vlcn/run.py

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2022-03-30 10:46:35 +02:00
# --------------------------------------------------------
# mcan-vqa (Deep Modular Co-Attention Networks)
# Licensed under The MIT License [see LICENSE for details]
# Written by Yuhao Cui https://github.com/cuiyuhao1996
# --------------------------------------------------------
from cfgs.base_cfgs import Cfgs
from core.exec import Execution
import argparse, yaml, os
def str2bool(v):
if isinstance(v, bool):
return v
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif v.lower() in ('no', 'false', 'f', 'n', '0'):
return False
else:
raise argparse.ArgumentTypeError('Boolean value expected.')
def parse_args():
'''
Parse input arguments
'''
parser = argparse.ArgumentParser(description='VLCN Args')
parser.add_argument('--RUN', dest='RUN_MODE',
default='train',
choices=['train', 'val', 'test'],
help='{train, val, test}',
type=str) # , required=True)
parser.add_argument('--MODEL', dest='MODEL',
choices=['small', 'large'],
help='{small, large}',
default='small', type=str)
parser.add_argument('--OPTIM', dest='OPTIM',
choices=['adam', 'rmsprop'],
help='The optimizer',
default='rmsprop', type=str)
parser.add_argument('--SPLIT', dest='TRAIN_SPLIT',
choices=['train', 'train+val'],
help="set training split, "
"eg.'train', 'train+val'"
"set 'train' can trigger the "
"eval after every epoch",
default='train',
type=str)
parser.add_argument('--EVAL_EE', dest='EVAL_EVERY_EPOCH',
default=True,
help='set True to evaluate the '
'val split when an epoch finished'
"(only work when train with "
"'train' split)",
type=bool)
parser.add_argument('--SAVE_PRED', dest='TEST_SAVE_PRED',
help='set True to save the '
'prediction vectors'
'(only work in testing)',
default=False,
type=bool)
parser.add_argument('--BS', dest='BATCH_SIZE',
help='batch size during training',
default=64,
type=int)
parser.add_argument('--MAX_EPOCH', dest='MAX_EPOCH',
default=30,
help='max training epoch',
type=int)
parser.add_argument('--PRELOAD', dest='PRELOAD',
help='pre-load the features into memory'
'to increase the I/O speed',
default=False,
type=bool)
parser.add_argument('--GPU', dest='GPU',
help="gpu select, eg.'0, 1, 2'",
default='0',
type=str)
parser.add_argument('--SEED', dest='SEED',
help='fix random seed',
default=42,
type=int)
parser.add_argument('--VERSION', dest='VERSION',
help='version control',
default='1.0.0',
type=str)
parser.add_argument('--RESUME', dest='RESUME',
default=False,
help='resume training',
type=str2bool)
parser.add_argument('--CKPT_V', dest='CKPT_VERSION',
help='checkpoint version',
type=str)
parser.add_argument('--CKPT_E', dest='CKPT_EPOCH',
help='checkpoint epoch',
type=int)
parser.add_argument('--CKPT_PATH', dest='CKPT_PATH',
help='load checkpoint path, we '
'recommend that you use '
'CKPT_VERSION and CKPT_EPOCH '
'instead',
type=str)
parser.add_argument('--ACCU', dest='GRAD_ACCU_STEPS',
help='reduce gpu memory usage',
type=int)
parser.add_argument('--NW', dest='NUM_WORKERS',
help='multithreaded loading',
default=0,
type=int)
parser.add_argument('--PINM', dest='PIN_MEM',
help='use pin memory',
type=bool)
parser.add_argument('--VERB', dest='VERBOSE',
help='verbose print',
type=bool)
parser.add_argument('--DATA_PATH', dest='DATASET_PATH',
default='/projects/abdessaied/data/MSRVTT-QA/',
help='Dataset root path',
type=str)
parser.add_argument('--EXP_NAME', dest='EXP_NAME',
help='The name of the experiment',
default="test",
type=str)
parser.add_argument('--DEBUG', dest='DEBUG',
help='Triggeres debug mode: small fractions of the data are loaded ',
default='0',
type=str2bool)
parser.add_argument('--ENABLE_TIME_MONITORING', dest='ENABLE_TIME_MONITORING',
help='Triggeres time monitoring when training',
default='0',
type=str2bool)
parser.add_argument('--MODEL_TYPE', dest='MODEL_TYPE',
help='The model type to be used\n 1: VLCN \n 2:VLCN-FLF \n 3: VLCN+LSTM \n 4: MCAN',
default=1,
type=int)
parser.add_argument('--PRETRAINED_PATH', dest='PRETRAINED_PATH',
help='Pretrained weights on msvd',
default='-',
type=str)
parser.add_argument('--TEST_EPOCH', dest='TEST_EPOCH',
help='',
default=7,
type=int)
args = parser.parse_args()
return args
if __name__ == '__main__':
args = parse_args()
os.chdir(os.path.dirname(os.path.abspath(__file__)))
__C = Cfgs(args.EXP_NAME, args.DATASET_PATH)
args_dict = __C.parse_to_dict(args)
cfg_file = "cfgs/{}_model.yml".format(args.MODEL)
with open(cfg_file, 'r') as f:
yaml_dict = yaml.load(f)
args_dict = {**yaml_dict, **args_dict}
__C.add_args(args_dict)
__C.proc()
print('Hyper Parameters:')
print(__C)
__C.check_path()
os.environ['CUDA_VISIBLE_DEVICES'] = __C.GPU
execution = Execution(__C)
execution.run(__C.RUN_MODE)
#execution.run('test', epoch=__C.TEST_EPOCH)