(self, x)
| 138 | return nn.Sequential(*layers) |
| 139 | |
| 140 | def forward(self, x): |
| 141 | x = self.conv1(x) |
| 142 | x = self.bn1(x) |
| 143 | x = self.relu(x) |
| 144 | x = self.maxpool(x) |
| 145 | |
| 146 | x = self.layer1(x) |
| 147 | x = self.layer2(x) |
| 148 | x = self.layer3(x) |
| 149 | x = self.layer4(x) |
| 150 | |
| 151 | if self.include_top: |
| 152 | x = self.avgpool(x) |
| 153 | x = torch.flatten(x, 1) |
| 154 | x = self.fc(x) |
| 155 | |
| 156 | return x |
| 157 | |
| 158 | |
| 159 | def resnet34(num_classes=1000, include_top=True): |
nothing calls this directly
no outgoing calls
no test coverage detected