if I want to change the neural networks, how can I do? about pytorch-gan-collections HOT 3 CLOSED

def initialize(self):
    for m in self.modules():
        if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d, nn.Linear)):
            init.normal_(m.weight, std=0.02)
            init.zeros_(m.bias)

def forward(self, z, *args, **kwargs):
    x = self.linear(z)
    x = x.view(x.size(0), -1, self.M, self.M)
    x = self.main(x)
    return x

    self.main = nn.Sequential(
        # M
        nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1),
        nn.LeakyReLU(0.1, inplace=True),
        nn.Conv2d(32, 64, kernel_size=4, stride=2, padding=1),
        nn.LeakyReLU(0.1, inplace=True),
        nn.BatchNorm2d(64),
        nn.Dropout2d(p=0.2),
        # M / 2
        nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
        nn.LeakyReLU(0.1, inplace=True),
        nn.BatchNorm2d(128),
        nn.Dropout2d(p=0.2),
        nn.Conv2d(128, 10, kernel_size=3, stride=1, padding=1),
        nn.ReLU(True))

    self.linear = nn.Linear(M // 2 * M // 2 * 10, 1) # here I am not sure
    self.initialize()

def initialize(self):
    for m in self.modules():
        if isinstance(m, (nn.Conv2d, nn.Linear)):
            init.normal_(m.weight, std=0.02)
            init.zeros_(m.bias)
            spectral_norm(m)

def forward(self, x, *args, **kwargs):
    x = self.main(x)
    x = torch.flatten(x, start_dim=1)
    x = self.linear(x)
    return x](url)

self.linear = nn.Linear(M // 16 * M // 16 * 10, 1)