Unable to use yolov5s in deepstream pipeline

Accelerated Computing Intelligent Video Analytics DeepStream SDK
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1

Description

I am unable to use Yolov5s in deepstream pipeline.

Environment

TensorRT Version: 8.2.5
GPU Type: Tesla T4
Nvidia Driver Version: 510.47.03
CUDA Version: 11.6
CUDNN Version:
Operating System + Version: Ubuntu
Python Version (if applicable): 3.8.10
TensorFlow Version (if applicable):
PyTorch Version (if applicable): 1.12.1
Baremetal or Container (if container which image + tag): deepstream6.1-dlevel

Relevant Files

Please attach or include links to any models, data, files, or scripts necessary to reproduce your issue. (Github repo, Google Drive, Dropbox, etc.)

Reference - Getting started with custom NVIDIA Deepstream 6.0 pipelines in Python | by Jules Talloen | ML6team

Steps To Reproduce

Please include:

  • Exact steps/commands to build your repro

  1. git clone GitHub - ml6team/deepstream-python: NVIDIA Deepstream 6.1 Python boilerplate

  2. install libraries:
    sudo apt install git-lfs
    cd deepstream-python
    git lfs install
    git lfs pull

  3. docker build -t deepstream .

  4. docker run -it --gpus all -v ~/deepstream-python/output:/app/output -v ~/deepstream-python/deepstream/app/:/app/app/ --entrypoint bash deepstream:latest

  5. Edit config and add:
    vi configs/pgies/pgie.txt
    replace:
    model-file=/opt/anpr/data/yolov5m/yolov5s.wts
    proto-file=/opt/anpr/data/yolov5m/yolov5s.cfg

  6. python3 run.py file:///opt/nvidia/deepstream/deepstream-6.1/samples/streams/sample_720p.h264.

  • Exact steps/commands to run your repro
    Mentioned above

  • Full traceback of errors encountered

INFO:app.pipeline.Pipeline:Creating Pipeline
INFO:app.pipeline.Pipeline:Creating Source bin
INFO:app.pipeline.Pipeline:Creating URI decode bin
INFO:app.pipeline.Pipeline:Creating Stream mux
INFO:app.pipeline.Pipeline:Creating PGIE
INFO:app.pipeline.Pipeline:Creating Tracker
INFO:app.pipeline.Pipeline:Creating Converter 1
INFO:app.pipeline.Pipeline:Creating Caps filter 1
INFO:app.pipeline.Pipeline:Creating Tiler
INFO:app.pipeline.Pipeline:Creating Converter 2
INFO:app.pipeline.Pipeline:Creating OSD
INFO:app.pipeline.Pipeline:Creating Queue 1
INFO:app.pipeline.Pipeline:Creating Converter 3
INFO:app.pipeline.Pipeline:Creating Caps filter 2
INFO:app.pipeline.Pipeline:Creating Encoder
INFO:app.pipeline.Pipeline:Creating Parser
INFO:app.pipeline.Pipeline:Creating Container
INFO:app.pipeline.Pipeline:Creating Sink
INFO:app.pipeline.Pipeline:Linking elements in the Pipeline: source-bin-00 → stream-muxer → primary-inference → tracker → convertor1 → capsfilter1 → nvtiler → convertor2 → onscreendisplay → queue1 → mp4-sink-bin
INFO:app.pipeline.Pipeline:Starting pipeline
gstnvtracker: Loading low-level lib at /opt/nvidia/deepstream/deepstream/lib/libnvds_nvmultiobjecttracker.so
gstnvtracker: Batch processing is ON
gstnvtracker: Past frame output is OFF
[NvMultiObjectTracker] Initialized
ERROR: [TRT]: CaffeParser: Could not parse binary model file
ERROR: [TRT]: CaffeParser: Could not parse model file
ERROR: …/nvdsinfer/nvdsinfer_model_builder.cpp:153 Failed while parsing caffe network: /opt/anpr/data/yolov5m/yolov5s.cfg
ERROR: …/nvdsinfer/nvdsinfer_model_builder.cpp:966 failed to build network since parsing model errors.
ERROR: …/nvdsinfer/nvdsinfer_model_builder.cpp:799 failed to build network.
[NvMultiObjectTracker] De-initialized
Error: gst-resource-error-quark: Failed to create NvDsInferContext instance (1): gstnvinfer.cpp(846): gst_nvinfer_start (): /GstPipeline:pipeline0/GstNvInfer:primary-inference:
Config file path: /app/app/…/configs/pgies/pgie.txt, NvDsInfer Error: NVDSINFER_CONFIG_FAILED
INFO:app.pipeline.Pipeline:Exiting pipeline

2

Hi,

This looks like a Deepstream related issue. We will move this post to the Deepstream forum.

Thanks!

4

Ok

6

Where and how did you get the model files? Please ask the guy who provide the model for help.

7

There is a default resnet model which is working fine because it has proto.txt and it is in caffe format. But i want to run yolov5 in either .pt or .engine format in this deepstream pipeline.

8

There is no update from you for a period, assuming this is not an issue anymore.
Hence we are closing this topic. If need further support, please open a new one.
Thanks

TAO has provided a yolov5 model. NVIDIA-AI-IOT/yolov5_gpu_optimization: This repository provides YOLOV5 GPU optimization sample (github.com)

And this model can work with DeepStream. NVIDIA-AI-IOT/deepstream_tao_apps: Sample apps to demonstrate how to deploy models trained with TAO on DeepStream (github.com)

10

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