def get_cfg(): args = parse_args() print("Called with args:") print(args) args = set_dataset_args(args) if args.cfg_file is not None: cfg_from_file(args.cfg_file) if args.set_cfgs is not None: cfg_from_list(args.set_cfgs) print("Using config:") pprint.pprint(cfg) # np.random.seed(cfg.RNG_SEED) setup_seed(cfg.RNG_SEED) return args
import _init_paths import torch from torch.autograd import Variable import torch.nn as nn from roi_data_layer.roidb import combined_roidb from roi_data_layer.roibatchLoader import roibatchLoader from model.utils.config import cfg, cfg_from_file, cfg_from_list, get_output_dir from model.utils.net_utils import weights_normal_init, save_net, load_net, \ adjust_learning_rate, save_checkpoint, clip_gradient, FocalLoss, sampler, calc_supp, EFocalLoss from model.utils.parser_func import parse_args, set_dataset_args if __name__ == '__main__': args = parse_args() print('Called with args:') print(args) args = set_dataset_args(args) if args.cfg_file is not None: cfg_from_file(args.cfg_file) if args.set_cfgs is not None: cfg_from_list(args.set_cfgs) print('Using config:') pprint.pprint(cfg) np.random.seed(cfg.RNG_SEED) # torch.backends.cudnn.benchmark = True if torch.cuda.is_available() and not args.cuda: