keep fixing compilation issues

bioimage-io · Sep 30, 2024 · cba377b · cba377b
1 parent 1938cd7
commit cba377b
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Showing 7 changed files with 71 additions and 45 deletions.
diff --git a/src/main/java/io/bioimage/modelrunner/bioimageio/bioengine/BioengineInterface.java b/src/main/java/io/bioimage/modelrunner/bioimageio/bioengine/BioengineInterface.java
@@ -167,12 +167,12 @@ void main(String[] args) throws LoadModelException, RunModelException {
 		final ImgFactory< FloatType > imgFactory = new ArrayImgFactory<>( new FloatType() );
 		final Img< FloatType > img1 = imgFactory.create( 1, 1, 512, 512 );
 		Tensor<FloatType> inpTensor = Tensor.build("input0", "bcyx", img1);
-		List<Tensor<T>> inputs = new ArrayList<Tensor<T>>();
-		inputs.add((Tensor<T>) inpTensor);
+		List<Tensor<FloatType>> inputs = new ArrayList<Tensor<FloatType>>();
+		inputs.add(inpTensor);
 		final Img< FloatType > img2 = imgFactory.create( 1, 2, 512, 512 );
 		Tensor<FloatType> outTensor = Tensor.build("output0", "bcyx", img2);
-		List<Tensor<R>> outputs = new ArrayList<Tensor<R>>();
-		outputs.add((Tensor<R>) outTensor);
+		List<Tensor<FloatType>> outputs = new ArrayList<Tensor<FloatType>>();
+		outputs.add(outTensor);
 		bi.run(inputs, outputs);
 		System.out.print(DECODE_JSON_VAL);
 	}

diff --git a/src/main/java/io/bioimage/modelrunner/example/ExampleLoadAndRunAllBmzModels.java b/src/main/java/io/bioimage/modelrunner/example/ExampleLoadAndRunAllBmzModels.java
@@ -127,10 +127,10 @@ public static void main(String[] args) {
 	void loadAndRunModel(String modelFolder, ModelDescriptor descriptor) throws Exception {
 		Model model = Model.createBioimageioModel(modelFolder, ENGINES_DIR);
 		model.loadModel();
-		List<Tensor<T>> inputs = createInputs(descriptor);
-		List<Tensor<R>> outputs = createOutputs(descriptor);
+		List<Tensor<FloatType>> inputs = createInputs(descriptor);
+		List<Tensor<FloatType>> outputs = createOutputs(descriptor);
 		model.runModel(inputs, outputs);
-		for (Tensor<?> tt : outputs) {
+		for (Tensor<FloatType> tt : outputs) {
 			if (tt.isEmpty())
 				throw new Exception(descriptor.getName() + ": Output tensor is empty");
 		}
@@ -147,8 +147,8 @@ void loadAndRunModel(String modelFolder, ModelDescriptor descriptor) throws Exce
 	 * 	file containing the information
 	 * @return the input Tensor list
 	 */
-	private static <T extends RealType<T> & NativeType<T>> List<Tensor<T>> createInputs(ModelDescriptor descriptor) {
-		List<Tensor<T>> inputs = new ArrayList<Tensor<T>>();
+	private static List<Tensor<FloatType>> createInputs(ModelDescriptor descriptor) {
+		List<Tensor<FloatType>> inputs = new ArrayList<Tensor<FloatType>>();
 		final ImgFactory< FloatType > imgFactory = new ArrayImgFactory<>( new FloatType() );
 
 		for ( TensorSpec it : descriptor.getInputTensors()) {
@@ -159,7 +159,7 @@ private static <T extends RealType<T> & NativeType<T>> List<Tensor<T>> createInp
 			long[] imSize = LongStream.range(0, step.length)
 					.map(i -> min[(int) i] + step[(int) i]).toArray();
 			Tensor<FloatType> tt = Tensor.build(name, axesStr, imgFactory.create(imSize));
-			inputs.add((Tensor<T>) tt);
+			inputs.add(tt);
 		}
 		return inputs;
 	}

diff --git a/src/main/java/io/bioimage/modelrunner/example/ExampleLoadTensorflow1Tensorflow2.java b/src/main/java/io/bioimage/modelrunner/example/ExampleLoadTensorflow1Tensorflow2.java
@@ -202,8 +202,8 @@ void loadAndRunTf1() throws LoadEngineException, Exception {
 		// Create the input tensor with the nameand axes given by the rdf.yaml file
 		// and add it to the list of input tensors
 		Tensor<FloatType> inpTensor = Tensor.build("input", "byxc", img1);
-		List<Tensor<T>> inputs = new ArrayList<Tensor<T>>();
-		inputs.add((Tensor<T>) inpTensor);
+		List<Tensor<FloatType>> inputs = new ArrayList<Tensor<FloatType>>();
+		inputs.add(inpTensor);
 
 		// Create the output tensors defined in the rdf.yaml file with their corresponding 
 		// name and axes and add them to the output list of tensors.
@@ -212,8 +212,8 @@ void loadAndRunTf1() throws LoadEngineException, Exception {
 		// defining the dimensions and data type
 		final Img< FloatType > img2 = imgFactory.create( 1, 512, 512, 33 );
 		Tensor<FloatType> outTensor = Tensor.build("output", "byxc", img2);
-		List<Tensor<R>> outputs = new ArrayList<Tensor<R>>();
-		outputs.add((Tensor<R>) outTensor);
+		List<Tensor<FloatType>> outputs = new ArrayList<Tensor<FloatType>>();
+		outputs.add(outTensor);
 
 		// Run the model on the input tensors. THe output tensors 
 		// will be rewritten with the result of the execution

diff --git a/src/main/java/io/bioimage/modelrunner/model/Model.java b/src/main/java/io/bioimage/modelrunner/model/Model.java
@@ -61,6 +61,7 @@
 import net.imglib2.type.NativeType;
 import net.imglib2.type.numeric.RealType;
 import net.imglib2.type.numeric.real.FloatType;
+import net.imglib2.util.Cast;
 
 /**
  * Class that manages a Deep Learning model to load it and run it.
@@ -534,7 +535,7 @@ void runModel( List< Tensor < T > > inTensors, List< Tensor < R > > outTensors )
 		ArrayList<Tensor<FloatType>> inTensorsFloat = new ArrayList<Tensor<FloatType>>();
 		for (Tensor<T> tt : inTensors) {
 			if (tt.getData().getAt(0) instanceof FloatType)
-				inTensorsFloat.add((Tensor<FloatType>) tt);
+				inTensorsFloat.add(Cast.unchecked(tt));
 			else
 				inTensorsFloat.add(Tensor.createCopyOfTensorInWantedDataType( tt, new FloatType() ));
 		}
@@ -707,9 +708,9 @@ List<Tensor<T>> runTiling(List<Tensor<R>> inputTensors, TileMaker tiles, TilingC
 	public static <T extends NativeType<T> & RealType<T>> void main(String[] args) throws IOException, ModelSpecsException, LoadEngineException, RunModelException, LoadModelException {
 
 		String mm = "/home/carlos/git/JDLL/models/NucleiSegmentationBoundaryModel_17122023_143125";
-		Img<T> im = (Img<T>) ArrayImgs.floats(new long[] {1, 1, 512, 512});
+		Img<T> im = Cast.unchecked(ArrayImgs.floats(new long[] {1, 1, 512, 512}));
 		List<Tensor<T>> l = new ArrayList<Tensor<T>>();
-		l.add((Tensor<T>) Tensor.build("input0", "bcyx", im));
+		l.add(Tensor.build("input0", "bcyx", im));
 		Model model = createBioimageioModel(mm);
 		model.loadModel();
 		TileInfo tile = TileInfo.build(l.get(0).getName(), new long[] {1, 1, 512, 512}, 

diff --git a/src/main/java/io/bioimage/modelrunner/transformations/ScaleLinearTransformation.java b/src/main/java/io/bioimage/modelrunner/transformations/ScaleLinearTransformation.java
@@ -25,15 +25,8 @@
 import net.imglib2.RandomAccessibleInterval;
 import net.imglib2.loops.LoopBuilder;
 import net.imglib2.type.NativeType;
+import net.imglib2.type.numeric.IntegerType;
 import net.imglib2.type.numeric.RealType;
-import net.imglib2.type.numeric.integer.ByteType;
-import net.imglib2.type.numeric.integer.IntType;
-import net.imglib2.type.numeric.integer.LongType;
-import net.imglib2.type.numeric.integer.ShortType;
-import net.imglib2.type.numeric.integer.UnsignedByteType;
-import net.imglib2.type.numeric.integer.UnsignedIntType;
-import net.imglib2.type.numeric.integer.UnsignedShortType;
-import net.imglib2.type.numeric.real.DoubleType;
 import net.imglib2.type.numeric.real.FloatType;
 import net.imglib2.util.Util;
 import net.imglib2.view.IntervalView;
@@ -227,9 +220,21 @@ private static long[][] getAllCombinations(long[] arr){
 		return allPoints;
 	}
 
-	@SuppressWarnings("unchecked")
 	public < R extends RealType< R > & NativeType< R > > 
 	void scaleLinear(RandomAccessibleInterval<R> rai, double gain, double offset) {
+
+        R type = Util.getTypeFromInterval(rai);
+        if (type instanceof IntegerType) {
+			LoopBuilder.setImages( rai )
+			.multiThreaded()
+			.forEachPixel( i -> i.setReal(Math.floor(i.getRealDouble() * gain + offset) ) );
+        } else {
+			LoopBuilder.setImages( rai )
+			.multiThreaded()
+			.forEachPixel( i -> i.setReal((i.getRealDouble() * gain + offset) ) );
+        }
+        /**
+         * TODO remove
 		if (rai.getAt(0) instanceof ByteType) {
 			LoopBuilder.setImages( (RandomAccessibleInterval<ByteType>) rai )
 			.multiThreaded()
@@ -269,5 +274,6 @@ void scaleLinear(RandomAccessibleInterval<R> rai, double gain, double offset) {
 		} else {
 			throw new IllegalArgumentException("Unsupported data type: " + Util.getTypeFromInterval(rai));
 		}
+         */
 	}
 }
diff --git a/src/main/java/io/bioimage/modelrunner/transformations/ScaleRangeTransformation.java b/src/main/java/io/bioimage/modelrunner/transformations/ScaleRangeTransformation.java
@@ -31,15 +31,8 @@
 import net.imglib2.img.basictypeaccess.array.FloatArray;
 import net.imglib2.loops.LoopBuilder;
 import net.imglib2.type.NativeType;
+import net.imglib2.type.numeric.IntegerType;
 import net.imglib2.type.numeric.RealType;
-import net.imglib2.type.numeric.integer.ByteType;
-import net.imglib2.type.numeric.integer.IntType;
-import net.imglib2.type.numeric.integer.LongType;
-import net.imglib2.type.numeric.integer.ShortType;
-import net.imglib2.type.numeric.integer.UnsignedByteType;
-import net.imglib2.type.numeric.integer.UnsignedIntType;
-import net.imglib2.type.numeric.integer.UnsignedShortType;
-import net.imglib2.type.numeric.real.DoubleType;
 import net.imglib2.type.numeric.real.FloatType;
 import net.imglib2.util.Util;
 import net.imglib2.view.IntervalView;
@@ -146,14 +139,21 @@ private < R extends RealType< R > & NativeType< R > > void globalScale( final Te
 		scaleRange(output.getData(), maxPercentileVal, minPercentileVal);
 	}
 
-	@SuppressWarnings("unchecked")
 	private < R extends RealType< R > & NativeType< R > > 
 	double findPercentileValue(RandomAccessibleInterval<R> rai, double percentile) {
 		final IterableInterval<R> flatImage = Views.iterable(rai);
 		long flatSize = Arrays.stream(flatImage.dimensionsAsLongArray()).reduce(1, (a, b) -> a * b);
 		double[] flatArr = new double[(int) flatSize];
 
 		int count = 0;
+		final Cursor<R> cursor = (Cursor<R>) flatImage.cursor();
+		while ( cursor.hasNext() )
+		{
+			cursor.next();
+			flatArr[count ++] = cursor.get().getRealDouble();
+		}
+		/* 
+		 * TODO remove
 		if (rai.getAt(0) instanceof ByteType) {
 			final Cursor<ByteType> cursor = (Cursor<ByteType>) flatImage.cursor();
 			while ( cursor.hasNext() )
@@ -220,6 +220,7 @@ private < R extends RealType< R > & NativeType< R > > void globalScale( final Te
 		} else {
 			throw new IllegalArgumentException("Unsupported data type: " + Util.getTypeFromInterval(rai));
 		}
+		 */
 		Arrays.sort(flatArr);
 
 		int percentilePos = (int) (flatSize * percentile);
@@ -306,10 +307,22 @@ public static void test2() {
 		 System.out.print(true);
 	}
 
-	@SuppressWarnings("unchecked")
 	public < R extends RealType< R > & NativeType< R > > 
 	void scaleRange(RandomAccessibleInterval<R> rai, double maxPercentileVal, double minPercentileVal) {
 		double diff = maxPercentileVal - minPercentileVal;
+
+        R type = Util.getTypeFromInterval(rai);
+        if (type instanceof IntegerType) {
+			LoopBuilder.setImages( rai )
+			.multiThreaded()
+			.forEachPixel( i -> i.setReal(Math.floor((i.getRealDouble() - minPercentileVal) / (diff + eps)) ) );
+        } else {
+			LoopBuilder.setImages( rai )
+			.multiThreaded()
+			.forEachPixel( i -> i.setReal(((i.getRealDouble() - minPercentileVal) / (diff + eps)) ) );
+        }
+        /**
+         * TODO remove
 		if (rai.getAt(0) instanceof ByteType) {
 			LoopBuilder.setImages( (RandomAccessibleInterval<ByteType>) rai )
 			.multiThreaded()
@@ -349,5 +362,6 @@ void scaleRange(RandomAccessibleInterval<R> rai, double maxPercentileVal, double
 		} else {
 			throw new IllegalArgumentException("Unsupported data type: " + Util.getTypeFromInterval(rai));
 		}
+		*/
 	}
 }
diff --git a/...main/java/io/bioimage/modelrunner/transformations/ZeroMeanUnitVarianceTransformation.java b/...main/java/io/bioimage/modelrunner/transformations/ZeroMeanUnitVarianceTransformation.java
@@ -29,15 +29,8 @@
 import net.imglib2.img.basictypeaccess.array.FloatArray;
 import net.imglib2.loops.LoopBuilder;
 import net.imglib2.type.NativeType;
+import net.imglib2.type.numeric.IntegerType;
 import net.imglib2.type.numeric.RealType;
-import net.imglib2.type.numeric.integer.ByteType;
-import net.imglib2.type.numeric.integer.IntType;
-import net.imglib2.type.numeric.integer.LongType;
-import net.imglib2.type.numeric.integer.ShortType;
-import net.imglib2.type.numeric.integer.UnsignedByteType;
-import net.imglib2.type.numeric.integer.UnsignedIntType;
-import net.imglib2.type.numeric.integer.UnsignedShortType;
-import net.imglib2.type.numeric.real.DoubleType;
 import net.imglib2.type.numeric.real.FloatType;
 import net.imglib2.util.Util;
 import net.imglib2.view.IntervalView;
@@ -384,9 +377,20 @@ public static void test3() {
 		System.out.print(true);
 	}
 
-	@SuppressWarnings("unchecked")
 	public < R extends RealType< R > & NativeType< R > > 
 	void zeroMeanUnitVariance(RandomAccessibleInterval<R> rai, double mean, double std) {
+        R type = Util.getTypeFromInterval(rai);
+        if (type instanceof IntegerType) {
+			LoopBuilder.setImages( rai )
+			.multiThreaded()
+			.forEachPixel( i -> i.setReal(Math.floor((i.getRealDouble() - mean) / (std + eps)) ) );
+        } else {
+			LoopBuilder.setImages( rai )
+			.multiThreaded()
+			.forEachPixel( i -> i.setReal(((i.getRealDouble() - mean) / (std + eps)) ) );
+        }
+        /**
+         * TODO remove
 		if (rai.getAt(0) instanceof ByteType) {
 			LoopBuilder.setImages( (RandomAccessibleInterval<ByteType>) rai )
 			.multiThreaded()
@@ -426,5 +430,6 @@ void zeroMeanUnitVariance(RandomAccessibleInterval<R> rai, double mean, double s
 		} else {
 			throw new IllegalArgumentException("Unsupported data type: " + Util.getTypeFromInterval(rai));
 		}
+		*/
 	}
 }