faq¶
如何加入技术交流QQ群？¶
编译¶
怎么交叉编译？cmake 工具链怎么设置啊？¶
参见 https://github.com/Tencent/ncnn/wiki/how-to-build
The submodules were not downloaded! Please update submodules with “git submodule update –init” and try again¶
如上，下载完整源码。或者按提示执行: git submodule update –init
Could NOT find Protobuf (missing: Protobuf_INCLUDE_DIR)¶
sudo apt-get install libprotobuf-dev protobuf-compiler
Could NOT find CUDA (missing: CUDA_TOOLKIT_ROOT_DIR CUDA_INCLUDE_DIRS CUDA_CUDART_LIBRARY)¶
https://github.com/Tencent/ncnn/issues/1873
Could not find a package configuration file provided by “OpenCV” with any of the following names: OpenCVConfig.cmake opencv-config.cmake¶
sudo apt-get install libopencv-dev
或者自行编译安装，set(OpenCV_DIR {OpenCVConfig.cmake所在目录})
Could not find a package configuration file provided by “ncnn” with any of the following names: ncnnConfig.cmake ncnn-config.cmake¶
set(ncnn_DIR {ncnnConfig.cmake所在目录})
如何安装 vulkan sdk¶
找不到库（需要根据系统/编译器指定）¶
undefined reference to __kmpc_for_static_init_4 __kmpc_for_static_fini __kmpc_fork_call …
需要链接openmp库
undefined reference to vkEnumerateInstanceExtensionProperties vkGetInstanceProcAddr vkQueueSubmit …
需要 vulkan-1.lib
undefined reference to glslang::InitializeProcess() glslang::TShader::TShader(EShLanguage) …
需要 glslang.lib OGLCompiler.lib SPIRV.lib OSDependent.lib
undefined reference to AAssetManager_fromJava AAssetManager_open AAsset_seek …
find_library和target_like_libraries中增加 android
find_package(ncnn)
undefined reference to typeinfo for ncnn::Layer¶
opencv rtti -> opencv-mobile
undefined reference to __cpu_model¶
升级编译器 / libgcc_s libgcc
unrecognized command line option “-mavx2”¶
升级 gcc
为啥自己编译的ncnn android库特别大？¶
https://github.com/Tencent/ncnn/wiki/build-for-android.zh 以及见 如何裁剪更小的 ncnn 库
ncnnoptimize和自定义层¶
先ncnnoptimize再增加自定义层，避免ncnnoptimize不能处理自定义层保存。
rtti/exceptions冲突¶
产生原因是项目工程中使用的库配置不一样导致冲突，根据自己的实际情况分析是需要开启还是关闭。ncnn默认是ON，在重新编译ncnn时增加以下2个参数即可：
开启：-DNCNN_DISABLE_RTTI=OFF -DNCNN_DISABLE_EXCEPTION=OFF
关闭：-DNCNN_DISABLE_RTTI=ON -DNCNN_DISABLE_EXCEPTION=ON
error: undefined symbol: ncnn::Extractor::extract(char const*, ncnn::Mat&)¶
可能的情况：
尝试升级 Android Studio 的 NDK 版本
怎样添加ncnn库到项目中？cmake方式怎么用？¶
编译ncnn，make install。linux/windows set/export ncnn_DIR 指向 isntall目录下下包含ncnnConfig.cmake 的目录
android¶
ios¶
linux¶
windows¶
macos¶
arm linux¶
转模型问题¶
caffe¶
./caffe2ncnn caffe.prototxt caffe.caffemodel ncnn.param ncnn.bin
mxnet¶
./mxnet2ncnn mxnet-symbol.json mxnet.params ncnn.param ncnn.bin
pytorch - onnx¶
tensorflow 1.x/2.x - keras¶
tensorflow 2.x - mlir¶
Shape not supported yet! Gather not supported yet! Cast not supported yet!¶
onnx-simplifier 静态shape
convertmodel¶
怎么生成有固定 shape 信息的模型？¶
Input 0=w 1=h 2=c
why gpu能更快¶
ncnnoptimize 怎么转成 fp16 模型¶
ncnnoptimize model.param model.bin yolov5s-opt.param yolov5s-opt.bin 65536
ncnnoptimize 怎样查看模型的 FLOPS / 内存占用情况¶
怎么修改模型支持动态 shape？¶
Interp Reshape
如何将模型转换为代码内嵌到程序里？¶
ncnn2mem
如何加密模型？¶
https://zhuanlan.zhihu.com/p/268327784
Linux下转的ncnn模型，Windows/MacOS/Android/.. 也能直接用吗？¶
Yes，全平台通用
如何去掉后处理，再导出 onnx？¶
检测：
参考up的一篇文章(https://zhuanlan.zhihu.com/p/128974102)，步骤三就是去掉后处理,再导出onnx,其中去掉后处理可以是项目内测试时去掉后续步骤的结果。
pytorch 有的层导不出 onnx 怎么办？¶
ONNX_ATEN_FALLBACK 完全自定义的op，先改成能导出的（如 concat slice），转到 ncnn 后再修改 param
使用¶
vkEnumeratePhysicalDevices failed -3¶
vkCreateInstance failed -9¶
驱动
ModuleNotFoundError: No module named ‘ncnn.ncnn’¶
python setup.py develop
fopen nanodet-m.param failed¶
文件路径 working dir
File not found or not readable. Make sure that XYZ.param/XYZ.bin is accessible.
