class UserPolicyVHUS(Policy):
def __init__(self):
with open(
os.path.join(os.path.dirname(os.path.abspath(__file__)),
'config.json'), 'r') as f:
config = json.load(f)
manager = UserDataManager()
voc_goal_size, voc_usr_size, voc_sys_size = manager.get_voc_size()
self.user = VHUS(config, voc_goal_size, voc_usr_size,
voc_sys_size).to(device=DEVICE)
self.goal_gen = GoalGenerator()
self.manager = manager
self.user.eval()
self.load(config['load'])
def init_session(self):
self.time_step = -1
self.topic = 'NONE'
self.goal = self.goal_gen.get_user_goal()
self.goal_input = torch.LongTensor(
self.manager.get_goal_id(self.manager.usrgoal2seq(self.goal)))
self.goal_len_input = torch.LongTensor([len(self.goal_input)
]).squeeze()
self.sys_da_id_stack = [] # to save sys da history
def predict(self, state):
"""Predict an user act based on state and preorder system action.
Args:
state (tuple):
Dialog state.
Returns:
usr_action (tuple):
User act.
session_over (boolean):
True to terminate session, otherwise session continues.
"""
sys_action = state['system_action']
sys_seq_turn = self.manager.sysda2seq(
self.manager.ref_data2stand(sys_action), self.goal)
self.sys_da_id_stack += self.manager.get_sysda_id([sys_seq_turn])
sys_seq_len = torch.LongTensor(
[max(len(sen), 1) for sen in self.sys_da_id_stack])
max_sen_len = sys_seq_len.max().item()
sys_seq = torch.LongTensor(padding(self.sys_da_id_stack, max_sen_len))
usr_a, terminal = self.user.select_action(self.goal_input,
self.goal_len_input, sys_seq,
sys_seq_len)
usr_action = self.manager.usrseq2da(self.manager.id2sentence(usr_a),
self.goal)
return capital(usr_action), terminal
def load(self, filename):
user_mdl = os.path.join(os.path.dirname(os.path.abspath(__file__)),
filename + '_simulator.mdl')
if os.path.exists(user_mdl):
self.user.load_state_dict(torch.load(user_mdl))
print('<<user simulator>> loaded checkpoint from file: {}'.format(
user_mdl))
class UserPolicyVHUS(Policy):
def __init__(
self,
archive_file=DEFAULT_ARCHIVE_FILE,
model_file='https://tatk-data.s3-ap-northeast-1.amazonaws.com/vhus_simulator_camrest.zip'
):
with open(
os.path.join(os.path.dirname(os.path.abspath(__file__)),
'config.json'), 'r') as f:
config = json.load(f)
manager = UserDataManager()
voc_goal_size, voc_usr_size, voc_sys_size = manager.get_voc_size()
self.user = VHUS(config, voc_goal_size, voc_usr_size,
voc_sys_size).to(device=DEVICE)
self.goal_gen = GoalGenerator()
self.manager = manager
self.user.eval()
if not os.path.isfile(archive_file):
if not model_file:
raise Exception("No model for VHUS Policy is specified!")
archive_file = cached_path(model_file)
model_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)),
'save')
if not os.path.exists(model_dir):
os.mkdir(model_dir)
if not os.path.exists(os.path.join(model_dir, 'best_simulator.mdl')):
archive = zipfile.ZipFile(archive_file, 'r')
archive.extractall(model_dir)
self.load(config['load'])
def init_session(self):
self.time_step = -1
self.topic = 'NONE'
self.goal = self.goal_gen.get_user_goal()
self.goal_input = torch.LongTensor(
self.manager.get_goal_id(self.manager.usrgoal2seq(self.goal)))
self.goal_len_input = torch.LongTensor([len(self.goal_input)
]).squeeze()
self.sys_da_id_stack = [] # to save sys da history
def predict(self, state):
"""Predict an user act based on state and preorder system action.
Args:
state (tuple):
Dialog state.
Returns:
usr_action (tuple):
User act.
session_over (boolean):
True to terminate session, otherwise session continues.
"""
sys_action = state
sys_seq_turn = self.manager.sysda2seq(
self.manager.ref_data2stand(sys_action), self.goal)
self.sys_da_id_stack += self.manager.get_sysda_id([sys_seq_turn])
sys_seq_len = torch.LongTensor(
[max(len(sen), 1) for sen in self.sys_da_id_stack])
max_sen_len = sys_seq_len.max().item()
sys_seq = torch.LongTensor(padding(self.sys_da_id_stack, max_sen_len))
usr_a, terminal = self.user.select_action(self.goal_input,
self.goal_len_input, sys_seq,
sys_seq_len)
usr_action = self.manager.usrseq2da(self.manager.id2sentence(usr_a),
self.goal)
return usr_action, terminal
def load(self, filename):
user_mdl = os.path.join(os.path.dirname(os.path.abspath(__file__)),
filename + '_simulator.mdl')
if os.path.exists(user_mdl):
self.user.load_state_dict(torch.load(user_mdl))
print('<<user simulator>> loaded checkpoint from file: {}'.format(
user_mdl))