def forward(self, in0, in1):
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 == 'Gray'):
(N, C, X, Y) = in0.size()
in0 = util.tensor2tensorGrayscale(in0)
in1 = util.tensor2tensorGrayscale(in1)
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'):
assert (in0.size()[0] == 1) # currently only supports batchSize 1
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