Integrating TAO Models into DeepStream#

The deep learning and computer vision models that you’ve trained can be deployed on edge devices, such as a Jetson Xavier or Jetson Nano, a discrete GPU, or in the cloud with NVIDIA GPUs. TAO has been designed to integrate with DeepStream SDK, so models trained with TAO will work out of the box with DeepStream SDK.

DeepStream SDK is a streaming analytic toolkit to accelerate building AI-based video analytic applications. This section will describe how to deploy your trained model to DeepStream SDK.

To deploy a model trained by TAO to DeepStream we have two options:

  • Option 1: Integrate the .etlt model directly in the DeepStream app. The model file is generated by export.

  • Option 2: Generate a device-specific optimized TensorRT engine using TAO Deploy. The generated TensorRT engine file can also be ingested by DeepStream.

  • Option 3 (Deprecated for x86 devices): Generate a device-specific optimized TensorRT engine using TAO Converter.

Machine-specific optimizations are done as part of the engine creation process, so a distinct engine should be generated for each environment and hardware configuration. If the TensorRT or CUDA libraries of the inference environment are updated (including minor version updates), or if a new model is generated, new engines need to be generated. Running an engine that was generated with a different version of TensorRT and CUDA is not supported and will cause unknown behavior that affects inference speed, accuracy, and stability, or it may fail to run altogether.

Option 1 is very straightforward. The .etlt file and calibration cache are directly used by DeepStream. DeepStream will automatically generate the TensorRT engine file and then run inference. TensorRT engine generation can take some time depending on size of the model and type of hardware.

Engine generation can be done ahead of time with Option 2: TAO Deploy is used to convert the .etlt file to TensorRT; this file is then provided directly to DeepStream. The TAO Deploy workflow is similar to TAO Converter, which is deprecated for x86 devices from TAO version 4.0.x but is still required for deployment to Jetson devices.

See the Exporting the Model section for more details on how to export a TAO model.

../../_images/tao_overview.png

The tables below capture the compatibility of the various open architectures supported in TAO and and pre-trained models distributed with TAO for deployment with respective versions of DeepStream SDK.

Open Model Deployment#

Model

Model output format

Prunable

INT8

Compatible with DS5.1

Compatible with DS6.0

TRT-OSS required

Image Classification

Encrypted UFF

Yes

Yes

Yes

Yes

No

MultiTask Classification

Encrypted UFF

Yes

Yes

Yes

Yes

No

DetectNet_v2

Encrypted UFF

Yes

Yes

Yes

Yes

No

EfficientDet

Encrypted ONNX

Yes

Yes

No

Yes

Yes

FasterRCNN

Encrypted UFF

Yes

Yes

Yes

Yes

Yes

SSD

Encrypted UFF

Yes

Yes

Yes

Yes

Yes

YOLOv3

Encrypted ONNX

Yes

Yes

Yes (with TRT 7.1)

Yes

Yes

YOLOv4

Encrypted ONNX

Yes

Yes

Yes (with TRT 7.1)

Yes

Yes

YOLOv4-tiny

Encrypted ONNX

Yes

Yes

Yes (with TRT 7.1)

