Does albumentations normalize mask?
When I pass an image and a mask to albumentations.Normalize(mean, std).
How would I go about incorporating this?
Should I just add it manually in dataset?
Grateful for any tips you have!

Edited:
Normalization works for three-channel images. If your mask image is grayscale image then probably you need to stack(image= np.stack((img,)*3, axis=-1))
it and make three channel image then apply albumentations's Normalization function. Official function for A.Normalize() is as following which deals with RGB images:
def normalize(img, mean, std, max_pixel_value=255.0):
    mean = np.array(mean, dtype=np.float32)
    mean *= max_pixel_value

    std = np.array(std, dtype=np.float32)
    std *= max_pixel_value

    denominator = np.reciprocal(std, dtype=np.float32)

    img = img.astype(np.float32)
    img -= mean
    img *= denominator
    return img

According to Albumentations's docs, you can make a composition of Transforms and use it within PyTorch dataset.
import albumentations as A
from albumentations.pytorch import ToTensorV2

train_transform = A.Compose(
    [
        A.SmallestMaxSize(max_size=160),
        A.ShiftScaleRotate(shift_limit=0.05, scale_limit=0.05, rotate_limit=15, p=0.5),
        A.RandomCrop(height=128, width=128),
        A.RGBShift(r_shift_limit=15, g_shift_limit=15, b_shift_limit=15, p=0.5),
        A.RandomBrightnessContrast(p=0.5),
        A.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
        ToTensorV2(),
    ]
)
train_dataset = CatsVsDogsDataset(images_filepaths=train_images_filepaths, transform=train_transform)


But I am not really sure that normalizing mask image is right way or not.
