(self, x)
| 353 | return nn.Sequential(*layers) |
| 354 | |
| 355 | def forward(self, x): |
| 356 | x = self.conv1(x) |
| 357 | x = self.bn1(x) |
| 358 | x = self.relu(x) |
| 359 | x = self.maxpool(x) |
| 360 | |
| 361 | x = self.layer1(x) |
| 362 | x = self.layer2(x) |
| 363 | x = self.layer3(x) |
| 364 | x = self.layer4(x) |
| 365 | |
| 366 | x = self.avgpool(x) |
| 367 | x = torch.flatten(x, 1) |
| 368 | # disable for contrastive learning |
| 369 | # if self.drop: |
| 370 | # x = self.drop(x) |
| 371 | # x = self.fc(x) |
| 372 | |
| 373 | return x |
| 374 | |
| 375 | |
| 376 | def resnest50(pretrained=False, **kwargs): |
nothing calls this directly
no outgoing calls
no test coverage detected