def list_collate()

in imnet_evaluate/samplers.py [0:0]

• 13 lines of code
• 10 McCabe index (conditional complexity)

def list_collate(batch):
    """
    Collate into a list instead of a tensor to deal with variable-sized inputs
    """
    elem_type = type(batch[0])
    if isinstance(batch[0], torch.Tensor):
        return batch
    elif elem_type.__module__ == 'numpy':
        if elem_type.__name__ == 'ndarray':
            return list_collate([torch.from_numpy(b) for b in batch])
    elif isinstance(batch[0], Mapping):
        return {key: list_collate([d[key] for d in batch]) for key in batch[0]}
    elif isinstance(batch[0], Sequence):
        transposed = zip(*batch)
        return [list_collate(samples) for samples in transposed]
    return default_collate(batch)