Pytorch implementation of Masked Auto-Encoder:

• Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick. Masked Autoencoders Are Scalable Vision Learners. arXiv 2021.

1. Clone to the local.

> git clone https://github.com/liujiyuan13/MAE-code.git MAE-code

2. Install required packages.

> cd MAE-code
> pip install requirements.txt

3. Prepare datasets.

• For Cifar10, Cifar100 and STL, skip this step for it will be done automatically;
• For ImageNet1K, download and unzip the train(val) set into ./data/ImageNet1K/train(val).

4. Set parameters.

• All parameters are kept in default_args() function of main_mae(eval).py file.

5. Run the code.

> python main_mae.py	# train MAE encoder
> python main_eval.py	# evaluate MAE encoder

6. Visualize the ouput.

> tensorboard --logdir=./log --port 8888

...
+ ckpt				# checkpoint
+ data 				# data folder
+ img 				# store images for README.md
+ log 				# log files
.gitignore 			
lars.py 			# LARS optimizer
main_eval.py 			# main file for evaluation
main_mae.py  			# main file for MAE training
model.py 			# model definitions of MAE and EvalNet
README.md 
util.py 			# helper functions
vit.py 				# definition of vision transformer

In the paper, ViT-Base, ViT-Large and ViT-Huge are used. You can switch between them by simply changing the parameters in default_args(). Details can be found here and are listed in following table.

I implement four network training strategies concerned in the paper, including

• pre-training is used to train MAE encoder and done in main_mae.py.
• linear probing is used to evaluate MAE encoder. During training, MAE encoder is fixed.
  • args.n_partial = 0
• partial fine-tuning is used to evaluate MAE encoder. During training, MAE encoder is partially fixed.
  • args.n_partial = 0.5 --> fine-tuning MLP sub-block with the transformer fixed
  • 1<=args.n_partial<=args.vit_depth-1 --> fine-tuning MLP sub-block and last layers of transformer
• end-to-end fine-tuning is used to evaluate MAE encoder. During training, MAE encoder is fully trainable.
  • args.n_partial = args.vit_depth

Note that the last three strategies are done in main_eval.py where parameter args.n_partial is located.

At the same time, I follow the parameter settings in the paper appendix. Note that partial fine-tuning and end-to-end fine-tuning use the same setting. Nevertheless, I replace RandAug(9, 0.5) with RandomResizedCrop and leave mixup, cutmix and drop path techniques in further implementation.

The experiment reproduce will takes a long time and I am unfortunately busy these days. If you get some results and are willing to contribute, please reach me via email. Thanks!

By the way, I have run the code from start to end. It works! So don't worry about the implementation errors. If you find any, please raise issues or email me.

This repository is under GPL V3.

Thanks project vit-pytorch, pytorch-lars and DeepLearningExamples for their codes contribute to this repository a lot!

Homepage: https://liujiyuan13.github.io

Email: liujiyuan13@163.com