Ssd inception v2 model ONNX to tensorrt error

How can I change the UINT8 to INT32 in tensorflow ssd inception v2 model.?
Is there any workaround for this model.?
Here is the model : http://download.tensorflow.org/models/object_detection/ssd_inception_v2_coco_2018_01_28.tar.gz:

I used tf2onnx command to convert saved model to onnx. It worked .

Then I got the below error when I run this command ./trtexec --onxx=inception.onnx

Error repeated for both custom trained and also standard ssd inception v2 model

WARNING: ONNX model has a newer ir_version (0.0.5) than this parser was built against (0.0.3).
Unsupported ONNX data type: UINT8 (2)
ERROR: ModelImporter.cpp:54 In function importInput:
[8] Assertion failed: convert_dtype(onnx_tensor_type.elem_type(), &trt_dtype)

Any suggestions for this??
I am using jetson nano with below specifications.

sudo apt-cache show nvidia-jetpack
Package: nvidia-jetpack
Version: 4.3-b134
Architecture: arm64
Maintainer: NVIDIA Corporation
Installed-Size: 194

Depends: nvidia-container-csv-cuda (= 10.0.326-1), libopencv-python (= 4.1.1-2-gd5a58aa75), libvisionworks-sfm-dev (= 0.90.4), libvisionworks-dev (= 1.6.0.500n), libvisionworks-samples (= 1.6.0.500n), libnvparsers6 (= 6.0.1-1+cuda10.0), libcudnn7-doc (= 7.6.3.28-1+cuda10.0), libcudnn7-dev (= 7.6.3.28-1+cuda10.0), libnvinfer-samples (= 6.0.1-1+cuda10.0), libnvinfer-bin (= 6.0.1-1+cuda10.0), nvidia-container-csv-cudnn (= 7.6.3.28-1+cuda10.0), libvisionworks-tracking-dev (= 0.88.2), vpi-samples (= 0.1.0), tensorrt (= 6.0.1.10-1+cuda10.0), libopencv (= 4.1.1-2-gd5a58aa75), libnvinfer-doc (= 6.0.1-1+cuda10.0), libnvparsers-dev (= 6.0.1-1+cuda10.0), libcudnn7 (= 7.6.3.28-1+cuda10.0), libnvidia-container0 (= 0.9.0~beta.1), cuda-toolkit-10-0 (= 10.0.326-1), nvidia-container-csv-visionworks (= 1.6.0.500n), graphsurgeon-tf (= 6.0.1-1+cuda10.0), libopencv-samples (= 4.1.1-2-gd5a58aa75), python-libnvinfer-dev (= 6.0.1-1+cuda10.0), libnvinfer-plugin-dev (= 6.0.1-1+cuda10.0), libnvinfer-plugin6 (= 6.0.1-1+cuda10.0), nvidia-container-toolkit (= 1.0.1-1), libnvinfer-dev (= 6.0.1-1+cuda10.0), libvisionworks (= 1.6.0.500n), libopencv-dev (= 4.1.1-2-gd5a58aa75), nvidia-l4t-jetson-multimedia-api (= 32.3.1-20191209225816), vpi-dev (= 0.1.0), vpi (= 0.1.0), python3-libnvinfer (= 6.0.1-1+cuda10.0), python3-libnvinfer-dev (= 6.0.1-1+cuda10.0), opencv-licenses (= 4.1.1-2-gd5a58aa75), nvidia-container-csv-tensorrt (= 6.0.1.10-1+cuda10.0), libnvinfer6 (= 6.0.1-1+cuda10.0), libnvonnxparsers-dev (= 6.0.1-1+cuda10.0), libnvonnxparsers6 (= 6.0.1-1+cuda10.0), uff-converter-tf (= 6.0.1-1+cuda10.0), nvidia-docker2 (= 2.2.0-1), libvisionworks-sfm (= 0.90.4), libnvidia-container-tools (= 0.9.0~beta.1), nvidia-container-runtime (= 3.1.0-1), python-libnvinfer (= 6.0.1-1+cuda10.0), libvisionworks-tracking (= 0.88.2)
Homepage: http://developer.nvidia.com/jetson
Priority: standard
Section: metapackages
Filename: pool/main/n/nvidia-jetpack/nvidia-jetpack_4.3-b134_arm64.deb
Size: 29742
SHA256: 1fd73e258509822b928b274f61a413038a29c3705ee8eef351a914b9b1b060ce
SHA1: a7c4ab8b241ab1d2016d2c42f183c295e66d67fe
MD5sum: de856bb9607db87fd298faf7f7cc320f
Description: NVIDIA Jetpack Meta Package
Description-md5: ad1462289bdbc54909ae109d1d32c0a8

hi @god_ra,
I tried the same and was able to re-produce the issue.
Our team will be working to resolve this issue.
Thank you for your patience.

Okay. let me know as soon as you get the solution.

Thank you

Did you find the solution for this??

I am not bale to convert tensorflow graph to ONNX and UFF both.
Let me know the possible solution for this.

I can provide my frozen graph if needed.:

It was trained on 38 classes.
Including background 39 classes.

I am still waiting for your response.
@AakankshaS

Hi @god_ra,
The Engineering team is working on the issue.
We will keep you posted on the solution.
Thanks!

Hi @AakankshaS
It is almost a month since I posted.

I want to use tensorflow model in tensorRT using c++ sample codes.
Standard incepiton models works fine with tf - uff - tensorrt (python and c++)
But custom trained models doesnot work with c++ samples code.
They work well with python code.

Atleast solve the problem of uff conversion if onnx is gettign delayed.

I am literally waitning everyday for your solution on this.

posted several times, but no respons efrom NVIDIA or tensorRT community.

Support is not as good as we think I suppose.

Just solve my problem of tf - uff - tensorrt(c++) or tf-onnx-tensorrt(c++) for custom trained ssd inception v2 models.