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#!python3
import argparse
import time
# import sys
# sys.path.append('/root/miniforge3/envs/DB/lib/python3.8/site-packages')
import torch
import yaml
from trainer import Trainer
# tagged yaml objects
from experiment import Structure, TrainSettings, ValidationSettings, Experiment
from concern.log import Logger
from data.data_loader import DataLoader
from data.image_dataset import ImageDataset
from training.checkpoint import Checkpoint
from training.model_saver import ModelSaver
from training.optimizer_scheduler import OptimizerScheduler
from concern.config import Configurable, Config
def main():
import sys
# sys.argv.append( 'experiments/seg_detector/td500_resnet18_deform_thre.yaml' )
sys.argv.append( 'experiments/seg_detector/ic15_resnet18_deform_thre.yaml' )
sys.argv.append( '--num_gpus' )
sys.argv.append( '1' )
sys.argv.append( '--batch_size' )
sys.argv.append( '6' )
sys.argv.append( '--epochs' )
sys.argv.append( '1200' )
#sys.argv.append( '--resume' ) # 继续上一次训练
#sys.argv.append( '/root/model_epoch_120_minibatch_12000' )
#sys.argv.append( '--start_iter' )
#sys.argv.append( '18000' )
#sys.argv.append( '--start_epoch' )
#sys.argv.append( '107' )
torch.backends.cudnn.enabled = False
parser = argparse.ArgumentParser(description='Text Recognition Training')
parser.add_argument('exp', type=str)
parser.add_argument('--name', type=str)
parser.add_argument('--batch_size', type=int, help='Batch size for training')
parser.add_argument('--resume', type=str, help='Resume from checkpoint')
parser.add_argument('--epochs', type=int, help='Number of training epochs')
parser.add_argument('--num_workers', type=int, help='Number of dataloader workers')
parser.add_argument('--start_iter', type=int, help='Begin counting iterations starting from this value (should be used with resume)')
parser.add_argument('--start_epoch', type=int, help='Begin counting epoch starting from this value (should be used with resume)')
parser.add_argument('--max_size', type=int, help='max length of label')
parser.add_argument('--lr', type=float, help='initial learning rate')
parser.add_argument('--optimizer', type=str, help='The optimizer want to use')
parser.add_argument('--thresh', type=float, help='The threshold to replace it in the representers')
parser.add_argument('--verbose', action='store_true', help='show verbose info')
parser.add_argument('--visualize', action='store_true', help='visualize maps in tensorboard')
parser.add_argument('--force_reload', action='store_true', dest='force_reload', help='Force reload data meta')
parser.add_argument('--no-force_reload', action='store_false', dest='force_reload', help='Force reload data meta')
parser.add_argument('--validate', action='store_true', dest='validate', help='Validate during training')
parser.add_argument('--no-validate', action='store_false', dest='validate', help='Validate during training')
parser.add_argument('--print-config-only', action='store_true', help='print config without actual training')
parser.add_argument('--debug', action='store_true', dest='debug', help='Run with debug mode, which hacks dataset num_samples to toy number')
parser.add_argument('--no-debug', action='store_false', dest='debug', help='Run without debug mode')
parser.add_argument('--benchmark', action='store_true', dest='benchmark', help='Open cudnn benchmark mode')
parser.add_argument('--no-benchmark', action='store_false', dest='benchmark', help='Turn cudnn benchmark mode off')
parser.add_argument('-d', '--distributed', action='store_true', dest='distributed', help='Use distributed training')
parser.add_argument('--local_rank', dest='local_rank', default=0, type=int, help='Use distributed training')
parser.add_argument('-g', '--num_gpus', dest='num_gpus', default=4, type=int, help='The number of accessible gpus')
parser.set_defaults(debug=False)
parser.set_defaults(benchmark=True)
args = parser.parse_args()
args = vars(args)
args = {k: v for k, v in args.items() if v is not None}
if args['distributed']:
torch.cuda.set_device(args['local_rank'])
torch.distributed.init_process_group(backend='nccl', init_method='env://')
conf = Config()
experiment_args = conf.compile(conf.load(args['exp']))['Experiment']
experiment_args.update(cmd=args)
experiment = Configurable.construct_class_from_config(experiment_args)
if not args['print_config_only']:
torch.backends.cudnn.benchmark = args['benchmark']
trainer = Trainer(experiment)
trainer.train()
if __name__ == '__main__':
main()
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