improve interprocessing communication

Author: carlosuc3m

src/main/java/io/bioimage/modelrunner/tensorflow/v2/api050/shm/ShmBuilder.java

@@ -28,7 +28,6 @@
 import java.nio.ByteBuffer;
 import java.util.Arrays;
 
-import org.tensorflow.Tensor;
 import org.tensorflow.types.TFloat32;
 import org.tensorflow.types.TFloat64;
 import org.tensorflow.types.TInt32;
@@ -101,13 +100,8 @@ private static void buildFromTensorUByte(TUint8 tensor, String memoryName) throw
 			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.asRawTensor().data().read(flatArr, flatArr.length - totalSize, totalSize);
-        shma.setBuffer(ByteBuffer.wrap(flatArr));
+        ByteBuffer buff = shma.getDataBufferNoHeader();
+        tensor.asRawTensor().data().read(buff.array(), 0, buff.capacity());
         if (PlatformDetection.isWindows()) shma.close();
     }
 
@@ -119,13 +113,8 @@ private static void buildFromTensorInt(TInt32 tensor, String memoryName) throws
 					+ " 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.asRawTensor().data().read(flatArr, flatArr.length - totalSize, totalSize);
-        shma.setBuffer(ByteBuffer.wrap(flatArr));
+        ByteBuffer buff = shma.getDataBufferNoHeader();
+        tensor.asRawTensor().data().read(buff.array(), 0, buff.capacity());
         if (PlatformDetection.isWindows()) shma.close();
     }
 
@@ -137,13 +126,8 @@ private static void buildFromTensorFloat(TFloat32 tensor, String memoryName) thr
 					+ " 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.asRawTensor().data().read(flatArr, flatArr.length - totalSize, totalSize);
-        shma.setBuffer(ByteBuffer.wrap(flatArr));
+        ByteBuffer buff = shma.getDataBufferNoHeader();
+        tensor.asRawTensor().data().read(buff.array(), 0, buff.capacity());
         if (PlatformDetection.isWindows()) shma.close();
     }
 
@@ -155,13 +139,8 @@ private static void buildFromTensorDouble(TFloat64 tensor, String memoryName) th
 					+ " 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.asRawTensor().data().read(flatArr, flatArr.length - totalSize, totalSize);
-        shma.setBuffer(ByteBuffer.wrap(flatArr));
+        ByteBuffer buff = shma.getDataBufferNoHeader();
+        tensor.asRawTensor().data().read(buff.array(), 0, buff.capacity());
         if (PlatformDetection.isWindows()) shma.close();
     }
 
@@ -174,13 +153,8 @@ private static void buildFromTensorLong(TInt64 tensor, String memoryName) throws
 
 
         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.asRawTensor().data().read(flatArr, flatArr.length - totalSize, totalSize);
-        shma.setBuffer(ByteBuffer.wrap(flatArr));
+        ByteBuffer buff = shma.getDataBufferNoHeader();
+        tensor.asRawTensor().data().read(buff.array(), 0, buff.capacity());
         if (PlatformDetection.isWindows()) shma.close();
     }
 }

src/main/java/io/bioimage/modelrunner/tensorflow/v2/api050/shm/TensorBuilder.java

@@ -118,9 +118,7 @@ private static 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);
 		TInt32 ndarray = TInt32.tensorOf(Shape.of(ogShape),
 			dataBuffer);
 		return ndarray;
@@ -137,9 +135,7 @@ private static 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);
 		TInt64 ndarray = TInt64.tensorOf(Shape.of(ogShape),
 			dataBuffer);
 		return ndarray;
@@ -156,9 +152,7 @@ private static 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);
 		TFloat32 ndarray = TFloat32.tensorOf(Shape.of(ogShape), dataBuffer);
 		return ndarray;
 	}
@@ -173,10 +167,8 @@ private static TFloat64 buildDouble(SharedMemoryArray tensor)
 		if (!tensor.isNumpyFormat())
 			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);
+		DoubleBuffer floatBuff = buff.asDoubleBuffer();
+		DoubleDataBuffer dataBuffer = RawDataBufferFactory.create(floatBuff.array(), false);
 		TFloat64 ndarray = TFloat64.tensorOf(Shape.of(ogShape), dataBuffer);
 		return ndarray;
 	}