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81 Commits
| Author | SHA1 | Message | Date | |
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 pepijn
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c80ddfe22c |
Merge remote-tracking branch 'origin/main' into feat/smolvla-on-steerable
Co-authored-by: Cursor <cursoragent@cursor.com> # Conflicts: # src/lerobot/configs/train.py # src/lerobot/datasets/__init__.py # src/lerobot/policies/factory.py # src/lerobot/policies/groot/groot_n1.py # src/lerobot/scripts/lerobot_eval.py # src/lerobot/scripts/lerobot_train.py # uv.lock |
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Caroline Pascal
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7ae12124b0 |
fix(save codec options): making sure codec options are always set via set_if (#3910)
* fix(save codec options): making sure codec options are always safely set through `set_if` * tests(update): updating tests |
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Pepijn
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4fa9578e3d |
refactor(pi052): trim PR — remove say tool, debug gates, dead code; move runtime
Cleanup pass over the language-support PR to cut LOC and scope creep. Removals: - SayTool + tools/ package (registry, Tool protocol, [tools] extra) and the runtime's tool-dispatch path. Kept <say> training supervision and inference stripping so speech-annotated datasets still train. - WeightedEpisodeAwareSampler + VQA oversampling wiring (_build_vqa_oversample_weights, vqa_target_fraction) — training uses plain EpisodeAwareSampler again. - Debug env-gates PI052_DEBUG_TENSORS, PI052_SUBTASK_USE_TASK, EVAL_TASK_OVERRIDE. - Dead code: broken _tp._DUMP_BUDGET block, unused imports (copy/Tensor, RevisionNotFoundError, LeRobotDataset, os), messages_for_vqa, steps.py shim (modeling imports pi052_adapter directly), duplicated _emit, builtins.type[T]. Moves: - Policy-agnostic runtime -> src/lerobot/runtime/ (LanguageConditionedRuntime + adapter Protocol + state); pi052 keeps only its adapter + CLI. Tests -> tests/runtime/. Other: - Compacted verbose AI-authored comments/docstrings across pi052 (kept the hard-won DDP / barrier-timeout / reduce-max / VQA-routing notes). - Relocated LM-head prediction debug helper to pi052/debug_utils.py. - Fixed test_render_messages: assert task-fallback render (current behavior) instead of the stale no-op expectation. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> |
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Caroline Pascal
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c746ca2df2 |
fix(depth unit): adding input depth unit storage in the dataset metadata (#3899)
* fix(depth unit): storing raw depth units in the dataset metadata for correct depth statistics and depth raw frames handling. The unit is stored as a string ("m","mm") under "depth_unit" at the same level as "is_depth_map". Unit is inferred from the depth frame type.
* feat(raw frame unit): adapting dataset reader so that raw depth frames are scaled according to the requested unit
* feat(stats units): rescaling stats when loading a dataset so that the stats are given in the requested unit
* tests(unit): adapting and extending depth tests to units manipulations
* chore(format): formating code
* feat(warning): adding a warning when depth unit is not specified in the dataset
* chore(infer_depth_unit): moving the depth unit inference utility in a more accessible location
* feat(rerun unit): adding correct depth unit display for rerun (foxglove does not support units yet)
* feat(unit getter): adding a proper output_depth_unit getter to LeRobotDataset for cleaner integration
* fix(streaming dataset): extending support for depth units to streaming datasets
* test(rerun): fixing rerun tests
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Caroline Pascal
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b961d2a8c5 | feat(libaom-av1): adding support for libaom-av1 codec (#3898) | ||
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Caroline Pascal
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3dd19d043e |
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
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Pepijn
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4dbe83d3bc |
Merge remote-tracking branch 'origin/main' into feat/smolvla-on-steerable
# Conflicts: # docs/source/annotation_pipeline.mdx # examples/annotations/run_hf_job.py # pyproject.toml # src/lerobot/annotations/steerable_pipeline/config.py # src/lerobot/annotations/steerable_pipeline/frames.py # src/lerobot/annotations/steerable_pipeline/modules/plan_subtasks_memory.py # src/lerobot/annotations/steerable_pipeline/vlm_client.py # src/lerobot/annotations/steerable_pipeline/writer.py # src/lerobot/datasets/__init__.py # src/lerobot/datasets/sampler.py # src/lerobot/scripts/lerobot_annotate.py # src/lerobot/scripts/lerobot_train.py # tests/annotations/test_frames.py # tests/annotations/test_modules.py # tests/annotations/test_writer.py # tests/datasets/test_sampler.py # tests/scripts/test_lerobot_annotate.py # uv.lock |
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Caroline Pascal
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287c823f13 |
fix(features copy): adding deepcopy on LeRobot dataset features to avoid shallow copy leaks (#3826)
* fix(features copy): adding deepcopy on LeRobot dataset features to avoid shallow copy leaks * tests(test): adding new test |
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Pepijn
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58ccc01508 |
fix(datasets): enforce one parquet row group per episode in v3 data writes (#3807)
* fix(datasets): enforce one parquet row group per episode in v3 data writes LeRobot v3 data shards must hold exactly one row group per episode so a reader can fetch episode i with pq.ParquetFile(path).read_row_group(i) (a byte-range read) instead of loading the whole shard. The recording writer already does this (one write_table per episode); the aggregate and lerobot-annotate re-write paths instead concatenated many episodes and wrote them in one shot, collapsing the file to a single row group. - io_utils: add write_table_one_row_group_per_episode (one ParquetWriter, one write_table per episode — same pattern as the recording writer); to_parquet_with_hf_images embeds images then writes per-episode row groups; to_parquet_one_row_group_per_episode wraps it for plain frames - aggregate: route non-image data writes through the per-episode writer; leave the episodes-metadata parquet untouched (already one row/episode) - annotate: rewrite shards via the per-episode writer instead of a single bulk pq.write_table - tests: invariant coverage through the aggregate (image + video) and annotate paths No change to on-disk schema, paths, naming, rollover thresholds, or compression. Readers stay backward-compatible (old collapsed files load). * Update src/lerobot/datasets/io_utils.py Co-authored-by: Caroline Pascal <caroline8.pascal@gmail.com> Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com> * Update src/lerobot/datasets/io_utils.py Co-authored-by: Caroline Pascal <caroline8.pascal@gmail.com> Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com> * fix(datasets): correct indentation and add strict= in row-group helper The web-edited numpy version of write_table_one_row_group_per_episode had an over-indented line (IndentationError, breaking pre-commit + test collection) and a zip() without strict=. Fix both; behaviour unchanged. --------- Signed-off-by: Pepijn <138571049+pkooij@users.noreply.github.com> Co-authored-by: Caroline Pascal <caroline8.pascal@gmail.com> |
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Altman
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8515d456be |
fix(datasets): avoid uint8 overflow in image stats (#3697)
* fix(datasets): avoid uint8 overflow in image stats * fix(datasets): promote stats batches dynamically |
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Mahbod
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30790de178 |
feat(edit-dataset): add concatenate_videos opt-out to merge (#3663)
* feat(edit-dataset): add `concatenate_videos` opt-out to merge
When merging datasets, source mp4s are concatenated into shards capped at
`video_files_size_in_mb` (default 200 MB). This is great for dataloader
throughput but destroys per-episode (or per-source) video boundaries,
which is undesirable when you want to inspect, ship, or reuse the
individual mp4s.
Add a `concatenate_videos: bool = True` knob plumbed through
`MergeConfig` → `merge_datasets` → `aggregate_datasets` → `aggregate_videos`.
When False, each source mp4 is copied 1:1 to its own destination mp4 with
no re-muxing, so the merge preserves source video boundaries.
Usage:
lerobot-edit-dataset \
--new_repo_id user/merged \
--operation.type=merge \
--operation.repo_ids "['user/a', 'user/b']" \
--operation.concatenate_videos=false
Defaults are unchanged; the dataloader path is unaffected because the
`episodes.parquet` `from_timestamp`/`to_timestamp` index keeps working
regardless of whether each mp4 holds one or many episodes.
