PyTorch now officially supports GroupNormlization. I highly suggest using the groupnorm from PyTorch instead of this one.

Updated

The code is updated accordinign to PyTorch v1.0rc1.

pytorch-groupnormalization

Pytorch implementation of group normalization in https://arxiv.org/abs/1803.08494 (Following the PyTorch Style)

This group normalization implementation is modified from the Instance Normalization in PyTorch.

ImageNet Validation results.

P.S. NCPG : Number Channels per Group.

Model	NCPG	Top1 Accuracy	Top5 Accuracy	Link
ResNet50	32	75.768%	92.552%	resnet50-groupnorm32
ResNet50	16	75.872%	92.780%	resnet50-groupnorm16

Training Script :

    python main.py IMAGENET_DIR --arch=resnet50 --group-norm=32 --epochs=100 --lr=0.1 --batch-size=256 --workers=8

Testing Script :

    wget www.cs.unc.edu/~cyfu/resnet50_groupnorm32.tar
    python main.py IMAGENET_DIR --evaluate --batch-size=250 --arch=resnet50 --group-norm=32  --resume=./resnet50_groupnorm32.tar   
    
    wget www.cs.unc.edu/~cyfu/resnet50_groupnorm16.tar
    python main.py IMAGENET_DIR --evaluate --batch-size=250 --arch=resnet50 --group-norm=16  --resume=./resnet50_groupnorm16.tar   

Reference:

@article{wu2018group,
  title={Group Normalization},
  author={Yuxin Wu, Kaiming He},
  journal={arXiv preprint arXiv:1803.08494},
  year={2018}
}