def demotest(sentence):
ckpt_file = tf.train.latest_checkpoint(model_path)
print(ckpt_file)
paths['model_path'] = ckpt_file
model = BiLSTM_CRF(args,
embeddings,
tag2label,
word2id,
paths,
config=config)
model.build_graph()
saver = tf.train.Saver()
with tf.Session(config=config) as sess:
print('============= demo =============')
saver.restore(sess, ckpt_file)
while (1):
#print('Please input your sentence:')
demo_sent = sentence
if demo_sent == '' or demo_sent.isspace():
print('语句为空')
PER = ['']
LOC = ['']
ORG = ['']
return (PER, LOC, ORG)
else:
demo_sent = list(demo_sent.strip())
demo_data = [(demo_sent, ['O'] * len(demo_sent))]
tag = model.demo_one(sess, demo_data)
PER, LOC, ORG = get_entity(tag, demo_sent)
print('PER: {}\nLOC: {}\nORG: {}'.format(PER, LOC, ORG))
return (PER, LOC, ORG)
def evaluate_words(lines):
print("start evaluate_words")
ckpt_file = tf.train.latest_checkpoint(model_path)
print(ckpt_file)
paths['model_path'] = ckpt_file
model = BiLSTM_CRF(args,
embeddings,
tag2label,
word2id,
paths,
config=config)
model.build_graph()
saver = tf.train.Saver()
with tf.Session(config=config) as sess:
print('============= demo =============')
saver.restore(sess, ckpt_file)
demo_sent = lines
print(demo_sent)
demo_sent = list(demo_sent.strip())
print(demo_sent)
demo_data = [(demo_sent, ['O'] * len(demo_sent))]
tag = model.demo_one(sess, demo_data)
PER, LOC, ORG = get_entity(tag, demo_sent)
print('PER: {}\nLOC: {}\nORG: {}'.format(PER, LOC, ORG))
def get_org(self, text):
demo_sent = text
demo_sent = list(demo_sent.strip())
demo_data = [(demo_sent, ['O'] * len(demo_sent))]
tag = self.model.demo_one(self.sess, demo_data)
per, pos, org = get_entity(tag, demo_sent)
return set(org)
def save_result(saver,sess,decoder,ckpt_file):
saver.restore(sess, ckpt_file)
f = open("./data/case/input.txt","r")
sentences = f.readlines()
f.close()
result=[]
for step, sen in enumerate(sentences):
sys.stdout.write(' processing: {} sentence / {} sentences.'.format(step + 1, len(sentences)) + '\r')
instance={}
sen_ = list(sen.strip().strip('\r\n'))
char_list = [(sen_, ['O'] * len(sen_))]
tag = decoder.demo_one(sess, char_list)
PER, LOC, ORG = get_entity(tag, sen_)
#print(sen)
#print("PER",PER)
#print("LOC",LOC)
#print("ORG",ORG)
instance['sen']=sen
instance['PER']=PER
instance['LOC']=LOC
instance['ORG']=ORG
result.append(instance)
with open('./data/case/result.json','w',encoding='utf-8')as fw:
fw.write(json.dumps(result,ensure_ascii=False))
print("********The result is saved in the ./data/case/result.json*********"+ '\r')
def getRest(input):
ckpt_file = tf.train.latest_checkpoint(model_path)
print(ckpt_file)
paths['model_path'] = ckpt_file
model = BiLSTM_CRF(args,
embeddings,
tag2label,
word2id,
paths,
config=config)
model.build_graph()
saver = tf.train.Saver()
with tf.Session(config=config) as sess:
print('============= demo =============')
saver.restore(sess, ckpt_file)
demo_sent = input
if demo_sent == '' or demo_sent.isspace():
return {'status': 'fail'}
else:
demo_sent = list(demo_sent.strip())
demo_data = [(demo_sent, ['O'] * len(demo_sent))]
tag = model.demo_one(sess, demo_data)
PER, LOC, ORG = get_entity(tag, demo_sent)
result = {'status': 'success', 'PER': PER, 'LOC': LOC, 'ORG': ORG}
return result
def handle_POST(self, request, context, scheme, value):
entity = context['entity'] = get_entity(scheme, value)
if entity.source.module_name != 'molly.providers.apps.maps.osm':
raise Http404
form = UpdateOSMForm(request.POST)
if form.is_valid():
new_metadata = copy.deepcopy(entity.metadata['osm'])
for k in ('name', 'operator', 'phone', 'opening_hours', 'url', 'cuisine', 'food', 'food__hours', 'atm', 'collection_times', 'ref', 'capacity'):
tag_name = k.replace('__', ':')
if tag_name in new_metadata and not form.cleaned_data[k]:
del new_metadata['osm']['tags'][tag_name]
elif form.cleaned_data[k]:
new_metadata['tags'][tag_name] = form.cleaned_data[k]
new_metadata['attrs']['version'] = str(int(new_metadata['attrs']['version'])+1)
osm_update = OSMUpdate(
contributor_name = form.cleaned_data['contributor_name'],
contributor_email = form.cleaned_data['contributor_email'],
contributor_attribute = form.cleaned_data['contributor_attribute'],
entity = entity,
old = simplejson.dumps(entity.metadata),
new = simplejson.dumps(new_metadata),
notes = form.cleaned_data['notes'],
)
osm_update.save()
return HttpResponseRedirect(reverse('places:entity_update', args=[scheme, value])+'?submitted=true')
else:
context['form'] = form
return self.render(request, context, 'places/update_osm')
def get_metadata(self, request, scheme, value):
entity = get_entity(scheme, value)
user_location = request.session.get("geolocation:location")
distance, bearing = entity.get_distance_and_bearing_from(user_location)
additional = "<strong>%s</strong>" % capfirst(entity.primary_type.verbose_name)
if distance:
additional += ", approximately %.3fkm %s" % (distance / 1000, bearing)
return {"title": entity.title, "additional": additional}
def initial_context(self, request, scheme, value):
context = super(EntityDetailView, self).initial_context(request)
entity = get_entity(scheme, value)
context.update({
'entity': entity,
'entity_types': entity.all_types.all(),
})
return context
def deploy_model(d):
vocab = read_dictionary(os.path.join(os.getcwd(),
'data_path/word2id1.pkl'))
with tf.Session() as sess:
signature_key = 'test_signature'
input_key = 'input_x'
input_key2 = 'sequence_length'
output_key = 'output'
output_key2 = 'transition_param'
meta_graph_def = tf.saved_model.loader.load(
sess, ['test_saved_model'],
os.path.join(os.getcwd(), 'model/1506177919'))
