def run(config_file):
# 命令行解析器
args = argparse.ArgumentParser(description='text classification')
# 添加命令：配置、重启、计算
args.add_argument('-c',
'--config',
default=config_file,
type=str,
help='config file path (default: None)')
args.add_argument('-r',
'--resume',
default=None,
type=str,
help='path to latest checkpoint (default: None)')
args.add_argument('-d',
'--device',
default='0,1',
type=str,
help='indices of GPUs to enable (default: all)')
# 客户参数：可修改的配置
CustomArgs = collections.namedtuple('CustomArgs', 'flags type target')
# 添加客户参数：学习率、批量长度
options = [
CustomArgs(['--lr', '--learning_rate'],
type=float,
target='optimizer;args;lr'),
CustomArgs(['--bs', '--batch_size'],
type=int,
target='data_process;args;batch_size')
]
# 配置解析器
config = ConfigParser.from_args(args, options)
# 打印训练的模型类型
print(config.config['model_arch']['type'].lower())
# 训练
if 'bert' in config.config['model_arch']['type'].lower():
main(config, use_transformers=True)
else:
main(config, use_transformers=False)
def run(config_file, model_path, text_list):
args = argparse.ArgumentParser(description='text classification')
# 配置文件、模型路径、计算代理
args.add_argument('-c', '--config', default=config_file, type=str, help='config file path (default: None)')
# default=model_path
args.add_argument('-r', '--resume', default=model_path, type=str, help='path to latest checkpoint (default: None)')
# default='0',
args.add_argument('-d', '--device', default='0', type=str, help='indices of GPUs to enable (default: all)')
CustomArgs = collections.namedtuple('CustomArgs', 'flags type target')
options = [
CustomArgs(['--lr', '--learning_rate'], type=float, target='optimizer;args;lr'),
CustomArgs(['--bs', '--batch_size'], type=int, target='data_process;args;batch_size')
]
config = ConfigParser.from_args(args, options)
print(config.config['model_arch']['type'].lower())
if 'bert' in config.config['model_arch']['type'].lower():
main(config, use_transformers=True, text_list=text_list)
else:
main(config, use_transformers=False, text_list=text_list)
help='path to the directory where parsed documents are saved'
'in case parsed files exist here, KNP is skipped')
parser.add_argument('--export-dir',
default=None,
type=str,
help='directory where analysis result is exported')
parser.add_argument('-tab',
'--tab',
action='store_true',
default=False,
help='whether to output details')
parser.add_argument(
'--remote-knp',
action='store_true',
default=False,
help='Use KNP running on remote host. '
'Make sure you specify host address and port in analyzer/config.ini')
parser.add_argument(
'--skip-untagged',
action='store_true',
default=False,
help='If set, do not export documents which failed to be analyzed')
parser.add_argument(
'--rel-only',
action='store_true',
default=False,
help='If set, do not add <述語項構造> tag besides <rel> tag to system output'
)
parsed_args = parser.parse_args()
main(ConfigParser.from_args(parsed_args, run_id=''), parsed_args)
args.add_argument('-c',
'--config',
default=None,
type=str,
help='config file path (default: None)')
args.add_argument('-r',
'--resume',
default=None,
type=str,
help='path to latest checkpoint (default: None)')
# custom cli options to modify configuration from default values given in json file.
CustomArgs = collections.namedtuple('CustomArgs', 'flags type target')
options = [
CustomArgs(['-n', '--name'], type=str, target='name'),
CustomArgs(['--lr', '--learning_rate'],
type=float,
target='optimizer;args;lr'),
CustomArgs(['--bs', '--batch_size'],
type=int,
target='dataloader;args;batch_size'),
CustomArgs(['--u2w', '--utt2wav_val'],
type=str,
target='valid_dataset;args;wav_scp'),
CustomArgs(['--u2l', '--utt2label_val'],
type=str,
target='valid_dataset;args;utt2label')
]
config = ConfigParser.from_args(args, options)
main(config)
prediction_writer = PredictionKNPWriter(dataset, logger)
with io.StringIO() as string:
_ = prediction_writer.write(arguments_set, string, skip_untagged=False)
knp_result = string.getvalue()
with log_dir.joinpath('pas.knp').open('wt') as f:
f.write(knp_result)
return knp_result
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-r', '--resume', '-m', '--model', default=None, type=str,
help='path to trained checkpoint')
parser.add_argument('--ens', '--ensemble', default=None, type=str,
help='path to directory where checkpoints to ensemble exist')
parser.add_argument('-d', '--device', default='', type=str,
help='indices of GPUs to enable (default: all)')
parser.add_argument('-c', '--config', default=None, type=str,
help='config file path (default: None)')
parser.add_argument('--host', default='0.0.0.0', type=str,
help='host ip address (default: 0.0.0.0)')
parser.add_argument('--port', default=12345, type=int,
help='host port number (default: 12345)')
args = parser.parse_args()
config = ConfigParser.from_args(args, run_id='')
analyzer = Analyzer(config, logger=logger)
server = SimpleXMLRPCServer((args.host, args.port))
server.register_function(analyze_raw_data_from_client)
server.serve_forever()
type=float,
help=
'threshold for argument existence. The higher you set, the higher precision gets. [0, 1]'
)
parser.add_argument(
'--recall-threshold',
default=0.0,
type=float,
help=
'threshold for argument non-existence. The higher you set, the higher recall gets [0, 1]'
)
parser.add_argument('--result-suffix',
default='',
type=str,
help='custom evaluation result directory name')
parser.add_argument('--run-id',
default=None,
type=str,
help='custom experiment directory name')
parser.add_argument('--oracle',
action='store_true',
default=False,
help='use oracle dependency labels')
parsed_args = parser.parse_args()
inherit_save_dir = (parsed_args.resume is not None
and parsed_args.run_id is None)
main(
ConfigParser.from_args(parsed_args,
run_id=parsed_args.run_id,
inherit_save_dir=inherit_save_dir), parsed_args)
if __name__ == '__main__':
print("n_gpu", str(torch.cuda.device_count()))
parser = argparse.ArgumentParser()
parser.add_argument('-c',
'--config',
default=None,
type=str,
help='config file path (default: None)')
parser.add_argument('-r',
'--resume',
default=None,
type=str,
help='path to latest checkpoint (default: None)')
parser.add_argument('-d',
'--device',
default='',
type=str,
help='indices of GPUs to enable (default: "")')
parser.add_argument('--seed',
type=int,
default=42,
help='random seed for initialization')
parser.add_argument("-f",
'--freeze_encoder',
action='store_true',
help='Freeze encoder during training')
parsed_args = parser.parse_args()
main(ConfigParser.from_args(parsed_args), parsed_args)