added dice score calculator function

utils/data_evaluation_utils.py

+"""Data Evaluation Functions
+
+Description:
+-------------
+This folder contains several functions which, either on their own or included in larger pieces of software, perform data evaluation tasks.
+
+Usage
+-------------
+To use content from this folder, import the functions and instantiate them as you wish to use them:
+
+    from utils.data_evaluation_utils import function_name
+
+"""
+
+import os
+import numpy as np
+import torch
+
+def dice_score_calculator(outputs, correct_labels, number_of_classes, number_of_samples=10, mode='train'):
+    """Dice Score Calculator
+
+    This function calculates the confusion matrix for the given data.
+    The function returns the average dice score and the confusion matrix
+
+    Args:
+        outputs (torch.tensor): Tensor of all the network outputs (Y-hat)
+        correct_labels (torch.tensor): Output ground-truth labelled data (Y)
+        number_of_classes (int): Number of classes
+        number_of_samples (int): Output shape for randomly generated samples
+        mode (str): Current run mode or phase
+
+    Returns:
+        dice_score (torch.tensor): Dice score value for each class
+
+    Raises:
+        None
+    """
+    
+    dice_score = torch.zeros(number_of_classes)
+
+    if mode == 'train':
+        samples = np.random.choice(len(outputs), number_of_samples)
+        outputs, correct_labels = outputs[samples], correct_labels[samples]
+
+    for i in range(number_of_classes):
+        ground_truth = (correct_labels == i).float()
+        predictions = (outputs == i).float()
+        intersection = 2.0 * torch.sum(torch.mul(ground_truth, predictions))
+        reunion = torch.sum(ground_truth) + torch.sum(predictions) + 1e-4
+        dice_score[i] = torch.div(intersection, reunion)
+
+    return dice_score
\ No newline at end of file