* feat(edit-dataset): extend concatenate opt-out to data files
Following review, add a concatenate_data flag mirroring concatenate_videos,
threaded through MergeConfig, merge_datasets, aggregate_datasets, aggregate_data
and append_or_create_parquet_file. Metadata index files still always concatenate.
Also trim the verbose docstrings and comments since the names are
self-explanatory, and extend the existing merge test to cover data files.
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Pepijn
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234c768dfb |
feat(datasets): deterministic, resumable shuffling for EpisodeAwareSampler (#3769)
* fix(datasets): expose a generator on EpisodeAwareSampler for distributed shuffle sync In distributed training, accelerate can only synchronize the shuffle permutation across ranks when the sampler exposes a generator attribute. EpisodeAwareSampler shuffled via the global torch RNG, so disjoint batch shards relied on every rank's global CPU RNG staying in lockstep forever; any rank-asymmetric RNG consumption (e.g. eval rollouts on the main process only) silently desynced the permutations and ranks trained on overlapping/missing samples. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * fix(train): seed sampler generator and gate dataset download per node - Pass a generator seeded with cfg.seed to EpisodeAwareSampler so accelerator.prepare registers it as the synchronized RNG and the shuffle order is reproducible. - Gate the initial make_dataset call on is_local_main_process instead of is_main_process: the global main process only exists on node 0, so on every other node all local ranks were downloading the dataset and building the Arrow cache concurrently. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * feat(datasets): add DeterministicEpisodeAwareSampler with O(1) memory and sample-exact resume Add a sampler that never materializes frame indices: it stores only per-episode boundaries (numpy, a few bytes per episode) and maps logical positions to frame indices on the fly with searchsorted. Shuffling uses a seeded Feistel permutation over [0, num_frames) (cycle-walking to the exact domain), so the data order is a pure function of (seed, epoch): - no RNG state to synchronize across distributed ranks, - constant memory and zero epoch-boundary cost at any dataset size, - O(1) seek to any position, enabling sample-exact resume. Opt in with --deterministic_sampler=true. On resume, lerobot-train maps the checkpointed step back to (epoch, start_index) via compute_sampler_state and continues at the exact sample where the run left off (up to accelerate's even_batches padding at epoch boundaries). The shuffle is pseudo-random rather than a true uniform permutation, the standard trade-off in large-scale training loaders. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * refactor(datasets): fold deterministic mode into EpisodeAwareSampler Instead of a parallel DeterministicEpisodeAwareSampler class, extend the existing EpisodeAwareSampler with a deterministic=True mode (seeded Feistel permutation, epoch auto-advance, state_dict/load_state_dict). The default mode is behavior-identical: same torch.randperm consumption and the same generator contract accelerate synchronizes; the O(N) Python index list is replaced by O(num_episodes) boundary arrays in both modes, with `indices` kept as a back-compat property. Passing a generator together with deterministic=True is rejected, and the state/seek methods raise outside deterministic mode. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * feat(train): enable deterministic_sampler by default Deterministic data order (sample-exact resume, no cross-rank RNG sync, O(1) sampler memory) is now the default for map-style training; set deterministic_sampler=false to restore the legacy RNG-based shuffle. Streaming datasets ignore the flag (the sampler path only applies to map-style datasets), replacing the previous hard validation error so streaming configs keep working with the new default. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * feat(datasets): default EpisodeAwareSampler to deterministic mode and trim comments deterministic=True is now the class default as well as the training default; the legacy RNG path requires an explicit deterministic=False (the train script's non-deterministic branch passes it). Docstrings and inline comments slimmed down across the changed files. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> * test(sampler): drain resumed trillion-frame sampler via iter() to avoid list() prealloc list(sampler) calls PyObject_LengthHint -> __len__ (the full 10**12 epoch length) and preallocates that many slots before iterating, OOMing even though the resumed epoch only yields 3 frames. Collect through the iterator (no length hint) so the test exercises the real O(1) seek/drain instead of CPython's list growth heuristic. * fix(datasets): guard Feistel cycle-walking loop against non-convergence Replace the unbounded while True in EpisodeAwareSampler._permute with a bounded for loop capped at _MAX_CYCLE_WALK_STEPS (100) and raise RuntimeError if the cycle-walk fails to land in [0, num_frames). The loop is expected to converge in <4 steps on the chosen power-of-two domain, so the bound is a safety net that should never trip in practice but prevents a pathological infinite loop. https://claude.ai/code/session_01HQ15tFrBsHYScjGWosEv22 * fix(datasets): make deterministic-sampler resume robust to world-size changes compute_sampler_state mapped a checkpointed step back to (epoch, start_index) using the *current* num_processes, but the number of sampler positions a step consumes scales with the world size that produced it. Resuming on a different GPU count therefore landed on the wrong epoch/offset, silently re-seeing or skipping data. Record num_processes in training_step.json at checkpoint time and feed the checkpoint's value into compute_sampler_state on resume, so the data order resumes at the right position regardless of the new world size. Warn when the world size changed (the global offset is correct, but per-rank sample-exactness needs the same topology). Old checkpoints without the field fall back to the current world size. Also document compute_sampler_state's assumptions explicitly: num_processes / batch_size must match the checkpointing run, and accelerate's even_batches=True padding is mirrored by the ceil(... / num_processes) term. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Co-authored-by: Cursor <cursoragent@cursor.com> * style: apply ruff-format to lerobot_train.py Collapse the compute_sampler_state(...) call onto one line so the ruff-format pre-commit hook passes (fixes the failing CI check). Co-authored-by: Cursor <cursoragent@cursor.com> * refactor(datasets): use seeded torch.randperm instead of Feistel in EpisodeAwareSampler Drop the Feistel permutation (and its SplitMix64 hash / cycle-walking) in favor of a torch.randperm seeded from (seed, epoch). The deterministic mode keeps its key properties - data order is a pure function of (seed, epoch), so it reproduces on every rank with no global-RNG synchronization, and - state_dict / load_state_dict still resume sample-exactly, now by regenerating the epoch's permutation and slicing from the saved offset. Construction stays O(num_episodes) (only episode boundaries are stored, never a per-frame index list). The trade-off vs Feistel: the per-epoch shuffle is again O(num_frames) memory (the randperm tensor) and no longer O(1)-seekable, in exchange for ~30 fewer LOC and a truly uniform shuffle. Tests updated: the trillion-frame O(1) test is replaced with a boundary-storage check and a scale resume-exactness test. Co-authored-by: Cursor <cursoragent@cursor.com> * refactor(datasets): make EpisodeAwareSampler always deterministic With Feistel gone, deterministic and legacy modes were both just torch.randperm and the deterministic path strictly dominated (reproducible across ranks via the (seed, epoch) seed, no accelerate generator sync, resumable). Collapse to a single path and drop the redundant flag: - remove the `deterministic` and `generator` constructor args, `_iter_default`, and `_require_deterministic`; `set_epoch` / `state_dict` / `load_state_dict` are now unconditional - remove the `deterministic_sampler` train config field and the legacy generator branch in lerobot_train.py (non-streaming map datasets always use the sampler) - drop the now-obsolete generator/legacy tests Note: removes the `generator` kwarg from EpisodeAwareSampler (back-compat break vs main); the order is now a pure function of (seed, epoch), so no cross-rank RNG sync is needed. Co-authored-by: Cursor <cursoragent@cursor.com> * fix(datasets): address sampler review (batch_size resume guard + docs) - Record batch_size in training_step.json alongside num_processes and feed the checkpoint's value into compute_sampler_state on resume; warn when it differs (per-rank sample-exactness needs the same batch size). - Document the set_epoch vs __iter__ auto-advance coupling on EpisodeAwareSampler (callers should rely on exactly one mechanism per run). - Note the broadened (reproducibility-breaking) sampler guard and the no-generator distributed sharding correctness in lerobot_train.py. - Add load_training_batch_size + parallel tests. Co-authored-by: Cursor <cursoragent@cursor.com> * fix(train): download dataset once on the global main process Gate the training dataset download on the global is_main_process (download once to the shared dataset root, barrier, then every other rank reads the already-populated copy) instead of per-node is_local_main_process. LeRobotDataset skips its snapshot_download when try_load() succeeds, so no rank re-downloads. Assumes the dataset root / HF cache is on storage shared across nodes. Co-authored-by: Cursor <cursoragent@cursor.com> * chore(datasets): trim sampler comment and drop duplicate tests Remove the verbose dataloader-guard comment and the two EpisodeAwareSampler tests that duplicated existing validation/warning coverage (no coverage loss). Co-authored-by: Cursor <cursoragent@cursor.com> --------- Co-authored-by: Claude Fable 5 <noreply@anthropic.com> Co-authored-by: Cursor <cursoragent@cursor.com> |