Yes

Yes

DSSD

Encrypted UFF

Yes

Yes

Yes

Yes

Yes

RetinaNet

Encrypted UFF

Yes

Yes

Yes

Yes

Yes

MaskRCNN

Encrypted UFF

No

Yes

Yes

Yes

Yes

UNET

Encrypted ONNX

No

Yes

Yes

Yes

No

Character Recognition

Encrypted ONNX

No

Yes

Yes

Yes

No

PointPillars

Encrypted ONNX

Yes

No

No

No

Yes
Pre-trained Model Deployment#

Model Name

Model arch

Model output format

Prunable

INT8

Compatible with DS5.1

Compatible with DS6.0

TRT-OSS required

PeopleNet

DetectNet_v2

Encrypted UFF

Yes

Yes

Yes

Yes

No

TrafficCamNet

DetectNet_v2

Encrypted UFF

Yes

Yes

Yes

Yes

No

DashCamNet

DetectNet_v2

Encrypted UFF

Yes

Yes

Yes

Yes

No

FaceDetect-IR

DetectNet_v2

Encrypted UFF

Yes

Yes

Yes

Yes

No

FaceDetect

DetectNet_v2

Encrypted UFF

Yes

Yes

Yes

Yes

No

VehicleMakeNet

Image Classification

Encrypted UFF

Yes

Yes

Yes

Yes

No

VehicleTypeNet

Image Classification

Encrypted UFF

Yes

Yes

Yes

Yes

No

LPDNet

DetectNet_v2

Encrypted UFF

Yes

Yes

Yes

Yes

No

LPRNet

Character Recognition

Encrypted ONNX

No

Yes

Yes

Yes

No

PeopleSegNet

MaskRCNN

Encrypted UFF

No

Yes

Yes

Yes

Yes

PeopleSemSegNet

UNET

Encrypted ONNX

No

Yes

Yes

Yes

Yes

BodyPoseNet

VGG Backbone with Custom Refinement Stages

Encrypted ONNX

Yes

Yes

No

Yes

No

EmotionNet

5 Fully Connected Layers

Encrypted ONNX

No

No

No

Yes

No

FPENet

Recombinator networks

Encrypted ONNX

No

Yes

No

Yes

No

GazeNet

Four branch AlexNet based model

Encrypted ONNX

No

No

No

Yes

No

GestureNet

ResNet18

Encrypted ONNX

No

Yes

No

Yes

No

HeartRateNet

Two branch model with attention

Encrypted ONNX

No

No

No

Yes

No

Action Recognition Net

Action Recognition Net

Encrypted ONNX

No

No

No

Yes

No

OCDNet

Optical Character Detection

ONNX

Yes

No

No

No

Yes

OCRNet

Optical Character Recognition

ONNX

Yes

No

No

No

Yes

Optical Inspection

Optical Inspection

ONNX

No

No

No

No

No

PCBInspection

Image Classification

ONNX

No

No

No

No

No

Retail Object Recognition

Metric Learning Recognition

ONNX

No

No

No

Yes

No

Note

Due to changes in the TensorRT API between versions 8.0.x and 7.2.x, the deployable models generated using the export task in TAO 3.0-21.11+ can only be deployed in DeepStream version 6.0. In order to deploy the models compatible with DeepStream 5.1 from the table above with DeepStream 5.1, you will need to run the corresponding tao model <model> export task using the TAO 3.0-21.08 package to re-generate a deployable model and calibration cache file that is compatible with TensorRT 7.2.

Similarly, if you have a model trained with TAO 3.0-21.08 package and want to deploy to DeepStream 6.0, please regenerate the deployable model.etlt and int8 calibration file using the corresponding tao model <model> export task in TAO 3.0-21.11+

TAO 3.0-21.11+ was built with TensorRT 8.0.1.6.

../../_images/ds_tao_interoperability.png

TAO -> DeepStream version interoperability#

To downgrade to the 3.0-21.08 or 3.0-21.11 package, please instantiate a new virtual environment by following the instructions in the Quick Start Guide and run the following commands

pip3 install nvidia-pyindex
pip3 install nvidia-tao==0.1.19    # for 3.0-21.08
pip3 install nvidia-tao==0.1.20    # for 3.0-21.11

Follow the instructions below to deploy TAO models to DeepStream.

Installation Prerequisites#

  1. Install Jetpack 4.6 for Jetson devices.

    Note: For Jetson devices, use the following commands to manually increase the Jetson Power mode and maximize performance further by using the Jetson Clocks mode:

    sudo nvpmodel -m 0
sudo /usr/bin/jetson_clocks

  1. Install Deepstream.

Deployment Files#

The following files are required to run each TAO model with Deepstream:

  • ds_tlt.c: The application main file

  • nvdsinfer_custombboxparser_tlt: A custom parser function on inference end nodes

  • Models: TAO models from NGC

  • Model configuration files: The Deepstream Inference configuration file

Sample Application#

We have provided several reference applications on GitHub.

