实现ResNet-34需要了解ResNet的基本架构。ResNet是一个深度残差神经网络，其基础结构是由多个残差块（Residual block）堆叠而成的。每个残差块包含两个卷积层和一个跳跃连接（shortcut connection），跳跃连接负责绕过这两个卷积层，直接连接两个卷积层的输入和输出，从而允许信息在深度网络中跳过某些层。这样可以避免梯度消失和梯度爆炸问题，使得更深的网络结构能够被训练。
实现ResNet34需要实现一些基本的模块，如卷积层、残差块等。以下是一个简单的实现代码，您可以参考：
#include <iostream>
#include <vector>
using namespace std;
// 定义一个二维矩阵类型表示卷积层的卷积核
typedef vector<vector<double>> Matrix;
// 定义卷积函数
vector<vector<double>> conv2d(vector<vector<double>>& input, Matrix& kernel, int stride = 1)
{
int in_channels = input.size();
int out_channels = kernel.size();
int kernel_size = kernel[0].size();
int input_size = input[0].size();
int output_size = (input_size - kernel_size) / stride + 1;
vector<vector<double>> output(out_channels, vector<double>(output_size, 0));
for (int l = 0; l < out_channels; l++)
{
for (int i = 0; i < output_size; i++)
{
for (int j = 0; j < output_size; j++)
{
double sum = 0.0;
for (int c = 0; c < in_channels; c++)
{
for (int m = 0; m < kernel_size; m++)
{
for (int n = 0; n < kernel_size; n++)
{
sum += input[c][i * stride + m][j * stride + n] * kernel[l][m][n];
}
}
}
output[l][i][j] = sum;
}
}
}
return output;
}
// 定义归一化函数
vector<vector<double>> normalize(vector<vector<double>>& input)
{
int size = input.size();
int rows = input[0].size();
int cols = input[0][0].size();
vector<double> mean(size, 0);
vector<double> std(size, 0);
for (int i = 0; i < size; i++)
{
double sum = 0.0;
double square_sum = 0.0;
for (int j = 0; j < rows; j++)
{
for (int k = 0; k < cols; k++)
{
sum += input[i][j][k];
square_sum += input[i][j][k] * input[i][j][k];
}
}
mean[i] = sum / (rows * cols);
std[i] = sqrt(square_sum / (rows * cols) - mean[i] * mean[i]);
}
vector<vector<double>> output(size, vector<double>(rows, 0));
for (int i = 0; i < size; i++)
{
for (int j = 0; j < rows; j++)
{
for (int k = 0; k < cols; k++)
{
output[i][j] = (input[i][j][k] - mean[i]) / std[i];
}
}
}
return output;
}
// 定义残差块
vector<vector<double>> residual(vector<vector<double>>& input, Matrix& kernel1, Matrix& kernel2, int stride = 1)
{
int in_channels = input.size();
int out_channels = kernel2.size();
int kernel_size = kernel2[0].size();
int input_size = input[0].size();
int output_size = (input_size - kernel_size) / stride + 1;
// 对输入进行归一化处理
input = normalize(input);
// 使用两个卷积层实现残差块
// 第一个卷积层
vector<vector<double>> conv1_output = conv2d(input, kernel1, stride);
// 使用ReLU激活函数
for (int i = 0; i < out_channels; i++)
{
for (int j = 0; j < output_size; j++)
{
for (int k = 0; k < output_size; k++)
{
conv1_output[i][j][k] = max(conv1_output[i][j][k], 0.0);
}
}
}
// 第二个卷积层
vector<vector<double>> conv2_output = conv2d(conv1_output, kernel2);
// 残差连接
vector<vector<double>> residual_output(out_channels, vector<double>(output_size, 0));
for (int i = 0; i < out_channels; i++)
{
for (int j = 0; j < output_size; j++)
{
for (int k = 0; k < output_size; k++)
{
residual_output[i][j][k] = conv2_output[i][j][k] + input[i][j * stride][k * stride];
}
}
}
return residual_output;
}
// 定义ResNet34模型
vector<vector<double>> resnet34(vector<vector<double>>& input)
{
// 定义卷积核
Matrix conv1_kernel = {
{
-1, -1, -1}, {
-1, 8, -1}, {
-1, -1, -1} };
Matrix conv2_kernel1 = {
{
-1, -1, -1}, {
-1, 0, -1}, {
-1, -1, -1} };
Matrix conv2_kernel2 = {
{
-1, -1, -1}, {
-1, 8, -1}, {
-1, -1, -1} };
Matrix conv3_kernel1 = {
{
-1, -1, -1}, {
-1, 0, -1}, {
-1, -1, -1} };
Matrix conv3_kernel2 = {
{
-1, -1, -1}, {
-1, 8, -1}, {
-1, -1, -1} };
Matrix conv4_kernel1 = {
{
-1, -1, -1}, {
-1, 0, -1}, {
-1, -1, -1} };
Matrix conv4_kernel2 = {
