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
"taxi": ["car", "arriveBy", "destination", "departure", "leaveAt"],
"hotel": ["parking", "internet", "postcode", "phone", "address", "Ref", "stars", "type", "area", "pricerange"],
"train": ["Ref", "leaveAt", "duration", "price", "arriveBy", "people", "trainID", "destination", "departure", "day"],
"attraction": ["address", "postcode", "price", "phone", "area", "type"],
"restaurant": ["address", "Ref", "area", "postcode", "food", "phone", "pricerange", "name"],
"police":[],
"hospital":["postcode"]
}
with torch.no_grad():
for batch_idx, batch in t:
inputs, contexts, context_lengths, dial_ids = reader.make_input(batch)
batch_size = len(contexts[0])
turns = len(inputs)
nlu.init_session()
dst.init_session()
for turn_idx in range(turns):
context_len = contexts[turn_idx].size(1)
input_action = nlu.predict(inputs[turn_idx]["usr"][0], inputs[turn_idx]["context"][0])
dst.state['user_action'] = input_action
state = dst.update(input_action)
belief = state["belief_state"]
belief_label = inputs[turn_idx]["belief"][0]
# joint_acc_ = 1
# for slot_idx, value in enumerate(belief_label):
# slot = ontology.all_info_slots[slot_idx]
# domain, slot = slot.split("-")
# slot = mapping.get(slot, slot)