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Caroline Pascal
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0e9bd9e6fb |
feat(trim): adding optional trimming option in reencode_video (#3779)
* feat(trim): adding optional trimming option in reencode_video * tests(trim): add triming test --------- Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com> |
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Pepijn
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41166b39fb |
fix(train): synchronize EpisodeAwareSampler shuffling across ranks and gate dataset download per node (#3768)
* fix(datasets): expose a generator on EpisodeAwareSampler for distributed shuffle sync In distributed training, accelerate can only synchronize the shuffle permutation across ranks when the sampler exposes a generator attribute. EpisodeAwareSampler shuffled via the global torch RNG, so disjoint batch shards relied on every rank's global CPU RNG staying in lockstep forever; any rank-asymmetric RNG consumption (e.g. eval rollouts on the main process only) silently desynced the permutations and ranks trained on overlapping/missing samples. * fix(train): seed sampler generator and gate dataset download per node - Pass a generator seeded with cfg.seed to EpisodeAwareSampler so accelerator.prepare registers it as the synchronized RNG and the shuffle order is reproducible. - Gate the initial make_dataset call on is_local_main_process instead of is_main_process: the global main process only exists on node 0, so on every other node all local ranks were downloading the dataset and building the Arrow cache concurrently. |
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Pepijn
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1e9a6d044d |
Merge remote-tracking branch 'origin/feat/language-annotation-pipeline' into feat/smolvla-on-steerable
# Conflicts: # src/lerobot/datasets/__init__.py # src/lerobot/policies/__init__.py # src/lerobot/policies/factory.py # src/lerobot/processor/render_messages_processor.py # uv.lock |
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Pepijn
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1ff10b935c |
Merge branch 'feat/language-annotation-pipeline' into feat/smolvla-on-steerable
Resolves conflicts from 66 commits on the base branch: * pyproject.toml — keep base's transformers>=5.4.0,<5.6.0; add the sentencepiece-dep entry pi052 (FAST action tokenizer) needs. * policies/__init__.py — keep pi052 export; drop the RewardClassifierConfig export that base removed. * policies/factory.py — docstring list resolution (keep pi052; drop reward_classifier, removed by base). * annotations/steerable_pipeline/executor.py — adopt base's renamed _ensure_annotation_metadata_in_info (it already advertises the say tool); drop pi052's older _ensure_tools_in_info call. * configs/train.py — keep pi052's vqa_target_fraction; adopt base's SampleWeightingConfig (legacy RA-BC inline params already covered by the migration shim base added). * scripts/lerobot_train.py — merge pi052's per-policy processor rebuild + dataset_repo_id pass-through with base's active_cfg / is_reward_model_training tightening, and re-route vqa-weighted sampler to active_cfg.drop_n_last_frames. * datasets/language_render.py — adopt base's _select_one + timestamp tolerance (drops pi052's stale _select_latest / per-style sort_key). * tests — adopt base's parametrized per-camera blend + tolerance test; drop pi052 tests that overlap with base's tighter rewrites; keep pi052's flow-only / VQA-blend coverage; add a test_canonical_recipe_loads check on subtask_mem_vqa_speech.yaml. * policies/pi052/processor_pi052.py — import RenderMessagesStep directly from render_messages_processor (base intentionally dropped it from lerobot.processor's re-exports). * uv.lock — regenerated cleanly from base + pi052's pocket-tts / beartype. All 67 touched tests pass (30 pi052 + 37 recipe / language-render / pipeline / render-messages). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Roham Z. Nobari
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dfdc48a7f1 |
fix(datasets): bound VideoDecoderCache to prevent OOM on large datasets (#3614)
VideoDecoderCache used an unbounded dict keyed on absolute path, with no eviction in the standard LeRobotDataset path. With shuffled iteration over datasets that have many distinct mp4 files, every DataLoader worker accumulated one cached (VideoDecoder, fsspec file handle) pair per distinct path it had ever touched. Per-entry cost is ~3-5 MB of host RAM plus one open FD; at ~8 k entries this is roughly 30 GB per worker. This was hit in the wild during a SmolVLA training run on a 4,195-episode SO-101 dataset (8,390 mp4s, two cameras per episode). dmesg showed anon-rss climbing to 34.9 GB on a single pt_data_worker before the OOM killer fired ~30 min into training; with --num_workers=8 the per-worker peak halved to 17.9 GB, which is the expected inverse-scaling signature when the leak is per-decode and the workload is split across workers. The working workaround on the affected platform was --dataset.video_backend=pyav, because the pyav path opens/closes per call and never touches this cache. Switch the backing store to an OrderedDict and evict LRU entries when the cap is reached, closing the evicted file handle inside the lock so we do not leak FDs either. Default cap is DEFAULT_DECODER_CACHE_SIZE = 100, overridable via LEROBOT_VIDEO_DECODER_CACHE_SIZE or by passing max_size= to the constructor; max_size=None restores the legacy unbounded behaviour for callers that need it. Validation on the original failing workload (decode_video_frames_torchcodec called over real mp4s from the affected SO-101 dataset): unbounded: 300 files -> +1087 MB host RSS, cache=300, still climbing cap=50: 500 files -> +266 MB host RSS, cache=50, stable cap=50: 2000 calls -> +312 MB host RSS, cache=50, stable cap=100: 1000 calls -> +470 MB host RSS, cache=100, stable Three independent seeded runs at cap=50 agreed to within 1% (263 / 266 / 265 MB delta), and the 2000-call multi-pass run shows RSS plateaus after the cap is reached instead of drifting. Tests in tests/datasets/test_video_decoder_cache.py cover: default-is-bounded, size cap, LRU ordering, FD close on eviction, FD close on clear(), cache-hit invariance, max_size=None fallback, and env-var override. No regressions in test_video_encoding.py, test_streaming.py, or test_dataset_reader.py (73 prior tests still pass alongside the 8 new ones). |
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|
四七
|
6a8878a639 |
fix(datasets): normalize shape=(1,) numeric values before HF encoding (#3344)
* fix(datasets): normalize shape=(1,) numeric values before save * test(datasets): cover shape=(1,) int/bool and finalize Co-authored-by: Copilot <copilot@github.com> |
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|
Caroline Pascal
|
d38eb89f71 |
feat(video re-encoding): Adding utility and dataset edition tool for video re-encoding (#3611)
* feat(utility): adding video re-encode utility * feat(edit): adding a new lerobot-edit-dataset tool to re-encode all the videos of a dataset * chore(format): formatting code * chore(review): fix Claude reviews * test(reencode dataset): adding missing test for reencode dataset |
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|
Pepijn
|
7ab4936b1b |
Add extensive language support (#3467)
