Please provide complete information as applicable to your setup.
• Hardware Platform (Jetson / GPU)
GPU
• DeepStream Version
5.0
• JetPack Version (valid for Jetson only)
• TensorRT Version
Version in nvcr.io/nvidia/deepstream:5.0-20.07-triton
• NVIDIA GPU Driver Version (valid for GPU only)
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 510.73.05 Driver Version: 510.73.05 CUDA Version: 11.6 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 NVIDIA RTX A5000 Off | 00000000:0A:00.0 On | Off |
| 30% 48C P8 24W / 230W | 2027MiB / 24564MiB | 19% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
• Issue Type( questions, new requirements, bugs)
Bug?
• How to reproduce the issue ? (This is for bugs. Including which sample app is using, the configuration files content, the command line used and other details for reproducing)
• Requirement details( This is for new requirement. Including the module name-for which plugin or for which sample application, the function description)
I am trying to recreate the results from the Deploying Models from TensorFlow Model Zoo Using NVIDIA DeepStream and NVIDIA Triton Inference Server Forum post and Blog post. Links below:
I have set up a code repository here that attempts to recreate it: https://github.com/thebruce87m/Tensorflow-On-Deepstream-With-Triton-Server
I have created four scripts:
001-download.sh
#!/bin/bash
cd ./downloads/
# Download the model
wget http://download.tensorflow.org/models/object_detection/faster_rcnn_inception_v2_coco_2018_01_28.tar.gz
tar xvf faster_rcnn_inception_v2_coco_2018_01_28.tar.gz
# Download the labels
wget https://raw.githubusercontent.com/NVIDIA-AI-IOT/deepstream_triton_model_deploy/master/faster_rcnn_inception_v2/config/labels.txt
002-run-docker.sh
#!/bin/bash
# Run the docker:
docker run \
--gpus all \
-it \
--rm \
--shm-size=1g \
--ulimit memlock=-1 \
--ulimit stack=67108864 \
--net=host \
--privileged \
-v /tmp/.X11-unix:/tmp/.X11-unix \
-v $(pwd):/code/ \
-e DISPLAY=$DISPLAY \
-e CUDA_VER=11.6 \
-w /code/ \
nvcr.io/nvidia/deepstream:5.0-20.07-triton
003-prepare.sh
#!/bin/bash
export DEEPSTREAM_DIR=/opt/nvidia/deepstream/deepstream-5.0/
# Copy the model
cd ${DEEPSTREAM_DIR}samples/trtis_model_repo
mkdir -p faster_rcnn_inception_v2 && cd faster_rcnn_inception_v2 && mkdir -p 1
cp /code/downloads/faster_rcnn_inception_v2_coco_2018_01_28/frozen_inference_graph.pb 1/model.graphdef
# Copy the config file
cp /code/files/config.pbtxt .
# Copy the labels
cp /code/downloads/labels.txt .
# Copy the deepstream configs
mkdir -p ${DEEPSTREAM_DIR}samples/configs/deepstream-app-trtis/
cp /code/files/config_infer_primary_faster_rcnn_inception_v2.txt ${DEEPSTREAM_DIR}samples/configs/deepstream-app-trtis/
cp /code/files/source1_primary_faster_rcnn_inception_v2.txt ${DEEPSTREAM_DIR}samples/configs/deepstream-app-trtis/
# Build the plugin
cd ${DEEPSTREAM_DIR}sources/libs/nvdsinfer_customparser
make all
cp ${DEEPSTREAM_DIR}sources/libs/nvdsinfer_customparser/libnvds_infercustomparser.so ${DEEPSTREAM_DIR}lib/
004-run.sh
#!/bin/bash
# Run the demo
export DEEPSTREAM_DIR=/opt/nvidia/deepstream/deepstream-5.0/
cd $DEEPSTREAM_DIR/samples/configs/deepstream-app-trtis
deepstream-app --gst-debug=3 -c source1_primary_faster_rcnn_inception_v2.txt
It does run the demo, however the boxes are not correct:
My questions:
-
What am I doing wrong in the example? Why do the boxes not match?
-
Every time I run the app it takes 25+ minutes to load before processing the video. Why? How can I avoid this?
Thanks!