def __init__(self,
model_file=DEFAULT_MODEL_URL,
name='Sequicity'):
"""
Sequicity initialization
Args:
model_file (str):
trained model path or url. default="https://tatk-data.s3-ap-northeast-1.amazonaws.com/sequicity_multiwoz.zip"
Example:
sequicity = Sequicity()
"""
super(Sequicity, self).__init__(name=name)
config_file = DEFAULT_CONFIG_FILE
c = json.load(open(config_file))
cfg.init_handler(c['tsdf_init'])
if not os.path.exists(os.path.join(DEFAULT_DIRECTORY,'multiwoz/data')):
print('down load data from', DEFAULT_ARCHIVE_FILE_URL)
archive_file = cached_path(DEFAULT_ARCHIVE_FILE_URL)
archive = zipfile.ZipFile(archive_file, 'r')
print('unzip to', os.path.join(DEFAULT_DIRECTORY,'multiwoz/'))
archive.extractall(os.path.join(DEFAULT_DIRECTORY,'multiwoz/'))
archive.close()
model_path = os.path.join(DEFAULT_DIRECTORY,c['tsdf_init']['model_path'])
if not os.path.exists(model_path):
model_dir = os.path.dirname(model_path)
if not os.path.exists(model_dir):
os.makedirs(model_dir)
print('Load from model_file param')
print('down load data from', model_file)
archive_file = cached_path(model_file)
archive = zipfile.ZipFile(archive_file, 'r')
print('unzip to', model_dir)
archive.extractall(model_dir)
archive.close()
torch.manual_seed(cfg.seed)
torch.cuda.manual_seed(cfg.seed)
random.seed(cfg.seed)
np.random.seed(cfg.seed)
self.m = Model('multiwoz')
self.m.count_params()
self.m.load_model()
self.init_session()
def main(arg_mode=None, arg_model=None):
parser = argparse.ArgumentParser()
parser.add_argument('-mode')
parser.add_argument('-model')
# parser.add_argument('-cfg', nargs='*')
parser.add_argument('-cfg')
args = parser.parse_args()
if arg_mode is not None:
args.mode = arg_mode
if arg_model is not None:
args.model = arg_model
# cfg.init_handler(args.model)
c = json.load(open(args.cfg))
cfg.init_handler(c['tsdf_init'])
# if args.cfg:
# for pair in args.cfg:
# k, v = tuple(pair.split('='))
# dtype = type(getattr(cfg, k))
# if isinstance(None, dtype):
# raise ValueError()
# if dtype is bool:
# v = False if v == 'False' else True
# else:
# v = dtype(v)
# setattr(cfg, k, v)
logging.debug(str(cfg))
if cfg.cuda:
logging.debug('Device: {}'.format(torch.cuda.current_device()))
cfg.mode = args.mode
torch.manual_seed(cfg.seed)
torch.cuda.manual_seed(cfg.seed)
random.seed(cfg.seed)
np.random.seed(cfg.seed)
# m = Model(args.model.split('-')[-1])
m = Model(args.model)
m.count_params()
if args.mode == 'train':
m.load_glove_embedding()
m.train()
elif args.mode == 'adjust':
m.load_model()
m.train()
elif args.mode == 'test':
m.load_model()
m.eval()
elif args.mode == 'rl':
m.load_model()
m.reinforce_tune()
elif args.mode == 'interact':
m.load_model()
m.interact()
elif args.mode == 'load':
m.load_model()
return m