{
-1, -1, -1}, {
-1, 8, -1}, {
-1, -1, -1} };
Matrix conv5_kernel1 = {
{
-1, -1, -1}, {
-1, 0, -1}, {
-1, -1, -1} };
Matrix conv5_kernel2 = {
{
-1, -1, -1}, {
-1, 8, -1}, {
-1, -1, -1} };
// 定义残差块的步长
int stride1 = 1;
int stride2 = 2;
// 定义4个残差块
vector<vector<double>> residual1_output = residual(input, conv1_kernel, conv2_kernel1, stride1);
vector<vector<double>> residual2_output = residual(residual1_output, conv2_kernel2, conv3_kernel1, stride2);
vector<vector<double>> residual3_output = residual(residual2_output, conv3_kernel2, conv4_kernel1, stride2);
vector<vector<double>> residual4_output = residual(residual3_output, conv4_kernel2, conv5_kernel1, stride2);
// 对最后一个残差块的输出进行池化
int size = residual4_output[0].size();
vector<vector<double>> pool_output(residual4_output.size(), vector<double>(1, 0));
for (int i = 0; i < residual4_output.size(); i++)
{
double sum = 0.0;
for (int j = 0; j < size; j++)
{
for (int k = 0; k < size; k++)
{
sum += residual4_output[i][j][k];
}
}
pool_output[i][0] = sum / (size * size);
}
return pool_output;
}
int main()
{
// 定义输入
int in_channels = 3;
int input_size = 224;
vector<vector<double>> input(in_channels, vector<double>(input_size, 0));
for (int i = 0; i < in_channels; i++)
{
for (int j = 0; j < input_size; j++)
{
for (int k = 0; k < input_size; k++)
{
input[i][j] = rand() % 256;
}
}
}
// 使用ResNet34模型进行预测
vector<vector<double>> output = resnet34(input);
// 输出结果
for (int i = 0; i < output.size(); i++)
{
cout << "Output channel " << i << ": " << output[i][0] << endl;
}
return 0;
}
上述代码实现了ResNet34模型的前向传播。请注意，这只是一个示例，实际上，ResNet34模型包含更多的层，每个层都需要设计相应的卷积核和步长。您可能需要花费更多的时间来实现一个完整的模型。
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <math.h>
// 定义卷积操作
void conv(float* input, float* output, float* weight, float* bias, int in_channel, int out_channel, int kernel_size, int stride, int padding) {
int output_size = (in_channel - kernel_size + 2 * padding) / stride + 1; // 计算卷积输出的大小
for (int i = 0; i < out_channel; i++) {
// 遍历输出通道
for (int j = 0; j < output_size; j++) {
// 遍历输出空间
for (int k = 0; k < output_size; k++) {
float sum = 0;
for (int l = 0; l < in_channel; l++) {
// 遍历输入通道
for (int m = 0; m < kernel_size; m++) {
// 遍历卷积核空间
for (int n = 0; n < kernel_size; n++) {
int input_row = j * stride + m - padding; // 计算输入位置
int input_col = k * stride + n - padding;
if (input_row >= 0 && input_row < in_channel && input_col >= 0 && input_col < in_channel) {
// 判断输入是否合法
sum += input[input_row * in_channel * in_channel + input_col * in_channel + l] *
weight[i * in_channel * kernel_size * kernel_size + l * kernel_size * kernel_size + m * kernel_size + n]; // 计算卷积
}
}
}
}
output[i * output_size * output_size + j * output_size + k] = sum + bias[i]; // 加上偏置项
}
}
}
}
// 定义BN操作
void batch_norm(float* input, float* output, float* weight, float* bias, float* running_mean, float* running_var, float eps, int size) {
for (int i = 0; i < size; i++) {
// 遍历所有元素
output[i] = (input[i] - running_mean[i]) / sqrt(running_var[i] + eps); // 进行标准化
output[i] = output[i] * weight[i] + bias[i]; // 进行缩放和平移
}
}
// 定义ReLU操作
void relu(float* input, float* output, int size) {
for (int i = 0; i < size; i++) {
// 遍历所有元素
output[i] = fmax(0, input[i]); // 取max(0, x)
}
}
// 定义池化操作
void max_pool(float* input, float* output, int in_channel, int out_channel, int kernel_size, int stride) {
int output_size = (in_channel - kernel_size) / stride + 1; // 计算池化输出的大小
for (int i = 0; i < out_channel; i++) {
// 遍历输出通道
for (int j = 0; j < output_size; j++) {
// 遍历输出空间
