Awesome

IterNorm

Code for reproducing the results in the following paper:

Iterative Normalization: Beyond Standardization towards Efficient Whitening

Lei Huang, Yi Zhou, Fan Zhu, Li Liu, Ling Shao

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. arXiv:1904.03441

This is the torch implementation (results of experimetns are based on this implementation). Other implementation are shown as follows:

1. Pytorch re-implementation

2. Tensorflow implementation by Lei Zhao.

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Requirements and Dependency

• Install Torch with CUDA (for GPU).
• Install cudnn.
• Install the dependency optnet by:

luarocks install optnet

Experiments

1. Reproduce the results of VGG-network on Cifar-10 datasets:

Prepare the data: download CIFAR-10 , and put the data files under ./data/.

• Run:

bash y_execute_vggE_base.sh               //basic configuration
bash y_execute_vggE_b1024.sh              //batch size of 1024
bash y_execute_vggE_b16.sh                //batch size of 16
bash y_execute_vggE_LargeLR.sh            //10x larger learning rate
bash y_execute_vggE_IterNorm_Iter.sh      //effect of iteration number
bash y_execute_vggE_IterNorm_Group.sh     //effect of group size

Note that the scripts don't inculde the setups of Decorrelated Batch Noarmalizaiton (DBN). To reproduce the results of DBN please follow the instructions of the DBN project, and the corresponding hyper-parameters described in the paper.

2. Reproduce the results of Wide-Residual-Networks on Cifar-10 datasets:

Prepare the data: same as in VGG-network on Cifar-10 experiments.

• Run:

bash y_execute_wr.sh               

3. Reproduce the ImageNet experiments.

• Download ImageNet and put it in: /data/lei/imageNet/input_torch/ (you can also customize the path in opts_imageNet.lua)
• Install the IterNorm module to Torch as a Lua package: go to the directory ./models/imagenet/cuSpatialDBN/ and run luarocks make cudbn-1.0-0.rockspec. (Note that the modules in ./models/imagenet/cuSpatialDBN/ are the same as in the ./module/, and the installation by luarocks is for convinience in training ImageNet with multithreads.)
• run the script with `z_execute_imageNet_***'

This project is based on the training scripts of Wide Residual Network repo and Facebook's ResNet repo.

Contact

Email: huanglei@nlsde.buaa.edu.cn.. Discussions and suggestions are welcome!