hangX/lerobot
1
0
Fork 0
forked from tangger/lerobot
Code Pull Requests Activity

Loads episode_data_index and stats during dataset __init__ (#85)

Browse Source 
Co-authored-by: Simon Alibert <75076266+aliberts@users.noreply.github.com>
Co-authored-by: Alexander Soare <alexander.soare159@gmail.com>
This commit is contained in:
Remi
2024-04-23 14:13:25 +02:00
committed by GitHub
parent e2168163cd
commit 1030ea0070
89 changed files with 1008 additions and 432 deletions
Download Patch File Download Diff File Expand all files Collapse all files

45
lerobot/scripts/eval.py
View File

@@ -47,6 +47,7 @@ from PIL import Image as PILImage
from tqdm import trange
from lerobot.common.datasets.factory import make_dataset
from lerobot.common.datasets.utils import hf_transform_to_torch
from lerobot.common.envs.factory import make_env
from lerobot.common.envs.utils import postprocess_action, preprocess_observation
from lerobot.common.logger import log_output_dir
@@ -208,11 +209,12 @@ def eval_policy(
max_rewards.extend(batch_max_reward.tolist())
all_successes.extend(batch_success.tolist())
# similar logic is implemented in dataset preprocessing
# similar logic is implemented when datasets are pushed to hub (see: `push_to_hub`)
ep_dicts = []
episode_data_index = {"from": [], "to": []}
num_episodes = dones.shape[0]
total_frames = 0
idx_from = 0
id_from = 0
for ep_id in range(num_episodes):
num_frames = done_indices[ep_id].item() + 1
total_frames += num_frames
@@ -222,19 +224,20 @@ def eval_policy(
if return_episode_data:
ep_dict = {
"action": actions[ep_id, :num_frames],
"episode_id": torch.tensor([ep_id] * num_frames),
"frame_id": torch.arange(0, num_frames, 1),
"episode_index": torch.tensor([ep_id] * num_frames),
"frame_index": torch.arange(0, num_frames, 1),
"timestamp": torch.arange(0, num_frames, 1) / fps,
"next.done": dones[ep_id, :num_frames],
"next.reward": rewards[ep_id, :num_frames].type(torch.float32),
"episode_data_index_from": torch.tensor([idx_from] * num_frames),
"episode_data_index_to": torch.tensor([idx_from + num_frames] * num_frames),
}
for key in observations:
ep_dict[key] = observations[key][ep_id][:num_frames]
ep_dicts.append(ep_dict)
idx_from += num_frames
episode_data_index["from"].append(id_from)
episode_data_index["to"].append(id_from + num_frames)
id_from += num_frames
# similar logic is implemented in dataset preprocessing
if return_episode_data:
@@ -247,14 +250,29 @@ def eval_policy(
if key not in data_dict:
data_dict[key] = []
for ep_dict in ep_dicts:
for x in ep_dict[key]:
# c h w -> h w c
img = PILImage.fromarray(x.permute(1, 2, 0).numpy())
for img in ep_dict[key]:
# sanity check that images are channel first
c, h, w = img.shape
assert c < h and c < w, f"expect channel first images, but instead {img.shape}"
# sanity check that images are float32 in range [0,1]
assert img.dtype == torch.float32, f"expect torch.float32, but instead {img.dtype=}"
assert img.max() <= 1, f"expect pixels lower than 1, but instead {img.max()=}"
assert img.min() >= 0, f"expect pixels greater than 1, but instead {img.min()=}"
# from float32 in range [0,1] to uint8 in range [0,255]
img *= 255
img = img.type(torch.uint8)
# convert to channel last and numpy as expected by PIL
img = PILImage.fromarray(img.permute(1, 2, 0).numpy())
data_dict[key].append(img)
data_dict["index"] = torch.arange(0, total_frames, 1)
hf_dataset = Dataset.from_dict(data_dict).with_format("torch")
hf_dataset = Dataset.from_dict(data_dict)
hf_dataset.set_transform(hf_transform_to_torch)
if max_episodes_rendered > 0:
batch_stacked_frames = np.stack(ep_frames, 1) # (b, t, *)
@@ -307,7 +325,10 @@ def eval_policy(
},
}
if return_episode_data:
info["episodes"] = hf_dataset
info["episodes"] = {
"hf_dataset": hf_dataset,
"episode_data_index": episode_data_index,
}
if max_episodes_rendered > 0:
info["videos"] = videos
return info