How to use the lpips.networks_basic.FakeNet function in lpips

def __init__(self, chn_in, chn_out=1, use_dropout=False):
        super(NetLinLayer, self).__init__()

        layers = [nn.Dropout(),] if(use_dropout) else []
        layers += [nn.Conv2d(chn_in, chn_out, 1, stride=1, padding=0, bias=False),]
        self.model = nn.Sequential(*layers)


# L2, DSSIM metrics
class FakeNet(nn.Module):
    def __init__(self, use_gpu=True, colorspace='Lab'):
        super(FakeNet, self).__init__()
        self.use_gpu = use_gpu
        self.colorspace=colorspace

class L2(FakeNet):

    def forward(self, in0, in1):
        assert(in0.size()[0]==1) # currently only supports batchSize 1

        if(self.colorspace=='RGB'):
            (N,C,X,Y) = in0.size()
            value = torch.mean(torch.mean(torch.mean((in0-in1)**2,dim=1).view(N,1,X,Y),dim=2).view(N,1,1,Y),dim=3).view(N)
            return value
        elif(self.colorspace=='Lab'):
            value = util.l2(util.tensor2np(util.tensor2tensorlab(in0.data,to_norm=False)),
                util.tensor2np(util.tensor2tensorlab(in1.data,to_norm=False)), range=100.).astype('float')
            ret_var = Variable( torch.Tensor((value,) ) )
            if(self.use_gpu):
                ret_var = ret_var.cuda()
            return ret_var

def forward(self, in0, in1):
        assert(in0.size()[0]==1) # currently only supports batchSize 1

        if(self.colorspace=='RGB'):
            (N,C,X,Y) = in0.size()
            value = torch.mean(torch.mean(torch.mean((in0-in1)**2,dim=1).view(N,1,X,Y),dim=2).view(N,1,1,Y),dim=3).view(N)
            return value
        elif(self.colorspace=='Lab'):
            value = util.l2(util.tensor2np(util.tensor2tensorlab(in0.data,to_norm=False)),
                util.tensor2np(util.tensor2tensorlab(in1.data,to_norm=False)), range=100.).astype('float')
            ret_var = Variable( torch.Tensor((value,) ) )
            if(self.use_gpu):
                ret_var = ret_var.cuda()
            return ret_var

class DSSIM(FakeNet):

    def forward(self, in0, in1):
        assert(in0.size()[0]==1) # currently only supports batchSize 1

        if(self.colorspace=='RGB'):
            value = util.dssim(1.*util.tensor2im(in0.data), 1.*util.tensor2im(in1.data), range=255.).astype('float')
        elif(self.colorspace=='Lab'):
            value = util.dssim(util.tensor2np(util.tensor2tensorlab(in0.data,to_norm=False)),
                util.tensor2np(util.tensor2tensorlab(in1.data,to_norm=False)), range=100.).astype('float')
        ret_var = Variable( torch.Tensor((value,) ) )
        if(self.use_gpu):
            ret_var = ret_var.cuda()
        return ret_var

def print_network(net):
    num_params = 0

def __init__(self, use_gpu=True, colorspace='Lab'):
        super(FakeNet, self).__init__()
        self.use_gpu = use_gpu
        self.colorspace=colorspace