This project provides source code for our CVPR19 paper AOGNets and an on-going work Mixture Normalization: A Lightweight Integration of Feature Normalization and Attention. The code is still under refactoring. Please stay tuned for more.
Ubuntu 16.04 LTS (althoug not tested, other OS should work too if PyTorch can be installed successfully)
Python 3 (Anaconda is recommended)
CUDA 9 or newer
PyTorch 0.4 or newer
NVIDIA APEX
NVIDIA NCCL.
$ git clone https://github.com/iVMCL/AOGNets.git
$ cd AOGNets
$ pip install requirements.txt
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
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Download the ImageNet dataset to YOUR_IMAGENET_PATH and move validation images to labeled subfolders
- The script may be helpful.
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Create a datasets subfolder under your cloned AOGNets and a symlink to the ImageNet dataset
$ cd AOGNets
$ mkdir datasets
$ ln -s PATH_TO_YOUR_IMAGENET ./datasets/
NVIDIA Apex is used for FP16 training.
E.g.,
$ cd AOGNets
$ ./examples/train_fp16.sh aognet_s configs/aognet_imagenet_12M.yaml first_try
See more configuration files in AOGNets/configs. Change the GPU settings in train_fp16.sh
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Pretrained AOGNet_12M in ImageNet on Google Drive
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Pretrained AOGNet_40M in ImageNet on Google Drive
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More coming soon
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Remarks: The provided pretrained models are obtained using the latest refactored code and the performance are slightly different from the results in the paper.
E.g.,
$ cd AOGNets
$ ./examples/test_fp16.sh aognet_s AOGNet_12M_PATH
Please consider citing the AOGNets paper in your publications if it helps your research.
@inproceedings{AOGNets,
author = {Xilai Li and Xi Song and Tianfu Wu},
title = {AOGNets: Compositional Grammatical Architectures for Deep Learning},
booktitle = {{IEEE} Conference on Computer Vision and Pattern Recognition, {CVPR}},
year = {2019},
url = {https://arxiv.org/pdf/1711.05847.pdf}
}
Please feel free to report issues and any related problems to Xilai Li (xli47 at ncsu dot edu), Xi Song (xsong.lhi at gmail.com) and Tianfu Wu (twu19 at ncsu dot edu).
AOGNets related codes are under RESEARCH ONLY LICENSE.