This repository contains the PyTorch implementation for
Dynamic Dual Gating Neural Networks
Fanrong Li, Gang Li, Xiangyu He, Jian Cheng
ICCV 2021 Oral
The main requirements of this work are:
- Python 3.7
- PyTorch == 1.5.0
- Torchvision == 0.6.0
- CUDA 10.2
We recommand using conda env to setup the experimental environments.
# Create environment
conda create -n DGNet python=3.7
conda activate DGNet
# Install PyTorch & Torchvision
pip install torch==1.5.0 torchvision==0.6.0
# Clone repo
git clone https://github.com/anonymous-9800/DGNet.git ./DGNet
cd ./DGNet
# Install other requirements
pip install -r requirements.txtOur trained models can be found here: Google Drive. And the pretrained cifar10 models can be found here: Google Drive. Unzip and place them into the DGNet folder.
# CIFAR-10
sh ./scripts/cifar_e.sh [ARCH] [PATH-TO-DATASET] [GPU-IDs] [PATH-TO-SAVE] [PATH-TO-TRAINED-MODEL]
# ResNet on ImageNet
sh ./scripts/imagenet_e.sh [ARCH] [PATH-TO-DATASET] [GPU-IDs] [PATH-TO-SAVE] [PATH-TO-TRAINED-MODEL]
# Example
sh ./scripts/imagenet_e.sh resdg34 [PATH-TO-DATASET] 0 imagenet/resdg34-04-e ./trained_models_cls/imagenet_results/resdg34/sparse06/resdg34_04.pth.tar# CIFAR-10
sh ./scripts/cifar_t.sh [ARCH] [PATH-TO-DATASET] [TARGET-DENSITY] [GPU-IDs] [PATH-TO-SAVE] [PATH-TO-PRETRAINED-MODEL]
# ResNet on ImageNet
sh ./scripts/imagenet_t.sh [ARCH] [PATH-TO-DATASET] [TARGET-DENSITY] [GPU-IDs] [PATH-TO-SAVE]
# Example
sh ./scripts/imagenet_t.sh resdg34 [PATH-TO-DATASET] 0.4 0,1 imagent/resdg34-04| Model | Method | Top-1 (%) | Top-5 (%) | FLOPs | Google Drive |
| ResNet-18 | DGNet (50%) | 70.12 | 89.22 | 9.54E8 | Link |
| DGNet (60%) | 69.38 | 88.94 | 7.88E8 | Link | |
| ResNet-34 | DGNet (60%) | 73.01 | 90.99 | 1.50E9 | Link |
| DGNet (70%) | 71.95 | 90.46 | 1.21E9 | Link | |
| ResNet-50 | DGNet (60%) | 76.41 | 93.05 | 1.65E9 | Link |
| DGNet (70%) | 75.12 | 92.34 | 1.31E9 | Link | |
| MobileNet-V2 | DGNet (50%) | 71.62 | 90.05 | 1.60E8 | Link |
If you find this project useful for your research, please use the following BibTeX entry.
@inproceedings{dgnet,
title={Dynamic Dual Gating Neural Networks},
author={Li, Fanrong and Li, Gang and He, Xiangyu and Cheng, Jian},
booktitle={Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
year={2021}
}
For any questions, feel free to contact: [email protected]
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