Rknn Tflite Cpp

7 min read Sep 30, 2024

Harnessing the Power of TensorFlow Lite with RKNN for Efficient Inference on Rockchip Devices

TensorFlow Lite is a powerful framework for running machine learning models on edge devices, offering incredible flexibility and performance. But sometimes, even with TensorFlow Lite, you need a boost to reach your full potential, especially when working with resource-constrained devices. Enter RKNN, Rockchip's dedicated library for optimizing TensorFlow Lite models for Rockchip SoCs.

Why Choose RKNN?

You might be wondering, why go through the extra step of using RKNN? Isn't TensorFlow Lite enough? Well, the answer is: it depends.

RKNN comes into play when you need:

Enhanced Performance: RKNN optimizes models for specific Rockchip SoCs, extracting the maximum performance from your device's hardware.
Reduced Memory Footprint: By converting your TensorFlow Lite model into an RKNN-compatible format, you can significantly reduce the memory required to store and run the model, making it ideal for devices with limited resources.
Simplified Deployment: RKNN provides a streamlined workflow for deploying your models on Rockchip devices, making the process smoother and less prone to errors.

Embracing the Power of RKNN

Let's dive deeper into the practical aspects of using RKNN:

1. Setting the Stage:

Install the RKNN library: The first step is to install the RKNN library on your development environment. Refer to the official Rockchip documentation for installation instructions.
Prepare your TensorFlow Lite model: You'll need a TensorFlow Lite model in the .tflite format. If you haven't already, use the TensorFlow Lite converter to convert your model from a TensorFlow SavedModel or Keras model.

2. The RKNN Conversion Process:

Create an RKNN object: Instantiate an RKNN object using the rknn.RKNN class.
Load your TensorFlow Lite model: Use the load_tflite method to load your .tflite model into the RKNN object.
Customize the RKNN configuration: You can configure various parameters, such as the target device, model input/output shapes, quantization settings, and more. This step allows you to fine-tune your model for optimal performance on your specific hardware.
Perform the model conversion: The build method is where the magic happens. It converts your TensorFlow Lite model into an RKNN-compatible format, taking into account your specified configurations.

3. Inference and Optimization:

Run inference: After conversion, you can use the run method to perform inference on your input data.
Performance analysis: RKNN provides tools to analyze the model's performance, helping you identify bottlenecks and optimize your model further.

Real-World Applications: C++ Integration

Let's see how RKNN seamlessly integrates with C++ code:

#include 
#include "rknn.h"

int main() {
    // Create an RKNN object
    rknn_context ctx;
    ctx = rknn_init();
    if (ctx == NULL) {
        std::cerr << "RKNN Initialization failed" << std::endl;
        return -1;
    }

    // Load the TensorFlow Lite model
    rknn_input_output_num io_num;
    rknn_input_output_node io_nodes[2];
    if (rknn_load_tflite(ctx, "model.tflite", &io_num, io_nodes) != RKNN_SUCC) {
        std::cerr << "RKNN model loading failed" << std::endl;
        return -1;
    }

    // Configure the RKNN object (example: setting input size)
    rknn_input_node input_node;
    input_node.index = 0; 
    input_node.name = "input_tensor"; 
    input_node.n_dims = 4; 
    input_node.dims[0] = 1; 
    input_node.dims[1] = 224;
    input_node.dims[2] = 224;
    input_node.dims[3] = 3;
    input_node.is_const = 0; 
    input_node.dtype = RKNN_TENSOR_TYPE_FLOAT32; 
    input_node.fmt = RKNN_TENSOR_FMT_NCHW;

    // Build the RKNN model
    if (rknn_build(ctx, &input_node, 1, NULL, 0) != RKNN_SUCC) {
        std::cerr << "RKNN model building failed" << std::endl;
        return -1;
    }

    // Run inference (example: assuming input data is in `input_data` array)
    rknn_input inputs[1];
    inputs[0].index = 0; 
    inputs[0].type = RKNN_TENSOR_TYPE_FLOAT32;
    inputs[0].size = 1 * 224 * 224 * 3 * sizeof(float);
    inputs[0].fmt = RKNN_TENSOR_FMT_NCHW;
    inputs[0].buf = input_data;

    rknn_output outputs[1];
    outputs[0].index = 0;
    outputs[0].type = RKNN_TENSOR_TYPE_FLOAT32;
    outputs[0].size = 1 * 1000 * sizeof(float);
    outputs[0].fmt = RKNN_TENSOR_FMT_NCHW;
    outputs[0].buf = output_data;

    if (rknn_run(ctx, inputs, 1, outputs, 1) != RKNN_SUCC) {
        std::cerr << "RKNN inference failed" << std::endl;
        return -1;
    }

    // Process output data 
    // ...

    // Release resources
    rknn_destroy(ctx);

    return 0;
}

This example demonstrates the basic steps involved in using RKNN within your C++ application. Remember to adapt the code to your specific model and data.

Conclusion

RKNN is a valuable tool in the TensorFlow Lite arsenal, empowering you to unlock the full potential of your Rockchip devices. By leveraging the efficiency and performance gains offered by RKNN, you can create robust and performant AI applications that run seamlessly on your edge devices.