TensorRT 8.5.2 conversion of RTDETR (object detection model) is failing

AI & Data Science Deep Learning (Training & Inference) TensorRT
1

Description

A clear and concise description of the bug or issue.

Environment

TensorRT Version: 8.5.2.2
GPU Type: RTX 3090
Nvidia Driver Version: 537.42
CUDA Version: 12.1
CUDNN Version: 8.9.6
Operating System + Version: ubuntu 20.04.5 LTS
Python Version (if applicable): 3.8.10
TensorFlow Version (if applicable):
PyTorch Version (if applicable): 2.0.1
Baremetal or Container (if container which image + tag): Container. From baseImage: nvcr.io/nvidia/pytorch:23.04-py3, upgrade pytorch

Relevant Files

The following command is used:
/usr/src/tensorrt/bin/trtexec --onnx=temp/rtdetr.onnx --workspace=4096 --shapes=images:1x3x640x640 --saveEngine=output/model.trt --avgRuns=10

Please fine the onnx files (opset 16 and 17), ErrorFile (that contains the errors), here: tensorrt_challenge - Google Drive

2

Hi @rajesh21 ,
Can you please help us with onnx model

Thanks

3

Hello,
Thank you for your response. I am not sure what you are asking. Are you asking where the location of the onnx file is?
It is located in two places that is already shared:

As indicated in the above post, the folder (tensorrt_challenge - Google Drive) also contains the error file as well.

4

Hello,
Just want to follow up? Do you need any other additional information from us?

5

After resetting few flags (E.g., specifying batch size), the tensortRT file gets created but is unusable.

with open(engine_path, "rb") as f:
    engine_data = f.read()
engine = trt_runtime.deserialize_cuda_engine(engine_data)
engine is None

The command we used to convert is as follows:

     command = (
            f"/usr/src/tensorrt/bin/trtexec "
            f"--onnx={conversion_config.temp_dir/'rtdetr.onnx'}  "
            f"--workspace=16096 "
            f"--minShapes=images:1x3x640x640,orig_target_sizes:1x2 "
            f"--optShapes=images:4x3x640x640,orig_target_sizes:4x2 "
            f"--maxShapes=images:4x3x640x640,orig_target_sizes:4x2 "
            f"--saveEngine=output/model.trt"
        )