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fix video paths and train.py

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This commit is contained in:
Jade Choghari (jchoghar)
2025-08-19 09:38:26 -04:00
parent 4ed6cf159d
commit ce83752f16
2 changed files with 13 additions and 6 deletions
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src/lerobot/scripts/eval.py
+5 -2
View File
@@ -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 # 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. # 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) n_batches = n_episodes // env.num_envs + int((n_episodes % env.num_envs) != 0)
print("n_batches", n_batches)
# Keep track of some metrics. # Keep track of some metrics.
sum_rewards = [] sum_rewards = []
@@ -565,12 +564,16 @@ def eval_policy_multitask(
def eval_task(task_group, task_id, env): def eval_task(task_group, task_id, env):
"""Evaluates a single task in parallel.""" """Evaluates a single task in parallel."""
print(f"Evaluating: task_group: {task_group}, task_id: {task_id} ...") 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( task_result = eval_policy(
env, env,
policy, policy,
n_episodes, n_episodes,
max_episodes_rendered, max_episodes_rendered,
videos_dir, task_videos_dir,
return_episode_data, return_episode_data,
start_seed, start_seed,
# verbose=verbose, # verbose=verbose,
src/lerobot/scripts/train.py
+7 -3
View File
@@ -262,17 +262,15 @@ def train(cfg: TrainPipelineConfig):
start_seed=cfg.seed, start_seed=cfg.seed,
max_parallel_tasks=cfg.env.max_parallel_tasks, max_parallel_tasks=cfg.env.max_parallel_tasks,
) )
aggregated_results = eval_info["overall"]["aggregated"] aggregated = eval_info["overall"]["aggregated"]
# Print per-suite stats # Print per-suite stats
for task_group, task_group_info in eval_info.items(): for task_group, task_group_info in eval_info.items():
if task_group == "overall": if task_group == "overall":
continue # Skip the overall stats since we already printed it continue # Skip the overall stats since we already printed it
print(f"\nAggregated Metrics for {task_group}:") print(f"\nAggregated Metrics for {task_group}:")
print(task_group_info["aggregated"]) print(task_group_info["aggregated"])
breakpoint()
else: else:
print("START EVAL") print("START EVAL")
breakpoint()
eval_info = eval_policy( eval_info = eval_policy(
eval_env, eval_env,
policy, policy,
@@ -302,6 +300,12 @@ def train(cfg: TrainPipelineConfig):
wandb_logger.log_video(eval_info["video_paths"][0], step, mode="eval") wandb_logger.log_video(eval_info["video_paths"][0], step, mode="eval")
if eval_env: if eval_env:
# 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() eval_env.close()
logging.info("End of training") logging.info("End of training")