correct errors in interprocessing communication

Author: carlosuc3m

Diff for: src/main/java/io/bioimage/modelrunner/tensorflow/v2/api020/shm/ShmBuilder.java

@@ -98,13 +98,8 @@ private static void buildFromTensorUByte(Tensor<TUint8> tensor, String memoryNam
 			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);
-        ByteBuffer buff = shma.getDataBuffer();
-        int totalSize = 1;
-		for (long i : arrayShape) {totalSize *= i;}
-    	byte[] flatArr = new byte[buff.capacity()];
-    	buff.get(flatArr);
-        tensor.rawData().read(flatArr, flatArr.length - totalSize, totalSize);
-        shma.setBuffer(ByteBuffer.wrap(flatArr));
+        ByteBuffer buff1 = shma.getDataBufferNoHeader();
+        tensor.rawData().read(buff1.array(), 0, buff1.capacity());
         if (PlatformDetection.isWindows()) shma.close();
     }
 
@@ -116,13 +111,8 @@ private static void buildFromTensorInt(Tensor<TInt32> tensor, String memoryName)
 					+ " is too big. Max number of elements per int output tensor supported: " + Integer.MAX_VALUE / 4);
 
         SharedMemoryArray shma = SharedMemoryArray.readOrCreate(memoryName, arrayShape, new IntType(), false, true);
-        ByteBuffer buff = shma.getDataBuffer();
-        int totalSize = 4;
-		for (long i : arrayShape) {totalSize *= i;}
-    	byte[] flatArr = new byte[buff.capacity()];
-    	buff.get(flatArr);
-        tensor.rawData().read(flatArr, flatArr.length - totalSize, totalSize);
-        shma.setBuffer(ByteBuffer.wrap(flatArr));
+        ByteBuffer buff1 = shma.getDataBufferNoHeader();
+        tensor.rawData().read(buff1.array(), 0, buff1.capacity());
         if (PlatformDetection.isWindows()) shma.close();
     }
 
@@ -134,13 +124,8 @@ private static void buildFromTensorFloat(Tensor<TFloat32> tensor, String memoryN
 					+ " is too big. Max number of elements per float output tensor supported: " + Integer.MAX_VALUE / 4);
 
         SharedMemoryArray shma = SharedMemoryArray.readOrCreate(memoryName, arrayShape, new FloatType(), false, true);
-        ByteBuffer buff = shma.getDataBuffer();
-        int totalSize = 4;
-		for (long i : arrayShape) {totalSize *= i;}
-    	byte[] flatArr = new byte[buff.capacity()];
-    	buff.get(flatArr);
-        tensor.rawData().read(flatArr, flatArr.length - totalSize, totalSize);
-        shma.setBuffer(ByteBuffer.wrap(flatArr));
+        ByteBuffer buff1 = shma.getDataBufferNoHeader();
+        tensor.rawData().read(buff1.array(), 0, buff1.capacity());
         if (PlatformDetection.isWindows()) shma.close();
     }
 
@@ -152,13 +137,8 @@ private static void buildFromTensorDouble(Tensor<TFloat64> tensor, String memory
 					+ " is too big. Max number of elements per double output tensor supported: " + Integer.MAX_VALUE / 8);
 
         SharedMemoryArray shma = SharedMemoryArray.readOrCreate(memoryName, arrayShape, new DoubleType(), false, true);
-        ByteBuffer buff = shma.getDataBuffer();
-        int totalSize = 8;
-		for (long i : arrayShape) {totalSize *= i;}
-    	byte[] flatArr = new byte[buff.capacity()];
-    	buff.get(flatArr);
-        tensor.rawData().read(flatArr, flatArr.length - totalSize, totalSize);
-        shma.setBuffer(ByteBuffer.wrap(flatArr));
+        ByteBuffer buff1 = shma.getDataBufferNoHeader();
+        tensor.rawData().read(buff1.array(), 0, buff1.capacity());
         if (PlatformDetection.isWindows()) shma.close();
     }
 
@@ -171,13 +151,8 @@ private static void buildFromTensorLong(Tensor<TInt64> tensor, String memoryName
 