# 从 meta_graph_def 中取出 SignatureDef 对象
signature = meta_graph_def.signature_def
# 从 signature 中找到具体输入输出的 tensor name
x_tensor_name = signature[signature_key].inputs[input_key].name
x_tensor_name2 = signature[signature_key].inputs[input_key2].name
y_tensor_name = signature[signature_key].outputs[output_key].name
y_tensor_name2 = signature[signature_key].outputs[output_key2].name
word_ids = sess.graph.get_tensor_by_name(x_tensor_name)
sequence_lengths = sess.graph.get_tensor_by_name(x_tensor_name2)
y = sess.graph.get_tensor_by_name(y_tensor_name)
y2 = sess.graph.get_tensor_by_name(y_tensor_name2)
sent = d['1']
input_sent = list(sent.strip())
input_data = [(input_sent, ['O'] * len(input_sent))]
for seqs, labels in batch_yield(input_data, vocab, tag2label):
feed_dict, seq_len_list = get_feed_dict(seqs, labels)
[logits, transition_params] = sess.run([y, y2],
feed_dict={
word_ids:
seqs,
sequence_lengths:
seq_len_list
})
label_list = []
for logit in logits:
viterbi_seq, _ = viterbi_decode(logit[:seq_len_list[0]],
transition_params)
label_list.append(viterbi_seq)
label2tag = {}
for tag, label in tag2label.items():
label2tag[label] = tag if label != 0 else label
tag = [label2tag[label] for label in label_list[0]]
PER, LOC, ORG, OTH = get_entity(tag, input_sent)
return ('PER: {}\nLOC: {}\nORG: {}\nOTH: {}'.format(
PER, LOC, ORG, OTH))
def predict(self, demo_sent):
if demo_sent == '' or demo_sent.isspace():
print('See you next time!')
return {}
else:
demo_sent = list(demo_sent.strip())
demo_data = [(demo_sent, ['O'] * len(demo_sent))]
tag = self.model.demo_one(self.sess, demo_data)
entities = get_entity(tag, demo_sent)
return entities
def do_lstm(sentence):
global sess, model
per = []
loc = []
org = []
# with tf.Session(config=config) as sess:
if sess is not None:
print('============= demo =============')
if len(sentence) > 10:
demo_sent = sentence.replace(u" ", u"，").replace(u"《", "").replace(
u"》", "").replace(" ", "").replace(",",
u"，").replace(u"［", "")
demo_sent = demo_sent.replace(u"］", u"").replace(u"（", "").replace(
u"）", "").replace(u"—", "").replace(u"〔",
" ").replace(u"〕", " ")
demo_sent = demo_sent.replace(u"＂", u"").replace(u"“", "").replace(
u"”", "").replace("...", "").replace(u"⒄", "")
_sent = [demo_sent]
for _s in _sent:
if len(_s) < 10:
continue
# print("{}".format(_s))
_sent = list(_s.strip())
_data = [(_sent, ['O'] * len(_sent))]
# print(_data)
tag = model.demo_one(sess, _data)
try:
PER, LOC, ORG = get_entity(tag, _sent)
# print('PER: {}\nLOC: {}\nORG: {}'.format(PER, LOC, ORG))
if len(PER) > 0:
for _p in PER:
if len(_p) > 1 and _p not in per:
print('PER: {}'.format(_p))
per.append(_p)
if len(LOC) > 0:
for _p in LOC:
if len(_p) > 1 and _p not in loc:
print('LOC: {}'.format(_p))
loc.append(_p)
if len(ORG) > 0:
for _p in ORG:
if len(_p) > 1 and _p not in org:
print('ORG: {}'.format(_p))
org.append(_p)
except Exception as e:
print(e)
return per, loc, org
def get_metadata(self, request, scheme, value):
entity = get_entity(scheme, value)
user_location = request.session.get('geolocation:location')
distance, bearing = entity.get_distance_and_bearing_from(user_location)
additional = '<strong>%s</strong>' % capfirst(entity.primary_type.verbose_name)
if distance:
additional += ', approximately %.3fkm %s' % (distance/1000, bearing)
return {
'title': entity.title,
'additional': additional,
}
def breadcrumb(self, request, context, scheme, value):
if request.session.get("geolocation:location"):
parent_view = "nearby-detail"
else:
parent_view = "category-detail"
entity = get_entity(scheme, value)
return Breadcrumb(
"places",
lazy_parent(parent_view, ptypes=entity.primary_type.slug),
context["entity"].title,
lazy_reverse("entity", args=[scheme, value]),
)
def breadcrumb(self, request, context, scheme, value):
if request.session.get('geolocation:location'):
parent_view = 'nearby-detail'
else:
parent_view = 'category-detail'
entity = get_entity(scheme, value)
return Breadcrumb(
'places',
lazy_parent(parent_view, ptypes=entity.primary_type.slug),
context['entity'].title,
lazy_reverse('entity', args=[scheme,value]),
)
def page(request, entity_type, entity_id, layout_slug=None): try: entity = get_entity(entity_type, pk=int(entity_id)) except: import traceback traceback.print_exc() raise Http404 try: __layout__ = get_layout(slug=layout_slug, entity_type=entity_type, entity=entity) except: __layout__ = Layout.get() output = locals() context = RequestContext(request) return render_to_response(__layout__.get_template(), output, context)
def handle_POST(self, request, context, scheme, value):
entity = context["entity"] = get_entity(scheme, value)
if entity.source.module_name != "molly.providers.apps.maps.osm":
raise Http404
form = UpdateOSMForm(request.POST)
if form.is_valid():
new_metadata = copy.deepcopy(entity.metadata["osm"])
for k in (
"name",
"operator",
"phone",
"opening_hours",
"url",
"cuisine",
"food",
"food__hours",
"atm",
"collection_times",
"ref",
"capacity",
):
tag_name = k.replace("__", ":")
if tag_name in new_metadata and not form.cleaned_data[k]:
del new_metadata["osm"]["tags"][tag_name]
elif form.cleaned_data[k]:
new_metadata["tags"][tag_name] = form.cleaned_data[k]
new_metadata["attrs"]["version"] = str(int(new_metadata["attrs"]["version"]) + 1)
osm_update = OSMUpdate(
contributor_name=form.cleaned_data["contributor_name"],
contributor_email=form.cleaned_data["contributor_email"],
contributor_attribute=form.cleaned_data["contributor_attribute"],
entity=entity,
old=simplejson.dumps(entity.metadata),
new=simplejson.dumps(new_metadata),
notes=form.cleaned_data["notes"],
