基于 TensorFlow Serving的TFslim（inceptionv3）模型部署（TF-slim系列3）
文章参考：
https://blog.csdn.net/u010333076/article/details/78454399
https://gyang274.github.io/tensorflow-serving-tutorial/0x02b00.slim.inception.v4.html
本文承接之前写过的TF-slim系列
原文并不完整，在此做了相关整理以及调试，以下做出说明。
TFSlim简介：
slim是tensorflow的一个高阶模块，能够简洁，快速的实现模型的构建，训练。tensorflow models工程里面还有一个slim构建分类项目，能够快速的对数据集进行整理，以及进行vgg,inception,resnet等分类任务。但是该项目生成的模型是ckpt文件，并不能直接的用于上线部署，目前TensorFlow官方所推荐的是使用SaveModel类来进行模型保存。这种保存方案使用TensorFlow Serving,可以很容易的进行上线部署。
前期准备工作：
以下依赖在2019年7月15日使用无误。
采用docker方式下载tensorflow serving的环境
docker pull tensorflow/serving:latest-devel-gpu进入tensorflow serving容器 git models模块。
git clone https://github.com/tensorflow/models.git将models文件夹下的models/research/slim/nets/文件夹拷贝到
/tensorflow-serving/tensorflow_serving/example下
ckpt转models脚本：
需要准备自己用TF-slim分类任务训练出来的ckpt文件
# Copyright 2016 Google Inc. All Rights Reserved.
#
# 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.
# ==============================================================================
#!/usr/bin/env python2.7
"""Export inception model given existing training checkpoints.
The model is exported as SavedModel with proper signatures that can be loaded by
standard tensorflow_model_server.
"""
from __future__ import print_function
import os.path
# This is a placeholder for a Google-internal import.
import tensorflow as tf
slim = tf.contrib.slim
import nets.inception as inception
#import tensorflow.models.research.slim.nets.inception
#from nets import inception
# from inception import inception_model
print("***********************************************************************************************")
tf.app.flags.DEFINE_string('checkpoint_dir', '/tensorflow-serving/tf_checkpoints/slim/my_inception_v3',
"""Directory where to read training checkpoints.""")
tf.app.flags.DEFINE_string('output_dir', '/tmp/inception_output',
"""Directory where to export inference model.""")
tf.app.flags.DEFINE_integer('model_version', 1,
"""Version number of the model.""")
tf.app.flags.DEFINE_integer('image_size', 299,
"""Needs to provide same value as in training.""")
FLAGS = tf.app.flags.FLAGS
NUM_CLASSES = 5
NUM_TOP_CLASSES = 5
WORKING_DIR = os.path.dirname(os.path.realpath(__file__))
print("checkpoint_dir is ",FLAGS.checkpoint_dir)
print("output_dir is ",FLAGS.output_dir)
print("model_version",FLAGS.model_version)
print("image_size is ",FLAGS.image_size)
print("***********************************************************************************************")
def export():
with tf.Graph().as_default():
# build inference model
# labels
f = open('/tensorflow-serving/tf_checkpoints/slim/my_inception_v3/labels.txt')
lines = f.readlines()
names = {0:'background'}
for line in lines:
i,label = line.split(':')
names[int(i)]=label
names_tensor = tf.constant(names.values())
names_lookup_table = tf.contrib.lookup.index_to_string_table_from_tensor(names_tensor)
# input transformation
serialized_tf_example = tf.placeholder(tf.string, name='tf_example')
feature_configs = {
'image/encoded': tf.FixedLenFeature(shape=[], dtype=tf.string),}
tf_example = tf.parse_example(serialized_tf_example, feature_configs)
jpegs = tf_example['image/encoded']
images = tf.map_fn(preprocess_image, jpegs, dtype=tf.float32)
# run inference
with slim.arg_scope(inception.inception_v3_arg_scope()):
# inception v3 models
logits, end_points = inception.inception_v3(
images, num_classes=NUM_CLASSES,
is_training=False)
# logits = tf.Print(logits, [logits])
probs = tf.nn.softmax(logits)
# transform output to topk result
topk_probs, topk_indices = tf.nn.top_k(probs, NUM_TOP_CLASSES)
topk_names = names_lookup_table.lookup(tf.to_int64(topk_indices))
init_fn = slim.assign_from_checkpoint_fn(
os.path.join(FLAGS.checkpoint_dir, 'model.ckpt-1000'),slim.get_model_variables('InceptionV3'))
config = tf.ConfigProto(
# device_count = {
# 'GPU': 0
# },
gpu_options={
'allow_growth': 1,
# 'per_process_gpu_memory_fraction': 0.01
},
allow_soft_placement=True,
log_device_placement=False,
)
with tf.Session(config=config) as sess:
init_fn(sess)
# init on 2017.08.05
# prelogits = sess.graph.get_tensor_by_name(
# 'InceptionV3/Logits/PreLogitsFlatten/Reshape:0'
# )
# update on 2017.10.22
# note: looks like the graphdef is updated for slim-inception-v3
print('Graph Node Tensor Name:')
for node_tensor in tf.get_default_graph().as_graph_def().node:
if str(node_tensor.name).startswith('InceptionV3/Logits'):
print(str(node_tensor.name))
prelogits = sess.graph.get_tensor_by_name('InceptionV3/Logits/Dropout_1b/Identity:0')
