Masked Autoencoders Are Scalable Vision Learners for self-supervised ViT

MAE for Self-supervised ViT

Introduction

This is an unofficial PyTorch implementation of Masked Autoencoders Are Scalable Vision Learners for self-supervised ViT.

This repo is mainly based on moco-v3, pytorch-image-models and BEiT

TODO

• visualization of reconstruction image
• linear prob
• more results
• transfer learning
• …

Main Results

The following results are based on ImageNet-1k self-supervised pre-training, followed by ImageNet-1k supervised training for linear evaluation or end-to-end fine-tuning.

Vit-Base

On 8 NVIDIA GeForce RTX 3090 GPUs, pretrain for 100 epochs needs about 9 hours, 4096 batch size needs about 24 GB GPU memory.

[1]. fine-tuning for 50 epochs;

Vit-Large

On 8 NVIDIA A40 GPUs, pretrain for 100 epochs needs about 34 hours, 4096 batch size needs about xx GB GPU memory.

Usage: Preparation

The code has been tested with CUDA 11.4, PyTorch 1.8.2.

Notes:

1. The batch size specified by -b is the total batch size across all GPUs from all nodes.
2. The learning rate specified by --lr is the base lr (corresponding to 256 batch-size), and is adjusted by the linear lr scaling rule.
3. In this repo, only multi-gpu, DistributedDataParallel training is supported; single-gpu or DataParallel training is not supported. This code is improved to better suit the multi-node setting, and by default uses automatic mixed-precision for pre-training.
4. Only pretraining and finetuning have been tested.

Usage: Self-supervised Pre-Training

Below is examples for MAE pre-training.

ViT-Base with 1-node (8-GPU, NVIDIA GeForce RTX 3090) training, batch 4096

python main_mae.py \
  -c cfgs/ViT-B16_ImageNet1K_pretrain.yaml \
  --multiprocessing-distributed --world-size 1 --rank 0 \
  [your imagenet-folder with train and val folders]

or

sh train_mae.sh

ViT-Large with 1-node (8-GPU, NVIDIA A40) pre-training, batch 2048

python main_mae.py \
  -c cfgs/ViT-L16_ImageNet1K_pretrain.yaml \
  --multiprocessing-distributed --world-size 1 --rank 0 \
  [your imagenet-folder with train and val folders]

Usage: End-to-End Fine-tuning ViT

Below is examples for MAE fine-tuning.

ViT-Base with 1-node (8-GPU, NVIDIA GeForce RTX 3090) training, batch 1024

python main_fintune.py \
  -c cfgs/ViT-B16_ImageNet1K_finetune.yaml \
  --multiprocessing-distributed --world-size 1 --rank 0 \
  [your imagenet-folder with train and val folders]

ViT-Large with 2-node (16-GPU, 8 NVIDIA GeForce RTX 3090 + 8 NVIDIA A40) training, batch 512

python main_fintune.py \
  -c cfgs/ViT-B16_ImageNet1K_finetune.yaml \
  --multiprocessing-distributed --world-size 2 --rank 0 \
  [your imagenet-folder with train and val folders]

On another node, run the same command with –rank 1.

Note:

1. We use --resume rather than --finetune in the DeiT repo, as its --finetune option trains under eval mode. When loading the pre-trained model, revise model_without_ddp.load_state_dict(checkpoint['model']) with strict=False.

[TODO] Usage: Linear Classification

By default, we use momentum-SGD and a batch size of 1024 for linear classification on frozen features/weights. This can be done with a single 8-GPU node.

python main_lincls.py \
  -a [architecture] --lr [learning rate] \
  --dist-url 'tcp://localhost:10001' \
  --multiprocessing-distributed --world-size 1 --rank 0 \
  --pretrained [your checkpoint path]/[your checkpoint file].pth.tar \
  [your imagenet-folder with train and val folders]

License

This project is under the CC-BY-NC 4.0 license. See LICENSE for details.

Citation

If you use the code of this repo, please cite the original papre and this repo:

@Article{he2021mae,
  author  = {Kaiming He* and Xinlei Chen* and Saining Xie and Yanghao Li and Piotr Dolla ́r and Ross Girshick},
  title   = {Masked Autoencoders Are Scalable Vision Learners},
  journal = {arXiv preprint arXiv:2111.06377},
  year    = {2021},
}

@misc{yang2021maepriv,
  author       = {Lu Yang* and Pu Cao* and Yang Nie and Qing Song},
  title        = {MAE-priv},
  howpublished = {\url{https://github.com/BUPT-PRIV/MAE-priv}},
  year         = {2021},
}

GitHub

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