RETFound - A foundation model for retinal imaging

This is the official repo for RETFound, which is heavily based on MAE:

Key features

• RETFound was self-supervised pre-trained on 1.6 million retinal images
• RETFound has been validated in multiple disease detection tasks
• RETFound can be efficiently adapted to customised tasks

Install enviroment

Create enviroment with conda:

conda create -n retfound python=3.6.15 -y

Install Pytorch 1.81 (cuda 11.1)

pip install torch==1.8.1+cu111 torchvision==0.9.1+cu111 torchaudio==0.8.1 -f https://download.pytorch.org/whl/torch_stable.html

Install others

pip install -r requirement.txt

Fine-tuning with RETFound weights

• RETFound pre-trained weights

	ViT-Large
Colour fundus image	download
OCT	download

• Organise data (using IDRiD as an example)

• Start fine-tuning (use IDRiD as example). A fine-tuned checkpoint will be saved during training. Evaluation will be run after training.

python -m torch.distributed.launch --nproc_per_node=1 --master_port=48798 main_finetune.py  
    --batch_size 16 \
    --world_size 1 \
    --model vit_large_patch16 \
    --epochs 50 \
    --blr 5e-3 --layer_decay 0.65 \
    --weight_decay 0.05 --drop_path 0.2 \
    --nb_classes 5 \
    --data_path ./IDRiD_data/ \
    --task ./finetune_IDRiD/ \
    --finetune ./RETFound_cfp_weights.pth

• For evaluation only

python -m torch.distributed.launch --nproc_per_node=1 --master_port=48798 main_finetune.py 
    --eval --batch_size 16 \
    --world_size 1 \
    --model vit_large_patch16 \
    --epochs 50 \
    --blr 5e-3 --layer_decay 0.65 \
    --weight_decay 0.05 --drop_path 0.2 \
    --nb_classes 5 \
    --data_path ./IDRiD_data/ \
    --task ./internal_IDRiD/ \
    --resume ./finetune_IDRiD/checkpoint-best.pth

Load the model and weights

import torch
import models_vit
from util.pos_embed import interpolate_pos_embed
from timm.models.layers import trunc_normal_

# call the model
model = models_vit.__dict__['vit_large_patch16'](
    num_classes=2,
    drop_path_rate=0.2,
    global_pool=True,
)

# load RETFound weights
checkpoint = torch.load('RETFound_cfp_weights.pth', map_location='cpu')
checkpoint_model = checkpoint['model']
state_dict = model.state_dict()
for k in ['head.weight', 'head.bias']:
    if k in checkpoint_model and checkpoint_model[k].shape != state_dict[k].shape:
        print(f"Removing key {k} from pretrained checkpoint")
        del checkpoint_model[k]

# interpolate position embedding
interpolate_pos_embed(model, checkpoint_model)

# load pre-trained model
msg = model.load_state_dict(checkpoint_model, strict=False)

assert set(msg.missing_keys) == {'head.weight', 'head.bias', 'fc_norm.weight', 'fc_norm.bias'}

# manually initialize fc layer
trunc_normal_(model.head.weight, std=2e-5)

print("Model = %s" % str(model))