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3672a14b42
Modified tensorflow parser for the new dnn engine #26394 ### Pull Request Readiness Checklist See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [ ] There is a reference to the original bug report and related work - [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [x] The feature is well documented and sample code can be built with the project CMake
120 lines
3.7 KiB
Java
120 lines
3.7 KiB
Java
package org.opencv.test.dnn;
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import java.io.File;
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import java.io.FileInputStream;
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import java.io.IOException;
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import java.util.ArrayList;
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import java.util.List;
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import org.opencv.core.Core;
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import org.opencv.core.Mat;
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import org.opencv.core.MatOfInt;
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import org.opencv.core.MatOfFloat;
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import org.opencv.core.MatOfByte;
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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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import org.opencv.dnn.Dnn;
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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.opencv.test.OpenCVTestCase;
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/*
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* regression test for #12324,
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* testing various java.util.List invocations,
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* which use the LIST_GET macro
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*/
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public class DnnListRegressionTest extends OpenCVTestCase {
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private final static String ENV_OPENCV_DNN_TEST_DATA_PATH = "OPENCV_DNN_TEST_DATA_PATH";
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private final static String ENV_OPENCV_TEST_DATA_PATH = "OPENCV_TEST_DATA_PATH";
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String modelFileName = "";
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String sourceImageFile = "";
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Net net;
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@Override
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protected void setUp() throws Exception {
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super.setUp();
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String envDnnTestDataPath = System.getenv(ENV_OPENCV_DNN_TEST_DATA_PATH);
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if(envDnnTestDataPath == null){
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isTestCaseEnabled = false;
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return;
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}
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File dnnTestDataPath = new File(envDnnTestDataPath);
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modelFileName = new File(dnnTestDataPath, "dnn/tensorflow_inception_graph.pb").toString();
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String envTestDataPath = System.getenv(ENV_OPENCV_TEST_DATA_PATH);
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if(envTestDataPath == null) throw new Exception(ENV_OPENCV_TEST_DATA_PATH + " has to be defined!");
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File testDataPath = new File(envTestDataPath);
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File f = new File(testDataPath, "dnn/grace_hopper_227.png");
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sourceImageFile = f.toString();
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if(!f.exists()) throw new Exception("Test image is missing: " + sourceImageFile);
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net = Dnn.readNetFromTensorflow(modelFileName);
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Mat image = Imgcodecs.imread(sourceImageFile);
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assertNotNull("Loading image from file failed!", image);
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Mat inputBlob = Dnn.blobFromImage(image, 1.0, new Size(224, 224), new Scalar(0), true, true);
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assertNotNull("Converting image to blob failed!", inputBlob);
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net.setInput(inputBlob, "");
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}
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/*public void testSetInputsNames() {
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List<String> inputs = new ArrayList();
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inputs.add("input");
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try {
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net.setInputsNames(inputs);
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} catch(Exception e) {
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fail("Net setInputsNames failed: " + e.getMessage());
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}
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}*/
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public void testForward() {
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List<Mat> outs = new ArrayList();
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List<String> outNames = new ArrayList();
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//outNames.add("");
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try {
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net.forward(outs,outNames);
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} catch(Exception e) {
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fail("Net forward failed: " + e.getMessage());
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}
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}
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public void testGetMemoryConsumption() {
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List<MatOfInt> netInputShapes = new ArrayList();
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netInputShapes.add(new MatOfInt(1, 3, 224, 224));
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MatOfInt netInputTypes = new MatOfInt(5);
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long[] weights=null;
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long[] blobs=null;
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try {
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net.getMemoryConsumption(netInputShapes, netInputTypes, weights, blobs);
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} catch(Exception e) {
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fail("Net getMemoryConsumption failed: " + e.getMessage());
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}
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}
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public void testGetFLOPS() {
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List<MatOfInt> netInputShapes = new ArrayList();
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netInputShapes.add(new MatOfInt(1, 3, 224, 224));
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MatOfInt netInputTypes = new MatOfInt(5);
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try {
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net.getFLOPS(netInputShapes, netInputTypes);
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} catch(Exception e) {
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fail("Net getFLOPS failed: " + e.getMessage());
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}
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}
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}
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