find_blob_index_by_name data / output / … failed¶
layer name vs blob name
param.bin 应该用 xxx.id.h 的枚举
parse magic failed¶
param is too old, please regenerate¶
模型本身有问题
Your model file is being the old format converted by an old caffe2ncnn tool.
Checkout the latest ncnn code, build it and regenerate param and model binary files, and that should work.
Make sure that your param file starts with the magic number 7767517.
you may find more info on use-ncnn-with-alexnet
set_vulkan_compute failed, network use_vulkan_compute disabled¶
你应该在 load_param / load_model 之前设置 net.opt.use_vulkan_compute = true;
多个blob输入，多个blob输出，怎么做？¶
多次执行ex.input() 和 ex.extract()
Extractor extract 多次会重复计算吗？¶
不会
如何看每一层的耗时？¶
cmake -DNCNN_BENCHMARK=ON ..
如何转换 cv::Mat CV_8UC3 BGR 图片¶
from_pixels to_pixels
如何转换 float 数据为 ncnn::Mat¶
首先，自己申请的内存需要自己管理，此时ncnn::Mat不会自动给你释放你传过来的float数据
std::vector<float> testData(60, 1.0); // 利用std::vector<float>自己管理内存的申请和释放
ncnn::Mat in1(60, (void*)testData.data()).reshape(4, 5, 3); // 把float数据的指针转成void*传过去即可，甚至还可以指定维度(up说最好使用reshape用来解决channel gap)
float* a = new float[60]; // 自己new一块内存，后续需要自己释放
ncnn::Mat in2 = ncnn::Mat(60, (void*)a).reshape(4, 5, 3).clone(); // 使用方法和上面相同，clone() to transfer data owner
如何初始化 ncnn::Mat 为全 0¶
mat.fill(0.f);
如何 resize crop rotate 图片¶
为什么我使用GPU，但是GPU占用为0¶
windows 10 任务管理器 - 性能选项卡 - GPU - 选择其中一个视图左上角的下拉箭头切换到 Compute_0 / Compute_1 / Cuda
你还可以安装软件：GPU-Z
layer XYZ not exists or registered¶
Your network contains some operations that are not implemented in ncnn.
You may implement them as custom layer followed in how-to-implement-custom-layer-step-by-step.
Or you could simply register them as no-op if you are sure those operations make no sense.
network graph not ready¶
You shall call Net::load_param() first, then Net::load_model().
This error may also happens when Net::load_param() failed, but not properly handled.
For more information about the ncnn model load api, see ncnn-load-model
memory not 32-bit aligned at XYZ¶
The pointer passed to Net::load_param() or Net::load_model() is not 32bit aligned.
In practice, the head pointer of std::vector is not guaranteed to be 32bit aligned.
you can store your binary buffer in ncnn::Mat structure, its internal memory is aligned.
crash on android with ‘__kmp_abort_process’¶
This usually happens if you bundle multiple shared library with openmp linked
It is actually an issue of the android ndk https://github.com/android/ndk/issues/1028
On old android ndk, modify the link flags as
-Wl,-Bstatic -lomp -Wl,-Bdynamic
For recent ndk >= 21
-fstatic-openmp
dlopen failed: library “libomp.so” not found¶
Newer android ndk defaults to dynamic openmp runtime
modify the link flags as
-fstatic-openmp -fopenmp
crash when freeing a ncnn dynamic library(.dll/.so) built with openMP¶
for optimal performance, the openmp threadpool spin waits for about a second prior to shutting down in case more work becomes available.
