This repo is re-produce for Channel_pruning, Framework is Caffe.
The original source code :yihui-he.
Two main function in my implementation :
Now, it is only for native ConvNet such as VGG, not work for multi-branch ConvNet such as ResNet. I don`t implementation channel decompose ,because it is unhelpful in my test experiment!!!! So ,I have 2C not 3C.
| File | Intro |
|---|---|
| __channel_pruning_one_layer.py__ | This is Channel Pruning reproduct，but only for pruning one layer, you should modify "prune_layer_name" in Line 49 for youself. |
| channel_pruning_reproduce.py | Such as pruning layer "conv2_1", This file tell you how to implement step-by-step. Including Get Feature(), Lasso Regression(), Linear Regression(), Generator New Protobuf(), Generator New Weights() |
| low_rank_and_channel_pruning.py | All funtion in here, such as Low Rank and Channel Pruning(layer-by-layer), It needs two configure file:config.json and __config_cratio.json__ , you can modify them in Line 45 and Line 49. |
| config.json | It is for Low rank configuration, the number obtained from the experiment. |
| __config_cratio.json__ | It is for Channel Pruning(layer-by-layer) configuration, the number means that how many channels will be keep. |
@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}
}