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feat(depth maps): adding support for depth in LeRobot (#3644)
* feat(depth): add depth quantization helpers and tests
* feat(video): add ffv1 to supported codecs
* feat(depth): persist depth metadata
* feat(depth): extend quantization tools to better fit the encoding/decoding pipeline
* feat(depth): plumb DepthEncoderConfig through LeRobotDataset and DatasetWriter
* feat(depth): wire StreamingVideoEncoder + writer to depth encoder
* feat(depth): wire DatasetReader to decode_depth_frames
* feat(cameras/realsense): expose async depth in metric meters
* feat(features): route 2D camera shapes to observation.depth.<key>
* feat(robots/so_follower): emit + populate depth keys when use_depth
* feat(record): plumb DepthEncoderConfig through lerobot-record
* feat(viz): render depth observations as rr.DepthImage in Viridis
* feat(depth maps writer): adding support for raw depth maps recording with image writer
* chore(format): format code
* feat(depth shape): ensuring depth maps shape is always including the channel
* feat(is_depth): simplifying is_depth nested name + legacy support
* fix(stop_event): fixing stop_event race condition in camera classes
* fix(plumbing): fixing missing parts in the depth maps pipeline
* chore(typos): fixing typos
* test(fix): fixing exisiting tests to still work with latest features
* tests(depth): adding new tests for depth integration validation
* feat(pix_fmt channels): use PyAv to check get pixel formats number of channels
* feat(refactor): refactor DepthEncoderConfig quantization pipeline, so that the methods do not live in the config class. Add pixel format - channels validation.Move the default pixel format for depth in the config file.
* fix(pre-commit): fixing mutable defautl value
* fix(info): fixing info metadata update when is_depth_map was set
* tests(typos): fixing typos in tests
* fix(realsense): fixing typo in realsense serial number
* fix(normalization): restricting 255 normalization to non depth/uint8 images only
* fix(typo): fixing typo
* fix(TIFF): add missing quantization and cleanup for TIFF files
* feat(batched dequantization): optimizing dequantize_depth for torch based batched dequantization
* feat(tools): adding depth support in LeRobotDataset edition tools
* test(aggregate): extending aggregation tests to depth frames
* test(cleaning): cleaning up tests
* fix(from_video_info): fixing early validation issue in from_video_info
* fix(typo): fixing typo
* fix(is_depth): adding missing doctrings and is_depth arguments in video decoding functions
Co-authored-by: Wensi (Vince) Ai <59036629+wensi-ai@users.noreply.github.com>
* fix(depth units): fixing depth units output for the realsense cameras
* feat(output unit): adding support for output unit specification at dataset reading/training time
Co-authored-by: Wensi (Vince) Ai <59036629+wensi-ai@users.noreply.github.com>
* test(depth): cleaning up depth tests
* test(depth encoding): updating and cleaning video/depth encoding tests
* chore(format): formatting code
* docs(depth): improving depth maps docs
* test(fix): fixing depth tests
* test(dataset tools): adding missing tests for new dataset edition tools features
* chore(format): formatting code
* fix(pyav check): fixing PyAV option validation for integer codec options by normalizing
numeric values before calling `is_integer()`
Co-authored-by: Wensi (Vince) Ai <59036629+wensi-ai@users.noreply.github.com>
* docs(mermaid): fixing mermaid diagram
* fix(rebase): rebase follow up corrections
* feat(dataset tools): adding missing docstrings and features for depth fill support in dataset edition tools
* docs(docstring): updating docstrings
* docs(dataset tools): updating docs
* fix(save images): fixing image saving in dataset tools
* fix(update video info): fixing update video info logic to match the recording and editing use cases
* test(reencode): fixing reencoding monkeypatch
* fix(review): add Claude review
* chore(format): format code
* fix(update video info): ditching the differentiated approahces for video info update - video info are always updated unless for preserved keys.
* chore(rebase): fixing rebase merge conflicts
* test(visualization): fixing visualization tests
* feat(docstrings): adding explicit docstring for encoding parameters. Docstrigns will now show up as description in the CLI --help.
