ncnn | performance neural network inference framework | Machine Learning library
kandi X-RAY | ncnn Summary
kandi X-RAY | ncnn Summary
ncnn is a high-performance neural network inference computing framework optimized for mobile platforms. ncnn is deeply considerate about deployment and uses on mobile phones from the beginning of design. ncnn does not have third party dependencies. it is cross-platform, and runs faster than all known open source frameworks on mobile phone cpu. Developers can easily deploy deep learning algorithm models to the mobile platform by using efficient ncnn implementation, create intelligent APPs, and bring the artificial intelligence to your fingertips. ncnn is currently being used in many Tencent applications, such as QQ, Qzone, WeChat, Pitu and so on. ncnn 是一个为手机端极致优化的高性能神经网络前向计算框架。ncnn 从设计之初深刻考虑手机端的部署和使用。无第三方依赖，跨平台，手机端 cpu 的速度快于目前所有已知的开源框架。基于 ncnn，开发者能够将深度学习算法轻松移植到手机端高效执行，开发出人工智能 APP，将 AI 带到你的指尖。ncnn 目前已在腾讯多款应用中使用，如 QQ，Qzone，微信，天天P图等。.
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ncnn Examples and Code Snippets
Lines of Code : 162License : Non-SPDX (NOASSERTION)
Copy#if NCNN_STDIO
int Convolution::load_model(FILE* binfp)
{
int nread;
union
{
struct
{
unsigned char f0;
unsigned char f1;
unsigned char f2;
unsigned char f3;
};
wget https://github.com/llvm/llvm-project/releases/download/llvmorg-11.0.0/openmp-11.0.0.src.tar.xz
tar -xf openmp-11.0.0.src.tar.xz
cd openmp-11.0.0.src
# apply some compilation fix
sed -i'' -e '/.size __kmp_unnamed_critical_addr/d' runtime/src/z_L // d寄存器全部使用 %P
// d reg matches %P
// a += b * c
float32x2_t _a = vld1_f32(a);
float32x2_t _b = vld1_f32(b);
float32x2_t _c = vld1_f32(c);
asm volatile(
"vmla.f32 %P0, %P2, %P3"
: "=w"(_a) // %0
: "0"(_a),
"w"(_b), // %2
"w"( Community Discussions
Trending Discussions on ncnn
QUESTION
I am implementing the nearest-neighborhood resizing algorithm for RGB image (unsigned char type). Considering the speed comparison with OpenCV's on Android ARMv8 platform, I find that OpenCV use cv::parallel_for_ for multi-threading speed up.
Thus, I dive into the corresponding source code of OpenCV's cv::resize(), copy and paste the code that actually run, put in my main.cpp. It contains a functor resizeNNInvoker, and cv::parallel_for_ that performs multi-thread calculation on this functor.
What makes me confuse is that cv::parallel_for_ version run faster than using my_parallel_for_, whose code keeps same as OpenCV's.
To make it more clear:
- Tested on Android armv8 platform
- **Compiling OpenCV with OpenMP multithread, turn of other parallel framwork
- Go to OpenCV's
cv::parallel_for_, change its source code to the same asmy_parallel_for_(see below) - Using 4 threads by
cv::setNumThreads(4), and binding 4 big cpu cores (using ncnn API) - All code compile under Release mode (via CMake)
- Test input image: width=7680,height=4320, target image size: 7680/3, 4320/3.
Time cost is as follow:
method time cost cv::parallel_for_ 3.24 ms my_parallel_for_ 7.67 ms inplace openmp 7.75 ms ...ANSWER
Answered 2021-Aug-11 at 06:54This is the code from OpenCV to select the actual threading framework:
QUESTION
I have these Images in a folder (~/Downloads/output_frames) I want to use the command I got from https://github.com/nihui/dain-ncnn-vulkan
ffmpeg -framerate 48 -i output_frames/%06d.png -i audio.m4a -c:a copy -crf 20 -c:v libx264 -pix_fmt yuv420p output.mp4
I get this error:
[image2 @ 0x14681b800] Could find no file with path 'output_frames/%06d.png' and index in the range 0-4
output_frames/%06d.png: No such file or directory
ANSWER
Answered 2021-May-27 at 15:43Use %08d.png as there are 8 digits.
QUESTION
I tried running realsr-ncnn-vulkan to just test upscaling a simple image on Google's Colab platform. https://github.com/nihui/realsr-ncnn-vulkan
The problem is that once I try to run it I'm getting the error
error while loading shared libraries: libvulkan.so.1: cannot open shared object file: No such file or directory
So what do I need to install prior before running it? Or is there no way to get vulkan working despite the pretty powerful GPU?
...ANSWER
Answered 2020-Oct-10 at 05:03You are missing the libraries:
depending on your setup you need:
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