From fb74170c00e8ed839e8a3a5476e41b970b7e396d Mon Sep 17 00:00:00 2001
From: Qiea <1310371422@qq.com>
Date: Sun, 10 Nov 2024 21:15:37 +0800
Subject: [PATCH] _tmp

---
 cnn.c       | 92 +++++++++++++++++++++++++----------------------------
 cnn_model.c |  6 ++++
 cnn_model.h |  4 +++
 3 files changed, 53 insertions(+), 49 deletions(-)

diff --git a/cnn.c b/cnn.c
index 3435f3c..4a3f872 100644
--- a/cnn.c
+++ b/cnn.c
@@ -19,56 +19,50 @@ float* expand(const float* old_matrix, u8 old_matrix_length, u8 layer){
     return new_matrix;
 }
 
-//num_kernels 卷积核的个数：32
-//area 卷积的面积：3*3
+//model 模型名字
 //input_matrix 输入图像
 //input_matrix_length 输入图像的边长：102
-//输出图像的边长：100
+//c_rl 输出图像的边长：100
 //返回卷积的结果
-float* convolution(u8 num_kernels, u8 area, const float* input_matrix, u8 input_matrix_length, u8 layer){
+float* convolution(Model model_w, Model model_b, const float* input_matrix, u8 input_matrix_length){
     // 初始化卷积层参数
+    u8 c_rl = input_matrix_length - 2;
     float conv_temp; // 临时变量，用于存储卷积计算的中间结果
-    float* conv_rlst = (float *) malloc(sizeof (float)*layer*num_kernels*(input_matrix_length-2)*(input_matrix_length-2));
-    memset(conv_rlst, 0, sizeof (float)*layer*num_kernels*(input_matrix_length-2)*(input_matrix_length-2));
+    float* conv_rlst = (float *) malloc(sizeof (float) * model_w.num_kernels * model_w.layer * (c_rl*c_rl));
+    memset(conv_rlst, 0, sizeof (float) * model_w.num_kernels * model_w.layer * (c_rl*c_rl));
     // 遍历30个卷积核（假设有30个通道）
-    for(u8 l=0;l<layer;l++){
-        for(u8 n=0; n<num_kernels; n++)
-        {
-            // 遍历输出图像的每一行（卷积输出大小为24x24）
-            for(u8 row=0; row<(input_matrix_length-2); row++)
-            {
-                // 遍历输出图像的每一列
-                for(u8 col=0; col<(input_matrix_length-2); col++)
-                {
-                    conv_temp = 0; // 每个输出像素初始化为0
-                    // 进行5x5的卷积操作
-                    for(u8 x=0; x<area; x++)
-                    {
-                        for(u8 y=0; y<area; 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[row*input_matrix_length+col+x*input_matrix_length+y +
-                                0
-                            ] * conv1_weight.array[x*area+y+n*(area*area)+
-                                0
-                            ];
+        for(u8 n=0; n<model_w.num_kernels; n++){
+            for(u8 l=0;l<model_w.layer;l++){
+                // 遍历输出图像的每一行（卷积输出大小为24x24）
+                for(u8 row=0; row<(c_rl); row++) {
+                    // 遍历输出图像的每一列
+                    for (u8 col = 0; col < (input_matrix_length - 2); col++) {
+                        conv_temp = 0; // 每个输出像素初始化为0
+                        // 进行3x3的卷积操作
+                        for (u8 x = 0; x < 3; x++) {
+                            for (u8 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[model_w.layer*(input_matrix_length*input_matrix_length)+row*28
+                                                          +col+(x*(input_matrix_length)+y)] * model_w.array[n*(model_w.layer*3*3)+l*(3*3)+x*3+y];
+                            }
                         }
+                        // 加上对应卷积核的偏置
+                        conv_temp += model_b.array[n];
+                        // 激活函数：ReLU（将小于0的值设为0）
+                        if (conv_temp > 0)
+                            conv_rlst[row * (input_matrix_length - 2) + col +
+                                      n * (input_matrix_length - 2) * (input_matrix_length - 2) +
+                                      0
+                            ] = conv_temp; // 如果卷积结果大于0，存入结果数组
+                        else
+                            conv_rlst[row * (input_matrix_length - 2) + col +
+                                      n * (input_matrix_length - 2) * (input_matrix_length - 2) +
+                                      0
+                            ] = 0; // 否则存入0
                     }
-                    // 加上对应卷积核的偏置
-                    conv_temp += conv1_bias.array[n];
-                    // 激活函数：ReLU（将小于0的值设为0）
-                    if(conv_temp > 0)
-                        conv_rlst[row*(input_matrix_length-2)+col+n*(input_matrix_length-2)*(input_matrix_length-2)+
-                                0
-                        ] = conv_temp; // 如果卷积结果大于0，存入结果数组
-                    else
-                        conv_rlst[row*(input_matrix_length-2)+col+n*(input_matrix_length-2)*(input_matrix_length-2)+
-                                0
-                        ] = 0; // 否则存入0
                 }
             }
         }
-    }
     return conv_rlst;
 }
 