* feat(mm as default): adding a global DEFAULT_DEPTH_UNIT variable setting mm as default depth unit
* fix(RGB <-> camera): renaming camera_encoder to rgb_encoder for clarity
* chore(TODO): removing deprecated TODO
* doc(write_u16_plane): improving docstrings for write_u16_plane
* feat(units): adding constants for depth frames units (m and mm)
* fix(spam): replacing spamming warning but a debug log
* feat(leagcy metadata): adding automatic metadata update for legacy 'video.is_depth_map' feature
* fix(copy&reindex): fixing metadat reshaping for single channel frames
* fix(ImageNet): excluding dpeth frames from ImageNet stats
* fix(PyAV container seek): fixing initial PyAV container seek to be robust againsy codec choice
* feat(lerobot-dataset-viz): adding support for depth in lerobot-dataset-viz
* fix(compress): removing rerun compression for DepthImages
* fix(signle channel squeeze): fixing single channel squeezing
* chore(format): format code
* fix(streaming): adding support for dequantization in streaming_dataset.py
* refactor(read depth): factorizing depth reading methods for realsense camera and adding support for depth-only usage
* chore(renaming): fixing missed RGBEncoderConfig renamings
* docs(renaming): reflecting renamings in a clearer way in the docs
* chore(annotation): excluding depth from the annotation pipeline
* feat(robots): adding depth support in compatible follower robots
* feat(LeSadKiwi): excluding LeKiwi from depth support (for now)
* chore(fail): removing misplaced file
* chore(fail): removing misplaced file
* fix(remove ffv1): removing ffv1 as it does not support MP4
* docs(cheat sheet): adding depth and video encoding to the cheat sheet
* fix(lossless): tuning depth encoding parameters for lossless depth storage
* test(fix): fixing failing tests
* depth(ZMQ): excluding ZMQ from depth support
* Revert "depth(ZMQ): excluding ZMQ from depth support"
This reverts commit b95cf4e4c2.
* fix(image transforms): excluding depth frames from images transforms
* fix(typo): typo
* fix(stats): fixing stats computation for depth frames
* fix(TIFF vs. pytorch): adding an extra uint16 to float32 conversion for depth maps stored as raw TIFF images
* fix(typos): fixing typos
* test(dtype): fixing stats computation typing tests
---------
Signed-off-by: Steven Palma <imstevenpmwork@ieee.org>
Co-authored-by: Wensi (Vince) Ai <59036629+wensi-ai@users.noreply.github.com>
Co-authored-by: Steven Palma <imstevenpmwork@ieee.org>
Co-authored-by: Wensi Ai <wsai@stanford.edu>
This commit is contained in:
@@ -29,7 +29,10 @@ from lerobot.configs import VIDEO_ENCODER_INFO_KEYS
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from lerobot.datasets.aggregate import aggregate_datasets
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from lerobot.datasets.feature_utils import features_equal_for_merge
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from lerobot.datasets.lerobot_dataset import LeRobotDataset
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from tests.fixtures.constants import DUMMY_REPO_ID
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from tests.fixtures.constants import (
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DUMMY_CAMERA_FEATURES_WITH_DEPTH,
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DUMMY_REPO_ID,
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)
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def assert_data_shards_one_row_group_per_episode(root):
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@@ -211,6 +214,26 @@ def assert_dataset_iteration_works(aggr_ds):
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pass
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def assert_depth_keys_preserved(aggr_ds, ds_0, ds_1):
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"""Test that depth keys are correctly preserved after aggregation.
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Ensures that the ``is_depth_map`` marker on visual features survives
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aggregation, so that downstream consumers (e.g. the dataset reader's
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depth decoding path) keep working on the merged dataset.
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"""
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expected_depth_keys = set(ds_0.meta.depth_keys)
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assert expected_depth_keys == set(ds_1.meta.depth_keys), (
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"Source datasets disagree on depth_keys; test setup is inconsistent"
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)
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actual_depth_keys = set(aggr_ds.meta.depth_keys)
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assert actual_depth_keys == expected_depth_keys, (
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f"Expected depth_keys {expected_depth_keys}, got {actual_depth_keys}"
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)
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for key in expected_depth_keys:
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info = aggr_ds.meta.info.features[key].get("info") or {}
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assert info.get("is_depth_map") is True, f"Depth marker lost on feature {key!r} after aggregation"
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def assert_video_timestamps_within_bounds(aggr_ds):
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"""Test that all video timestamps are within valid bounds for their respective video files.
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@@ -260,7 +283,11 @@ def assert_video_timestamps_within_bounds(aggr_ds):
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def test_aggregate_datasets(tmp_path, lerobot_dataset_factory):
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"""Test basic aggregation functionality with standard parameters."""
