def forward(self, x, with_feature=True):
f_list, x = unpack_feature(x)
if self.use_res_connect:
ret = x + self.conv(x)
else:
ret = self.conv(x)
return pack_feature(f_list, ret, with_feature)
def forward(self, x, with_feature=False):
x = self.features(x)
f_list, x = unpack_feature(x)
x = x.mean([2, 3])
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)
f_list, features = unpack_feature(features)
out = F.relu(features, inplace=True)
out = F.adaptive_avg_pool2d(out, (1, 1)).view(features.size(0), -1)
f_list.append(out)
out = self.classifier(out)
return pack_feature(f_list, out, with_feature)
def forward(self, x, with_feature=True):
f_list, x_last = unpack_feature(x)
x = F.relu(self.bn1(self.conv1(x_last)))
x = self.bn2(self.conv2(x))
x += self.shortcut(x_last)
x = F.relu(x)
return pack_feature(f_list, x, with_feature)
def forward(self, x, with_feature=True):
f_list, x = unpack_feature(x)
if not self.equalInOut:
x = self.relu1(self.bn1(x))
else:
out = self.relu1(self.bn1(x))
out = self.relu2(self.bn2(self.conv1(out if self.equalInOut else x)))
if self.droprate > 0:
out = F.dropout(out, p=self.droprate, training=self.training)
out = self.conv2(out)
out = torch.add(x if self.equalInOut else self.convShortcut(x), out)
return pack_feature(f_list, out, with_feature)
def forward(self, x, with_feature=False):
out = self.conv1(x)
out = self.block1(out)
out = self.block2(out)
out = self.block3(out)
f_list, out = unpack_feature(out)
out = self.relu(self.bn1(out))
out = F.avg_pool2d(out, 8)
out = out.view(-1, self.nChannels)
f_list.append(out)
out = self.fc(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 = F.relu(self.bn1(self.conv1(x)))
# x = pack_feature(*unpack_feature(x))
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
f_list, x = unpack_feature(x)
x = F.avg_pool2d(x, x.size()[3])
x = x.view(x.size(0), -1)
f_list.append(x)
x = self.linear(x)
return pack_feature(f_list, x, 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)