Is there a way to convert detectron2 to a more compact model for faster inferencing ?
TensorRT Version :GPU Type :Nvidia Driver Version :CUDA Version :CUDNN Version :Operating System + Version :Python Version (if applicable) :TensorFlow Version (if applicable) :PyTorch Version (if applicable) :Baremetal or Container (if container which image + tag) :
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NVES
April 29, 2022, 8:07pm
3
Hi,
You can refer below link for all the supported operators list, in case any operator is not supported you need to create a custom plugin to support that operation
<!--- SPDX-License-Identifier: Apache-2.0 -->
# Supported ONNX Operators
TensorRT 8.4 supports operators up to Opset 17. Latest information of ONNX operators can be found [here](https://github.com/onnx/onnx/blob/master/docs/Operators.md)
TensorRT supports the following ONNX data types: DOUBLE, FLOAT32, FLOAT16, INT8, and BOOL
> Note: There is limited support for INT32, INT64, and DOUBLE types. TensorRT will attempt to cast down INT64 to INT32 and DOUBLE down to FLOAT, clamping values to `+-INT_MAX` or `+-FLT_MAX` if necessary.
See below for the support matrix of ONNX operators in ONNX-TensorRT.
## Operator Support Matrix
| Operator | Supported | Supported Types | Restrictions |
|---------------------------|------------|-----------------|------------------------------------------------------------------------------------------------------------------------|
| Abs | Y | FP32, FP16, INT32 |
| Acos | Y | FP32, FP16 |
| Acosh | Y | FP32, FP16 |
| Add | Y | FP32, FP16, INT32 |
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Also, request you to share your model and script if not shared already so that we can help you better.
Meanwhile, for some common errors and queries please refer to below link:
Thanks!