "max_decel_mps2": 4.0051, "max_yaw_rate_radps": 3.552, "max_frame_displacement_m": 1.5546, "offroad_before_collision": false, "clean_frame_counts": {"1": 24, "5": 21, "0": 24, "2": 24, "3": 24, "4": 24, "15": 2, "18": 24}, "artifact_warnings": ["high_yaw_rate", "short_clean_track"]}, "interaction_checks": {"risk_agent_in_primary_pair": true, "primary_pair_has_collision_agent": true, "primary_pair_has_offroad_agent": false, "distance_trend_label": "mostly_decreasing"}, "context_agent_summaries": [{"agent_id": 1, "roles": ["collided_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.1013, "width_m": 1.4463, "clean_frames": 24, "speed_at_event_mps": 6.0534}, {"agent_id": 5, "roles": ["risk_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.9674, "width_m": 1.661, "clean_frames": 21, "speed_at_event_mps": 6.6373}, {"agent_id": 0, "roles": ["context_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.7002, "width_m": 1.6324, "clean_frames": 24, "speed_at_event_mps": 6.9943}, {"agent_id": 2, "roles": ["collided_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.1365, "width_m": 1.4227, "clean_frames": 24, "speed_at_event_mps": 7.8256}, {"agent_id": 3, "roles": ["risk_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.0947, "width_m": 1.3817, "clean_frames": 24, "speed_at_event_mps": 11.5457}, {"agent_id": 4, "roles": ["context_agent"], "is_controlled": false, "response_type": 0, "raw_agent_type": 7, "length_m": 3.446, "width_m": 1.566, "clean_frames": 24, "speed_at_event_mps": 0.615}, {"agent_id": 15, "roles": ["risk_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.3256, "width_m": 1.4787, "clean_frames": 2, "speed_at_event_mps": null}, {"agent_id": 18, "roles": ["context_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.5207, "width_m": 1.5406, "clean_frames": 24, "speed_at_event_mps": 1.8233}], "map_summary": {"center_xy": [-0.183, -1.5919], "radius_m": 80.0, "num_points": 256, "type_counts": {"0": 256}}, "timeline": [{"t_to_event_s": -1.3, "frame_index": 0, "target_offset_s": -5.0, "distance_m": 18.0927, "ttc_s": null, "closing_speed_mps": null, "relative_speed_mps": null, "longitudinal_gap_m": 10.2637, "lateral_offset_m": 14.8997, "relative_heading_rad": -1.4789, "agent_a_speed_mps": null, "agent_b_speed_mps": null, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -1.0, "frame_index": 3, "target_offset_s": -1.0, "distance_m": 17.1211, "ttc_s": 4.3971, "closing_speed_mps": 3.8937, "relative_speed_mps": 3.9041, "longitudinal_gap_m": 10.5967, "lateral_offset_m": 13.4477, "relative_heading_rad": -1.5486, "agent_a_speed_mps": 2.7704, "agent_b_speed_mps": 2.1864, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -0.5, "frame_index": 8, "target_offset_s": -0.5, "distance_m": 14.1242, "ttc_s": 1.9034, "closing_speed_mps": 7.4206, "relative_speed_mps": 7.4352, "longitudinal_gap_m": 10.7507, "lateral_offset_m": 9.1606, "relative_heading_rad": -1.7431, "agent_a_speed_mps": 4.771, "agent_b_speed_mps": 4.1194, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": 0.0, "frame_index": 13, "target_offset_s": 0.0, "distance_m": 9.7953, "ttc_s": 0.9155, "closing_speed_mps": 10.699, "relative_speed_mps": 10.7531, "longitudinal_gap_m": 7.8116, "lateral_offset_m": 5.9097, "relative_heading_rad": -1.8041, "agent_a_speed_mps": 6.6373, "agent_b_speed_mps": 6.0534, "agent_a_collision": false, "agent_b_collision": true, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": 0.5, "frame_index": 18, "target_offset_s": 0.5, "distance_m": 4.9008, "ttc_s": 1.4791, "closing_speed_mps": 3.3133, "relative_speed_mps": 3.5048, "longitudinal_gap_m": 3.771, "lateral_offset_m": 3.1301, "relative_heading_rad": -1.6544, "agent_a_speed_mps": 8.5718, "agent_b_speed_mps": 7.3868, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": 0.7, "frame_index": 20, "target_offset_s": 1.0, "distance_m": 3.6077, "ttc_s": 0.2709, "closing_speed_mps": 13.3196, "relative_speed_mps": 13.3704, "longitudinal_gap_m": 2.7416, "lateral_offset_m": 2.3451, "relative_heading_rad": -1.4164, "agent_a_speed_mps": 9.3049, "agent_b_speed_mps": 7.7867, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}], "llm_task": {"question": "Judge whether this is a natural and safety-critical long-tail driving scenario.", "return_format": "strict_json", "expected_keys": ["accepted", "danger_level", "naturalness_score", "interaction_score", "scenario_value_score", "scenario_type", "artifact_flags", "reason"]}} diff --git a/gpudrive-main/search_outputs/llm_cases_shard_000/summary.json b/gpudrive-main/search_outputs/llm_cases_shard_000/summary.json new file mode 100644 index 0000000000000000000000000000000000000000..34488e711fd3a79932e52763ef69f15690265247 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_shard_000/summary.json @@ -0,0 +1,28 @@ +{ + "input": "server_resource\\event_traces.jsonl", + "output_dir": "search_outputs\\llm_cases_shard_000", + "records_read": 1247, + "cases_written": 1247, + "cases_skipped_no_primary_pair": 0, + "semantic_counts": { + "same_direction_side_conflict_or_cut_in": 447, + "generic_close_interaction": 135, + "crossing_or_intersection_conflict": 544, + "rear_end_or_following_conflict": 121 + }, + "primary_pair_reason_counts": { + "risk_agent_directly_collided_with_partner": 504, + "event_min_distance_risk_partner": 735, + "risk_partner_low_ttc_or_distance_fallback": 4, + "collided_agents_pair": 4 + }, + "artifact_warning_counts": { + "high_yaw_rate": 1153, + "offroad_before_collision": 243, + "extreme_speed": 9, + "hard_deceleration": 63, + "high_acceleration": 216, + "large_frame_displacement": 1, + "short_clean_track": 427 + } +} \ No newline at end of file diff --git a/gpudrive-main/search_outputs/llm_cases_test/case_cards/shard000_batch0000_world0002_line000000.md b/gpudrive-main/search_outputs/llm_cases_test/case_cards/shard000_batch0000_world0002_line000000.md new file mode 100644 index 0000000000000000000000000000000000000000..fec4a1cbf2fde065998b25e3d3d1e10206524319 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_test/case_cards/shard000_batch0000_world0002_line000000.md @@ -0,0 +1,57 @@ +# shard000_batch0000_world0002_line000000 + +## Outcome +- collision: True +- near_miss: True +- offroad: True +- event_time_s: 2.9 +- raw_min_distance_m: 1.9574 +- clean_min_distance_m: 1.9575 + +## Primary Pair +- agent_a: 5 (risk_agent,collided_agent) +- agent_b: 7 (collided_agent,offroad_agent) +- selection_reason: risk_agent_directly_collided_with_partner +- confidence: high +- risk_directly_involved: True +- semantic_candidate: same_direction_side_conflict_or_cut_in + +## Primary Interaction Metrics +- num_clean_pair_frames: 34 +- min_ttc_s: 0.4335 +- time_ttc_below_1s: 1.2 +- time_ttc_below_2s: 2.3 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 0.9655, 'distance_start_m': 29.5168, 'distance_end_m': 1.9575} + +## Naturalness Checks +- max_speed_mps: 23.926 +- max_accel_mps2: 4.0166 +- max_decel_mps2: 4.0081 +- max_yaw_rate_radps: 6.665 +- max_frame_displacement_m: 2.3926 +- artifact_warnings: ['high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 5 | risk_agent,collided_agent | True | 34 | 10.645 | 3.0827 | 1.391 | +| 7 | collided_agent,offroad_agent | True | 39 | 14.2738 | 3.1654 | 1.4256 | +| 0 | risk_agent,offroad_agent | True | 40 | 17.2471 | 3.7002 | 1.6324 | +| 2 | context_agent | True | 40 | 19.003 | 3.2432 | 1.5205 | +| 3 | context_agent | True | 40 | 16.8588 | 3.2124 | 1.4202 | +| 4 | risk_agent | True | 23 | | 3.5435 | 1.5256 | +| 6 | context_agent | True | 40 | 13.7644 | 3.2835 | 1.4368 | +| 17 | context_agent | True | 32 | 23.6592 | 2.8417 | 1.3018 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -2.9 | 29.5168 | | | | 1.8873 | 29.4564 | | | +| -2.0 | 20.5942 | 1.6912 | 12.177 | 12.8039 | -6.3909 | 19.5775 | 5.179 | 10.1396 | +| -1.0 | 7.7682 | 0.6847 | 11.3453 | 11.6026 | -6.5408 | 4.1909 | 9.179 | 13.3404 | +| -0.5 | 3.6239 | 0.4436 | 8.1688 | 8.9406 | -3.4811 | 1.0071 | 10.6456 | 14.8069 | +| 0.0 | 1.9575 | 0.4496 | 4.3538 | 7.2043 | -1.5368 | 1.2124 | 10.645 | 14.2738 | +| 0.4 | 3.1975 | 12.4782 | 0.2562 | 2.2195 | -3.1686 | 0.4287 | 12.2457 | 14.4066 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_test/case_cards/shard000_batch0000_world0009_line000001.md b/gpudrive-main/search_outputs/llm_cases_test/case_cards/shard000_batch0000_world0009_line000001.md new file mode 100644 index 0000000000000000000000000000000000000000..0a38e4de331728a8e76a8811633c47877c96032d --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_test/case_cards/shard000_batch0000_world0009_line000001.md @@ -0,0 +1,57 @@ +# shard000_batch0000_world0009_line000001 + +## Outcome +- collision: True +- near_miss: False +- offroad: True +- event_time_s: 5.4 +- raw_min_distance_m: 3.1167 +- clean_min_distance_m: 3.1166 + +## Primary Pair +- agent_a: 4 (risk_agent) +- agent_b: 8 (context_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: False +- semantic_candidate: same_direction_side_conflict_or_cut_in + +## Primary Interaction Metrics +- num_clean_pair_frames: 50 +- min_ttc_s: 2.5086 +- time_ttc_below_1s: 0.0 +- time_ttc_below_2s: 0.0 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 0.7931, 'distance_start_m': 6.4593, 'distance_end_m': 3.4719} + +## Naturalness Checks +- max_speed_mps: 33.7012 +- max_accel_mps2: 4.0153 +- max_decel_mps2: 3.9965 +- max_yaw_rate_radps: 14.554 +- max_frame_displacement_m: 3.3701 +- artifact_warnings: ['high_yaw_rate', 'offroad_before_collision'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 4 | risk_agent | True | 55 | 23.9944 | 4.3251 | 1.9082 | +| 8 | context_agent | True | 50 | 21.5161 | 3.219 | 1.473 | +| 0 | context_agent | True | 58 | 21.8984 | 3.7002 | 1.6324 | +| 3 | risk_agent | True | 55 | 20.7969 | 3.1886 | 1.4024 | +| 5 | collided_agent | True | 61 | 33.3017 | 3.2417 | 1.3969 | +| 6 | risk_agent | True | 30 | | 3.1494 | 1.4529 | +| 11 | collided_agent | True | 51 | 23.837 | 3.0509 | 1.4043 | +| 18 | offroad_agent | True | 43 | | 3.2048 | 1.388 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -5.0 | 12.2163 | | | | -4.3129 | 11.4296 | | | +| -3.0 | 6.4593 | 3.31 | 1.9514 | 2.2334 | -3.8266 | 5.2038 | 19.7301 | 17.5167 | +| -2.0 | 4.4688 | 2.5086 | 1.7814 | 2.503 | -3.5204 | 2.7526 | 21.4632 | 18.9835 | +| -1.0 | 3.1972 | 5.7704 | 0.5541 | 2.5069 | -3.1861 | 0.2657 | 22.7958 | 20.3163 | +| -0.5 | 3.1616 | | -0.4216 | 2.5091 | -3.0067 | -0.9775 | 23.4626 | 20.9832 | +| -0.1 | 3.4719 | | -1.1008 | 2.5098 | -2.8573 | -1.9722 | 23.9944 | 21.5161 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_test/case_cards/shard000_batch0000_world0012_line000002.md b/gpudrive-main/search_outputs/llm_cases_test/case_cards/shard000_batch0000_world0012_line000002.md new file mode 100644 index 0000000000000000000000000000000000000000..56047ffb03a8b749ba9ec7c2518df0dcf54948b2 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_test/case_cards/shard000_batch0000_world0012_line000002.md @@ -0,0 +1,57 @@ +# shard000_batch0000_world0012_line000002 + +## Outcome +- collision: True +- near_miss: False +- offroad: False +- event_time_s: 3.8 +- raw_min_distance_m: 3.3008 +- clean_min_distance_m: 3.3008 + +## Primary Pair +- agent_a: 4 (risk_agent,collided_agent) +- agent_b: 7 (risk_agent,collided_agent) +- selection_reason: risk_agent_directly_collided_with_partner +- confidence: high +- risk_directly_involved: True +- semantic_candidate: same_direction_side_conflict_or_cut_in + +## Primary Interaction Metrics +- num_clean_pair_frames: 41 +- min_ttc_s: 0.8114 +- time_ttc_below_1s: 0.4 +- time_ttc_below_2s: 1.4 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 1.0, 'distance_start_m': 16.6732, 'distance_end_m': 3.3008} + +## Naturalness Checks +- max_speed_mps: 654.3072 +- max_accel_mps2: 6488.7513 +- max_decel_mps2: 18.4696 +- max_yaw_rate_radps: 30.336 +- max_frame_displacement_m: 65.4307 +- artifact_warnings: ['extreme_speed', 'hard_deceleration', 'high_acceleration', 'high_yaw_rate', 'large_frame_displacement'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 4 | risk_agent,collided_agent | True | 43 | 20.9505 | 3.3869 | 1.5994 | +| 7 | risk_agent,collided_agent | True | 41 | 25.0195 | 3.101 | 1.4958 | +| 0 | context_agent | False | 49 | 5.4707 | 3.7002 | 1.6324 | +| 1 | context_agent | True | 44 | 21.0514 | 2.9684 | 1.3518 | +| 2 | context_agent | False | 42 | 5.3768 | 3.1209 | 1.4512 | +| 3 | risk_agent | True | 44 | 20.9943 | 2.9235 | 1.3487 | +| 6 | context_agent | True | 43 | 20.0458 | 3.1435 | 1.4294 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -3.8 | 20.0383 | | | | -2.1553 | -19.922 | | | +| -3.0 | 16.6732 | 3.9614 | 4.2089 | 4.212 | -1.9318 | -16.5609 | 13.2175 | 17.4198 | +| -2.0 | 12.4874 | 2.8728 | 4.3468 | 4.3496 | -1.5683 | -12.3885 | 16.9507 | 21.2861 | +| -1.0 | 7.8288 | 1.697 | 4.6132 | 4.62 | -1.1481 | -7.7441 | 19.6161 | 24.22 | +| -0.5 | 5.5233 | 1.1982 | 4.6096 | 4.6211 | -0.9224 | -5.4457 | 20.2829 | 24.8864 | +| 0.0 | 3.3008 | 0.8114 | 4.0682 | 4.0894 | -0.6924 | -3.2274 | 20.9505 | 25.0195 | +| 0.2 | 6.3712 | 2.5986 | 2.4517 | 4.3242 | -1.5354 | -6.1834 | 21.4824 | 24.8857 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_test/case_cards/shard000_batch0000_world0017_line000003.md b/gpudrive-main/search_outputs/llm_cases_test/case_cards/shard000_batch0000_world0017_line000003.md new file mode 100644 index 0000000000000000000000000000000000000000..0e662c8d551d4670a5b3db2ed66ba8adbcfaaf47 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_test/case_cards/shard000_batch0000_world0017_line000003.md @@ -0,0 +1,50 @@ +# shard000_batch0000_world0017_line000003 + +## Outcome +- collision: True +- near_miss: False +- offroad: False +- event_time_s: 3.2 +- raw_min_distance_m: 4.0969 +- clean_min_distance_m: 4.0743 + +## Primary Pair +- agent_a: 1 (risk_agent) +- agent_b: 0 (context_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: False +- semantic_candidate: generic_close_interaction + +## Primary Interaction Metrics +- num_clean_pair_frames: 9 +- min_ttc_s: None +- time_ttc_below_1s: 0.0 +- time_ttc_below_2s: 0.0 +- distance_trend_before_event: {'label': 'not_decreasing', 'decreasing_ratio': 0.0, 'distance_start_m': 4.131, 'distance_end_m': 4.5567} + +## Naturalness Checks +- max_speed_mps: 22.3423 +- max_accel_mps2: 4.0106 +- max_decel_mps2: -0.6604 +- max_yaw_rate_radps: 4.5679 +- max_frame_displacement_m: 2.2342 +- artifact_warnings: ['high_yaw_rate', 'short_clean_track'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 0 | context_agent | True | 41 | | 3.7002 | 1.6324 | +| 1 | risk_agent | True | 9 | | 3.4315 | 1.5318 | +| 6 | risk_agent | True | 37 | | 3.01 | 1.4046 | +| 9 | risk_agent | True | 2 | | 3.3504 | 1.4788 | +| 15 | collided_agent | True | 33 | | 3.3941 | 1.5125 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -3.2 | 4.0743 | | | | 4.0742 | 0.0218 | | | +| -3.0 | 4.131 | | -0.3919 | 2.0784 | 4.1079 | 0.4362 | 4.1743 | 6.2478 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_test/case_cards/shard000_batch0000_world0020_line000004.md b/gpudrive-main/search_outputs/llm_cases_test/case_cards/shard000_batch0000_world0020_line000004.md new file mode 100644 index 0000000000000000000000000000000000000000..7e113111f5ebe998fcbe7a2acf196fd093f73850 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_test/case_cards/shard000_batch0000_world0020_line000004.md @@ -0,0 +1,58 @@ +# shard000_batch0000_world0020_line000004 + +## Outcome +- collision: True +- near_miss: False +- offroad: False +- event_time_s: 4.9 +- raw_min_distance_m: 2.1322 +- clean_min_distance_m: 2.1322 + +## Primary Pair +- agent_a: 10 (risk_agent,collided_agent) +- agent_b: 0 (context_agent) +- selection_reason: event_min_distance_risk_partner +- confidence: medium +- risk_directly_involved: True +- semantic_candidate: same_direction_side_conflict_or_cut_in + +## Primary Interaction Metrics +- num_clean_pair_frames: 60 +- min_ttc_s: 0.0973 +- time_ttc_below_1s: 1.2 +- time_ttc_below_2s: 1.9 +- distance_trend_before_event: {'label': 'mostly_decreasing', 'decreasing_ratio': 0.9333, 'distance_start_m': 42.7184, 'distance_end_m': 2.1322} + +## Naturalness Checks +- max_speed_mps: 31.1746 +- max_accel_mps2: 130.8809 +- max_decel_mps2: 5.9354 +- max_yaw_rate_radps: 12.724 +- max_frame_displacement_m: 3.1175 +- artifact_warnings: ['high_acceleration', 'high_yaw_rate'] + +## Context Agents +| agent | roles | controlled | clean_frames | speed_event | length | width | +| --- | --- | --- | --- | --- | --- | --- | +| 0 | context_agent | False | 60 | 6.4672 | 3.7002 | 1.6324 | +| 10 | risk_agent,collided_agent | True | 60 | 28.041 | 3.3421 | 1.4774 | +| 2 | risk_agent | True | 52 | 25.8953 | 3.2188 | 1.4623 | +| 3 | context_agent | True | 60 | 26.3787 | 4.1701 | 1.7598 | +| 5 | context_agent | False | 60 | 8.3336 | 3.4134 | 1.5414 | +| 6 | context_agent | False | 60 | 13.1443 | 3.2981 | 1.4325 | +| 7 | risk_agent | True | 36 | | 3.1299 | 1.3812 | + +## Timeline +| t_to_event | distance | ttc | closing | rel_speed | lat_offset | long_gap | a_speed | b_speed | +| --- | --- | --- | --- | --- | --- | --- | --- | --- | +| -4.9 | 55.212 | | | | 3.1922 | 55.1196 | | | +| -3.0 | 42.7184 | 4.4631 | 9.5715 | 9.6031 | 3.3206 | 42.5891 | 20.375 | 10.772 | +| -2.0 | 30.5419 | 2.1306 | 14.3347 | 14.4364 | 3.455 | 30.3458 | 24.2421 | 9.8062 | +| -1.0 | 18.8455 | 1.0754 | 17.5236 | 17.6025 | -2.2811 | 18.7069 | 25.7746 | 8.2282 | +| -0.5 | 9.264 | 0.4591 | 20.1769 | 20.423 | -1.8203 | 9.0834 | 27.7078 | 7.3543 | +| 0.0 | 2.1322 | 0.0973 | 21.9152 | 24.68 | -1.8846 | 0.9973 | 28.041 | 6.4672 | +| 0.5 | 8.9179 | | -23.9606 | 24.1971 | -2.0314 | -8.6835 | 29.8414 | 5.6582 | +| 1.0 | 3.7519 | 2.3531 | 1.5944 | 35.6212 | 3.7299 | 0.4056 | 31.1746 | 4.9242 | + +## LLM Task +Judge whether this is a natural and safety-critical long-tail driving scenario. Return strict JSON. diff --git a/gpudrive-main/search_outputs/llm_cases_test/llm_cases.jsonl b/gpudrive-main/search_outputs/llm_cases_test/llm_cases.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..0e50f48e9b908a44e55637e5e4303308cdb9f91e --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_test/llm_cases.jsonl @@ -0,0 +1,5 @@ +{"case_id": "shard000_batch0000_world0002_line000000", "schema_version": "llm_longtail_case_v1", "source": {"event_trace_line_index": 0, "scenario_file": "tfrecord-00005-of-01000_436.json", "shard_id": 0, "batch_idx": 0, "world_idx": 2}, "outcome": {"collision": true, "near_miss": true, "offroad": true, "first_collision_time_s": 2.9, "first_offroad_time_s": 3.0, "min_distance_time_s": 2.9, "event_time_s": 2.9, "raw_min_distance_m": 1.9574, "clean_min_distance_m": 1.9575}, "primary_pair": {"agent_a": 5, "agent_b": 7, "agent_a_role": ["risk_agent", "collided_agent"], "agent_b_role": ["collided_agent", "offroad_agent"], "selection_reason": "risk_agent_directly_collided_with_partner", "confidence": "high", "risk_directly_involved": true, "semantic_candidate": "same_direction_side_conflict_or_cut_in"}, "scene_summary": {"controlled_agents": 13, "risk_agents": [0, 4, 5], "collided_agents": [5, 7], "offroad_agents": [0, 7], "goal_agents": 13, "context_agents": [5, 7, 0, 2, 3, 4, 6, 17]}, "primary_interaction_metrics": {"num_clean_pair_frames": 34, "min_clean_distance_m": 1.9575, "min_clean_distance_time_to_event_s": 0.0, "min_ttc_s": 0.4335, "time_ttc_below_1s": 1.2, "time_ttc_below_2s": 2.3, "distance_trend_before_event": {"label": "mostly_decreasing", "decreasing_ratio": 0.9655, "distance_start_m": 29.5168, "distance_end_m": 1.9575}, "metrics_at_event_or_nearest_clean_frame": {"pair_valid": true, "distance_m": 1.9575, "ttc_s": 0.4496, "closing_speed_mps": 4.3538, "relative_speed_mps": 7.2043, "longitudinal_gap_m": 1.2124, "lateral_offset_m": -1.5368, "relative_heading_rad": 0.4045, "agent_a_speed_mps": 10.645, "agent_b_speed_mps": 14.2738}, "metrics_at_min_clean_distance": {"pair_valid": true, "distance_m": 1.9575, "ttc_s": 0.4496, "closing_speed_mps": 4.3538, "relative_speed_mps": 7.2043, "longitudinal_gap_m": 1.2124, "lateral_offset_m": -1.5368, "relative_heading_rad": 0.4045, "agent_a_speed_mps": 10.645, "agent_b_speed_mps": 14.2738}}, "naturalness_checks": {"max_speed_mps": 23.926, "max_accel_mps2": 4.0166, "max_decel_mps2": 4.0081, "max_yaw_rate_radps": 6.665, "max_frame_displacement_m": 2.3926, "offroad_before_collision": false, "clean_frame_counts": {"5": 34, "7": 39, "0": 40, "2": 40, "3": 40, "4": 23, "6": 40, "17": 32}, "artifact_warnings": ["high_yaw_rate"]}, "interaction_checks": {"risk_agent_in_primary_pair": true, "primary_pair_has_collision_agent": true, "primary_pair_has_offroad_agent": true, "distance_trend_label": "mostly_decreasing"}, "context_agent_summaries": [{"agent_id": 5, "roles": ["risk_agent", "collided_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.0827, "width_m": 1.391, "clean_frames": 34, "speed_at_event_mps": 10.645}, {"agent_id": 7, "roles": ["collided_agent", "offroad_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.1654, "width_m": 1.4256, "clean_frames": 39, "speed_at_event_mps": 14.2738}, {"agent_id": 0, "roles": ["risk_agent", "offroad_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.7002, "width_m": 1.6324, "clean_frames": 40, "speed_at_event_mps": 17.2471}, {"agent_id": 2, "roles": ["context_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.2432, "width_m": 1.5205, "clean_frames": 40, "speed_at_event_mps": 19.003}, {"agent_id": 3, "roles": ["context_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.2124, "width_m": 1.4202, "clean_frames": 40, "speed_at_event_mps": 16.8588}, {"agent_id": 4, "roles": ["risk_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.5435, "width_m": 1.5256, "clean_frames": 23, "speed_at_event_mps": null}, {"agent_id": 6, "roles": ["context_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.2835, "width_m": 1.4368, "clean_frames": 40, "speed_at_event_mps": 13.7644}, {"agent_id": 17, "roles": ["context_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 2.8417, "width_m": 1.3018, "clean_frames": 32, "speed_at_event_mps": 23.6592}], "map_summary": {"center_xy": [20.7116, -14.1158], "radius_m": 80.0, "num_points": 256, "type_counts": {"1": 31, "3": 70, "2": 27, "4": 1, "0": 127}}, "timeline": [{"t_to_event_s": -2.9, "frame_index": 0, "target_offset_s": -5.0, "distance_m": 29.5168, "ttc_s": null, "closing_speed_mps": null, "relative_speed_mps": null, "longitudinal_gap_m": 29.4564, "lateral_offset_m": 1.8873, "relative_heading_rad": 2.6233, "agent_a_speed_mps": null, "agent_b_speed_mps": null, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -2.0, "frame_index": 9, "target_offset_s": -2.0, "distance_m": 20.5942, "ttc_s": 1.6912, "closing_speed_mps": 12.177, "relative_speed_mps": 12.8039, "longitudinal_gap_m": 19.5775, "lateral_offset_m": -6.3909, "relative_heading_rad": 2.1197, "agent_a_speed_mps": 5.179, "agent_b_speed_mps": 10.1396, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -1.0, "frame_index": 19, "target_offset_s": -1.0, "distance_m": 7.7682, "ttc_s": 0.6847, "closing_speed_mps": 11.3453, "relative_speed_mps": 11.6026, "longitudinal_gap_m": 4.1909, "lateral_offset_m": -6.5408, "relative_heading_rad": 1.1884, "agent_a_speed_mps": 9.179, "agent_b_speed_mps": 13.3404, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -0.5, "frame_index": 24, "target_offset_s": -0.5, "distance_m": 3.6239, "ttc_s": 0.4436, "closing_speed_mps": 8.1688, "relative_speed_mps": 8.9406, "longitudinal_gap_m": 1.0071, "lateral_offset_m": -3.4811, "relative_heading_rad": 0.6411, "agent_a_speed_mps": 10.6456, "agent_b_speed_mps": 14.8069, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": 0.0, "frame_index": 29, "target_offset_s": 0.0, "distance_m": 1.9575, "ttc_s": 0.4496, "closing_speed_mps": 4.3538, "relative_speed_mps": 7.2043, "longitudinal_gap_m": 1.2124, "lateral_offset_m": -1.5368, "relative_heading_rad": 0.4045, "agent_a_speed_mps": 10.645, "agent_b_speed_mps": 14.2738, "agent_a_collision": true, "agent_b_collision": true, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": 0.4, "frame_index": 33, "target_offset_s": 0.5, "distance_m": 3.1975, "ttc_s": 12.4782, "closing_speed_mps": 0.2562, "relative_speed_mps": 2.2195, "longitudinal_gap_m": 0.4287, 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"naturalness_score", "interaction_score", "scenario_value_score", "scenario_type", "artifact_flags", "reason"]}} +{"case_id": "shard000_batch0000_world0017_line000003", "schema_version": "llm_longtail_case_v1", "source": {"event_trace_line_index": 3, "scenario_file": "tfrecord-00009-of-01000_359.json", "shard_id": 0, "batch_idx": 0, "world_idx": 17}, "outcome": {"collision": true, "near_miss": false, "offroad": false, "first_collision_time_s": 3.2, "first_offroad_time_s": null, "min_distance_time_s": 0.1, "event_time_s": 3.2, "raw_min_distance_m": 4.0969, "clean_min_distance_m": 4.0743}, "primary_pair": {"agent_a": 1, "agent_b": 0, "agent_a_role": ["risk_agent"], "agent_b_role": ["context_agent"], "selection_reason": "event_min_distance_risk_partner", "confidence": "medium", "risk_directly_involved": false, "semantic_candidate": "generic_close_interaction"}, "scene_summary": {"controlled_agents": 11, "risk_agents": [1, 6, 9], "collided_agents": [15], "offroad_agents": [], "goal_agents": 11, "context_agents": [0, 1, 6, 9, 15]}, "primary_interaction_metrics": {"num_clean_pair_frames": 9, "min_clean_distance_m": 4.0743, "min_clean_distance_time_to_event_s": -3.2, "min_ttc_s": null, "time_ttc_below_1s": 0.0, "time_ttc_below_2s": 0.0, "distance_trend_before_event": {"label": "not_decreasing", "decreasing_ratio": 0.0, "distance_start_m": 4.131, "distance_end_m": 4.5567}, "metrics_at_event_or_nearest_clean_frame": {"pair_valid": true, "distance_m": 4.0743, "ttc_s": null, "closing_speed_mps": null, "relative_speed_mps": null, "longitudinal_gap_m": 0.0218, "lateral_offset_m": 4.0742, "relative_heading_rad": 0.0277, "agent_a_speed_mps": null, "agent_b_speed_mps": null}, "metrics_at_min_clean_distance": {"pair_valid": true, "distance_m": 4.0743, "ttc_s": null, "closing_speed_mps": null, "relative_speed_mps": null, "longitudinal_gap_m": 0.0218, "lateral_offset_m": 4.0742, "relative_heading_rad": 0.0277, "agent_a_speed_mps": null, "agent_b_speed_mps": null}}, "naturalness_checks": {"max_speed_mps": 22.3423, "max_accel_mps2": 4.0106, "max_decel_mps2": -0.6604, "max_yaw_rate_radps": 4.5679, "max_frame_displacement_m": 2.2342, "offroad_before_collision": false, "clean_frame_counts": {"0": 41, "1": 9, "6": 37, "9": 2, "15": 33}, "artifact_warnings": ["high_yaw_rate", "short_clean_track"]}, "interaction_checks": {"risk_agent_in_primary_pair": true, "primary_pair_has_collision_agent": false, "primary_pair_has_offroad_agent": false, "distance_trend_label": "not_decreasing"}, "context_agent_summaries": [{"agent_id": 0, "roles": ["context_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.7002, "width_m": 1.6324, "clean_frames": 41, "speed_at_event_mps": null}, {"agent_id": 1, "roles": ["risk_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.4315, "width_m": 1.5318, "clean_frames": 9, "speed_at_event_mps": null}, {"agent_id": 6, "roles": ["risk_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.01, "width_m": 1.4046, "clean_frames": 37, "speed_at_event_mps": null}, {"agent_id": 9, "roles": ["risk_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.3504, "width_m": 1.4788, "clean_frames": 2, "speed_at_event_mps": null}, {"agent_id": 15, "roles": ["collided_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.3941, "width_m": 1.5125, "clean_frames": 33, "speed_at_event_mps": null}], "map_summary": {"center_xy": [34.1139, 4.452], "radius_m": 80.0, "num_points": 256, "type_counts": {"3": 58, "1": 25, "5": 1, "2": 11, "6": 1, "0": 160}}, "timeline": [{"t_to_event_s": -3.2, "frame_index": 0, "target_offset_s": -5.0, "distance_m": 4.0743, "ttc_s": null, "closing_speed_mps": null, "relative_speed_mps": null, "longitudinal_gap_m": 0.0218, "lateral_offset_m": 4.0742, "relative_heading_rad": 0.0277, "agent_a_speed_mps": null, "agent_b_speed_mps": null, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -3.0, "frame_index": 2, "target_offset_s": -3.0, "distance_m": 4.131, "ttc_s": null, "closing_speed_mps": -0.3919, "relative_speed_mps": 2.0784, "longitudinal_gap_m": 0.4362, "lateral_offset_m": 4.1079, "relative_heading_rad": 0.0277, "agent_a_speed_mps": 4.1743, "agent_b_speed_mps": 6.2478, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}], "llm_task": {"question": "Judge whether this is a natural and safety-critical long-tail driving scenario.", "return_format": "strict_json", "expected_keys": ["accepted", "danger_level", "naturalness_score", "interaction_score", "scenario_value_score", "scenario_type", "artifact_flags", "reason"]}} +{"case_id": "shard000_batch0000_world0020_line000004", "schema_version": "llm_longtail_case_v1", "source": {"event_trace_line_index": 4, "scenario_file": "tfrecord-00000-of-01000_190.json", "shard_id": 0, "batch_idx": 0, "world_idx": 20}, "outcome": {"collision": true, "near_miss": false, "offroad": false, "first_collision_time_s": 4.9, "first_offroad_time_s": null, "min_distance_time_s": 4.9, "event_time_s": 4.9, "raw_min_distance_m": 2.1322, "clean_min_distance_m": 2.1322}, "primary_pair": {"agent_a": 10, "agent_b": 