先提供两个网址

关于DAIN的介绍，大家可以点击这条链接

本人研究方向也不在 视频插帧这个方向上，只是想用一用最新的深度学习视频插帧算法。DAIN在linux下配置十分复杂，复现比较困难，贴上github教程：

第一波

linux下实现DAIN的方法

Installation

Download repository:

$ git clone https://github.com/baowenbo/DAIN.git

Before building Pytorch extensions, be sure you have pytorch >= 1.0.0:

$ python -c "import torch; print(torch.__version__)"

Generate our PyTorch extensions:

$ cd DAIN
$ cd my_package 
$ ./build.sh

Generate the Correlation package required by :

$ cd ../PWCNet/correlation_package_pytorch1_0
$ ./build.sh

Testing Pre-trained Models

Make model weights dir and Middlebury dataset dir:

$ cd DAIN
$ mkdir model_weights
$ mkdir MiddleBurySet

Download pretrained models,

$ cd model_weights
$ wget http://vllab1.ucmerced.edu/~wenbobao/DAIN/best.pth

and Middlebury dataset:

$ cd ../MiddleBurySet
$ wget http://vision.middlebury.edu/flow/data/comp/zip/other-color-allframes.zip
$ unzip other-color-allframes.zip
$ wget http://vision.middlebury.edu/flow/data/comp/zip/other-gt-interp.zip
$ unzip other-gt-interp.zip
$ cd ..

preinstallations:

$ cd PWCNet/correlation_package_pytorch1_0
$ sh build.sh
$ cd ../my_package
$ sh build.sh
$ cd ..

We are good to go by:

$ CUDA_VISIBLE_DEVICES=0 python demo_MiddleBury.py

The interpolated results are under MiddleBurySet/other-result-author/[random number]/, where the random number is used to distinguish different runnings.

Downloading Results

Our DAIN model achieves the state-of-the-art performance on the UCF101, Vimeo90K, and Middlebury ( and other). Download our interpolated results with:

$ wget http://vllab1.ucmerced.edu/~wenbobao/DAIN/UCF101_DAIN.zip
$ wget http://vllab1.ucmerced.edu/~wenbobao/DAIN/Vimeo90K_interp_DAIN.zip
$ wget http://vllab1.ucmerced.edu/~wenbobao/DAIN/Middlebury_eval_DAIN.zip
$ wget http://vllab1.ucmerced.edu/~wenbobao/DAIN/Middlebury_other_DAIN.zip

Slow-motion Generation

Our model is fully capable of generating slow-motion effect with minor modification on the network architecture. Run the following code by specifying time_step = 0.25 to generate x4 slow-motion effect:

$ CUDA_VISIBLE_DEVICES=0 python demo_MiddleBury_slowmotion.py --netName DAIN_slowmotion --time_step 0.25

or set time_step to 0.125 or 0.1 as follows

$ CUDA_VISIBLE_DEVICES=0 python demo_MiddleBury_slowmotion.py --netName DAIN_slowmotion --time_step 0.125
$ CUDA_VISIBLE_DEVICES=0 python demo_MiddleBury_slowmotion.py --netName DAIN_slowmotion --time_step 0.1

to generate x8 and x10 slow-motion respectively. Or if you would like to have x100 slow-motion for a little fun.

$ CUDA_VISIBLE_DEVICES=0 python demo_MiddleBury_slowmotion.py --netName DAIN_slowmotion --time_step 0.01

You may also want to create gif animations by:

$ cd MiddleBurySet/other-result-author/[random number]/Beanbags
$ convert -delay 1 *.png -loop 0 Beanbags.gif //1*10ms delay

Have fun and enjoy yourself!

windows使用DAIN-APP

打开选一个版本下载下来。我是3070的显卡，所以看到这个适配30x0的更新版本，毫不犹豫就选他了： 这是一个免安装的软件，直接解压安装包后，就可以打开应用程序了： 软件打开是这样一个界面：（我正在把一个25FPS的视频插帧到200FPS） 需要注意的一点是：batch size这一项可能得自己根据视频的质量来试一试，过大可能会导致爆显存。我这个视频是640*512 25fps 一分多钟，64batch16batch都试过都爆显存，4可以正常运行。 软件的具体使用方法很简单，我就不多介绍了，看看插帧的效果再来做补充吧…

第二波来了

25fps插帧到200fps完成了，1分半的视频跑了一下午。 现在我把200fps的视频又输入了，准备弄个1600fps的视频 试了试 batch size=8，发现显存接近跑满，没有报错

没有后续了，等了好久之后报错内存不够了。只有把视频拆成多个视频进行处理了。