remove policy is None eval end-to-end tests

.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

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
+    logging.info("Making policy.")
-    policy = make_policy(cfg) if cfg.policy.pretrained_model_path else None
+    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?