在文章NLP（三十四）使用keras-bert實現序列標註任務中，我們使用Keras和Keras-bert進行模型訓練、模型評估和模型預測。我們對人民日報實體數據集進行模型訓練，保存後的模型文件為example.h5，h5是Keras保存模型的一種文件格式。
在文章Keras入門（七）使用Flask+Keras-bert構建模型預測服務，我們也介紹了如何使用Flask和example.h5文件來實現模型預測的HTTP服務。
本文將會介紹如何將h5文件轉化為pb文件並利用tensorflow/serving實現模型部署。
在Github項目keras_to_tensorflow（網址為：https://github.com/amir-abdi/keras_to_tensorflow）中，有專門介紹如何將普通的keras模型轉化為tensorflow模型的辦法。本文在此基礎上，略微修改轉換的腳本（change_keras_h5_file_to_pb_models.py）如下：
# -*- coding: utf-8 -*-
import os
import tensorflow as tf
from tensorflow.python.framework import graph_util
from tensorflow.python.framework import graph_io
from pathlib import Path
from absl import app
from absl import flags
from absl import logging
import keras
from keras import backend as K
from keras.models import model_from_json
from keras_bert import get_custom_objects
from keras_contrib.layers import CRF
from keras_contrib.losses import crf_loss
from keras_contrib.metrics import crf_accuracy
custom_objects = get_custom_objects()
for key, value in {'CRF': CRF, 'crf_loss': crf_loss, 'crf_accuracy': crf_accuracy}.items():
custom_objects[key] = value
K.set_learning_phase(0)
FLAGS = flags.FLAGS
flags.DEFINE_string('input_model', "../example_ner.h5", 'Path to the input model.')
flags.DEFINE_string('input_model_json', None, 'Path to the input model '
'architecture in json format.')
flags.DEFINE_string('output_model', "./example_ner.pb", 'Path where the converted model will '
'be stored.')
flags.DEFINE_boolean('save_graph_def', False,
'Whether to save the graphdef.pbtxt file which contains '
'the graph definition in ASCII format.')
flags.DEFINE_string('output_nodes_prefix', None,
'If set, the output nodes will be renamed to '
'`output_nodes_prefix`+i, where `i` will numerate the '
'number of of output nodes of the network.')
flags.DEFINE_boolean('quantize', False,
'If set, the resultant TensorFlow graph weights will be '
'converted from float into eight-bit equivalents. See '
'documentation here: '
'https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/graph_transforms')
flags.DEFINE_boolean('channels_first', False,
'Whether channels are the first dimension of a tensor. '
'The default is TensorFlow behaviour where channels are '
'the last dimension.')
flags.DEFINE_boolean('output_meta_ckpt', False,
'If set to True, exports the model as .meta, .index, and '
'.data files, with a checkpoint file. These can be later '
'loaded in TensorFlow to continue training.')
flags.mark_flag_as_required('input_model')
flags.mark_flag_as_required('output_model')
def load_model(input_model_path, input_json_path):
if not Path(input_model_path).exists():
raise FileNotFoundError(
'Model file `{}` does not exist.'.format(input_model_path))
try:
# 下面一行已經修改，在改回普通的Keras加載模型時，需要去掉custom_objects
model = keras.models.load_model(input_model_path, custom_objects=custom_objects)
return model
except FileNotFoundError as err:
logging.error('Input mode file (%s) does not exist.', FLAGS.input_model)
raise err
except ValueError as wrong_file_err:
if input_json_path:
if not Path(input_json_path).exists():
raise FileNotFoundError(
'Model description json file `{}` does not exist.'.format(
input_json_path))
try:
model = model_from_json(open(str(input_json_path)).read())
model.load_weights(input_model_path)
return model
except Exception as err:
logging.error("Couldn't load model from json.")
