Integrating TAO Models into DeepStream

TAO Toolkit v5.2.0

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 Toolkit has been designed to integrate with DeepStream SDK, so models trained with TAO Toolkit 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 Toolkit 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.

tao_overview.png

The tables below capture the compatibility of the various open architectures supported in TAO Toolkit and and pre-trained models distributed with TAO Toolkit 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 Toolkit 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 Toolkit 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 Toolkit 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 Toolkit 3.0-21.11+

TAO Toolkit 3.0-21.11+ was built with TensorRT 8.0.1.6.

ds_tao_interoperability.png

TAO Toolkit -> 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

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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.

  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:

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

  1. Install Deepstream.

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

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

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

Download the Models

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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.

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./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

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make cd deepstream-lpr-app

For US car plate recognition:

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cp dict_us.txt dict.txt

Start to run the application:

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./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:

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    mkdir -p $HOME/peoplenet && \ wget https://api.ngc.nvidia.com/v2/models/nvidia/tao/peoplenet/versions/pruned_quantized_v2.3/files/resnet34_peoplenet_pruned.etlt \ -O $HOME/peoplenet/resnet34_peoplenet_pruned.etlt


  2. Run the application:

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    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:

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    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:

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    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:

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    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:

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    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:

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    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:

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    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:

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


  2. Download the model:

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

    or

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    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 https://github.com/NVIDIA-AI-IOT/deepstream_tlt_apps/tree/master#1-build-trt-oss-plugin

  4. Build the application:

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


  5. Run the application:

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    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:

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    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:

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    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,

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    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

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    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,

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    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.

Previous TRTEXEC with VisualChangeNet
Next Deploying to DeepStream for Classification TF1/TF2/PyTorch
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