From b91bb81d81e1dce4a95ba3393a487d20d2f362de Mon Sep 17 00:00:00 2001
From: Qiea <1310371422@qq.com>
Date: Mon, 11 Nov 2024 11:04:38 +0800
Subject: [PATCH] tmp

---
 cnn.c       | 58 +++++++++++++++++++++++++++++++++++++++--------------
 cnn_model.c |  6 +++---
 cnn_model.h |  2 +-
 3 files changed, 47 insertions(+), 19 deletions(-)

diff --git a/cnn.c b/cnn.c
index 49873a2..fed1873 100644
--- a/cnn.c
+++ b/cnn.c
@@ -30,12 +30,17 @@ float* convolution(Model model_w, Model model_b, const float* input_matrix, int
     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
+
+    //用于合并前的数组，具有32*64*50*50(第二层)的大小
+    float* _conv_rlst = (float *) malloc(sizeof (float) * model_w.channel * model_w.num_kernels * (cr_l * cr_l));
+    memset(_conv_rlst, 0, sizeof (float) * model_w.channel * model_w.num_kernels * (cr_l * cr_l));
+    //子图合并后的数组
     float* conv_rlst = (float *) malloc(sizeof (float) * model_w.num_kernels * (cr_l * cr_l));
     memset(conv_rlst, 0, sizeof (float) * model_w.num_kernels * (cr_l * cr_l));
-    // 遍历30个卷积核（假设有30个通道）
 
-    for(int l=0;l<model_w.layer;l++){
-        for(int n=0; n<model_w.num_kernels; n++){
+    // 遍历30个卷积核（假设有30个通道）
+    for(int c=0; c < model_w.channel; c++){
+        for(int k=0; k < model_w.num_kernels; k++){
             for(int row = 0; row < cr_l; row++) {
                 for (int col = 0; col < cr_l; col++) {
                     conv_temp = 0; // 每个输出像素初始化为0
@@ -43,25 +48,48 @@ float* convolution(Model model_w, Model model_b, const float* input_matrix, int
                     for (int x = 0; x < 3; x++) {
                         for (int y = 0; y < 3; y++) {
                             // 将输入图像的对应像素与卷积核权重相乘，并累加到conv_temp
-                            conv_temp += input_matrix[(l*(im_l*im_l)) + (row*im_l+col) + (x*im_l+y)] *
-                                    model_w.array[(l+n*(model_w.layer))*(3*3) + (x*3) + y];
+                            conv_temp += input_matrix[(c*im_l*im_l) + (row*(im_l)+col) + (x*(im_l)+y)]
+                                    * model_w.array[() + (x*3+y) + (k*3*3)];
                         }
                     }
-                    if(_debug < 10 && strcmp(model_w.name, "conv2_weight") == 0){
-                        printf("[%d]%f\r\n",_debug,conv_temp);
-                    }
-                    _debug++;
+
+//                    if(model_w.channel>1){
+//                        float sum=0;
+//                        for(int _l=0 ; _l < model_w.channel ; _l++){
+//                            sum += input_matrix[
+//                                    (_l*(im_l*im_l)) + ((row+1)*(im_l)+(col+1))
+//                            ];
+//                        }
+//                        conv_temp = sum;
+//                    }
+//
+//                    if(_debug < 10 && strcmp(model_w.name, "conv2_weight") == 0){
+//                        printf("[%d]%f, input_matrix:[%f]\r\k",_debug,conv_temp,input_matrix[
+//                                (0*(im_l*im_l)) + ((row+1)*(im_l)+(col+1))
+//                        ]);
+//                    }
+//                    _debug++;
+
                     // 加上对应卷积核的偏置
-                    conv_temp += model_b.array[n];
+                    conv_temp += model_b.array[k];
                     // 激活函数：ReLU（将小于0的值设为0）
                     if (conv_temp > 0)
-                        conv_rlst[(n*(cr_l*cr_l)) + (row*cr_l) + (col)] = conv_temp; // 如果卷积结果大于0，存入结果数组
+                        conv_rlst[(k * (cr_l * cr_l)) + (row * cr_l) + (col)] = conv_temp; // 如果卷积结果大于0，存入结果数组
                     else
-                        conv_rlst[(n*(cr_l*cr_l)) + (row*cr_l) + (col)] = 0; // 否则存入0
+                        conv_rlst[(k * (cr_l * cr_l)) + (row * cr_l) + (col)] = 0; // 否则存入0
                 }
            }
         }
+
+
+
+
+
+
+
     }
+
+
     return conv_rlst;
 }
 
