def main():
#加载日志
logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt = '%m/%d/%Y %H:%M:%S',
level = logging.INFO)
logger = logging.getLogger(__name__)
# 'opt' means options
opt = None
parser = argparse.ArgumentParser(description='multi-qa')
parser.add_argument('mode', help='mode: train')
parser.add_argument('conf_file', help='path to conf file.')
parser.add_argument('dataset', help='dataset for train')
#得到参数实例
args = parser.parse_args()
mode = args.mode # train mode
#得到conf文件的参数
conf_file = args.conf_file
dataset = args.dataset
conf_args = Arguments(conf_file)
opt = conf_args.readArguments()
opt['cuda'] = torch.cuda.is_available()
opt['confFile'] = conf_file
opt['datadir'] = os.path.dirname(conf_file) # .
for key, val in args.__dict__.items():
if val is not None and key not in ['command', 'conf_file']:
opt[key] = val
#使用GPU
# opt['cuda'] = False
device = torch.device("cuda" if opt['cuda'] else 'cpu')
logger.info("device %s is using for training!", device)
model = ConvQA_CN_NetTrainer(opt)
print("Select mode----", mode)
print("Using dataset ----", dataset)
model.train()
def main(conf_file, model_file, data_file, output_path, mask, indicator,
answer_span_in_context, no_ans_bit):
conf_args = Arguments(conf_file)
opt = conf_args.readArguments()
opt['cuda'] = torch.cuda.is_available()
opt['confFile'] = conf_file
opt['datadir'] = os.path.dirname(conf_file)
opt['PREV_ANS_MASK'] = mask
opt['PREV_ANS_INDICATOR'] = indicator
opt['OFFICIAL'] = True
opt['OFFICIAL_TEST_FILE'] = data_file
if answer_span_in_context:
opt['ANSWER_SPAN_IN_CONTEXT_FEATURE'] = None
if no_ans_bit:
opt['NO_PREV_ANS_BIT'] = None
trainer = SDNetTrainer(opt)
test_data = trainer.preproc.preprocess('test')
predictions, confidence, final_json = trainer.official(
model_file, test_data)
with output_path.open(mode='w') as f:
json.dump(final_json, f)
from Models.SDNetTrainer import SDNetTrainer
from Utils.Arguments import Arguments
if __name__ == "__main__":
# multiprocessing.set_start_method('spawn')
opt = None
parser = argparse.ArgumentParser(description='SDNet')
parser.add_argument('--command', default='train', help='Command: train')
parser.add_argument('--conf_file', default='conf_stvqa', help='Path to conf file.')
parser.add_argument('--log_file', default='', help='Path to log file.')
cmdline_args = parser.parse_args()
command = cmdline_args.command
conf_file = cmdline_args.conf_file
conf_args = Arguments(conf_file)
opt = conf_args.readArguments()
opt['cuda'] = torch.cuda.is_available()
opt['confFile'] = conf_file
opt['datadir'] = os.path.dirname(conf_file) # conf_file specifies where the data folder is
if cmdline_args.log_file != '':
if not os.path.exists('myLog'):
os.makedirs('myLog')
file_handle = logging.FileHandler(os.path.join('myLog', cmdline_args.log_file +'.txt'))
log.addHandler(file_handle)
for key,val in cmdline_args.__dict__.items():
if val is not None and key not in ['command', 'conf_file']:
opt[key] = val
model = SDNetTrainer(opt)