diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..4b6ebe5ff71ddf0402c7083d1325c4d4bcf1b045 --- /dev/null +++ b/.gitignore @@ -0,0 +1 @@ +*.pt diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..039ea20e9afa09e2cff4347db634e8560781ccf3 --- /dev/null +++ b/README.md @@ -0,0 +1,27 @@ +--- +title: scatteringNet +emoji: 🧊 +colorFrom: gray +colorTo: yellow +sdk: gradio +app_file: src/gradio/app.py +pinned: false +license: mit +short_description: "Occupancy fill: label inside points on a 3D mesh." +--- + +# scatteringNet β€” occupancy fill + +Public Gradio demo. Upload an OBJ (or pick a sample) and **Run model** to label query points inside the solid. + +[Code](https://github.com/PerryGu/scatteringNet) + +[Video](https://youtu.be/vU45O0Mu0o4) + +These sample meshes were not in the training catalog. + +**Weights.** After this slim tree is on the Space, upload: + +`models/2026-09-14_07-43-34_prim_extruded_nr45_knn24_n2048_n6/best.pt` + +This folder is infer + Gradio only. It is not the training repo. diff --git a/config.yaml b/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..17be67afdcc19c7eaa4e265c7c5f90c53dac845d --- /dev/null +++ b/config.yaml @@ -0,0 +1,73 @@ +# Experiment settings for the occupancy MLP MVP. +# `device` is still detected at runtime in src/config.py (CUDA vs CPU). + +# --- Model Architecture --- +hidden: 64 # Number of hidden units (channels) per layer in the OccupancyMLP +depth: 4 # Number of hidden linear layers in the MLP network +seed: 1 # Same seed as the previous extrude_nr1 train + +# --- Path Configuration --- +data_dir: "E:/Work_stuff/scatteringNet/data" # Base root directory where all NPZ dataset files are stored + +# --- Training Hyperparameters --- +epochs: 20 # Same length as the knn24-n2048 G5 clone (compare IoU to that best.pt) +lr: 0.001 # Initial learning rate +batch_size: 1024 # Points per optimizer step (GPU mini-batch) +optimizer: adam # adam | adamw | sgd +# BCE inside-class weight. auto = n_outside / n_inside on the train split. +# Omit or null = unweighted BCE (legacy / 18-09-31 clone). Head is unchanged. +pos_weight: auto + +# --- Split --- +# Whole meshes: 80% train / 20% val of unique OBJs. All NPZs of one OBJ stay on one side. +# This split selects best.pt (not a locked holdout). Phase 2 Step 11 adds that. +val_fraction: 0.20 + +# --- Geometry --- +# none = xyz-only OccupancyMLP. surface = envelope (XYZ + face normal) + OccupancyEncoder. +# Occupancy labels stay in the NPZ. New trains write envelope_dim=6 on best.pt. +shape_encoder: surface # none / surface +n_surface: 2048 # Envelope samples on the joined OBJ (area-weighted face darts) +knn_k: 24 # Neighbors per query (not envelope count). Same head as knn24-n2048 inspect / G5 clone. Fresh train; do not resume that mix-75 best.pt. +# knn_local_dim: 64 # z_local width; omit to use latent_dim / hidden +# latent_dim: 64 # OccupancyEncoder z width; omit to use hidden + +# --- Catalog --- +# npz_catalog wins over npz_glob. Each row is all meshes for that glob unless +# max_shapes is set (unique OBJs, sampled with seed; still max_files_per_shape NPZs). +# Varied/organic and combo* are omitted on purpose. +# Fallback glob is unused while npz_catalog is set. +# Smooth extruded_* is back (same glob as knn24-n2048 inspect). High-round +# extrude_* stay s0.08. Primitives and nr1 stay s0.15. nr3 is omitted. +# max_files_per_shape keeps lattice + one jitter. +npz_glob: "exports/dataset/*.npz" +npz_catalog: + - glob: "exports/dataset/cone_*.npz" + - glob: "exports/dataset/cube_*.npz" + - glob: "exports/dataset/cyl_*.npz" + - glob: "exports/dataset/gear_*.npz" + - glob: "exports/dataset/helix_*.npz" + - glob: "exports/dataset/pipe_*.npz" + - glob: "exports/dataset/platonic_*.npz" + - glob: "exports/dataset/prism_*.npz" + - glob: "exports/dataset/sphere_*.npz" + - glob: "exports/dataset/torus_*.npz" + - glob: "exports/dataset/extruded_*occupancy_s0.08*.npz" + - glob: "exports/dataset/extrude_*_nr1_*.npz" + max_shapes: 40 + - glob: "exports/dataset/extrude_*_nr4_*occupancy_s0.08*.npz" + max_shapes: 90 + - glob: "exports/dataset/extrude_*_nr5_*occupancy_s0.08*.npz" +max_files_per_shape: 2 + +# --- Run logs --- +# Suffix for runs/_/. Device is detected at runtime, not stored here. +run_name: prim_extruded_nr45_knn24_n2048_n6_pw + +# --- Checkpoints --- +# Scalar used to decide models//best.pt (strict improve). No last.pt. +# The run snapshot (runs//config.yaml) records: +# total β€” planned epoch count (copied from epochs) +# checkpoint β€” epoch index stored in best.pt (runtime; not a project knob) +# val_iou = keep the epoch with the best inside overlap (not overall accuracy). +checkpoint_metric: val_iou diff --git a/docs/inspect_checkpoint.yaml b/docs/inspect_checkpoint.yaml new file mode 100644 index 0000000000000000000000000000000000000000..94d9ae59850da30c2c035b5668d2e578df213f0a --- /dev/null +++ b/docs/inspect_checkpoint.yaml @@ -0,0 +1,5 @@ +# INSPECT checkpoint alias (single source of truth). +# Both the Three.js inspect viewer and Gradio default to this run when +# models//best.pt exists. Do not call this alias "best". +# 2026-09-16 area-only and the pos_weight run are documented A/B logs only. +run_id: "2026-09-14_07-43-34_prim_extruded_nr45_knn24_n2048_n6" diff --git a/models/.gitkeep b/models/.gitkeep new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000000000000000000000000000000000000..0e766c1a781e5aab964a440d9a8b93ca617e14e2 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,22 @@ +[build-system] +requires = ["setuptools>=61"] +build-backend = "setuptools.build_meta" + +[project] +name = "scatteringnet" +version = "0.1.0" +description = "Occupancy fill demo (Gradio Space)" +requires-python = ">=3.10" +dependencies = [ + "numpy", + "pyyaml", + "trimesh>=4.0", +] + +[tool.setuptools] +package-dir = { "scatteringnet" = "src" } +packages = [ + "scatteringnet", + "scatteringnet.geometry", + "scatteringnet.viewer", +] diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..6f00f2d526096ebd8bb51d13445718f38e7534ff --- /dev/null +++ b/requirements.txt @@ -0,0 +1,4 @@ +numpy +pyyaml +trimesh>=4.0 +torch diff --git a/scatteringnet/__init__.py b/scatteringnet/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..7eb40be1805d5e5e5a4a9b4f4afff417b16b44b8 --- /dev/null +++ b/scatteringnet/__init__.py @@ -0,0 +1 @@ +"""Occupancy fill: catalog training, encoder, and inspect helper.""" diff --git a/scatteringnet/config.py b/scatteringnet/config.py new file mode 100644 index 0000000000000000000000000000000000000000..3d39a70a0b4cc93945548e151b37bd1df7f9b8b8 --- /dev/null +++ b/scatteringnet/config.py @@ -0,0 +1,611 @@ +"""Hybrid config loader for catalog occupancy training. + +Static experiment knobs live in ``config.yaml``. ``device`` is resolved +here from CUDA availability. Training knobs (epochs, lr, batch_size, +optimizer, catalog, val split) are YAML-owned so ``train_multi_npz`` +does not hardcode them. + +This module does not read ``.env`` and does not open NPZ files. +""" + +from __future__ import annotations + +import os +from dataclasses import dataclass +from pathlib import Path +from typing import Any, Mapping, TypedDict + +import torch +import yaml + +# Repo root: src/config.py β†’ parents[1]. +_REPO_ROOT = Path(__file__).resolve().parents[1] +_DEFAULT_YAML = _REPO_ROOT / "config.yaml" + +_REQUIRED_YAML_KEYS = ( + "hidden", + "depth", + "seed", + "data_dir", + "epochs", + "lr", +) + + +class YamlKnobs(TypedDict): + """Subset of OccupancyConfig that is stored in YAML (paths as strings).""" + + hidden: int + depth: int + seed: int + data_dir: str + epochs: int + lr: float + val_fraction: float + latent_dim: int | None + npz_glob: str + npz_paths: tuple[str, ...] + npz_catalog: tuple[tuple[str, int | None], ...] + max_files_per_shape: int | None + run_name: str + checkpoint_metric: str + batch_size: int + optimizer: str + n_surface: int + knn_k: int + knn_local_dim: int | None + shape_encoder: str + # Explicit BCE pos_weight; None when omitted or when auto is set. + pos_weight: float | None + pos_weight_auto: bool + + +def get_device() -> torch.device: + """ + CUDA when a GPU is visible; otherwise CPU. + + Hugging Face CPU Spaces have no CUDA: infer still runs (slower). + ``SCATTERINGNET_DEVICE=cpu`` forces CPU even if a GPU exists. + ``SCATTERINGNET_DEVICE=cuda`` uses CUDA only when ``is_available()``; + otherwise it falls back to CPU (no crash). + + Training scripts should still warn on a long catalog run on CPU. + """ + forced = os.environ.get("SCATTERINGNET_DEVICE", "").strip().lower() + if forced == "cpu": + return torch.device("cpu") + if forced == "cuda": + if torch.cuda.is_available(): + return torch.device("cuda") + return torch.device("cpu") + if torch.cuda.is_available(): + return torch.device("cuda") + return torch.device("cpu") + + +def repo_root() -> Path: + """Git / project root (folder that contains ``src/`` and ``config.yaml``).""" + return _REPO_ROOT + + +def gpu_name(device: torch.device | None = None) -> str | None: + """ + Human GPU name for the run snapshot (``None`` on CPU). + + Uses ``cfg.device`` when given so a forced-CPU train does not stamp a + card that was not used. + """ + dev = device if device is not None else get_device() + if dev.type != "cuda" or not torch.cuda.is_available(): + return None + index = 0 if dev.index is None else int(dev.index) + if index < 0 or index >= torch.cuda.device_count(): + return None + name = str(torch.cuda.get_device_name(index)).strip() + return name or None + + +def _as_positive_int(name: str, value: Any) -> int: + """YAML may yield int or (rarely) str; occupancy dims must be int >= 1.""" + try: + parsed = int(value) + except (TypeError, ValueError) as exc: + raise ValueError(f"{name} must be an integer, got {value!r}") from exc + if parsed < 1: + raise ValueError(f"{name} must be >= 1, got {parsed}") + return parsed + + +def _as_int_in_range(name: str, value: Any, lo: int, hi: int) -> int: + """Inclusive integer range (YAML ``knn_k`` is 0–4096).""" + try: + parsed = int(value) + except (TypeError, ValueError) as exc: + raise ValueError(f"{name} must be an integer, got {value!r}") from exc + if parsed < lo or parsed > hi: + raise ValueError(f"{name} must be in [{lo}, {hi}], got {parsed}") + return parsed + + +def _as_positive_float(name: str, value: Any) -> float: + """Learning-rate style knobs must be a finite float > 0.""" + try: + parsed = float(value) + except (TypeError, ValueError) as exc: + raise ValueError(f"{name} must be a float, got {value!r}") from exc + if parsed <= 0.0 or parsed != parsed: + raise ValueError(f"{name} must be > 0, got {parsed}") + return parsed + + +def _as_pos_weight_pair(raw: Mapping[str, Any]) -> tuple[float | None, bool]: + """ + YAML ``pos_weight``: omit / null β†’ unweighted BCE. + + ``auto`` β†’ compute n_outside / n_inside on the train split at train time. + A finite float > 0 is used as-is (1.0 is unweighted). + """ + if "pos_weight" not in raw: + return None, False + value = raw["pos_weight"] + if value is None or value is False: + return None, False + if isinstance(value, str): + text = value.strip().lower() + if text in ("", "none", "off", "false"): + return None, False + if text == "auto": + return None, True + parsed = _as_positive_float("pos_weight", value) + return parsed, False + + +def _as_open_unit_interval(name: str, value: Any) -> float: + """Hold-out fractions must be in (0, 1) so both splits are non-empty.""" + try: + parsed = float(value) + except (TypeError, ValueError) as exc: + raise ValueError(f"{name} must be a float, got {value!r}") from exc + if parsed != parsed or parsed <= 0.0 or parsed >= 1.0: + raise ValueError(f"{name} must be in (0, 1), got {parsed}") + return parsed + + +def _as_nonempty_path_string(name: str, value: Any) -> str: + if value is None or (isinstance(value, str) and not value.strip()): + raise ValueError(f"{name} must be a non-empty path string in config.yaml") + return str(value).strip() + + +def _as_data_dir_string(value: Any) -> str: + return _as_nonempty_path_string("data_dir", value) + + +def _as_optional_positive_int(name: str, value: Any) -> int | None: + """YAML null β†’ unlimited catalog cap; otherwise int >= 1.""" + if value is None: + return None + return _as_positive_int(name, value) + + +def _as_run_name(value: Any) -> str: + """Optional YAML suffix for ``runs/_/``; empty β†’ ``run``.""" + if value is None: + return "run" + text = str(value).strip() + return text if text else "run" + + +_CHECKPOINT_METRIC_ALIASES = { + "test_acc": "val_acc", + "test_iou": "val_iou", +} + + +def _as_checkpoint_metric(value: Any) -> str: + """Name of the scalar used to decide ``best.pt`` (strict improve).""" + if value is None or (isinstance(value, str) and not value.strip()): + raise ValueError("checkpoint_metric must be a non-empty string") + name = str(value).strip() + return _CHECKPOINT_METRIC_ALIASES.get(name, name) + + +def _as_val_fraction(raw: Mapping[str, Any]) -> float: + """Prefer ``val_fraction``; accept legacy ``test_fraction``.""" + if "val_fraction" in raw: + return _as_open_unit_interval("val_fraction", raw["val_fraction"]) + if "test_fraction" in raw: + return _as_open_unit_interval("test_fraction", raw["test_fraction"]) + raise ValueError("Config YAML missing keys: val_fraction") + + +def _as_optional_latent_dim(raw: Mapping[str, Any]) -> int | None: + """YAML omit / null β†’ use ``hidden`` at train time.""" + if "latent_dim" not in raw or raw["latent_dim"] is None: + return None + return _as_positive_int("latent_dim", raw["latent_dim"]) + + +# Names accepted in config.yaml ``optimizer``. Used by train_multi_npz. +_ALLOWED_OPTIMIZERS = ("adam", "adamw", "sgd") +# ``none`` = OccupancyMLP (xyz only). ``surface`` = envelope encoder. +_ALLOWED_SHAPE_ENCODERS = ("none", "surface") + + +def _as_optimizer(value: Any) -> str: + """Optimizer family for multi-NPZ train; default Adam.""" + if value is None or (isinstance(value, str) and not str(value).strip()): + return "adam" + name = str(value).strip().lower() + if name not in _ALLOWED_OPTIMIZERS: + allowed = ", ".join(_ALLOWED_OPTIMIZERS) + raise ValueError(f"optimizer must be one of {allowed}, got {value!r}") + return name + + +def _as_shape_encoder(value: Any) -> str: + """Occupancy head family; default xyz-only so older YAML still loads.""" + if value is None or (isinstance(value, str) and not str(value).strip()): + return "none" + name = str(value).strip().lower() + if name not in _ALLOWED_SHAPE_ENCODERS: + allowed = ", ".join(_ALLOWED_SHAPE_ENCODERS) + raise ValueError(f"shape_encoder must be one of {allowed}, got {value!r}") + return name + + +def _as_npz_paths(value: Any) -> tuple[str, ...]: + """Explicit NPZ list relative to data_dir (empty β†’ use glob).""" + if value is None: + return () + if isinstance(value, str): + item = value.strip() + return (item,) if item else () + if not isinstance(value, list): + raise ValueError(f"npz_paths must be a list of strings or null, got {type(value).__name__}") + out: list[str] = [] + for i, raw in enumerate(value): + text = _as_nonempty_path_string(f"npz_paths[{i}]", raw) + out.append(text) + return tuple(out) + + +def _as_npz_catalog(value: Any) -> tuple[tuple[str, int | None], ...]: + """Union of globs; optional ``max_shapes`` is unique meshes per glob.""" + if value is None: + return () + if not isinstance(value, list): + raise ValueError( + f"npz_catalog must be a list or null, got {type(value).__name__}" + ) + out: list[tuple[str, int | None]] = [] + for i, raw in enumerate(value): + if isinstance(raw, str): + glob_s = _as_nonempty_path_string(f"npz_catalog[{i}]", raw) + out.append((glob_s, None)) + continue + if not isinstance(raw, Mapping): + raise ValueError( + f"npz_catalog[{i}] must be a glob string or mapping, " + f"got {type(raw).__name__}" + ) + if "glob" not in raw: + raise ValueError(f"npz_catalog[{i}] missing glob") + glob_s = _as_nonempty_path_string(f"npz_catalog[{i}].glob", raw["glob"]) + max_shapes: int | None = None + if "max_shapes" in raw and raw["max_shapes"] is not None: + max_shapes = _as_positive_int( + f"npz_catalog[{i}].max_shapes", raw["max_shapes"] + ) + out.append((glob_s, max_shapes)) + return tuple(out) + + +def as_repo_relative(path: Path | str, *, root: Path | None = None) -> str: + """ + POSIX string relative to the git repo when ``path`` is inside it. + + Already-relative inputs are returned as POSIX. Absolute paths on another + drive (the dataset disk) cannot be repo-relative and stay absolute POSIX. + """ + text = str(path).strip() + if not text: + return text + parsed = Path(text) + if not parsed.is_absolute(): + return parsed.as_posix() + base = (root or _REPO_ROOT).resolve() + try: + return parsed.resolve().relative_to(base).as_posix() + except ValueError: + return parsed.resolve().as_posix() + + +def as_data_relative(path: Path | str, data_dir: Path | str) -> str: + """ + POSIX string relative to ``data_dir`` (``exports/...``, not ``E:/...``). + + Already-relative inputs are returned as POSIX. Paths outside ``data_dir`` + (unit-test temp trees) fall back to absolute POSIX. + """ + text = str(path).strip() + if not text: + return text + parsed = Path(text) + if not parsed.is_absolute(): + return parsed.as_posix() + root = Path(data_dir).expanduser().resolve() + resolved = parsed.expanduser().resolve() + try: + return resolved.relative_to(root).as_posix() + except ValueError: + return resolved.as_posix() + + +def load_yaml_knobs(path: Path) -> YamlKnobs: + """ + Read YAML settings. Does not check that data_dir exists on disk. + + Parameters + ---------- + path: + Path to ``config.yaml``. + + Returns + ------- + YamlKnobs + Typed dict of experiment knobs (paths still strings). + """ + if not path.is_file(): + raise FileNotFoundError(f"Config YAML not found: {path}") + raw = yaml.safe_load(path.read_text(encoding="utf-8")) + if not isinstance(raw, Mapping): + raise ValueError(f"Config YAML must be a mapping, got {type(raw).__name__}") + missing = [k for k in _REQUIRED_YAML_KEYS if k not in raw] + if missing: + raise ValueError(f"Config YAML missing keys: {', '.join(missing)}") + pos_weight, pos_weight_auto = _as_pos_weight_pair(raw) + return { + "hidden": _as_positive_int("hidden", raw["hidden"]), + "depth": _as_positive_int("depth", raw["depth"]), + "seed": _as_positive_int("seed", raw["seed"]), + "data_dir": _as_data_dir_string(raw["data_dir"]), + "epochs": _as_positive_int("epochs", raw["epochs"]), + "lr": _as_positive_float("lr", raw["lr"]), + "val_fraction": _as_val_fraction(raw), + "latent_dim": _as_optional_latent_dim(raw), + "npz_glob": ( + _as_nonempty_path_string("npz_glob", raw["npz_glob"]) + if "npz_glob" in raw + else "exports/dataset/*.npz" + ), + "npz_paths": _as_npz_paths(raw.get("npz_paths")), + "npz_catalog": _as_npz_catalog(raw.get("npz_catalog")), + "max_files_per_shape": ( + _as_optional_positive_int("max_files_per_shape", raw["max_files_per_shape"]) + if "max_files_per_shape" in raw + else 2 + ), + "run_name": ( + _as_run_name(raw["run_name"]) if "run_name" in raw else "run" + ), + "checkpoint_metric": ( + _as_checkpoint_metric(raw["checkpoint_metric"]) + if "checkpoint_metric" in raw + else "val_acc" + ), + "batch_size": ( + _as_positive_int("batch_size", raw["batch_size"]) + if "batch_size" in raw + else 1024 + ), + "optimizer": ( + _as_optimizer(raw["optimizer"]) if "optimizer" in raw else "adam" + ), + "n_surface": ( + _as_positive_int("n_surface", raw["n_surface"]) + if "n_surface" in raw + else 1024 + ), + "knn_k": ( + _as_int_in_range("knn_k", raw["knn_k"], 0, 4096) + if "knn_k" in raw + else 0 + ), + "knn_local_dim": ( + _as_positive_int("knn_local_dim", raw["knn_local_dim"]) + if "knn_local_dim" in raw + else None + ), + "shape_encoder": ( + _as_shape_encoder(raw["shape_encoder"]) + if "shape_encoder" in raw + else "none" + ), + "pos_weight": pos_weight, + "pos_weight_auto": pos_weight_auto, + } + + +def _warn_missing_data_dir(data_dir: Path, yaml_path: Path) -> None: + """Print a terminal hint so the user can fix config.yaml (no .env involved).""" + print( + "\n" + "Dataset folder not found.\n" + f" Looked for: {data_dir}\n" + "\n" + "Update `data_dir` in config.yaml to the folder that contains " + "`exports/` and `meshes/`.\n" + f" Config file: {yaml_path}\n" + ) + + +def require_data_dir(data_dir: Path, *, yaml_path: Path) -> None: + """Validate the dataset root before training or NPZ loading.""" + if data_dir.is_dir(): + return + _warn_missing_data_dir(data_dir, yaml_path) + raise FileNotFoundError( + f"Dataset directory does not exist: {data_dir}. " + f"Set data_dir in {yaml_path}." + ) + + +@dataclass(frozen=True) +class OccupancyConfig: + """Resolved experiment settings from YAML plus detected device.""" + + data_dir: Path + device: torch.device + hidden: int + depth: int + seed: int + epochs: int + lr: float + # Fraction of catalog **meshes** held out as val (selection split, not a locked test). + val_fraction: float + # Catalog knobs (optional in YAML; omitted keys keep these defaults). + npz_glob: str = "exports/dataset/*.npz" + npz_paths: tuple[str, ...] = () + # ``(glob, max_shapes)`` rows. Empty β†’ use ``npz_glob``. ``max_shapes`` + # None keeps every mesh that glob hits (after ``max_files_per_shape``). + npz_catalog: tuple[tuple[str, int | None], ...] = () + max_files_per_shape: int | None = 2 + # Suffix for runs/_/ (device stays runtime-only). + run_name: str = "run" + # Which logged scalar selects best.pt (strict improve). + checkpoint_metric: str = "val_acc" + # Encoder latent width; None β†’ use ``hidden`` at train / infer time. + latent_dim: int | None = None + # Mini-batch size and optimizer family (train_multi_npz). + batch_size: int = 1024 + optimizer: str = "adam" + # Envelope sample count (YAML). Used when ``shape_encoder`` is ``surface``. + n_surface: int = 1024 + # 0 = global envelope z only. >0 = that many nearest envelope dots per query. + knn_k: int = 0 + # Width of z_local; None β†’ same as occupancy latent_dim / hidden. + knn_local_dim: int | None = None + # ``none`` keeps OccupancyMLP; ``surface`` uses the envelope PointNet. + shape_encoder: str = "none" + # BCE inside-class weight. None + auto=False = unweighted (legacy). + pos_weight: float | None = None + pos_weight_auto: bool = False + + +def load_config( + yaml_path: Path | None = None, + *, + require_existing_data_dir: bool = True, +) -> OccupancyConfig: + """ + Compose OccupancyConfig from ``config.yaml`` (not from ``.env``). + + When ``require_existing_data_dir`` is True (default), a missing folder + prints a short instruction and then raises FileNotFoundError β€” used for + training and data loading. + + Parameters + ---------- + yaml_path: + Config file; default is repo-root ``config.yaml``. + require_existing_data_dir: + If True, refuse to return a config whose ``data_dir`` is missing. + + Returns + ------- + OccupancyConfig + YAML knobs plus detected ``device``. + """ + cfg_path = yaml_path or _DEFAULT_YAML + knobs = load_yaml_knobs(cfg_path) + data_dir = Path(knobs["data_dir"]) + if require_existing_data_dir: + require_data_dir(data_dir, yaml_path=cfg_path) + return OccupancyConfig( + data_dir=data_dir, + device=get_device(), + hidden=knobs["hidden"], + depth=knobs["depth"], + seed=knobs["seed"], + epochs=knobs["epochs"], + lr=knobs["lr"], + val_fraction=knobs["val_fraction"], + latent_dim=knobs["latent_dim"], + npz_glob=knobs["npz_glob"], + npz_paths=knobs["npz_paths"], + npz_catalog=knobs["npz_catalog"], + max_files_per_shape=knobs["max_files_per_shape"], + run_name=knobs["run_name"], + checkpoint_metric=knobs["checkpoint_metric"], + batch_size=knobs["batch_size"], + optimizer=knobs["optimizer"], + n_surface=knobs["n_surface"], + knn_k=knobs["knn_k"], + knn_local_dim=knobs["knn_local_dim"], + shape_encoder=knobs["shape_encoder"], + pos_weight=knobs["pos_weight"], + pos_weight_auto=knobs["pos_weight_auto"], + ) + + +def encoder_latent_dim(cfg: OccupancyConfig) -> int: + """OccupancyEncoder ``z`` width: YAML ``latent_dim`` or ``hidden``.""" + if cfg.latent_dim is None: + return int(cfg.hidden) + return int(cfg.latent_dim) + + +def encoder_knn_local_dim(cfg: OccupancyConfig) -> int: + """Local envelope code width: YAML ``knn_local_dim`` or global latent.""" + if cfg.knn_local_dim is None: + return encoder_latent_dim(cfg) + return int(cfg.knn_local_dim) + + +def format_config(cfg: OccupancyConfig) -> str: + """ + Pretty-print for CLI smoke checks. + + Parameters + ---------- + cfg: + Resolved config. + + Returns + ------- + str + Multi-line ``OccupancyConfig(...)`` dump. + """ + return ( + f"OccupancyConfig(\n" + f" data_dir={cfg.data_dir}\n" + f" device={cfg.device}\n" + f" gpu={gpu_name(cfg.device)}\n" + f" hidden={cfg.hidden}\n" + f" depth={cfg.depth}\n" + f" seed={cfg.seed}\n" + f" epochs={cfg.epochs}\n" + f" lr={cfg.lr}\n" + f" val_fraction={cfg.val_fraction}\n" + f" latent_dim={cfg.latent_dim}\n" + f" npz_glob={cfg.npz_glob}\n" + f" npz_paths={list(cfg.npz_paths)}\n" + f" npz_catalog={list(cfg.npz_catalog)}\n" + f" max_files_per_shape={cfg.max_files_per_shape}\n" + f" run_name={cfg.run_name}\n" + f" checkpoint_metric={cfg.checkpoint_metric}\n" + f" batch_size={cfg.batch_size}\n" + f" optimizer={cfg.optimizer}\n" + f" n_surface={cfg.n_surface}\n" + f" knn_k={cfg.knn_k}\n" + f" knn_local_dim={cfg.knn_local_dim}\n" + f" shape_encoder={cfg.shape_encoder}\n" + f" pos_weight={cfg.pos_weight}\n" + f" pos_weight_auto={cfg.pos_weight_auto}\n" + f")" + ) + + +if __name__ == "__main__": + print(format_config(load_config())) diff --git a/scatteringnet/data_npz.py b/scatteringnet/data_npz.py new file mode 100644 index 0000000000000000000000000000000000000000..1e738cc8bb6c69273c21a7d6cbec2f407f775497 --- /dev/null +++ b/scatteringnet/data_npz.py @@ -0,0 +1,460 @@ +"""Load occupancy query points and labels from scatter NPZ files. + +:func:`load_points_labels` reads **one** file. A catalog resolver lists +many NPZs (glob or explicit paths) without training. + +``load_points_labels`` still returns only ``points`` and ``labels``. +``load_points_labels_mesh`` also resolves ``mesh_path`` against ``data_dir``. +""" + +from __future__ import annotations + +import glob as globlib +import random +from pathlib import Path +from typing import Sequence + +import numpy as np +from numpy.typing import NDArray + +# Labels are converted here (not deferred to the Dataset) so every caller gets +# the same dtypes: float32 XYZ and float32 {0, 1} occupancy. +PointsArray = NDArray[np.float32] +LabelsArray = NDArray[np.float32] + + +def load_points_labels(path: Path) -> tuple[PointsArray, LabelsArray]: + """ + Read query coordinates and inside/outside labels from one NPZ file. + + Parameters + ---------- + path: + Path to a ``.npz`` with arrays ``points`` ``(N, 3)`` and + ``labels`` ``(N,)`` (typically uint8 0/1). + + Returns + ------- + points: + ``float32`` array of shape ``(N, 3)``. + labels: + ``float32`` array of shape ``(N,)`` with values in ``{0.0, 1.0}`` + (0 = outside, 1 = inside). + """ + npz_path = Path(path) + if not npz_path.is_file(): + raise FileNotFoundError(f"NPZ not found: {npz_path}") + + # allow_pickle=False: we only need numeric arrays, not object payloads. + with np.load(npz_path, allow_pickle=False) as raw: + files = set(raw.files) + if "points" not in files or "labels" not in files: + raise KeyError( + f"NPZ must contain 'points' and 'labels', got {sorted(files)} " + f"in {npz_path}" + ) + points = np.asarray(raw["points"]) + labels = np.asarray(raw["labels"]) + + if points.ndim != 2 or points.shape[1] != 3: + raise ValueError( + f"points must have shape (N, 3), got {tuple(points.shape)} in {npz_path}" + ) + n = int(points.shape[0]) + if labels.shape != (n,): + raise ValueError( + f"labels must have shape (N,) with N={n}, got {tuple(labels.shape)} " + f"in {npz_path}" + ) + + points_f32 = np.asarray(points, dtype=np.float32) + labels_f32 = np.asarray(labels, dtype=np.float32) + unique = np.unique(labels_f32) + if not np.all((unique == 0.0) | (unique == 1.0)): + raise ValueError( + f"labels must be in {{0, 1}}, got unique={unique.tolist()} in {npz_path}" + ) + return points_f32, labels_f32 + + +def count_npz_points(path: Path | str) -> int: + """ + Query count in one NPZ without keeping the arrays. + + Catalog construct uses this so ``len(part)`` / ``n_points`` do not + require loading every lattice into RAM. + """ + npz_path = Path(path) + if not npz_path.is_file(): + raise FileNotFoundError(f"NPZ not found: {npz_path}") + with np.load(npz_path, allow_pickle=False) as raw: + files = set(raw.files) + if "points" not in files or "labels" not in files: + raise KeyError( + f"NPZ must contain 'points' and 'labels', got {sorted(files)} " + f"in {npz_path}" + ) + n = int(np.asarray(raw["points"]).shape[0]) + n_y = int(np.asarray(raw["labels"]).shape[0]) + if n_y != n: + raise ValueError( + f"labels must have shape (N,) with N={n}, got N={n_y} in {npz_path}" + ) + return n + + +def count_npz_labels(path: Path | str) -> tuple[int, int]: + """ + Outside / inside counts in one NPZ without keeping the point cloud. + + Used for ``pos_weight: auto`` (n_outside / n_inside on the train split). + """ + npz_path = Path(path) + if not npz_path.is_file(): + raise FileNotFoundError(f"NPZ not found: {npz_path}") + with np.load(npz_path, allow_pickle=False) as raw: + if "labels" not in set(raw.files): + raise KeyError(f"NPZ must contain 'labels', got {sorted(raw.files)} in {npz_path}") + labels = np.asarray(raw["labels"]).reshape(-1) + # Labels are 0/1 occupancy; nonzero is inside. + n_in = int(np.count_nonzero(labels)) + n_out = int(labels.size) - n_in + return n_out, n_in + + +def read_npz_mesh_path(path: Path | str) -> str: + """ + Read the stored ``mesh_path`` string from one occupancy NPZ. + + Step 2 writes a ``data_dir``-relative POSIX path (for example + ``meshes/Primitives/Sphere/sphere_r0p5_sa16_sh16.obj``). + + Parameters + ---------- + path: + Occupancy ``.npz`` that contains ``mesh_path``. + + Returns + ------- + str + Stored path string (relative or absolute). Not resolved here. + """ + npz_path = Path(path) + if not npz_path.is_file(): + raise FileNotFoundError(f"NPZ not found: {npz_path}") + # allow_pickle=True: some exports store a 0-d string / object array. + with np.load(npz_path, allow_pickle=True) as raw: + if "mesh_path" not in raw.files: + raise KeyError(f"NPZ has no 'mesh_path' in {npz_path}") + stored = str(np.asarray(raw["mesh_path"]).item()).strip() + if not stored: + raise ValueError(f"mesh_path is empty in {npz_path}") + return stored + + +def resolve_mesh_path(stored: str, data_dir: Path | str) -> Path: + """ + Resolve a stored ``mesh_path`` against ``data_dir``. + + Relative entries are joined to ``data_dir``. Absolute entries are + used as-is. Missing files raise ``FileNotFoundError``. + + Parameters + ---------- + stored: + Value from :func:`read_npz_mesh_path`. + data_dir: + Dataset root (``config.yaml`` ``data_dir``). + + Returns + ------- + Path + Existing resolved mesh file. + """ + text = str(stored).strip() + if not text: + raise ValueError("mesh_path is empty") + item = Path(text) + root = Path(data_dir) + resolved = item if item.is_absolute() else (root / item) + resolved = resolved.resolve() + if not resolved.is_file(): + raise FileNotFoundError(f"mesh not found: {resolved} (stored={text!r})") + return resolved + + +def load_points_labels_mesh( + path: Path | str, + data_dir: Path | str, +) -> tuple[PointsArray, LabelsArray, Path]: + """ + Read occupancy arrays and resolve the source OBJ. + + Keeps :func:`load_points_labels` unchanged (xyz + labels only). + + Parameters + ---------- + path: + Occupancy ``.npz`` with ``points``, ``labels``, and ``mesh_path``. + data_dir: + Root used to resolve a relative ``mesh_path``. + + Returns + ------- + points, labels, mesh_path: + Same arrays as :func:`load_points_labels`, plus the existing OBJ. + """ + npz_path = Path(path) + points, labels = load_points_labels(npz_path) + stored = read_npz_mesh_path(npz_path) + mesh_path = resolve_mesh_path(stored, data_dir) + return points, labels, mesh_path + + +def _is_combo_npz(path: Path) -> bool: + """True when the filename looks like a combo dump (excluded from the catalog).""" + return "combo" in path.name.lower() + + +def shape_key(path: Path) -> str: + """ + Group NPZs that belong to the same mesh. + + Uses the stem before ``__`` (dataset_builder tag), else the full stem. + + Parameters + ---------- + path: + NPZ path. + + Returns + ------- + str + Stable key for ``max_files_per_shape``. + """ + stem = Path(path).stem + if "__" in stem: + return stem.split("__", 1)[0] + return stem + + +def _cap_per_shape( + paths: Sequence[Path], + max_files_per_shape: int | None, +) -> list[Path]: + """Keep at most ``max_files_per_shape`` files per :func:`shape_key` (sorted order).""" + if max_files_per_shape is None: + return list(paths) + if max_files_per_shape < 1: + raise ValueError(f"max_files_per_shape must be >= 1 or None, got {max_files_per_shape}") + counts: dict[str, int] = {} + out: list[Path] = [] + for path in paths: + key = shape_key(path) + taken = counts.get(key, 0) + if taken >= max_files_per_shape: + continue + counts[key] = taken + 1 + out.append(path) + return out + + +def _glob_npz(root: Path, pattern: str) -> list[Path]: + """Match ``pattern`` under ``root`` (``*`` / ``**``).""" + full = str(root / pattern) + recursive = "**" in pattern.replace("\\", "/") + found = globlib.glob(full, recursive=recursive) + return [Path(p).resolve() for p in found if Path(p).is_file()] + + +def _is_parameterized_stem(path: Path) -> bool: + """ + Maya catalog names are ``family_param_...``. Varied one-off stems + (``Cone.obj`` β†’ ``Cone__occupancy.npz``) have no ``_`` in the shape key + and must not ride along when Windows glob is case-insensitive. + """ + return "_" in shape_key(path) + + +def _subsample_shapes( + paths: Sequence[Path], + max_shapes: int, + seed: int, +) -> list[Path]: + """Keep NPZs for at most ``max_shapes`` unique :func:`shape_key` values.""" + if max_shapes < 1: + raise ValueError(f"max_shapes must be >= 1, got {max_shapes}") + keys: list[str] = [] + seen: set[str] = set() + for path in paths: + key = shape_key(path) + if key in seen: + continue + seen.add(key) + keys.append(key) + if max_shapes >= len(keys): + return list(paths) + # Sort then sample so the same seed always picks the same meshes. + chosen_keys = set(random.Random(int(seed)).sample(sorted(keys), max_shapes)) + return [path for path in paths if shape_key(path) in chosen_keys] + + +def resolve_npz_catalog( + data_dir: Path | str, + *, + npz_glob: str = "exports/dataset/*.npz", + npz_paths: Sequence[str | Path] | None = None, + npz_catalog: Sequence[tuple[str, int | None]] | None = None, + max_files_per_shape: int | None = 2, + exclude_combo: bool = True, + seed: int = 1, +) -> list[Path]: + """ + Resolve occupancy NPZ paths under ``data_dir`` (no point loading). + + Priority: explicit ``npz_paths``, else ``npz_catalog`` (union of globs), + else ``npz_glob``. Relative entries are joined to ``data_dir``. + Missing files in ``npz_paths`` raise ``FileNotFoundError``. + + Parameters + ---------- + data_dir: + Dataset root (``config.yaml`` ``data_dir``). + npz_glob: + Single glob relative to ``data_dir`` (``*`` and ``**`` allowed). + npz_paths: + Explicit relative or absolute NPZ paths. Empty / None β†’ use glob(s). + npz_catalog: + ``(glob, max_shapes)`` rows. ``max_shapes`` is unique meshes after + the per-shape file cap; ``None`` keeps every mesh the glob hits. + max_files_per_shape: + Cap per :func:`shape_key` after sort. ``None`` = no cap. + exclude_combo: + Drop filenames containing ``combo``. + seed: + RNG for ``max_shapes`` subsampling (YAML ``seed``). + + Returns + ------- + list[Path] + Sorted existing ``.npz`` files. + """ + root = Path(data_dir) + chosen: list[Path] + if npz_paths: + chosen = [] + for raw in npz_paths: + item = Path(raw) + resolved = item if item.is_absolute() else (root / item) + if not resolved.is_file(): + raise FileNotFoundError(f"NPZ not found: {resolved}") + chosen.append(resolved.resolve()) + elif npz_catalog: + # Union in YAML order. Same file from two globs is kept once. + seen: set[Path] = set() + chosen = [] + for pattern, max_shapes in npz_catalog: + hit = _glob_npz(root, str(pattern)) + if exclude_combo: + hit = [p for p in hit if not _is_combo_npz(p)] + hit = [ + p + for p in hit + if p.suffix.lower() == ".npz" and _is_parameterized_stem(p) + ] + hit = sorted(hit) + hit = _cap_per_shape(hit, max_files_per_shape) + if max_shapes is not None: + hit = _subsample_shapes(hit, int(max_shapes), int(seed)) + for path in hit: + if path in seen: + continue + seen.add(path) + chosen.append(path) + else: + chosen = _glob_npz(root, npz_glob) + + npz_only = [p for p in chosen if p.suffix.lower() == ".npz"] + if exclude_combo: + npz_only = [p for p in npz_only if not _is_combo_npz(p)] + if npz_catalog and not npz_paths: + # Already capped per glob; keep YAML union order (not a global sort). + capped = npz_only + else: + npz_only = sorted(npz_only) + capped = _cap_per_shape(npz_only, max_files_per_shape) + if not capped: + raise FileNotFoundError( + f"No occupancy NPZ files matched under {root} " + f"(glob={npz_glob!r}, catalog={bool(npz_catalog)}, " + f"explicit={bool(npz_paths)})" + ) + return capped + + +def _summarize(points: PointsArray, labels: LabelsArray) -> str: + n = int(points.shape[0]) + inside = float(labels.mean()) if n else float("nan") + xyz_min = points.min(axis=0) if n else np.full(3, np.nan, dtype=np.float32) + xyz_max = points.max(axis=0) if n else np.full(3, np.nan, dtype=np.float32) + return ( + f"N={n}\n" + f"inside_fraction={inside:.6f}\n" + f"xyz_min={xyz_min.tolist()}\n" + f"xyz_max={xyz_max.tolist()}" + ) + + +# Default smoke-check file from the v2 plan (dataset_test sphere). +_SAMPLE_RELATIVE = Path("exports") / "dataset_test" / "sphere__raycast_z_raut_s0.15_inout.npz" + + +if __name__ == "__main__": + import sys + + from scatteringnet.config import load_config + + cfg = load_config() + if "--catalog" in sys.argv: + paths = resolve_npz_catalog( + cfg.data_dir, + npz_glob=cfg.npz_glob, + npz_paths=cfg.npz_paths or None, + npz_catalog=cfg.npz_catalog or None, + max_files_per_shape=cfg.max_files_per_shape, + seed=cfg.seed, + ) + print(f"data_dir={cfg.data_dir}") + print(f"npz_glob={cfg.npz_glob}") + print(f"npz_catalog={list(cfg.npz_catalog)}") + print(f"max_files_per_shape={cfg.max_files_per_shape}") + print(f"files={len(paths)}") + # Load a few files only β€” the full catalog can be thousands of NPZs. + preview = paths[:3] + n_all = 0 + n_in = 0 + for path in preview: + pts, labs = load_points_labels(path) + n = int(pts.shape[0]) + inside = int((labs == 1.0).sum()) + n_all += n + n_in += inside + print( + f" {path.name} N={n} inside={inside} outside={n - inside}" + ) + print( + f"preview_files={len(preview)} preview_N={n_all} " + f"preview_inside={n_in} preview_outside={n_all - n_in}" + ) + from scatteringnet.dataset import OccupancyMultiNpzDataset, make_dataloader + + ds = OccupancyMultiNpzDataset(paths) + loader = make_dataloader(ds.parts[0], batch_size=8, shuffle=False) + xyz, y = next(iter(loader)) + print(f"files={len(ds)} n_points={ds.n_points} parts={len(ds.parts)}") + print(f"batch xyz={tuple(xyz.shape)} y={tuple(y.shape)}") + else: + sample = cfg.data_dir / _SAMPLE_RELATIVE + pts, labs = load_points_labels(sample) + print(f"file={sample}") + print(_summarize(pts, labs)) diff --git a/scatteringnet/geometry/__init__.py b/scatteringnet/geometry/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..fd84e5cfbf2762a4dff82672dd530c2c8e4f17de --- /dev/null +++ b/scatteringnet/geometry/__init__.py @@ -0,0 +1,10 @@ +"""Training-side geometry: OBJ triangles and envelope samples.""" + +from scatteringnet.geometry.mesh_io import clear_triangle_cache +from scatteringnet.geometry.surface import clear_envelope_cache + + +def clear_geometry_caches() -> None: + """Drop OBJ triangle and envelope process caches.""" + clear_triangle_cache() + clear_envelope_cache() diff --git a/scatteringnet/geometry/mesh_io.py b/scatteringnet/geometry/mesh_io.py new file mode 100644 index 0000000000000000000000000000000000000000..7b3489ba3a46ca0d357e1e32facb22f708b12706 --- /dev/null +++ b/scatteringnet/geometry/mesh_io.py @@ -0,0 +1,80 @@ +"""Load OBJ triangle meshes for the NPZ ↔ mesh join. + +This module only returns ``vertices (V, 3)`` and ``faces (T, 3)``. +It does **not** sample the envelope. +""" + +from __future__ import annotations + +from pathlib import Path +import numpy as np +import trimesh +from numpy.typing import NDArray + +from scatteringnet.geometry.trimesh_util import as_trimesh + +VerticesArray = NDArray[np.float32] +FacesArray = NDArray[np.int32] + +# Same resolved OBJ can back several NPZs (``max_files_per_shape``). Cache +# the triangle arrays so catalog load does not re-parse the file. +_TRIANGLE_CACHE: dict[str, tuple[VerticesArray, FacesArray]] = {} + + +def clear_triangle_cache() -> None: + """Drop cached OBJ arrays (tests / long-lived notebooks).""" + _TRIANGLE_CACHE.clear() + + +def load_obj_triangles( + path: Path | str, + *, + cache: bool = True, +) -> tuple[VerticesArray, FacesArray]: + """ + Read one OBJ as triangle vertices and face indices. + + Parameters + ---------- + path: + Existing ``.obj`` file. + cache: + Reuse arrays for the same resolved path (catalog load). + + Returns + ------- + vertices: + ``float32`` array of shape ``(V, 3)``. + faces: + ``int32`` array of shape ``(T, 3)`` (0-based vertex indices). + """ + obj_path = Path(path) + if not obj_path.is_file(): + raise FileNotFoundError(f"OBJ not found: {obj_path}") + if obj_path.suffix.lower() != ".obj": + raise ValueError(f"expected .obj, got {obj_path.suffix!r} ({obj_path})") + + cache_key = str(obj_path.resolve()) + if cache and cache_key in _TRIANGLE_CACHE: + return _TRIANGLE_CACHE[cache_key] + + # process=False keeps the authored vertices; we only need the join. + loaded = trimesh.load(obj_path, force=None, process=False) + mesh = as_trimesh(loaded) + vertices = np.asarray(mesh.vertices, dtype=np.float32) + faces = np.asarray(mesh.faces, dtype=np.int32) + if vertices.ndim != 2 or vertices.shape[1] != 3: + raise ValueError( + f"vertices must have shape (V, 3), got {tuple(vertices.shape)} in {obj_path}" + ) + if faces.ndim != 2 or faces.shape[1] != 3: + raise ValueError( + f"faces must have shape (T, 3), got {tuple(faces.shape)} in {obj_path}" + ) + if int(faces.shape[0]) < 1: + raise ValueError(f"OBJ has no triangles: {obj_path}") + + arrays = (vertices, faces) + if cache: + _TRIANGLE_CACHE[cache_key] = arrays + return arrays diff --git a/scatteringnet/geometry/surface.py b/scatteringnet/geometry/surface.py new file mode 100644 index 0000000000000000000000000000000000000000..8644f11327230b9849b20958516b5408e5b80f88 --- /dev/null +++ b/scatteringnet/geometry/surface.py @@ -0,0 +1,154 @@ +"""Surface envelope samples from a triangle mesh. + +Area-weighted face darts (larger triangles get more samples). Each +sample is ``(x, y, z, nx, ny, nz)``: position plus the unit normal of +the triangle it sits on. World clouds are cached per +``(mesh_key, n_surface, seed)``. AABB is applied to XYZ only. +""" + +from __future__ import annotations + +import numpy as np +import trimesh +from numpy.typing import NDArray +from trimesh.sample import sample_surface + +from scatteringnet.normalize import apply_normalization + +PointsArray = NDArray[np.float32] +ENVELOPE_XYZ_DIM = 3 +ENVELOPE_FEAT_DIM = 6 + +# Same OBJ + count + seed β†’ same world samples (always 6-D). +_ENVELOPE_CACHE: dict[tuple[str, int, int], PointsArray] = {} + + +def clear_envelope_cache() -> None: + """Drop cached world-space envelopes (tests / long-lived notebooks).""" + _ENVELOPE_CACHE.clear() + + +def _triangle_normals(verts: np.ndarray, faces: np.ndarray) -> NDArray[np.float64]: + v0 = verts[faces[:, 0]] + v1 = verts[faces[:, 1]] + v2 = verts[faces[:, 2]] + cross = np.cross(v1 - v0, v2 - v0) + length = np.linalg.norm(cross, axis=1, keepdims=True) + ok = length[:, 0] > 1e-12 + normals = np.zeros_like(cross) + normals[ok] = cross[ok] / length[ok] + return normals + + +def _pack_xyz_normal(xyz: np.ndarray, normals: np.ndarray) -> PointsArray: + """Concatenate XYZ with unit face normals β†’ ``(N, 6)``.""" + pos = np.asarray(xyz, dtype=np.float32) + nrm = np.asarray(normals, dtype=np.float32) + if pos.shape != nrm.shape or pos.ndim != 2 or pos.shape[1] != 3: + raise ValueError( + f"xyz/normals must be (N, 3), got {tuple(pos.shape)} / {tuple(nrm.shape)}" + ) + length = np.linalg.norm(nrm, axis=1, keepdims=True) + ok = length[:, 0] > 1e-12 + unit = np.zeros_like(nrm) + unit[ok] = nrm[ok] / length[ok] + return np.concatenate([pos, unit], axis=1) + + +def apply_envelope_aabb( + env: np.ndarray, center: np.ndarray, scale: float +) -> PointsArray: + """AABB-normalize XYZ; leave normals as unit directions.""" + arr = np.asarray(env, dtype=np.float32) + if arr.ndim != 2 or arr.shape[1] not in (ENVELOPE_XYZ_DIM, ENVELOPE_FEAT_DIM): + raise ValueError( + f"envelope must be (N, 3) or (N, 6), got {tuple(arr.shape)}" + ) + out = arr.copy() + out[:, :3] = apply_normalization(out[:, :3], center, scale) + return out + + +def undo_envelope_aabb( + env: np.ndarray, center: np.ndarray, scale: float +) -> PointsArray: + """Undo AABB on XYZ only.""" + arr = np.asarray(env, dtype=np.float32) + if arr.ndim != 2 or arr.shape[1] not in (ENVELOPE_XYZ_DIM, ENVELOPE_FEAT_DIM): + raise ValueError( + f"envelope must be (N, 3) or (N, 6), got {tuple(arr.shape)}" + ) + out = arr.copy() + c = np.asarray(center, dtype=np.float32).reshape(3) + out[:, :3] = out[:, :3] * np.float32(scale) + c + return out + + +def project_envelope_dim(env: np.ndarray, dim: int) -> PointsArray: + """Keep XYZ+normal or drop to XYZ for an older checkpoint.""" + arr = np.asarray(env, dtype=np.float32) + want = int(dim) + if want == ENVELOPE_FEAT_DIM: + if arr.ndim != 2 or arr.shape[1] != ENVELOPE_FEAT_DIM: + raise ValueError(f"expected (N, 6) envelope, got {tuple(arr.shape)}") + return arr + if want == ENVELOPE_XYZ_DIM: + if arr.ndim != 2 or arr.shape[1] not in (ENVELOPE_XYZ_DIM, ENVELOPE_FEAT_DIM): + raise ValueError(f"expected (N, 3|6) envelope, got {tuple(arr.shape)}") + return arr[:, :3] + raise ValueError(f"envelope dim must be 3 or 6, got {want}") + + +def _sample_area( + verts: np.ndarray, tris: np.ndarray, count: int, seed: int +) -> PointsArray: + mesh = trimesh.Trimesh(vertices=verts, faces=tris, process=False) + points, face_idx = sample_surface(mesh, count, seed=int(seed)) + nrm = _triangle_normals(verts, tris)[np.asarray(face_idx, dtype=np.int64)] + out = _pack_xyz_normal(points, nrm) + if out.shape != (count, ENVELOPE_FEAT_DIM): + raise ValueError( + f"expected envelope shape {(count, ENVELOPE_FEAT_DIM)}, got {tuple(out.shape)}" + ) + return out + + +def sample_surface_points( + vertices: np.ndarray, + faces: np.ndarray, + n_surface: int, + *, + seed: int = 1, + cache_key: str | None = None, +) -> PointsArray: + """ + Sample ``n_surface`` envelope points as ``(N, 6)`` XYZ + unit normal. + + Face-area weighted: larger triangles receive more darts. No crease + or fold path β€” unused ``envelope_mix`` on old checkpoints is ignored. + """ + count = int(n_surface) + if count < 1: + raise ValueError(f"n_surface must be >= 1, got {count}") + verts = np.asarray(vertices, dtype=np.float64) + tris = np.asarray(faces, dtype=np.int64) + if verts.ndim != 2 or verts.shape[1] != 3: + raise ValueError(f"vertices must have shape (V, 3), got {tuple(verts.shape)}") + if tris.ndim != 2 or tris.shape[1] != 3: + raise ValueError(f"faces must have shape (T, 3), got {tuple(tris.shape)}") + + key: tuple[str, int, int] | None = None + if cache_key is not None: + key = (str(cache_key), count, int(seed)) + cached = _ENVELOPE_CACHE.get(key) + if cached is not None: + return cached + + out = _sample_area(verts, tris, count, int(seed)) + if out.shape != (count, ENVELOPE_FEAT_DIM): + raise ValueError( + f"expected envelope shape {(count, ENVELOPE_FEAT_DIM)}, got {tuple(out.shape)}" + ) + if key is not None: + _ENVELOPE_CACHE[key] = out + return out diff --git a/scatteringnet/geometry/trimesh_util.py b/scatteringnet/geometry/trimesh_util.py new file mode 100644 index 0000000000000000000000000000000000000000..ccb7a340d053c6923e36b7ceddb62c692f19ecb8 --- /dev/null +++ b/scatteringnet/geometry/trimesh_util.py @@ -0,0 +1,19 @@ +"""Shared trimesh flatten. No Open3D β€” safe for train-side mesh_io.""" + +from __future__ import annotations + +from typing import Any + +import trimesh + + +def as_trimesh(mesh: Any) -> trimesh.Trimesh: + """Flatten a Trimesh or a Scene of triangle meshes to one Trimesh.""" + if isinstance(mesh, trimesh.Scene): + geoms = [g for g in mesh.geometry.values() if isinstance(g, trimesh.Trimesh)] + if not geoms: + raise ValueError("Scene contains no triangle meshes") + mesh = trimesh.util.concatenate(geoms) + if not isinstance(mesh, trimesh.Trimesh): + raise TypeError(f"Unsupported mesh type: {type(mesh)!r}") + return mesh diff --git a/scatteringnet/infer_multi_npz.py b/scatteringnet/infer_multi_npz.py new file mode 100644 index 0000000000000000000000000000000000000000..7267b09bd8d04d9e917c8dd197971bc9a3fc6164 --- /dev/null +++ b/scatteringnet/infer_multi_npz.py @@ -0,0 +1,202 @@ +"""Classify occupancy NPZs from ``models//best.pt``. + +Rebuilds ``OccupancyMLP`` or ``OccupancyEncoder`` from the checkpoint +``kind``. Query XYZ (and envelope, when conditioned) use the stored +per-mesh AABB, not a fresh map. Face-token checkpoints are rejected. +""" + +from __future__ import annotations + +from pathlib import Path +from typing import Any + +import numpy as np +import torch + +from scatteringnet.config import OccupancyConfig, as_data_relative, load_config, repo_root +from scatteringnet.data_npz import load_points_labels, load_points_labels_mesh +from scatteringnet.geometry.mesh_io import load_obj_triangles +from scatteringnet.geometry.surface import apply_envelope_aabb, project_envelope_dim, sample_surface_points +from scatteringnet.metrics import occupancy_metrics +from scatteringnet.normalize import apply_normalization +from scatteringnet.occupancy_encoder import ( + OccupancyEncoder, + envelope_dim_from_ckpt, + envelope_seed_from_ckpt, +) +from scatteringnet.occupancy_encoder import CHECKPOINT_KIND as ENCODER_KIND +from scatteringnet.occupancy_mlp import OccupancyMLP +from scatteringnet.occupancy_mlp import CHECKPOINT_KIND as MLP_KIND + + +def resolve_best_pt( + *, + checkpoint: Path | str | None = None, + run_id: str | None = None, + root: Path | None = None, +) -> Path: + """Resolve ``best.pt`` from an explicit path or ``runs//checkpoint_dir.txt``.""" + base = (root or repo_root()).resolve() + if checkpoint is not None: + path = Path(checkpoint) + if not path.is_absolute(): + path = base / path + if not path.is_file(): + raise FileNotFoundError(f"checkpoint not found: {path}") + return path + if not run_id: + raise ValueError("pass checkpoint= or run_id=") + pointer = base / "runs" / str(run_id) / "checkpoint_dir.txt" + if not pointer.is_file(): + raise FileNotFoundError(f"run pointer not found: {pointer}") + rel = pointer.read_text(encoding="utf-8").strip() + path = Path(rel) + if not path.is_absolute(): + path = base / path + best = path / "best.pt" if path.is_dir() else path + if not best.is_file(): + raise FileNotFoundError(f"best.pt not found: {best}") + return best + + +def load_occupancy_model( + ckpt: dict[str, Any], + device: torch.device, +) -> OccupancyMLP | OccupancyEncoder: + """Rebuild the head recorded in ``ckpt['kind']`` and load weights.""" + hidden = int(ckpt["hidden"]) + depth = int(ckpt["depth"]) + kind = str(ckpt["kind"]) + if kind == MLP_KIND: + model: OccupancyMLP | OccupancyEncoder = OccupancyMLP(hidden=hidden, depth=depth) + elif kind == ENCODER_KIND: + latent = int(ckpt["latent_dim"]) if ckpt.get("latent_dim") is not None else hidden + enc = str(ckpt.get("shape_encoder") or "surface").strip().lower() + if enc == "mesh": + raise ValueError( + "face-token occupancy checkpoints (shape_encoder='mesh') " + "are no longer supported" + ) + knn_k = int(ckpt["knn_k"]) if ckpt.get("knn_k") is not None else 0 + knn_local = None + if knn_k > 0 and ckpt.get("knn_local_dim") is not None: + knn_local = int(ckpt["knn_local_dim"]) + model = OccupancyEncoder( + hidden=hidden, + depth=depth, + latent_dim=latent, + shape_encoder=enc, + knn_k=knn_k, + knn_local_dim=knn_local, + envelope_dim=envelope_dim_from_ckpt(ckpt), + ) + else: + raise ValueError(f"unsupported checkpoint kind {kind!r}") + model.load_state_dict(ckpt["state_dict"]) + return model.to(device).eval() + + +def _part_for_npz(ckpt: dict[str, Any], npz_path: Path, data_dir: Path) -> dict[str, Any]: + rel = as_data_relative(npz_path, data_dir) + name = npz_path.name + for part in ckpt.get("parts") or []: + stored = str(part.get("npz", "")) + if stored == rel or Path(stored).name == name: + return part + raise KeyError(f"no AABB part for {npz_path.name} in checkpoint") + + +def infer_npz( + cfg: OccupancyConfig, + *, + checkpoint: Path | str | None = None, + run_id: str | None = None, + npz_path: Path | str | None = None, + root: Path | None = None, +) -> dict[str, float]: + """ + Score one NPZ with ``best.pt``. Returns accuracy / IoU / F1. + """ + ckpt_path = resolve_best_pt(checkpoint=checkpoint, run_id=run_id, root=root) + ckpt = torch.load(ckpt_path, map_location="cpu", weights_only=False) + model = load_occupancy_model(ckpt, cfg.device) + if npz_path is None: + rels = ckpt.get("npz_paths") or [] + if not rels: + raise ValueError("checkpoint has no npz_paths; pass npz_path=") + npz = cfg.data_dir / str(rels[0]) + else: + npz = Path(npz_path) + if not npz.is_absolute(): + npz = cfg.data_dir / npz + part = _part_for_npz(ckpt, npz, cfg.data_dir) + center = np.asarray(part["center"], dtype=np.float32) + scale = float(part["scale"]) + geom = None + shape_id = None + enc = str(ckpt.get("shape_encoder", "none")).strip().lower() + if enc == "mesh": + raise ValueError( + "face-token occupancy checkpoints (shape_encoder='mesh') " + "are no longer supported" + ) + if enc == "surface": + points, labels, mesh_path = load_points_labels_mesh(npz, cfg.data_dir) + vertices, faces = load_obj_triangles(mesh_path) + cache_key = str(mesh_path.resolve()) + n_surface = int(ckpt.get("n_surface") or cfg.n_surface) + world = sample_surface_points( + vertices, + faces, + n_surface, + seed=envelope_seed_from_ckpt(ckpt), + cache_key=cache_key, + ) + arr = apply_envelope_aabb(world, center, scale) + arr = project_envelope_dim(arr, envelope_dim_from_ckpt(ckpt)) + geom = torch.from_numpy(arr).unsqueeze(0) + shape_id = torch.zeros((), dtype=torch.long) + else: + points, labels = load_points_labels(npz) + xyz = torch.from_numpy(apply_normalization(points, center, scale)) + y = torch.from_numpy(np.asarray(labels, dtype=np.float32)).unsqueeze(1) + logits_rows: list[torch.Tensor] = [] + with torch.no_grad(): + for start in range(0, int(xyz.shape[0]), int(cfg.batch_size)): + sl = slice(start, start + int(cfg.batch_size)) + batch_xyz = xyz[sl].to(cfg.device) + if geom is None: + logits_rows.append(model(batch_xyz).cpu()) + else: + b = int(batch_xyz.shape[0]) + geom_b = geom.expand(b, -1, -1).to(cfg.device) + sid = shape_id.expand(b).to(cfg.device) + logits_rows.append(model(batch_xyz, geom_b, sid).cpu()) + logits = torch.cat(logits_rows, dim=0) + scores = occupancy_metrics(logits, y) + print( + f"checkpoint={ckpt_path} npz={npz.name} " + f"acc={scores.accuracy:.4f} iou={scores.inside_iou:.4f} " + f"f1={scores.inside_f1:.4f}" + ) + return { + "accuracy": scores.accuracy, + "inside_iou": scores.inside_iou, + "inside_f1": scores.inside_f1, + } + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser(description="Infer occupancy from models//best.pt") + parser.add_argument("--checkpoint", default=None, help="Path to best.pt") + parser.add_argument("--run-id", default=None, help="runs/ folder name") + parser.add_argument("--npz", default=None, help="NPZ path (data_dir-relative or absolute)") + args = parser.parse_args() + infer_npz( + load_config(), + checkpoint=args.checkpoint, + run_id=args.run_id, + npz_path=args.npz, + ) diff --git a/scatteringnet/metrics.py b/scatteringnet/metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..9c6ca16f12d884585b0558ab4ce026e86903a75f --- /dev/null +++ b/scatteringnet/metrics.py @@ -0,0 +1,156 @@ +"""Occupancy classification metrics from logits vs labels. + +The model emits raw logits (no sigmoid in ``forward``). Metrics apply +sigmoid only at decision time so they stay consistent with +``BCEWithLogitsLoss`` and with inference (threshold ``0.5``). + +Inside (label ``1``) is the product-relevant class: points that belong +in the interior. Precision / recall are therefore reported for that +class only. No extra packages (sklearn, etc.). +""" + +from __future__ import annotations + +from dataclasses import dataclass + +import torch +from torch import Tensor + + +@dataclass(frozen=True) +class OccupancyMetrics: + """Pointwise occupancy scores for one batch or a full set of queries.""" + + accuracy: float + inside_precision: float + inside_recall: float + inside_iou: float + inside_f1: float + + +def _safe_div(numerator: float, denominator: float) -> float: + """Return ``0.0`` when the count in the denominator is zero (no sklearn).""" + if denominator <= 0.0: + return 0.0 + return numerator / denominator + + +def _flatten_pair(logits: Tensor, labels: Tensor) -> tuple[Tensor, Tensor]: + """ + Collapse ``(B, 1)`` or ``(B,)`` logits / labels to a shared 1-D view. + + Last dim of logits is 1 when it comes from ``OccupancyMLP``; labels from + the Dataset match that. A 1-D label vector is accepted so callers do not + have to unsqueeze. + """ + if logits.numel() != labels.numel(): + raise ValueError( + f"logits and labels must have the same number of elements, " + f"got logits={tuple(logits.shape)} labels={tuple(labels.shape)}" + ) + return logits.reshape(-1), labels.reshape(-1) + + +def occupancy_metrics( + logits: Tensor, + labels: Tensor, + *, + threshold: float = 0.5, +) -> OccupancyMetrics: + """ + Accuracy plus inside precision / recall from occupancy logits. + + Parameters + ---------- + logits: + Unnormalized scores, shape ``(B, 1)`` or ``(B,)``. Positive β†’ inside. + labels: + Float ``{0, 1}`` with the same number of elements as ``logits``. + threshold: + Decision cut on ``sigmoid(logit)``. Default ``0.5`` matches inference. + + Returns + ------- + OccupancyMetrics + Scalar floats on CPU (safe to print or average across batches). + """ + # Sigmoid here only: training still uses BCE-with-logits on raw logits. + tp, fp, fn, correct, n = occupancy_counts(logits, labels, threshold=threshold) + return occupancy_metrics_from_counts(tp=tp, fp=fp, fn=fn, correct=correct, n=n) + + +def occupancy_counts( + logits: Tensor, + labels: Tensor, + *, + threshold: float = 0.5, +) -> tuple[float, float, float, float, float]: + """Return ``(tp, fp, fn, correct, n)`` for a micro-average over points.""" + logits_flat, labels_flat = _flatten_pair(logits, labels) + pred_inside = logits_flat.sigmoid() >= threshold + true_inside = labels_flat > 0.5 + pred_f = pred_inside.to(dtype=torch.float32) + true_f = true_inside.to(dtype=torch.float32) + tp = float((pred_f * true_f).sum().item()) + fp = float((pred_f * (1.0 - true_f)).sum().item()) + fn = float(((1.0 - pred_f) * true_f).sum().item()) + correct = float((pred_inside == true_inside).to(dtype=torch.float32).sum().item()) + n = float(pred_inside.numel()) + return tp, fp, fn, correct, n + + +def occupancy_metrics_from_counts( + *, + tp: float, + fp: float, + fn: float, + correct: float, + n: float, +) -> OccupancyMetrics: + """Build metrics from accumulated confusion counts (point micro-average).""" + precision = _safe_div(tp, tp + fp) + recall = _safe_div(tp, tp + fn) + return OccupancyMetrics( + accuracy=_safe_div(correct, n), + inside_precision=precision, + inside_recall=recall, + inside_iou=_safe_div(tp, tp + fp + fn), + inside_f1=_safe_div(2.0 * precision * recall, precision + recall), + ) + + +def accuracy_from_logits( + logits: Tensor, + labels: Tensor, + *, + threshold: float = 0.5, +) -> float: + """Fraction of points whose thresholded sigmoid matches the 0/1 label. + + Parameters + ---------- + logits, labels, threshold: + Same meaning as :func:`occupancy_metrics`. + + Returns + ------- + float + Accuracy in ``[0, 1]``. + """ + return occupancy_metrics(logits, labels, threshold=threshold).accuracy + + +if __name__ == "__main__": + # Deterministic 8-point batch: 4 true insides, 4 true outsides, all correct. + demo_logits = torch.tensor( + [[4.0], [3.0], [2.0], [1.0], [-1.0], [-2.0], [-3.0], [-4.0]] + ) + demo_labels = torch.tensor( + [[1.0], [1.0], [1.0], [1.0], [0.0], [0.0], [0.0], [0.0]] + ) + scores = occupancy_metrics(demo_logits, demo_labels) + print(f"accuracy={scores.accuracy:.4f}") + print(f"inside_precision={scores.inside_precision:.4f}") + print(f"inside_recall={scores.inside_recall:.4f}") + print(f"inside_iou={scores.inside_iou:.4f}") + print(f"inside_f1={scores.inside_f1:.4f}") diff --git a/scatteringnet/normalize.py b/scatteringnet/normalize.py new file mode 100644 index 0000000000000000000000000000000000000000..a3ea0e24b82f8fd4c10c6d01299d823117caa365 --- /dev/null +++ b/scatteringnet/normalize.py @@ -0,0 +1,104 @@ +"""AABB normalization for occupancy XYZ. + +Maps a point cloud into a roughly ``[-1, 1]^3`` cube so the MLP sees +comparable coordinates across differently sized meshes. + + center = midpoint of the axis-aligned bounding box + scale = maximum half-extent (longest AABB side / 2) + +Normalized point: ``(xyz - center) / scale``. +This module does not touch the occupancy model. +""" + +from __future__ import annotations + +import numpy as np +from numpy.typing import NDArray + +PointsArray = NDArray[np.float32] + + +def compute_center_scale(points: np.ndarray) -> tuple[PointsArray, float]: + """ + AABB center and max half-extent for an ``(N, 3)`` point array. + + Parameters + ---------- + points: + Query XYZ, shape ``(N, 3)``, at least one row. + + Returns + ------- + center: + ``float32`` vector of shape ``(3,)``. + scale: + Positive float (max half-extent). Raises if the cloud has no extent. + """ + pts = np.asarray(points, dtype=np.float32) + if pts.ndim != 2 or pts.shape[1] != 3: + raise ValueError(f"points must have shape (N, 3), got {tuple(pts.shape)}") + if pts.shape[0] == 0: + raise ValueError("points must contain at least one row") + + xyz_min = pts.min(axis=0) + xyz_max = pts.max(axis=0) + center = 0.5 * (xyz_min + xyz_max) + half_extents = 0.5 * (xyz_max - xyz_min) + scale = float(np.max(half_extents)) + if scale <= 0.0: + raise ValueError( + "scale must be > 0; all points appear to share the same location" + ) + return center.astype(np.float32, copy=False), scale + + +def apply_normalization( + points: np.ndarray, + center: np.ndarray, + scale: float, +) -> PointsArray: + """ + Return ``(points - center) / scale`` as ``float32 (N, 3)``. + + Parameters + ---------- + points: + Query XYZ, shape ``(N, 3)``. + center: + AABB midpoint, shape ``(3,)``. + scale: + Positive max half-extent. + + Returns + ------- + ndarray + Normalized points, ``float32 (N, 3)``. + """ + if scale <= 0.0: + raise ValueError(f"scale must be > 0, got {scale}") + pts = np.asarray(points, dtype=np.float32) + if pts.ndim != 2 or pts.shape[1] != 3: + raise ValueError(f"points must have shape (N, 3), got {tuple(pts.shape)}") + c = np.asarray(center, dtype=np.float32).reshape(3) + return (pts - c) / np.float32(scale) + + +if __name__ == "__main__": + from scatteringnet.config import load_config + from scatteringnet.data_npz import load_points_labels + + sample = ( + load_config().data_dir + / "exports" + / "dataset_test" + / "sphere__raycast_z_raut_s0.15_inout.npz" + ) + points, _labels = load_points_labels(sample) + center, scale = compute_center_scale(points) + normed = apply_normalization(points, center, scale) + recovered = normed[:3] * np.float32(scale) + center + print(f"file={sample}") + print(f"center={center.tolist()} scale={scale:.6f}") + print(f"normed_min={normed.min(axis=0).tolist()}") + print(f"normed_max={normed.max(axis=0).tolist()}") + print(f"inverse_ok={np.allclose(recovered, points[:3], rtol=1e-5, atol=1e-5)}") diff --git a/scatteringnet/occupancy_encoder.py b/scatteringnet/occupancy_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..053feb3f1e9331703a03aeb3bebcf6e0b9f5adc1 --- /dev/null +++ b/scatteringnet/occupancy_encoder.py @@ -0,0 +1,248 @@ +"""Geometry-conditioned occupancy: query XYZ plus a shape latent. + +``OccupancyMLP`` stays xyz-only. ``shape_encoder: surface`` is the +envelope PointNet over ``(N, 6)`` XYZ + face normal (or ``(N, 3)`` +for older checkpoints). ``knn_k > 0`` adds per-query nearest-neighbor +features (XYZ offset, plus the neighbor normal when the cloud is 6-D). +The face-token head (``shape_encoder: mesh``) was removed. +""" + +from __future__ import annotations + +import torch +import torch.nn as nn +from torch import Tensor + +from scatteringnet.occupancy_mlp import build_mlp + +# Distinct from OccupancyMLP so infer can tell the checkpoint apart. +CHECKPOINT_KIND = "occupancy_encoder" + + +class SurfaceEncoder(nn.Module): + """ + Per-point MLP + max-pool (PointNet) over an envelope cloud. + + Shapes + ------ + envelope: ``(U, N, C)`` unique meshes; ``C`` is 3 (XYZ) or 6 (XYZ+n) + output: ``(U, D)`` one latent per mesh + """ + + def __init__( + self, latent_dim: int = 64, hidden: int = 64, *, in_dim: int = 3 + ) -> None: + super().