* Add extensive language support * Address review: split persistent/event schemas, drop event timestamps - recipe.py: derive _VALID_ROLES/_VALID_STREAMS from MessageRole/MessageStream Literals - dataset_metadata.py: keep CODEBASE_VERSION at v3.0 - language.py: remove RESERVED_STYLES; split arrow/feature schemas into persistent (with timestamp) and event (without timestamp); add docstrings - language_render.py: events use frame-row timestamp implicitly; no per-event timestamp filtering or sorting - converters.py: drop unused subtask_key passthrough - add docstrings to new public APIs (recipe, render_messages_processor, collate) - update tests for split schemas; revert uv.lock Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * Add docstrings to all new helpers; revert uv.lock Covers private helpers in recipe.py, language.py, language_render.py, and render_messages_processor.py. Also reverts uv.lock to main (it was re-generated by `uv run` during local checks). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * feat(language): add motion (persistent) and trace (event-only) styles Promote the previously-reserved motion/trace styles to first-class core styles. motion routes to language_persistent (it tracks robot state over time); trace routes to language_events (single-moment annotations). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * feat(language): per-camera tagging on view-dependent styles Adds a nullable `camera` field to the language row struct (both persistent and event variants) so view-dependent styles like `vqa` can carry which `observation.images.*` view they were grounded against. Without this, multi-camera datasets ended up with multiple `(vqa, role)` rows at the same timestamp that the resolver could not disambiguate. - `language.py`: add `camera` to PERSISTENT_ROW_FIELDS / EVENT_ROW_FIELDS, to both Arrow struct types and the HF datasets feature mappings; introduce VIEW_DEPENDENT_STYLES = {vqa, motion, trace} plus `is_view_dependent_style` and `validate_camera_field` helpers (camera required iff style is view-dependent). - `language_render.py`: thread an optional `camera=` kwarg through every resolver (`active_at`, `emitted_at`, `nth_prev`, `nth_next`) and through `_matching_rows` / `_select_*`, so recipes can disambiguate per-camera VQA with `emitted_at(t, style=vqa, role=assistant, camera=...)`. Without a `camera` filter, multi-row matches keep raising the existing ambiguity error — which is the desired behaviour on multi-camera data. - `recipes/pi05_hirobot.yaml`: replace the single `ask_vqa` branch with `ask_vqa_top` and `ask_vqa_wrist` per-camera sub-recipes (each carrying the matching image block), keeping the original 0.20 budget and documenting the customization point for datasets with different cameras. - Tests: schema test asserts the new field order; new tests cover `is_view_dependent_style`, `validate_camera_field` (both required and forbidden directions), per-camera `emitted_at` filtering, and the ambiguity error when two cameras emit `(vqa, assistant)` at the same timestamp without a `camera=` filter. RenderMessagesStep + dataset passthrough fixtures updated to include the new field. - `docs/source/language_and_recipes.mdx`: document the `camera` field, the per-camera resolver pattern, and the canonical recipe convention. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * fix(language): drop motion from VIEW_DEPENDENT_STYLES Motion primitives are described in robot-frame (joint / Cartesian) terms, not pixel space, so they are camera-agnostic. Only `vqa` (event) and `trace` (event, pixel-trajectory) are view-dependent. The `camera` field stays on PERSISTENT_ROW_FIELDS for schema symmetry — the validator, resolver, and HF feature mapping behave identically across the two columns regardless of which styles populate `camera` today — but persistent rows now always have `camera=None` in practice. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * feat(language): task_aug style + automatic ${task} rephrasing rotation Adds task-prompt diversity (Xiao 2022 / CAST) without touching ``meta/tasks.parquet`` or forcing recipes to opt in. The plan reserved ``task_aug`` as a future style; this lands it now. - ``language.py``: add ``task_aug`` to ``CORE_STYLES`` and ``PERSISTENT_STYLES``. ``column_for_style("task_aug")`` returns ``language_persistent`` so PR 2 writers route it correctly. - ``language_render.py``: ``_resolve_task`` now consults the persistent slice for rows of ``style="task_aug", role="user"``. When any exist it picks one deterministically by ``sample_idx`` (blake2b-keyed, not Python's randomized hash) so an epoch sees every rephrasing of every episode while the same sample still resolves identically across reruns. Falls back to the canonical ``meta/tasks.parquet`` task when no rephrasings are present, so existing datasets and unannotated runs keep their behaviour. Explicit ``task=`` overrides still win. - Tests: rephrasing coverage across samples, determinism on repeat ``sample_idx``, fallback when persistent has no ``task_aug`` rows, and explicit override priority. Recipes get this for free: any ``${task}`` placeholder rotates through the available rephrasings. Recipes that want the literal canonical task can override the binding. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * feat(language): tool catalog in meta/info.json + LeRobotDatasetMetadata.tools Stores OpenAI-style function schemas at ``meta/info.json["tools"]`` so datasets can declare which tools are available (today: just ``say``; tomorrow: per-dataset extensions). The ``DEFAULT_TOOLS`` constant fills in for unannotated datasets so chat-template consumers don't have to special-case anything. Three pieces: - ``language.py``: ``SAY_TOOL_SCHEMA`` and ``DEFAULT_TOOLS`` constants. Single source of truth — PR 2's writer and PR 3's runtime tool registry will both import from here instead of duplicating the dict. - ``dataset_metadata.py``: ``LeRobotDatasetMetadata.tools`` property reads ``info.json["tools"]`` and falls back to ``DEFAULT_TOOLS``. Returns deep-copied dicts so callers can mutate the result safely. - ``docs/source/tools.mdx``: spec page covering the catalog, per-row invocations, and the three-step "how to add a new tool" workflow (declare schema, implement, register). Linked from the docs toctree under the Datasets section. This lays the groundwork for PR 2's pipeline writing the catalog out during annotation, and PR 3's ``src/lerobot/tools/`` package shipping runnable implementations (one file per tool — first up: ``say.py`` wrapping Kyutai's pocket-tts). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * Apply ruff and prettier formatting after merge Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * refactor(language): unify resolver dispatch and prune redundant test scaffolding * Drop the unused `events` kwarg from `active_at`/`nth_prev`/`nth_next`; only `emitted_at` actually consults events. The dispatcher in `_resolve_spec` now passes events conditionally. * Replace the dual `_persistent_sort_key`/`_event_sort_key` pair with a single `_row_sort_key` and drop the `sort_key` parameter from `_select_one`. Event rows lack `timestamp` (it is implicit in the frame) and now default to `0.0` for sort purposes — the `(style, role)` tiebreaker is unchanged. * Inline `_select_latest` into `active_at` (its only caller). * Collapse `emitted_at`'s dual-branch into one `_select_one` call. * Tighten `_validate_persistent_resolver` to a single `column_for_style(style) != LANGUAGE_PERSISTENT` check. * Parameterize `test_per_camera_blend_renders_both_views` over the two cameras and factor the sub-recipe builder into `_vqa_subrecipe` so the test no longer hand-rolls two near-identical recipe blocks. Net -98 LOC; behavior, public resolver names, and test expectations unchanged. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * fix(language): always raise on ambiguous resolver matches `_select_one` previously skipped its ambiguity check whenever any of `role`/`tool_name`/`camera` was set, on the assumption that the caller had already pinned down a unique row. That left a real ambiguity hole for VQA: with two cameras emitting `(vqa, assistant)` at the same frame, `emitted_at(..., role="assistant")` silently picked the first sorted row instead of telling the recipe to add `camera=...`. The existing `test_emitted_at_raises_on_ambiguous_per_camera_vqa` test already encoded the desired behavior. Tighten the check: any time `len(rows) > 1` we now raise with the selectors echoed back, so users see exactly which fields they passed and that more is needed to disambiguate. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * chore: fix CI — collapse short ValueError to one line, refresh uv.lock * `ruff format` on CI (newer version) wants the short `camera=None` ValueError on a single line. * `uv.lock` was stale relative to `pyproject.toml`'s `datasets>=4.7.0` pin (and picked up upstream `s390x` marker fixes for cuda packages). CI runs `uv sync --locked` which rejected the divergence. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * fix(language): keep base install green — drop processor re-export, gate dataset-extra tests `lerobot.processor` re-exported `RenderMessagesStep` at the package level, so importing anything from `lerobot.processor` pulled in `lerobot.datasets.language` → `lerobot.datasets/__init__.py` → `require_package("datasets")`, which fails in the Tier 1 base install that intentionally omits the `[dataset]` extra. The chain bricked collection for unrelated suites (`tests/policies/pi0_pi05/...`, `tests/envs/...`, etc.). * Stop re-exporting `RenderMessagesStep` from `lerobot.processor`. The only consumer (the test) already imports from the submodule. Document the deliberate omission in the module docstring. * Add `pytest.importorskip("datasets", ...)