#parser.add_argument('-cfg', '--cfg', default='deepmedic_ce_50_50_check', type=str)
parser.add_argument('-cfg',
'--cfg',
default='deepmedic_ce_50_50_redo',
type=str)
#parser.add_argument('-cfg', '--cfg', default='deepmedic_ce_50_50_c25_redo', type=str)
#parser.add_argument('-cfg', '--cfg', default='deepmedic_ce_50_50_all', type=str)
parser.add_argument('-gpu', '--gpu', default='0', type=str)
parser.add_argument('-out', '--out', default='', type=str)
path = os.path.dirname(__file__)
## parse arguments
args = parser.parse_args()
args = Parser(args.cfg, log='train').add_args(args)
args.gpu = str(args.gpu)
ckpts = args.makedir()
resume = os.path.join(ckpts, 'model_last.tar')
if not args.resume and os.path.exists(resume):
args.resume = resume
def main():
# setup environments and seeds
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
random.seed(args.seed)
np.random.seed(args.seed)
from models import criterions
from data import datasets
from data.dataloader import DataLoader
from utils import Parser
path = os.path.dirname(__file__)
cfg_name = 'deepmedic'
mode = 'train'
args = Parser().add_cfg(cfg_name)
ckpts = args.getdir()
args.gpu = str(args.gpu)
args.gpu = '0'
out_dir = os.path.join('output', cfg_name, mode)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
# setup logs
log = os.path.join(out_dir, 'log.txt')
fmt = '%(asctime)s %(message)s'
logging.basicConfig(level=logging.INFO, format=fmt, filename=log)
console = logging.StreamHandler()
console.setLevel(logging.INFO)
console.setFormatter(logging.Formatter(fmt))
logging.getLogger('').addHandler(console)