__init__() + if latent_dim < 1: + raise ValueError(f"latent_dim must be >= 1, got {latent_dim}") + if hidden < 1: + raise ValueError(f"hidden must be >= 1, got {hidden}") + dim = int(in_dim) + if dim not in (3, 6): + raise ValueError(f"in_dim must be 3 or 6, got {dim}") + self.latent_dim = latent_dim + self.hidden = hidden + self.in_dim = dim + self.point_mlp = nn.Sequential( + nn.Linear(dim, hidden), + nn.ReLU(inplace=True), + nn.Linear(hidden, latent_dim), + ) + + def forward(self, envelope: Tensor) -> Tensor: + """Max-pool per-point features β†’ one vector per unique mesh.""" + if envelope.ndim != 3 or envelope.shape[-1] != self.in_dim: + raise ValueError( + f"envelope must have shape (U, N, {self.in_dim}), " + f"got {tuple(envelope.shape)}" + ) + features = self.point_mlp(envelope) + return features.max(dim=1).values + + +def knn_offsets(xyz: Tensor, envelope: Tensor, k: int) -> Tensor: + """ + Offsets from each query to its ``k`` nearest envelope points. + + Distances use XYZ only (AABB Euclidean). ``k`` is clamped to N. + If the cloud is ``(B, N, 6)``, each neighbor is + ``(dx, dy, dz, nx, ny, nz)`` β€” relative position plus that + neighbor's stored normal. Query points have no normal. + + Shapes: ``xyz (B, 3)``, ``envelope (B, N, 3|6)`` β†’ ``(B, k, 3|6)``. + """ + if xyz.ndim != 2 or xyz.shape[-1] != 3: + raise ValueError(f"xyz must have shape (B, 3), got {tuple(xyz.shape)}") + if envelope.ndim != 3 or envelope.shape[-1] not in (3, 6): + raise ValueError( + f"envelope must have shape (B, N, 3 or 6), got {tuple(envelope.shape)}" + ) + if int(xyz.shape[0]) != int(envelope.shape[0]): + raise ValueError( + f"xyz/envelope batch mismatch: {tuple(xyz.shape)} vs {tuple(envelope.shape)}" + ) + n_env = int(envelope.shape[1]) + if n_env < 1: + raise ValueError("envelope length N must be >= 1") + take = min(int(k), n_env) + if take < 1: + raise ValueError(f"k must be >= 1, got {k}") + feat = int(envelope.shape[-1]) + # k-NN is position-only; extras (normals) ride along after the gather. + pos = envelope[..., :3] + dist = torch.linalg.norm(pos - xyz.unsqueeze(1), dim=-1) + idx = dist.topk(take, dim=-1, largest=False).indices + nbrs = torch.gather(envelope, 1, idx.unsqueeze(-1).expand(-1, -1, feat)) + rel_xyz = nbrs[..., :3] - xyz.unsqueeze(1) + if feat == 3: + return rel_xyz + return torch.cat([rel_xyz, nbrs[..., 3:]], dim=-1) + + +# Catalog trains used YAML seed 1. Old best.pt files omit ``seed``. +DEFAULT_ENVELOPE_SEED = 1 + + +def envelope_seed_from_ckpt(ckpt: dict) -> int: + """ + Envelope RNG seed this checkpoint was trained with. + + New trains store ``seed`` on ``best.pt``. Older files omit it; do + not fall back to live YAML (that knob may have changed since train). + """ + raw = ckpt.get("seed") + if raw is None: + return DEFAULT_ENVELOPE_SEED + return int(raw) + + +def envelope_dim_from_ckpt(ckpt: dict) -> int: + """ + Envelope channel count this checkpoint was trained with. + + New trains store ``envelope_dim``. Older XYZ-only ``best.pt`` files + omit it; the first SurfaceEncoder Linear in-features is then 3. + """ + raw = ckpt.get("envelope_dim") + if raw is not None: + dim = int(raw) + if dim not in (3, 6): + raise ValueError(f"envelope_dim must be 3 or 6, got {dim}") + return dim + weight = (ckpt.get("state_dict") or {}).get("surface.point_mlp.0.weight") + if weight is not None: + dim = int(weight.shape[1]) + if dim in (3, 6): + return dim + return 3 + + +class OccupancyEncoder(nn.Module): + """ + Occupancy logits from query XYZ and an envelope code. + + Unique ``shape_id`` values are encoded **once** per batch, then + broadcast. ``knn_k > 0`` concatenates a local envelope code. + + Shapes + ------ + xyz: ``(B, 3)`` + geom: ``(B, N, C)`` envelope; ``C`` is ``envelope_dim`` (3 or 6) + shape_id: ``(B,)`` long + output: ``(B, 1)`` logits + """ + + def __init__( + self, + hidden: int = 64, + depth: int = 4, + latent_dim: int = 64, + *, + shape_encoder: str = "surface", + knn_k: int = 0, + knn_local_dim: int | None = None, + envelope_dim: int = 6, + ) -> None: + super().__init__() + if hidden < 1: + raise ValueError(f"hidden must be >= 1, got {hidden}") + if depth < 1: + raise ValueError(f"depth must be >= 1, got {depth}") + if latent_dim < 1: + raise ValueError(f"latent_dim must be >= 1, got {latent_dim}") + kind = str(shape_encoder).strip().lower() + if kind != "surface": + raise ValueError( + "OccupancyEncoder only supports shape_encoder='surface' " + f"(face-token 'mesh' was removed), got {shape_encoder!r}" + ) + k = int(knn_k) + if k < 0: + raise ValueError(f"knn_k must be >= 0, got {k}") + local_dim = int(knn_local_dim) if knn_local_dim is not None else int(latent_dim) + if k > 0 and local_dim < 1: + raise ValueError(f"knn_local_dim must be >= 1, got {local_dim}") + ed = int(envelope_dim) + if ed not in (3, 6): + raise ValueError(f"envelope_dim must be 3 or 6, got {ed}") + self.hidden = hidden + self.depth = depth + self.latent_dim = latent_dim + self.shape_encoder = kind + self.knn_k = k + self.knn_local_dim = local_dim if k > 0 else 0 + self.envelope_dim = ed + # Name ``surface`` is load-stable for existing envelope checkpoints. + self.surface = SurfaceEncoder(latent_dim=latent_dim, hidden=hidden, in_dim=ed) + self.token_dim = ed + head_in = 3 + latent_dim + if k > 0: + # Same PointNet block as the global envelope, over k neighbor features. + self.local = SurfaceEncoder(latent_dim=local_dim, hidden=hidden, in_dim=ed) + head_in += local_dim + self.head = build_mlp(head_in, hidden, depth) + + def encode_unique(self, geom: Tensor, shape_id: Tensor) -> Tensor: + """ + Encode each distinct ``shape_id`` once and scatter back to ``(B, D)``. + + Parameters + ---------- + geom, shape_id: + Batched envelope clouds and integer mesh ids (same length B). + """ + if shape_id.ndim != 1 or int(shape_id.shape[0]) != int(geom.shape[0]): + raise ValueError( + f"shape_id must be (B,), got {tuple(shape_id.shape)} " + f"for geom {tuple(geom.shape)}" + ) + unique_ids, inverse = torch.unique(shape_id, sorted=True, return_inverse=True) + hits = shape_id.unsqueeze(0) == unique_ids.unsqueeze(1) + first = hits.to(dtype=torch.int64).argmax(dim=1) + z_unique = self.surface(geom[first]) + return z_unique[inverse] + + def forward( + self, + xyz: Tensor, + geom: Tensor, + shape_id: Tensor, + ) -> Tensor: + """``cat(xyz, z_global[, z_local])`` β†’ occupancy logit.""" + if xyz.ndim != 2 or xyz.shape[-1] != 3: + raise ValueError(f"xyz must have shape (B, 3), got {tuple(xyz.shape)}") + if geom.ndim != 3 or geom.shape[-1] != self.token_dim: + raise ValueError( + f"geom must have shape (B, K, {self.token_dim}), got {tuple(geom.shape)}" + ) + if int(xyz.shape[0]) != int(geom.shape[0]): + raise ValueError( + f"xyz/geom batch mismatch: {tuple(xyz.shape)} vs {tuple(geom.shape)}" + ) + ids = shape_id.reshape(-1) + z_shape = self.encode_unique(geom, ids) + pieces = [xyz, z_shape] + if self.knn_k > 0: + rel = knn_offsets(xyz, geom, self.knn_k) + pieces.append(self.local(rel)) + return self.head(torch.cat(pieces, dim=-1)) diff --git a/scatteringnet/occupancy_mlp.py b/scatteringnet/occupancy_mlp.py new file mode 100644 index 0000000000000000000000000000000000000000..24355a02366dfaacc5ec3fe15658881df356c228 --- /dev/null +++ b/scatteringnet/occupancy_mlp.py @@ -0,0 +1,87 @@ +"""Occupancy MLP: raw XYZ β†’ inside/outside logit.""" + +from __future__ import annotations + +import torch +import torch.nn as nn +from torch import Tensor + +# Checkpoint schema tag (not a YAML knob). +CHECKPOINT_KIND = "occupancy_mlp" + + +def build_mlp(in_dim: int, hidden: int, depth: int) -> nn.Sequential: + """Linearβ†’ReLU Γ— ``depth`` then a 1-logit head. Shared by OccupancyMLP.""" + if in_dim < 1: + raise ValueError(f"in_dim must be >= 1, got {in_dim}") + if hidden < 1: + raise ValueError(f"hidden must be >= 1, got {hidden}") + if depth < 1: + raise ValueError(f"depth must be >= 1, got {depth}") + layers: list[nn.Module] = [] + dim = in_dim + for _ in range(depth): + layers.append(nn.Linear(dim, hidden)) + layers.append(nn.ReLU(inplace=True)) + dim = hidden + layers.append(nn.Linear(dim, 1)) + return nn.Sequential(*layers) + + +class OccupancyMLP(nn.Module): + """ + Tiny fully-connected occupancy field. + + Maps a batch of 3D query coordinates to a single unnormalized logit per + point. A later training step will apply ``binary_cross_entropy_with_logits`` + (do not softmax / sigmoid inside ``forward``). + + Shapes + ------ + xyz: ``(B, 3)`` batch of query points (device follows the caller) + output: ``(B, 1)`` logits; positive β†’ inside, negative β†’ outside + + Device + ------ + Parameters live on whatever device the module was moved to + (``.to(device)`` / ``.cuda()``). ``xyz`` must already be on that same + device; this module does not copy tensors. + """ + + def __init__(self, hidden: int = 64, depth: int = 4) -> None: + """ + Build Linearβ†’ReLU blocks then a 1-logit head. + + Parameters + ---------- + hidden: + Channel width of each hidden Linear (must be ``>= 1``). + depth: + Number of hidden Linear+ReLU blocks (must be ``>= 1``). + """ + super().__init__() + self.hidden = hidden + self.depth = depth + # First Linear is 3 β†’ H; remaining blocks are H β†’ H. + self.net = build_mlp(3, hidden, depth) + + def forward(self, xyz: Tensor) -> Tensor: + """ + Evaluate occupancy logits at query coordinates. + + Parameters + ---------- + xyz: + Float tensor of shape ``(B, 3)``. Last dim is Cartesian XYZ. + + Returns + ------- + Tensor + Float tensor of shape ``(B, 1)`` on the same device as ``xyz``. + """ + if xyz.ndim != 2 or xyz.shape[-1] != 3: + raise ValueError( + f"xyz must have shape (B, 3), got {tuple(xyz.shape)}" + ) + # Sequential Linear layers require matching dtype/device with parameters. + return self.net(xyz) diff --git a/scatteringnet/viewer/__init__.py b/scatteringnet/viewer/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e62eda26b2188b4e3da68cdb91f8f55fdae47c07 --- /dev/null +++ b/scatteringnet/viewer/__init__.py @@ -0,0 +1 @@ +"""Three.js inspect helper (HTTP + Job A / Job B).""" diff --git a/scatteringnet/viewer/infer_job.py b/scatteringnet/viewer/infer_job.py new file mode 100644 index 0000000000000000000000000000000000000000..b0cefc8947a268db3b9bfeac0b5d2d90a8f65507 --- /dev/null +++ b/scatteringnet/viewer/infer_job.py @@ -0,0 +1,536 @@ +"""Job A / B: classify uploaded points with a ``models/*/best.pt``. + +Reuses ``load_occupancy_model`` and AABB helpers. Envelope tokens follow +the **checkpoint**, not live ``config.yaml``. Face-token checkpoints +are rejected. Does not modify occupancy train/infer modules. +""" + +from __future__ import annotations + +import base64 +import threading +import time +from pathlib import Path +from typing import Any, TYPE_CHECKING + +if TYPE_CHECKING: + from scatteringnet.config import OccupancyConfig + +import numpy as np + + +from scatteringnet.infer_multi_npz import load_occupancy_model # noqa: E402 +from scatteringnet.geometry.mesh_io import load_obj_triangles # noqa: E402 +from scatteringnet.geometry.surface import ( # noqa: E402 + apply_envelope_aabb, + project_envelope_dim, + sample_surface_points, +) +from scatteringnet.metrics import occupancy_metrics # noqa: E402 +from scatteringnet.normalize import apply_normalization, compute_center_scale # noqa: E402 +from scatteringnet.occupancy_encoder import CHECKPOINT_KIND as ENCODER_KIND # noqa: E402 +from scatteringnet.occupancy_encoder import envelope_dim_from_ckpt # noqa: E402 +from scatteringnet.occupancy_encoder import envelope_seed_from_ckpt # noqa: E402 + +from scatteringnet.viewer.mesh_access import resolve_viewer_mesh # noqa: E402 +from scatteringnet.viewer.model_access import match_checkpoint_part, resolve_viewer_checkpoint # noqa: E402 +from scatteringnet.viewer.obj_fill import triangles_from_obj_text # noqa: E402 + +MAX_POINTS = 2_000_000 + +# One occupancy head in this process. Same checkpoint + device β†’ skip torch.load. +_CACHE_LOCK = threading.Lock() +_MODEL_CACHE: tuple[tuple[str, int, str], Any, dict[str, Any]] | None = None + + +def clear_model_cache() -> None: + """Drop the cached head (tests / swapped GPU).""" + global _MODEL_CACHE + with _CACHE_LOCK: + _MODEL_CACHE = None + + +def _model_cache_key(ckpt_path: Path, device: Any) -> tuple[str, int, str]: + stat = ckpt_path.stat() + return (str(ckpt_path.resolve()), int(stat.st_mtime_ns), str(device)) + + +def load_cached_occupancy( + run_id: str, + models_root: Path, + device: Any, +) -> tuple[Any, dict[str, Any], Path, bool]: + """ + Load ``best.pt`` once per (path, mtime, device). + + Returns ``(model, ckpt, path, cache_hit)``. Switching run_id replaces + the slot so GPU RAM does not keep every inspect checkpoint. + """ + import torch + + ckpt_path = resolve_viewer_checkpoint(run_id, models_root) + key = _model_cache_key(ckpt_path, device) + global _MODEL_CACHE + with _CACHE_LOCK: + slot = _MODEL_CACHE + if slot is not None and slot[0] == key: + return slot[1], slot[2], ckpt_path, True + ckpt = torch.load(ckpt_path, map_location="cpu", weights_only=False) + model = load_occupancy_model(ckpt, device) + with _CACHE_LOCK: + _MODEL_CACHE = (key, model, ckpt) + return model, ckpt, ckpt_path, False + + +def _sync_if_cuda(device) -> None: + """Wait for GPU work so lap times are not just the CPU launch.""" + import torch + + dev = device if hasattr(device, "type") else torch.device(str(device)) + if str(dev.type) == "cuda": + torch.cuda.synchronize() + + +def _round_s(t0: float) -> float: + return round(time.perf_counter() - t0, 3) + + +def pred_from_probs(probs: np.ndarray, threshold: float = 0.5) -> np.ndarray: + """Hard inside labels: 1 iff sigmoid probability is at least ``threshold``.""" + t = float(threshold) + if not np.isfinite(t): + t = 0.5 + t = min(1.0, max(0.0, t)) + return (np.asarray(probs, dtype=np.float32).reshape(-1) >= t).astype(np.uint8) + + +def _sigmoid_probs_and_pred(logits: Any) -> tuple[np.ndarray, np.ndarray]: + """ + Float32 sigmoid of a 1-D logit tensor, plus a 0.5-cut pred for tests/compat. + + The inspect page re-cuts from ``prob_b64``; occupancy train metrics stay at 0.5. + """ + import torch + + probs = ( + torch.sigmoid(logits.reshape(-1)) + .detach() + .cpu() + .numpy() + .astype(np.float32, copy=False) + ) + return probs, pred_from_probs(probs, 0.5) + + +def aabb_for_viewer( + ckpt: dict[str, Any], + *, + npz_name: str, + mesh_path: str, + points: np.ndarray, + data_dir: Path | None, + vertices: np.ndarray | None = None, + uploaded_obj: bool = False, +) -> tuple[np.ndarray, float, str]: + """ + Checkpoint AABB when this catalog NPZ/mesh was trained; else mesh vertices. + + Uploaded OBJ Fill (Job B) always uses this mesh's vertices. Catalog + ``parts`` matched by filename would apply another cube's box. + """ + if not uploaded_obj: + part = match_checkpoint_part(ckpt, npz_name, mesh_path) + if part is not None: + center = np.asarray(part["center"], dtype=np.float32).reshape(3) + scale = float(part["scale"]) + return center, scale, "checkpoint" + if vertices is not None and int(np.asarray(vertices).shape[0]) > 0: + center, scale = compute_center_scale(np.asarray(vertices, dtype=np.float32)) + return center, scale, "mesh" + if mesh_path and data_dir is not None: + try: + resolved = resolve_viewer_mesh(mesh_path, data_dir) + vertices, _faces = load_obj_triangles(resolved) + center, scale = compute_center_scale(vertices) + return center, scale, "mesh" + except (PermissionError, FileNotFoundError, ValueError): + pass + center, scale = compute_center_scale(points) + return center, scale, "points" + + +def _ckpt_shape_encoder(ckpt: dict[str, Any]) -> str: + """ + Encoder stored on ``best.pt``, not live YAML. + + Missing ``shape_encoder`` on an occupancy-encoder checkpoint is the + original envelope head. + """ + raw = str(ckpt.get("shape_encoder") or "").strip().lower() + if raw in ("surface", "none"): + return raw + if raw == "mesh": + raise ValueError( + "face-token occupancy checkpoints (shape_encoder='mesh') " + "are no longer supported" + ) + kind = str(ckpt.get("kind") or "") + if kind == ENCODER_KIND: + return "surface" + return "none" + + +def _runtime_cfg(cfg: OccupancyConfig | None): + """Use the caller cfg (tests) or load repo YAML for device / batch / seed.""" + if cfg is not None: + return cfg + from scatteringnet.config import load_config + + return load_config() + + +def _forward_logits(model, cfg, xyz, geom, shape_id): + """Batched occupancy logits. ``geom`` is envelope ``(1, N, 3)`` or ``None``.""" + import torch + + n = int(xyz.shape[0]) + logits_rows: list[Any] = [] + with torch.no_grad(): + for start in range(0, n, int(cfg.batch_size)): + sl = slice(start, start + int(cfg.batch_size)) + batch_xyz = xyz[sl].to(cfg.device) + if geom is None: + logits_rows.append(model(batch_xyz).cpu()) + else: + b = int(batch_xyz.shape[0]) + geom_b = geom.expand(b, -1, -1).to(cfg.device) + sid = shape_id.reshape(()).expand(b).to(cfg.device) + logits_rows.append(model(batch_xyz, geom_b, sid).cpu()) + return torch.cat(logits_rows, dim=0) + + +def _geom_from_mesh(ckpt, cfg, vertices, faces, center, scale, cache_key: str): + """ + Build the envelope this checkpoint was trained with. + + ``surface`` β†’ ``(1, n_surface, C)`` envelope (C from checkpoint). + ``none`` β†’ no geometry (xyz-only MLP). + Count comes from the checkpoint. Sampling is always area-weighted + (old ``envelope_mix`` on ``best.pt`` is ignored). + """ + import torch + + enc = _ckpt_shape_encoder(ckpt) + seed = envelope_seed_from_ckpt(ckpt) + if enc == "surface": + n_surface = ( + int(ckpt["n_surface"]) if ckpt.get("n_surface") is not None else 1024 + ) + world = sample_surface_points( + vertices, + faces, + n_surface, + seed=seed, + cache_key=cache_key, + ) + env = apply_envelope_aabb(world, center, scale) + env = project_envelope_dim(env, envelope_dim_from_ckpt(ckpt)) + geom = torch.from_numpy(env).unsqueeze(0) + shape_id = torch.zeros(1, dtype=torch.long) + return geom, shape_id + return None, None + + +def decode_points_b64(text: str) -> np.ndarray: + """Little-endian float32 XYZ from standard base64.""" + raw = base64.b64decode(text) + pts = np.frombuffer(raw, dtype=np.float32) + if pts.size % 3 != 0: + raise ValueError("points buffer length is not a multiple of 3") + return np.ascontiguousarray(pts.reshape(-1, 3)) + + +def decode_labels_b64(text: str, n: int) -> np.ndarray: + """uint8 {0,1} labels, length ``n``.""" + raw = base64.b64decode(text) + labels = np.frombuffer(raw, dtype=np.uint8) + if int(labels.shape[0]) != int(n): + raise ValueError( + f"labels length {labels.shape[0]} does not match points {n}" + ) + return np.ascontiguousarray(labels) + + +def infer_uploaded_npz( + *, + run_id: str, + models_root: Path, + data_dir: Path | None, + npz_name: str, + mesh_path: str, + points: np.ndarray, + labels: np.ndarray, + cfg: OccupancyConfig | None = None, +) -> dict[str, Any]: + """ + Classify ``points`` with ``best.pt``. Returns pred bytes (base64) and scores. + + Envelope rebuild follows ``ckpt['shape_encoder']``. + """ + import torch + + n = int(points.shape[0]) + if n < 1: + raise ValueError("no query points") + if n > MAX_POINTS: + raise ValueError(f"too many points ({n}; max {MAX_POINTS})") + if points.ndim != 2 or points.shape[1] != 3: + raise ValueError(f"points must be (N, 3), got {tuple(points.shape)}") + if int(labels.shape[0]) != n: + raise ValueError("labels length does not match points") + + t_all = time.perf_counter() + t0 = time.perf_counter() + cfg = _runtime_cfg(cfg) + model, ckpt, ckpt_path, cache_hit = load_cached_occupancy( + run_id, models_root, cfg.device + ) + _sync_if_cuda(cfg.device) + load_s = _round_s(t0) + center, scale, aabb_src = aabb_for_viewer( + ckpt, + npz_name=npz_name, + mesh_path=mesh_path, + points=points, + data_dir=data_dir, + ) + enc = _ckpt_shape_encoder(ckpt) + geom = None + shape_id = None + parse_s = 0.0 + envelope_s = 0.0 + if enc in ("surface", "mesh"): + if not mesh_path: + raise ValueError("this checkpoint needs a mesh_path for the geometry encoder") + if data_dir is None: + raise ValueError("helper has no data_dir; cannot load the OBJ for the encoder") + t0 = time.perf_counter() + mesh_file = resolve_viewer_mesh(mesh_path, data_dir) + vertices, faces = load_obj_triangles(mesh_file) + parse_s = _round_s(t0) + t0 = time.perf_counter() + geom, shape_id = _geom_from_mesh( + ckpt, cfg, vertices, faces, center, scale, str(mesh_file.resolve()) + ) + envelope_s = _round_s(t0) + if geom is None: + raise ValueError(f"checkpoint shape_encoder={enc!r} built no geometry tokens") + + t0 = time.perf_counter() + xyz = torch.from_numpy(apply_normalization(points, center, scale)) + y = torch.from_numpy(labels.astype(np.float32)).unsqueeze(1) + logits = _forward_logits(model, cfg, xyz, geom, shape_id) + _sync_if_cuda(cfg.device) + forward_s = _round_s(t0) + scores = occupancy_metrics(logits, y) + probs, pred = _sigmoid_probs_and_pred(logits) + timings = { + "parse_obj": parse_s, + "load_model": load_s, + "envelope": envelope_s, + "forward": forward_s, + "server_total": _round_s(t_all), + "load_cached": cache_hit, + "device": str(cfg.device), + } + print( + "viewer infer-npz timings (s) n=%s parse=%.3f load=%.3f envelope=%.3f " + "forward=%.3f total=%.3f cached=%s