I’m working on a project with TensorRT due to some speed issue.
As far as I know, the whole process should be like this…
(keras).h5/hdf5 -> (tensorflow).pb -> .uff -> .engine
So far, I’ve run through the whole process with the same model structure (UNET) I used before as I use in this project.
The only difference between these two models is the model input/output size:
old model - input: 1664 x 288 x 1, output:1664 x 288 x 6
current model - input: 1920 x 1920 x 1, output:1920 x 1920 x 2
However, when I go through the whole process as same as I’ve done with the old model on the current one,
it terminates without any error before running inference with engine file.
It terminates at
createMnistCudaBuffer() and left the output before that function on the terminal…
I thought it might be the memory issue, so I’ve tried to adjust the declaration of
MAX_WORKSPACE, but it didn’t work.
Did I miss something important which may cause this situation?
I installed tensorRT python’s wheel under Anaconda with TRT18.104.22.168, and install TRT5.1.5 under my OS
GPU Type: RTX 2080 Ti (11G)
Nvidia Driver Version: (Sorry I forgot to check, maybe 417.xx?)
CUDA Version: 10.0
CUDNN Version: 7.4.2
Operating System + Version: Windows 10
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): -
Any help or advice would be appreciated!