# use: TensorFlow pre-trained model
# use pre-trained model from PyTorch
# we use: resnet_v1_50 on the ImageNet validation setl, pytorch pretrained resnet_50 is 76.15%
trainloader = imagenet_traindata(args.batch_size)
testloader = imagenet_testdata(args.batch_size)
MainModel = imp.load_source('MainModel', "tf_resnetv1_50_to_pth.py")
# load pre-trained model from PyTorch
model = torch.load('tf_resnetv1_50_to_pth.pth')
model = nn.DataParallel(model)
model = model.cuda()
print(model)
trainloader = imagenet_traindata(args.batch_size)
testloader = imagenet_testdata(args.batch_size)
MainModel = imp.load_source('MainModel', "tf_resnetv1_50_to_pth.py")
model = torch.load('tf_resnetv1_50_to_pth.pth')
model = nn.DataParallel(model)
model = model.cuda()
print(model)
criterion = nn.CrossEntropyLoss()
