--- a
+++ b/code/config.py
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+"""
+DeepSlide
+Contains all hyperparameters for the entire repository.
+
+Authors: Jason Wei, Behnaz Abdollahi, Saeed Hassanpour
+"""
+
+import argparse
+from pathlib import Path
+
+import torch
+
+from compute_stats import compute_stats
+from utils import (get_classes, get_log_csv_name)
+
+# Source: https://stackoverflow.com/questions/12151306/argparse-way-to-include-default-values-in-help
+parser = argparse.ArgumentParser(
+    description="DeepSlide",
+    formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+
+###########################################
+#               USER INPUTS               #
+###########################################
+# Input folders for training images.
+# Must contain subfolders of images labelled by class.
+# If your two classes are 'a' and 'n', you must have a/*.jpg with the images in class a and
+# n/*.jpg with the images in class n.
+parser.add_argument(
+    "--all_wsi",
+    type=Path,
+    default=Path("all_wsi"),
+    help="Location of the WSI organized in subfolders by class")
+# For splitting into validation set.
+parser.add_argument("--val_wsi_per_class",
+                    type=int,
+                    default=20,
+                    help="Number of WSI per class to use in validation set")
+# For splitting into testing set, remaining images used in train.
+parser.add_argument("--test_wsi_per_class",
+                    type=int,
+                    default=30,
+                    help="Number of WSI per class to use in test set")
+# When splitting, do you want to move WSI or copy them?
+parser.add_argument(
+    "--keep_orig_copy",
+    type=bool,
+    default=True,
+    help=
+    "Whether to move or copy the WSI when splitting into training, validation, and test sets"
+)
+
+#######################################
+#               GENERAL               #
+#######################################
+# Number of processes to use.
+parser.add_argument("--num_workers",
+                    type=int,
+                    default=8,
+                    help="Number of workers to use for IO")
+# Default shape for ResNet in PyTorch.
+parser.add_argument("--patch_size",
+                    type=int,
+                    default=224,
+                    help="Size of the patches extracted from the WSI")
+
+##########################################
+#               DATA SPLIT               #
+##########################################
+# The names of your to-be folders.
+parser.add_argument("--wsi_train",
+                    type=Path,
+                    default=Path("wsi_train"),
+                    help="Location to be created to store WSI for training")
+parser.add_argument("--wsi_val",
+                    type=Path,
+                    default=Path("wsi_val"),
+                    help="Location to be created to store WSI for validation")
+parser.add_argument("--wsi_test",
+                    type=Path,
+                    default=Path("wsi_test"),
+                    help="Location to be created to store WSI for testing")
+
+# Where the CSV file labels will go.
+parser.add_argument("--labels_train",
+                    type=Path,
+                    default=Path("labels_train.csv"),
+                    help="Location to store the CSV file labels for training")
+parser.add_argument(
+    "--labels_val",
+    type=Path,
+    default=Path("labels_val.csv"),
+    help="Location to store the CSV file labels for validation")
+parser.add_argument("--labels_test",
+                    type=Path,
+                    default=Path("labels_test.csv"),
+                    help="Location to store the CSV file labels for testing")
+
+###############################################################
+#               PROCESSING AND PATCH GENERATION               #
+###############################################################
+# This is the input for model training, automatically built.
+parser.add_argument(
+    "--train_folder",
+    type=Path,
+    default=Path("train_folder"),
+    help="Location of the automatically built training input folder")
+
+# Folders of patches by WSI in training set, used for finding training accuracy at WSI level.
+parser.add_argument(
+    "--patches_eval_train",
+    type=Path,
+    default=Path("patches_eval_train"),
+    help=
+    "Folders of patches by WSI in training set, used for finding training accuracy at WSI level"
+)
+# Folders of patches by WSI in validation set, used for finding validation accuracy at WSI level.
+parser.add_argument(
+    "--patches_eval_val",
+    type=Path,
+    default=Path("patches_eval_val"),
+    help=
+    "Folders of patches by WSI in validation set, used for finding validation accuracy at WSI level"
+)
+# Folders of patches by WSI in test set, used for finding test accuracy at WSI level.
+parser.add_argument(
+    "--patches_eval_test",
+    type=Path,
+    default=Path("patches_eval_test"),
+    help=
+    "Folders of patches by WSI in testing set, used for finding test accuracy at WSI level"
+)
+
+# Target number of training patches per class.
+parser.add_argument("--num_train_per_class",
+                    type=int,
+                    default=80000,
+                    help="Target number of training samples per class")
+
+# Only looks for purple images and filters whitespace.
