32d40eb01a5a61304720fb1c425abef0a1f5b5a0,model.py,Generator,__init__,#Generator#,24

Before Change

class Generator(nn.Module):
    def __init__(self):
        super(Generator, self).__init__()
        self.main = nn.Sequential(
            // input is Z, going into a convolution
            nn.ConvTranspose2d(100, 32 * 8, 4, 1, 0, bias=False),
            nn.BatchNorm2d(32 * 8),
            nn.ReLU(True),
            // state size. (32*8) x 4 x 4
            nn.ConvTranspose2d(32 * 8, 32 * 4, 4, 2, 1, bias=False),
            nn.BatchNorm2d(32 * 4),
            nn.ReLU(True),
            // state size. (32*4) x 8 x 8
            nn.ConvTranspose2d(32 * 4, 32 * 2, 4, 2, 1, bias=False),
            nn.BatchNorm2d(32 * 2),
            nn.ReLU(True),
            // state size. (32*2) x 16 x 16
            nn.ConvTranspose2d(32 * 2, 3, 4, 2, 1, bias=False),
            nn.Tanh()
            // state size. (3) x 32 x 32
        )

    def forward(self, x):
        output = self.main(x)
        return output

After Change

        super(Generator, self).__init__()

        self.conv1 = nn.Conv2d(3, 64, (5, 5), (1, 1), (2, 2))
        self.conv2 = nn.Conv2d(64, 64, (3, 3), (1, 1), (1, 1))
        self.conv3 = nn.Conv2d(64, 32, (3, 3), (1, 1), (1, 1))
        self.conv4 = nn.Conv2d(32, 3 * (upscale_factor ** 2), (3, 3), (1, 1), (1, 1))
        self.pixel_shuffle = nn.PixelShuffle(upscale_factor)

Instances