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Pre-training Using MAE

We adopt the framework of MAE for pre-training. The code is heavily borrowed from Masked Autoencoders: A PyTorch Implementation.

1. Installation

cd mae/
conda create -n mae python=3.8
conda activate mae
pip install torch==1.8.1+cu111 torchvision==0.9.1+cu111 -f https://download.pytorch.org/whl/torch_stable.html
pip install -r requirements.txt
  • Attention: The pre-training code is based on timm==0.3.2, for which a fix is needed to work with PyTorch 1.8.1+. Add the below code to timm/models/layers/helpers.py: 
    import torch

TORCH_MAJOR = int(torch.__version__.split('.')[0])
TORCH_MINOR = int(torch.__version__.split('.')[1])

if TORCH_MAJOR == 1 and TORCH_MINOR < 8:
    from torch._six import container_abcs
else:
    import collections.abc as container_abcs

2. Prepare dataset

  • We support two types of datasets: ImageFolder and LMDB.
    • torchvision.datasets.ImageFolder format: 
      |--dataset
    |--book32
        |--image1.jpg
        |--image2.jpg
        |--...
    |--openvino
        |--image1.jpg
        |--image2.jpg
        |--...
    • LMDB format. To know more about LMDB structure and how to create LMDB, you should not miss this repo. 
      |--dataset
    |--book32
      |--data.mdb
      |--lock.mdb
    |--openvino
      |--data.mdb
      |--lock.mdb
    |--cc
      |--data.mdb
      |--lock.mdb

3. Pre-training

  • Pre-training ViT-Small on Union14M-U with 4 gpus: 
    CUDA_VISIBLE_DEVICES=0,1,2,3 python3 -m torch.distributed.launch \
    --nproc_per_node=4 main_pretrain.py \
    --batch_size 256 \
    --model mae_vit_small_patch4 \
    --mask_ratio 0.75 \
    --epochs 20 \
    --warmup_epochs 2 \
    --norm_pix_loss \
    --blr 1.5e-4 \
    --weight_decay 0.05 \
    --data_path ../data/Union14M-U/book32_lmdb ../data/Union14M-U/cc_lmdb ../data/Union14M-U/openvino_lmdb
  • To pretrain ViT-Base, use --model mae_vit_base_patch4.
  • Here the effective batch size is 256 (batch_size per gpu) * 1 (nodes) * 4 (gpus per node) = 1024. If memory or # gpus is limited, use --accum_iter to maintain the effective batch size, which is batch_size (per gpu) * nodes * 8 (gpus per node) * accum_iter.
  • Here we use --norm_pix_loss as the target for better representation learning. To train a baseline model (e.g., for visualization), use pixel-based construction and turn off --norm_pix_loss.
  • To train ViT-Base set --model mae_vit_base_patch4
  • We also support tensorboard for visualization during pre-training. The learning rate, loss, and reconstructed images are logged every 200 iterations. Note that when using norm_pix_loss, the reconstructed images are not the original images, but the images after normalization. To use it: 
    tensorboard --logdir=output_dir
  • The pre-training takes about 2 hours each epoch on 4 A6000 GPUs (48G).