SavedModelBundle.load is now working
This commit is contained in:
davpapp
parent
bd3d07d7ac
commit
8ccafa8c63
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@ -0,0 +1,171 @@
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import java.io.IOException;
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import java.nio.file.Files;
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import java.nio.file.Path;
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import java.nio.file.Paths;
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import org.opencv.core.Core;
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import org.tensorflow.DataType;
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import org.tensorflow.Graph;
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import org.tensorflow.Output;
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import org.tensorflow.SavedModelBundle;
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import org.tensorflow.Session;
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import org.tensorflow.Tensor;
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import org.tensorflow.TensorFlow;
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import org.tensorflow.types.UInt8;
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public class ObjectDetectionViaSavedModelBundle {
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public static void main( String[] args ) throws Exception {
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/*System.out.println("Reading model from TensorFlow...");
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System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
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ObjectDetector objectDetector = new ObjectDetector();
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objectDetector.testGetLayerTypes();
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objectDetector.testGetLayer();
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objectDetector.testImage();*/
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final int IMG_SIZE = 128;
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final String value = "Hello from " + TensorFlow.version();
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System.out.println(value);
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byte[] imageBytes = readAllBytesOrExit(Paths.get("/home/dpapp/tensorflow-1.5.0/models/raccoon_dataset/test_images/ironOre_test_9.jpg"));
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Tensor image = constructAndExecuteGraphToNormalizeImage(imageBytes);
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SavedModelBundle load = SavedModelBundle.load("/home/dpapp/tensorflow-1.5.0/models/raccoon_dataset/results/checkpoint_23826/saved_model/", "serve");
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try (Graph g = load.graph()) {
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try (Session s = load.session();
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Tensor result = s.runner()
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.feed("image_tensor:0", image)
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.fetch("detection_boxes:0").run().get(0))
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{
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System.out.println(result.floatValue());
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}
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}
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load.close();
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System.out.println("Done...");
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}
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private static byte[] readAllBytesOrExit(Path path) {
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try {
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return Files.readAllBytes(path);
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} catch (IOException e) {
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System.err.println("Failed to read [" + path + "]: " + e.getMessage());
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System.exit(1);
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}
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return null;
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}
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private static Tensor<Float> constructAndExecuteGraphToNormalizeImage(byte[] imageBytes) {
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try (Graph g = new Graph()) {
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GraphBuilder b = new GraphBuilder(g);
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// Some constants specific to the pre-trained model at:
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// https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip
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//
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// - The model was trained with images scaled to 224x224 pixels.
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// - The colors, represented as R, G, B in 1-byte each were converted to
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// float using (value - Mean)/Scale.
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final int H = 224;
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final int W = 224;
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final float mean = 117f;
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final float scale = 1f;
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// Since the graph is being constructed once per execution here, we can use a constant for the
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// input image. If the graph were to be re-used for multiple input images, a placeholder would
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// have been more appropriate.
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final Output<String> input = b.constant("input", imageBytes);
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final Output<Float> output =
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b.div(
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b.sub(
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b.resizeBilinear(
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b.expandDims(
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b.cast(b.decodeJpeg(input, 3), Float.class),
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b.constant("make_batch", 0)),
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b.constant("size", new int[] {H, W})),
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b.constant("mean", mean)),
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b.constant("scale", scale));
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try (Session s = new Session(g)) {
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return s.runner().fetch(output.op().name()).run().get(0).expect(Float.class);
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}
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}
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}
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static class GraphBuilder {
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GraphBuilder(Graph g) {
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this.g = g;
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}
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Output<Float> div(Output<Float> x, Output<Float> y) {
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return binaryOp("Div", x, y);
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}
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<T> Output<T> sub(Output<T> x, Output<T> y) {
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return binaryOp("Sub", x, y);
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}
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<T> Output<Float> resizeBilinear(Output<T> images, Output<Integer> size) {
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return binaryOp3("ResizeBilinear", images, size);
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}
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<T> Output<T> expandDims(Output<T> input, Output<Integer> dim) {
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return binaryOp3("ExpandDims", input, dim);
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}
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<T, U> Output<U> cast(Output<T> value, Class<U> type) {
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DataType dtype = DataType.fromClass(type);
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return g.opBuilder("Cast", "Cast")
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.addInput(value)
