def fine_tune(pos_action, neg_action, tokenizer, model):
nlg_usr = TemplateNLG(is_user=True)
nlg_sys = TemplateNLG(is_user=False)
pos_train_usr_utter = []
pos_train_sys_utter = []
neg_train_usr_utter = []
neg_train_sys_utter = []
for turn in pos_action:
if turn[0] != [] and turn[1] != []:
s_u = nlg_usr.generate(turn[0])
s_a = nlg_sys.generate(turn[1])
pos_train_usr_utter.append(s_u)
pos_train_sys_utter.append(s_a)
for turn in neg_action:
if turn[0] != [] and turn[1] != []:
s_u = nlg_usr.generate(turn[0])
s_a = nlg_sys.generate(turn[1])
neg_train_usr_utter.append(s_u)
neg_train_sys_utter.append(s_a)
train_usr_utter = pos_train_usr_utter + neg_train_usr_utter
train_sys_utter = pos_train_sys_utter + neg_train_sys_utter
train_encoding = tokenizer(train_usr_utter,
train_sys_utter,
padding=True,
truncation=True,
max_length=80)
train_encoding['label'] = [1] * len(pos_train_usr_utter) + [0] * len(
neg_train_usr_utter)
train_dataset = Dataset.from_dict(train_encoding)
train_dataset.set_format('torch',
columns=['input_ids', 'attention_mask', 'label'])
save_dir = os.path.join(root_dir,
'convlab2/policy/dqn/NLE/save/script_fine_tune')
log_dir = os.path.join(
root_dir, 'convlab2/policy/dqn/NLE/save/script_fine_tune/logs')
training_args = TrainingArguments(
output_dir=save_dir,
num_train_epochs=2,
per_device_train_batch_size=32,
per_device_eval_batch_size=128,
warmup_steps=500,
weight_decay=0.01,
evaluate_during_training=False,
logging_dir=log_dir,
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
)
trainer.train()
trainer.save_model(os.path.join(save_dir, 'fine_tune_checkpoint'))
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