)
osm_update.save()
return HttpResponseRedirect(reverse("places:entity-update", args=[scheme, value]) + "?submitted=true")
else:
context["form"] = form
return self.render(request, context, "places/update_osm")
def demo(model, config, word2id):
ckpt_file = tf.train.latest_checkpoint(config.model_dir)
session = tf.Session()
session.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(sess=session, save_path=ckpt_file) # 读取保存的模型
while True:
print('Please input your sentence:')
demo_sent = input()
if demo_sent == '' or demo_sent.isspace():
print('See you next time!')
break
else:
demo_sent = list(demo_sent.strip())
demo_data = [(demo_sent, ['O'] * len(demo_sent))]
tag = demo_one(session, model, config, word2id, demo_data)
PER, LOC, ORG = get_entity(tag, demo_sent)
print('PER: {}\nLOC: {}\nORG: {}'.format(PER, LOC, ORG))
def test101(**kwargs):
import argparse
from utils import str2bool
from data import read_dictionary, tag2label
print('test101', kwargs)
##
parser = argparse.ArgumentParser(
description='BiLSTM-CRF for Chinese NER task')
parser.add_argument('--train_data',
type=str,
default='data_path',
help='train data source')
parser.add_argument('--demo_model',
type=str,
default='1521112368',
help='model for test and demo')
parser.add_argument('--batch_size',
type=int,
default=64,
help='#sample of each minibatch')
args = parser.parse_args([])
##
word2id = read_dictionary(os.path.join('.', args.train_data,
'word2id.pkl'))
client = BiLSTM_CRF_Client(args, tag2label, word2id)
demo_sent = kwargs.get("demo_sent")
demo_sent = list(demo_sent.strip())
print('demo_sent', len(demo_sent))
demo_data = [(demo_sent, ['O'] * len(demo_sent))]
ret1 = client.demo_one(kwargs.get("server"), demo_data, verbose=False)
print('result-1', ret1)
from utils import get_entity
PER, LOC, ORG = get_entity(ret1, demo_sent)
print('PER: {}\nLOC: {}\nORG: {}'.format(PER, LOC, ORG))
def ner_predict(cases, encrypt=False):
result = []
for case_str in cases:
pc = prpcrypt(get_config().get("encryptKey", "key"))
#process the string or listinfo
if encrypt:
case_str = pc.decrypt(bytes(case_str, encoding='utf-8'))
case_str = re.sub("\|", "", case_str)
case_str = re.sub(" ", "", case_str)
case_str = re.sub("\n", "", case_str)
case_str = re.sub(" ", "", case_str)
predict_sent = list(case_str.strip())
demo_data = [(predict_sent, ['O'] * len(predict_sent))]
tag = model.demo_one(sess, demo_data)
LOC = get_entity(tag, predict_sent)
res = {
"loc": list(set(LOC)),
}
result.append(res["loc"])
return result
def entity_favourite(request, type_slug, id):
entity = get_entity(type_slug, id)
if request.method != 'POST':
return HttpResponse('', mimetype='text/plain', status=405)
try:
value = request.POST['is_favourite'] == 'true'
except KeyError:
return HttpResponse('', mimetype='text/plain', status=400)
make_favourite(request, entity, value)
if 'no_redirect' in request.POST:
return HttpResponse('', mimetype='text/plain', status=400)
if 'return_url' in request.POST:
return HttpResponseRedirect(request.POST['return_url'])
else:
return HttpResponseRedirect(entity.get_absolute_url())
def entity_favourite(request, type_slug, id):
entity = get_entity(type_slug, id)
if request.method != "POST":
return HttpResponse("", mimetype="text/plain", status=405)
try:
value = request.POST["is_favourite"] == "true"
except KeyError:
return HttpResponse("", mimetype="text/plain", status=400)
make_favourite(request, entity, value)
if "no_redirect" in request.POST:
return HttpResponse("", mimetype="text/plain", status=400)
if "return_url" in request.POST:
return HttpResponseRedirect(request.POST["return_url"])
else:
return HttpResponseRedirect(entity.get_absolute_url())
def main(test_sent):
start_time = time.time()
channel = implementations.insecure_channel('192.168.1.210', 5075)
stub = prediction_service_pb2.beta_create_PredictionService_stub(channel)
test_sent = list(test_sent.strip())
test_data = [(test_sent, ['O'] * len(test_sent))]
label_list = []
for seqs, labels in batch_yield(test_data,
batch_size=64,
vocab=word2id,
tag2label=tag2label,
shuffle=False):
label_list_, _ = predict_one_batch(seqs, stub)
label_list.extend(label_list_)
# label2tag = {}
# for tag, label in tag2label.items():
# label2tag[label] = tag if label != 0 else label
tag = [label2tag[label] for label in label_list[0]]
print 'tag', tag
PER, LOC, ORG = get_entity(tag, test_sent)
time_used = time.time() - start_time
print 'tim_used', time_used
return PER, LOC, ORG
def handle_GET(self, request, context, scheme, value, ptype):
entity = get_entity(scheme, value)
return super(NearbyEntityDetailView, self).handle_GET(request, context, ptype, entity)
def get_metadata(self, request, scheme, value, ptype):
entity = get_entity(type_slug, id)
return super(NearbyEntityDetailView, self).get_metadata(request, ptype, entity)
def initial_context(self, request, scheme, value, ptype):
entity = get_entity(scheme, value)
context = super(NearbyEntityDetailView, self).initial_context(request, ptype, entity)
context["entity"] = entity
return context
print("test data: {}".format(test_size))
model.test(test_data)
## demo
elif args.mode == 'demo':
ckpt_file = tf.train.latest_checkpoint(model_path)
print(ckpt_file)
paths['model_path'] = ckpt_file
model = BiLSTM_CRF(args,
embeddings,
tag2label,
word2id,
paths,
config=config)
model.build_graph()
saver = tf.train.Saver()
with tf.Session(config=config) as sess:
print('============= demo =============')
saver.restore(sess, ckpt_file)
while (1):
print('Please input your sentence:')
demo_sent = input()
if demo_sent == '' or demo_sent.isspace():
print('See you next time!')
