199 lines
6.8 KiB
Python
199 lines
6.8 KiB
Python
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# --------------------------------------------------------
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# mcan-vqa (Deep Modular Co-Attention Networks)
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# Licensed under The MIT License [see LICENSE for details]
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# Written by Yuhao Cui https://github.com/cuiyuhao1996
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# --------------------------------------------------------
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from cfgs.base_cfgs import Cfgs
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from core.exec import Execution
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import argparse, yaml, os
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def str2bool(v):
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if isinstance(v, bool):
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return v
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if v.lower() in ('yes', 'true', 't', 'y', '1'):
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return True
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elif v.lower() in ('no', 'false', 'f', 'n', '0'):
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return False
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else:
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raise argparse.ArgumentTypeError('Boolean value expected.')
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def parse_args():
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'''
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Parse input arguments
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'''
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parser = argparse.ArgumentParser(description='VLCN Args')
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parser.add_argument('--RUN', dest='RUN_MODE',
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default='train',
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choices=['train', 'val', 'test'],
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help='{train, val, test}',
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type=str) # , required=True)
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parser.add_argument('--MODEL', dest='MODEL',
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choices=['small', 'large'],
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help='{small, large}',
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default='small', type=str)
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parser.add_argument('--OPTIM', dest='OPTIM',
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choices=['adam', 'rmsprop'],
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help='The optimizer',
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default='rmsprop', type=str)
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parser.add_argument('--SPLIT', dest='TRAIN_SPLIT',
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choices=['train', 'train+val'],
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help="set training split, "
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"eg.'train', 'train+val'"
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"set 'train' can trigger the "
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"eval after every epoch",
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default='train',
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type=str)
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parser.add_argument('--EVAL_EE', dest='EVAL_EVERY_EPOCH',
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default=True,
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help='set True to evaluate the '
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'val split when an epoch finished'
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"(only work when train with "
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"'train' split)",
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type=bool)
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parser.add_argument('--SAVE_PRED', dest='TEST_SAVE_PRED',
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help='set True to save the '
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'prediction vectors'
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'(only work in testing)',
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default=False,
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type=bool)
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parser.add_argument('--BS', dest='BATCH_SIZE',
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help='batch size during training',
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default=64,
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type=int)
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parser.add_argument('--MAX_EPOCH', dest='MAX_EPOCH',
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default=30,
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help='max training epoch',
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type=int)
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parser.add_argument('--PRELOAD', dest='PRELOAD',
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help='pre-load the features into memory'
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'to increase the I/O speed',
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default=False,
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type=bool)
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parser.add_argument('--GPU', dest='GPU',
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help="gpu select, eg.'0, 1, 2'",
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default='0',
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type=str)
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parser.add_argument('--SEED', dest='SEED',
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help='fix random seed',
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default=42,
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type=int)
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parser.add_argument('--VERSION', dest='VERSION',
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help='version control',
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default='1.0.0',
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type=str)
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parser.add_argument('--RESUME', dest='RESUME',
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default=False,
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help='resume training',
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type=str2bool)
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parser.add_argument('--CKPT_V', dest='CKPT_VERSION',
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help='checkpoint version',
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type=str)
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parser.add_argument('--CKPT_E', dest='CKPT_EPOCH',
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help='checkpoint epoch',
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type=int)
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parser.add_argument('--CKPT_PATH', dest='CKPT_PATH',
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help='load checkpoint path, we '
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'recommend that you use '
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'CKPT_VERSION and CKPT_EPOCH '
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'instead',
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type=str)
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parser.add_argument('--ACCU', dest='GRAD_ACCU_STEPS',
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help='reduce gpu memory usage',
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type=int)
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parser.add_argument('--NW', dest='NUM_WORKERS',
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help='multithreaded loading',
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default=0,
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type=int)
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parser.add_argument('--PINM', dest='PIN_MEM',
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help='use pin memory',
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type=bool)
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parser.add_argument('--VERB', dest='VERBOSE',
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help='verbose print',
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type=bool)
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parser.add_argument('--DATA_PATH', dest='DATASET_PATH',
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default='/projects/abdessaied/data/MSRVTT-QA/',
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help='Dataset root path',
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type=str)
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parser.add_argument('--EXP_NAME', dest='EXP_NAME',
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help='The name of the experiment',
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default="test",
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type=str)
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parser.add_argument('--DEBUG', dest='DEBUG',
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help='Triggeres debug mode: small fractions of the data are loaded ',
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default='0',
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type=str2bool)
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parser.add_argument('--ENABLE_TIME_MONITORING', dest='ENABLE_TIME_MONITORING',
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help='Triggeres time monitoring when training',
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default='0',
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type=str2bool)
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parser.add_argument('--MODEL_TYPE', dest='MODEL_TYPE',
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help='The model type to be used\n 1: VLCN \n 2:VLCN-FLF \n 3: VLCN+LSTM \n 4: MCAN',
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default=1,
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type=int)
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parser.add_argument('--PRETRAINED_PATH', dest='PRETRAINED_PATH',
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help='Pretrained weights on msvd',
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default='-',
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type=str)
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parser.add_argument('--TEST_EPOCH', dest='TEST_EPOCH',
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help='',
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default=7,
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type=int)
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args = parser.parse_args()
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return args
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if __name__ == '__main__':
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args = parse_args()
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os.chdir(os.path.dirname(os.path.abspath(__file__)))
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__C = Cfgs(args.EXP_NAME, args.DATASET_PATH)
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args_dict = __C.parse_to_dict(args)
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cfg_file = "cfgs/{}_model.yml".format(args.MODEL)
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with open(cfg_file, 'r') as f:
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yaml_dict = yaml.load(f)
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args_dict = {**yaml_dict, **args_dict}
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__C.add_args(args_dict)
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__C.proc()
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print('Hyper Parameters:')
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print(__C)
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__C.check_path()
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os.environ['CUDA_VISIBLE_DEVICES'] = __C.GPU
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execution = Execution(__C)
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execution.run(__C.RUN_MODE)
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#execution.run('test', epoch=__C.TEST_EPOCH)
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