I am using LightCNN 1 to extract a face features vector out of an image generated from a GAN. I want to compute the loss between the generated image from the GAN and the original face before going inside the GAN. Below is an example of the code I am using:
import torch
from util.util import get_image
import torchvision.transforms as transforms
from LightCNN.light_cnn import LightCNN_29Layers_v2

face_model = torch.nn.DataParallel(LightCNN_29Layers_v2(num_classes=80013)).cuda()
checkpoint = torch.load('LightCNN_29Layers_V2_checkpoint.pth')
face_model.load_state_dict(checkpoint['state_dict'])
face_model.eval()

transform = transforms.Compose([transforms.ToTensor()])
																	   #get_image returns an image as if it was read from disk, check https://github.com/AlfredXiangWu/LightCNN/blob/master/extract_features.py#L76
tmp_rec_A=transform(get_image(rec_A.detach())).unsqueeze(0)   #rec_A is the output generated from a GAN after giving it an input image A
tmp_real_A=transform(get_image(real_A)).unsqueeze(0)          #real_A is the original image A                  
tmp_rec_B=transform(get_image(rec_B.detach())).unsqueeze(0)   #rec_B is the output generated from a GAN after giving it an input image B
tmp_real_B=transform(get_image(real_B)).unsqueeze(0)          #real_B is the original image B                   


rec_A_feat =face_model(tmp_rec_A)[1]      #face feature vector of the output generated from a GAN after giving it an input image A
real_A_feat=face_model(tmp_real_A)[1] 	  #face feature vector of the original image A
rec_B_feat =face_model(tmp_rec_B)[1]      #face feature vector of the output generated from a GAN after giving it an input image B
real_B_feat=face_model(tmp_real_B)[1]     #face feature vector of the original image B

criterionFeatureMatch = torch.nn.MSELoss()

loss_face_feature_A = criterionFeatureMatch(rec_A_feat, real_A_feat)
loss_face_feature_B = criterionFeatureMatch(rec_B_feat, real_B_feat)           
loss_G = loss_face_feature_A + loss_face_feature_B
loss_G.backward() 						  #should backpropagate the gradients for the generator network

However, when the last line is executed I get the following error:
  File "anaconda3/envs/temp/lib/python3.6/site-packages/torch/tensor.py", line 102, in backward
    torch.autograd.backward(self, gradient, retain_graph, create_graph)
  File "anaconda3/envs/temp/lib/python3.6/site-packages/torch/autograd/__init__.py", line 90, in backward
    allow_unreachable=True)  # allow_unreachable flag
RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn

My PyTorch version is 1.0.1 and Python version is 3.6.2
I do not know what is wrong with this code. Any help is appreciated. Thanks!