If you unload a dynamic library that’s in the process of spin-waiting, it will crash in the manner you see (most of the time).
Just set OMP_WAIT_POLICY=passive in your environment, before calling loadlibrary. or Just wait a few seconds before calling freelibrary.
You can also use the following method to set environment variables in your code:
for msvc++:
SetEnvironmentVariable(_T("OMP_WAIT_POLICY"), _T("passive"));
for g++:
setenv("OMP_WAIT_POLICY", "passive", 1)
reference: https://stackoverflow.com/questions/34439956/vc-crash-when-freeing-a-dll-built-with-openmp
如何打印 ncnn::Mat 的值？¶
如何可视化 ncnn::Mat 的值？¶
总是输出第一张图的结果¶
复用 Extractor？！
启用fp16时的精度有差异¶
net.opt.use_fp16_packed = false;
net.opt.use_fp16_storage = false;
net.opt.use_fp16_arithmetic = false;
如何跑得更快？内存占用更少？库体积更小？¶
fp32 fp16¶
大小核绑定¶
ncnn::set_cpu_powersave(int)绑定大核或小核 注意windows系统不支持绑核。 ncnn支持不同的模型运行在不同的核心。假设硬件平台有2个大核，4个小核，你想把netA运行在大核，netB运行在小核。 可以通过std::thread or pthread创建两个线程，运行如下代码： 0:全部 1:小核 2:大核
ncnnoptimize¶
使用方式一：
./ncnnoptimize ncnn.param ncnn.bin new.param new.bin flag
注意这里的flag指的是fp32和fp16，其中0指的是fp32，1指的是fp16
使用方式二：
./ncnnoptimize ncnn.param ncnn.bin new.param new.bin flag cutstartname cutendname
cutstartname：模型截取的起点
cutendname：模型截取的终点
如何使用量化工具？¶
如何设置线程数？¶
opt.num_threads
如何 batch inference？¶
partial graph inference¶
先 extract 分类，判断后，再 extract bbox
如何启用 bf16s 加速？¶
如何裁剪更小的 ncnn 库？¶
net.opt sgemm winograd fp16_storage 各是有什么作用？¶
对内存消耗的影响
白嫖项目¶
nanodet¶
其他¶
up主用的什么系统/编辑器/开发环境？¶
ex.input("data1", in);
ex.input("data2", in);
ex.extract("output1", out);
ex.extract("output2", out);
// 计算变换矩阵 并且求逆变换
int type = 0; // 0->区域外填充为v[0],v[1],v[2], -233->区域外不处理
unsigned int v = 0;
float tm[6];
float tm_inv[6];
// 人脸区域在原图上的坐标和宽高
float src_x = target->det.rect.x / target->det.w * pIveImageU8C3->u32Width;
float src_y = target->det.rect.y / target->det.h * pIveImageU8C3->u32Height;
float src_w = target->det.rect.w / target->det.w * pIveImageU8C3->u32Width;
float src_h = target->det.rect.h / target->det.h * pIveImageU8C3->u32Height;
float point_src[10] = {
src_x + src_w * target->attr.land[0][0], src_x + src_w * target->attr.land[0][1],
src_x + src_w * target->attr.land[1][0], src_x + src_w * target->attr.land[1][1],
src_x + src_w * target->attr.land[2][0], src_x + src_w * target->attr.land[2][1],
src_x + src_w * target->attr.land[3][0], src_x + src_w * target->attr.land[3][1],
src_x + src_w * target->attr.land[4][0], src_x + src_w * target->attr.land[4][1],
};
float point_dst[10] = { // +8 是因为我们处理112*112的图
30.2946f + 8.0f, 51.6963f,
65.5318f + 8.0f, 51.5014f,
48.0252f + 8.0f, 71.7366f,
33.5493f + 8.0f, 92.3655f,
62.7299f + 8.0f, 92.2041f,
};
// 第一种方式：先计算变换在求逆
AffineTrans::get_affine_transform(point_src, point_dst, 5, tm);
