def use_log():
log_info("Start")
try:
log_warning("Below code has error:")
a = 1 / 0
except Exception as e:
log_error("Division by 0") # 输出单行错误信息
log_exception(e) # 输出堆栈错误信息
log_error_pro("Division by 0", e) # log_error_pro = log_error + log_exception 同时输出自定义错误消息和堆栈信息
parser.add_argument('--img_size',
type=int,
default=512,
help='Size of input images')
parser.add_argument('--stride',
type=float,
default=0.5,
help='From 0 to 1')
parser.add_argument('--lr', type=float, default=1e-3, help='Learning rate')
parser.add_argument('--wsize',
type=int,
default=60,
help='Size of windows for bagging')
args = parser.parse_args()
log_info('Params: ' + str(args))
train_tr, test_tr = get_basic_transforms()
if args.test:
train_ds = MnistBags(wsize=(args.wsize, args.wsize))
test_ds = MnistBags(wsize=(args.wsize, args.wsize), train=False)
log_info('Mnist dataset is used')
elif args.arti:
train_ds = GENdataset(transform=train_tr,
inp_size=args.img_size,
wsize=(args.wsize, args.wsize),
crop=True,
stride=args.stride)
test_ds = GENdataset(transform=test_tr,
inp_size=args.img_size,
parser.add_argument('--batch_size', type=int, default=1, help='Batch size')
parser.add_argument('--id',
type=str,
default='default',
help='Unique net id to save')
parser.add_argument('--save_each',
type=int,
default=50,
help='Save model weights each n epochs')
parser.add_argument('--save_best',
action='store_false',
help='Save best test model?')
args = parser.parse_args()
log_info('Params: ' + str(args))
# log_info('GIT revision: ' + subprocess.check_output('git rev-parse HEAD', shell=True).decode("utf-8"))
# DATASETS INITIALIZATION
train_tr, test_tr = get_basic_transforms()
if args.test:
train_ds = CentriollesDatasetOn(transform=train_tr,
pos_dir='dataset/mnist/1',
neg_dir='dataset/mnist/0',
inp_size=args.img_size)
test_ds = CentriollesDatasetOn(transform=test_tr,
pos_dir='dataset/mnist/1',
neg_dir='dataset/mnist/0',
inp_size=args.img_size,
train=False)
parser.add_argument('--wd', type=float, default=1e-6, help='Weight decay')
parser.add_argument('--ld', type=float, default=0.95, help='Learning rate multipliyer for every 10 epoches')
parser.add_argument('--batch_size', type=int, default=1, help='Batch size')
parser.add_argument('--epoch', type=int, default=0, help='Number of epoches')
parser.add_argument('--test', action='store_true', help='Test this model on simpler dataset')
parser.add_argument('--artif', action='store_true', help='Artificial dataset')
parser.add_argument('--crop', action='store_true', help='Crop only the central cell')
parser.add_argument('--stride', type=float, default=0.5, help='From 0 to 1')
parser.add_argument('--pyramid_layers', type=int, default=28, help='Number of layers in da pyramid')
parser.add_argument('--id', type=str, default='default', help='Unique net id to save')
parser.add_argument('--save_each', type=int, default=0, help='Save model weights each n epochs')
parser.add_argument('--save_best', action='store_true', help='Save best test model?')
args = parser.parse_args()
log_info('Params: ' + str(args))
# log_info('GIT revision: ' + subprocess.check_output('git rev-parse HEAD', shell=True).decode("utf-8"))
# DATASETS INITIALIZATION
train_tr, test_tr = get_basic_transforms()
if args.use_bags:
if args.artif:
train_ds = GENdataset(transform=train_tr,
inp_size=args.img_size, wsize=(args.wsize, args.wsize),
crop=args.crop, stride=args.stride, pyramid_layers=args.pyramid_layers)
test_ds = GENdataset(transform=test_tr,
inp_size=args.img_size, wsize=(args.wsize, args.wsize),
crop=args.crop, stride=args.stride, train=False,
pyramid_layers=args.pyramid_layers)
real_test_ds = CentriollesDatasetBags(transform=test_tr,
inp_size=args.img_size, wsize=(args.wsize, args.wsize),