0, "agent_a_role": ["risk_agent", "collided_agent"], "agent_b_role": ["context_agent"], "selection_reason": "event_min_distance_risk_partner", "confidence": "medium", "risk_directly_involved": true, "semantic_candidate": "same_direction_side_conflict_or_cut_in"}, "scene_summary": {"controlled_agents": 9, "risk_agents": [2, 7, 10], "collided_agents": [10], "offroad_agents": [], "goal_agents": 9, "context_agents": [0, 10, 2, 3, 5, 6, 7]}, "primary_interaction_metrics": {"num_clean_pair_frames": 60, "min_clean_distance_m": 2.1322, "min_clean_distance_time_to_event_s": 0.0, "min_ttc_s": 0.0973, "time_ttc_below_1s": 1.2, "time_ttc_below_2s": 1.9, "distance_trend_before_event": {"label": "mostly_decreasing", "decreasing_ratio": 0.9333, "distance_start_m": 42.7184, "distance_end_m": 2.1322}, "metrics_at_event_or_nearest_clean_frame": {"pair_valid": true, "distance_m": 2.1322, "ttc_s": 0.0973, "closing_speed_mps": 21.9152, "relative_speed_mps": 24.68, "longitudinal_gap_m": 0.9973, "lateral_offset_m": -1.8846, "relative_heading_rad": 0.5289, "agent_a_speed_mps": 28.041, "agent_b_speed_mps": 6.4672}, "metrics_at_min_clean_distance": {"pair_valid": true, "distance_m": 2.1322, "ttc_s": 0.0973, "closing_speed_mps": 21.9152, "relative_speed_mps": 24.68, "longitudinal_gap_m": 0.9973, "lateral_offset_m": -1.8846, "relative_heading_rad": 0.5289, "agent_a_speed_mps": 28.041, "agent_b_speed_mps": 6.4672}}, "naturalness_checks": {"max_speed_mps": 31.1746, "max_accel_mps2": 130.8809, "max_decel_mps2": 5.9354, "max_yaw_rate_radps": 12.724, "max_frame_displacement_m": 3.1175, "offroad_before_collision": false, "clean_frame_counts": {"0": 60, "10": 60, "2": 52, "3": 60, "5": 60, "6": 60, "7": 36}, "artifact_warnings": ["high_acceleration", "high_yaw_rate"]}, "interaction_checks": {"risk_agent_in_primary_pair": true, "primary_pair_has_collision_agent": true, "primary_pair_has_offroad_agent": false, "distance_trend_label": "mostly_decreasing"}, "context_agent_summaries": [{"agent_id": 0, "roles": ["context_agent"], "is_controlled": false, "response_type": 0, "raw_agent_type": 7, "length_m": 3.7002, "width_m": 1.6324, "clean_frames": 60, "speed_at_event_mps": 6.4672}, {"agent_id": 10, "roles": ["risk_agent", "collided_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.3421, "width_m": 1.4774, "clean_frames": 60, "speed_at_event_mps": 28.041}, {"agent_id": 2, "roles": ["risk_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.2188, "width_m": 1.4623, "clean_frames": 52, "speed_at_event_mps": 25.8953}, {"agent_id": 3, "roles": ["context_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 4.1701, "width_m": 1.7598, "clean_frames": 60, "speed_at_event_mps": 26.3787}, {"agent_id": 5, "roles": ["context_agent"], "is_controlled": false, "response_type": 0, "raw_agent_type": 7, "length_m": 3.4134, "width_m": 1.5414, "clean_frames": 60, "speed_at_event_mps": 8.3336}, {"agent_id": 6, "roles": ["context_agent"], "is_controlled": false, "response_type": 0, "raw_agent_type": 7, "length_m": 3.2981, "width_m": 1.4325, "clean_frames": 60, "speed_at_event_mps": 13.1443}, {"agent_id": 7, "roles": ["risk_agent"], "is_controlled": true, "response_type": 0, "raw_agent_type": 7, "length_m": 3.1299, "width_m": 1.3812, "clean_frames": 36, "speed_at_event_mps": null}], "map_summary": {"center_xy": [3.971, -11.1785], "radius_m": 80.0, "num_points": 256, "type_counts": {"1": 10, "3": 48, "2": 3, "5": 1, "0": 194}}, "timeline": [{"t_to_event_s": -4.9, "frame_index": 0, "target_offset_s": -5.0, "distance_m": 55.212, "ttc_s": null, "closing_speed_mps": null, "relative_speed_mps": null, "longitudinal_gap_m": 55.1196, "lateral_offset_m": 3.1922, "relative_heading_rad": -0.0028, "agent_a_speed_mps": null, "agent_b_speed_mps": null, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -3.0, "frame_index": 19, "target_offset_s": -3.0, "distance_m": 42.7184, "ttc_s": 4.4631, "closing_speed_mps": 9.5715, "relative_speed_mps": 9.6031, "longitudinal_gap_m": 42.5891, "lateral_offset_m": 3.3206, "relative_heading_rad": 0.0039, "agent_a_speed_mps": 20.375, "agent_b_speed_mps": 10.772, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -2.0, "frame_index": 29, "target_offset_s": -2.0, "distance_m": 30.5419, "ttc_s": 2.1306, "closing_speed_mps": 14.3347, "relative_speed_mps": 14.4364, "longitudinal_gap_m": 30.3458, "lateral_offset_m": 3.455, "relative_heading_rad": 0.0091, "agent_a_speed_mps": 24.2421, "agent_b_speed_mps": 9.8062, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -1.0, "frame_index": 39, "target_offset_s": -1.0, "distance_m": 18.8455, "ttc_s": 1.0754, "closing_speed_mps": 17.5236, "relative_speed_mps": 17.6025, "longitudinal_gap_m": 18.7069, "lateral_offset_m": -2.2811, "relative_heading_rad": 0.1155, "agent_a_speed_mps": 25.7746, "agent_b_speed_mps": 8.2282, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": -0.5, "frame_index": 44, "target_offset_s": -0.5, "distance_m": 9.264, "ttc_s": 0.4591, "closing_speed_mps": 20.1769, "relative_speed_mps": 20.423, "longitudinal_gap_m": 9.0834, "lateral_offset_m": -1.8203, "relative_heading_rad": 0.1175, "agent_a_speed_mps": 27.7078, "agent_b_speed_mps": 7.3543, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": 0.0, "frame_index": 49, "target_offset_s": 0.0, "distance_m": 2.1322, "ttc_s": 0.0973, "closing_speed_mps": 21.9152, "relative_speed_mps": 24.68, "longitudinal_gap_m": 0.9973, "lateral_offset_m": -1.8846, "relative_heading_rad": 0.5289, "agent_a_speed_mps": 28.041, "agent_b_speed_mps": 6.4672, "agent_a_collision": true, "agent_b_collision": true, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": 0.5, "frame_index": 54, "target_offset_s": 0.5, "distance_m": 8.9179, "ttc_s": null, "closing_speed_mps": -23.9606, "relative_speed_mps": 24.1971, "longitudinal_gap_m": -8.6835, "lateral_offset_m": -2.0314, "relative_heading_rad": 0.1699, "agent_a_speed_mps": 29.8414, "agent_b_speed_mps": 5.6582, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}, {"t_to_event_s": 1.0, "frame_index": 59, "target_offset_s": 1.0, "distance_m": 3.7519, "ttc_s": 2.3531, "closing_speed_mps": 1.5944, "relative_speed_mps": 35.6212, "longitudinal_gap_m": 0.4056, "lateral_offset_m": 3.7299, "relative_heading_rad": 2.6672, "agent_a_speed_mps": 31.1746, "agent_b_speed_mps": 4.9242, "agent_a_collision": false, "agent_b_collision": false, "agent_a_offroad": false, "agent_b_offroad": false}], "llm_task": {"question": "Judge whether this is a natural and safety-critical long-tail driving scenario.", "return_format": "strict_json", "expected_keys": ["accepted", "danger_level", "naturalness_score", "interaction_score", "scenario_value_score", "scenario_type", "artifact_flags", "reason"]}} diff --git a/gpudrive-main/search_outputs/llm_cases_test/summary.json b/gpudrive-main/search_outputs/llm_cases_test/summary.json new file mode 100644 index 0000000000000000000000000000000000000000..39ac227a0a12573720cd7007bd71712357c8afe0 --- /dev/null +++ b/gpudrive-main/search_outputs/llm_cases_test/summary.json @@ -0,0 +1,24 @@ +{ + "input": "server_resource\\event_traces.jsonl", + "output_dir": "search_outputs\\llm_cases_test", + "records_read": 5, + "cases_written": 5, + "cases_skipped_no_primary_pair": 0, + "semantic_counts": { + "same_direction_side_conflict_or_cut_in": 4, + "generic_close_interaction": 1 + }, + "primary_pair_reason_counts": { + "risk_agent_directly_collided_with_partner": 2, + "event_min_distance_risk_partner": 3 + }, + "artifact_warning_counts": { + "high_yaw_rate": 5, + "offroad_before_collision": 1, + "extreme_speed": 1, + "hard_deceleration": 1, + "high_acceleration": 2, + "large_frame_displacement": 1, + "short_clean_track": 1 + } +} \ No newline at end of file diff --git a/gpudrive-main/server_resource/event_traces.jsonl b/gpudrive-main/server_resource/event_traces.jsonl new file mode 100644 index 0000000000000000000000000000000000000000..a24a639733c0951b0906503d596411c072a76562 --- /dev/null +++ b/gpudrive-main/server_resource/event_traces.jsonl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6f3969b7bc4e95b31c97f2210282e709cdc5bb27e0e7f180523cbb06cc907b26 +size 219173853 diff --git a/gpudrive-main/setup.py b/gpudrive-main/setup.py new file mode 100644 index 0000000000000000000000000000000000000000..913fd6cbb8b029f5983096a76cc4bd91a5038e2b --- /dev/null +++ b/gpudrive-main/setup.py @@ -0,0 +1,12 @@ +from setuptools import setup, find_packages + +setup( + name="gpudrive", + version="0.1.0", + packages=find_packages(include=["pygpudrive"]), + package_data={ + "gpudrive": ["cpython-31*-*.so"], + "pygpudrive" + }, + include_package_data=True, +) diff --git a/gpudrive-main/src/CMakeLists.txt b/gpudrive-main/src/CMakeLists.txt new file mode 100644 index 0000000000000000000000000000000000000000..5eec975fde501a7355a4f8a6adf6a2e926cb5802 --- /dev/null +++ b/gpudrive-main/src/CMakeLists.txt @@ -0,0 +1,91 @@ +set(SIMULATOR_SRCS + types.hpp + sim.hpp sim.cpp + init.hpp rng.hpp + level_gen.hpp level_gen.cpp + obb.hpp + utils.hpp + rasterizer.hpp + binary_heap.hpp + knn.hpp + dynamics.hpp +) + +add_library(gpudrive_cpu_impl STATIC + ${SIMULATOR_SRCS} +) + +target_link_libraries(gpudrive_cpu_impl + PUBLIC + madrona_mw_core + PRIVATE + madrona_common + madrona_mw_physics + madrona_rendering_system +) + +add_library(gpudrive_mgr STATIC + mgr.hpp mgr.cpp + MapReader.hpp MapReader.cpp +) + +target_link_libraries(gpudrive_mgr + PUBLIC + madrona_python_utils + PRIVATE + nlohmann_json::nlohmann_json + gpudrive_cpu_impl + madrona_mw_cpu + madrona_common + madrona_importer + madrona_physics_loader + madrona_render +) + +if (TARGET madrona_mw_gpu) + madrona_build_compile_defns( + OUT_TARGET + gpudrive_gpu_srcs + SOURCES_DEFN + GPU_HIDESEEK_SRC_LIST + FLAGS_DEFN + GPU_HIDESEEK_COMPILE_FLAGS + SRCS + ${SIMULATOR_SRCS} + ) + target_link_libraries(gpudrive_mgr PRIVATE + gpudrive_gpu_srcs + madrona_mw_gpu + ) +endif () + +target_compile_definitions(gpudrive_mgr PRIVATE + -DDATA_DIR="${CMAKE_CURRENT_SOURCE_DIR}/../assets/" +) + +madrona_python_module(madrona_gpudrive + bindings.cpp +) + +target_link_libraries(madrona_gpudrive PRIVATE + gpudrive_mgr +) + +if (TARGET madrona_viz) + add_executable(viewer viewer.cpp) + target_link_libraries(viewer PRIVATE + madrona_mw_core gpudrive_mgr madrona_viz) + + target_compile_definitions(viewer PRIVATE + -DDATA_DIR="${CMAKE_CURRENT_SOURCE_DIR}/../assets/" + ) + + if (TARGET madrona_cuda) + target_link_libraries(viewer PRIVATE + madrona_cuda + ) + endif() +endif() + +add_executable(headless headless.cpp) +target_link_libraries(headless madrona_mw_core gpudrive_mgr) diff --git a/gpudrive-main/src/MapReader.cpp b/gpudrive-main/src/MapReader.cpp new file mode 100644 index 0000000000000000000000000000000000000000..8c933edd91e1f5c9c8afdaa279849f6af58a9b21 --- /dev/null +++ b/gpudrive-main/src/MapReader.cpp @@ -0,0 +1,63 @@ +#include "MapReader.hpp" +#include "json_serialization.hpp" + +#ifdef MADRONA_CUDA_SUPPORT +#include +#endif + +namespace { +madrona_gpudrive::Map *copyToArrayOnHostOrDevice(const madrona_gpudrive::Map *in, + madrona::ExecMode hostOrDevice) { + madrona_gpudrive::Map *map = nullptr; + + if (hostOrDevice == madrona::ExecMode::CUDA) { +#ifdef MADRONA_CUDA_SUPPORT + map = static_cast(madrona::cu::allocGPU(sizeof(madrona_gpudrive::Map))); + cudaMemcpy(map, in, sizeof(madrona_gpudrive::Map), cudaMemcpyHostToDevice); + auto error = cudaGetLastError(); + assert (error == cudaSuccess); + +#else + FATAL("Madrona was not compiled with CUDA support"); +#endif + } else { + assert(hostOrDevice == madrona::ExecMode::CPU); + + // This is a copy from CPU to CPU and can be avoided by extracting and + // releasing in's backing array. For the sake of symmetry with the CUDA + // scenario, we nevertheless opt to copy the data. + map = new madrona_gpudrive::Map(); + std::memcpy(map, in, sizeof(madrona_gpudrive::Map)); + } + + return map; +} +} // namespace + +namespace madrona_gpudrive { + +MapReader::MapReader(const std::string &pathToFile) : in_(pathToFile) { + assert(in_.is_open()); + map_ = new madrona_gpudrive::Map(); +} + +MapReader::~MapReader() { + delete map_; +} + +void MapReader::doParse(float polylineReductionThreshold) { + nlohmann::json rawJson; + in_ >> rawJson; + + from_json(rawJson, *map_, polylineReductionThreshold); +} + +madrona_gpudrive::Map* MapReader::parseAndWriteOut(const std::string &path, + madrona::ExecMode executionMode, float polylineReductionThreshold) { + MapReader reader(path); + reader.doParse(polylineReductionThreshold); + + return copyToArrayOnHostOrDevice(reader.map_, executionMode); + +} +} // namespace madrona_gpudrive diff --git a/gpudrive-main/src/MapReader.hpp b/gpudrive-main/src/MapReader.hpp new file mode 100644 index 0000000000000000000000000000000000000000..e42a3f6c6bb4c9a09865389a59336e94e62cad98 --- /dev/null +++ b/gpudrive-main/src/MapReader.hpp @@ -0,0 +1,25 @@ +#pragma once + +#include +#include + +#include "init.hpp" + +namespace madrona_gpudrive { + +struct AgentInit; + +class MapReader { +public: + static madrona_gpudrive::Map* parseAndWriteOut(const std::string &path, madrona::ExecMode executionMode, float polylineReductionThreshold); + +private: + MapReader(const std::string &pathToFile); + ~MapReader(); + void doParse(float polylineReductionThreshold); + + std::ifstream in_; + madrona_gpudrive::Map *map_; +}; + +} // namespace madrona_gpudrive diff --git a/gpudrive-main/src/binary_heap.hpp b/gpudrive-main/src/binary_heap.hpp new file mode 100644 index 0000000000000000000000000000000000000000..05bc3e03f0568da5c5c150914ee500e578554915 --- /dev/null +++ b/gpudrive-main/src/binary_heap.hpp @@ -0,0 +1,192 @@ +/* + * + * Copyright (c) 1994 + * Hewlett-Packard Company + * + * Permission to use, copy, modify, distribute and sell this software + * and its documentation for any purpose is hereby granted without fee, + * provided that the above copyright notice appear in all copies and + * that both that copyright notice and this permission notice appear + * in supporting documentation. Hewlett-Packard Company makes no + * representations about the suitability of this software for any + * purpose. It is provided "as is" without express or implied warranty. + * + * Copyright (c) 1997 + * Silicon Graphics Computer Systems, Inc. + * + * Permission to use, copy, modify, distribute and sell this software + * and its documentation for any purpose is hereby granted without fee, + * provided that the above copyright notice appear in all copies and + * that both that copyright notice and this permission notice appear + * in supporting documentation. Silicon Graphics makes no + * representations about the suitability of this software for any + * purpose. It is provided "as is" without express or implied warranty. + */ + +/* NOTE: This is an internal header file, included by other STL headers. + * You should not attempt to use it directly. + */ +#pragma once + +// Heap-manipulation functions: push_heap, pop_heap, make_heap, sort_heap. + +template + +inline void __push_heap(_RandomAccessIterator __first, _Distance __holeIndex, + _Distance __topIndex, _Tp __x, _Compare __comp) { + _Distance __parent = (__holeIndex - 1) / 2; + while (__holeIndex > __topIndex && __comp(*(__first + __parent), __x)) { + *(__first + __holeIndex) = *(__first + __parent); + __holeIndex = __parent; + __parent = (__holeIndex - 1) / 2; + } + *(__first + __holeIndex) = __x; +} + +template + +inline void __push_heap_aux(_RandomAccessIterator __first, + _RandomAccessIterator __last, _Compare __comp) { + int hole_index = (__last - __first) - 1; + int top_index = 0; + __push_heap(__first, hole_index, top_index, *(__last - 1), __comp); +} + +template + +inline void push_heap(_RandomAccessIterator __first, + _RandomAccessIterator __last, _Compare __comp) { + __push_heap_aux(__first, __last, __comp); +} + +template + +void __adjust_heap(_RandomAccessIterator __first, _Distance __holeIndex, + _Distance __len, _Tp __x) { + _Distance __topIndex = __holeIndex; + _Distance __secondChild = 2 * __holeIndex + 2; + while (__secondChild < __len) { + if (*(__first + __secondChild) < *(__first + (__secondChild - 1))) + __secondChild--; + *(__first + __holeIndex) = *(__first + __secondChild); + __holeIndex = __secondChild; + __secondChild = 2 * (__secondChild + 1); + } + if (__secondChild == __len) { + *(__first + __holeIndex) = *(__first + (__secondChild - 1)); + __holeIndex = __secondChild - 1; + } + __push_heap(__first, __holeIndex, __topIndex, __x); +} + +template + +inline void __pop_heap(_RandomAccessIterator __first, + _RandomAccessIterator __last, + _RandomAccessIterator __result, _Tp __x) { + *__result = *__first; + __adjust_heap(__first, 0, __last - __first, __x); +} + +template + +inline void __pop_heap_aux(_RandomAccessIterator __first, + _RandomAccessIterator __last) { + __pop_heap(__first, __last - 1, __last - 1, *(__last - 1)); +} + +template + +inline void pop_heap(_RandomAccessIterator __first, + _RandomAccessIterator __last) { + __pop_heap_aux(__first, __last, __VALUE_TYPE(__first)); +} + +template + +void __adjust_heap(_RandomAccessIterator __first, _Distance __holeIndex, + _Distance __len, _Tp __x, _Compare __comp) { + _Distance __topIndex = __holeIndex; + _Distance __secondChild = 2 * __holeIndex + 2; + while (__secondChild < __len) { + if (__comp(*(__first + __secondChild), *(__first + (__secondChild - 1)))) + __secondChild--; + *(__first + __holeIndex) = *(__first + __secondChild); + __holeIndex = __secondChild; + __secondChild = 2 * (__secondChild + 1); + } + if (__secondChild == __len) { + *(__first + __holeIndex) = *(__first + (__secondChild - 1)); + __holeIndex = __secondChild - 1; + } + __push_heap(__first, __holeIndex, __topIndex, __x, __comp); +} + +template + +inline void +__pop_heap(_RandomAccessIterator __first, _RandomAccessIterator __last, + _RandomAccessIterator __result, _Tp __x, _Compare __comp) { + *__result = *__first; + + int hole_index = 0; + int len = __last - __first; + + __adjust_heap(__first, hole_index, len, __x, __comp); +} + +template + +inline void __pop_heap_aux(_RandomAccessIterator __first, + _RandomAccessIterator __last, _Compare __comp) { + __pop_heap(__first, __last - 1, __last - 1, *(__last - 1), __comp); +} + +template + +inline void pop_heap(_RandomAccessIterator __first, + _RandomAccessIterator __last, _Compare __comp) { + __pop_heap_aux(__first, __last, __comp); +} + +template + +void __make_heap(_RandomAccessIterator __first, _RandomAccessIterator __last) { + if (__last - __first < 2) + return; + int __len = __last - __first; + int __parent = (__len - 2) / 2; + + while (true) { + __adjust_heap(__first, __parent, __len, *(__first + __parent)); + if (__parent == 0) + return; + __parent--; + } +} + +template + +void __make_heap(_RandomAccessIterator __first, _RandomAccessIterator __last, + _Compare __comp) { + if (__last - __first < 2) + return; + int __len = __last - __first; + int __parent = (__len - 2) / 2; + + while (true) { + __adjust_heap(__first, __parent, __len, *(__first + __parent), __comp); + if (__parent == 0) + return; + __parent--; + } +} + +template + +inline void make_heap(_RandomAccessIterator __first, + _RandomAccessIterator __last, _Compare __comp) { + __make_heap(__first, __last, __comp); +} diff --git a/gpudrive-main/src/bindings.cpp b/gpudrive-main/src/bindings.cpp new file mode 100644 index 0000000000000000000000000000000000000000..6b7414ddb85cc59c2b0334a27c830f10799ed5f1 --- /dev/null +++ b/gpudrive-main/src/bindings.cpp @@ -0,0 +1,152 @@ +#include "mgr.hpp" + +#include +#include + +#include +#include + +namespace nb = nanobind; + +namespace madrona_gpudrive +{ + + // This file creates the python bindings used by the learning code. + // Refer to the nanobind documentation for more details on these functions. + NB_MODULE(madrona_gpudrive, m) + { + // Each simulator has a madrona submodule that includes base types + // like madrona::py::Tensor and madrona::py::PyExecMode. + madrona::py::setupMadronaSubmodule(m); + + // Add bindings for constants defined in src/consts.hpp + m.attr("kMaxAgentCount") = consts::kMaxAgentCount; + m.attr("kMaxRoadEntityCount") = consts::kMaxRoadEntityCount; + m.attr("kMaxAgentMapObservationsCount") = consts::kMaxAgentMapObservationsCount; + m.attr("episodeLen") = consts::episodeLen; + m.attr("numLidarSamples") = consts::numLidarSamples; + m.attr("vehicleScale") = consts::vehicleLengthScale; + + // Define RewardType enum + nb::enum_(m, "RewardType") + .value("DistanceBased", RewardType::DistanceBased) + .value("OnGoalAchieved", RewardType::OnGoalAchieved) + .value("Dense", RewardType::Dense); + + // Define RewardParams class + nb::class_(m, "RewardParams") + .def(nb::init<>()) // Default constructor + .def_rw("rewardType", &RewardParams::rewardType) + .def_rw("distanceToGoalThreshold", &RewardParams::distanceToGoalThreshold) + .def_rw("distanceToExpertThreshold", &RewardParams::distanceToExpertThreshold); + + nb::enum_(m, "FindRoadObservationsWith") + .value("KNearestEntitiesWithRadiusFiltering", FindRoadObservationsWith::KNearestEntitiesWithRadiusFiltering) + .value("AllEntitiesWithRadiusFiltering", FindRoadObservationsWith::AllEntitiesWithRadiusFiltering); + + // Define Parameters class + nb::class_(m, "Parameters") + .def(nb::init<>()) // Default constructor + .def_rw("polylineReductionThreshold", &Parameters::polylineReductionThreshold) + .def_rw("observationRadius", &Parameters::observationRadius) + .def_rw("rewardParams", &Parameters::rewardParams) + .def_rw("collisionBehaviour", &Parameters::collisionBehaviour) + .def_rw("maxNumControlledAgents", &Parameters::maxNumControlledAgents) + .def_rw("IgnoreNonVehicles", &Parameters::IgnoreNonVehicles) + .def_rw("roadObservationAlgorithm", &Parameters::roadObservationAlgorithm) + .def_rw("initOnlyValidAgentsAtFirstStep", &Parameters::initOnlyValidAgentsAtFirstStep) + .def_rw("dynamicsModel", &Parameters::dynamicsModel) + .def_rw("enableLidar", &Parameters::enableLidar) + .def_rw("disableClassicalObs", &Parameters::disableClassicalObs) + .def_rw("isStaticAgentControlled", &Parameters::isStaticAgentControlled) + .def_rw("readFromTracksToPredict", &Parameters::readFromTracksToPredict); + + // Define CollisionBehaviour enum + nb::enum_(m, "CollisionBehaviour") + .value("AgentStop", CollisionBehaviour::AgentStop) + .value("AgentRemoved", CollisionBehaviour::AgentRemoved) + .value("Ignore", CollisionBehaviour::Ignore); + + nb::enum_(m, "DynamicsModel") + .value("Classic", DynamicsModel::Classic) + .value("InvertibleBicycle", DynamicsModel::InvertibleBicycle) + .value("DeltaLocal", DynamicsModel::DeltaLocal) + .value("State", DynamicsModel::State); + + nb::enum_(m, "EntityType") + .value("_None", EntityType::None) + .value("RoadEdge", EntityType::RoadEdge) + .value("RoadLine", EntityType::RoadLine) + .value("RoadLane", EntityType::RoadLane) + .value("CrossWalk", EntityType::CrossWalk) + .value("SpeedBump", EntityType::SpeedBump) + .value("StopSign", EntityType::StopSign) + .value("Vehicle", EntityType::Vehicle) + .value("Pedestrian", EntityType::Pedestrian) + .value("Cyclist", EntityType::Cyclist) + .value("Padding", EntityType::Padding) + .value("NumTypes", EntityType::NumTypes); + + // Bindings for Manager class + nb::class_(m, "SimManager") + .def( + "__init__", [](Manager *self, madrona::py::PyExecMode exec_mode, int64_t gpu_id, std::vector scenes, Parameters params, bool enable_batch_renderer, uint32_t batch_render_view_width, uint32_t batch_render_view_height) + { new (self) Manager(Manager::Config{ + .execMode = exec_mode, + .gpuID = (int)gpu_id, + .scenes = scenes, + .params = params, + .enableBatchRenderer = enable_batch_renderer, + .batchRenderViewWidth = batch_render_view_width, + .batchRenderViewHeight = batch_render_view_height});}, + nb::arg("exec_mode"), + nb::arg("gpu_id"), + nb::arg("scenes"), + nb::arg("params"), + nb::arg("enable_batch_renderer") = false, + nb::arg("batch_render_view_width") = 64, + nb::arg("batch_render_view_height") = 64) + .def("step", &Manager::step) + .def("reset", &Manager::reset) + .def("action_tensor", &Manager::actionTensor) + .def("reward_tensor", &Manager::rewardTensor) + .def("done_tensor", &Manager::doneTensor) + .def("self_observation_tensor", &Manager::selfObservationTensor) + .def("map_observation_tensor", &Manager::mapObservationTensor) + .def("partner_observations_tensor", &Manager::partnerObservationsTensor) + .def("lidar_tensor", &Manager::lidarTensor) + .def("steps_remaining_tensor", &Manager::stepsRemainingTensor) + .def("shape_tensor", &Manager::shapeTensor) + .def("controlled_state_tensor", &Manager::controlledStateTensor) + .def("agent_roadmap_tensor", &Manager::agentMapObservationsTensor) + .def("absolute_self_observation_tensor", + &Manager::absoluteSelfObservationTensor) + .def("bev_observation_tensor", &Manager::bevObservationTensor) + .def("valid_state_tensor", &Manager::validStateTensor) + .def("info_tensor", &Manager::infoTensor) + .def("rgb_tensor", &Manager::rgbTensor) + .def("depth_tensor", &Manager::depthTensor) + .def("response_type_tensor", &Manager::responseTypeTensor) + .def("expert_trajectory_tensor", &Manager::expertTrajectoryTensor) + .def("set_maps", &Manager::setMaps) + .def("world_means_tensor", &Manager::worldMeansTensor) + .def("metadata_tensor", &Manager::metadataTensor) + .def("map_name_tensor", &Manager::mapNameTensor) + .def("deleteAgents", [](Manager &self, nb::dict py_agents_to_delete) { + std::unordered_map> agents_to_delete; + + // Convert Python dict to C++ unordered_map + for (auto item : py_agents_to_delete) { + int32_t key = nb::cast(item.first); + std::vector value = nb::cast>(item.second); + agents_to_delete[key] = value; + } + + self.deleteAgents(agents_to_delete); + }) + .def("deleted_agents_tensor", &Manager::deletedAgentsTensor) + .def("map_name_tensor", &Manager::mapNameTensor) + .def("scenario_id_tensor", &Manager::scenarioIdTensor); + } + +} diff --git a/gpudrive-main/src/consts.hpp b/gpudrive-main/src/consts.hpp new file mode 100644 index 0000000000000000000000000000000000000000..41d7a528206c0393b445868ba78c72f26a12f5c2 --- /dev/null +++ b/gpudrive-main/src/consts.hpp @@ -0,0 +1,68 @@ +#pragma once + +#include +#include +#include + +namespace madrona_gpudrive { + +namespace consts { + +inline constexpr madrona::CountT kMaxAgentCount = 64; +inline constexpr madrona::CountT kMaxRoadEntityCount = 10000; +inline constexpr madrona::CountT kMaxAgentMapObservationsCount = 200; + +inline constexpr bool useEstimatedYaw = true; + +inline constexpr float staticThreshold = 0.2f; + +// Various world / entity size parameters +inline constexpr float worldLength = 40.f; + +// This factor rescales the length of the vehicles by a tiny amount +// To account for the fact that noise occasionally puts vehicles into initial +// collisions. This is a dataset artifact that we are handling here like this. +inline constexpr float vehicleLengthScale = 0.7f; + +// Each unit of distance forward (+ y axis) rewards the agents by this amount +inline constexpr float rewardPerDist = 0.05f; +// Each step that the agents don't make additional progress they get a small +// penalty reward +inline constexpr float slackReward = -0.005f; + +// Steps per episode +inline constexpr int32_t episodeLen = 91; + +// Number of lidar samples, arranged in circle around agent +inline constexpr madrona::CountT numLidarSamples = 50; + +// These offsets define the offset in the z-axis to throw the lidar rays +// Different objects are on different heights in the world so as to show in the lidar rays +// In total we throw 3*numLidarSamples rays, 1 for the car, 1 for the road edge, 1 for the road line +inline constexpr float lidarCarOffset = 0.5f; +inline constexpr float lidarRoadEdgeOffset = 0.1f; +inline constexpr float lidarRoadLineOffset = -0.1f; +inline constexpr float lidarDistance = 200.f; +inline constexpr float lidarAngle = madrona::math::pi / 3; // The angle between the normal and the lidar ray in the extreme. By default we define a 120 degree view cone. + +// Bev observation constants +inline constexpr int bev_rasterization_resolution = 200; + +// Time (seconds) per step +inline constexpr float deltaT = 0.04f; + +// Number of physics substeps +inline constexpr madrona::CountT numPhysicsSubsteps = 0.f; + +inline constexpr float zDimensionScale = 1; +inline constexpr float xDimensionScaleRoadSegment = 1; + +inline constexpr madrona::CountT kTrajectoryLength = 91; // Nocturne has 90 timesteps per episode. making it 91 as a buffer. + +inline constexpr madrona::CountT kMaxRoadGeometryLength = 1810; + +inline constexpr madrona::math::Vector3 kPaddingPosition = { + -11000, -11000, std::numeric_limits::max()}; +} + +} diff --git a/gpudrive-main/src/dynamics.hpp b/gpudrive-main/src/dynamics.hpp new file mode 100644 index 0000000000000000000000000000000000000000..41d98d8f2755a8d1b3ddf95ab58d21138357eb29 --- /dev/null +++ b/gpudrive-main/src/dynamics.hpp @@ -0,0 +1,195 @@ +#pragma once + +#include +#include + +using namespace madrona; +using namespace madrona::math; +namespace madrona_gpudrive +{ + + inline void forwardKinematics(const Action &action, VehicleSize &size, Rotation &rotation, Position &position, Velocity &velocity) + { + const float maxSpeed{std::numeric_limits::max()}; + const float dt{0.1}; + + auto clipSpeed = [maxSpeed](float speed) + { + return