Deploying to DeepStream for Deformable DETR

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.

TensorRT Open Source Software (OSS)

The TensorRT OSS build is required for Deformable DETR models because several prerequisite TensorRT plugins are only available in the TensorRT open source repo and not in the general TensorRT release. Specifically, you need the MultiscaleDeformableAttnPlugin_TRT for Deformable DETR.

If your deployment platform is an x86 PC with an NVIDIA GPU, follow the TensorRT OSS on x86 instructions; if your deployment platform is NVIDIA Jetson, follow the TensorRT OSS on Jetson (ARM64) instructions.

TensorRT OSS on x86

Building TensorRT OSS on x86:

Install Cmake (>=3.13).

Note

TensorRT OSS requires cmake >= v3.13, so install cmake 3.13 if your cmake version is lower than 3.13c

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            sudo apt remove --purge --auto-remove cmake
wget https://github.com/Kitware/CMake/releases/download/v3.13.5/cmake-3.13.5.tar.gz
tar xvf cmake-3.13.5.tar.gz
cd cmake-3.13.5/
./configure
make -j$(nproc)
sudo make install
sudo ln -s /usr/local/bin/cmake /usr/bin/cmake

Get GPU architecture. The GPU_ARCHS value can be retrieved by the deviceQuery CUDA sample:

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            cd /usr/local/cuda/samples/1_Utilities/deviceQuery
sudo make
./deviceQuery

If the /usr/local/cuda/samples doesn’t exist in your system, you could download deviceQuery.cpp from this GitHub repo. Compile and run deviceQuery.

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            nvcc deviceQuery.cpp -o deviceQuery
./deviceQuery

This command will output something like this, which indicates the GPU_ARCHS is 75 based on CUDA Capability major/minor version.

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            Detected 2 CUDA Capable device(s)

Device 0: "Tesla T4"
  CUDA Driver Version / Runtime Version          10.2 / 10.2
  CUDA Capability Major/Minor version number:    7.5

Build TensorRT OSS:

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            git clone -b 21.08 https://github.com/nvidia/TensorRT
cd TensorRT/
git submodule update --init --recursive
export TRT_SOURCE=`pwd`
cd $TRT_SOURCE
mkdir -p build && cd build

Note

Make sure your GPU_ARCHS from step 2 is in TensorRT OSS CMakeLists.txt. If GPU_ARCHS is not in TensorRT OSS CMakeLists.txt, add -DGPU_ARCHS=<VER> as below, where <VER> represents GPU_ARCHS from step 2.

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            /usr/local/bin/cmake .. -DGPU_ARCHS=xy  -DTRT_LIB_DIR=/usr/lib/x86_64-linux-gnu/ -DCMAKE_C_COMPILER=/usr/bin/gcc -DTRT_BIN_DIR=`pwd`/out
make nvinfer_plugin -j$(nproc)

After building ends successfully, libnvinfer_plugin.so* will be generated under \`pwd\`/out/.

Replace the original libnvinfer_plugin.so*:

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            sudo mv /usr/lib/x86_64-linux-gnu/libnvinfer_plugin.so.8.x.y ${HOME}/libnvinfer_plugin.so.8.x.y.bak   // backup original libnvinfer_plugin.so.x.y
sudo cp $TRT_SOURCE/`pwd`/out/libnvinfer_plugin.so.8.m.n  /usr/lib/x86_64-linux-gnu/libnvinfer_plugin.so.8.x.y
sudo ldconfig

TensorRT OSS on Jetson (ARM64)

Install Cmake (>=3.13)

Note

TensorRT OSS requires cmake >= v3.13, while the default cmake on Jetson/Ubuntu 18.04 is cmake 3.10.2.

Upgrade TensorRT OSS using:

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            sudo apt remove --purge --auto-remove cmake
wget https://github.com/Kitware/CMake/releases/download/v3.13.5/cmake-3.13.5.tar.gz
tar xvf cmake-3.13.5.tar.gz
cd cmake-3.13.5/
./configure
make -j$(nproc)
sudo make install
sudo ln -s /usr/local/bin/cmake /usr/bin/cmake

Get GPU architecture based on your platform. The GPU_ARCHS for different Jetson platform are given in the following table.

