def forward(self, x, with_feature=False):
x = self.features(x)
f_list, x = unpack_feature(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
f_list.append(x)
x = self.classifier(x)
return pack_feature(f_list, x, with_feature)
def forward(self, x, with_feature=False):
features = self.features(x)
out = F.relu(features, inplace=True)
out = F.adaptive_avg_pool2d(out, (1, 1))
out = torch.flatten(out, 1)
out = self.classifier(out)
f_list, out = unpack_feature(out)
return pack_feature(f_list, out, with_feature)
def forward(self, x, with_feature=True):
f_list, x = unpack_feature(x)
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return pack_feature(f_list, out, with_feature)
def forward(self, x, with_feature=False):
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)
f_list, x = unpack_feature(x)
x = self.avgpool(x)
x = x.reshape(x.size(0), -1)
f_list.append(x)
x = self.fc(x)
return pack_feature(f_list, x, with_feature)