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How to compare the precision layer by layer with engine(for example, tensorRT) if I have a custom operator in my onnx (and a corresponding plugin in TensorRT)?
#293
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MyraYu2022 opened this issue
Nov 22, 2024
· 0 comments
I used to check my engine(tensorRT) layer by layer with onnx using polygraphy tool. But I have a custom operator writen by myself in my onnx, and a corresponding plugin in my tensorRT now and how can I compare them layer by layer using polygraphy?
I write the .sh script like this:
polygraphy run my_model_with_custom_operator.onnx \
--onnxrt --trt \
--trt-outputs mark all \
--onnx-outputs mark all \
--atol 1e-3 --rtol 1e-3 \
--fail-fast \
--val-range [0,1] \
--verbose
And I get error as below: onnxruntime.capi.onnxruntime_pybind11_state.Fail: [ONNXRuntimeError] : 1 : FAIL : Fatal error: customPlugin(-1) is not a registered function/op
Thank you very much.
Environment
TensorRT Version:
8.4.0.6
NVIDIA Driver Version:
535.183.01
CUDA Version:
11.3
CUDNN Version:
8.3.2.44
Operating System:
Python Version (if applicable):
3.9.7
PyTorch Version (if applicable):
1.13.0
The text was updated successfully, but these errors were encountered:
MyraYu2022
changed the title
Polygraphy: how to compare the precision layer by layer with engine(for example, tensorRT) if I have a custom operator in my onnx (and a corresponding plugin in TensorRT)?
How to compare the precision layer by layer with engine(for example, tensorRT) if I have a custom operator in my onnx (and a corresponding plugin in TensorRT)?
Nov 22, 2024
Description
I used to check my engine(tensorRT) layer by layer with onnx using polygraphy tool. But I have a custom operator writen by myself in my onnx, and a corresponding plugin in my tensorRT now and how can I compare them layer by layer using polygraphy?
I write the .sh script like this:
And I get error as below:
onnxruntime.capi.onnxruntime_pybind11_state.Fail: [ONNXRuntimeError] : 1 : FAIL : Fatal error: customPlugin(-1) is not a registered function/op
Thank you very much.
Environment
TensorRT Version:
8.4.0.6
NVIDIA Driver Version:
535.183.01
CUDA Version:
11.3
CUDNN Version:
8.3.2.44
Operating System:
Python Version (if applicable):
3.9.7
PyTorch Version (if applicable):
1.13.0
The text was updated successfully, but these errors were encountered: