import argparse import os def demo(): parser = argparse.ArgumentParser(description='Demo for Ev2Hands') parser.add_argument('--batch_size', dest='batch_size', required=False, help='Set the batch_size (default: 128)', default='32') parser.add_argument('--checkpoint_path', dest='checkpoint', required=False, help='path of checkpoint_path', default='./savedmodels/best_model_state_dict.pth') args = parser.parse_args() os.environ['CHECKPOINT_PATH'] = args.checkpoint os.environ['BATCH_SIZE'] = args.batch_size return args def evaluate(): parser = argparse.ArgumentParser(description='Evaluation of Ev2Hands') parser.add_argument('--batch_size', dest='batch_size', required=False, help='Set the batch_size (default: 128)', default='128') parser.add_argument('--checkpoint_path', dest='checkpoint', required=False, help='path of checkpoint', default='./savedmodels/best_model_state_dict.pth') args = parser.parse_args() os.environ['CHECKPOINT_PATH'] = args.checkpoint os.environ['BATCH_SIZE'] = args.batch_size return args def train(): parser = argparse.ArgumentParser(description='Trainer of Ev2Hands') parser.add_argument('--batch_size', dest='batch_size', required=False, help='Set the batch_size (default: 8)', default='8') parser.add_argument('--checkpoint_path', dest='checkpoint', required=False, help='path of checkpoint', default='') args = parser.parse_args() os.environ['CHECKPOINT_PATH'] = args.checkpoint os.environ['BATCH_SIZE'] = args.batch_size return args def finetune(): parser = argparse.ArgumentParser(description='FineTuner of Ev2Hands for real data') parser.add_argument('--batch_size', dest='batch_size', required=False, help='Set the batch_size (default: 8)', default='8') parser.add_argument('--checkpoint_path', dest='checkpoint', required=False, help='path of checkpoint', default='./savedmodels/best_model_state_dict.pth') args = parser.parse_args() os.environ['CHECKPOINT_PATH'] = args.checkpoint os.environ['BATCH_SIZE'] = args.batch_size return args