def forward(self, in0, in1, retPerLayer=None): 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 forward(self, in0, in1, retPerLayer=None): 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, retPerLayer=None): assert in0.size()[0] == 1 # currently only supports batchSize 1 if self.colorspace == "RGB": value = util.dssim( 1.0 * util.tensor2im(in0.data), 1.0 * util.tensor2im(in1.data), range=255.0, ).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=100.0, ).astype("float") ret_var = Variable(torch.Tensor((value, ))) if self.use_gpu: ret_var = ret_var.cuda() return ret_var