break
else:
demo_sent = list(demo_sent.strip())
demo_data = [(demo_sent, ['O'] * len(demo_sent))]
tag = model.demo_one(sess, demo_data)
ENT, HYPER = get_entity(tag, demo_sent)
print('ENT: {}\nHYPER: {}'.format(ENT, HYPER))
model.test(test_data)
## demo
elif args.mode == 'demo':
ckpt_file = tf.train.latest_checkpoint(model_path)
print(ckpt_file)
paths['model_path'] = ckpt_file
model = model.BiLSTM_CRF(args,
embeddings,
data.tag2label,
word2id,
paths,
config=config)
model.build_graph()
saver = tf.train.Saver()
with tf.Session(config=config) as sess:
print('============= demo =============')
saver.restore(sess, ckpt_file)
while (1):
print('Please input your sentence:')
demo_sent = input()
if demo_sent == '' or demo_sent.isspace():
print('See you next time!')
break
else:
demo_sent = list(demo_sent.strip())
demo_data = [(demo_sent, ['O'] * len(demo_sent))]
tag = model.demo_one(sess, demo_data)
PER, LOC, ORG = utils.get_entity(tag, demo_sent)
print('PER: {}\nLOC: {}\nORG: {}'.format(PER, LOC, ORG))
charge = one_text['charge'] # 犯罪原因
judgementId = one_text['judgementId'] # 判决Id，唯一标示
keywords = one_text['keywords'] # 关键词
court = one_text['court'] # 法院信息
judge_text = one_text['judge_text'] # 判决结果，是一个列表，继续循环
proponents = one_text['proponents'] # 原告
opponents = one_text['opponents'] # 被告
for text in judge_text: # 处理判决结果
text = re.sub("<a.+?</a>", '', text)
if text is '':
continue
print('judge_text: ', text)
demo_data = [(text, ['O'] * len(text))]
tag = model.demo_one(sess, demo_data)
PER, LOC, ORG = get_entity(tag, text)
MON = get_MON_entity(text)
print('PER: {}\nLOC: {}\nORG: {}\nMON: {}\n'.format(
PER, LOC, ORG, MON))
# 将数据写入es
es.index(index='zhizhuxia_sichuan',
doc_type='ner_type',
body={
'addr': addr,
'charge': charge,
'judgementId': judgementId,
'keywords': keywords,
'court': court,
'judge_text': text,
'PER': PER,
word2id,
paths,
config=config)
model.build_graph()
saver = tf.train.Saver()
with tf.Session(config=config) as sess:
print('============= demo =============')
saver.restore(sess, ckpt_file)
while (1):
print('输入待识别句子:')
demo_sent = input()
if demo_sent == '' or demo_sent.isspace():
print('See you next time!')
break
else:
# 邱实在实验室买戴尔的显示屏
# ['邱', '实', '在', '实', '验', '室', '买', '戴', '尔', '的', '显', '示', '屏']
demo_sent = list(demo_sent.strip())
# [(
# ['邱', '实', '在', '实', '验', '室', '买', '戴', '尔', '的', '显', '示', '屏'],
# ['O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O']
# )]
demo_data = [(demo_sent, ['O'] * len(demo_sent))]
# ['B-PER', 'I-PER', 0, 0, 0, 0, 0, 'B-PER', 'I-PER', 0, 0, 0, 0]
tag = model.demo_one(sess, demo_data)
# PER, LOC, ORG = get_entity(tag, demo_sent)
targets = get_entity(tag, demo_sent)
print('属性词: {}'.format(targets))
# 2019.03.07 15:45 start train
def initial_context(self, request, scheme, value):
return dict(super(EntityUpdateView, self).initial_context(request), entity=get_entity(scheme, value))
saver = tf.train.Saver()
with tf.Session(config=config) as sess:
print('============= demo =============')
saver.restore(sess, ckpt_file)
while (1):
print('Please input your sentence:')
demo_sent = input()
if demo_sent == '' or demo_sent.isspace():
print('See you next time!')
