Working with 1D in TensorRT

I imported my ONNX model using a parser in TensorRT.
My ONNX model include two conv1d layers.

  1. I did not see any 1D convolution layer in the TensorRT layer list (see 2. Layers and Features) :
    Support Matrix :: NVIDIA Deep Learning TensorRT Documentation
    There is only IConvolutionLayer for 2D and 3D convolution.
    I would like to know if TensorRT uses a specific conv1d layer or if it adapts his conv2d layer to 1D.

  2. I also would like to know if there are some specific features with 1D (caveats, thinks to avoid, differents ways to optimize than if it would be 2D).

Hi @julie.fraysse

We don’t natively support it. If we see one in ONNX we convert the 1D conv into a 2D conv, perform the operation, and convert the result back.


Thanks for your reply,

  1. Is this why I see a lot of “copyPackedKernel” during batch inference execution?
  2. These kernels seem to cousume a lot of time (nearly half of the time). Is this normal ?