dataset_mono: Implement validate_percentage + test_percentage support

This removes the train_percentage argument

TODO: map this forwards to enable support in deeplabv3_plus_test_rainfall

...thinking about it, it's really not a test now, is it? Updating the filename would be such a /hassle/ though....

aimodel/src/lib/dataset/dataset_mono.py

@@ -16,6 +16,23 @@ from .primitives.remove_isolated_pixels import remove_isolated_pixels
 
 # TO PARSE:
 def parse_item(metadata, output_size=100, input_size="same", water_threshold=0.1, water_bins=2, heightmap=None, rainfall_scale_up=1, do_remove_isolated_pixels=True):
+	"""
+	Parse a single TFRecord item from the dataset.
+
+	Args:
+		metadata (dict): Metadata about the shapes of the dataset - rainfall radar, water depth data etc. This should be read automaticallyfrom the metadata.json file that's generated by previous pipeline steps that I forget at this time.
+		output_size (int): The desired output size of the water depth data.
+		input_size (str or int): The desired input size of the rainfall radar data. If "same", it will be set to the same as the output_size.
+		water_threshold (float): The threshold to use for binarizing the water depth data.
+		water_bins (int): The number of bins to use for the water depth data (e.g. for one-hot encoding).
+		heightmap (tf.Tensor): An optional heightmap to include as an additional channel in the rainfall radar data.
+		rainfall_scale_up (int): A factor to scale up the rainfall radar data.
+		do_remove_isolated_pixels (bool): Whether to remove isolated pixels from the water depth data or not. Isolated pixels are binaried [=1] pixels that are surrounded on (4|8 TODO FIGURE OUT) sides.
+
+	Returns:
+		A function that takes a single TFRecord item and returns the parsed rainfall radar and water depth data.
+	"""
+ 	
 	if input_size == "same":
 		input_size = output_size # This is almost always the case with e.g. the DeepLabV3+ model
 	
@@ -144,22 +161,31 @@ def get_filepaths(dirpath_input, do_shuffle=True):
 	return result
 
 # TODO refactor this to validate_percentage=0.2 and test_percentage=0, but DON'T FORGET TO CHECK ***ALL*** usages of this FIRST and update them afterwards!
-def dataset_mono(dirpath_input, train_percentage=0.8, **kwargs):
+def dataset_mono(dirpath_input, validate_percentage=0.2, test_percentage=0, **kwargs):
 	filepaths = get_filepaths(dirpath_input)
 	filepaths_count = len(filepaths)
-	dataset_splitpoint = math.floor(filepaths_count * train_percentage)
 	
-	filepaths_train = filepaths[:dataset_splitpoint]
-	filepaths_validate = filepaths[dataset_splitpoint:]
+	split_trainvalidate=math.floor(filepaths_count * (1-(validate_percentage+test_percentage)))
+	split_validatetest=math.floor(filepaths * (1 - test_percentage))
 	
-	print("DEBUG:dataset_mono filepaths_train", filepaths_train, "filepaths_validate", filepaths_validate)
+	
+	filepaths_train = filepaths[:split_trainvalidate]
+	filepaths_validate = filepaths[split_trainvalidate:split_validatetest]
+	filepaths_test = []
+	if test_percentage > 0:
+		filepaths_test = filepaths[split_validatetest:]
+	
+	print("DEBUG:dataset_mono filepaths_train", filepaths_train, "filepaths_validate", filepaths_validate, "filepaths_test", filepaths_test)
 	
 	metadata = read_metadata(dirpath_input)
 	
 	dataset_train = make_dataset(filepaths_train, metadata=metadata, **kwargs)
 	dataset_validate = make_dataset(filepaths_validate, metadata=metadata, **kwargs)
+	dataset_test = None
+	if test_percentage > 0:
+		dataset_test = make_dataset(filepaths_test, metadata=metadata, **kwargs)
 	
-	return dataset_train, dataset_validate #, filepaths
+	return dataset_train, dataset_validate, dataset_test #, filepaths
 
 def dataset_mono_predict(dirpath_input, batch_size=64, **kwargs):
 	"""Creates a tf.data.Dataset() for prediction using the contrastive learning model.