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bug remove

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Jade Choghari (jchoghar)
2025-08-17 16:40:11 -04:00
parent 14a59f576b
commit 7626d26e6a
1 changed files with 6 additions and 32 deletions
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src/lerobot/scripts/eval.py
+6 -32
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@@ -46,7 +46,6 @@ Note that in both examples, the repo/folder should contain at least `config.json
You can learn about the CLI options for this script in the `EvalPipelineConfig` in lerobot/configs/eval.py You can learn about the CLI options for this script in the `EvalPipelineConfig` in lerobot/configs/eval.py
""" """
import concurrent
import json import json
import logging import logging
import threading import threading
@@ -57,7 +56,7 @@ from copy import deepcopy
from dataclasses import asdict from dataclasses import asdict
from pathlib import Path from pathlib import Path
from pprint import pformat from pprint import pformat
import concurrent
import einops import einops
import gymnasium as gym import gymnasium as gym
import numpy as np import numpy as np
@@ -157,29 +156,9 @@ def rollout(
# Infer "task" from attributes of environments. # Infer "task" from attributes of environments.
# TODO: works with SyncVectorEnv but not AsyncVectorEnv # TODO: works with SyncVectorEnv but not AsyncVectorEnv
observation = add_envs_task(env, observation) observation = add_envs_task(env, observation)
if step % 100 == 0:
import imageio.v2 as imageio
img = observation["observation.images.image"] # (1, 3, 256, 256)
if isinstance(img, torch.Tensor):
img = img.detach().cpu().numpy()
# remove batch → (3, 256, 256)
img = img[0]
# transpose → (256, 256, 3)
img = np.transpose(img, (1, 2, 0))
# scale + convert to uint8
img = (img * 255).clip(0, 255).astype(np.uint8)
# now works
imageio.imwrite(f"obs_{step:06d}.png", img)
with torch.inference_mode(): with torch.inference_mode():
action = policy.select_action(observation) action = policy.select_action(observation)
observation["observation.images.image"] observation['observation.images.image']
# Convert to CPU / numpy. # Convert to CPU / numpy.
action = action.to("cpu").numpy() action = action.to("cpu").numpy()
assert action.ndim == 2, "Action dimensions should be (batch, action_dim)" assert action.ndim == 2, "Action dimensions should be (batch, action_dim)"
@@ -198,11 +177,12 @@ def rollout(
# Keep track of which environments are done so far. # Keep track of which environments are done so far.
# done = terminated | truncated | done # done = terminated | truncated | done
# TODO: jadechoghari changed, this is cleaner #TODO: jadechoghari changed, this is cleaner
done = terminated | truncated | done done = terminated | truncated | done
if step + 1 == max_steps: if step + 1 == max_steps:
done = np.ones_like(done, dtype=bool) done = np.ones_like(done, dtype=bool)
all_actions.append(torch.from_numpy(action)) all_actions.append(torch.from_numpy(action))
all_rewards.append(torch.from_numpy(reward)) all_rewards.append(torch.from_numpy(reward))
all_dones.append(torch.from_numpy(done)) all_dones.append(torch.from_numpy(done))
@@ -398,7 +378,7 @@ def eval_policy(
# Wait till all video rendering threads are done. # Wait till all video rendering threads are done.
for thread in threads: for thread in threads:
thread.join() thread.join()
# Compile eval info. # Compile eval info.
info = { info = {
"per_episode": [ "per_episode": [
@@ -480,22 +460,16 @@ def _compile_episode_data(
return data_dict return data_dict
def set_global_seed(seed): def set_global_seed(seed):
"""Set seed for reproducibility.""" """Set seed for reproducibility."""
import random import random
random.seed(seed) random.seed(seed)
np.random.seed(seed) np.random.seed(seed)
torch.manual_seed(seed) torch.manual_seed(seed)
if torch.cuda.is_available(): if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed) torch.cuda.manual_seed_all(seed)
def log_output_dir(out_dir): def log_output_dir(out_dir):
logging.info("Output dir:" + f" {out_dir}") logging.info("Output dir:"+ f" {out_dir}")
@parser.wrap() @parser.wrap()
def eval(cfg: EvalPipelineConfig): def eval(cfg: EvalPipelineConfig):
logging.info(pformat(asdict(cfg))) logging.info(pformat(asdict(cfg)))