全功能实现，验证通过，暂未精简

cnn.c

@@ -14,11 +14,14 @@ void print_rslt(float* rslt, u8 input_matrix_length, u32 length){
     printf("\r\n");
 }
 
-
-
 // 将原始矩阵复制到填充后的矩阵中央
 float* expand(const float* old_matrix, int old_matrix_length, int layer){
     float* new_matrix = (float *)malloc(sizeof(float)*layer*(old_matrix_length+2)*(old_matrix_length+2));
+    if (new_matrix == NULL) {
+        // 处理 malloc 失败的情况，例如打印错误并退出
+        perror("Memory allocation failed");
+        exit(1); // 退出程序
+    }
     memset(new_matrix, 0, sizeof(float)*layer*(old_matrix_length+2)*(old_matrix_length+2));
     for(int l=0; l < layer; l++){
         for (int i = 0; i < old_matrix_length; i++) {
@@ -33,8 +36,6 @@ float* expand(const float* old_matrix, int old_matrix_length, int layer){
     return new_matrix;
 }
 
-
-
 //model 模型名字
 //input_matrix 输入图像
 //input_matrix_length 输入图像的边长：102
@@ -42,7 +43,6 @@ float* expand(const float* old_matrix, int old_matrix_length, int layer){
 //返回卷积的结果
 float* convolution(Model model_w, Model model_b, const float* input_matrix, int input_matrix_length){
     // 初始化卷积层参数
-    int _debug=0;
     int im_l = input_matrix_length;
     int cr_l = input_matrix_length - 2;
     float conv_temp; // 临时变量，用于存储卷积计算的中间结果
@@ -93,11 +93,10 @@ float* convolution(Model model_w, Model model_b, const float* input_matrix, int
             }
         }
     }
+    free(_conv_rlst);
     return conv_rlst;
 }
 
-
-
 //num_kernels 卷积核的个数：32
 //area 池化的面积：2*2
 //input_matrix 输入图像
@@ -137,8 +136,6 @@ float* pooling(Model model_w, const float* input_matrix, u8 input_matrix_length)
     return pool_rslt;
 }
 
-
-
 float* hidden(const float* input_matrix){
     float affine1_temp; // 临时变量，用于存储全连接层的中间结果
     float *affine1_rslt = (float *) malloc(sizeof(float)*128);
@@ -170,8 +167,6 @@ float* hidden(const float* input_matrix){
     return affine1_rslt;
 }
 
-
-
 float* output(const float* input_matrix){
     float affine2_temp; // 临时变量，用于存储输出层的中间结果
     float *affine2_rslt = (float *) malloc(sizeof(float)*7);
@@ -212,22 +207,107 @@ float* output(const float* input_matrix){
 
 
 
-void cnn_run(){
+void calculate_statistics(Model model, float* value)
+{
+    value[0] = fabsf(model.array[0]);
+    float sum = 0;
+    float sum_sq = 0;
 
-//第一层：填充102 * 102
+    for (int i = 0; i < model.maxlength; i++) {
-    float* expand_matrix_1 = expand(data.array, 100, 1);
+        float abs_val = fabsf(model.array[i]);
-    float* conv_rlst_1 = convolution(conv1_weight,conv1_bias,expand_matrix_1, 102);
-    float* pool_rslt_1 = pooling(conv1_weight, conv_rlst_1, 100);
-//第二层：填充32 * 52 * 52
-    float* expand_matrix_2 = expand(pool_rslt_1, 50, 32);
-    float* conv_rlst_2 = convolution(conv2_weight,conv2_bias,expand_matrix_2, 52);
-    float* pool_rslt_2 = pooling(conv2_weight, conv_rlst_2, 50);
-//第三层：填充 64 * 27 * 27
-    float* expand_matrix_3 = expand(pool_rslt_2, 25, 64);
-    float* conv_rlst_3 = convolution(conv3_weight,conv3_bias,expand_matrix_3, 27);
-    float* pool_rslt_3 = pooling(conv3_weight, conv_rlst_3, 25);
 
-    float* affine1_rslt = hidden(pool_rslt_3);
+        if (abs_val > value[0]) {
-    float* affine2_rslt = output(affine1_rslt);
+            value[0] = abs_val;
+        }
+        sum += abs_val;
+        sum_sq += abs_val * abs_val;
+    }
 
