Deepstream

[1] DeepStream SDK on Jetson uses Jetpack, which includes L4T, Multimedia APIs, CUDA, and TensorRT. The SDK offers a rich collection of plug-ins and libraries, built using the Gstreamer framework to enable developers to build flexible applications for transforming video into valuable insights. DeepStream also comes with sample applications including source code and an application adaptation guide to help developers jumpstart their builds.

For this wiki used Jetson TX1 for testing. Required:

Jetpack 3.2 which includes L4T R28.2, CUDA 9.0, TensorRT 3.0 GA, cuDNN 7.0.5, VisionWorks 1.6
Download Deepstream for Jetson https://developer.nvidia.com/deepstream-jetson You need to sign and download.
Deepstream download for Jetson and Tesla, available at: ftp://10.251.101.2/docs/Installers/Nvidia/

Ridgerun offers GstInference, GstInference is the GStreamer front-end for R²Inference, the actual project that handles the abstraction for different back-ends and frameworks. R²Inference will know how to deal with different vendor frameworks such as TensorFlow (x86, iMX8), OpenVX (x86, iMX8), Caffe (x86, NVidia), TensorRT (Nvidia) or NCSDK (Intel), while exposing a generic/easy interface to the user.

More information please check:
- GstInference Work in progress
- PDF Slides
- Presentation video recording
- Please check https://www.ridgerun.com/contact for more information.
- Contact us is you have questions or doubts: https://www.ridgerun.com/contact

Using Deepstream demo at Jetson

This wiki is for Deepstream 1.5 at Jetson (and tested at TX1) Deepstream 3.0 is available for Xavier not covered on this wiki.

tar xpvf DeepStream_SDK_on_Jetson_1.5_pre-release.tbz2
sudo tar xpvf deepstream_sdk_on_jetson.tbz2 -C /
sudo tar xpvf deepstream_sdk_on_jetson_models.tbz2 -C /
sudo ldconfig

Run the demo: Video will be displayed at HDMI output

nvgstiva-app -c ${HOME}/configs/PGIE-FP16-CarType-CarMake-CarColor.txt

Building the demo

Install and build:

sudo apt-get install libgstreamer-plugins-base1.0-dev libgstreamer1.0-dev

sudo ln -s /usr/lib/aarch64-linux-gnu/tegra/libnvid_mapper.so.1.0.0 \
             /usr/lib/aarch64-linux-gnu/libnvid_mapper.so

cd ${HOME}/nvgstiva-app_sources/nvgstiva-app

make

#Run the App with ./nvgstiva-app -c <config-file>
./nvgstiva-app -c ./${HOME}/configs/PGIE-FP16-CarType-CarMake-CarColor.txt

Doing some analysis

The sample application is a Gstreamer application that uses NVidia elements, by obtaining the DOT file we can see used elements and its configurations, since decodebins and other similar elements are used the pipeline is extensive. Check the Generated Dot file at:

Pipeline graphic for filesrc pipeline: Deepstream filesrc Tegra Pipeline
Pipeline graphic for nvcamerasrc pipeline: Deepstream nvcamerasrc Tegra Pipeline

Basically the pipeline is composed with (in order as elements appear):

Filesrc
Decodebin from mp3 to 720p NV12
nvvconv
nvcaffegie (this element receives as parameters profile file, caffe model and caffe model cache)
nvtracker
tee (with 4 outputs)
Three more nvcaffegie plugins, each one with a different model (car color, vehicle type, secondary make)
each one of this nvcaffegie goes into a fakesink
Fourth tee goes to nvvconv
nvosd
nvoverlaysink

* Note: NVidia elements are provided as binaries:

libnvcaffegie.so.1.0.0
libgstnvtracker.so
libgstnvclrdetector.so
libgstnvcaffegie.so

Testing with gst-launch

Pipeline with nvcamerasrc, one model:

