def forward(self, z, y):
y_onehot = one_hot_embedding(y, self.num_classes)
z_in = torch.cat([z, y_onehot], dim=1)
output = self.fc(z_in)
output = output.view(-1, self.z_dim, 1, 1)
output =self.TS(output)
output = pixel_norm(output)
output = self.deconv1(output)
output = self.BN_1(output)
output = self.TS(output)
output = pixel_norm(output)
output = self.deconv2(output)
output = self.BN_2(output)
output = self.TS(output)
output = pixel_norm(output)
output = self.deconv3(output)
output = self.BN_3(output)
output = self.TS(output)
output = pixel_norm(output)
output = self.deconv4(output)
output = self.outact(output)
return output.view(-1, 32 * 32)
def forward(self, z, y):
y_onehot = one_hot_embedding(y, self.num_classes)
z_in = torch.cat([z, y_onehot], dim=1)
output = self.fc(z_in)
output = output.view(-1, 4 * self.model_dim, 4, 4)
output = self.relu(output)
output = pixel_norm(output)
output = self.block1(output)
output = self.block2(output)
output = self.block3(output)
output = self.outact(self.output(output))
output = output[:, :, :-2, :-2]
output = torch.reshape(output, [-1, IMG_H * IMG_W])
return output
def forward(self, z, y):
y_onehot = one_hot_embedding(y, self.num_classes)
z_in = torch.cat([z, y_onehot], dim=1)
output = self.fc(z_in)
output = output.view(-1, 4 * self.model_dim, 4, 4)
output = self.relu(output)
output = pixel_norm(output)
output = self.deconv1(output)
output = output[:, :, :7, :7]
output = self.relu(output)
output = pixel_norm(output)
output = self.deconv2(output)
output = self.relu(output).contiguous()
output = pixel_norm(output)
output = self.deconv3(output)
output = self.outact(output)
return output.view(-1, IMG_W * IMG_H)