break
else:
demo_sent = list(demo_sent.strip())
demo_data = [(demo_sent, ['O'] * len(demo_sent))]
tag = model.demo_one(sess, demo_data)
# name 参照 data.py tag2label
PER = get_entity(tag, demo_sent, 'PER')
SEX = get_entity(tag, demo_sent, 'SEX')
TIT = get_entity(tag, demo_sent, 'TIT')
REA = get_entity(tag, demo_sent, 'PER')
print('PER: {}\nSEX: {}\nTIT: {}\nREA: {}'.format(
PER, SEX, TIT, REA))
## predict
elif args.mode == "predict":
ckpt_file = tf.train.latest_checkpoint(model_path)
print(ckpt_file)
paths['model_path'] = ckpt_file
model = BiLSTM_CRF(args,
embeddings,
tag2label,
word2id,
ckpt_file = tf.train.latest_checkpoint(model_path)
model = BLC(batch_size=args['batch_size'],
epoch_num=args['epoch'],
hidden_dim=args['hidden_dim'],
embeddings=embeddings,
dropout_keep=args['dropout'],
optimizer=args['optimizer'],
lr=args['lr'],
clip_grad=args['clip'],
tag2label=tag2label,
vocab=word2id,
shuffle=args['shuffle'],
model_path=ckpt_file,
summary_path=summary_path,
result_path=result_path,
update_embedding=args['update_embedding'])
model.build_graph()
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess, ckpt_file)
while (1):
print('输入待识别的句子: ')
sent = input()
if sent == '' or sent.isspace():
break
else:
sent = list(sent.strip())
data = [(sent, ['O'] * len(sent))]
tag = model.test(sess, data)
ENT = get_entity(tag, sent)
print('ENT: {}\n'.format(ENT))
model.build_graph()
saver = tf.train.Saver()
with tf.Session(config=config) as sess:
print('============= demo =============')
saver.restore(sess, ckpt_file)
while(1):
print('Please input your sentence:')
demo_sent = input()
if demo_sent == '' or demo_sent.isspace():
print('See you next time!')
break
else:
demo_sent = list(demo_sent.strip())
demo_data = [(demo_sent, ['O'] * len(demo_sent))]
tag = model.demo_one(sess, demo_data)
ENT, EVA, ALL = get_entity(tag, demo_sent)
print('ENT: {}\nEVA: {}\nALL: {}\n'.format(ENT, EVA, ALL))
elif args.mode == 'all':
ckpt_file = tf.train.latest_checkpoint(model_path)
print('ckpt_file:',ckpt_file)
paths['model_path'] = ckpt_file
model = BiLSTM_CRF(args, embeddings, tag2label, word2id, paths, config=config)
model.build_graph()
saver = tf.train.Saver()
with tf.Session(config=config) as sess:
print('============= demo =============')
saver.restore(sess, ckpt_file)
result = open('result.txt', 'w',encoding='utf8')
with open('content.txt', encoding='utf8') as f:
count = 0
error_count = 0
print(ckpt_file)
paths['model_path'] = ckpt_file
model = BiLSTM_CRF(args, embeddings, tag2label, word2id, paths, config=config)
model.build_graph()
print("test data: {}".format(test_size))
model.test(test_data)
## demo
elif args.mode == 'demo':
ckpt_file = tf.train.latest_checkpoint(model_path)
print(ckpt_file)
paths['model_path'] = ckpt_file
model = BiLSTM_CRF(args, embeddings, tag2label, word2id, paths, config=config)
model.build_graph()
saver = tf.train.Saver()
with tf.Session(config=config) as sess:
print('============= demo =============')
saver.restore(sess, ckpt_file)
while(1):
print('Please input your sentence:')
demo_sent = input()
if demo_sent == '' or demo_sent.isspace():
print('See you next time!')
break
else:
demo_sent = list(demo_sent.strip())
demo_data = [(demo_sent, ['O'] * len(demo_sent))]
tag = model.demo_one(sess, demo_data)
PER, LOC, ORG, UNI, JOB = get_entity(tag, demo_sent)
print('PER: {}\nLOC: {}\nORG: {}\nUNI: {}\nJOB: {}'.format(PER, LOC, ORG, UNI, JOB))
i = 0
with open('data_path/es_data/es_weibo_data',
'r',
encoding='gb18030') as f:
for line in f:
i += 1
print(i)
demo_sent = line.strip()
demo_sent = list(demo_sent)
#print (demo_sent)
demo_data = [(demo_sent, ['Neg_B'] * len(demo_sent))]
#print (demo_data)
#print (len(demo_sent))
tag = model.demo_one(sess, demo_data)
#print (tag)
Neg, Pos, Neu = get_entity(tag, demo_sent)
flag = 0
posList = []
negList = []
for n in Neg:
if Neg_dic.get(n) is None and len(n) > 1:
#fo.write(line.strip()+'\t'+'Neg:')
flag = 1
Neg_dic[n] = -1
negList.append(n)
#fo.write(n +' ')
for p in Pos:
if Pos_dic.get(p) is None and len(p) > 1:
flag = 1
Pos_dic[p] = 1
posList.append(p)
print(ckpt_file)
paths['model_path'] = ckpt_file
model = BiLSTM_CRF(args, embeddings, tag2label, word2id, paths, config=config)
model.build_graph()
print("test data: {}".format(test_size))
model.test(test_data)
## demo
elif args.mode == 'demo':
ckpt_file = tf.train.latest_checkpoint(model_path)
print(ckpt_file)
paths['model_path'] = ckpt_file
model = BiLSTM_CRF(args, embeddings, tag2label, word2id, paths, config=config)
model.build_graph()
saver = tf.train.Saver()
with tf.Session(config=config) as sess:
print('============= demo =============')
saver.restore(sess, ckpt_file)
while(1):
print('Please input your sentence:')
demo_sent = input()
if demo_sent == '' or demo_sent.isspace():
print('See you next time!')
break
else:
demo_sent = list(demo_sent.strip())
demo_data = [(demo_sent, ['O'] * len(demo_sent))]
tag = model.demo_one(sess, demo_data)
PER, LOC, ORG = get_entity(tag, demo_sent)
print('PER: {}\nLOC: {}\nORG: {}'.format(PER, LOC, ORG))
embeddings=embeddings,
dropout_keep=args['dropout'],
optimizer=args['optimizer'],
lr=args['lr'],
clip_grad=args['clip'],
tag2label=tag2label,
vocab=word2id,
shuffle=args['shuffle'],
model_path=ckpt_file,
summary_path=summary_path,
result_path=result_path,
update_embedding=args['update_embedding'])
model.build_graph()
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess, ckpt_file)
while (1):
print('输入待识别的句子: ')
sent = input()
if sent == '' or sent.isspace():
break
else:
sent = list(sent.strip())
data = [(sent, ['O'] * len(sent))]
tag = model.test(sess, data)
#ENT = get_entity(tag, sent)
PER, LOC, ORG = get_entity(tag, sent)
#print('ENT: {}\n'.format(ENT))
print('PER: {}\n'.format(PER))
print('LOC: {}\n'.format(LOC))
print('ORG: {}\n'.format(ORG))
def get_metadata(self, request, scheme, value):
entity = get_entity(scheme, value)
return super(NearbyEntityListView, self).get_metadata(request, entity)
print('model_path:', model_path)
ckpt_file = tf.train.latest_checkpoint(model_path)
print(ckpt_file)
paths['model_path'] = ckpt_file
model = BiLSTM_CRF(FLAGS=FLAGS,
embeddings=embeddings,
server=None,
num_workers=None,
word2id=word2id,
tag2label=tag2label,
paths=paths,
train_data_len=None)
model.build_graph()
saver = tf.train.Saver()
with tf.Session() as sess:
print('============= demo =============')
saver.restore(sess, ckpt_file)
while 1:
print('Please input your sentence:')
demo_sent = input('input:')
if demo_sent == '' or demo_sent.isspace():
print('see you next time!')
break
else:
demo_sent = list(demo_sent.strip())
demo_data = [(demo_sent, ['O'] * len(demo_sent))]
tag = model.demo_one(sess, demo_data)
PER, LOC, ORG, DUTY = get_entity(tag, demo_sent)
print('PER: {}\nLOC: {}\nORG: {}\nDUTY: {}'.format(
PER, LOC, ORG, DUTY))
def predictor(_):
"""Triggered by HTTP.
"""
start_time = time.time()
client_datastore = datastore.Client()
# Then get by key for this entity
query_queue = client_datastore.query(kind='Queue')
queue = list(query_queue.fetch())
if len(queue) == 0:
print("No frames to process")
return
print("{} frames to process".format(len(queue)))
# test
print('Number of duplicates in the queue',
count_duplicates(list(map(lambda x: dict(x)['frame'], queue))))
# Above a certain amount of frames in the queue we pick batch instead of online predictions
# Or if there is currently a batch being prepared and other input files are waiting
# to be written (checking if any queued frame has the key 'batch')
if len(queue) >= TRESHOLD or any(map(lambda x: 'batch' in dict(x), queue)):
if any_job_running(
PROJECT_ID
): # TODO: think about how to plan for later a job launch (after end of this one)
print("A job is already running, exiting")
return
# Instantiates a GCS client
storage_client = storage.Client()
# Required in case building an input takes multiple predictor execution,
# We want to keep the same jobId than previous execution because
# jobId is used for the input / output folder (we want all inputs in the same folder)
jobIds = list(filter(lambda x: 'batch' in dict(x), queue))
# The model to prepare inputs is either the first of the list
# either it's the first element of the list which has 'batch' property (means that a job was being prepared already)
model = jobIds[0]['model'] if len(jobIds) > 0 else dict(
queue[0])['model']
body = make_batch_job_body(project_name=PROJECT_ID,
bucket_name=BUCKET_NAME,
model_name=MODEL_NAME,
region=REGION,
version_name=model,
max_worker_count=72)
# Filtering the queue to launch a batch job only for the asked model
filtered_queue = list(
filter(lambda x: model in dict(x)['model'], queue))
if len(jobIds) > 0:
body['jobId'] = jobIds[0]['batch']
# Also need to update paths
body['predictionInput'][
'inputPaths'] = 'gs://{}/{}/batches/*'.format(
BUCKET_NAME, body['jobId'])
body['predictionInput'][
'outputPath'] = 'gs://{}/{}/batch_results'.format(
BUCKET_NAME, body['jobId'])
else: # Optimization, so we do it only once
# We want to signal that all these frames have to be put into input files
# Doing it first right away because it takes some execution time
# Actually we just need to tag the last element of the batch
filtered_queue[-1]['batch'] = body['jobId']
client_datastore.put(filtered_queue[-1])
# Creating multiple small input files (better scalability)
for i, chunk in enumerate(chunks(filtered_queue, BATCH_CHUNK)):
print('Chunk n°{}'.format(i + 1))
elapsed_time = time.time() - start_time
print('Elapsed time {0:.2f}'.format(elapsed_time))
# Avoid timeout (40s)
if elapsed_time > 40:
# Resursive until everything into input files
get_no_response(
'https://{}-{}.cloudfunctions.net/predictor'.format(
REGION, PROJECT_ID))
print(
f'{len(filtered_queue) - (1 + i) * BATCH_CHUNK} frames left to write to input file for model {model}'
)
return
random_file_id = random_id()
for i, q in enumerate(chunk):
frame_entity = get_entity(client_datastore, 'Frame',
dict(q)['frame'])
json_frame = frame_to_input(frame_entity)
# Random name, must be different from other input files
file_name = "inputs-{}.json".format(random_file_id)
file_path = os.path.join("/tmp", file_name)
# Open file with "a" = append the file
with open(file_path, "a+") as json_file:
json_file.write(json.dumps(json_frame) + "\n")
client_datastore.delete(q.key)
bucket = storage_client.get_bucket(BUCKET_NAME)
blob = bucket.blob(
os.path.join(body['jobId'], 'batches', file_name))
# Upload the input
blob.upload_from_filename(file_path)
# Launch the batch prediction job
response = batch_predict(PROJECT_ID, body)
# Dismiss processed messages from the queue in case the job has been queued only
if 'QUEUED' in response:
pass
return
else:
import socket
# https://stackoverflow.com/questions/48969145/how-to-set-the-request-timeout-in-google-ml-api-python-client
socket.setdefaulttimeout(ONLINE_TIMEOUT)
# Iterate through the frames to process
for i, q in enumerate(queue):
elapsed_time = time.time() - start_time
print('Elapsed time {0:.2f}'.format(elapsed_time))
# Avoid timeout (40s)
if elapsed_time > 30: # TODO: handle timeout treshold in relation to image size (model takes longer for bigger image)
# Resursive until everything processed
get_no_response(
'https://{}-{}.cloudfunctions.net/predictor'.format(
REGION, PROJECT_ID))
print('{} frames left to process'.format(len(queue) - i))
return
frame_entity = get_entity(client_datastore, 'Frame',
dict(q)['frame'])
json_frame = frame_to_input(frame_entity)
instances = [json_frame]
# Query AI Platform with the input
result = online_predict(PROJECT_ID, MODEL_NAME, instances,
dict(q)['model'])
# Put the prediction in Datastore
key_prediction = client_datastore.key('Prediction')
entity_prediction = datastore.Entity(key=key_prediction)
keys_object = list()
