def forward(self, in0, in1, retPerLayer=False):
if self.pnet_type == 'vggface':
in0 = (in0 + 1) * 255. / 2
in0_sc = in0 - self.shift.expand_as(in0)
in1 = (in1 + 1) * 255. / 2
in1_sc = in1 - self.shift.expand_as(in1)
else:
in0_sc = (in0 -
self.shift.expand_as(in0)) / self.scale.expand_as(in0)
in1_sc = (in1 -
self.shift.expand_as(in0)) / self.scale.expand_as(in0)
outs0 = self.net.forward(in0_sc)
outs1 = self.net.forward(in1_sc)
if (retPerLayer):
all_scores = []
st = 3
en = 4
for i in range(st, en + 1):
cur_score = (1. - util.cos_sim(outs0[i], outs1[i]))
if (i == st):
val = 1. * cur_score
else:
val = val + cur_score
if (retPerLayer):
all_scores += [cur_score]
if (retPerLayer):
return (val, all_scores)
else:
return val
def forward(self, in0, in1, retPerLayer=False):
in0_sc = (in0 - self.shift.expand_as(in0)) / self.scale.expand_as(in0)
in1_sc = (in1 - self.shift.expand_as(in0)) / self.scale.expand_as(in0)
if self.colorspace == 'Gray':
in0_sc = util.tensor2tensorGrayscaleLazy(in0_sc)
in1_sc = util.tensor2tensorGrayscaleLazy(in1_sc)
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 + self.lambda_feat_layers[kk]*cur_score
val = val + cur_score
if (retPerLayer):
all_scores += [cur_score]
if (retPerLayer):
return (val, all_scores)
else:
return val
def forward_layers(self, in0, in1, st, en):
in0_sc = (in0 - self.shift.expand_as(in0)) / self.scale.expand_as(in0)
in1_sc = (in1 - self.shift.expand_as(in0)) / self.scale.expand_as(in0)
outs0 = self.net.forward(in0_sc)
outs1 = self.net.forward(in1_sc)
for i in range(st, en + 1):
cur_score = (1. - util.cos_sim(outs0[i], outs1[i]))
if (i == st):
val = 1. * cur_score
else:
val = val + cur_score
return val
def forward(self, in0, in1, retPerLayer=False):
in0_sc = (in0 - self.shift.expand_as(in0))/self.scale.expand_as(in0)
in1_sc = (in1 - self.shift.expand_as(in0))/self.scale.expand_as(in0)
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 + self.lambda_feat_layers[kk]*cur_score
val = val + cur_score
if(retPerLayer):
all_scores+=[cur_score]
if(retPerLayer):
return (val, all_scores)
else:
return val
