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
Thanks for your response, but I still dont understand what the solution is...
except for decomposing the tuple into elementary arguments.
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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
Hello, I am also struggling the tvm.transform.Sequential API usage，could you
please help me to understand the following code piece ?
# driver/build_module.py下面的_build_for_device函数
opt_mixed += [
tvm.tir.transform.ThreadSync("shared"),
tvm.tir.transform.ThreadSync
@dmitriy-arm might know.
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