Merge pull request #4 from davehoadley/R2024b

R2024b support

.github/workflows/run-matlab-tests.yml

@@ -1,16 +1,16 @@
-name: MATLAB matrix tests
+name: Generate Test and Coverage Artifacts on GitHub-Hosted Runner
 on:
   push:
   pull_request:
   workflow_dispatch:
   schedule:
     - cron:  '24 16 * * 2' # schedule a weekly build to keep caches warm
 jobs:
-  test-job:
+  my-job:
     strategy: 
         matrix:
           os: [ubuntu-latest, windows-latest, macos-13, macos-14]
-    name: Run MATLAB Tests and Generate Artifacts
+    name: Build Toolbox
     runs-on: ${{ matrix.os }}
     steps:
       - name: Support long paths
@@ -24,6 +24,7 @@ jobs:
         uses: matlab-actions/setup-matlab@v2
         with:
           cache: true
+          release: R2024b
           products: |
             Deep_Learning_Toolbox
             Computer_Vision_Toolbox
@@ -38,13 +39,23 @@ jobs:
             Deep_Learning_Toolbox_Model_for_ResNet-50_Network
             Deep_Learning_Toolbox_Model_for_VGG-16_Network
             Deep_Learning_Toolbox_Model_for_VGG-19_Network
-      - name: Run tests and generate artifacts
-        uses: matlab-actions/run-tests@v2
-        with:
-          test-results-junit: test-results/results.xml
-          logging-level: detailed
-      - name: Upload Test Results for ${{ matrix.os }}
+      - name: Run MATLAB build
+        uses: matlab-actions/run-build@v2
+      - name: Upload Test and Coverage Results
         uses: actions/upload-artifact@v4
         with:
           name: ${{ matrix.os }} Results
           path: test-results          
+      - name: Upload Code Analysis Results
+        if: ${{ matrix.os == 'ubuntu-latest' }}
+        uses: actions/upload-artifact@v4
+        with:
+          name: Results
+          path: analysis-results          
+      - name: Upload Toolbox (mltbx)
+        if: ${{ matrix.os == 'ubuntu-latest' }}
+        uses: actions/upload-artifact@v4
+        with:
+          name: Tool Validation Kit
+          path: release/Tool Validation Kit.mltbx
+

.gitignore

@@ -1 +1,2 @@
 # List of untracked files to ignore
+derived/

UnitTestFolder/ComputerVisionToolbox/DeepLabV3PlusTests.m

@@ -21,11 +21,11 @@
             'xception','block14_sepconv2_act');
 
         ExpAdditionalLayer = struct(...
-            'resnet18', 33, ...
-            'inceptionresnetv2', 33, ...
-            'mobilenetv2', 39', ...
-            'resnet50', 33, ...
-            'xception', 39);
+            'resnet18', 32, ...
+            'inceptionresnetv2', 32, ...
+            'mobilenetv2', 38', ...
+            'resnet50', 32, ...
+            'xception', 38);
     end
 
     %% Test Methods
@@ -36,7 +36,7 @@ function checkDeepLabv3PlusLayersTest(test, SpPkgName, LayerMatch, ExpAdditional
             % Check the layer is created/replaced properly
             imageSize = [512 512];
             numClasses = 21;
-            lgraph = deeplabv3plusLayers(imageSize, numClasses, SpPkgName, "Downsampling", 8);
+            lgraph = deeplabv3plus(imageSize, numClasses, SpPkgName, "Downsampling", 8);
 
             % Verify default values
             dlayerNames = {lgraph.Layers.Name};

UnitTestFolder/ComputerVisionToolbox/UnetTests.m

@@ -98,22 +98,19 @@ function testDefaultValues(test)
             for i=1:length(idxConvLayer)
                 paddingModes{i} = unet1.Layers(idxConvLayer(i)).PaddingMode;
             end
-            if strcmp(padding, "valid")
-                test.verifyEqual(paddingModes,repmat({'manual'},[1,i]));
-            else
-                test.verifyEqual(paddingModes,repmat({padding},[1,i]));
-            end
+
+            test.verifyEqual(paddingModes,repmat({padding},[1,i]));
         end
 
         function verifyEncoderAndDecoderSections(test)
             % Testpoint to verify that there are only EncoderDepth number
             % of Encoder and Decoder sections.
 
