用pip3之前也应该pip3 install --upgrade pip来升级pip3命令 用pip3的时候，可以在命令后面加  -i https:// 的国内源，来加快下载速度.(注意，现在国内源都是https！)

ICCV 2017, by Yihui He, Xiangyu Zhang and Jian Sun

In this repository, we released code for the following models:

Structured simplification methods	Channel pruning (d)

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}
}

1. Requirements
2. Installation
3. Channel Pruning and finetuning
4. Pruned models for download
5. Pruning faster RCNN
6. FAQ

1. Python3 packages you might not have: scipy, sklearn, easydict, use sudo pip3 install to install.
2. For finetuning with 128 batch size, 4 GPUs (~11G of memory)

1. Clone the repository

   # Make sure to clone with --recursive
   git clone --recursive https://github.com/yihui-he/channel-pruning.git

2. 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

3. Download ImageNet classification dataset http://www.image-net.org/download-images

4. Specify imagenet source path in temp/vgg.prototxt (line 12 and 36)

For fast testing, you can directly download pruned model. See next section

1. Download the original VGG-16 model http://www.robots.ox.ac.uk/~vgg/software/very_deep/caffe/VGG_ILSVRC_16_layers.caffemodel
   move it to temp/vgg.caffemodel (or create a softlink instead)

2. 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

3. Combine some factorized layers for further compression, and calculate the acceleration ratio. Replace the ImageData layer of temp/cb_3c_3C4x_mem_bn_vgg.prototxt with temp/vgg.prototxt's

   ./combine.sh | xargs ./calflop.sh

4. 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

5. 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

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

For fast testing, you can directly download pruned model from release
Or you can:

1. clone my py-faster-rcnn repo: https://github.com/yihui-he/py-faster-rcnn
2. 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

You can find answers of some commonly asked questions in our Github wiki, or just create a new issue