improve javadoc

bioimage-io · Oct 1, 2024 · f2b7ce3 · f2b7ce3
1 parent 4dfa7d3
commit f2b7ce3
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diff --git a/src/main/java/io/bioimage/modelrunner/pytorch/javacpp/JavaWorker.java b/src/main/java/io/bioimage/modelrunner/pytorch/javacpp/JavaWorker.java
@@ -22,6 +22,12 @@ public class JavaWorker {
 
 	private boolean cancelRequested = false;
 
+	/**
+	 * Method in the child process that is in charge of keeping the process open and calling the model load,
+	 * model inference and model closing
+	 * @param args
+	 * 	args of the parent process
+	 */
 	public static void main(String[] args) {
 
     	try(Scanner scanner = new Scanner(System.in)){

diff --git a/src/main/java/io/bioimage/modelrunner/pytorch/javacpp/tensor/ImgLib2Builder.java b/src/main/java/io/bioimage/modelrunner/pytorch/javacpp/tensor/ImgLib2Builder.java
@@ -25,17 +25,12 @@
 
 import io.bioimage.modelrunner.tensor.Utils;
 import io.bioimage.modelrunner.utils.CommonUtils;
-import io.bioimage.modelrunner.utils.IndexingUtils;
-import net.imglib2.Cursor;
 import net.imglib2.RandomAccessibleInterval;
-import net.imglib2.img.Img;
-import net.imglib2.img.array.ArrayImgFactory;
 import net.imglib2.img.array.ArrayImgs;
 import net.imglib2.type.Type;
 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.UnsignedByteType;
 import net.imglib2.type.numeric.real.DoubleType;
 import net.imglib2.type.numeric.real.FloatType;
 
@@ -78,37 +73,7 @@ public static <T extends Type<T>> RandomAccessibleInterval<T> build(org.bytedeco
     	}
     }
 
-	/**
-	 * Builds a {@link RandomAccessibleInterval} from a unsigned byte-typed {@link org.bytedeco.pytorch.Tensor}.
-	 * 
-	 * @param tensor 
-	 * 	The {@link org.bytedeco.pytorch.Tensor} data is read from.
-	 * @return The {@link RandomAccessibleInterval} built from the tensor of type {@link UnsignedByteType}.
-	 */
-    public static RandomAccessibleInterval<UnsignedByteType> buildFromTensorUByte(org.bytedeco.pytorch.Tensor tensor) {
-    	long[] arrayShape = tensor.shape();
-		if (CommonUtils.int32Overflows(arrayShape, 1))
-			throw new IllegalArgumentException("Model output tensor with shape " + Arrays.toString(arrayShape) 
-					+ " is too big. Max number of elements per ubyte output tensor supported: " + Integer.MAX_VALUE / 1);
-		long[] tensorShape = new long[arrayShape.length];
-		for (int i = 0; i < arrayShape.length; i ++) tensorShape[i] = arrayShape[arrayShape.length - 1 - i];
-		long flatSize = 1;
-    	for (long l : tensorShape) {flatSize *= l;}
-    	byte[] flatArr = new byte[(int) flatSize];
-    	tensor.data_ptr_byte().get(flatArr);
-		RandomAccessibleInterval<UnsignedByteType> rai = ArrayImgs.unsignedBytes(flatArr, tensorShape);
-		return Utils.transpose(rai);
-	}
-
-
-	/**
-	 * Builds a {@link RandomAccessibleInterval} from a signed byte-typed {@link org.bytedeco.pytorch.Tensor}.
-	 * 
-	 * @param tensor 
-	 * 	The {@link org.bytedeco.pytorch.Tensor} data is read from.
-	 * @return The {@link RandomAccessibleInterval} built from the tensor of type {@link ByteType}.
-	 */
-    public static RandomAccessibleInterval<ByteType> buildFromTensorByte(org.bytedeco.pytorch.Tensor tensor)
+    private static RandomAccessibleInterval<ByteType> buildFromTensorByte(org.bytedeco.pytorch.Tensor tensor)
     {
     	long[] arrayShape = tensor.shape();
 		if (CommonUtils.int32Overflows(arrayShape, 1))
@@ -124,14 +89,7 @@ public static RandomAccessibleInterval<ByteType> buildFromTensorByte(org.bytedec
 		return Utils.transpose(rai);
 	}
 
-	/**
-	 * Builds a {@link RandomAccessibleInterval} from a signed integer-typed {@link org.bytedeco.pytorch.Tensor}.
-	 * 
-	 * @param tensor 
-	 * 	The {@link org.bytedeco.pytorch.Tensor} data is read from.
-	 * @return The {@link RandomAccessibleInterval} built from the tensor of type {@link IntType}.
-	 */
-    public static RandomAccessibleInterval<IntType> buildFromTensorInt(org.bytedeco.pytorch.Tensor tensor)
+    private static RandomAccessibleInterval<IntType> buildFromTensorInt(org.bytedeco.pytorch.Tensor tensor)
     {
     	long[] arrayShape = tensor.shape();
 		if (CommonUtils.int32Overflows(arrayShape, 4))
@@ -147,14 +105,7 @@ public static RandomAccessibleInterval<IntType> buildFromTensorInt(org.bytedeco.
 		return Utils.transpose(rai);
     }
 
-	/**
-	 * Builds a {@link RandomAccessibleInterval} from a signed float-typed {@link org.bytedeco.pytorch.Tensor}.
-	 * 
-	 * @param tensor 
-	 * 	The {@link org.bytedeco.pytorch.Tensor} data is read from.
-	 * @return The {@link RandomAccessibleInterval} built from the tensor of type {@link FloatType}.
-	 */
-    public static RandomAccessibleInterval<FloatType> buildFromTensorFloat(org.bytedeco.pytorch.Tensor tensor)
+    private static RandomAccessibleInterval<FloatType> buildFromTensorFloat(org.bytedeco.pytorch.Tensor tensor)
     {
     	long[] arrayShape = tensor.shape();
 		if (CommonUtils.int32Overflows(arrayShape, 4))
@@ -170,14 +121,7 @@ public static RandomAccessibleInterval<FloatType> buildFromTensorFloat(org.byted
 		return Utils.transpose(rai);
     }
 
-	/**
-	 * Builds a {@link RandomAccessibleInterval} from a signed double-typed {@link org.bytedeco.pytorch.Tensor}.
-	 * 
-	 * @param tensor 
-	 * 	The {@link org.bytedeco.pytorch.Tensor} data is read from.
-	 * @return The {@link RandomAccessibleInterval} built from the tensor of type {@link DoubleType}.
-	 */
-    public static RandomAccessibleInterval<DoubleType> buildFromTensorDouble(org.bytedeco.pytorch.Tensor tensor)
+    private static RandomAccessibleInterval<DoubleType> buildFromTensorDouble(org.bytedeco.pytorch.Tensor tensor)
     {
     	long[] arrayShape = tensor.shape();
 		if (CommonUtils.int32Overflows(arrayShape, 8))
@@ -193,14 +137,7 @@ public static RandomAccessibleInterval<DoubleType> buildFromTensorDouble(org.byt
 		return Utils.transpose(rai);
     }
 
-	/**
-	 * Builds a {@link RandomAccessibleInterval} from a signed long-typed {@link org.bytedeco.pytorch.Tensor}.
-	 * 
-	 * @param tensor 
-	 * 	The {@link org.bytedeco.pytorch.Tensor} data is read from.
-	 * @return The {@link RandomAccessibleInterval} built from the tensor of type {@link LongType}.
-	 */
-    public static RandomAccessibleInterval<LongType> buildFromTensorLong(org.bytedeco.pytorch.Tensor tensor)
+    private static RandomAccessibleInterval<LongType> buildFromTensorLong(org.bytedeco.pytorch.Tensor tensor)
     {
     	long[] arrayShape = tensor.shape();
 		if (CommonUtils.int32Overflows(arrayShape, 8))

diff --git a/src/main/java/io/bioimage/modelrunner/pytorch/javacpp/tensor/JavaCPPTensorBuilder.java b/src/main/java/io/bioimage/modelrunner/pytorch/javacpp/tensor/JavaCPPTensorBuilder.java
@@ -88,14 +88,6 @@ public static < T extends RealType< T > & NativeType< T > > org.bytedeco.pytorch
     	}
     }
 
-	/**
-	 * Builds a {@link org.bytedeco.pytorch.Tensor} from a signed byte-typed
-	 * {@link RandomAccessibleInterval}.
-	 * 
-	 * @param tensor 
-	 * 	the {@link RandomAccessibleInterval} that will be copied into an {@link org.bytedeco.pytorch.Tensor}
-	 * @return The {@link org.bytedeco.pytorch.Tensor} built from the tensor of type {@link ByteType}.
-	 */
     private static org.bytedeco.pytorch.Tensor buildFromTensorByte(RandomAccessibleInterval<ByteType> tensor)
     {
 		long[] ogShape = tensor.dimensionsAsLongArray();
@@ -121,14 +113,6 @@ private static org.bytedeco.pytorch.Tensor buildFromTensorByte(RandomAccessibleI
 	 	return ndarray;
 	}
 
-	/**
-	 * Builds a {@link org.bytedeco.pytorch.Tensor} from a signed integer-typed
-	 * {@link RandomAccessibleInterval}.
-	 * 
-	 * @param tensor 
-	 * 	the {@link RandomAccessibleInterval} that will be copied into an {@link org.bytedeco.pytorch.Tensor}
-	 * @return The {@link org.bytedeco.pytorch.Tensor} built from the tensor of type {@link IntType}.
-	 */
     private static org.bytedeco.pytorch.Tensor buildFromTensorInt(RandomAccessibleInterval<IntType> tensor)
     {
 		long[] ogShape = tensor.dimensionsAsLongArray();
@@ -154,14 +138,6 @@ private static org.bytedeco.pytorch.Tensor buildFromTensorInt(RandomAccessibleIn
 	 	return ndarray;
     }
 
-	/**
-	 * Builds a {@link org.bytedeco.pytorch.Tensor} from a signed float-typed
-	 * {@link RandomAccessibleInterval}.
-	 * 
-	 * @param tensor 
-	 * 	the {@link RandomAccessibleInterval} that will be copied into an {@link org.bytedeco.pytorch.Tensor}
-	 * @return The {@link org.bytedeco.pytorch.Tensor} built from the tensor of type {@link FloatType}.
-	 */
     private static org.bytedeco.pytorch.Tensor buildFromTensorFloat(RandomAccessibleInterval<FloatType> tensor)
     {
 		long[] ogShape = tensor.dimensionsAsLongArray();
@@ -187,14 +163,6 @@ private static org.bytedeco.pytorch.Tensor buildFromTensorFloat(RandomAccessible
 	 	return ndarray;
     }
 
-	/**
-	 * Builds a {@link org.bytedeco.pytorch.Tensor} from a signed double-typed
-	 * {@link RandomAccessibleInterval}.
-	 * 
-	 * @param tensor 
-	 * 	the {@link RandomAccessibleInterval} that will be copied into an {@link NDArray}
-	 * @return The {@link org.bytedeco.pytorch.Tensor} built from the tensor of type {@link DoubleType}.
-	 */
     private static org.bytedeco.pytorch.Tensor buildFromTensorDouble(RandomAccessibleInterval<DoubleType> tensor)
     {
 		long[] ogShape = tensor.dimensionsAsLongArray();