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Detect Objects Using YOLO v3 Network Deployed to FPGA
This example shows how to deploy a trained you only look once (YOLO) v3 object detector to a target FPGA board. You then use MATLAB® to retrieve the object classification from the FPGA board.
Compared to YOLO v2 networks, YOLO v3 networks have additional detection heads that help detect smaller objects.
Create YOLO v3 Detector Object
In this example, you use a pretrained YOLO v3 object detector. To construct and train a custom YOLO v3 detector, see Object Detection Using YOLO v3 Deep Learning (Computer Vision Toolbox).
Use the downloadPretrainedYOLOv3Detector function to generate a dlnetwork object. To get the code for this function, see the downloadPretrainedYOLOv3Detector Function section.
preTrainedDetector = downloadPretrainedYOLOv3Detector;
Downloaded pretrained detector
The generated network uses training data to estimate the anchor boxes, which help the detector learn to predict the boxes. For more information about anchor boxes, see Anchor Boxes for Object Detection (Computer Vision Toolbox). The downloadPretrainedYOLOv3Detector function creates this YOLO v3 network:
Load the Pretrained network
Extract the network from the pretrained YOLO v3 detector object.
yolov3Detector = preTrainedDetector; net = yolov3Detector.Network;
Extract the attributes of the network as variables.
anchorBoxes = yolov3Detector.AnchorBoxes; outputNames = yolov3Detector.Network.OutputNames; inputSize = yolov3Detector.InputSize; classNames = yolov3Detector.ClassNames;
Use the analyzeNetwork function to obtain information about the network layers. the function returns a graphical representation of the network that contains detailed parameter information for every layer in the network.
analyzeNetwork(net);
Define FPGA Board Interface
Define the target FPGA board programming interface by using the dlhdl.Target object. Create a programming interface with custom name for your target device and an Ethernet interface to connect the target device to the host computer.
hTarget = dlhdl.Target('Xilinx','Interface','Ethernet');
Prepare Network for Deployment
Prepare the network for deployment by creating a dlhdl.Workflow object. Specify the network and bitstream name. Ensure that the bitstream name matches the data type and the FPGA board that you are targeting. In this example, the target FPGA board is the Xilinx® Zynq® UltraScale+™ MPSoC ZCU102 board and the bitstream uses the single data type.
hW = dlhdl.Workflow('Network',net,'Bitstream','zcu102_single','Target',hTarget);
Compile Network
Run the compile method of the dlhdl.Workflow object to compile the network and generate the instructions, weights, and biases for deployment.
dn = compile(hW);
### Compiling network for Deep Learning FPGA prototyping ...
### Targeting FPGA bitstream zcu102_single.
### An output layer called 'Output1_customOutputConv1' of type 'nnet.cnn.layer.RegressionOutputLayer' has been added to the provided network. This layer performs no operation during prediction and thus does not affect the output of the network.
### An output layer called 'Output2_customOutputConv2' of type 'nnet.cnn.layer.RegressionOutputLayer' has been added to the provided network. This layer performs no operation during prediction and thus does not affect the output of the network.
### Optimizing network: Fused 'nnet.cnn.layer.BatchNormalizationLayer' into 'nnet.cnn.layer.Convolution2DLayer'
### The network includes the following layers:
1 'data' Image Input 227×227×3 images (SW Layer)
