type=int,
default=10000,
help='number of evaluation')
parser.add_argument('--vis_num',
type=int,
default=60,
help='number of visible evaluation')
parser.add_argument('--multiscale',
type=bool,
default=False,
help='enable multiscale_search')
args = parser.parse_args()
Config.set_model_name(args.model_name)
Config.set_model_type(Config.MODEL[args.model_type])
Config.set_model_backbone(Config.BACKBONE[args.model_backbone])
Config.set_dataset_type(Config.DATA[args.dataset_type])
Config.set_dataset_path(args.dataset_path)
Config.set_dataset_version(args.dataset_version)
config = Config.get_config()
model = Model.get_model(config)
evaluate = Model.get_evaluate(config)
dataset = Dataset.get_dataset(config)
evaluate(model,
dataset,
vis_num=args.vis_num,
total_eval_num=args.eval_num,
enable_multiscale_search=args.multiscale)
type=str,
default="Default",
help=
"model backbone, available options: Mobilenet, Vggtiny, Vgg19, Resnet18, Resnet50"
)
parser.add_argument(
"--model_name",
type=str,
default="default_name",
help="model name,to distinguish model and determine model dir")
parser.add_argument(
"--dataset_path",
type=str,
default="./data",
help="dataset path,to determine the path to load the dataset")
args = parser.parse_args()
#config model
Config.set_model_name(args.model_name)
Config.set_model_type(Config.MODEL[args.model_type])
Config.set_model_backbone(Config.BACKBONE[args.model_backbone])
Config.set_pretrain(True)
#config dataset
Config.set_pretrain_dataset_path(args.dataset_path)
config = Config.get_config()
#train
model = Model.get_model(config)
pretrain = Model.get_pretrain(config)
dataset = Dataset.get_pretrain_dataset(config)
pretrain(model, dataset)