Pad the spatial dimensions of the given array.
(
np_arr: np.array,
pad_value: float,
padding_before: list[int],
padding_after: list[int],
dtype: str,
)
| 100 | |
| 101 | |
| 102 | def pad_tensor( |
| 103 | np_arr: np.array, |
| 104 | pad_value: float, |
| 105 | padding_before: list[int], |
| 106 | padding_after: list[int], |
| 107 | dtype: str, |
| 108 | ) -> np.array: |
| 109 | """Pad the spatial dimensions of the given array.""" |
| 110 | orig_shape = list(np_arr.shape) |
| 111 | padded_shape = list(np_arr.shape) |
| 112 | n = len(orig_shape) |
| 113 | for dim in range(2, n): |
| 114 | i = dim - 2 |
| 115 | padded_shape[dim] += padding_after[i] + padding_before[i] |
| 116 | |
| 117 | pad_np = (np.zeros(shape=padded_shape) + pad_value).astype(dtype) |
| 118 | ranges_it = [range(padded_shape[0]), range(padded_shape[1])] |
| 119 | for dim in range(2, n): |
| 120 | i = dim - 2 |
| 121 | ranges_it.append(range(padding_before[i], padding_before[i] + orig_shape[dim])) |
| 122 | pad_np[np.ix_(*ranges_it)] = np_arr |
| 123 | return pad_np |
| 124 | |
| 125 | |
| 126 | def poolnd_python( |
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