mirror of
https://github.com/Tavish9/any4lerobot.git
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Qizhi Chen
67091bc4a7
* Add generic converter adapter hooks Co-authored-by: Codex <codex@openai.com> * Require conversion task repo ids Co-authored-by: Codex <codex@openai.com> * Remove conversion task runtime repo id check Co-authored-by: Codex <codex@openai.com> * Apply suggestion from @gemini-code-assist[bot] Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com> --------- Co-authored-by: Codex <codex@openai.com> Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
111 lines
3.2 KiB
Markdown
111 lines
3.2 KiB
Markdown
# Generic Converter
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Shared conversion flow for turning task-based source datasets into LeRobot
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datasets.
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The generic package owns the execution mechanics:
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- create one temporary `LeRobotDataset` per `ConversionTask`
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- run tasks with a local or Ray Datatrove executor
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- aggregate temporary datasets into the adapter output directory
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- remove temporary task outputs by default
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- optionally push the aggregated dataset to the Hub
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Dataset-specific converters own the adapter logic:
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- where raw inputs come from
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- how tasks are discovered or loaded
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- how one raw input is converted into LeRobot episodes
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- how task metadata, such as language instructions, is represented
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## Files
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- `adapter.py`: `BaseAdapter`, the class dataset adapters inherit from.
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- `pipeline.py`: the reusable conversion, executor, aggregation, cleanup, and push flow.
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- `utils.py`: shared types and small helpers.
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## Adapter Contract
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A dataset converter should subclass `BaseAdapter`, pass `output_path` to the
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base constructor, and provide dataset-level metadata as class attributes.
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Required attributes:
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- `dataset_type`
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- `fps`
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- `robot_type`
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- `features`
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Optional attributes:
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- `tags`
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Required methods:
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- `load_tasks(self) -> list[ConversionTask]`
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- `load_subset(self, task: ConversionTask) -> Iterable[Any]`
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Optional hooks:
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- `create_dataset(self, task: ConversionTask)`
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- `save_episode(self, dataset, episode_data, task) -> bool`
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- `get_episode_length(self, episode_data) -> int`
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`run_converter` reads `adapter.output_path` and calls `adapter.load_tasks()`
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without arguments. Store paths, task manifests, or other adapter options on the
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adapter instance in `__init__`.
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Use `adapter.temp_output_path` when building task-level temporary output paths.
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`load_subset` receives the full `ConversionTask`, not just an input path. Use
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`task.input_path` for raw data and `task.metadata` for dataset-specific values
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such as language instructions. By default, each yielded episode must be a
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sequence of frame dictionaries accepted by `LeRobotDataset.add_frame`; each
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frame should include the LeRobot `task` field when language tasks are needed.
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Adapters that need custom dataset classes or extra per-episode arguments can
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override the optional hooks.
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## ConversionTask
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`ConversionTask` describes one independently convertible raw input:
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- `input_path`: source file or directory
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- `output_path`: temporary LeRobot dataset directory for this task
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- `local_repo_id`: required repo id used while writing the temporary dataset
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- `metadata`: adapter-owned metadata
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Keep dataset-specific values in `metadata`; the generic pipeline does not know
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about task-file schemas or instruction formats.
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## Usage Sketch
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```python
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from generic_converter import BaseAdapter, ConversionTask, run_converter
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class MyAdapter(BaseAdapter):
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dataset_type = "my_dataset"
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fps = 20
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robot_type = "my_robot"
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features = MY_FEATURES
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tags = ["my_dataset"]
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def __init__(self, output_path):
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super().__init__(output_path)
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def load_tasks(self) -> list[ConversionTask]:
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...
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def load_subset(self, task: ConversionTask):
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...
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run_converter(
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adapter=adapter,
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executor="local",
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cpus_per_task=1,
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tasks_per_job=1,
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workers=-1,
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)
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```
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