(self, x)
| 207 | return nn.Sequential(*layers) |
| 208 | |
| 209 | def forward(self, x): |
| 210 | x = self.conv1(x) |
| 211 | x = self.bn1(x) |
| 212 | x = self.relu(x) |
| 213 | x = self.maxpool(x) |
| 214 | |
| 215 | x = self.layer1(x) |
| 216 | x = self.layer2(x) |
| 217 | x = self.layer3(x) |
| 218 | x = self.layer4(x) |
| 219 | |
| 220 | x = self.avgpool(x) |
| 221 | x = torch.flatten(x, 1) |
| 222 | |
| 223 | return x |
| 224 | |
| 225 | |
| 226 | def _resnet(arch, block, layers, pretrained, progress, **kwargs): |
nothing calls this directly
no outgoing calls
no test coverage detected