raise err
else:
logging.error(
'Input file specified only holds the weights, and not '
'the model definition. Save the model using '
'model.save(filename.h5) which will contain the network '
'architecture as well as its weights. If the model is '
'saved using model.save_weights(filename), the flag '
'input_model_json should also be set to the '
'architecture which is exported separately in a '
'json format. Check the keras documentation for more details '
'(https://keras.io/getting-started/faq/)')
raise wrong_file_err
def main(args):
logging.info("begin====================================================")
# If output_model path is relative and in cwd, make it absolute from root
output_model = FLAGS.output_model
if str(Path(output_model).parent) == '.':
output_model = str((Path.cwd() / output_model))
output_fld = Path(output_model).parent
output_model_name = Path(output_model).name
output_model_stem = Path(output_model).stem
output_model_pbtxt_name = output_model_stem + '.pbtxt'
# Create output directory if it does not exist
# print (Path(output_model).parent)
if not os.path.exists(str(Path(output_model).parent)):
Path(output_model).parent.mkdir(parents=True)
if FLAGS.channels_first:
K.set_image_data_format('channels_first')
else:
K.set_image_data_format('channels_last')
model = load_model(FLAGS.input_model, FLAGS.input_model_json)
input_node_names = [node.op.name for node in model.inputs]
logging.info('Input nodes names are: %s', str(input_node_names))
# TODO(amirabdi): Support networks with multiple inputs
orig_output_node_names = [node.op.name for node in model.outputs]
if FLAGS.output_nodes_prefix: # 給模型節點編號
num_output = len(orig_output_node_names)
pred = [None] * num_output
converted_output_node_names = [None] * num_output
# Create dummy tf nodes to rename output
for i in range(num_output):
converted_output_node_names[i] = '{}{}'.format(
FLAGS.output_nodes_prefix, i)
pred[i] = tf.identity(model.outputs[i],
name=converted_output_node_names[i])
else:
converted_output_node_names = orig_output_node_names
logging.info('Converted output node names are: %s',
str(converted_output_node_names))
sess = K.get_session()
if FLAGS.output_meta_ckpt: # 讓轉化的模型可以繼續被訓練
saver = tf.train.Saver()
saver.save(sess, str(output_fld / output_model_stem))
if FLAGS.save_graph_def: # 以ascii形式存儲模型
tf.train.write_graph(sess.graph.as_graph_def(), str(output_fld),
output_model_pbtxt_name, as_text=True)
logging.info('Saved the graph definition in ascii format at %s',
str(Path(output_fld) / output_model_pbtxt_name))
if FLAGS.quantize: # 將權重從float轉為八位比特
from tensorflow.tools.graph_transforms import TransformGraph
transforms = ["quantize_weights", "quantize_nodes"]
transformed_graph_def = TransformGraph(sess.graph.as_graph_def(), [],
converted_output_node_names,
transforms)
constant_graph = graph_util.convert_variables_to_constants(
sess,
transformed_graph_def,
converted_output_node_names)
else: # float形式存儲權重
constant_graph = graph_util.convert_variables_to_constants(
sess,
sess.graph.as_graph_def(),
converted_output_node_names)
graph_io.write_graph(constant_graph, str(output_fld), output_model_name,
as_text=False)
logging.info('Saved the freezed graph at %s',
str(Path(output_fld) / output_model_name))
if __name__ == "__main__":
app.run(main)
在該腳本中，input_model（輸入模型）的路徑為../example_ner.h5，output_model（輸出模型）的路徑為./example_ner.pb，同時加載了keras-bert訓練好的模型example_ner.h5。運行上述腳本，會在當前路徑下生成example_ner.pb，但很可惜，只有這一個腳本還無法實現在tensorflow/serving上的部署。
再接再厲！我們將上述生成的pb文件轉化為tensorflow/serving支持的文件格式。轉化的腳本（get_tf_serving_file.py）如下：
# -*- coding: utf-8 -*-
import os
import tensorflow as tf
from tensorflow.python.saved_model import signature_constants
from tensorflow.python.saved_model import tag_constants
from keras_bert import get_custom_objects
from keras_contrib.layers import CRF
from keras_contrib.losses import crf_loss