` (and `pandas` where needed) at the top of the four PR-added tests that exercise the language stack: - tests/datasets/test_language.py - tests/datasets/test_language_render.py - tests/processor/test_render_messages_processor.py - tests/utils/test_collate.py Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * fix(language): address review — tools accessor, motion docs, conditional collate * **`meta.tools` actually reads `info.json["tools"]`.** `DatasetInfo` had no `tools` field, so `from_dict` silently dropped the key (it warned about unknown fields then discarded them) and the property always returned `DEFAULT_TOOLS`. Added `tools: list[dict] | None` to the dataclass; `to_dict()` drops it when unset so existing datasets keep a clean `info.json`. Fixed the accessor to read `self.info.tools` (the previous `.get(...)` would have raised AttributeError on the dataclass anyway). Added regression tests: fallback when absent, round-trip from disk, and round-trip through `DatasetInfo.from_dict` / `to_dict`. * **`motion` is not view-dependent — fix the docs.** The mdx claimed rows of style `motion` must carry `camera`, but `VIEW_DEPENDENT_STYLES = {"vqa", "trace"}` and the validator agrees: motion primitives are joint/Cartesian-frame, not pixel-space. Updated both call-out paragraphs in `language_and_recipes.mdx`. * **Conditional `collate_fn` swap.** Added `meta.has_language_columns` and gate the `lerobot_collate_fn` swap in `lerobot_train.py` on it, so non-language datasets keep PyTorch's `default_collate`. Also added a pass-through test in `test_collate.py` that asserts on a plain tensor batch the custom collate matches `default_collate` key-for-key, plus a test for the `None`-sample drop path. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * review: dedupe regex, centralize column names, harden collate, more tests * **#2 — dedupe `_PLACEHOLDER_RE`.** The same regex was compiled in `recipe.py` and `language_render.py`. Promote to module-level `PLACEHOLDER_RE` in `recipe.py` (its primary owner — declares template syntax) and import from `language_render.py`. * **#3 — centralize language column names.** `io_utils.py` had hardcoded `{"language_persistent", "language_events"}` literals at two sites. Replace with `LANGUAGE_COLUMNS` import so a future column rename can't silently desync. * **#4 — defensive collate preserved-keys.** `lerobot_collate_fn` silently filtered language fields from samples that didn't have them, which would hand downstream consumers a preserved list shorter than the tensor batch. Now: if any sample carries a key, every sample in the batch must carry it; otherwise raise a `ValueError` so the upstream rendering bug surfaces at the boundary. * **#5 — `_scalar` rejects non-singleton lists.** Previously a zero- or multi-element list fell through and triggered confusing `float([])` errors downstream. Now raises `ValueError` with the actual length. * **#6 — refactor `_extract_complementary_data`.** Replace 11 lines of `key = {... if ... else {}}` plus an 11-line splat dict with a single `_COMPLEMENTARY_KEYS` tuple iterated once. * **#7 — document `EXTENDED_STYLES`.** Was an empty `set()` with no comment. Add a docstring explaining it's an intentional extension point: downstream modules append project-local styles before `column_for_style` is called. * **#9 — `tools.mdx` notes the runtime layer is future work.** The page referenced `src/lerobot/tools/`, `registry.py`, and `get_tools(meta)` — none exist in this PR. Added a callout at the start of "How to add your own tool" plus a note on the implementations paragraph. * **#10 — tests for YAML round-trip, malformed rows, blend validation.** `test_recipe.py` grew from 1 case to 12 covering: blend-or-messages exclusivity, target-turn requirement, blend emptiness, weight presence/positivity, nested-blend rejection, `from_dict` with nested blends, `from_yaml` / `load_recipe` agreement, top-level non-mapping rejection. Added a malformed-row test for `_normalize_rows` that asserts non-dict entries raise `TypeError`. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * review: emitted_at uses 0.1s tolerance; MessageTurn requires stream at construction * **Float tolerance in `emitted_at` for persistent styles.** The ``_timestamp(row) == t`` exact-equality check silently missed any caller that derived ``t`` arithmetically (e.g. ``frame_idx / fps``) even though the parquet timestamp would only differ by ULPs. Added ``EMITTED_AT_TOLERANCE_S = 0.1`` and check ``abs(...) <= tolerance`` instead, with a docstring explaining why exact equality wasn't enough and why 0.1 s is safe at typical 30–100 Hz control rates. Test asserts the new behavior at half-window (matches) and double-window (no match) using the constant so it stays in sync. * **`MessageTurn.stream` is required at construction.** It was typed ``MessageStream | None = None`` so YAML could omit ``stream:`` and pass the dataclass invariant — but ``_validate_rendered`` rejected ``None`` streams later, surfacing the error at the first sample instead of at recipe load. Now ``__post_init__`` raises ``ValueError`` if ``stream`` is ``None``, with the list of valid streams in the message. The redundant late-stage check in ``_validate_rendered`` is replaced with a one-line comment that cites the upstream invariant. Test pins the new construction-time rejection. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * docs(tools): drop follow-up-PR references Reword the two callouts in `tools.mdx` to describe the runtime layer in present tense ("not part of the catalog layer shipped today", "those modules don't yet exist in the tree") instead of pointing at a specific follow-up PR. Keeps the doc honest about what works now without coupling it to a particular release order. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> * review: address CarolinePascal feedback - language timestamps: float64 -> float32 to match LeRobotDataset frame timestamps (Arrow struct + HF feature) - dataset_metadata: hoist `.language` imports to module top — language.py has no lerobot imports, so there is no circular-import risk - dataset_metadata: add a `meta.tools` setter that persists the catalog to info.json and reloads `meta.info` - feature_utils: validate the `language` dtype instead of returning "" — warn (non-fatal) when a non-empty value is written at record time - centralize the scalar-unwrap helper as `lerobot.utils.utils.unwrap_scalar`, shared by render_messages_processor and language_render - docs: move `## Layer 2 — recipe anatomy` ahead of the resolver sections, which describe recipe bindings rather than dataset layout - language_render: note in EMITTED_AT_TOLERANCE_S that persistent rows change on a human-action timescale, not the camera frame rate Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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|
Pepijn
|
0f5f0e4091 |
refactor(recipes): rename recipes, drop pi05_hirobot
- hirobot.yaml -> subtasks_vqa.yaml - hirobot_memory.yaml -> subtask_mem_vqa_speech.yaml - pi05_hirobot.yaml -> deleted (stale: uses plan, top-camera names; superseded by the two recipes above) - smolvla2_hirobot.yaml -> deleted (was untracked stale junk) Updated the smolvla2 / pi052 `recipe_path` config defaults, all docstring / comment references, the annotation-pipeline + recipe docs, and the three tests that loaded pi05_hirobot.yaml (repointed to the renamed recipes; the low-level-branch and pipeline-render assertions now accept a flow-only `low_level` stream as valid supervision, since the new recipes' low_level_execution has no text-CE target). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Pepijn
|
fbcb9225f5 |
feat: oversample sparse VQA annotations (recipe consumption + weighted sampler)
VQA annotations are sparse, so VQA was badly underrepresented in training: its effective share was weight x density, and blend draws that picked an ask_vqa* sub-recipe for a non-VQA frame were wasted entirely. Two pieces: 1. Recipe-side consumption (language_render.py): render_sample now routes any frame that carries a VQA annotation to a matching ask_vqa* sub-recipe, regardless of the weighted blend draw. No VQA annotation is wasted and no draw lands on a non-renderable VQA recipe — VQA's recipe-side share now equals the VQA-annotation density. 2. Dataset-side oversampling (WeightedEpisodeAwareSampler + vqa_target_fraction): a new weighted, episode-aware sampler draws frames with replacement by per-frame weight. When TrainPipelineConfig.vqa_target_fraction is set, the train script scans language_events, weights VQA frames so they make up ~that fraction of the training stream, and uses the weighted sampler. This is what actually lets VQA exceed its natural density. Default None keeps uniform episode-aware sampling unchanged. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Pepijn
|
bf996c7938 |
fix(datasets): render flow-only low_level recipes instead of dropping them
A recipe whose only supervision is the action-expert flow loss (e.g.