2 'conv1' 2-D Convolution 64 3×3×3 convolutions with stride [2 2] and padding [0 0 0 0] (HW Layer)
3 'relu_conv1' ReLU ReLU (HW Layer)
4 'pool1' 2-D Max Pooling 3×3 max pooling with stride [2 2] and padding [0 0 0 0] (HW Layer)
5 'fire2-squeeze1x1' 2-D Convolution 16 1×1×64 convolutions with stride [1 1] and padding [0 0 0 0] (HW Layer)
6 'fire2-relu_squeeze1x1' ReLU ReLU (HW Layer)
7 'fire2-expand1x1' 2-D Convolution 64 1×1×16 convolutions with stride [1 1] and padding [0 0 0 0] (HW Layer)
8 'fire2-relu_expand1x1' ReLU ReLU (HW Layer)
9 'fire2-expand3x3' 2-D Convolution 64 3×3×16 convolutions with stride [1 1] and padding [1 1 1 1] (HW Layer)
10 'fire2-relu_expand3x3' ReLU ReLU (HW Layer)
11 'fire2-concat' Depth concatenation Depth concatenation of 2 inputs (HW Layer)
12 'fire3-squeeze1x1' 2-D Convolution 16 1×1×128 convolutions with stride [1 1] and padding [0 0 0 0] (HW Layer)
13 'fire3-relu_squeeze1x1' ReLU ReLU (HW Layer)
14 'fire3-expand1x1' 2-D Convolution 64 1×1×16 convolutions with stride [1 1] and padding [0 0 0 0] (HW Layer)
15 'fire3-relu_expand1x1' ReLU ReLU (HW Layer)
16 'fire3-expand3x3' 2-D Convolution 64 3×3×16 convolutions with stride [1 1] and padding [1 1 1 1] (HW Layer)
17 'fire3-relu_expand3x3' ReLU ReLU (HW Layer)
18 'fire3-concat' Depth concatenation Depth concatenation of 2 inputs (HW Layer)
19 'pool3' 2-D Max Pooling 3×3 max pooling with stride [2 2] and padding [0 1 0 1] (HW Layer)
20 'fire4-squeeze1x1' 2-D Convolution 32 1×1×128 convolutions with stride [1 1] and padding [0 0 0 0] (HW Layer)
21 'fire4-relu_squeeze1x1' ReLU ReLU (HW Layer)
22 'fire4-expand1x1' 2-D Convolution 128 1×1×32 convolutions with stride [1 1] and padding [0 0 0 0] (HW Layer)
23 'fire4-relu_expand1x1' ReLU ReLU (HW Layer)
24 'fire4-expand3x3' 2-D Convolution 128 3×3×32 convolutions with stride [1 1] and padding [1 1 1 1] (HW Layer)
25 'fire4-relu_expand3x3' ReLU ReLU (HW Layer)
26 'fire4-concat' Depth concatenation Depth concatenation of 2 inputs (HW Layer)
27 'fire5-squeeze1x1' 2-D Convolution 32 1×1×256 convolutions with stride [1 1] and padding [0 0 0 0] (HW Layer)
28 'fire5-relu_squeeze1x1' ReLU ReLU (HW Layer)
29 'fire5-expand1x1' 2-D Convolution 128 1×1×32 convolutions with stride [1 1] and padding [0 0 0 0] (HW Layer)
30 'fire5-relu_expand1x1' ReLU ReLU (HW Layer)
31 'fire5-expand3x3' 2-D Convolution 128 3×3×32 convolutions with stride [1 1] and padding [1 1 1 1] (HW Layer)
32 'fire5-relu_expand3x3' ReLU ReLU (HW Layer)
33 'fire5-concat' Depth concatenation Depth concatenation of 2 inputs (HW Layer)
34 'pool5' 2-D Max Pooling 3×3 max pooling with stride [2 2] and padding [0 1 0 1] (HW Layer)
35 'fire6-squeeze1x1' 2-D Convolution 48 1×1×256 convolutions with stride [1 1] and padding [0 0 0 0] (HW Layer)
36 'fire6-relu_squeeze1x1' ReLU ReLU (HW Layer)
37 'fire6-expand1x1' 2-D Convolution 192 1×1×48 convolutions with stride [1 1] and padding [0 0 0 0] (HW Layer)
38 'fire6-relu_expand1x1' ReLU ReLU (HW Layer)
