(self, x, return_features=False)
| 308 | self.fc = nn.Linear(768, num_classes) |
| 309 | |
| 310 | def forward(self, x, return_features=False): |
| 311 | |
| 312 | features = self.base_vit(x) |
| 313 | features = torch.nn.functional.normalize(features, dim=-1) |
| 314 | logits = self.fc(features) |
| 315 | |
| 316 | if return_features: |
| 317 | return logits, features |
| 318 | else: |
| 319 | return logits |
| 320 | |
| 321 | @torch.no_grad() |
| 322 | def normalize_prototypes(self): |
nothing calls this directly
no outgoing calls
no test coverage detected