meaniou: implement one-hot version

it expects sparse, but our output is one-hot.

aimodel/src/deeplabv3_plus_test_rainfall.py

@@ -18,9 +18,10 @@ 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 metric_dice_coefficient
+from lib.ai.components.MetricDice import metric_dice_coefficient as dice_coefficient
 from lib.ai.components.MetricSensitivity import sensitivity
 from lib.ai.components.MetricSpecificity import specificity
+from lib.ai.components.MetricMeanIoU import one_hot_mean_iou as mean_iou
 
 time_start = datetime.now()
 logger.info(f"Starting at {str(datetime.now().isoformat())}")
@@ -165,6 +166,7 @@ else:
 		"MetricDice": MetricDice,
 		"MetricSensitivity": MetricSensitivity,
 		"MetricSpecificity": MetricSpecificity
+		"MetricMeanIoU": MetricMeanIoU
 	})
 
 
@@ -189,8 +191,8 @@ if PATH_CHECKPOINT is None:
 		loss=loss_fn,
 		metrics=[
 			"accuracy",
-			metric_dice_coefficient,
-			tf.keras.metrics.MeanIoU(num_classes=2),
+			dice_coefficient,
+			mean_iou,
 			sensitivity, # How many true positives were accurately predicted
 			specificity # How many true negatives were accurately predicted?
 			# TODO: Add IoU, F1, Precision, Recall,  here. 

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

@@ -0,0 +1,23 @@
+import math
+
+import tensorflow as tf
+
+
+def one_hot_mean_iou(y_true, y_pred, classes=2):
+	"""Compute the mean IoU for one-hot tensors.
+	Args:
+		y_true (tf.Tensor): The ground truth label.
+		y_pred (tf.Tensor): The output predicted by the model.
+	
+	Returns:
+		tf.Tensor: The computed mean IoU.
+	"""
+	
+	y_pred = tf.math.argmax(y_pred, axis=-1)
+	y_true = tf.cast(y_true, dtype=tf.float32)
+	y_pred = tf.cast(y_pred, dtype=tf.float32)
+	
+	
+	iou = tf.keras.metrics.MeanIoU(classes=classes)
+	iou.update_state(y_true, y_pred)
+	return iou.result()