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"