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

Related tags

Deep Learning LegoNet
Overview

LegoNet

This code is the implementation of ICML2019 paper LegoNet: Efficient Convolutional Neural Networks with Lego Filters

Run

python train.py

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

LegoConv2d

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}
}
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Comments
  • No speedup. Training is slower using Lego filters

    No speedup. Training is slower using Lego filters

    The current given implementation does not provide any speed up in training. The usage of lego filters actually slows down the training process compared to regular convolutional filters. Can you explain why this is happening?

    opened by GiannisPikoulis 0
  • Question about Lego-Res50 implementation

    Question about Lego-Res50 implementation

    Hello. I was wondering whether in your implementation of the Lego version of the ResNet50 which is described in your paper, you just replaced all regular convolutional layers with their Lego counterparts with corresponding kernel sizes (just like in VGG16). Also would it be possible to share your code for implementing LegoNet based on ResNet and MobileNet architectures for reproducibility reasons.

    Thanks in advance.

    opened by GiannisPikoulis 0
Owner
YangZhaohui
PhD candidate at Peking University.
YangZhaohui
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