add pre and post processing to model run

src/main/java/io/bioimage/modelrunner/model/Model.java

@@ -52,6 +52,7 @@
 import io.bioimage.modelrunner.exceptions.LoadEngineException;
 import io.bioimage.modelrunner.exceptions.LoadModelException;
 import io.bioimage.modelrunner.exceptions.RunModelException;
+import io.bioimage.modelrunner.model.processing.Processing;
 import io.bioimage.modelrunner.tensor.Tensor;
 import io.bioimage.modelrunner.utils.Constants;
 import io.bioimage.modelrunner.versionmanagement.InstalledEngines;
@@ -530,8 +531,14 @@ void runModel( List< Tensor < T > > inTensors, List< Tensor < R > > outTensors )
 	{
 		DeepLearningEngineInterface engineInstance = engineClassLoader.getEngineInstance();
 		engineClassLoader.setEngineClassLoader();
-		inTensors.stream().forEach( tt -> tt = (Tensor<T>) Tensor.createCopyOfTensorInWantedDataType( tt, new FloatType() ) );
-		engineInstance.run( inTensors, outTensors );
+		ArrayList<Tensor<FloatType>> inTensorsFloat = new ArrayList<Tensor<FloatType>>();
+		for (Tensor<T> tt : inTensors) {
+			if (tt.getData().getAt(0) instanceof FloatType)
+				inTensorsFloat.add((Tensor<FloatType>) tt);
+			else
+				inTensorsFloat.add(Tensor.createCopyOfTensorInWantedDataType( tt, new FloatType() ));
+		}
+		engineInstance.run( inTensorsFloat, outTensors );
 		engineClassLoader.setBaseClassLoader();
 	}
 
@@ -595,8 +602,8 @@ else if (descriptor == null)
 				.map(tt -> new ImageInfo(tt.getName(), tt.getAxesOrderString(), tt.getData().dimensionsAsLongArray()))
 				.collect(Collectors.toList());
 		List<TileInfo> inputTiles = calc.getOptimalTileSize(imageInfos);
-		TileMaker tiles = TileMaker.build(descriptor, inputTiles);
-		return runTiling(inputTensors, tiles, tileCounter);
+		TileMaker maker = TileMaker.build(descriptor, inputTiles);
+		return runBMZ(inputTensors, maker, tileCounter);
 	}
 
 	/**
@@ -663,8 +670,15 @@ else if (descriptor == null) {
 			}
 		}
 		TileMaker maker = TileMaker.build(descriptor, tiles);
-
-		return runTiling(inputTensors, maker, tileCounter);
+		return runBMZ(inputTensors, maker, tileCounter);
+	}
+
+	private <T extends RealType<T> & NativeType<T>, R extends RealType<R> & NativeType<R>> 
+	List<Tensor<T>> runBMZ(List<Tensor<R>> inputTensors, TileMaker tiles, TilingConsumer tileCounter) throws RunModelException {
+		Processing processing = Processing.init(descriptor);
+		inputTensors = processing.preprocess(inputTensors, false);
+		List<Tensor<R>> outputTensors = runTiling(inputTensors, tiles, tileCounter);
+		return processing.postprocess(outputTensors, true);
 	}
 
 	@SuppressWarnings("unchecked")