Python utils.str2bool() Examples

def parse_args():
    parser = argparse.ArgumentParser(description='Demo for LEDNet from a given image')

    parser.add_argument('--input-pic', type=str, default=os.path.join(cur_path, 'png/demo.png'),
                        help='path to the input picture')
    parser.add_argument('--pretrained', type=str,
                        default=os.path.expanduser('~/cbb/own/pretrained/seg/lednet/LEDNet_final.pth'),
                        help='Default Pre-trained model root.')
    parser.add_argument('--cuda', type=ptutil.str2bool, default='true',
                        help='demo with GPU')

    opt = parser.parse_args()
    return opt 

def parse_args():
    parser = argparse.ArgumentParser(description='Eval Segmentation.')
    parser.add_argument('--batch-size', type=int, default=1,
                        help='Training mini-batch size')
    parser.add_argument('--num-workers', '-j', dest='num_workers', type=int,
                        default=4, help='Number of data workers')
    parser.add_argument('--dataset', type=str, default='citys',
                        help='Select dataset.')
    parser.add_argument('--split', type=str, default='val',
                        help='Select val|test, evaluate in val or test data')
    parser.add_argument('--mode', type=str, default='testval',
                        help='Select testval|val, w/o corp and with crop')
    parser.add_argument('--base-size', type=int, default=1024,
                        help='base image size')
    parser.add_argument('--crop-size', type=int, default=768,
                        help='crop image size')

    parser.add_argument('--pretrained', type=str,
                        default='./LEDNet_iter_073600.pth',
                        help='Default Pre-trained model root.')

    # device
    parser.add_argument('--cuda', type=ptutil.str2bool, default='true',
                        help='Training with GPUs.')
    parser.add_argument('--local_rank', type=int, default=0)
    parser.add_argument('--init-method', type=str, default="env://")

    args = parser.parse_args()
    return args 

def main():
    parser = argparse.ArgumentParser()

    parser.add_argument('--log_dir', default='logdir-tacotron')
    
    parser.add_argument('--data_paths', default='.\\data\\moon,.\\data\\son')
    
    
    parser.add_argument('--load_path', default=None)   # 아래의 'initialize_path'보다 우선 적용
    #parser.add_argument('--load_path', default='logdir-tacotron/moon+son_2018-12-25_19-03-21')
    
    
    parser.add_argument('--initialize_path', default=None)   # ckpt로 부터 model을 restore하지만, global step은 0에서 시작

    parser.add_argument('--batch_size', type=int, default=32)
    parser.add_argument('--num_test_per_speaker', type=int, default=2)
    parser.add_argument('--random_seed', type=int, default=123)
    parser.add_argument('--summary_interval', type=int, default=100000)
    parser.add_argument('--test_interval', type=int, default=500)  # 500
    parser.add_argument('--checkpoint_interval', type=int, default=2000) # 2000
    parser.add_argument('--skip_path_filter', type=str2bool, default=False, help='Use only for debugging')

    parser.add_argument('--slack_url', help='Slack webhook URL to get periodic reports.')
    parser.add_argument('--git', action='store_true', help='If set, verify that the client is clean.')  # The store_true option automatically creates a default value of False.

    config = parser.parse_args()
    config.data_paths = config.data_paths.split(",")
    setattr(hparams, "num_speakers", len(config.data_paths))

    prepare_dirs(config, hparams)

    log_path = os.path.join(config.model_dir, 'train.log')
    infolog.init(log_path, config.model_dir, config.slack_url)

    tf.set_random_seed(config.random_seed)
    print(config.data_paths)

    if any("krbook" not in data_path for data_path in config.data_paths) and  hparams.sample_rate != 20000:
        warning("Detect non-krbook dataset. May need to set sampling rate from {} to 20000".format(hparams.sample_rate))
        
    if any('LJ' in data_path for data_path in config.data_paths) and  hparams.sample_rate != 22050:
        warning("Detect LJ Speech dataset. Set sampling rate from {} to 22050".format(hparams.sample_rate))

    if config.load_path is not None and config.initialize_path is not None:
        raise Exception(" [!] Only one of load_path and initialize_path should be set")

    train(config.model_dir, config) 

def main():
    parser = argparse.ArgumentParser()

    parser.add_argument('--log_dir', default='logdir-tacotron2')
    
    parser.add_argument('--data_paths', default='D:\\hccho\\Tacotron-Wavenet-Vocoder-hccho\\data\\moon,D:\\hccho\\Tacotron-Wavenet-Vocoder-hccho\\data\\son')
    #parser.add_argument('--data_paths', default='D:\\hccho\\Tacotron-Wavenet-Vocoder-hccho\\data\\small1,D:\\hccho\\Tacotron-Wavenet-Vocoder-hccho\\data\\small2')
    
    
    #parser.add_argument('--load_path', default=None)   # 아래의 'initialize_path'보다 우선 적용
    parser.add_argument('--load_path', default='logdir-tacotron2/moon+son_2019-03-01_10-35-44')
    
    
    parser.add_argument('--initialize_path', default=None)   # ckpt로 부터 model을 restore하지만, global step은 0에서 시작

    parser.add_argument('--batch_size', type=int, default=32)
    parser.add_argument('--num_test_per_speaker', type=int, default=2)
    parser.add_argument('--random_seed', type=int, default=123)
    parser.add_argument('--summary_interval', type=int, default=100)
    
    parser.add_argument('--test_interval', type=int, default=500)  # 500
    
    parser.add_argument('--checkpoint_interval', type=int, default=2000) # 2000
    parser.add_argument('--skip_path_filter', type=str2bool, default=False, help='Use only for debugging')

    parser.add_argument('--slack_url', help='Slack webhook URL to get periodic reports.')
    parser.add_argument('--git', action='store_true', help='If set, verify that the client is clean.')  # The store_true option automatically creates a default value of False.

    config = parser.parse_args()
    config.data_paths = config.data_paths.split(",")
    setattr(hparams, "num_speakers", len(config.data_paths))

    prepare_dirs(config, hparams)

    log_path = os.path.join(config.model_dir, 'train.log')
    infolog.init(log_path, config.model_dir, config.slack_url)

    tf.set_random_seed(config.random_seed)
    print(config.data_paths)


    if config.load_path is not None and config.initialize_path is not None:
        raise Exception(" [!] Only one of load_path and initialize_path should be set")

    train(config.model_dir, config)