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
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