def __proportional_function(self, doc, token, new_topic):
if isinstance(doc, LDADoc) and isinstance(token, Token):
old_topic = token.topic
dt_alpha = doc.topic_sum(new_topic) + self.__model.alpha()
wt_beta = self.__model.word_topic_value(token.id, new_topic) + self.__model.beta()
t_sum_beta_sum = self.__model.topic_sum_value(new_topic) + self.__model.beta_sum()
if new_topic == old_topic and wt_beta > 1:
if dt_alpha > 1:
dt_alpha -= 1
wt_beta -= 1
t_sum_beta_sum -= 1
return dt_alpha * wt_beta / t_sum_beta_sum
elif isinstance(doc, SLDADoc) and isinstance(token, Sentence):
sent = token
old_topic = sent.topic
result = doc.topic_sum(new_topic) + self.__model.alpha()
if new_topic == old_topic:
result -= 1
for word_id in sent.tokens:
wt_beta = self.__model.word_topic_value(word_id, new_topic) + self.__model.beta()
t_sum_beta_sum = self.__model.topic_sum_value(new_topic) + self.__model.beta_sum()
if new_topic == old_topic and wt_beta > 1:
wt_beta -= 1
t_sum_beta_sum -= 1
result *= wt_beta / t_sum_beta_sum
return result
else:
logger.error("Wrong input argument type!")
def infer(self, input, doc):
"""Perform LDA topic inference on input, and store the results in doc.
Args:
input: a list of strings after tokenization.
doc: LDADoc type or SLDADoc type.
"""
fix_random_seed()
if isinstance(doc, LDADoc) and not isinstance(doc, SLDADoc):
doc.init(self.__model.num_topics())
doc.set_alpha(self.__model.alpha())
for token in input:
id_ = self.__model.term_id(token)
if id_ != OOV:
init_topic = rand_k(self.__model.num_topics())
doc.add_token(Token(init_topic, id_))
self.lda_infer(doc, 20, 50)
elif isinstance(doc, SLDADoc):
doc.init(self.__model.num_topics())
doc.set_alpha(self.__model.alpha())
for sent in input:
words = []
for token in sent:
id_ = self.__model.term_id(token)
if id_ != OOV:
words.append(id_)
init_topic = rand_k(self.__model.num_topics())
doc.add_sentence(Sentence(init_topic, words))
self.slda_infer(doc, 20, 50)
else:
logger.error("Wrong Doc Type!")
def check_server(self):
for server in self.server_list:
client = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
server_ip = server.split(':')[0]
server_port = int(server.split(':')[1])
client.connect((server_ip, server_port))
client.send(b'pending server')
response = client.recv(1024).decode()
response_list = response.split('\t')
status_code = int(response_list[0].split(':')[1])
if status_code == 0:
server_model = response_list[1].split(':')[1]
if server_model == self.model_name:
serving_port = response_list[2].split(':')[1]
serving_ip = server_ip
self.serving_list.append(serving_ip + ':' + serving_port)
else:
logger.error(
'model_name not match, server {} using : {} '.format(
server, server_model))
else:
error_msg = response_list[1]
logger.error('connect server {} failed. {}'.format(
server, error_msg))
def get_face_landmark(self, image):
"""
预测人脸的68个关键点坐标
images(ndarray): 单张图片的像素数据
"""
try:
# 选择GPU运行，use_gpu=True，并且在运行整个教程代码之前设置CUDA_VISIBLE_DEVICES环境变量
res = self.module.keypoint_detection(images=[image], use_gpu=False)
img = image
tmp_img = image.copy()
for index, point in enumerate(res[0]['data'][0]):
# cv2.putText(img, str(index), (int(point[0]), int(point[1])), cv2.FONT_HERSHEY_COMPLEX, 3, (0,0,255), -1)
cv2.circle(tmp_img, (int(point[0]), int(point[1])), 2, (0, 0, 255), -1)
res_img_path = 'face_landmark.jpg'
cv2.imwrite(res_img_path, tmp_img)
img = mpimg.imread(res_img_path)
# 展示预测68个关键点结果
#下面用于绘图
# plt.figure(figsize=(10,10))
# plt.imshow(img)
# plt.axis('off')
#plt.show()
return True, res[0]['data'][0],img
except Exception as e:
logger.error("Get face landmark localization failed! Exception: %s " % e)
return False, None , None
def encode(self, text):
if len(self.serving_list) == 0:
logger.error('No match server.')
return -1
if type(text) != list:
raise TypeError('Only support list')
request_msg = self.prepare_data(text)
response_msg = self.request_server(request_msg)
retry = 0
while type(response_msg) == str and response_msg == 'retry':
if retry < self.retry:
retry += 1
logger.info('Try to connect another servers')
response_msg = self.request_server(request_msg)
else:
logger.error('Request failed after {} times retry'.format(
self.retry))
break
result = []
for msg in response_msg["instances"]:
for sample in msg["instances"]:
result.append(sample["values"])
return result
def _init_with_url(self, url):
utils.check_url(url)
result, tips, module_dir = default_downloader.download_file_and_uncompress(
url, save_path=".")
if not result:
logger.error(tips)
exit(1)
self._init_with_module_file(module_dir)
def check_requirements(self):
try:
import shapely, pyclipper
except:
logger.error(
'This module requires the shapely, pyclipper tools. The running enviroment does not meet the requirments. Please install the two packages.'
)
exit()
def get_face_landmark(self, image, use_gpu=False):
try:
res = self.module.keypoint_detection(images=[image],
use_gpu=use_gpu)
return True, res[0]['data'][0]
except Exception as e:
logger.error(
"Get face landmark localization failed! Exception: %s " % e)
return False, None
def _init_with_name(self, name, version=None):
log_msg = "Installing %s module" % name
if version:
log_msg += "-%s" % version
logger.info(log_msg)
result, tips, module_dir = default_module_manager.install_module(
module_name=name, module_version=version)
if not result:
logger.error(tips)
exit(1)
logger.info(tips)
self._init_with_module_file(module_dir[0])
def get_face_landmark(self, image):
"""
预测人脸的68个关键点坐标
images(ndarray): 单张图片的像素数据
"""
try:
res = self.module.keypoint_detection(images=[image])
return True, res[0]['data'][0]
except Exception as e:
logger.error(
"Get face landmark localization failed! Exception: %s " % e)
return False, None
def get_face_landmark(self, image):
"""
预测人脸的68个关键点坐标
images(ndarray): 单张图片的像素数据
"""
try:
# 选择GPU运行，use_gpu=True，并且在运行整个教程代码之前设置CUDA_VISIBLE_DEVICES环境变量
res = self.module.keypoint_detection(images=[image], use_gpu=False)
return True, res[0]['data'][0]
except Exception as e:
logger.error(
"Get face landmark localization failed! Exception: %s " % e)
return False, None
def _init_with_name(self, name, version=None):
log_msg = "Installing %s module" % name
if version:
log_msg += "-%s" % version
logger.info(log_msg)
extra = {"command": "install"}
result, tips, module_dir = default_module_manager.install_module(
module_name=name, module_version=version, extra=extra)
if not result:
logger.error(tips)
raise RuntimeError(tips)
else:
logger.info(tips)
self._init_with_module_file(module_dir[0])
def load(self, vocab_file):
self.__term2id = {}
self.__id2term = {}
with open(vocab_file, 'r', encoding='utf-8') as fin:
for line in fin.readlines():
fields = line.strip().split('\t')
assert len(
fields) == 5, "Vocabulary file [%s] format error!" % (
vocab_file)
term = fields[1]
id_ = int(fields[2])
if term in self.__term2id:
logger.error("Duplicate word [%s] in vocab file!" % (term))
continue
self.__term2id[term] = id_
self.__id2term[id_] = term
def init_with_name(cls, name, version=None, **kwargs):
fp_lock = open(os.path.join(CACHE_HOME, name), "a")
lock.flock(fp_lock, lock.LOCK_EX)
log_msg = "Installing %s module" % name
if version:
log_msg += "-%s" % version
logger.info(log_msg)
extra = {"command": "install"}
result, tips, module_dir = default_module_manager.install_module(
module_name=name, module_version=version, extra=extra)
if not result:
logger.error(tips)
raise RuntimeError(tips)
logger.info(tips)
lock.flock(fp_lock, lock.LOCK_UN)
return cls.init_with_directory(directory=module_dir[0], **kwargs)
def show_topic_keywords(self, topic_id, k=10):
"""
This interface returns first k keywords under specific topic.
Args:
topic_id(int): topic information we want to know.
k(int): top k keywords.
Returns:
results(dict): contains specific topic's keywords and corresponding
probability.
