def forward(self, x: Tensor) -> Tensor: x = self.conv(x) x = x.view(-1, 512 * 2 * 2) x = self.linear(x) return x
def forward(self, x: Tensor) -> Tensor: x = self.bn1(self.fc1(x)).relu() x = self.bn2(self.fc2(x)).relu() x = x.view(-1, 16, self.img_size_h, self.img_size_w) x = self.up1(self.cbn1(self.conv1(x)).relu()) if self.dataset == 'CelebA': x = self.up2(self.cbn2(self.conv2(x)).relu()) x = self.cbn3(self.conv3(x)).relu() x = self.conv4(x).sigmoid() return x
def forward(self, x: Tensor) -> Tuple[Tensor]: x = self.down1(self.cbn1(self.conv1(x)).relu()) if self.dataset == 'CelebA': x = self.down2(self.cbn2(self.conv2(x)).relu()) x = self.cbn3(self.conv3(x)).relu() x = x.view(-1, 16*self.img_size_h*self.img_size_w) x = self.bn1(self.fc1(x)).relu() d = self.out_d(x).sigmoid() q_cat = self.out_q_cat(x) q_non_cat_mean, q_non_cat_logvar = self.out_q_non_cat_mean(x), self.out_q_non_cat_logvar(x) return d, q_cat, q_non_cat_mean, q_non_cat_logvar
def forward(self, img: Tensor, labels: int): """Forward pass of the Discriminator. Args: img: the image that should be classified in fake or real. labels: the label of the image. Returns: """ d_in = torch.cat( (img.view(img.size(0), -1), self.label_embedding(labels)), -1) score = self.model(d_in) return score
def forward(self, input: Tensor, mask: Tensor = None, hx: Tuple[Tensor, Tensor] = None) -> Tuple[Tensor, Tensor]: batch_size = input.size(0) if self.batch_first else input.size(1) if hx is None: num_directions = 2 if self.bidirectional else 1 hx = input.new_zeros((self.num_layers * num_directions, batch_size, self.hidden_size)) hx = (hx, hx) func = rnn_f.autograd_var_masked_rnn(num_layers=self.num_layers, batch_first=self.batch_first, bidirectional=self.bidirectional, lstm=True) self.reset_noise(batch_size) output, hidden = func(input, self.all_cells, hx, None if mask is None else mask.view(mask.size() + (1,))) return output, hidden
def forward(self, x: Tensor) -> Tensor: y: Tensor = self.conv1(x) x = self.conv128(y) x += y y = self.conv128to256(x) x = self.conv256(y) x += y y = self.conv256to512(x) x = self.conv512(y) x += y x = x.view(-1, 512 * 4 * 4) return self.linear(x)
def forward(self, x: Tensor) -> Tensor: x = self.conv(x) x = x.view(-1, 256 * 3 * 3) x = self.linear(x) return x
def forward(self, x: Tensor) -> Tensor: x = self.conv(x) x = x.view(-1, 512 * 4 * 4) # -1 means infer this dimension x = self.linear(x) return x