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"""Test basic aggregation functionality with standard parameters.
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Source datasets include both RGB and depth video features so the same
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aggregation flow is exercised on the ``is_depth_map`` branch.
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"""
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ds_0_num_frames = 400
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ds_1_num_frames = 800
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ds_0_num_episodes = 10
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@@ -272,14 +299,21 @@ def test_aggregate_datasets(tmp_path, lerobot_dataset_factory):
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repo_id=f"{DUMMY_REPO_ID}_0",
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total_episodes=ds_0_num_episodes,
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total_frames=ds_0_num_frames,
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camera_features=DUMMY_CAMERA_FEATURES_WITH_DEPTH,
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)
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ds_1 = lerobot_dataset_factory(
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root=tmp_path / "test_1",
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repo_id=f"{DUMMY_REPO_ID}_1",
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total_episodes=ds_1_num_episodes,
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total_frames=ds_1_num_frames,
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camera_features=DUMMY_CAMERA_FEATURES_WITH_DEPTH,
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)
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# Confirm depth was actually wired into the source datasets so the
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# rest of the assertions exercise the depth aggregation path.
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assert len(ds_0.meta.depth_keys) > 0, "ds_0 should expose at least one depth key"
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assert len(ds_1.meta.depth_keys) > 0, "ds_1 should expose at least one depth key"
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aggregate_datasets(
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repo_ids=[ds_0.repo_id, ds_1.repo_id],
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roots=[ds_0.root, ds_1.root],
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@@ -306,6 +340,7 @@ def test_aggregate_datasets(tmp_path, lerobot_dataset_factory):
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assert_episode_indices_updated_correctly(aggr_ds, ds_0, ds_1)
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assert_video_frames_integrity(aggr_ds, ds_0, ds_1)
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assert_video_timestamps_within_bounds(aggr_ds)
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assert_depth_keys_preserved(aggr_ds, ds_0, ds_1)
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assert_dataset_iteration_works(aggr_ds)
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@@ -423,7 +458,11 @@ def test_aggregate_incomplete_video_encoder_info_warns_and_nuls_encoders(
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def test_aggregate_with_low_threshold(tmp_path, lerobot_dataset_factory):
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"""Test aggregation with small file size limits to force file rotation/sharding."""
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"""Test aggregation with small file size limits to force file rotation/sharding.
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Depth video features are included to verify that file rotation/concat
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correctly handles depth-marked features alongside regular RGB ones.
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"""
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ds_0_num_episodes = ds_1_num_episodes = 10
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ds_0_num_frames = ds_1_num_frames = 400
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@@ -432,14 +471,19 @@ def test_aggregate_with_low_threshold(tmp_path, lerobot_dataset_factory):
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repo_id=f"{DUMMY_REPO_ID}_small_0",
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total_episodes=ds_0_num_episodes,
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total_frames=ds_0_num_frames,
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camera_features=DUMMY_CAMERA_FEATURES_WITH_DEPTH,
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)
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ds_1 = lerobot_dataset_factory(
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root=tmp_path / "small_1",
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repo_id=f"{DUMMY_REPO_ID}_small_1",
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total_episodes=ds_1_num_episodes,
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total_frames=ds_1_num_frames,
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camera_features=DUMMY_CAMERA_FEATURES_WITH_DEPTH,
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)
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assert len(ds_0.meta.depth_keys) > 0, "ds_0 should expose at least one depth key"
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assert len(ds_1.meta.depth_keys) > 0, "ds_1 should expose at least one depth key"
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# Use the new configurable parameters to force file rotation
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aggregate_datasets(
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repo_ids=[ds_0.repo_id, ds_1.repo_id],
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@@ -470,6 +514,7 @@ def test_aggregate_with_low_threshold(tmp_path, lerobot_dataset_factory):
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assert_episode_indices_updated_correctly(aggr_ds, ds_0, ds_1)
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assert_video_frames_integrity(aggr_ds, ds_0, ds_1)
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assert_video_timestamps_within_bounds(aggr_ds)
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assert_depth_keys_preserved(aggr_ds, ds_0, ds_1)
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assert_dataset_iteration_works(aggr_ds)
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# Check that multiple files were actually created due to small size limits
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@@ -489,7 +534,8 @@ def test_video_timestamps_regression(tmp_path, lerobot_dataset_factory):
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"""Regression test for video timestamp bug when merging datasets.
