Question

Tensorflow C ++版本：1.13.1
平台：Ubuntu16.04
工具：日食
我已经构建了libtensorflow_cc.so并通过一些示例（例如加载mnist数据集）成功对其进行了测试。 enter image description here

即使打印一些警告，上图也能返回正确的结果。最近，我尝试将python代码转换为c ++版本，下面是一段python代码。

import tensorflow as tf
import  numpy as np
from skimage import io, transform
import cv2
sess = tf.Session()
new_saver = tf.train.import_meta_graph(r'D:\CNN_test_result_sizhuangzao_model\v2_101_ziji_9\model\model.meta')                      new_saver.restore(sess,tf.train.latest_checkpoint(r'D:\CNN_test_result_sizhuangzao_model\v2_101_ziji_9\model'))

graph = tf.get_default_graph()
#Here is confusing to me."input:0" and "is_training:0" are two tensors in python
#How does tensorflow c++ put in 2 tensors as the first argument of session->run()?
input_x = graph.get_tensor_by_name("input:0")
is_training_x=graph.get_tensor_by_name("is_training:0")

jpg_path=r"D:\CNN_test_result_sizhuangzao\chaodacesiji1\pos.txt"
file = open(jpg_path)
lines = file.readlines()
num=0
for line in lines: 
    img0 = io.imread(line.strip())
    l=img0.shape
    k=l[0]
    c=l[1]
    num+=1
    print(num)
    if  c/k<5:
        continue
    img = transform.resize(img0, (48,160, 1))
    feed_dict={input_x:np.reshape(img, [-1, 48, 160, 1]),is_training_x:False}
    #And here 'output' is an operation and "output:0" is a tensor in python.How does tensorflow c++ describe them ?
    prob_op = graph.get_operation_by_name('output')
    out_softmax = graph.get_tensor_by_name("output:0")
    img_out_softmax = sess.run(out_softmax,feed_dict)
    prediction_labels = np.argmax(img_out_softmax,1)

我已经尝试过Tensorflow C ++作为以下代码（此功能根据https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/label_image/main.cc进行了略微更改） ''' int main（） { 会话*会话；状态状态= NewSession（SessionOptions（），＆session）;

  const std::string graph_fn = "/tensorflowtest/model-0617/model.meta";
  MetaGraphDef graphdef;
  Status status_load = ReadBinaryProto(Env::Default(), graph_fn, &graphdef); 
  if (!status_load.ok()) {
        return -1;
  }
Status status_create = session->Create(graphdef.graph_def()); 
if (!status_create.ok()) {
   std::cout << "ERROR: Creating graph in session failed..." << status_create.ToString() << std::endl;
        return -1;
  }
  cout << "Session successfully created.Load model successfully!"<< endl;


  const std::string checkpointPath = "/media/root/Ubuntu311/projects/Ecology_projects/tensorflowtest/model-0617/model";
  Tensor checkpointPathTensor(DT_STRING, TensorShape());
  checkpointPathTensor.scalar<std::string>()() = checkpointPath;
  status = session->Run(
      {{ graphdef.saver_def().filename_tensor_name(), checkpointPathTensor },},
      {},{graphdef.saver_def().restore_op_name()},nullptr);
  if (!status.ok())
  {
      throw runtime_error("Error loading checkpoint from " + checkpointPath + ": " + status.ToString());
  }
  cout << "Load weights successfully!"<< endl;


  //read image for prediction...
  string image_path= "/media/root/Ubuntu311/projects/Ecology_projects/copy/cnn-imgs/AABW.jpg";
   int input_height =48;
   int input_width=160;
   int input_mean=0;
   int input_std=1;
   std::vector<Tensor> resized_tensors;
   Status read_tensor_status =
   ReadImageFile(image_path, input_height, input_width, input_mean,
                           input_std, &resized_tensors);
   if (!read_tensor_status.ok()) {
     LOG(ERROR) << read_tensor_status;
     cout<<"resing error"<<endl;
     return -1;
   }

   const Tensor& resized_tensor = resized_tensors[0];
   std::cout <<"Read image successfully: "<< resized_tensor.DebugString()<<endl;
   //I do not know if it is wrong that making "input" and "is_training" together to the variable--resized_tensor.
   std::string Input1Name = "input";
   std::string Input2Name = "is_training";
   vector<std::pair<string, Tensor> > inputs;
   inputs.push_back(std::make_pair(Input1Name, resized_tensor));
   inputs.push_back(std::make_pair(Input2Name, resized_tensor));

   vector<tensorflow::Tensor> outputs;
   //I do not know if i should make "out_softmax" or "output" as the 2nd argument in session->Run and the 3rd should be null or not.
   string output2="out_softmax";
   string output_ = "output";
   //session->Run returns the error information--"model_pruner failed: Invalid argument: Invalid input graph." 
   Status status_run = session->Run(inputs, {output2}, {}, &outputs);
   if (!status_run.ok()) {
       std::cout << "ERROR: RUN failed..."  << std::endl;
       std::cout << status_run.ToString() << "\n";
       return -1;
   }
   ...
}

以下信息是整个印刷品：

Session successfully created.Load model successfully!
Load weights successfully!
Read image successfully: Tensor<type: float shape: [1,48,160,1] values: [[[0.592156887][0.603921592][0.603921592]]]...>
2019-07-03 14:47:02.087086: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:486] model_pruner failed: Invalid argument: Invalid input graph.

有帮助吗？

model_pruner失败：无效参数：运行tensorflow c ++ API Ubuntu16.04

0 个答案: