239 lines
8.4 KiB
Python
239 lines
8.4 KiB
Python
import json
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import logging
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import os
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from copy import deepcopy
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from pathlib import Path
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import einops
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import pytest
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import torch
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from datasets import Dataset
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import lerobot
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from lerobot.common.datasets.factory import make_dataset
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from lerobot.common.datasets.utils import (
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compute_stats,
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flatten_dict,
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get_stats_einops_patterns,
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hf_transform_to_torch,
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load_previous_and_future_frames,
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unflatten_dict,
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)
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from lerobot.common.utils.utils import init_hydra_config
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from .utils import DEFAULT_CONFIG_PATH, DEVICE
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@pytest.mark.parametrize("env_name, dataset_id, policy_name", lerobot.env_dataset_policy_triplets)
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def test_factory(env_name, dataset_id, policy_name):
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cfg = init_hydra_config(
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DEFAULT_CONFIG_PATH,
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overrides=[
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f"env={env_name}",
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f"dataset_id={dataset_id}",
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f"policy={policy_name}",
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f"device={DEVICE}",
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],
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)
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dataset = make_dataset(cfg)
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delta_timestamps = dataset.delta_timestamps
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image_keys = dataset.image_keys
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item = dataset[0]
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keys_ndim_required = [
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("action", 1, True),
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("episode_index", 0, True),
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("frame_index", 0, True),
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("timestamp", 0, True),
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# TODO(rcadene): should we rename it agent_pos?
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("observation.state", 1, True),
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("next.reward", 0, False),
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("next.done", 0, False),
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]
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# test number of dimensions
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for key, ndim, required in keys_ndim_required:
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if key not in item:
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if required:
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assert key in item, f"{key}"
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else:
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logging.warning(f'Missing key in dataset: "{key}" not in {dataset}.')
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continue
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if delta_timestamps is not None and key in delta_timestamps:
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assert item[key].ndim == ndim + 1, f"{key}"
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assert item[key].shape[0] == len(delta_timestamps[key]), f"{key}"
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else:
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assert item[key].ndim == ndim, f"{key}"
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if key in image_keys:
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assert item[key].dtype == torch.float32, f"{key}"
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# TODO(rcadene): we assume for now that image normalization takes place in the model
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assert item[key].max() <= 1.0, f"{key}"
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assert item[key].min() >= 0.0, f"{key}"
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if delta_timestamps is not None and key in delta_timestamps:
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# test t,c,h,w
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assert item[key].shape[1] == 3, f"{key}"
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else:
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# test c,h,w
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assert item[key].shape[0] == 3, f"{key}"
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if delta_timestamps is not None:
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# test missing keys in delta_timestamps
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for key in delta_timestamps:
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assert key in item, f"{key}"
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def test_compute_stats_on_xarm():
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"""Check that the statistics are computed correctly according to the stats_patterns property.
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We compare with taking a straight min, mean, max, std of all the data in one pass (which we can do
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because we are working with a small dataset).
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"""
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from lerobot.common.datasets.xarm import XarmDataset
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data_dir = Path(os.environ["DATA_DIR"]) if "DATA_DIR" in os.environ else None
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dataset = XarmDataset(
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dataset_id="xarm_lift_medium",
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root=data_dir,
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)
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# Note: we set the batch size to be smaller than the whole dataset to make sure we are testing batched
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# computation of the statistics. While doing this, we also make sure it works when we don't divide the
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# dataset into even batches.
