Unable to get the Deepstream pose example to work on jetson nano

Accelerated Computing Intelligent Video Analytics DeepStream SDK
1

Jetson nano 4GB
DeepStream Version 5
JetPack Version 4.5.1
TensorRT Version 7.1.3

I have been trying to get the pose estimation example (Link) to run on a jetson nano 4gb, but when starting it up i never get further than the ‘killed’ message from the log below. I’ve tried this with and without the engine file in the configuration file, but no difference, ‘killed’ at the same point during the flow.

sudo ./deepstream-pose-estimation-app WIN_20210302_00_32_30_Pro.mp4 test
Now playing: WIN_20210302_00_32_30_Pro.mp4
Opening in BLOCKING MODE
Opening in BLOCKING MODE
Opening in BLOCKING MODE
Opening in BLOCKING MODE
ERROR: Deserialize engine failed because file path: /opt/nvidia/deepstream/deepstream-5.0/sources/apps/sample_apps/deepstream_pose_estimation/pose_estimation.onnx_b1_gpu0_fp16.engine open error
0:00:07.053459871 25167   0x55b2212b00 WARN                 nvinfer gstnvinfer.cpp:616:gst_nvinfer_logger:<primary-nvinference-engine> NvDsInferContext[UID 1]: Warning from NvDsInferContextImpl::deserializeEngineAndBackend() <nvdsinfer_context_impl.cpp:1690> [UID = 1]: deserialize engine from file :/opt/nvidia/deepstream/deepstream-5.0/sources/apps/sample_apps/deepstream_pose_estimation/pose_estimation.onnx_b1_gpu0_fp16.engine failed
0:00:07.053565655 25167   0x55b2212b00 WARN                 nvinfer gstnvinfer.cpp:616:gst_nvinfer_logger:<primary-nvinference-engine> NvDsInferContext[UID 1]: Warning from NvDsInferContextImpl::generateBackendContext() <nvdsinfer_context_impl.cpp:1797> [UID = 1]: deserialize backend context from engine from file :/opt/nvidia/deepstream/deepstream-5.0/sources/apps/sample_apps/deepstream_pose_estimation/pose_estimation.onnx_b1_gpu0_fp16.engine failed, try rebuild
0:00:07.053599041 25167   0x55b2212b00 INFO                 nvinfer gstnvinfer.cpp:619:gst_nvinfer_logger:<primary-nvinference-engine> NvDsInferContext[UID 1]: Info from NvDsInferContextImpl::buildModel() <nvdsinfer_context_impl.cpp:1715> [UID = 1]: Trying to create engine from model files
----------------------------------------------------------------
Input filename:   /opt/nvidia/deepstream/deepstream-5.0/sources/apps/sample_apps/deepstream_pose_estimation/resnet18_baseline_att_224x224_A_epoch_249.onnx
ONNX IR version:  0.0.6
Opset version:    9
Producer name:    pytorch
Producer version: 1.7
Domain:
Model version:    0
Doc string:
----------------------------------------------------------------
Killed

This is the content of the config file (with engine file disabled here):

[property]
gpu-id=0
net-scale-factor=0.0174292
offsets=123.675;116.28;103.53
##onnx-file=resnet18_baseline_att_224x224_A_epoch_249.onnx
onnx-file=pose_estimation.onnx
labelfile-path=labels.txt
batch-size=1
process-mode=1
model-color-format=0
## 0=FP32, 1=INT8, 2=FP16 mode
network-mode=2
num-detected-classes=4
interval=0
gie-unique-id=1
## model-engine-file=pose_estimation.onnx_b1_gpu0_fp16.engine
network-type=100
workspace-size=3000

I’ve seen other posts where people share their .onnx file, but i tried those and the system complained about those being for int64 and they didn’t work for me. any help would be appreciated.

3

Hi,

“killed” error is usually caused by the out of memory problem.
Could you monitor the device with tegrastats to check the memory usage first?

$ sudo tegrastats

Thanks.

4

In my case…
I can passed this issue by making tensorrt engine file.

/usr/src/tensorrt/bin/trtexec --onnx=pose_estimation.onnx --saveEngine=pose_estimation.onnx_b1_gpu0_fp16.engine

6

i checked and memory may be the problem, however at a certain moment tegrastats freezes, to only resume after the killed error appears. 2 relevant lines, before and after the freeze indicate that it is indeed a low on memory condition.

RAM 3647/3956MB (lfb 58x4MB) SWAP 1559/1978MB (cached 0MB) IRAM 0/252kB(lfb 252kB) CPU [52%@1479,56%@1479,95%@1479,100%@1479] EMC_FREQ 2%@1600 GR3D_FREQ 0%@76 VIC_FREQ 0%@140 APE 25 PLL@34.5C CPU@37.5C PMIC@100C GPU@36.5C AO@42C thermal@37C POM_5V_IN 4115/3909 POM_5V_GPU 0/28 POM_5V_CPU 1848/1723
RAM 928/3956MB (lfb 191x4MB) SWAP 842/1978MB (cached 4MB) IRAM 0/252kB(lfb 252kB) CPU [55%@710,63%@710,80%@710,89%@710] EMC_FREQ 3%@1600 GR3D_FREQ 0%@76 VIC_FREQ 0%@140 APE 25 PLL@33.5C CPU@36.5C PMIC@100C GPU@35.5C AO@42C thermal@35.25C POM_5V_IN 2695/3884 POM_5V_GPU 0/27 POM_5V_CPU 538/1699

any workarounds for when this happens?

7

This allowed me to generate the file, however the results when running it with one of the sample videos included in the sdk gave me garbage results, did it do the same for you?

8

In my case…

using onnx file which made by my own follows guide from blog post → doesn’t work correctly

using onnx file which attached on github → works correctly.

So now I am analyzing the cause… ;)

9

I managed to get it working by using the export_for_isaac.py script found in this post: link
Hopfuly this can help you too.

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