I’ve created a topic here
(It’s about failed to use tensorRT to run inference with uff parser)
As @AakankshaS suggested, I change to use onnx parser, but I still couldn’t get the correct result…
I’ve describe this situation in that post but didn’t get response, so I think I should create another topic here…
Here is the thing:
the result of the inference after post-processing should be an image(let’s call it as ‘A’) with size 1920 x 1920,
but it turned out to be an image consisted of 4 ‘A’ in each row and column, and the image’s size is still 1920 x 1920.
(i.e., there are 16 small ‘A’ in an image)
Furthermore, each small ‘A’ seems to get lighter from top left to bottom right(?).
I’m not sure if it’s the problem of model or the post-processing…
Could anyone help me out with this issue?
I’ve done these process during the conversion of model:
- Add a permute layer after my output layer while converting .h5 to .onnx
(Since ONNX work with NCHW order of tensor’s dimension, and my model’s is NHWC)
- Set the batch dimension to 1 in my onnx model
(Or else I’ll have to set optimization profile(which I’ve tried and still failed to make it Q_Q),
since there’s a dimension for batch with “?” in my original model, it’ll recognized as dynamic input)
Here’s the information of my onnx to engine’s conversion:
[07/17/2020-11:51:10] [I] Building and running a GPU inference engine for Onnx MNIST
Input filename: trial_multi_batch1.onnx
ONNX IR version: 0.0.6
Opset version: 11
Producer name: keras2onnx
Producer version: 1.6.0
Model version: 0
Any help or advice would be appreciated!
TensorRT Version: 188.8.131.52
GPU Type: RTX 2080 Ti
Nvidia Driver Version: 432.00
CUDA Version: 10.0
CUDNN Version: 7.4.2
Operating System + Version: Windows10
Python Version (if applicable): 3.6.8
TensorFlow Version (if applicable): 1.13.1
PyTorch Version (if applicable): -
Baremetal or Container (if container which image + tag): -
Steps To Reproduce
Run the cpp file with the same setting as SampleOnnxMNIST.cpp, and you can compare the result I provided in the zip file.