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