gan.py

#md | DCGAN Generator
#mdi | https://miro.medium.com/max/1400/1*5ALjnfAqwcWbOsledTBXsw.png
class generator(nn.Module):
    # initializers
    def __init__(self, d=128):
        super(generator, self).__init__()
        self.deconv1 = nn.ConvTranspose2d(100, d*8, 4, 1, 0) #md | Deconvolution | sub
        self.deconv1_bn = nn.BatchNorm2d(d*8) #md | Batch Normalization | sub
        self.deconv2 = nn.ConvTranspose2d(d*8, d*4, 4, 2, 1)
        self.deconv2_bn = nn.BatchNorm2d(d*4)
        self.deconv3 = nn.ConvTranspose2d(d*4, d*2, 4, 2, 1)
        self.deconv3_bn = nn.BatchNorm2d(d*2)
        self.deconv4 = nn.ConvTranspose2d(d*2, d, 4, 2, 1)
        self.deconv4_bn = nn.BatchNorm2d(d)
        self.deconv5 = nn.ConvTranspose2d(d, 3, 4, 2, 1)

    #md | Model Benchmarks
        for m in self._modules:
            normal_init(self._modules[m], mean, std)

    # forward method
    def forward(self, input):
        # x = F.relu(self.deconv1(input))
        x = F.relu(self.deconv1_bn(self.deconv1(input)))
        x = F.relu(self.deconv2_bn(self.deconv2(x)))
        x = F.relu(self.deconv3_bn(self.deconv3(x)))
        x = F.relu(self.deconv4_bn(self.deconv4(x)))
        x = F.tanh(self.deconv5(x))

        return x