`low_level_execution`: `user(${subtask})` with `stream: low_level` and no
`target` turn) was rejected at render time by `_render_message_recipe` and
`_validate_rendered`, both of which required at least one target turn.
The result: every blend draw of the flow-only recipe rendered to `None`,
`predict_actions` was never set, `run_flow` never fired, and the action
expert received no flow loss — leaving it at random init. Both gates now
also accept a `low_level`-stream turn as valid supervision.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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|
Caroline Pascal
|
bd9619dfc3 |
feat(encoding parameters): adding support for user provided video encoding parameters (#3455)
* chore(video backend): renaming codec into video_backend in get_safe_default_video_backend() * feat(pyav utils): adding suport for PyAV encoding parameters validation * feat(VideoEncoderConfig): creating a VideoEncoderConfig to encapsulate encoding parameters * feat(VideoEncoderConfig): propagating the VideoEncoderConfig in the codebase * chore(docs): updating the docs * feat(metadata): adding encoding parameters in dataset metadata * fix(concatenation compatibility): adding compatibility check when concatenating video files * feat(VideoEncoderConfig init): making VideoEncoderConfig more robust and adaptable to multiple backends * feat(pyav checks): making pyav parameters checks more robust * chore(duplicate): removing duplicate get_codec_options definition * test(existing): adapting existing tests * test(new): adding new tests for encoding related features * chore(format): fixing formatting issues * chore(PyAV): cleaning up PyAV utils and encoding parameters checks to stick to the minimun required tooling. * chore(format): formatting code * chore(doctrings): updating docstrings * fix(camera_encoder_config): Removing camera_encoder_config from LeRobotDataset, as it's only required in LeRobotDatasetWriter. * feat(default values): applying a consistent naming convention for default RGB cameras video encoder parameters * fix(rollout): propagating VideoEncoderConfig to the latest recording modes * chore(format): formatting code, fixing error messages and variable names * fix(arguments order): reverting changes in arguments order in StreamingVideoEncoder * chore(relative imports): switching to relative local imports within lerobot.datasets * test(artifacts): cleaning up artifacts for the video encoding tests * chore(docs): updating docs * chore(fromat): formatting code * fix(imports): refactoring the file architecture to avoid circular imports. VideoEncoderConfig is now defined in lerobot.configs and lazily imports av at runtime. * fix(typos): fixing typos and small mistakes * test(factories): updating factories * feat(aggregate): updating dataset aggregation procedure. Encoding tuning paramters (crf, g,...) are ignored for validation and changed to None in the aggregated dataset if incompatible. * docs(typos): fixing typos * fix(deletion): reverting unwanted deletion * fix(typos): fixing multiple typos * feat(codec options): passing codec options to lerobot_edit_dataset episode deletion tool * typo(typo): typo * fix(typos): fixing remaining typos * chore(rename): renaming camera_encoder_config to camera_encoder * docs(clean): cleaning and formating docs * docs(dataset): addind details about datasets * chore(format): formatting code * docs(warning): adding warning regarding encoding parameters modification * fix(re-encoding): removing inconsistent re-encoding option in lerobot_edit_dataset * typos(typos): typos * chore(format): resolving prettier issues * fix(h264_nvenc): fixing crf handling for h264_nvenc * docs(clean): removing too technical parts of the docs * fix(imports): fixing imports at the __init__ level * fix(imports): fixing not very pretty imports in video config file |
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|
Caroline Pascal
|
f218d5ab30 |
feat(episodes): adding support for metadata based episodes filtering (#3530)
* feat(episode filtering): adding support for episodes filtering at initialization time in LeRobotDataset * test(tests): adding tests * chore(format): formatting code * feat(performance): improving implementation for better performances on big datasets * chores(warning): improving warnings and errors for episodes filtering * test(invalid key): adding test for invalid filtering key * chore(format): formatting code |
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Pepijn
|
e7e5fca5de |
review: emitted_at uses 0.1s tolerance; MessageTurn requires stream at construction
* **Float tolerance in `emitted_at` for persistent styles.** The ``_timestamp(row) == t`` exact-equality check silently missed any caller that derived ``t`` arithmetically (e.g. ``frame_idx / fps``) even though the parquet timestamp would only differ by ULPs. Added ``EMITTED_AT_TOLERANCE_S = 0.1`` and check ``abs(...) <= tolerance`` instead, with a docstring explaining why exact equality wasn't enough and why 0.1 s is safe at typical 30–100 Hz control rates. Test asserts the new behavior at half-window (matches) and double-window (no match) using the constant so it stays in sync. * **`MessageTurn.stream` is required at construction.** It was typed ``MessageStream | None = None`` so YAML could omit ``stream:`` and pass the dataclass invariant — but ``_validate_rendered`` rejected ``None`` streams later, surfacing the error at the first sample instead of at recipe load. Now ``__post_init__`` raises ``ValueError`` if ``stream`` is ``None``, with the list of valid streams in the message. The redundant late-stage check in ``_validate_rendered`` is replaced with a one-line comment that cites the upstream invariant. Test pins the new construction-time rejection. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Pepijn
|
beb22afd81 |
review: dedupe regex, centralize column names, harden collate, more tests
* **#2 — dedupe `_PLACEHOLDER_RE`.** The same regex was compiled in
`recipe.py` and `language_render.py`. Promote to module-level
`PLACEHOLDER_RE` in `recipe.py` (its primary owner — declares
template syntax) and import from `language_render.py`.
* **#3 — centralize language column names.** `io_utils.py` had
hardcoded `{"language_persistent", "language_events"}` literals at
two sites. Replace with `LANGUAGE_COLUMNS` import so a future column
rename can't silently desync.
* **#4 — defensive collate preserved-keys.** `lerobot_collate_fn`
silently filtered language fields from samples that didn't have
them, which would hand downstream consumers a preserved list
shorter than the tensor batch. Now: if any sample carries a key,
every sample in the batch must carry it; otherwise raise a
`ValueError` so the upstream rendering bug surfaces at the boundary.
* **#5 — `_scalar` rejects non-singleton lists.** Previously a zero-
or multi-element list fell through and triggered confusing
`float([])` errors downstream. Now raises `ValueError` with the
actual length.
* **#6 — refactor `_extract_complementary_data`.** Replace 11 lines
of `key = {... if ... else {}}` plus an 11-line splat dict with a
single `_COMPLEMENTARY_KEYS` tuple iterated once.
* **#7 — document `EXTENDED_STYLES`.** Was an empty `set()` with no
comment. Add a docstring explaining it's an intentional extension
point: downstream modules append project-local styles before
`column_for_style` is called.
* **#9 — `tools.mdx` notes the runtime layer is future work.** The
page referenced `src/lerobot/tools/`, `registry.py`, and
`get_tools(meta)` — none exist in this PR. Added a callout at the
start of "How to add your own tool" plus a note on the
implementations paragraph.
* **#10 — tests for YAML round-trip, malformed rows, blend
validation.** `test_recipe.py` grew from 1 case to 12 covering:
blend-or-messages exclusivity, target-turn requirement, blend
emptiness, weight presence/positivity, nested-blend rejection,
`from_dict` with nested blends, `from_yaml` / `load_recipe`
agreement, top-level non-mapping rejection. Added a malformed-row
test for `_normalize_rows` that asserts non-dict entries raise
`TypeError`.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Pepijn
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d55b581ca1 |
fix(language): address review — tools accessor, motion docs, conditional collate
* **`meta.tools` actually reads `info.json["tools"]`.** `DatasetInfo`
had no `tools` field, so `from_dict` silently dropped the key (it
warned about unknown fields then discarded them) and the property
always returned `DEFAULT_TOOLS`. Added `tools: list[dict] | None`
to the dataclass; `to_dict()` drops it when unset so existing
datasets keep a clean `info.json`. Fixed the accessor to read
`self.info.tools` (the previous `.get(...)` would have raised
AttributeError on the dataclass anyway). Added regression tests:
fallback when absent, round-trip from disk, and round-trip
through `DatasetInfo.from_dict` / `to_dict`.