# For each object detected ...
# Assuming there is only one prediction possible even though there is a 's' at predictions ?
for i in range(int(result['predictions'][0]['num_detections'])):
if result['predictions'][0]['detection_scores'][i] > 0.1:
# Create a new dict that will be put in datastore in a clean format
object_detected = dict()
object_detected['detection_classes'] = int(
result['predictions'][0]['detection_classes'][i])
object_detected['detection_boxes'] = result['predictions'][
0]['detection_boxes'][i]
object_detected['detection_scores'] = result[
'predictions'][0]['detection_scores'][i]
# Put the information about the object into a new table row ...
key_object = client_datastore.key('Object')
entity_object = datastore.Entity(key=key_object)
entity_object.update(object_detected)
client_datastore.put(entity_object)
# Store the id generated for reference in Prediction table
keys_object.append(entity_object.id)
# Put a list of objects detected in prediction row
entity_prediction.update({
'model': dict(q)['model'],
'objects': keys_object
})
client_datastore.put(entity_prediction)
# Update the predictions properties of the Frame row
if 'processing' in frame_entity['predictions']:
frame_entity['predictions'] = [] # Reset it
# If it doesn't go in the if, it means that it already has predictions from another model
frame_entity['predictions'].append(entity_prediction.id)
# Push into datastore
client_datastore.put(frame_entity)
# Dismiss processed messages from the queue
# Remove from datastore
client_datastore.delete(q.key)
return
def initial_context(self, request, scheme, value):
return {"entity": get_entity(scheme, value)}
with tf.Session(config=model.config) as sess:
sess.run(tf.global_variables_initializer())
model.train(sess=sess, train=train_data, dev=train_data, saver=saver)
elif args.mode == 'demo':
ckpt_file = tf.train.latest_checkpoint(model_path)
print(ckpt_file)
log_path['model_path'] = ckpt_file
model = BiLSTM_CRF(args, tag2label, vocab, log_path, logger, config=config)
model.build_graph()
saver = tf.train.Saver()
with tf.Session(config=config) as sess:
print('Start demo ...')
saver.restore(sess, ckpt_file)
while True:
print('Please input sentence(pause enter or space to exit):')
demo_sent = input()
if demo_sent == '' or demo_sent.isspace():
print('Error for input format, see you next time!')
break
else:
try:
demo_sent = list(demo_sent.strip())
demo_data = [(demo_sent, ['O'] * len(demo_sent))]
tag = model.demo_one(sess, demo_data)
NOR, VER, ENG, OTH = get_entity(tag, demo_sent)
print('NOR: {}, VER: {}, ENG: {}, OTH: {}'.format(
NOR, VER, ENG, OTH))
except:
print('Please switch to manual service ...')
def trainAll(args):
if args.mode == 'train':
model = BiLSTM_CRF(args,
embeddings,
tag2label,
word2id,
paths,
config=config)
model.build_graph()
## hyperparameters-tuning, split train/dev
# dev_data = train_data[:5000]; dev_size = len(dev_data)
# train_data = train_data[5000:]; train_size = len(train_data)
# print("train data: {0}\ndev data: {1}".format(train_size, dev_size))
# model.train(train=train_data, dev=dev_data)
## train model on the whole training data
print("train data: {}".format(len(train_data)))
model.train(
train=train_data, dev=test_data
) # use test_data as the dev_data to see overfitting phenomena
## testing model
elif args.mode == 'test':
ckpt_file = tf.train.latest_checkpoint(model_path)
print(ckpt_file)
paths['model_path'] = ckpt_file
model = BiLSTM_CRF(args,
embeddings,
tag2label,
word2id,
paths,
config=config)
model.build_graph()
print("test data: {}".format(test_size))
model.test(test_data)
## demo
elif args.mode == 'demo':
ckpt_file = tf.train.latest_checkpoint(model_path)
print(ckpt_file)
paths['model_path'] = ckpt_file
model = BiLSTM_CRF(args,
embeddings,
tag2label,
word2id,
paths,
config=config)
model.build_graph()
saver = tf.train.Saver()
with tf.Session(config=config) as sess:
print('============= demo =============')
saver.restore(sess, ckpt_file)
while (1):
print('Please input your sentence:')
demo_sent = input()
if demo_sent == '' or demo_sent.isspace():
print('See you next time!')