 
         SharedMemoryArray shma = SharedMemoryArray.readOrCreate(memoryName, arrayShape, new LongType(), false, true);
-        ByteBuffer buff = shma.getDataBuffer();
-        int totalSize = 8;
-		for (long i : arrayShape) {totalSize *= i;}
-    	byte[] flatArr = new byte[buff.capacity()];
-    	buff.get(flatArr);
-        tensor.rawData().read(flatArr, flatArr.length - totalSize, totalSize);
-        shma.setBuffer(ByteBuffer.wrap(flatArr));
+        ByteBuffer buff1 = shma.getDataBufferNoHeader();
+        tensor.rawData().read(buff1.array(), 0, buff1.capacity());
         if (PlatformDetection.isWindows()) shma.close();
     }
 }

Diff for: src/main/java/io/bioimage/modelrunner/tensorflow/v2/api020/shm/TensorBuilder.java

@@ -23,13 +23,6 @@
 
 import io.bioimage.modelrunner.tensor.shm.SharedMemoryArray;
 import io.bioimage.modelrunner.utils.CommonUtils;
-import net.imglib2.RandomAccessibleInterval;
-import net.imglib2.img.Img;
-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;
 import net.imglib2.util.Cast;
 
 import java.nio.ByteBuffer;
@@ -125,9 +118,7 @@ private static Tensor<TInt32> buildInt(SharedMemoryArray tensor)
 			throw new IllegalArgumentException("Shared memory arrays must be saved in numpy format.");
 		ByteBuffer buff = tensor.getDataBufferNoHeader();
 		IntBuffer intBuff = buff.asIntBuffer();
-		int[] intArray = new int[intBuff.capacity()];
-		intBuff.get(intArray);
-		IntDataBuffer dataBuffer = RawDataBufferFactory.create(intArray, false);
+		IntDataBuffer dataBuffer = RawDataBufferFactory.create(intBuff.array(), false);
 		Tensor<TInt32> ndarray = TInt32.tensorOf(Shape.of(ogShape), dataBuffer);
 		return ndarray;
 	}
@@ -143,9 +134,7 @@ private static Tensor<TInt64> buildLong(SharedMemoryArray tensor)
 			throw new IllegalArgumentException("Shared memory arrays must be saved in numpy format.");
 		ByteBuffer buff = tensor.getDataBufferNoHeader();
 		LongBuffer longBuff = buff.asLongBuffer();
-		long[] longArray = new long[longBuff.capacity()];
-		longBuff.get(longArray);
-		LongDataBuffer dataBuffer = RawDataBufferFactory.create(longArray, false);
+		LongDataBuffer dataBuffer = RawDataBufferFactory.create(longBuff.array(), false);
 		Tensor<TInt64> ndarray = TInt64.tensorOf(Shape.of(ogShape), dataBuffer);
 		return ndarray;
 	}
@@ -161,9 +150,7 @@ private static Tensor<TFloat32> buildFloat(SharedMemoryArray tensor)
 			throw new IllegalArgumentException("Shared memory arrays must be saved in numpy format.");
 		ByteBuffer buff = tensor.getDataBufferNoHeader();
 		FloatBuffer floatBuff = buff.asFloatBuffer();
-		float[] floatArray = new float[floatBuff.capacity()];
-		floatBuff.get(floatArray);
-		FloatDataBuffer dataBuffer = RawDataBufferFactory.create(floatArray, false);
+		FloatDataBuffer dataBuffer = RawDataBufferFactory.create(floatBuff.array(), false);
 		Tensor<TFloat32> ndarray = TFloat32.tensorOf(Shape.of(ogShape), dataBuffer);
 		return ndarray;
 	}
@@ -179,9 +166,7 @@ private static Tensor<TFloat64> buildDouble(SharedMemoryArray tensor)
 			throw new IllegalArgumentException("Shared memory arrays must be saved in numpy format.");
 		ByteBuffer buff = tensor.getDataBufferNoHeader();
 		DoubleBuffer doubleBuff = buff.asDoubleBuffer();
-		double[] doubleArray = new double[doubleBuff.capacity()];
-		doubleBuff.get(doubleArray);
-		DoubleDataBuffer dataBuffer = RawDataBufferFactory.create(doubleArray, false);
+		DoubleDataBuffer dataBuffer = RawDataBufferFactory.create(doubleBuff.array(), false);
 		Tensor<TFloat64> ndarray = TFloat64.tensorOf(Shape.of(ogShape), dataBuffer);
 		return ndarray;
 	}