def load_expert_trajectory(data_name: str, info_name: str,
data_keyword: str) -> EvaluationTrajectoryGroup:
param = Parameter()
param.model_level = 'high'
param.eval_keyword = data_keyword
param.eval_data_name = data_name
param.eval_info_name = info_name
data_frame_list, sentences = load_evaluation_dataset(param)
info_list = [
EvaluationUnitInfo(data_keyword, -1, '', -1, i)
for i in range(len(data_frame_list))
]
trajectories = [
EvaluationTrajectory(i, d, False)
for i, d in zip(info_list, data_frame_list)
]
info = EvaluationUnitInfo(data_keyword, -1, '', -1, -1)
traj_group = EvaluationTrajectoryGroup(info, trajectories)
return traj_group
def load_param_and_evaluator(eval_keyword: str, args, model_type: str):
param = Parameter()
low_level = model_type in ['control', 'stop']
if model_type == 'control':
exp_index = args.control_model_index
exp_name = args.control_model_name
exp_step = args.control_model_step
elif model_type == 'stop':
exp_index = args.stop_model_index
exp_name = args.stop_model_name
exp_step = args.stop_model_step
elif model_type == 'high':
exp_index = args.high_level_index
exp_name = args.high_level_name
exp_step = args.high_level_step
elif model_type == 'single':
exp_index = args.single_model_index
exp_name = args.single_model_name
exp_step = args.single_model_step
else:
raise TypeError('invalid model type {}'.format(model_type))
param.exp_name = exp_name
param.exp_index = exp_index
param.load()
param.batch_size = 1
param.eval_keyword = eval_keyword
param.eval_data_name = args.eval_data_name
param.eval_info_name = args.eval_info_name
logger.info('model type: {}'.format(param.model_type))
cls = LowLevelEvaluator if low_level else (
SingleEvaluator if model_type == 'single' else HighLevelEvaluator)
logger.info((model_type, cls, param.model_level, param.encoder_type))
eval_arg = args.exp_cmd if low_level else eval_keyword
evaluator = cls(param, eval_arg)
evaluator.load(step=exp_step)
return param, evaluator
