Function list_data_collate

monai/data/utils.py:455–509

Enhancement for PyTorch DataLoader default collate. If dataset already returns a list of batch data that generated in transforms, need to merge all data to 1 list. Then it's same as the default collate behavior. Note: Need to use this collate if apply some transforms that c

(batch: Sequence)

def list_data_collate(batch: Sequence):
    """
    Enhancement for PyTorch DataLoader default collate.
    If dataset already returns a list of batch data that generated in transforms, need to merge all data to 1 list.
    Then it's same as the default collate behavior.

    Note:
        Need to use this collate if apply some transforms that can generate batch data.

    """
    from torch.utils.data._utils.collate import default_collate_fn_map

    from monai.data.meta_tensor import MetaTensor

    default_collate_fn_map.update({MetaTensor: collate_meta_tensor_fn})
    elem = batch[0]
    data = [i for k in batch for i in k] if isinstance(elem, list) else batch
    key = None
    collate_fn = default_collate
    try:
        # if config.USE_META_DICT:
        # data = pickle_operations(data)  # bc 0.9.0
        if isinstance(elem, Mapping):
            ret = {}
            for k in elem:
                key = k
                data_for_batch = [d[key] for d in data]
                ret[key] = collate_fn(data_for_batch)
        else:
            ret = collate_fn(data)
        return ret
    except RuntimeError as re:
        re_str = str(re)
        if "equal size" in re_str:
            if key is not None:
                re_str += f"\nCollate error on the key '{key}' of dictionary data."
            re_str += (
                "\n\nMONAI hint: if your transforms intentionally create images of different shapes, creating your "
                + "`DataLoader` with `collate_fn=pad_list_data_collate` might solve this problem (check its "
                + "documentation)."
            )
        _ = dev_collate(data)
        raise RuntimeError(re_str) from re
    except TypeError as re:
        re_str = str(re)
        if "numpy" in re_str and "Tensor" in re_str:
            if key is not None:
                re_str += f"\nCollate error on the key '{key}' of dictionary data."
            re_str += (
                "\n\nMONAI hint: if your transforms intentionally create mixtures of torch Tensor and numpy ndarray, "
                + "creating your `DataLoader` with `collate_fn=pad_list_data_collate` might solve this problem "
                + "(check its documentation)."
            )
        _ = dev_collate(data)
        raise TypeError(re_str) from re


def _non_zipping_check(batch_data: Mapping | Iterable, detach: bool, pad: bool, fill_value):