/HCGNet

Official implementations of HCGNets：Gated Convolutional Networks with Hybrid Connectivity for Image Classification (AAAI-2020)

Primary LanguagePython

HCGNets: Gated Convolutional Networks with Hybrid Connectivity for Image Classification

This project provides source code for our AAAI-2020 paper HCGNet.

Installation

Requirements

Ubuntu 16.04 LTS

Python 3 (Anaconda is recommended)

CUDA 9 or newer

PyTorch 0.4 or newer

NVIDIA NCCL

Foolbox

MMDection

Clone the repo

$ git clone https://github.com/winycg/HCGNet.git
$ cd HCGNet

Some tweaks

USE pillow-simd to speed up pytorch image loader (assume Anaconda is used)

$ pip uninstall pillow
$ conda uninstall --force jpeg libtiff -y
$ conda install -c conda-forge libjpeg-turbo
$ CC="cc -mavx2" pip install --no-cache-dir -U --force-reinstall --no-binary :all: --compile pillow-simd

Perform training on CIFAR dataset

Training

python main_cifar.py --dataset cifar10 --arch HCGNet_A1

Results

Model Params FLOPS CIFAR-10 CIFAR-100
HCGNet-A1 1.1M 0.16G 3.15 18.13
HCGNet-A2 3.1M 0.50G 2.29 16.54
HCGNet-A3 11.4M 1.99G 2.14 15.96

Perform training and evaluation on ImageNet dataset

Dataset preparation

  • Download the ImageNet dataset to YOUR_IMAGENET_PATH and move validation images to labeled subfolders

  • Create a datasets subfolder under your cloned HCGNet and a symlink to the ImageNet dataset

$ cd HCGNet
$ mkdir data
$ ln -s PATH_TO_YOUR_IMAGENET ./data/

Training

python -m torch.distributed.launch --nproc_per_node=2 main_imagenet.py --arch HCGNet_B --gpu-id 0,1

Need NCCL library:

  • --nproc_per_node : Your GPU number for training.

Evaluation

When the training finishes, the best model file HCGNet_B_best.pth.tar is saved in ./checkpoint/.

python -m torch.distributed.launch --nproc_per_node=2 main_imagenet.py --arch HCGNet_B --gpu-id 0,1 --resume --checkpoint ./checkpoint/HCGNet_B_best.pth.tar --evaluate

Results

Model Params FLOPS Top-1 Top-5 Pretrained model
HCGNet-B 12.9M 2.0G 21.5 5.9 HCGNet_B_best.pth.tar

Interpretability by network dissection

Reference

Network dissection method is proposed by Network Dissection: Quantifying Interpretability of Deep Visual Representations paper. Our experiemnts refer the official implementation: https://github.com/CSAILVision/NetDissect-Lite

Evaluation

Evaluation on interpretability based on the ImageNet pretrained model HCGNet-B:

cd network_dissection/
python main.py

You may only modify the settings.py and loader/model_loader.py to implement your requirements.

Results

Generated result files of HCGNet_B can be downloaded Here. We compare the unique detectors of HCGNet-B against other popular SOTA models as follows:

interpretability

Adversarial robustness by FGSM attack

Reference

FGSM (Fast gradient sign mthod) is proposed by Explaining and harnessing adversarial examples paper. Our experiemnts refer the implementation by foolbox .

Evaluation

Evaluation on adversarial robustness based on ImageNet pretrained models: HCGNet-B and other popular networks:

cd FGSM_attack/
python attack.py

Results

Attack models with different perturbation energies：0.001~0.005. We report top-1 accuracy on ImageNet validation set after attacking.

Model 0.000 0.001 0.002 0.003 0.004 0.005
ResNet-50 0.762 0.384 0.228 0.158 0.121 0.102
DenseNet-169 0.760 0.353 0.194 0.125 0.094 0.077
ResNeXt-50 0.776 0.382 0.239 0.177 0.148 0.131
Wide ResNet-50 0.785 0.439 0.279 0.202 0.162 0.139
HCGNet-B 0.785 0.480 0.415 0.386 0.370 0.360

Object detection and segmentation

We experiment HCGNet-B pretrained on ImageNet as a backbone on the Mask-RCNN system to implement object detection and instance segmentation tasks.

Prerequisites:

You need to install MMDection at first and obtain a mmdetection folder. Then you need to move the detection/hcgnet.py and detection/__init__.py to mmdetection/mmdet/models/backbones/ , as well as detection/hcgnet_config.py to mmdetection/configs/ in your folder.

Training

./tools/dist_train.sh configs/hcgnet_config.py 2 --validate
  • 2 means your GPU number

Evaluation

You can download our pretrained detection and segmentation model file from HCGNet_detection.pth. You need to configure the path of HCGNet-B pretrained model by pretrained key in hcgnet_config.py .

./tools/dist_test.sh configs/hcgnet_config.py \ checkpoints/HCGNet_detection.pth \
2 --out results.pkl --eval bbox segm

Results

Model Params FLOPs box AP box AP_50 box AP_75 seg AP seg AP_50 seg AP_75
ResNet-50-FPN 44.2M 275.6G 37.3 59.9 40.2 34.2 55.9 36.2
HCGNet-B-FPN 32.1M 230.4G 38.3 60.6 41.3 35.2 57.5 37.1

Citation

@inproceedings{yang2020gated,
  title={Gated Convolutional Networks with Hybrid Connectivity for Image Classification},
  author={Yang, Chuanguang and An, Zhulin and Zhu, Hui and Hu, Xiaolong and Zhang, Kun and Xu, Kaiqiang and Li, Chao and Xu, Yongjun},
  booktitle={Thirty-Fourth AAAI Conference on Artificial Intelligence},
  pages = {12581--12588},
  year={2020}
}

Contact

Please feel free to report issues and any related problems to Chuanguang Yang (yangchuanguang@ict.ac.cn).

License

HCGNets related codes are under RESEARCH ONLY LICENSE.