Deploying to DeepStream for MaskRCNN

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.

MaskRCNN, requires the generateDetectionPlugin, multilevelCropAndResizePlugin, resizeNearestPlugin and multilevelProposeROI plugins from the TensorRT OSS build.

If the deployment platform is x86 with an NVIDIA GPU, follow the TensorRT OSS on x86 instructions. On the other hand, if your deployment is on NVIDIA Jetson platform, 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
           

For MaskRCNN, 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 will need the following:

• The DeepStream SDK (download from the DeepStream SDK Download Page). 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 7+ OSS Plugins

• A labels.txt file containing the labels for classes in the order in which the networks produce 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.

TDeepStream SDK ships with an end-to-end reference application that is fully configurable. You can configure input sources, the 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:

Typically, two or more configuration files 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. This will set the input source and resolution, number of inferences, tracker, and output sinks. The other supporting config files are for each individual inference engine. The inference-specific configuration files are used to specify the models, inference resolution, batch size, number of classes, and other customizations. The main configuration file will call all the supporting configuration files.

Here are some configuration files in samples/configs/deepstream-app for reference:

• source4_1080p_dec_infer-resnet_tracker_sgie_tiled_display_int8.txt: The main configuration file

• config_infer_primary.txt: The supporting configuration file for the primary detector in the pipeline above

• config_infer_secondary_*.txt: The supporting configuration 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 a MaskRCNN Model

To run a MaskRCNN model in DeepStream, you need a label file and a DeepStream configuration file. In addition, you need to compile the TensorRT 7+ open source software and MaskRCNN output parser for DeepStream.

See here for a GitHub page containing a DeepStream sample with documentation on how to run inference using the trained MaskRCNN models from TAO Toolkit.

Prerequisites for MaskRCNN Model

• MaskRCNN requires the generateDetectionPlugin, multilevelCropAndResizePlugin, resizeNearestPlugin and multilevelProposeROI plugins, which are available in the TensorRT open source repo. Detailed instructions to build TensorRT OSS can be found in the TensorRT Open Source Software (OSS) section.

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

If the COCO annotation file has the following in “categories”:

            

            
[{'supercategory': 'person', 'id': 1, 'name': 'person'},
 {'supercategory': 'car', 'id': 2, 'name': 'car'}]
        

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

            

            
BG
person
car
        

Run deepstream-app as follows:

            

            
deepstream-app -c <deepstream-app config file>
        

Also, you can use deepstream-mrcnn-test to run the MaskRCNN model. See the README under $DS_TOP/source/apps/sample_apps/deepstream-mrcnn-test/.

The configuration file is used by deepstream-app (see the Deepstream Configuration Guide for more details). You need to enable the display-mask under the osd group to see the mask visual view:

            

            
[osd]
enable=1
gpu-id=0
border-width=3
text-size=15
text-color=1;1;1;1;
text-bg-color=0.3;0.3;0.3;1
font=Serif
display-mask=1
display-bbox=0
display-text=0
Nvinfer config file
        

The Nvinfer configuration file is used in the nvinfer plugin; see the Deepstream plugin manual for more details. The following are key parameters for running the MaskRCNN model:

            

            
tlt-model-key=<tlt_encode or TLT Key used during model export>
tlt-encoded-model=<Path to TLT model>
parse-bbox-instance-mask-func-name=<post process parser name>
custom-lib-path=<path to post process parser lib>
network-type=3 ## 3 is for instance segmentation network
output-instance-mask=1
labelfile-path=<Path to label file>
int8-calib-file=<Path to optional INT8 calibration cache>
infer-dims=<Inference resolution if different than provided>
num-detected-classes=<# of classes if different than default>
        

Here’s an example:

            

            
[property]
gpu-id=0
net-scale-factor=0.017507
offsets=123.675;116.280;103.53
model-color-format=0
tlt-model-key=<tlt_encode or TLT Key used during model export>
tlt-encoded-model=<Path to TLT model>
parse-bbox-instance-mask-func-name=NvDsInferParseCustomMrcnnTLT
custom-lib-path=<path to post process parser lib>
network-type=3 ## 3 is for instance segmentation network
labelfile-path=<Path to MaskRCNN label file>
int8-calib-file=<Path to optional INT8 calibration cache>
infer-dims=<Inference resolution if different than provided>
num-detected-classes=3
uff-input-blob-name=Input
output-blob-names=generate_detections;mask_fcn_logits/BiasAdd
batch-size=1
## 0=FP32, 1=INT8, 2=FP16 mode
network-mode=2
interval=0
gie-unique-id=1
#no cluster
## 0=Group Rectangles, 1=DBSCAN, 2=NMS, 3= DBSCAN+NMS Hybrid, 4 = None(No clustering)
## MRCNN supports only cluster-mode=4; Clustering is done by the model itself
cluster-mode=4
output-instance-mask=1

[class-attrs-all]
pre-cluster-threshold=0.8