An pytorch implementation of Masked Autoencoders Are Scalable Vision Learners

This is a coarse version for MAE, only make the pretrain model, the finetune and linear is comming soon.

1. Introduction

This repo is the MAE-vit model which impelement with pytorch, no reference any reference code so this is a non-official version. Because of the limitation of time and machine, I only trained the vit-tiny model for encoder.

2. Enveriments

• python 3.7+
• pytorch 1.7.1
• pillow
• timm
• opencv-python

3. Model Config

Pretrain Config

• BaseConfig

  img_size = 224,
  patch_size = 16,

• Encoder
  The encoder if follow the Vit-tiny model config

  encoder_dim = 192,
  encoder_depth = 12,
  encoder_heads = 3,

• Decoder
  The decoder is followed the kaiming paper config.

  decoder_dim = 512,
  decoder_depth = 8,
  decoder_heads = 16, 

• Mask
  1. We use the shuffle patch after Sin-Cos position embeeding for encoder.
  2. Mask the shuffle patch, keep the mask index.
  3. Unshuffle the mask patch and combine with the encoder embeeding before the position embeeding for decoder.
  4. Restruction decoder embeeidng by convtranspose.
  5. Build the mask map with mask index for cal the loss(only consider the mask patch).

Finetune Config

Wait for the results

TODO:

• Finetune Trainig
• Linear Training

4. Results

Restruction the imagenet validation image from pretrain model, compare with the kaiming results, restruction quality is less than he.
May be the encoder model is too small TT.

The Mae-Vit-tiny pretrain models is here, you can download to test the restruction result. Put the ckpt in weights folder.

5. Training & Inference

• dataset prepare

  /data/home/imagenet/xxx.jpeg, 0
  /data/home/imagenet/xxx.jpeg, 1
  ...
  /data/home/imagenet/xxx.jpeg, 999
  

• Training

  1. Pretrain

     #!/bin/bash
     OMP_NUM_THREADS=1
     MKL_NUM_THREADS=1
     export OMP_NUM_THREADS
     export MKL_NUM_THREADS
     cd MAE-Pytorch;
     CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -W ignore -m torch.distributed.launch --nproc_per_node 8 train_mae.py \
     --batch_size 256 \
     --num_workers 32 \
     --lr 1.5e-4 \
     --optimizer_name "adamw" \
     --cosine 1 \
     --max_epochs 300 \
     --warmup_epochs 40 \
     --num-classes 1000 \
     --crop_size 224 \
     --patch_size 16 \
     --color_prob 0.0 \
     --calculate_val 0 \
     --weight_decay 5e-2 \
     --lars 0 \
     --mixup 0.0 \
     --smoothing 0.0 \
     --train_file $train_file \
     --val_file $val_file \
     --checkpoints-path $ckpt_folder \
     --log-dir $log_folder

  2. Finetune
     TODO:

     • training

  3. Linear
     TODO:

     • training

• Inference

  1. pretrian

  python mae_test.py --test_image xxx.jpg --ckpt weights.pth

  2. classification
     TODO:
     • training

6. TODO

• VIT-BASE model training.
• SwinTransformers for MAE.
• Finetune & Linear training.

Finetune is trainig, the weights may be comming soon.

GitHub

View Github