Deploying to DeepStream for EfficientDet
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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 in TAO version 4.0.0 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 EfficientDet models because several prerequisite TensorRT plugins are only available in the TensorRT open source repo. Specifically, batchTilePlugin and EfficientNMSPlugin are required for EfficientDet.

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:

  1. 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

  2. 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

  3. 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/.

  4. 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)

  1. 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

  2. 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

  3. 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/.

  4. 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 EfficientDet, 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.

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 .etlt 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.

arch_ref_appl.png


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 EfficientDet Model

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

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

Prerequisite for EfficientDet Model

  1. EfficientDet requires ResizeNearest_TRT and EfficientNMS_TRT. These plugins are available in the TensorRT open source repo. Detailed instructions to build TensorRT OSS can be found in TensorRT Open Source Software (OSS).

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

    1. 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

    2. 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

    3. 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': 'car', 'id': 2, 'name': 'car'}]

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

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BG
person
car

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.

dstream_deploy_options2.png


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

For this option, users will need to add the following parameters in the configuration file. The int8-calib-file is only required for INT8 precision.

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tlt-encoded-model=<TLT exported .etlt>
tlt-model-key=<Model export key>
int8-calib-file=<Calibration cache file>

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.

  1. Generate the device-specific TensorRT engine using TAO Deploy.

  2. 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 the [property] section of config_infer_primary.txt file:

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

Add the label file generated above using the following:

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labelfile-path=<efficientdet 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
net-scale-factor=1.0
offsets=0;0;0
model-color-format=0
network-input-order=1
labelfile-path=efficientdet_d0_labels.txt
model-engine-file=./d0_avlp_bs1_int8.engine
int8-calib-file=d0.cal
tlt-encoded-model=d0_avlp.etlt
tlt-model-key=nvidia_tlt
infer-dims=3;512;512
maintain-aspect-ratio=1
uff-input-blob-name=image_arrays:0
batch-size=1
## 0=FP32, 1=INT8, 2=FP16 mode
network-mode=2
num-detected-classes=1
interval=0
gie-unique-id=1
is-classifier=0
#network-type=0
cluster-mode=4
output-blob-names=num_detections;detection_boxes;detection_scores;detection_classes
parse-bbox-func-name=NvDsInferParseCustomEfficientDetTAO
custom-lib-path=nvdsinfer_custombboxparser_efficientdet_tao.so

[class-attrs-all]
pre-cluster-threshold=0.3
roi-top-offset=0
roi-bottom-offset=0
detected-min-w=0
detected-min-h=0
detected-max-w=0
detected-max-h=0

© Copyright 2022, NVIDIA.. Last updated on Mar 23, 2023.