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test.py
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test.py
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# coding=utf-8
# Copyright 2019 The Google UDA Team Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Runner for UDA that uses BERT."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import time
import tensorflow as tf
from queue import Queue
from cffex_uda import uda, preprocess
from cffex_uda.albert import modeling
from cffex_uda.utils import raw_data_utils, tokenization
from django_uda.conf import *
def _decode_record(record, name_to_features):
"""Decodes a record to a TensorFlow example."""
example = tf.parse_single_example(record, name_to_features)
# tf.Example only supports tf.int64, but the TPU only supports tf.int32.
# So cast all int64 to int32.
for name in list(example.keys()):
t = example[name]
if t.dtype == tf.int64:
t = tf.to_int32(t)
example[name] = t
return example
def timeCounter(text):
def wraper():
print('-'*100)
print(text, wraper.cnt, time.time()-wraper.t)
print('-'*100)
wraper.t = time.time()
wraper.cnt += 1
wraper.t = time.time()
wraper.cnt = 0
return wraper
def get_sup_feature_specs():
"""Get supervised feature."""
feature_specs = collections.OrderedDict()
feature_specs["input_ids"] = tf.FixedLenFeature(
[conf["max_seq_length"]], tf.int64)
feature_specs["input_mask"] = tf.FixedLenFeature(
[conf["max_seq_length"]], tf.int64)
feature_specs["input_type_ids"] = tf.FixedLenFeature(
[conf["max_seq_length"]], tf.int64)
feature_specs["label_ids"] = tf.FixedLenFeature(
[1], tf.int64)
return feature_specs
def prediction_input_fn_builder(G_input, tokenizer, processor):
def gen():
while True:
data = G_input.get()
feature = preprocess.proc_single_sentence(data, 256, tokenizer, processor)
dic = feature.get_dict_features()
yield {
'input_ids':[[int(i) for i in dic['input_ids'].int64_list.value]],
'input_mask':[[int(i) for i in dic['input_mask'].int64_list.value]],
'input_type_ids':[[int(i) for i in dic['input_type_ids'].int64_list.value]],
'label_ids':[[int(dic['label_ids'].int64_list.value[0])]],
}
'''
feature = preprocess.proc_single_sentence(data, 256, tokenizer, processor)
tf_example = tf.train.Example(
features=tf.train.Features(feature=feature.get_dict_features()))
d = tf_example.SerializeToString()
d = _decode_record(d, get_sup_feature_specs())
print(d)
yield d
'''
def input_fn(params):
return (tf.data.Dataset.from_generator(gen,
output_types={'input_ids':tf.int32, 'input_mask':tf.int32, 'input_type_ids':tf.int32, 'label_ids':tf.int32},
output_shapes={'input_ids':(None, conf["max_seq_length"]), 'input_mask':(None, conf['max_seq_length']), 'input_type_ids': (None, conf['max_seq_length']), 'label_ids': (None, 1)}).
prefetch(10))
return input_fn
class Get_UDA(object):
def __init__(self, conf, tokenizer, processor):
tf.logging.set_verbosity(tf.logging.INFO)
self.conf = conf
self.ckpt_path = tf.train.latest_checkpoint(self.conf['model_dir'])
print(self.ckpt_path)
self.processor = raw_data_utils.get_processor(conf['task_name'])
# for example, IMDB processor returns dataset as ['pos', 'neg']
label_list = self.processor.get_labels()
self.bert_config = modeling.AlbertConfig.from_json_file(
conf['bert_config_file'],)
tf.gfile.MakeDirs(conf['model_dir'])
tf.logging.info("warmup steps {}/{}".format(
conf['num_warmup_steps'], conf['num_train_steps']))
tpu_cluster_resolver = None
is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2
run_config = tf.contrib.tpu.RunConfig(
cluster=tpu_cluster_resolver,
master=None,
model_dir=conf['model_dir'],
save_checkpoints_steps=500,
keep_checkpoint_max=1000,
# train_distribute=train_distribute,
tpu_config=tf.contrib.tpu.TPUConfig(
iterations_per_loop=conf['iterations_per_loop'],
per_host_input_for_training=is_per_host))
model_fn = uda.model_fn_builder(
bert_config=self.bert_config,
init_checkpoint=conf['init_checkpoint'],
learning_rate=conf['learning_rate'],
clip_norm=conf['clip_norm'],
num_train_steps=conf['num_train_steps'],
num_warmup_steps=conf['num_warmup_steps'],
use_tpu=False,
use_one_hot_embeddings=conf['use_one_hot_embeddings'],
label_list=label_list,
num_labels=len(label_list),
unsup_ratio=conf['unsup_ratio'],
uda_coeff=conf['uda_coeff'],
tsa=conf['tsa'],
print_feature=False,
print_structure=False,
)
# If TPU is not available, this will fall back to normal Estimator on CPU
# or GPU.
self.estimator = tf.contrib.tpu.TPUEstimator(
use_tpu=False,
model_fn=model_fn,
config=run_config,
params={"model_dir": conf['model_dir']},
train_batch_size=conf['train_batch_size'],
eval_batch_size=conf['eval_batch_size'])
self.q = Queue()
self.predictor = self.estimator.predict(
prediction_input_fn_builder(self.q, tokenizer, processor),
checkpoint_path=self.ckpt_path,
)
def predict(self, str):
try:
counter=timeCounter('predict stage')
counter()
self.q.put(str)
predict_result = self.predictor.next()
counter()
except Exception as e:
print(repr(e))
return predict_result
if __name__ == "__main__":
tokenizer = tokenization.FullTokenizer(
vocab_file=conf['vocab_file'], do_lower_case=True)
processor = raw_data_utils.get_processor(conf['task_name'])
tokenizer = tokenization.FullTokenizer(
vocab_file=conf['vocab_file'], do_lower_case=True)
uda = Get_UDA(conf,tokenizer,processor)
tf.logging.info("***** Running prediction *****")
str = '股指期货就像孙悟空。人跟神，不能做亲密的小伙伴，实在不好愉快的玩耍。'
predict_result = uda.predict(str)
# for key in predict_result.keys():
# predict_result[key] = predict_result[key].item()
p = predict_result
print(type(p['probabilities']))
print(type(list(p['probabilities'])))
# for p in predict_result:
# print(p)
# break
# print(predict_result)