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)