Files
lerobot_piper/lerobot/common/datasets/pusht.py
2024-04-23 14:13:25 +02:00

77 lines
2.2 KiB
Python

from pathlib import Path
import torch
from lerobot.common.datasets.utils import (
load_episode_data_index,
load_hf_dataset,
load_previous_and_future_frames,
load_stats,
)
class PushtDataset(torch.utils.data.Dataset):
"""
https://huggingface.co/datasets/lerobot/pusht
Arguments
----------
delta_timestamps : dict[list[float]] | None, optional
Loads data from frames with a shift in timestamps with a different strategy for each data key (e.g. state, action or image)
If `None`, no shift is applied to current timestamp and the data from the current frame is loaded.
"""
# Copied from lerobot/__init__.py
available_datasets = ["pusht"]
fps = 10
image_keys = ["observation.image"]
def __init__(
self,
dataset_id: str = "pusht",
version: str | None = "v1.1",
root: Path | None = None,
split: str = "train",
transform: callable = None,
delta_timestamps: dict[list[float]] | None = None,
):
super().__init__()
self.dataset_id = dataset_id
self.version = version
self.root = root
self.split = split
self.transform = transform
self.delta_timestamps = delta_timestamps
# 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:
return len(self.hf_dataset)
@property
def num_episodes(self) -> int:
return len(self.episode_data_index["from"])
def __len__(self):
return self.num_samples
def __getitem__(self, idx):
item = self.hf_dataset[idx]
if self.delta_timestamps is not None:
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
)
if self.transform is not None:
item = self.transform(item)
return item