loss cel+dice: fixup

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

@@ -22,17 +22,19 @@ def dice_loss(y_true, y_pred):
 class LossCrossEntropyDice(tf.keras.losses.Loss):
 	"""Cross-entropy loss and dice loss combined together into one nice neat package.
 	Combines the two with mean.
+	The ground truth labels should sparse, NOT one-hot. The predictions should be one-hot, NOT sparse.
 	@source https://lars76.github.io/2018/09/27/loss-functions-for-segmentation.html#9
 	"""
-
+	
 	def __init__(self, **kwargs):
 		super(LossCrossEntropyDice, self).__init__(**kwargs)
-
+	
 	def call(self, y_true, y_pred):
 		y_true = tf.cast(y_true, tf.float32)
+		y_true = tf.one_hot(y_true, 2) # Input is sparse
 		o = tf.nn.sigmoid_cross_entropy_with_logits(y_true, y_pred) + dice_loss(y_true, y_pred)
 		return tf.reduce_mean(o)
-
+	
 	def get_config(self):
 		config = super(LossCrossEntropyDice, self).get_config()
 		config.update({