def parse_args()

in florence2-VQA/src_train/train_mlflow.py [0:0]

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

def parse_args():
    # setup argparse
    parser = argparse.ArgumentParser()
    # curr_time = datetime.now().strftime("%Y-%m-%d_%H:%M:%S")

    # hyperparameters
    parser.add_argument("--model_name_or_path", default="microsoft/Florence-2-base-ft", type=str, help="Model name or path")
    parser.add_argument("--train_dir", default="../dataset", type=str, help="Input directory for training")
    parser.add_argument("--model_dir", default="./model", type=str, help="output directory for model")
    parser.add_argument("--epochs", default=1, type=int, help="number of epochs")
    parser.add_argument("--output_dir", default="./output_dir", type=str, help="directory to temporarily store when training a model")    
    parser.add_argument("--train_batch_size", default=10, type=int, help="training - mini batch size for each gpu/process")
    parser.add_argument("--eval_batch_size", default=10, type=int, help="evaluation - mini batch size for each gpu/process")
    parser.add_argument("--learning_rate", default=1e-06, type=float, help="learning rate")
    parser.add_argument("--logging_steps", default=5, type=int, help="logging steps")
    parser.add_argument("--save_steps", default=20, type=int, help="save steps")    
    parser.add_argument("--grad_accum_steps", default=1, type=int, help="gradient accumulation steps")
    parser.add_argument("--lr_scheduler_type", default="linear", type=str)
    parser.add_argument("--seed", default=0, type=int, help="seed")
    parser.add_argument("--warmup_ratio", default=0.2, type=float, help="warmup ratio")

    # parse args
    args = parser.parse_args()

    # return args
    return args