rknn tflite cpp

Guri Bee

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23-10-2024

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Running TensorFlow Lite Models with RKNN on Raspberry Pi 4: A Comprehensive Guide

The Raspberry Pi 4, a powerful and versatile single-board computer, has found numerous applications in embedded systems, robotics, and edge computing. Combining the efficiency of TensorFlow Lite (TFLite) with the optimized performance of Rockchip's RKNN framework unlocks significant potential for deploying machine learning models on the Pi.

This article explores the integration of RKNN with TFLite models in C++ for Raspberry Pi 4, offering a practical guide for developers seeking to leverage this powerful combination.

What is RKNN?

RKNN, developed by Rockchip, is a lightweight, efficient inference engine specifically designed for Rockchip SoCs. It optimizes TFLite models for execution on Rockchip hardware, offering significant performance gains compared to running models directly through TensorFlow Lite.

Why Use RKNN with TFLite?

Combining RKNN with TFLite presents several advantages:

• Optimized Performance: RKNN accelerates model execution by taking advantage of the specific hardware capabilities of the Rockchip SoC, leading to faster inference speeds.
• Reduced Resource Consumption: RKNN minimizes memory usage and CPU overhead, making it ideal for resource-constrained devices like the Raspberry Pi.
• Ease of Deployment: The framework offers a straightforward API for model conversion and execution, simplifying the deployment process.

Getting Started with RKNN and TFLite on Raspberry Pi

1. Setting up the Environment

• Install Necessary Packages:

  sudo apt-get update
  sudo apt-get install -y python3-pip
  sudo pip3 install rpi.gpio
  sudo pip3 install tensorflow-cpu 
  

• Download and Install RKNN: Download the RKNN SDK from the Rockchip website. Extract the archive and follow the provided installation instructions.

2. Converting Your TFLite Model

• Use the RKNN Converter: The RKNN SDK includes a converter tool for optimizing TFLite models. Use the following command to convert your model:

  rknn_converter -model your_model.tflite -mean_values 0 0 0 0 -scale_values 1 1 1 1 -output model.rknn
  

  Replace your_model.tflite with the path to your TFLite model.

3. Running the Model with RKNN in C++

• Create a C++ Project: Create a new C++ project in your preferred IDE and include the RKNN header files in your project.

• Initialize and Load the Model:

  #include "rknn.h"
  
  int main() {
    rknn_context ctx;
    rknn_init(&ctx, "model.rknn"); 
    // ... further code ...
  }
  

• Prepare Input Data: Load your input data and format it according to the model's input requirements.

• Run Inference:

  rknn_input inputs[1];
  inputs[0].index = 0;
  inputs[0].type = RKNN_TENSOR_TYPE_FLOAT32;
  inputs[0].fmt = RKNN_TENSOR_FMT_NCHW;
  // ... set input data ...
  rknn_run(ctx, inputs, NULL); 
  

• Retrieve Output Data: Extract the output data from the model.

• Post-processing: Apply any necessary post-processing steps to the output data, such as scaling or classification.

Example: Object Detection with RKNN

#include "rknn.h"
#include <iostream>

int main() {
    rknn_context ctx;
    rknn_init(&ctx, "model.rknn"); 

    // Prepare input data
    float input_data[1 * 3 * 224 * 224];
    // ... load input data ...

    // Set input
    rknn_input inputs[1];
    inputs[0].index = 0;
    inputs[0].type = RKNN_TENSOR_TYPE_FLOAT32;
    inputs[0].fmt = RKNN_TENSOR_FMT_NCHW;
    inputs[0].size = sizeof(input_data);
    inputs[0].data = input_data;

    // Run inference
    rknn_run(ctx, inputs, NULL);

    // Get output
    rknn_output outputs[1];
    outputs[0].index = 0;
    rknn_get_output(ctx, &outputs[0]);

    // Process output
    float *output_data = (float *)outputs[0].data;
    // ... extract detected objects and bounding boxes ...

    rknn_destroy(ctx);

    return 0;
}

Key Considerations

• Model Optimization: Optimize your TFLite model for size and efficiency before converting it with RKNN. Techniques like quantization can significantly reduce model size and improve performance.
• Memory Management: Pay close attention to memory management, especially when handling large datasets.
• Performance Tuning: Experiment with different RKNN settings to find optimal performance for your specific model and application.

Conclusion

RKNN provides a powerful framework for running TFLite models on Raspberry Pi 4, enabling efficient and robust machine learning deployments on edge devices. By following the steps outlined in this guide and considering the provided considerations, developers can leverage this combination to create compelling and impactful applications.