argmax for sensitivity & specificity too

aimodel/src/lib/ai/components/MetricDice.py

@@ -15,9 +15,6 @@ def dice_coefficient(y_true, y_pred):
 		tf.Tensor: The computed Dice coefficient.
 	"""
 	
-	y_true = tf.cast(y_true, dtype=tf.float32)
-	y_pred = tf.cast(y_pred, dtype=tf.float32)
-	
 	y_pred = tf.math.sigmoid(y_pred)
 	numerator = 2 * tf.reduce_sum(y_true * y_pred)
 	denominator = tf.reduce_sum(y_true + y_pred)
@@ -26,5 +23,8 @@ def dice_coefficient(y_true, y_pred):
 
 
 def metric_dice_coefficient(y_true, y_pred):
+	y_true = tf.cast(y_true, dtype=tf.float32)
+	y_pred = tf.cast(y_pred, dtype=tf.float32)
+	
 	y_pred = tf.math.argmax(y_pred, axis=-1)
 	return dice_coefficient(y_true, y_pred)

aimodel/src/lib/ai/components/MetricSensitivity.py

@@ -3,8 +3,10 @@ import math
 import tensorflow as tf
 
 def sensitivity(y_true, y_pred):
-	ground_truth = tf.cast(y_true, dtype=tf.float32)
+	y_true = tf.cast(y_true, dtype=tf.float32)
-	prediction = tf.cast(y_pred, dtype=tf.float32)
+	y_pred = tf.cast(y_pred, dtype=tf.float32)
+	
+	y_pred = tf.math.argmax(y_pred, axis=-1)
 	
 	recall = tf.keras.metrics.Recall()
 	recall.update_state(y_true, y_pred)

aimodel/src/lib/ai/components/MetricSpecificity.py

@@ -13,10 +13,11 @@ def specificity(y_pred, y_true):
 	Returns:
 	Specificity score
 	"""
-	
 	y_true = tf.cast(y_true, dtype=tf.float32)
 	y_pred = tf.cast(y_pred, dtype=tf.float32)
 	
+	y_pred = tf.math.argmax(y_pred, axis=-1)
+	
 	neg_y_true = 1 - y_true
 	neg_y_pred = 1 - y_pred
 	fp = K.sum(neg_y_true * y_pred)