()
return parser.parse_args()
Parse all the arguments provided from the CLI.

    Returns:
      A list of parsed arguments.
Parse all the arguments provided from the CLI.
Parse all the arguments provided from the CLI .
def get_arguments():
    """Parse all the arguments provided from the CLI.

    Returns:
      A list of parsed arguments.
    """
    parser = argparse.ArgumentParser(description="CE2P Network")
    parser.add_argument("--batch-size", type=int, default=BATCH_SIZE,
                        help="Number of images sent to the network in one step.")
    parser.add_argument("--data-dir", type=str, default=DATA_DIRECTORY,
                        help="Path to the directory containing the dataset.")
    parser.add_argument("--dataset", type=str, default='train', choices=['train', 'val', 'trainval', 'test'],
                        help="Path to the file listing the images in the dataset.")
    parser.add_argument("--ignore-label", type=int, default=IGNORE_LABEL,
                        help="The index of the label to ignore during the training.")
    parser.add_argument("--input-size", type=str, default=INPUT_SIZE,
                        help="Comma-separated string with height and width of images.")
    parser.add_argument("--learning-rate", type=float, default=LEARNING_RATE,
                        help="Base learning rate for training with polynomial decay.")
    parser.add_argument("--momentum", type=float, default=MOMENTUM,
                        help="Momentum component of the optimiser.")
    parser.add_argument("--num-classes", type=int, default=NUM_CLASSES,
                        help="Number of classes to predict (including background).") 
    parser.add_argument("--start-iters", type=int, default=0,
                        help="Number of classes to predict (including background).") 
    parser.add_argument("--power", type=float, default=POWER,
                        help="Decay parameter to compute the learning rate.")
    parser.add_argument("--weight-decay", type=float, default=WEIGHT_DECAY,
                        help="Regularisation parameter for L2-loss.")
    parser.add_argument("--random-mirror", action="store_true",
                        help="Whether to randomly mirror the inputs during the training.")
    parser.add_argument("--random-scale", action="store_true",
                        help="Whether to randomly scale the inputs during the training.")
    parser.add_argument("--random-seed", type=int, default=RANDOM_SEED,
                        help="Random seed to have reproducible results.")
    parser.add_argument("--restore-from", type=str, default=RESTORE_FROM,
                        help="Where restore model parameters from.")
    parser.add_argument("--save-num-images", type=int, default=SAVE_NUM_IMAGES,
                        help="How many images to save.")
    parser.add_argument("--snapshot-dir", type=str, default=SNAPSHOT_DIR,
                        help="Where to save snapshots of the model.")
    parser.add_argument("--gpu", type=str, default='None',
                        help="choose gpu device.")
    parser.add_argument("--start-epoch", type=int, default=0,
                        help="choose the number of recurrence.")
    parser.add_argument("--epochs", type=int, default=150,
                        help="choose the number of recurrence.")
    return parser.parse_args()
def get_arguments ( ) : parser = argparse . ArgumentParser ( description = "CE2P Network" ) parser . add_argument ( "--batch-size" , type = int , default = BATCH_SIZE , help = "Number of images sent to the network in one step." ) parser . add_argument ( "--data-dir" , type = str , default = DATA_DIRECTORY , help = "Path to the directory containing the dataset." ) parser . add_argument ( "--dataset" , type = str , default = 'train' , choices = [ 'train' , 'val' , 'trainval' , 'test' ] , help = "Path to the file listing the images in the dataset." ) parser . add_argument ( "--ignore-label" , type = int , default = IGNORE_LABEL , help = "The index of the label to ignore during the training." ) parser . add_argument ( "--input-size" , type = str , default = INPUT_SIZE , help = "Comma-separated string with height and width of images." ) parser . add_argument ( "--learning-rate" , type = float , default = LEARNING_RATE , help = "Base learning rate for training with polynomial decay." ) parser . add_argument ( "--momentum" , type = float , default = MOMENTUM , help = "Momentum component of the optimiser." ) parser . add_argument ( "--num-classes" , type = int , default = NUM_CLASSES , help = "Number of classes to predict (including background)." ) parser . add_argument ( "--start-iters" , type = int , default = 0 , help = "Number of classes to predict (including background)." ) parser . add_argument ( "--power" , type = float , default = POWER , help = "Decay parameter to compute the learning rate." ) parser . add_argument ( "--weight-decay" , type = float , default = WEIGHT_DECAY , help = "Regularisation parameter for L2-loss." ) parser . add_argument ( "--random-mirror" , action = "store_true" , help = "Whether to randomly mirror the inputs during the training." ) parser . add_argument ( "--random-scale" , action = "store_true" , help = "Whether to randomly scale the inputs during the training." ) parser . add_argument ( "--random-seed" , type = int , default = RANDOM_SEED , help = "Random seed to have reproducible results." ) parser . add_argument ( "--restore-from" , type = str , default = RESTORE_FROM , help = "Where restore model parameters from." ) parser . add_argument ( "--save-num-images" , type = int , default = SAVE_NUM_IMAGES , help = "How many images to save." ) parser . add_argument ( "--snapshot-dir" , type = str , default = SNAPSHOT_DIR , help = "Where to save snapshots of the model." ) parser . add_argument ( "--gpu" , type = str , default = 'None' , help = "choose gpu device." ) parser . add_argument ( "--start-epoch" , type = int , default = 0 , help = "choose the number of recurrence." ) parser . add_argument ( "--epochs" , type = int , default = 150 , help = "choose the number of recurrence." ) return parser . parse_args ( )