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.setAttr("DstT", dtype)
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.build()
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.<U>output(0);
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}
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Output<UInt8> decodeJpeg(Output<String> contents, long channels) {
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return g.opBuilder("DecodeJpeg", "DecodeJpeg")
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.addInput(contents)
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.setAttr("channels", channels)
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.build()
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.<UInt8>output(0);
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}
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<T> Output<T> constant(String name, Object value, Class<T> type) {
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try (Tensor<T> t = Tensor.<T>create(value, type)) {
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return g.opBuilder("Const", name)
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.setAttr("dtype", DataType.fromClass(type))
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.setAttr("value", t)
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.build()
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.<T>output(0);
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}
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}
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Output<String> constant(String name, byte[] value) {
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return this.constant(name, value, String.class);
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}
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Output<Integer> constant(String name, int value) {
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return this.constant(name, value, Integer.class);
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}
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Output<Integer> constant(String name, int[] value) {
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return this.constant(name, value, Integer.class);
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}
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Output<Float> constant(String name, float value) {
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return this.constant(name, value, Float.class);
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}
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private <T> Output<T> binaryOp(String type, Output<T> in1, Output<T> in2) {
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return g.opBuilder(type, type).addInput(in1).addInput(in2).build().<T>output(0);
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}
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private <T, U, V> Output<T> binaryOp3(String type, Output<U> in1, Output<V> in2) {
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return g.opBuilder(type, type).addInput(in1).addInput(in2).build().<T>output(0);
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}
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private Graph g;
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}
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}
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@ -4,11 +4,16 @@ import static org.junit.Assert.assertNotNull;
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import java.awt.List;
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import java.io.File;
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import java.io.IOException;
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import java.nio.file.Files;
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import java.nio.file.Path;
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import java.nio.file.Paths;
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import java.util.ArrayList;
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import org.opencv.core.Core;
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import org.opencv.core.CvType;
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import org.opencv.core.Mat;
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import org.opencv.core.Point;
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import org.opencv.core.Scalar;
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import org.opencv.core.Size;
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import org.opencv.dnn.DictValue;
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@ -17,6 +22,14 @@ import org.opencv.dnn.Layer;
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import org.opencv.dnn.Net;
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import org.opencv.imgcodecs.Imgcodecs;
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import org.opencv.imgproc.Imgproc;
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import org.tensorflow.DataType;
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import org.tensorflow.Graph;
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import org.tensorflow.Output;
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import org.tensorflow.SavedModelBundle;
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import org.tensorflow.Session;
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import org.tensorflow.Tensor;
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import org.tensorflow.TensorFlow;
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import org.tensorflow.types.UInt8;
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public class ObjectDetector {
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@ -29,7 +42,7 @@ public class ObjectDetector {
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public ObjectDetector() throws Exception {
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this.inputImagePath = "/home/dpapp/tensorflow-1.5.0/models/raccoon_dataset/test_images/ironOre_test_9.jpg";
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this.inputModelPath = "/home/dpapp/tensorflow-1.5.0/models/raccoon_dataset/results/checkpoint_23826/frozen_graph_inference.pb";
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this.inputModelArgumentsPath = "/home/dpapp/tensorflow-1.5.0/models/raccoon_dataset/generated_graph.pbtxt";
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this.inputModelArgumentsPath = "/home/dpapp/tensorflow-1.5.0/models/raccoon_dataset/training/graph.pbtxt";
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File f = new File(inputImagePath);
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if(!f.exists()) throw new Exception("Test image is missing: " + inputImagePath);
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@ -41,6 +54,9 @@ public class ObjectDetector {
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net = Dnn.readNetFromTensorflow(inputModelPath, inputModelArgumentsPath);
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}
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public void testGetLayerTypes() {
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ArrayList<String> layertypes = new ArrayList();
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net.getLayerTypes(layertypes);
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@ -64,31 +80,74 @@ public class ObjectDetector {
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assertEquals("Layer name does not match the expected value!", testLayerName, layer.get_name());
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}
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public void testImage() throws Exception {
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Mat rawImage = Imgcodecs.imread(inputImagePath);
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Mat grayImage = new Mat();
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Imgproc.cvtColor(rawImage, grayImage, Imgproc.COLOR_RGB2GRAY);
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assertNotNull("Loading image from file failed!", rawImage);
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public Mat testImage() throws Exception {
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final int IN_WIDTH = 300;
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final int IN_HEIGHT = 300;
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final float WH_RATIO = (float)IN_WIDTH / IN_HEIGHT;
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final double IN_SCALE_FACTOR = 0.007843;
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final double MEAN_VAL = 127.5;
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final double THRESHOLD = 0.2;
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Mat frame = Imgcodecs.imread(inputImagePath);
