Implement initial UNTESTED support for split_validation and split_test

aimodel/slurm-TEST-deeplabv3p-rainfall.job

@@ -43,6 +43,8 @@ show_help() {
 	echo -e "    PATH_CHECKPOINT 	The path to a checkpoint 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 "    UPSAMPLE           How much to upsample by at the beginning of the model. A value of disables upscaling. Default: 2." >&2;
+	echo -e "    SPLIT_VALIDATE     Percentage of the available files in the dataset to be allocated to the validation split. Default: 0.2" >&2;
+	echo -e "    SPLIT_TEST         Percentage of the available files in the dataset to be allocated to the test split. Default: 0.2" >&2;
 	echo -e "    STEPS_PER_EXECUTION How many steps to perform before surfacing from the GPU to e.g. do callbacks. Default: 16." >&2;
 	echo -e "    RANDSEED           The random seed to use when shuffling filepaths. Default: unset, which means use a random value." >&2;
 	echo -e "    JIT_COMPILE        Set to any value to compile the model with XLA." >&2;
@@ -77,7 +79,7 @@ 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 DICE_LOG_COSH WATER_THRESHOLD UPSAMPLE STEPS_PER_EXECUTION JIT_COMPILE RANDSEED PREDICT_AS_ONE;
+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 WATER_THRESHOLD UPSAMPLE STEPS_PER_EXECUTION JIT_COMPILE RANDSEED PREDICT_AS_ONE SPLIT_VALIDATE SPLIT_TEST;
 
 echo ">>> Installing requirements";
 conda run -n py38 pip install -q -r requirements.txt;

aimodel/src/deeplabv3_plus_test_rainfall.py

@@ -56,13 +56,13 @@ UPSAMPLE = env.read("UPSAMPLE", int, 2)
 SPLIT_VALIDATE = env.read("SPLIT_VALIDATE", float, 0.2)
 SPLIT_TEST = env.read("SPLIT_TEST", float, 0)
 
-
 STEPS_PER_EXECUTION = env.read("STEPS_PER_EXECUTION", int, 1)
 JIT_COMPILE = env.read("JIT_COMPILE", bool, False)
 DIR_OUTPUT = env.read("DIR_OUTPUT", str, f"output/{datetime.utcnow().date().isoformat()}_deeplabv3plus_rainfall_TEST")
 PATH_CHECKPOINT = env.read("PATH_CHECKPOINT", str, None)
 PREDICT_COUNT = env.read("PREDICT_COUNT", int, 25)
 PREDICT_AS_ONE = env.read("PREDICT_AS_ONE", bool, False)
+
 # ~~~
 
 env.val_dir_exists(os.path.join(DIR_OUTPUT, "checkpoints"), create=True)
@@ -82,7 +82,7 @@ env.print_all(False)
 # ██████  ██   ██    ██    ██   ██ ███████ ███████    ██    
 
 if not PREDICT_AS_ONE:
-	dataset_train, dataset_validate = dataset_mono(
+	dataset_train, dataset_validate, dataset_test = dataset_mono(
 		dirpath_input=DIR_RAINFALLWATER,
 		batch_size=BATCH_SIZE,
 		water_threshold=WATER_THRESHOLD,
@@ -91,11 +91,14 @@ if not PREDICT_AS_ONE:
 		input_size="same",
 		filepath_heightmap=PATH_HEIGHTMAP,
 		do_remove_isolated_pixels=REMOVE_ISOLATED_PIXELS,
-		parallel_reads_multiplier=PARALLEL_READS
+		parallel_reads_multiplier=PARALLEL_READS,
+		percentage_validate=SPLIT_VALIDATE,
+		percentage_test=SPLIT_TESTs
 	)
 
 	logger.info("Train Dataset:", dataset_train)
 	logger.info("Validation Dataset:", dataset_validate)
+	logger.info("Test Dataset:", dataset_test)
 else:
 	dataset_train = dataset_mono_predict(
 		dirpath_input=DIR_RAINFALLWATER,
@@ -253,6 +256,7 @@ if PATH_CHECKPOINT is None:
 	logger.info(">>> Beginning training")
 	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=[
 			tf.keras.callbacks.CSVLogger(
@@ -395,5 +399,12 @@ if not PREDICT_AS_ONE:
 		colormap,
 		model=model
 	)
+	if dataset_test is not None:
+		plot_predictions(
+			os.path.join(DIR_OUTPUT, "predict_test_$$.png"),
+			get_from_batched(dataset_test, PREDICT_COUNT),
+			colormap,
+			model=model
+		)
 
 logger.info(f"Complete at {str(datetime.now().isoformat())}, elapsed {str((datetime.now() - time_start).total_seconds())} seconds")

aimodel/src/lib/dataset/dataset_mono.py

@@ -160,19 +160,18 @@ 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, percentage_validate=0.2, percentage_test=0, **kwargs):
-def dataset_mono(dirpath_input, validate_percentage=0.2, test_percentage=0, **kwargs):
 	filepaths = get_filepaths(dirpath_input)
 	filepaths_count = len(filepaths)
 	
-	split_trainvalidate=math.floor(filepaths_count * (1-(validate_percentage+test_percentage)))
+	split_trainvalidate=math.floor(filepaths_count * (1-(percentage_validate+percentage_test)))
-	split_validatetest=math.floor(filepaths * (1 - test_percentage))
+	split_validatetest=math.floor(filepaths * (1 - percentage_test))
 	
 	
 	filepaths_train = filepaths[:split_trainvalidate]
 	filepaths_validate = filepaths[split_trainvalidate:split_validatetest]
 	filepaths_test = []
-	if test_percentage > 0:
+	if percentage_test > 0:
 		filepaths_test = filepaths[split_validatetest:]
 	
 	print("DEBUG:dataset_mono filepaths_train", filepaths_train, "filepaths_validate", filepaths_validate, "filepaths_test", filepaths_test)
@@ -182,7 +181,7 @@ def dataset_mono(dirpath_input, validate_percentage=0.2, test_percentage=0, **kw
 	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:
+	if percentage_test > 0:
 		dataset_test = make_dataset(filepaths_test, metadata=metadata, **kwargs)
 	
 	return dataset_train, dataset_validate, dataset_test #, filepaths