Tensorflow在C中导出和运行图形的不同方法

2 回答

• 33

  这是我使用TF 0.12中引入的V2检查点的解决方案 .

  无需将所有变量转换为常量或freeze the graph .

  为了清楚起见，V2检查点在我的目录_2512720中看起来像这样：

  checkpoint  # some information on the name of the files in the checkpoint
  my-model.data-00000-of-00001  # the saved weights
  my-model.index  # probably definition of data layout in the previous file
  my-model.meta  # protobuf of the graph (nodes and topology info)
  

  Python part (saving)

  with tf.Session() as sess:
      tf.train.Saver(tf.trainable_variables()).save(sess, 'models/my-model')
  

  如果使用 tf.trainable_variables() 创建 Saver ，则可以节省一些头痛和存储空间 . 但也许一些更复杂的模型需要保存所有数据，然后将此参数删除到 Saver ，只需确保您创建的图形已创建 Saver after . 给所有变量/图层赋予唯一名称也是非常明智的，否则你可以在different problems中运行 .

  Python part (inference)

  with tf.Session() as sess:
      saver = tf.train.import_meta_graph('models/my-model.meta')
      saver.restore(sess, tf.train.latest_checkpoint('models/'))
      outputTensors = sess.run(outputOps, feed_dict=feedDict)
  

  C++ part (inference)

  请注意， checkpointPath 不是任何现有文件的路径，只是它们的公共前缀 . 如果您错误地将路径放到了 .index 文件中，TF将不会告诉您这是错误的，但是由于未初始化的变量，它会在推理期间死亡 .

  #include <tensorflow/core/public/session.h>
  #include <tensorflow/core/protobuf/meta_graph.pb.h>
  
  using namespace std;
  using namespace tensorflow;
  
  ...
  // set up your input paths
  const string pathToGraph = "models/my-model.meta"
  const string checkpointPath = "models/my-model";
  ...
  
  auto session = NewSession(SessionOptions());
  if (session == nullptr) {
      throw runtime_error("Could not create Tensorflow session.");
  }
  
  Status status;
  
  // Read in the protobuf graph we exported
  MetaGraphDef graph_def;
  status = ReadBinaryProto(Env::Default(), pathToGraph, &graph_def);
  if (!status.ok()) {
      throw runtime_error("Error reading graph definition from " + pathToGraph + ": " + status.ToString());
  }
  
  // Add the graph to the session
  status = session->Create(graph_def.graph_def());
  if (!status.ok()) {
      throw runtime_error("Error creating graph: " + status.ToString());
  }
  
  // Read weights from the saved checkpoint
  Tensor checkpointPathTensor(DT_STRING, TensorShape());
  checkpointPathTensor.scalar<std::string>()() = checkpointPath;
  status = session->Run(
          {{ graph_def.saver_def().filename_tensor_name(), checkpointPathTensor },},
          {},
          {graph_def.saver_def().restore_op_name()},
          nullptr);
  if (!status.ok()) {
      throw runtime_error("Error loading checkpoint from " + checkpointPath + ": " + status.ToString());
  }
  
  // and run the inference to your liking
  auto feedDict = ...
  auto outputOps = ...
  std::vector<tensorflow::Tensor> outputTensors;
  status = session->Run(feedDict, outputOps, {}, &outputTensors);
  

  回复于 2024-05-22T00:34:56+08:00
• 17

  对于预测（以及所有其他操作），您可以执行以下操作：

  首先在python中你应该 name 你的变量或操作以供将来使用

  self.init = tf.initialize_variables(tf.all_variables(), name="nInit")
  

  训练之后，如果你已经分配了变量，那么计算得到了所有这些变量并将其作为常量保存到图表中 . （几乎可以使用该冻结工具完成相同的操作，但我通常自己做，请在py和cpp下面检查“name = nWeights”）

  def save(self, filename):
      for variable in tf.trainable_variables():
          tensor = tf.constant(variable.eval())
          tf.assign(variable, tensor, name="nWeights")
  
      tf.train.write_graph(self.sess.graph_def, 'graph/', 'my_graph.pb', as_text=False)
  

  现在去c并加载我们的图并从保存的常量中加载变量：

  void load(std::string my_model) {
          auto load_graph_status =
                  ReadBinaryProto(tensorflow::Env::Default(), my_model, &graph_def);
  
          auto session_status = session->Create(graph_def);
  
          std::vector<tensorflow::Tensor> out;
          std::vector<string> vNames;
  
          int node_count = graph_def.node_size();
          for (int i = 0; i < node_count; i++) {
              auto n = graph_def.node(i);
  
              if (n.name().find("nWeights") != std::string::npos) {
                  vNames.push_back(n.name());
              }
          }
  
          session->Run({}, vNames, {}, &out);
  

  现在你已经加载了所有的神经净重或其他变量 .

  同样，您可以执行其他操作（记住名称？）;制作适当大小的输入和输出张量，用数据填充输入张量并运行会话，如下所示：

  auto operationStatus = session->Run(input, {"put_your_operation_here"}, {}, &out);
  

  回复于 2024-05-22T00:34:56+08:00