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add batch support #124

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add batch support #124

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13 changes: 12 additions & 1 deletion lpips/lpips.py
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Original file line number Diff line number Diff line change
Expand Up @@ -110,6 +110,9 @@ def __init__(self, pretrained=True, net='alex', version='0.1', lpips=True, spati
self.eval()

def forward(self, in0, in1, retPerLayer=False, normalize=False):
batch_size0 = in0.shape[0]
batch_size1 = in1.shape[0]

if normalize: # turn on this flag if input is [0,1] so it can be adjusted to [-1, +1]
in0 = 2 * in0 - 1
in1 = 2 * in1 - 1
Expand All @@ -121,7 +124,9 @@ 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][:, None] - feats1[kk][None]) ** 2
# put all pairwise differences in batch dimension
diffs[kk] = diffs[kk].view(batch_size0 * batch_size1, *diffs[kk].shape[2:])

if(self.lpips):
if(self.spatial):
Expand All @@ -134,6 +139,12 @@ def forward(self, in0, in1, retPerLayer=False, normalize=False):
else:
res = [spatial_average(diffs[kk].sum(dim=1,keepdim=True), keepdim=True) for kk in range(self.L)]

# unpack again to get batch_size0 x batch_size1 outputs for all pairwise distances
if(self.spatial):
res = [r.view(batch_size0, batch_size1, *r.shape[1:]) for r in res]
else:
res = [r.view(batch_size0, batch_size1) for r in res]

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