https://github.com/ben-louis/fisherpruning-pytorch

An implementation of <Group Fisher Pruning for Practical Network Compression> based on pytorch and mmcv
https://github.com/ben-louis/fisherpruning-pytorch

Last synced: about 1 month ago
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An implementation of <Group Fisher Pruning for Practical Network Compression> based on pytorch and mmcv

• Host: GitHub
• URL: https://github.com/ben-louis/fisherpruning-pytorch
• Owner: Ben-Louis
• Created: 2021-11-16T11:55:05.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2021-11-21T15:26:03.000Z (almost 3 years ago)
• Last Synced: 2023-03-01T05:05:54.010Z (over 1 year ago)
• Language: Python
• Size: 54.7 KB
• Stars: 15
• Watchers: 1
• Forks: 4
• Open Issues: 3
• Metadata Files:
  • Readme: README.md

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README

        
# FisherPruning-Pytorch

An implementation of <Group Fisher Pruning for Practical Network Compression> based on pytorch and mmcv 

---

### Main Functions

- Pruning for fully-convolutional structures, 

  such as one-stage detectors; (copied from the [official code](https://github.com/jshilong/FisherPruning))

  

- Pruning for networks combining convolutional layers and fully-connected layers, such as faster-RCNN and ResNet;

- Pruning for networks which involve group convolutions, such as ResNeXt and RegNet.

### Usage

#### Requirements

```text

torch

torchvision

mmcv / mmcv-full

mmcls 

mmdet 

```

#### Compatibility

This code is tested with 

```text

pytorch=1.3

torchvision=0.4

cudatoolkit=10.0

mmcv-full==1.3.14

mmcls=0.16 

mmdet=2.17

```

and 

```text

pytorch=1.8

torchvision=0.9

cudatoolkit=11.1

mmcv==1.3.16

mmcls=0.16 

mmdet=2.17

```

#### Data

Download [ImageNet](https://image-net.org/download.php) and [COCO](https://cocodataset.org/), 

then extract them and organize the folders as 

  ```

  - detection

    |- tools

    |- configs

    |- data

    |   |- coco

    |   |   |- train2017

    |   |   |- val2017

    |   |   |- test2017

    |   |   |- annotations

    |

  - classification

    |- tools

    |- configs

    |- data

    |   |- imagenet

    |   |   |- train

    |   |   |- val

    |   |   |- test 

    |   |   |- meta

    |

  - ...

  ```

#### Commands

e.g. Classification

```bash

cd classification

```

1. Pruning

   ```bash

   # single GPU

   python tools/train.py configs/xxx_pruning.py --gpus=1

   # multi GPUs (e.g. 4 GPUs)

   python -m torch.distributed.launch --nproc_per_node=4 tools/train.py configs/xxx_pruning.py --launch pytorch

   ```

   

2. Fine-tune

   

   In the config file, modify the `deploy_from` to the pruned model, and modify the `samples_per_gpu` to 256/#GPUs. Then

   ```bash

   # single GPU

   python tools/train.py configs/xxx_finetune.py --gpus=1

   # multi GPUs (e.g. 4 GPUs)

   python -m torch.distributed.launch --nproc_per_node=4 tools/train.py configs/xxx_finetune.py --launch pytorch

   ```

   

3. Test

   In the config file, add the attribute `load_from` to the finetuned model. Then

   ```bash

   python tools/test.py configs/xxx_finetune.py --metrics=accuracy

   ```

   

The commands for pruning and finetuning of detection models are similar to that of classification models. 

Instructions will be added soon.

## Acknowledgments

My project acknowledges the official code [FisherPruning](https://github.com/jshilong/FisherPruning).