Alexander Hirner <[email protected]> added the comment:
We construct a computational graph and need to validate (or search for possible
new) edges at runtime. The data is specific to computer vision. One example is
clipping geometry within 2D boundaries. A minimal implementation of this data
would be:
@dataclass
class FramedGeometry:
width: PositiveInt
height: PositiveInt
geometry: Geometry
However, different properties are manifest in different encodings. Height,
width can be meta data from an image database or inherent to a decoded image
(as np.ndarray.shape). The transform will then
`dataclasses.replace(geometry=...)` its attribute(s).. If width and height are
not implemented, another transition is needed to produce them whilst only
looking into the image header, not fully decoding potentially large and many
images.
The read-only interface ensures that transitions are generic wrt some forms of
inputs. The replace interface preserves runtime types.
Inputs and outputs are annotated with @dataclass or tuples of them. Those
dataclasses are a mixin of base dataclasses that declare concrete properties
like a URI of an image and ABCs that declare accessors like get_width(self) ->
PositiveInt. We use @pydantic.dataclass to parse, validate and deserialize
concrete classes to great extent [0].
In order to not implement accessors on top of dataclasses, we'd want that
abstract properties are compatible with dataclasses and issubclass works with
data protocols (given the necessary constraints).
PS:
Polymorphism for computer-vision data is an interesting problem. Other
approaches exist, each with their own struggle to model "traits" the right way
[1]. E.g., scaling would be valid for `FramedGeometry` since no image data is
included but invalid when images are referenced but cannot be resized, like:
class EncodedSizedAnnotatedFrame:
width: PositiveInt
height: PositiveInt
image_bin: bytes
geometry: Geometry
Thanks!
[0] https://pydantic-docs.helpmanual.io/usage/dataclasses/
[1] https://github.com/pytorch/vision/issues/1406
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