mirror of
https://github.com/huggingface/lerobot.git
synced 2026-08-08 17:39:44 +00:00
Hiroaki.Ishikawa
31fedfd9dd
* fix(stats): use conservative bounds for quantile aggregation instead of incorrect weighted mean * docs: add --overwrite/--skip-images/--root options to augment_dataset_quantile_stats usage * fix(dataset): clarify quantile aggregation semantics * fix(augment): handle quantile stats edge cases
219 lines
8.2 KiB
Python
219 lines
8.2 KiB
Python
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from types import SimpleNamespace
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import numpy as np
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import pytest
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import torch
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pytest.importorskip("datasets", reason="datasets is required (install lerobot[dataset])")
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from lerobot.scripts.augment_dataset_quantile_stats import (
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compute_quantile_stats_for_dataset,
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has_quantile_stats,
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)
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def _numeric_keys(dataset):
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return [
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k for k, v in dataset.features.items() if v["dtype"] not in ("image", "video", "string", "language")
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]
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def _image_keys(dataset):
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return [k for k, v in dataset.features.items() if v["dtype"] in ("image", "video")]
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def test_numeric_stats_are_unaffected_by_sampling(tmp_path, lerobot_dataset_factory):
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"""Sampling only touches image/video frames; numeric features are read in
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full either way, so their stats must be identical with and without sampling."""
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dataset = lerobot_dataset_factory(
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root=tmp_path / "ds", total_episodes=2, total_frames=400, use_videos=False
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)
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exact = compute_quantile_stats_for_dataset(dataset, use_sampling=False)
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sampled = compute_quantile_stats_for_dataset(dataset, use_sampling=True)
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numeric_keys = _numeric_keys(dataset)
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assert numeric_keys, "fixture should expose numeric features"
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for key in numeric_keys:
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if key not in exact:
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continue
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for stat in ("mean", "std", "q01", "q50", "q99"):
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if stat in exact[key]:
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np.testing.assert_allclose(
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sampled[key][stat],
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exact[key][stat],
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rtol=1e-6,
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atol=1e-6,
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err_msg=f"numeric feature '{key}' stat '{stat}' changed under sampling",
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)
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def test_image_sampling_reduces_data_but_keeps_stats_close(tmp_path, lerobot_dataset_factory):
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"""For images, sampling should reduce the number of samples considered while
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keeping the resulting statistics close to the exact ones."""
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dataset = lerobot_dataset_factory(
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root=tmp_path / "ds", total_episodes=2, total_frames=400, use_videos=False
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)
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exact = compute_quantile_stats_for_dataset(dataset, use_sampling=False)
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sampled = compute_quantile_stats_for_dataset(dataset, use_sampling=True)
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image_keys = _image_keys(dataset)
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assert image_keys, "fixture should expose at least one image feature"
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for key in image_keys:
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# sampling actually looked at fewer pixels
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assert sampled[key]["count"][0] < exact[key]["count"][0]
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# but per-channel mean stays close
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np.testing.assert_allclose(
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sampled[key]["mean"],
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exact[key]["mean"],
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rtol=0.15,
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err_msg=f"image feature '{key}' mean drifted too far under sampling",
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)
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def test_short_episodes_use_all_frames(tmp_path, lerobot_dataset_factory):
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"""With episodes shorter than the sampling floor, sampling is a no-op and
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must produce exactly the same stats as the exact path."""
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dataset = lerobot_dataset_factory(
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root=tmp_path / "ds", total_episodes=2, total_frames=40, use_videos=False
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)
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exact = compute_quantile_stats_for_dataset(dataset, use_sampling=False)
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sampled = compute_quantile_stats_for_dataset(dataset, use_sampling=True)
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for key in _image_keys(dataset):
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assert sampled[key]["count"][0] == exact[key]["count"][0]
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def test_quantile_stats_present_after_compute(tmp_path, lerobot_dataset_factory):
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"""The computed stats should contain quantile keys for the dataset."""
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dataset = lerobot_dataset_factory(
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root=tmp_path / "ds", total_episodes=2, total_frames=200, use_videos=False
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)
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stats = compute_quantile_stats_for_dataset(dataset, use_sampling=True)
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assert has_quantile_stats(stats)
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class FakeHFDataset:
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"""Minimal stand-in exposing the column slicing used by the augment script."""
