dlr: HACK: argmax to convert [64,128,128, 2] → [64,128,128]

aimodel/src/deeplabv3_plus_test_rainfall.py

@@ -18,7 +18,7 @@ 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 dice_coefficient
+from lib.ai.components.MetricDice import metric_dice_coefficient
 from lib.ai.components.MetricSensitivity import sensitivity
 from lib.ai.components.MetricSpecificity import specificity
 
@@ -189,7 +189,7 @@ if PATH_CHECKPOINT is None:
 		loss=loss_fn,
 		metrics=[
 			"accuracy",
-			dice_coefficient,
+			metric_dice_coefficient,
 			tf.keras.metrics.MeanIoU(num_classes=2),
 			sensitivity, # How many true positives were accurately predicted
 			specificity # How many true negatives were accurately predicted?

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

@@ -14,32 +14,17 @@ def dice_coefficient(y_true, y_pred):
 	Returns:
 		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)
-
+	
 	return numerator / denominator
 
-class MetricDice(tf.keras.metrics.Metric):
-	"""An implementation of the dice loss function.
-	@source 
-	Args:
-		smooth (float): The batch size (currently unused).
-	"""
-	def __init__(self, name="dice_coefficient", smooth=100, **kwargs):
-		super(MetricDice, self).__init__(name=name, **kwargs)
-		
-		self.param_smooth = smooth
-	
-	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 dice_coef(ground_truth, prediction, smooth=self.param_smooth)
-	
-	def get_config(self):
-		config = super(MetricDice, self).get_config()
-		config.update({
-			"smooth": self.param_smooth,
-		})
-		return config
+
+def metric_dice_coefficient(y_true, y_pred):
+	y_pred = tf.math.argmax(y_pred)
+	return dice_coefficient(y_true, y_pred)