device=%s" + % ( + n, + parse_s, + load_s, + envelope_s, + forward_s, + timings["server_total"], + cache_hit, + cfg.device, + ), + flush=True, + ) + gt = labels > 0 + pred_bool = pred > 0 + n_fn = int(np.count_nonzero(gt & ~pred_bool)) + n_fp = int(np.count_nonzero(~gt & pred_bool)) + return { + "pred_b64": base64.b64encode(np.ascontiguousarray(pred)).decode("ascii"), + "prob_b64": base64.b64encode(np.ascontiguousarray(probs)).decode("ascii"), + "n": n, + "accuracy": float(scores.accuracy), + "inside_iou": float(scores.inside_iou), + "inside_f1": float(scores.inside_f1), + "kind": str(ckpt.get("kind") or ""), + "shape_encoder": enc, + "aabb": aabb_src, + "n_error": n_fn + n_fp, + "n_fn": n_fn, + "n_fp": n_fp, + "run_id": Path(ckpt_path).parent.name, + "timings": timings, + } + + +def infer_uploaded_obj( + *, + run_id: str, + models_root: Path, + data_dir: Path | None, + obj_name: str, + obj_text: str, + points: np.ndarray, + cfg: OccupancyConfig | None = None, +) -> dict[str, Any]: + """ + Classify fill-lattice XYZ with ``best.pt``. + + Envelope tokens are built from the uploaded OBJ when the + checkpoint is ``surface``. No file labels (Job B): metrics are omitted. + """ + import torch + + n = int(points.shape[0]) + if n < 1: + raise ValueError("no query points; Fill points first") + if n > MAX_POINTS: + raise ValueError(f"too many points ({n}; max {MAX_POINTS})") + if points.ndim != 2 or points.shape[1] != 3: + raise ValueError(f"points must be (N, 3), got {tuple(points.shape)}") + + t_all = time.perf_counter() + t0 = time.perf_counter() + vertices, faces = triangles_from_obj_text(obj_text) + parse_s = _round_s(t0) + t0 = time.perf_counter() + cfg = _runtime_cfg(cfg) + model, ckpt, ckpt_path, cache_hit = load_cached_occupancy( + run_id, models_root, cfg.device + ) + _sync_if_cuda(cfg.device) + load_s = _round_s(t0) + center, scale, aabb_src = aabb_for_viewer( + ckpt, + npz_name="", + mesh_path=str(obj_name or ""), + points=points, + data_dir=data_dir, + vertices=vertices, + uploaded_obj=True, + ) + enc = _ckpt_shape_encoder(ckpt) + t0 = time.perf_counter() + geom, shape_id = _geom_from_mesh( + ckpt, cfg, vertices, faces, center, scale, "upload:" + str(obj_name or "obj") + ) + envelope_s = _round_s(t0) + if enc in ("surface", "mesh") and geom is None: + raise ValueError(f"checkpoint shape_encoder={enc!r} built no geometry tokens") + t0 = time.perf_counter() + xyz = torch.from_numpy(apply_normalization(points, center, scale)) + logits = _forward_logits(model, cfg, xyz, geom, shape_id) + _sync_if_cuda(cfg.device) + forward_s = _round_s(t0) + probs, pred = _sigmoid_probs_and_pred(logits) + n_in = int(np.count_nonzero(pred > 0)) + timings = { + "parse_obj": parse_s, + "load_model": load_s, + "envelope": envelope_s, + "forward": forward_s, + "server_total": _round_s(t_all), + "load_cached": cache_hit, + "device": str(cfg.device), + } + print( + "viewer infer-obj timings (s) n=%s parse=%.3f load=%.3f envelope=%.3f " + "forward=%.3f total=%.3f cached=%s device=%s" + % ( + n, + parse_s, + load_s, + envelope_s, + forward_s, + timings["server_total"], + cache_hit, + cfg.device, + ), + flush=True, + ) + return { + "pred_b64": base64.b64encode(np.ascontiguousarray(pred)).decode("ascii"), + "prob_b64": base64.b64encode(np.ascontiguousarray(probs)).decode("ascii"), + "n": n, + "n_inside": n_in, + "n_outside": n - n_in, + "kind": str(ckpt.get("kind") or ""), + "shape_encoder": enc, + "aabb": aabb_src, + "run_id": Path(ckpt_path).parent.name, + "timings": timings, + } + + +_WARM_OBJ = ( + "v 0 0 0\nv 1 0 0\nv 0 1 0\nv 0 0 1\n" + "f 1 2 3\nf 1 2 4\nf 1 3 4\nf 2 3 4\n" +) + + +def warmup_viewer_helper(models_root: Path) -> None: + """ + Pay first-click costs before the browser opens: torch, CUDA, trimesh + fill, load the INSPECT ``best.pt`` (else newest), tiny envelope + forward. + """ + t0 = time.perf_counter() + print("warmup: torch / CUDA…", flush=True) + import torch + + from scatteringnet.config import load_config + from scatteringnet.viewer.obj_fill import fill_aabb_lattice + + cfg = load_config() + print("warmup: occupancy device=" + str(cfg.device), flush=True) + if str(cfg.device.type) == "cuda": + torch.zeros(1, device=cfg.device) + torch.cuda.synchronize() + print("warmup: CUDA " + str(cfg.device), flush=True) + else: + print("warmup: CPU only", flush=True) + + print("warmup: tiny fill…", flush=True) + verts, faces = triangles_from_obj_text(_WARM_OBJ) + points, _used, _grid = fill_aabb_lattice(verts, 0.40) + if int(points.shape[0]) < 1: + points = np.array([[0.2, 0.2, 0.2], [0.8, 0.2, 0.2]], dtype=np.float32) + + from scatteringnet.viewer.model_access import inspect_run_id, list_viewer_models + + rows = list_viewer_models(models_root) + if not rows: + print("warmup: no models/; skip infer", flush=True) + print("warmup: done in %.1fs" % (time.perf_counter() - t0), flush=True) + return + ids = [str(row["id"]) for row in rows] + wanted = inspect_run_id() + # Warm the inspect alias when that folder exists; otherwise newest. + run_id = wanted if wanted in ids else ids[0] + print("warmup: infer " + run_id + "…", flush=True) + infer_uploaded_obj( + run_id=run_id, + models_root=models_root, + data_dir=None, + obj_name="warmup.obj", + obj_text=_WARM_OBJ, + points=points, + cfg=cfg, + ) + print("warmup: done in %.1fs" % (time.perf_counter() - t0), flush=True) diff --git a/scatteringnet/viewer/mesh_access.py b/scatteringnet/viewer/mesh_access.py new file mode 100644 index 0000000000000000000000000000000000000000..9aa9f2a2d87d6f23ef9d69b93047bed158e1195c --- /dev/null +++ b/scatteringnet/viewer/mesh_access.py @@ -0,0 +1,52 @@ +"""Resolve NPZ ``mesh_path`` for the occupancy viewer helper. + +Uses :func:`data_npz.resolve_mesh_path` and then requires the file to sit +under ``data_dir`` (no arbitrary filesystem reads). +""" + +from __future__ import annotations + +from pathlib import Path + +from scatteringnet.data_npz import resolve_mesh_path + + +def resolve_viewer_mesh(stored: str, data_dir: Path | str) -> Path: + """ + Return an existing ``.obj`` under ``data_dir`` for a stored mesh_path. + + Parameters + ---------- + stored: + Relative or absolute path from the NPZ ``mesh_path`` array. + data_dir: + Dataset root (``config.yaml`` ``data_dir``). + + Returns + ------- + Path + Resolved OBJ path. + + Raises + ------ + ValueError + Empty path or not an OBJ. + FileNotFoundError + Mesh does not exist (from :func:`resolve_mesh_path`). + PermissionError + Resolved path is outside ``data_dir``. + """ + text = str(stored).strip() + if not text: + raise ValueError("mesh_path is empty") + root = Path(data_dir).resolve() + resolved = resolve_mesh_path(text, root) + try: + resolved.relative_to(root) + except ValueError as exc: + raise PermissionError( + f"mesh is outside data_dir: {resolved} (stored={text!r})" + ) from exc + if resolved.suffix.lower() != ".obj": + raise ValueError(f"mesh is not an OBJ: {resolved}") + return resolved diff --git a/scatteringnet/viewer/model_access.py b/scatteringnet/viewer/model_access.py new file mode 100644 index 0000000000000000000000000000000000000000..67e1217e376616c8df0d7aed43505cdcc850dbc0 --- /dev/null +++ b/scatteringnet/viewer/model_access.py @@ -0,0 +1,183 @@ +"""List and resolve ``models//best.pt`` for the occupancy viewer. + +Read-only: paths must stay under the repo ``models/`` folder. +Does not import torch. +""" + +from __future__ import annotations + +from pathlib import Path + +import yaml + +# Repo root: this file lives at src/viewer/model_access.py. +_REPO_ROOT = Path(__file__).resolve().parents[2] +# Pointers (YAML). Occupancy train / infer math does not read these. +INSPECT_POINTER = _REPO_ROOT / "docs" / "inspect_checkpoint.yaml" +HOLDOUT_POINTER = _REPO_ROOT / "docs" / "locked_holdout_objs.yaml" +# Same id as the committed pointer; used if that file is missing. +_INSPECT_FALLBACK = "2026-09-14_07-43-34_prim_extruded_nr45_knn24_n2048_n6" + + +def inspect_run_id() -> str: + """INSPECT alias: ``run_id`` in ``docs/inspect_checkpoint.yaml``. + + Viewers use this as the default ``models//best.pt`` when that file + exists. Occupancy train / infer math is unchanged. + """ + try: + raw = yaml.safe_load(INSPECT_POINTER.read_text(encoding="utf-8")) + except OSError: + return _INSPECT_FALLBACK + if not isinstance(raw, dict): + return _INSPECT_FALLBACK + token = str(raw.get("run_id") or "").strip() + return token[:200] if token else _INSPECT_FALLBACK + + +def locked_holdout_objs() -> list[str]: + """OBJ basenames from ``docs/locked_holdout_objs.yaml``. + + Train / catalog construction does not consult this list. It is the + locked inspect set for humans and for tests. + """ + try: + raw = yaml.safe_load(HOLDOUT_POINTER.read_text(encoding="utf-8")) + except OSError: + return [] + if not isinstance(raw, dict): + return [] + objs = raw.get("objs") or [] + if not isinstance(objs, list): + return [] + names: list[str] = [] + for item in objs: + name = str(item or "").strip() + if name: + names.append(name) + return names + + +def _shape_encoder_from_run(runs_root: Path | None, run_id: str) -> str: + """Read ``shape_encoder`` from ``runs//config.yaml`` (no torch).""" + if runs_root is None: + return "" + path = Path(runs_root) / run_id / "config.yaml" + try: + if not path.is_file(): + return "" + for line in path.read_text(encoding="utf-8").splitlines(): + stripped = line.strip() + if stripped.startswith("#") or not stripped.startswith("shape_encoder:"): + continue + raw = stripped.split(":", 1)[1].split("#", 1)[0].strip().strip("\"'") + kind = raw.lower() + if kind in ("surface", "mesh", "none"): + return kind + return "" + except OSError: + return "" + return "" + + +def list_viewer_models( + models_root: Path | str, + *, + runs_root: Path | str | None = None, +) -> list[dict[str, str | int]]: + """ + Return ``best.pt`` checkpoints one level under ``models_root``. + + Each item: ``id`` (folder name), ``path`` (repo-relative), ``mtime``, + and ``shape_encoder`` when ``runs//config.yaml`` is present. + Newest first. Missing folder β†’ empty list. + """ + root = Path(models_root) + try: + root = root.resolve() + except OSError: + return [] + if not root.is_dir(): + return [] + runs = Path(runs_root) if runs_root is not None else None + items: list[dict[str, str | int]] = [] + for best in root.glob("*/best.pt"): + if not best.is_file(): + continue + run_id = best.parent.name + try: + mtime = int(best.stat().st_mtime) + except OSError: + mtime = 0 + enc = _shape_encoder_from_run(runs, run_id) + row: dict[str, str | int] = { + "id": run_id, + "path": "models/" + run_id + "/best.pt", + "mtime": mtime, + } + if enc: + row["shape_encoder"] = enc + items.append(row) + items.sort(key=lambda row: (-int(row["mtime"]), str(row["id"]))) + return items + + +def resolve_viewer_checkpoint(run_id: str, models_root: Path | str) -> Path: + """ + Return ``models_root / / best.pt``. + + ``run_id`` is a single folder name (no slashes). Also accepts + ``models//best.pt`` and strips it down to the folder name. + + Raises + ------ + ValueError + Empty or unsafe id. + FileNotFoundError + ``best.pt`` is missing. + PermissionError + Resolved path is outside ``models_root``. + """ + raw = str(run_id or "").strip().replace("\\", "/") + if raw.endswith("/best.pt"): + raw = raw[: -len("/best.pt")] + if raw.startswith("models/"): + raw = raw[len("models/") :] + name = raw.strip("/") + if not name or "/" in name or name in (".", "..") or ".." in name: + raise ValueError("invalid model id") + root = Path(models_root).resolve() + best = (root / name / "best.pt").resolve() + try: + best.relative_to(root) + except ValueError as exc: + raise PermissionError( + f"checkpoint is outside models/: {best} (id={run_id!r})" + ) from exc + if not best.is_file(): + raise FileNotFoundError(f"best.pt not found for {name!r}") + return best + + +def match_checkpoint_part(ckpt: dict, npz_name: str, mesh_path: str) -> dict | None: + """ + Catalog AABB from ``ckpt['parts']``. + + NPZ: full stored path or the occupancy filename (those stems are unique). + Mesh: exact stored path only. Basename matches (``cube.obj``) steal a + catalog box for an OOD upload of the same name β€” Job B must not do that. + """ + parts = ckpt.get("parts") or [] + name = Path(str(npz_name or "")).name + mesh = str(mesh_path or "").replace("\\", "/").strip() + if name: + for part in parts: + stored = str(part.get("npz") or "").replace("\\", "/") + if stored == name or Path(stored).name == name: + return part + if mesh: + for part in parts: + stored_m = str(part.get("mesh") or "").replace("\\", "/").strip() + if stored_m and stored_m == mesh: + return part + return None diff --git a/scatteringnet/viewer/obj_fill.py b/scatteringnet/viewer/obj_fill.py new file mode 100644 index 0000000000000000000000000000000000000000..2621ac959eba6962741fb310ddd85e49507aeb50 --- /dev/null +++ b/scatteringnet/viewer/obj_fill.py @@ -0,0 +1,138 @@ +"""Job B: unlabeled AABB lattice inside an uploaded OBJ (no occupancy GT). + +Lattice math matches ``scatter_generation.raycast_scatter`` occupancy grid +(``_padded_bounds`` / ``_uniform_grid_points``) without importing that module +(Open3D / package ``__init__``). Does not import torch and does not raycast-label. +""" + +from __future__ import annotations + +import base64 +import os +import tempfile +from pathlib import Path + +import numpy as np + + +from scatteringnet.geometry.mesh_io import load_obj_triangles # noqa: E402 + +MAX_FILL_POINTS = 200_000 +SPACING_MIN = 0.04 +SPACING_MAX = 0.50 +# Slider 0 = coarse (0.40), 100 = dense (0.05). Training occupancy often uses 0.15. +SPACING_COARSE = 0.40 +SPACING_FINE = 0.05 + + +def spacing_from_slider(value: float) -> float: + """Map UI density 0–100 to lattice spacing (higher = denser = smaller step).""" + t = min(1.0, max(0.0, float(value) / 100.0)) + return float(SPACING_COARSE + (SPACING_FINE - SPACING_COARSE) * t) + + +def clamp_spacing(spacing: float) -> float: + s = float(spacing) + if s < SPACING_MIN or s > SPACING_MAX: + raise ValueError( + f"spacing must be between {SPACING_MIN} and {SPACING_MAX}, got {s}" + ) + return s + + +def _padded_bounds(bounds: np.ndarray, pad: float) -> np.ndarray: + """Expand AABB by ``pad`` on every side (same as occupancy lattice).""" + bounds = np.asarray(bounds, dtype=np.float64) + out = bounds.copy() + out[0] -= pad + out[1] += pad + return out + + +def _uniform_grid_points( + bounds: np.ndarray, + spacing: float, + *, + max_points: int = MAX_FILL_POINTS, +) -> tuple[np.ndarray, float, tuple[int, int, int]]: + """Regular XYZ lattice; coarsen spacing by 1.25 until under ``max_points``.""" + if spacing <= 0: + raise ValueError("point_spacing must be > 0") + bmin = bounds[0].astype(np.float64) + bmax = bounds[1].astype(np.float64) + extents = np.maximum(bmax - bmin, 1e-12) + used = float(spacing) + + def counts(step: float) -> tuple[int, int, int]: + return tuple(max(2, int(np.floor(extents[i] / step)) + 1) for i in range(3)) + + nx, ny, nz = counts(used) + while nx * ny * nz > max_points: + used *= 1.25 + nx, ny, nz = counts(used) + + xs = np.linspace(bmin[0], bmax[0], nx, dtype=np.float64) + ys = np.linspace(bmin[1], bmax[1], ny, dtype=np.float64) + zs = np.linspace(bmin[2], bmax[2], nz, dtype=np.float64) + xx, yy, zz = np.meshgrid(xs, ys, zs, indexing="ij") + points = np.column_stack([xx.ravel(), yy.ravel(), zz.ravel()]) + return points, used, (nx, ny, nz) + + +def triangles_from_obj_text(text: str) -> tuple[np.ndarray, np.ndarray]: + """Parse Wavefront text via a temp file (same loader as training).""" + raw = str(text or "") + if not raw.strip(): + raise ValueError("OBJ is empty") + fd, path = tempfile.mkstemp(suffix=".obj") + try: + os.write(fd, raw.encode("utf-8")) + os.close(fd) + fd = -1 + return load_obj_triangles(path, cache=False) + finally: + if fd >= 0: + try: + os.close(fd) + except OSError: + pass + try: + os.unlink(path) + except OSError: + pass + + +def fill_aabb_lattice( + vertices: np.ndarray, + spacing: float, + *, + max_points: int = MAX_FILL_POINTS, +) -> tuple[np.ndarray, float, tuple[int, int, int]]: + """ + Regular grid in a padded mesh AABB. Pad = spacing (outside shell), no jitter. + """ + step = clamp_spacing(spacing) + verts = np.asarray(vertices, dtype=np.float64) + if verts.ndim != 2 or verts.shape[1] != 3 or verts.shape[0] < 1: + raise ValueError(f"vertices must be (V, 3), got {tuple(verts.shape)}") + bounds = np.stack([verts.min(axis=0), verts.max(axis=0)]) + sample_bounds = _padded_bounds(bounds, step) + points, used, grid = _uniform_grid_points( + sample_bounds, step, max_points=int(max_points) + ) + return np.ascontiguousarray(points, dtype=np.float32), float(used), grid + + +def fill_from_obj_text( + obj_text: str, + spacing: float, +) -> dict: + """Return lattice points (float32) and grid metadata.""" + vertices, _faces = triangles_from_obj_text(obj_text) + points, used, grid = fill_aabb_lattice(vertices, spacing) + return { + "n": int(points.shape[0]), + "used_spacing": used, + "grid": [int(grid[0]), int(grid[1]), int(grid[2])], + "points_b64": base64.b64encode(np.ascontiguousarray(points)).decode("ascii"), + } diff --git a/src/__init__.py b/src/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..7eb40be1805d5e5e5a4a9b4f4afff417b16b44b8 --- /dev/null +++ b/src/__init__.py @@ -0,0 +1 @@ +"""Occupancy fill: catalog training, encoder, and inspect helper.""" diff --git a/src/config.py b/src/config.py new file mode 100644 index 0000000000000000000000000000000000000000..3d39a70a0b4cc93945548e151b37bd1df7f9b8b8 --- /dev/null +++ b/src/config.py @@ -0,0 +1,611 @@ +"""Hybrid config loader for catalog occupancy training. + +Static experiment knobs live in ``config.yaml``. ``device`` is resolved +here from CUDA availability. Training knobs (epochs, lr, batch_size, +optimizer, catalog, val split) are YAML-owned so ``train_multi_npz`` +does not hardcode them. + +This module does not read ``.env`` and does not open NPZ files. +""" + +from __future__ import annotations + +import os +from dataclasses import dataclass +from pathlib import Path +from typing import Any, Mapping, TypedDict + +import torch +import yaml + +# Repo root: src/config.py β†’ parents[1]. +_REPO_ROOT = Path(__file__).resolve().parents[1] +_DEFAULT_YAML = _REPO_ROOT / "config.yaml" + +_REQUIRED_YAML_KEYS = ( + "hidden", + "depth", + "seed", + "data_dir", + "epochs", + "lr", +) + + +class YamlKnobs(TypedDict): + """Subset of OccupancyConfig that is stored in YAML (paths as strings).""" + + hidden: int + depth: int + seed: int + data_dir: str + epochs: int + lr: float + val_fraction: float + latent_dim: int | None + npz_glob: str + npz_paths: tuple[str, ...] + npz_catalog: tuple[tuple[str, int | None], ...] + max_files_per_shape: int | None + run_name: str + checkpoint_metric: str + batch_size: int + optimizer: str + n_surface: int + knn_k: int + knn_local_dim: int | None + shape_encoder: str + # Explicit BCE pos_weight; None when omitted or when auto is set. + pos_weight: float | None + pos_weight_auto: bool + + +def get_device() -> torch.device: + """ + CUDA when a GPU is visible; otherwise CPU. + + Hugging Face CPU Spaces have no CUDA: infer still runs (slower). + ``SCATTERINGNET_DEVICE=cpu`` forces CPU even if a GPU exists. + ``SCATTERINGNET_DEVICE=cuda`` uses CUDA only when ``is_available()``; + otherwise it falls back to CPU (no crash). + + Training scripts should still warn on a long catalog run on CPU. + """ + forced = os.environ.get("SCATTERINGNET_DEVICE", "").strip().lower() + if forced == "cpu": + return torch.device("cpu") + if forced == "cuda": + if torch.cuda.is_available(): + return torch.device("cuda") + return torch.device("cpu") + if torch.cuda.is_available(): + return torch.device("cuda") + return torch.device("cpu") + + +def repo_root() -> Path: + """Git / project root (folder that contains ``src/`` and ``config.yaml``).""" + return _REPO_ROOT + + +def gpu_name(device: torch.device | None = None) -> str | None: + """ + Human GPU name for the run snapshot (``None`` on CPU). + + Uses ``cfg.device`` when given so a forced-CPU train does not stamp a + card that was not used. + """ + dev = device if device is not None else get_device() + if dev.type != "cuda" or not torch.cuda.is_available(): + return None + index = 0 if dev.index is None else int(dev.index) + if index < 0 or index >= torch.cuda.device_count(): + return None + name = str(torch.cuda.get_device_name(index)).strip() + return name or None + + +def _as_positive_int(name: str, value: Any) -> int: + """YAML may yield int or (rarely) str; occupancy dims must be int >= 1.""" + try: + parsed = int(value) + except (TypeError, ValueError) as exc: + raise ValueError(f"{name} must be an integer, got {value!r}") from exc + if parsed < 1: + raise ValueError(f"{name} must be >= 1, got {parsed}") + return parsed + + +def _as_int_in_range(name: str, value: Any, lo: int, hi: int) -> int: + """Inclusive integer range (YAML ``knn_k`` is 0–4096).""" + try: + parsed = int(value) + except (TypeError, ValueError) as exc: + raise ValueError(f"{name} must be an integer, got {value!r}") from exc + if parsed < lo or parsed > hi: + raise ValueError(f"{name} must be in [{lo}, {hi}], got {parsed}") + return parsed + + +def _as_positive_float(name: str, value: Any) -> float: + """Learning-rate style knobs must be a finite float > 0.""" + try: + parsed = float(value) + except (TypeError, ValueError) as exc: + raise ValueError(f"{name} must be a float, got {value!r}") from exc + if parsed <= 0.0 or parsed != parsed: + raise ValueError(f"{name} must be > 0, got {parsed}") + return parsed + + +def _as_pos_weight_pair(raw: Mapping[str, Any]) -> tuple[float | None, bool]: + """ + YAML ``pos_weight``: omit / null β†’ unweighted BCE. + + ``auto`` β†’ compute n_outside / n_inside on the train split at train