Please provide the following information when requesting support. • Hardware RTX 3060 • Network Type LPRNet • TAO toolkit_version: 3.21.11 • Training spec file my_spec.txt (2.5 KB) To continue LPRNet can't use exported engine file . I managed to convert .etlt lprnet to .engine, yet I still get…

[image] s.levsov: [TRT] [E] 1: [stdArchiveReader.cpp::StdArchiveReader::35] Error Code 1: Serialization (Serialization assertion safeVersionRead == safeSerializationVersion failed.Version tag does not match. Note: Current Version: 0, Serialized Engine Version: 43) [01/04/2022-14:20:27] [TRT] [E…

When I run docker I get this message: Running command in container: nvcr.io/nvidia/tao/tao-toolkit-tf:v3.21.11-tf1.15.5-py3 Not sure how to check full info on it. One thing though, I run inference on my Ubuntu 20.04 PC with RTX 3060 on it , not in docker. And if I try to use engine in deepstream_…

May I know where did you run below command, inside docker or outside docker? $ ./tao-converter custom_lprnet.etlt -k nvidia_tlt -p image_input,1x3x48x96,4x3x48x96,16x3x48x96 -t fp16 -e custom_lprnet.engine

[image] s.levsov: [01/04/2022-14:20:27] [TRT] [E] 1: [stdArchiveReader.cpp::StdArchiveReader::35] Error Code 1: Serialization (Serialization assertion safeVersionRead == safeSerializationVersion failed.Version tag does not match. Note: Current Version: 0, Serialized Engine Version: 43) [01/04/20…

I get this message if I run this code in non-virtual environment: def load_engine(trt_runtime, engine_path): with open(engine_path, 'rb') as f: engine_data = f.read() engine = trt_runtime.deserialize_cuda_engine(engine_data) return engine TRT_LOGGER = trt.Logger(trt.Logger.WAR…

OK, you can run tao-converter to generate a new tensorrt engine again under non-virtual environment. Then please check again.

That’s exactly what I did before. For the current project I don’t use venv.

For your current non-virtual environment, could you run below and share the result? $ dpkg -l |grep cuda

$ dpkg -l |grep cuda ii cuda 11.5.1-1 amd64 CUDA meta-package ii cuda-11-5 11.5.1-1 amd64 CUDA 11.5 meta-package ii cuda…

LPRNet can’t use exported engine file

Accelerated Computing Intelligent Video Analytics TAO Toolkit

Morganh January 5, 2022, 1:08pm 15

For other inference ways, refer to GitHub - NVIDIA-AI-IOT/tao-toolkit-triton-apps at dev-morgan/add-lprnet-triton or Python run LPRNet with TensorRT show pycuda._driver.MemoryError: cuMemHostAlloc failed: out of memory - #8 by Morganh or Not Getting Correct output while running inference using TensorRT on LPRnet fp16 Model

Topic		Replies	Views
LPRNet can't use exported engine file TAO Toolkit	17	2851	December 9, 2021
Lprnet: Failed to run the tensorrt engine verification TAO Toolkit	7	1399	July 27, 2021
Can't load trt engine and throwing an instance of 'nvinfer1::MyelinError' TAO Toolkit	16	3044	May 6, 2021
LPRNet gives different results when running on Jetson TAO Toolkit jetson	27	1275	March 27, 2024
Falure to do inference TAO Toolkit tensorrt	8	1244	December 28, 2021
Convert tensorrt engine from version 7 to 8 TAO Toolkit tensorrt	66	5405	May 11, 2021
Cannot use TensorRT model exported by NVIDIA TAO TAO Toolkit	7	1365	April 27, 2022
LPRNet custom trained model ERROR: [TRT]: UffParser: Could not read buffer TAO Toolkit	3	861	September 16, 2021
Deepstream-lpr-python-version TAO Toolkit	14	1613	June 14, 2022
Not Getting Correct output while running inference using TensorRT on LPRnet fp16 Model TAO Toolkit	22	1878	July 22, 2021

LPRNet can’t use exported engine file

Related topics