correct gigabug creating tensors

Author: carlosuc3m

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

@@ -29,7 +29,11 @@
 
 import org.bytedeco.pytorch.Tensor;
 
-import net.imglib2.type.numeric.integer.UnsignedByteType;
+import net.imglib2.type.numeric.integer.IntType;
+import net.imglib2.type.numeric.integer.LongType;
+import net.imglib2.type.numeric.integer.ByteType;
+import net.imglib2.type.numeric.real.DoubleType;
+import net.imglib2.type.numeric.real.FloatType;
 
 /**
  * A utility class that converts {@link Tensor}s into {@link SharedMemoryArray}s for
@@ -79,7 +83,7 @@ private static void buildFromTensorByte(Tensor tensor, String memoryName) throws
 		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);
-		SharedMemoryArray shma = SharedMemoryArray.readOrCreate(memoryName, arrayShape, new UnsignedByteType(), false, true);
+		SharedMemoryArray shma = SharedMemoryArray.readOrCreate(memoryName, arrayShape, new ByteType(), false, true);
         shma.getDataBufferNoHeader().put(tensor.asByteBuffer());
         if (PlatformDetection.isWindows()) shma.close();
     }
@@ -90,7 +94,7 @@ private static void buildFromTensorInt(Tensor tensor, String memoryName) throws
 		if (CommonUtils.int32Overflows(arrayShape, 4))
 			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);
+		SharedMemoryArray shma = SharedMemoryArray.readOrCreate(memoryName, arrayShape, new IntType(), false, true);
         shma.getDataBufferNoHeader().put(tensor.asByteBuffer());
         if (PlatformDetection.isWindows()) shma.close();
     }
@@ -101,7 +105,7 @@ private static void buildFromTensorFloat(Tensor tensor, String memoryName) throw
 		if (CommonUtils.int32Overflows(arrayShape, 4))
 			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);
+		SharedMemoryArray shma = SharedMemoryArray.readOrCreate(memoryName, arrayShape, new FloatType(), false, true);
         shma.getDataBufferNoHeader().put(tensor.asByteBuffer());
         if (PlatformDetection.isWindows()) shma.close();
     }
@@ -112,7 +116,7 @@ private static void buildFromTensorDouble(Tensor tensor, String memoryName) thro
 		if (CommonUtils.int32Overflows(arrayShape, 8))
 			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);
+		SharedMemoryArray shma = SharedMemoryArray.readOrCreate(memoryName, arrayShape, new DoubleType(), false, true);
         shma.getDataBufferNoHeader().put(tensor.asByteBuffer());
         if (PlatformDetection.isWindows()) shma.close();
     }
@@ -123,7 +127,7 @@ private static void buildFromTensorLong(Tensor tensor, String memoryName) throws
 		if (CommonUtils.int32Overflows(arrayShape, 8))
 			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);
+		SharedMemoryArray shma = SharedMemoryArray.readOrCreate(memoryName, arrayShape, new LongType(), false, true);
         shma.getDataBufferNoHeader().put(tensor.asByteBuffer());
         if (PlatformDetection.isWindows()) shma.close();
     }