loss cel+dice: fixup

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

@@ -22,6 +22,7 @@ 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
 	"""
 	
@@ -30,6 +31,7 @@ class LossCrossEntropyDice(tf.keras.losses.Loss):
 	
 	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)