gistfile1.txt

package org.deeplearning4j.examples;

import org.datavec.image.loader.NativeImageLoader;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.nn.layers.objdetect.DetectedObject;
import org.deeplearning4j.nn.layers.objdetect.YoloUtils;
import org.deeplearning4j.parallelism.ParallelInference;
import org.deeplearning4j.parallelism.inference.InferenceMode;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.api.preprocessor.ImagePreProcessingScaler;
import org.nd4j.linalg.factory.Nd4j;

import java.io.File;
import java.util.List;

public class Debug6730 {

    public static final double[][] DEFAULT_PRIOR_BOXES = {{0.57273, 0.677385}, {1.87446, 2.06253}, {3.33843, 5.47434}, {7.88282, 3.52778}, {9.77052, 9.16828}};

    public static void main(String[] args) throws Exception {
//        int height = 224;
//        int width = 224;
        int height = 416;
        int width = 416;

//        ComputationGraph cg = ComputationGraph.load(new File("C:/Users/Alex/Downloads/tiny_224.zip"), false);
        ComputationGraph cg = ComputationGraph.load(new File("C:/Users/Alex/Downloads/tiny_416.zip"), false);
        ParallelInference pi = new ParallelInference.Builder(cg).inferenceMode(InferenceMode.BATCHED).batchLimit(8).workers(4).build();


        NativeImageLoader loader = new NativeImageLoader(height, width, 3);
        ImagePreProcessingScaler imagePreProcessingScaler = new ImagePreProcessingScaler(0, 1);

        File[] fileList = new File("E:\\Data\\VOC\\VOCtrainval_11-May-2012\\VOCdevkit\\VOC2012\\JPEGImages").listFiles();

        for(File img : fileList) {
            System.out.println(img);
            INDArray indArray = loader.asMatrix(img);
            imagePreProcessingScaler.transform(indArray);


            INDArray out = cg.outputSingle(indArray);

            INDArray outPi = pi.output(indArray);

            boolean equals = out.equals(outPi);


            List<DetectedObject> predictedObjects = YoloUtils.getPredictedObjects(Nd4j.create(DEFAULT_PRIOR_BOXES), out, 0.5, 0);
            List<DetectedObject> predictedObjectsPI = YoloUtils.getPredictedObjects(Nd4j.create(DEFAULT_PRIOR_BOXES), outPi, 0.5, 0);
            if(!equals || predictedObjects.size() > 0){
                if(!equals){
                    System.out.println("NOT EQUAL");
                }
                System.out.println(predictedObjects);
                System.out.println(predictedObjectsPI);
            }
        }
    }

}