dlr: fix metrics

aimodel/src/deeplabv3_plus_test_rainfall.py

@@ -18,9 +18,9 @@ import tensorflow as tf
 
 from lib.dataset.dataset_mono import dataset_mono
 from lib.ai.components.LossCrossEntropyDice import LossCrossEntropyDice
-from lib.ai.components.MetricDice import MetricDice
+from lib.ai.components.MetricDice import dice_coefficient
-from lib.ai.components.MetricSensitivity import MetricSensitivity
+from lib.ai.components.MetricSensitivity import sensitivity
-from lib.ai.components.MetricSpecificity import MetricSpecificity
+from lib.ai.components.MetricSpecificity import specificity
 
 time_start = datetime.now()
 logger.info(f"Starting at {str(datetime.now().isoformat())}")
@@ -189,10 +189,10 @@ if PATH_CHECKPOINT is None:
 		loss=loss_fn,
 		metrics=[
 			"accuracy",
-			MetricDice(),
+			dice_coefficient,
 			tf.keras.metrics.MeanIoU(num_classes=2),
-			MetricSensitivity(), # How many true positives were accurately predicted
+			sensitivity, # How many true positives were accurately predicted
-			MetricSpecificity() # How many true negatives were accurately predicted?
+			specificity # How many true negatives were accurately predicted?
 			# TODO: Add IoU, F1, Precision, Recall,  here. 
 		],
 	)

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

@@ -2,7 +2,13 @@ import math
 
 import tensorflow as tf
 
+def sensitivity(y_true, y_pred):
+	ground_truth = tf.cast(y_true, dtype=tf.float32)
+	prediction = tf.cast(y_pred, dtype=tf.float32)
 	
+	recall = tf.keras.metrics.Recall()
+	recall.update_state(y_true, y_pred)
+	return recall.result()
 
 class MetricSensitivity(tf.keras.metrics.Metric):
 	"""An implementation of the sensitivity.

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

@@ -4,7 +4,8 @@ import tensorflow as tf
 
 
 def specificity(y_pred, y_true):
-	"""
+	"""An implementation of the specificity.
+	In other words, a measure of how many of the true negatives were accurately predicted
 	@source https://datascience.stackexchange.com/a/40746/86851
 	param:
 	y_pred - Predicted labels
@@ -12,34 +13,13 @@ 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)
+	
 	neg_y_true = 1 - y_true
 	neg_y_pred = 1 - y_pred
 	fp = K.sum(neg_y_true * y_pred)
 	tn = K.sum(neg_y_true * neg_y_pred)
 	specificity = tn / (tn + fp + K.epsilon())
 	return specificity
-
-
-class MetricSpecificity(tf.keras.metrics.Metric):
-	"""An implementation of the specificity.
-	In other words, a measure of how many of the true negatives were accurately predicted
-	@source 
-	Args:
-		smooth (float): The batch size (currently unused).
-	"""
-
-	def __init__(self, name="specificity", **kwargs):
-		super(MetricSpecificity, self).__init__(name=name, **kwargs)
-
-	def call(self, y_true, y_pred):
-		ground_truth = tf.cast(y_true, dtype=tf.float32)
-		prediction = tf.cast(y_pred, dtype=tf.float32)
-
-		return specificity(ground_truth, prediction)
-
-	def get_config(self):
-		config = super(MetricSpecificity, self).get_config()
-		config.update({
-			
-		})
-		return config