Mask isn't transformed the same way as image

Somehow the masks are transformed differently than the respective images in my implementation. Everything without transforms works fine though.

data = (src.transform(vision.get_transforms(), size=IMG_SHAPE, tfm_y=True)

def imshow(idx):
    fig, (ax1, ax2) = plt.subplots(1, 2, figsize = (8, 4))


Even basic flip with dihedral_affine in most cases results in different images. Is there something to consider? The only difference to other examples is the way I load masks, instead of using the default open_mask in SegmentationLabelList I use a custom function to load them from run-length encodings.

def masks_as_image(masks, shape):
    # Decode ship masks into image segments and overlay them
    mask_img = np.zeros(shape, dtype=np.uint8)
    for mask in masks:
        if isinstance(mask, str):
            mask_img += vision.rle_decode(mask, shape).T.astype(np.uint8)
    mask_tensor = basic_data.FloatTensor(mask_img)
    mask_tensor = mask_tensor.view(shape[1], shape[0], -1)
    return vision.ImageSegment(mask_tensor.permute(2,0,1))

def open_mask(fn):
    img_id = fn.stem + fn.suffix
    masks = masks_df[masks_df['ImageId'] == img_id]['EncodedPixels']
    mask_img = masks_as_image(masks, IMG_SHAPE)
    return mask_img

classes = ['no_ship', 'ship']
src = (SegmentationItemList.from_folder(path/'train_v2')
       .label_from_func(lambda x: x, classes=classes))

But it shouldn’t have any effect on later transformations since my mask tensors are in valid shape.

Someone else posted on this example, and it looks like the problem lies in rle_decode. This dataset seems to use a differnet convention than the fastai function.

That’s why I transposed the output of rle_decode so that mask and image perfectly overlay, so the problem may lay somewhere later in the code. I’ll try to do some more tests, such as save the mask to disk and use the default open_mask on it.

Hi Polakowo,

How did you transpose the output of rle_decode, external libraries kinda break the downstream processes.


Please somebody answer rony’s question.

Sorry for late response. You can look at my implementation here

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