39 'fire6-expand3x3' 2-D Convolution 192 3×3×48 convolutions with stride [1 1] and padding [1 1 1 1] (HW Layer)
40 'fire6-relu_expand3x3' ReLU ReLU (HW Layer)
41 'fire6-concat' Depth concatenation Depth concatenation of 2 inputs (HW Layer)
42 'fire7-squeeze1x1' 2-D Convolution 48 1×1×384 convolutions with stride [1 1] and padding [0 0 0 0] (HW Layer)
43 'fire7-relu_squeeze1x1' ReLU ReLU (HW Layer)
44 'fire7-expand1x1' 2-D Convolution 192 1×1×48 convolutions with stride [1 1] and padding [0 0 0 0] (HW Layer)
45 'fire7-relu_expand1x1' ReLU ReLU (HW Layer)
46 'fire7-expand3x3' 2-D Convolution 192 3×3×48 convolutions with stride [1 1] and padding [1 1 1 1] (HW Layer)
47 'fire7-relu_expand3x3' ReLU ReLU (HW Layer)
48 'fire7-concat' Depth concatenation Depth concatenation of 2 inputs (HW Layer)
49 'fire8-squeeze1x1' 2-D Convolution 64 1×1×384 convolutions with stride [1 1] and padding [0 0 0 0] (HW Layer)
50 'fire8-relu_squeeze1x1' ReLU ReLU (HW Layer)
51 'fire8-expand1x1' 2-D Convolution 256 1×1×64 convolutions with stride [1 1] and padding [0 0 0 0] (HW Layer)
52 'fire8-relu_expand1x1' ReLU ReLU (HW Layer)
53 'fire8-expand3x3' 2-D Convolution 256 3×3×64 convolutions with stride [1 1] and padding [1 1 1 1] (HW Layer)
54 'fire8-relu_expand3x3' ReLU ReLU (HW Layer)
55 'fire8-concat' Depth concatenation Depth concatenation of 2 inputs (HW Layer)
56 'fire9-squeeze1x1' 2-D Convolution 64 1×1×512 convolutions with stride [1 1] and padding [0 0 0 0] (HW Layer)
57 'fire9-relu_squeeze1x1' ReLU ReLU (HW Layer)
58 'fire9-expand1x1' 2-D Convolution 256 1×1×64 convolutions with stride [1 1] and padding [0 0 0 0] (HW Layer)
59 'fire9-relu_expand1x1' ReLU ReLU (HW Layer)
60 'fire9-expand3x3' 2-D Convolution 256 3×3×64 convolutions with stride [1 1] and padding [1 1 1 1] (HW Layer)
61 'fire9-relu_expand3x3' ReLU ReLU (HW Layer)
62 'fire9-concat' Depth concatenation Depth concatenation of 2 inputs (HW Layer)
63 'customConv1' 2-D Convolution 1024 3×3×512 convolutions with stride [1 1] and padding 'same' (HW Layer)
64 'customRelu1' ReLU ReLU (HW Layer)
65 'customOutputConv1' 2-D Convolution 18 1×1×1024 convolutions with stride [1 1] and padding 'same' (HW Layer)
66 'featureConv2' 2-D Convolution 128 1×1×512 convolutions with stride [1 1] and padding 'same' (HW Layer)
67 'featureRelu2' ReLU ReLU (HW Layer)
68 'Output1_customOutputConv1' Regression Output mean-squared-error (SW Layer)
69 'featureResize2' dnnfpga.custom.Resize2DLayer dnnfpga.custom.Resize2DLayer (HW Layer)
70 'depthConcat2' Depth concatenation Depth concatenation of 2 inputs (HW Layer)
71 'customConv2' 2-D Convolution 256 3×3×384 convolutions with stride [1 1] and padding 'same' (HW Layer)
72 'customRelu2' ReLU ReLU (HW Layer)
73 'customOutputConv2' 2-D Convolution 18 1×1×256 convolutions with stride [1 1] and padding 'same' (HW Layer)
74 'Output2_customOutputConv2' Regression Output mean-squared-error (SW Layer)
### Notice: The layer 'data' with type 'nnet.cnn.layer.ImageInputLayer' is implemented in software.
### Notice: The layer 'Output1_customOutputConv1' with type 'nnet.cnn.layer.RegressionOutputLayer' is implemented in software.
### Notice: The layer 'Output2_customOutputConv2' with type 'nnet.cnn.layer.RegressionOutputLayer' is implemented in software.
