add model loaded check

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

@@ -37,6 +37,8 @@
 
 import javax.xml.bind.ValidationException;
 
+import ij.IJ;
+import ij.ImagePlus;
 import io.bioimage.modelrunner.bioimageio.bioengine.BioEngineAvailableModels;
 import io.bioimage.modelrunner.bioimageio.bioengine.BioengineInterface;
 import io.bioimage.modelrunner.bioimageio.description.ModelDescriptor;
@@ -59,6 +61,7 @@
 import net.imglib2.img.array.ArrayImg;
 import net.imglib2.img.array.ArrayImgs;
 import net.imglib2.img.basictypeaccess.array.FloatArray;
+import net.imglib2.img.display.imagej.ImageJFunctions;
 import net.imglib2.type.NativeType;
 import net.imglib2.type.numeric.RealType;
 import net.imglib2.type.numeric.real.FloatType;
@@ -73,6 +76,10 @@
  */
 public class Model
 {
+	/**
+	 * Whether the model is loaded or not
+	 */
+	boolean loaded = false;
 	/**
 	 * ClassLoader containing all the classes needed to use the corresponding
 	 * Deep Learning framework (engine).
@@ -458,6 +465,7 @@ public void loadModel() throws LoadModelException
 		if (engineClassLoader.isBioengine())
 			((BioengineInterface) engineInstance).addServer(engineInfo.getServer());
 		engineClassLoader.setBaseClassLoader();
+		loaded = true;
 	}
 
 	/**
@@ -473,6 +481,7 @@ public void closeModel()
 		engineInstance = null;
 		engineClassLoader.setBaseClassLoader();
 		engineClassLoader = null;
+		loaded = false;
 	}
 
 	/**
@@ -509,20 +518,21 @@ public void runModel( List< Tensor < ? > > inTensors, List< Tensor < ? > > outTe
 	 */
 	public <T extends RealType<T> & NativeType<T>, R extends RealType<R> & NativeType<R>> 
 	List<Tensor<T>> runBioimageioModelOnImgLib2WithTiling(List<Tensor<R>> inputTensors) throws ValidationException, RunModelException {
+		if (!this.isLoaded())
+			throw new RunModelException("Please first load the model.");
 		if (descriptor == null && modelFolder == null)
 			throw new IllegalArgumentException("");
 		else if (descriptor == null && !(new File(modelFolder, Constants.RDF_FNAME).isFile()))
 			throw new IllegalArgumentException("");
 		else if (descriptor == null)
 			descriptor = ModelDescriptor.readFromLocalFile(modelFolder + File.separator + Constants.RDF_FNAME);
 		PatchGridCalculator<R> tileGrid = PatchGridCalculator.build(descriptor, inputTensors);
-		runTiling(inputTensors, tileGrid);
-		return null;
+		return runTiling(inputTensors, tileGrid);
 	}
 
 	@SuppressWarnings("unchecked")
 	private <T extends RealType<T> & NativeType<T>, R extends RealType<R> & NativeType<R>> 
-	void runTiling(List<Tensor<R>> inputTensors, PatchGridCalculator<R> tileGrid) throws RunModelException {
+	List<Tensor<T>> runTiling(List<Tensor<R>> inputTensors, PatchGridCalculator<R> tileGrid) throws RunModelException {
 		LinkedHashMap<String, PatchSpec> inTileSpecs = tileGrid.getInputTensorsTileSpecs();
 		LinkedHashMap<String, PatchSpec> outTileSpecs = tileGrid.getOutputTensorsTileSpecs();
 		List<Tensor<T>> outputTensors = new ArrayList<Tensor<T>>();
@@ -536,6 +546,7 @@ void runTiling(List<Tensor<R>> inputTensors, PatchGridCalculator<R> tileGrid) th
 																	new FloatType()));
 		}
 		doTiling(inputTensors, outputTensors, tileGrid);
+		return outputTensors;
 	}
 
 	private <T extends RealType<T> & NativeType<T>, R extends RealType<R> & NativeType<R>> 
@@ -579,19 +590,24 @@ void doTiling(List<Tensor<R>> inputTensors, List<Tensor<T>> outputTensors, Patch
 
 			this.runModel(inputTileList, outputTileList);
 		}
-
 	}
 
-	public static <T extends NativeType<T> & RealType<T>> void main(String[] args) throws IOException {
+	public static <T extends NativeType<T> & RealType<T>> void main(String[] args) throws IOException, ValidationException, LoadEngineException, RunModelException {
 		String mm = "C:\\Users\\angel\\OneDrive\\Documentos\\pasteur\\git\\model-runner-java\\models\\StarDist H&E Nuclei Segmentation_06092023_020924\\";
 		Img<FloatType> im = ArrayImgs.floats(new long[] {1, 511, 512, 3});
+		ImagePlus imp = IJ.openImage(mm + File.separator + "sample_input_0.tif");
+		imp.show();
+		RandomAccessibleInterval<FloatType> wrapImg = ImageJFunctions.convertFloat(imp);
+		wrapImg = (RandomAccessibleInterval<FloatType>) Views.addDimension(wrapImg, 0, 0);
+		wrapImg = (RandomAccessibleInterval<FloatType>) Views.permute(wrapImg, 2, 3);
+		wrapImg = (RandomAccessibleInterval<FloatType>) Views.permute(wrapImg, 1, 2);
+		wrapImg = (RandomAccessibleInterval<FloatType>) Views.permute(wrapImg, 0, 1);
 		List<Tensor<T>> l = new ArrayList<Tensor<T>>();
-		l.add((Tensor<T>) Tensor.build("input", "bxyc", im));
-		PatchGridCalculator<T> tileGrid = PatchGridCalculator.build(mm, l);
-		LinkedHashMap<String, PatchSpec> inTileSpecs = tileGrid.getInputTensorsTileSpecs();
-		LinkedHashMap<String, PatchSpec> outTileSpecs = tileGrid.getOutputTensorsTileSpecs();
-        TileGrid aa = TileGrid.create(inTileSpecs.get("input"));
-        TileGrid bb = TileGrid.create(outTileSpecs.get("output"));
+		l.add((Tensor<T>) Tensor.build("input", "bxyc", wrapImg));
+		Model model = createBioimageioModel(mm);
+		model.loadModel();
+		List<Tensor<T>> out = model.runBioimageioModelOnImgLib2WithTiling(l);
+		ImageJFunctions.show(Views.dropSingletonDimensions(out.get(0).getData()));
 		System.out.println(false);
 	}
 
@@ -652,4 +668,12 @@ public boolean isBioengine() {
 	public EngineInfo getEngineInfo() {
 		return engineInfo;
 	}
+
+	/**
+	 * Whether the model is loaded or not
+	 * @return
+	 */
+	public boolean isLoaded() {
+		return loaded;
+	}
 }