"""
EPS = 1e-8
results = {}
if 0 <= topic_id < self.config.num_topics:
k = min(k, len(self.topic_words[topic_id]))
for i in range(k):
prob = self.topic_words[topic_id][i].count / \
(self.topic_sum_table[topic_id] + EPS)
results[self.vocabulary[self.topic_words[topic_id][i].word_id]] = prob
return results
else:
logger.error("%d is out of range!" % topic_id)
def request_server(self, request_msg):
if self.load_balance == 'round_robin':
try:
cur_con = httplib.HTTPConnection(
self.serving_list[self.con_index])
cur_con.request('POST', "/BertService/inference", request_msg,
{"Content-Type": "application/json"})
response = cur_con.getresponse()
response_msg = response.read()
response_msg = ujson.loads(response_msg)
self.con_index += 1
self.con_index = self.con_index % len(self.serving_list)
return response_msg
except BaseException as err:
logger.warning("Infer Error with server {} : {}".format(
self.serving_list[self.con_index], err))
if len(self.serving_list) == 0:
logger.error('All server failed, process will exit')
return 'fail'
else:
self.con_index += 1
return 'retry'
elif self.load_balance == 'random':
try:
random.seed()
self.con_index = random.randint(0, len(self.serving_list) - 1)
logger.info(self.con_index)
cur_con = httplib.HTTPConnection(
self.serving_list[self.con_index])
cur_con.request('POST', "/BertService/inference", request_msg,
{"Content-Type": "application/json"})
response = cur_con.getresponse()
response_msg = response.read()
response_msg = ujson.loads(response_msg)
return response_msg
except BaseException as err:
logger.warning("Infer Error with server {} : {}".format(
self.serving_list[self.con_index], err))
if len(self.serving_list) == 0:
logger.error('All server failed, process will exit')
return 'fail'
else:
self.con_index = random.randint(0,
len(self.serving_list) - 1)
return 'retry'
elif self.load_balance == 'bind':
try:
self.con_index = int(self.process_id) % len(self.serving_list)
cur_con = httplib.HTTPConnection(
self.serving_list[self.con_index])
cur_con.request('POST', "/BertService/inference", request_msg,
{"Content-Type": "application/json"})
response = cur_con.getresponse()
response_msg = response.read()
response_msg = ujson.loads(response_msg)
return response_msg
except BaseException as err:
logger.warning("Infer Error with server {} : {}".format(
self.serving_list[self.con_index], err))
if len(self.serving_list) == 0:
logger.error('All server failed, process will exit')
return 'fail'
else:
self.con_index = int(self.process_id) % len(
self.serving_list)
return 'retry'
def _load_test_examples(self,
version_2_with_negative=False,
is_training=False):
self.test_file = None
logger.error("not test_file")
def __init__(
self,
num_classes,
dataset=None,
feed_list=None, # Deprecated
data_reader=None, # Deprecated
feature=None,
token_feature=None,
network=None,
startup_program=None,
config=None,
hidden_units=None,
metrics_choices="default"):
"""
Args:
num_classes: total labels of the text classification task.
feed_list(list): the variable name that will be feeded to the main program, Deprecated in paddlehub v1.8.
data_reader(object): data reader for the task. It must be one of ClassifyReader and LACClassifyReader, Deprecated in paddlehub v1.8..
feature(Variable): the `feature` will be used to classify texts. It must be the sentence-level feature, shape as [-1, emb_size]. `Token_feature` and `feature` couldn't be setted at the same time. One of them must be setted as not None. Default None.
token_feature(Variable): the `feature` will be used to connect the pre-defined network. It must be the token-level feature, shape as [-1, seq_len, emb_size]. Default None.
network(str): the pre-defined network. Choices: 'bilstm', 'bow', 'cnn', 'dpcnn', 'gru' and 'lstm'. Default None. If network is setted, then `token_feature` must be setted and `feature` must be None.
startup_program (object): the customized startup program, default None.
config (RunConfig): run config for the task, such as batch_size, epoch, learning_rate setting and so on. Default None.
hidden_units(list): the element of `hidden_units` list is the full-connect layer size. It will add the full-connect layers to the program. Default None.
metrics_choices(list): metrics used to the task, default ["acc"]. Choices: acc, precision, recall, f1, matthews.
"""
if (not feature) and (not token_feature):
logger.error(
'Both token_feature and feature are None, one of them must be set.'
)
exit(1)
elif feature and token_feature:
logger.error(
'Both token_feature and feature are set. One should be set, the other should be None.'
)
exit(1)
if network:
assert network in [
'bilstm', 'bow', 'cnn', 'dpcnn', 'gru', 'lstm'
], 'network (%s) choice must be one of bilstm, bow, cnn, dpcnn, gru, lstm!' % network
assert token_feature and (
not feature
), 'If you wanna use network, you must set token_feature ranther than feature for TextClassifierTask!'
assert len(
token_feature.shape
) == 3, 'When you use network, the parameter token_feature must be the token-level feature([batch_size, max_seq_len, embedding_size]), shape as [-1, 128, 200].'
else:
assert feature and (
not token_feature
), 'If you do not use network, you must set feature ranther than token_feature for TextClassifierTask!'
assert len(
feature.shape
) == 2, 'When you do not use network, the parameter feture must be the sentence-level feature ([batch_size, hidden_size]), such as the pooled_output of ERNIE, BERT, RoBERTa and ELECTRA module.'
self.network = network
if metrics_choices == "default":
metrics_choices = ["acc"]
super(TextClassifierTask,
self).__init__(dataset=dataset,
data_reader=data_reader,
feature=feature if feature else token_feature,
num_classes=num_classes,
feed_list=feed_list,
startup_program=startup_program,
config=config,
hidden_units=hidden_units,
metrics_choices=metrics_choices)