std::max(std::min(speed, maxSpeed), -maxSpeed); + }; + // TODO(samk): hoist into Vector2::PolarToVector2D + auto polarToVector2D = [](float r, float theta) + { + return math::Vector2{r * cosf(theta), r * sinf(theta)}; + }; + + float speed = velocity.linear.length(); + float yaw = utils::quatToYaw(rotation); + // Average speed + const float v{clipSpeed(speed + 0.5f * action.classic.acceleration * dt)}; + const float tanDelta{tanf(action.classic.steering)}; + // Assume center of mass lies at the middle of length, then l / L == 0.5. + const float beta{std::atan(0.5f * tanDelta)}; + const math::Vector2 d{polarToVector2D(v, yaw + beta)}; + const float w{v * std::cos(beta) * tanDelta / size.length}; + + // model.position += d * dt; + // model.heading = utils::AngleAdd(model.heading, w * dt); + // model.speed = clipSpeed(model.speed + action.acceleration * dt); + float new_yaw = utils::AngleAdd(yaw, w * dt); + float new_speed = clipSpeed(speed + action.classic.acceleration * dt); + position.x += d.x * dt; + position.y += d.y * dt; + position.z = 1; + rotation = Quat::angleAxis(new_yaw, madrona::math::up); + velocity.linear.x = new_speed * cosf(new_yaw); + velocity.linear.y = new_speed * sinf(new_yaw); + velocity.linear.z = 0; + velocity.angular = Vector3::zero(); + velocity.angular.z = w; + } + + inline void forwardBicycleModel(Action &action, Rotation &rotation, Position &position, Velocity &velocity) + { + // Clip acceleration and steering + action.classic.acceleration = fmaxf(-6.0, fminf(action.classic.acceleration, 6.0)); + action.classic.steering = fmaxf(-3.0, fminf(action.classic.steering, 3.0)); + + const float dt{0.1}; + float yaw = utils::quatToYaw(rotation); + float speed = velocity.linear.length(); + //new_x = x + vel_x * t + 0.5 * accel * jnp.cos(yaw) * t**2 + position.x = position.x + velocity.linear.x * dt + 0.5 * action.classic.acceleration * cosf(yaw) * dt * dt; + // new_y = y + vel_y * t + 0.5 * accel * jnp.sin(yaw) * t**2 + position.y = position.y + velocity.linear.y * dt + 0.5 * action.classic.acceleration * sinf(yaw) * dt * dt; + // delta_yaw = steering * (speed * t + 0.5 * accel * t**2) + float delta_yaw = action.classic.steering * (speed * dt + 0.5 * action.classic.acceleration * dt * dt); + // new_yaw = geometry.wrap_yaws(yaw + delta_yaw) + float new_yaw = utils::AngleAdd(yaw, delta_yaw); + // new_vel = speed + accel * t + float new_speed = speed + action.classic.acceleration * dt; + // new_vel_x = new_vel * jnp.cos(new_yaw) + velocity.linear.x = new_speed * cosf(new_yaw); + // new_vel_y = new_vel * jnp.sin(new_yaw) + velocity.linear.y = new_speed * sinf(new_yaw); + velocity.linear.z = 0; + + velocity.angular = Vector3::zero(); + velocity.angular.z = delta_yaw / dt; // Is this correct ? + + rotation = Quat::angleAxis(new_yaw, madrona::math::up); + } + + inline void forwardDeltaModel(Action &action, Rotation &rotation, Position &position, Velocity &velocity) + { + // start delta model + // start DeltaLocal + const float dt{0.1}; + float yaw = utils::quatToYaw(rotation); + // rotated_xy = jnp.matmul(rotation_mat, action.data[..., :2, jnp.newaxis], precision='float32')[..., 0] + // From https://en.wikipedia.org/wiki/Rotation_matrix + float cos = std::cos(yaw); + float sin = std::sin(yaw); + + // x = c * x - s * y + // y = s * x + c * y + float dx = action.delta.dx * cos - action.delta.dy * sin; + float dy = action.delta.dx * sin + action.delta.dy * cos; + // end DeltaLocal + // start DeltaGlobal + position.x = position.x + dx; + position.y = position.y + dy; + + + velocity.linear.x = dx / dt; + velocity.linear.y = dy / dt; + velocity.linear.z = 0; + velocity.angular = Vector3::zero(); + velocity.angular.z = action.delta.dyaw / dt; // Is this correct ? + // end DeltaGlobal + float new_yaw = utils::AngleAdd(yaw, action.delta.dyaw); + // end delta model + + rotation = Quat::angleAxis(new_yaw, madrona::math::up); + + } + + inline Action inverseBicycleModel(const Rotation &rotation, const Velocity &velocity, const Rotation &targetRotation, const Velocity &targetVelocity) + { + const float dt{0.1}; + + Action action = {.classic = {0, 0, 0}}; + float speed = velocity.linear.length(); + float target_speed = targetVelocity.linear.length(); + + // accel = (new_vel - vel) / dt + action.classic.acceleration = (target_speed - speed) / dt; + + float yaw = utils::NormalizeAngle(utils::quatToYaw(rotation)); + float target_yaw = utils::NormalizeAngle(utils::quatToYaw(targetRotation)); + + if(consts::useEstimatedYaw) + { + target_yaw = atan2f(targetVelocity.linear.y, targetVelocity.linear.x); + } + + // steering = (new_yaw - yaw) / (speed * dt + 1/2 * accel * dt ** 2) + float denominator = speed * dt + 0.5 * action.classic.acceleration * dt * dt; + if (denominator != 0) + { + action.classic.steering = (target_yaw - yaw) / denominator; + } + else + { + action.classic.steering = 0; + } + + return action; + + } + + inline Action inverseDeltaModel(const Rotation &rotation, const Position &position, const Rotation &targetRotation, const Position &targetPosition) + { + Action action{.delta = {0, 0, 0}}; + float yaw = utils::quatToYaw(rotation); + float target_yaw = utils::quatToYaw(targetRotation); + // start delta model + // start DeltaGlobal + action.delta.dx = targetPosition.x - position.x; + action.delta.dy = targetPosition.y - position.y; + action.delta.dyaw = target_yaw - yaw; + + action.delta.dx = fmaxf(-6.0, fminf(action.delta.dx, 6.0)); + action.delta.dy = fmaxf(-6.0, fminf(action.delta.dy, 6.0)); + // end DeltaGlobal + // start DeltaLocal + + // rotated_xy = jnp.matmul(rotation_mat, action.data[..., :2, jnp.newaxis], precision='float32')[..., 0] + // From https://en.wikipedia.org/wiki/Rotation_matrix + float cos = std::cos(-yaw); + float sin = std::sin(-yaw); + // x = c * x - s * y + // y = s * x + c * y + float local_dx= action.delta.dx * cos - action.delta.dy * sin; + float local_dy = action.delta.dx * sin + action.delta.dy * cos; + + action.delta.dx = fmaxf(-6.0, fminf(local_dx, 6.0)); + action.delta.dy = fmaxf(-6.0, fminf(local_dy, 6.0)); + action.delta.dyaw = utils::NormalizeAngle(action.delta.dyaw); + // end DeltaLocal + // end delta model + + return action; + + } + + inline void forwardStateModel(Action &action, Rotation &rotation, Position &position, Velocity &velocity) + { + // No clipping happening here. + // This can go out of bounds with invalid actions + position = action.state.position; + velocity = action.state.velocity; + + rotation = Quat::angleAxis(action.state.yaw, madrona::math::up); + } +} \ No newline at end of file diff --git a/gpudrive-main/src/headless.cpp b/gpudrive-main/src/headless.cpp new file mode 100644 index 0000000000000000000000000000000000000000..3bd1aa5b51892fe16562a59825a987d6c6770c8e --- /dev/null +++ b/gpudrive-main/src/headless.cpp @@ -0,0 +1,156 @@ +#include "mgr.hpp" +#include "consts.hpp" +#include "types.hpp" + +#include +#include +#include +#include +#include +#include +#include +#include + +using namespace madrona; + +int main(int argc, char *argv[]) +{ + using namespace madrona_gpudrive; + + if (argc < 3) { + fprintf(stderr, "%s TYPE NUM_STEPS [--rand-actions]\n", argv[0]); + return -1; + } + std::string type(argv[1]); + + ExecMode exec_mode; + if (type == "CPU") { + exec_mode = ExecMode::CPU; + } else if (type == "CUDA") { + exec_mode = ExecMode::CUDA; + } else { + fprintf(stderr, "Invalid ExecMode\n"); + return -1; + } + + uint64_t num_steps = std::stoul(argv[2]); + std::vector scenes = { + "../data/processed/examples/tfrecord-00000-of-00150_78.json", + "../data/processed/examples/tfrecord-00043-of-00150_223.json", + "../data/processed/examples/tfrecord-00149-of-00150_111.json" + }; + uint64_t num_worlds = scenes.size(); + + bool rand_actions = false; + if (argc >= 4) { + if (std::string(argv[3]) == "--rand-actions") { + rand_actions = true; + } + } + + Manager mgr({ + .execMode = exec_mode, + .gpuID = 0, + .scenes = scenes, + .params = { + .polylineReductionThreshold = 1.0, + .observationRadius = 100.0, + .rewardParams = { + .rewardType = RewardType::DistanceBased, + .distanceToGoalThreshold = 0.5, + .distanceToExpertThreshold = 0.5 + }, + .maxNumControlledAgents = 0, + } + }); + + std::random_device rd; + std::mt19937 rand_gen(rd()); + std::uniform_real_distribution acc_gen(-3.0,2.0); + std::uniform_real_distribution steer_gen(-0.7,0.7); + + auto action_printer = mgr.actionTensor().makePrinter(); + auto self_printer = mgr.selfObservationTensor().makePrinter(); + auto partner_obs_printer = mgr.partnerObservationsTensor().makePrinter(); + auto map_obs_printer = mgr.mapObservationTensor().makePrinter(); + auto shapePrinter = mgr.shapeTensor().makePrinter(); + auto rewardPrinter = mgr.rewardTensor().makePrinter(); + auto donePrinter = mgr.doneTensor().makePrinter(); + auto controlledStatePrinter = mgr.controlledStateTensor().makePrinter(); + auto agent_map_obs_printer = mgr.agentMapObservationsTensor().makePrinter(); + auto info_printer = mgr.infoTensor().makePrinter(); + auto means_printer = mgr.worldMeansTensor().makePrinter(); + auto metadata_printer = mgr.metadataTensor().makePrinter(); + + auto printObs = [&]() { + // printf("Self\n"); + // self_printer.print(); + + // printf("Actions\n"); + // action_printer.print(); + + // printf("Partner Obs\n"); + // partner_obs_printer.print(); + + // printf("Map Obs\n"); + // map_obs_printer.print(); + + // printf("Shape\n"); + // shapePrinter.print(); + + // printf("Reward\n"); + // rewardPrinter.print(); + + // printf("Done\n"); + // donePrinter.print(); + + // printf("Controlled State\n"); + // controlledStatePrinter.print(); + + // printf("Agent Map Obs\n"); + // agent_map_obs_printer.print(); + + // printf("Info\n"); + // info_printer.print(); + + // printf("Means\n"); + // means_printer.print(); + + metadata_printer.print(); + }; + + auto worldToShape = + mgr.getShapeTensorFromDeviceMemory(); + + const auto start = std::chrono::steady_clock::now(); + for (CountT i = 0; i < (CountT)num_steps; i++) { + if (rand_actions) { + for (CountT j = 0; j < (CountT)num_worlds; j++) { + auto agentCount = worldToShape.at(j).agentEntityCount; + for (CountT k = 0; k < agentCount; k++) { + float acc = acc_gen(rand_gen); + float steer = steer_gen(rand_gen); + float head = 0; + + mgr.setAction(j, k, acc, steer, head); + } + } + } + mgr.step(); + } + const auto end = std::chrono::steady_clock::now(); + const std::chrono::duration elapsed = end - start; + printObs(); + + float fps = (double)num_steps * (double)num_worlds / elapsed.count(); + printf("FPS %f\n", fps); + + uint64_t totalAgentCount{0}; + for (CountT j = 0; j < (CountT)num_worlds; j++) { + auto agentCount = worldToShape.at(j).agentEntityCount; + totalAgentCount += agentCount; + } + + float fpsNormalized = fps * totalAgentCount; + printf("Agent-Normalized FPS %f\n", fpsNormalized); +} diff --git a/gpudrive-main/src/init.hpp b/gpudrive-main/src/init.hpp new file mode 100644 index 0000000000000000000000000000000000000000..253e1d06eb424c63b6847a5bf3f3398affd4e79f --- /dev/null +++ b/gpudrive-main/src/init.hpp @@ -0,0 +1,137 @@ +#pragma once + +#include +#include "types.hpp" + +namespace madrona_gpudrive +{ + // Constants computed from train files. + constexpr size_t MAX_OBJECTS = 515; + constexpr size_t MAX_ROADS = 956; + constexpr size_t MAX_POSITIONS = 91; + constexpr size_t MAX_GEOMETRY = 1746; + + // Cannot use Madrona::math::Vector2 because it is not a POD type. + // Getting all zeros if using any madrona types. + struct MapVector2 + { + float x; + float y; + }; + + struct MapObject + { + MapVector2 position[MAX_POSITIONS]; + VehicleSize vehicle_size; + float heading[MAX_POSITIONS]; + MapVector2 velocity[MAX_POSITIONS]; + bool valid[MAX_POSITIONS]; + MapVector2 goalPosition; + EntityType type; + MetaData metadata; + + uint32_t numPositions; + uint32_t numHeadings; + uint32_t numVelocities; + uint32_t numValid; + uint32_t id; + MapVector2 mean; + bool markAsExpert{false}; + }; + + struct MapRoad + { + // std::array geometry; + MapVector2 geometry[MAX_GEOMETRY]; + uint32_t id; + MapType mapType; + EntityType type; + uint32_t numPoints; + MapVector2 mean; + }; + + struct Map + { + MapObject objects[MAX_OBJECTS]; + MapRoad roads[MAX_ROADS]; + + uint32_t numObjects; + uint32_t numRoads; + uint32_t numRoadSegments; + MapVector2 mean; + + char mapName[32]; + + char scenarioId[32]; + + // Constructor + Map() = default; + }; + + struct EpisodeManager + { + madrona::AtomicU32 curEpisode; + }; + + enum class RewardType : uint32_t + { + DistanceBased, // negative distance to goal + OnGoalAchieved, // 1 if on goal, 0 otherwise + Dense // negative distance to expert trajectory + }; + + struct RewardParams + { + RewardType rewardType; + float distanceToGoalThreshold; + float distanceToExpertThreshold; + }; + + enum class CollisionBehaviour : uint32_t + { + AgentStop, + AgentRemoved, + Ignore + }; + + enum class DynamicsModel : uint32_t + { + Classic, + InvertibleBicycle, + DeltaLocal, + State + }; + + enum class FindRoadObservationsWith + { + KNearestEntitiesWithRadiusFiltering, + AllEntitiesWithRadiusFiltering + }; + + struct Parameters + { + float polylineReductionThreshold; + float observationRadius; + RewardParams rewardParams; + CollisionBehaviour collisionBehaviour = CollisionBehaviour::AgentStop; // Default: AgentStop + uint32_t maxNumControlledAgents = 10000; // Arbitrary high number to by default control all vehicles + bool IgnoreNonVehicles = false; // Default: false + FindRoadObservationsWith roadObservationAlgorithm{ + FindRoadObservationsWith::KNearestEntitiesWithRadiusFiltering}; + bool initOnlyValidAgentsAtFirstStep = true; // Default: true + bool isStaticAgentControlled = false; // Default: false + bool enableLidar = false; + bool disableClassicalObs = false; + DynamicsModel dynamicsModel = DynamicsModel::Classic; + bool readFromTracksToPredict = false; // Default: false - for womd_tracks_to_predict initialization mode + }; + + struct WorldInit + { + EpisodeManager *episodeMgr; + madrona::phys::ObjectManager *rigidBodyObjMgr; + Map *map; + const Parameters *params; + }; + +} diff --git a/gpudrive-main/src/json_serialization.hpp b/gpudrive-main/src/json_serialization.hpp new file mode 100644 index 0000000000000000000000000000000000000000..43cffb8378271df72a6bdc66d0a0c9cf285f9641 --- /dev/null +++ b/gpudrive-main/src/json_serialization.hpp @@ -0,0 +1,415 @@ +#pragma once + +#include "init.hpp" +#include "types.hpp" +#include "consts.hpp" +#include +#include +#include + +namespace madrona_gpudrive +{ + void from_json(const nlohmann::json &j, MapVector2 &p) + { + p.x = j.at("x").get(); + p.y = j.at("y").get(); + } + + void from_json(const nlohmann::json &j, MapObject &obj) + { + obj.mean = {0,0}; + uint32_t i = 0; + for (const auto &pos : j.at("position")) + { + if (i < MAX_POSITIONS) + { + from_json(pos, obj.position[i]); + obj.mean.x += (obj.position[i].x - obj.mean.x)/(i+1); + obj.mean.y += (obj.position[i].y - obj.mean.y)/(i+1); + ++i; + } + else + { + break; // Avoid overflow + } + } + obj.numPositions = i; + j.at("width").get_to(obj.vehicle_size.width); + j.at("length").get_to(obj.vehicle_size.length); + j.at("height").get_to(obj.vehicle_size.height); + j.at("id").get_to(obj.id); + + i = 0; + for (const auto &h : j.at("heading")) + { + if (i < MAX_POSITIONS) + { + h.get_to(obj.heading[i]); + ++i; + } + else + { + break; // Avoid overflow + } + } + obj.numHeadings = i; + + i = 0; + for (const auto &v : j.at("velocity")) + { + if (i < MAX_POSITIONS) + { + from_json(v, obj.velocity[i]); + ++i; + } + else + { + break; // Avoid overflow + } + } + obj.numVelocities = i; + + i = 0; + for (const auto &v : j.at("valid")) + { + if (i < MAX_POSITIONS) + { + v.get_to(obj.valid[i]); + ++i; + } + else + { + break; // Avoid overflow + } + } + obj.numValid = i; + + + from_json(j.at("goalPosition"), obj.goalPosition); + std::string type = j.at("type"); + if(type == "vehicle") + obj.type = EntityType::Vehicle; + else if(type == "pedestrian") + obj.type = EntityType::Pedestrian; + else if(type == "cyclist") + obj.type = EntityType::Cyclist; + else + obj.type = EntityType::None; + + std::string markAsExpertKey = "mark_as_expert"; + if (j.contains(markAsExpertKey)) { + from_json(j.at("mark_as_expert"), obj.markAsExpert); + } + + // Initialize metadata fields to 0 + obj.metadata.isSdc = 0; + obj.metadata.isObjectOfInterest = 0; + obj.metadata.isTrackToPredict = 0; + obj.metadata.difficulty = 0; + } + + void from_json(const nlohmann::json &j, MapRoad &road, float polylineReductionThreshold = 0.0) + { + road.mean = {0,0}; + std::string type = j.at("type"); + if(type == "road_edge") + road.type = EntityType::RoadEdge; + else if(type == "road_line") + road.type = EntityType::RoadLine; + else if(type == "lane") + road.type = EntityType::RoadLane; + else if(type == "crosswalk") + road.type = EntityType::CrossWalk; + else if(type == "speed_bump") + road.type = EntityType::SpeedBump; + else if(type == "stop_sign") + road.type = EntityType::StopSign; + else + road.type = EntityType::None; + + + std::vector geometry_points_; + for(const auto &point: j.at("geometry")) + { + MapVector2 p; + from_json(point, p); + geometry_points_.push_back(p); + } + + const int64_t num_segments = j["geometry"].size() - 1; + const int64_t sample_every_n_ = 1; + const int64_t num_sampled_points = (num_segments + sample_every_n_ - 1) / sample_every_n_ + 1; + if (num_segments >= 10 && (road.type == EntityType::RoadLane || road.type == EntityType::RoadEdge || road.type == EntityType::RoadLine)) + { + std::vector skip(num_sampled_points, false); // This list tracks the points that are skipped + int64_t k = 0; + bool skipChanged = true; // This is used to check if the skip list has changed in the last iteration + while (skipChanged) // This loop runs O(N^2) in worst case, but it is very fast in practice probably O(NlogN) + { + skipChanged = false; // Reset the skipChanged flag + k = 0; + while (k < num_sampled_points - 1) + { + int64_t k_1 = k + 1; // k_1 is the next point that is not skipped + while (k_1 < num_sampled_points - 1 && skip[k_1]) + { + k_1++; // Keep incrementing k_1 until we find a point that is not skipped + } + if (k_1 >= num_sampled_points - 1) + break; + int64_t k_2 = k_1 + 1; + while (k_2 < num_sampled_points && skip[k_2]) + { + k_2++; // Keep incrementing k_2 until we find a point that is not skipped + } + if (k_2 >= num_sampled_points) + break; + auto point1 = geometry_points_[k * sample_every_n_]; + auto point2 = geometry_points_[k_1 * sample_every_n_]; + auto point3 = geometry_points_[k_2 * sample_every_n_]; + float_t area = 0.5 * std::abs((point1.x - point3.x) * (point2.y - point1.y) - (point1.x - point2.x) * (point3.y - point1.y)); + if (area < polylineReductionThreshold) + { // If the area is less than the threshold, then we skip the middle point + skip[k_1] = true; // Mark the middle point as skipped + k = k_2; // Skip the middle point and start from the next point + skipChanged = true; // Set the skipChanged flag to true + } + else + { + k = k_1; // If the area is greater than the threshold, then we don't skip the middle point and start from the next point + } + } + } + + // Create the road lines + k = 0; + skip[0] = false; + skip[num_sampled_points - 1] = false; + std::vector new_geometry_points; // This list stores the points that are not skipped + while (k < num_sampled_points) + { + if (!skip[k]) + { + new_geometry_points.push_back(geometry_points_[k * sample_every_n_]); // Add the point to the list if it is not skipped + } + k++; + } + for (size_t i = 0; i < new_geometry_points.size(); i++) + { + if(i==MAX_GEOMETRY) + break; + road.geometry[i] = new_geometry_points[i]; // Create the road lines + } + road.numPoints = new_geometry_points.size(); + } + else + { + for (int64_t i = 0; i < num_sampled_points ; ++i) + { + if(i==MAX_GEOMETRY) + break; + road.geometry[i] = geometry_points_[i * sample_every_n_]; + } + road.numPoints = num_sampled_points; + } + + if (j.contains("id")) { + road.id = j.at("id").get(); + } + + if (j.contains("map_element_id")) + { + auto mapElementId = j.at("map_element_id").get(); + + if(mapElementId == 4 or mapElementId >= static_cast(MapType::NUM_TYPES) or mapElementId < -1) + { + road.mapType = MapType::UNKNOWN; + } + else + { + road.mapType = static_cast(mapElementId); + } + } + else + { + road.mapType = MapType::UNKNOWN; + } + + for (int i = 0; i < road.numPoints; i++) + { + road.mean.x += (road.geometry[i].x - road.mean.x)/(i+1); + road.mean.y += (road.geometry[i].y - road.mean.y)/(i+1); + } + + } + + std::pair calc_mean(const nlohmann::json &j) + { + std::pair mean = {0, 0}; + int64_t numEntities = 0; + for (const auto &obj : j["objects"]) + { + int i = 0; + for (const auto &pos : obj["position"]) + { + if(obj["valid"][i++] == false) + continue; + numEntities++; + float newX = pos["x"]; + float newY = pos["y"]; + // Update mean incrementally + mean.first += (newX - mean.first) / numEntities; + mean.second += (newY - mean.second) / numEntities; + } + } + for (const auto &obj : j["roads"]) + { + for (const auto &point : obj["geometry"]) + { + numEntities++; + float newX = point["x"]; + float newY = point["y"]; + + // Update mean incrementally + mean.first += (newX - mean.first) / numEntities; + mean.second += (newY - mean.second) / numEntities; + } + } + return mean; + } + + void from_json(const nlohmann::json &j, Map &map, float polylineReductionThreshold) + { + std::string name = j.at("name").get(); + std::strncpy(map.mapName, name.c_str(), sizeof(map.mapName)); + + std::string scenario_id = j.at("scenario_id").get(); + std::strncpy(map.scenarioId, scenario_id.c_str(), sizeof(map.scenarioId)); + + auto mean = calc_mean(j); + map.mean = {mean.first, mean.second}; + map.numObjects = std::min(j.at("objects").size(), static_cast(MAX_OBJECTS)); + + const auto& metadata = j.at("metadata"); + int sdc_index = metadata.at("sdc_track_index").get(); + + // Create id to object index mapping + std::unordered_map idToObjIdx; + size_t idx = 0; + + // First, identify which objects are tracks_to_predict and objects_of_interest + std::unordered_set tracks_to_predict_indices; + std::unordered_set objects_of_interest_ids; + + // Collect tracks_to_predict indices + for (const auto& track : metadata.at("tracks_to_predict")) { + int track_index = track.at("track_index").get(); + if (track_index >= 0 && track_index < j.at("objects").size()) { + tracks_to_predict_indices.insert(track_index); + } else { + std::cerr << "Warning: Invalid track_index " << track_index << " in scene " << j.at("name").get() << std::endl; + } + } + + // Collect objects_of_interest IDs + for (const auto& obj_id : metadata.at("objects_of_interest")) { + objects_of_interest_ids.insert(obj_id.get()); + } + + // Initialize SDC first if valid + if (sdc_index >= 0 && sdc_index < j.at("objects").size()) { + j.at("objects")[sdc_index].get_to(map.objects[0]); + map.objects[0].metadata.isSdc = 1; + + // Set additional metadata if needed + int sdc_id = map.objects[0].id; + if (tracks_to_predict_indices.find(sdc_index) != tracks_to_predict_indices.end()) { + map.objects[0].metadata.isTrackToPredict = 1; + // Find and set difficulty + for (const auto& track : metadata.at("tracks_to_predict")) { + if (track.at("track_index").get() == sdc_index) { + map.objects[0].metadata.difficulty = track.at("difficulty").get(); + break; + } + } + } + if (objects_of_interest_ids.find(sdc_id) != objects_of_interest_ids.end()) { + map.objects[0].metadata.isObjectOfInterest = 1; + } + + idToObjIdx[sdc_id] = 0; + idx = 1; + + // Remove SDC from sets to avoid double processing + tracks_to_predict_indices.erase(sdc_index); + objects_of_interest_ids.erase(sdc_id); + } + + // Initialize tracks_to_predict objects (excluding SDC) + for (size_t i = 0; i < j.at("objects").size() && idx < map.numObjects; i++) { + if (i == sdc_index) continue; // Skip SDC as it's already initialized + + if (tracks_to_predict_indices.find(i) != tracks_to_predict_indices.end()) { + j.at("objects")[i].get_to(map.objects[idx]); + map.objects[idx].metadata.isTrackToPredict = 1; + + // Find and set difficulty + for (const auto& track : metadata.at("tracks_to_predict")) { + if (track.at("track_index").get() == static_cast(i)) { + map.objects[idx].metadata.difficulty = track.at("difficulty").get(); + break; + } + } + + // Check if also object of interest + if (objects_of_interest_ids.find(map.objects[idx].id) != objects_of_interest_ids.end()) { + map.objects[idx].metadata.isObjectOfInterest = 1; + objects_of_interest_ids.erase(map.objects[idx].id); + } + + idToObjIdx[map.objects[idx].id] = idx; + idx++; + } + } + + // Initialize objects_of_interest (excluding those already processed) + for (size_t i = 0; i < j.at("objects").size() && idx < map.numObjects; i++) { + if (i == sdc_index) continue; + + int obj_id = j.at("objects")[i].at("id").get(); + if (objects_of_interest_ids.find(obj_id) != objects_of_interest_ids.end()) { + j.at("objects")[i].get_to(map.objects[idx]); + map.objects[idx].metadata.isObjectOfInterest = 1; + + idToObjIdx[map.objects[idx].id] = idx; + idx++; + } + } + + // Initialize all remaining objects + for (size_t i = 0; i < j.at("objects").size() && idx < map.numObjects; i++) { + if (i == sdc_index) continue; + + int obj_id = j.at("objects")[i].at("id").get(); + if (idToObjIdx.find(obj_id) == idToObjIdx.end()) { // Check if not already processed + j.at("objects")[i].get_to(map.objects[idx]); + idToObjIdx[map.objects[idx].id] = idx; + idx++; + } + } + + // Process roads + map.numRoads = std::min(j.at("roads").size(), static_cast(MAX_ROADS)); + size_t countRoadPoints = 0; + idx = 0; + for (const auto &road : j.at("roads")) { + if (idx >= map.numRoads) + break; + from_json(road, map.roads[idx], polylineReductionThreshold); + size_t roadPoints = map.roads[idx].numPoints; + countRoadPoints += (map.roads[idx].type <= EntityType::RoadLane) ? (roadPoints - 1) : 1; + ++idx; + } + map.numRoadSegments = countRoadPoints; + } +} diff --git a/gpudrive-main/src/knn.hpp b/gpudrive-main/src/knn.hpp new file mode 100644 index 0000000000000000000000000000000000000000..cad6a6c0858175d27097b23e789ea605b3470423 --- /dev/null +++ b/gpudrive-main/src/knn.hpp @@ -0,0 +1,160 @@ +#pragma once + +#include "binary_heap.hpp" +#include "types.hpp" +#include +#include +#include +#include "utils.hpp" + +#ifndef MADRONA_GPU_MODE +#include +#endif + +namespace { +bool cmp(const madrona_gpudrive::MapObservation &lhs, const madrona_gpudrive::MapObservation &rhs) { + return lhs.position.length2() < rhs.position.length2(); +} + +void fillZeros(madrona_gpudrive::MapObservation *begin, + madrona_gpudrive::MapObservation *beyond) { + while (begin < beyond) { + *begin++ = + madrona_gpudrive::MapObservation{.position = {0, 0}, + .scale = madrona::math::Diag3x3{0, 0, 0}, + .heading = 0.f, + .type = (float)madrona_gpudrive::EntityType::None}; + } +} + +madrona_gpudrive::MapObservation +relativeObservation(const madrona_gpudrive::MapObservation &absoluteObservation, + const madrona::base::Rotation &referenceRotation, + const madrona::math::Vector2 &referencePosition) { + auto relativePosition = + madrona::math::Vector2{.x = absoluteObservation.position.x, + .y = absoluteObservation.position.y} - + referencePosition; + + return madrona_gpudrive::MapObservation{ + .position = referenceRotation.inv() + .rotateVec({relativePosition.x, relativePosition.y, 0}) + .xy(), + .scale = absoluteObservation.scale, + .heading = madrona_gpudrive::utils::quatToYaw(referenceRotation.inv() * madrona::math::Quat::angleAxis(absoluteObservation.heading,madrona::math::up)), + .type = absoluteObservation.type}; +} + + +bool isObservationsValid(madrona_gpudrive::Engine &ctx, + madrona_gpudrive::MapObservation *observations, + madrona::CountT K, + const madrona::base::Rotation &referenceRotation, + const madrona::math::Vector2 &referencePosition) { +#ifdef MADRONA_GPU_MODE + return true; +#else + const auto roadCount = ctx.data().numRoads; + + std::vector sortedObservations; + sortedObservations.reserve(roadCount); + + for (madrona::CountT roadIdx = 0; roadIdx < roadCount; ++roadIdx) { + auto &road_iface = ctx.get(ctx.data().roads[roadIdx]).e; + const auto ¤tObservation = + ctx.get(road_iface); + sortedObservations.emplace_back(relativeObservation( + currentObservation, referenceRotation, referencePosition)); + } + + std::sort(sortedObservations.begin(), sortedObservations.end(), cmp); + std::sort(observations, observations + K, cmp); + + return std::equal(observations, observations + K, sortedObservations.begin(), + sortedObservations.begin() + K, + [](const madrona_gpudrive::MapObservation &lhs, + const madrona_gpudrive::MapObservation &rhs) { + return lhs.position.x == rhs.position.x && + lhs.position.y == rhs.position.y; + }); +#endif +} + +madrona::CountT radiusFilter(madrona_gpudrive::MapObservation *heap, madrona::CountT K, float radius) { + madrona::CountT newBeyond{K}; + + madrona::CountT idx{0}; + while (idx < newBeyond) { + if (heap[idx].position.length() <= radius) { + ++idx; + continue; + } + + heap[idx] = heap[--newBeyond]; + } + + return newBeyond; +} + +} // namespace + +namespace madrona_gpudrive { + +template +void selectKNearestRoadEntities(Engine &ctx, const Rotation &referenceRotation, + const madrona::math::Vector2 &referencePosition, + madrona_gpudrive::MapObservation *heap) { + const Entity *roads = ctx.data().roads; + const auto roadCount = ctx.data().numRoads; + + utils::ReferenceFrame referenceFrame(referencePosition, referenceRotation); + + for (madrona::CountT i = 0; i < std::min(roadCount, K); ++i) { + heap[i] = + referenceFrame.observationOf(ctx.get(roads[i]), + ctx.get(roads[i]), + ctx.get(roads[i]), + ctx.get(roads[i]), + static_cast(ctx.get(roads[i]).id), + ctx.get(roads[i])); + } + + if (roadCount < K) { + auto newBeyond = radiusFilter(heap, roadCount, ctx.data().params.observationRadius); + fillZeros(heap + newBeyond, heap + K); + return; + } + + make_heap(heap, heap + K, cmp); + + for (madrona::CountT roadIdx = K; roadIdx < roadCount; ++roadIdx) { + auto currentObservation = referenceFrame.observationOf( + ctx.get(roads[roadIdx]), + ctx.get(roads[roadIdx]), + ctx.get(roads[roadIdx]), + ctx.get(roads[roadIdx]), + static_cast(ctx.get(roads[roadIdx]).id), + ctx.get(roads[roadIdx])); + + const auto &kthNearestObservation = heap[0]; + bool isCurrentObservationCloser = + cmp(currentObservation, kthNearestObservation); + + if (not isCurrentObservationCloser) { + continue; + } + + pop_heap(heap, heap + K, cmp); + + heap[K - 1] = currentObservation; + push_heap(heap, heap + K, cmp); + } + + assert( + isObservationsValid(ctx, heap, K, referenceRotation, referencePosition)); + + auto newBeyond = radiusFilter(heap, K, ctx.data().params.observationRadius); + fillZeros(heap + newBeyond, heap + K); +} + +} // namespace madrona_gpudrive diff --git a/gpudrive-main/src/level_gen.cpp b/gpudrive-main/src/level_gen.cpp new file mode 100644 index 0000000000000000000000000000000000000000..30c01467e9cb078a21f252934389d3ba96cf28b0 --- /dev/null +++ b/gpudrive-main/src/level_gen.cpp @@ -0,0 +1,530 @@ +#include "level_gen.hpp" +#include "dynamics.hpp" +#include "init.hpp" + +namespace madrona_gpudrive { +using namespace madrona; +using namespace madrona::math; +using namespace madrona::phys; + +// Register the entity with the broadphase system +// This is needed for every entity with all the physics components. +// Not registering an entity will cause a crash because the broadphase +// systems will still execute over entities with the physics components. +static void registerRigidBodyEntity( + Engine &ctx, + Entity e, + SimObject sim_obj) +{ + ObjectID obj_id { (int32_t)sim_obj }; + ctx.get(e) = PhysicsSystem::registerEntity(ctx, e, obj_id); +} + +static inline void resetAgentInterface(Engine &ctx, Entity agent_iface, EntityType type, ResponseType resp_type, int32_t steps_remaining= consts::episodeLen, int32_t done = 0) { + ctx.get(agent_iface).t = steps_remaining; + ctx.get(agent_iface).v = done; + ctx.get(agent_iface).v = 0; + ctx.get(agent_iface) = Info{}; + ctx.get(agent_iface).type = (int32_t)type; + ctx.get(agent_iface) = resp_type; +} + +static inline void resetAgent(Engine &ctx, Entity agent) { + auto agent_iface = ctx.get(agent).e; + auto xCoord = ctx.get(agent_iface).positions[0].x; + auto yCoord = ctx.get(agent_iface).positions[0].y; + auto xVelocity = ctx.get(agent_iface).velocities[0].x; + auto yVelocity = ctx.get(agent_iface).velocities[0].y; + auto heading = ctx.get(agent_iface).headings[0]; + + ctx.get(agent) = Vector3{.x = xCoord, .y = yCoord, .z = 1}; + ctx.get(agent) = Quat::angleAxis(heading, madrona::math::up); + if (ctx.get(agent) == ResponseType::Static) { + ctx.get(agent) = Velocity{Vector3::zero(), Vector3::zero()}; + } else { + ctx.get(agent) = Velocity{Vector3{.x = xVelocity, .y = yVelocity, .z = 0}, Vector3::zero()}; + } + ctx.get(agent_iface) = getZeroAction(ctx.data().params.dynamicsModel); + + resetAgentInterface(ctx, agent_iface, ctx.get(agent), ctx.get(agent)); + +#ifndef GPUDRIVE_DISABLE_NARROW_PHASE + ctx.get(agent).hasCollided.store_release(0); +#endif +} + +static inline void populateExpertTrajectory(Engine &ctx, const Entity &agent, const MapObject &agentInit) { + const auto &agent_iface = ctx.get(agent).e; + auto &trajectory = ctx.get(agent_iface); + for(CountT i = 0; i < agentInit.numPositions; i++) + { + trajectory.positions[i] = Vector2{.x = agentInit.position[i].x - ctx.singleton().mean.x, .y = agentInit.position[i].y - ctx.singleton().mean.y}; + trajectory.velocities[i] = Vector2{.x = agentInit.velocity[i].x, .y = agentInit.velocity[i].y}; + trajectory.headings[i] = agentInit.heading[i]; + trajectory.valids[i] = (float)agentInit.valid[i]; + trajectory.inverseActions[i] = getZeroAction(ctx.data().params.dynamicsModel); + } + if (ctx.data().params.dynamicsModel == DynamicsModel::Classic || ctx.data().params.dynamicsModel == DynamicsModel::State){ + return; + } + for(CountT i = agentInit.numPositions - 2; i >=0; i--) + { + if(!trajectory.valids[i] || !trajectory.valids[i+1]) + { + trajectory.inverseActions[i] = getZeroAction(ctx.data().params.dynamicsModel); + } + + Rotation rot = Quat::angleAxis(trajectory.headings[i], madrona::math::up); + Position pos = Vector3{.x = trajectory.positions[i].x, .y = trajectory.positions[i].y, .z = 1}; + Velocity vel = {Vector3{.x = trajectory.velocities[i].x, .y = trajectory.velocities[i].y, .z = 0}, Vector3::zero()}; + Rotation targetRot = Quat::angleAxis(trajectory.headings[i+1], madrona::math::up); + switch (ctx.data().params.dynamicsModel) { + case DynamicsModel::Classic: + case DynamicsModel::State: + // No inverse action model for classic model + break; + + case DynamicsModel::InvertibleBicycle: { + Velocity targetVel = {Vector3{.x = trajectory.velocities[i+1].x, .y = trajectory.velocities[i+1].y, .z = 0}, Vector3::zero()}; + trajectory.inverseActions[i] = inverseBicycleModel(rot, vel, targetRot, targetVel); + break; + } + + case DynamicsModel::DeltaLocal: { + Position targetPos = Vector3{.x = trajectory.positions[i+1].x, .y = trajectory.positions[i+1].y, .z = 1}; + trajectory.inverseActions[i] = inverseDeltaModel(rot, pos, targetRot, targetPos); + break; + } + } + } +} + +static inline bool isAgentStatic(Engine &ctx, Entity agent) { + auto agent_iface = ctx.get(agent).e; + + // Static agents are those that are not tracks to predict + if (ctx.data().params.readFromTracksToPredict and ctx.get(agent_iface).isTrackToPredict != -1) { + return false; + } + + // Original logic for other initialization modes + bool isStatic = (ctx.get(agent).position - ctx.get(agent_iface).positions[0]).length() < consts::staticThreshold; + return !ctx.data().params.isStaticAgentControlled and isStatic; +} + +static inline bool isAgentControllable(Engine &ctx, Entity agent, bool markAsExpert = false) { + auto agent_iface = ctx.get(agent).e; + + // If readFromTracksToPredict is true, base controllability on isTrackToPredict flag + if (ctx.data().params.readFromTracksToPredict) { + return ctx.data().numControlledAgents < ctx.data().params.maxNumControlledAgents && + ctx.get(agent_iface).isTrackToPredict != -1; + } + + // Original logic for other initialization modes + return ctx.data().numControlledAgents < ctx.data().params.maxNumControlledAgents && + ctx.get(agent_iface).valids[0] && + ctx.get(agent) == ResponseType::Dynamic && + !markAsExpert; +} + +static inline Entity createAgent(Engine &ctx, const MapObject &agentInit) { + assert(agentInit.type >= EntityType::Vehicle && agentInit.type <= EntityType::Cyclist); + + // The following components do not vary within an episode and so need only + // be set once + auto agent = ctx.makeRenderableEntity(); + auto agent_iface = ctx.get(agent).e = ctx.makeEntity(); + + ctx.get(agent) = agentInit.vehicle_size; + ctx.get(agent) = Diag3x3{.d0 = agentInit.vehicle_size.length/2, .d1 = agentInit.vehicle_size.width/2, .d2 = 1}; + ctx.get(agent) *= consts::vehicleLengthScale; + ctx.get(agent) = ObjectID{(int32_t)SimObject::Agent}; + ctx.get(agent) = agentInit.type; + ctx.get(agent)= Goal{.position = Vector2{.x = agentInit.goalPosition.x - ctx.singleton().mean.x, .y = agentInit.goalPosition.y - ctx.singleton().mean.y}}; + ctx.get(agent_iface) = AgentID{.id = static_cast(agentInit.id)}; + + populateExpertTrajectory(ctx, agent, agentInit); + + //Applying custom rules + ctx.get(agent) = isAgentStatic(ctx, agent) ? ResponseType::Static : ResponseType::Dynamic; + ctx.get(agent_iface) = ControlledState{.controlled = isAgentControllable(ctx, agent, agentInit.markAsExpert)}; + ctx.data().numControlledAgents += ctx.get(agent_iface).controlled; + + ctx.get(agent_iface) = agentInit.metadata; + + if (ctx.data().enableRender) { + render::RenderingSystem::attachEntityToView(ctx, + agent, + 90.f, 0.001f, + 1.5f * math::up); + } + + return agent; +} + +static Entity makeRoadEdge(Engine &ctx, const MapRoad &roadInit, CountT j) { + const MapVector2 &p1 = roadInit.geometry[j]; + const MapVector2 &p2 = roadInit.geometry[j+1]; // This is guaranteed to be within bounds + + float z = 1 + (roadInit.type == EntityType::RoadEdge ? consts::lidarRoadEdgeOffset : consts::lidarRoadLineOffset); + + Vector3 start{.x = p1.x - ctx.singleton().mean.x, .y = p1.y - ctx.singleton().mean.y, .z = z}; + Vector3 end{.x = p2.x - ctx.singleton().mean.x, .y = p2.y - ctx.singleton().mean.y, .z = z}; + + auto road_edge = ctx.makeRenderableEntity(); + ctx.get(road_edge).e = ctx.makeEntity(); + + auto pos = Vector3{.x = (start.x + end.x)/2, .y = (start.y + end.y)/2, .z = z}; + auto rot = Quat::angleAxis(atan2(end.y - start.y, end.x - start.x), madrona::math::up); + auto scale = Diag3x3{.d0 = start.distance(end)/2, .d1 = 0.1, .d2 = 0.1}; + setRoadEntitiesProps(ctx, road_edge, pos, rot, scale, roadInit.type, ObjectID{(int32_t)SimObject::Cube}, ResponseType::Static, roadInit.id, roadInit.mapType); + registerRigidBodyEntity(ctx, road_edge, SimObject::Cube); + + return road_edge; +} + +float calculateDistance(float x1, float y1, float x2, float y2) { + return sqrt(pow(x2 - x1, 2) + pow(y2 - y1, 2)); +} + +static Entity makeCube(Engine &ctx, const MapRoad &roadInit) { + + MapVector2 points[] = { + roadInit.geometry[0], + roadInit.geometry[1], + roadInit.geometry[2], + roadInit.geometry[3] + }; + + // Calculate distances between consecutive points + float lengths[4]; + for (int i = 0; i < 4; ++i) + { + MapVector2 &p_start = points[i]; + MapVector2 &p_end = points[(i + 1) % 4]; // Wrap around to the first point + lengths[i] = calculateDistance(p_start.x, p_start.y, p_end.x, p_end.y); + } + + int maxLength_i = 0; + int minLength_i = 0; + for (int i = 1; i < 4; ++i) { + if (lengths[i] > lengths[maxLength_i]) + maxLength_i = i; + if (lengths[i] < lengths[minLength_i]) + minLength_i = i; + } + + MapVector2 &start = points[maxLength_i]; + MapVector2 &end = points[(maxLength_i + 1) % 4]; + + // Calculate rotation angle (assuming longer side is used to calculate angle) + float angle = atan2(end.y - start.y, end.x - start.x); + + auto speed_bump = ctx.makeRenderableEntity(); + ctx.get(speed_bump).e = ctx.makeEntity(); + + float sum_x = 0.0f; + float sum_y = 0.0f; + + for (const auto& point : points) { + sum_x += point.x; + sum_y += point.y; + } + + auto pos = Vector3{.x = sum_x/4 - ctx.singleton().mean.x, .y = sum_y/4 - ctx.singleton().mean.y, .z = 1 + consts::lidarRoadLineOffset}; + auto rot = Quat::angleAxis(angle, madrona::math::up); + auto scale = Diag3x3{.d0 = lengths[maxLength_i]/2, .d1 = lengths[minLength_i]/2, .d2 = 0.1}; + setRoadEntitiesProps(ctx, speed_bump, pos, rot, scale, roadInit.type, ObjectID{(int32_t)SimObject::SpeedBump}, ResponseType::Static, roadInit.id, roadInit.mapType); + registerRigidBodyEntity(ctx, speed_bump, SimObject::SpeedBump); + return speed_bump; +} + +static Entity makeStopSign(Engine &ctx, const MapRoad &roadInit) { + float x1 = roadInit.geometry[0].x; + float y1 = roadInit.geometry[0].y; + + auto stop_sign = ctx.makeRenderableEntity(); + ctx.get(stop_sign).e = ctx.makeEntity(); + + auto pos = Vector3{.x = x1 - ctx.singleton().mean.x, .y = y1 - ctx.singleton().mean.y, .z = 1}; + auto rot = Quat::angleAxis(0, madrona::math::up); + auto scale = Diag3x3{.d0 = 0.2, .d1 = 0.2, .d2 = 1}; + setRoadEntitiesProps(ctx, stop_sign, pos, rot, scale, EntityType::StopSign, ObjectID{(int32_t)SimObject::StopSign}, ResponseType::Static, roadInit.id, roadInit.mapType); + registerRigidBodyEntity(ctx, stop_sign, SimObject::StopSign); + return stop_sign; +} + +static inline void createRoadEntities(Engine &ctx, const MapRoad &roadInit, CountT &idx) { + if (idx >= consts::kMaxRoadEntityCount) + return; + switch (roadInit.type) + { + case EntityType::RoadEdge: + case EntityType::RoadLine: + case EntityType::RoadLane: + { + size_t numPoints = roadInit.numPoints; + for (size_t j = 1; j <= numPoints - 1; j++) + { + auto road = ctx.data().roads[idx] = makeRoadEdge(ctx, roadInit, j-1); + ctx.data().road_ifaces[idx++] = ctx.get(road).e; + if (idx >= consts::kMaxRoadEntityCount) return; + } + break; + } + case EntityType::CrossWalk: + case EntityType::SpeedBump: + { + assert(roadInit.numPoints >= 4); + // TODO: Speed Bump are not guranteed to have 4 points. Need to handle this case. + auto road = ctx.data().roads[idx] = makeCube(ctx, roadInit); + ctx.data().road_ifaces[idx++] = ctx.get(road).e; + break; + } + case EntityType::StopSign: + { + assert(roadInit.numPoints >= 1); + // TODO: Stop Sign are not guranteed to have 1 point. Need to handle this case. + auto road = ctx.data().roads[idx] = makeStopSign(ctx, roadInit); + ctx.data().road_ifaces[idx++] = ctx.get(road).e; + break; + } + default: + return; + } +} + +static void createFloorPlane(Engine &ctx) +{ + ctx.data().floorPlane = ctx.makeRenderableEntity(); + setRoadEntitiesProps(ctx, ctx.data().floorPlane, Vector3{.x = 0, .y = 0, .z = 0}, + Quat::angleAxis(0, madrona::math::up), + Diag3x3{.d0 = 100, .d1 = 100, .d2 = 0.1}, + EntityType::None, ObjectID{(int32_t)SimObject::Plane}, ResponseType::Static, 0, MapType::UNKNOWN); + registerRigidBodyEntity(ctx, ctx.data().floorPlane, SimObject::Plane); +} + +void createPaddingEntities(Engine &ctx) { + for (CountT agentIdx = ctx.data().numAgents; + agentIdx < consts::kMaxAgentCount; ++agentIdx) { + Entity &agent_iface = ctx.data().agent_ifaces[agentIdx] = ctx.makeEntity(); + ctx.get(agent_iface) = AgentID{.id = -1}; + resetAgentInterface(ctx, agent_iface, EntityType::None, ResponseType::Static, 0, 1); + ctx.get(agent_iface) = ControlledState{.controlled = 0}; + auto &agent_map_obs = ctx.get(agent_iface); + for (CountT i = 0; i < consts::kMaxAgentMapObservationsCount; i++) { + agent_map_obs.obs[i] = MapObservation::zero(); + } + auto &self_obs = ctx.get(agent_iface); + self_obs = SelfObservation::zero(); + + auto &abs_self_obs = ctx.get(agent_iface); + abs_self_obs.position = Vector3::zero(); + abs_self_obs.rotation = AbsoluteRotation{.rotationAsQuat = Quat{1, 0, 0, 0}, .rotationFromAxis = 0}; + abs_self_obs.goal = Goal{.position = {0, 0}}; + abs_self_obs.vehicle_size = VehicleSize{.length = 0, .width = 0, .height = 0}; + abs_self_obs.id = -1.0f; + + auto &partner_obs = ctx.get(agent_iface); + for (CountT i = 0; i < consts::kMaxAgentCount-1; i++) { + partner_obs.obs[i] = PartnerObservation::zero(); + } + + Trajectory::zero(ctx.get(agent_iface)); + MetaData::zero(ctx.get(agent_iface)); + } + + for (CountT roadIdx = ctx.data().numRoads; + roadIdx < consts::kMaxRoadEntityCount; ++roadIdx) { + Entity &e = ctx.data().road_ifaces[roadIdx] = ctx.makeEntity(); + ctx.get(e) = MapObservation::zero(); + } +} + +void createCameraEntity(Engine &ctx) +{ + auto camera = ctx.makeRenderableEntity(); + ctx.get(camera) = Vector3{.x = 0, .y = 0, .z = 20}; + ctx.get(camera) = (math::Quat::angleAxis(0, math::up) * + math::Quat::angleAxis(-math::pi / 2.f, math::right)).normalize(); + + render::RenderingSystem::attachEntityToView(ctx, + camera, + 150.f, 0.001f, + 1.5f * math::up); + + ctx.data().camera_agent = camera; +} + +static inline bool shouldAgentBeCreated(Engine &ctx, const MapObject &agentInit) +{ + // When readFromTracksToPredict is enabled, we want to create all agents + // This overrides all other rules except for the check against deleted agents + if (ctx.data().params.readFromTracksToPredict) { + // Only check the deleted agents list + auto& deletedAgents = ctx.singleton().deletedAgents; + for (CountT i = 0; i < consts::kMaxAgentCount; i++) + { + if(deletedAgents[i] == agentInit.id) + { + return false; + } + } + + return true; + } + + // Original logic for other initialization modes + if (ctx.data().params.IgnoreNonVehicles && + (agentInit.type == EntityType::Pedestrian || agentInit.type == EntityType::Cyclist)) + { + return false; + } + + if (ctx.data().params.initOnlyValidAgentsAtFirstStep && !agentInit.valid[0]) + { + return false; + } + + // Check the deleted agents list + auto& deletedAgents = ctx.singleton().deletedAgents; + for (CountT i = 0; i < consts::kMaxAgentCount; i++) + { + if(deletedAgents[i] == agentInit.id) + { + return false; + } + } + + return true; +} + +void createPersistentEntities(Engine &ctx) { + // createFloorPlane(ctx); + const auto& map = ctx.singleton(); + + auto& mapName = ctx.singleton(); + for (int i = 0; i < 32; i++) { + mapName.mapName[i] = map.mapName[i]; + } + + auto& scenarioId = ctx.singleton(); + for (int i = 0; i < 32; i++) { + scenarioId.scenarioId[i] = map.scenarioId[i]; + } + + if (ctx.data().enableRender) + { + createCameraEntity(ctx); + } + + ctx.data().numControlledAgents = 0; + ctx.singleton().reset = 0; + + auto& means = ctx.singleton().mean; + means = {map.mean.x, map.mean.y, 0}; // TODO: Add z to the map + + CountT agentIdx = 0; + for (CountT agentCtr = 0; agentCtr < map.numObjects && agentIdx < consts::kMaxAgentCount; ++agentCtr) { + const auto &agentInit = map.objects[agentCtr]; + + if (not shouldAgentBeCreated(ctx, agentInit)) + { + continue; + } + + auto agent = createAgent(ctx, agentInit); + ctx.data().agent_ifaces[agentIdx] = ctx.get(agent).e; + ctx.data().agents[agentIdx++] = agent; + } + ctx.data().numAgents = agentIdx; + + CountT roadIdx = 0; + for(CountT roadCtr = 0; roadCtr < map.numRoads && roadIdx < consts::kMaxRoadEntityCount; roadCtr++) + { + const auto &roadInit = map.roads[roadCtr]; + createRoadEntities(ctx, roadInit, roadIdx); + } + ctx.data().numRoads = roadIdx; + + auto &shape = ctx.singleton(); + shape.agentEntityCount = ctx.data().numAgents; + shape.roadEntityCount = ctx.data().numRoads; + + createPaddingEntities(ctx); + + for (CountT i = 0; i < ctx.data().numAgents; i++) { + Entity cur_agent = ctx.data().agents[i]; + OtherAgents &other_agents = ctx.get(cur_agent); + CountT out_idx = 0; + for (CountT j = 0; j < ctx.data().numAgents; j++) + { + if (i == j) + { + continue; + } + + Entity other_agent = ctx.data().agents[j]; + other_agents.e[out_idx++] = other_agent; + } + } +} + +static void resetPersistentEntities(Engine &ctx) +{ + for (CountT idx = 0; idx < ctx.data().numAgents; ++idx) + { + Entity agent = ctx.data().agents[idx]; + resetAgent(ctx, agent); + registerRigidBodyEntity(ctx, agent, SimObject::Agent); + } + + for (CountT idx = 0; idx < ctx.data().numRoads; idx++) + { + Entity road = ctx.data().roads[idx]; + if(road == Entity::none()) break; + SimObject simObjType = static_cast(ctx.get(road).idx); + registerRigidBodyEntity(ctx, road, simObjType); + } +} + +void destroyWorld(Engine &ctx) +{ + for (CountT idx = 0; idx < ctx.data().numAgents; ++idx) + { + Entity agent = ctx.data().agents[idx]; + ctx.destroyRenderableEntity(agent); + } + for (CountT idx = 0; idx < ctx.data().numRoads; idx++) + { + Entity road = ctx.data().roads[idx]; + ctx.destroyRenderableEntity(road); + } + if (ctx.data().enableRender) + { + ctx.destroyRenderableEntity(ctx.data().camera_agent); + } + for (CountT idx = 0; idx < consts::kMaxAgentCount; ++idx) + { + Entity agent_iface = ctx.data().agent_ifaces[idx]; + ctx.destroyEntity(agent_iface); + } + for (CountT idx = 0; idx < consts::kMaxRoadEntityCount; ++idx) + { + Entity road_iface = ctx.data().road_ifaces[idx]; + ctx.destroyEntity(road_iface); + } + ctx.data().numAgents = 0; + ctx.data().numRoads = 0; + ctx.data().numControlledAgents = 0; + ctx.singleton().mean = Vector3::zero(); +} + + +void resetWorld(Engine &ctx) +{ + resetPersistentEntities(ctx); +} + +} diff --git a/gpudrive-main/src/level_gen.hpp b/gpudrive-main/src/level_gen.hpp new file mode 100644 index 0000000000000000000000000000000000000000..0fead22805056879a58d6ccc120c5494ec148958 --- /dev/null +++ b/gpudrive-main/src/level_gen.hpp @@ -0,0 +1,68 @@ +#pragma once + +#include "sim.hpp" +#include "utils.hpp" + +namespace madrona_gpudrive +{ + + void createPersistentEntities(Engine &ctx); + +void resetWorld(Engine &ctx); + +// Destroys all entities in the world +void destroyWorld(Engine &ctx); + + static inline Action getZeroAction(DynamicsModel model) + { + switch (model) + { + case DynamicsModel::Classic: + case DynamicsModel::InvertibleBicycle: + { + return Action{.classic = {0, 0, 0}}; + break; + } + + case DynamicsModel::DeltaLocal: + { + return Action{.delta{.dx = 0, .dy = 0, .dyaw = 0}}; + } + case DynamicsModel::State: + { + return Action{.state = {.position = madrona::math::Vector3{0, 0, 1}, .yaw = 0, .velocity = {.linear = madrona::math::Vector3::zero(), .angular = madrona::math::Vector3::zero()}}}; + } + default: + return Action{.classic = {0, 0, 0}}; + } + } + + static inline void setRoadEntitiesProps(Engine &ctx, + Entity road, + madrona::math::Vector3 pos, + madrona::math::Quat rot, + Scale scale, + EntityType type, + ObjectID objId, + ResponseType responseType, + uint32_t roadIdx, + MapType mapType) + { + ctx.get(road) = pos; + ctx.get(road) = rot; + ctx.get(road) = Velocity{madrona::math::Vector3::zero(), madrona::math::Vector3::zero()}; + ctx.get(road) = scale; + ctx.get(road) = type; + ctx.get(road) = objId; + ctx.get(road) = responseType; + ctx.get(road).id = roadIdx; + ctx.get(road) = mapType; + ctx.get(ctx.get(road).e) = MapObservation{.position = pos.xy(), + .scale = scale, + .heading = utils::quatToYaw(rot), + .type = (float)type, + .id = static_cast(roadIdx), + .mapType = static_cast(mapType)}; + } + +} // namespace madrona_gpudrive \ No newline at end of file diff --git a/gpudrive-main/src/mgr.cpp b/gpudrive-main/src/mgr.cpp new file mode 100644 index 0000000000000000000000000000000000000000..e314735a4d67eb51fcb49a30ec453cc7fbb71720 --- /dev/null +++ b/gpudrive-main/src/mgr.cpp @@ -0,0 +1,997 @@ +#include "mgr.hpp" +#include "MapReader.hpp" +#include "sim.hpp" + +#include +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#ifdef MADRONA_CUDA_SUPPORT +#include +#include +#endif + +using namespace madrona; +using namespace madrona::math; +using namespace madrona::phys; +using namespace madrona::py; + +namespace madrona_gpudrive { + +struct RenderGPUState { + render::APILibHandle apiLib; + render::APIManager apiMgr; + render::GPUHandle gpu; +}; + + +static inline Optional initRenderGPUState( + const Manager::Config &mgr_cfg) +{ + if (mgr_cfg.extRenderDev || !mgr_cfg.enableBatchRenderer) { + return Optional::none(); + } + + auto render_api_lib = render::APIManager::loadDefaultLib(); + render::APIManager render_api_mgr(render_api_lib.lib()); + render::GPUHandle gpu = render_api_mgr.initGPU(mgr_cfg.gpuID); + + return RenderGPUState { + .apiLib = std::move(render_api_lib), + .apiMgr = std::move(render_api_mgr), + .gpu = std::move(gpu), + }; +} + +static inline Optional initRenderManager( + const Manager::Config &mgr_cfg, + const Optional &render_gpu_state) +{ + if (!mgr_cfg.extRenderDev && !mgr_cfg.enableBatchRenderer) { + return Optional::none(); + } + + render::APIBackend *render_api; + render::GPUDevice *render_dev; + + if (render_gpu_state.has_value()) { + render_api = render_gpu_state->apiMgr.backend(); + render_dev = render_gpu_state->gpu.device(); + } else { + render_api = mgr_cfg.extRenderAPI; + render_dev = mgr_cfg.extRenderDev; + } + + return render::RenderManager(render_api, render_dev, { + .enableBatchRenderer = mgr_cfg.enableBatchRenderer, + .agentViewWidth = mgr_cfg.batchRenderViewWidth, + .agentViewHeight = mgr_cfg.batchRenderViewHeight, + .numWorlds = static_cast(mgr_cfg.scenes.size()), + .maxViewsPerWorld = consts::kMaxAgentCount + 1, // FIXME? + .maxInstancesPerWorld = 3000, + .execMode = mgr_cfg.execMode, + .voxelCfg = {}, + }); +} + +struct Manager::Impl { + Config cfg; + PhysicsLoader physicsLoader; + EpisodeManager *episodeMgr; + WorldReset *worldResetBuffer; + Action *agentActionsBuffer; + Optional renderGPUState; + Optional renderMgr; + int64_t numWorlds{0}; + + inline Impl(const Manager::Config &mgr_cfg, + PhysicsLoader &&phys_loader, + EpisodeManager *ep_mgr, + WorldReset *reset_buffer, + Action *action_buffer, + Optional &&render_gpu_state, + Optional &&render_mgr, + int64_t numWorlds) + : cfg(mgr_cfg), + physicsLoader(std::move(phys_loader)), + episodeMgr(ep_mgr), + worldResetBuffer(reset_buffer), + agentActionsBuffer(action_buffer), + renderGPUState(std::move(render_gpu_state)), + renderMgr(std::move(render_mgr)), + numWorlds(numWorlds) {} + + inline virtual ~Impl() {} + + virtual void step() = 0; + virtual void reset() = 0; + + virtual Tensor exportTensor(ExportID slot, + TensorElementType type, + madrona::Span dimensions) const = 0; + + static inline Impl * init(const Config &cfg); +}; + +struct Manager::CPUImpl final : Manager::Impl { + using TaskGraphT = + TaskGraphExecutor; + + TaskGraphT cpuExec; + + inline CPUImpl(const Manager::Config &mgr_cfg, + PhysicsLoader &&phys_loader, + EpisodeManager *ep_mgr, + WorldReset *reset_buffer, + Action *action_buffer, + TaskGraphT &&cpu_exec, + Optional &&render_gpu_state, + Optional &&render_mgr, + int64_t numWorlds) + : Impl(mgr_cfg, std::move(phys_loader), ep_mgr, reset_buffer, action_buffer, + std::move(render_gpu_state), std::move(render_mgr), numWorlds), + cpuExec(std::move(cpu_exec)) + {} + + inline virtual ~CPUImpl() final + { + delete episodeMgr; + } + + inline virtual void step() { cpuExec.runTaskGraph(TaskGraphID::Step); } + + inline virtual void reset() { cpuExec.runTaskGraph(TaskGraphID::Reset); } + + virtual inline Tensor exportTensor(ExportID slot, + TensorElementType type, + madrona::Span dims) const final + { + void *dev_ptr = cpuExec.getExported((uint32_t)slot); + return Tensor(dev_ptr, type, dims, Optional::none()); + } +}; + +#ifdef MADRONA_CUDA_SUPPORT +struct Manager::CUDAImpl final : Manager::Impl { + MWCudaExecutor gpuExec; + MWCudaLaunchGraph stepGraph; + MWCudaLaunchGraph resetGraph; + + inline CUDAImpl(const Manager::Config &mgr_cfg, + PhysicsLoader &&phys_loader, + EpisodeManager *ep_mgr, + WorldReset *reset_buffer, + Action *action_buffer, + MWCudaExecutor &&gpu_exec, + Optional &&render_gpu_state, + Optional &&render_mgr, + int64_t numWorlds) + : Impl(mgr_cfg, std::move(phys_loader), + ep_mgr, reset_buffer, action_buffer, + std::move(render_gpu_state), std::move(render_mgr), numWorlds), + gpuExec(std::move(gpu_exec)), + stepGraph(gpuExec.buildLaunchGraph(TaskGraphID::Step)), + resetGraph(gpuExec.buildLaunchGraph(TaskGraphID::Reset)) {} + + inline virtual ~CUDAImpl() final + { + REQ_CUDA(cudaFree(episodeMgr)); + } + + inline virtual void step() { gpuExec.run(stepGraph); } + + inline virtual void reset() { gpuExec.run(resetGraph); } + + virtual inline Tensor exportTensor(ExportID slot, + TensorElementType type, + madrona::Span dims) const final + { + void *dev_ptr = gpuExec.getExported((uint32_t)slot); + return Tensor(dev_ptr, type, dims, cfg.gpuID); + } +}; +#endif + +static void loadRenderObjects(render::RenderManager &render_mgr) +{ + std::array render_asset_paths; + render_asset_paths[(size_t)SimObject::Cube] = + (std::filesystem::path(DATA_DIR) / "cube_render.obj").string(); + render_asset_paths[(size_t)SimObject::Agent] = + (std::filesystem::path(DATA_DIR) / "agent_render.obj").string(); + render_asset_paths[(size_t)SimObject::Plane] = + (std::filesystem::path(DATA_DIR) / "plane.obj").string(); + render_asset_paths[(size_t)SimObject::StopSign] = + (std::filesystem::path(DATA_DIR) / "cube_render.obj").string(); + render_asset_paths[(size_t)SimObject::SpeedBump] = + (std::filesystem::path(DATA_DIR) / "cube_render.obj").string(); + + std::array render_asset_cstrs; + for (size_t i = 0; i < render_asset_paths.size(); i++) { + render_asset_cstrs[i] = render_asset_paths[i].c_str(); + } + + std::array import_err; + auto render_assets = imp::ImportedAssets::importFromDisk( + render_asset_cstrs, Span(import_err.data(), import_err.size())); + + if (!render_assets.has_value()) { + FATAL("Failed to load render assets: %s", import_err); + } + + auto materials = std::to_array({ + { render::rgb8ToFloat(191, 108, 10), -1, 0.8f, 1.0f }, + { math::Vector4{0.4f, 0.4f, 0.4f, 0.0f}, -1, 0.8f, 0.2f,}, + { math::Vector4{1.f, 1.f, 1.f, 0.0f}, 1, 0.5f, 1.0f,}, + { render::rgb8ToFloat(230, 230, 230), -1, 0.8f, 1.0f }, + { math::Vector4{0.5f, 0.3f, 0.3f, 0.0f}, 0, 0.8f, 0.2f,}, + { render::rgb8ToFloat(230, 20, 20), -1, 0.8f, 1.0f }, + { render::rgb8ToFloat(230, 230, 20), -1, 0.8f, 1.0f }, + { render::rgb8ToFloat(255,0,0), -1, 0.8f, 1.0f}, + { render::rgb8ToFloat(0,0,0), -1, 0.8f, 0.2f} + }); + + // Override materials + render_assets->objects[(CountT)SimObject::Cube].meshes[0].materialIDX = 0; + render_assets->objects[(CountT)SimObject::Agent].meshes[0].materialIDX = 2; + render_assets->objects[(CountT)SimObject::Agent].meshes[1].materialIDX = 3; + render_assets->objects[(CountT)SimObject::Agent].meshes[2].materialIDX = 3; + render_assets->objects[(CountT)SimObject::Plane].meshes[0].materialIDX = 