5576495ae2d425855fb06920e8ce0f574e6ac212,model.py,CapsuleGenerator,forward,#CapsuleGenerator#Any#,29

Before Change

        self.pixel_shuffle = nn.PixelShuffle(upscale_factor)

    def forward(self, x):
        x = self.lrelu(self.conv1(x))
        x = self.lrelu(self.conv2(x))
        x = self.lrelu(self.conv3(x))
        x = self.lrelu(self.conv4(x))

        x = self.conv5(x)
        // capsules squash
        x = torch.cat([squash(capsule) for capsule in torch.chunk(x, chunks=16, dim=1)], dim=1)
        x = self.lrelu(self.conv6(x))

        x = F.sigmoid(self.pixel_shuffle(x))
        return x


class CapsuleDiscriminator(nn.Module):
    def __init__(self):

After Change

        block2 = self.block2(block1)
        block3 = self.block3(block2)
        block4 = self.block4(block3)
        block5 = self.block5(block4)
        block6 = self.block6(block5)
        block7 = self.block7(block6)
        block8 = self.block8(block1 + block7)

        return (F.tanh(block8) + 1) / 2


class Discriminator(nn.Module):
    def __init__(self):

Instances