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https://github.com/huggingface/lerobot.git
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Pepijn
cec8ee0be6
Steerable annotation pipeline (lerobot-annotate) that populates the language_persistent and language_events columns introduced in PR 1 (#3467) directly into data/chunk-*/file-*.parquet. This is PR 2 of the three-PR plan: PR 1 (Add extensive language support #3467): schema + DSL + rendering, base of this PR PR 2 (this PR): annotation pipeline writing into PR 1's columns PR 3: model with language prediction and runtime A VLM (Qwen-VL family, served on vLLM) watches each episode's video and emits grounded language annotations: subtasks, plans, memory, task rephrasings, interjections + speech, and per-camera VQA. The pipeline is built for production annotation at scale — single-camera grounding, embedded-frame inputs, a describe-then-segment grounding flow, and a deterministic full-episode coverage guarantee — informed by Scale's dense-captioning findings (representation > sampling, rules > reasoning, model capacity is the biggest lever, two-pass systems compound errors)
87 lines
2.9 KiB
Python
87 lines
2.9 KiB
Python
#!/usr/bin/env python
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# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import json
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from types import SimpleNamespace
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import pytest
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# ``lerobot.scripts.lerobot_annotate`` (and the ``_push_to_hub`` path it
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# exercises) imports ``lerobot.datasets``, which only ships under the
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# ``dataset`` extra. Skip in tiers without it instead of erroring.
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pytest.importorskip("datasets", reason="datasets is required (install lerobot[dataset])")
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def test_push_to_hub_tags_uploaded_dataset_revision(tmp_path, monkeypatch):
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from lerobot.scripts.lerobot_annotate import _push_to_hub
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root = tmp_path / "dataset"
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(root / "meta").mkdir(parents=True)
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(root / "meta" / "info.json").write_text(json.dumps({"codebase_version": "v3.0"}))
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calls = {}
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class FakeHfApi:
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def create_repo(self, **kwargs):
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calls["create_repo"] = kwargs
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def upload_folder(self, **kwargs):
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calls["upload_folder"] = kwargs
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return SimpleNamespace(oid="abc123")
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def delete_tag(self, repo_id, **kwargs):
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import requests
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from huggingface_hub.errors import RevisionNotFoundError
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calls["delete_tag"] = {"repo_id": repo_id, **kwargs}
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# Simulate the common case: no stale tag to delete.
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raise RevisionNotFoundError("no such tag", response=requests.Response())
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def create_tag(self, **kwargs):
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calls["create_tag"] = kwargs
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monkeypatch.setattr("huggingface_hub.HfApi", FakeHfApi)
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cfg = SimpleNamespace(
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repo_id="source/dataset",
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new_repo_id="annotated/dataset",
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push_private=True,
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push_commit_message=None,
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)
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_push_to_hub(root, cfg)
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assert calls["create_repo"] == {
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"repo_id": "annotated/dataset",
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"repo_type": "dataset",
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"private": True,
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"exist_ok": True,
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}
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assert calls["upload_folder"]["repo_id"] == "annotated/dataset"
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# A stale tag (e.g. from a previous annotation run) is deleted first so
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# the new tag always points at the upload we just made.
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assert calls["delete_tag"] == {
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"repo_id": "annotated/dataset",
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"tag": "v3.0",
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"repo_type": "dataset",
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}
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assert calls["create_tag"] == {
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"repo_id": "annotated/dataset",
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"tag": "v3.0",
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"repo_type": "dataset",
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"revision": "abc123",
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}
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