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Tensorflow

Build and Use Google TensorFlow C++ API: A Guide

By Ondřej Dolanský on 12/08/2024

Learn how to build high-performance machine learning applications by harnessing the power of the Google TensorFlow C++ API.

Build and Use Google TensorFlow C++ API: A Guide

Table of Contents

Introduction

This guide provides a step-by-step example of how to load and run a TensorFlow model using the TensorFlow C API. This API allows you to integrate TensorFlow functionality into C/C++ applications.

Step-by-Step Guide

  1. Install TensorFlow C API: Follow the instructions at https://www.tensorflow.org/install/lang_c to install the TensorFlow C API on your system.

  2. Include TensorFlow headers:

#include "tensorflow/core/public/c/c_api.h"
  1. Load a TensorFlow Graph:
TF_Graph* graph = TF_NewGraph();
TF_Status* status = TF_NewStatus();
TF_Buffer* graph_def = TF_LoadFile("path/to/model.pb", status);
TF_ImportGraphDefOptions* opts = TF_ImportGraphDefOptionsNew();
TF_GraphImportGraphDef(graph, graph_def, opts, status);
  1. Create a Session:
TF_SessionOptions* options = TF_NewSessionOptions();
TF_Session* session = TF_NewSession(graph, options, status);
  1. Prepare Input Data:
float input_data[] = {1.0f, 2.0f, 3.0f};
TF_Tensor* input_tensor = TF_NewTensor(
    TF_FLOAT, /* data type */
    {1, 3}, /* dimensions */
    input_data, /* data */
    sizeof(input_data) /* data size */
);
  1. Run the Session:
const char* input_op_name = "input_tensor";
const char* output_op_name = "output_tensor";
TF_Output input_op = {TF_GraphOperationByName(graph, input_op_name), 0};
TF_Output output_op = {TF_GraphOperationByName(graph, output_op_name), 0};
TF_Tensor* output_tensor = nullptr;
TF_SessionRun(
    session,
    nullptr, /* run options */
    &input_op, &input_tensor, 1, /* input tensors */
    &output_op, &output_tensor, 1, /* output tensors */
    nullptr, 0, /* target operations */
    nullptr, /* run metadata */
    status /* status */
);
  1. Process Output Data:
float* output_data = static_cast<float*>(TF_TensorData(output_tensor));
  1. Cleanup:
TF_DeleteTensor(input_tensor);
TF_DeleteTensor(output_tensor);
TF_DeleteSession(session, status);
TF_DeleteGraph(graph);
TF_DeleteStatus(status);

Note: This is a basic example and may require modifications depending on your specific model and use case. Refer to the TensorFlow C++ API documentation for more details.

Code Example

This C++ code demonstrates how to load a pre-trained TensorFlow model, prepare input data, run the model inference, process the output, and clean up resources using the TensorFlow C API. It includes loading a graph from a .pb file, creating a session, defining input data as a tensor, running the session with specified input and output operations, extracting output data from the output tensor, and deleting allocated TensorFlow objects.

#include "tensorflow/core/public/c/c_api.h"
#include <iostream>

int main() {
  // 1. Load the TensorFlow Graph
  TF_Graph* graph = TF_NewGraph();
  TF_Status* status = TF_NewStatus();
  TF_Buffer* graph_def = TF_LoadFile("path/to/model.pb", status);
  if (TF_GetCode(status) != TF_OK) {
    std::cerr << "Error loading graph: " << TF_Message(status) << std::endl;
    return 1;
  }
  TF_ImportGraphDefOptions* opts = TF_ImportGraphDefOptionsNew();
  TF_GraphImportGraphDef(graph, graph_def, opts, status);
  if (TF_GetCode(status) != TF_OK) {
    std::cerr << "Error importing graph: " << TF_Message(status) << std::endl;
    return 1;
  }

  // 2. Create a Session
  TF_SessionOptions* options = TF_NewSessionOptions();
  TF_Session* session = TF_NewSession(graph, options, status);
  if (TF_GetCode(status) != TF_OK) {
    std::cerr << "Error creating session: " << TF_Message(status) << std::endl;
    return 1;
  }

  // 3. Prepare Input Data
  float input_data[] = {1.0f, 2.0f, 3.0f};
  int64_t dims[] = {1, 3};
  TF_Tensor* input_tensor = TF_NewTensor(
      TF_FLOAT, dims, 2, input_data, sizeof(input_data), nullptr, nullptr);

  // 4. Run the Session
  const char* input_op_name = "input_tensor";
  const char* output_op_name = "output_tensor";
  TF_Output input_op = {TF_GraphOperationByName(graph, input_op_name), 0};
  TF_Output output_op = {TF_GraphOperationByName(graph, output_op_name), 0};
  TF_Tensor* output_tensor = nullptr;
  TF_SessionRun(
      session, nullptr, &input_op, &input_tensor, 1, &output_op,
      &output_tensor, 1, nullptr, 0, nullptr, status);
  if (TF_GetCode(status) != TF_OK) {
    std::cerr << "Error running session: " << TF_Message(status) << std::endl;
    return 1;
  }

