about results about pytorch-cyclegan HOT 2 OPEN

class UpBlock(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(UpBlock, self).__init__()
        # Replace upconv with an interpolation followed by a convolution.
        self.upsample = nn.Upsample(scale_factor=2, mode='bilinear')  # You can also try mode='bilinear'
        self.conv = nn.Conv2d(in_channels, in_channels // 2, kernel_size=3, padding=1)
        self.conv_block = ConvBlock(in_channels // 2 + in_channels // 2, out_channels)  # Adjust for the concatenated channels

    def forward(self, x, skip):
        x = self.upsample(x)
        x = self.conv(x)
        x = torch.cat([x, skip], dim=1)
        return self.conv_block(x)