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Imgproc.cvtColor(frame, frame, Imgproc.COLOR_RGBA2RGB);
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assertNotNull("Loading image from file failed!", frame);
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Mat image = new Mat();
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Imgproc.resize(grayImage, image, new Size(224, 224));
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Mat blob = Dnn.blobFromImage(frame, IN_SCALE_FACTOR,
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new Size(IN_WIDTH, IN_HEIGHT),
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new Scalar(MEAN_VAL, MEAN_VAL, MEAN_VAL), false, false);
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net.setInput(blob);
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Mat detections = net.forward();
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Mat inputBlob = Dnn.blobFromImage(image);
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assertNotNull("Converting image to blob failed!", inputBlob);
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int cols = frame.cols();
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int rows = frame.rows();
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Mat inputBlobP = new Mat();
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Core.subtract(inputBlob, new Scalar(117.0), inputBlobP);
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net.setInput(inputBlobP);
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Size cropSize;
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if ((float)cols / rows > WH_RATIO) {
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cropSize = new Size(rows * WH_RATIO, rows);
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} else {
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cropSize = new Size(cols, cols / WH_RATIO);
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}
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Mat result = net.forward();
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int y1 = (int)(rows - cropSize.height) / 2;
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int y2 = (int)(y1 + cropSize.height);
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int x1 = (int)(cols - cropSize.width) / 2;
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int x2 = (int)(x1 + cropSize.width);
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Mat subFrame = frame.submat(y1, y2, x1, x2);
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assertNotNull("Net returned no result!", result);
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cols = subFrame.cols();
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rows = subFrame.rows();
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detections = detections.reshape(1, (int)detections.total() / 7);
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for (int i = 0; i < detections.rows(); ++i) {
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double confidence = detections.get(i, 2)[0];
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if (confidence > THRESHOLD) {
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int classId = (int)detections.get(i, 1)[0];
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int xLeftBottom = (int)(detections.get(i, 3)[0] * cols);
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int yLeftBottom = (int)(detections.get(i, 4)[0] * rows);
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int xRightTop = (int)(detections.get(i, 5)[0] * cols);
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int yRightTop = (int)(detections.get(i, 6)[0] * rows);
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// Draw rectangle around detected object.
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Imgproc.rectangle(subFrame, new Point(xLeftBottom, yLeftBottom),
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new Point(xRightTop, yRightTop),
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new Scalar(0, 255, 0));
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String label = "ironOre" + ": " + confidence;
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int[] baseLine = new int[1];
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Size labelSize = Imgproc.getTextSize(label, Core.FONT_HERSHEY_SIMPLEX, 0.5, 1, baseLine);
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// Draw background for label.
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Imgproc.rectangle(subFrame, new Point(xLeftBottom, yLeftBottom - labelSize.height),
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new Point(xLeftBottom + labelSize.width, yLeftBottom + baseLine[0]),
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new Scalar(255, 255, 255), Core.FILLED);
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// Write class name and confidence.
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Imgproc.putText(subFrame, label, new Point(xLeftBottom, yLeftBottom),
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Core.FONT_HERSHEY_SIMPLEX, 0.5, new Scalar(0, 0, 0));
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}
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}
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return frame;
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}
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@ -103,7 +162,147 @@ public class ObjectDetector {
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objectDetector.testGetLayerTypes();
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objectDetector.testGetLayer();
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objectDetector.testImage();
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/*
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final int IMG_SIZE = 128;
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final String value = "Hello from " + TensorFlow.version();
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byte[] imageBytes = readAllBytesOrExit(Paths.get("/home/dpapp/tensorflow-1.5.0/models/raccoon_dataset/test_images/ironOre_test_9.jpg"));
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Tensor image = constructAndExecuteGraphToNormalizeImage(imageBytes);
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SavedModelBundle load = SavedModelBundle.load("/home/dpapp/tensorflow-1.5.0/models/raccoon_dataset/SavedModel/saved_model.pb");
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long[] sitio2;
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try (Graph g = load.graph()) {
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try (Session s = load.session();
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Tensor result = s.runner()
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.feed("image_tensor", image)
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.fetch("detection_boxes").run().get(0))
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{
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sitio2 = (long[]) result.copyTo(new long[1]);
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System.out.print(sitio2[0]+"\n");
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}
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}
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load.close();
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*/
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System.out.println("Done...");
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}
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private static byte[] readAllBytesOrExit(Path path) {
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try {
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return Files.readAllBytes(path);
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} catch (IOException e) {
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System.err.println("Failed to read [" + path + "]: " + e.getMessage());
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System.exit(1);
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}
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return null;
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}
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private static Tensor<Float> constructAndExecuteGraphToNormalizeImage(byte[] imageBytes) {
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try (Graph g = new Graph()) {
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GraphBuilder b = new GraphBuilder(g);
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// Some constants specific to the pre-trained model at:
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// https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip
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//
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// - The model was trained with images scaled to 224x224 pixels.