GST_DEBUG=3 gst-launch-1.0 nvcamerasrc queue-size=6 sensor-id=0 fpsRange='30 30' \
! 'video/x-raw(memory:NVMM), width=(int)1920, height=(int)1080, framerate=(fraction)30/1, format=(string)I420' \
! queue ! nvvidconv ! nvcaffegie  model-path="/home/nvidia/Model/ResNet_18/ResNet_18_threeClass_VGA_pruned.caffemodel" \
protofile-path="/home/nvidia/Model/ResNet_18/ResNet_18_threeClass_VGA_deploy_pruned.prototxt" \
model-cache="/home/nvidia/Model/ResNet_18/ResNet_18_threeClass_VGA_pruned.caffemodel_b2_fp16.cache" \
labelfile-path="/home/nvidia/Model/ResNet_18/labels.txt" net-stride=16 batch-size=2 roi-top-offset="0,0:1,0:2,0:" \
roi-bottom-offset="0,0:1,0:2,0:" detected-min-w-h="0,0,0:1,0,0:2,0,0" detected-max-w-h="0,1920,1080:1,100,1080:2,1920,1080:" \
interval=1 parse-func=4 net-scale-factor=0.0039215697906911373 \
class-thresh-params="0,0.200000,0.100000,3,0:1,0.200000,0.100000,3,0:2,0.200000,0.100000,3,0:" \
output-bbox-layer-name=Layer11_bbox output-coverage-layer-names=Layer11_cov ! queue ! nvtracker \
! queue ! nvosd x-clock-offset=800 y-clock-offset=820 hw-blend-color-attr="3,1.000000,1.000000,0.000000:" \
! queue ! nvoverlaysink sync=false enable-last-sample=false

Pipeline with nvcamerasrc and two caffe models, it is better to put pipeline at script and execute, video runs and boxes are draw, but no labels.

gst-launch-1.0 nvcamerasrc queue-size=10 sensor-id=0 fpsRange='30 30' ! \
'video/x-raw(memory:NVMM), width=(int)1920, height=(int)1080, \
framerate=(fraction)30/1, format=(string)I420' \
! queue ! nvvidconv ! \
nvcaffegie  \
class-thresh-params="0,0.200000,0.100000,3,0:1,0.200000,0.100000,3,0:2,0.200000,0.100000,3,0:" \
model-path="/home/nvidia/Model/ResNet_18/ResNet_18_threeClass_VGA_pruned.caffemodel" \
protofile-path="/home/nvidia/Model/ResNet_18/ResNet_18_threeClass_VGA_deploy_pruned.prototxt" \
model-cache="/home/nvidia/Model/ResNet_18/ResNet_18_threeClass_VGA_pruned.caffemodel_b2_fp16.cache" \
labelfile-path="/home/nvidia/Model/ResNet_18/labels.txt"  \
batch-size=2 \
roi-top-offset="0,0:1,0:2,0:" \
roi-bottom-offset="0,0:1,0:2,0:" \
detected-min-w-h="0,0,0:1,0,0:2,0,0" \
detected-max-w-h="0,1920,1080:1,100,1080:2,1920,1080:" \
interval=1 \
parse-func=4 \
net-scale-factor=0.0039215697906911373 \
output-bbox-layer-name=Layer11_bbox \
output-coverage-layer-names=Layer11_cov ! \
queue ! \
nvtracker \
! queue ! tee name=t ! queue ! nvosd x-clock-offset=800 y-clock-offset=820 hw-blend-color-attr="3,1.000000,1.000000,0.000000:" \
! nvvidconv ! nvoverlaysink sync=false async=false enable-last-sample=false \
t. ! queue ! \
nvcaffegie  \
gie-mode = 2 \
gie-unique-id=5 \
infer-on-gie-id=1 \
class-thresh-params="0,1.000000,0.100000,3,2" \
infer-on-class-ids="2:" \
model-path="/home/nvidia/Model/IVA_secondary_carcolor_V1/CarColorPruned.caffemodel" \
protofile-path="/home/nvidia/Model/IVA_secondary_carcolor_V1/deploy.prototxt" \
model-cache="/home/nvidia/Model/IVA_secondary_carcolor_V1/CarColorPruned.caffemodel_b2_fp16.cache" \
batch-size=2 \
detected-min-w-h="11,0,0:" \
detected-max-w-h="3,1920,1080:" \
roi-top-offset="0,0:" \
roi-bottom-offset="0,0:" \
model-color-format=1 \
meanfile-path="/home/nvidia/Model/IVA_secondary_carcolor_V1/mean.ppm" \
detect-clr="0:" \
labelfile-path="/home/nvidia/Model/IVA_secondary_carcolor_V1/labels.txt" \
sec-class-threshold=0.510000 \
parse-func=0 \
is-classifier=TRUE \
offsets="" \
output-coverage-layer-names="softmax" \
sgie-async-mode=TRUE  \
! fakesink async=false sync=false enable-last-sample=false

