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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 |