这篇教程keras h5模型转化tensorflow serving模型脚本和restful client访问脚本写得很实用，希望能帮到您。
h5_2_pd.py这是将h5模型转化为saved模型的脚本。
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Date : 2019-07-26 17:19:07
# @Author :
# @Email : None
# @Version : py3
from keras.models import load_model
import tensorflow as tf
import os
from os import *
import os.path as osp
from keras import backend as K
import tensorflow.keras.backend as K
from tensorflow.keras.losses import categorical_crossentropy
from tensorflow.keras.optimizers import Adadelta
# from mymodel import captcha_model as model
def export_model(model,
export_model_dir,
model_version
):
"""
:param export_model_dir: type string, save dir for exported model
:param model_version: type int best
:return:no return
"""
with tf.get_default_graph().as_default():
# prediction_signature
tensor_info_input = tf.saved_model.utils.build_tensor_info(model.input)
tensor_info_input = tf.saved_model.utils.build_tensor_info(model.output)
prediction_signature = (
tf.saved_model.signature_def_utils.build_signature_def(
inputs={'images': tensor_info_input}, # Tensorflow.TensorInfo
outputs={'result': tensor_info_input},
method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME))
print('step1 => prediction_signature created successfully')
# set-up a builder
export_path_base = export_model_dir
export_path = os.path.join(
tf.compat.as_bytes(export_path_base),
tf.compat.as_bytes(str(model_version)))
builder = tf.saved_model.builder.SavedModelBuilder(export_path)
builder.add_meta_graph_and_variables(
# tags:SERVING,TRAINING,EVAL,GPU,TPU
sess=K.get_session(),
tags=[tf.saved_model.tag_constants.SERVING],
signature_def_map={'prediction_signature': prediction_signature,},
)
print('step2 => Export path(%s) ready to export trained model' % export_path, '\n starting to export model...')
builder.save(as_text=True)
print('Done exporting!')
def export_model2(model,
export_model_dir,
model_version
):
"""
:param export_model_dir: type string, save dir for exported model url
:param model_version: type int best
:return:no return
"""
with tf.get_default_graph().as_default():
# prediction_signature
tensor_info_input = tf.saved_model.utils.build_tensor_info(model.input)
tensor_info_output = tf.saved_model.utils.build_tensor_info(model.output)
print(model.output.shape, '**', tensor_info_output)
prediction_signature = (
tf.saved_model.signature_def_utils.build_signature_def(
inputs={'images': tensor_info_input}, # Tensorflow.TensorInfo
outputs={'result': tensor_info_output},
#method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME)
method_name= "tensorflow/serving/predict")
)
print('step1 => prediction_signature created successfully')
# set-up a builder
mkdir(export_model_dir)
export_path_base = export_model_dir
export_path = os.path.join(
tf.compat.as_bytes(export_path_base),
tf.compat.as_bytes(str(model_version)))
builder = tf.saved_model.builder.SavedModelBuilder(export_path)
builder.add_meta_graph_and_variables(
# tags:SERVING,TRAINING,EVAL,GPU,TPU
sess=K.get_session(),
tags=[tf.saved_model.tag_constants.SERVING],
signature_def_map={
'predict':
prediction_signature,
tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
prediction_signature,
},
)
print('step2 => Export path(%s) ready to export trained model' % export_path, '\n starting to export model...')
#builder.save(as_text=True)
builder.save()
print('Done exporting!')
if __name__ == '__main__':
# 指定路径和模型版本号
model = tf.keras.models.load_model('..../model.h5')
export_model2(
model,
'./export_modeltest',
1
)
client脚本。这是对模型进行restful API访问的脚本。个人觉得这里面的关键点是搞清楚你的模型输入输出数据格式。所谓输出就是神经网络的input tendor的形状、数据格式。输出就是神经网络输出的数据格式。可以通过tensorflow的命令saved_model_cli 查看，使用格式如下，这个命令要在python环境安装tensorflow下才能使用。
saved_model_cli show --dir ...../resnet/1 --all
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Date : 2019-07-26 17:19:07
# @Author :
# @Email : None
# @Version : py3
from __future__ import print_function
import base64
import requests
import cv2
import numpy as np
import json
import pandas as pd
# The server URL specifies the endpoint of your server running the ResNet
# model with the name "resnet" and using the predict interface.
SERVER_URL = 'http://localhost:8501/v1/models/resnet:predict'
# get_inputs函数处理模型预测数据，要和训练模型时的数据预处过程相同
def get_inputs(src=[]):
pre_x = []
for s in src:
input = cv2.imread(s)
input = cv2.resize(input, (224, 224))
input = cv2.cvtColor(input, cv2.COLOR_BGR2GRAY)
pre_x.append(input) # input一张图片
pre_x = np.asarray(pre_x) / 255.0
return pre_x
class NumpyEncoder(json.JSONEncoder):
def default(self, obj):
if isinstance(obj, np.ndarray):
return obj.tolist()
return json.JSONEncoder.default(self, obj)
def main():
# Compose a JSON Predict request (send JPG image in base64).
#jpg_bytes = base64.b64encode(dl_request.content).decode('utf-8')
#predict_request = '{"instances" : [%s]}' % (jpeg_bytes)
images = [imglist]
pre_x = get_inputs(images)
pre_x = np.reshape(pre_x, ( pre_x.shape[1], pre_x.shape[2], 1))
input_data = np.expand_dims(pre_x,axis =0)
# change the format of input data suit the model
p = {'inputs': input_data}
predict_request = {"instances":p}
# to json
param = json.dumps(p, cls=NumpyEncoder)
# Send few requests to warm-up the model.
for _ in range(3):
response=requests.post(SERVER_URL, data=param)
response.raise_for_status()
# Send few actual requests
response = requests.post(SERVER_URL, data=param)
response.raise_for_status()
prediction = response.json()['outputs'][0]
prediction = np.array(prediction)
# 输出最终预测结果，就是得分最高的类别
pre_result_index = np.argsort(-prediction)[0]
if __name__ == '__main__':
main()
原文：https://blog.csdn.net/A_pinkpig/article/details/105833811
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