break
else:
demo_sent = list(demo_sent.strip())
demo_data = [(demo_sent, ['O'] * len(demo_sent))]
tag = model.demo_one(sess, demo_data)
PER, LOC, ORG = get_entity(tag, demo_sent)
print('PER: {}\nLOC: {}\nORG: {}'.format(PER, LOC, ORG))
elif args.mode == 'savemodel':
ckpt_file = tf.train.latest_checkpoint(model_path)
print(ckpt_file)
paths['model_path'] = ckpt_file
model = BiLSTM_CRF(args,
embeddings,
tag2label,
word2id,
paths,
config=config)
model.build_graph()
saver = tf.train.Saver()
with tf.Session(config=config) as sess:
saver.restore(sess, ckpt_file)
demo_sent = tf.placeholder(tf.string, name='input')
demo_sent = list(str(demo_sent).strip())
demo_data = [(demo_sent, ['O'] * len(demo_sent))]
tag = model.demo_one(sess, demo_data)
PER, LOC, ORG = get_entity(tag, demo_sent)
result = {'PER': PER, 'LOC': LOC, 'ORG': ORG}
print('PER: {}\nLOC: {}\nORG: {}'.format(PER, LOC, ORG))
# #保存SavedModel模型
builder = tf.saved_model.builder.SavedModelBuilder('./savemodels')
signature = predict_signature_def(inputs={'input': demo_sent},
outputs={'output': result})
builder.add_meta_graph_and_variables(
sess, [tf.saved_model.tag_constants.SERVING],
signature_def_map={'predict': signature})
builder.save()
print('savemodel saves')
def handle_GET(self, request, context, scheme, value):
entity = get_entity(scheme, value)
return super(NearbyEntityListView, self).handle_GET(request, context, entity)
file_name_list = file_name('../data/test2')#读取测试集
output_path = '../data/test_ori'
if not os.path.exists(output_path): os.makedirs(output_path)
for name in file_name_list:
name_dir = '../data/test2/' + name
write_dir = '../data/test_ori/rec_' + name#预测完后写入的文件
f = open(name_dir, 'r', encoding = 'utf-8')
fw = open(write_dir, 'w', encoding = 'utf-8')
while True:
line = f.readline()
if line == '':
break
line = list(line.strip())
demo_data = [(line, ['O'] * len(line))]
tag = model.demo_one(sess, demo_data)
result = get_entity(tag, line)
#result = sorted(result1[0].items(), key=lambda x:x[1], reverse = False)
entity_list = result
#print(entity_list)
#j_sort = []
i = 0
for en in entity_list:
en_write1 = en['start']
en_write2 = en['end']
en_write3 = en['type']
en_write4 = en['word']
i += 1
fw.write(str(en_write1)+'\t'+str(en_write2)+'\t'+en_write3+'\t'+en_write4+'\n')
print(entity_list)
fw.close()
## demo
elif args.mode == 'demo':
ckpt_file = tf.train.latest_checkpoint(model_path)
print(ckpt_file)
paths['model_path'] = ckpt_file
model = BiLSTM_CRF(args,
embeddings,
tag2label,
word2id,
paths,
config=config)
model.build_graph()
saver = tf.train.Saver()
with tf.Session(config=config) as sess:
print('============= demo =============')
#使用 saver.restore() 方法，重载模型的参数，继续训练或用于测试数据。
saver.restore(sess, ckpt_file)
while (1):
print('Please input your sentence:')
demo_sent = input()
if demo_sent == '' or demo_sent.isspace():
print('See you next time!')
break
else:
demo_sent = list(demo_sent.strip())
demo_data = [(demo_sent, ['O'] * len(demo_sent))]
tag = model.demo_one(sess, demo_data)
entities = get_entity(tag, demo_sent)
print({i: entities[i] for i in entities.keys()})
#print('PER: {}\nLOC: {}\nORG: {}\nTIME: {}\nROLE: {}'.format(PER, LOC, ORG, TIME, ROLE))
model.test(test_data)
## demo
elif args.mode == 'demo':
ckpt_file = tf.train.latest_checkpoint(model_path)
print(ckpt_file)
paths['model_path'] = ckpt_file
model = BiLSTM_CRF(args,
embeddings,
tag2label,
word2id,
paths,
config=config)
model.build_graph()
saver = tf.train.Saver()
with tf.Session(config=config) as sess:
print('============= demo =============')
saver.restore(sess, ckpt_file)
while (1):
print('Please input your sentence:')
demo_sent = input()
if demo_sent == '' or demo_sent.isspace():
print('See you next time!')
break
else:
demo_sent = list(demo_sent.strip())
demo_data = [(demo_sent, ['O'] * len(demo_sent))]
tag = model.demo_one(sess, demo_data)
PER, LOC, ORG = get_entity(tag, demo_sent)
print('PER: {}\nLOC: {}\nORG: {}'.format(PER, LOC, ORG))
## demo
elif args.mode == 'demo':
ckpt_file = tf.train.latest_checkpoint(model_path)
print(ckpt_file)
paths['model_path'] = ckpt_file
model = BiLSTM_CRF(args,
embeddings,
dictname2id,
word2id,
paths,
config=config)
model.build_graph()
saver = tf.train.Saver()
with tf.Session(config=config) as sess:
print('============= demo =============')
saver.restore(sess, ckpt_file)
while (1):
print('Please input your sentence:')
demo_sent = input()
if demo_sent == '' or demo_sent.isspace():
print('See you next time!')
break
else:
demo_sent = list(demo_sent.strip())
demo_data = [(sentence2id(demo_sent,
word2id), [0] * len(demo_sent))]
tag = model.demo_one(sess, demo_data)
res = get_entity(tag[0], demo_sent, dictname2id)
print(res)