def compute_vgg_loss(self, vgg, img, target):
img_vgg = vgg_preprocess(img)
target_vgg = vgg_preprocess(target)
img_fea = vgg(img_vgg)
target_fea = vgg(target_vgg)
return torch.mean(
(self.instancenorm(img_fea) - self.instancenorm(target_fea))**2)
def forward(self, x, out_keys, preprocess=True):
'''
NOTE: input tensor should range in [0,1]
'''
out = {}
if preprocess:
x = vgg_preprocess(x, vgg_normal_correct=self.vgg_normal_correct)
out['r11'] = F.relu(self.conv1_1(x))
out['r12'] = F.relu(self.conv1_2(out['r11']))
out['p1'] = self.pool1(out['r12'])
out['r21'] = F.relu(self.conv2_1(out['p1']))
out['r22'] = F.relu(self.conv2_2(out['r21']))
out['p2'] = self.pool2(out['r22'])
out['r31'] = F.relu(self.conv3_1(out['p2']))
out['r32'] = F.relu(self.conv3_2(out['r31']))
out['r33'] = F.relu(self.conv3_3(out['r32']))
out['r34'] = F.relu(self.conv3_4(out['r33']))
out['p3'] = self.pool3(out['r34'])
out['r41'] = F.relu(self.conv4_1(out['p3']))
out['r42'] = F.relu(self.conv4_2(out['r41']))
out['r43'] = F.relu(self.conv4_3(out['r42']))
out['r44'] = F.relu(self.conv4_4(out['r43']))
out['p4'] = self.pool4(out['r44'])
out['r51'] = F.relu(self.conv5_1(out['p4']))
out['r52'] = F.relu(self.conv5_2(out['r51']))
out['r53'] = F.relu(self.conv5_3(out['r52']))
out['r54'] = F.relu(self.conv5_4(out['r53']))
out['p5'] = self.pool5(out['r54'])
return [out[key] for key in out_keys]