from keras_contrib.metrics import crf_accuracy
from keras.models import load_model
custom_objects = get_custom_objects()
for key, value in {'CRF': CRF, 'crf_loss': crf_loss, 'crf_accuracy': crf_accuracy}.items():
custom_objects[key] = value
export_dir = '../example_ner/1'
graph_pb = './example_ner.pb'
model = load_model('../example_ner.h5', custom_objects=custom_objects)
builder = tf.saved_model.builder.SavedModelBuilder(export_dir)
with tf.gfile.GFile(graph_pb, "rb") as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
sigs = {}
with tf.Session(graph=tf.Graph()) as sess:
tf.import_graph_def(graph_def, name="")
g = tf.get_default_graph()
sigs[signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY] = \
tf.saved_model.signature_def_utils.predict_signature_def(
inputs={"input_1": g.get_operation_by_name('input_1').outputs[0], "input_2": g.get_operation_by_name('input_2').outputs[0]},
outputs={"output": g.get_operation_by_name('crf_1/one_hot').outputs[0]}
)
builder.add_meta_graph_and_variables(sess,
[tag_constants.SERVING],
signature_def_map = sigs)
builder.save()
運行上述腳本，會在上級目錄生成example_ner/1文件夾，結構如下：
example_ner
└── 1
├── saved_model.pb
└── variables
2 directories, 1 file
至此，我們已經生成了tensorflow/serving支持的模型部署的文件格式。
利用tensorflow/serving實現模型部署 在文章tensorflow(5)將ckpt轉化為pb文件並利用tensorflow/serving實現模型部署及預測、tensorflow(6)利用tensorflow/serving實現模型部署及預測、tensorflow(7)利用tensorflow/serving實現BERT模型部署中，筆者已經給出了不少tensorflow/serving的使用說明，這裡不再詳細講述。
利用tensorflow/serving部署example_ner模型的命令如下：
docker run -t --rm -p 8561:8501 -v "$path/example_ner:/models/example_ner" -e MODEL_NAME=example_ner tensorflow/serving:1.14.0
模型調用腳本如下：
# -*- coding: utf-8 -*-
import json
import requests
import numpy as np
from pprint import pprint
from keras_bert import Tokenizer
# 讀取label2id字典
with open("../example_label2id.json", "r", encoding="utf-8") as h:
label_id_dict = json.loads(h.read())
id_label_dict = {v: k for k, v in label_id_dict.items()}
# 載入數據
dict_path = '../chinese_L-12_H-768_A-12/vocab.txt'
token_dict = {}
with open(dict_path, 'r', encoding='utf-8') as reader:
for line in reader:
token = line.strip()
token_dict[token] = len(token_dict)
class OurTokenizer(Tokenizer):
def _tokenize(self, text):
R = []
for c in text:
if c in self._token_dict:
R.append(c)
else:
R.append('[UNK]')
return R
# 將BIO序列轉化為JSON格式
def bio_to_json(string, tags):
item = {"string": string, "entities": []}
entity_name = ""
entity_start = 0
iCount = 0
entity_tag = ""
for c_idx in range(min(len(string), len(tags))):
c, tag = string[c_idx], tags[c_idx]
if c_idx < len(tags)-1:
tag_next = tags[c_idx+1]
else:
tag_next = ''
if tag[0] == 'B':
entity_tag = tag[2:]
entity_name = c
entity_start = iCount
if tag_next[2:] != entity_tag:
item["entities"].append({"word": c, "start": iCount, "end": iCount + 1, "type": tag[2:]})
elif tag[0] == "I":
if tag[2:] != tags[c_idx-1][2:] or tags[c_idx-1][2:] == 'O':
tags[c_idx] = 'O'
pass
else:
entity_name = entity_name + c
if tag_next[2:] != entity_tag:
item["entities"].append({"word": entity_name, "start": entity_start, "end": iCount + 1, "type": entity_tag})
entity_name = ''
iCount += 1
return item
tokenizer = OurTokenizer(token_dict)
# 測試HTTP響應時間
sentence = "井上雄彥的《灌籃高手》是一部作品，也是他自己的修行之路。"
token_ids, segment_is = tokenizer.encode(sentence, max_len=128)
tensor = {"instances": [{"input_1": token_ids, "input_2": segment_is}]}
url = "http://192.168.1.193:8561/v1/models/example_ner:predict"
req = requests.post(url, json=tensor)
if req.status_code == 200:
t = np.asarray(req.json()['predictions'][0]).argmax(axis=1)
tags = [id_label_dict[_] for _ in t]
pprint(bio_to_json(sentence, tags[1:-1]))
輸出結果如下：
{'entities': [{'end': 4, 'start': 0, 'type': 'PER', 'word': '井上雄彥'}],
'string': '井上雄彥的《灌籃高手》是一部作品，也是他自己的修行之路。'}
本文演示的所有腳本已經上傳至https://github.com/percent4/keras_bert_sequence_labeling/tree/master/h5_2_tensorflow_serving 。
2021.1.16於上海浦東