Reference app for YOLOv3/YOLOv4/YOLOv4-tiny, FasterRCNN, SSD/DSSD, RetinaNet, EfficientDet, MaskRCNN, UNet - DeepStream TAO reference app

Reference app for License plate detection and Recognition - DeepStream LPR app

Pre-trained models - License Plate Detection (LPDNet) and Recognition (LPRNet)#

The following steps outline how to run the License Plate Detection and Recognition application: DeepStream LPR app

Download the Repository#

git clone https://github.com/NVIDIA-AI-IOT/deepstream_lpr_app.git

Download the Models#

cd deepstream_lpr_app

mkdir -p ./models/tlt_pretrained_models/trafficcamnet
cd ./models/tlt_pretrained_models/trafficcamnet
wget https://api.ngc.nvidia.com/v2/models/nvidia/tao/trafficcamnet/versions/pruned_v1.0/files/trafficnet_int8.txt
wget https://api.ngc.nvidia.com/v2/models/nvidia/tao/trafficcamnet/versions/pruned_v1.0/files/resnet18_trafficcamnet_pruned.etlt
cd -

mkdir -p ./models/LP/LPD
cd ./models/LP/LPD
wget https://api.ngc.nvidia.com/v2/models/nvidia/tao/lpdnet/versions/pruned_v1.0/files/usa_pruned.etlt
wget https://api.ngc.nvidia.com/v2/models/nvidia/tao/lpdnet/versions/pruned_v1.0/files/usa_lpd_cal.bin
wget https://api.ngc.nvidia.com/v2/models/nvidia/tao/lpdnet/versions/pruned_v1.0/files/usa_lpd_label.txt
cd -

mkdir -p ./models/LP/LPR
cd ./models/LP/LPR
wget https://api.ngc.nvidia.com/v2/models/nvidia/tao/lprnet/versions/deployable_v1.0/files/us_lprnet_baseline18_deployable.etlt
touch labels_us.txt
cd -

Convert the Models to TRT Engine#

See the TAO Converter section.

./tao-converter -k nvidia_tlt -p image_input,1x3x48x96,4x3x48x96,16x3x48x96 models/LP/LPR/us_lprnet_baseline18_deployable.etlt -t fp16 -e models/LP/LPR/lpr_us_onnx_b16.engine

Build and Run#

make
cd deepstream-lpr-app

For US car plate recognition:

cp dict_us.txt dict.txt

Start to run the application:

./deepstream-lpr-app <1:US car plate model|2: Chinese car plate model> <1: output as h264 file| 2:fakesink 3:display output>
[0:ROI disable|1:ROI enable] [input mp4 file path and name] [input mp4 file path and name] ... [input mp4 file path and name] [output 264 file path and name]

For detailed instructions about running this application, refer to this GitHub repository.

Pre-trained models - PeopleNet, TrafficCamNet, DashCamNet, FaceDetectIR, Vehiclemakenet, Vehicletypenet, PeopleSegNet, PeopleSemSegNet#

PeopleNet#

Follow these instructions to run the PeopleNet model in DeepStream:

  1. Download the model:

    mkdir -p $HOME/peoplenet && \
wget https://api.ngc.nvidia.com/v2/models/nvidia/tao/peoplenet/versions/pruned_quantized_v2.3/files/resnet34_peoplenet_pruned_int8.etlt \
-O $HOME/peoplenet/resnet34_peoplenet_pruned_int8.etlt

  2. Run the application:

    xhost +
docker run --gpus all -it --rm -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$DISPLAY -v $HOME:/opt/nvidia/deepstream/deepstream-6.0/samples/models/tao_pretrained_models \
-w /opt/nvidia/deepstream/deepstream-6.0/samples/configs/tao_pretrained_models nvcr.io/nvidia/deepstream:6.0-samples \
deepstream-app -c deepstream_app_source1_peoplenet.txt

TrafficCamNet#

Follow these instructions to run the TrafficCamNet model in DeepStream:

  1. Download the model:

    mkdir -p $HOME/trafficcamnet && \
wget https://api.ngc.nvidia.com/v2/models/nvidia/tao/trafficcamnet/versions/pruned_v1.0/files/resnet18_trafficcamnet_pruned.etlt \
-O $HOME/trafficcamnet/resnet18_trafficcamnet_pruned.etlt && \
wget https://api.ngc.nvidia.com/v2/models/nvidia/tao/trafficcamnet/versions/pruned_v1.0/files/trafficnet_int8.txt \
-O $HOME/trafficcamnet/trafficnet_int8.txt

  2. Run the application:

    xhost +
docker run --gpus all -it --rm -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$DISPLAY -v $HOME:/opt/nvidia/deepstream/deepstream-6.0/samples/models/tao_pretrained_models \
-w /opt/nvidia/deepstream/deepstream-6.0/samples/configs/tao_pretrained_models nvcr.io/nvidia/deepstream:6.0-samples \
deepstream-app -c deepstream_app_source1_trafficcamnet.txt

