conv upsampling

conv_upsampling.py

class ConvUpsampling(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0):
        super(ConvUpsampling, self).__init__()
        
        self.scale_factor = kernel_size
        self.conv = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding, bias=False),
            nn.BatchNorm2d(out_channels),
            nn.LeakyReLU()
        )

    def forward(self, x):
        x = F.interpolate(x, scale_factor=self.scale_factor, mode='bilinear')
        return self.conv(x)