}
}
# # user_goal = goal
goal = Goal(goal_generator)
goal.set_user_goal(user_goal)
#
# user_policy.init_session(ini_goal=goal)
# sys_policy.init_session()
#
# goal = user_policy.get_goal()
#
# pprint(goal)
sys_response = ''
# sess.init_session(ini_goal=goal)
user_policy.init_session(ini_goal=goal)
print('init goal:')
# pprint(user_policy.get_goal())
pprint(user_agent.policy.get_goal())
# pprint(sess.evaluator.goal)
# print('-' * 50)
# for i in range(20):
# sys_response, user_response, session_over, reward = sess.next_turn(sys_response)
# print('user:'******'sys:', sys_response)
# print()
# if session_over is True:
# break
# print('task success:', sess.evaluator.task_success())
# print('book rate:', sess.evaluator.book_rate())
# print('inform precision/recall/f1:', sess.evaluator.inform_F1())
class Dialog(Agent):
def __init__(self, model_file=DEFAULT_MODEL_URL, name="Dialog"):
super(Dialog, self).__init__(name=name)
if not os.path.exists(os.path.join(DEFAULT_DIRECTORY,'multiwoz/data')):
os.mkdir(os.path.join(DEFAULT_DIRECTORY,'multiwoz/data'))
### download multiwoz data
print('down load data from', DEFAULT_ARCHIVE_FILE_URL)
if not os.path.exists(os.path.join(DEFAULT_DIRECTORY,'multiwoz/save')):
os.mkdir(os.path.join(DEFAULT_DIRECTORY,'multiwoz/save'))
### download trained model
print('down load model from', DEFAULT_MODEL_URL)
model_path = ""
config = Config()
parser = config.parser
config = parser.parse_args()
with open("assets/never_split.txt") as f:
never_split = f.read().split("\n")
self.tokenizer = BertTokenizer("assets/vocab.txt", never_split=never_split)
self.nlu = BERTNLU()
self.dst_ = DST(config).cuda()
ckpt = torch.load("save/model_Sun_Jun_21_07:08:48_2020.pt", map_location = lambda storage, loc: storage.cuda(local_rank))
self.dst_.load_state_dict(ckpt["model"])
self.dst_.eval()
self.policy = RulePolicy()
self.nlg = TemplateNLG(is_user=False)
self.init_session()
self.slot_mapping = {
"leave": "leaveAt",
"arrive": "arriveBy"
}
def init_session(self):
self.nlu.init_session()
self.policy.init_session()
self.nlg.init_session()
self.history = []
self.state = default_state()
pass
def response(self, user):
self.history.append(["user", user])
user_action = []
self.input_action = self.nlu.predict(user, context=[x[1] for x in self.history[:-1]])
self.input_action = deepcopy(self.input_action)
for act in self.input_action:
intent, domain, slot, value = act
if intent == "Request":
user_action.append(act)
if not self.state["request_state"].get(domain):
self.state["request_state"][domain] = {}
if slot not in self.state["request_state"][domain]:
self.state['request_state'][domain][slot] = 0
context = " ".join([utterance[1] for utterance in self.history])
context = context[-MAX_CONTEXT_LENGTH:]
context = self.tokenizer.encode(context)
context = torch.tensor(context, dtype=torch.int64).unsqueeze(dim=0).cuda() # [1, len]
belief_gen = self.dst_(None, context, 0, test=True)[0] # [slots, len]
for slot_idx, domain_slot in enumerate(ontology.all_info_slots):
domain, slot = domain_slot.split("-")
slot = self.slot_mapping.get(slot, slot)
value = belief_gen[slot_idx][:-1] # remove <EOS>
value = self.tokenizer.decode(value)
if value != "none":
if slot in self.state["belief_state"][domain]["book"].keys():
if self.state["belief_state"][domain]["book"][slot] == "":
action = ["Inform", domain.capitalize(), REF_USR_DA[domain].get(slot, slot), value]
user_action.append(action)
self.state["belief_state"][domain]["book"][slot] = value
elif slot in self.state["belief_state"][domain]["semi"].keys():
if self.state["belief_state"][domain]["semi"][slot] == "":
action = ["Inform", domain.capitalize(), REF_USR_DA[domain].get(slot, slot), value]
user_action.append(action)
self.state["belief_state"][domain]["semi"][slot] = value
self.state["user_action"] = user_action
self.output_action = deepcopy(self.policy.predict(self.state))
model_response = self.nlg.generate(self.output_action)
self.history.append(["sys", model_response])
return model_response