* **`motion` is not view-dependent — fix the docs.** The mdx claimed
rows of style `motion` must carry `camera`, but `VIEW_DEPENDENT_STYLES
= {"vqa", "trace"}` and the validator agrees: motion primitives are
joint/Cartesian-frame, not pixel-space. Updated both call-out
paragraphs in `language_and_recipes.mdx`.
* **Conditional `collate_fn` swap.** Added `meta.has_language_columns`
and gate the `lerobot_collate_fn` swap in `lerobot_train.py` on it,
so non-language datasets keep PyTorch's `default_collate`. Also
added a pass-through test in `test_collate.py` that asserts on a
plain tensor batch the custom collate matches `default_collate`
key-for-key, plus a test for the `None`-sample drop path.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Pepijn
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24d2ffe3c6 |
fix(language): keep base install green — drop processor re-export, gate dataset-extra tests
`lerobot.processor` re-exported `RenderMessagesStep` at the package
level, so importing anything from `lerobot.processor` pulled in
`lerobot.datasets.language` → `lerobot.datasets/__init__.py` →
`require_package("datasets")`, which fails in the Tier 1 base install
that intentionally omits the `[dataset]` extra. The chain bricked
collection for unrelated suites (`tests/policies/pi0_pi05/...`,
`tests/envs/...`, etc.).
* Stop re-exporting `RenderMessagesStep` from `lerobot.processor`. The
only consumer (the test) already imports from the submodule.
Document the deliberate omission in the module docstring.
* Add `pytest.importorskip("datasets", ...)` (and `pandas` where
needed) at the top of the four PR-added tests that exercise the
language stack:
- tests/datasets/test_language.py
- tests/datasets/test_language_render.py
- tests/processor/test_render_messages_processor.py
- tests/utils/test_collate.py
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Pepijn
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e8327b8e62 |
refactor(language): unify resolver dispatch and prune redundant test scaffolding
* Drop the unused `events` kwarg from `active_at`/`nth_prev`/`nth_next`; only `emitted_at` actually consults events. The dispatcher in `_resolve_spec` now passes events conditionally. * Replace the dual `_persistent_sort_key`/`_event_sort_key` pair with a single `_row_sort_key` and drop the `sort_key` parameter from `_select_one`. Event rows lack `timestamp` (it is implicit in the frame) and now default to `0.0` for sort purposes — the `(style, role)` tiebreaker is unchanged. * Inline `_select_latest` into `active_at` (its only caller). * Collapse `emitted_at`'s dual-branch into one `_select_one` call. * Tighten `_validate_persistent_resolver` to a single `column_for_style(style) != LANGUAGE_PERSISTENT` check. * Parameterize `test_per_camera_blend_renders_both_views` over the two cameras and factor the sub-recipe builder into `_vqa_subrecipe` so the test no longer hand-rolls two near-identical recipe blocks. Net -98 LOC; behavior, public resolver names, and test expectations unchanged. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Pepijn
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c450298147 |
Apply ruff and prettier formatting after merge
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Pepijn
|
5c30b14929 | Merge remote-tracking branch 'origin/main' into feat/language-columns | ||
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Pepijn
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c1a0c601e2 |
feat(language): task_aug style + automatic ${task} rephrasing rotation
Adds task-prompt diversity (Xiao 2022 / CAST) without touching
``meta/tasks.parquet`` or forcing recipes to opt in. The plan reserved
``task_aug`` as a future style; this lands it now.
- ``language.py``: add ``task_aug`` to ``CORE_STYLES`` and
``PERSISTENT_STYLES``. ``column_for_style("task_aug")`` returns
``language_persistent`` so PR 2 writers route it correctly.
- ``language_render.py``: ``_resolve_task`` now consults the persistent
slice for rows of ``style="task_aug", role="user"``. When any exist
it picks one deterministically by ``sample_idx`` (blake2b-keyed, not
Python's randomized hash) so an epoch sees every rephrasing of every
episode while the same sample still resolves identically across
reruns. Falls back to the canonical ``meta/tasks.parquet`` task when
no rephrasings are present, so existing datasets and unannotated runs
keep their behaviour. Explicit ``task=`` overrides still win.
- Tests: rephrasing coverage across samples, determinism on repeat
``sample_idx``, fallback when persistent has no ``task_aug`` rows,
and explicit override priority.
Recipes get this for free: any ``${task}`` placeholder rotates through
the available rephrasings. Recipes that want the literal canonical task
can override the binding.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Pepijn
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1ca38d9748 |
fix(language): drop motion from VIEW_DEPENDENT_STYLES
Motion primitives are described in robot-frame (joint / Cartesian) terms, not pixel space, so they are camera-agnostic. Only `vqa` (event) and `trace` (event, pixel-trajectory) are view-dependent. The `camera` field stays on PERSISTENT_ROW_FIELDS for schema symmetry — the validator, resolver, and HF feature mapping behave identically across the two columns regardless of which styles populate `camera` today — but persistent rows now always have `camera=None` in practice. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Pepijn
|
5a6aa64570 |
feat(language): per-camera tagging on view-dependent styles
Adds a nullable `camera` field to the language row struct (both persistent
and event variants) so view-dependent styles like `vqa` can carry which
`observation.images.*` view they were grounded against. Without this,
multi-camera datasets ended up with multiple `(vqa, role)` rows at the
same timestamp that the resolver could not disambiguate.
- `language.py`: add `camera` to PERSISTENT_ROW_FIELDS / EVENT_ROW_FIELDS,
to both Arrow struct types and the HF datasets feature mappings;
introduce VIEW_DEPENDENT_STYLES = {vqa, motion, trace} plus
`is_view_dependent_style` and `validate_camera_field` helpers (camera
required iff style is view-dependent).
- `language_render.py`: thread an optional `camera=` kwarg through every
resolver (`active_at`, `emitted_at`, `nth_prev`, `nth_next`) and through
`_matching_rows` / `_select_*`, so recipes can disambiguate per-camera
VQA with `emitted_at(t, style=vqa, role=assistant, camera=...)`.
Without a `camera` filter, multi-row matches keep raising the existing
ambiguity error — which is the desired behaviour on multi-camera data.
- `recipes/pi05_hirobot.yaml`: replace the single `ask_vqa` branch with
`ask_vqa_top` and `ask_vqa_wrist` per-camera sub-recipes (each carrying
the matching image block), keeping the original 0.20 budget and
documenting the customization point for datasets with different cameras.
- Tests: schema test asserts the new field order; new tests cover
`is_view_dependent_style`, `validate_camera_field` (both required and
forbidden directions), per-camera `emitted_at` filtering, and the
ambiguity error when two cameras emit `(vqa, assistant)` at the same
timestamp without a `camera=` filter. RenderMessagesStep + dataset
passthrough fixtures updated to include the new field.
