(self, data)
| 77 | self.featuresampler = featuresampler |
| 78 | |
| 79 | def embed(self, data): |
| 80 | if isinstance(data, torch.utils.data.DataLoader): |
| 81 | features = [] |
| 82 | for image in data: |
| 83 | if isinstance(image, dict): |
| 84 | image = image["image"] |
| 85 | with torch.no_grad(): |
| 86 | input_image = image.to(torch.float).to(self.device) |
| 87 | features.append(self._embed(input_image)) |
| 88 | return features |
| 89 | return self._embed(data) |
| 90 | |
| 91 | def _embed(self, images, detach=True, provide_patch_shapes=False): |
| 92 | """Returns feature embeddings for images.""" |