### Compiling layer group: conv1>>fire2-relu_squeeze1x1 ...
### Compiling layer group: conv1>>fire2-relu_squeeze1x1 ... complete.
### Compiling layer group: fire2-expand1x1>>fire2-relu_expand1x1 ...
### Compiling layer group: fire2-expand1x1>>fire2-relu_expand1x1 ... complete.
### Compiling layer group: fire2-expand3x3>>fire2-relu_expand3x3 ...
### Compiling layer group: fire2-expand3x3>>fire2-relu_expand3x3 ... complete.
### Compiling layer group: fire3-squeeze1x1>>fire3-relu_squeeze1x1 ...
### Compiling layer group: fire3-squeeze1x1>>fire3-relu_squeeze1x1 ... complete.
### Compiling layer group: fire3-expand1x1>>fire3-relu_expand1x1 ...
### Compiling layer group: fire3-expand1x1>>fire3-relu_expand1x1 ... complete.
### Compiling layer group: fire3-expand3x3>>fire3-relu_expand3x3 ...
### Compiling layer group: fire3-expand3x3>>fire3-relu_expand3x3 ... complete.
### Compiling layer group: pool3>>fire4-relu_squeeze1x1 ...
### Compiling layer group: pool3>>fire4-relu_squeeze1x1 ... complete.
### Compiling layer group: fire4-expand1x1>>fire4-relu_expand1x1 ...
### Compiling layer group: fire4-expand1x1>>fire4-relu_expand1x1 ... complete.
### Compiling layer group: fire4-expand3x3>>fire4-relu_expand3x3 ...
### Compiling layer group: fire4-expand3x3>>fire4-relu_expand3x3 ... complete.
### Compiling layer group: fire5-squeeze1x1>>fire5-relu_squeeze1x1 ...
### Compiling layer group: fire5-squeeze1x1>>fire5-relu_squeeze1x1 ... complete.
### Compiling layer group: fire5-expand1x1>>fire5-relu_expand1x1 ...
### Compiling layer group: fire5-expand1x1>>fire5-relu_expand1x1 ... complete.
### Compiling layer group: fire5-expand3x3>>fire5-relu_expand3x3 ...
### Compiling layer group: fire5-expand3x3>>fire5-relu_expand3x3 ... complete.
### Compiling layer group: pool5>>fire6-relu_squeeze1x1 ...
### Compiling layer group: pool5>>fire6-relu_squeeze1x1 ... complete.
### Compiling layer group: fire6-expand1x1>>fire6-relu_expand1x1 ...
### Compiling layer group: fire6-expand1x1>>fire6-relu_expand1x1 ... complete.
### Compiling layer group: fire6-expand3x3>>fire6-relu_expand3x3 ...
### Compiling layer group: fire6-expand3x3>>fire6-relu_expand3x3 ... complete.
### Compiling layer group: fire7-squeeze1x1>>fire7-relu_squeeze1x1 ...
### Compiling layer group: fire7-squeeze1x1>>fire7-relu_squeeze1x1 ... complete.
### Compiling layer group: fire7-expand1x1>>fire7-relu_expand1x1 ...
### Compiling layer group: fire7-expand1x1>>fire7-relu_expand1x1 ... complete.
### Compiling layer group: fire7-expand3x3>>fire7-relu_expand3x3 ...
### Compiling layer group: fire7-expand3x3>>fire7-relu_expand3x3 ... complete.
### Compiling layer group: fire8-squeeze1x1>>fire8-relu_squeeze1x1 ...
### Compiling layer group: fire8-squeeze1x1>>fire8-relu_squeeze1x1 ... complete.
### Compiling layer group: fire8-expand1x1>>fire8-relu_expand1x1 ...
### Compiling layer group: fire8-expand1x1>>fire8-relu_expand1x1 ... complete.
### Compiling layer group: fire8-expand3x3>>fire8-relu_expand3x3 ...
### Compiling layer group: fire8-expand3x3>>fire8-relu_expand3x3 ... complete.
### Compiling layer group: fire9-squeeze1x1>>fire9-relu_squeeze1x1 ...
### Compiling layer group: fire9-squeeze1x1>>fire9-relu_squeeze1x1 ... complete.
### Compiling layer group: fire9-expand1x1>>fire9-relu_expand1x1 ...
### Compiling layer group: fire9-expand1x1>>fire9-relu_expand1x1 ... complete.
### Compiling layer group: fire9-expand3x3>>fire9-relu_expand3x3 ...
### Compiling layer group: fire9-expand3x3>>fire9-relu_expand3x3 ... complete.
### Compiling layer group: customConv1>>customOutputConv1 ...
### Compiling layer group: customConv1>>customOutputConv1 ... complete.
### Compiling layer group: featureConv2>>featureRelu2 ...
### Compiling layer group: featureConv2>>featureRelu2 ... complete.
### Compiling layer group: customConv2>>customOutputConv2 ...
### Compiling layer group: customConv2>>customOutputConv2 ... complete.
### Allocating external memory buffers:
offset_name offset_address allocated_space
_______________________ ______________ _________________
"InputDataOffset" "0x00000000" "24.0 MB"
"OutputResultOffset" "0x01800000" "4.0 MB"
"SchedulerDataOffset" "0x01c00000" "4.0 MB"
"SystemBufferOffset" "0x02000000" "28.0 MB"
"InstructionDataOffset" "0x03c00000" "8.0 MB"
"ConvWeightDataOffset" "0x04400000" "104.0 MB"
"EndOffset" "0x0ac00000" "Total: 172.0 MB"
### Network compilation complete.
Program Bitstream onto FPGA and Download Network Weights
To deploy the network on the Xilinx® Zynq® UltraScale+ MPSoC ZCU102 hardware, run the deploy method of the dlhdl.Workflow object. This method programs the FPGA board using the output of the compile method and the programming file, downloads the network weights and biases, displays progress messages, and the time it takes to deploy the network.
