https://github.com/jacobgil/pytorch-pruning

PyTorch Implementation of [1611.06440] Pruning Convolutional Neural Networks for Resource Efficient Inference
https://github.com/jacobgil/pytorch-pruning

deep-learning pruning pytorch

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PyTorch Implementation of [1611.06440] Pruning Convolutional Neural Networks for Resource Efficient Inference

• Host: GitHub
• URL: https://github.com/jacobgil/pytorch-pruning
• Owner: jacobgil
• Created: 2017-06-23T11:43:52.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2019-07-12T20:39:41.000Z (over 5 years ago)
• Last Synced: 2024-11-20T03:38:05.747Z (23 days ago)
• Topics: deep-learning, pruning, pytorch
• Language: Python
• Homepage:
• Size: 11.7 KB
• Stars: 875
• Watchers: 22
• Forks: 202
• Open Issues: 30
• Metadata Files:
  • Readme: README.md

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• awesome-pytorch - pytorch-pruning
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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.