             encoderDepth = 4;
-            unet = unetLayers([96 96], 2, 'EncoderDepth', encoderDepth);
+            unet1 = unetLayers([96 96], 2, 'EncoderDepth', encoderDepth);
 
             % Get the names of the layers.
-            layerNames = {unet.Layers(:).Name};
+            layerNames = {unet1.Layers(:).Name};
 
             encDecFlag = false([1 encoderDepth]);
 
@@ -143,7 +140,7 @@ function verifyNumChannelsInEncoderAndDecoderSections(test)
             % Testpoint to verify same number of channels for last
             % convolution layer of each encoder and decoder section.
             numFirstEncoderFilters = 128;
-            unet = unetLayers([96 96], 2, 'NumFirstEncoderFilters', ...
+            unet1 = unetLayers([96 96], 2, 'NumFirstEncoderFilters', ...
                 numFirstEncoderFilters);
 
             encoderLevel = 1;
@@ -152,19 +149,19 @@ function verifyNumChannelsInEncoderAndDecoderSections(test)
             encoderChannel = [];
             decoderChannel = [];
 
-            for i = 1:length(unet.Layers)
+            for i = 1:length(unet1.Layers)
 
-                if contains(unet.Layers(i).Name, ['Encoder-Stage-' ...
+                if contains(unet1.Layers(i).Name, ['Encoder-Stage-' ...
                         num2str(encoderLevel) '-Conv-2'])
                     encoderChannel = [encoderChannel ...
-                        unet.Layers(i).NumChannels];
+                        unet1.Layers(i).NumChannels];
 
                     encoderLevel = encoderLevel+1;
                 end
 
-                if contains(unet.Layers(i).Name, ['Decoder-Stage-' ...
+                if contains(unet1.Layers(i).Name, ['Decoder-Stage-' ...
                         num2str(decoderLevel) '-Conv-2'])
-                    decoderChannel = [unet.Layers(i).NumChannels...
+                    decoderChannel = [unet1.Layers(i).NumChannels...
                         decoderChannel];
 
                     decoderLevel = decoderLevel+1;
@@ -180,14 +177,14 @@ function verifyValidConvolutionPadding(test)
             encoderDepth = 1;
             numFirstEncoderFilters = 32;
             padding = 'valid';
-            unet = unetLayers([100 100 3], 3, 'EncoderDepth', ...
+            unet1 = unetLayers([100 100 3], 3, 'EncoderDepth', ...
                 encoderDepth, 'FilterSize', filterSize, ...
                 'NumFirstEncoderFilters', numFirstEncoderFilters, ...
                 'ConvolutionPadding', padding);
             decoderLevel = 1;
             numCrop2dLayers = 0;
-            for i = 1:length(unet.Layers)
-                if contains(unet.Layers(i).Name, ...
+            for i = 1:length(unet1.Layers)
+                if contains(unet1.Layers(i).Name, ...
                         ['Crop2d-' num2str(decoderLevel)])
                     decoderLevel = decoderLevel+1;
                     numCrop2dLayers = numCrop2dLayers + 1;
@@ -201,7 +198,7 @@ function testDifferentParameters(test, InputSize, EncoderDepth, ...
 
             flag = true;
             try
-                unet = unetLayers(InputSize, 4,...
+                unet1 = unetLayers(InputSize, 4,...
                     'EncoderDepth', EncoderDepth, ...
                     'NumFirstEncoderFilters', ...
                     NumFirstEncoderFilters, 'FilterSize', FilterSize); %#ok<NASGU> 

UnitTestFolder/DeepLearningToolbox/ImageDataAugmenterTests.m

@@ -43,11 +43,10 @@ function testDefaultValues(testcase)
 
         function basicBehaviorRGB(testcase)
 
-            augmenter = imageDataAugmenter('RandRotation',[-20 20]);
+            augmenter = instrumentedImageDataAugmenter('RandRotation',[-20 20]);
             A = testcase.peppersImage;
             out = augment(augmenter,testcase.peppersImage);
-
-            exp = imwarp(A,affine2d(augmenter.AffineTransforms),'OutputView',imref2d(size(A)));
+            exp = imwarp(A,affine2d(augmenter.getAffineTransform),'OutputView',imref2d(size(A)));
 
