README.md

Loading videos to Tensor in PyTorch

中文博客地址link

There are many ways to load videos, but in oder to accelerate the training speed in video models, I tried different packages to see which one is the best.

The test videos is come from the MSVD dataset.

Structure

$load_video_as_tensor
    |──test_videos
        |──big_video
            |──00000.jpg
            |── ...
        |──long_video
            |──00000.jpg
            |── ...
        |──short_video
            |──00000.jpg
            |── ...
        |──small_video
            |──00000.jpg
            |── ...
        |──big_video.avi
        |──long_video.avi
        |──short_video.avi
        |──small_video.avi
    |──test_load_video_speed.py  # run experiment(all methods)
    |──video2jpg.py              # transfer videos to frame by frame images
    |──requirements.txt
    |──README.md

Install

1. Clone the project

git clone https://github.com/Horizon2333/load_video_as_tensor
cd load_video_as_tensor

2. Install dependencies

pip install -r requirements.txt

Usage

1. Transform the video to images (Because one experiment try to load frames) :

python video2jpg.py

2. Run the experiment:

python test_load_video_speed.py

Results

method	initial datatype	intermediate datatype	final datatype	short video cost /s	long video cost /s	small video cost /s	big video cost /s
opencv	ndarray	-	Tensor	0.11	1.09	0.07	12.82
opencv	ndarray	PILImage	Tensor	0.24	1.82	0.10	15.54
scikit-video	ndarray	-	Tensor	0.52	1.10	0.18	15.63
imageio	imageio.core.util.Array	ndarray	Tensor	0.74	3.03	0.30	16.51
vidgear	ndarray	-	Tensor	1.87	56.30	2.82	20.21
pyav	av.video.frame.VideoFrame	PILImage → ndarray	Tensor	0.50	1.91	0.11	13.91
frames	ndarray	-	Tensor	0.26	4.41	0.19	17.28
torchvision.io	Tensor	-	Tensor	0.42	2.64	0.09	13.80

(Test on computer with: Intel Core i7-9750H; 16G RAM; Nvidia GeForce GTX 1660Ti with Max-Q Design)

Seems like the best choice is just to use OpenCV!

---

If there are something wrong with my experiments or there are better ways to load videos in RAM, please tell me, thanks a lot!