time. + A finite float > 0 is used as-is (1.0 is unweighted). + """ + if "pos_weight" not in raw: + return None, False + value = raw["pos_weight"] + if value is None or value is False: + return None, False + if isinstance(value, str): + text = value.strip().lower() + if text in ("", "none", "off", "false"): + return None, False + if text == "auto": + return None, True + parsed = _as_positive_float("pos_weight", value) + return parsed, False + + +def _as_open_unit_interval(name: str, value: Any) -> float: + """Hold-out fractions must be in (0, 1) so both splits are non-empty.""" + try: + parsed = float(value) + except (TypeError, ValueError) as exc: + raise ValueError(f"{name} must be a float, got {value!r}") from exc + if parsed != parsed or parsed <= 0.0 or parsed >= 1.0: + raise ValueError(f"{name} must be in (0, 1), got {parsed}") + return parsed + + +def _as_nonempty_path_string(name: str, value: Any) -> str: + if value is None or (isinstance(value, str) and not value.strip()): + raise ValueError(f"{name} must be a non-empty path string in config.yaml") + return str(value).strip() + + +def _as_data_dir_string(value: Any) -> str: + return _as_nonempty_path_string("data_dir", value) + + +def _as_optional_positive_int(name: str, value: Any) -> int | None: + """YAML null β†’ unlimited catalog cap; otherwise int >= 1.""" + if value is None: + return None + return _as_positive_int(name, value) + + +def _as_run_name(value: Any) -> str: + """Optional YAML suffix for ``runs/_/``; empty β†’ ``run``.""" + if value is None: + return "run" + text = str(value).strip() + return text if text else "run" + + +_CHECKPOINT_METRIC_ALIASES = { + "test_acc": "val_acc", + "test_iou": "val_iou", +} + + +def _as_checkpoint_metric(value: Any) -> str: + """Name of the scalar used to decide ``best.pt`` (strict improve).""" + if value is None or (isinstance(value, str) and not value.strip()): + raise ValueError("checkpoint_metric must be a non-empty string") + name = str(value).strip() + return _CHECKPOINT_METRIC_ALIASES.get(name, name) + + +def _as_val_fraction(raw: Mapping[str, Any]) -> float: + """Prefer ``val_fraction``; accept legacy ``test_fraction``.""" + if "val_fraction" in raw: + return _as_open_unit_interval("val_fraction", raw["val_fraction"]) + if "test_fraction" in raw: + return _as_open_unit_interval("test_fraction", raw["test_fraction"]) + raise ValueError("Config YAML missing keys: val_fraction") + + +def _as_optional_latent_dim(raw: Mapping[str, Any]) -> int | None: + """YAML omit / null β†’ use ``hidden`` at train time.""" + if "latent_dim" not in raw or raw["latent_dim"] is None: + return None + return _as_positive_int("latent_dim", raw["latent_dim"]) + + +# Names accepted in config.yaml ``optimizer``. Used by train_multi_npz. +_ALLOWED_OPTIMIZERS = ("adam", "adamw", "sgd") +# ``none`` = OccupancyMLP (xyz only). ``surface`` = envelope encoder. +_ALLOWED_SHAPE_ENCODERS = ("none", "surface") + + +def _as_optimizer(value: Any) -> str: + """Optimizer family for multi-NPZ train; default Adam.""" + if value is None or (isinstance(value, str) and not str(value).strip()): + return "adam" + name = str(value).strip().lower() + if name not in _ALLOWED_OPTIMIZERS: + allowed = ", ".join(_ALLOWED_OPTIMIZERS) + raise ValueError(f"optimizer must be one of {allowed}, got {value!r}") + return name + + +def _as_shape_encoder(value: Any) -> str: + """Occupancy head family; default xyz-only so older YAML still loads.""" + if value is None or (isinstance(value, str) and not str(value).strip()): + return "none" + name = str(value).strip().lower() + if name not in _ALLOWED_SHAPE_ENCODERS: + allowed = ", ".join(_ALLOWED_SHAPE_ENCODERS) + raise ValueError(f"shape_encoder must be one of {allowed}, got {value!r}") + return name + + +def _as_npz_paths(value: Any) -> tuple[str, ...]: + """Explicit NPZ list relative to data_dir (empty β†’ use glob).""" + if value is None: + return () + if isinstance(value, str): + item = value.strip() + return (item,) if item else () + if not isinstance(value, list): + raise ValueError(f"npz_paths must be a list of strings or null, got {type(value).__name__}") + out: list[str] = [] + for i, raw in enumerate(value): + text = _as_nonempty_path_string(f"npz_paths[{i}]", raw) + out.append(text) + return tuple(out) + + +def _as_npz_catalog(value: Any) -> tuple[tuple[str, int | None], ...]: + """Union of globs; optional ``max_shapes`` is unique meshes per glob.""" + if value is None: + return () + if not isinstance(value, list): + raise ValueError( + f"npz_catalog must be a list or null, got {type(value).__name__}" + ) + out: list[tuple[str, int | None]] = [] + for i, raw in enumerate(value): + if isinstance(raw, str): + glob_s = _as_nonempty_path_string(f"npz_catalog[{i}]", raw) + out.append((glob_s, None)) + continue + if not isinstance(raw, Mapping): + raise ValueError( + f"npz_catalog[{i}] must be a glob string or mapping, " + f"got {type(raw).__name__}" + ) + if "glob" not in raw: + raise ValueError(f"npz_catalog[{i}] missing glob") + glob_s = _as_nonempty_path_string(f"npz_catalog[{i}].glob", raw["glob"]) + max_shapes: int | None = None + if "max_shapes" in raw and raw["max_shapes"] is not None: + max_shapes = _as_positive_int( + f"npz_catalog[{i}].max_shapes", raw["max_shapes"] + ) + out.append((glob_s, max_shapes)) + return tuple(out) + + +def as_repo_relative(path: Path | str, *, root: Path | None = None) -> str: + """ + POSIX string relative to the git repo when ``path`` is inside it. + + Already-relative inputs are returned as POSIX. Absolute paths on another + drive (the dataset disk) cannot be repo-relative and stay absolute POSIX. + """ + text = str(path).strip() + if not text: + return text + parsed = Path(text) + if not parsed.is_absolute(): + return parsed.as_posix() + base = (root or _REPO_ROOT).resolve() + try: + return parsed.resolve().relative_to(base).as_posix() + except ValueError: + return parsed.resolve().as_posix() + + +def as_data_relative(path: Path | str, data_dir: Path | str) -> str: + """ + POSIX string relative to ``data_dir`` (``exports/...``, not ``E:/...``). + + Already-relative inputs are returned as POSIX. Paths outside ``data_dir`` + (unit-test temp trees) fall back to absolute POSIX. + """ + text = str(path).strip() + if not text: + return text + parsed = Path(text) + if not parsed.is_absolute(): + return parsed.as_posix() + root = Path(data_dir).expanduser().resolve() + resolved = parsed.expanduser().resolve() + try: + return resolved.relative_to(root).as_posix() + except ValueError: + return resolved.as_posix() + + +def load_yaml_knobs(path: Path) -> YamlKnobs: + """ + Read YAML settings. Does not check that data_dir exists on disk. + + Parameters + ---------- + path: + Path to ``config.yaml``. + + Returns + ------- + YamlKnobs + Typed dict of experiment knobs (paths still strings). + """ + if not path.is_file(): + raise FileNotFoundError(f"Config YAML not found: {path}") + raw = yaml.safe_load(path.read_text(encoding="utf-8")) + if not isinstance(raw, Mapping): + raise ValueError(f"Config YAML must be a mapping, got {type(raw).__name__}") + missing = [k for k in _REQUIRED_YAML_KEYS if k not in raw] + if missing: + raise ValueError(f"Config YAML missing keys: {', '.join(missing)}") + pos_weight, pos_weight_auto = _as_pos_weight_pair(raw) + return { + "hidden": _as_positive_int("hidden", raw["hidden"]), + "depth": _as_positive_int("depth", raw["depth"]), + "seed": _as_positive_int("seed", raw["seed"]), + "data_dir": _as_data_dir_string(raw["data_dir"]), + "epochs": _as_positive_int("epochs", raw["epochs"]), + "lr": _as_positive_float("lr", raw["lr"]), + "val_fraction": _as_val_fraction(raw), + "latent_dim": _as_optional_latent_dim(raw), + "npz_glob": ( + _as_nonempty_path_string("npz_glob", raw["npz_glob"]) + if "npz_glob" in raw + else "exports/dataset/*.npz" + ), + "npz_paths": _as_npz_paths(raw.get("npz_paths")), + "npz_catalog": _as_npz_catalog(raw.get("npz_catalog")), + "max_files_per_shape": ( + _as_optional_positive_int("max_files_per_shape", raw["max_files_per_shape"]) + if "max_files_per_shape" in raw + else 2 + ), + "run_name": ( + _as_run_name(raw["run_name"]) if "run_name" in raw else "run" + ), + "checkpoint_metric": ( + _as_checkpoint_metric(raw["checkpoint_metric"]) + if "checkpoint_metric" in raw + else "val_acc" + ), + "batch_size": ( + _as_positive_int("batch_size", raw["batch_size"]) + if "batch_size" in raw + else 1024 + ), + "optimizer": ( + _as_optimizer(raw["optimizer"]) if "optimizer" in raw else "adam" + ), + "n_surface": ( + _as_positive_int("n_surface", raw["n_surface"]) + if "n_surface" in raw + else 1024 + ), + "knn_k": ( + _as_int_in_range("knn_k", raw["knn_k"], 0, 4096) + if "knn_k" in raw + else 0 + ), + "knn_local_dim": ( + _as_positive_int("knn_local_dim", raw["knn_local_dim"]) + if "knn_local_dim" in raw + else None + ), + "shape_encoder": ( + _as_shape_encoder(raw["shape_encoder"]) + if "shape_encoder" in raw + else "none" + ), + "pos_weight": pos_weight, + "pos_weight_auto": pos_weight_auto, + } + + +def _warn_missing_data_dir(data_dir: Path, yaml_path: Path) -> None: + """Print a terminal hint so the user can fix config.yaml (no .env involved).""" + print( + "\n" + "Dataset folder not found.\n" + f" Looked for: {data_dir}\n" + "\n" + "Update `data_dir` in config.yaml to the folder that contains " + "`exports/` and `meshes/`.\n" + f" Config file: {yaml_path}\n" + ) + + +def require_data_dir(data_dir: Path, *, yaml_path: Path) -> None: + """Validate the dataset root before training or NPZ loading.""" + if data_dir.is_dir(): + return + _warn_missing_data_dir(data_dir, yaml_path) + raise FileNotFoundError( + f"Dataset directory does not exist: {data_dir}. " + f"Set data_dir in {yaml_path}." + ) + + +@dataclass(frozen=True) +class OccupancyConfig: + """Resolved experiment settings from YAML plus detected device.""" + + data_dir: Path + device: torch.device + hidden: int + depth: int + seed: int + epochs: int + lr: float + # Fraction of catalog **meshes** held out as val (selection split, not a locked test). + val_fraction: float + # Catalog knobs (optional in YAML; omitted keys keep these defaults). + npz_glob: str = "exports/dataset/*.npz" + npz_paths: tuple[str, ...] = () + # ``(glob, max_shapes)`` rows. Empty β†’ use ``npz_glob``. ``max_shapes`` + # None keeps every mesh that glob hits (after ``max_files_per_shape``). + npz_catalog: tuple[tuple[str, int | None], ...] = () + max_files_per_shape: int | None = 2 + # Suffix for runs/_/ (device stays runtime-only). + run_name: str = "run" + # Which logged scalar selects best.pt (strict improve). + checkpoint_metric: str = "val_acc" + # Encoder latent width; None β†’ use ``hidden`` at train / infer time. + latent_dim: int | None = None + # Mini-batch size and optimizer family (train_multi_npz). + batch_size: int = 1024 + optimizer: str = "adam" + # Envelope sample count (YAML). Used when ``shape_encoder`` is ``surface``. + n_surface: int = 1024 + # 0 = global envelope z only. >0 = that many nearest envelope dots per query. + knn_k: int = 0 + # Width of z_local; None β†’ same as occupancy latent_dim / hidden. + knn_local_dim: int | None = None + # ``none`` keeps OccupancyMLP; ``surface`` uses the envelope PointNet. + shape_encoder: str = "none" + # BCE inside-class weight. None + auto=False = unweighted (legacy). + pos_weight: float | None = None + pos_weight_auto: bool = False + + +def load_config( + yaml_path: Path | None = None, + *, + require_existing_data_dir: bool = True, +) -> OccupancyConfig: + """ + Compose OccupancyConfig from ``config.yaml`` (not from ``.env``). + + When ``require_existing_data_dir`` is True (default), a missing folder + prints a short instruction and then raises FileNotFoundError β€” used for + training and data loading. + + Parameters + ---------- + yaml_path: + Config file; default is repo-root ``config.yaml``. + require_existing_data_dir: + If True, refuse to return a config whose ``data_dir`` is missing. + + Returns + ------- + OccupancyConfig + YAML knobs plus detected ``device``. + """ + cfg_path = yaml_path or _DEFAULT_YAML + knobs = load_yaml_knobs(cfg_path) + data_dir = Path(knobs["data_dir"]) + if require_existing_data_dir: + require_data_dir(data_dir, yaml_path=cfg_path) + return OccupancyConfig( + data_dir=data_dir, + device=get_device(), + hidden=knobs["hidden"], + depth=knobs["depth"], + seed=knobs["seed"], + epochs=knobs["epochs"], + lr=knobs["lr"], + val_fraction=knobs["val_fraction"], + latent_dim=knobs["latent_dim"], + npz_glob=knobs["npz_glob"], + npz_paths=knobs["npz_paths"], + npz_catalog=knobs["npz_catalog"], + max_files_per_shape=knobs["max_files_per_shape"], + run_name=knobs["run_name"], + checkpoint_metric=knobs["checkpoint_metric"], + batch_size=knobs["batch_size"], + optimizer=knobs["optimizer"], + n_surface=knobs["n_surface"], + knn_k=knobs["knn_k"], + knn_local_dim=knobs["knn_local_dim"], + shape_encoder=knobs["shape_encoder"], + pos_weight=knobs["pos_weight"], + pos_weight_auto=knobs["pos_weight_auto"], + ) + + +def encoder_latent_dim(cfg: OccupancyConfig) -> int: + """OccupancyEncoder ``z`` width: YAML ``latent_dim`` or ``hidden``.""" + if cfg.latent_dim is None: + return int(cfg.hidden) + return int(cfg.latent_dim) + + +def encoder_knn_local_dim(cfg: OccupancyConfig) -> int: + """Local envelope code width: YAML ``knn_local_dim`` or global latent.""" + if cfg.knn_local_dim is None: + return encoder_latent_dim(cfg) + return int(cfg.knn_local_dim) + + +def format_config(cfg: OccupancyConfig) -> str: + """ + Pretty-print for CLI smoke checks. + + Parameters + ---------- + cfg: + Resolved config. + + Returns + ------- + str + Multi-line ``OccupancyConfig(...)`` dump. + """ + return ( + f"OccupancyConfig(\n" + f" data_dir={cfg.data_dir}\n" + f" device={cfg.device}\n" + f" gpu={gpu_name(cfg.device)}\n" + f" hidden={cfg.hidden}\n" + f" depth={cfg.depth}\n" + f" seed={cfg.seed}\n" + f" epochs={cfg.epochs}\n" + f" lr={cfg.lr}\n" + f" val_fraction={cfg.val_fraction}\n" + f" latent_dim={cfg.latent_dim}\n" + f" npz_glob={cfg.npz_glob}\n" + f" npz_paths={list(cfg.npz_paths)}\n" + f" npz_catalog={list(cfg.npz_catalog)}\n" + f" max_files_per_shape={cfg.max_files_per_shape}\n" + f" run_name={cfg.run_name}\n" + f" checkpoint_metric={cfg.checkpoint_metric}\n" + f" batch_size={cfg.batch_size}\n" + f" optimizer={cfg.optimizer}\n" + f" n_surface={cfg.n_surface}\n" + f" knn_k={cfg.knn_k}\n" + f" knn_local_dim={cfg.knn_local_dim}\n" + f" shape_encoder={cfg.shape_encoder}\n" + f" pos_weight={cfg.pos_weight}\n" + f" pos_weight_auto={cfg.pos_weight_auto}\n" + f")" + ) + + +if __name__ == "__main__": + print(format_config(load_config())) diff --git a/src/data_npz.py b/src/data_npz.py new file mode 100644 index 0000000000000000000000000000000000000000..1e738cc8bb6c69273c21a7d6cbec2f407f775497 --- /dev/null +++ b/src/data_npz.py @@ -0,0 +1,460 @@ +"""Load occupancy query points and labels from scatter NPZ files. + +:func:`load_points_labels` reads **one** file. A catalog resolver lists +many NPZs (glob or explicit paths) without training. + +``load_points_labels`` still returns only ``points`` and ``labels``. +``load_points_labels_mesh`` also resolves ``mesh_path`` against ``data_dir``. +""" + +from __future__ import annotations + +import glob as globlib +import random +from pathlib import Path +from typing import Sequence + +import numpy as np +from numpy.typing import NDArray + +# Labels are converted here (not deferred to the Dataset) so every caller gets +# the same dtypes: float32 XYZ and float32 {0, 1} occupancy. +PointsArray = NDArray[np.float32] +LabelsArray = NDArray[np.float32] + + +def load_points_labels(path: Path) -> tuple[PointsArray, LabelsArray]: + """ + Read query coordinates and inside/outside labels from one NPZ file. + + Parameters + ---------- + path: + Path to a ``.npz`` with arrays ``points`` ``(N, 3)`` and + ``labels`` ``(N,)`` (typically uint8 0/1). + + Returns + ------- + points: + ``float32`` array of shape ``(N, 3)``. + labels: + ``float32`` array of shape ``(N,)`` with values in ``{0.0, 1.0}`` + (0 = outside, 1 = inside). + """ + npz_path = Path(path) + if not npz_path.is_file(): + raise FileNotFoundError(f"NPZ not found: {npz_path}") + + # allow_pickle=False: we only need numeric arrays, not object payloads. + with np.load(npz_path, allow_pickle=False) as raw: + files = set(raw.files) + if "points" not in files or "labels" not in files: + raise KeyError( + f"NPZ must contain 'points' and 'labels', got {sorted(files)} " + f"in {npz_path}" + ) + points = np.asarray(raw["points"]) + labels = np.asarray(raw["labels"]) + + if points.ndim != 2 or points.shape[1] != 3: + raise ValueError( + f"points must have shape (N, 3), got {tuple(points.shape)} in {npz_path}" + ) + n = int(points.shape[0]) + if labels.shape != (n,): + raise ValueError( + f"labels must have shape (N,) with N={n}, got {tuple(labels.shape)} " + f"in {npz_path}" + ) + + points_f32 = np.asarray(points, dtype=np.float32) + labels_f32 = np.asarray(labels, dtype=np.float32) + unique = np.unique(labels_f32) + if not np.all((unique == 0.0) | (unique == 1.0)): + raise ValueError( + f"labels must be in {{0, 1}}, got unique={unique.tolist()} in {npz_path}" + ) + return points_f32, labels_f32 + + +def count_npz_points(path: Path | str) -> int: + """ + Query count in one NPZ without keeping the arrays. + + Catalog construct uses this so ``len(part)`` / ``n_points`` do not + require loading every lattice into RAM. + """ + npz_path = Path(path) + if not npz_path.is_file(): + raise FileNotFoundError(f"NPZ not found: {npz_path}") + with np.load(npz_path, allow_pickle=False) as raw: + files = set(raw.files) + if "points" not in files or "labels" not in files: + raise KeyError( + f"NPZ must contain 'points' and 'labels', got {sorted(files)} " + f"in {npz_path}" + ) + n = int(np.asarray(raw["points"]).shape[0]) + n_y = int(np.asarray(raw["labels"]).shape[0]) + if n_y != n: + raise ValueError( + f"labels must have shape (N,) with N={n}, got N={n_y} in {npz_path}" + ) + return n + + +def count_npz_labels(path: Path | str) -> tuple[int, int]: + """ + Outside / inside counts in one NPZ without keeping the point cloud. + + Used for ``pos_weight: auto`` (n_outside / n_inside on the train split). + """ + npz_path = Path(path) + if not npz_path.is_file(): + raise FileNotFoundError(f"NPZ not found: {npz_path}") + with np.load(npz_path, allow_pickle=False) as raw: + if "labels" not in set(raw.files): + raise KeyError(f"NPZ must contain 'labels', got {sorted(raw.files)} in {npz_path}") + labels = np.asarray(raw["labels"]).reshape(-1) + # Labels are 0/1 occupancy; nonzero is inside. + n_in = int(np.count_nonzero(labels)) + n_out = int(labels.size) - n_in + return n_out, n_in + + +def read_npz_mesh_path(path: Path | str) -> str: + """ + Read the stored ``mesh_path`` string from one occupancy NPZ. + + Step 2 writes a ``data_dir``-relative POSIX path (for example + ``meshes/Primitives/Sphere/sphere_r0p5_sa16_sh16.obj``). + + Parameters + ---------- + path: + Occupancy ``.npz`` that contains ``mesh_path``. + + Returns + ------- + str + Stored path string (relative or absolute). Not resolved here. + """ + npz_path = Path(path) + if not npz_path.is_file(): + raise FileNotFoundError(f"NPZ not found: {npz_path}") + # allow_pickle=True: some exports store a 0-d string / object array. + with np.load(npz_path, allow_pickle=True) as raw: + if "mesh_path" not in raw.files: + raise KeyError(f"NPZ has no 'mesh_path' in {npz_path}") + stored = str(np.asarray(raw["mesh_path"]).item()).strip() + if not stored: + raise ValueError(f"mesh_path is empty in {npz_path}") + return stored + + +def resolve_mesh_path(stored: str, data_dir: Path | str) -> Path: + """ + Resolve a stored ``mesh_path`` against ``data_dir``. + + Relative entries are joined to ``data_dir``. Absolute entries are + used as-is. Missing files raise ``FileNotFoundError``. + + Parameters + ---------- + stored: + Value from :func:`read_npz_mesh_path`. + data_dir: + Dataset root (``config.yaml`` ``data_dir``). + + Returns + ------- + Path + Existing resolved mesh file. + """ + text = str(stored).strip() + if not text: + raise ValueError("mesh_path is empty") + item = Path(text) + root = Path(data_dir) + resolved = item if item.is_absolute() else (root / item) + resolved = resolved.resolve() + if not resolved.is_file(): + raise FileNotFoundError(f"mesh not found: {resolved} (stored={text!r})") + return resolved + + +def load_points_labels_mesh( + path: Path | str, + data_dir: Path | str, +) -> tuple[PointsArray, LabelsArray, Path]: + """ + Read occupancy arrays and resolve the source OBJ. + + Keeps :func:`load_points_labels` unchanged (xyz + labels only). + + Parameters + ---------- + path: + Occupancy ``.npz`` with ``points``, ``labels``, and ``mesh_path``. + data_dir: + Root used to resolve a relative ``mesh_path``. + + Returns + ------- + points, labels, mesh_path: + Same arrays as :func:`load_points_labels`, plus the existing OBJ. + """ + npz_path = Path(path) + points, labels = load_points_labels(npz_path) + stored = read_npz_mesh_path(npz_path) + mesh_path = resolve_mesh_path(stored, data_dir) + return points, labels, mesh_path + + +def _is_combo_npz(path: Path) -> bool: + """True when the filename looks like a combo dump (excluded from the catalog).""" + return "combo" in path.name.lower() + + +def shape_key(path: Path) -> str: + """ + Group NPZs that belong to the same mesh. + + Uses the stem before ``__`` (dataset_builder tag), else the full stem. + + Parameters + ---------- + path: + NPZ path. + + Returns + ------- + str + Stable key for ``max_files_per_shape``. + """ + stem = Path(path).stem + if "__" in stem: + return stem.split("__", 1)[0] + return stem + + +def _cap_per_shape( + paths: Sequence[Path], + max_files_per_shape: int | None, +) -> list[Path]: + """Keep at most ``max_files_per_shape`` files per :func:`shape_key` (sorted order).""" + if max_files_per_shape is None: + return list(paths) + if max_files_per_shape < 1: + raise ValueError(f"max_files_per_shape must be >= 1 or None, got {max_files_per_shape}") + counts: dict[str, int] = {} + out: list[Path] = [] + for path in paths: + key = shape_key(path) + taken = counts.get(key, 0) + if taken >= max_files_per_shape: + continue + counts[key] = taken + 1 + out.append(path) + return out + + +def _glob_npz(root: Path, pattern: str) -> list[Path]: + """Match ``pattern`` under ``root`` (``*`` / ``**``).""" + full = str(root / pattern) + recursive = "**" in pattern.replace("\\", "/") + found = globlib.glob(full, recursive=recursive) + return [Path(p).resolve() for p in found if Path(p).is_file()] + + +def _is_parameterized_stem(path: Path) -> bool: + """ + Maya catalog names are ``family_param_...