mixup only uses positive samples and does not mixup the same samples …

…multiple times (#280)

utils.py

@@ -248,18 +248,32 @@ def mixup(x, y, augmentation_ratio=0.25, alpha=0.2):
     # Set numpy random seed
     np.random.seed(cfg.RANDOM_SEED)
 
+    # Get indices of all positive samples
+    positive_indices = np.unique(np.where(y[:, :] == 1)[0])
+
     # Calculate the number of samples to augment based on the ratio
-    num_samples_to_augment = int(len(x) * augmentation_ratio)
+    num_samples_to_augment = int(len(positive_indices) * augmentation_ratio)
+
+    # Indices of samples, that are already mixed up
+    mixed_up_indices = []
 
     for _ in range(num_samples_to_augment):
-        # Randomly choose one instance from the dataset
-        index = np.random.choice(len(x))
+
+        # Randomly choose one instance from the positive samples
+        index = np.random.choice(positive_indices)
+
+        # Choose another one, when the chosen one was already mixed up
+        while index in mixed_up_indices:
+            index = np.random.choice(positive_indices)
+
         x1, y1 = x[index], y[index]
 
         # Randomly choose a different instance from the dataset
-        second_index = np.random.choice(len(x))
-        while second_index == index:
-            second_index = np.random.choice(len(x))
+        second_index = np.random.choice(positive_indices)
+
+        # Choose again, when the same or an already mixed up sample was selected
+        while second_index == index or second_index in mixed_up_indices:
+            second_index = np.random.choice(positive_indices)
         x2, y2 = x[second_index], y[second_index]
 
         # Generate a random mixing coefficient (lambda)
@@ -273,6 +287,9 @@ def mixup(x, y, augmentation_ratio=0.25, alpha=0.2):
         x[index] = mixed_x
         y[index] = mixed_y
 
+        # Mark the sample as already mixed up
+        mixed_up_indices.append(index)
+
     del mixed_x
     del mixed_y