fix video paths and train.py

src/lerobot/scripts/eval.py

@@ -257,7 +257,6 @@ def eval_policy(
     # Determine how many batched rollouts we need to get n_episodes. Note that if n_episodes is not evenly
     # divisible by env.num_envs we end up discarding some data in the last batch.
     n_batches = n_episodes // env.num_envs + int((n_episodes % env.num_envs) != 0)
-    print("n_batches", n_batches)
 
     # Keep track of some metrics.
     sum_rewards = []
@@ -565,12 +564,16 @@ def eval_policy_multitask(
     def eval_task(task_group, task_id, env):
         """Evaluates a single task in parallel."""
         print(f"Evaluating: task_group: {task_group}, task_id: {task_id} ...")
+        # jadechoghari : added multi video eval support 
+        if videos_dir is not None:
+            task_videos_dir = videos_dir / f"{task_group}_{task_id}"
+            task_videos_dir.mkdir(parents=True, exist_ok=True)
         task_result = eval_policy(
             env,
             policy,
             n_episodes,
             max_episodes_rendered,
-            videos_dir,
+            task_videos_dir,
             return_episode_data,
             start_seed,
             # verbose=verbose,

src/lerobot/scripts/train.py

@@ -262,17 +262,15 @@ def train(cfg: TrainPipelineConfig):
                         start_seed=cfg.seed,
                         max_parallel_tasks=cfg.env.max_parallel_tasks,
                     )
-                    aggregated_results = eval_info["overall"]["aggregated"]
+                    aggregated = eval_info["overall"]["aggregated"]
                     # Print per-suite stats
                     for task_group, task_group_info in eval_info.items():
                         if task_group == "overall":
                             continue  # Skip the overall stats since we already printed it
                         print(f"\nAggregated Metrics for {task_group}:")
                         print(task_group_info["aggregated"])
-                    breakpoint()
                 else:
                     print("START EVAL")
-                    breakpoint()
                     eval_info = eval_policy(
                         eval_env,
                         policy,
@@ -302,7 +300,13 @@ def train(cfg: TrainPipelineConfig):
                 wandb_logger.log_video(eval_info["video_paths"][0], step, mode="eval")
 
     if eval_env:
-        eval_env.close()
+        # added by jade, close all env in multi eval setup
+        if cfg.env.multitask_eval:
+            for task_group, envs_dict in eval_env.items():
+                for idx, env in envs_dict.items():
+                    env.close()
+        else:
+            eval_env.close()
     logging.info("End of training")
 
     if cfg.policy.push_to_hub: