stable working version

Author: carlosuc3m

src/main/java/io/bioimage/modelrunner/pytorch/javacpp/PytorchJavaCPPInterface.java

@@ -253,18 +253,15 @@ protected void runFromShmas(List<String> inputs, List<String> outputs) throws IO
         IValue output = model.forward(inputsVector);
         TensorVector outputTensorVector = null;
         if (output.isTensorList()) {
-        	System.out.println("entered 1");
         	outputTensorVector = output.toTensorVector();
         } else {
-        	System.out.println("entered 2");
         	outputTensorVector = new TensorVector();
         	outputTensorVector.put(output.toTensor());
         }
 
 		// Fill the agnostic output tensors list with data from the inference result
 		int c = 0;
 		for (String ee : outputs) {
-			System.out.println(ee);
 			Map<String, Object> decoded = Types.decode(ee);
 			ShmBuilder.build(outputTensorVector.get(c ++), (String) decoded.get(MEM_NAME_KEY));
 		}

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

@@ -20,21 +20,22 @@
  */
 package io.bioimage.modelrunner.pytorch.javacpp.shm;
 
-import io.bioimage.modelrunner.pytorch.javacpp.tensor.ImgLib2Builder;
 import io.bioimage.modelrunner.system.PlatformDetection;
 import io.bioimage.modelrunner.tensor.shm.SharedMemoryArray;
 import io.bioimage.modelrunner.utils.CommonUtils;
 
 import java.io.IOException;
 import java.nio.ByteBuffer;
+import java.nio.DoubleBuffer;
 import java.nio.FloatBuffer;
+import java.nio.IntBuffer;
+import java.nio.LongBuffer;
 import java.util.Arrays;
 
 import org.bytedeco.pytorch.Tensor;
 
 import net.imglib2.type.numeric.integer.IntType;
 import net.imglib2.type.numeric.integer.LongType;
-import net.imglib2.RandomAccessibleInterval;
 import net.imglib2.type.numeric.integer.ByteType;
 import net.imglib2.type.numeric.real.DoubleType;
 import net.imglib2.type.numeric.real.FloatType;
@@ -88,7 +89,14 @@ 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 ByteType(), false, true);
-        shma.getDataBufferNoHeader().put(tensor.asByteBuffer());
+		long flatSize = 1;
+    	for (long l : arrayShape) {flatSize *= l;}
+    	byte[] flat = new byte[(int) flatSize];
+    	ByteBuffer byteBuffer = ByteBuffer.allocateDirect((int) (flatSize));
+    	tensor.data_ptr_byte().get(flat);
+    	byteBuffer.put(flat);
+    	byteBuffer.rewind();
+        shma.getDataBufferNoHeader().put(byteBuffer);
         if (PlatformDetection.isWindows()) shma.close();
     }
 
@@ -99,8 +107,15 @@ 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 IntType(), false, true);
-		RandomAccessibleInterval<?> rai = shma.getSharedRAI();
-		rai = ImgLib2Builder.build(tensor);
+		long flatSize = 1;
+    	for (long l : arrayShape) {flatSize *= l;}
+    	int[] flat = new int[(int) flatSize];
+    	ByteBuffer byteBuffer = ByteBuffer.allocateDirect((int) (flatSize * Integer.BYTES));
+    	IntBuffer floatBuffer = byteBuffer.asIntBuffer();
+    	tensor.data_ptr_int().get(flat);
+    	floatBuffer.put(flat);
+    	byteBuffer.rewind();
+        shma.getDataBufferNoHeader().put(byteBuffer);
         if (PlatformDetection.isWindows()) shma.close();
     }
 
@@ -130,7 +145,15 @@ 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 DoubleType(), false, true);
-        shma.getDataBufferNoHeader().put(tensor.asByteBuffer());
+		long flatSize = 1;
+    	for (long l : arrayShape) {flatSize *= l;}
+    	double[] flat = new double[(int) flatSize];
+    	ByteBuffer byteBuffer = ByteBuffer.allocateDirect((int) (flatSize * Double.BYTES));
+    	DoubleBuffer floatBuffer = byteBuffer.asDoubleBuffer();
+    	tensor.data_ptr_double().get(flat);
+    	floatBuffer.put(flat);
+    	byteBuffer.rewind();
+        shma.getDataBufferNoHeader().put(byteBuffer);
         if (PlatformDetection.isWindows()) shma.close();
     }
 
@@ -141,7 +164,15 @@ 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 LongType(), false, true);
-        shma.getDataBufferNoHeader().put(tensor.asByteBuffer());
+		long flatSize = 1;
+    	for (long l : arrayShape) {flatSize *= l;}
+    	long[] flat = new long[(int) flatSize];
+    	ByteBuffer byteBuffer = ByteBuffer.allocateDirect((int) (flatSize * Long.BYTES));
+    	LongBuffer floatBuffer = byteBuffer.asLongBuffer();
+    	tensor.data_ptr_long().get(flat);
+    	floatBuffer.put(flat);
+    	byteBuffer.rewind();
+        shma.getDataBufferNoHeader().put(byteBuffer);
         if (PlatformDetection.isWindows()) shma.close();
     }
 }