+parser.add_argument(
+    "--type_histopath",
+    type=bool,
+    default=True,
+    help="Only look for purple histopathology images and filter whitespace")
+
+# Number of purple points for region to be considered purple.
+parser.add_argument(
+    "--purple_threshold",
+    type=int,
+    default=100,
+    help="Number of purple points for region to be considered purple.")
+
+# Scalar to use for reducing image to check for purple.
+parser.add_argument(
+    "--purple_scale_size",
+    type=int,
+    default=15,
+    help="Scalar to use for reducing image to check for purple.")
+
+# Sliding window overlap factor (for testing).
+# For generating patches during the training phase, we slide a window to overlap by some factor.
+# Must be an integer. 1 means no overlap, 2 means overlap by 1/2, 3 means overlap by 1/3.
+# Recommend 2 for very high resolution, 3 for medium, and 5 not extremely high resolution images.
+parser.add_argument("--slide_overlap",
+                    type=int,
+                    default=3,
+                    help="Sliding window overlap factor for the testing phase")
+
+# Overlap factor to use when generating validation patches.
+parser.add_argument(
+    "--gen_val_patches_overlap_factor",
+    type=float,
+    default=1.5,
+    help="Overlap factor to use when generating validation patches.")
+
+parser.add_argument("--image_ext",
+                    type=str,
+                    default="jpg",
+                    help="Image extension for saving patches")
+
+# Produce patches for testing and validation by folder.  The code only works
+# for now when testing and validation are split by folder.
+parser.add_argument(
+    "--by_folder",
+    type=bool,
+    default=True,
+    help="Produce patches for testing and validation by folder.")
+
+#########################################
+#               TRANSFORM               #
+#########################################
+parser.add_argument(
+    "--color_jitter_brightness",
+    type=float,
+    default=0.5,
+    help=
+    "Random brightness jitter to use in data augmentation for ColorJitter() transform"
+)
+parser.add_argument(
+    "--color_jitter_contrast",
+    type=float,
+    default=0.5,
+    help=
+    "Random contrast jitter to use in data augmentation for ColorJitter() transform"
+)
+parser.add_argument(
+    "--color_jitter_saturation",
+    type=float,
+    default=0.5,
+    help=
+    "Random saturation jitter to use in data augmentation for ColorJitter() transform"
+)
+parser.add_argument(
+    "--color_jitter_hue",
+    type=float,
+    default=0.2,
+    help=
+    "Random hue jitter to use in data augmentation for ColorJitter() transform"
+)
+
+########################################
+#               TRAINING               #
+########################################
+# Model hyperparameters.
+parser.add_argument("--num_epochs",
+                    type=int,
+                    default=20,
+                    help="Number of epochs for training")
+# Choose from [18, 34, 50, 101, 152].
+parser.add_argument(
+    "--num_layers",
+    type=int,
+    default=18,
+    help=
+    "Number of layers to use in the ResNet model from [18, 34, 50, 101, 152]")
+parser.add_argument("--learning_rate",
+                    type=float,
+                    default=0.001,
+                    help="Learning rate to use for gradient descent")
+parser.add_argument("--batch_size",
+                    type=int,
+                    default=16,
+                    help="Mini-batch size to use for training")
+parser.add_argument("--weight_decay",
+                    type=float,
+                    default=1e-4,
+                    help="Weight decay (L2 penalty) to use in optimizer")
+parser.add_argument("--learning_rate_decay",
+                    type=float,
+                    default=0.85,
+                    help="Learning rate decay amount per epoch")
+parser.add_argument("--resume_checkpoint",
+                    type=bool,
+                    default=False,
+                    help="Resume model from checkpoint file")
+parser.add_argument("--save_interval",
+                    type=int,
+                    default=1,
+                    help="Number of epochs between saving checkpoints")
+# Where models are saved.
+parser.add_argument("--checkpoints_folder",
+                    type=Path,
+                    default=Path("checkpoints"),
+                    help="Directory to save model checkpoints to")
+
+# Name of checkpoint file to load from.
+parser.add_argument(
+    "--checkpoint_file",
+    type=Path,
+    default=Path("xyz.pt"),
+    help="Checkpoint file to load if resume_checkpoint_path is True")
+# ImageNet pretrain?
+parser.add_argument("--pretrain",
+                    type=bool,
+                    default=False,
+                    help="Use pretrained ResNet weights")
+parser.add_argument("--log_folder",
+                    type=Path,
+                    default=Path("logs"),
+                    help="Directory to save logs to")
+
+##########################################
+#               PREDICTION               #
+##########################################
+# Selecting the best model.
