# 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()