DataBlock: batch_tfms=aug_transforms() is applied to both training and validation images

Image segmentation with Unet. I make a Pandas dataframe with image and mask attributes. I create a DataBlock with getters and augmentation. Code excerpt:

def make_mask(row):
    Is called by DataBlock(getters=).
    Takes a list of paths to mask files from a Pandas column.
    Makes sure all masks are 8 bits per pixel.
    If there are multiple masks, merges them.
    Returns a PILMask.create() mask image.
    f = ColReader("mask")
    # PILMask.create() probably forces 8 bits per pixel
    all_images = [np.asarray(PILMask.create(x)) for x in f(row)]
    image_stack = np.stack(all_images)
    image_union = np.amax(image_stack, axis=0)
    return PILMask.create(image_union)

src_datablock = DataBlock(
    blocks=(ImageBlock, MaskBlock),
    getters=[ColReader("image"), make_mask],
    splitter=TrainTestSplitter(stratify=src_df["dataset"].to_list(), random_state=42),
    item_tfms=Resize(size=input_image_size, method="squish"),

src_dataloader = src_datablock.dataloaders(src_df, bs=src_batch_size)


The problem is, the same aug_transforms() are applied to both training and validation images. This is definitely not what I want.

Is there a way to prevent aug_transforms() from being applied in validation?

No, this is wrong, sorry about that.

It was an artifact of image transforms previously applied that made it seem like aug_transforms() was applied in both cases.