DashCamNet + Vehiclemakenet + Vehicletypenet#

Follow these instructions to run the DashCamNet model as primary detector and Vehiclemakenet and Vehicletypenet as secondary classifier in DeepStream:

  1. Download the model:

    mkdir -p $HOME/dashcamnet && \
wget https://api.ngc.nvidia.com/v2/models/nvidia/tao/dashcamnet/versions/pruned_v1.0/files/resnet18_dashcamnet_pruned.etlt \
-O $HOME/dashcamnet/resnet18_dashcamnet_pruned.etlt && \
wget https://api.ngc.nvidia.com/v2/models/nvidia/tao/dashcamnet/versions/pruned_v1.0/files/dashcamnet_int8.txt \
-O $HOME/dashcamnet/dashcamnet_int8.txt
mkdir -p $HOME/vehiclemakenet && \
wget https://api.ngc.nvidia.com/v2/models/nvidia/tao/vehiclemakenet/versions/pruned_v1.0/files/resnet18_vehiclemakenet_pruned.etlt \
-O $HOME/vehiclemakenet/resnet18_vehiclemakenet_pruned.etlt && \
wget https://api.ngc.nvidia.com/v2/models/nvidia/tao/vehiclemakenet/versions/pruned_v1.0/files/vehiclemakenet_int8.txt \
-O $HOME/vehiclemakenet/vehiclemakenet_int8.txt
mkdir -p $HOME/vehicletypenet && \
wget https://api.ngc.nvidia.com/v2/models/nvidia/tao/vehicletypenet/versions/pruned_v1.0/files/resnet18_vehicletypenet_pruned.etlt \
-O $HOME/vehicletypenet/resnet18_vehicletypenet_pruned.etlt && \
wget https://api.ngc.nvidia.com/v2/models/nvidia/tao/vehicletypenet/versions/pruned_v1.0/files/vehicletypenet_int8.txt \
-O $HOME/vehicletypenet/vehicletypenet_int8.txt

  2. Run the application:

    xhost +
docker run --gpus all -it --rm -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$DISPLAY -v $HOME:/opt/nvidia/deepstream/deepstream-6.0/samples/models/tao_pretrained_models \
-w /opt/nvidia/deepstream/deepstream-6.0/samples/configs/tao_pretrained_models nvcr.io/nvidia/deepstream:6.0-samples \
deepstream-app -c deepstream_app_source1_dashcamnet_vehiclemakenet_vehicletypenet.txt

FaceDetectIR#

Follow these instructions to run the FaceDetectIR model in DeepStream:

  1. Download the model:

    mkdir -p $HOME/facedetectir && \
wget https://api.ngc.nvidia.com/v2/models/nvidia/tao/facedetectir/versions/pruned_v1.0/files/resnet18_facedetectir_pruned.etlt \
-O $HOME/facedetectir/resnet18_facedetectir_pruned.etlt && \
wget https://api.ngc.nvidia.com/v2/models/nvidia/tao/facedetectir/versions/pruned_v1.0/files/facedetectir_int8.txt \
-O $HOME/facedetectir/facedetectir_int8.txt

  2. Run the application:

    xhost +
docker run --gpus all -it --rm -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$DISPLAY -v $HOME:/opt/nvidia/deepstream/deepstream-6.0/samples/models/tao_pretrained_models \
-w /opt/nvidia/deepstream/deepstream-6.0/samples/configs/tao_pretrained_models nvcr.io/nvidia/deepstream:6.0-samples \
deepstream-app -c deepstream_app_source1_facedetectir.txt

PeopleSegNet#

Follow these instructions to run the PeopleSegNet model in DeepStream:

  1. Download the Repository:

    git clone https://github.com/NVIDIA-AI-IOT/deepstream_tlt_apps.git

  2. Download the model:

    ngc registry model download-version "nvidia/tao/peoplesegnet:deployable_v2.0"

    or

    wget --content-disposition https://api.ngc.nvidia.com/v2/models/nvidia/tao/peoplesegnet/versions/deployable_v2.0/zip \
-O peoplesegnet_deployable_v2.0.zip

  3. Build TRT OSS Plugin:

    TRT-OSS instructions are provided in NVIDIA-AI-IOT/deepstream_tlt_apps

  4. Build the application:

    export CUDA_VER=xy.z              // xy.z is CUDA version, e.g. 10.2
make

  5. Run the application:

    SHOW_MASK=1 ./apps/ds-tlt -c configs/peopleSegNet_tlt/pgie_peopleSegNetv2_tlt_config.txt -i \
/opt/nvidia/deepstream/deepstream-5.1/samples/streams/sample_720p.h264 -d

