scripts/compute_norm_stats.py

"""Compute normalization statistics for a config.

This script is used to compute the normalization statistics for a given config. It
will compute the mean and standard deviation of the data in the dataset and save it
to the config assets directory.
"""

from __future__ import annotations

import pathlib

import numpy as np
import tqdm
import tyro
from datasets import load_dataset

import openpi.models.model as _model
import openpi.shared.normalize as normalize
import openpi.training.config as _config
import openpi.training.data_loader as _data_loader
import openpi.transforms as transforms


class RemoveStrings(transforms.DataTransformFn):
    def __call__(self, x: dict) -> dict:
        return {k: v for k, v in x.items() if not np.issubdtype(np.asarray(v).dtype, np.str_)}


def create_torch_dataloader(
    data_config: _config.DataConfig,
    action_horizon: int,
    batch_size: int,
    model_config: _model.BaseModelConfig,
    num_workers: int,
    max_frames: int | None = None,
) -> tuple[_data_loader.Dataset, int]:
    if data_config.repo_id is None:
        raise ValueError("Data config must have a repo_id")
    dataset = _data_loader.create_torch_dataset(data_config, action_horizon, model_config)
    dataset = _data_loader.TransformedDataset(
        dataset,
        [
            *data_config.repack_transforms.inputs,
            *data_config.data_transforms.inputs,
            # Remove strings since they are not supported by JAX and are not needed to compute norm stats.
            RemoveStrings(),
        ],
    )
    if max_frames is not None and max_frames < len(dataset):
        num_batches = max_frames // batch_size
        shuffle = True
    else:
        num_batches = len(dataset) // batch_size
        shuffle = False
    data_loader = _data_loader.TorchDataLoader(
        dataset,
        local_batch_size=batch_size,
        num_workers=num_workers,
        shuffle=shuffle,
        num_batches=num_batches,
    )
    return data_loader, num_batches


def create_rlds_dataloader(
    data_config: _config.DataConfig,
    action_horizon: int,
    batch_size: int,
    max_frames: int | None = None,
) -> tuple[_data_loader.Dataset, int]:
    dataset = _data_loader.create_rlds_dataset(data_config, action_horizon, batch_size, shuffle=False)
    dataset = _data_loader.IterableTransformedDataset(
        dataset,
        [
            *data_config.repack_transforms.inputs,
            *data_config.data_transforms.inputs,
            # Remove strings since they are not supported by JAX and are not needed to compute norm stats.
            RemoveStrings(),
        ],
        is_batched=True,
    )
    if max_frames is not None and max_frames < len(dataset):
        num_batches = max_frames // batch_size
    else:
        # NOTE: this length is currently hard-coded for DROID.
        num_batches = len(dataset) // batch_size
    data_loader = _data_loader.RLDSDataLoader(
        dataset,
        num_batches=num_batches,
    )
    return data_loader, num_batches


def _iter_parquet_batches(
    dataset_root: pathlib.Path,
    *,
    batch_size: int,
    max_frames: int | None,
):
    """Iterate parquet data in batches without decoding videos.

    Expects a LeRobot-format dataset directory at `dataset_root` containing `data/`.
    Only reads `observation.state` and `action` columns.
    """
    data_dir = dataset_root / "data"
    if not data_dir.exists():
        raise FileNotFoundError(f"Expected parquet directory at {data_dir}")

    ds = load_dataset("parquet", data_dir=str(data_dir), split="train")
    total = len(ds)
    limit = total if max_frames is None else min(total, max_frames)

    # Access only required columns to reduce work.
    state_col = "observation.state"
    action_col = "action"

    for start in range(0, limit, batch_size):
        end = min(limit, start + batch_size)
        batch = ds[start:end]
        # HF datasets returns lists; convert to numpy arrays.
        yield {
            "state": np.asarray(batch[state_col]),
            "actions": np.asarray(batch[action_col]),
        }


def main(config_name: str, max_frames: int | None = None, parquet_only: bool = False):
    config = _config.get_config(config_name)
    data_config = config.data.create(config.assets_dirs, config.model)

    if parquet_only:
        if data_config.rlds_data_dir is not None:
            raise ValueError("--parquet-only is only supported for LeRobot (parquet) datasets, not RLDS.")
        if data_config.repo_id is None:
            raise ValueError("Data config must have a repo_id")
        if data_config.lerobot_root is None:
            raise ValueError(
                "Data config must set `lerobot_root` for --parquet-only (path to the LeRobot dataset directory)."
            )
        # NOTE: In LeRobot, `root` is the dataset directory containing `meta/`, `data/`, and `videos/`.
        dataset_root = pathlib.Path(data_config.lerobot_root)
        data_loader = _iter_parquet_batches(
            dataset_root,
            batch_size=config.batch_size,
            max_frames=max_frames,
        )
        if max_frames is not None:
            num_batches = max_frames // config.batch_size
        else:
            # Derive total batches from parquet dataset length (requires reading dataset metadata once).
            num_examples = len(load_dataset("parquet", data_dir=str(dataset_root / "data"), split="train"))
            num_batches = num_examples // config.batch_size
    elif data_config.rlds_data_dir is not None:
        data_loader, num_batches = create_rlds_dataloader(
            data_config, config.model.action_horizon, config.batch_size, max_frames
        )
    else:
        data_loader, num_batches = create_torch_dataloader(
            data_config, config.model.action_horizon, config.batch_size, config.model, config.num_workers, max_frames
        )

    keys = ["state", "actions"]
    stats = {key: normalize.RunningStats() for key in keys}

    for batch in tqdm.tqdm(data_loader, total=num_batches, desc="Computing stats"):
        for key in keys:
            stats[key].update(np.asarray(batch[key]))

    norm_stats = {key: stats.get_statistics() for key, stats in stats.items()}

    output_path = config.assets_dirs / data_config.repo_id
    print(f"Writing stats to: {output_path}")
    normalize.save(output_path, norm_stats)


if __name__ == "__main__":
    tyro.cli(main)