Deploying to DeepStream for YOLOv4_tiny

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) 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. With option 2, the tao-deploy is used to convert the .etlt file to TensorRT; this file is then provided directly to DeepStream. The tao-converter follows the similar workflow as tao-deploy. This option is deprecated for 4.0.0 and will not be available in the future release.

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

TensorRT Open Source Software (OSS)

Important

As of 4.0.0, tao converter is deprecated. This method may not be available in the future releases. This section is only applicable if you’re still using tao converter for legacy. For tao-deploy, please jump to Integrating YOLOv4-tiny Model.

The TensorRT OSS build is required for YOLOv4-tiny models. This is required because several TensorRT plugins that are required by these models are only available in TensorRT open source repo and not in the general TensorRT release. Specifically, for YOLOv4-tiny, we need the batchTilePlugin and batchedNMSPlugin.

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

Copy
Copied!

            
            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:

Copy
Copied!

            
            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.

Copy
Copied!

            
            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.

Copy
Copied!

            
            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:

Copy
Copied!

            
            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.

Copy
Copied!

            
            /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*:

Copy
Copied!

            
            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:

Copy
Copied!

            
            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:

Copy
Copied!

            
            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.

Copy
Copied!

            
            /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.

Copy
Copied!

            
            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

Generating an Engine Using tao-converter

The tao-converter tool is provided with the TAO Toolkit to facilitate the deployment of TAO trained models on TensorRT and/or Deepstream. This section elaborates on how to generate a TensorRT engine using tao-converter.

For deployment platforms with an x86-based CPU and discrete GPUs, the tao-converter is distributed within the TAO docker. Therefore, we suggest using the docker to generate the engine. However, this requires that the user adhere to the same minor version of TensorRT as distributed with the docker. The TAO docker includes TensorRT version 8.0.

Instructions for x86

For an x86 platform with discrete GPUs, the default TAO package includes the tao-converter built for TensorRT 8.2.5.1 with CUDA 11.4 and CUDNN 8.2. However, for any other version of CUDA and TensorRT, please refer to the overview section for download. Once the tao-converter is downloaded, follow the instructions below to generate a TensorRT engine.

Unzip the zip file on the target machine.

Install the OpenSSL package using the command:

Copy
Copied!

            
            sudo apt-get install libssl-dev

Export the following environment variables:

Copy
Copied!

            
            $ export TRT_LIB_PATH=”/usr/lib/x86_64-linux-gnu”
$ export TRT_INC_PATH=”/usr/include/x86_64-linux-gnu”

Run the tao-converter using the sample command below and generate the engine.
Instructions to build TensorRT OSS on Jetson can be found in the TensorRT OSS on x86 section above or in this GitHub repo.

Note

Make sure to follow the output node names as mentioned in Exporting the Model section of the respective model.

Instructions for Jetson

For the Jetson platform, the tao-converter is available to download in the NVIDIA developer zone. You may choose the version you wish to download as listed in the overview section. Once the tao-converter is downloaded, please follow the instructions below to generate a TensorRT engine.

Unzip the zip file on the target machine.

Install the OpenSSL package using the command:

Copy
Copied!

            
            sudo apt-get install libssl-dev

Export the following environment variables:

Copy
Copied!

            
            $ export TRT_LIB_PATH=”/usr/lib/aarch64-linux-gnu”
$ export TRT_INC_PATH=”/usr/include/aarch64-linux-gnu”

For Jetson devices, TensorRT comes pre-installed with Jetpack. If you are using older JetPack, upgrade to JetPack-5.0DP.
Instructions to build TensorRT OSS on Jetson can be found in the TensorRT OSS on Jetson (ARM64) section above or in this GitHub repo.
Run the tao-converter using the sample command below and generate the engine.

Note

Make sure to follow the output node names as mentioned in Exporting the Model section of the respective model.

Using the tao-converter

Copy
Copied!

            
            tao-converter [-h] -k <encryption_key>
              -d <input_dimensions>
              -o <comma separated output nodes>
              [-c <path to calibration cache file>]
              [-e <path to output engine>]
              [-b <calibration batch size>]
              [-m <maximum batch size of the TRT engine>]
              [-t <engine datatype>]
              [-w <maximum workspace size of the TRT Engine>]
              [-i <input dimension ordering>]
              [-p <optimization_profiles>]
              [-s]
              [-u <DLA_core>]
              input_file

Required Arguments

input_file: The path to the .etlt model exported using tao yolo_v4_tiny export.
-k: The key used to encode the .tlt model when training.
-d: A comma-separated list of input dimensions that should match the dimensions used for tao yolo_v4_tiny export.
-o: A comma-separated list of output blob names that should match the output configuration used for tao yolo_v4_tiny export. For YOLOv4-tiny, set this argument to BatchedNMS.
-p: Optimization profiles for .etlt models with dynamic shape. Use a comma-separated list of optimization profile shapes in the format <input_name>,<min_shape>,<opt_shape>,<max_shape>, where each shape has the format: <n>x<c>x<h>x<w>. The input name for YOLOv4-tiny is Input

Optional Arguments

-e: The path to save the engine to. The default path is ./saved.engine.
-t: The desired engine data type. The options are {fp32, fp16, int8}. The default value is fp32. A calibration cache is generated in INT8 mode.
-w: The maximum workspace size for the TensorRT engine. The default value is 1073741824(1<<30).
-i: The input dimension ordering; all other TAO commands use NCHW. The options are {nchw, nhwc, nc}.The default value is nchw, so you can omit this argument for YOLOv4-tiny.
-s: TensorRT strict type constraints. A Boolean to apply TensorRT strict type constraints when building the TensorRT engine.
-u: Specifies the DLA core index when building the TensorRT engine on Jetson devices (only needed if using DLA core).

