A bug in Keras where it resizes first and then applies the preprocessing function

keras-preprocessing-bug.py

'''
Keras's ImageDataGenerator is buggy. It first resizes based on `target_size`, 
and then applies the preprocessing function you specified. This sucks.
Who wants that to happen anyway. 
'''

def crop_image(img):
    print(img.shape)
    return img


def load_data():
    train_datagen = ImageDataGenerator(rotation_range=90,
                                       rescale=1. / 255,
                                       preprocessing_function=crop_image)

    test_datagen = ImageDataGenerator(rescale=1. / 255,
                                      preprocessing_function=crop_image)

    train_gen = train_datagen.flow_from_directory(
        '%s/train' % base,
        target_size=(256, 256),
        color_mode='grayscale',
        batch_size=batch_size,
        class_mode='categorical')

    test_gen = test_datagen.flow_from_directory(
        '%s/test' % base,
        target_size=(256, 256),
        color_mode='grayscale',
        batch_size=batch_size,
        class_mode='categorical')

    return train_gen, test_gen


def plot_data_gen(train_gen, test_gen):
    for X, y in train_gen:
        plt.figure(figsize=(16, 16))
        for i in range(25):
            plt.subplot(5, 5, i + 1)
            plt.axis('off')
            plt.title('Label: %d' % np.argmax(y[i]))
            img = np.uint8(255 * X[i, :, :, 0])
            plt.imshow(img, cmap='gray')
        break
    plt.show()


if __name__ == '__main__':
    model = load_model()
    train_gen, test_gen = load_data()
    plot_data_gen(train_gen, test_gen)

Is this bug corrected yet? Reading the current documentation of preprocessing_function seems like it is not. (https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/image/ImageDataGenerator)

Is there a workaround to this bug? I need to apply the preprocessing function first to make my images square after filling them with empty spaces on the edges and then resize it.

Thank you,