code/dataloader_bak/lerobot_datasets.py

# Copyright 2025 NVIDIA Corp. and affiliates. All rights reserved.
# Modified by [Fangjing Wang/ SUST University] in [2025]. 
# Modification: [return raw data and suport multi-dataset mixture].
# Modified by [Jinhui YE/ HKUST University] in [2025]. 
# Modification: [suport topdowm processing, suport param from config].

from pathlib import Path
from typing import Sequence
from omegaconf import OmegaConf

from starVLA.dataloader.gr00t_lerobot.datasets import LeRobotSingleDataset, LeRobotMixtureDataset
from starVLA.dataloader.gr00t_lerobot.mixtures import DATASET_NAMED_MIXTURES
from starVLA.dataloader.gr00t_lerobot.data_config import get_robot_type_config_map
from starVLA.dataloader.gr00t_lerobot.embodiment_tags import ROBOT_TYPE_TO_EMBODIMENT_TAG, EmbodimentTag

def collate_fn(batch):
    return batch

def make_LeRobotSingleDataset(
    data_root_dir: Path | str,
    data_name: str,
    robot_type: str,
    delete_pause_frame: bool = False,
    data_cfg: dict | None = None,
) -> LeRobotSingleDataset:
    """
    Make a LeRobotSingleDataset object.

    :param data_root_dir: The root directory of the dataset.
    :param data_name: The name of the dataset.
    :param robot_type: The robot type config to use.
    :param crop_obs_camera: Whether to crop the observation camera images.
    :return: A LeRobotSingleDataset object.
    """
    chunk_size = data_cfg.get("chunk_size")
    state_use_action_chunk = data_cfg.get("state_use_action_chunk")
    data_config = get_robot_type_config_map(chunk_size=chunk_size, state_use_action_chunk=state_use_action_chunk)[robot_type]
    modality_config = data_config.modality_config()
    transforms = data_config.transform()
    dataset_path = data_root_dir / data_name
    if robot_type not in ROBOT_TYPE_TO_EMBODIMENT_TAG:
        print(f"Warning: Robot type {robot_type} not found in ROBOT_TYPE_TO_EMBODIMENT_TAG, using {EmbodimentTag.NEW_EMBODIMENT} as default")
        embodiment_tag = EmbodimentTag.NEW_EMBODIMENT
    else:
        embodiment_tag = ROBOT_TYPE_TO_EMBODIMENT_TAG[robot_type]
    
    video_backend = data_cfg.get("video_backend", "decord") if data_cfg else "decord"
    
    return LeRobotSingleDataset(
        dataset_path=dataset_path,
        modality_configs=modality_config,
        transforms=transforms,
        embodiment_tag=embodiment_tag,
        video_backend=video_backend, # decord is more efficiency | torchvision_av for video.av1
        delete_pause_frame=delete_pause_frame,
        data_cfg=data_cfg,
    )

def get_vla_dataset(
    data_cfg: dict,
    mode: str = "train",
    balance_dataset_weights: bool = False,
    balance_trajectory_weights: bool = False,
    seed: int = 42,
    delete_pause_frame: bool = True,
    **kwargs: dict,
) -> LeRobotMixtureDataset:
    """
    Get a LeRobotMixtureDataset object.
    """
    data_root_dir = data_cfg.data_root_dir
    data_mix = data_cfg.data_mix
    mixture_spec = DATASET_NAMED_MIXTURES[data_mix]
    included_datasets, filtered_mixture_spec = set(), []
    for d_name, d_weight, robot_type in mixture_spec:  
        dataset_key = (d_name, robot_type)  
        if dataset_key in included_datasets:
            print(f"Skipping Duplicate Dataset: `{(d_name, d_weight, robot_type)}`")
            continue

        included_datasets.add(dataset_key)
        filtered_mixture_spec.append((d_name, d_weight, robot_type))

    dataset_mixture = []
    for d_name, d_weight, robot_type in filtered_mixture_spec:
        dataset_mixture.append((make_LeRobotSingleDataset(Path(data_root_dir), d_name, robot_type, delete_pause_frame=delete_pause_frame, data_cfg=data_cfg), d_weight))

    return LeRobotMixtureDataset(
        dataset_mixture,
        mode=mode,
        balance_dataset_weights=balance_dataset_weights,
        balance_trajectory_weights=balance_trajectory_weights,
        seed=seed,
        data_cfg=data_cfg,
        **kwargs,
    )



if __name__ == "__main__":

    import argparse
    parser = argparse.ArgumentParser()
    parser.add_argument("--config_yaml", type=str, default="./starVLA/config/training/starvla_cotrain_behavior.yaml", help="Path to YAML config")
    args, clipargs = parser.parse_known_args()

    args.config_yaml = "examples/LIBERO/train_files/starvla_cotrain_libero.yaml"
    cfg = OmegaConf.load(args.config_yaml)

    vla_dataset_cfg = cfg.datasets.vla_data
    # vla_dataset_cfg.data_root_dir = "./playground/Datasets/behavior-1k"
    # vla_dataset_cfg.include_state = True
    # vla_dataset_cfg.data_mix = "BEHAVIOR_dual_base_depth"
    vla_dataset_cfg.task_id = 1
    for task_id in ["all"]:
        # 11,26,36,37
        # 5,11,13,26,36,27,43,44,45,46
        # 2,3,5,11,13,25,26,27,
        # 3,5,11,13, / 14,15,16,17, / 19,20,23,25, / 26,27,30,34, / 36,37,38,39, 41,42,43,44,45,46,47,49
        vla_dataset_cfg.task_id = task_id
        print(f"Testing Task ID: {task_id}")
        dataset = get_vla_dataset(data_cfg=vla_dataset_cfg)
        # dataset
    from torch.utils.data import DataLoader
    train_dataloader = DataLoader(
        dataset,
        batch_size=2,
        num_workers=1, # For Debug
        collate_fn=collate_fn,
    )

    from tqdm import tqdm
    count = 1
    for batch in tqdm(train_dataloader, desc="Processing Batches"):
        # print(batch)
        # print(1)
        if count > 1:
            break
        count += 1
        pass