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This test specifically checks that video timestamps are correctly calculated
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and accumulated when merging multiple datasets.
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and accumulated when merging multiple datasets. Depth video features are
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included so depth timestamps are also covered by the regression.
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"""
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datasets = []
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for i in range(3):
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@@ -498,9 +544,13 @@ def test_video_timestamps_regression(tmp_path, lerobot_dataset_factory):
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repo_id=f"{DUMMY_REPO_ID}_regression_{i}",
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total_episodes=2,
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total_frames=100,
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camera_features=DUMMY_CAMERA_FEATURES_WITH_DEPTH,
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)
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datasets.append(ds)
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for i, ds in enumerate(datasets):
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assert len(ds.meta.depth_keys) > 0, f"Dataset {i} should expose at least one depth key"
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aggregate_datasets(
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repo_ids=[ds.repo_id for ds in datasets],
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roots=[ds.root for ds in datasets],
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@@ -517,12 +567,21 @@ def test_video_timestamps_regression(tmp_path, lerobot_dataset_factory):
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aggr_ds = LeRobotDataset(f"{DUMMY_REPO_ID}_regression_aggr", root=tmp_path / "regression_aggr")
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assert_video_timestamps_within_bounds(aggr_ds)
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# Depth keys must survive the merge for the regression to cover the
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# ``is_depth_map`` decoding branch.
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assert set(aggr_ds.meta.depth_keys) == set(datasets[0].meta.depth_keys)
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depth_keys = set(aggr_ds.meta.depth_keys)
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for i in range(len(aggr_ds)):
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item = aggr_ds[i]
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for key in aggr_ds.meta.video_keys:
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assert key in item, f"Video key {key} missing from item {i}"
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assert item[key].shape[0] == 3, f"Expected 3 channels for video key {key}"
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# Depth frames are single-channel (1, H, W) after dequantization;
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# standard RGB frames keep the 3-channel layout.
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expected_channels = 1 if key in depth_keys else 3
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assert item[key].shape[0] == expected_channels, (
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f"Expected {expected_channels} channels for video key {key}, got {item[key].shape}"
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)
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def assert_image_schema_preserved(aggr_ds):
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@@ -639,25 +698,31 @@ def test_aggregate_image_datasets(tmp_path, lerobot_dataset_factory):
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ds_0_num_episodes = 2
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ds_1_num_episodes = 3
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# Create two image-based datasets (use_videos=False)
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# Create two image-based datasets (use_videos=False) with a mix of RGB
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# and depth-marked cameras so the depth path is exercised in image mode.
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ds_0 = lerobot_dataset_factory(
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root=tmp_path / "image_0",
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repo_id=f"{DUMMY_REPO_ID}_image_0",
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total_episodes=ds_0_num_episodes,
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total_frames=ds_0_num_frames,
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use_videos=False, # Image-based dataset
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use_videos=False,
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camera_features=DUMMY_CAMERA_FEATURES_WITH_DEPTH,
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)
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ds_1 = lerobot_dataset_factory(
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root=tmp_path / "image_1",
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repo_id=f"{DUMMY_REPO_ID}_image_1",
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total_episodes=ds_1_num_episodes,
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total_frames=ds_1_num_frames,
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use_videos=False, # Image-based dataset
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use_videos=False,
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camera_features=DUMMY_CAMERA_FEATURES_WITH_DEPTH,
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)
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# Verify source datasets have image keys
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assert len(ds_0.meta.image_keys) > 0, "ds_0 should have image keys"
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assert len(ds_1.meta.image_keys) > 0, "ds_1 should have image keys"
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# And that the depth marker actually made it onto an image feature.
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assert len(ds_0.meta.depth_keys) > 0, "ds_0 should expose at least one depth key"
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assert len(ds_1.meta.depth_keys) > 0, "ds_1 should expose at least one depth key"
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# Aggregate the datasets
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aggregate_datasets(
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@@ -692,6 +757,7 @@ def test_aggregate_image_datasets(tmp_path, lerobot_dataset_factory):
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# Image-specific assertions
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assert_image_schema_preserved(aggr_ds)
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assert_image_frames_integrity(aggr_ds, ds_0, ds_1)
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assert_depth_keys_preserved(aggr_ds, ds_0, ds_1)
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# Verify images can be accessed and have correct shape
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sample_item = aggr_ds[0]
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