4; + render_assets->objects[(CountT)SimObject::StopSign].meshes[0].materialIDX = 7; + render_assets->objects[(CountT)SimObject::SpeedBump].meshes[0].materialIDX = 8; + // render_assets->objects[(CountT)SimObject::Cylinder].meshes[0].materialIDX = 7; + + render_mgr.loadObjects(render_assets->objects, materials, { + { (std::filesystem::path(DATA_DIR) / + "green_grid.png").string().c_str() }, + { (std::filesystem::path(DATA_DIR) / + "smile.png").string().c_str() }, + }); + + render_mgr.configureLighting({ + { true, math::Vector3{1.0f, 1.0f, -2.0f}, math::Vector3{50.0f, 50.0f, 1.0f} } + }); +} + +static void loadPhysicsObjects(PhysicsLoader &loader) +{ + std::array asset_paths; + asset_paths[(size_t)SimObject::Cube] = + (std::filesystem::path(DATA_DIR) / "cube_collision.obj").string(); + asset_paths[(size_t)SimObject::Agent] = + (std::filesystem::path(DATA_DIR) / "agent_collision_simplified.obj").string(); + asset_paths[(size_t)SimObject::StopSign] = + (std::filesystem::path(DATA_DIR) / "cube_collision.obj").string(); + asset_paths[(size_t)SimObject::SpeedBump] = + (std::filesystem::path(DATA_DIR) / "cube_collision.obj").string(); + // asset_paths[(size_t)SimObject::Cylinder] = + // (std::filesystem::path(DATA_DIR) / "cylinder_collision.obj").string(); + + std::array asset_cstrs; + for (size_t i = 0; i < asset_paths.size(); i++) { + asset_cstrs[i] = asset_paths[i].c_str(); + } + + char import_err_buffer[4096]; + auto imported_src_hulls = imp::ImportedAssets::importFromDisk( + asset_cstrs, import_err_buffer, true); + + if (!imported_src_hulls.has_value()) { + FATAL("%s", import_err_buffer); + } + + DynArray src_convex_hulls( + imported_src_hulls->objects.size()); + + DynArray> prim_arrays(0); + HeapArray src_objs( + (CountT)SimObject::NumObjects); + + auto setupHull = [&](SimObject obj_id, + float inv_mass, + RigidBodyFrictionData friction) { + auto meshes = imported_src_hulls->objects[(CountT)obj_id].meshes; + DynArray prims(meshes.size()); + + for (const imp::SourceMesh &mesh : meshes) { + src_convex_hulls.push_back(mesh); + prims.push_back({ + .type = CollisionPrimitive::Type::Hull, + .hullInput = { + .hullIDX = uint32_t(src_convex_hulls.size() - 1), + }, + }); + } + + prim_arrays.emplace_back(std::move(prims)); + + src_objs[(CountT)obj_id] = SourceCollisionObject { + .prims = Span(prim_arrays.back()), + .invMass = inv_mass, + .friction = friction, + }; + }; + + setupHull(SimObject::Cube, 0.075f, { + .muS = 0.5f, + .muD = 0.75f, + }); + + setupHull(SimObject::Agent, 1.f, { + .muS = 0.5f, + .muD = 0.5f, + }); + + setupHull(SimObject::StopSign, 1.f, { + .muS = 0.5f, + .muD = 0.5f, + }); + + setupHull(SimObject::SpeedBump, 1.f, { + .muS = 0.5f, + .muD = 0.5f, + }); + + SourceCollisionPrimitive plane_prim { + .type = CollisionPrimitive::Type::Plane, + }; + + src_objs[(CountT)SimObject::Plane] = { + .prims = Span(&plane_prim, 1), + .invMass = 0.f, + .friction = { + .muS = 0.5f, + .muD = 0.5f, + }, + }; + + StackAlloc tmp_alloc; + RigidBodyAssets rigid_body_assets; + CountT num_rigid_body_data_bytes; + void *rigid_body_data = RigidBodyAssets::processRigidBodyAssets( + src_convex_hulls, + src_objs, + false, + tmp_alloc, + &rigid_body_assets, + &num_rigid_body_data_bytes); + + if (rigid_body_data == nullptr) { + FATAL("Invalid collision hull input"); + } + + // This is a bit hacky, but in order to make sure the agents + // remain controllable by the policy, they are only allowed to + // rotate around the Z axis (infinite inertia in x & y axes) + rigid_body_assets.metadatas[ + (CountT)SimObject::Agent].mass.invInertiaTensor.x = 0.f; + rigid_body_assets.metadatas[ + (CountT)SimObject::Agent].mass.invInertiaTensor.y = 0.f; + + loader.loadRigidBodies(rigid_body_assets); + free(rigid_body_data); +} + +bool isRoadObservationAlgorithmValid(FindRoadObservationsWith algo) { + madrona::CountT roadObservationsCount = + sizeof(AgentMapObservations) / sizeof(MapObservation); + + return algo == + FindRoadObservationsWith::KNearestEntitiesWithRadiusFiltering || + (algo == + FindRoadObservationsWith::AllEntitiesWithRadiusFiltering && + roadObservationsCount == consts::kMaxAgentMapObservationsCount); +} + +Manager::Impl * Manager::Impl::init(const Manager::Config &mgr_cfg) { + Sim::Config sim_cfg; + sim_cfg.enableLidar = mgr_cfg.params.enableLidar; + + assert(isRoadObservationAlgorithmValid( + mgr_cfg.params.roadObservationAlgorithm)); + + const int64_t numWorlds = mgr_cfg.scenes.size(); + + switch (mgr_cfg.execMode) { + case ExecMode::CUDA: { +#ifdef MADRONA_CUDA_SUPPORT + CUcontext cu_ctx = MWCudaExecutor::initCUDA(mgr_cfg.gpuID); + + EpisodeManager *episode_mgr = + (EpisodeManager *)cu::allocGPU(sizeof(EpisodeManager)); + REQ_CUDA(cudaMemset(episode_mgr, 0, sizeof(EpisodeManager))); + + PhysicsLoader phys_loader(ExecMode::CUDA, 10); + loadPhysicsObjects(phys_loader); + + ObjectManager *phys_obj_mgr = &phys_loader.getObjectManager(); + + HeapArray world_inits(numWorlds); + + + Parameters* paramsDevicePtr = (Parameters*)cu::allocGPU(sizeof(Parameters)); + REQ_CUDA(cudaMemcpy(paramsDevicePtr, &(mgr_cfg.params), sizeof(Parameters), cudaMemcpyHostToDevice)); + + int64_t worldIdx{0}; + for (auto const &scene : mgr_cfg.scenes) { + Map *map = (Map *)MapReader::parseAndWriteOut(scene, + ExecMode::CUDA, mgr_cfg.params.polylineReductionThreshold); + world_inits[worldIdx++] = WorldInit{episode_mgr, phys_obj_mgr, map, paramsDevicePtr}; + } + assert(worldIdx == numWorlds); + + Optional render_gpu_state = + initRenderGPUState(mgr_cfg); + + Optional render_mgr = + initRenderManager(mgr_cfg, render_gpu_state); + + if (render_mgr.has_value()) { + loadRenderObjects(*render_mgr); + sim_cfg.renderBridge = render_mgr->bridge(); + } else { + sim_cfg.renderBridge = nullptr; + } + + MWCudaExecutor gpu_exec({ + .worldInitPtr = world_inits.data(), + .numWorldInitBytes = sizeof(WorldInit), + .userConfigPtr = (void *)&sim_cfg, + .numUserConfigBytes = sizeof(Sim::Config), + .numWorldDataBytes = sizeof(Sim), + .worldDataAlignment = alignof(Sim), + .numWorlds = static_cast(numWorlds), + .numTaskGraphs = (uint32_t)TaskGraphID::NumTaskGraphs, + .numExportedBuffers = (uint32_t)ExportID::NumExports, + }, { + { GPU_HIDESEEK_SRC_LIST }, + { GPU_HIDESEEK_COMPILE_FLAGS }, + CompileConfig::OptMode::LTO, + }, cu_ctx); + + WorldReset *world_reset_buffer = + (WorldReset *)gpu_exec.getExported((uint32_t)ExportID::Reset); + + Action *agent_actions_buffer = + (Action *)gpu_exec.getExported((uint32_t)ExportID::Action); + madrona::cu::deallocGPU(paramsDevicePtr); + for (int64_t i = 0; i < numWorlds; i++) { + auto &init = world_inits[i]; + madrona::cu::deallocGPU(init.map); + } + + return new CUDAImpl { + mgr_cfg, + std::move(phys_loader), + episode_mgr, + world_reset_buffer, + agent_actions_buffer, + std::move(gpu_exec), + std::move(render_gpu_state), + std::move(render_mgr), + numWorlds + }; + +#else + FATAL("Madrona was not compiled with CUDA support"); +#endif + } break; + case ExecMode::CPU: { + EpisodeManager *episode_mgr = new EpisodeManager { 0 }; + + PhysicsLoader phys_loader(ExecMode::CPU, 10); + loadPhysicsObjects(phys_loader); + + ObjectManager *phys_obj_mgr = &phys_loader.getObjectManager(); + + HeapArray world_inits(numWorlds); + + int64_t worldIdx{0}; + + for (auto const &scene : mgr_cfg.scenes) + { + Map *map_ = (Map *)MapReader::parseAndWriteOut(scene, + ExecMode::CPU, mgr_cfg.params.polylineReductionThreshold); + world_inits[worldIdx++] = WorldInit{episode_mgr, phys_obj_mgr, map_, &(mgr_cfg.params)}; + } + assert(worldIdx == numWorlds); + + + + Optional render_gpu_state = + initRenderGPUState(mgr_cfg); + + Optional render_mgr = + initRenderManager(mgr_cfg, render_gpu_state); + + if (render_mgr.has_value()) { + loadRenderObjects(*render_mgr); + sim_cfg.renderBridge = render_mgr->bridge(); + } else { + sim_cfg.renderBridge = nullptr; + } + + CPUImpl::TaskGraphT cpu_exec { + ThreadPoolExecutor::Config { + .numWorlds = static_cast(mgr_cfg.scenes.size()), + .numExportedBuffers = (uint32_t)ExportID::NumExports, + }, + sim_cfg, + world_inits.data(), + (uint32_t)TaskGraphID::NumTaskGraphs, + }; + + WorldReset *world_reset_buffer = + (WorldReset *)cpu_exec.getExported((uint32_t)ExportID::Reset); + + Action *agent_actions_buffer = + (Action *)cpu_exec.getExported((uint32_t)ExportID::Action); + auto cpu_impl = new CPUImpl { + mgr_cfg, + std::move(phys_loader), + episode_mgr, + world_reset_buffer, + agent_actions_buffer, + std::move(cpu_exec), + std::move(render_gpu_state), + std::move(render_mgr), + numWorlds + }; + + for (size_t i = 0; i < mgr_cfg.scenes.size(); i++) { + auto &init = world_inits[i]; + delete init.map; + } + + return cpu_impl; + } break; + default: MADRONA_UNREACHABLE(); + } +} + +Manager::Manager(const Config &cfg) : impl_(Impl::init(cfg)) { reset({}); } + +Manager::~Manager() {} + +void Manager::step() +{ + impl_->step(); + + if (impl_->renderMgr.has_value()) { + impl_->renderMgr->readECS(); + } + + if (impl_->cfg.enableBatchRenderer) { + impl_->renderMgr->batchRender(); + } +} + +void Manager::reset(std::vector worldsToReset) { + for (const auto &worldIdx : worldsToReset) { + triggerReset(worldIdx); + } + + impl_->reset(); +} + +void Manager::setMaps(const std::vector &maps) +{ + assert(impl_->cfg.scenes.size() == maps.size()); + impl_->cfg.scenes = maps; + + ResetMap resetmap{ + 1, + }; + + if (impl_->cfg.execMode == madrona::ExecMode::CUDA) + { +#ifdef MADRONA_CUDA_SUPPORT + auto &gpu_exec = static_cast(impl_.get())->gpuExec; + for (size_t world_idx = 0; world_idx < maps.size(); world_idx++) + { + Map *map = static_cast(MapReader::parseAndWriteOut(maps[world_idx], + ExecMode::CUDA, impl_->cfg.params.polylineReductionThreshold)); + Map *mapDevicePtr = (Map *)gpu_exec.getExported((uint32_t)ExportID::Map) + world_idx; + REQ_CUDA(cudaMemcpy(mapDevicePtr, map, sizeof(Map), cudaMemcpyHostToDevice)); + madrona::cu::deallocGPU(map); + + auto resetMapPtr = (ResetMap *)gpu_exec.getExported((uint32_t)ExportID::ResetMap) + world_idx; + REQ_CUDA(cudaMemcpy(resetMapPtr, &resetmap, sizeof(ResetMap), cudaMemcpyHostToDevice)); + + // reset agents to delete + auto agentsToDeleteDevicePtr = (int32_t *)gpu_exec.getExported((uint32_t)ExportID::DeletedAgents); + int32_t *agentsToDeletePtr = agentsToDeleteDevicePtr + world_idx * consts::kMaxAgentCount; + REQ_CUDA(cudaMemset(agentsToDeletePtr, -1, consts::kMaxAgentCount * sizeof(int32_t))); + } + +#else + // Handle the case where CUDA support is not available + FATAL("Madrona was not compiled with CUDA support"); +#endif + } + else + { + + auto &cpu_exec = static_cast(impl_.get())->cpuExec; + + for (size_t world_idx = 0; world_idx < maps.size(); world_idx++) + { + // Parse the map string into your MapData structure + Map *map = static_cast(MapReader::parseAndWriteOut(maps[world_idx], + ExecMode::CPU, impl_->cfg.params.polylineReductionThreshold)); + + Map *mapDevicePtr = (Map *)cpu_exec.getExported((uint32_t)ExportID::Map) + world_idx; + memcpy(mapDevicePtr, map, sizeof(Map)); + delete map; + + auto resetMapPtr = (ResetMap *)cpu_exec.getExported((uint32_t)ExportID::ResetMap) + world_idx; + memcpy(resetMapPtr, &resetmap, sizeof(ResetMap)); + + // reset agents to delete + auto agentsToDeleteDevicePtr = (int32_t *)cpu_exec.getExported((uint32_t)ExportID::DeletedAgents); + int32_t *agentsToDeletePtr = agentsToDeleteDevicePtr + world_idx * consts::kMaxAgentCount; + memset(agentsToDeletePtr, -1, consts::kMaxAgentCount * sizeof(int32_t)); + } + } + + // Vector of range on integers from 0 to the number of worlds + std::vector worldIndices(impl_->cfg.scenes.size()); + std::iota(worldIndices.begin(), worldIndices.end(), 0); + reset(worldIndices); +} + +Tensor Manager::deletedAgentsTensor() const +{ + return impl_->exportTensor(ExportID::DeletedAgents, TensorElementType::Int32, + { + impl_->numWorlds, + consts::kMaxAgentCount, + }); +} + +void Manager::deleteAgents(const std::unordered_map> &agentsToDelete) +{ + + ResetMap resetmap{ + 1, + }; + + if (impl_->cfg.execMode == madrona::ExecMode::CUDA) + { +#ifdef MADRONA_CUDA_SUPPORT + auto &gpu_exec = static_cast(impl_.get())->gpuExec; + auto agentsToDeleteDevicePtr = (int32_t *)gpu_exec.getExported((uint32_t)ExportID::DeletedAgents); + for (const auto &[worldIdx, agents] : agentsToDelete) + { + assert(worldIdx < impl_->cfg.scenes.size()); + assert(agents.size() <= consts::kMaxAgentCount); + int32_t *agentsToDeletePtr = agentsToDeleteDevicePtr + worldIdx * consts::kMaxAgentCount; + for (size_t i = 0; i < agents.size(); i++) + { + REQ_CUDA(cudaMemcpy(agentsToDeletePtr + i, &agents[i], sizeof(int32_t), cudaMemcpyHostToDevice)); + } + auto resetMapPtr = (ResetMap *)gpu_exec.getExported((uint32_t)ExportID::ResetMap) + worldIdx; + REQ_CUDA(cudaMemcpy(resetMapPtr, &resetmap, sizeof(ResetMap), cudaMemcpyHostToDevice)); + } +#else + // Handle the case where CUDA support is not available + FATAL("Madrona was not compiled with CUDA support"); +#endif + } + else + { + auto &cpu_exec = static_cast(impl_.get())->cpuExec; + auto agentsToDeleteDevicePtr = (int32_t *)cpu_exec.getExported((uint32_t)ExportID::DeletedAgents); + for (const auto &[worldIdx, agents] : agentsToDelete) + { + assert(worldIdx < impl_->cfg.scenes.size()); + assert(agents.size() <= consts::kMaxAgentCount); + int32_t *agentsToDeletePtr = agentsToDeleteDevicePtr + worldIdx * consts::kMaxAgentCount; + for (size_t i = 0; i < agents.size(); i++) + { + memcpy(agentsToDeletePtr + i, &agents[i], sizeof(int32_t)); + } + auto resetMapPtr = (ResetMap *)cpu_exec.getExported((uint32_t)ExportID::ResetMap) + worldIdx; + memcpy(resetMapPtr, &resetmap, sizeof(ResetMap)); + } + } + + std::vector worldIndices(impl_->cfg.scenes.size()); + std::iota(worldIndices.begin(), worldIndices.end(), 0); + reset(worldIndices); +} + + +Tensor Manager::actionTensor() const +{ + return impl_->exportTensor(ExportID::Action, TensorElementType::Float32, + { + impl_->numWorlds, + consts::kMaxAgentCount, + ActionExportSize, // Num_actions + }); +} + + +Tensor Manager::rewardTensor() const +{ + return impl_->exportTensor(ExportID::Reward, TensorElementType::Float32, + { + impl_->numWorlds, + consts::kMaxAgentCount, + 1, + }); +} + +Tensor Manager::worldMeansTensor() const +{ + return impl_->exportTensor(ExportID::WorldMeans, TensorElementType::Float32, + { + impl_->numWorlds, + WorldMeansExportSize, + }); +} + +Tensor Manager::doneTensor() const +{ + return impl_->exportTensor(ExportID::Done, TensorElementType::Int32, + { + impl_->numWorlds, + consts::kMaxAgentCount, + 1, + }); +} + +Tensor Manager::infoTensor() const +{ + return impl_->exportTensor(ExportID::Info, TensorElementType::Int32, + { + impl_->numWorlds, + consts::kMaxAgentCount, + InfoExportSize + }); +} + +Tensor Manager::selfObservationTensor() const +{ + return impl_->exportTensor(ExportID::SelfObservation, + TensorElementType::Float32, + { + impl_->numWorlds, + consts::kMaxAgentCount, + SelfObservationExportSize + }); +} + +Tensor Manager::mapObservationTensor() const +{ + return impl_->exportTensor(ExportID::MapObservation, + TensorElementType::Float32, + { + impl_->numWorlds, + consts::kMaxRoadEntityCount, + MapObservationExportSize + }); +} + +Tensor Manager::partnerObservationsTensor() const +{ + return impl_->exportTensor(ExportID::PartnerObservations, + TensorElementType::Float32, + { + impl_->numWorlds, + consts::kMaxAgentCount, + consts::kMaxAgentCount - 1, + PartnerObservationExportSize + }); +} + +Tensor Manager::agentMapObservationsTensor() const +{ + return impl_->exportTensor(ExportID::AgentMapObservations, + TensorElementType::Float32, + { + impl_->numWorlds, + consts::kMaxAgentCount, + consts::kMaxAgentMapObservationsCount, + AgentMapObservationExportSize, + }); + +} + +Tensor Manager::lidarTensor() const +{ + return impl_->exportTensor(ExportID::Lidar, TensorElementType::Float32, + { + impl_->numWorlds, + consts::kMaxAgentCount, + 3, // Trace lidars on 3 planes + consts::numLidarSamples, + LidarExportSize / (3 * consts::numLidarSamples), + }); +} + +Tensor Manager::bevObservationTensor() const +{ + return impl_->exportTensor(ExportID::BevObservations, TensorElementType::Float32, + { + impl_->numWorlds, + consts::kMaxAgentCount, + consts::bev_rasterization_resolution, + consts::bev_rasterization_resolution, + BevObservationExportSize, + }); +} + +Tensor Manager::stepsRemainingTensor() const +{ + return impl_->exportTensor(ExportID::StepsRemaining, + TensorElementType::Int32, + { + impl_->numWorlds, + consts::kMaxAgentCount, + 1, + }); +} + +Tensor Manager::shapeTensor() const { + return impl_->exportTensor(ExportID::Shape, TensorElementType::Int32, + {impl_->numWorlds, 2}); +} + +Tensor Manager::controlledStateTensor() const { + return impl_->exportTensor(ExportID::ControlledState, TensorElementType::Int32, + {impl_->numWorlds, consts::kMaxAgentCount, 1}); +} + +Tensor Manager::responseTypeTensor() const { + return impl_->exportTensor(ExportID::ResponseType, TensorElementType::Int32, + {impl_->numWorlds, consts::kMaxAgentCount, 1}); +} + +Tensor Manager::absoluteSelfObservationTensor() const { + return impl_->exportTensor( + ExportID::AbsoluteSelfObservation, TensorElementType::Float32, + {impl_->numWorlds, consts::kMaxAgentCount, AbsoluteSelfObservationExportSize}); +} + +Tensor Manager::validStateTensor() const { + return impl_->exportTensor( + ExportID::ValidState, TensorElementType::Int32, + {impl_->numWorlds, consts::kMaxAgentCount, 1}); +} + +Tensor Manager::expertTrajectoryTensor() const { + return impl_->exportTensor( + ExportID::Trajectory, TensorElementType::Float32, + {impl_->numWorlds, consts::kMaxAgentCount, TrajectoryExportSize}); +} + +Tensor Manager::mapNameTensor() const { + return impl_->exportTensor( + ExportID::MapName, TensorElementType::Int32, + {impl_->numWorlds, MapNameExportSize} + ); +} + +Tensor Manager::scenarioIdTensor() const { + return impl_->exportTensor( + ExportID::ScenarioId, TensorElementType::Int32, + {impl_->numWorlds, ScenarioIdExportSize} + ); +} + +Tensor Manager::metadataTensor() const { + return impl_->exportTensor( + ExportID::MetaData, TensorElementType::Int32, + {impl_->numWorlds, consts::kMaxAgentCount, MetaDataExportSize} + ); +} + +void Manager::triggerReset(int32_t world_idx) +{ + WorldReset reset { + 1, + }; + + auto *reset_ptr = impl_->worldResetBuffer + world_idx; + + if (impl_->cfg.execMode == ExecMode::CUDA) { +#ifdef MADRONA_CUDA_SUPPORT + cudaMemcpy(reset_ptr, &reset, sizeof(WorldReset), + cudaMemcpyHostToDevice); +#endif + } else { + *reset_ptr = reset; + } +} + +Tensor Manager::rgbTensor() const +{ + const uint8_t *rgb_ptr = impl_->renderMgr->batchRendererRGBOut(); + + assert(rgb_ptr != nullptr); + + return Tensor((void*)rgb_ptr, TensorElementType::UInt8, { + impl_->numWorlds, + consts::kMaxAgentCount, + impl_->cfg.batchRenderViewHeight, + impl_->cfg.batchRenderViewWidth, + 4, + }, impl_->cfg.gpuID); +} + +Tensor Manager::depthTensor() const +{ + const float *depth_ptr = impl_->renderMgr->batchRendererDepthOut(); + + return Tensor((void *)depth_ptr, TensorElementType::Float32, { + impl_->numWorlds, + consts::kMaxAgentCount, + impl_->cfg.batchRenderViewHeight, + impl_->cfg.batchRenderViewWidth, + 1, + }, impl_->cfg.gpuID); +} + +void Manager::setAction(int32_t world_idx, int32_t agent_idx, + float acceleration, float steering, float headAngle) { + Action action{.classic = {acceleration, steering, headAngle}}; + + auto *action_ptr = impl_->agentActionsBuffer + world_idx * consts::kMaxAgentCount + agent_idx; + + if (impl_->cfg.execMode == ExecMode::CUDA) { +#ifdef MADRONA_CUDA_SUPPORT + cudaMemcpy(action_ptr, &action, sizeof(Action), cudaMemcpyHostToDevice); +#endif + } else { + *action_ptr = action; + } +} + +std::vector +Manager::getShapeTensorFromDeviceMemory() { + const uint32_t numWorlds = impl_->numWorlds; + const auto &tensor = shapeTensor(); + + const std::size_t floatsPerShape{2}; + const std::size_t tensorByteCount{sizeof(float) * floatsPerShape * + numWorlds}; + + std::vector worldToShape(numWorlds); + switch (impl_->cfg.execMode) { + case ExecMode::CUDA: +#ifdef MADRONA_CUDA_SUPPORT + cudaMemcpy(worldToShape.data(), tensor.devicePtr(), tensorByteCount, + cudaMemcpyDeviceToHost); +#else + FATAL("Madrona was not compiled with CUDA support"); +#endif + break; + case ExecMode::CPU: + std::memcpy(worldToShape.data(), tensor.devicePtr(), tensorByteCount); + break; + } + + return worldToShape; +} + +render::RenderManager & Manager::getRenderManager() +{ + return *impl_->renderMgr; +} + +} diff --git a/gpudrive-main/src/mgr.hpp b/gpudrive-main/src/mgr.hpp new file mode 100644 index 0000000000000000000000000000000000000000..d9613b61b5dc77a865888a4adc5edcec953c3c94 --- /dev/null +++ b/gpudrive-main/src/mgr.hpp @@ -0,0 +1,102 @@ +#pragma once +#ifdef madrona_3d_example_mgr_EXPORTS +#define MGR_EXPORT MADRONA_EXPORT +#else +#define MGR_EXPORT MADRONA_IMPORT +#endif + +#include +#include +#include + +#include +#include + +#include + +#include "init.hpp" +#include "types.hpp" + +namespace madrona_gpudrive { + +// The Manager class encapsulates the linkage between the outside training +// code and the internal simulation state (src/sim.hpp / src/sim.cpp) +// +// Manager is responsible for initializing the simulator, loading physics +// and rendering assets off disk, and mapping ECS components to tensors +// for learning +class Manager { +public: + struct Config { + madrona::ExecMode execMode; // CPU or CUDA + int gpuID; // Which GPU for CUDA backend? + // TODO(sk): Use nanobind filesystem.h? + std::vector scenes; + Parameters params; + + + // Rendering settings + bool enableBatchRenderer = false; + uint32_t batchRenderViewWidth = 64; + uint32_t batchRenderViewHeight = 64; + madrona::render::APIBackend *extRenderAPI = nullptr; + madrona::render::GPUDevice *extRenderDev = nullptr; + }; + + MGR_EXPORT Manager(const Config &cfg); + MGR_EXPORT ~Manager(); + + MGR_EXPORT void step(); + MGR_EXPORT void reset(std::vector worldsToReset); + + // These functions export Tensor objects that link the ECS + // simulation state to the python bindings / PyTorch tensors (src/bindings.cpp) + MGR_EXPORT madrona::py::Tensor actionTensor() const; + MGR_EXPORT madrona::py::Tensor rewardTensor() const; + MGR_EXPORT madrona::py::Tensor doneTensor() const; + MGR_EXPORT madrona::py::Tensor selfObservationTensor() const; + MGR_EXPORT madrona::py::Tensor mapObservationTensor() const; + MGR_EXPORT madrona::py::Tensor partnerObservationsTensor() const; + MGR_EXPORT madrona::py::Tensor agentMapObservationsTensor() const; + MGR_EXPORT madrona::py::Tensor lidarTensor() const; + MGR_EXPORT madrona::py::Tensor bevObservationTensor() const; + MGR_EXPORT madrona::py::Tensor stepsRemainingTensor() const; + MGR_EXPORT madrona::py::Tensor shapeTensor() const; + MGR_EXPORT madrona::py::Tensor controlledStateTensor() const; + MGR_EXPORT madrona::py::Tensor absoluteSelfObservationTensor() const; + MGR_EXPORT madrona::py::Tensor validStateTensor() const; + MGR_EXPORT madrona::py::Tensor infoTensor() const; + MGR_EXPORT madrona::py::Tensor responseTypeTensor() const; + MGR_EXPORT madrona::py::Tensor expertTrajectoryTensor() const; + MGR_EXPORT madrona::py::Tensor worldMeansTensor() const; + MGR_EXPORT madrona::py::Tensor metadataTensor() const; + MGR_EXPORT madrona::py::Tensor deletedAgentsTensor() const; + MGR_EXPORT madrona::py::Tensor mapNameTensor() const; + MGR_EXPORT madrona::py::Tensor scenarioIdTensor() const; + madrona::py::Tensor rgbTensor() const; + madrona::py::Tensor depthTensor() const; + // These functions are used by the viewer to control the simulation + // with keyboard inputs in place of DNN policy actions + MGR_EXPORT void triggerReset(int32_t world_idx); + MGR_EXPORT void setAction(int32_t world_idx, int32_t agent_idx, + float acceleration, float steering, + float headAngle); + MGR_EXPORT void setMaps(const std::vector &maps); + + MGR_EXPORT void deleteAgents(const std::unordered_map> &agentsToDelete); + + // TODO: remove parameters + MGR_EXPORT std::vector + getShapeTensorFromDeviceMemory(); + + madrona::render::RenderManager & getRenderManager(); + + private: + struct Impl; + struct CPUImpl; + struct CUDAImpl; + + std::unique_ptr impl_; +}; + +} diff --git a/gpudrive-main/src/obb.hpp b/gpudrive-main/src/obb.hpp new file mode 100644 index 0000000000000000000000000000000000000000..fa206f9c452921bea146a11a53a03ae4f5ec1303 --- /dev/null +++ b/gpudrive-main/src/obb.hpp @@ -0,0 +1,94 @@ +#pragma once + +#include "utils.hpp" +#include +#include + +namespace madrona_gpudrive { + +// This code is based on +// https://www.flipcode.com/archives/2D_OBB_Intersection.shtml +struct OrientedBoundingBox2D { + static OrientedBoundingBox2D from(const madrona::base::Position &position, + const madrona::base::Rotation &rotation, + const madrona::base::Scale &scale) { + float theta{utils::quatToYaw(rotation)}; + madrona::math::Vector2 X{cosf(theta), sinf(theta)}; + madrona::math::Vector2 Y{-sinf(theta), cosf(theta)}; + + X *= scale.d0; + Y *= scale.d1; + + madrona::math::Vector2 center{.x = position.x, .y = position.y}; + + OrientedBoundingBox2D obb; + obb.corners[0] = center - X - Y; + obb.corners[1] = center + X - Y; + obb.corners[2] = center + X + Y; + obb.corners[3] = center - X + Y; + + obb.updateAxes(); + + return obb; + } + static bool hasCollided(const OrientedBoundingBox2D &obbA, + const OrientedBoundingBox2D &obbB) { + return obbA.overlaps(obbB) && obbB.overlaps(obbA); + } + + void updateAxes() { + axes[0] = corners[1] - corners[0]; + axes[1] = corners[3] - corners[0]; + + // Make the length of each axes 1/edge length so we know any + // dot product must be less than 1 to fall within the edge. + + for (int a = 0; a < 2; ++a) { + axes[a] /= axes[a].length2(); + origin[a] = corners[0].dot(axes[a]); + } + } + bool overlaps(const OrientedBoundingBox2D &other) const { + for (int a = 0; a < 2; ++a) { + float t = other.corners[0].dot(axes[a]); + + // Find the extent of box 2 on axis a + float tMin = t; + float tMax = t; + + for (int c = 1; c < 4; ++c) { + t = other.corners[c].dot(axes[a]); + + if (t < tMin) { + tMin = t; + } else if (t > tMax) { + tMax = t; + } + } + + // We have to subtract off the origin + + // See if [tMin, tMax] intersects [0, 1] + if ((tMin > 1 + origin[a]) || (tMax < origin[a])) { + // There was no intersection along this dimension; + // the boxes cannot possibly overlap. + return false; + } + } + + // There was no dimension along which there is no intersection. + // Therefore the boxes overlap. + return true; + } + + // Corners of OBB in the global coordinate system + madrona::math::Vector2 corners[4]; + + // Two edges of the box extended away from corner[0] + madrona::math::Vector2 axes[2]; + + // origin[a] = corner[0].dot(axis[a]) + float origin[2]; +}; + +} // namespace madrona_gpudrive diff --git a/gpudrive-main/src/rasterizer.hpp b/gpudrive-main/src/rasterizer.hpp new file mode 100644 index 0000000000000000000000000000000000000000..6dfa20c0042cc5e9ae03d3710fa98842bb0f39d3 --- /dev/null +++ b/gpudrive-main/src/rasterizer.hpp @@ -0,0 +1,80 @@ +#pragma once + +#include "types.hpp" +#include +#include +#include + +namespace madrona_gpudrive { +namespace rasterizer { + +// Convert ego-relative position (meters) to grid coordinates +inline std::pair toGridCoords( + const madrona::math::Vector2 &rel_pos, + float radius, + int resolution) +{ + float scale_m = resolution / (2 * radius); + int x_grid = static_cast((rel_pos.x + radius) * scale_m); + int y_grid = static_cast((rel_pos.y + radius) * scale_m); + return { + std::min(std::max(0, x_grid), resolution - 1), + std::min(std::max(0, y_grid), resolution - 1) + }; +} + +// Rasterize rectangle into grid +inline void rasterizeRotatedRectangle( + BevObservations &grid, + const madrona::math::Vector2 ¢er, + float yaw, + float length, + float width, + size_t type, + float radius, + int resolution) +{ + float half_w = width / 2.0f; + float half_l = length / 2.0f; + + float scale_px = (2 * radius) / resolution; + + auto [gx, gy] = toGridCoords(center, radius, resolution); + + // Sub-optimal method which finds maximum bounding box of object + float max_side = std::max(half_w, half_l); + int box_radius = static_cast(std::ceil( + std::sqrt(2 * (max_side * max_side)) / scale_px)); + + float cos_yaw = std::cos(-yaw); + float sin_yaw = std::sin(-yaw); + + for (int dy = -box_radius; dy <= box_radius; dy++) { + for (int dx = -box_radius; dx <= box_radius; dx++) { + int x = gx + dx; + int y = gy + dy; + + if (x < 0 || x >= resolution || y < 0 || y >= resolution) + continue; + + float px = x * scale_px - radius; + float py = y * scale_px - radius; + + float local_dx = px - center.x; + float local_dy = py - center.y; + + float local_x = local_dx * cos_yaw - local_dy * sin_yaw; + float local_y = local_dx * sin_yaw + local_dy * cos_yaw; + + constexpr float epsilon = 1e-3f; + + if (std::abs(local_x) <= half_l + epsilon && + std::abs(local_y) <= half_w + epsilon) + { + grid.obs[y][x].type = type; + } + } + } +} +} +} \ No newline at end of file diff --git a/gpudrive-main/src/rng.hpp b/gpudrive-main/src/rng.hpp new file mode 100644 index 0000000000000000000000000000000000000000..730127802608ca0ff90b875d35ee22557d8af877 --- /dev/null +++ b/gpudrive-main/src/rng.hpp @@ -0,0 +1,45 @@ +#pragma once + +#include + +namespace madrona_gpudrive { + +// Simple PRNG intended to