deploy(hW);
### Programming FPGA Bitstream using Ethernet... ### Attempting to connect to the hardware board at 192.168.1.101... ### Connection successful ### Programming FPGA device on Xilinx SoC hardware board at 192.168.1.101... ### Copying FPGA programming files to SD card... ### Setting FPGA bitstream and devicetree for boot... # Copying Bitstream zcu102_single.bit to /mnt/hdlcoder_rd # Set Bitstream to hdlcoder_rd/zcu102_single.bit # Copying Devicetree devicetree_dlhdl.dtb to /mnt/hdlcoder_rd # Set Devicetree to hdlcoder_rd/devicetree_dlhdl.dtb # Set up boot for Reference Design: 'AXI-Stream DDR Memory Access : 3-AXIM' ### Rebooting Xilinx SoC at 192.168.1.101... ### Reboot may take several seconds... ### Attempting to connect to the hardware board at 192.168.1.101... ### Connection successful ### Programming the FPGA bitstream has been completed successfully. ### Loading weights to Conv Processor. ### Conv Weights loaded. Current time is 27-Oct-2022 13:44:50
Test Network
Load the example image and convert the image into a dlarray. Then classify the image on the FPGA by using the predict method of the dlhdl.Workflow object and display the results.
img = imread('vehicle_image.jpg'); I = single(rescale(img)); I = imresize(I, yolov3Detector.InputSize(1:2)); dlX = dlarray(I,'SSC');
Store the output of each detection head of the network in the features variable. Pass features to the post-processing function processYOLOv3Output to combine the multiple outputs and compute the final results. To get the code for this function, see the processYOLOv3Output Function section.
features = cell(size(net.OutputNames'));
[features{:}] = hW.predict(dlX, 'Profiler', 'on');### Finished writing input activations.
### Running single input activation.
Deep Learning Processor Profiler Performance Results
LastFrameLatency(cycles) LastFrameLatency(seconds) FramesNum Total Latency Frames/s
------------- ------------- --------- --------- ---------
Network 34469645 0.15668 1 34473970 6.4
conv1 673148 0.00306
pool1 509022 0.00231
fire2-squeeze1x1 308280 0.00140
fire2-expand1x1 305546 0.00139
fire2-expand3x3 305227 0.00139
fire3-squeeze1x1 628018 0.00285
fire3-expand1x1 305219 0.00139
fire3-expand3x3 305220 0.00139
pool3 286781 0.00130
fire4-squeeze1x1 264346 0.00120
fire4-expand1x1 264777 0.00120
fire4-expand3x3 264750 0.00120
fire5-squeeze1x1 749166 0.00341
fire5-expand1x1 264800 0.00120
fire5-expand3x3 264880 0.00120
pool5 219686 0.00100
fire6-squeeze1x1 195193 0.00089
fire6-expand1x1 145091 0.00066
fire6-expand3x3 145075 0.00066
fire7-squeeze1x1 290001 0.00132
fire7-expand1x1 144830 0.00066
fire7-expand3x3 145390 0.00066
fire8-squeeze1x1 369605 0.00168
fire8-expand1x1 245085 0.00111
fire8-expand3x3 245208 0.00111
fire9-squeeze1x1 490784 0.00223
fire9-expand1x1 244864 0.00111
fire9-expand3x3 245458 0.00112
customConv1 17592876 0.07997
customOutputConv1 952889 0.00433
featureConv2 913457 0.00415
featureResize2 57819 0.00026
customConv2 5600648 0.02546
customOutputConv2 526143 0.00239
* The clock frequency of the DL processor is: 220MHz
[bboxes, scores, labels] = processYOLOv3Output(anchorBoxes, inputSize, classNames, features, I);
resultImage = insertObjectAnnotation(I,'rectangle',bboxes,scores);
imshow(resultImage)
The FPGA returns a score prediction of 0.89605 with a bounding box drawn around the object in the image. The FPGA also returns a prediction of vehicle to the labels variable.
downloadPretrainedYOLOv3Detector Function
The downloadPretrainedYOLOv3Detector function to download the pretrained YOLO v3 detector network:
function detector = downloadPretrainedYOLOv3Detector if ~exist('yolov3SqueezeNetVehicleExample_21aSPKG.mat', 'file') if ~exist('yolov3SqueezeNetVehicleExample_21aSPKG.zip', 'file') zipFile = matlab.internal.examples.downloadSupportFile('vision/data', 'yolov3SqueezeNetVehicleExample_21aSPKG.zip'); copyfile(zipFile); end unzip('yolov3SqueezeNetVehicleExample_21aSPKG.zip'); end pretrained = load("yolov3SqueezeNetVehicleExample_21aSPKG.mat"); detector = pretrained.detector; disp('Downloaded pretrained detector'); end
processYOLOv3Output Function
The processYOLOv3Output function is attached as a helper file in this example's directory. This function converts the feature maps from multiple detection heads to bounding boxes, scores and labels. A code snippet of the function is shown below.
