1. 安装
- 目前只关心在linux上的安装,内容可以参考 官方教程
- 步骤:
git clone https://github.com/Tencent/ncnn.git
cd ncnn
git submodule update --init
wget https://sdk.lunarg.com/sdk/download/1.2.154.0/linux/vulkansdk-linux-x86_64-1.2.154.0.tar.gz?Human=true -O vulkansdk-linux-x86_64-1.2.154.0.tar.gz
tar -xf vulkansdk-linux-x86_64-1.2.154.0.tar.gz
export VULKAN_SDK=$(pwd)/1.2.154.0/x86_64
cd ncnn
mkdir -p build
cd build
cmake -DCMAKE_BUILD_TYPE=Release -DNCNN_VULKAN=ON -DNCNN_SYSTEM_GLSLANG=ON -DNCNN_BUILD_EXAMPLES=ON ..
make -j
- 这些步骤都很简单,有问题也是一些C++、CMake的问题
2. 第一个样例 squeezenet
- 运行:在编译好后,在
/path/to/ncnn/examples
目录下运行 ../build/examples/squeezenet ../images/256-ncnn.png
即可。
- 程序入口在
/path/to/ncnn/examples/squeezenet.cpp
,源码如下
#include "net.h"
#include <algorithm>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <stdio.h>
#include <vector>
static int detect_squeezenet(const cv::Mat& bgr, std::vector<float>& cls_scores)
{
ncnn::Net squeezenet;
squeezenet.opt.use_vulkan_compute = true;
squeezenet.load_param("squeezenet_v1.1.param");
squeezenet.load_model("squeezenet_v1.1.bin");
ncnn::Mat in = ncnn::Mat::from_pixels_resize(bgr.data, ncnn::Mat::PIXEL_BGR, bgr.cols, bgr.rows, 227, 227);
const float mean_vals[3] = {
104.f, 117.f, 123.f};
in.substract_mean_normalize(mean_vals, 0);
ncnn::Extractor ex = squeezenet.create_extractor();
ex.input("data", in);
ncnn::Mat out;
ex.extract("prob", out);
cls_scores.resize(out.w);
for (int j = 0; j < out.w; j++)
{
cls_scores[j] = out[j];
}
return 0;
}
static int print_topk(const std::vector<float>& cls_scores, int topk)
{
int size = cls_scores.size();
std::vector<std::pair<float, int> > vec;
vec.resize(size);
for (int i = 0; i < size; i++)
{
vec[i] = std::make_pair(cls_scores[i], i);
}
std::partial_sort(vec.begin(), vec.begin() + topk, vec.end(),
std::greater<std::pair<float, int> >());
for (int i = 0; i < topk; i++)
{
float score = vec[i].first;
int index = vec[i].second;
fprintf(stderr, "%d = %f\n", index, score);
}
return 0;
}
int main(int argc, char** argv)
{
if (argc != 2)
{
fprintf(stderr, "Usage: %s [imagepath]\n", argv[0]);
return -1;
}
const char* imagepath = argv[1];
cv::Mat m = cv::imread(imagepath, 1);
if (m.empty())
{
fprintf(stderr, "cv::imread %s failed\n", imagepath);
return -1;
}
std::vector<float> cls_scores;
detect_squeezenet(m, cls_scores);
print_topk(cls_scores, 3);
return 0;
}