7b34113cc3b5529a127bd02667de9de0b5b75df9,models/networks_basic.py,PNet,forward,#PNet#Any#Any#Any#,40

Before Change

    def forward(self, in0, in1, retPerLayer=False):
        in0_sc = self.scaling_layer(in0)
        in1_sc = self.scaling_layer(in1)

        outs0 = self.net.forward(in0_sc)
        outs1 = self.net.forward(in1_sc)

        if(retPerLayer):
            all_scores = []
        for (kk,out0) in enumerate(outs0):
            cur_score = (1.-util.cos_sim(outs0[kk],outs1[kk]))
            if(kk==0):
                val = 1.*cur_score
            else:
                val = val + cur_score
            if(retPerLayer):
                all_scores+=[cur_score]

        if(retPerLayer):
            return (val, all_scores)
        else:
            return val

After Change

        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

Instances