function [bboxes, scores, labels] = processYOLOv3Output(anchorBoxes, inputSize, classNames, features, img) % This function converts the feature maps from multiple detection heads to bounding boxes, scores and labels % processYOLOv3Output is C code generatable % Breaks down the raw output from predict function into Confidence score, X, Y, Width, % Height and Class probabilities for each output from detection head predictions = iYolov3Transform(features, anchorBoxes); % Initialize parameters for post-processing inputSize2d = inputSize(1:2); info.PreprocessedImageSize = inputSize2d(1:2); info.ScaleX = size(img,1)/inputSize2d(1); info.ScaleY = size(img,2)/inputSize2d(1); params.MinSize = [1 1]; params.MaxSize = size(img(:,:,1)); params.Threshold = 0.5; params.FractionDownsampling = 1; params.DetectionInputWasBatchOfImages = false; params.NetworkInputSize = inputSize; params.DetectionPreprocessing = "none"; params.SelectStrongest = 1; bboxes = []; scores = []; labels = []; % Post-process the predictions to get bounding boxes, scores and labels [bboxes, scores, labels] = iPostprocessMultipleDetection(anchorBoxes, inputSize, classNames, predictions, info, params); end function [bboxes, scores, labels] = iPostprocessMultipleDetection (anchorBoxes, inputSize, classNames, YPredData, info, params) % Post-process the predictions to get bounding boxes, scores and labels % YpredData is a (x,8) cell array, where x = number of detection heads % Information in each column is: % column 1 -> confidence scores % column 2 to column 5 -> X offset, Y offset, Width, Height of anchor boxes % column 6 -> class probabilities % column 7-8 -> copy of width and height of anchor boxes % Initialize parameters for post-processing classes = classNames; predictions = YPredData; extractPredictions = cell(size(predictions)); % Extract dlarray data for i = 1:size(extractPredictions,1) for j = 1:size(extractPredictions,2) extractPredictions{i,j} = extractdata(predictions{i,j}); end end % Storing the values of columns 2 to 5 of extractPredictions % Columns 2 to 5 represent information about X-coordinate, Y-coordinate, Width and Height of predicted anchor boxes extractedCoordinates = cell(size(predictions,1),4); for i = 1:size(predictions,1) for j = 2:5 extractedCoordinates{i,j-1} = extractPredictions{i,j}; end end % Convert predictions from grid cell coordinates to box coordinates. boxCoordinates = anchorBoxGenerator(anchorBoxes, inputSize, classNames, extractedCoordinates, params.NetworkInputSize); % Replace grid cell coordinates in extractPredictions with box coordinates for i = 1:size(YPredData,1) for j = 2:5 extractPredictions{i,j} = single(boxCoordinates{i,j-1}); end end % 1. Convert bboxes from spatial to pixel dimension % 2. Combine the prediction from different heads. % 3. Filter detections based on threshold. % Reshaping the matrices corresponding to confidence scores and bounding boxes detections = cell(size(YPredData,1),6); for i = 1:size(detections,1) for j = 1:5 detections{i,j} = reshapePredictions(extractPredictions{i,j}); end end % Reshaping the matrices corresponding to class probablities numClasses = repmat({numel(classes)},[size(detections,1),1]); for i = 1:size(detections,1) detections{i,6} = reshapeClasses(extractPredictions{i,6},numClasses{i,1}); end % cell2mat converts the cell of matrices into one matrix, this combines the % predictions of all detection heads detections = cell2mat(detections); % Getting the most probable class and corresponding index [classProbs, classIdx] = max(detections(:,6:end),[],2); detections(:,1) = detections(:,1).*classProbs; detections(:,6) = classIdx; % Keep detections whose confidence score is greater than threshold. detections = detections(detections(:,1) >= params.Threshold,:); [bboxes, scores, labels] = iPostProcessDetections(detections, classes, info, params); end function [bboxes, scores, labels] = iPostProcessDetections(detections, classes, info, params) % Resizes the anchor boxes, filters anchor boxes based on size and apply % NMS to eliminate overlapping anchor boxes if ~isempty(detections) % Obtain bounding boxes and class data for pre-processed image scorePred = detections(:,1); bboxesTmp = detections(:,2:5); classPred = detections(:,6); inputImageSize = ones(1,2); inputImageSize(2) = info.ScaleX.