SRmodel.py

def conv(ni, nf, kernel_size = 3, actn=True):
    layers = [nn.Conv2d(ni, nf, kernel_size, padding=kernel_size//2)]
    if actn: layers.append(nn.ReLU(True))
    return nn.Sequential(*layers)
#----------------------  
class ResSequential(nn.Module):
    def __init__(self, layers, res_scale=1.0):
        super().__init__()
        self.res_scale = res_scale # A factor less than 1.0 will help stabelize training
        self.layers = nn.Sequential(*layers)
    def forward(self, x): return x + self.layers(x) * self.res_scale