shuffle=True, **kwargs) test_loader = torch.utils.data.DataLoader(datasets.MNIST( 'data', train=False, transform=transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307, ), (0.3081, )) ])), batch_size=args.test_batch_size, shuffle=True, **kwargs) # generate the model if args.arch == 'LeNet_5': model = models.LeNet_5() else: print('ERROR: specified arch is not suppported') exit() if not args.pretrained: best_acc = 0.0 else: pretrained_model = torch.load(args.pretrained) best_acc = pretrained_model['acc'] model.load_state_dict( pretrained_model['state_dict']) #将预训练过的模型的参数状态加载到model里 if args.cuda: model.cuda() #网络移植到gpu上
transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)) ])), batch_size=args.batch_size, shuffle=True, **kwargs) test_loader = torch.utils.data.DataLoader( datasets.MNIST('data', train=False, transform=transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)) ])), batch_size=args.test_batch_size, shuffle=True, **kwargs) # generate the model if args.arch == 'LeNet_300_100': model = models.LeNet_300_100(args.prune) elif args.arch == 'LeNet_5': model = models.LeNet_5(args.prune) else: print('ERROR: specified arch is not suppported') exit() if not args.pretrained: best_acc = 0.0 else: pretrained_model = torch.load(args.pretrained) best_acc = pretrained_model['acc'] load_state(model, pretrained_model['state_dict']) if args.cuda: model.cuda() print(model)