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def __init__(self, columns: dict[str, list]):
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self._columns = columns
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def select_columns(self, keys):
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return FakeHFDataset({key: self._columns[key] for key in keys})
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def __getitem__(self, index):
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return {key: values[index] for key, values in self._columns.items()}
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def test_compute_quantile_stats_skips_language_features():
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class FakeDataset:
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num_episodes = 1
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features = {
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"action": {"dtype": "float32"},
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"observation.language": {"dtype": "language"},
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}
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meta = SimpleNamespace(episodes=[{"dataset_from_index": 0, "dataset_to_index": 2}])
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hf_dataset = FakeHFDataset(
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{
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"action": [[0.0], [1.0]],
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"observation.language": [
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[{"role": "user", "content": "pick"}],
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[{"role": "assistant", "content": "done"}],
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],
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}
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)
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stats = compute_quantile_stats_for_dataset(FakeDataset())
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assert set(stats) == {"action"}
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def test_compute_quantile_stats_skip_images_avoids_decoding():
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class FakeDataset:
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num_episodes = 1
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features = {
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"action": {"dtype": "float32"},
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"observation.images.cam": {"dtype": "video"},
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}
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meta = SimpleNamespace(episodes=[{"dataset_from_index": 0, "dataset_to_index": 2}])
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hf_dataset = FakeHFDataset({"action": [[0.0], [1.0]]})
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def __getitem__(self, index):
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raise AssertionError(f"video frame {index} was decoded despite skip_images=True")
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stats = compute_quantile_stats_for_dataset(FakeDataset(), skip_images=True)
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assert set(stats) == {"action"}
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def test_compute_quantile_stats_handles_single_frame():
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class FakeDataset:
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num_episodes = 1
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features = {"action": {"dtype": "float32"}}
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meta = SimpleNamespace(episodes=[{"dataset_from_index": 0, "dataset_to_index": 1}])
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hf_dataset = FakeHFDataset({"action": [[5.0, 7.0]]})
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stats = compute_quantile_stats_for_dataset(FakeDataset())
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np.testing.assert_array_equal(stats["action"]["count"], np.array([1]))
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for key in ("min", "max", "mean", "q01", "q10", "q50", "q90", "q99"):
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np.testing.assert_allclose(stats["action"][key], np.array([5.0, 7.0]))
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def test_compute_quantile_stats_image_count_uses_frames():
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frames = [torch.zeros(3, 2, 2), torch.ones(3, 2, 2)]
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class FakeDataset:
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num_episodes = 1
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features = {"observation.images.cam": {"dtype": "video"}}
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meta = SimpleNamespace(episodes=[{"dataset_from_index": 0, "dataset_to_index": 2}])
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hf_dataset = FakeHFDataset({})
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def __getitem__(self, index):
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return {"observation.images.cam": frames[index]}
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stats = compute_quantile_stats_for_dataset(FakeDataset(), use_sampling=False)
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image_stats = stats["observation.images.cam"]
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np.testing.assert_array_equal(image_stats["count"], np.array([2]))
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assert image_stats["mean"].shape == (3, 1, 1)
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np.testing.assert_allclose(image_stats["mean"], np.full((3, 1, 1), 0.5))
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def test_compute_quantile_stats_accumulates_across_episodes():
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values = [[float(value)] for value in range(100)] + [[float(value)] for value in range(1000, 1010)]
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class FakeDataset:
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num_episodes = 2
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features = {"action": {"dtype": "float32"}}
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meta = SimpleNamespace(
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episodes=[
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{"dataset_from_index": 0, "dataset_to_index": 100},
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{"dataset_from_index": 100, "dataset_to_index": 110},
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]
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)
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hf_dataset = FakeHFDataset({"action": values})
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stats = compute_quantile_stats_for_dataset(FakeDataset())
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np.testing.assert_array_equal(stats["action"]["count"], np.array([110]))
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expected_q90 = np.percentile(np.asarray(values), 90, axis=0)
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np.testing.assert_allclose(stats["action"]["q90"], expected_q90, atol=0.1)
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