Caffe源码中Pooling Layer文件分析

Caffe源码(caffe version commit: 09868ac , date: 2015.08.15)中有一些重要的头文件，这里介绍下include/caffe/vision_layers文件中PoolingLayer类，在最新版caffe中，PoolingLayer类被单独放在了include/caffe/layers/pooling_layer.hpp文件中，这两个文件中PoolingLayer类的内容及实现是完全一致的：

1．  include文件：

(1)、<caffe/blob.hpp>：此文件的介绍可以参考：http://blog.csdn.net/fengbingchun/article/details/59106613

(2)、<caffe/layer.hpp>：此文件的介绍可以参考：http://blog.csdn.net/fengbingchun/article/details/60871052

(3)、<caffe/proto/caffe.pb.h>：此文件的介绍可以参考：http://blog.csdn.net/fengbingchun/article/details/55267162

2．  类PoolingLayer：池化层，Layer类的子类

Pooling layer的主要作用是降维，缩小feature map，图像降采样，方法有：

(1)、均值采样：取区域平均值作为降采样值；

(2)、最大值采样：取区域最大值作为降采样值；

(3)、随机采样：取区域内随机一个像素值。

<caffe/layers/pooling_layer.hpp>文件的详细介绍如下：

#ifndef CAFFE_POOLING_LAYER_HPP_ #define CAFFE_POOLING_LAYER_HPP_ #include <vector> #include "caffe/blob.hpp" #include "caffe/layer.hpp" #include "caffe/proto/caffe.pb.h" namespace caffe { /** * @brief Pools the input image by taking the max, average, etc. within regions. * * TODO(dox): thorough documentation for Forward, Backward, and proto params. */ // 池化层，Layer类的子类，图像降采样，有三种Pooling方法：Max、Avx、Stochastic template <typename Dtype> class PoolingLayer : public Layer<Dtype> { public: // 显示构造函数 explicit PoolingLayer(const LayerParameter& param) : Layer<Dtype>(param) {} // 参数初始化，通过类PoolingParameter获取成员变量值，包括： // global_pooling_、kernel_h_、kernel_w_、pad_h_、pad_w_、stride_h_、stride_w_ virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top); // 调整top blobs的shape，并有可能会reshape rand_idx_或max_idx_； // 获取成员变量值，包括：channels_、height_、width_、pooled_height_、pooled_width_ virtual void Reshape(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top); // 获得Pooling layer的类型: Pooling virtual inline const char* type() const { return "Pooling"; } // 获得Pooling layer所需的bottom blobs的个数: 1 virtual inline int ExactNumBottomBlobs() const { return 1; } // 获得Pooling layer所需的bottom blobs的最少个数: 1 virtual inline int MinTopBlobs() const { return 1; } // MAX POOL layers can output an extra top blob for the mask; // others can only output the pooled inputs. // 获得Pooling layer所需的bottom blobs的最多个数: Max为2，其它(Avg, Stochastic)为1 virtual inline int MaxTopBlobs() const { return (this->layer_param_.pooling_param().pool() == PoolingParameter_PoolMethod_MAX) ? 2 : 1; } protected: // CPU实现Pooling layer的前向传播，仅有Max和Ave两种方法实现 virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top); // GPU实现Pooling layer的前向传播，Max、Ave、Stochastic三种方法实现 virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top); // CPU实现Pooling layer的反向传播，仅有Max和Ave两种方法实现 virtual void Backward_cpu(const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom); // GPU实现Pooling layer的反向传播，Max、Ave、Stochastic三种方法实现 virtual void Backward_gpu(const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom); // Caffe中类的成员变量名都带有后缀"_"，这样就容易区分临时变量和类成员变量 int kernel_h_, kernel_w_; // 滤波器(卷积核)大小 int stride_h_, stride_w_; // 步长大小 int pad_h_, pad_w_; // 图像扩充大小 int channels_; // 图像通道数 int height_, width_; // 图像高、宽 // 池化后图像高、宽 // pooled_height_ = (height_ + 2 * pad_h_ - kernel_h_) / stride_h_)) + 1 // pooled_width_ = (width_ + 2 * pad_w_ - kernel_w_) / stride_w_)) + 1 int pooled_height_, pooled_width_; bool global_pooling_; // 是否全区域池化(将整幅图像降采样为1*1) Blob<Dtype> rand_idx_; // 随机采样索引,Pooling方法为STOCHASTIC时用到并会Reshape Blob<int> max_idx_; // 最大值采样索引，Pooling方法为MAX时用到并会Reshape }; } // namespace caffe #endif // CAFFE_POOLING_LAYER_HPP_ 在caffe.proto文件中，有一个message是与pooling layer 相关的，如下： message PoolingParameter { // Pooling层参数类 enum PoolMethod { // 枚举类型，Pooling的方法：Max(最大值采样)、AVE(均值采样)、STOCHASTIC(随机采样) MAX = 0; AVE = 1; STOCHASTIC = 2; } optional PoolMethod pool = 1 [default = MAX]; // The pooling method, pooling方法 // Pad, kernel size, and stride are all given as a single value for equal // dimensions in height and width or as Y, X pairs. optional uint32 pad = 4 [default = 0]; // The padding size (equal in Y, X)，图像扩充大小(添加图像边界的像素大小) optional uint32 pad_h = 9 [default = 0]; // The padding height,图像扩充大小，Y optional uint32 pad_w = 10 [default = 0]; // The padding width，图像扩充大小，X optional uint32 kernel_size = 2; // The kernel size (square)，滤波器(卷积核、滑动窗)的大小(高=宽) optional uint32 kernel_h = 5; // The kernel height，滤波器(卷积核、滑动窗)的高 optional uint32 kernel_w = 6; // The kernel width，滤波器(卷积核、滑动窗)的宽 optional uint32 stride = 3 [default = 1]; // The stride (equal in Y, X),滑动步长(高=宽)，卷积核卷积时平移的步幅 optional uint32 stride_h = 7; // The stride height,滑动步长，高 optional uint32 stride_w = 8; // The stride width，滑动步长，宽 enum Engine { DEFAULT = 0; CAFFE = 1; CUDNN = 2; } optional Engine engine = 11 [default = DEFAULT]; // // If global_pooling then it will pool over the size of the bottom by doing // kernel_h = bottom->height and kernel_w = bottom->width optional bool global_pooling = 12 [default = false]; // 是否是全区域池化 } pooling layer的测试代码如下：