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// - The colors, represented as R, G, B in 1-byte each were converted to
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// float using (value - Mean)/Scale.
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final int H = 224;
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final int W = 224;
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final float mean = 117f;
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final float scale = 1f;
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// Since the graph is being constructed once per execution here, we can use a constant for the
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// input image. If the graph were to be re-used for multiple input images, a placeholder would
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// have been more appropriate.
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final Output<String> input = b.constant("input", imageBytes);
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final Output<Float> output =
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b.div(
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b.sub(
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b.resizeBilinear(
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b.expandDims(
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b.cast(b.decodeJpeg(input, 3), Float.class),
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b.constant("make_batch", 0)),
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b.constant("size", new int[] {H, W})),
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b.constant("mean", mean)),
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b.constant("scale", scale));
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try (Session s = new Session(g)) {
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return s.runner().fetch(output.op().name()).run().get(0).expect(Float.class);
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}
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}
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}
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static class GraphBuilder {
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GraphBuilder(Graph g) {
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this.g = g;
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}
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Output<Float> div(Output<Float> x, Output<Float> y) {
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return binaryOp("Div", x, y);
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}
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<T> Output<T> sub(Output<T> x, Output<T> y) {
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return binaryOp("Sub", x, y);
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}
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<T> Output<Float> resizeBilinear(Output<T> images, Output<Integer> size) {
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return binaryOp3("ResizeBilinear", images, size);
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}
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<T> Output<T> expandDims(Output<T> input, Output<Integer> dim) {
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return binaryOp3("ExpandDims", input, dim);
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}
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<T, U> Output<U> cast(Output<T> value, Class<U> type) {
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DataType dtype = DataType.fromClass(type);
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return g.opBuilder("Cast", "Cast")
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.addInput(value)
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.setAttr("DstT", dtype)
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.build()
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.<U>output(0);
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}
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Output<UInt8> decodeJpeg(Output<String> contents, long channels) {
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return g.opBuilder("DecodeJpeg", "DecodeJpeg")
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.addInput(contents)
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.setAttr("channels", channels)
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.build()
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.<UInt8>output(0);
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}
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<T> Output<T> constant(String name, Object value, Class<T> type) {
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try (Tensor<T> t = Tensor.<T>create(value, type)) {
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return g.opBuilder("Const", name)
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.setAttr("dtype", DataType.fromClass(type))
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.setAttr("value", t)
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.build()
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.<T>output(0);
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}
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}
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Output<String> constant(String name, byte[] value) {
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return this.constant(name, value, String.class);
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}
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Output<Integer> constant(String name, int value) {
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return this.constant(name, value, Integer.class);
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}
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Output<Integer> constant(String name, int[] value) {
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return this.constant(name, value, Integer.class);
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}
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Output<Float> constant(String name, float value) {
|
||||
return this.constant(name, value, Float.class);
|
||||
}
|
||||
|
||||
private <T> Output<T> binaryOp(String type, Output<T> in1, Output<T> in2) {
|
||||
return g.opBuilder(type, type).addInput(in1).addInput(in2).build().<T>output(0);
|
||||
}
|
||||
|
||||
private <T, U, V> Output<T> binaryOp3(String type, Output<U> in1, Output<V> in2) {
|
||||
return g.opBuilder(type, type).addInput(in1).addInput(in2).build().<T>output(0);
|
||||
}
|
||||
private Graph g;
|
||||
}
|
||||
|
||||
}
|
||||
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Reference in New Issue