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PyTorch-Filter Response Normalization Layer(FRN)

PyTorch implementation of Filter Response Normalization Layer(FRN)

[1911.09737] Filter Response Normalization Layer: Eliminating Batch Dependence in the Training of Deep Neural Networks

0. How to apply FRN to your model

Replace BatchNorm2d + ReLU in the model with FRN + TLU yourself. Currently, it is difficult to easily replace them with functions. Because many models use the same ReLU in various places.

1. Experiment(Classification)

We use Best Artworks of All Time | Kaggle dataset. This dataset contains 49 artists and their pictures.
In this experiment, we classify artist by picture.

1.0 Assumed libraries

• torch==1.3.1
• catalyst==19.11.6
• albumentations==0.4.3
• NVIDIA/apex
  • If you use --fp16 option

1.1 Get dataset

If you can use kaggle API command, you can download easily

$ cd input
$ kaggle datasets download -d ikarus777/best-artworks-of-all-time
$ unzip best-artworks-of-all-time.zip -d artworks

Or download directly from Best Artworks of All Time | Kaggle

I assume the following directory structure.

input
├── artworks
│   ├── artists.csv
│   ├── images
│   │   └── images
│   │       ├── Alfred_Sisley
│   │       │   ├── Alfred_Sisley_1.jpg
│   │       │   ├── Alfred_Sisley_10.jpg
│   │       │   ├── ...

1.2 Train(and Valid)

You can use --fp16 if you installed nvidia/apex. But FRN is not tuned for FP16, you should turn off --fp16 when use --frn.

$ python train_cls.py --fp16
$ python train_cls.py --frn