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https://github.com/foolwood/pytorch-slimming
Learning Efficient Convolutional Networks through Network Slimming, In ICCV 2017.
https://github.com/foolwood/pytorch-slimming
deep-learning fast-inference l1-regularization pytorch weight-pruning
Last synced: 19 days ago
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Learning Efficient Convolutional Networks through Network Slimming, In ICCV 2017.
- Host: GitHub
- URL: https://github.com/foolwood/pytorch-slimming
- Owner: foolwood
- License: mit
- Created: 2018-01-13T06:38:28.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2019-05-13T09:04:47.000Z (over 5 years ago)
- Last Synced: 2024-11-16T04:03:24.232Z (27 days ago)
- Topics: deep-learning, fast-inference, l1-regularization, pytorch, weight-pruning
- Language: Python
- Homepage:
- Size: 12.7 KB
- Stars: 565
- Watchers: 11
- Forks: 95
- Open Issues: 16
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-AutoML-and-Lightweight-Models - foolwood/pytorch-slimming
README
# pytorch-slimming
This is a **[PyTorch](http://pytorch.org/)** _re_-implementation of algorithm presented in "[Learning Efficient Convolutional Networks Through Network Slimming](http://openaccess.thecvf.com/content_iccv_2017/html/Liu_Learning_Efficient_Convolutional_ICCV_2017_paper.html) (ICCV2017)." . The official source code is based on Torch. For more info, visit the author's [webpage](https://github.com/liuzhuang13/slimming)!.
| CIFAR10-VGG16BN | Baseline | Trained with Sparsity (1e-4) | Pruned (0.7 Pruned) | Fine-tuned (40epochs) |
| :---------------: | :------: | :--------------------------: | :-----------------: | :-------------------: |
| Top1 Accuracy (%) | 93.62 | 93.77 | 10.00 | 93.56 |
| Parameters | 20.04M | 20.04M | 2.42M | 2.42M |
| Pruned Ratio | 0 | 0.1 | 0.2 | 0.3 | 0.4 | 0.5 | 0.6 | 0.7 |
| :----------------------------------: | :-----------: | :----------: | :-----------: | :----------: | :----------: | :----------: | :----------: | :----------: |
| Top1 Accuracy (%) without Fine-tuned | 93.77 | 93.72 | 93.76 | 93.75 | 93.75 | 93.40 | 37.83 | 10.00 |
| Parameters(M) / macc(M) | 20.04/ 398.44 | 15.9/ 349.22 | 12.28/ 307.78 | 9.12/ 272.94 | 6.74/ 247.86 | 4.62/ 231.86 | 3.14/ 222.17 | 2.42/ 210.84 |
| Pruned Ratio | architecture |
| :----------: | :--------------------------------------: |
| 0 | [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512] |
| 0.1 | [60, 64, 'M', 128, 128, 'M', 256, 255, 253, 245, 'M', 436, 417, 425, 462, 'M', 463, 465, 472, 424] |
| 0.2 | [58, 64, 'M', 128, 128, 'M', 256, 255, 250, 233, 'M', 360, 336, 329, 398, 'M', 420, 412, 435, 341] |
| 0.3 | [56, 64, 'M', 128, 128, 'M', 256, 254, 249, 227, 'M', 284, 239, 244, 351, 'M', 369, 364, 384, 255] |
| 0.4 | [52, 64, 'M', 128, 128, 'M', 256, 254, 247, 218, 'M', 218, 162, 166, 294, 'M', 317, 315, 318, 165] |
| 0.5 | [52, 64, 'M', 128, 128, 'M', 256, 254, 245, 214, 'M', 179, 117, 116, 229, 'M', 228, 220, 210, 111] |
| 0.6 | [51, 64, 'M', 128, 128, 'M', 256, 254, 245, 213, 'M', 165, 85, 92, 153, 'M', 83, 86, 87, 111] |
| 0.7 | [49, 64, 'M', 128, 128, 'M', 256, 254, 234, 198, 'M', 114, 41, 24, 11, 'M', 14, 13, 19, 104] |
## Baseline
```shell
python main.py
```
## Trained with Sparsity
```shell
python main.py -sr --s 0.0001
```
## Pruned
```shell
python prune.py --model model_best.pth.tar --save pruned.pth.tar --percent 0.7
```
## Fine-tuned
```shell
python main.py -refine pruned.pth.tar --epochs 40
```
## Reference
```
@InProceedings{Liu_2017_ICCV,
author = {Liu, Zhuang and Li, Jianguo and Shen, Zhiqiang and Huang, Gao and Yan, Shoumeng and Zhang, Changshui},
title = {Learning Efficient Convolutional Networks Through Network Slimming},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {Oct},
year = {2017}
}
```