def test_datasetGenFromDataframe(): dsfolder = '/home/aditya/AIBH_Intern_09_Aditya/data' print( 10 * '-', 'Checking True or Default Augmentation', 10 * '-', ) trainGen, testGen = ai.datasetGenFromDataframe(dsfolder).load_generator() #print('DS GenType:', trainGen.STEP_SIZE, type(trainGen.STEP_SIZE) ) assert isinstance(trainGen.STEP_SIZE, float), "datasetGenFromDataframe: Failed" print( 10 * '-', 'Checking False or No Augmentation', 10 * '-', ) trainGen, testGen = ai.datasetGenFromDataframe( dsfolder, augmentation=False).load_generator() assert isinstance(trainGen.STEP_SIZE, float), "datasetGenFromDataframe: Failed" print( 10 * '-', 'Checking Augmentation Passing', 10 * '-', ) v = ai.AUGMENTATION() trainGen, testGen = ai.datasetGenFromDataframe( dsfolder, augmentation=v).load_generator() assert isinstance(trainGen.STEP_SIZE, float), "datasetGenFromDataframe: Failed"
def test_augmentation(): v = ai.AUGMENTATION() print('AUG Type:', type(v.trainAug)) assert isinstance( v.trainAug, tf.keras.preprocessing.image.ImageDataGenerator ), "augmentation: TrainAug - Default Initialization Failed" assert isinstance( v.testAug, tf.keras.preprocessing.image.ImageDataGenerator ), "augmentation: TestAug - Default Initialization Failed"