def respond(self, req):
# 对req进行初步处理
inputs, dec_input = data_utils.preprocess_request(sentence=req, start_sign=self.start_sign,
end_sign=self.end_sign, token=self.token,
max_length=self.max_length)
self.beam_search_container.init_all_inner_variables(inputs=inputs, dec_input=dec_input)
inputs, dec_input = self.beam_search_container.expand_beam_size_inputs()
for t in range(self.max_length):
predictions = self._create_predictions(inputs, dec_input)
self.beam_search_container.add(predictions=predictions, end_sign=self.token.get(self.end_sign))
if self.beam_search_container.beam_size == 0:
break
inputs, dec_input = self.beam_search_container.expand_beam_size_inputs()
dec_input = torch.unsqueeze(dec_input[:, -1], dim=-1)
beam_search_result = self.beam_search_container.get_result(top_k=1)
result = ''
# 从容器中抽取序列，生成最终结果
for i in range(len(beam_search_result)):
temp = beam_search_result[i].numpy()
text = data_utils.sequences_to_texts(temp, self.token)
text[0] = text[0].replace(self.start_sign, '').replace(self.end_sign, '').replace(' ', '')
result = '<' + text[0] + '>' + result
return result
def respond(self, req, input_dict_fn, target_dict_fn):
# 对req进行初步处理
input_token, target_token = _data.load_token_dict(
input_dict_fn=input_dict_fn, target_dict_fn=target_dict_fn)
inputs, dec_input = self._pre_treat_inputs(req, input_token,
target_token)
self.beam_search_container.init_variables(inputs=inputs,
dec_input=dec_input)
inputs, dec_input = self.beam_search_container.get_variables()
for t in range(_config.max_length_tar):
predictions = self._create_predictions(inputs, dec_input, t)
self.beam_search_container.add(predictions=predictions,
end_sign=target_token.get('end'))
if self.beam_search_container.beam_size == 0:
break
inputs, dec_input = self.beam_search_container.get_variables()
beam_search_result = self.beam_search_container.get_result()
result = ''
# 从容器中抽取序列，生成最终结果
for i in range(len(beam_search_result)):
temp = beam_search_result[i].numpy()
text = _data.sequences_to_texts(temp, target_token)
text[0] = text[0].replace('start',
'').replace('end', '').replace(' ', '')
result = '<' + text[0] + '>' + result
return result
def respond(self, req):
# 对req进行初步处理
inputs, dec_input = _data.preprocess_request(sentence=req,
token=self.token)
self.beam_search_container.init_all_inner_variables(
inputs=inputs, dec_input=dec_input)
inputs, dec_input = self.beam_search_container.expand_beam_size_inputs(
)
for t in range(_config.max_length_tar):
predictions = self._create_predictions(inputs, dec_input, t)
self.beam_search_container.add(predictions=predictions,
end_sign=self.token.get('end'))
if self.beam_search_container.beam_size == 0:
break
inputs, dec_input = self.beam_search_container.expand_beam_size_inputs(
)
beam_search_result = self.beam_search_container.get_result(top_k=3)
result = ''
# 从容器中抽取序列，生成最终结果
for i in range(len(beam_search_result)):
temp = beam_search_result[i].numpy()
text = _data.sequences_to_texts(temp, self.token)
text[0] = text[0].replace(_config.start_sign,
'').replace(_config.end_sign,
'').replace(' ', '')
result = '<' + text[0] + '>' + result
return result
def response(sentence):
inputs = " ".join(jieba.cut("cls" + sentence + "sep"))
config = get_config()
tokenizer = data_utils.load_dict(config["gpt2_dict"])
inputs = [tokenizer.get(i, 1) for i in inputs.split(' ')]
# inputs = tf.keras.preprocessing.sequence.pad_sequences([inputs], maxlen=config["max_length"], padding="post")
inputs = tf.convert_to_tensor(inputs)
inputs = tf.cast(tf.expand_dims(inputs, axis=0), dtype=tf.int64)
checkpoint_dir = config["checkpoint_dir"]
model = gpt2.gpt2(vocab_size=config["vocab_size"],
num_layers=config["num_layers"],
units=config["units"],
deep=config["deep"],
num_heads=config["num_heads"],
dropout=config["dropout"])
learning_rate = gpt2.CustomSchedule(config["deep"])
optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate,
beta_1=0.9,
beta_2=0.98,
epsilon=1e-9)
checkpoint = tf.train.Checkpoint(model=model, optimizer=optimizer)
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir, exist_ok=True)
if os.listdir(checkpoint_dir):
checkpoint.restore(
tf.train.latest_checkpoint(checkpoint_dir)).expect_partial()
result = []
for _ in range(config["max_length"]):
# print(inputs)
# exit(0)
predictions = model(inputs=inputs, training=False)
predictions = tf.nn.softmax(predictions, axis=-1)
predictions = predictions[:, -1:, :]
predictions = tf.squeeze(predictions, axis=1)
# print(predictions)
# exit(0)
pred = tf.argmax(input=predictions, axis=-1)
print(inputs)
print(pred)
# exit(0)
if pred.numpy()[0] == 2:
break
result.append(pred.numpy()[0])
inputs = tf.concat([inputs, tf.expand_dims(pred, axis=0)], axis=-1)
print(inputs)
print(result)
return data_utils.sequences_to_texts(result, tokenizer)
def respond(self, req: str):
"""
对外部聊天请求进行回复
子类需要利用模型进行推断和搜索以产生回复。
Args:
req: 输入的语句
Returns: 系统回复字符串
"""
# 对req进行初步处理
inputs, dec_input = data_utils.preprocess_request(
sentence=req,
token=self.token,
max_length=self.max_length,
start_sign=self.start_sign,
end_sign=self.end_sign)
self.beam_search_container.reset(inputs=inputs, dec_input=dec_input)
inputs, dec_input = self.beam_search_container.get_search_inputs()
for t in range(self.max_length):
predictions = self._create_predictions(inputs, dec_input, t)
self.beam_search_container.expand(predictions=predictions,
end_sign=self.token.get(
self.end_sign))
# 注意了，如果BeamSearch容器里的beam_size为0了，说明已经找到了相应数量的结果，直接跳出循环
if self.beam_search_container.beam_size == 0:
break
inputs, dec_input = self.beam_search_container.get_search_inputs()
beam_search_result = self.beam_search_container.get_result(top_k=3)
result = ''
# 从容器中抽取序列，生成最终结果
for i in range(len(beam_search_result)):
temp = beam_search_result[i].numpy()
text = data_utils.sequences_to_texts(temp, self.token)
text[0] = text[0].replace(self.start_sign,
'').replace(self.end_sign,
'').replace(' ', '')
result = '<' + text[0] + '>' + result
return result