从Python导出Tensorflow图以在C ++中使用 [英] Export Tensorflow graphs from Python for use in C++

问题描述

究竟应该如何导出python模型以在c ++中使用？

Exactly how should python models be exported for use in c++?

我正在尝试执行类似于本教程的操作：
https://www.tensorflow.org/versions/r0.8/tutorials/image_recognition /index.html

I'm trying to do something similar to this tutorial: https://www.tensorflow.org/versions/r0.8/tutorials/image_recognition/index.html

我正在尝试将自己的TF模型导入c ++ API中，而不是最初的TF模型中。我调整了输入大小和路径，但是奇怪的错误不断弹出。我整天都在阅读堆栈溢出和其他论坛，但无济于事。

I'm trying to import my own TF model in the c++ API in stead of the inception one. I adjusted input size and the paths, but strange errors keep popping up. I spent all day reading stack overflow and other forums but to no avail.

我尝试了两种方法来导出图形。

I've tried two methods for exporting the graph.

方法1：元符号。

...loading inputs, setting up the model, etc....

sess = tf.InteractiveSession()
sess.run(tf.initialize_all_variables())


for i in range(num_steps):  
  x_batch, y_batch = batch(50)  
  if i%10 == 0:
        train_accuracy = accuracy.eval(feed_dict={
        x:x_batch, y_: y_batch, keep_prob: 1.0})
        print("step %d, training accuracy %g"%(i, train_accuracy))
  train_step.run(feed_dict={x: x_batch, y_: y_batch, keep_prob: 0.5})

print("test accuracy %g"%accuracy.eval(feed_dict={
    x: features_test, y_: labels_test, keep_prob: 1.0}))

saver = tf.train.Saver(tf.all_variables())
checkpoint = 
   '/home/sander/tensorflow/tensorflow/examples/cat_face/data/model.ckpt'
    saver.save(sess, checkpoint)

   tf.train.export_meta_graph(filename=
   '/home/sander/tensorflow/tensorflow/examples/cat_face/data/cat_graph.pb',  
    meta_info_def=None,
    graph_def=sess.graph_def,
    saver_def=saver.restore(sess, checkpoint),
    collection_list=None, as_text=False)

尝试运行程序时方法1产生以下错误：

Method 1 yields the following error when trying to run the program:

[libprotobuf ERROR 
google/protobuf/src/google/protobuf/wire_format_lite.cc:532] String field 
'tensorflow.NodeDef.op' contains invalid UTF-8 data when parsing a protocol 
buffer. Use the 'bytes' type if you intend to send raw bytes. 
E tensorflow/examples/cat_face/main.cc:281] Not found: Failed to load 
compute graph at 'tensorflow/examples/cat_face/data/cat_graph.pb'

我还尝试了另一种导出图形的方法：

I also tried another method of exporting the graph:

方法2： write_graph：

Method 2: write_graph:

tf.train.write_graph(sess.graph_def, 
'/home/sander/tensorflow/tensorflow/examples/cat_face/data/', 
'cat_graph.pb', as_text=False)

此版本实际上似乎加载了某些内容，但是我收到有关未初始化变量的错误：

This version actually seems to load something, but I get an error about variables not being initialized:

Running model failed: Failed precondition: Attempting to use uninitialized  
value weight1
[[Node: weight1/read = Identity[T=DT_FLOAT, _class=["loc:@weight1"], 
_device="/job:localhost/replica:0/task:0/cpu:0"](weight1)]]

推荐答案

首先，您需要使用以下命令来图形定义文件：

At first, you need to graph definition to file by using following command

with tf.Session() as sess:
//Build network here 
tf.train.write_graph(sess.graph.as_graph_def(), "C:\\output\\", "mymodel.pb")

然后，使用保护程序保存模型

Then, save your model by using saver

saver = tf.train.Saver(tf.global_variables()) 
saver.save(sess, "C:\\output\\mymodel.ckpt")

然后，您将有2个文件在您的输出中，mymodel.ckpt，mymodel.pb

Then, you will have 2 files at your output, mymodel.ckpt, mymodel.pb

从此处，并在C：\output\中运行以下命令。如果与您不同，请更改输出节点名称。

Download freeze_graph.py from here and run following command in C:\output\. Change output node name if it is different for you.

python Frozen_graph.py --input_graph mymodel.pb --input_checkpoint mymodel.ckpt --output_node_names softmax / Reshape_1 --output_graph mymodelforc.pb

python freeze_graph.py --input_graph mymodel.pb --input_checkpoint mymodel.ckpt --output_node_names softmax/Reshape_1 --output_graph mymodelforc.pb

您可以直接从C使用mymodelforc.pb。

You can use mymodelforc.pb directly from C.

您可以使用以下C代码加载原型文件

You can use following C code to load the proto file

#include "tensorflow/core/public/session.h"
#include "tensorflow/core/platform/env.h"
#include "tensorflow/cc/ops/image_ops.h"

Session* session;
NewSession(SessionOptions(), &session);

GraphDef graph_def;
ReadBinaryProto(Env::Default(), "C:\\output\\mymodelforc.pb", &graph_def);

session->Create(graph_def);

现在您可以使用会话进行推理了。

Now you can use session for inference.

您可以按以下方式应用推断参数：

You can apply inference parameter as following:

// Same dimension and type as input of your network
tensorflow::Tensor input_tensor(tensorflow::DT_FLOAT, tensorflow::TensorShape({ 1, height, width, channel }));
std::vector<tensorflow::Tensor> finalOutput;

// Fill input tensor with your input data

std::string InputName = "input"; // Your input placeholder's name
std::string OutputName = "softmax/Reshape_1"; // Your output placeholder's name

session->Run({ { InputName, input_tensor } }, { OutputName }, {}, &finalOutput);

// finalOutput will contain the inference output that you search for

这篇关于从Python导出Tensorflow图以在C ++中使用的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！