INT8 Mode Arguments

-c: The path to the calibration cache file (only used in INT8 mode). The default value is ./cal.bin.
-b: The batch size used during the export step for INT8-calibration cache generation (default: 8).
-m: The maximum batch size for the TensorRT engine (default: 16). If you encounter an out-of-memory issue, decrease the batch size accordingly. This parameter is not required for .etlt models generated with dynamic shape (which is only possible for new models introduced since version 3.0).

Sample Output Log

Here is a sample log for exporting a YOLOv4-tiny model:

Copy
Copied!

            
            tao-converter -k $KEY  \
              -p Input,1x3x384x1248,8x3x384x1248,16x3x384x1248 \
                    -e /export/trt.fp16.engine \
                    -t fp16 \
                    /ws/yolov4_cspdarknet_tiny_epoch_100.etlt

Integrating the model with DeepStream

To integrate a model trained by TAO Toolkit with DeepStream, you should generate a device-specific optimized TensorRT engine using tao-converter. The generated TensorRT engine file can then be ingested by DeepStream. DeepStream can also generate TensorRT engine on-the-fly for YOLOv4-tiny if only etlt models are provided.

For YOLOv4-tiny, you will need to build the TensorRT open-source plugins and custom bounding-box parser. The instructions to build TensorRT open-source plugins are provided in the TensorRT Open Source Software (OSS) section above. The instructions to build a custom bounding-box parser are provided in the Prerequisites for YOLOv4-tiny Model section below, and the required code can be found in this GitHub repo.

To integrate the models with DeepStream, you need the following:

Download and install 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 7+ 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.

DeepStream SDK ships with an end-to-end reference application which is fully configurable. Users can configure 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 2 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. This would set 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 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 your reference.

source4_1080p_dec_infer-resnet_tracker_sgie_tiled_display_int8.txt: Main config file
config_infer_primary.txt: Supporting config file for primary detector in the pipeline above
config_infer_secondary_*.txt: Supporting config file for 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 will little to no change. User will only have to modify or create config_infer_primary.txt and config_infer_secondary_*.txt.

Integrating a YOLOv4-tiny Model

To run a YOLOv4-tiny 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 YOLOv4-tiny bounding box parser for DeepStream.

A DeepStream sample with documentation on how to run inference using the trained YOLOv4-tiny models from TAO Toolkit is provided on GitHub repo..

Prerequisites for YOLOv4-tiny Model

YOLOv4-tiny requires batchTilePlugin, resizeNearestPlugin, and batchedNMSPlugin. These plugins are available in the TensorRT open source repo, but not in TensorRT 7.0. Detailed instructions to build TensorRT OSS can be found in TensorRT Open Source Software (OSS).
YOLOv4-tiny requires YOLOv3 custom bounding box parsers that are not built-in inside the DeepStream SDK. The source code to build YOLOv3 custom bounding box parsers is available in GitHub repo. The following instructions can be used to build bounding box parser:

Step1: Install git-lfs (git >= 1.8.2)

Copy
Copied!

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

Step 2: Download Source Code with SSH or HTTPS

Copy
Copied!

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

Step 3: Build

Copy
Copied!

            
            // 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

The label file is a text file containing the names of the classes that the YOLOv4-tiny model is trained to detect. The order in which the classes are listed here must match the order in which the model predicts the output. During the training, TAO YOLOv4-tiny will specify all class names in lower case and sort them in alphabetical order. For example, if the dataset_config is:

Copy
Copied!

            
            dataset_config {
  data_sources: {
      label_directory_path: "/workspace/tao-experiments/data/training/label_2"
      image_directory_path: "/workspace/tao-experiments/data/training/image_2"
  }
  target_class_mapping {
    key: "car"
    value: "car"
  }
  target_class_mapping {
    key: "person"
    value: "person"
  }
  target_class_mapping {
    key: "bicycle"
    value: "bicycle"
  }
  validation_data_sources: {
      label_directory_path: "/workspace/tao-experiments/data/val/label"
      image_directory_path: "/workspace/tao-experiments/data/val/image"
  }
}

Then the corresponding yolov4_tiny_labels.txt file would be:

Copy
Copied!

            
            bicycle
car
person

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.

Integrate the TensorRT engine file with the DeepStream app

Step 1: Generate TensorRT engine using tao-converter. Detailed instructions are provided in the Generating an engine using tao-converter section above.

Step 2: Once the engine file is generated successfully, modify the following parameters to use this engine with DeepStream.

Copy
Copied!

            
            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 primary infer configuration file:

Copy
Copied!

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

Add the label file generated above using:

Copy
Copied!

            
            labelfile-path=<YOLOv4-tiny labels>

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

Here’s a sample config file, pgie_yolov4_tiny_config.txt:

Copy
Copied!

            
            [property]
gpu-id=0
net-scale-factor=1.0
offsets=103.939;116.779;123.68
model-color-format=1
labelfile-path=<Path to yolov4_tiny_labels.txt>
model-engine-file=<PATH to generated TensorRT engine>
tlt-model-key=<Key to decrypt model>
infer-dims=3;384;1248
maintain-aspect-ratio=1
uff-input-order=0
uff-input-blob-name=Input
batch-size=1
## 0=FP32, 1=INT8, 2=FP16 mode
network-mode=0
num-detected-classes=3
interval=0
gie-unique-id=1
is-classifier=0
#network-type=0
#no cluster
cluster-mode=3
output-blob-names=BatchedNMS
parse-bbox-func-name=NvDsInferParseCustomBatchedNMSTLT
custom-lib-path=<Path to libnvds_infercustomparser_tlt.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

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