def forward(self, x, reverse=False):
if reverse:
x = grad_reverse(x, self.lambd)
x = F.relu(self.bn2_fc(self.fc2(x)))
x = F.dropout(x, training=self.training)
x = self.fc3(x)
return x
def forward(self, x_feat, reverse=False):
x = self.relu(self.bn1_fc(self.fc1(x_feat)))
x = F.dropout(x, training=self.training)
if reverse:
x = grad_reverse(x, self.lambd)
x = self.relu(self.bn2_fc(self.fc2(x)))
output = self.fc3(x)
return output, x
def forward(self, x, reverse=False):
x = self.model.avgpool(self.model.features(x))
x_feat = x.view(x.size(0), 256 * 6 * 6)
x = self.relu(self.bn1_fc(self.fc1(x_feat)))
x = F.dropout(x, training=self.training)
if reverse:
x = grad_reverse(x, self.lambd)
x = self.relu(self.bn2_fc(self.fc2(x)))
return x, x_feat
def forward(self, x, reverse=False): # GOOD x_feat PLACE ??? IMPORTANT!!!
x = self.mp1(self.relu(self.bn1(self.conv1(x))))
x = self.mp2(self.relu(self.bn2(self.conv2(x))))
x = self.mp3(self.relu(self.bn3(self.conv3(x))))
x = self.mp4(self.relu(self.bn4(self.conv4(x))))
x_feat = x.view(x.size(0), 128 * 8 * 8)
x = self.relu(self.bn1_fc(self.fc1(x_feat)))
x = F.dropout(x, training=self.training)
if reverse:
x = grad_reverse(x, self.lambd)
x = self.relu(self.bn2_fc(self.fc2(x)))
return x, x_feat
def forward(self, x, reverse=False):
#print(x.shape)
x = self.pool(F.relu(self.bn1(self.conv1(x))))
#print(x.shape)
x = self.pool(F.relu(self.bn2(self.conv2(x))))
#print(x.shape)
x = F.relu(self.bn3(self.conv3(x)))
#print(x.shape)
x = x.view(x.size(0), 64896)
#x = x.view(x.size(0),864)
x = F.relu(self.bn1_fc(self.fc1(x)))
x = F.dropout(x, training=self.training)
if reverse:
x = grad_reverse(x, self.lambd)
x = F.relu(self.bn2_fc(self.fc2(x)))
return x
def forward(self, x, reverse=False):
x = F.max_pool2d(F.relu(self.bn1(self.conv1(x))),
stride=2,
kernel_size=3,
padding=1)
x = F.max_pool2d(F.relu(self.bn2(self.conv2(x))),
stride=2,
kernel_size=3,
padding=1)
x = F.relu(self.bn3(self.conv3(x)))
x_feat = x.view(x.size(0), 8192)
x = F.relu(self.bn1_fc(self.fc1(x_feat)))
x = F.dropout(x, training=self.training)
if reverse:
x = grad_reverse(x, self.lambd)
x = F.relu(self.bn2_fc(self.fc2(x)))
return x, x_feat
def forward(self, x, reverse=False):
if reverse:
x = grad_reverse(x, self.lambd)
# x = F.relu(self.bn2_fc(self.fc2(x)))
x = self.fc3(x)
return x