README.md

Masked Distillation Pretraining

PyTorch reimplementation of MaskDistill from "A Unified View of Masked Image Modeling". This repo provides many different masked distillation pretraining configurations explored in the original work.

Requirements

• Python 3.8+
• pip install -r requirements

Usage

To pretrain a ViT-b/16 network following MaskDistil's default configuration run:

python train.py --accelerator gpu --devices 1 --precision 16  --data.root path/to/data
--max_epochs 300 --data.batch_size 256 --model.student_model vit_base_patch16 
--model.teacher_model clip_vit_large_patch14 --model.head_model fc --model.lr 2.5e-4
--data.mask_ratio 0.4

• Run python train.py --help for descriptions of all options.

• --model.student_name can be one of:

  • vit_tiny_patch16, vit_small_patch16, vit_base_patch16, vit_large_patch16, vit_huge_patch14
  • swin_tiny_patch4, swin_small_patch4, swin_base_patch4, swin_large_patch4

• --model.teacher_name can be one of:

  • clip_vit_base_patch32, clip_vit_base_patch16, clip_vit_large_patch14
  • openclip_vit_base_patch32, openclip_vit_large_patch14, openclip_vit_giant_patch14, openclip_vit_huge_patch14
  • pixel (predict masked patches' pixel values)

• --model.head can be one of fc, vit.

Using a Pretrained Model

Student weights can be extracted from a pretraining checkpoint file by running:

python scripts/extract_student_weights.py -c path/to/checkpoint/file

You can then initialize a ViT model with these weights with the following:

import torch
from timm.models.vision_transformer import VisionTransformer

weights = torch.load("path/to/weights/file")

# Assuming weights are for a ViT-b/16 model
model = VisionTransformer(
    patch_size=16,
    embed_dim=768,
    depth=12,
    num_heads=12,
)
model.load_state_dict(weights)

• Note: VisionTransformer arguments should match the those used during pretraining (e.g. ViT-b/16, ViT-l/16, etc.).

If you want to keep the pretrained relative position embeddings and/or the Layer Scale gamma parameters you can run:

python scripts/extract_student_weights.py -c path/to/checkpoint/file --keep_rel_pos_bias 
--keep_layer_scale_gamma 

To load the relative position embeddings you will need to initialize a BEiT model with the following:

import torch
from timm.models.beit import Beit

weights = torch.load("path/to/weights/file")

model = Beit(
    patch_size=16,
    embed_dim=768,
    depth=12,
    num_heads=12,
    use_rel_pos_bias=True,
    init_values=0.1,
)
model.load_state_dict(weights)

Citation

@article{peng2022unified,
  title={A unified view of masked image modeling},
  author={Peng, Zhiliang and Dong, Li and Bao, Hangbo and Ye, Qixiang and Wei, Furu},
  journal={arXiv preprint arXiv:2210.10615},
  year={2022}
}