Channel Pruning for Accelerating Very Deep Neural Networks
ICCV 2017, by Yihui He, Xiangyu Zhang and Jian Sun
Please have a look our new works on compressing deep models:
- AMC: AutoML for Model Compression and Acceleration on Mobile Devices ECCV'18, which combines channel pruning and reinforcement learning to further accelerate CNN. code and models are available!
- AddressNet: Shift-Based Primitives for Efficient Convolutional Neural Networks WACV'19. We propose a family of efficient networks based on Shift operation.
- MoBiNet: A Mobile Binary Network for Image Classification WACV'20 Binarized MobileNets.
In this repository, we released code for the following models:
| model | Speed-up | Accuracy |
|---|---|---|
| VGG-16 channel pruning | 5x | 88.1 (Top-5), 67.8 (Top-1) |
| VGG-16 3C1 | 4x | 89.9 (Top-5), 70.6 (Top-1) |
| ResNet-50 | 2x | 90.8 (Top-5), 72.3 (Top-1) |
| faster RCNN | 2x | 36.7 ([email protected]:.05:.95) |
| faster RCNN | 4x | 35.1 ([email protected]:.05:.95) |
1 3C method combined spatial decomposition (Speeding up Convolutional Neural Networks with Low Rank Expansions) and channel decomposition (Accelerating Very Deep Convolutional Networks for Classification and Detection) (mentioned in 4.1.2)
 |
 |
|---|---|
| Structured simplification methods | Channel pruning (d) |
Citation
If you find the code useful in your research, please consider citing:
@InProceedings{He_2017_ICCV,
author = {He, Yihui and Zhang, Xiangyu and Sun, Jian},
title = {Channel Pruning for Accelerating Very Deep Neural Networks},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {Oct},
year = {2017}
}
Contents
- Requirements
- Installation
- Channel Pruning and finetuning
- Pruned models for download
- Pruning faster RCNN
- FAQ
requirements
- Python3 packages you might not have:
scipy,sklearn,easydict, usesudo pip3 installto install. - For finetuning with 128 batch size, 4 GPUs (~11G of memory)
Installation (sufficient for the demo)
-
Clone the repository
# Make sure to clone with --recursive git clone --recursive https://github.com/yihui-he/channel-pruning.git -
Build my Caffe fork (which support bicubic interpolation and resizing image shorter side to 256 then crop to 224x224)
cd caffe # If you're experienced with Caffe and have all of the requirements installed, then simply do: make all -j8 && make pycaffe # Or follow the Caffe installation instructions here: # http://caffe.berkeleyvision.org/installation.html # you might need to add pycaffe to PYTHONPATH, if you've already had a caffe before
-
Download ImageNet classification dataset http://www.image-net.org/download-images
-
Specify imagenet
sourcepath intemp/vgg.prototxt(line 12 and 36)
Channel Pruning
For fast testing, you can directly download pruned model. See next section
-
Download the original VGG-16 model http://www.robots.ox.ac.uk/~vgg/software/very_deep/caffe/VGG_ILSVRC_16_layers.caffemodel
move it totemp/vgg.caffemodel(or create a softlink instead) -
Start Channel Pruning
python3 train.py -action c3 -caffe [GPU0] # or log it with ./run.sh python3 train.py -action c3 -caffe [GPU0] # replace [GPU0] with actual GPU device like 0,1 or 2
-
Combine some factorized layers for further compression, and calculate the acceleration ratio. Replace the ImageData layer of
temp/cb_3c_3C4x_mem_bn_vgg.prototxtwithtemp/vgg.prototxt's./combine.sh | xargs ./calflop.sh -
Finetuning
caffe train -solver temp/solver.prototxt -weights temp/cb_3c_vgg.caffemodel -gpu [GPU0,GPU1,GPU2,GPU3] # replace [GPU0,GPU1,GPU2,GPU3] with actual GPU device like 0,1,2,3 -
Testing
Though testing is done while finetuning, you can test anytime with:
caffe test -model path/to/prototxt -weights path/to/caffemodel -iterations 5000 -gpu [GPU0] # replace [GPU0] with actual GPU device like 0,1 or 2
Pruned models (for download)
For fast testing, you can directly download pruned model from release: VGG-16 3C 4X, VGG-16 5X, ResNet-50 2X. Or follow Baidu Yun Download link
Test with:
caffe test -model channel_pruning_VGG-16_3C4x.prototxt -weights channel_pruning_VGG-16_3C4x.caffemodel -iterations 5000 -gpu [GPU0]
# replace [GPU0] with actual GPU device like 0,1 or 2
Pruning faster RCNN
For fast testing, you can directly download pruned model from release
Or you can:
- clone my py-faster-rcnn repo: https://github.com/yihui-he/py-faster-rcnn
- use the pruned models from this repo to train faster RCNN 2X, 4X, solver prototxts are in https://github.com/yihui-he/py-faster-rcnn/tree/master/models/pascal_voc
FAQ
You can find answers of some commonly asked questions in our Github wiki, or just create a new issue
请看一下是boost还是多线程读取数据的问题
12 Dec 27, 2022
16 Jul 18, 2022
63 May 22, 2021
9 Oct 18, 2022
1.2k Jan 03, 2023
0 Jun 09, 2022
12 Feb 02, 2022
98 Dec 21, 2022
146 Nov 21, 2022
356 Dec 23, 2022
33 Mar 23, 2022
76 Dec 07, 2022
48 Dec 20, 2022
222 Jan 01, 2023
2 Mar 02, 2022
1 Feb 26, 2022
6 Sep 21, 2022
7 Jul 15, 2022
14 Oct 11, 2022
43 Nov 07, 2022