for (int k = 0; k < output_size; k++) {
float max_val = -INFINITY;
for (int l = 0; l < kernel_size; l++) {
// 遍历池化核空间
for (int m = 0; m < kernel_size; m++) {
int input_row = j * stride + l; // 计算输入位置
int input_col = k * stride + m;
if (input_row >= 0 && input_row < in_channel && input_col >= 0 && input_col < in_channel) {
// 判断输入是否合法
max_val = fmax(max_val, input[input_row * in_channel * in_channel + input_col * in_channel + i]); // 取最大值
}
}
}
output[i * output_size * output_size + j * output_size + k] = max_val; // 将最大值存入输出
}
}
}
}
// 定义ResNet34的一个BasicBlock
void basic_block(float* input, float* output, float* weight1, float* bias1, float* weight2, float* bias2, float* weight3, float* bias3, float* weight4, float* bias4, float* running_mean1, float* running_var1, float* running_mean2, float* running_var2, float eps, int in_channel, int out_channel, int stride) {
float* bn1 = (float*)malloc(sizeof(float) * in_channel * in_channel * in_channel); // 创建中间变量
float* relu1 = (float*)malloc(sizeof(float) * in_channel * in_channel * in_channel);
float* conv1 = (float*)malloc(sizeof(float) * out_channel * in_channel * 3 * 3);
float* bn2 = (float*)malloc(sizeof(float) * out_channel * in_channel * in_channel);
float* relu2 = (float*)malloc(sizeof(float) * out_channel * in_channel * in_channel);
float* conv2 = (float*)malloc(sizeof(float) * out_channel * out_channel * 3 * 3);
float* shortcut = input;
if (in_channel != out_channel || stride != 1) {
// 如果输入和输出维度不同或步长不同，使用1x1卷积调整输入的维度
shortcut = (float*)malloc(sizeof(float) * out_channel * in_channel * in_channel);
conv(input, shortcut, weight3, bias3, in_channel, out_channel, 1, stride, 0);
}
batch_norm(input, bn1, weight1, bias1, running_mean1, running_var1, eps, in_channel * in_channel * in_channel); // BN操作
relu(bn1, relu1, in_channel * in_channel * in_channel); // ReLU操作
conv(relu1, conv1, weight2, bias2, in_channel, in_channel, 3, stride, 1); // 3x3卷积
batch_norm(conv1, bn2, weight4, bias4, running_mean2, running_var2, eps, out_channel * in_channel * in_channel); // BN操作
relu(bn2, relu2, out_channel * in_channel * in_channel); // ReLU操作
conv(relu2, conv2, weight2 + in_channel * in_channel * 3 * 3, bias2 + out_channel, out_channel, out_channel, 3, 1, 1); // 3x3卷积
for (int i = 0; i < out_channel; i++) {
// 将两个卷积结果加起来
for (int j = 0; j < in_channel; j++) {
for (int k = 0; k < in_channel; k++) {
output[i * in_channel * in_channel + j * in_channel + k] = conv2[i * in_channel * in_channel + j * in_channel + k] + shortcut[i * in_channel * in_channel + j * in_channel + k];
}
}
}
free(bn1); // 释放中间变量
free(relu1);
free(conv1);
if (in_channel != out_channel || stride != 1) {
free(shortcut);
}
free(bn2);
free(relu2);
free(conv2);
}
// 定义ResNet34的整个网络
void resnet34(float* input, float* output, float* weight, float* bias, float* running_mean1, float* running_var1, float* running_mean2, float* running_var2, float* running_mean3, float* running_var3, float* running_mean4, float* running_var4, float* running_mean5, float* running_var5, float eps) {
float* bn1 = (float*)malloc(sizeof(float) * 64 * 224 * 224); // 创建中间变量
float* relu1 = (float*)malloc(sizeof(float) * 64 * 224 * 224);
float* conv1 = (float*)malloc(sizeof(float) * 64 * 3 * 3 * 7 * 7);
float* pool1 = (float*)malloc(sizeof(float) * 64 * 112 * 112);
float* block1 = (float*)malloc(sizeof(float) * 64 * 112 * 112);
float* block2 = (float*)malloc(sizeof(float) * 128 * 56 * 56);
float* block3 = (float*)malloc(sizeof(float) * 256 * 28 * 28);
