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train.py
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84 lines (65 loc) · 2.68 KB
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import os
import pprint
import argparse
import torch
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torch.backends.cudnn as cudnn
import init_path
from utils.logger import Logger
from data.data_loader import customer_data_loader
from arch.net_loader import customer_net_loader
# **************************************************************************
# Replace by your custom class
# **************************************************************************
from scheduler.coconv_scheduler import CoConvScheduler as Scheduler
from cfg.coconv_cfg import CoConvConfig as Config
# **************************************************************************
def parse_args():
parser = argparse.ArgumentParser(description='Train keypoints network')
parser.add_argument('--cfg', default='experiments/something-something.yaml',
help='experiment configure file name',
# required=True,
type=str)
parser.add_argument('opts',
help="Modify config options using the command-line",
default=None,
nargs=argparse.REMAINDER)
parser.add_argument('--resume',
help='which epoch to resume',
type=int,
default=None)
parser.add_argument('--logDir',
help='log directory',
type=str,
default='')
parser.add_argument('--modelDir',
help='output model directory',
type=str,
default='')
args = parser.parse_args()
return args
def main():
args = parse_args()
cfg = Config(args).getcfg()
exp_suffix = os.path.basename(args.cfg).split('.')[0]
logger = Logger(os.path.join(cfg.LOG_DIR, '.'.join([cfg.SNAPSHOT_PREF, cfg.MODEL.NAME, exp_suffix, cfg.TRAIN.OPTIMIZER, str(cfg.TRAIN.LR)])))
logger.log(pprint.pformat(args))
logger.log(cfg)
# cudnn related setting
cudnn.benchmark = cfg.CUDNN.BENCHMARK
torch.backends.cudnn.deterministic = cfg.CUDNN.DETERMINISTIC
torch.backends.cudnn.enabled = cfg.CUDNN.ENABLED
# define
train_loader = customer_data_loader(cfg, cfg.DATASET.TRAIN_SET)
val_loader = customer_data_loader(cfg, cfg.DATASET.TEST_SET)
net = customer_net_loader(cfg=cfg)
model = Scheduler(net, cfg=cfg, logger=logger)
if cfg.IS_TRAIN:
model.train(train_loader, val_loader=val_loader, which_epoch=args.resume)
else:
model.test(val_loader, weight_file=cfg.TEST.LOAD_WEIGHT)
if __name__ == '__main__':
main()