#include "funset.hpp" #include <string> #include <vector> #include "common.hpp" int test_caffe_layer_pooling() { caffe::Caffe::set_mode(caffe::Caffe::CPU); // set run caffe mode // set layer parameter caffe::LayerParameter layer_param; layer_param.set_phase(caffe::Phase::TRAIN); // cv::Mat -> caffe::Blob std::string image_name = "E:/GitCode/Caffe_Test/test_data/images/a.jpg"; cv::Mat mat1 = cv::imread(image_name, 1); if (!mat1.data) { fprintf(stderr, "read image fail: %s\n", image_name.c_str()); return -1; } mat1.convertTo(mat1, CV_32FC3); std::vector<cv::Mat> mat2; cv::split(mat1, mat2); std::vector<int> mat_reshape{ 1, (int)mat2.size(), mat2[0].rows, mat2[0].cols }; caffe::Blob<float> blob; blob.Reshape(mat_reshape); size_t size = mat2[0].rows * mat2[0].cols; float* data = new float[mat2.size() * size]; memcpy(data, mat2[0].data, size * sizeof(float)); memcpy(data + size, mat2[1].data, size * sizeof(float)); memcpy(data + 2 * size, mat2[2].data, size * sizeof(float)); blob.set_cpu_data(data); for (int method = 0; method < 2; ++method) { // set pooling parameter caffe::PoolingParameter* pooling_param = layer_param.mutable_pooling_param(); if (method == 0) pooling_param->set_pool(caffe::PoolingParameter::MAX); else pooling_param->set_pool(caffe::PoolingParameter::AVE); pooling_param->set_kernel_size(3); pooling_param->set_pad(2); pooling_param->set_stride(2); pooling_param->set_global_pooling(false); std::vector<caffe::Blob<float>*> bottom_blob{ &blob }, top_blob{ &caffe::Blob<float>()/*, &caffe::Blob<float>() */ }; // test PoolingLayer function caffe::PoolingLayer<float> pooling_layer(layer_param); pooling_layer.SetUp(bottom_blob, top_blob); fprintf(stderr, "top blob info: channels: %d, height: %d, width: %d\n", top_blob[0]->channels(), top_blob[0]->height(), top_blob[0]->width()); pooling_layer.Forward(bottom_blob, top_blob); int height = top_blob[0]->height(); int width = top_blob[0]->width(); const float* p = top_blob[0]->cpu_data(); std::vector<cv::Mat> mat3{ cv::Mat(height, width, CV_32FC1, (float*)p), cv::Mat(height, width, CV_32FC1, (float*)(p + height * width)), cv::Mat(height, width, CV_32FC1, (float*)(p + height * width * 2)) }; cv::Mat mat4; cv::merge(mat3, mat4); mat4.convertTo(mat4, CV_8UC3); if (method == 0) image_name = "E:/GitCode/Caffe_Test/test_data/images/forward0.jpg"; else image_name = "E:/GitCode/Caffe_Test/test_data/images/forward1.jpg"; cv::imwrite(image_name, mat4); for (int i = 0; i < bottom_blob[0]->count(); ++i) bottom_blob[0]->mutable_cpu_diff()[i] = bottom_blob[0]->cpu_data()[i]; for (int i = 0; i < top_blob[0]->count(); ++i) top_blob[0]->mutable_cpu_diff()[i] = top_blob[0]->cpu_data()[i]; std::vector<bool> propagate_down{ true }; pooling_layer.Backward(top_blob, propagate_down, bottom_blob); height = bottom_blob[0]->height(); width = bottom_blob[0]->width(); p = bottom_blob[0]->cpu_diff(); std::vector<cv::Mat> mat5{ cv::Mat(height, width, CV_32FC1, (float*)p), cv::Mat(height, width, CV_32FC1, (float*)(p + height * width)), cv::Mat(height, width, CV_32FC1, (float*)(p + height * width * 2)) }; cv::Mat mat6; cv::merge(mat5, mat6); mat6.convertTo(mat6, CV_8UC3); if (method == 0) image_name = "E:/GitCode/Caffe_Test/test_data/images/backward0.jpg"; else image_name = "E:/GitCode/Caffe_Test/test_data/images/backward1.jpg"; cv::imwrite(image_name, mat6); } delete[] data; return 0; } 执行结果如下图：

图像结果(Lena.jpg)如下：前向传播、反向传播，前两幅为Max，后两幅为Avg方法

GitHub：https://github.com/fengbingchun/Caffe_Test