dlr: implement stage 2 WIP of opt regression support via rmse

....this is so stupid. There's a reason why diffusion models are a thing and we don't use image segmentation models for this!!

aimodel/src/deeplabv3_plus_test_rainfall.py

@@ -234,47 +234,55 @@ def plot_metric(train, val, name, dir_output):
 
 if PATH_CHECKPOINT is None:
 	loss_fn = None
-	if LOSS == "cross-entropy-dice":
+	metrics = [
-		loss_fn = LossCrossEntropyDice(log_cosh=DICE_LOG_COSH)
-	elif LOSS == "cross-entropy":
-		loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
-	else:
-		raise Exception(f"Error: Unknown loss function '{LOSS}' (possible values: cross-entropy, cross-entropy-dice).")
-	
-	model.compile(
-		optimizer=tf.keras.optimizers.Adam(learning_rate=LEARNING_RATE),
-		loss=loss_fn,
-		metrics=[
 		"accuracy",
 		dice_coefficient,
 		mean_iou(),
 		sensitivity(),  # How many true positives were accurately predicted
-			specificity # How many true negatives were accurately predicted?
+		specificity,  # How many true negatives were accurately predicted?
-		],
+	]
+	if LOSS == "cross-entropy-dice":
+		loss_fn = LossCrossEntropyDice(log_cosh=DICE_LOG_COSH)
+	elif LOSS == "cross-entropy":
+		loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
+	elif LOSS == "root-mean-squared-error":
+		loss_fn = tf.keras.metrics.RootMeanSquaredError()
+		metrics = [tf.keras.metrics.RootMeanSquaredError()] # Others don't make sense w/o this
+	else:
+		raise Exception(
+			f"Error: Unknown loss function '{LOSS}' (possible values: cross-entropy, cross-entropy-dice)."
+		)
+
+	model.compile(
+		optimizer=tf.keras.optimizers.Adam(learning_rate=LEARNING_RATE),
+		loss=loss_fn,
+		metrics=metrics,
 		steps_per_execution=STEPS_PER_EXECUTION,
-		jit_compile=JIT_COMPILE
+		jit_compile=JIT_COMPILE,
 	)
 	logger.info(">>> Beginning training")
-	history = model.fit(dataset_train,
+	history = model.fit(
+		dataset_train,
 		validation_data=dataset_validate,
 		# test_data=dataset_test, # Nope, it doesn't have a param like this so it's time to do this the *hard* way
 		epochs=EPOCHS,
 		callbacks=[
-			CallbackExtraValidation({  # `model,` removed 'ref apparently exists by default????? ehhhh...???
+			CallbackExtraValidation(
+				{  # `model,` removed 'ref apparently exists by default????? ehhhh...???
 					"test": dataset_test  # Can be None because it handles that
-			}),
+				}
+			),
 			tf.keras.callbacks.CSVLogger(
-				filename=os.path.join(DIR_OUTPUT, "metrics.tsv"),
+				filename=os.path.join(DIR_OUTPUT, "metrics.tsv"), separator="\t"
-				separator="\t"
 			),
 			CallbackCustomModelCheckpoint(
 				model_to_checkpoint=model,
 				filepath=os.path.join(
 					DIR_OUTPUT,
 					"checkpoints",
-					"checkpoint_e{epoch:d}_loss{loss:.3f}.hdf5"
+					"checkpoint_e{epoch:d}_loss{loss:.3f}.hdf5",
 				),
-				monitor="loss"
+				monitor="loss",
 			),
 		],
 		steps_per_epoch=STEPS_PER_EPOCH,
@@ -283,12 +291,39 @@ if PATH_CHECKPOINT is None:
 	logger.info(">>> Training complete")
 	logger.info(">>> Plotting graphs")
 
-	plot_metric(history.history["loss"], history.history["val_loss"], "loss", DIR_OUTPUT)
+	plot_metric(
-	plot_metric(history.history["accuracy"], history.history["val_accuracy"], "accuracy", DIR_OUTPUT)
+		history.history["loss"], history.history["val_loss"], "loss", DIR_OUTPUT
-	plot_metric(history.history["metric_dice_coefficient"], history.history["val_metric_dice_coefficient"], "dice", DIR_OUTPUT)
+	)
-	plot_metric(history.history["one_hot_mean_iou"], history.history["val_one_hot_mean_iou"], "mean iou", DIR_OUTPUT)
+	plot_metric(
-	plot_metric(history.history["sensitivity"], history.history["val_sensitivity"], "sensitivity", DIR_OUTPUT)
+		history.history["accuracy"],
-	plot_metric(history.history["specificity"], history.history["val_specificity"], "specificity", DIR_OUTPUT)
+		history.history["val_accuracy"],
+		"accuracy",
+		DIR_OUTPUT,
+	)
+	plot_metric(
+		history.history["metric_dice_coefficient"],
+		history.history["val_metric_dice_coefficient"],
+		"dice",
+		DIR_OUTPUT,
+	)
+	plot_metric(
+		history.history["one_hot_mean_iou"],
+		history.history["val_one_hot_mean_iou"],
+		"mean iou",
+		DIR_OUTPUT,
+	)
+	plot_metric(
+		history.history["sensitivity"],
+		history.history["val_sensitivity"],
+		"sensitivity",
+		DIR_OUTPUT,
+	)
+	plot_metric(
+		history.history["specificity"],
+		history.history["val_specificity"],
+		"specificity",
+		DIR_OUTPUT,
+	)
 	
 
 # ██ ███    ██ ███████ ███████ ██████  ███████ ███    ██  ██████ ███████ 

aimodel/src/lib/dataset/dataset_mono.py

@@ -114,6 +114,8 @@ def parse_item(metadata, output_size=100, input_size="same", water_threshold=0.1
 		if water_threshold is not None: # if water_threshold=None, then regression mode
 			water = tf.cast(tf.math.greater_equal(water, water_threshold), dtype=tf.float32)
 			# BUG it may be a problem we're [height, width, channel] here rather than [height, width], depending on how dlr works
+		else:
+			water = tf.expand_dims(water, axis=-1) # Stack to have a channel, since if water_threshold=None then we would end up with [height, width] instead of [height, width, channel] otherwise
 		if do_remove_isolated_pixels:
 			water = remove_isolated_pixels(water)