Okay thanks alot for you fast and helpful answers :slight_smile:
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It doesn't make sense to convert a model with only one Resampler op. Think
about it. What's the layout of an input variable? An input variable only has
shape and type but no layout. As a result, InferCorrectLayout basically does
nothing.
Based on that, ConvertLayout actually propagates the kn
Thank you for your response @comaniac !
When testing your statement by *uncommenting* the `InferCorrectLayout` property
of nn.resampler and feeding a simple conv2D->Resampler model (applying a
LayoutTransform) i actually get the desired behaviour (it updated the
layout-attribute of my resam
My impression is actually opposite to yours. My impression is if you return
Undef or incompatible layout, then layout_transform will be inserted to
guarantee the correctness. For example:
```
conv2d(NCHW, OIHW) -(NCHW)-> transpose(axis=[0, 2, 3, 1]) -(NHWC)->
```
The output layout of `conv2d`
Hello everyone,
i have been implementing my version of the
[Resampler](https://www.tensorflow.org/addons/api_docs/python/tfa/image/resampler)
OP (from TF Frontend) to our TVM Stack.
Now (to my understanding) by adding the "InferCorrectLayout" Attribute to the
RelayCall Node i should be able
