Iam using runtime data augmentation using generators in keras for segmentation problem..
Here is my data generator
data_gen_args = dict(
width_shift_range=0.1,
height_shift_range=0.1,
zoom_range=0.2,
horizontal_flip=True,
validation_split=0.2
)
image_datagen = ImageDataGenerator(**data_gen_args)
def generate_data_generator(generator, Xi, Yi):
genXi = generator.flow(Xi, seed=7, batch_size=32)
genYi = generator.flow(Yi, seed=7,batch_size=32)
while True:
Xi = genXi.next()
Yi = genYi.next()
print(Yi.dtype)
print(np.unique(Yi))
yield (Xi, Yi)
train_generator = generate_data_generator(image_datagen,
x_train,
y_train)
My labels are in a numpy array with data type float 32 and value 0.0 and 1.0.
#Output of np.unique(y_train)
array([0., 1.], dtype=float32
However, the data generator seems to modifies pixel values as shown below:-
#Output of print(np.unique(Yi))
[0.00000000e+00 1.01742386e-04 1.74021334e-04 ... 9.99918878e-01
9.99988437e-01 1.00000000e+00]
It is supposed to have same values(0.0 and 1.0) after data geneartion.. Also, the the official documentation shows an example using same augmentation arguments for generating mask and images together.
However when i remove shift and zoom iam getting (0.0 and 1.0) as output. Keras verion 2.2.4,Python 3.6.8
UPDATE:-
I saved those images as numpy array and plotted it using matplotlib.It looks like the edges are smoothly interpolated (0.0-1.0) somehow upon including shifts and zoom augmentation. I can round these values in my custom generator as a hack; but i still don't understand the root cause (in case of normal images this is quite unnoticeable and has no adverse effects; but in masks we don't want to change label values )!!!
Still wondering.. is this a bug (nobody has mentioned it so far)or problem with my custom code ??