float* block4 = (float*)malloc(sizeof(float) * 512 * 14 * 14);
max_pool(input, pool1, 3, 64, 3, 2); // 第一层：3x3最大池化层
conv(pool1, bn1, weight, bias, 3, 64, 7, 2, 3); // 第二层：7x7卷积层
batch_norm(bn1, relu1, weight + 64 * 7 * 7 * 3, bias + 64, running_mean1, running_var1, eps, 64 * 224 * 224); // BN操作
relu(relu1, block1, 64 * 224 * 224); // ReLU操作
basic_block(block1, block2, weight + 64 * 7 * 7 * 3 + 64 * 3 * 3, bias + 64 + 64, weight + 64 * 7 * 7 * 3 + 64 * 3 * 3 + 128 * 3 * 3, bias + 128, weight + 64 * 7 * 7 * 3 + 128 * 3 * 3, bias + 128, weight + 64 * 7 * 7 * 3 + 128 * 3 * 3 + 128 * 3 * 3, bias + 128, running_mean2, running_var2, running_mean3, running_var3, eps, 64, 128, 2); // 第三层：两个BasicBlock
basic_block(block2, block3, weight + 64 * 7 * 7 * 3 + 128 * 3 * 3 * 2, bias + 128 * 2, weight + 64 * 7 * 7 * 3 + 128 * 3 * 3 * 3, bias + 256, weight + 64 * 7 * 7 * 3 + 256 * 3 * 3, bias + 256, weight + 64 * 7 * 7 * 3 + 256 * 3 * 3 * 2, bias + 256, running_mean4, running_var4, running_mean5, running_var5, eps, 128, 256, 2); // 第四层：两个BasicBlock
basic_block(block3, block4, weight + 64 * 7 * 7 * 3 + 256 * 3 * 3 * 2, bias + 256 * 2, weight + 64 * 7 * 7 * 3 + 256 * 3 * 3 * 3, bias + 512, weight + 64 * 7 * 7 * 3 + 512 * 3 * 3, bias + 512, weight + 64 * 7 * 7 * 3 + 512 * 3 * 3 * 2, bias + 512, running_mean5, running_var5, running_mean5, running_var5, eps, 256, 512, 2); // 第五层：两个BasicBlock
memcpy(output, block4, sizeof(float) * 512 * 14 * 14); // 输出结果
free(bn1); // 释放中间变量
free(relu1);
free(conv1);
free(pool1);
free(block1);
free(block2);
free(block3);
free(block4);
}
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#define MAX_CHANNELS 512
// 定义卷积层的结构体
typedef struct {
int input_channels; // 输入通道数
int output_channels; // 输出通道数
int kernel_size; // 卷积核大小
float *weights; // 权重矩阵
float *biases; // 偏置向量
} ConvLayer;
// 初始化卷积层的函数
void InitConvLayer(ConvLayer *layer, int input_channels, int output_channels, int kernel_size) {
layer->input_channels = input_channels;
layer->output_channels = output_channels;
layer->kernel_size = kernel_size;
int weights_size = input_channels * output_channels * kernel_size * kernel_size;
int biases_size = output_channels;
layer->weights = (float *)malloc(weights_size * sizeof(float));
layer->biases = (float *)malloc(biases_size * sizeof(float));
for (int i = 0; i < weights_size; i++) {
layer->weights[i] = (float)rand() / RAND_MAX;
}
for (int i = 0; i < biases_size; i++) {
layer->biases[i] = (float)rand() / RAND_MAX;
}
}
// 卷积层的前向传播函数
void ConvLayerForward(ConvLayer *layer, float *input, float *output, int input_width, int input_height) {
int output_width = input_width - layer->kernel_size + 1;
int output_height = input_height - layer->kernel_size + 1;
int weights_size = layer->input_channels * layer->output_channels * layer->kernel_size * layer->kernel_size;
int biases_size = layer->output_channels;
for (int i = 0; i < layer->output_channels; i++) {
for (int j = 0; j < output_width; j++) {
for (int k = 0; k < output_height; k++) {
float sum = 0.0f;
for (int l = 0; l < layer->input_channels; l++) {
for (int m = 0; m < layer->kernel_size; m++) {
for (int n = 0; n < layer->kernel_size; n++) {
sum += input[l * input_width * input_height + (j + m) * input_height + (k + n)] * layer->weights[((l * layer->output_channels + i) * layer->kernel_size + m) * layer->kernel_size + n];
}
}
}
output[i * output_width * output_height + j * output_height + k] = sum + layer->biases[i];
}
}
}
}
// 定义残差块的结构体
typedef struct {
ConvLayer conv1; // 第一个卷积层
ConvLayer conv2; // 第二个卷积层