be initialized with episode ID. +class RNG { +public: + static inline RNG make(uint32_t idx) + { + uint32_t v0 = idx; + uint32_t v1 = 0; + uint32_t s0 = 0; + +#pragma unroll + for (int n = 0; n < 8; n++) { + s0 += 0x9e3779b9; + v0 += ((v1 << 4) + 0xa341316c) ^ + (v1 + s0) ^ ((v1 >> 5) + 0xc8013ea4); + v1 += ((v0 << 4) + 0xad90777d) ^ (v0 + s0) ^ + ((v0 >> 5) + 0x7e95761e); + } + + RNG rng; + rng.v_ = v0; + + return rng; + } + + inline float rand() + { + const uint32_t LCG_A = 1664525u; + const uint32_t LCG_C = 1013904223u; + v_ = (LCG_A * v_ + LCG_C); + + uint32_t next = v_ & 0x00FFFFFF; + return float(next) / float(0x01000000); + } + +private: + uint32_t v_; +}; + +} diff --git a/gpudrive-main/src/sim.cpp b/gpudrive-main/src/sim.cpp new file mode 100644 index 0000000000000000000000000000000000000000..bcc0fe51e7a24c740a364a4e7978d60f5d6213d6 --- /dev/null +++ b/gpudrive-main/src/sim.cpp @@ -0,0 +1,1013 @@ +#include +#include +#include +#include +#include + +#include "level_gen.hpp" +#include "obb.hpp" +#include "sim.hpp" +#include "utils.hpp" +#include "rasterizer.hpp" +#include "knn.hpp" +#include "dynamics.hpp" + +using namespace madrona; +using namespace madrona::math; +using namespace madrona::phys; + +namespace RenderingSystem = madrona::render::RenderingSystem; + +namespace madrona_gpudrive { + +CountT getCurrentStep(const StepsRemaining &stepsRemaining) { + return consts::episodeLen - stepsRemaining.t; +} + +// Register all the ECS components and archetypes that will be +// used in the simulation +void Sim::registerTypes(ECSRegistry ®istry, const Config &cfg) +{ + base::registerTypes(registry); + phys::PhysicsSystem::registerTypes(registry); + + RenderingSystem::registerTypes(registry, cfg.renderBridge); + + registry.registerComponent(); + registry.registerComponent(); + registry.registerComponent(); + registry.registerComponent(); + registry.registerComponent(); + registry.registerComponent(); + registry.registerComponent(); + registry.registerComponent(); + registry.registerComponent(); + registry.registerComponent(); + registry.registerComponent(); + registry.registerComponent(); + registry.registerComponent(); + registry.registerComponent(); + registry.registerComponent(); + registry.registerComponent(); + registry.registerComponent(); + registry.registerComponent(); + registry.registerComponent(); + registry.registerComponent(); + registry.registerComponent(); + registry.registerComponent(); + registry.registerComponent(); + registry.registerComponent(); + registry.registerComponent(); + registry.registerComponent(); + + registry.registerSingleton(); + registry.registerSingleton(); + registry.registerSingleton(); + registry.registerSingleton(); + registry.registerSingleton(); + registry.registerSingleton(); + registry.registerSingleton(); + registry.registerSingleton(); + + registry.registerArchetype(); + registry.registerArchetype(); + registry.registerArchetype(); + registry.registerArchetype(); + registry.registerArchetype(); + + registry.exportSingleton((uint32_t)ExportID::Reset); + registry.exportSingleton((uint32_t)ExportID::Shape); + registry.exportSingleton((uint32_t)ExportID::Map); + registry.exportSingleton((uint32_t)ExportID::ResetMap); + registry.exportSingleton((uint32_t)ExportID::WorldMeans); + registry.exportSingleton((uint32_t)ExportID::DeletedAgents); + registry.exportSingleton((uint32_t)ExportID::MapName); + registry.exportSingleton((uint32_t)ExportID::ScenarioId); + + registry.exportColumn( + (uint32_t)ExportID::Action); + registry.exportColumn( + (uint32_t)ExportID::SelfObservation); + registry.exportColumn( + (uint32_t)ExportID::AgentMapObservations); + registry.exportColumn( + (uint32_t)ExportID::MapObservation); + + registry.exportColumn( + (uint32_t)ExportID::PartnerObservations); + registry.exportColumn( + (uint32_t)ExportID::Lidar); + registry.exportColumn( + (uint32_t)ExportID::BevObservations); + registry.exportColumn( + (uint32_t)ExportID::StepsRemaining); + registry.exportColumn( + (uint32_t)ExportID::Reward); + registry.exportColumn( + (uint32_t)ExportID::Done); + registry.exportColumn( + (uint32_t) ExportID::ControlledState); + registry.exportColumn( + (uint32_t)ExportID::AbsoluteSelfObservation); + registry.exportColumn( + (uint32_t)ExportID::Info); + registry.exportColumn( + (uint32_t)ExportID::ResponseType); + registry.exportColumn( + (uint32_t)ExportID::Trajectory); + registry.exportColumn( + (uint32_t)ExportID::MetaData); +} + +static inline void cleanupWorld(Engine &ctx) { + destroyWorld(ctx); +} + +static inline void initWorld(Engine &ctx) +{ + phys::PhysicsSystem::reset(ctx); + + // Assign a new episode ID + EpisodeManager &episode_mgr = *ctx.data().episodeMgr; + int32_t episode_idx = episode_mgr.curEpisode.fetch_add(1); + ctx.data().rng = RNG::make(episode_idx); + ctx.data().curEpisodeIdx = episode_idx; + + if(ctx.singleton().reset == 1) + { + createPersistentEntities(ctx); + ctx.singleton().reset = 0; + phys::PhysicsSystem::reset(ctx); + } + + // Defined in src/level_gen.hpp / src/level_gen.cpp + resetWorld(ctx); +} + +// This system runs in TaskGraphID::Reset and checks if the code external to the +// application has forced a reset by writing to the WorldReset singleton. If a +// reset is needed, cleanup the existing world and generate a new one. +inline void resetSystem(Engine &ctx, WorldReset &reset) +{ + if (reset.reset == 0) + { + return; + } + + reset.reset = 0; + + auto resetMap = ctx.singleton(); + + if (resetMap.reset == 1) + { + cleanupWorld(ctx); + } + initWorld(ctx); +} + +inline void collectSelfObsSystem(Engine &ctx, + const VehicleSize &size, + const Position &pos, + const Rotation &rot, + const Velocity &vel, + const Goal &goal, + const CollisionDetectionEvent& collisionEvent, + const AgentInterfaceEntity &agent_iface) +{ + auto &self_obs = ctx.get(agent_iface.e); + self_obs.speed = vel.linear.length(); + self_obs.vehicle_size = size; + auto goalPos = goal.position - pos.xy(); + self_obs.goal.position = rot.inv().rotateVec({goalPos.x, goalPos.y, 0}).xy(); + + auto hasCollided = collisionEvent.hasCollided.load_relaxed(); + self_obs.collisionState = hasCollided ? 1.f : 0.f; + self_obs.id = ctx.get(agent_iface.e).id; +} + +inline void collectPartnerObsSystem(Engine &ctx, + const Position &pos, + const Rotation &rot, + const OtherAgents &other_agents, + const AgentInterfaceEntity &agent_iface) +{ + if(ctx.data().params.disableClassicalObs) + return; + + auto &partner_obs = ctx.get(agent_iface.e); + + CountT arrIndex = 0; CountT agentIdx = 0; + while(agentIdx < ctx.data().numAgents - 1) + { + Entity other = other_agents.e[agentIdx++]; + + const Position &other_position = ctx.get(other); + const Velocity &other_velocity = ctx.get(other); + const Rotation &other_rot = ctx.get(other); + const VehicleSize &other_size = ctx.get(other); + + Vector2 relative_pos = (other_position - pos).xy(); + relative_pos = rot.inv().rotateVec({relative_pos.x, relative_pos.y, 0}).xy(); + float relative_speed = other_velocity.linear.length(); // Design decision: return the speed of the other agent directly + + Rotation relative_orientation = rot.inv() * other_rot; + + float relative_heading = utils::quatToYaw(relative_orientation); + + if(relative_pos.length() > ctx.data().params.observationRadius) + { + continue; + } + partner_obs.obs[arrIndex++] = { + .speed = relative_speed, + .position = relative_pos, + .heading = relative_heading, + .vehicle_size = other_size, + .type = (float)ctx.get(other), + .id = (float)ctx.get(ctx.get(other).e).id + }; + } + while(arrIndex < consts::kMaxAgentCount - 1) { + partner_obs.obs[arrIndex++] = PartnerObservation::zero(); + } +} + +inline void collectMapObservationsSystem(Engine &ctx, + const Position &pos, + const Rotation &rot, + const AgentInterfaceEntity &agent_iface) +{ + if(ctx.data().params.disableClassicalObs) + return; + + auto &map_obs = ctx.get(agent_iface.e); + + const auto alg = ctx.data().params.roadObservationAlgorithm; + if (alg == FindRoadObservationsWith::KNearestEntitiesWithRadiusFiltering) { + selectKNearestRoadEntities( + ctx, rot, pos.xy(), map_obs.obs); + return; + } + + assert(alg == FindRoadObservationsWith::AllEntitiesWithRadiusFiltering); + + utils::ReferenceFrame referenceFrame(pos.xy(), rot); + CountT arrIndex = 0; CountT roadIdx = 0; + while(roadIdx < ctx.data().numRoads && arrIndex < consts::kMaxAgentMapObservationsCount) { + Entity road = ctx.data().roads[roadIdx++]; + auto roadPos = ctx.get(road); + auto roadRot = ctx.get(road); + + auto dist = referenceFrame.distanceTo(roadPos); + if (dist > ctx.data().params.observationRadius) { + continue; + } + + map_obs.obs[arrIndex] = referenceFrame.observationOf( + roadPos, roadRot, ctx.get(road), ctx.get(road), static_cast(ctx.get(road).id), ctx.get(road)); + arrIndex++; + } + while (arrIndex < consts::kMaxAgentMapObservationsCount) { + map_obs.obs[arrIndex++] = MapObservation::zero(); + } +} + +// Make the agents easier to control by zeroing out their velocity +// after each step. +inline void agentZeroVelSystem(Engine &, + Velocity &vel) +{ + vel.linear.x = 0; + vel.linear.y = 0; + vel.linear.z = 0; + vel.angular = Vector3::zero(); +} + + +inline void movementSystem(Engine &e, + const AgentInterfaceEntity &agent_iface, + VehicleSize &size, + Rotation &rotation, + Position &position, + Velocity &velocity, + CollisionDetectionEvent& collisionEvent, + const ResponseType &responseType) { + if (collisionEvent.hasCollided.load_relaxed()) { + switch (e.data().params.collisionBehaviour) { + case CollisionBehaviour::AgentStop: + e.get(agent_iface.e).v = 1; + agentZeroVelSystem(e, velocity); + break; + + case CollisionBehaviour::AgentRemoved: + e.get(agent_iface.e).v = 1; + position = consts::kPaddingPosition; + agentZeroVelSystem(e, velocity); + break; + + case CollisionBehaviour::Ignore: + // Reset collision state at the start of each timestep. + // This ensures the collision state is only true if the agent collided in the current timestep. + collisionEvent.hasCollided.store_relaxed(0); // Reset the collision state. + Info& info = e.get(agent_iface.e); + info.collidedWithRoad = info.collidedWithVehicle = info.collidedWithNonVehicle = 0; + break; + } + } + + const auto &controlledState = e.get(agent_iface.e); + + if (responseType == ResponseType::Static) { + // Do nothing. The agent is static. + // Agent can only be static if isStaticAgentControlled is set to true. + return; + } + + if (e.get(agent_iface.e).v && responseType != ResponseType::Static) { + // Case: Agent has not collided but is done. + // This can only happen if the agent has reached goal or the episode has ended. + // In that case we teleport the agent. The agent will not collide with anything. + position = consts::kPaddingPosition; + velocity.linear.x = 0; + velocity.linear.y = 0; + velocity.linear.z = 0; + velocity.angular = Vector3::zero(); + return; + } + + if (controlledState.controlled) { + Action &action = e.get(agent_iface.e); + switch (e.data().params.dynamicsModel) { + + case DynamicsModel::InvertibleBicycle: + { + forwardBicycleModel(action, rotation, position, velocity); + break; + } + case DynamicsModel::DeltaLocal: + { + forwardDeltaModel(action, rotation, position, velocity); + break; + } + case DynamicsModel::Classic: + { + forwardKinematics(action, size, rotation, position, velocity); + break; + } + case DynamicsModel::State: + { + forwardStateModel(action, rotation, position, velocity); + break; + } + } + } else { + // Follow expert trajectory + const Trajectory &trajectory = e.get(agent_iface.e); + CountT curStepIdx = getCurrentStep(e.get(agent_iface.e)); + position.x = trajectory.positions[curStepIdx].x; + position.y = trajectory.positions[curStepIdx].y; + position.z = 1; + velocity.linear.x = trajectory.velocities[curStepIdx].x; + velocity.linear.y = trajectory.velocities[curStepIdx].y; + velocity.linear.z = 0; + velocity.angular = Vector3::zero(); + rotation = Quat::angleAxis(trajectory.headings[curStepIdx], madrona::math::up); + } +} + +static inline float encodeType(EntityType type) +{ + return (float)type; +} + +// Launches consts::numLidarSamples per agent. +// This system is specially optimized in the GPU version: +// a warp of threads is dispatched for each invocation of the system +// and each thread in the warp traces one lidar ray for the agent. +inline void lidarSystem(Engine &ctx, Entity e, const AgentInterfaceEntity &agent_iface, + EntityType &entityType) { + Lidar &lidar = ctx.get(agent_iface.e); + const Action &action = ctx.get(agent_iface.e); + + Vector3 pos = ctx.get(e); + Quat rot = ctx.get(e); + auto &bvh = ctx.singleton(); + + Vector3 agent_fwd = rot.rotateVec(math::fwd); + Vector3 right = rot.rotateVec(math::right); + + auto traceRay = [&](int32_t idx, float offset, LidarSample *samples) { + // float theta = 2.f * math::pi * ( + // float(idx) / float(consts::numLidarSamples)); + float head_angle = ctx.get(agent_iface.e).controlled ? action.classic.headAngle : 0.f; + float theta = consts::lidarAngle * (2 * float(idx) / float(consts::numLidarSamples) - 1) + head_angle; + float x = cosf(theta); + float y = sinf(theta); + + Vector3 ray_dir = (x * right + y * agent_fwd).normalize(); + + float hit_t; + Vector3 hit_normal; + Entity hit_entity = + bvh.traceRay(pos + offset * math::up, ray_dir, &hit_t, + &hit_normal, consts::lidarDistance); + + if (hit_entity == Entity::none()) { + samples[idx] = { + .depth = 0.f, + .encodedType = encodeType(EntityType::None), + .position = {0.f, 0.f}, + }; + } else { + EntityType entity_type = ctx.get(hit_entity); + + samples[idx] = { + .depth = hit_t, + .encodedType = encodeType(entity_type), + .position = {hit_t * x, + hit_t * y}, + }; + } + }; + + + // MADRONA_GPU_MODE guards GPU specific logic +#ifdef MADRONA_GPU_MODE + // Can use standard cuda variables like threadIdx for + // warp level programming + int32_t idx = threadIdx.x % 32; + + while (idx < consts::numLidarSamples) { + traceRay(idx, consts::lidarCarOffset, lidar.samplesCars); + traceRay(idx, consts::lidarRoadEdgeOffset, lidar.samplesRoadEdges); + traceRay(idx, consts::lidarRoadLineOffset, lidar.samplesRoadLines); + idx += 32; + } +#else + for (CountT i = 0; i < consts::numLidarSamples; i++) { + traceRay(i, consts::lidarCarOffset, lidar.samplesCars); + traceRay(i, consts::lidarRoadEdgeOffset, lidar.samplesRoadEdges); + traceRay(i, consts::lidarRoadLineOffset, lidar.samplesRoadLines); + } +#endif +} + +inline void collectBevObservationsSystem(Engine &ctx, + const Position &pos, + const Rotation &rot, + const OtherAgents &other_agents, + const AgentInterfaceEntity &agent_iface) +{ + if(ctx.data().params.disableClassicalObs) + return; + + auto &bev_obs = ctx.get(agent_iface.e); + + for (size_t i = 0; i < consts::bev_rasterization_resolution; i++) + { + for (size_t j = 0; j < consts::bev_rasterization_resolution; j++) + { + bev_obs.obs[i][j].type = 0; + } + } + + utils::ReferenceFrame referenceFrame(pos.xy(), rot); + + // Roads + CountT roadIdx = 0; + CountT arrIndex = 0; + while (roadIdx < ctx.data().numRoads && arrIndex < consts::kMaxAgentMapObservationsCount) + { + Entity road = ctx.data().roads[roadIdx++]; + auto roadPos = ctx.get(road); + auto roadRot = ctx.get(road); + const MapObservation &map_obs = referenceFrame.observationOf( + roadPos, + roadRot, + ctx.get(road), + ctx.get(road), + static_cast(ctx.get(road).id), + ctx.get(road) + ); + + auto dist = referenceFrame.distanceTo(roadPos); + if (dist > ctx.data().params.observationRadius) + continue; + + madrona::math::Vector2 rel_pos = map_obs.position; + float rel_yaw = map_obs.heading; + auto new_scale = map_obs.scale; + new_scale.d1 = std::max( //Ensure minimum segment width + map_obs.scale.d1, + (2 * ctx.data().params.observationRadius / consts::bev_rasterization_resolution) + ); + + rasterizer::rasterizeRotatedRectangle( + bev_obs, + rel_pos, + rel_yaw, + new_scale.d0, + new_scale.d1, + map_obs.type, + ctx.data().params.observationRadius, + consts::bev_rasterization_resolution + ); + arrIndex++; + } + + // Other agents + CountT agentIdx = 0; + while (agentIdx < ctx.data().numAgents - 1) + { + Entity other = other_agents.e[agentIdx++]; + + const Position &other_position = ctx.get(other); + const Rotation &other_rot = ctx.get(other); + const VehicleSize &other_size = ctx.get(other); + const auto type = static_cast(ctx.get(other)); + Vector2 relative_pos = (other_position - pos).xy(); + relative_pos = rot.inv().rotateVec({relative_pos.x, relative_pos.y, 0}).xy(); + Rotation relative_orientation = rot.inv() * other_rot; + + float relative_heading = utils::quatToYaw(relative_orientation); + + if(relative_pos.length() > ctx.data().params.observationRadius) + continue; + + rasterizer::rasterizeRotatedRectangle( + bev_obs, + relative_pos, + relative_heading, + other_size.length, + other_size.width, + type, + ctx.data().params.observationRadius, + consts::bev_rasterization_resolution + ); + } +} + +// Computes reward for each agent and keeps track of the max distance achieved +// so far through the challenge. Continuous reward is provided for any new +// distance achieved. +inline void rewardSystem(Engine &ctx, + const Position &position, + const Goal &goal, + const AgentInterfaceEntity &agent_iface) +{ + Reward &out_reward = ctx.get(agent_iface.e); + const auto &rewardType = ctx.data().params.rewardParams.rewardType; + if(rewardType == RewardType::DistanceBased) + { + float dist = (position.xy() - goal.position).length(); + float reward = -dist; + out_reward.v = reward; + } + else if(rewardType == RewardType::OnGoalAchieved) + { + float dist = (position.xy() - goal.position).length(); + float reward = (dist < ctx.data().params.rewardParams.distanceToGoalThreshold) ? 1.f : 0.f; + out_reward.v = reward; + } + else if(rewardType == RewardType::Dense) + { + // TODO: Implement full trajectory reward + assert(false); + } + + // Just in case agents do something crazy, clamp total reward + // out_reward.v = fmaxf(fminf(out_reward.v, 1.f), 0.f); +} + +inline void stepTrackerSystem(Engine &ctx, const AgentInterfaceEntity &agent_iface) { + StepsRemaining &stepsRemaining = ctx.get(agent_iface.e); + --stepsRemaining.t; +} + +// Keep track of the number of steps remaining in the episode and +// notify training that an episode has completed by +// setting done = 1 on the final step of the episode +inline void doneSystem(Engine &ctx, + const Position &position, + const Goal &goal, + AgentInterfaceEntity &agent_iface) +{ + StepsRemaining &steps_remaining = ctx.get(agent_iface.e); + Done &done = ctx.get(agent_iface.e); + Info &info = ctx.get(agent_iface.e); + int32_t num_remaining = steps_remaining.t; + if (num_remaining == consts::episodeLen && done.v != 1) + { // Make sure to not reset an agent's done flag + done.v = 0; + return; + } + else if (num_remaining == 0) + { + done.v = 1; + } + + // An agent can be done early if it reaches the goal + if (done.v != 1 || info.reachedGoal != 1) + { + float dist = (position.xy() - goal.position).length(); + if (dist < ctx.data().params.rewardParams.distanceToGoalThreshold) + { + done.v = 1; + info.reachedGoal = 1; + } + } +} + +void collisionDetectionSystem(Engine &ctx, + const CandidateCollision &candidateCollision) { + + auto isInvalidExpertOrDone = [&](const Loc &candidate) -> bool + { + auto agent_iface = ctx.getCheck(candidate); + if (agent_iface.valid()) + { + auto controlledState = ctx.get(agent_iface.value().e).controlled; + // Case: If an expert agent is in an invalid state, we need to ignore the collision detection for it. + if (controlledState == false) + { + auto currStep = getCurrentStep(ctx.get(agent_iface.value().e)); + auto &validState = ctx.get(agent_iface.value().e).valids[currStep]; + if (!validState) + { + return true; + } + } + else + { + // Case: If a controlled agent gets done, we teleport it to the padding position + // Hence we need to ignore the collision detection for it. + // The agent can also be done because it collided. + // In that case, we dont want to ignore collision. Especially if AgentStop is set. + auto &done = ctx.get(agent_iface.value().e); + auto &collisionEvent = ctx.get(candidate); + if (done.v && !collisionEvent.hasCollided.load_relaxed()) + { + return true; + } + } + } + return false; + }; + + if (isInvalidExpertOrDone(candidateCollision.a) || + isInvalidExpertOrDone(candidateCollision.b)) { + + return; + } + + const CountT PositionColumn{2}; + const CountT RotationColumn{3}; + const CountT ScaleColumn{4}; + + const Loc locationA{candidateCollision.a}; + const Position positionA{ + ctx.getDirect(PositionColumn, locationA)}; + const Rotation rotationA{ + ctx.getDirect(RotationColumn, locationA)}; + const Scale scaleA{ctx.getDirect(ScaleColumn, locationA)}; + + const Loc locationB{candidateCollision.b}; + const Position positionB{ + ctx.getDirect(PositionColumn, locationB)}; + const Rotation rotationB{ + ctx.getDirect(RotationColumn, locationB)}; + const Scale scaleB{ctx.getDirect(ScaleColumn, locationB)}; + + auto obbA = OrientedBoundingBox2D::from(positionA, rotationA, scaleA); + auto obbB = OrientedBoundingBox2D::from(positionB, rotationB, scaleB); + + bool hasCollided = OrientedBoundingBox2D::hasCollided(obbA, obbB); + if (not hasCollided) { + return; + } + + EntityType aEntityType = ctx.get(candidateCollision.a); + EntityType bEntityType = ctx.get(candidateCollision.b); + + for(auto &pair : ctx.data().collisionPairs) + { + if((pair.first == aEntityType && pair.second == bEntityType) || + (pair.first == bEntityType && pair.second == aEntityType)) + { + return; + } + } + + auto maybeCollisionDetectionEventA = + ctx.getCheck(candidateCollision.a); + if (maybeCollisionDetectionEventA.valid()) { + maybeCollisionDetectionEventA.value().hasCollided.store_relaxed(1); + auto agent_iface = ctx.get(candidateCollision.a).e; + if(bEntityType > EntityType::None && bEntityType <= EntityType::StopSign) + { + ctx.get(agent_iface).collidedWithRoad = 1; + } + else if(bEntityType == EntityType::Vehicle) + { + ctx.get(agent_iface).collidedWithVehicle = 1; + } + else if(bEntityType <= EntityType::Cyclist) + { + ctx.get(agent_iface).collidedWithNonVehicle = 1; + } + } + + auto maybeCollisionDetectionEventB = + ctx.getCheck(candidateCollision.b); + if (maybeCollisionDetectionEventB.valid()) { + maybeCollisionDetectionEventB.value().hasCollided.store_relaxed(1); + auto agent_iface = ctx.get(candidateCollision.b).e; + if(aEntityType > EntityType::None && aEntityType <= EntityType::StopSign) + { + ctx.get(agent_iface).collidedWithRoad = 1; + } + else if(aEntityType == EntityType::Vehicle) + { + ctx.get(agent_iface).collidedWithVehicle = 1; + } + else if(aEntityType <= EntityType::Cyclist) + { + ctx.get(agent_iface).collidedWithNonVehicle = 1; + } + } + + +} + +// Helper function for sorting nodes in the taskgraph. +// Sorting is only supported / required on the GPU backend, +// since the CPU backend currently keeps separate tables for each world. +// This will likely change in the future with sorting required for both +// environments +#ifdef MADRONA_GPU_MODE +template +TaskGraph::NodeID queueSortByWorld(TaskGraph::Builder &builder, + Span deps) +{ + auto sort_sys = + builder.addToGraph>( + deps); + auto post_sort_reset_tmp = + builder.addToGraph({sort_sys}); + + return post_sort_reset_tmp; +} +#endif + +inline void collectAbsoluteObservationsSystem(Engine &ctx, + const Position &position, + const Rotation &rotation, + const Goal &goal, + const VehicleSize &vehicleSize, + AgentInterfaceEntity &agent_iface) { + + auto &out = ctx.get(agent_iface.e); + out.position = position; + out.rotation.rotationAsQuat = rotation; + out.rotation.rotationFromAxis = utils::quatToYaw(rotation); + out.goal = goal; + out.vehicle_size = vehicleSize; + out.id = ctx.get(agent_iface.e).id; +} + +void setupRestOfTasks(TaskGraphBuilder &builder, const Sim::Config &cfg, + Span dependencies, + bool decrementStep) { + // setupBroadphaseTasks consists of the following sub-tasks: + // 1. updateLeafPositionsEntry + // 2. broadphase::updateBVHEntry + // 3. broadphase::refitEntry + auto broadphase_setup_sys = + phys::PhysicsSystem::setupBroadphaseTasks(builder, dependencies); + + auto findOverlappingEntities = + phys::PhysicsSystem::setupStandaloneBroadphaseOverlapTasks( + builder, {broadphase_setup_sys}); + + auto detectCollisions = builder.addToGraph< + ParallelForNode>( + {findOverlappingEntities}); + + // Finalize physics subsystem work + auto phys_done = phys::PhysicsSystem::setupStandaloneBroadphaseCleanupTasks( + builder, {detectCollisions}); + + phys_done = phys::PhysicsSystem::setupCleanupTasks( + builder, {detectCollisions}); + + auto reward_sys = builder.addToGraph>({phys_done}); + + auto previousSystem = reward_sys; + if (decrementStep) { + previousSystem = builder.addToGraph< + ParallelForNode>( + {reward_sys}); + } + + // Check if the episode is over + auto done_sys = + builder.addToGraph>( + {previousSystem}); + + auto clear_tmp = builder.addToGraph({done_sys}); + (void)clear_tmp; + + +#ifdef MADRONA_GPU_MODE + // RecycleEntitiesNode is required on the GPU backend in order to reclaim + // deleted entity IDs. + auto recycle_sys = builder.addToGraph({done_sys}); + (void)recycle_sys; +#endif + + // Finally, collect observations for the next step. + // auto collect_obs = builder.addToGraph>({clear_tmp}); + + auto collect_self_obs = builder.addToGraph>({clear_tmp}); + + auto collect_partner_obs = builder.addToGraph>({clear_tmp}); + + auto collect_map_obs = builder.addToGraph>({clear_tmp}); + + auto collect_bev_obs = builder.addToGraph>({clear_tmp}); + + auto collectAbsoluteSelfObservations = builder.addToGraph< + ParallelForNode>( + {clear_tmp}); + + if (cfg.renderBridge) { + RenderingSystem::setupTasks(builder, dependencies); + } + + TaskGraphNodeID lidar; + if(cfg.enableLidar) { + // The lidar system +#ifdef MADRONA_GPU_MODE + // Note the use of CustomParallelForNode to create a taskgraph node + // that launches a warp of threads (32) for each invocation (1). + // The 32, 1 parameters could be changed to 32, 32 to create a system + // that cooperatively processes 32 entities within a warp. + lidar = builder.addToGraph>({clear_tmp}); + } + +#ifdef MADRONA_GPU_MODE + TaskGraphNodeID sort_agents; + if(cfg.enableLidar) + { + sort_agents = queueSortByWorld(builder, {lidar, collect_self_obs, collect_partner_obs, collect_map_obs, collectAbsoluteSelfObservations}); + } else { + sort_agents = queueSortByWorld(builder, {collect_self_obs, collect_partner_obs, collect_map_obs, collectAbsoluteSelfObservations}); + } + // Sort entities, this could be conditional on reset like the second + // BVH build above. + + auto sort_phys_objects = queueSortByWorld( + builder, {sort_agents}); + + auto sort_agent_ifaces = queueSortByWorld( + builder, {sort_phys_objects}); + + auto sort_road_ifaces = queueSortByWorld( + builder, {sort_agent_ifaces}); + (void)sort_road_ifaces; +#else + (void)lidar; + (void)collect_self_obs; + (void)collect_partner_obs; + (void)collect_map_obs; + (void)collectAbsoluteSelfObservations; + (void)collect_bev_obs; +#endif +} + +static void setupStepTasks(TaskGraphBuilder &builder, const Sim::Config &cfg) { + auto moveSystem = builder.addToGraph>({}); + + setupRestOfTasks(builder, cfg, {moveSystem}, true); +} + +static void setupResetTasks(TaskGraphBuilder &builder, const Sim::Config &cfg) { + auto reset = + builder.addToGraph>( + {}); + + setupRestOfTasks(builder, cfg, {reset}, false); +} + +void Sim::setupTasks(TaskGraphManager &taskgraph_mgr, const Config &cfg) { + setupResetTasks(taskgraph_mgr.init(TaskGraphID::Reset), cfg); + setupStepTasks(taskgraph_mgr.init(TaskGraphID::Step), cfg); +} + +Sim::Sim(Engine &ctx, + const Config &cfg, + const WorldInit &init) + : WorldBase(ctx), + episodeMgr(init.episodeMgr), + params(*init.params) +{ + // Below check is used to ensure that the map is not empty due to incorrect WorldInit copy to GPU + assert(init.map->numObjects); + assert(init.map->numRoadSegments <= consts::kMaxRoadEntityCount); + + // Currently the physics system needs an upper bound on the number of + // entities that will be stored in the BVH. We plan to fix this in + // a future release. + // auto max_total_entities = init.map->numObjects + init.map->numRoadSegments; + auto max_total_entities = consts::kMaxAgentCount + consts::kMaxRoadEntityCount; + + phys::PhysicsSystem::init(ctx, init.rigidBodyObjMgr, + consts::deltaT, consts::numPhysicsSubsteps, -9.8f * math::up, + max_total_entities); + + enableRender = cfg.renderBridge != nullptr; + + if (enableRender) { + RenderingSystem::init(ctx, cfg.renderBridge); + } + + auto& map = ctx.singleton(); + map = *(init.map); + + auto& deletedAgents = ctx.singleton(); + for (auto i = 0; i < consts::kMaxAgentCount; i++) { + deletedAgents.deletedAgents[i] = -1; + } + // Creates agents, walls, etc. + createPersistentEntities(ctx); + + // Generate initial world state + initWorld(ctx); +} + +// This declaration is needed for the GPU backend in order to generate the +// CUDA kernel for world initialization, which needs to be specialized to the +// application's world data type (Sim) and config and initialization types. +// On the CPU it is a no-op. +MADRONA_BUILD_MWGPU_ENTRY(Engine, Sim, Sim::Config, WorldInit); + +} \ No newline at end of file diff --git a/gpudrive-main/src/sim.hpp