help="the model file to be evaluated. Usually it is of the form model_X.pth"
)
parser.add_argument('--outfile',
type=str,
default='experiment/kaggle.csv',
metavar='D',
help="name of the output csv file")
args = parser.parse_args()
use_cuda = torch.cuda.is_available()
state_dict = torch.load(args.model)
model.load_state_dict(state_dict)
model.eval()
if use_cuda:
print('Using GPU')
model.cuda()
else:
print('Using CPU')
from data import data_transform_val
test_dir = args.data + '/test_images/mistery_category'
def pil_loader(path):
# open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835)
with open(path, 'rb') as f:
with Image.open(f) as img:
return img.convert('RGB')
default=0.001,
help='initial learning rate')
parser.add_argument('-s',
type=bool,
default=True,
help='whether shuffle the dataset')
parser.add_argument('-a',
type=bool,
default=False,
help='test the filter, reconstructed')
parser.add_argument('-b', type=bool, default=False, help='test the acc')
parser.add_argument('-c', type=bool, default=True, help='train')
args = parser.parse_args()
net = Resnet(BasicBlock)
net = net.cuda() #参数和模型应该都放在cuda上
cifar10_training_loader = get_training_dataloader(
batch_size=args.batchsize, shuffle=args.s)
cifar10_test_loader = get_test_dataloader(batch_size=args.batchsize,
shuffle=args.s)
cifar10_image_loader = get_test_dataloader(batch_size=1, shuffle=args.s)
loss_function = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=args.lr)
#TIME_NOW = datetime.now().isoformat() #这个文件命名有点问题，Windows下文件夹名称不允许有：
TIME_NOW = '20191025'
checkpoint_path = os.path.join('checkpoint', TIME_NOW)