  // 5. Process Output Data
  float* output_data = static_cast<float*>(TF_TensorData(output_tensor));
  int output_size = TF_NumElements(output_tensor);
  std::cout << "Output: ";
  for (int i = 0; i < output_size; ++i) {
    std::cout << output_data[i] << " ";
  }
  std::cout << std::endl;

  // 6. Cleanup
  TF_DeleteTensor(input_tensor);
  TF_DeleteTensor(output_tensor);
  TF_DeleteSession(session, status);
  TF_DeleteGraph(graph);
  TF_DeleteStatus(status);
  TF_DeleteBuffer(graph_def);
  TF_DeleteImportGraphDefOptions(opts);
  TF_DeleteSessionOptions(options);

  return 0;
}

Explanation:

  1. Include Headers: Include the necessary TensorFlow C API header file.
  2. Load Graph: Load the pre-trained TensorFlow model from a .pb file.
  3. Create Session: Create a TensorFlow session to execute the graph.
  4. Prepare Input: Create a TensorFlow tensor to hold the input data.
  5. Run Session: Execute the graph with the provided input tensor and fetch the output tensor.
  6. Process Output: Access and process the output data from the output tensor.
  7. Cleanup: Release all allocated TensorFlow resources.

Before running this code:

  • Make sure you have installed the TensorFlow C API.
  • Replace "path/to/model.pb" with the actual path to your TensorFlow model file.
  • Replace "input_tensor" and "output_tensor" with the correct names of the input and output operations in your model.

This code provides a basic framework for loading and running a TensorFlow model using the C API. You can modify and extend it based on your specific model and requirements.

Additional Notes

General Considerations:

  • Performance: While the C API offers more control and can be beneficial for performance-critical applications, it also comes with a steeper learning curve compared to Python. Consider whether the potential performance gains outweigh the added complexity for your use case.
  • Memory Management: Be meticulous with memory management. The C API requires manual allocation and deallocation of TensorFlow objects using functions like TF_New* and TF_Delete*. Memory leaks are a real concern if not handled properly.
  • Error Handling: Always check the status returned by TensorFlow functions (TF_Status) to catch and handle errors gracefully.
  • Threading: The C API is not inherently thread-safe. If you need to use TensorFlow from multiple threads, you'll need to implement appropriate synchronization mechanisms.

Beyond the Basics:

  • Custom Operations: The C API allows you to define and register your own custom operations (kernels) written in C++. This enables extending TensorFlow with specialized functionality.
  • Mobile and Embedded Deployment: The C API is well-suited for deploying TensorFlow models on mobile and embedded devices with limited resources.
  • Other Language Bindings: The C API serves as the foundation for TensorFlow bindings in other languages like Go, Rust, and Java.

Troubleshooting:

  • Debugging: Debugging C++ code can be more challenging than Python. Consider using a debugger like GDB and logging strategically to identify and resolve issues.
  • Community Support: While the C API documentation might be less extensive than the Python API, the TensorFlow community is active and can provide valuable assistance through forums and online resources.

Alternatives:

  • TensorFlow Lite: If your target is mobile or embedded deployment, TensorFlow Lite might be a more suitable option, offering optimized inference for resource-constrained devices.
  • Higher-Level APIs: If you don't require the low-level control of the C API, consider using higher-level APIs like the C++ API or Python API, which offer greater ease of use.

Summary

This guide provides a concise walkthrough of using the TensorFlow C API to execute a pre-trained TensorFlow model.

Steps:

  1. Installation: Begin by installing the TensorFlow C API on your system using the instructions provided in the TensorFlow documentation.

  2. Initialization: Include the necessary TensorFlow header file (c_api.h) and load your pre-trained model from a .pb file.

  3. Session Setup: Create a TensorFlow session, which is responsible for executing the model graph.

  4. Input Preparation: Prepare your input data as a TF_Tensor object, specifying its data type, dimensions, and values.

  5. Execution: Run the TensorFlow session, providing it with the input tensor, output tensor, and the names of the input and output operations in your model graph.

  6. Output Processing: Access the output data from the output tensor, which now contains the model's predictions.

  7. Resource Management: Ensure proper cleanup by deleting all allocated TensorFlow objects (tensors, session, graph, status).

Key Points:

  • This example demonstrates a basic inference workflow and might need adjustments based on your model and use case.
  • Consult the TensorFlow C++ API documentation for comprehensive details and advanced usage scenarios.

Conclusion

By following these steps, you can effectively leverage the TensorFlow C API to integrate machine learning models into your C/C++ applications, enabling you to perform tasks like image classification, object detection, and natural language processing. Remember to consult the TensorFlow C API documentation for detailed information and advanced usage scenarios.

References

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