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computed_stats = compute_stats(dataset.hf_dataset, batch_size=int(len(dataset) * 0.25))
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# get einops patterns to aggregate batches and compute statistics
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stats_patterns = get_stats_einops_patterns(dataset.hf_dataset)
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# get all frames from the dataset in the same dtype and range as during compute_stats
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dataloader = torch.utils.data.DataLoader(
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dataset,
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num_workers=8,
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batch_size=len(dataset),
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shuffle=False,
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)
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full_batch = next(iter(dataloader))
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# compute stats based on all frames from the dataset without any batching
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expected_stats = {}
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for k, pattern in stats_patterns.items():
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full_batch[k] = full_batch[k].float()
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expected_stats[k] = {}
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expected_stats[k]["mean"] = einops.reduce(full_batch[k], pattern, "mean")
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expected_stats[k]["std"] = torch.sqrt(
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einops.reduce((full_batch[k] - expected_stats[k]["mean"]) ** 2, pattern, "mean")
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)
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expected_stats[k]["min"] = einops.reduce(full_batch[k], pattern, "min")
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expected_stats[k]["max"] = einops.reduce(full_batch[k], pattern, "max")
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# test computed stats match expected stats
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for k in stats_patterns:
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assert torch.allclose(computed_stats[k]["mean"], expected_stats[k]["mean"])
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assert torch.allclose(computed_stats[k]["std"], expected_stats[k]["std"])
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assert torch.allclose(computed_stats[k]["min"], expected_stats[k]["min"])
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assert torch.allclose(computed_stats[k]["max"], expected_stats[k]["max"])
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# load stats used during training which are expected to match the ones returned by computed_stats
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loaded_stats = dataset.stats # noqa: F841
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# TODO(rcadene): we can't test this because expected_stats is computed on a subset
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# # test loaded stats match expected stats
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# for k in stats_patterns:
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# assert torch.allclose(loaded_stats[k]["mean"], expected_stats[k]["mean"])
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# assert torch.allclose(loaded_stats[k]["std"], expected_stats[k]["std"])
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# assert torch.allclose(loaded_stats[k]["min"], expected_stats[k]["min"])
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# assert torch.allclose(loaded_stats[k]["max"], expected_stats[k]["max"])
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def test_load_previous_and_future_frames_within_tolerance():
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hf_dataset = Dataset.from_dict(
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{
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"timestamp": [0.1, 0.2, 0.3, 0.4, 0.5],
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"index": [0, 1, 2, 3, 4],
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"episode_index": [0, 0, 0, 0, 0],
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}
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)
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hf_dataset.set_transform(hf_transform_to_torch)
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episode_data_index = {
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"from": torch.tensor([0]),
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"to": torch.tensor([5]),
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}
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delta_timestamps = {"index": [-0.2, 0, 0.139]}
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tol = 0.04
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item = hf_dataset[2]
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item = load_previous_and_future_frames(item, hf_dataset, episode_data_index, delta_timestamps, tol)
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data, is_pad = item["index"], item["index_is_pad"]
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assert torch.equal(data, torch.tensor([0, 2, 3])), "Data does not match expected values"
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assert not is_pad.any(), "Unexpected padding detected"
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def test_load_previous_and_future_frames_outside_tolerance_inside_episode_range():
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hf_dataset = Dataset.from_dict(
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{
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"timestamp": [0.1, 0.2, 0.3, 0.4, 0.5],
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"index": [0, 1, 2, 3, 4],
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"episode_index": [0, 0, 0, 0, 0],
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}
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)
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hf_dataset.set_transform(hf_transform_to_torch)
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episode_data_index = {
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"from": torch.tensor([0]),
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"to": torch.tensor([5]),
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}
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delta_timestamps = {"index": [-0.2, 0, 0.141]}
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tol = 0.04
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item = hf_dataset[2]
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with pytest.raises(AssertionError):
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load_previous_and_future_frames(item, hf_dataset, episode_data_index, delta_timestamps, tol)
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def test_load_previous_and_future_frames_outside_tolerance_outside_episode_range():
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hf_dataset = Dataset.from_dict(
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{
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"timestamp": [0.1, 0.2, 0.3, 0.4, 0.5],
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"index": [0, 1, 2, 3, 4],
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"episode_index": [0, 0, 0, 0, 0],
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}
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)
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hf_dataset.set_transform(hf_transform_to_torch)
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episode_data_index = {
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"from": torch.tensor([0]),
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"to": torch.tensor([5]),
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}
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delta_timestamps = {"index": [-0.3, -0.24, 0, 0.26, 0.3]}
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tol = 0.04
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item = hf_dataset[2]
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item = load_previous_and_future_frames(item, hf_dataset, episode_data_index, delta_timestamps, tol)
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data, is_pad = item["index"], item["index_is_pad"]
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assert torch.equal(data, torch.tensor([0, 0, 2, 4, 4])), "Data does not match expected values"
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assert torch.equal(
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is_pad, torch.tensor([True, False, False, True, True])
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), "Padding does not match expected values"
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def test_flatten_unflatten_dict():
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d = {
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"obs": {
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"min": 0,
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"max": 1,
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"mean": 2,
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"std": 3,
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},
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"action": {
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"min": 4,
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"max": 5,
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"mean": 6,
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"std": 7,
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},
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}
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original_d = deepcopy(d)
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d = unflatten_dict(flatten_dict(d))
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# test equality between nested dicts
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assert json.dumps(original_d, sort_keys=True) == json.dumps(d, sort_keys=True), f"{original_d} != {d}"
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