mirror of
https://github.com/huggingface/lerobot.git
synced 2026-05-19 18:49:52 +00:00
Pepijn Kooijmans
fd18beb3a1
- name the three modules everywhere (plan / interjections / vqa) instead of module_1/2/3 — config classes, config fields, executor params, staging keys and phase names now carry the module name - rename examples/annotation -> examples/annotations; add the Apache header to run_hf_job.py - drop the unused GeneralVqaModule._generate_one - remove "PR 1" references from comments/docstrings - frames.py: rely on the always-defined LeRobotDatasetMetadata.camera_keys - executor.py: read/write meta/info.json via load_info / write_info - reader.py: load meta/tasks.parquet via io_utils.load_tasks - make --push_to_hub a bool; push the annotated dataset back to --repo_id - move the on-disk test dataset builder into tests/fixtures (build_annotation_dataset); run_e2e_smoke reuses it - clarify in the docs that the vqa module grounds each pair on a single frame (K = per-tick anchor count) - hoist stdlib dynamic imports to module scope Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
102 lines
4.0 KiB
Python
102 lines
4.0 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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"""Opt-in E2E smoke run for ``make annotation-e2e``.
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Builds the shared annotation fixture (:func:`build_annotation_dataset`),
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runs the full annotation pipeline against it with a stub VLM, and prints a
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short report. This is intentionally not a pytest test — it exercises the
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CLI plumbing — but it reuses the same on-disk dataset builder as the pytest
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fixtures so there is no duplicated fixture code.
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"""
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from __future__ import annotations
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import sys
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import tempfile
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from pathlib import Path
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from lerobot.annotations.steerable_pipeline.config import AnnotationPipelineConfig
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from lerobot.annotations.steerable_pipeline.executor import Executor
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from lerobot.annotations.steerable_pipeline.modules import (
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GeneralVqaModule,
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InterjectionsAndSpeechModule,
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PlanSubtasksMemoryModule,
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)
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from lerobot.annotations.steerable_pipeline.validator import StagingValidator
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from lerobot.annotations.steerable_pipeline.vlm_client import StubVlmClient
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from lerobot.annotations.steerable_pipeline.writer import LanguageColumnsWriter
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from tests.fixtures.dataset_factories import build_annotation_dataset
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def _stub_responder(messages):
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text = ""
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for m in messages:
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if m.get("role") == "user":
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content = m.get("content")
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if isinstance(content, list):
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for block in content:
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if isinstance(block, dict) and block.get("type") == "text":
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text = block.get("text", "")
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elif isinstance(content, str):
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text = content
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if "atomic subtasks" in text:
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return {
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"subtasks": [
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{"text": "grasp the bottle", "start": 0.0, "end": 1.0},
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{"text": "pour into the cup", "start": 1.0, "end": 2.0},
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{"text": "place the bottle down", "start": 2.0, "end": 3.0},
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]
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}
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if "concise hierarchical PLAN" in text:
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return {"plan": "1. grasp\n2. pour\n3. place"}
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if "Update the memory" in text:
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return {"memory": "poured once"}
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if "acknowledgement the robot" in text:
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return {"text": "Sure."}
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if "ONE realistic interruption" in text:
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return {"interjection": "use less water", "speech": "Using less water."}
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if "frame-grounded visual question" in text:
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return {"question": "How many cups?", "answer": {"label": "cup", "count": 1}}
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return None
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def main() -> int:
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with tempfile.TemporaryDirectory() as tmp:
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root = build_annotation_dataset(
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Path(tmp) / "ds",
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episode_specs=[(0, 30, "Pour water into the cup.")],
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fps=10,
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)
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vlm = StubVlmClient(responder=_stub_responder)
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cfg = AnnotationPipelineConfig()
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executor = Executor(
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config=cfg,
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plan=PlanSubtasksMemoryModule(vlm=vlm, config=cfg.plan),
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interjections=InterjectionsAndSpeechModule(vlm=vlm, config=cfg.interjections, seed=cfg.seed),
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vqa=GeneralVqaModule(vlm=vlm, config=cfg.vqa, seed=cfg.seed),
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writer=LanguageColumnsWriter(),
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validator=StagingValidator(),
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)
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summary = executor.run(root)
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print(f"phases={[(p.name, p.episodes_processed) for p in summary.phases]}")
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print(f"validation: {summary.validation_report.summary()}")
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print(f"shards rewritten: {len(summary.written_paths)}")
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return 0
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if __name__ == "__main__":
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sys.exit(main())
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