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dino-vit-pcam

This repository makes use of vit-pytorch: https://github.com/lucidrains/vit-pytorch

In order to run:

conda create -n dino_env python=3
  • Clone this repository to any directory, in this case C:\ml_code\dino-vit-pcam\
cd C:\ml_code\
git clone https://github.com/d-f/dino-vit-pcam.git
  • Download dependencies
cd dino-vit-pcam
pip install -r requirements.txt --find-links https://download.pytorch.org/whl/torch_stable.html
  • Set up directories
    • Depending on OS, run either create_dirs.ps1 (for Windows) or create_dirs.sh (for Linux) and choose a "project directory" for everything to be added to, in this case "C:\ml_projects\fcn_segmentation\"
C:\ml_code\dino-vit-pcam\create_dirs.ps1 "C:\\ml_projects\\dino_vit\\"

or

bash C:\ml_code\dino-vit-pcam\create_dirs.sh
"/C/ml_projects/dino_vit/"
  • Train DINO (using hyperparameters included in paper)
    • See create_argparser() on line 57 in pytorch_ViT_DINO.py for more details on adjustable hyperparameters
python pytorch_ViT_DINO.py -project_directory C:\ml_projects\dino-vit\ -model_save_name "dino_vit_model_1.pth.tar" -data_root C:\ml_projects\PCAM\
  • Fine-tune classifier
    • See create_argparser() on line 133 in pytorch_ViT_finetune.py for more details on adjustable hyperparameters
    • param_str set to "just_classifier" only trains a fully connected layer at the end of the network, "all" sets requires_grad to True for all parameters
python pytorch_ViT_finetune.py -project_directory C:\ml_projects\dino-vit -model_save_name "dino_vit_model_1.pth.tar" -param_str "just_classifer"
  • Calculate model performance (may need to add R to PATH)
Rscript C:\ml_code\dino_vit_pcam\utils\calc_model_performance.R
C:\ml_projects\dino_vit\results\dino_vit_model_1_test_results.json
C:\ml_projects\dino_vit\results\dino_vit_model_1_performance.json

For demonstration / debugging purposes a model was trained with DINO with a batch size of 120 and a learning rate of 3e-4 for 17 epochs, resulting in a loss value of 5.34.

A MLP classifier was added to this model and fine tuned for 4 epochs with a batch size of 16 and a learning rate of 1e-3, resulting in the following performance on the PCAM test dataset:

Accuracy Sensitivity (Recall) Specificity
73.21% 75.80% 70.63%

The loss values throughout training indicate this model would benefit from more training during both phases of training, but these two short training phases were just used as a sanity check.