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 resampler node and having the output tensor of conv2d in
NCHW layout instead of NHWC).
Now when trying to run an even simpler model containing just a Resampler OP
(without Conv2D) i try to convert the layout from NHWC to NCHW aswell. I have
implemented a convert_function in `python/tvm/relay/op/nn/_nn.py` and defined a
`InferCorrectLayout` for the call node.
But when trying to convert the layouts by using the following code lines, i
dont get any triggers and yet no layout_transform:
desired_layouts={"nn.resampler": ["NCHW"]}
seq = tvm.transform.Sequential([relay.transform.RemoveUnusedFunctions(),
relay.transform.ConvertLayout(desired_layouts)])
with tvm.transform.PassContext(opt_level=3):
irmodule = seq(irmodule)
I compared my changes to the ones mentioned in the "official" Layout Pass guide
([this](https://tvm.apache.org/docs/dev/convert_layout.html) one).
Are there additional hooks that need to be set or strategies (or something
similar) that need to be defined in order for transform_layout to start running
the layout pass?
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