# LegoNet

**Repository Path**: mirrors_huawei-noah/LegoNet

## Basic Information

- **Project Name**: LegoNet
- **Description**: A Pytorch implementation of "LegoNet: Efficient Convolutional Neural Networks with Lego Filters" (ICML 2019).
- **Primary Language**: Unknown
- **License**: BSD-3-Clause
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-09-24
- **Last Updated**: 2026-03-08

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## 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}
	}