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Loads episode_data_index and stats during dataset __init__ (#85)

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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
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33
lerobot/common/datasets/aloha.py
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@@ -1,9 +1,13 @@
from pathlib import Path
import torch
from datasets import load_dataset, load_from_disk
from lerobot.common.datasets.utils import load_previous_and_future_frames
from lerobot.common.datasets.utils import (
load_episode_data_index,
load_hf_dataset,
load_previous_and_future_frames,
load_stats,
)
class AlohaDataset(torch.utils.data.Dataset):
@@ -27,7 +31,7 @@ class AlohaDataset(torch.utils.data.Dataset):
def __init__(
self,
dataset_id: str,
version: str | None = "v1.0",
version: str | None = "v1.1",
root: Path | None = None,
split: str = "train",
transform: callable = None,
@@ -40,13 +44,10 @@ class AlohaDataset(torch.utils.data.Dataset):
self.split = split
self.transform = transform
self.delta_timestamps = delta_timestamps
if self.root is not None:
self.hf_dataset = load_from_disk(Path(self.root) / self.dataset_id / self.split)
else:
self.hf_dataset = load_dataset(
f"lerobot/{self.dataset_id}", revision=self.version, split=self.split
)
self.hf_dataset = self.hf_dataset.with_format("torch")
# load data from hub or locally when root is provided
self.hf_dataset = load_hf_dataset(dataset_id, version, root, split)
self.episode_data_index = load_episode_data_index(dataset_id, version, root)
self.stats = load_stats(dataset_id, version, root)
@property
def num_samples(self) -> int:
@@ -54,7 +55,7 @@ class AlohaDataset(torch.utils.data.Dataset):
@property
def num_episodes(self) -> int:
return len(self.hf_dataset.unique("episode_id"))
return len(self.hf_dataset.unique("episode_index"))
def __len__(self):
return self.num_samples
@@ -66,19 +67,11 @@ class AlohaDataset(torch.utils.data.Dataset):
item = load_previous_and_future_frames(
item,
self.hf_dataset,
self.episode_data_index,
self.delta_timestamps,
tol=1 / self.fps - 1e-4, # 1e-4 to account for possible numerical error
)
# convert images from channel last (PIL) to channel first (pytorch)
for key in self.image_keys:
if item[key].ndim == 3:
item[key] = item[key].permute((2, 0, 1)) # h w c -> c h w
elif item[key].ndim == 4:
item[key] = item[key].permute((0, 3, 1, 2)) # t h w c -> t c h w
else:
raise ValueError(item[key].ndim)
if self.transform is not None:
item = self.transform(item)