给出一个CMSIS-NN调用模型并实现预测的完整的代码

以下是一个使用CMSIS-NN调用模型并实现预测的完整代码示例。这个例子使用的是一个预训练的MNIST手写数字识别模型。你需要根据你的模型修改一些参数和调整代码。

#include "arm_math.h"
#include "arm_nnfunctions.h"
#include "arm_nnexamples_cifar10_parameter.h"
#include "arm_nnexamples_cifar10_weights.h"
#include <stdio.h>

#define IMG_SIZE 28*28
#define NUM_CLASSES 10
#define NUM_OUTPUT 10

static q7_t image_data[IMG_SIZE];
static q15_t output_data[NUM_OUTPUT];

int main()
{
    arm_status status;
    int index;

    // Load image data
    FILE* fp = fopen("test_image.bin", "rb");
    fread(image_data, sizeof(q7_t), IMG_SIZE, fp);
    fclose(fp);

    // Allocate memory for the convolutional neural network
    q7_t* buffer = (q7_t*) malloc(2*CNN_BUFF_SIZE*sizeof(q7_t));
    q7_t* scratch = buffer;
    q7_t* conv_out = scratch;
    scratch += CONV_OUT_SIZE;
    q7_t* pool_out = scratch;
    scratch += POOL_OUT_SIZE;
    q7_t* fc_out = scratch;
    scratch += FC_OUT_SIZE;

    // Load convolutional neural network parameters
    const uint16_t conv_dims[4] = {28, 28, 1, 16};
    const uint16_t conv_strides[2] = {1, 1};
    const uint16_t conv_padding[2] = {1, 1};
    const uint16_t pool_dims[4] = {14, 14, 16, 16};
    const uint16_t pool_strides[2] = {2, 2};
    const uint16_t pool_padding[2] = {0, 0};
    const uint16_t fc_dims[4] = {5, 5, 16, 10};

    // Perform convolutional neural network operations
    arm_convolve_HWC_q7_basic_nonsquare(image_data, conv_dims, conv_weights, CONV_BIAS, conv_strides, conv_padding, CONV_OUT_CH, CONV_OUT_DIM, conv_out, buffer);
    arm_relu_q7(conv_out, CONV_OUT_SIZE);
    arm_maxpool_q7_HWC(conv_out, pool_dims, POOL_OUT_CH, pool_strides, pool_padding, pool_out, buffer);
    arm_fully_connected_q7_opt(pool_out, fc_weights, FC_BIAS, FC_OUT_DIM, fc_dims, 1, 0, fc_out, buffer);
    arm_softmax_q7(fc_out, NUM_CLASSES, output_data);

    // Print prediction
    float32_t max_val = -3.4028235E+38;
    for (index = 0; index < NUM_OUTPUT; index++) {
        printf("%d: %d\n", index, output_data[index]);
        if (max_val < output_data[index]) {
            max_val = output_data[index];
        }
    }

    // Free memory
    free(buffer);

    return 0;
}

你需要根据自己的模型修改以下参数：

- `IMG_SIZE`: 输入图像的大小
- `NUM_CLASSES`: 分类数
- `NUM_OUTPUT`: 输出的大小
- `image_data`: 存储输入图像的数组
- `conv_dims`, `conv_strides`, `conv_padding`, `pool_dims`, `pool_strides`, `pool_padding`, `fc_dims`: 卷积神经网络的参数
- `conv_weights`, `CONV_BIAS`, `fc_weights`, `FC_BIAS`: 卷积神经网络的权重和偏置

你需要将这些参数和模型的权重和偏置导入到你的代码中。最后，你需要编译并运行你的代码，它将输出模型对输入图像的预测结果。