dlr CHANGE: Add optional log(cosh(dice_loss))

Ref https://doi.org/10.1109/cibcb48159.2020.9277638

aimodel/slurm-TEST-deeplabv3p-rainfall.job

@@ -37,6 +37,7 @@ show_help() {
 	echo -e "    NO_REMOVE_ISOLATED_PIXELS	Set to any value to avoid the engine from removing isolated pixels - that is, water pixels with no other surrounding pixels, either side to side to diagonally." >&2;
 	echo -e "    EPOCHS				The number of epochs to train for." >&2;
 	echo -e "    LOSS               The loss function to use. Default: cross-entropy (possible values: cross-entropy, cross-entropy-dice)." >&2;
+	echo -e "    DICE_LOG_COSH      When in cross-entropy-dice mode, in addition do loss = cel + log(cosh(dice_loss)) instead of just loss = cel + dice_loss." >&2;
 	echo -e "    PATH_CHECKPOINT 	The path to a checkcpoint to load. If specified, a model will be loaded instead of being trained." >&2;
 	echo -e "    LEARNING_RATE      The learning rate to use. Default: 0.001." >&2;
 	echo -e "    PREDICT_COUNT		The number of items from the (SCRAMBLED) dataset to make a prediction for." >&2;
@@ -69,7 +70,7 @@ echo -e ">>> DIR_OUTPUT: ${DIR_OUTPUT}";
 echo -e ">>> Additional args: ${ARGS}";
 
 export PATH=$HOME/software/bin:$PATH;
-export IMAGE_SIZE BATCH_SIZE DIR_RAINFALLWATER PATH_HEIGHTMAP PATH_COLOURMAP STEPS_PER_EPOCH DIR_OUTPUT PATH_CHECKPOINT EPOCHS PREDICT_COUNT NO_REMOVE_ISOLATED_PIXELS LOSS LEARNING_RATE;
+export IMAGE_SIZE BATCH_SIZE DIR_RAINFALLWATER PATH_HEIGHTMAP PATH_COLOURMAP STEPS_PER_EPOCH DIR_OUTPUT PATH_CHECKPOINT EPOCHS PREDICT_COUNT NO_REMOVE_ISOLATED_PIXELS LOSS LEARNING_RATE DICE_LOG_COSH;
 
 echo ">>> Installing requirements";
 conda run -n py38 pip install -q -r requirements.txt;

aimodel/src/deeplabv3_plus_test_rainfall.py

@@ -47,6 +47,7 @@ STEPS_PER_EPOCH = int(os.environ["STEPS_PER_EPOCH"]) if "STEPS_PER_EPOCH" in os.
 REMOVE_ISOLATED_PIXELS = False if "NO_REMOVE_ISOLATED_PIXELS" in os.environ else True
 EPOCHS = int(os.environ["EPOCHS"]) if "EPOCHS" in os.environ else 50
 LOSS = os.environ["LOSS"] if "LOSS" in os.environ else "cross-entropy-dice"
+DICE_LOG_COSH = True if "DICE_LOG_COSH" in os.environ else False
 LEARNING_RATE = float(os.environ["LEARNING_RATE"]) if "LEARNING_RATE" in os.environ else 0.001
 
 DIR_OUTPUT=os.environ["DIR_OUTPUT"] if "DIR_OUTPUT" in os.environ else f"output/{datetime.utcnow().date().isoformat()}_deeplabv3plus_rainfall_TEST"
@@ -184,7 +185,7 @@ else:
 if PATH_CHECKPOINT is None:
 	loss_fn = None
 	if LOSS == "cross-entropy-dice":
-		loss_fn = LossCrossEntropyDice()
+		loss_fn = LossCrossEntropyDice(log_cosh=DICE_LOG_COSH)
 	elif LOSS == "cross-entropy":
 		loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
 	else:

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

@@ -16,7 +16,6 @@ def dice_loss(y_true, y_pred):
 	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 1 - numerator / denominator
 
 class LossCrossEntropyDice(tf.keras.losses.Loss):
@@ -24,20 +23,27 @@ class LossCrossEntropyDice(tf.keras.losses.Loss):
 	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
+	log_cosh (bool): Whether to do log(cosh(dice_loss)) instead of just dice_loss on its own. Ref https://doi.org/10.1109/cibcb48159.2020.9277638
 	"""
 	
-	def __init__(self, **kwargs):
+	def __init__(self, log_cosh=True, **kwargs):
 		super(LossCrossEntropyDice, self).__init__(**kwargs)
 		
+		self.param_log_cosh = log_cosh
+	
 	def call(self, y_true, y_pred):
 		y_true = tf.cast(y_true, tf.float32)
 		y_true = tf.one_hot(tf.cast(y_true, dtype=tf.int32), 2)  # Input is sparse
-		o = tf.nn.sigmoid_cross_entropy_with_logits(y_true, y_pred) + dice_loss(y_true, y_pred)
+		
+		cel = tf.nn.sigmoid_cross_entropy_with_logits(y_true, y_pred)
+		dice = dice_loss(y_true, y_pred)
+		
+		o = cel + dice
 		return tf.reduce_mean(o)
 	
 	def get_config(self):
 		config = super(LossCrossEntropyDice, self).get_config()
 		config.update({
-			
+			"log_cosh": self.param_log_cosh
 		})
 		return config