- `docs/source/language_and_recipes.mdx`: document the `camera` field,
the per-camera resolver pattern, and the canonical recipe convention.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Maxime Ellerbach
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cb0a944941 |
refactor(datasets): replace untyped dict with typed DatasetInfo dataclass (#3472)
* refactor(datasets): replace untyped dict with typed DatasetInfo dataclass Introduce typed DatasetInfo dataclass to replace untyped dict representation of info.json. Changes: - Add DatasetInfo dataclass with explicit fields and validation - Implement __post_init__ for shape conversion (list ↔ tuple) - Add dict-style compatibility layer (__getitem__, __setitem__, .get()) - Add from_dict() and to_dict() for JSON serialization - Update io_utils to use load_info/write_info with DatasetInfo - Update dataset utilities and metadata to use attribute access - Remove aggregate.py dict-style field access - Add tests fixture support for DatasetInfo Benefits: - Type safety with IDE auto-completion - Validation at construction time - Explicit schema documentation * fix pre-commit * update docstring inside DatasetInfo.from_dict() * sorts the unknown to have deterministic output Signed-off-by: Maxime Ellerbach <maxime@ellerbach.net> * refactoring the last few old fieds * fix crop dataset roi type mismatch * use consistantly int for data and video_files_size_in_mb --------- Signed-off-by: Maxime Ellerbach <maxime@ellerbach.net> Co-authored-by: jjolla93 <jjolla93@gmail.com> |
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Steven Palma
|
ca87ccd941 |
feat(rollout): decouple policy deployment from data recording with new lerobot-rollout CLI (#3413)
* feat(scripts): lerobot-rollout * fix(rollout) require dataset in dagger + use duration too * fix(docs): dagger num_episodes * test(rollout): fix expectations * fix(rollout): features check * fix(rollout): device and task propagation + feature pos + warn fps + move rename_map config * docs(rollout): edit rename_map instructions * chore(rollout): multiple minor improvements * chore(rollout): address coments + minor improvements * fix(rollout): enable default * fix(tests): default value RTCConfig * fix(rollout): robot_observation_processor and notify_observation at policy frequency instead of interpolator rate Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com> * fix(rollout): prevent relativeactions with sync inference engine Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com> * fix(rollout): rtc reanchor to non normalized state Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com> * fix(rollout): fixing the episode length to use hwc (#3469) also reducing default length to 5 minutes * feat(rollout): go back to initial position is now a config * fix(rollout): properly propagating video_files_size_in_mb to lerobot_dataset (#3470) * chore(rollout): note about dagger correction stage * chore(docs): update comments and docstring * fix(test): move rtc relative out of rollout module * fix(rollout): address the review comments --------- Co-authored-by: Pepijn <138571049+pkooij@users.noreply.github.com> Co-authored-by: Maxime Ellerbach <maxime.ellerbach@huggingface.co> |
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Pepijn
|
0b06790da0 |
feat(language): add motion (persistent) and trace (event-only) styles
Promote the previously-reserved motion/trace styles to first-class core styles. motion routes to language_persistent (it tracks robot state over time); trace routes to language_events (single-moment annotations). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Pepijn
|
2b71221194 |
Address review: split persistent/event schemas, drop event timestamps
- recipe.py: derive _VALID_ROLES/_VALID_STREAMS from MessageRole/MessageStream Literals - dataset_metadata.py: keep CODEBASE_VERSION at v3.0 - language.py: remove RESERVED_STYLES; split arrow/feature schemas into persistent (with timestamp) and event (without timestamp); add docstrings - language_render.py: events use frame-row timestamp implicitly; no per-event timestamp filtering or sorting - converters.py: drop unused subtask_key passthrough - add docstrings to new public APIs (recipe, render_messages_processor, collate) - update tests for split schemas; revert uv.lock Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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Pepijn
|
8833d735a1 | Add extensive language support | ||
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whats2000
|
52f508c51c | fix(dataset): cleanup_interrupted_episode wipes image temp dirs (#3405) | ||
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Steven Palma
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df0763a2bc | feat(dependencies): minimal default tag install (#3362) | ||
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Caroline Pascal
|
d762f4bfe8 |
fix(dataset): adding metadata loading when reading from a dataset after writing (#3305)
* fix(one shot load): adding metadata loading when reading from a dataset after writing * refactor(one shot load): move metadata reload to ensure_readable() on LeRobotDatasetMetadata Move the metadata reload from DatasetReader.load_and_activate() to a new public ensure_readable() method on LeRobotDatasetMetadata, called from LeRobotDataset._ensure_reader(). This places lifecycle management in the right layer: metadata owns its readiness check, the dataset orchestrates the write-to-read transition, and the reader stays clean. Also adds a regression test using delta_timestamps to exercise the meta.episodes access path in the create -> write -> finalize -> read flow. Co-authored-by: Steven Palma <imstevenpmwork@users.noreply.github.com> --------- Co-authored-by: claude[bot] <41898282+claude[bot]@users.noreply.github.com> Co-authored-by: Steven Palma <imstevenpmwork@users.noreply.github.com> |
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Francesco Capuano
|
7c032f19fc |
feat(dataset): registering torchvision transforms (#3153)
* add: a flexible transformation registry * fix: image transforms can be set both at init and after * add: tests * fix: take in review * feat(datasets): add image transform setters * fix: pre-commit * fix: CI --------- Signed-off-by: Francesco Capuano <74058581+fracapuano@users.noreply.github.com> |
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Steven Palma
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4e45acca52 |
fix(dataset): use revision-safe Hub cache for downloaded datasets (#3233)
* refactor(dataset): enhance dataset root directory handling and introduce hub cache support - Updated DatasetConfig and LeRobotDatasetMetadata to clarify root directory behavior and introduce a dedicated hub cache for downloads. - Refactored LeRobotDataset and StreamingLeRobotDataset to utilize the new hub cache and improve directory management. - Added tests to ensure correct behavior when using the hub cache and handling different revisions without a specified root directory. * refactor(dataset): improve root directory handling in LeRobotDataset - Updated LeRobotDataset to store the requested root path separately from the actual root path. - Adjusted metadata loading to use the requested root, enhancing clarity and consistency in directory management. * refactor(dataset): minor improvements for hub cache support * chore(datasets): guard in resume + assertion test --------- Co-authored-by: AdilZouitine <adilzouitinegm@gmail.com> Co-authored-by: mickaelChen <mickael.chen.levinson@gmail.com> |
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Steven Palma
|
123495250b |
refactor(dataset): split LeRobotDataset into DatasetReader & DatasetWriter (+ API cleanup) (#3180)
* refactor(dataset): split reader and writer * chore(dataset): remove proxys * refactor(dataset): better reader & writer encapsulation * refactor(datasets): clean API + reduce leaky implementations * refactor(dataset): API cleaning for writer, reader and meta * refactor(dataset): expose writer & reader + other minor improvements * refactor(dataset): improve teardown routine * refactor(dataset): add hf_dataset property at the facade level * chore(dataset): add init for datasset module * docs(dataset): add docstrings for public API of the dataset classes * tests(dataset): add tests for new classes * fix(dataset): remove circular dependecy |
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Steven Palma
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d90e4bcfd3 |
refactor(dataset): modular files (#3171)
* refactor(dataset): modular files * refactor(dataset): update imports across the codebase |
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Steven Palma
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9d3b62aa61 | chore(dataset): basic house-keeping (#3170) | ||
|
Steven Palma
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7c2ec31793 | refactor(datasets): module cleanup (#3169) | ||
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Steven Palma
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e96339a3b4 |
feat(dataset): add streaming video encoding + HW encoder support (#2974)
* feat(dataset): init stream encoding * feat(dataset): use threads to fix frame pickle latency * refactor(dataset): remove HW encoded related changes * add lp (#2977) * feat(dataset): add Hw encoding + log drop frames (#2978) * chore(docs): add streaming video encoding guide * fix(dataset): style docs + testing * chore(docs): simplify sttreaming video encoding guide * chore(dataset): add commands + streaming encoding default false + print note if false + queue default is now 30 * chore(docs): add verification note advice * chore(dataset): adjusting defaults & docs for streaming encoding * docs(scripts): improve docstrings * test(dataset): polish streaming encoding tests * chore(dataset): move FYI log related to streaming * chore(dataset): add arg vcodec to suggestions * refactor(dataset): better handling for auto and available vcodec * chore(dataset): change log level * docs(dataset): add note related to training performance vcodec * docs(dataset): add more notes to streaming encoding --------- Co-authored-by: Caroline Pascal <caroline8.pascal@gmail.com> Co-authored-by: Pepijn <pepijn@huggingface.co> |