Jetson Platform GPU_ARCHS

Nano/Tx1 53

Tx2 62

AGX Xavier/Xavier NX 72

Build TensorRT OSS:

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            git clone -b 21.03 https://github.com/nvidia/TensorRT
cd TensorRT/
git submodule update --init --recursive
export TRT_SOURCE=`pwd`
cd $TRT_SOURCE
mkdir -p build && cd build

Note

The -DGPU_ARCHS=72 below is for Xavier or NX, for other Jetson platform, change 72 referring to GPU_ARCHS from step 2.

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            /usr/local/bin/cmake .. -DGPU_ARCHS=72  -DTRT_LIB_DIR=/usr/lib/aarch64-linux-gnu/ -DCMAKE_C_COMPILER=/usr/bin/gcc -DTRT_BIN_DIR=`pwd`/out
make nvinfer_plugin -j$(nproc)

After building ends successfully, libnvinfer_plugin.so* will be generated under ‘pwd’/out/.

Replace "libnvinfer_plugin.so*" with the newly generated.

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            sudo mv /usr/lib/aarch64-linux-gnu/libnvinfer_plugin.so.8.x.y ${HOME}/libnvinfer_plugin.so.8.x.y.bak   // backup original libnvinfer_plugin.so.x.y
sudo cp `pwd`/out/libnvinfer_plugin.so.8.m.n  /usr/lib/aarch64-linux-gnu/libnvinfer_plugin.so.8.x.y
sudo ldconfig

Integrating the model with DeepStream

For Deformable DETR, you will need to build the TensorRT open source plugins and custom bounding box parser. The instructions are provided in the TensorRT Open Source Software (OSS)`_ section above, and the required code can be found in this GitHub repo.

In order to integrate the models with DeepStream, you need the following:

The DeepStream SDK. The installation instructions for DeepStream are provided in the DeepStream Development Guide.
An exported .onnx model file and optional calibration cache for INT8 precision.
TensorRT 8+ OSS Plugins .
A labels.txt file containing the labels for classes in the order in which the networks produces outputs.
A sample config_infer_*.txt file to configure the nvinfer element in DeepStream. The nvinfer element handles everything related to TensorRT optimization and engine creation in DeepStream.

The DeepStream SDK ships with an end-to-end reference application that is fully configurable. You can configure the input sources, inference model, and output sinks. The app requires a primary object detection model, followed by an optional secondary classification model. The reference application is installed as deepstream-app. The graphic below shows the architecture of the reference application.

There are typically two or more configuration files that are used with this app. In the install directory, the config files are located in samples/configs/deepstream-app or sample/configs/tlt_pretrained_models. The main config file configures all the high level parameters in the pipeline above, setting the input source and resolution, number of inferences, tracker and output sinks. The supporting config files are for each individual inference engine. The inference-specific config files are used to specify models, inference resolution, batch size, number of classes and other customization. The main config file will call all the supporting config files. Here are some config files in samples/configs/deepstream-app for reference:

source4_1080p_dec_infer-resnet_tracker_sgie_tiled_display_int8.txt: The main config file
config_infer_primary.txt: The supporting config file for the primary detector in the pipeline above
config_infer_secondary_*.txt: The supporting config file for the secondary classifier in the pipeline above

The deepstream-app will only work with the main config file. This file will most likely remain the same for all models and can be used directly from the DeepStream SDK with little to no change. You will only need to modify or create config_infer_primary.txt and config_infer_secondary_*.txt.

Integrating an Deformable DETR Model

To run an Deformable DETR model in DeepStream, you need a label file and a DeepStream configuration file. In addition, you need to compile the TensorRT 8+ OSS and Deformable DETR bounding box parser for DeepStream.

