def concatenate_cache()

in tools/cache_preds.py [0:0]

• 31 lines of code
• 23 McCabe index (conditional complexity)

def concatenate_cache(batch):
    r"""Puts each data field into a tensor with outer dimension batch size"""
    elem_type = type(batch[0])
    if isinstance(batch[0], torch.Tensor):
        out = None
        return torch.cat(batch, 0, out=out)  # the main difference is here
    elif elem_type.__module__ == 'numpy' and elem_type.__name__ != 'str_' \
            and elem_type.__name__ != 'string_':
        elem = batch[0]
        if elem_type.__name__ == 'ndarray':
            # array of string classes and object
            if np_str_obj_array_pattern.search(elem.dtype.str) is not None:
                raise TypeError(error_msg_fmt.format(elem.dtype))
            return concatenate_cache([torch.from_numpy(b) for b in batch])
        if elem.shape == ():  # scalars
            py_type = float if elem.dtype.name.startswith('float') else int
            return numpy_type_map[elem.dtype.name](list(map(py_type, batch)))
    elif isinstance(batch[0], float):
        return torch.tensor(batch, dtype=torch.float64)
    elif isinstance(batch[0], int_classes):
        return torch.tensor(batch)
    elif isinstance(batch[0], string_classes):
        return batch
    elif isinstance(batch[0], container_abcs.Mapping):
        return {key: concatenate_cache([d[key] for d in batch])
                for key in batch[0]}
    elif isinstance(batch[0], tuple) and hasattr(batch[0], '_fields'):
        return type(batch[0])(*(concatenate_cache(samples)
                                for samples in zip(*batch)))
    elif isinstance(batch[0], container_abcs.Sequence):  # also some diffs here
        # just unpack
        return [s_ for s in batch for s_ in s]

    raise TypeError((error_msg_fmt.format(type(batch[0]))))