+# Automatically select the model with the highest validation accuracy.
+parser.add_argument(
+    "--auto_select",
+    type=bool,
+    default=True,
+    help="Automatically select the model with the highest validation accuracy")
+# Where to put the training prediction CSV files.
+parser.add_argument(
+    "--preds_train",
+    type=Path,
+    default=Path("preds_train"),
+    help="Directory for outputting training prediction CSV files")
+# Where to put the validation prediction CSV files.
+parser.add_argument(
+    "--preds_val",
+    type=Path,
+    default=Path("preds_val"),
+    help="Directory for outputting validation prediction CSV files")
+# Where to put the testing prediction CSV files.
+parser.add_argument(
+    "--preds_test",
+    type=Path,
+    default=Path("preds_test"),
+    help="Directory for outputting testing prediction CSV files")
+
+##########################################
+#               EVALUATION               #
+##########################################
+# Folder for outputting WSI predictions based on each threshold.
+parser.add_argument(
+    "--inference_train",
+    type=Path,
+    default=Path("inference_train"),
+    help=
+    "Folder for outputting WSI training predictions based on each threshold")
+parser.add_argument(
+    "--inference_val",
+    type=Path,
+    default=Path("inference_val"),
+    help=
+    "Folder for outputting WSI validation predictions based on each threshold")
+parser.add_argument(
+    "--inference_test",
+    type=Path,
+    default=Path("inference_test"),
+    help="Folder for outputting WSI testing predictions based on each threshold"
+)
+
+# For visualization.
+parser.add_argument(
+    "--vis_train",
+    type=Path,
+    default=Path("vis_train"),
+    help="Folder for outputting the WSI training prediction visualizations")
+parser.add_argument(
+    "--vis_val",
+    type=Path,
+    default=Path("vis_val"),
+    help="Folder for outputting the WSI validation prediction visualizations")
+parser.add_argument(
+    "--vis_test",
+    type=Path,
+    default=Path("vis_test"),
+    help="Folder for outputting the WSI testing prediction visualizations")
+
+#######################################################
+#               ARGUMENTS FROM ARGPARSE               #
+#######################################################
+args = parser.parse_args()
+
+# Device to use for PyTorch code.
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+# Automatically read in the classes.
+classes = get_classes(folder=args.all_wsi)
+num_classes = len(classes)
+
+# This is the input for model training, automatically built.
+train_patches = args.train_folder.joinpath("train")
+val_patches = args.train_folder.joinpath("val")
+
+# Compute the mean and standard deviation of the image patches from the specified folder.
+path_mean, path_std = compute_stats(folderpath=train_patches,
+                                    image_ext=args.image_ext)
+
+# Only used is resume_checkpoint is True.
+resume_checkpoint_path = args.checkpoints_folder.joinpath(args.checkpoint_file)
+
+# Named with date and time.
+log_csv = get_log_csv_name(log_folder=args.log_folder)
+
+# Does nothing if auto_select is True.
+eval_model = args.checkpoints_folder.joinpath(args.checkpoint_file)
+
+# Find the best threshold for filtering noise (discard patches with a confidence less than this threshold).
+threshold_search = (0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9)
+
+# For visualization.
+# This order is the same order as your sorted classes.
+colors = ("red", "white", "blue", "green", "purple", "orange", "black", "pink",
+          "yellow")
+
+# Print the configuration.
+# Source: https://stackoverflow.com/questions/44689546/how-to-print-out-a-dictionary-nicely-in-python/44689627
+# chr(10) and chr(9) are ways of going around the f-string limitation of
+# not allowing the '\' character inside.
+print(f"###############     CONFIGURATION     ###############\n"
+      f"{chr(10).join(f'{k}:{chr(9)}{v}' for k, v in vars(args).items())}\n"
+      f"device:\t{device}\n"
+      f"classes:\t{classes}\n"
+      f"num_classes:\t{num_classes}\n"
+      f"train_patches:\t{train_patches}\n"
+      f"val_patches:\t{val_patches}\n"
+      f"path_mean:\t{path_mean}\n"
+      f"path_std:\t{path_std}\n"
+      f"resume_checkpoint_path:\t{resume_checkpoint_path}\n"
+      f"log_csv:\t{log_csv}\n"
+      f"eval_model:\t{eval_model}\n"
+      f"threshold_search:\t{threshold_search}\n"
+      f"colors:\t{colors}\n"
+      f"\n#####################################################\n\n\n")