# pytorch-pruning

**Repository Path**: gvraky/pytorch-pruning

## Basic Information

- **Project Name**: pytorch-pruning
- **Description**: 通道剪枝：直接考虑特征图Aj的有无对loss的影响（含论文）
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 1
- **Created**: 2020-09-24
- **Last Updated**: 2021-11-02

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

## PyTorch implementation of  [\[1611.06440 Pruning Convolutional Neural Networks for Resource Efficient Inference\]](https://arxiv.org/abs/1611.06440) ##

This demonstrates pruning a VGG16 based classifier that classifies a small dog/cat dataset.


This was able to reduce the CPU runtime by x3 and the model size by x4.

For more details you can read the [blog post](https://jacobgil.github.io/deeplearning/pruning-deep-learning).

At each pruning step 512 filters are removed from the network.


Usage
-----

This repository uses the PyTorch ImageFolder loader, so it assumes that the images are in a different directory for each category.

Train

......... dogs

......... cats


Test


......... dogs

......... cats


The images were taken from [here](https://www.kaggle.com/c/dogs-vs-cats) but you should try training this on your own data and see if it works!

Training:
`python finetune.py --train`

Pruning:
`python finetune.py --prune`

TBD
---

 - Change the pruning to be done in one pass. Currently each of the 512 filters are pruned sequentually. 
	`
	for layer_index, filter_index in prune_targets:
			model = prune_vgg16_conv_layer(model, layer_index, filter_index)
		`


 	This is inefficient since allocating new layers, especially fully connected layers with lots of parameters, is slow.
	
	In principle this can be done in a single pass.



 - Change prune_vgg16_conv_layer to support additional architectures.
 	The most immediate one would be VGG with batch norm.