``. Varied one-off stems + (``Cone.obj`` β†’ ``Cone__occupancy.npz``) have no ``_`` in the shape key + and must not ride along when Windows glob is case-insensitive. + """ + return "_" in shape_key(path) + + +def _subsample_shapes( + paths: Sequence[Path], + max_shapes: int, + seed: int, +) -> list[Path]: + """Keep NPZs for at most ``max_shapes`` unique :func:`shape_key` values.""" + if max_shapes < 1: + raise ValueError(f"max_shapes must be >= 1, got {max_shapes}") + keys: list[str] = [] + seen: set[str] = set() + for path in paths: + key = shape_key(path) + if key in seen: + continue + seen.add(key) + keys.append(key) + if max_shapes >= len(keys): + return list(paths) + # Sort then sample so the same seed always picks the same meshes. + chosen_keys = set(random.Random(int(seed)).sample(sorted(keys), max_shapes)) + return [path for path in paths if shape_key(path) in chosen_keys] + + +def resolve_npz_catalog( + data_dir: Path | str, + *, + npz_glob: str = "exports/dataset/*.npz", + npz_paths: Sequence[str | Path] | None = None, + npz_catalog: Sequence[tuple[str, int | None]] | None = None, + max_files_per_shape: int | None = 2, + exclude_combo: bool = True, + seed: int = 1, +) -> list[Path]: + """ + Resolve occupancy NPZ paths under ``data_dir`` (no point loading). + + Priority: explicit ``npz_paths``, else ``npz_catalog`` (union of globs), + else ``npz_glob``. Relative entries are joined to ``data_dir``. + Missing files in ``npz_paths`` raise ``FileNotFoundError``. + + Parameters + ---------- + data_dir: + Dataset root (``config.yaml`` ``data_dir``). + npz_glob: + Single glob relative to ``data_dir`` (``*`` and ``**`` allowed). + npz_paths: + Explicit relative or absolute NPZ paths. Empty / None β†’ use glob(s). + npz_catalog: + ``(glob, max_shapes)`` rows. ``max_shapes`` is unique meshes after + the per-shape file cap; ``None`` keeps every mesh the glob hits. + max_files_per_shape: + Cap per :func:`shape_key` after sort. ``None`` = no cap. + exclude_combo: + Drop filenames containing ``combo``. + seed: + RNG for ``max_shapes`` subsampling (YAML ``seed``). + + Returns + ------- + list[Path] + Sorted existing ``.npz`` files. + """ + root = Path(data_dir) + chosen: list[Path] + if npz_paths: + chosen = [] + for raw in npz_paths: + item = Path(raw) + resolved = item if item.is_absolute() else (root / item) + if not resolved.is_file(): + raise FileNotFoundError(f"NPZ not found: {resolved}") + chosen.append(resolved.resolve()) + elif npz_catalog: + # Union in YAML order. Same file from two globs is kept once. + seen: set[Path] = set() + chosen = [] + for pattern, max_shapes in npz_catalog: + hit = _glob_npz(root, str(pattern)) + if exclude_combo: + hit = [p for p in hit if not _is_combo_npz(p)] + hit = [ + p + for p in hit + if p.suffix.lower() == ".npz" and _is_parameterized_stem(p) + ] + hit = sorted(hit) + hit = _cap_per_shape(hit, max_files_per_shape) + if max_shapes is not None: + hit = _subsample_shapes(hit, int(max_shapes), int(seed)) + for path in hit: + if path in seen: + continue + seen.add(path) + chosen.append(path) + else: + chosen = _glob_npz(root, npz_glob) + + npz_only = [p for p in chosen if p.suffix.lower() == ".npz"] + if exclude_combo: + npz_only = [p for p in npz_only if not _is_combo_npz(p)] + if npz_catalog and not npz_paths: + # Already capped per glob; keep YAML union order (not a global sort). + capped = npz_only + else: + npz_only = sorted(npz_only) + capped = _cap_per_shape(npz_only, max_files_per_shape) + if not capped: + raise FileNotFoundError( + f"No occupancy NPZ files matched under {root} " + f"(glob={npz_glob!r}, catalog={bool(npz_catalog)}, " + f"explicit={bool(npz_paths)})" + ) + return capped + + +def _summarize(points: PointsArray, labels: LabelsArray) -> str: + n = int(points.shape[0]) + inside = float(labels.mean()) if n else float("nan") + xyz_min = points.min(axis=0) if n else np.full(3, np.nan, dtype=np.float32) + xyz_max = points.max(axis=0) if n else np.full(3, np.nan, dtype=np.float32) + return ( + f"N={n}\n" + f"inside_fraction={inside:.6f}\n" + f"xyz_min={xyz_min.tolist()}\n" + f"xyz_max={xyz_max.tolist()}" + ) + + +# Default smoke-check file from the v2 plan (dataset_test sphere). +_SAMPLE_RELATIVE = Path("exports") / "dataset_test" / "sphere__raycast_z_raut_s0.15_inout.npz" + + +if __name__ == "__main__": + import sys + + from scatteringnet.config import load_config + + cfg = load_config() + if "--catalog" in sys.argv: + paths = resolve_npz_catalog( + cfg.data_dir, + npz_glob=cfg.npz_glob, + npz_paths=cfg.npz_paths or None, + npz_catalog=cfg.npz_catalog or None, + max_files_per_shape=cfg.max_files_per_shape, + seed=cfg.seed, + ) + print(f"data_dir={cfg.data_dir}") + print(f"npz_glob={cfg.npz_glob}") + print(f"npz_catalog={list(cfg.npz_catalog)}") + print(f"max_files_per_shape={cfg.max_files_per_shape}") + print(f"files={len(paths)}") + # Load a few files only β€” the full catalog can be thousands of NPZs. + preview = paths[:3] + n_all = 0 + n_in = 0 + for path in preview: + pts, labs = load_points_labels(path) + n = int(pts.shape[0]) + inside = int((labs == 1.0).sum()) + n_all += n + n_in += inside + print( + f" {path.name} N={n} inside={inside} outside={n - inside}" + ) + print( + f"preview_files={len(preview)} preview_N={n_all} " + f"preview_inside={n_in} preview_outside={n_all - n_in}" + ) + from scatteringnet.dataset import OccupancyMultiNpzDataset, make_dataloader + + ds = OccupancyMultiNpzDataset(paths) + loader = make_dataloader(ds.parts[0], batch_size=8, shuffle=False) + xyz, y = next(iter(loader)) + print(f"files={len(ds)} n_points={ds.n_points} parts={len(ds.parts)}") + print(f"batch xyz={tuple(xyz.shape)} y={tuple(y.shape)}") + else: + sample = cfg.data_dir / _SAMPLE_RELATIVE + pts, labs = load_points_labels(sample) + print(f"file={sample}") + print(_summarize(pts, labs)) diff --git a/src/geometry/__init__.py b/src/geometry/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..fd84e5cfbf2762a4dff82672dd530c2c8e4f17de --- /dev/null +++ b/src/geometry/__init__.py @@ -0,0 +1,10 @@ +"""Training-side geometry: OBJ triangles and envelope samples.""" + +from scatteringnet.geometry.mesh_io import clear_triangle_cache +from scatteringnet.geometry.surface import clear_envelope_cache + + +def clear_geometry_caches() -> None: + """Drop OBJ triangle and envelope process caches.""" + clear_triangle_cache() + clear_envelope_cache() diff --git a/src/geometry/mesh_io.py b/src/geometry/mesh_io.py new file mode 100644 index 0000000000000000000000000000000000000000..7b3489ba3a46ca0d357e1e32facb22f708b12706 --- /dev/null +++ b/src/geometry/mesh_io.py @@ -0,0 +1,80 @@ +"""Load OBJ triangle meshes for the NPZ ↔ mesh join. + +This module only returns ``vertices (V, 3)`` and ``faces (T, 3)``. +It does **not** sample the envelope. +""" + +from __future__ import annotations + +from pathlib import Path +import numpy as np +import trimesh +from numpy.typing import NDArray + +from scatteringnet.geometry.trimesh_util import as_trimesh + +VerticesArray = NDArray[np.float32] +FacesArray = NDArray[np.int32] + +# Same resolved OBJ can back several NPZs (``max_files_per_shape``). Cache +# the triangle arrays so catalog load does not re-parse the file. +_TRIANGLE_CACHE: dict[str, tuple[VerticesArray, FacesArray]] = {} + + +def clear_triangle_cache() -> None: + """Drop cached OBJ arrays (tests / long-lived notebooks).""" + _TRIANGLE_CACHE.clear() + + +def load_obj_triangles( + path: Path | str, + *, + cache: bool = True, +) -> tuple[VerticesArray, FacesArray]: + """ + Read one OBJ as triangle vertices and face indices. + + Parameters + ---------- + path: + Existing ``.obj`` file. + cache: + Reuse arrays for the same resolved path (catalog load). + + Returns + ------- + vertices: + ``float32`` array of shape ``(V, 3)``. + faces: + ``int32`` array of shape ``(T, 3)`` (0-based vertex indices). + """ + obj_path = Path(path) + if not obj_path.is_file(): + raise FileNotFoundError(f"OBJ not found: {obj_path}") + if obj_path.suffix.lower() != ".obj": + raise ValueError(f"expected .obj, got {obj_path.suffix!r} ({obj_path})") + + cache_key = str(obj_path.resolve()) + if cache and cache_key in _TRIANGLE_CACHE: + return _TRIANGLE_CACHE[cache_key] + + # process=False keeps the authored vertices; we only need the join. + loaded = trimesh.load(obj_path, force=None, process=False) + mesh = as_trimesh(loaded) + vertices = np.asarray(mesh.vertices, dtype=np.float32) + faces = np.asarray(mesh.faces, dtype=np.int32) + if vertices.ndim != 2 or vertices.shape[1] != 3: + raise ValueError( + f"vertices must have shape (V, 3), got {tuple(vertices.shape)} in {obj_path}" + ) + if faces.ndim != 2 or faces.shape[1] != 3: + raise ValueError( + f"faces must have shape (T, 3), got {tuple(faces.shape)} in {obj_path}" + ) + if int(faces.shape[0]) < 1: + raise ValueError(f"OBJ has no triangles: {obj_path}") + + arrays = (vertices, faces) + if cache: + _TRIANGLE_CACHE[cache_key] = arrays + return arrays diff --git a/src/geometry/surface.py b/src/geometry/surface.py new file mode 100644 index 0000000000000000000000000000000000000000..8644f11327230b9849b20958516b5408e5b80f88 --- /dev/null +++ b/src/geometry/surface.py @@ -0,0 +1,154 @@ +"""Surface envelope samples from a triangle mesh. + +Area-weighted face darts (larger triangles get more samples). Each +sample is ``(x, y, z, nx, ny, nz)``: position plus the unit normal of +the triangle it sits on. World clouds are cached per +``(mesh_key, n_surface, seed)``. AABB is applied to XYZ only. +""" + +from __future__ import annotations + +import numpy as np +import trimesh +from numpy.typing import NDArray +from trimesh.sample import sample_surface + +from scatteringnet.normalize import apply_normalization + +PointsArray = NDArray[np.float32] +ENVELOPE_XYZ_DIM = 3 +ENVELOPE_FEAT_DIM = 6 + +# Same OBJ + count + seed β†’ same world samples (always 6-D). +_ENVELOPE_CACHE: dict[tuple[str, int, int], PointsArray] = {} + + +def clear_envelope_cache() -> None: + """Drop cached world-space envelopes (tests / long-lived notebooks).""" + _ENVELOPE_CACHE.clear() + + +def _triangle_normals(verts: np.ndarray, faces: np.ndarray) -> NDArray[np.float64]: + v0 = verts[faces[:, 0]] + v1 = verts[faces[:, 1]] + v2 = verts[faces[:, 2]] + cross = np.cross(v1 - v0, v2 - v0) + length = np.linalg.norm(cross, axis=1, keepdims=True) + ok = length[:, 0] > 1e-12 + normals = np.zeros_like(cross) + normals[ok] = cross[ok] / length[ok] + return normals + + +def _pack_xyz_normal(xyz: np.ndarray, normals: np.ndarray) -> PointsArray: + """Concatenate XYZ with unit face normals β†’ ``(N, 6)``.""" + pos = np.asarray(xyz, dtype=np.float32) + nrm = np.asarray(normals, dtype=np.float32) + if pos.shape != nrm.shape or pos.ndim != 2 or pos.shape[1] != 3: + raise ValueError( + f"xyz/normals must be (N, 3), got {tuple(pos.shape)} / {tuple(nrm.shape)}" + ) + length = np.linalg.norm(nrm, axis=1, keepdims=True) + ok = length[:, 0] > 1e-12 + unit = np.zeros_like(nrm) + unit[ok] = nrm[ok] / length[ok] + return np.concatenate([pos, unit], axis=1) + + +def apply_envelope_aabb( + env: np.ndarray, center: np.ndarray, scale: float +) -> PointsArray: + """AABB-normalize XYZ; leave normals as unit directions.""" + arr = np.asarray(env, dtype=np.float32) + if arr.ndim != 2 or arr.shape[1] not in (ENVELOPE_XYZ_DIM, ENVELOPE_FEAT_DIM): + raise ValueError( + f"envelope must be (N, 3) or (N, 6), got {tuple(arr.shape)}" + ) + out = arr.copy() + out[:, :3] = apply_normalization(out[:, :3], center, scale) + return out + + +def undo_envelope_aabb( + env: np.ndarray, center: np.ndarray, scale: float +) -> PointsArray: + """Undo AABB on XYZ only.""" + arr = np.asarray(env, dtype=np.float32) + if arr.ndim != 2 or arr.shape[1] not in (ENVELOPE_XYZ_DIM, ENVELOPE_FEAT_DIM): + raise ValueError( + f"envelope must be (N, 3) or (N, 6), got {tuple(arr.shape)}" + ) + out = arr.copy() + c = np.asarray(center, dtype=np.float32).reshape(3) + out[:, :3] = out[:, :3] * np.float32(scale) + c + return out + + +def project_envelope_dim(env: np.ndarray, dim: int) -> PointsArray: + """Keep XYZ+normal or drop to XYZ for an older checkpoint.""" + arr = np.asarray(env, dtype=np.float32) + want = int(dim) + if want == ENVELOPE_FEAT_DIM: + if arr.ndim != 2 or arr.shape[1] != ENVELOPE_FEAT_DIM: + raise ValueError(f"expected (N, 6) envelope, got {tuple(arr.shape)}") + return arr + if want == ENVELOPE_XYZ_DIM: + if arr.ndim != 2 or arr.shape[1] not in (ENVELOPE_XYZ_DIM, ENVELOPE_FEAT_DIM): + raise ValueError(f"expected (N, 3|6) envelope, got {tuple(arr.shape)}") + return arr[:, :3] + raise ValueError(f"envelope dim must be 3 or 6, got {want}") + + +def _sample_area( + verts: np.ndarray, tris: np.ndarray, count: int, seed: int +) -> PointsArray: + mesh = trimesh.Trimesh(vertices=verts, faces=tris, process=False) + points, face_idx = sample_surface(mesh, count, seed=int(seed)) + nrm = _triangle_normals(verts, tris)[np.asarray(face_idx, dtype=np.int64)] + out = _pack_xyz_normal(points, nrm) + if out.shape != (count, ENVELOPE_FEAT_DIM): + raise ValueError( + f"expected envelope shape {(count, ENVELOPE_FEAT_DIM)}, got {tuple(out.shape)}" + ) + return out + + +def sample_surface_points( + vertices: np.ndarray, + faces: np.ndarray, + n_surface: int, + *, + seed: int = 1, + cache_key: str | None = None, +) -> PointsArray: + """ + Sample ``n_surface`` envelope points as ``(N, 6)`` XYZ + unit normal. + + Face-area weighted: larger triangles receive more darts. No crease + or fold path β€” unused ``envelope_mix`` on old checkpoints is ignored. + """ + count = int(n_surface) + if count < 1: + raise ValueError(f"n_surface must be >= 1, got {count}") + verts = np.asarray(vertices, dtype=np.float64) + tris = np.asarray(faces, dtype=np.int64) + if verts.ndim != 2 or verts.shape[1] != 3: + raise ValueError(f"vertices must have shape (V, 3), got {tuple(verts.shape)}") + if tris.ndim != 2 or tris.shape[1] != 3: + raise ValueError(f"faces must have shape (T, 3), got {tuple(tris.shape)}") + + key: tuple[str, int, int] | None = None + if cache_key is not None: + key = (str(cache_key), count, int(seed)) + cached = _ENVELOPE_CACHE.get(key) + if cached is not None: + return cached + + out = _sample_area(verts, tris, count, int(seed)) + if out.shape != (count, ENVELOPE_FEAT_DIM): + raise ValueError( + f"expected envelope shape {(count, ENVELOPE_FEAT_DIM)}, got {tuple(out.shape)}" + ) + if key is not None: + _ENVELOPE_CACHE[key] = out + return out diff --git a/src/geometry/trimesh_util.py b/src/geometry/trimesh_util.py new file mode 100644 index 0000000000000000000000000000000000000000..ccb7a340d053c6923e36b7ceddb62c692f19ecb8 --- /dev/null +++ b/src/geometry/trimesh_util.py @@ -0,0 +1,19 @@ +"""Shared trimesh flatten. No Open3D β€” safe for train-side mesh_io.""" + +from __future__ import annotations + +from typing import Any + +import trimesh + + +def as_trimesh(mesh: Any) -> trimesh.Trimesh: + """Flatten a Trimesh or a Scene of triangle meshes to one Trimesh.""" + if isinstance(mesh, trimesh.Scene): + geoms = [g for g in mesh.geometry.values() if isinstance(g, trimesh.Trimesh)] + if not geoms: + raise ValueError("Scene contains no triangle meshes") + mesh = trimesh.util.concatenate(geoms) + if not isinstance(mesh, trimesh.Trimesh): + raise TypeError(f"Unsupported mesh type: {type(mesh)!r}") + return mesh diff --git a/src/gradio/app.py b/src/gradio/app.py new file mode 100644 index 0000000000000000000000000000000000000000..b6cd72eaa3d5a9067315d428b5a29144e00f7c9c --- /dev/null +++ b/src/gradio/app.py @@ -0,0 +1,503 @@ +"""Gradio occupancy demo: upload an OBJ, fill the AABB, classify with best.pt. + +The 3D pane is a persistent Babylon canvas (``orbit.js`` via ``launch(js=)``). +Gradio ``Model3D`` remounts WebGL on every new GLB β€” that is the gray flash +and the camera snap. ``gr.HTML`` scripts are stripped, so the viewer JS is +not put in the HTML component. The inspect tool remains ``src/viewer``. + +Launch (conda env scatteringNet): + + python src/gradio/app.py +""" + +from __future__ import annotations + +import html +import os +import sys +import tempfile +from pathlib import Path + +# ZeroGPU scans for ``@spaces.GPU`` at import. Import this before torch. +try: + import spaces +except ImportError: + spaces = None + +import numpy as np + +# Pip package first β€” this directory must not shadow it. +import gradio as gr + +_HERE = Path(__file__).resolve().parent +# Repo root (Space ships ``scatteringnet/`` here; local uses pip ``-e .``). +_ROOT = _HERE.parents[1] +if str(_ROOT) not in sys.path: + sys.path.insert(0, str(_ROOT)) +if str(_HERE) not in sys.path: + sys.path.insert(0, str(_HERE)) + +from figure import ( # noqa: E402 + DEFAULT_MESH_OPACITY, + DEFAULT_POINT_SIZE, + empty_figure, + occupancy_figure, +) +from pipeline import ( # noqa: E402 + DEFAULT_CUT, + DEFAULT_DENSITY, + apply_cut, + default_run_id, + fill_and_infer, +) +from scatteringnet.viewer.obj_fill import triangles_from_obj_text # noqa: E402 + +# Shipped sample meshes in examples/ (not in the training catalog). +_EXAMPLE_OBJS = ( + "Obese.obj", + "horse.obj", + "Player.obj", + "dog.obj", + "Helix_bend.obj", + "TorusX3_box.obj", +) + +# Inspect viewer: scene.background = 0x2a2a32 (not near-black). +_BG_HEX = "#2a2a32" +_ORBIT_JS = _HERE / "orbit.js" +# Host never goes in event outputs β€” remounting it would flash again. +_ORBIT_HOST = ( + f'
' + '' + '
Drop an OBJ file here
' + "
" +) +# Shared by Blocks (Hugging Face finds ``demo``) and local launch(). +_ORBIT_CSS = ( + "#sn-cmd-wrap { display: none !important; }" + "#sn-obj-file-slot {" + " position: fixed !important; left: -100vw !important; top: 0 !important;" + " width: 8px !important; height: 8px !important; overflow: hidden !important;" + " opacity: 0 !important; pointer-events: none !important;" + "}" + "#sn-obj-file-slot .file-preview-holder, #sn-obj-file-slot table," + " #sn-obj-file-slot .filename { display: none !important; }" + "#sn-orbit-host.sn-drop-over { outline: 2px solid #f5a623; outline-offset: -2px; }" + "#sn-drop-hint {" + " position: absolute; left: 0; right: 0; top: 12px;" + " text-align: center; pointer-events: none; z-index: 2;" + " font-size: 13px; line-height: 1.3; color: #aabbcc;" + " text-shadow: 0 1px 2px #1a1a20;" + "}" +) + + +def _make_blocks() -> gr.Blocks: + """Gradio 6 moved ``js`` / ``css`` off Blocks onto ``launch()``.""" + return gr.Blocks(title="scatteringNet occupancy") + + +def _patch_launch(blocks: gr.Blocks) -> None: + """HF calls ``demo.launch()`` with no kwargs; inject orbit JS / CSS.""" + orbit_js = _ORBIT_JS.read_text(encoding="utf-8") + orig = blocks.launch + + def launch(*args, **kwargs): + kwargs.setdefault("js", orbit_js) + kwargs.setdefault("css", _ORBIT_CSS) + kwargs.setdefault("allowed_paths", [str(_glb_dir())]) + try: + return orig(*args, **kwargs) + except TypeError: + kwargs.pop("js", None) + return orig(*args, **kwargs) + + blocks.launch = launch + + +def _glb_dir() -> Path: + """Same folder ``figure._export_glb`` writes; ``allowed_paths`` serves it.""" + folder = Path(tempfile.gettempdir()) / "scatteringnet_gradio" + folder.mkdir(parents=True, exist_ok=True) + return folder + + +def _cmd_html( + glb: str, + reset_n: int, + point_size: float = DEFAULT_POINT_SIZE, + wire: bool = False, + force_default: bool = False, +) -> str: + """Tiny span orbit.js polls. Remounting this does not remount the canvas. + + ``force_default`` (last field) is **Reset view** only. Sample / drop / + Load OBJ / Run keep the current orbit β€” same as the original load fix. + """ + path = html.escape(str(Path(glb).resolve())) + px = max(1, min(24, int(round(float(point_size))))) + w = 1 if wire else 0 + d = 1 if force_default else 0 + return f'{path}|{int(reset_n)}|{px}|{w}|{d}' + + +def _held_path(file_obj) -> str | None: + """File widget / UploadButton β†’ filepath string for later Run / restyle.""" + if file_obj is None: + return None + return str(getattr(file_obj, "name", None) or file_obj) + + +def _read_upload(file_obj) -> tuple[str, str]: + """Gradio File (filepath) β†’ (basename, OBJ text).""" + if file_obj is None: + raise ValueError("upload an .obj") + path = Path(getattr(file_obj, "name", None) or str(file_obj)) + if path.suffix.lower() != ".obj": + raise ValueError("file must be an .obj") + return path.name, path.read_text(encoding="utf-8", errors="replace") + + +def _status_md(result: dict, *, cut: float, shown_out: bool) -> str: + """Short inspect-style summary under the view.""" + t = result.get("timings") or {} + extra = " (outside hidden)" if not shown_out else "" + return ( + f"**{result.get('obj_name', 'OBJ')}** Β· `{result.get('run_id', '')}` \n" + f"Inside **{result['n_inside']}** / outside **{result['n_outside']}** " + f"(cut {cut:.2f}){extra} \n" + f"Lattice `{result['n']}` pts Β· spacing `{result['used_spacing']:.3f}` Β· " + f"grid `{result['grid']}` \n" + f"Times s: fill+forward total `{t.get('server_total', 0):.3f}` Β· " + f"envelope `{t.get('envelope', 0):.3f}` Β· run `{t.get('forward', 0):.3f}` Β· " + f"device `{t.get('device', '?')