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README.txt

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+This code package implements the prototypical part network (ProtoPNet)
+from the paper "This Looks Like That: Deep Learning for Interpretable
+Image Recognition."
+
+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
+5. Augment the training set using img_aug.py (included in this code package)
+
+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
+(2) train_dir is the directory containing the augmented training set (the output of
+Step 5 in the instructions for preparing the data)
+(3) test_dir is the directory containing the test set
+(4) train_push_dir is the directory containing the original (unaugmented) training set
+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