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import cv2
import yaml
import argparse
import os
from torch.utils.data import DataLoader
from datasets.gl3d_dataset import GL3DDataset
from trainer import Trainer
from trainer_single_norel import SingleTrainerNoRel
from trainer_single import SingleTrainer
if __name__ == '__main__':
# add argument parser
parser = argparse.ArgumentParser()
parser.add_argument('--config', type=str, default='./configs/config.yaml')
parser.add_argument('--dataset_dir', type=str, default='/mnt/nvme2n1/hyz/data/GL3D')
parser.add_argument('--data_split', type=str, default='comb')
parser.add_argument('--is_training', type=bool, default=True)
parser.add_argument('--job_name', type=str, default='')
parser.add_argument('--gpu', type=str, default='0')
parser.add_argument('--start_cnt', type=int, default=0)
parser.add_argument('--stage', type=int, default=1)
args = parser.parse_args()
# load global config
with open(args.config, 'r') as f:
config = yaml.load(f, Loader=yaml.FullLoader)
# setup dataloader
dataset = GL3DDataset(args.dataset_dir, config['network'], args.data_split, is_training=args.is_training)
data_loader = DataLoader(dataset, batch_size=2, shuffle=True, num_workers=4)
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
if args.stage == 1:
trainer = SingleTrainerNoRel(config, f'cuda:0', data_loader, args.job_name, args.start_cnt)
elif args.stage == 2:
trainer = SingleTrainer(config, f'cuda:0', data_loader, args.job_name, args.start_cnt)
elif args.stage == 3:
trainer = Trainer(config, f'cuda:0', data_loader, args.job_name, args.start_cnt)
else:
raise NotImplementedError()
trainer.train()
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