ResNet Generator

vittorio: <p>Hi Guys, i am trying to create a ResNet GANs architecture for images of dimension 224x224 pixels.<br>
I’ve found plenty of resources online on creating a ResNet classifier so this is how my discriminator would look like:</p>
<pre><code class="lang-auto">class ResidualBlock(nn.Module):
    expansion = 1
    def __init__(self, inchannel, outchannel, stride=1):
        super(ResidualBlock, self).__init__()
        self.conv1 = nn.Sequential(
                        nn.Conv2d(inchannel, outchannel, kernel_size=3, stride=stride, padding=1, bias=False),
                        nn.BatchNorm2d(outchannel),
                        )
        self.conv2  = nn.Sequential(
                        nn.Conv2d(outchannel, outchannel, kernel_size=3, stride=1, padding=1, bias=False),
                        nn.BatchNorm2d(outchannel)
                    )
        self.skip = nn.Sequential()
        if stride != 1 or inchannel != self.expansion * outchannel:
            self.skip = nn.Sequential(
                nn.Conv2d(inchannel, self.expansion * outchannel, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(self.expansion * outchannel)
            )

    def forward(self, X):
        out = F.relu(self.conv1(X))
        out = self.conv2(out)
        out += self.skip(X)
        out = F.relu(out)
        return out

class Discriminator(nn.Module):
    def __init__(self, ResidualBlock, num_classes):
        super(Discriminator, self).__init__()
        self.inchannel = 64
        self.conv1 = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(64),
        )
        self.layer1 = self.make_layer(ResidualBlock, 64,  2, stride=1)
        self.layer2 = self.make_layer(ResidualBlock, 128, 2, stride=2)
        self.layer3 = self.make_layer(ResidualBlock, 256, 2, stride=2)
        self.layer4 = self.make_layer(ResidualBlock, 512, 2, stride=2)
        self.fc = nn.Linear(512*ResidualBlock.expansion, num_classes)

    def make_layer(self, block, channels, num_blocks, stride):
        strides = [stride] + [1] * (num_blocks - 1)
        layers = []
        for stride in strides:
            layers.append(block(self.inchannel, channels, stride))
            self.inchannel = channels * block.expansion
        return nn.Sequential(*layers)

    def forward(self, x):
        out = F.relu(self.conv1(x))
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = F.avg_pool2d(out, out.size()[3])
        out = torch.flatten(out,1 )
        out = self.fc(out)
        return out
</code></pre>
<p>Now this discriminator has 4 layers with two residual blocks each and i would like to create a generator with more or less the same depth. The problem is that i am extremely confused on how i should create this network. I wish to start from a noise dimension of 100 and reach a image dimension of 224x224x3. I don’t understand how i should include the upsampling into my basic block, if someone could give me an input on how one of these generator’s layer should look like i would really appreciate it.</p>