def parse_args()

in object-detection/train_yolov3.py [0:0]

• 77 lines of code
• 1 McCabe index (conditional complexity)

def parse_args():
    parser = argparse.ArgumentParser(description='Train YOLO networks with random input shape.')
    parser.add_argument('--model-dir', type=str, default=os.environ['SM_MODEL_DIR'])
    parser.add_argument('--train', type=str, default=os.environ['SM_CHANNEL_TRAIN'])
    parser.add_argument('--test', type=str, default=os.environ['SM_CHANNEL_TEST'])
    parser.add_argument('--val', type=str, default=os.environ['SM_CHANNEL_VAL'])
    parser.add_argument("--checkpoint-dir",type=str,default="/opt/ml/checkpoints",help="Path where checkpoints will be saved.")
    parser.add_argument('--dataset', type=str, choices=['custom','coco','voc'],default='custom',
                        help='Training dataset. Now support voc.')
    parser.add_argument('--current-host', type=str, default=os.environ['SM_CURRENT_HOST'])
    parser.add_argument('--hosts', type=list, default=json.loads(os.environ['SM_HOSTS']))
    parser.add_argument('--network', type=str, choices=['darknet53','mobilenet1.0'], default='darknet53',
                        help="Base network name which serves as feature extraction base.")
    parser.add_argument('--data-shape', type=int, default=512,
                        help="Input data shape for evaluation, use 320, 416, 608... " +
                             "Training is with random shapes from (320 to 608).")
    parser.add_argument('--batch-size', type=int, default=24, help='Training mini-batch size')
    parser.add_argument('--num-workers', '-j', dest='num_workers', type=int,
                        default=8, help='Number of data workers, you can use larger '
                        'number to accelerate data loading, if you CPU and GPUs are powerful.')
    parser.add_argument('--gpus',  type=int, default=os.environ['SM_NUM_GPUS'],
                        help='Training with GPUs, you can specify 1,3 for example.')
    parser.add_argument('--epochs', type=int, default=1,
                        help='Training epochs.')
    parser.add_argument('--resume', type=str, default='',
                        help='Resume from previously saved parameters if not None. '
                        'For example, you can resume from ./yolo3_xxx_0123.params')
    parser.add_argument('--start-epoch', type=int, default=0,
                        help='Starting epoch for resuming, default is 0 for new training.'
                        'You can specify it to 100 for example to start from 100 epoch.')
    parser.add_argument('--lr', type=float, default=0.001,
                        help='Learning rate, default is 0.001')
    parser.add_argument('--lr-mode', type=str, default='step',
                        help='learning rate scheduler mode. options are step, poly and cosine.')
    parser.add_argument('--lr-decay', type=float, default=0.1,
                        help='decay rate of learning rate. default is 0.1.')
    parser.add_argument('--lr-decay-period', type=int, default=0,
                        help='interval for periodic learning rate decays. default is 0 to disable.')
    parser.add_argument('--lr-decay-epoch', type=str, default='160,180',
                        help='epochs at which learning rate decays. default is 160,180.')
    parser.add_argument('--warmup-lr', type=float, default=0.0,
                        help='starting warmup learning rate. default is 0.0.')
    parser.add_argument('--warmup-epochs', type=int, default=2,
                        help='number of warmup epochs.')
    parser.add_argument('--momentum', type=float, default=0.9,
                        help='SGD momentum, default is 0.9')
    parser.add_argument('--wd', type=float, default=0.0005,
                        help='Weight decay, default is 5e-4')
    parser.add_argument('--log-interval', type=int, default=100,
                        help='Logging mini-batch interval. Default is 100.')
    parser.add_argument('--save-prefix', type=str, default='',
                        help='Saving parameter prefix')
    parser.add_argument('--save-interval', type=int, default=10,
                        help='Saving parameters epoch interval, best model will always be saved.')
    parser.add_argument('--val-interval', type=int, default=5,
                        help='Epoch interval for validation, increase the number will reduce the '
                             'training time if validation is slow.')
    parser.add_argument('--seed', type=int, default=233,
                        help='Random seed to be fixed.')
    parser.add_argument('--num-samples', type=int, default=-1,
                        help='Training images. Use -1 to automatically get the number.')
    parser.add_argument('--syncbn', action='store_true',
                        help='Use synchronize BN across devices.')
    parser.add_argument('--no-random-shape', action='store_true',
                        help='Use fixed size(data-shape) throughout the training, which will be faster '
                        'and require less memory. However, final model will be slightly worse.')
    parser.add_argument('--no-wd', action='store_true',
                        help='whether to remove weight decay on bias, and beta/gamma for batchnorm layers.')
    parser.add_argument('--mixup', type=bool, default=True,
                        help='whether to enable mixup.')
    parser.add_argument('--no-mixup-epochs', type=int, default=20,
                        help='Disable mixup training if enabled in the last N epochs.')
    parser.add_argument('--pretrained-model', type=str, choices=['Coco', 'None'], default='Coco',
                       help='Use a pre-trained model on Coco')
    parser.add_argument('--label-smooth', action='store_true', help='Use label smoothing.')
    args = parser.parse_args()
    return args