Pipeline with nvcamerasrc and two caffe models, it is better to put pipeline at script and execute, video runs and boxes are draw, but no labels, not using tee.

gst-launch-1.0 nvcamerasrc queue-size=10 sensor-id=0 fpsRange='30 30' ! \
'video/x-raw(memory:NVMM), width=(int)1920, height=(int)1080, \
framerate=(fraction)30/1, format=(string)I420' \
! queue ! nvvidconv ! \
nvcaffegie  \
class-thresh-params="0,0.200000,0.100000,3,0:1,0.200000,0.100000,3,0:2,0.200000,0.100000,3,0:" \
model-path="/home/nvidia/Model/ResNet_18/ResNet_18_threeClass_VGA_pruned.caffemodel" \
protofile-path="/home/nvidia/Model/ResNet_18/ResNet_18_threeClass_VGA_deploy_pruned.prototxt" \
model-cache="/home/nvidia/Model/ResNet_18/ResNet_18_threeClass_VGA_pruned.caffemodel_b2_fp16.cache" \
labelfile-path="/home/nvidia/Model/ResNet_18/labels.txt"  \
batch-size=2 \
roi-top-offset="0,0:1,0:2,0:" \
roi-bottom-offset="0,0:1,0:2,0:" \
detected-min-w-h="0,0,0:1,0,0:2,0,0" \
detected-max-w-h="0,1920,1080:1,100,1080:2,1920,1080:" \
interval=1 \
parse-func=4 \
net-scale-factor=0.0039215697906911373 \
output-bbox-layer-name=Layer11_bbox \
output-coverage-layer-names=Layer11_cov ! \
queue ! \
nvtracker \
! queue ! \
nvcaffegie  \
gie-mode = 2 \
gie-unique-id=5 \
infer-on-gie-id=1 \
class-thresh-params="0,1.000000,0.100000,3,2" \
infer-on-class-ids="2:" \
model-path="/home/nvidia/Model/IVA_secondary_carcolor_V1/CarColorPruned.caffemodel" \
protofile-path="/home/nvidia/Model/IVA_secondary_carcolor_V1/deploy.prototxt" \
model-cache="/home/nvidia/Model/IVA_secondary_carcolor_V1/CarColorPruned.caffemodel_b2_fp16.cache" \
batch-size=2 \
detected-min-w-h="11,0,0:" \
detected-max-w-h="3,1920,1080:" \
roi-top-offset="0,0:" \
roi-bottom-offset="0,0:" \
model-color-format=1 \
meanfile-path="/home/nvidia/Model/IVA_secondary_carcolor_V1/mean.ppm" \
detect-clr="0:" \
labelfile-path="/home/nvidia/Model/IVA_secondary_carcolor_V1/labels.txt" \
sec-class-threshold=0.510000 \
parse-func=0 \
is-classifier=TRUE \
offsets="" \
output-coverage-layer-names="softmax" \
sgie-async-mode=TRUE  \
! nvosd x-clock-offset=800 y-clock-offset=820 hw-blend-color-attr="3,1.000000,1.000000,0.000000:" \
! nvvidconv ! nvoverlaysink sync=false async=false enable-last-sample=false

GstInference and NVIDIA DeepStream 1.5 nvcaffegie

Contents

Deepstream

Using Deepstream demo at Jetson

Building the demo

Doing some analysis

Testing with gst-launch

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