b/gpudrive-main/src/sim.hpp new file mode 100644 index 0000000000000000000000000000000000000000..16ddc2e81d325a77015bfa14c95a21ecb0ae64ac --- /dev/null +++ b/gpudrive-main/src/sim.hpp @@ -0,0 +1,161 @@ +#pragma once + +#include +#include + +#include "consts.hpp" +#include "types.hpp" +#include "init.hpp" +#include "rng.hpp" + +namespace madrona_gpudrive { + +class Engine; + +// This enum is used by the Sim and Manager classes to track the export slots +// for each component exported to the training code. +enum class ExportID : uint32_t { + Reset, + Action, + Reward, + Done, + SelfObservation, + PartnerObservations, + AgentMapObservations, + Lidar, + BevObservations, + StepsRemaining, + BicycleModel, + MapObservation, + Shape, + ControlledState, + AbsoluteSelfObservation, + ValidState, + Info, + ResponseType, + Trajectory, + Map, + ResetMap, + WorldMeans, + MetaData, + DeletedAgents, + MapName, + ScenarioId, + NumExports +}; + +// Stores values for the ObjectID component that links entities to +// render / physics assets. +enum class SimObject : uint32_t { + Cube, + Agent, + StopSign, + SpeedBump, + Plane, + NumObjects, +}; + +enum class TaskGraphID : uint32_t { + Step, + Reset, + NumTaskGraphs, +}; + +// The Sim class encapsulates the per-world state of the simulation. +// Sim is always available by calling ctx.data() given a reference +// to the Engine / Context object that is passed to each ECS system. +// +// Per-World state that is frequently accessed but only used by a few +// ECS systems should be put in a singleton component rather than +// in this class in order to ensure efficient access patterns. +struct Sim : public madrona::WorldBase { + struct Config { + const madrona::render::RenderECSBridge *renderBridge; + bool enableLidar = false; + }; + + // Sim::registerTypes is called during initialization + // to register all components & archetypes with the ECS. + static void registerTypes(madrona::ECSRegistry ®istry, + const Config &cfg); + + // Sim::setupTasks is called during initialization to build + // the system task graph that will be invoked by the + // Manager class (src/mgr.hpp) for each step. + static void setupTasks(madrona::TaskGraphManager &taskgraph_mgr, + const Config &cfg); + + const std::pair collisionPairs[20] = { + {EntityType::Pedestrian, EntityType::RoadEdge}, + {EntityType::Pedestrian, EntityType::RoadLine}, + {EntityType::Pedestrian, EntityType::RoadLane}, + {EntityType::Pedestrian, EntityType::CrossWalk}, + {EntityType::Pedestrian, EntityType::SpeedBump}, + {EntityType::Cyclist, EntityType::RoadEdge}, + {EntityType::Cyclist, EntityType::RoadLine}, + {EntityType::Cyclist, EntityType::RoadLane}, + {EntityType::Cyclist, EntityType::CrossWalk}, + {EntityType::Cyclist, EntityType::SpeedBump}, + {EntityType::Vehicle, EntityType::CrossWalk}, + {EntityType::Vehicle, EntityType::SpeedBump}, + {EntityType::Vehicle, EntityType::RoadLine}, + {EntityType::Vehicle, EntityType::RoadLane}}; + + // The constructor is called for each world during initialization. + // Config is global across all worlds, while WorldInit (src/init.hpp) + // can contain per-world initialization data, created in (src/mgr.cpp) + Sim(Engine &ctx, + const Config &cfg, + const WorldInit &init); + + // EpisodeManager globally tracks episode IDs with an atomic across the + // simulation. + EpisodeManager *episodeMgr; + + // Simple random number generator seeded with episode ID. + RNG rng; + + // Floor plane entity, constant across all episodes. + Entity floorPlane; + + // Border wall entities: 3 walls to the left, up and down that define + // play area. These are constant across all episodes. + Entity borders[3]; + + // Agent entity references. This entities live across all episodes + // and are just reset to the start of the level on reset. + madrona::CountT numAgents; + Entity agents[consts::kMaxAgentCount]; + madrona::CountT numRoads; + Entity roads[consts::kMaxRoadEntityCount]; + + Entity agent_ifaces[consts::kMaxAgentCount]; + Entity road_ifaces[consts::kMaxRoadEntityCount]; + + Entity camera_agent; + + madrona::CountT numControlledAgents; + + Parameters params; + + // Episode ID number + int32_t curEpisodeIdx; + + // Are we visualizing the simulation in the viewer? + bool enableRender; +}; + +class Engine : public ::madrona::CustomContext { +public: + using CustomContext::CustomContext; + + // These are convenience helpers for creating renderable + // entities when rendering isn't necessarily enabled + template + inline madrona::Entity makeRenderableEntity(); + inline void destroyRenderableEntity(Entity e); +}; + +} + +#include "sim.inl" diff --git a/gpudrive-main/src/sim.inl b/gpudrive-main/src/sim.inl new file mode 100644 index 0000000000000000000000000000000000000000..31ad7c495be55c93bbcc6f3b5ff9f26112a68e6d --- /dev/null +++ b/gpudrive-main/src/sim.inl @@ -0,0 +1,22 @@ +namespace madrona_gpudrive { + +template +madrona::Entity Engine::makeRenderableEntity() +{ + Entity e = makeEntity(); + if (data().enableRender) { + madrona::render::RenderingSystem::makeEntityRenderable(*this, e); + } + + return e; +} + +inline void Engine::destroyRenderableEntity(Entity e) +{ + if (data().enableRender) { + madrona::render::RenderingSystem::cleanupRenderableEntity(*this, e); + } + destroyEntity(e); +} + +} diff --git a/gpudrive-main/src/types.hpp b/gpudrive-main/src/types.hpp new file mode 100644 index 0000000000000000000000000000000000000000..0ac27d418d360f1b22ab70bf2c1d1e5bf62efbd7 --- /dev/null +++ b/gpudrive-main/src/types.hpp @@ -0,0 +1,535 @@ +#pragma once + +#include +#include +#include +#include + +#include "consts.hpp" + +namespace madrona_gpudrive +{ + // Include several madrona types into the simulator namespace for convenience + using madrona::Entity; + using madrona::base::ObjectID; + using madrona::base::Position; + using madrona::base::Rotation; + using madrona::base::Scale; + using madrona::phys::ResponseType; + using madrona::phys::Velocity; + + // This enum is used to track the type of each entity + // The order of the enum is important and should not be changed + // The order is {Road types that can be reduced, Road types that cannot be reduced, agent types, other types} + enum class EntityType : uint32_t + { + None, + RoadEdge, + RoadLine, + RoadLane, + CrossWalk, + SpeedBump, + StopSign, + Vehicle, + Pedestrian, + Cyclist, + Padding, + NumTypes, + }; + + enum class MapType : int32_t + { + LANE_UNDEFINED = 0, + LANE_FREEWAY = 1, + LANE_SURFACE_STREET = 2, + LANE_BIKE_LANE = 3, + // Original definition skips 4 + ROAD_LINE_UNKNOWN = 5, + ROAD_LINE_BROKEN_SINGLE_WHITE = 6, + ROAD_LINE_SOLID_SINGLE_WHITE = 7, + ROAD_LINE_SOLID_DOUBLE_WHITE = 8, + ROAD_LINE_BROKEN_SINGLE_YELLOW = 9, + ROAD_LINE_BROKEN_DOUBLE_YELLOW = 10, + ROAD_LINE_SOLID_SINGLE_YELLOW = 11, + ROAD_LINE_SOLID_DOUBLE_YELLOW = 12, + ROAD_LINE_PASSING_DOUBLE_YELLOW = 13, + ROAD_EDGE_UNKNOWN = 14, + ROAD_EDGE_BOUNDARY = 15, + ROAD_EDGE_MEDIAN = 16, + STOP_SIGN = 17, + CROSSWALK = 18, + SPEED_BUMP = 19, + DRIVEWAY = 20, // New datatype in v1.2.0: Driveway entrances + UNKNOWN = -1, + NUM_TYPES = 21, + }; + + struct AgentID + { + int32_t id; + }; + + struct VehicleSize + { + float length; + float width; + float height; + }; + + struct Goal + { + madrona::math::Vector2 position; + }; + + // WorldReset is a per-world singleton component that causes the current + // episode to be terminated and the world regenerated + // (Singleton components like WorldReset can be accessed via Context::singleton + // (eg ctx.singleton().reset = 1) + struct WorldReset + { + int32_t reset; + }; + + struct ResetMap { + int32_t reset; + }; + + struct DeletedAgents { + int32_t deletedAgents[consts::kMaxAgentCount]; + }; + + struct WorldMeans { + madrona::math::Vector3 mean; // TODO: Z is 0 for now, but can be used for 3D in future + }; + + const size_t WorldMeansExportSize = 3; + + static_assert(sizeof(WorldMeans) == sizeof(float) * WorldMeansExportSize); + + struct ClassicAction + { + float acceleration; + float steering; + float headAngle; + }; + + struct DeltaAction + { + float dx; + float dy; + float dyaw; + }; + + struct StateAction + { + Position position; // 3 floats + float yaw; // 1 float + Velocity velocity; // 6 floats + }; + + union Action + { + ClassicAction classic; + DeltaAction delta; + StateAction state; + + static inline Action zero() + { + return Action{ + .classic = {.acceleration = 0, .steering = 0, .headAngle = 0}}; + } + }; + + const size_t ActionExportSize = 3 + 1 + 6; + + static_assert(sizeof(Action) == sizeof(float) * ActionExportSize); + + // Per-agent reward + // Exported as an [N * A, 1] float tensor to training code + struct Reward + { + float v; + }; + + // Per-agent component that indicates that the agent's episode is finished + // This is exported per-agent for simplicity in the training code + struct Done + { + // Currently bool components are not supported due to + // padding issues, so Done is an int32_t + int32_t v; + }; + + struct Info + { + int collidedWithRoad; + int collidedWithVehicle; + int collidedWithNonVehicle; + int reachedGoal; + int type; + + static inline Info zero() + { + return Info{ + .collidedWithRoad = 0, + .collidedWithVehicle = 0, + .collidedWithNonVehicle = 0, + .reachedGoal = 0, + .type = static_cast(EntityType::Padding)}; + } + }; + + const size_t InfoExportSize = 5; + + static_assert(sizeof(Info) == sizeof(int) * InfoExportSize); + + // Observation state for the current agent. + // Positions are rescaled to the bounds of the play area to assist training. + struct SelfObservation + { + float speed; + VehicleSize vehicle_size; + Goal goal; + float collisionState; + float id; + static inline SelfObservation zero() + { + return SelfObservation{ + .speed = 0, + .vehicle_size = {0, 0, 0}, + .goal = {.position = {0, 0}}, + .collisionState = 0, + .id = -1}; + } + }; + + const size_t SelfObservationExportSize = 8; // 1 + 3 + 2 + 1 + 1 + + static_assert(sizeof(SelfObservation) == sizeof(float) * SelfObservationExportSize); + + struct MapObservation + { + madrona::math::Vector2 position; + Scale scale; + float heading; + float type; + float id; + float mapType; + + static inline MapObservation zero() + { + return MapObservation{ + .position = {0, 0}, + .scale = madrona::math::Diag3x3{0, 0, 0}, + .heading = 0, + .type = static_cast(EntityType::None), + .id = -1, + .mapType = static_cast(MapType::UNKNOWN) + }; + } + }; + + const size_t MapObservationExportSize = 9; // 2 + 3 + 1 + 1 + 1 + 1 + + static_assert(sizeof(MapObservation) == sizeof(float) * MapObservationExportSize); + + struct PartnerObservation + { + float speed; + madrona::math::Vector2 position; + float heading; + VehicleSize vehicle_size; + float type; + float id; + + static inline PartnerObservation zero() { + return PartnerObservation{ + .speed = 0, + .position = {0, 0}, + .heading = 0, + .vehicle_size = {0, 0, 0}, + .type = static_cast(EntityType::None), + .id = -1}; + } + }; + + // Egocentric observations of other agents + struct PartnerObservations + { + PartnerObservation obs[consts::kMaxAgentCount - 1]; + }; + + const size_t PartnerObservationExportSize = 9; // 1 + 2 + 1 + 3 + 1 + 1 + + static_assert(sizeof(PartnerObservations) == sizeof(float) * + (consts::kMaxAgentCount - 1) * PartnerObservationExportSize); + + struct RoadMapId{ + int32_t id; + }; + + const size_t RoadMapIdExportSize = 1; + + static_assert(sizeof(RoadMapId) == sizeof(int) * RoadMapIdExportSize); + + struct AgentMapObservations + { + MapObservation obs[consts::kMaxAgentMapObservationsCount]; + }; + + const size_t AgentMapObservationExportSize = MapObservationExportSize; + + static_assert(sizeof(AgentMapObservations) == + sizeof(float) * consts::kMaxAgentMapObservationsCount * + AgentMapObservationExportSize); + + struct LidarSample + { + float depth; + float encodedType; + madrona::math::Vector2 position; + }; + + // Linear depth values and entity type in a circle around the agent + struct Lidar + { + LidarSample samplesCars[consts::numLidarSamples]; + LidarSample samplesRoadEdges[consts::numLidarSamples]; + LidarSample samplesRoadLines[consts::numLidarSamples]; + }; + + const size_t LidarExportSize = 3 * consts::numLidarSamples * 4; + + static_assert(sizeof(Lidar) == sizeof(float) * LidarExportSize); + + struct BevObservation + { + float type; + }; + struct BevObservations + { + BevObservation obs[consts::bev_rasterization_resolution][consts::bev_rasterization_resolution]; + }; + + const size_t BevObservationExportSize = 1; + + static_assert(sizeof(BevObservations) == BevObservationExportSize * sizeof(float) * consts::bev_rasterization_resolution * consts::bev_rasterization_resolution); + + // Number of steps remaining in the episode. Allows non-recurrent policies + // to track the progression of time. + struct StepsRemaining + { + uint32_t t; + }; + + // Can be refactored for rewards + struct Progress + { + float maxY; + }; + + // Per-agent component storing Entity IDs of the other agents. Used to + // build the egocentric observations of their state. + struct OtherAgents + { + madrona::Entity e[consts::kMaxAgentCount - 1]; + }; + + struct Trajectory + { + madrona::math::Vector2 positions[consts::kTrajectoryLength]; + madrona::math::Vector2 velocities[consts::kTrajectoryLength]; + float headings[consts::kTrajectoryLength]; + float valids[consts::kTrajectoryLength]; + Action inverseActions[consts::kTrajectoryLength]; + + static inline void zero(Trajectory& traj) + { + for (int i = 0; i < consts::kTrajectoryLength; i++) + { + traj.positions[i] = {0, 0}; + traj.velocities[i] = {0, 0}; + traj.headings[i] = 0; + traj.valids[i] = 0; + traj.inverseActions[i] = Action::zero(); + } + } + }; + + const size_t TrajectoryExportSize = 2 * 2 * consts::kTrajectoryLength + 2 * consts::kTrajectoryLength + ActionExportSize * consts::kTrajectoryLength; + + static_assert(sizeof(Trajectory) == sizeof(float) * TrajectoryExportSize); + + struct Shape + { + int32_t agentEntityCount; + int32_t roadEntityCount; + }; + + struct ControlledState + { + int32_t controlled; // default: 1 + }; + + struct CollisionDetectionEvent + { + madrona::AtomicI32 hasCollided{false}; + }; + + struct AbsoluteRotation + { + Rotation rotationAsQuat; // x, y, z, w + float rotationFromAxis; + }; + + struct AbsoluteSelfObservation + { + Position position; + AbsoluteRotation rotation; + Goal goal; + VehicleSize vehicle_size; + float id; + }; + + const size_t AbsoluteSelfObservationExportSize = 14; // 3 + 5 + 2 + 3 + 1 + + static_assert(sizeof(AbsoluteSelfObservation) == sizeof(float) * AbsoluteSelfObservationExportSize); + + struct MapName + { + char32_t mapName[32]; + }; + + const size_t MapNameExportSize = 32; + static_assert(sizeof(MapName) == sizeof(char32_t) * MapNameExportSize); + + struct ScenarioId + { + char32_t scenarioId[32]; + }; + + const size_t ScenarioIdExportSize = 32; + static_assert(sizeof(ScenarioId) == sizeof(char32_t) * ScenarioIdExportSize); + + //Metadata struct : using agent IDs. + struct MetaData + { + int32_t isSdc; + int32_t isObjectOfInterest; + int32_t isTrackToPredict; + int32_t difficulty; + + static inline void zero(MetaData& metadata) + { + metadata.isSdc = -1; + metadata.isObjectOfInterest = -1; + metadata.isTrackToPredict = -1; + metadata.difficulty = -1; + } + }; + const size_t MetaDataExportSize = 4; + static_assert(sizeof(MetaData) == sizeof(int32_t) * MetaDataExportSize); + + struct AgentInterface : public madrona::Archetype< + Action, + Reward, + Done, + Info, + // Observations + SelfObservation, + AbsoluteSelfObservation, + PartnerObservations, + AgentMapObservations, + Lidar, + BevObservations, + StepsRemaining, + ResponseType, + Trajectory, + AgentID, + MetaData, + + ControlledState // Drive Logic + + > + { + }; + + struct AgentInterfaceEntity + { + madrona::Entity e; + }; + + // Needed so that the taskgraph doesnt run on InterfaceEntity from roads + struct RoadInterfaceEntity + { + madrona::Entity e; + }; + + /* ECS Archetypes for the game */ + + struct CameraAgent : public madrona::Archetype< + Position, + Rotation, + madrona::render::RenderCamera, + madrona::render::Renderable> + { + }; + + // There are 2 Agents in the environment trying to get to the destination + struct Agent : public madrona::Archetype< + // Basic components required for physics. Note that the current physics + // implementation requires archetypes to have these components first + // in this exact order. + Position, + Rotation, + Scale, + ObjectID, + ResponseType, + madrona::phys::broadphase::LeafID, + Velocity, + CollisionDetectionEvent, + + // Internal logic state. + Progress, + OtherAgents, + EntityType, + + VehicleSize, + Goal, + // Interface + AgentInterfaceEntity, + // Visualization: In addition to the fly camera, src/viewer.cpp can + // view the scene from the perspective of entities with this component + madrona::render::RenderCamera, + // All entities with the Renderable component will be drawn by the + // viewer and batch renderer + madrona::render::Renderable + + > + { + }; + + struct RoadInterface : public madrona::Archetype< + MapObservation> + { + }; + + // Generic archetype for entities that need physics but don't have custom + // logic associated with them. + struct PhysicsEntity : public madrona::Archetype< + Position, + Rotation, + Scale, + ObjectID, + ResponseType, + madrona::phys::broadphase::LeafID, + Velocity, + RoadInterfaceEntity, + EntityType, + RoadMapId, + MapType, + madrona::render::Renderable> + { + }; + +} diff --git a/gpudrive-main/src/utils.hpp b/gpudrive-main/src/utils.hpp new file mode 100644 index 0000000000000000000000000000000000000000..d8e6956eb434684b79ce5f76e905905c2a9ce0e3 --- /dev/null +++ b/gpudrive-main/src/utils.hpp @@ -0,0 +1,66 @@ +#pragma once + +#include "types.hpp" +#include +#include +#include + +namespace madrona_gpudrive { +namespace utils { + +template inline float NormalizeAngle(T angle) { +const T ret = fmod(angle, madrona::math::pi_m2); + return ret > madrona::math::pi ? ret - madrona::math::pi_m2 : (ret < -madrona::math::pi ? ret + madrona::math::pi_m2 : ret); +} + +template inline T AngleAdd(T lhs, T rhs) { + return NormalizeAngle(lhs + rhs); +} + +inline float quatToYaw(madrona::base::Rotation q) { + // From + // https://en.wikipedia.org/wiki/Conversion_between_quaternions_and_Euler_angles#Quaternion_to_Euler_angles_(in_3-2-1_sequence)_conversion + return atan2(2.0f * (q.w * q.z + q.x * q.y), + 1.0f - 2.0f * (q.y * q.y + q.z * q.z)); +} + +class ReferenceFrame { +public: + ReferenceFrame(const madrona::math::Vector2 &position, + const madrona::base::Rotation &rotation) + : referenceRotation(rotation), referencePosition(position) {} + + madrona_gpudrive::MapObservation + observationOf(const madrona::math::Vector3 &position, + const madrona::base::Rotation &rotation, const Scale &scale, + madrona_gpudrive::EntityType type, float id, MapType mapType = MapType::UNKNOWN) const { + return madrona_gpudrive::MapObservation{.position = relative(position), + .scale = scale, + .heading = relative(rotation), + .type = static_cast(type), + .id = static_cast(id), + .mapType = static_cast(mapType)}; + } + + float distanceTo(const madrona::math::Vector3 &position) const { + return relative(position).length(); + } + +private: + madrona::math::Vector2 + relative(const madrona::math::Vector3 &absolutePos) const { + auto relativePosition = absolutePos.xy() - referencePosition; + + return referenceRotation.inv() + .rotateVec({relativePosition.x, relativePosition.y, 0}) + .xy(); + } + + float relative(const madrona::base::Rotation &absoluteRot) const { + return madrona_gpudrive::utils::quatToYaw(referenceRotation.inv() * absoluteRot); + } + + madrona::math::Vector2 referencePosition; + madrona::base::Rotation referenceRotation; +}; +}} diff --git a/gpudrive-main/src/viewer.cpp b/gpudrive-main/src/viewer.cpp new file mode 100644 index 0000000000000000000000000000000000000000..5e25837aac6c2e20896c0fe740cacdceebd700fa --- /dev/null +++ b/gpudrive-main/src/viewer.cpp @@ -0,0 +1,210 @@ +#include + +#include "sim.hpp" +#include "mgr.hpp" +#include "types.hpp" + +#include +#include +#include + +#include + +using namespace madrona; +using namespace madrona::viz; + +static HeapArray readReplayLog(const char *path) { + std::ifstream replay_log(path, std::ios::binary); + replay_log.seekg(0, std::ios::end); + int64_t size = replay_log.tellg(); + replay_log.seekg(0, std::ios::beg); + + HeapArray log(size / sizeof(float)); + + replay_log.read((char *)log.data(), (size / sizeof(float)) * sizeof(float)); + + return log; +} + +int main(int argc, char *argv[]) +{ + using namespace madrona_gpudrive; + + constexpr int64_t num_views = 2; + + uint32_t num_worlds = 1; + if (argc >= 2) { + num_worlds = (uint32_t)atoi(argv[1]); + } + + ExecMode exec_mode = ExecMode::CPU; + if (argc >= 3) { + if (!strcmp("--cpu", argv[2])) { + exec_mode = ExecMode::CPU; + } else if (!strcmp("--cuda", argv[2])) { + exec_mode = ExecMode::CUDA; + } + } + + const char *replay_log_path = nullptr; + if (argc >= 4) { + replay_log_path = argv[3]; + } + + std::optional> replay_log; + uint32_t cur_replay_step = 0; + uint32_t num_replay_steps = 0; + if (replay_log_path != nullptr) { + replay_log = readReplayLog(replay_log_path); + num_replay_steps = replay_log->size() / (num_worlds * num_views * 4); + } + + bool enable_batch_renderer = +#ifdef MADRONA_MACOS + false; +#else + true; +#endif + + WindowManager wm {}; + WindowHandle window = wm.makeWindow("GPUDrive", 1920, 1080); + render::GPUHandle render_gpu = wm.initGPU(0, { window.get() }); + + Manager mgr({ + .execMode = exec_mode, + .gpuID = 0, + .scenes = {"../data/processed/examples/tfrecord-00001-of-01000_307.json"}, + .params = { + .polylineReductionThreshold = 1.0, + .observationRadius = 100.0, + .maxNumControlledAgents = 0 + }, + .enableBatchRenderer = enable_batch_renderer, + .extRenderAPI = wm.gpuAPIManager().backend(), + .extRenderDev = render_gpu.device(), + }); + + madrona::CountT stepCtr = 0; + // math::Quat initial_camera_rotation = math::Quat::angleAxis(0, math::up).normalize(); + math::Quat initial_camera_rotation = + (math::Quat::angleAxis(0, math::up) * + math::Quat::angleAxis(-math::pi / 2.f, math::right)).normalize(); + + Viewer viewer(mgr.getRenderManager(), window.get(), { + .numWorlds = num_worlds, + .simTickRate = 20, + .cameraMoveSpeed = 20.f, + .cameraPosition = 100.f * math::up, + .cameraRotation = initial_camera_rotation, + }); + + auto replayStep = [&]() { + if (cur_replay_step == num_replay_steps - 1) { + return true; + } + + printf("Step: %u\n", cur_replay_step); + + for (uint32_t i = 0; i < num_worlds; i++) { + for (uint32_t j = 0; j < num_views; j++) { + uint32_t base_idx = 0; + base_idx = 4 * (cur_replay_step * num_views * num_worlds + + i * num_views + j); + + auto acceleration = (*replay_log)[base_idx]; + auto steering = (*replay_log)[base_idx + 1]; + auto headAngle = (*replay_log)[base_idx + 2]; + + + printf("%d, %d: %f %f %f\n", i, j, acceleration, steering, + headAngle); + mgr.setAction(i, j, acceleration, steering, headAngle); + } + } + + cur_replay_step++; + + return false; + }; + + auto self_printer = mgr.selfObservationTensor().makePrinter(); + auto partner_printer = mgr.partnerObservationsTensor().makePrinter(); + auto lidar_printer = mgr.lidarTensor().makePrinter(); + auto steps_remaining_printer = mgr.stepsRemainingTensor().makePrinter(); + auto reward_printer = mgr.rewardTensor().makePrinter(); + + auto printObs = [&]() { + printf("Self\n"); + self_printer.print(); + + printf("Partner\n"); + partner_printer.print(); + + printf("Lidar\n"); + lidar_printer.print(); + + printf("Steps Remaining\n"); + steps_remaining_printer.print(); + + printf("Reward\n"); + reward_printer.print(); + + printf("\n"); + }; + + viewer.loop( + [&mgr](CountT world_idx, const Viewer::UserInput &input) { + + using Key = Viewer::KeyboardKey; + if (input.keyHit(Key::R)) { + mgr.reset({(int)world_idx}); + } + (void)world_idx; + }, + [&mgr](CountT world_idx, CountT agent_idx, + const Viewer::UserInput &input) { + using Key = Viewer::KeyboardKey; + + float steering{0}; + const float steeringDelta{math::pi / 8}; + + float acceleration{0}; + const float accelerationDelta{1}; + + bool shift_pressed = input.keyPressed(Key::Shift); + + if (input.keyPressed(Key::W)) { + acceleration += accelerationDelta; + } + if (input.keyPressed(Key::S)) { + acceleration -= accelerationDelta; + } + + if (input.keyPressed(Key::D)) { + steering += steeringDelta; + } + if (input.keyPressed(Key::A)) { + steering -= steeringDelta; + } + + mgr.setAction(world_idx, agent_idx, acceleration, steering, 0); + + }, [&]() { + if (replay_log.has_value()) { + bool replay_finished = replayStep(); + + if (replay_finished) { + viewer.stopLoop(); + } + } + + mgr.step(); + stepCtr++; + + if(stepCtr % consts::episodeLen == 0) { + mgr.reset({0}); + } + + // printObs(); + }, []() {}); +} diff --git a/gpudrive-main/tests/CMakeLists.txt b/gpudrive-main/tests/CMakeLists.txt new file mode 100644 index 0000000000000000000000000000000000000000..3e4fe6570a21114436e0386cb2dd46e2b879e828 --- /dev/null +++ b/gpudrive-main/tests/CMakeLists.txt @@ -0,0 +1,36 @@ +# Assuming you're in a sub-directory "tests" in your project directory + +set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}) +set(CMAKE_LIBRARY_OUTPUT_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}) + +# Create the testJsons directory if it doesn't exist +file(MAKE_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/testJsons) + +# Copy test.json to the testJsons directory +configure_file(${CMAKE_SOURCE_DIR}/tests/test.json ${CMAKE_CURRENT_BINARY_DIR}/testJsons/test.json COPYONLY) + +include_directories( + ${CMAKE_SOURCE_DIR}/src + ${CMAKE_SOURCE_DIR}/external/madrona +) + +# Add your test file(s) to the add_executable command +add_executable(my_tests + bicyclemodel.cpp + test_utils.cpp + CollisionDetectionTests.cpp + observationTest.cpp + EgocentricRoadObservationTests.cpp +) + +# Link against required libraries. Ensure that the paths and names are correct. +target_link_libraries(my_tests + gtest_main + gpudrive_mgr + nlohmann_json::nlohmann_json + madrona_libcxx +) + +# Include GoogleTest module functions and discover tests in the executable +include(GoogleTest) +gtest_discover_tests(my_tests) diff --git a/gpudrive-main/tests/CollisionDetectionTests.cpp b/gpudrive-main/tests/CollisionDetectionTests.cpp new file mode 100644 index 0000000000000000000000000000000000000000..278c4d5ff8aaa35e9334cabb69ebbbcc9a8d7e5c --- /dev/null +++ b/gpudrive-main/tests/CollisionDetectionTests.cpp @@ -0,0 +1,85 @@ +#include "obb.hpp" +#include +#include +#include + +using namespace madrona; +using namespace madrona::base; +using namespace madrona::math; +using madrona_gpudrive::OrientedBoundingBox2D; + +TEST(CollisionDetectionAxisAligned, Colliding) { + Position origin{{.x = 0, .y = 0, .z = 0}}; + Position offset{{.x = 1, .y = 1, .z = 1}}; + + Scale unitCubeScale{{.d0 = 1, .d1 = 1, .d2 = 1}}; + + Rotation noRotation{Quat::angleAxis(0, madrona::math::up)}; + + auto obbA = OrientedBoundingBox2D::from(origin, noRotation, unitCubeScale); + auto obbB = + OrientedBoundingBox2D::from(origin + offset, noRotation, unitCubeScale); + + EXPECT_TRUE(OrientedBoundingBox2D::hasCollided(obbA, obbB)); +} + +TEST(CollisionDetectionAxisAligned, NotColliding) { + Position origin{{.x = 0, .y = 0, .z = 0}}; + Position offset{{.x = 2, .y = 2, .z = 0}}; + + Scale unitCubeScale{{.d0 = 0.5, .d1 = 0.5, .d2 = 1}}; + + Rotation noRotation{Quat::angleAxis(0, madrona::math::up)}; + + auto obbA = OrientedBoundingBox2D::from(origin, noRotation, unitCubeScale); + auto obbB = + OrientedBoundingBox2D::from(origin + offset, noRotation, unitCubeScale); + + EXPECT_FALSE(OrientedBoundingBox2D::hasCollided(obbA, obbB)); +} + +TEST(CollisionDetectionAxisAligned, PointIntersection) { + Position origin{{.x = 0, .y = 0, .z = 0}}; + Position offset{{.x = 1, .y = 1, .z = 0}}; + + Scale unitCubeScale{{.d0 = 0.5, .d1 = 0.5, .d2 = 0.5}}; + + Rotation noRotation{Quat::angleAxis(0, madrona::math::up)}; + + auto obbA = OrientedBoundingBox2D::from(origin, noRotation, unitCubeScale); + auto obbB = + OrientedBoundingBox2D::from(origin + offset, noRotation, unitCubeScale); + + EXPECT_TRUE(OrientedBoundingBox2D::hasCollided(obbA, obbB)); +} + +TEST(CollisionDetectionAxisAligned, OneInsideOther) { + Position origin{{.x = 0, .y = 0, .z = 0}}; + + Scale unitCubeScale{{.d0 = 1, .d1 = 1, .d2 = 1}}; + Scale halfUnitCubeScale{{.d0 = 0.5, .d1 = 0.5, .d2 = 0.5}}; + + Rotation noRotation{Quat::angleAxis(0, madrona::math::up)}; + + auto obbA = OrientedBoundingBox2D::from(origin, noRotation, unitCubeScale); + auto obbB = + OrientedBoundingBox2D::from(origin, noRotation, halfUnitCubeScale); + + EXPECT_TRUE(OrientedBoundingBox2D::hasCollided(obbA, obbB)); +} + +TEST(CollisionDetectionNonAxisAligned, ExhaustiveRotations) { + Position posA{{.x = 0, .y = 0, .z = 0}}; + Position posB{{.x = 0.5, .y = 0.5, .z = 0}}; + Scale unitCubeScale{{.d0 = 1, .d1 = 1, .d2 = 1}}; + Rotation rotA{Quat::angleAxis(0, madrona::math::up)}; + + for (float degrees = 0; degrees < 360; degrees += 15) { + Rotation rotB{Quat::angleAxis(toRadians(degrees), madrona::math::up)}; + + auto obbA = OrientedBoundingBox2D::from(posA, rotA, unitCubeScale); + auto obbB = OrientedBoundingBox2D::from(posB, rotB, unitCubeScale); + + EXPECT_TRUE(OrientedBoundingBox2D::hasCollided(obbA, obbB)); + } +} diff --git a/gpudrive-main/tests/EgocentricRoadObservationTests.cpp b/gpudrive-main/tests/EgocentricRoadObservationTests.cpp new file mode 100644 index 0000000000000000000000000000000000000000..48cbf0fe2cb33f26313656874af7fec02969b5a0 --- /dev/null +++ b/gpudrive-main/tests/EgocentricRoadObservationTests.cpp @@ -0,0 +1,22 @@ +#include "utils.hpp" +#include + +using namespace madrona; +using namespace madrona::base; +using namespace madrona::math; +using madrona_gpudrive::utils::ReferenceFrame; + +TEST(EgocentricRoadObservationTests, Relative) { + ReferenceFrame rf{Vector2{.x = 3, .y = 0}, + Quat::angleAxis(toRadians(90), madrona::math::up)}; + + auto mapObs = + rf.observationOf(Vector3{.x = 3, .y = 3, .z = 0}, + Quat::angleAxis(toRadians(270), madrona::math::up), + Scale{{.d0 = 10, .d1 = 0.1, .d2 = 0.1}}, + static_cast(0), 0); + + EXPECT_LT(mapObs.position.x - 3, 0.000001); + EXPECT_LT(mapObs.position.y - 0, 0.000001); + EXPECT_EQ(mapObs.heading, toRadians(180)); +} diff --git a/gpudrive-main/tests/agent-agent-collision.json b/gpudrive-main/tests/agent-agent-collision.json new file mode 100644 index 0000000000000000000000000000000000000000..23a43710818d016c98d3d196b1fb7d7d754ede50 --- /dev/null +++ b/gpudrive-main/tests/agent-agent-collision.json @@ -0,0 +1,79 @@ +{ + "name": "tfrecord-00100-of-00150_139.json", + "objects": [ + { + "position": [ + { + "x": 0, + "y": 0 + } + ], + "width": 1, + "length": 1, + "heading": [ + 90 + ], + "velocity": [ + { + "x": 0, + "y": 1 + } + ], + "valid": [ + true + ], + "goalPosition": { + "x": 606.9762573242188, + "y": -4803.1611328125 + }, + "type": "vehicle" + }, + { + "position": [ + { + "x": 0, + "y": 5 + } + ], + "width": 1, + "length": 1, + "heading": [ + 0.0 + ], + "velocity": [ + { + "x": 0, + "y": 0 + } + ], + "valid": [ + true + ], + "goalPosition": { + "x": 606.9762573242188, + "y": -4803.1611328125 + }, + "type": "vehicle" + } + ], + "roads": [ + { + "geometry": [ + { + "x": 1230.7761931898476, + "y": -10766.941661909548 + }, + { + "x": 1231.2750720307201, + "y": -10766.94119611562 + }, + { + "x": 1231.773950879533, + "y": -10766.940739173213 + } + ], + "type": "road_edge" + } + ], + "tl_states": {} +} diff --git a/gpudrive-main/tests/agent-road-edge-collision.json b/gpudrive-main/tests/agent-road-edge-collision.json new file mode 100644 index 0000000000000000000000000000000000000000..2c38d1a5263738ce3386f075cc55f267154c26c0 --- /dev/null +++ b/gpudrive-main/tests/agent-road-edge-collision.json @@ -0,0 +1,48 @@ +{ + "name": "tfrecord-00100-of-00150_139.json", + "objects": [ + { + "position": [ + { + "x": 0, + "y": 0 + } + ], + "width": 1, + "length": 1, + "heading": [ + 0 + ], + "velocity": [ + { + "x": 0, + "y": 1 + } + ], + "valid": [ + true + ], + "goalPosition": { + "x": 606.9762573242188, + "y": -4803.1611328125 + }, + "type": "vehicle" + } + ], + "roads": [ + { + "geometry": [ + { + "x": 10, + "y": 10 + }, + { + "x": -10, + "y": -10 + } + ], + "type": "road_edge" + } + ], + "tl_states": {} +} diff --git a/gpudrive-main/tests/bicyclemodel.cpp b/gpudrive-main/tests/bicyclemodel.cpp new file mode 100644 index 0000000000000000000000000000000000000000..df629f6eb6c7fb775b4acca4aa97883212f02557 --- /dev/null +++ b/gpudrive-main/tests/bicyclemodel.cpp @@ -0,0 +1,242 @@ +#include "gtest/gtest.h" +#include "consts.hpp" +#include "mgr.hpp" +#include "test_utils.hpp" + +#include +#include +#include +#include +#include + + +using namespace madrona; +using nlohmann::json; + +// TODO: Add the dynamic files here to be able to test from any json file. + +class BicycleKinematicModelTest : public ::testing::Test { +protected: + madrona_gpudrive::Manager mgr = madrona_gpudrive::Manager({ + .execMode = ExecMode::CPU, + .gpuID = 0, + .scenes = {"testJsons/test.json"}, + .params = { + .polylineReductionThreshold = 0.0, + .observationRadius = 100.0, + .collisionBehaviour = madrona_gpudrive::CollisionBehaviour::Ignore, + .initOnlyValidAgentsAtFirstStep = false, + .dynamicsModel = madrona_gpudrive::DynamicsModel::Classic + } + }); + + uint32_t num_agents = madrona_gpudrive::consts::kMaxAgentCount; + int64_t num_roads = madrona_gpudrive::consts::kMaxRoadEntityCount; + int64_t num_steps = 10; + int64_t num_worlds = 1; + int64_t numEntities = 0; + + std::pair mean = {0, 0}; + + std::unordered_map agent_length_map; + std::unordered_map agent_width_map; + std::ifstream data = std::ifstream("testJsons/test.json"); + std::vector initialState; + std::default_random_engine generator; + std::uniform_real_distribution acc_distribution; + std::uniform_real_distribution steering_distribution; + madrona::py::Tensor::Printer absolute_obs_printer = mgr.absoluteSelfObservationTensor().makePrinter(); + void SetUp() override { + json rawJson; + data >> rawJson; + mean = test_utils::calcMean(rawJson); + std::cout<<"CTEST Mean x: "< M_PI ? theta - M_PI*2 : (theta < - M_PI ? theta + M_PI*2 : theta); + initialState.push_back(theta); + initialState.push_back(math::Vector2{.x = obj["velocity"][0]["x"], .y = obj["velocity"][0]["y"]}.length()); + n_agents++; + } + acc_distribution = std::uniform_real_distribution(-3.0, 2.0); + steering_distribution = std::uniform_real_distribution(-0.7, 0.7); + generator = std::default_random_engine(42); + + auto shape_tensor = mgr.shapeTensor(); + int32_t *ptr = static_cast(shape_tensor.devicePtr()); + num_agents = 1; + } +}; + + +std::tuple StepBicycleModel(float x, float y, float theta, float speed_curr, float acceleration, float steering_action, float dt = 0.1, float L = 1) { + + float v = speed_curr + 0.5 * acceleration * dt; //Nocturne uses average speed + + float beta = atan(tan(steering_action) * (L/2) / L); + + float w = v * cos(beta) * tan(steering_action) / L; + + float x_next = x + v * cos(theta + beta) * dt; + float y_next = y + v * sin(theta + beta) * dt; + + float theta_next = std::fmod(theta + w * dt, M_PI*2); // Clipping necessary to follow the implementation in madrona + theta_next = theta_next > M_PI ? theta_next - M_PI*2 : (theta_next < - M_PI ? theta_next + M_PI*2 : theta_next); + + float speed_next = abs(speed_curr + acceleration * dt); + return std::make_tuple(x_next, y_next, theta_next, speed_next); +} + +std::pair validateBicycleModel(const py::Tensor &abs_obs, const py::Tensor &self_obs, const std::vector &expected, const uint32_t num_agents) +{ + int64_t num_elems = 1; + for (int i = 0; i < abs_obs.numDims(); i++) + { + num_elems *= abs_obs.dims()[i]; + } + + if (num_agents * madrona_gpudrive::AbsoluteSelfObservationExportSize > num_elems) + { + return {false, "Expected number of elements is less than the number of agents."}; + } + + num_elems = 1; + for (int i = 0; i < self_obs.numDims(); i++) + { + num_elems *= self_obs.dims()[i]; + } + + if (num_agents * madrona_gpudrive::SelfObservationExportSize > num_elems) + { + return {false, "Expected number of elements is less than the number of agents."}; + } + + float *ptr = static_cast(abs_obs.devicePtr()); + + for (int64_t i = 0, agent_idx = 0; i < num_agents * madrona_gpudrive::AbsoluteSelfObservationExportSize;) + { + auto x = static_cast(ptr[i]); + auto y = static_cast(ptr[i + 1]); + auto rot = static_cast(ptr[i + 7]); + auto x_exp = expected[agent_idx]; + auto y_exp = expected[agent_idx + 1]; + auto rot_exp = expected[agent_idx + 2]; + + i += madrona_gpudrive::AbsoluteSelfObservationExportSize; + agent_idx += 4; + + if (std::abs(x - x_exp) > test_utils::EPSILON || std::abs(y - y_exp) > test_utils::EPSILON || std::abs(rot - rot_exp) > test_utils::EPSILON) + { + return {false, "Value mismatch."}; + } + } + + ptr = static_cast(self_obs.devicePtr()); + for (int64_t i = 0, agent_idx = 0; i < num_agents * madrona_gpudrive::SelfObservationExportSize;) + { + auto speed = static_cast(ptr[i]); + auto speed_exp = expected[agent_idx+3]; + + if(std::abs(speed - speed_exp) > test_utils::EPSILON) + { + return {false, "Value mismatch."}; + } + + agent_idx += 4; + i += madrona_gpudrive::SelfObservationExportSize; + } + + return {true, ""}; +} + +std::vector parseBicycleModel(const py::Tensor &abs_obs, const py::Tensor &self_obs, const uint32_t num_agents) +{ + std::vector obs; + obs.resize(num_agents * 4); + float *ptr = static_cast(abs_obs.devicePtr()); + for (int i = 0, agent_idx = 0; i < num_agents * madrona_gpudrive::AbsoluteSelfObservationExportSize;) + { + obs[agent_idx] = static_cast(ptr[i]); + obs[agent_idx+1] = static_cast(ptr[i+1]); + obs[agent_idx+2] = static_cast(ptr[i+7]); + agent_idx += 4; + i+=madrona_gpudrive::AbsoluteSelfObservationExportSize; + } + ptr = static_cast(self_obs.devicePtr()); + for (int i = 0, agent_idx = 0; i < num_agents * madrona_gpudrive::SelfObservationExportSize;) + { + obs[agent_idx+3] = static_cast(ptr[i]); + agent_idx += 4; + i+=madrona_gpudrive::SelfObservationExportSize; + } + return obs; +} + +TEST_F(BicycleKinematicModelTest, TestModelEvolution) { + + auto printObs = [&]() { + printf("Absolute: \n"); + absolute_obs_printer.print(); + printf("\n"); + }; + + auto printVector = [](const std::vector& v) { + std::cout << "Vector: \n"; + for(auto i : v) { + std::cout << i << " "; + } + std::cout << "\n"; + }; + std::vector expected; + //Check first step - + for(int i = 0; i < num_agents; i++) + { + auto [x_next, y_next, theta_next, speed_next] = StepBicycleModel(initialState[4*i], initialState[4*i+1], initialState[4*i+2], initialState[4*i+3], 0, 0, 0.1, agent_length_map[i]); + expected.push_back(x_next); + expected.push_back(y_next); + expected.push_back(theta_next); + expected.push_back(speed_next); + } + auto abs_obs = mgr.absoluteSelfObservationTensor(); + auto self_obs = mgr.selfObservationTensor(); + auto [valid, errorMsg] = validateBicycleModel(abs_obs, self_obs, initialState, num_agents); + ASSERT_TRUE(valid); + printObs(); + + for(int i = 0; i < num_steps; i++) + { + expected.clear(); + printObs(); + auto prev_state = parseBicycleModel(abs_obs, self_obs, num_agents); // Due to floating point errors, we cannot use the expected values from the previous step so as not to accumulate errors. + printVector(prev_state); + for(int j = 0; j < num_agents; j++) + { + float acc = acc_distribution(generator); + float steering = steering_distribution(generator); + mgr.setAction(0,j,acc,steering,0); + auto [x_next, y_next, theta_next, speed_next] = StepBicycleModel(prev_state[4*j], prev_state[4*j+1], prev_state[4*j+2], prev_state[4*j+3], acc, steering, 0.1, agent_length_map[j]); + expected.push_back(x_next); + expected.push_back(y_next); + expected.push_back(theta_next); + expected.push_back(speed_next); + } + mgr.step(); + abs_obs = mgr.absoluteSelfObservationTensor(); + self_obs = mgr.selfObservationTensor(); + std::tie(valid, errorMsg) = validateBicycleModel(abs_obs, self_obs, expected, num_agents); + ASSERT_TRUE(valid); + } + +} diff --git a/gpudrive-main/tests/observationTest.cpp b/gpudrive-main/tests/observationTest.cpp new file mode 100644 index 0000000000000000000000000000000000000000..5c946236e62f86fa15aaa022f499b4f5d83deacc --- /dev/null +++ b/gpudrive-main/tests/observationTest.cpp @@ -0,0 +1,138 @@ +#include "gtest/gtest.h" +#include "consts.hpp" +#include "mgr.hpp" +#include "test_utils.hpp" + +#include +#include +#include +#include +#include + + +using namespace madrona; +using nlohmann::json; + +class ObservationsTest : public ::testing::Test { +protected: + uint32_t num_worlds = 1; + + madrona_gpudrive::Manager mgr = madrona_gpudrive::Manager({ + .execMode = ExecMode::CPU, + .gpuID = 0, + .scenes = {"testJsons/test.json"}, + .params = { + .polylineReductionThreshold = 0.0, + .observationRadius = 100.0, + .collisionBehaviour = madrona_gpudrive::CollisionBehaviour::Ignore, + .roadObservationAlgorithm = madrona_gpudrive::FindRoadObservationsWith::KNearestEntitiesWithRadiusFiltering + } + }); + + + std::pair mean = {0, 0}; + + std::ifstream data = std::ifstream("testJsons/test.json"); + std::vector>> roadGeoms; + std::vector roadTypes; + + void SetUp() override { + json rawJson; + data >> rawJson; + mean = test_utils::calcMean(rawJson); + + std::cout<<"CTEST Mean x: "<> roadGeom; + for (const auto &point: obj["geometry"]) + { + roadGeom.push_back({point["x"], point["y"]}); + } + roadGeoms.push_back(roadGeom); + + if(obj["type"] == "road_edge") + { + roadTypes.push_back((float)madrona_gpudrive::EntityType::RoadEdge); + } + else if(obj["type"] == "road_line") + { + roadTypes.push_back((float)madrona_gpudrive::EntityType::RoadLine); + } + else if(obj["type"] == "lane") + { + roadTypes.push_back((float)madrona_gpudrive::EntityType::RoadLane); + } + else if(obj["type"] == "crosswalk") + { + roadTypes.push_back((float)madrona_gpudrive::EntityType::CrossWalk); + } + else if(obj["type"] == "speed_bump") + { + roadTypes.push_back((float)madrona_gpudrive::EntityType::SpeedBump); + } + else if(obj["type"] == "stop_sign") + { + roadTypes.push_back((float)madrona_gpudrive::EntityType::StopSign); + } + else if(obj["type"] == "invalid") + { + roadTypes.push_back(0); + } + } + } +}; + +TEST_F(ObservationsTest, TestObservations) { + auto obs = mgr.mapObservationTensor(); + auto flat_obs = test_utils::flatten_obs(obs); + + int64_t idx = 0; + for(int64_t i = 0; i < roadGeoms.size(); i++) + { + std::vector> roadGeom = roadGeoms[i]; + float roadType = roadTypes[i]; + for(int64_t j = 0; j < roadGeom.size() - 1; j++) + { + if(roadType > (float)madrona_gpudrive::EntityType::RoadLane && roadType < (float)madrona_gpudrive::EntityType::StopSign) + { + float x = (roadGeom[j].first + roadGeom[j+1].first + roadGeom[j+2].first + roadGeom[j+3].first)/4 - mean.first; + float y = (roadGeom[j].second + roadGeom[j+1].second + roadGeom[j+2].second + roadGeom[j+3].second)/4 - mean.second; + + ASSERT_NEAR(flat_obs[idx], x, test_utils::EPSILON); + ASSERT_NEAR(flat_obs[idx+1], y, test_utils::EPSILON); + ASSERT_EQ(flat_obs[idx+6], roadType); + idx += madrona_gpudrive::MapObservationExportSize; + break; + } + else if(roadType == (float)madrona_gpudrive::EntityType::StopSign) + { + float x = roadGeom[j].first - mean.first; + float y = roadGeom[j].second - mean.second; + + ASSERT_NEAR(flat_obs[idx], x, test_utils::EPSILON); + ASSERT_NEAR(flat_obs[idx+1], y, test_utils::EPSILON); + ASSERT_EQ(flat_obs[idx+6], roadType); + idx += 7; + break; + } + + float x1 = roadGeom[j].first - mean.first; + float y1 = roadGeom[j].second - mean.second; + float x2 = roadGeom[j+1].first - mean.first; + float y2 = roadGeom[j+1].second - mean.second; + float dx = (x2 + x1)/2; + float dy = (y2 + y1)/2; + + ASSERT_NEAR(flat_obs[idx], dx, test_utils::EPSILON); + ASSERT_NEAR(flat_obs[idx+1], dy, test_utils::EPSILON); + ASSERT_FLOAT_EQ(flat_obs[idx+6], roadType) << "i = " << i << " j = " << j << " idx = " << idx; + idx += madrona_gpudrive::MapObservationExportSize; + } + if(idx >= flat_obs.size()) + { + break; + } + } +} diff --git a/gpudrive-main/tests/pytest_data/test.json b/gpudrive-main/tests/pytest_data/test.json new file mode 100644 index 0000000000000000000000000000000000000000..4fa10f98ae867040d7d435299935d8f5af7f6b70 --- /dev/null +++ b/gpudrive-main/tests/pytest_data/test.json @@ -0,0 +1 @@ +{"name": "tfrecord-00000-of-01000_222.json", "scenario_id": "1c365f15b70ebdbf", "objects": [{"position": [{"x": 4339.291015625, "y": 709.6715087890625, "z": -122.79333128679495}, {"x": 4339.291015625, "y": 709.6715087890625, "z": -122.7933909901561}, {"x": 4339.291015625, "y": 709.6715087890625, "z": -122.79345080060156}, {"x": 4339.291015625, "y": 709.6715087890625, "z": 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"type": "driveway", "map_element_id": 20, "id": 160}], "tl_states": {}, "metadata": {"sdc_track_index": 24, "objects_of_interest": [1847, 1415], "tracks_to_predict": [{"track_index": 7, "difficulty": 0}, {"track_index": 24, "difficulty": 0}]}} \ No newline at end of file diff --git a/gpudrive-main/tests/test_delta_model.py b/gpudrive-main/tests/test_delta_model.py new file mode 100644 index 0000000000000000000000000000000000000000..1002a036eb4914def1176f81f7c5b0bfd4f88a8b --- /dev/null +++ b/gpudrive-main/tests/test_delta_model.py @@ -0,0 +1,61 @@ +import madrona_gpudrive +import pytest +import torch + +@pytest.fixture(scope="module") +def sim_init(): + reward_params = madrona_gpudrive.RewardParams() + reward_params.rewardType = madrona_gpudrive.RewardType.DistanceBased + reward_params.distanceToGoalThreshold = 1.0 + reward_params.distanceToExpertThreshold = 1.0 + + params = madrona_gpudrive.Parameters() + params.polylineReductionThreshold = 0.5 + params.observationRadius = 10.0 + params.collisionBehaviour = madrona_gpudrive.CollisionBehaviour.AgentStop + params.rewardParams = reward_params + params.maxNumControlledAgents = 2 # we are going to use the second vehicle as the controlled vehicle + params.IgnoreNonVehicles = True + params.dynamicsModel = madrona_gpudrive.DynamicsModel.DeltaLocal + + sim = madrona_gpudrive.SimManager( + exec_mode=madrona_gpudrive.madrona.ExecMode.CPU, + gpu_id=0, + scenes=["tests/pytest_data/test.json"], + params=params + ) + + return sim + +def test_forward_inverse_delta_dynamics(sim_init): + sim = sim_init + + valid_agent_idx = 1 + done_tensor = sim.done_tensor().to_torch() + expert_trajectory_tensor = sim.expert_trajectory_tensor().to_torch() + action_tensor = sim.action_tensor().to_torch() + absolute_obs = sim.absolute_self_observation_tensor().to_torch() + self_obs = sim.self_observation_tensor().to_torch() + actions = torch.zeros_like(action_tensor) + + traj = expert_trajectory_tensor[:,valid_agent_idx].squeeze() + pos = traj[:2*91].view(91,2) + vel = traj[2*91:4*91].view(91,2) + headings = traj[4*91:5*91].view(91,1) + invActions = traj[6*91:16*91].view(91,10) + print('invActions', invActions[:2]) + position = absolute_obs[0,valid_agent_idx,:2] + heading = absolute_obs[0,valid_agent_idx,7] + speed = self_obs[0, valid_agent_idx, 0] + + assert torch.allclose(position, pos[0], atol=1e-2), f"Position mismatch: {position} vs {pos[0]}" + assert pytest.approx(heading.item(), abs=1e-2) == headings[0].item(), f"Heading mismatch: {heading.item()} vs {headings[0].item()}" + assert pytest.approx(speed.item(), abs=1e-2) == torch.norm(vel[0]).item(), f"Speed mismatch: {speed.item()} vs {torch.norm(vel[0]).item()}" + + actions[:,valid_agent_idx,:3] = invActions[0,:3] + action_tensor.copy_(actions) + sim.step() + + assert torch.allclose(position, pos[1], atol=2e-2), f"Position mismatch: {position} vs {pos[1]} at step {1}" + assert pytest.approx(heading.item(), abs=3e-3) == headings[1].item(), f"Heading mismatch: {heading.item()} vs {headings[1].item()}" + assert pytest.approx(speed.item(), abs=1e-3) == torch.norm(vel[1]).item(), f"Speed mismatch: {speed.item()} vs {torch.norm(vel[1]).item()}" \ No newline at end of file diff --git a/gpudrive-main/tests/test_expert.py b/gpudrive-main/tests/test_expert.py new file mode 100644 index 0000000000000000000000000000000000000000..e521bd5562b9a4d3a379d6b6fab9d07daa11a1a7 --- /dev/null +++ b/gpudrive-main/tests/test_expert.py @@ -0,0 +1,60 @@ +import collections +import madrona_gpudrive +import pytest + +@pytest.fixture(scope="module") +def simulation_results(): + # Create an instance of RewardParams + reward_params = madrona_gpudrive.RewardParams() + reward_params.rewardType = madrona_gpudrive.RewardType.DistanceBased # Or any other value from the enum + reward_params.distanceToGoalThreshold = 1.0 # Set appropriate values + reward_params.distanceToExpertThreshold = 1.0 # Set appropriate values + + # Create an instance of Parameters + params = madrona_gpudrive.Parameters() + params.polylineReductionThreshold = 0.5 # Set appropriate value + params.observationRadius = 10.0 # Set appropriate value + params.collisionBehaviour = madrona_gpudrive.CollisionBehaviour.AgentStop # Set appropriate value + params.rewardParams = reward_params # Set the rewardParams attribute to the instance created above + params.maxNumControlledAgents = 0 + params.IgnoreNonVehicles = True + params.isStaticAgentControlled = False + # Now use the 'params' instance when creating SimManager + sim = madrona_gpudrive.SimManager( + exec_mode=madrona_gpudrive.madrona.ExecMode.CPU, + gpu_id=0, + scenes=["tests/pytest_data/test.json"], + params=params, + enable_batch_renderer=False, # Optional parameter + batch_render_view_width=1024, + batch_render_view_height=1024 + ) + + done = sim.done_tensor().to_torch() + + while not done.all(): + sim.step() + + return sim + +def test_goal_reaching(simulation_results): + sim = simulation_results + info, shape = sim.info_tensor().to_torch(), sim.shape_tensor().to_torch() + info_valid = info[info[:, :, -1] == float(madrona_gpudrive.EntityType.Vehicle)] + goal_reached = info_valid[:, -2].sum().item() + assert goal_reached == shape[:, 0].sum().item() + print("Test passed!") + +def test_collision_rate(simulation_results): + sim = simulation_results + info = sim.info_tensor().to_torch() + + info_valid = info[info[:, :, -1] == float(madrona_gpudrive.EntityType.Vehicle)] + collisions = info_valid[:, :3].sum().item() + try: + assert collisions == 0 + except AssertionError: + print("Assertion failed! Info tensor:") + print(info_valid) + raise + print("Test passed!") diff --git a/gpudrive-main/tests/test_gpudrive.py b/gpudrive-main/tests/test_gpudrive.py new file mode 100644 index 0000000000000000000000000000000000000000..418c71b1f001ca1217783599cfa05793bc5f573a --- /dev/null +++ b/gpudrive-main/tests/test_gpudrive.py @@ -0,0 +1,11 @@ +import pufferlib + +from gpudrive.env.env_puffer import PufferGPUDrive + +env = PufferGPUDrive() +env.reset() + +for i in range(10): + actions = env.action_space.sample() + env.step(actions) + diff --git a/gpudrive-main/tests/test_nuscenes_adapter.py b/gpudrive-main/tests/test_nuscenes_adapter.py new file mode 100644 index 0000000000000000000000000000000000000000..54cdda3dbc4f6b46ff53ca4ca104c67dc61f48e5 --- /dev/null +++ b/gpudrive-main/tests/test_nuscenes_adapter.py @@ -0,0 +1,90 @@ +import math + +import numpy as np + +from data_utils.nuscenes_adapter import ( + ERR_VAL, + NuScenesAdapterConfig, + category_to_gpudrive, + interpolate_track, +) + + +def _state(time_sec, x, y, yaw=0.0, size=None): + return { + "time_sec": time_sec, + "translation": [x, y, 0.0], + "yaw": yaw, + "size": size or [2.0, 4.5, 1.7], + } + + +def test_interpolate_track_resamples_to_fixed_grid(): + config = NuScenesAdapterConfig(target_hz=2.0, num_steps=3) + target_times = np.array([0.0, 0.5, 1.0]) + obj = interpolate_track( + [_state(0.0, 0.0, 0.0), _state(1.0, 1.0, 0.0)], + target_times, + config, + ) + + assert obj is not None + assert [pos["x"] for pos in obj["position"]] == [0.0, 0.5, 1.0] + assert obj["valid"] == [True, True, True] + assert [vel["x"] for vel in obj["velocity"]] == [1.0, 1.0, 1.0] + assert obj["goalPosition"]["x"] == 1.0 + + +def test_interpolate_track_wraps_yaw_across_pi_boundary(): + config = NuScenesAdapterConfig(target_hz=2.0, num_steps=3) + target_times = np.array([0.0, 0.5, 1.0]) + obj = interpolate_track( + [ + _state(0.0, 0.0, 0.0, yaw=math.radians(179.0)), + _state(1.0, 1.0, 0.0, yaw=math.radians(-179.0)), + ], + target_times, + config, + ) + + assert obj is not None + assert abs(abs(obj["heading"][1]) - math.pi) < 1e-6 + + +def test_interpolate_track_does_not_bridge_large_annotation_gaps(): + config = NuScenesAdapterConfig( + target_hz=1.0, + num_steps=3, + max_annotation_gap_sec=1.1, + min_valid_steps=2, + ) + target_times = np.array([0.0, 1.0, 2.0]) + obj = interpolate_track( + [_state(0.0, 0.0, 0.0), _state(2.0, 2.0, 0.0)], + target_times, + config, + ) + + assert obj is not None + assert obj["valid"] == [True, False, True] + assert obj["position"][1]["x"] == ERR_VAL + assert obj["goalPosition"]["x"] == 2.0 + + +def test_interpolate_track_can_require_start_validity(): + config = NuScenesAdapterConfig(target_hz=1.0, num_steps=3) + target_times = np.array([0.0, 1.0, 2.0]) + obj = interpolate_track( + [_state(1.0, 1.0, 0.0), _state(2.0, 2.0, 0.0)], + target_times, + config, + ) + + assert obj is None + + +def test_category_to_gpudrive_filters_supported_dynamic_types(): + assert category_to_gpudrive("vehicle.car") == "vehicle" + assert category_to_gpudrive("vehicle.bicycle") == "cyclist" + assert category_to_gpudrive("pedestrian.adult") == "pedestrian" + assert category_to_gpudrive("movable_object.barrier") is None diff --git a/gpudrive-main/tests/test_utils.cpp b/gpudrive-main/tests/test_utils.cpp new file mode 100644 index 0000000000000000000000000000000000000000..9f6a487d5501c996daebf0773c465d0f01a35d86 --- /dev/null +++ b/gpudrive-main/tests/test_utils.cpp @@ -0,0 +1,55 @@ +#include "test_utils.hpp" + +namespace test_utils +{ + std::vector flatten_obs(const py::Tensor &obs) + { + int64_t num_elems = 1; + for (int i = 0; i < obs.numDims(); i++) + { + num_elems *= obs.dims()[i]; + } + float *ptr = static_cast(obs.devicePtr()); + std::vector flattened; + for (int i = 0; i < num_elems; i++) + { + flattened.push_back(static_cast(ptr[i])); + } + return flattened; + } + + std::pair calcMean(const nlohmann::json &rawJson) + { + std::pair mean = {0, 0}; + int64_t numEntities = 0; + for (const auto &obj : rawJson["objects"]) + { + int i = 0; + for (const auto &pos : obj["position"]) + { + if(obj["valid"][i++] == false) + continue; + numEntities++; + float newX = pos["x"]; + float newY = pos["y"]; + // Update mean incrementally + mean.first += (newX - mean.first) / numEntities; + mean.second += (newY - mean.second) / numEntities; + } + } + for (const auto &obj : rawJson["roads"]) + { + for (const auto &point : obj["geometry"]) + { + numEntities++; + float newX = point["x"]; + float newY = point["y"]; + + // Update mean incrementally + mean.first += (newX - mean.first) / numEntities; + mean.second += (newY - mean.second) / numEntities; + } + } + return mean; + } +} \ No newline at end of file diff --git a/gpudrive-main/tests/test_utils.hpp b/gpudrive-main/tests/test_utils.hpp new file mode 100644 index 0000000000000000000000000000000000000000..7482e2bc98f5280323d63e024202247347dc26ec --- /dev/null +++ b/gpudrive-main/tests/test_utils.hpp @@ -0,0 +1,16 @@ +#include "mgr.hpp" +#include +#include +#include +#include + +using namespace madrona; + +namespace test_utils +{ + const float EPSILON = 0.001f; // Decreased epsilon to account for floating point errors. TODO: increase floating point precision and ideally use 1e-6 as epsilon. + + std::vector flatten_obs(const py::Tensor &obs); + + std::pair calcMean(const nlohmann::json &rawJson); +} // namespace utils diff --git a/gpudrive-main/tests/test_waymax_model.py b/gpudrive-main/tests/test_waymax_model.py new file mode 100644 index 0000000000000000000000000000000000000000..d622cb51845564bd0236d0c83d03dba06091db24 --- /dev/null +++ b/gpudrive-main/tests/test_waymax_model.py @@ -0,0 +1,59 @@ +import madrona_gpudrive +import pytest +import torch + +@pytest.fixture(scope="module") +def sim_init(): + reward_params = madrona_gpudrive.RewardParams() + reward_params.rewardType = madrona_gpudrive.RewardType.DistanceBased + reward_params.distanceToGoalThreshold = 1.0 + reward_params.distanceToExpertThreshold = 1.0 + + params = madrona_gpudrive.Parameters() + params.polylineReductionThreshold = 0.5 + params.observationRadius = 10.0 + params.collisionBehaviour = madrona_gpudrive.CollisionBehaviour.AgentStop + params.rewardParams = reward_params + params.maxNumControlledAgents = 2 # we are going to use the second vehicle as the controlled vehicle + params.IgnoreNonVehicles = True + params.dynamicsModel = madrona_gpudrive.DynamicsModel.InvertibleBicycle + sim = madrona_gpudrive.SimManager( + exec_mode=madrona_gpudrive.madrona.ExecMode.CPU, + gpu_id=0, + scenes=["tests/pytest_data/test.json"], + params=params + ) + + return sim + +def test_forward_inverse_dynamics(sim_init): + sim = sim_init + + valid_agent_idx = 1 + done_tensor = sim.done_tensor().to_torch() + expert_trajectory_tensor = sim.expert_trajectory_tensor().to_torch() + action_tensor = sim.action_tensor().to_torch() + absolute_obs = sim.absolute_self_observation_tensor().to_torch() + self_obs = sim.self_observation_tensor().to_torch() + actions = torch.zeros_like(action_tensor) + + traj = expert_trajectory_tensor[:,valid_agent_idx].squeeze() + pos = traj[:2*91].view(91,2) + vel = traj[2*91:4*91].view(91,2) + headings = traj[4*91:5*91].view(91,1) + invActions = traj[6*91:16*91].view(91,10) + print('invActions', invActions[:2]) + position = absolute_obs[0,valid_agent_idx,:2] + heading = absolute_obs[0,valid_agent_idx,7] + speed = self_obs[0, valid_agent_idx, 0] + assert torch.allclose(position, pos[0], atol=1e-2), f"Position mismatch: {position} vs {pos[0]}" + assert pytest.approx(heading.item(), abs=1e-2) == headings[0].item(), f"Heading mismatch: {heading.item()} vs {headings[0].item()}" + assert pytest.approx(speed.item(), abs=1e-2) == torch.norm(vel[0]).item(), f"Speed mismatch: {speed.item()} vs {torch.norm(vel[0]).item()}" + + actions[:,valid_agent_idx,:3] = invActions[0,:3] + action_tensor.copy_(actions) + sim.step() + + assert torch.allclose(position, pos[1], atol=2e-2), f"Position mismatch: {position} vs {pos[1]} at step {1}" + assert pytest.approx(heading.item(), abs=3e-3) == headings[1].item(), f"Heading mismatch: {heading.item()} vs 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