Code Generation for Object Detection by Using Single Shot Multibox Detector

This example uses:

Open Script

This example shows how to generate CUDA® code for an SSD network (ssdObjectDetector object) and take advantage of the NVIDIA® cuDNN and TensorRT libraries. An SSD network is based on a feed-forward convolutional neural network that detect multiple objects within the image in a single shot. SSD network can be thought of as having two sub-networks. A feature extraction network, followed by a detection network.

This example generates code for the network trained in the Object Detection Using SSD Deep Learning example from Computer Vision Toolbox™. For more information, see Object Detection Using SSD Deep Learning. The Object Detection Using SSD Deep Learning example uses ResNet-50 for feature extraction. The detection sub-network is a small CNN compared to the feature extraction network and is composed of a few convolutional layers and layers specific to SSD.

Prerequisites

  • CUDA enabled NVIDIA® GPU with compute capability 3.2 or higher.

  • NVIDIA CUDA toolkit and driver.

  • NVIDIA cuDNN library.

  • Environment variables for the compilers and libraries. For information on the supported versions of the compilers and libraries, see Third-party Products (GPU Coder). For setting up the environment variables, see Setting Up the Prerequisite Products (GPU Coder).

  • GPU Coder Interface for Deep Learning Libraries support package. To install this support package, use the Add-On Explorer.

Verify GPU Environment

Use the coder.checkGpuInstall function to verify that the compilers and libraries necessary for running this example are set up correctly.

envCfg = coder.gpuEnvConfig('host');
envCfg.DeepLibTarget = 'cudnn';
envCfg.DeepCodegen = 1;
envCfg.Quiet = 1;
coder.checkGpuInstall(envCfg);

Get Pretrained DAGNetwork

net = getSSDNW();

The DAG network contains 180 layers including convolution, ReLU, and batch normalization layers, anchor box, SSD merge, focal loss, and other layers. To display an interactive visualization of the deep learning network architecture, use the analyzeNetwork function.

analyzeNetwork(net);

The ssdObj_detect Entry-Point Function

The ssdObj_detect.m entry-point function takes an image input and runs the detector on the image using the deep learning network saved in the ssdResNet50VehicleExample_20a.mat file. The function loads the network object from the ssdResNet50VehicleExample_20a.mat file into a persistent variable ssdObj and reuses the persistent object on subsequent detection calls.

type('ssdObj_detect.m')

function outImg = ssdObj_detect(in)

%   Copyright 2019-2020 The MathWorks, Inc.

persistent ssdObj;

if isempty(ssdObj)
    ssdObj = coder.loadDeepLearningNetwork('ssdResNet50VehicleExample_20a.mat');
end

% Pass in input
[bboxes,~,labels] = ssdObj.detect(in,'Threshold',0.7);

% Convert categorical labels to cell array of charactor vectors for 
% execution
labels = cellstr(labels);

% Annotate detections in the image.
if ~isempty(labels)
    outImg = insertObjectAnnotation(in,'rectangle',bboxes,labels);
else
    outImg = in;
end

Run MEX Code Generation

To generate CUDA code for the ssdObj_detect.m entry-point function, create a GPU code configuration object for a MEX target and set the target language to C++. Use the coder.DeepLearningConfig function to create a CuDNN deep learning configuration object and assign it to the DeepLearningConfig property of the GPU code configuration object. Run the codegen command specifying an input size of [300,300,3]. This value corresponds to the input layer size of SSD Network.

cfg = coder.gpuConfig('mex');
cfg.TargetLang = 'C++';
cfg.DeepLearningConfig = coder.DeepLearningConfig('cudnn');
codegen -config cfg ssdObj_detect -args {ones(300,300,3,'uint8')} -report

Code generation successful: To view the report, open('codegen/mex/ssdObj_detect/html/report.mldatx').

Run Generated MEX

Set up the video file reader and read the input video. Create a video player to display the video and the output detections.

videoFile = 'highway_lanechange.mp4';
videoFreader = vision.VideoFileReader(videoFile,'VideoOutputDataType','uint8');
depVideoPlayer = vision.DeployableVideoPlayer('Size','Custom','CustomSize',[640 480]);

Read the video input frame-by-frame and detect the vehicles in the video using the detector.

cont = ~isDone(videoFreader);
while cont
    I = step(videoFreader);
    in = imresize(I,[300,300]);
    out = ssdObj_detect_mex(in);
    step(depVideoPlayer, out);
    cont = ~isDone(videoFreader) && isOpen(depVideoPlayer); % Exit the loop if the video player figure window is closed
end

References

[1] Liu, Wei, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng Yang Fu, and Alexander C. Berg. "SSD: Single shot multibox detector." In 14th European Conference on Computer Vision, ECCV 2016. Springer Verlag, 2016.

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