AffineTrans::invert_affine_transform(tm, tm_inv);
// 第二种方式：直接拿到求逆的结果
// AffineTrans::get_affine_transform(point_dst, point_src, 5, tm_inv);
// rgb 分离的，所以要单独处理
for(int c = 0; c < 3; c++)
{
unsigned char* pSrc = malloc(xxx);
unsigned char* pDst = malloc(xxx);
ncnn::warpaffine_bilinear_c1(pSrc, SrcWidth, SrcHeight, SrcStride[c], pDst, DstWidth, DstHeight, DstStride[c], tm_inv, type, v);
}
// rgb packed则可以一次处理
ncnn::warpaffine_bilinear_c3(pSrc, SrcWidth, SrcHeight, SrcStride, pDst, DstWidth, DstHeight, DstStride, tm_inv, type, v);
class Noop : public ncnn::Layer {};
DEFINE_LAYER_CREATOR(Noop)
net.register_custom_layer("LinearRegressionOutput", Noop_layer_creator);
net.register_custom_layer("MAERegressionOutput", Noop_layer_creator);
void pretty_print(const ncnn::Mat& m)
{
for (int q=0; q<m.c; q++)
{
const float* ptr = m.channel(q);
for (int y=0; y<m.h; y++)
{
for (int x=0; x<m.w; x++)
{
printf("%f ", ptr[x]);
}
ptr += m.w;
printf("\n");
}
printf("------------------------\n");
}
}
In Android Studio, printf will not work, you can use __android_log_print instead. Example :
#include <android/log.h> // Don't forget this
void pretty_print(const ncnn::Mat& m)
{
for (int q=0; q<m.c; q++)
{
for (int y=0; y<m.h; y++)
{
for (int x=0; x<m.w; x++)
{
__android_log_print(ANDROID_LOG_DEBUG,"LOG_TAG","ncnn Mat is : %f", m.channel(q).row(y)[x]);
}
}
}
}
void visualize(const char* title, const ncnn::Mat& m)
{
std::vector<cv::Mat> normed_feats(m.c);
for (int i=0; i<m.c; i++)
{
cv::Mat tmp(m.h, m.w, CV_32FC1, (void*)(const float*)m.channel(i));
cv::normalize(tmp, normed_feats[i], 0, 255, cv::NORM_MINMAX, CV_8U);
cv::cvtColor(normed_feats[i], normed_feats[i], cv::COLOR_GRAY2BGR);
// check NaN
for (int y=0; y<m.h; y++)
{
const float* tp = tmp.ptr<float>(y);
uchar* sp = normed_feats[i].ptr<uchar>(y);
for (int x=0; x<m.w; x++)
{
float v = tp[x];
if (v != v)
{
sp[0] = 0;
sp[1] = 0;
sp[2] = 255;
}
sp += 3;
}
}
}
int tw = m.w < 10 ? 32 : m.w < 20 ? 16 : m.w < 40 ? 8 : m.w < 80 ? 4 : m.w < 160 ? 2 : 1;
int th = (m.c - 1) / tw + 1;
cv::Mat show_map(m.h * th, m.w * tw, CV_8UC3);
show_map = cv::Scalar(127);
// tile
for (int i=0; i<m.c; i++)
{
int ty = i / tw;
int tx = i % tw;
normed_feats[i].copyTo(show_map(cv::Rect(tx * m.w, ty * m.h, m.w, m.h)));
}
cv::resize(show_map, show_map, cv::Size(0,0), 2, 2, cv::INTER_NEAREST);
cv::imshow(title, show_map);
}
void thread_1()
{
ncnn::set_cpu_powersave(2); // bind to big cores
netA.opt.num_threads = 2;
}
void thread_2()
{
ncnn::set_cpu_powersave(1); // bind to little cores
netB.opt.num_threads = 4;
}
net.opt.use_packing_layout = true;
net.opt.use_bf16_storage = true;
A53
| 软件类型 | 软件名称 |
|---|---|
| 系统 | Fedora |
| 桌面环境 | KDE |
| 编辑器 | Kate |
| 画草图 | kolourpaint |
| 画函数图像 | kmplot |
| bilibili直播 | OBS |