A DeepStream sample with documentation on how to run inference using the trained Deformable DETR models from TAO Toolkit is provided on GitHub here.

Prerequisite for Deformable DETR Model

Deformable DETR requires MultiscaleDeformableAttnPlugin_TRT. This plugin is available in the TensorRT open source repo. Detailed instructions to build TensorRT OSS can be found in TensorRT Open Source Software (OSS).

Deformable DETR requires custom bounding-box parsers that are not built-in inside the DeepStream SDK. The source code to build custom bounding-box parsers for Deformable DETR is available here. The following instructions can be used to build the bounding-box parser:

Install git-lfs (git >= 1.8.2)

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            curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | sudo bash
sudo apt-get install git-lfs
git lfs install

Download the source code with SSH or HTTPS:

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            git clone -b release/tlt3.0 https://github.com/NVIDIA-AI-IOT/deepstream_tlt_apps

Build the custom bounding-box parser:

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            // or Path for DS installation
export CUDA_VER=10.2         // CUDA version, e.g. 10.2
make

This generates libnvds_infercustomparser_tlt.so in the directory post_processor.

Label File

If the COCO annotation file has the following in categories:

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            [{'supercategory': 'Person', 'id': 1, 'name': 'Person'},
 {'supercategory': 'Face', 'id': 1, 'name': 'Face'},
 {'supercategory': 'Bag', 'id': 2, 'name': 'Bag'}]

Then the corresponding ddetr_labels.txt file will be as follows:

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            BG
Person
Face
Bag

DeepStream Configuration File

The detection model is typically used as a primary inference engine. It can also be used as a secondary inference engine. To run this model in the sample deepstream-app, you must modify the existing config_infer_primary.txt file to point to this model.

Option 1: Integrate the model (.onnx) directly in the DeepStream app.

For this option, users will need to add the following parameters in the configuration file.

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            onnx-file=<TAO Toolkit exported .onnx>
int8-calib-file=<Calibration cache file>

From TAO 5.0.0, .etlt is deprecated. To integrate .etlt directly in the DeepStream app, you need following parmaters in the configuration file.

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            tlt-encoded-model=<TLT exported .etlt>
tlt-model-key=<Model export key>

The tlt-encoded-model parameter points to the exported model (.etlt) from TLT. The tlt-model-key is the encryption key used during model export.

Option 2: Integrate the TensorRT engine file with the DeepStream app.

Generate the device-specific TensorRT engine using TAO Deploy.

After the engine file is generated, modify the following parameter to use this engine with DeepStream:

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            model-engine-file=<PATH to generated TensorRT engine>

All other parameters are common between the two approaches. To use the custom bounding box parser instead of the default parsers in DeepStream, modify the following parameters in [property] section of config_infer_primary.txt file:

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            parse-bbox-func-name=NvDsInferParseCustomDDETRTAO
custom-lib-path=<PATH to libnvds_infercustomparser_tlt.so>

Add the label file generated above using:

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            labelfile-path=<deformable_detr labels>

For all the options, see the sample configuration file below. To learn about what all the parameters are used for, refer to the DeepStream Development Guide.

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            [property]
gpu-id=0
offsets=123.675;116.28;103.53
net-scale-factor=0.0173520735728
labelfile-path=<Path to ddetr_labels.txt>
onnx-file=<Path to Deformable DETR onnx model>
model-engine-file=<Path to Deformable DETR engine file>
batch-size=1
## 0=FP32, 1=INT8, 2=FP16 mode
network-mode=2
num-detected-classes=4
interval=0
gie-unique-id=1
output-blob-names=pred_boxes;pred_logits
infer-dims=3;544;960
cluster-mode=4
parse-bbox-func-name=NvDsInferParseCustomDDETRTAO
custom-lib-path=libnvds_infercustomparser_tao.so

[class-attrs-all]
pre-cluster-threshold=0.5
topk=100

Jetson Platform	GPU_ARCHS
Nano/Tx1	53
Tx2	62
AGX Xavier/Xavier NX	72