PeopleSemSegNet#

Follow these instructions to run the PeopleSemSegNet model in DeepStream:

  1. Download tao-converter and the model:

    mkdir $HOME/deepstream
cd $HOME/deepstream
wget https://developer.nvidia.com/cuda111-cudnn80-trt72
unzip cuda111-cudnn80-trt72
cp cuda11.1_cudnn8.0_trt7.2/tao-converter ./
chmod 0777 tao-converter
ngc registry model download-version "nvidia/tao/peoplesemsegnet:deployable_v1.0"

  2. Run the application:

    xhost +
docker run --gpus all -it --rm -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$DISPLAY  -v $HOME:$HOME -w $HOME/deepstream \
nvcr.io/nvidia/deepstream:5.1-21.02-devel ./tao-converter -k tlt_encode -p input_1,1x3x544x960,1x3x544x960,1x3x544x960 -t fp16 -e \
peoplesemsegnet_vdeployable_v1.0/unet_resnet18.etlt_b1_gpu0_fp16.engine peoplesemsegnet_vdeployable_v1.0/peoplesemsegnet.etlt ; \
git clone https://github.com/NVIDIA-AI-IOT/deepstream_tlt_apps.git ; cd deepstream_tlt_apps ; export CUDA_VER=11.1 ; export SHOW_MASK=1 ; make ; \
sed -i "s/..\/..\/models\/unet\/unet_resnet18.etlt_b1_gpu0_fp16.engine/..\/..\/..\/peoplesemsegnet_vdeployable_v1.0\/unet_resnet18.etlt_b1_gpu0_fp16.engine/g" \
configs/unet_tlt/pgie_unet_tlt_config.txt ; sed -i "s/infer-dims=3;608;960/infer-dims=3;544;960/g"  configs/unet_tlt/pgie_unet_tlt_config.txt ; \
sed -i "s/unet_labels.txt/..\/..\/..\/peoplesemsegnet_vdeployable_v1.0\/labels.txt/g"  configs/unet_tlt/pgie_unet_tlt_config.txt ; \
sed -i "s/num-detected-classes=3/num-detected-classes=2/g"  configs/unet_tlt/pgie_unet_tlt_config.txt ; ./apps/ds-tlt -c configs/unet_tlt/pgie_unet_tlt_config.txt \
-i /opt/nvidia/deepstream/deepstream-5.1/samples/streams/sample_720p.h264 -d

Pre-trained models - BodyPoseNet, EmotionNet, FPENet, GazeNet, GestureNet, HeartRateNet#

  1. Follow the prerequisites for the Deepstream-TAO Other apps README such as installing DeepStream SDK 6.0.

  2. Download the TAO Converter tool for relevant platform. For example,

    mkdir $HOME/deepstream
cd $HOME/deepstream
wget https://developer.nvidia.com/cuda111-cudnn80-trt72
unzip cuda111-cudnn80-trt72
cp cuda11.1_cudnn8.0_trt7.2/tao-converter ./
chmod 0777 tao-converter

  3. Download the Deepstream-TAO Other apps repository.

  4. Download the all the pre-trained models with the provided utility script. This will place the etlt models in pre-determined locations so that DeepStream configs can properly locate them. Replace these models with custom

    cd deepstream_tao_apps
chmod 755 download_models.sh
export TAO_CONVERTER=the file path of tao-converter
export MODEL_PRECISION=fp16
./download_models.sh

  5. Build and run the sample applications per the Deepstream-TAO Other apps README. For example, to run the BodyPoseNet sample application,

    cd deepstream-bodypose2d-app
./deepstream-bodypose2d-app [1:file sink|2:fakesink|3:display sink]  \
<bodypose2d model config file> <input uri> ... <input uri> <out filename>

General purpose CV model architecture - Classification, Object detection and Segmentation#

A sample DeepStream app to run a classification, object detection, and semantic and instance segmentation network as well as TRT-OSS instructions are provided here.

For more information about each individual model architecture, refer to the following sections.

Image classification#

Multitask Classification#

Object Detection#

Instance Segmentation#

Semantic Segmentation#

Character Recognition Using nvOCDR#

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