This code package implements the prototypical part network (ProtoPNet)
from the paper "This Looks Like That: Deep Learning for Interpretable Image Recognition"
(to appear at NeurIPS 2019), by Chaofan Chen* (Duke University), Oscar Li* (Duke University),
Chaofan Tao (Duke University), Alina Jade Barnett (Duke University),
Jonathan Su (MIT Lincoln Laboratory), and Cynthia Rudin (Duke University)
(* denotes equal contribution).

This code package was SOLELY developed by the authors at Duke University,
and licensed under MIT License (see LICENSE for more information regarding the use
and the distribution of this code package).

Prerequisites: PyTorch, NumPy, cv2, Augmentor (https://github.com/mdbloice/Augmentor)
Recommended hardware: 4 NVIDIA Tesla P-100 GPUs or 8 NVIDIA Tesla K-80 GPUs

Instructions for preparing the data:
1. Download the dataset CUB_200_2011.tgz from http://www.vision.caltech.edu/visipedia/CUB-200-2011.html
2. Unpack CUB_200_2011.tgz
3. Crop the images using information from bounding_boxes.txt (included in the dataset)
4. Split the cropped images into training and test sets, using train_test_split.txt (included in the dataset)
5. Put the cropped training images in the directory "./datasets/cub200_cropped/train_cropped/"
6. Put the cropped test images in the directory "./datasets/cub200_cropped/test_cropped/"
7. Augment the training set using img_aug.py (included in this code package)
   -- this will create an augmented training set in the following directory:
      "./datasets/cub200_cropped/train_cropped_augmented/"

Instructions for training the model:
1. In settings.py, provide the appropriate strings for data_path, train_dir, test_dir,
train_push_dir:
(1) data_path is where the dataset resides
    -- if you followed the instructions for preparing the data, data_path should be "./datasets/cub200_cropped/"
(2) train_dir is the directory containing the augmented training set
    -- if you followed the instructions for preparing the data, train_dir should be data_path + "train_cropped_augmented/"
(3) test_dir is the directory containing the test set
    -- if you followed the instructions for preparing the data, test_dir should be data_path + "test_cropped/"
(4) train_push_dir is the directory containing the original (unaugmented) training set
    -- if you followed the instructions for preparing the data, train_push_dir should be data_path + "train_cropped/"
2. Run main.py

Instructions for finding the nearest prototypes to a test image:
1. Run local_analysis.py and supply the following arguments:
-gpuid is the GPU device ID(s) you want to use (optional, default '0')
-modeldir is the directory containing the model you want to analyze
-model is the filename of the saved model you want to analyze
-imgdir is the directory containing the image you want to analyze
-img is the filename of the image you want to analyze
-imgclass is the (0-based) index of the correct class of the image

Instructions for finding the nearest patches to each prototype:
1. Run global_analysis.py and supply the following arguments:
-gpuid is the GPU device ID(s) you want to use (optional, default '0')
-modeldir is the directory containing the model you want to analyze
-model is the filename of the saved model you want to analyze

Instructions for pruning the prototypes from a saved model:
1. Run run_pruning.py and supply the following arguments:
-gpuid is the GPU device ID(s) you want to use (optional, default '0')
-modeldir is the directory containing the model you want to prune prototypes from
-model is the filename of the saved model you want to prune prototypes from
Note: the prototypes in the model must already have been projected (pushed) onto
the nearest latent training patches, before running this script

Instructions for combining several ProtoPNet models (Jupyter Notebook required):
1. Run the Jupyter Notebook combine_models.ipynb