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 to also automatically change the Layout of my Custom OP's Inputs/Outputs when the layout is changed for nn.conv2d (see [here](https://tvm.apache.org/docs/dev/convert_layout.html). So my problem is: The Resampler OP has a data tensor of a certain layout (NHWC, NCHW etc.) which i want to adapt depending on the desired_layout. But Resampler also takes a so called warp tensor with n Dimensions. The same applies for the output shape. In theory i want a layout_transform insertion ONLY on the first input Tensor and leave the other input tensor and the output tensor **untouched** with regards to its layout. From my intuition Array<Array<Layout>> ResamplerInferCorrectLayout(...){ /*similar code to UpsamplingInferCorrectLayout*/ return Array<Array<Layout>>{{inferred_layout, Layout::Undef()}, {Layout::Undef()}}; } Sadly this does not work and i dont yield any layout_transform near my Resampler OP. If i understand correctly that has to do with the fact, that a Layout::Undef() tells TVM to overall not insert any layout_transform. Do you have any idea how i can achieve the "passthrough" of the 2nd Input tensor and outputtensor with regards to layout_transform? --- [Visit Topic](https://discuss.tvm.apache.org/t/infercorrectlayout/9116/1) to respond. You are receiving this because you enabled mailing list mode. To unsubscribe from these emails, [click here](https://discuss.tvm.apache.org/email/unsubscribe/6f10e8f6696cabe0b13373b0dd1aed9c243b2a2354c92f969d9815d5d6852af0).
