(self, inputs)
| 31 | """ |
| 32 | |
| 33 | def apply(self, inputs): |
| 34 | elem = inputs[0] |
| 35 | elem_type = type(elem) |
| 36 | if ( |
| 37 | elem_type.__module__ == "numpy" |
| 38 | and elem_type.__name__ != "str_" |
| 39 | and elem_type.__name__ != "string_" |
| 40 | ): |
| 41 | elem = inputs[0] |
| 42 | if elem_type.__name__ == "ndarray": |
| 43 | # array of string classes and object |
| 44 | if np_str_obj_array_pattern.search(elem.dtype.str) is not None: |
| 45 | raise TypeError(default_collate_err_msg_format.format(elem.dtype)) |
| 46 | |
| 47 | return np.ascontiguousarray(np.stack(inputs)) |
| 48 | elif elem.shape == (): # scalars |
| 49 | return np.array(inputs) |
| 50 | elif isinstance(elem, float): |
| 51 | return np.array(inputs, dtype=np.float64) |
| 52 | elif isinstance(elem, int): |
| 53 | return np.array(inputs) |
| 54 | elif isinstance(elem, (str, bytes)): |
| 55 | return inputs |
| 56 | elif isinstance(elem, collections.abc.Mapping): |
| 57 | return {key: self.apply([d[key] for d in inputs]) for key in elem} |
| 58 | elif isinstance(elem, tuple) and hasattr(elem, "_fields"): # namedtuple |
| 59 | return elem_type(*(self.apply(samples) for samples in zip(*inputs))) |
| 60 | elif isinstance(elem, collections.abc.Sequence): |
| 61 | transposed = zip(*inputs) |
| 62 | return [self.apply(samples) for samples in transposed] |
| 63 | |
| 64 | raise TypeError(default_collate_err_msg_format.format(elem_type)) |
no test coverage detected