*info.PreprocessedImageSize(2); inputImageSize(1) = info.ScaleY.*info.PreprocessedImageSize(1); % Resize boxes to actual image size. scale = [inputImageSize(2) inputImageSize(1) inputImageSize(2) inputImageSize(1)]; bboxPred = bboxesTmp.*scale; % Convert x and y position of detections from centre to top-left. bboxPred = iConvertCenterToTopLeft(bboxPred); % Filter boxes based on MinSize, MaxSize. [bboxPred, scorePred, classPred] = filterBBoxes(params.MinSize, params.MaxSize, bboxPred, scorePred, classPred); % Apply NMS to eliminate boxes having significant overlap if params.SelectStrongest [bboxes, scores, classNames] = selectStrongestBboxMulticlass(bboxPred, scorePred, classPred ,... 'RatioType', 'Union', 'OverlapThreshold', 0.4); else bboxes = bboxPred; scores = scorePred; classNames = classPred; end % Limit width detections detectionsWd = min((bboxes(:,1) + bboxes(:,3)),inputImageSize(1,2)); bboxes(:,3) = detectionsWd(:,1) - bboxes(:,1); % Limit height detections detectionsHt = min((bboxes(:,2) + bboxes(:,4)),inputImageSize(1,1)); bboxes(:,4) = detectionsHt(:,1) - bboxes(:,2); bboxes(bboxes<1) = 1; % Convert classId to classNames. labels = categorical(classes,cellstr(classes)); labels = labels(classNames); else % If detections are empty then bounding boxes, scores and labels should % be empty bboxes = zeros(0,4,'single'); scores = zeros(0,1,'single'); labels = categorical(classes); end end function x = reshapePredictions(pred) % Reshapes the matrices corresponding to scores, X, Y, Width and Height to % make them compatible for combining the outputs of different detection % heads [h,w,c,n] = size(pred); x = reshape(pred,h*w*c,1,n); end function x = reshapeClasses(pred,numClasses) % Reshapes the matrices corresponding to the class probabilities, to make it % compatible for combining the outputs of different detection heads [h,w,c,n] = size(pred); numAnchors = c/numClasses; x = reshape(pred,h*w,numClasses,numAnchors,n); x = permute(x,[1,3,2,4]); [h,w,c,n] = size(x); x = reshape(x,h*w,c,n); end function bboxes = iConvertCenterToTopLeft(bboxes) % Convert x and y position of detections from centre to top-left. bboxes(:,1) = bboxes(:,1) - bboxes(:,3)/2 + 0.5; bboxes(:,2) = bboxes(:,2) - bboxes(:,4)/2 + 0.5; bboxes = floor(bboxes); bboxes(bboxes<1) = 1; end function tiledAnchors = anchorBoxGenerator(anchorBoxes, inputSize, classNames,YPredCell,inputImageSize) % Convert grid cell coordinates to box coordinates. % Generate tiled anchor offset. tiledAnchors = cell(size(YPredCell)); for i = 1:size(YPredCell,1) anchors = anchorBoxes{i,:}; [h,w,~,n] = size(YPredCell{i,1}); [tiledAnchors{i,2},tiledAnchors{i,1}] = ndgrid(0:h-1,0:w-1,1:size(anchors,1),1:n); [~,~,tiledAnchors{i,3}] = ndgrid(0:h-1,0:w-1,anchors(:,2),1:n); [~,~,tiledAnchors{i,4}] = ndgrid(0:h-1,0:w-1,anchors(:,1),1:n); end for i = 1:size(YPredCell,1) [h,w,~,~] = size(YPredCell{i,1}); tiledAnchors{i,1} = double((tiledAnchors{i,1} + YPredCell{i,1})./w); tiledAnchors{i,2} = double((tiledAnchors{i,2} + YPredCell{i,2})./h); tiledAnchors{i,3} = double((tiledAnchors{i,3}.*YPredCell{i,3})./inputImageSize(2)); tiledAnchors{i,4} = double((tiledAnchors{i,4}.*YPredCell{i,4})./inputImageSize(1)); end end function predictions = iYolov3Transform(YPredictions, anchorBoxes) % This function breaks down the raw output from predict function into Confidence score, X, Y, Width, % Height and Class probabilities for each output from detection head predictions = cell(size(YPredictions,1),size(YPredictions,2) + 2); for idx = 1:size(YPredictions,1) % Get the required info on feature size. numChannelsPred = size(YPredictions{idx},3); %number of channels in a feature map numAnchors = size(anchorBoxes{idx},1); %number of anchor boxes per grid numPredElemsPerAnchors = numChannelsPred/numAnchors; channelsPredIdx = 1:numChannelsPred; predictionIdx = ones([1,numAnchors.