(self, data: torch.Tensor, labels: torch.Tensor | None = None, randomize=True)
| 153 | return mixweight * labels + (1 - mixweight) * labels[perm, ...] |
| 154 | |
| 155 | def __call__(self, data: torch.Tensor, labels: torch.Tensor | None = None, randomize=True): |
| 156 | data_t = convert_to_tensor(data, track_meta=get_track_meta()) |
| 157 | augmented_label = None |
| 158 | if labels is not None: |
| 159 | labels_t = convert_to_tensor(labels, track_meta=get_track_meta()) |
| 160 | if randomize: |
| 161 | self.randomize(data) |
| 162 | augmented = convert_to_dst_type(self.apply(data_t), dst=data)[0] |
| 163 | |
| 164 | if labels is not None: |
| 165 | augmented_label = convert_to_dst_type(self.apply(labels_t), dst=labels)[0] |
| 166 | return (augmented, augmented_label) if labels is not None else augmented |
| 167 | |
| 168 | |
| 169 | class CutOut(Mixer): |
nothing calls this directly
no test coverage detected