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CAIN ncnn Vulkan

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ncnn implementation of CAIN, Channel Attention Is All You Need for Video Frame Interpolation.

cain-ncnn-vulkan uses ncnn project as the universal neural network inference framework.

Download

Download Windows/Linux/MacOS Executable for Intel/AMD/Nvidia GPU

https://github.com/nihui/cain-ncnn-vulkan/releases

This package includes all the binaries and models required. It is portable, so no CUDA or PyTorch runtime environment is needed :)

About CAIN

CAIN (Channel Attention Is All You Need for Video Frame Interpolation) (AAAI 2020)

https://github.com/myungsub/CAIN

Myungsub Choi, Heewon Kim, Bohyung Han, Ning Xu, Kyoung Mu Lee

2nd place in [AIM 2019 ICCV Workshop] - Video Temporal Super-Resolution Challenge

Project | Paper-AAAI (Download the paper [here] in case the AAAI link is broken) | Poster

Usages

Input two frame images, output one interpolated frame image.

Example Command

./cain-ncnn-vulkan -0 0.jpg -1 1.jpg -o 01.jpg
./cain-ncnn-vulkan -i input_frames/ -o output_frames/

Video Interpolation with FFmpeg

mkdir input_frames
mkdir output_frames

# find the source fps and format with ffprobe, for example 24fps, AAC
ffprobe input.mp4

# extract audio
ffmpeg -i input.mp4 -vn -acodec copy audio.m4a

# decode all frames
ffmpeg -i input.mp4 input_frames/frame_%06d.png

# interpolate 2x frame count
./cain-ncnn-vulkan -i input_frames -o output_frames

# encode interpolated frames in 48fps with audio
ffmpeg -framerate 48 -i output_frames/%06d.png -i audio.m4a -c:a copy -crf 20 -c:v libx264 -pix_fmt yuv420p output.mp4

Full Usages

Usage: cain-ncnn-vulkan -0 infile -1 infile1 -o outfile [options]...
       cain-ncnn-vulkan -i indir -o outdir [options]...

  -h                   show this help
  -v                   verbose output
  -0 input0-path       input image0 path (jpg/png/webp)
  -1 input1-path       input image1 path (jpg/png/webp)
  -i input-path        input image directory (jpg/png/webp)
  -o output-path       output image path (jpg/png/webp) or directory
  -m model-path        cain model path (default=cain)
  -g gpu-id            gpu device to use (default=auto) can be 0,1,2 for multi-gpu
  -j load:proc:save    thread count for load/proc/save (default=1:2:2) can be 1:2,2,2:2 for multi-gpu
  -f pattern-format    output image filename pattern format (%08d.jpg/png/webp, default=ext/%08d.png)

If you encounter a crash or error, try upgrading your GPU driver:

Build from Source

  1. Download and setup the Vulkan SDK from https://vulkan.lunarg.com/
dnf install vulkan-headers vulkan-loader-devel
apt-get install libvulkan-dev
pacman -S vulkan-headers vulkan-icd-loader
  1. Clone this project with all submodules
git clone https://github.com/nihui/cain-ncnn-vulkan.git
cd cain-ncnn-vulkan
git submodule update --init --recursive
  1. Build with CMake
mkdir build
cd build
cmake ../src
cmake --build . -j 4

TODO

Sample Images

Original Image

origin0 origin1

Interpolate with cain

cain-ncnn-vulkan.exe -0 0.png -1 1.png -o out.png

cain

Original CAIN Project

Other Open-Source Code Used