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)
