mono: switch loss from crossentropy to dice

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

@@ -0,0 +1,53 @@
+import math
+
+import tensorflow as tf
+
+def dice_coef(y_true, y_pred, smooth=100):        
+	"""Calculates the Dice coefficient.
+	@source https://stackoverflow.com/a/72264322/1460422
+	Args:
+		y_true (Tensor): The ground truth.
+		y_pred (Tensor): The predicted output.
+		smooth (float, optional): The smoothness of the output. Lower values = penalise the model more for mistakes to make it better at fine detail. Defaults to 100.
+	Returns:
+		Tensor: The dice coefficient.
+	"""
+	y_true_f = tf.flatten(y_true)
+	y_pred_f = K.flatten(y_pred)
+	intersection = K.sum(y_true_f * y_pred_f)
+	dice = (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)
+	return dice
+
+def dice_coef_loss(y_true, y_pred, **kwargs):
+	"""Turns the dice coefficient into a loss value.
+	NOTE: This is not the only option here. See also the other options in the source.
+	@source https://stackoverflow.com/a/72264322/1460422
+	Args:
+		y_true (Tensor): The ground truth
+		y_pred (Tensor): The predicted output.
+	Returns:
+		Tensor: The Dice coefficient, but as a loss value that decreases instead fo increases as the model learns.
+	"""
+	return -dice_coef(y_true, y_pred, **kwargs)
+
+
+
+class LossDice(tf.keras.losses.Loss):
+	"""An implementation of the dice loss function.
+	Args:
+		smooth (float): The batch size (currently unused).
+	"""
+	def __init__(self, smooth=100, **kwargs):
+		super(LossCrossentropy, self).__init__(**kwargs)
+		
+		self.param_smooth = smooth
+	
+	def call(self, y_true, y_pred):
+		return dice_coef_loss(y_true, y_pred, smooth=self.param_smooth)
+	
+	def get_config(self):
+		config = super(LossCrossentropy, self).get_config()
+		config.update({
+			"smooth": self.param_smooth,
+		})
+		return config

aimodel/src/lib/ai/model_rainfallwater_mono.py

@@ -69,8 +69,11 @@ def model_rainfallwater_mono(metadata, model_arch_enc="convnext_xtiny", model_ar
 	# TODO: An attention layer here instead of a dense layer, with a skip connection perhaps?
 	logger.warning("Warning: TODO implement attention from https://ieeexplore.ieee.org/document/9076883")
 	layer_next = tf.keras.layers.Dense(32, activation="gelu")(layer_next)
-	layer_next = tf.keras.layers.Conv2D(water_bins, activation="gelu", kernel_size=1, padding="same")(layer_next)
-	layer_next = tf.keras.layers.Softmax(axis=-1)(layer_next)
+	# LOSS cross entropy
+	# layer_next = tf.keras.layers.Conv2D(water_bins, activation="gelu", kernel_size=1, padding="same")(layer_next)
+	# layer_next = tf.keras.layers.Softmax(axis=-1)(layer_next)
+	# LOSS dice
+	layer_next = tf.keras.layers.Conv2D(1, activation="gelu", kernel_size=1, padding="same")(layer_next)
 	
 	model = tf.keras.Model(
 		inputs = layer_input,

aimodel/src/lib/dataset/dataset_mono.py

@@ -69,9 +69,11 @@ def parse_item(metadata, shape_water_desired=[100,100], water_threshold=0.1, wat
 		print("DEBUG:dataset BEFORE_SQUEEZE water", water.shape)
 		water = tf.squeeze(water)
 		print("DEBUG:dataset AFTER_SQUEEZE water", water.shape)
-		water = tf.cast(tf.math.greater_equal(water, water_threshold), dtype=tf.int32)
-		water = tf.one_hot(water, water_bins, axis=-1, dtype=tf.int32)
-		
+		# LOSS cross entropy
+		# water = tf.cast(tf.math.greater_equal(water, water_threshold), dtype=tf.int32)
+		# water = tf.one_hot(water, water_bins, axis=-1, dtype=tf.int32)
+		# LOSS dice
+		water = tf.cast(tf.math.greater_equal(water, water_threshold), dtype=tf.float32)
 		
 		print("DEBUG DATASET_OUT:rainfall shape", rainfall.shape)
 		print("DEBUG DATASET_OUT:water shape", water.shape)