I have converted the FPEnet file with tao. But now when I want to inference it, it gives following error…
(env) eren@erennx:~/FPEnet$ /home/eren/env/bin/python /home/eren/FPEnet/test.py
[07/09/2022-22:37:39] [TRT] [W] Using an engine plan file across different models of devices is not recommended and is likely to affect performance or even cause errors.
Traceback (most recent call last):
File “/home/eren/FPEnet/test.py”, line 150, in
fpenet_obj = FpeNet(‘/home/eren/FPEnet/model32.trt’)
File “/home/eren/FPEnet/test.py”, line 35, in init
self._allocate_buffers()
File “/home/eren/FPEnet/test.py”, line 61, in _allocate_buffers
host_mem = cuda.pagelocked_empty(size, dtype)
NameError: name ‘dtype’ is not defined
[07/09/2022-22:37:43] [TRT] [E] 1: [defaultAllocator.cpp::deallocate::35] Error Code 1: Cuda Runtime (invalid argument)
Segmentation fault (core dumped)
My code is adapted from the previous forum topic “how to inference with fpenet” test.py and is as follows
It looks like you deserialize a TensorRT engine directly.
Did you generate that engine on the XavierNX and the same JetPack version?
Please noted that TensorRT is not portable since it is optimized based on the hardware resources.
So you will need to generate it on the same GPU architecture and TensorRT software version.
Yes I have converted it with various following commands on the same device(jetson nx)
tao-converter
-k nvidia_tlt
-t fp16 (I converted also with fp32)
-p input_face_images:0,1x1x80x80,1x1x80x80,2x1x80x80
-e /home/eren/FPEnet/model.engine (and used also various other names, I think it is not important)
-m 1
-w 1000000000 (tried also without -w)
/home/eren/FPEnet/model.etlt
I had downloaded the deepstream_tao_apps folder I,
$cd apps/tao_others/deepstream-faciallandmark-app
$export CUDA_VER=10.2
but
$make
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/nvidia/deepstream/deepstream/lib/cvcore_libs
./deepstream-faciallandmark-app 2 …/…/…/configs/facial_tao/sample_faciallandmarks_config.txt file:///usr/data/faciallandmarks_test.jpg ./landmarks
cannot compile as:
make
g++ -c -o deepstream_faciallandmark_app.o -fpermissive -Wall -Werror -DPLATFORM_TEGRA -I/opt/nvidia/deepstream/deepstream/sources/includes -I/opt/nvidia/deepstream/deepstream/sources/includes/cvcore_headers -I /usr/local/cuda-10.2/include -I …/common pkg-config --cflags gstreamer-1.0 -D_GLIBCXX_USE_CXX11_ABI=1 -Wno-sign-compare -Wno-deprecated-declarations deepstream_faciallandmark_app.cpp
deepstream_faciallandmark_app.cpp:46:10: fatal error: nvds_yml_parser.h: No such file or directory #include “nvds_yml_parser.h”
^~~~~~~~~~~~~~~~~~~
compilation terminated.
Makefile:70: recipe for target ‘deepstream_faciallandmark_app.o’ failed
make: *** [deepstream_faciallandmark_app.o] Error 1
I tried to find the file nvds_yml_parser.h, which did not exist.
I had downloaded the deepstream_tao_apps two months ago
deepstream and deepstream-6.0 otherwise works…
jetson_release -v
NVIDIA Jetson Xavier NX (Developer Kit Version)
Jetpack UNKNOWN [L4T 32.7.2]
NV Power Mode: MODE_20W_4CORE - Type: 7
jetson_stats.service: active
Board info:
Type: Xavier NX (Developer Kit Version)
SOC Family: tegra194 - ID:25
Module: P3668 - Board: P3509-000
Code Name: jakku
CUDA GPU architecture (ARCH_BIN): 7.2
Serial Number: 1421520056113
Libraries:
CUDA: 10.2.300
cuDNN: 8.2.1.32
TensorRT: 8.2.1.8
Visionworks: 1.6.0.501
OpenCV: 4.1.1 compiled CUDA: NO
VPI: ii libnvvpi1 1.2.3 arm64 NVIDIA Vision Programming Interface library
deepstream-app: error while loading shared libraries: libyaml-cpp.so.0.6: cannot open shared object file: No such file or directory
as i have jetpack 4.6…Do I have to install jetpack 5 and install tao and then try to convert fpenet to trt and run the code again, as the ds6.1 is reinstalled now?
Does tao-converter jetson nx work with jetpack 5 and ds6.1? should I reformat and switch to jetpack 5?
Second problem was: So the models were not downloaded…
After running:
:~/deepstream_tao_apps$ ./download_models.sh
and downloading models folder
and pointing to a test.jpg deepstrea_facial_app could inference, and while deepstream was trying to use the etlt file it ceated multiple engine files for INT8 INT16 engine files under its models folder…
So coming to the first question again, I have used the ‘facenet.etlt_b1_gpu0_int8.engine’ that the deepstream had ceated during inference with the test.py file for FPRnet, but in the end it gave the same error in the beginning…
~/FPEnet$ /home/eren/env/bin/python /home/eren/FPEnet/test.py
Traceback (most recent call last):
File “/home/eren/FPEnet/test.py”, line 150, in
fpenet_obj = FpeNet(‘/home/eren/FPEnet/facenet.etlt_b1_gpu0_int8.engine’)
File “/home/eren/FPEnet/test.py”, line 35, in init
self._allocate_buffers()
File “/home/eren/FPEnet/test.py”, line 61, in _allocate_buffers
host_mem = cuda.pagelocked_empty(size, dtype)
NameError: name ‘dtype’ is not defined
[07/13/2022-13:11:56] [TRT] [E] 1: [defaultAllocator.cpp::deallocate::35] Error Code 1: Cuda Runtime (invalid argument)
[07/13/2022-13:11:56] [TRT] [E] 1: [cudaDriverHelpers.cpp::operator()::29] Error Code 1: Cuda Driver (invalid device context)
Segmentation fault (core dumped)
Can I not use that engine files? what else could be the problem?
(env) eren@erennx:~/FPEnet$ sudo docker run --gpus all -it -v /workspace/tlt-experiments/:/workspace/tlt-experiments -p 8888:8888 nvcr.io/nvidia/tao/tao-toolkit-tf:v3.22.05-tf1.15.4-py3 /bin/bash
WARNING: The requested image’s platform (linux/amd64) does not match the detected host platform (linux/arm64/v8) and no specific platform was requested
exec /usr/local/bin/install_ngc_cli.sh: no such file or directory
To run it on my jetson nx what should I do?Is the L4 Base the right container?Hoe can I run my files without any internet connection on jetson nx? Is it possible to run on jetson nx without any containers?
There is no update from you for a period, assuming this is not an issue any more.
Hence we are closing this topic. If need further support, please open a new one.
Thanks
