lerobot/tests/test_datasets.py

import pytest
import torch

from lerobot.common.utils import init_hydra_config
import logging
from lerobot.common.datasets.factory import make_dataset

from .utils import DEVICE, DEFAULT_CONFIG_PATH


@pytest.mark.parametrize(
    "env_name,dataset_id",
    [
        ("simxarm", "lift"),
        ("pusht", "pusht"),
        ("aloha", "sim_insertion_human"),
        ("aloha", "sim_insertion_scripted"),
        ("aloha", "sim_transfer_cube_human"),
        ("aloha", "sim_transfer_cube_scripted"),
    ],
)
def test_factory(env_name, dataset_id):
    cfg = init_hydra_config(
        DEFAULT_CONFIG_PATH,
        overrides=[f"env={env_name}", f"env.task={dataset_id}", f"device={DEVICE}"]
    )
    dataset = make_dataset(cfg)

    item = dataset[0]

    assert "action" in item
    assert "episode" in item
    assert "frame_id" in item
    assert "timestamp" in item
    assert "next.done" in item
    # TODO(rcadene): should we rename it agent_pos?
    assert "observation.state" in item
    for key in dataset.image_keys:
        img = item.get(key)
        assert img.dtype == torch.float32
        # TODO(rcadene): we assume for now that image normalization takes place in the model
        assert img.max() <= 1.0
        assert img.min() >= 0.0

    if "next.reward" not in item:
        logging.warning(f'Missing "next.reward" key in dataset {dataset}.')
    if "next.done" not in item:
        logging.warning(f'Missing "next.done" key in dataset {dataset}.')


# def test_compute_stats():
#     """Check that the statistics are computed correctly according to the stats_patterns property.

#     We compare with taking a straight min, mean, max, std of all the data in one pass (which we can do
#     because we are working with a small dataset).
#     """
#     cfg = init_hydra_config(
#         DEFAULT_CONFIG_PATH, overrides=["env=aloha", "env.task=sim_transfer_cube_human"]
#     )
#     dataset = make_dataset(cfg)
#     # Get all of the data.
#     all_data = TensorDictReplayBuffer(
#             storage=buffer._storage,
#             batch_size=len(buffer),
#             sampler=SamplerWithoutReplacement(),
#     ).sample().float()
#     # Note: we set the batch size to be smaller than the whole dataset to make sure we are testing batched
#     # computation of the statistics. While doing this, we also make sure it works when we don't divide the
#     # dataset into even batches. 
#     computed_stats = buffer._compute_stats(batch_size=int(len(all_data) * 0.75))
#     for k, pattern in buffer.stats_patterns.items():
#         expected_mean = einops.reduce(all_data[k], pattern, "mean")
#         assert torch.allclose(computed_stats[k]["mean"], expected_mean)
#         assert torch.allclose(
#             computed_stats[k]["std"],
#             torch.sqrt(einops.reduce((all_data[k] - expected_mean) ** 2, pattern, "mean"))
#         )
#         assert torch.allclose(computed_stats[k]["min"], einops.reduce(all_data[k], pattern, "min"))
#         assert torch.allclose(computed_stats[k]["max"], einops.reduce(all_data[k], pattern, "max"))