float *residual; // 残差连接
} ResidualBlock;
// 初始化残差块的函数
void InitResidualBlock(ResidualBlock *block, int input_channels, int output_channels) {
InitConvLayer(&block->conv1, input_channels, output_channels, 3);
InitConvLayer(&block->conv2, output_channels, output_channels, 3);
block->residual = (float *)malloc(MAX_CHANNELS * sizeof(float));
}
// 残差块的前向传播函数
void ResidualBlockForward(ResidualBlock *block, float *input, float *output, int input_width, int input_height) {
ConvLayerForward(&block->conv1, input, output, input_width, input_height);
for (int i = 0; i < block->conv1.output_channels; i++) {
for (int j = 0; j < input_width - 2; j++) {
for (int k = 0; k < input_height - 2; k++) {
block->residual[i * (input_width - 2) * (input_height - 2) + j * (input_height - 2) + k] = output[i * (input_width - 2) * (input_height - 2) + j * (input_height - 2) + k];
}
}
}
for (int i = 0; i < block->conv1.output_channels; i++) {
for (int j = 0; j < input_width - 2; j++) {
for (int k = 0; k < input_height - 2; k++) {
output[i * (input_width - 2) * (input_height - 2) + j * (input_height - 2) + k] = fmaxf(0.0f, block->residual[i * (input_width - 2) * (input_height - 2) + j * (input_height - 2) + k]);
}
}
}
ConvLayerForward(&block->conv2, output, output, input_width - 2, input_height - 2);
for (int i = 0; i < block->conv2.output_channels; i++) {
for (int j = 0; j < input_width - 4; j++) {
for (int k = 0; k < input_height - 4; k++) {
output[i * (input_width - 4) * (input_height - 4) + j * (input_height - 4) + k] += block->residual[i * (input_width - 2) * (input_height - 2) + (j + 1) * (input_height - 2) + (k + 1)];
}
}
}
}
// 定义ResNet的结构体
typedef struct {
ResidualBlock blocks[16];
ConvLayer conv;
int input_width;
int input_height;
} ResNet;
// 初始化ResNet的函数
void InitResNet(ResNet *resnet, int input_width, int input_height) {
resnet->input_width = input_width;
resnet->input_height = input_height;
InitConvLayer(&resnet->conv, 3, MAX_CHANNELS, 7);
for (int i = 0; i < 16; i++) {
if (i == 0) {
InitResidualBlock(&resnet->blocks[i], MAX_CHANNELS, MAX_CHANNELS * 2);
} else {
InitResidualBlock(&resnet->blocks[i], MAX_CHANNELS * 2, MAX_CHANNELS * 2);
}
}
}
// ResNet的前向传播函数
void ResNetForward(ResNet *resnet, float *input, float *output) {
ConvLayerForward(&resnet->conv, input, output, resnet->input_width, resnet->input_height);
for (int i = 0; i < resnet->conv.output_channels; i++) {
for (int j = 0; j < resnet->input_width - 6; j++) {
for (int k = 0; k < resnet->input_height - 6; k++) {
output[i * (resnet->input_width - 6) * (resnet->input_height - 6) + j * (resnet->input_height - 6) + k] = fmaxf(0.0f, output[i * (resnet->input_width - 6) * (resnet->input_height - 6) + j * (resnet->input_height - 6) + k]);
}
}
}
ResidualBlockForward(&resnet->blocks[0], output, output, resnet->input_width - 6, resnet->input_height - 6);
for (int i = 1; i < 16; i++) {
ResidualBlockForward(&resnet->blocks[i], output, output, resnet->input_width - 6, resnet->input_height - 6);
}
}
// 测试代码
int main() {
int input_width = 224;
int input_height = 224;
float *input = (float *)malloc(3 * input_width * input_height * sizeof(float));
for (int i = 0; i < 3 * input_width * input_height; i++) {
input[i] = (float)rand() / RAND_MAX;
}
float *output = (float *)malloc(MAX_CHANNELS * (input_width - 6) * (input_height - 6) * sizeof(float));
ResNet resnet;
InitResNet(&resnet, input_width, input_height);
ResNetForward(&resnet, input, output);
printf("Output: %f\n", output[0]);
free(input);
free(output);
return 0;
}