+    value[1] = sum / (float)model.maxlength;
+
+    float variance = (sum_sq / (float)model.maxlength) - (value[1] * value[1]);
+    value[2] = sqrtf(variance);
+}
+
+u8 check_threshold(Model model, const float* value)
+{
+    const float threshold = 20;
+
+    for (int i = 0; i < model.maxlength; i++) {
+        float K = (fabsf(model.array[i]) - value[1]) / value[2];
+        if (K > threshold) {
+            return 1;
+        }
+    }
+    return 0;
+}
+
+float* generateMatrix(Model model, const float* value)
+{
+    float* CNN_data = (float*) malloc(sizeof(float)*100*100);
+    memset(CNN_data, 0, sizeof(float)*100*100);
+
+    u16 x = model.maxlength / 100;
+    float y = value[0] / 100;
+
+    for (int i = 0; i < model.maxlength; i++) {
+        float absolutevalue = fabsf(model.array[i]);
+
+        if (!absolutevalue) {
+            continue;
+        }
+
+        int xIndex = i / x;
+        if (xIndex >= 100) xIndex = 99;
+
+        int yIndex = (int)(absolutevalue / y);
+        if (yIndex < 0) yIndex = 0;
+
+        CNN_data[yIndex * 100 + xIndex]++;
+    }
+    return CNN_data;
+}
+
+void cnn_run(){
+    float value[3] = {0};
+    calculate_statistics(data,&value[0]);
+    if (check_threshold(data,&value[0])){
+
+        float* _data = generateMatrix(data,&value[0]);
+
+//        printf("最大值:%f 平均值:%f 标准差:%f\r\n",value[0],value[1],value[2]);
+//        print_rslt(_data,100,1*100*100);
+
+        //第一层：填充102 * 102
+        float* expand_matrix_1 = expand(_data, 100, 1);
+        float* conv_rlst_1 = convolution(conv1_weight,conv1_bias,expand_matrix_1, 102);
+        float* pool_rslt_1 = pooling(conv1_weight, conv_rlst_1, 100);
+
+        free(_data);
+        free(expand_matrix_1);
+        free(conv_rlst_1);
+        //第二层：填充32 * 52 * 52
+        float* expand_matrix_2 = expand(pool_rslt_1, 50, 32);
+        float* conv_rlst_2 = convolution(conv2_weight,conv2_bias,expand_matrix_2, 52);
+        float* pool_rslt_2 = pooling(conv2_weight, conv_rlst_2, 50);
+
+        free(pool_rslt_1);
+        free(expand_matrix_2);
+        free(conv_rlst_2);
+        //第三层：填充 64 * 27 * 27
+        float* expand_matrix_3 = expand(pool_rslt_2, 25, 64);
+        float* conv_rlst_3 = convolution(conv3_weight,conv3_bias,expand_matrix_3, 27);
+        float* pool_rslt_3 = pooling(conv3_weight, conv_rlst_3, 25);
+
+        free(pool_rslt_2);
+        free(expand_matrix_3);
+        free(conv_rlst_3);
+
+        float* affine1_rslt = hidden(pool_rslt_3);
+        float* affine2_rslt = output(affine1_rslt);
+
+        free(pool_rslt_3);
+        free(affine1_rslt);
+        free(affine2_rslt);
+    }
 }

cnn.h

@@ -2,6 +2,7 @@
 #define _CNN_H_
 #include "cnn_model.h"
 #include <stdio.h>
+#include <math.h>
 
 
 

cnn_model.h

@@ -8,6 +8,7 @@
 #include <stdint.h>
 
 
+
 typedef struct {
 	char* 	name;
 	char* 	dname;
@@ -33,7 +34,11 @@ typedef struct {
 #define FC1_WEIGHT_ARRSIZE (128*18432)		//2359296
 #define FC2_BIAS_ARRSIZE (7)
 #define FC2_WEIGHT_ARRSIZE (7*128)			//896
-#define DATA_ARRSIZE (100*100)				//1250000
+#if 1
+    #define DATA_ARRSIZE (1250000)
+#else
+    #define DATA_ARRSIZE (100 * 100)
+#endif
 
 
 

main.c

@@ -83,6 +83,8 @@ int main(){
     model_write("all");
 
     run_dataset();
+//    model_switchdata("C1autosave00095_right_new_2");
+//    cnn_run();
 
     DEBUG_PRINTF("\r\nEnd结束");
 }