@@ -138,17 +132,17 @@ int main(){
 //第一层：填充102 * 102
     float* expand_matrix_1 = expand(data.array, 100, 1);
         print_rslt(expand_matrix_1, 102, (0*102*102));
-    float* conv_rlst_1 = convolution(32, 3, expand_matrix_1, 102,1);
+    float* conv_rlst_1 = convolution(conv1_weight,conv1_bias,expand_matrix_1, 102);
         print_rslt(conv_rlst_1, 32, (1*100));
-    float* pool_rslt_1 = pooling(32, 2, conv_rlst_1, 100);
-
-//第二层：填充102 * 102
-    float* expand_matrix_2 = expand(pool_rslt_1, 50, 32);
-        print_rslt(expand_matrix_2, 52, (0*52*52));
-    float* conv_rlst_2 = convolution(64, 3, expand_matrix_2, 52,1);
-        print_rslt(conv_rlst_2, 64, (1*50*50));
-    float* pool_rslt_2 = pooling(64, 2, conv_rlst_2, 50);
-
+//    float* pool_rslt_1 = pooling(32, 2, conv_rlst_1, 100);
+//
+////第二层：填充102 * 102
+//    float* expand_matrix_2 = expand(pool_rslt_1, 50, 32);
+//        print_rslt(expand_matrix_2, 52, (0*52*52));
+//    float* conv_rlst_2 = convolution(conv2_weight,conv2_bias,expand_matrix_2, 102);
+//        print_rslt(conv_rlst_2, 64, (1*50*50));
+//    float* pool_rslt_2 = pooling(64, 2, conv_rlst_2, 50);
+//
 
 
 
diff --git a/cnn_model.c b/cnn_model.c
index d3eb79f..4a81e9d 100644
--- a/cnn_model.c
+++ b/cnn_model.c
@@ -341,6 +341,8 @@ 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.num_kernels = 32;
 
 	conv2_bias.name = "conv2_bias";
 	conv2_bias.array = modelmym_init(conv2_bias.name);
@@ -349,6 +351,8 @@ 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.num_kernels = 64;
 
 	conv3_bias.name = "conv3_bias";
 	conv3_bias.array = modelmym_init(conv3_bias.name);
@@ -357,6 +361,8 @@ void model_init(){
 	conv3_weight.name = "conv3_weight";
 	conv3_weight.array = modelmym_init(conv3_weight.name);
 	conv3_weight.maxlength = CONV3_WEIGHT_ARRSIZE;
+    conv2_weight.layer = 64;
+    conv2_weight.num_kernels = 128;
 
 	fc1_bias.name = "fc1_bias";
 	fc1_bias.array = modelmym_init(fc1_bias.name);
diff --git a/cnn_model.h b/cnn_model.h
index aa7ef5a..459f13d 100644
--- a/cnn_model.h
+++ b/cnn_model.h
@@ -14,6 +14,10 @@ typedef struct {
 	float* 	array;
 	u32 	maxlength;
 	u32		realength;
+
+    u8      layer;
+    u8      num_kernels;
+
 } Model;