| import torch |
| import pandas as pd |
| from box import Box |
| import numpy as np |
| import os |
| import logging |
| from gpudrive.env.dataset import SceneDataLoader |
| from eval_utils import ( |
| load_config, |
| make_env, |
| load_policy, |
| evaluate_policy, |
| ) |
|
|
| import random |
| import torch |
| import numpy as np |
|
|
| def set_seed(seed: int): |
| random.seed(seed) |
| np.random.seed(seed) |
| torch.manual_seed(seed) |
| torch.cuda.manual_seed_all(seed) |
| torch.backends.cudnn.deterministic = True |
|
|
| logging.basicConfig(level=logging.INFO) |
| SEED = 42 |
| set_seed(SEED) |
|
|
| if __name__ == "__main__": |
|
|
| |
| eval_config = load_config("examples/experimental/config/eval_config") |
| model_config = load_config("examples/experimental/config/model_config") |
|
|
| train_loader = SceneDataLoader( |
| root=eval_config.train_dir, |
| batch_size=eval_config.num_worlds, |
| dataset_size=eval_config.num_worlds, |
| sample_with_replacement=False, |
| ) |
|
|
| |
| env = make_env(eval_config, train_loader) |
|
|
| for model in model_config.models: |
|
|
| logging.info(f"Evaluating model {model.name}") |
|
|
| |
| policy = load_policy( |
| path_to_cpt=model_config.models_path, |
| model_name=model.name, |
| device=eval_config.device, |
| env=env, |
| ) |
|
|
| |
| train_loader = SceneDataLoader( |
| root=eval_config.train_dir, |
| batch_size=eval_config.num_worlds, |
| dataset_size=model.train_dataset_size |
| if model.name != "random_baseline" |
| else 1000, |
| sample_with_replacement=False, |
| shuffle=False, |
| ) |
|
|
| test_loader = SceneDataLoader( |
| root=eval_config.test_dir, |
| batch_size=eval_config.num_worlds, |
| dataset_size=eval_config.test_dataset_size |
| if model.name != "random_baseline" |
| else 1000, |
| sample_with_replacement=False, |
| shuffle=True, |
| ) |
|
|
| |
| logging.info( |
| f"Rollouts on {len(set(train_loader.dataset))} train scenes / {len(set(test_loader.dataset))} test scenes" |
| ) |
|
|
| df_res_train = evaluate_policy( |
| env=env, |
| policy=policy, |
| data_loader=train_loader, |
| dataset_name="train", |
| deterministic=False, |
| render_sim_state=False, |
| ) |
|
|
| df_res_test = evaluate_policy( |
| env=env, |
| policy=policy, |
| data_loader=test_loader, |
| dataset_name="test", |
| deterministic=False, |
| render_sim_state=False, |
| ) |
|
|
| |
| df_res = pd.concat([df_res_train, df_res_test]) |
|
|
| |
| df_res["model_name"] = model.name |
| df_res["train_dataset_size"] = model.train_dataset_size |
|
|
| |
| if not os.path.exists(eval_config.res_path): |
| os.makedirs(eval_config.res_path) |
|
|
| tab_agg_perf = df_res.groupby("dataset")[ |
| [ |
| "goal_achieved_frac", |
| "collided_frac", |
| "off_road_frac", |
| "other_frac", |
| ] |
| ].agg(["mean", "std"]) |
| tab_agg_perf = tab_agg_perf * 100 |
| tab_agg_perf = tab_agg_perf.round(1) |
|
|
| print("Scene-based metrics \n") |
| print(tab_agg_perf) |
| print("") |
|
|
| print("Agent-based metrics \n") |
| total_agents = df_res["controlled_agents_in_scene"].sum() |
| collision_rate = (df_res["collided_count"].sum() / total_agents) * 100 |
| offroad_rate = (df_res["off_road_count"].sum() / total_agents) * 100 |
| goal_rate = (df_res["goal_achieved_count"].sum() / total_agents) * 100 |
| other_rate = (df_res["other_count"].sum() / total_agents) * 100 |
|
|
| print(f"Total agents: {total_agents} in {df_res.shape[0]} scenes") |
| print(f"Collision rate: {collision_rate}") |
| print(f"Offroad rate: {offroad_rate}") |
| print(f"Goal rate: {goal_rate}") |
| print(f"Other rate: {other_rate}") |
|
|
| df_res.to_csv(f"{eval_config.res_path}/{model.name}.csv", index=False) |
|
|
| logging.info(f"Saved at {eval_config.res_path}/{model.name}.csv \n") |
|
|
