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Fixed where the square() to preserve distance properties #73

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Fixed where the square() to preserve distance properties #73

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Fixed where the square() to preserve distance properties
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@GuillaumeRochette
GuillaumeRochette authored Jul 10, 2021
commit 779e2982d0e963c66349ba88a09467a66aa67867
10 changes: 5 additions & 5 deletions lpips/lpips.py
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Original file line number Diff line number Diff line change
Expand Up @@ -89,18 +89,18 @@ def forward(self, in0, in1, retPerLayer=False, normalize=False):

for kk in range(self.L):
feats0[kk], feats1[kk] = lpips.normalize_tensor(outs0[kk]), lpips.normalize_tensor(outs1[kk])
diffs[kk] = (feats0[kk]-feats1[kk])**2
diffs[kk] = feats0[kk]-feats1[kk]

if(self.lpips):
if(self.spatial):
res = [upsample(self.lins[kk](diffs[kk]), out_HW=in0.shape[2:]) for kk in range(self.L)]
res = [upsample(self.lins[kk](diffs[kk]).square(), out_HW=in0.shape[2:]) for kk in range(self.L)]
else:
res = [spatial_average(self.lins[kk](diffs[kk]), keepdim=True) for kk in range(self.L)]
res = [spatial_average(self.lins[kk](diffs[kk]).square(), keepdim=True) for kk in range(self.L)]
else:
if(self.spatial):
res = [upsample(diffs[kk].sum(dim=1,keepdim=True), out_HW=in0.shape[2:]) for kk in range(self.L)]
res = [upsample(diffs[kk].square().sum(dim=1,keepdim=True), out_HW=in0.shape[2:]) for kk in range(self.L)]
else:
res = [spatial_average(diffs[kk].sum(dim=1,keepdim=True), keepdim=True) for kk in range(self.L)]
res = [spatial_average(diffs[kk].square().sum(dim=1,keepdim=True), keepdim=True) for kk in range(self.L)]

val = res[0]
for l in range(1,self.L):
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