}`" + ) + + +def _view(result: dict | None, cut: float, show_outside: bool, mesh_opacity: float): + """Rebuild the occupancy GLB. The canvas stays; only the cmd span changes.""" + if not result: + return str(empty_figure()), "Upload an OBJ, then **Run model**." + pred, n_in, n_out = apply_cut(result["probs"], float(cut)) + view = dict(result) + view["pred"] = pred + view["n_inside"] = n_in + view["n_outside"] = n_out + glb = occupancy_figure( + view["vertices"], + view["faces"], + view["points"], + pred, + show_outside=bool(show_outside), + title=str(view.get("obj_name") or "occupancy fill"), + mesh_opacity=float(mesh_opacity), + ) + return str(glb), _status_md(view, cut=float(cut), shown_out=bool(show_outside)) + + +def run_model(file_obj, obj_held, density, cut, show_outside, mesh_opacity): + """Fill + infer with the single default checkpoint (no model picker).""" + src = file_obj or obj_held + held = _held_path(src) + try: + obj_name, obj_text = _read_upload(src) + result = fill_and_infer( + obj_text, + obj_name=obj_name, + run_id=default_run_id(), + density=float(density), + ) + glb, md = _view(result, float(cut), bool(show_outside), float(mesh_opacity)) + return result, glb, md, None, held + except Exception as exc: + return None, str(empty_figure(str(exc))), f"**Error:** {exc}", None, held + + +def preview_upload(file_obj, mesh_opacity=DEFAULT_MESH_OPACITY): + """Show the uploaded mesh on the floor immediately (no occupancy yet).""" + if file_obj is None: + return None, str(empty_figure()), "Upload an OBJ, then click **Run model**." + try: + obj_name, obj_text = _read_upload(file_obj) + vertices, faces = triangles_from_obj_text(obj_text) + glb = occupancy_figure( + vertices, + faces, + np.zeros((0, 3), dtype=np.float32), + np.zeros((0,), dtype=np.uint8), + title=obj_name, + mesh_opacity=float(mesh_opacity), + ) + return ( + None, + str(glb), + f"**{obj_name}** is on the floor. Click **Run model** to classify the fill.", + ) + except Exception as exc: + return None, str(empty_figure(str(exc))), f"**Error:** {exc}" + + +def accept_obj( + file_obj, + mesh_opacity=DEFAULT_MESH_OPACITY, + reset_n=0, + current_glb=None, +): + """ + Load an OBJ, then clear the File box so the next drop can fire change. + + Keep ``reset_n`` so orbit.js does not reframe. Clearing the picker + (``file_obj is None``) must not write a new empty GLB β€” that changed + the cmd path and made the camera reload. **Reset view** is the only bump. + """ + keep = int(reset_n or 0) + if file_obj is None: + glb = current_glb or str(empty_figure()) + yield None, str(glb), keep, "Upload an OBJ, then click **Run model**.", None, None + return + held = _held_path(file_obj) + state, glb, md = preview_upload(file_obj, mesh_opacity) + yield state, str(glb), keep, md, None, held + + +def restyle(result, file_obj, obj_held, cut, show_outside, mesh_opacity): + """Rebuild GLB after cut / opacity / outside. Reset counter is not touched.""" + try: + if result: + return _view(result, float(cut), bool(show_outside), float(mesh_opacity)) + src = file_obj or obj_held + if src is None: + return str(empty_figure()), "Upload an OBJ, then **Run model**." + _, glb, md = preview_upload(src, mesh_opacity) + return str(glb), md + except Exception as exc: + return str(empty_figure(str(exc))), f"**Error:** {exc}" + + +def bump_reset(glb_held, reset_n, point_size, wire): + """Reset view: same GLB, increment token so JS frames 45Β° / 70Β°.""" + nxt = int(reset_n or 0) + 1 + path = glb_held or str(empty_figure()) + return nxt, _cmd_html(path, nxt, point_size, wire, force_default=True) + + +def accept_obj_ui( + file_obj, + mesh_opacity=DEFAULT_MESH_OPACITY, + reset_n=0, + point_size=DEFAULT_POINT_SIZE, + wire=False, + glb_held=None, +): + """accept_obj plus the #sn-cmd span orbit.js reads.""" + for state, glb, nxt, md, cleared, held in accept_obj( + file_obj, mesh_opacity, reset_n, current_glb=glb_held + ): + yield state, glb, nxt, _cmd_html(glb, nxt, point_size, wire), md, cleared, held + + +def _maybe_gpu(fn): + """ZeroGPU requires at least one ``@spaces.GPU`` at startup. No-op locally.""" + if spaces is None: + return fn + return spaces.GPU(duration=30)(fn) + + +@_maybe_gpu +def run_model_ui( + file_obj, obj_held, density, cut, show_outside, mesh_opacity, reset_n, point_size, wire +): + """Run: new GLB, same reset token, so the canvas keeps the orbit.""" + result, glb, md, cleared, held = run_model( + file_obj, obj_held, density, cut, show_outside, mesh_opacity + ) + return result, glb, _cmd_html(glb, int(reset_n or 0), point_size, wire), md, cleared, held + + +def restyle_ui( + result, file_obj, obj_held, cut, show_outside, mesh_opacity, reset_n, point_size, wire +): + """Restyle: new GLB, same reset token.""" + glb, md = restyle(result, file_obj, obj_held, cut, show_outside, mesh_opacity) + return glb, _cmd_html(glb, int(reset_n or 0), point_size, wire), md + + +def set_view_flags(glb_held, reset_n, point_size, wire): + """Dot size / wireframe β€” no new GLB, orbit.js applies both.""" + path = glb_held or str(empty_figure()) + return _cmd_html(path, int(reset_n or 0), point_size, wire) + + +def build_demo() -> gr.Blocks: + """One-page Gradio occupancy fill.""" + # Only list files that are actually on disk (partial checkout still launches). + example_list = [ + str(path) + for name in _EXAMPLE_OBJS + if (path := _HERE / "examples" / name).is_file() + ] or None + empty_glb = empty_figure() + + with _make_blocks() as demo: + gr.Markdown( + """ +# scatteringNet β€” occupancy fill + +A trained occupancy network that fills a 3D mesh with points it labels +**inside** the solid (not outside). + +[Code](https://github.com/PerryGu/scatteringNet) + +[Video](https://youtu.be/vU45O0Mu0o4) + """.strip() + ) + state = gr.State(None) + obj_held = gr.State(None) + glb_held = gr.State(str(Path(empty_glb).resolve())) + reset_n = gr.State(0) + with gr.Row(): + with gr.Column(scale=1): + load_btn = gr.Button("Load OBJ", elem_id="sn-load-obj") + density_in = gr.Slider( + 0, + 100, + value=DEFAULT_DENSITY, + step=1, + label="Density (higher = denser lattice)", + ) + cut_in = gr.Slider( + 0.05, + 0.95, + value=DEFAULT_CUT, + step=0.01, + label="Inside cut", + ) + opacity_in = gr.Slider( + 0, + 100, + value=DEFAULT_MESH_OPACITY, + step=1, + label="Mesh opacity", + info="Default 50% so occupancy points show through the shell.", + ) + psize_in = gr.Slider( + 1, + 24, + value=DEFAULT_POINT_SIZE, + step=1, + label="Dot size", + info="Occupancy points in the 3D view (pixels).", + ) + with gr.Row(): + show_out = gr.Checkbox(label="Show outside points", value=False) + wire_in = gr.Checkbox(label="Wireframe", value=False) + with gr.Row(): + run_btn = gr.Button("Run model", variant="primary") + reset_btn = gr.Button("Reset view") + status = gr.Markdown( + "Drop an OBJ on the **3D view**, or click **Load OBJ**. " + "**Run model** fills the volume." + ) + with gr.Column(scale=2): + # Never list this HTML in outputs β€” a new value remounts the canvas. + gr.HTML(value=_ORBIT_HOST, elem_id="sn-orbit-wrap") + # Plumbing only. The cmd span is a temp GLB path; if it sits in the + # layout it paints through Load OBJ (even with height:0). + with gr.Column(elem_id="sn-obj-file-slot"): + cmd = gr.HTML( + value=_cmd_html(str(empty_glb), 0, DEFAULT_POINT_SIZE), + elem_id="sn-cmd-wrap", + visible="hidden", + container=False, + ) + obj_in = gr.File( + label="OBJ", + file_types=[".obj"], + type="filepath", + show_label=False, + container=False, + elem_id="sn-obj-file", + ) + _accept_out = [state, glb_held, reset_n, cmd, status, obj_in, obj_held] + _run_out = [state, glb_held, cmd, status, obj_in, obj_held] + _style_out = [glb_held, cmd, status] + if example_list: + # One file column; the other inputs keep the live sliders / Wireframe. + # inputs=obj_in alone called accept_obj_ui with wire=False. + gr.Markdown( + "These geometries were not in the model's training catalog." + ) + gr.Examples( + examples=[[p] for p in example_list], + inputs=[obj_in, opacity_in, reset_n, psize_in, wire_in, glb_held], + outputs=_accept_out, + fn=accept_obj_ui, + run_on_click=True, + cache_examples=False, + label="Sample OBJ", + ) + # Picker is a plain Button: UploadButton paints the path over the label. + # orbit.js clicks #sn-obj-file's input; File.upload runs accept. + # Do not bind File.clear β€” we empty the box on purpose so DND stays. + obj_in.upload( + accept_obj_ui, + inputs=[obj_in, opacity_in, reset_n, psize_in, wire_in, glb_held], + outputs=_accept_out, + ) + run_btn.click( + run_model_ui, + inputs=[ + obj_in, + obj_held, + density_in, + cut_in, + show_out, + opacity_in, + reset_n, + psize_in, + wire_in, + ], + outputs=_run_out, + ) + reset_btn.click( + bump_reset, + inputs=[glb_held, reset_n, psize_in, wire_in], + outputs=[reset_n, cmd], + ) + _style_in = [ + state, + obj_in, + obj_held, + cut_in, + show_out, + opacity_in, + reset_n, + psize_in, + wire_in, + ] + cut_in.release(restyle_ui, inputs=_style_in, outputs=_style_out) + opacity_in.release(restyle_ui, inputs=_style_in, outputs=_style_out) + show_out.change(restyle_ui, inputs=_style_in, outputs=_style_out) + _flag_in = [glb_held, reset_n, psize_in, wire_in] + psize_in.release(set_view_flags, inputs=_flag_in, outputs=cmd) + wire_in.change(set_view_flags, inputs=_flag_in, outputs=cmd) + return demo + + +# HF / ZeroGPU look for a module-level ``demo`` and for ``@spaces.GPU``. +demo = build_demo() +demo.queue() +_patch_launch(demo) + + +def main() -> None: + """Local binds localhost; Spaces set ``PORT`` and need ``0.0.0.0``.""" + on_space = bool(os.environ.get("SPACE_ID") or os.environ.get("PORT")) + port = int(os.environ["PORT"]) if os.environ.get("PORT") else 7860 + host = "0.0.0.0" if on_space else "127.0.0.1" + demo.launch(server_name=host, server_port=port) + + +if __name__ == "__main__": + main() diff --git a/src/gradio/examples/Helix_bend.obj b/src/gradio/examples/Helix_bend.obj new file mode 100644 index 0000000000000000000000000000000000000000..28d55314ab849cc8900be7936fc0d1e0c99e6c33 --- /dev/null +++ b/src/gradio/examples/Helix_bend.obj @@ -0,0 +1,14774 @@ +# This file uses centimeters as units for non-parametric coordinates. + +mtllib Helix_bend.mtl +g bend1Set tweakSet1 +v 1.444975 -8.160144 0.000000 +v 0.950000 -8.365170 0.000000 +v 0.455025 -8.160144 0.000000 +v 0.250000 -7.665169 0.000000 +v 0.455025 -7.170195 -0.000000 +v 0.950000 -6.965170 -0.000000 +v 1.444975 -7.170195 -0.000000 +v 1.650000 -7.665169 0.000000 +v 1.435398 -8.110811 -0.166081 +v 0.943704 -8.315836 -0.109190 +v 0.452010 -8.110811 -0.052299 +v 0.248343 -7.615836 -0.028734 +v 0.452010 -7.120862 -0.052299 +v 0.943704 -6.915836 -0.109190 +v 1.435398 -7.120862 -0.166081 +v 1.639065 -7.615836 -0.189646 +v 1.406797 -8.061477 -0.329961 +v 0.924900 -8.266502 -0.216933 +v 0.443003 -8.061477 -0.103905 +v 0.243395 -7.566503 -0.057088 +v 0.443003 -7.071528 -0.103905 +v 0.924900 -6.866503 -0.216933 +v 1.406797 -7.071528 -0.329961 +v 1.606405 -7.566503 -0.376779 +v 1.359549 -8.012144 -0.489468 +v 0.893837 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a/src/gradio/examples/Player.obj b/src/gradio/examples/Player.obj new file mode 100644 index 0000000000000000000000000000000000000000..e133e6e2a4f7fca96bc955dff35021e6e7704a08 --- /dev/null +++ b/src/gradio/examples/Player.obj @@ -0,0 +1,53717 @@ +# This file uses centimeters as units for non-parametric coordinates. + +mtllib Player.mtl +g default +v 5.159797 11.435051 -0.499438 +v 5.305791 11.240304 -0.520365 +v 5.301744 11.239517 -0.296644 +v 5.187854 11.467460 -0.310342 +v 5.190524 11.466210 -0.709082 +v 5.339725 11.331635 -0.706197 +v 5.410063 11.054366 -0.496747 +v 5.339104 11.026116 -0.369634 +v 5.476545 11.253510 -0.815133 +v 5.407432 11.207855 -0.720148 +v 5.462671 11.422363 -0.817610 +v 5.382171 11.166001 -0.617041 +v 5.465013 11.067299 -0.599757 +v 5.530209 11.108116 -0.810722 +v 5.480333 11.071754 -0.715183 +v 5.545472 11.267075 -0.865312 +v 5.595604 11.158295 -0.875027 +v 5.662352 11.031128 -0.868246 +v 5.600051 10.983436 -0.799287 +v 5.660284 11.231560 -0.896644 +v 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0000000000000000000000000000000000000000..2725e0f09f2e57fc08f8047ca889ebcd12cb99bd --- /dev/null +++ b/src/gradio/figure.py @@ -0,0 +1,255 @@ +"""GLB scene for Gradio ``Model3D`` (Babylon.js orbit, Y-up). + +Plotly 3D was the wrong tool: its camera is a unit-cube, not world Y-up, so +the floor never sat on the ground. This writes a real mesh (floor grid + +OBJ + occupancy points) and lets Gradio's 3D viewer frame it. +""" + +from __future__ import annotations + +import tempfile +import uuid +from pathlib import Path + +import numpy as np +import trimesh +from trimesh.path.entities import Line +from trimesh.path.path import Path3D +from trimesh.visual.material import PBRMaterial +from trimesh.visual.texture import TextureVisuals + +# Inspect COLOR_INSIDE is 0xffaa00. Babylon treats GLB vertex colors as +# linear, so that same RGB reads as lemon-yellow. Drop the green channel +# so the on-screen fill matches the inspect orange. +COLOR_INSIDE = np.array([255, 136, 0, 255], dtype=np.uint8) +COLOR_OUTSIDE = np.array([74, 109, 140, 220], dtype=np.uint8) +COLOR_MESH_RGB = (0.60, 0.64, 0.70) +# THREE.GridHelper(span, 20, 0xaabbcc, 0x556677) β€” center vs cells. +COLOR_GRID = np.array([85, 102, 119, 255], dtype=np.uint8) +COLOR_GRID_CENTER = np.array([170, 187, 204, 255], dtype=np.uint8) +DEFAULT_MESH_OPACITY = 50 +# Babylon POINTS are 1 px in the GLB; orbit.js applies this pixel size. +DEFAULT_POINT_SIZE = 8 +# Match pipeline.MAX_FILL_POINTS so the GLB is not a random subset of the lattice. +MAX_PLOT_POINTS = 80_000 +FLOOR_DIVS = 20 + + +def _floor_bounds(vertices: np.ndarray | None) -> tuple[float, float, float, float, float]: + """XZ rectangle and Y of the floor (content ymin, else y=0).""" + if vertices is None or int(np.asarray(vertices).size) == 0: + return -2.0, 2.0, -2.0, 2.0, 0.0 + verts = np.asarray(vertices, dtype=np.float64) + vmin = verts.min(axis=0) + vmax = verts.max(axis=0) + radius = 0.5 * float(np.max(vmax - vmin)) + span = max(radius * 4.0, 4.0) + cx = 0.5 * float(vmin[0] + vmax[0]) + cz = 0.5 * float(vmin[2] + vmax[2]) + y = float(vmin[1]) + half = 0.5 * span + return cx - half, cx + half, cz - half, cz + half, y + + +def _clamp_opacity(value: float) -> float: + """Slider 0–100 β†’ 0–1. Missing / NaN β†’ default 50%.""" + try: + t = float(value) + except (TypeError, ValueError): + t = float(DEFAULT_MESH_OPACITY) + if not np.isfinite(t): + t = float(DEFAULT_MESH_OPACITY) + return min(1.0, max(0.0, t / 100.0)) + + +def _shell_mesh(vertices: np.ndarray, faces: np.ndarray, opacity: float) -> trimesh.Trimesh: + """ + OBJ shell with a GLTF BLEND material. + + Vertex-color alpha is ignored by Babylon Model3D (that is why the + torus looked solid). ``alphaMode=BLEND`` + baseColorFactor.a is the + channel the viewer actually uses. + """ + mesh = trimesh.Trimesh(vertices=vertices, faces=faces, process=False) + alpha = _clamp_opacity(opacity) + mesh.visual = TextureVisuals( + material=PBRMaterial( + name="shell", + baseColorFactor=[COLOR_MESH_RGB[0], COLOR_MESH_RGB[1], COLOR_MESH_RGB[2], alpha], + metallicFactor=0.0, + roughnessFactor=0.85, + alphaMode="BLEND", + doubleSided=True, + ) + ) + mesh.metadata["name"] = "occ_shell" + return mesh + + +def _grid_mesh(vertices: np.ndarray | None, *, n: int = FLOOR_DIVS) -> Path3D: + """ + XZ floor as GL_LINES (1 px in Babylon), not extruded boxes. + + Boxes read as a thick waffle. The inspect viewer uses + ``THREE.GridHelper(span, 20, 0xaabbcc, 0x556677)`` β€” same count + and the lighter centre cross. + """ + x0, x1, z0, z1, y = _floor_bounds(vertices) + n = max(2, int(n)) + mid = n // 2 + verts: list[list[float]] = [] + entities: list[Line] = [] + colors: list[np.ndarray] = [] + for i in range(n + 1): + t = i / n + x = x0 + (x1 - x0) * t + z = z0 + (z1 - z0) * t + color = COLOR_GRID_CENTER if i == mid else COLOR_GRID + i0 = len(verts) + verts.extend(([x, y, z0], [x, y, z1])) + entities.append(Line(points=[i0, i0 + 1])) + colors.append(color) + i1 = len(verts) + verts.extend(([x0, y, z], [x1, y, z])) + entities.append(Line(points=[i1, i1 + 1])) + colors.append(color) + return Path3D( + entities=entities, + vertices=np.asarray(verts, dtype=np.float64), + colors=np.asarray(colors, dtype=np.uint8), + process=False, + ) + + +def _axis_mesh(vertices: np.ndarray | None) -> Path3D: + """ + RGB triad as GL_LINES β€” same idea as ``THREE.AxesHelper``. + + Length is ``max(radius * 0.45, 1)`` with ``span = 4 * radius``, + matching the inspect helper. Boxes looked like fat sticks. + """ + x0, x1, z0, z1, y = _floor_bounds(vertices) + span = max(x1 - x0, z1 - z0, 1.0) + length = max(0.45 * (span / 4.0), 1.0) + origin = np.array([0.5 * (x0 + x1), y, 0.5 * (z0 + z1)], dtype=np.float64) + # AxesHelper: +X red, +Y green, +Z blue. + specs = ( + (np.array([length, 0.0, 0.0]), (255, 0, 0, 255)), + (np.array([0.0, length, 0.0]), (0, 255, 0, 255)), + (np.array([0.0, 0.0, length]), (0, 0, 255, 255)), + ) + verts: list[np.ndarray] = [] + entities: list[Line] = [] + colors: list[tuple[int, int, int, int]] = [] + for delta, rgba in specs: + i0 = len(verts) + verts.extend((origin, origin + delta)) + entities.append(Line(points=[i0, i0 + 1])) + colors.append(rgba) + return Path3D( + entities=entities, + vertices=np.asarray(verts, dtype=np.float64), + colors=np.asarray(colors, dtype=np.uint8), + process=False, + ) + + +def _points_mesh(xyz: np.ndarray, rgba: np.ndarray) -> trimesh.Trimesh | None: + """Occupancy dots as a vertex cloud (GLB point primitive).""" + if xyz.ndim != 2 or xyz.shape[0] < 1: + return None + cloud = trimesh.points.PointCloud(xyz.astype(np.float64), colors=rgba) + # Node name survives GLB import so orbit.js can set pointSize. + cloud.metadata["name"] = "occ_points" + return cloud + + +def _subsample(points: np.ndarray, pred: np.ndarray, cap: int) -> tuple[np.ndarray, np.ndarray]: + n = int(points.shape[0]) + if n <= cap: + return points, pred + rng = np.random.default_rng(0) + inside = np.flatnonzero(pred > 0) + outside = np.flatnonzero(pred == 0) + n_in = min(int(inside.shape[0]), cap) + take_in = rng.choice(inside, size=n_in, replace=False) if n_in else np.empty(0, dtype=int) + remain = cap - n_in + n_out = min(int(outside.shape[0]), remain) + take_out = ( + rng.choice(outside, size=n_out, replace=False) if n_out else np.empty(0, dtype=int) + ) + idx = np.concatenate([take_in, take_out]) + return points[idx], pred[idx] + + +def _placeholder_geom() -> trimesh.Trimesh: + """Tiny triangle so trimesh can export when the draw list is empty.""" + dummy = trimesh.Trimesh( + vertices=np.array([[0.0, 0.0, 0.0], [1e-4, 0.0, 0.0], [0.0, 0.0, 1e-4]]), + faces=np.array([[0, 1, 2]], dtype=np.int64), + process=False, + ) + dummy.metadata["name"] = "occ_empty" + return dummy + + +def _export_glb(geoms: list) -> Path: + """Write a unique GLB so the orbit fetch is not served from a stale cache.""" + folder = Path(tempfile.gettempdir()) / "scatteringnet_gradio" + folder.mkdir(parents=True, exist_ok=True) + path = folder / f"view_{uuid.uuid4().hex[:10]}.glb" + scene = trimesh.Scene() + added = 0 + for i, geom in enumerate(geoms): + if geom is None: + continue + meta = getattr(geom, "metadata", None) or {} + name = str(meta["name"]) if isinstance(meta, dict) and meta.get("name") else f"g{i}" + scene.add_geometry(geom, node_name=name) + added += 1 + if added < 1: + scene.add_geometry(_placeholder_geom(), node_name="occ_empty") + scene.export(path) + return path + + +def empty_figure(message: str = "", **_kwargs) -> Path: + """ + Placeholder GLB so the cmd path can change. The visible floor lives + in orbit.js and is never replaced (a per-mesh grid was the camera jump). + """ + _ = message + return _export_glb([]) + + +def occupancy_figure( + vertices: np.ndarray, + faces: np.ndarray, + points: np.ndarray, + pred: np.ndarray, + *, + show_outside: bool = False, + title: str = "", + mesh_opacity: float = DEFAULT_MESH_OPACITY, + **_kwargs, +) -> Path: + """Mesh + occupancy points as one GLB. Floor stays in orbit.js.""" + _ = title + verts = np.asarray(vertices, dtype=np.float32) if vertices is not None else np.zeros((0, 3), np.float32) + tris = np.asarray(faces, dtype=np.int32) if faces is not None else np.zeros((0, 3), np.int32) + geoms: list = [] + if verts.ndim == 2 and verts.shape[0] > 0: + if tris.ndim == 2 and tris.shape[0] > 0 and tris.shape[1] == 3: + geoms.append(_shell_mesh(verts, tris, mesh_opacity)) + xyz = np.asarray(points, dtype=np.float32) + if xyz.ndim == 2 and xyz.shape[0] > 0: + labels = np.asarray(pred, dtype=np.uint8).reshape(-1) + if not show_outside: + keep = labels > 0 + xyz = xyz[keep] + labels = labels[keep] + xyz, labels = _subsample(xyz, labels, MAX_PLOT_POINTS) + if xyz.shape[0] > 0: + colors = np.where(labels[:, None] > 0, COLOR_INSIDE, COLOR_OUTSIDE) + geoms.append(_points_mesh(xyz, colors)) + return _export_glb(geoms) diff --git a/src/gradio/orbit.js b/src/gradio/orbit.js new file mode 100644 index 0000000000000000000000000000000000000000..f3d8cbe0cb7f9f5da6303f5abd95e217c40d809c --- /dev/null +++ b/src/gradio/orbit.js @@ -0,0 +1,564 @@ +/** + * Persistent occupancy orbit (Y-up). Gradio Model3D remounts the whole + * WebGL context on every new GLB β€” that is the gray flash + camera snap. + * This script owns one Engine for the page lifetime and only replaces meshes. + * + * Gradio 6 strips