Method forward

UIC/models/resnet50.py:248–285

(self, x)

        return nn.Sequential(*layers)

    def forward(self, x):
        if not self.linear_eval:
            with torch.no_grad():
                if self.gblur:
                    x = self.gblur(x)
            x = self.conv1(x)
            x = self.bn1(x)
            x = self.relu(x)
            x = self.maxpool(x)

            x = self.layer1(x)
            x = self.layer2(x)
            x = self.layer3(x)
            x = self.layer4(x)

            x = self.avgpool(x)
            if self.extra_mlp:
                x = self.head(x)
            x = torch.flatten(x, 1)
            x = self.fc(x)
        else:
            with torch.no_grad():
                x = self.conv1(x)
                x = self.bn1(x)
                x = self.relu(x)
                x = self.maxpool(x)
                x = self.layer1(x)
                x = self.layer2(x)
                x = self.layer3(x)
                x = self.layer4(x)
                x = self.avgpool(x)
                if self.extra_mlp:
                    x = self.head(x)
                x = torch.flatten(x, 1)
                x = x.detach()
            x = self.linear(x)

        return x

def _resnet(block, layers, out=1000, dim=3, **kwargs):
    model = ResNet(block, layers, num_classes=out, dim=dim, **kwargs)