             % Use SSIM to give us some wiggle room re: resizing algorithm,
             % intermediate datatype, etc. We just want to know if the same
@@ -60,25 +59,16 @@ function basicBehaviorRGB(testcase)
 
         function basicBehaviorGrayscale(testcase)
 
-            augmenter = imageDataAugmenter('RandRotation',[-20 20],'RandXTranslation',[0 10]);
+            augmenter = instrumentedImageDataAugmenter('RandRotation',[-20 20],'RandXTranslation',[0 10]);
 
             A = testcase.cameramanImage;
             out = augment(augmenter,A);
 
-            rot = augmenter.Rotation;
-            xtrans = augmenter.XTranslation;
-
-            testcase.verifyGreaterThanOrEqual(rot,-20);
-            testcase.verifyLessThanOrEqual(rot,20);
-            testcase.verifyGreaterThanOrEqual(xtrans,0);
-            testcase.verifyLessThanOrEqual(xtrans,10);
-
-            exp = imwarp(A,affine2d(augmenter.AffineTransforms),'OutputView',imref2d(size(A)));
+            exp = imwarp(A,affine2d(augmenter.getAffineTransform()),'OutputView',imref2d(size(A)));
 
             ssimObserved = ssim(out,exp);
 
-            testcase.verifyGreaterThan(ssimObserved,0.9,'Incorrect augmentation for RGB rotation.');
-
+            testcase.verifyGreaterThan(ssimObserved,0.9,'Incorrect augmentation for RGB rotation.');            
         end
 
         function testRotation(testcase)
@@ -152,27 +142,67 @@ function testScale(testcase)
 
         end
 
-        function testReflection(testcase)
-
+        function testXReflection(testcase)
+
+            import matlab.unittest.constraints.IsEqualTo;
+
             A = testcase.cameramanImage;
             augmenter = imageDataAugmenter('RandXReflection',true);
-
-            rng(1);
-            act = augmenter.augment(A);
-            exp = fliplr(A);
-
-            testcase.verifyTrue(isequal(act,exp),'Incorrect augmentation for X reflection.');
-
+
+            % Every result should be either flipped or not.
+            Aflipped = fliplr(A);
+            con = IsEqualTo(A) | IsEqualTo(Aflipped);
+
+            % Use a fixed random sequence to ensure test repeatability.
+            orig = rng(1);
+            testcase.addTeardown(@rng, orig)
+
+            flipped = false(1,100);
+            for n = 1:100
+                act = augmenter.augment(A);
+                testcase.verifyThat(act, con, 'Incorrect augmentation for X reflection.');
+
+                flipped(n) = all(act==Aflipped, 'all');
+            end
+
+            % The number flipped ought to be close to the expected mean, 50.  The exact
+            % number is repeatable in this test due to the fixed seed, but may alter if
+            % the imageDataAugmenter changes how it samples from the random stream.  To
+            % allow for this we test that the flipping happens within the 2 sigma
+            % level.
+            testcase.verifyLessThanOrEqual(abs(sum(flipped)-50), 10, 'Incorrect augmentation for X reflection.');
+        end
+
+        function testYReflection(testcase)
+
+            import matlab.unittest.constraints.IsEqualTo;
+
+            A = testcase.cameramanImage;
             augmenter = imageDataAugmenter('RandYReflection',true);
 
-            rng(1);
-            act = augmenter.augment(A);
-            exp = flipud(A);
-
-            testcase.verifyTrue(isequal(act,exp),'Incorrect augmentation for Y reflection.');
-
+            Aflipped = flipud(A);
+            con = IsEqualTo(A) | IsEqualTo(Aflipped);
+
+            % Use a fixed random sequence to ensure test repeatability.
+            orig = rng(1);
+            testcase.addTeardown(@rng, orig)
+
+            flipped = false(1,100);
+            for n = 1:100
+                act = augmenter.augment(A);
+                testcase.verifyThat(act, con, 'Incorrect augmentation for Y reflection.');
+
+                flipped(n) = all(act==Aflipped, 'all');
+            end
+
+            % The number flipped ought to be close to the expected mean, 50.  The exact
+            % number is repeatable in this test due to the fixed seed, but may alter if
+            % the imageDataAugmenter changes how it samples from the random stream.  To
+            % allow for this we test that the flipping happens within the 2 sigma
+            % level.
+            testcase.verifyLessThanOrEqual(abs(sum(flipped)-50), 10, 'Incorrect augmentation for Y reflection.');
         end
-        
+
         function testShear(testcase)
 
             A = testcase.peppersImage;

UnitTestFolder/DeepLearningToolbox/TrainingOptionsTests.m

@@ -41,7 +41,7 @@ function passingInvalidSolverNameThrowsError(test)
             % be thrown.
 
             invalidSolverName = 'NotARealSolverName';
-            errorID = 'nnet_cnn:trainingOptions:InvalidSolverName';
+            errorID = 'nnet_cnn:trainingOptions:InvalidTrainingOptions';
 
             try
                 trainingOptions(invalidSolverName);

UnitTestFolder/DeepLearningToolbox/instrumentedImageDataAugmenter.m

@@ -0,0 +1,27 @@
+classdef instrumentedImageDataAugmenter < imageDataAugmenter
+    % imageDataAugmenter subclass which provides access to intermediate
+    % transform values for testing.
+
+    % Copyright 2024 The MathWorks, Inc.
+
+    methods
+        function obj = instrumentedImageDataAugmenter(varargin)
+            obj@imageDataAugmenter(varargin{:});
+        end
+
+        function T = getAffineTransform(obj, varargin)
+            %getAffineTransform Return the affine transform matrix
+            %
+            %  getAffineTransform(obj) returns the full 3x3 matrix that the
+            %  imageDataAugmenter used for the most recent augment() call.
+            %
+            %  getAffineTransform(obj, rowidx, colidx, pageidx) indexes the above
+            %  matrix and returns only the requested elements.
+
+            T = obj.AffineTransforms;
+            if nargin>1
+                T = T(varargin{:});
+            end
+        end
+    end
+end

app/Tool Validation Kit App.prj

@@ -1,5 +1,5 @@
 <deployment-project plugin="plugin.apptool" plugin-version="1.0">
-  <configuration build-checksum="3995440563" file="C:\projects\tool-validation-kit\app\Tool Validation Kit App.prj" location="C:\projects\tool-validation-kit\app" name="Tool Validation Kit App" target="target.mlapps" target-name="Package App">
+  <configuration build-checksum="2546190291" file="C:\projects\tool-validation-kit\app\Tool Validation Kit App.prj" location="C:\projects\tool-validation-kit\app" name="Tool Validation Kit App" target="target.mlapps" target-name="Package App">
     <param.appname>Tool Validation Kit App</param.appname>
     <param.authnamewatermark>MathWorks Consulting</param.authnamewatermark>
     <param.email />
@@ -13,12 +13,12 @@
     <param.summary />
     <param.description />
     <param.screenshot />
-    <param.version>4.2.1</param.version>
+    <param.version>4.2.2</param.version>
     <param.products.name />
     <param.products.id />
     <param.products.version />
     <param.platforms />
-    <param.output>${PROJECT_ROOT}</param.output>
+    <param.output>C:\projects\tool-validation-kit\release</param.output>
     <param.guid>22279144-5ed8-4032-b62b-bf792b595b21</param.guid>
     <unset>
       <param.email />
@@ -29,7 +29,6 @@
       <param.products.id />
       <param.products.version />
       <param.platforms />
-      <param.output />
     </unset>
     <fileset.main>
       <file>${PROJECT_ROOT}\runToolValidationKitApp.m</file>
@@ -54,11 +53,11 @@
     </fileset.resources>
     <fileset.package />
     <build-deliverables>
-      <file location="C:\projects\tool-validation-kit" name="app" optional="false">C:\projects\tool-validation-kit\app</file>
+      <file location="C:\projects\tool-validation-kit" name="release" optional="false">C:\projects\tool-validation-kit\release</file>
     </build-deliverables>
     <workflow />
     <matlab>
-      <root>C:\Program Files\MATLAB\R2023b</root>
+      <root>C:\Program Files\MATLAB\R2024b</root>
       <toolboxes>
         <toolbox name="matlabcoder" />
         <toolbox name="embeddedcoder" />