increase robustness creating pytorch tensors

Author: carlosuc3m

src/main/java/io/bioimage/modelrunner/pytorch/javacpp/tensor/JavaCPPTensorBuilder.java

@@ -20,16 +20,22 @@
  */
 package io.bioimage.modelrunner.pytorch.javacpp.tensor;
 
+import java.nio.ByteBuffer;
+import java.util.Arrays;
+
 import io.bioimage.modelrunner.tensor.Tensor;
 import io.bioimage.modelrunner.tensor.Utils;
+import net.imglib2.Cursor;
 import net.imglib2.RandomAccessibleInterval;
 import net.imglib2.blocks.PrimitiveBlocks;
 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.UnsignedByteType;
 import net.imglib2.type.numeric.real.DoubleType;
 import net.imglib2.type.numeric.real.FloatType;
 import net.imglib2.util.Util;
+import net.imglib2.view.Views;
 
 /**
  * Class that manages the creation of JAvaCPP Pytorch tensors  
@@ -101,16 +107,24 @@ public static <T extends Type<T>> org.bytedeco.pytorch.Tensor build(RandomAccess
     private static org.bytedeco.pytorch.Tensor buildFromTensorByte(RandomAccessibleInterval<ByteType> tensor)
     {
 		long[] ogShape = tensor.dimensionsAsLongArray();
+		if (CommonUtils.int32Overflows(ogShape))
+			throw new IllegalArgumentException("Provided tensor with shape " + Arrays.toString(ogShape) 
+								+ " is too big. Max number of elements per tensor supported: " + Integer.MAX_VALUE);
 		tensor = Utils.transpose(tensor);
-		PrimitiveBlocks< ByteType > blocks = PrimitiveBlocks.of( tensor );
 		long[] tensorShape = tensor.dimensionsAsLongArray();
 		int size = 1;
 		for (long ll : tensorShape) size *= ll;
 		final byte[] flatArr = new byte[size];
 		int[] sArr = new int[tensorShape.length];
 		for (int i = 0; i < sArr.length; i ++)
 			sArr[i] = (int) tensorShape[i];
-		blocks.copy( tensor.minAsLongArray(), flatArr, sArr );
+
+		Cursor<ByteType> cursor = Views.flatIterable(tensor).cursor();
+		int i = 0;
+		while (cursor.hasNext()) {
+			cursor.fwd();
+			flatArr[i ++] = cursor.get().get();
+		}
 	 	org.bytedeco.pytorch.Tensor ndarray = org.bytedeco.pytorch.Tensor.create(flatArr, ogShape);
 	 	return ndarray;
 	}
@@ -126,17 +140,25 @@ private static org.bytedeco.pytorch.Tensor buildFromTensorByte(RandomAccessibleI
     private static org.bytedeco.pytorch.Tensor buildFromTensorInt(RandomAccessibleInterval<IntType> tensor)
     {
 		long[] ogShape = tensor.dimensionsAsLongArray();
+		if (CommonUtils.int32Overflows(ogShape))
+			throw new IllegalArgumentException("Provided tensor with shape " + Arrays.toString(ogShape) 
+								+ " is too big. Max number of elements per tensor supported: " + Integer.MAX_VALUE);
 		tensor = Utils.transpose(tensor);
-		PrimitiveBlocks< IntType > blocks = PrimitiveBlocks.of( tensor );
 		long[] tensorShape = tensor.dimensionsAsLongArray();
 		int size = 1;
 		for (long ll : tensorShape) size *= ll;
 		final int[] flatArr = new int[size];
 		int[] sArr = new int[tensorShape.length];
 		for (int i = 0; i < sArr.length; i ++)
 			sArr[i] = (int) tensorShape[i];
-		blocks.copy( tensor.minAsLongArray(), flatArr, sArr );
-		org.bytedeco.pytorch.Tensor ndarray = org.bytedeco.pytorch.Tensor.create(flatArr, ogShape);
+
+		Cursor<IntType> cursor = Views.flatIterable(tensor).cursor();
+		int i = 0;
+		while (cursor.hasNext()) {
+			cursor.fwd();
+			flatArr[i ++] = cursor.get().get();
+		}
+	 	org.bytedeco.pytorch.Tensor ndarray = org.bytedeco.pytorch.Tensor.create(flatArr, ogShape);
 	 	return ndarray;
     }
 
@@ -151,17 +173,25 @@ private static org.bytedeco.pytorch.Tensor buildFromTensorInt(RandomAccessibleIn
     private static org.bytedeco.pytorch.Tensor buildFromTensorFloat(RandomAccessibleInterval<FloatType> tensor)
     {
 		long[] ogShape = tensor.dimensionsAsLongArray();
+		if (CommonUtils.int32Overflows(ogShape))
+			throw new IllegalArgumentException("Provided tensor with shape " + Arrays.toString(ogShape) 
+								+ " is too big. Max number of elements per tensor supported: " + Integer.MAX_VALUE);
 		tensor = Utils.transpose(tensor);
-		PrimitiveBlocks< FloatType > blocks = PrimitiveBlocks.of( tensor );
 		long[] tensorShape = tensor.dimensionsAsLongArray();
 		int size = 1;
 		for (long ll : tensorShape) size *= ll;
 		final float[] flatArr = new float[size];
 		int[] sArr = new int[tensorShape.length];
 		for (int i = 0; i < sArr.length; i ++)
 			sArr[i] = (int) tensorShape[i];
-		blocks.copy( tensor.minAsLongArray(), flatArr, sArr );
-		org.bytedeco.pytorch.Tensor ndarray = org.bytedeco.pytorch.Tensor.create(flatArr, ogShape);
+
+		Cursor<FloatType> cursor = Views.flatIterable(tensor).cursor();
+		int i = 0;
+		while (cursor.hasNext()) {
+			cursor.fwd();
+			flatArr[i ++] = cursor.get().get();
+		}
+	 	org.bytedeco.pytorch.Tensor ndarray = org.bytedeco.pytorch.Tensor.create(flatArr, ogShape);
 	 	return ndarray;
     }
 
@@ -176,17 +206,25 @@ private static org.bytedeco.pytorch.Tensor buildFromTensorFloat(RandomAccessible
     private static org.bytedeco.pytorch.Tensor buildFromTensorDouble(RandomAccessibleInterval<DoubleType> tensor)
     {
 		long[] ogShape = tensor.dimensionsAsLongArray();
+		if (CommonUtils.int32Overflows(ogShape))
+			throw new IllegalArgumentException("Provided tensor with shape " + Arrays.toString(ogShape) 
+								+ " is too big. Max number of elements per tensor supported: " + Integer.MAX_VALUE);
 		tensor = Utils.transpose(tensor);
-		PrimitiveBlocks< DoubleType > blocks = PrimitiveBlocks.of( tensor );
 		long[] tensorShape = tensor.dimensionsAsLongArray();
 		int size = 1;
 		for (long ll : tensorShape) size *= ll;
 		final double[] flatArr = new double[size];
 		int[] sArr = new int[tensorShape.length];
 		for (int i = 0; i < sArr.length; i ++)
 			sArr[i] = (int) tensorShape[i];
-		blocks.copy( tensor.minAsLongArray(), flatArr, sArr );
-		org.bytedeco.pytorch.Tensor ndarray = org.bytedeco.pytorch.Tensor.create(flatArr, ogShape);
+
+		Cursor<DoubleType> cursor = Views.flatIterable(tensor).cursor();
+		int i = 0;
+		while (cursor.hasNext()) {
+			cursor.fwd();
+			flatArr[i ++] = cursor.get().get();
+		}
+	 	org.bytedeco.pytorch.Tensor ndarray = org.bytedeco.pytorch.Tensor.create(flatArr, ogShape);
 	 	return ndarray;
     }
 }