# an optional alternative
# prelogits = end_points['PreLogitsFlatten']
# export inference model.
output_path = os.path.join(
tf.compat.as_bytes(FLAGS.output_dir),
tf.compat.as_bytes(str(FLAGS.model_version))
)
print ('Exporting trained model to', FLAGS.output_dir)
#yuankun
builder = tf.saved_model.builder.SavedModelBuilder(FLAGS.output_dir)
#builder = tf.saved_model.builder.SavedModelBuilder(output_path)
# build the signature_def_map.
predict_inputs_tensor_info = tf.saved_model.utils.build_tensor_info(jpegs)
classes_output_tensor_info = tf.saved_model.utils.build_tensor_info(topk_names)
scores_output_tensor_info = tf.saved_model.utils.build_tensor_info(topk_probs)
prelogits_output_tensor_info = tf.saved_model.utils.build_tensor_info(prelogits)
prediction_signature = (tf.saved_model.signature_def_utils.build_signature_def(
inputs={
'images': predict_inputs_tensor_info },
outputs={'classes': classes_output_tensor_info,
'scores': scores_output_tensor_info,
'prelogits': prelogits_output_tensor_info},
method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME
)
)
legacy_init_op = tf.group(tf.tables_initializer(), name='legacy_init_op')
builder.add_meta_graph_and_variables(sess, [tf.saved_model.tag_constants.SERVING],
signature_def_map={'predict_images':prediction_signature,},
legacy_init_op=legacy_init_op
)
builder.save()
print('Successfully exported model to %s' % FLAGS.output_dir)
def preprocess_image(image_buffer):
"""Preprocess JPEG encoded bytes to 3D float Tensor."""
# Decode the string as an RGB JPEG.
# Note that the resulting image contains an unknown height and width
# that is set dynamically by decode_jpeg. In other words, the height
# and width of image is unknown at compile-time.
image = tf.image.decode_jpeg(image_buffer, channels=3)
# After this point, all image pixels reside in [0,1)
# until the very end, when they're rescaled to (-1, 1). The various
# adjust_* ops all require this range for dtype float.
image = tf.image.convert_image_dtype(image, dtype=tf.float32)
# Crop the central region of the image with an area containing 87.5% of
# the original image.
image = tf.image.central_crop(image, central_fraction=0.875)
# Resize the image to the original height and width.
image = tf.expand_dims(image, 0)
image = tf.image.resize_bilinear(
image, [FLAGS.image_size, FLAGS.image_size], align_corners=False)
image = tf.squeeze(image, [0])
# Finally, rescale to [-1,1] instead of [0, 1)
image = tf.subtract(image, 0.5)
image = tf.multiply(image, 2.0)
return image
def main(unused_argv=None):
export()
if __name__ == '__main__':
tf.app.run()
执行以下操作，ckpt开始转SaveModel生成的pb文件
python my_inception_v3_saved_model.py \
--checkpoint_dir=/tensorflow-serving/tf_checkpoints/slim/my_inception_v3 \
--output_dir=/tmp/test3 \
--model_version=3 \
--image_size=299执行后生成文件
部署模型：
tensorflow_model_server \
--model_name=my_inception_v3 \
--model_base_path=/tmp/test3 \
--port=9000
这样就成功的将模型部署在9000端口上了。
后续有时间会写上client客户端程序