@@ -130,7 +158,7 @@ int main(){
 
 //第一层：填充102 * 102
     float* expand_matrix_1 = expand(data.array, 100, 1);
-//        print_rslt(expand_matrix_1, 102, (1*102*102));
+//        print_rslt(expand_matrix_1, 102, (1*10*102));
     float* conv_rlst_1 = convolution(conv1_weight,conv1_bias,expand_matrix_1, 102);
 //        print_rslt(conv_rlst_1, 100*0.01, (0.01*10*100));
     float* pool_rslt_1 = pooling(conv1_weight, conv_rlst_1, 100);
@@ -138,9 +166,9 @@ int main(){
 
 //第二层：填充32 * 52 * 52
     float* expand_matrix_2 = expand(pool_rslt_1, 50, 32);
-//        print_rslt(expand_matrix_2, 52, (1*52*52));
+        print_rslt(expand_matrix_2, 52, (1*10*52));
     float* conv_rlst_2 = convolution(conv2_weight,conv2_bias,expand_matrix_2, 52);
-//        print_rslt(conv_rlst_2, 50*0.02, (0.02*10*50));
+        print_rslt(conv_rlst_2, 50, (1*10*50));
     float* pool_rslt_2 = pooling(conv2_weight, conv_rlst_2, 50);
 //        print_rslt(pool_rslt_2, 25, (1*25*25));
 
diff --git a/cnn_model.c b/cnn_model.c
index cb87d66..e866b5c 100644
--- a/cnn_model.c
+++ b/cnn_model.c
@@ -341,7 +341,7 @@ void model_init(){
 	conv1_weight.name = "conv1_weight";
 	conv1_weight.array = modelmym_init(conv1_weight.name);
 	conv1_weight.maxlength = CONV1_WEIGHT_ARRSIZE;
-    conv1_weight.layer = 1;
+    conv1_weight.channel = 1;
     conv1_weight.num_kernels = 32;
 
 	conv2_bias.name = "conv2_bias";
@@ -351,7 +351,7 @@ void model_init(){
 	conv2_weight.name = "conv2_weight";
 	conv2_weight.array = modelmym_init(conv2_weight.name);
 	conv2_weight.maxlength = CONV2_WEIGHT_ARRSIZE;
-    conv2_weight.layer = 32;
+    conv2_weight.channel = 32;
     conv2_weight.num_kernels = 64;
 
 	conv3_bias.name = "conv3_bias";
@@ -361,7 +361,7 @@ void model_init(){
 	conv3_weight.name = "conv3_weight";
 	conv3_weight.array = modelmym_init(conv3_weight.name);
 	conv3_weight.maxlength = CONV3_WEIGHT_ARRSIZE;
-    conv3_weight.layer = 64;
+    conv3_weight.channel = 64;
     conv3_weight.num_kernels = 128;
 
 	fc1_bias.name = "fc1_bias";
diff --git a/cnn_model.h b/cnn_model.h
index 459f13d..65207cc 100644
--- a/cnn_model.h
+++ b/cnn_model.h
@@ -15,7 +15,7 @@ typedef struct {
 	u32 	maxlength;
 	u32		realength;
 
-    u8      layer;
+    u8      channel;
     u8      num_kernels;
 
 } Model;