*5]); % X positions. startIdx = 1; endIdx = numChannelsPred; stride = numPredElemsPerAnchors; predictions{idx,2} = YPredictions{idx}(:,:,startIdx:stride:endIdx,:); predictionIdx = [predictionIdx startIdx:stride:endIdx]; % Y positions. startIdx = 2; endIdx = numChannelsPred; stride = numPredElemsPerAnchors; predictions{idx,3} = YPredictions{idx}(:,:,startIdx:stride:endIdx,:); predictionIdx = [predictionIdx startIdx:stride:endIdx]; % Width. startIdx = 3; endIdx = numChannelsPred; stride = numPredElemsPerAnchors; predictions{idx,4} = YPredictions{idx}(:,:,startIdx:stride:endIdx,:); predictionIdx = [predictionIdx startIdx:stride:endIdx]; % Height. startIdx = 4; endIdx = numChannelsPred; stride = numPredElemsPerAnchors; predictions{idx,5} = YPredictions{idx}(:,:,startIdx:stride:endIdx,:); predictionIdx = [predictionIdx startIdx:stride:endIdx]; % Confidence scores. startIdx = 5; endIdx = numChannelsPred; stride = numPredElemsPerAnchors; predictions{idx,1} = YPredictions{idx}(:,:,startIdx:stride:endIdx,:); predictionIdx = [predictionIdx startIdx:stride:endIdx]; % Class probabilities. classIdx = setdiff(channelsPredIdx,predictionIdx); predictions{idx,6} = YPredictions{idx}(:,:,classIdx,:); end for i = 1:size(predictions,1) predictions{i,7} = predictions{i,4}; predictions{i,8} = predictions{i,5}; end % Apply activation to the predicted cell array % Apply sigmoid activation to columns 1-3 (Confidence score, X, Y) for i = 1:size(predictions,1) for j = 1:3 predictions{i,j} = sigmoid(predictions{i,j}); end end % Apply exponentiation to columns 4-5 (Width, Height) for i = 1:size(predictions,1) for j = 4:5 predictions{i,j} = exp(predictions{i,j}); end end % Apply sigmoid activation to column 6 (Class probabilities) for i = 1:size(predictions,1) for j = 6 predictions{i,j} = sigmoid(predictions{i,j}); end end end function [bboxPred, scorePred, classPred] = filterBBoxes(minSize, maxSize, bboxPred, scorePred, classPred) % Filter boxes based on MinSize, MaxSize [bboxPred, scorePred, classPred] = filterSmallBBoxes(minSize, bboxPred, scorePred, classPred); [bboxPred, scorePred, classPred] = filterLargeBBoxes(maxSize, bboxPred, scorePred, classPred); end function varargout = filterSmallBBoxes(minSize, varargin) % Filter boxes based on MinSize bboxes = varargin{1}; tooSmall = any((bboxes(:,[4 3]) < minSize),2); for ii = 1:numel(varargin) varargout{ii} = varargin{ii}(~tooSmall,:); end end function varargout = filterLargeBBoxes(maxSize, varargin) % Filter boxes based on MaxSize bboxes = varargin{1}; tooBig = any((bboxes(:,[4 3]) > maxSize),2); for ii = 1:numel(varargin) varargout{ii} = varargin{ii}(~tooBig,:); end end function m = cell2mat(c) % Converts the cell of matrices into one matrix by concatenating % the output corresponding to each feature map elements = numel(c); % If number of elements is 0 return an empty array if elements == 0 m = []; return end % If number of elements is 1, return same element as matrix if elements == 1 if isnumeric(c{1}) || ischar(c{1}) || islogical(c{1}) || isstruct(c{1}) m = c{1}; return end end % Error out for unsupported cell content ciscell = iscell(c{1}); cisobj = isobject(c{1}); if cisobj || ciscell disp('CELL2MAT does not support cell arrays containing cell arrays or objects.'); end % If input input is struct, extract field names of structure into a cell if isstruct(c{1}) cfields = cell(elements,1); for n = 1:elements cfields{n} = fieldnames(c{n}); end if ~isequal(cfields{:}) disp('The field names of each cell array element must be consistent and in consistent order.'); end end % If number of dimensions is 2 if ndims(c) == 2 rows = size(c,1); cols = size(c,2); if (rows < cols) % If rows is less than columns first concatenate each column into 1 % row then concatenate all the rows m = cell(rows,1); for n = 1:rows m{n} = cat(2,c{n,:}); end m = cat(1,m{:}); else % If columns is less than rows, first concatenate each corresponding % row into columns, then combine all columns into 1 m = cell(1,cols); for n = 1:cols m{n} = cat(1,c{:,n}); end m = cat(2,m{:}); end return end end
References
[1] Redmon, Joseph, and Ali Farhadi. “YOLOv3: An Incremental Improvement.” Preprint, submitted April 8, 2018. https://arxiv.org/abs/1804.02767.
See Also
dlhdl.Target | dlhdl.Workflow | compile | deploy | predict | classify
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