Squeezenet, AxPy, TensorRT

Is there an existing conversion of the AxPy layer used in Squeezenet to TensorRT?

I see some notes from a couple of years ago, but nothing about AxPy.

Thanks!

– Samson

hello,

AxPy is an unsupported layer. Recommend TensorRT plugin API; it is for implementing custom layers (i.e. in a CUDA kernel)

See AastaNV’s TensorRT plugin sample here: https://github.com/AastaNV/Face-Recognition

I understand it is un-supported, and I may have to write a custom layer.

That said: I have to think someone has written this layer – squeezenet is a pretty common network. Has no one done this layer?

Also, could I potentially try to divide up the layer? Could I divide the layer in two, into an multiplication layer (Ax) and an addition layer (Py)? I’d prefer to do that then write a custom layer. Which layers should I use if I want to do that?

If I divided the layers, I would thin tensor rt would then be smart enough to merge them together during optimization.

Thanks!

Were you able to find solution to splitting the output of a layer into parts, as is done using view (PyTorch) or using slicing in numpy?