diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index 8d0b6851..b3411e11 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -160,17 +160,6 @@ jobs: device=cpu \ policy.pretrained_model_path=tests/outputs/act/models/2.pt - # TODO(aliberts): This takes ~2mn to run, needs to be improved - # - name: Test eval ACT on ALOHA end-to-end (policy is None) - # run: | - # source .venv/bin/activate - # python lerobot/scripts/eval.py \ - # --config lerobot/configs/default.yaml \ - # policy=act \ - # env=aloha \ - # eval_episodes=1 \ - # device=cpu - - name: Test train Diffusion on PushT end-to-end run: | source .venv/bin/activate @@ -197,17 +186,6 @@ jobs: device=cpu \ policy.pretrained_model_path=tests/outputs/diffusion/models/2.pt - - name: Test eval Diffusion on PushT end-to-end (policy is None) - run: | - source .venv/bin/activate - python lerobot/scripts/eval.py \ - --config lerobot/configs/default.yaml \ - policy=diffusion \ - env=pusht \ - eval_episodes=1 \ - env.episode_length=8 \ - device=cpu - - name: Test train TDMPC on Simxarm end-to-end run: | source .venv/bin/activate @@ -233,13 +211,3 @@ jobs: env.episode_length=8 \ device=cpu \ policy.pretrained_model_path=tests/outputs/tdmpc/models/2.pt - - - name: Test eval TDPMC on Simxarm end-to-end (policy is None) - run: | - source .venv/bin/activate - python lerobot/scripts/eval.py \ - --config lerobot/configs/default.yaml \ - policy=tdmpc \ - env=xarm \ - eval_episodes=1 \ - device=cpu diff --git a/lerobot/scripts/eval.py b/lerobot/scripts/eval.py index fa7e1096..0e4f7bb3 100644 --- a/lerobot/scripts/eval.py +++ b/lerobot/scripts/eval.py @@ -57,7 +57,7 @@ def write_video(video_path, stacked_frames, fps): def eval_policy( env: gym.vector.VectorEnv, - policy, + policy: torch.nn.Module, max_episodes_rendered: int = 0, video_dir: Path = None, # TODO(rcadene): make it possible to overwrite fps? we should use env.fps @@ -312,12 +312,12 @@ def eval(cfg: dict, out_dir=None, stats_path=None): logging.info("Making environment.") env = make_env(cfg, num_parallel_envs=cfg.eval_episodes) - # when policy is None, rollout a random policy - policy = make_policy(cfg) if cfg.policy.pretrained_model_path else None + logging.info("Making policy.") + policy = make_policy(cfg) info = eval_policy( env, - policy=policy, + policy, max_episodes_rendered=10, video_dir=Path(out_dir) / "eval", # TODO(rcadene): what should we do with the transform?