https://github.com/zhaohui-yang/LegoNet

A Pytorch implementation of "LegoNet: Efficient Convolutional Neural Networks with Lego Filters" (ICML 2019).
https://github.com/zhaohui-yang/LegoNet

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A Pytorch implementation of "LegoNet: Efficient Convolutional Neural Networks with Lego Filters" (ICML 2019).

• Host: GitHub
• URL: https://github.com/zhaohui-yang/LegoNet
• Owner: zhaohui-yang
• License: bsd-3-clause
• Created: 2019-09-05T12:43:46.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2020-10-30T07:17:55.000Z (over 4 years ago)
• Last Synced: 2024-08-01T13:32:23.530Z (7 months ago)
• Language: Python
• Size: 7.81 KB
• Stars: 142
• Watchers: 7
• Forks: 15
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: License.txt

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README

        
# LegoNet

This code is the implementation of ICML2019 paper [LegoNet: Efficient Convolutional Neural Networks with Lego Filters](http://proceedings.mlr.press/v97/yang19c/yang19c.pdf)

## Run

```python

python train.py

```

You could achieve an VGG16 with 93.88% accuracy on CIFAR10 dataset, the lego filters occupy ~3.8M parameters.

## LegoConv2d

```python

self.lego = nn.Parameter(nn.init.kaiming_normal_(torch.rand(self.n_lego, self.basic_channels, self.kernel_size, self.kernel_size)))

self.aux_coefficients = nn.Parameter(init.kaiming_normal_(torch.rand(self.n_split, self.out_channels, self.n_lego, 1, 1)))

self.aux_combination = nn.Parameter(init.kaiming_normal_(torch.rand(self.n_split, self.out_channels, self.n_lego, 1, 1)))

```

lego: Lego Filters

aux_coefficients: combination coefficients used during combination

aux_combination: combination index

## Note

The aux_coefficients and aux_combination should be saved as sparse matrix for saving memory. This code does not include this part.

## Citation

	@inproceedings{legonet,

		title={LegoNet: Efficient Convolutional Neural Networks with Lego Filters},

		author={Yang, Zhaohui and Wang, Yunhe and Liu, Chuanjian and Chen, Hanting and Xu, Chunjing and Shi, Boxin and Xu, Chao and Xu, Chang},

		booktitle={International Conference on Machine Learning},

		pages={7005--7014},

		year={2019}

	}