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:
