correct support for shm to tensor creation

Author: carlosuc3m

src/main/java/io/bioimage/modelrunner/pytorch/javacpp/shm/ShmBuilder.java

@@ -25,6 +25,7 @@
 import io.bioimage.modelrunner.utils.CommonUtils;
 
 import java.io.IOException;
+import java.nio.ByteBuffer;
 import java.util.Arrays;
 
 import org.bytedeco.pytorch.Tensor;
@@ -80,7 +81,8 @@ private static void buildFromTensorByte(Tensor tensor, String memoryName) throws
 			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);
 		SharedMemoryArray shma = SharedMemoryArray.readOrCreate(memoryName, arrayShape, new UnsignedByteType(), false, true);
-    	tensor.data_ptr_byte().get(shma.getDataBufferNoHeader().array());
+        ByteBuffer buff = shma.getDataBufferNoHeader();
+        buff = tensor.asByteBuffer();
         if (PlatformDetection.isWindows()) shma.close();
     }
 
@@ -91,7 +93,8 @@ private static void buildFromTensorInt(Tensor tensor, String memoryName) throws
 			throw new IllegalArgumentException("Model output tensor with shape " + Arrays.toString(arrayShape) 
 					+ " is too big. Max number of elements per int output tensor supported: " + Integer.MAX_VALUE / 4);
 		SharedMemoryArray shma = SharedMemoryArray.readOrCreate(memoryName, arrayShape, new UnsignedByteType(), false, true);
-    	tensor.data_ptr_byte().get(shma.getDataBufferNoHeader().array());
+        ByteBuffer buff = shma.getDataBufferNoHeader();
+        buff = tensor.asByteBuffer();
         if (PlatformDetection.isWindows()) shma.close();
     }
 
@@ -102,7 +105,8 @@ private static void buildFromTensorFloat(Tensor tensor, String memoryName) throw
 			throw new IllegalArgumentException("Model output tensor with shape " + Arrays.toString(arrayShape) 
 					+ " is too big. Max number of elements per float output tensor supported: " + Integer.MAX_VALUE / 4);
 		SharedMemoryArray shma = SharedMemoryArray.readOrCreate(memoryName, arrayShape, new UnsignedByteType(), false, true);
-    	tensor.data_ptr_byte().get(shma.getDataBufferNoHeader().array());
+        ByteBuffer buff = shma.getDataBufferNoHeader();
+        buff = tensor.asByteBuffer();
         if (PlatformDetection.isWindows()) shma.close();
     }
 
@@ -113,7 +117,8 @@ private static void buildFromTensorDouble(Tensor tensor, String memoryName) thro
 			throw new IllegalArgumentException("Model output tensor with shape " + Arrays.toString(arrayShape) 
 					+ " is too big. Max number of elements per double output tensor supported: " + Integer.MAX_VALUE / 8);
 		SharedMemoryArray shma = SharedMemoryArray.readOrCreate(memoryName, arrayShape, new UnsignedByteType(), false, true);
-    	tensor.data_ptr_byte().get(shma.getDataBufferNoHeader().array());
+        ByteBuffer buff = shma.getDataBufferNoHeader();
+        buff = tensor.asByteBuffer();
         if (PlatformDetection.isWindows()) shma.close();
     }
 
@@ -124,7 +129,8 @@ private static void buildFromTensorLong(Tensor tensor, String memoryName) throws
 			throw new IllegalArgumentException("Model output tensor with shape " + Arrays.toString(arrayShape) 
 					+ " is too big. Max number of elements per long output tensor supported: " + Integer.MAX_VALUE / 8);
 		SharedMemoryArray shma = SharedMemoryArray.readOrCreate(memoryName, arrayShape, new UnsignedByteType(), false, true);
-    	tensor.data_ptr_byte().get(shma.getDataBufferNoHeader().array());
+        ByteBuffer buff = shma.getDataBufferNoHeader();
+        buff = tensor.asByteBuffer();
         if (PlatformDetection.isWindows()) shma.close();
     }
 }

src/main/java/io/bioimage/modelrunner/pytorch/javacpp/shm/TensorBuilder.java

@@ -85,7 +85,10 @@ private static Tensor buildByte(SharedMemoryArray shmArray)
 		if (!shmArray.isNumpyFormat())
 			throw new IllegalArgumentException("Shared memory arrays must be saved in numpy format.");
 		ByteBuffer buff = shmArray.getDataBufferNoHeader();
-		Tensor ndarray = Tensor.create(buff.array(), ogShape);
+		byte[] flat = new byte[buff.capacity()];
+		buff.get(flat);
+		buff.rewind();
+		Tensor ndarray = Tensor.create(flat, ogShape);
 		return ndarray;
 	}
 
@@ -99,7 +102,10 @@ private static Tensor buildInt(SharedMemoryArray shmaArray)
 		if (!shmaArray.isNumpyFormat())
 			throw new IllegalArgumentException("Shared memory arrays must be saved in numpy format.");
 		ByteBuffer buff = shmaArray.getDataBufferNoHeader();
-		Tensor ndarray = Tensor.create(buff.asIntBuffer().array(), ogShape);
+		int[] flat = new int[buff.capacity() / 4];
+		buff.asIntBuffer().get(flat);
+		buff.rewind();
+		Tensor ndarray = Tensor.create(flat, ogShape);
 		return ndarray;
 	}
 
@@ -113,7 +119,10 @@ private static org.bytedeco.pytorch.Tensor buildLong(SharedMemoryArray shmArray)
 		if (!shmArray.isNumpyFormat())
 			throw new IllegalArgumentException("Shared memory arrays must be saved in numpy format.");
 		ByteBuffer buff = shmArray.getDataBufferNoHeader();
-		Tensor ndarray = Tensor.create(buff.asLongBuffer().array(), ogShape);
+		long[] flat = new long[buff.capacity() / 8];
+		buff.asLongBuffer().get(flat);
+		buff.rewind();
+		Tensor ndarray = Tensor.create(flat, ogShape);
 		return ndarray;
 	}
 
@@ -127,7 +136,10 @@ private static org.bytedeco.pytorch.Tensor buildFloat(SharedMemoryArray shmArray
 		if (!shmArray.isNumpyFormat())
 			throw new IllegalArgumentException("Shared memory arrays must be saved in numpy format.");
 		ByteBuffer buff = shmArray.getDataBufferNoHeader();
-		Tensor ndarray = Tensor.create(buff.asFloatBuffer().array(), ogShape);
+		float[] flat = new float[buff.capacity() / 4];
+		buff.asFloatBuffer().get(flat);
+		buff.rewind();
+		Tensor ndarray = Tensor.create(flat, ogShape);
 		return ndarray;
 	}
 
@@ -141,7 +153,10 @@ private static org.bytedeco.pytorch.Tensor buildDouble(SharedMemoryArray shmArra
 		if (!shmArray.isNumpyFormat())
 			throw new IllegalArgumentException("Shared memory arrays must be saved in numpy format.");
 		ByteBuffer buff = shmArray.getDataBufferNoHeader();
-		Tensor ndarray = Tensor.create(buff.asDoubleBuffer().array(), ogShape);
+		double[] flat = new double[buff.capacity() / 8];
+		buff.asDoubleBuffer().get(flat);
+		buff.rewind();
+		Tensor ndarray = Tensor.create(flat, ogShape);
 		return ndarray;
 	}
 }