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Complete Stacktrace for Yolo attempt
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| package faultrecordreader; | |
| import static org.nd4j.linalg.indexing.NDArrayIndex.all; | |
| import static org.nd4j.linalg.indexing.NDArrayIndex.point; | |
| import java.io.DataInputStream; | |
| import java.io.IOException; | |
| import java.net.URI; | |
| import java.util.ArrayList; | |
| import java.util.Arrays; | |
| import java.util.Collection; | |
| import java.util.Collections; | |
| import java.util.HashSet; | |
| import java.util.List; | |
| import java.util.Set; | |
| import org.datavec.api.conf.Configuration; | |
| import org.datavec.api.records.Record; | |
| import org.datavec.api.records.listener.RecordListener; | |
| import org.datavec.api.records.metadata.RecordMetaData; | |
| import org.datavec.api.records.metadata.RecordMetaDataURI; | |
| import org.datavec.api.records.reader.RecordReader; | |
| import org.datavec.api.split.InputSplit; | |
| import org.datavec.api.writable.Writable; | |
| import org.datavec.api.writable.batch.NDArrayRecordBatch; | |
| import org.datavec.image.util.ImageUtils; | |
| import org.nd4j.linalg.api.ndarray.INDArray; | |
| import org.nd4j.linalg.factory.Nd4j; | |
| import org.nd4j.linalg.indexing.INDArrayIndex; | |
| import faults.Fault; | |
| import faults.FaultFactory; | |
| import faults.FaultNames; | |
| /** | |
| * An fault record reader for object detection. | |
| * <p> | |
| * Format of returned values: 4d array, with dimensions [minibatch, 4+C, h, w] | |
| * Where the image is quantized into h x w grid locations. | |
| * <p> | |
| * Note that this is a modified version of Alex Black's | |
| * ObjectDetectionRecordReader.java | |
| * | |
| * @author Michael C. Kunkel | |
| */ | |
| public class FaultObjectDetectionRecordReader implements RecordReader { | |
| private final int gridW; | |
| private final int gridH; | |
| private int height; | |
| private int width; | |
| private int channels; | |
| protected List<String> labels; | |
| protected FaultFactory factory = null; | |
| private int label; | |
| // args for FaultFactory constructor | |
| private int superLayer; | |
| private int maxFaults; | |
| private FaultNames desiredFault; | |
| private boolean singleFaultGeneration; | |
| private boolean blurredFaults; | |
| /** | |
| * | |
| * @param height | |
| * Height of the output images | |
| * @param width | |
| * Width of the output images | |
| * @param channels | |
| * Number of channels for the output images | |
| * @param gridH | |
| * Grid/quantization size (along height dimension) - Y axis | |
| * @param gridW | |
| * Grid/quantization size (along height dimension) - X axis | |
| * @param labelProvider | |
| * ImageObjectLabelProvider - used to look up which objects are | |
| * in each image | |
| */ | |
| public FaultObjectDetectionRecordReader(int superLayer, int maxFaults, FaultNames desiredFault, | |
| boolean singleFaultGeneration, boolean blurredFaults, int height, int width, int channels, int gridH, | |
| int gridW) { | |
| this.superLayer = superLayer; | |
| this.maxFaults = maxFaults; | |
| this.desiredFault = desiredFault; | |
| this.singleFaultGeneration = singleFaultGeneration; | |
| this.blurredFaults = blurredFaults; | |
| this.factory = new FaultFactory(superLayer, maxFaults, desiredFault, singleFaultGeneration, blurredFaults); | |
| this.height = height; | |
| this.width = width; | |
| this.channels = channels; | |
| this.gridW = gridW; | |
| this.gridH = gridH; | |
| initialize(); | |
| } | |
| private void initialize() { | |
| Set<String> labelSet = new HashSet<>(); | |
| /** | |
| * OK, we need all the faults loaded at once otherwise it doesn't make | |
| * sense with the one-hot representation | |
| */ | |
| for (FaultNames d : FaultNames.values()) { | |
| labelSet.add(d.getSaveName()); | |
| } | |
| labels = new ArrayList<>(labelSet); | |
| // To ensure consistent order for label assignment (irrespective of file | |
| // iteration order), we want to sort the list of labels | |
| Collections.sort(labels); | |
| } | |
| @Override | |
| public boolean batchesSupported() { | |
| // I might want to set this to true so that I train in batches, reduces | |
| // memory | |
| // will get back to this after impl of modes | |
| return true; | |
| } | |
| @Override | |
| public boolean hasNext() { | |
| // since this is batch mode, and the data is generated on the fly, this | |
| // should always be true | |
| return true; | |
| } | |
| @Override | |
| public List<Writable> next() { | |
| return next(1).get(0); | |
| } | |
| @Override | |
| public void reset() { | |
| this.factory = new FaultFactory(this.superLayer, this.maxFaults, this.desiredFault, this.singleFaultGeneration, | |
| this.blurredFaults); | |
| } | |
| @Override | |
| public boolean resetSupported() { | |
| // Why would we need to reset in this type of training? | |
| return true; | |
| } | |
| @Override | |
| public List<List<Writable>> next(int num) { | |
| List<INDArray> faultData = new ArrayList<>(num); | |
| List<List<Fault>> objects = new ArrayList<>(num); | |
| for (int i = 0; i < num && hasNext(); i++) { | |
| faultData.add(factory.getFeatureVectorAsMatrix()); | |
| // faultData.add(factory.getFeatureVector()); | |
| objects.add(factory.getFaultList()); | |
| } | |
| int nClasses = labels.size(); | |
| INDArray outImg = Nd4j.create(faultData.size(), channels, height, width); | |
| INDArray outLabel = Nd4j.create(faultData.size(), 4 + nClasses, gridH, gridW); | |
| int exampleNum = 0; | |
| for (int i = 0; i < faultData.size(); i++) { | |
| INDArray imageFile = faultData.get(i); | |
| outImg.put(new INDArrayIndex[] { point(exampleNum), all(), all(), all() }, imageFile); | |
| List<Fault> objectsThisImg = objects.get(exampleNum); | |
| label(imageFile, objectsThisImg, outLabel, exampleNum); | |
| exampleNum++; | |
| } | |
| reset(); | |
| return new NDArrayRecordBatch(Arrays.asList(outImg, outLabel)); | |
| } | |
| private void label(INDArray image, List<Fault> objectsThisImg, INDArray outLabel, int exampleNum) { | |
| int oW = image.columns(); // should be 6 | |
| int oH = image.rows(); // should be 112 | |
| int W = oW; | |
| int H = oH; | |
| // put the label data into the output label array | |
| for (Fault io : objectsThisImg) { | |
| /** | |
| * OK here is a little nuance. The locations of the Faults are in | |
| * natural CLAS x->wires; y->layers coordinates. But the featured | |
| * data itself is in columns->x->layers; rows->y->wires so we should | |
| * SWITCH XCenter <-> YCenter XMin <-> YMin XMax <-> YMax | |
| * | |
| */ | |
| double cx = io.getFaultCoordinates().getYCenterPixels(); | |
| double cy = io.getFaultCoordinates().getXCenterPixels(); | |
| double[] cxyPostScaling = ImageUtils.translateCoordsScaleImage(cx, cy, W, H, width, height); | |
| double[] tlPost = ImageUtils.translateCoordsScaleImage(io.getFaultCoordinates().getyMin(), | |
| io.getFaultCoordinates().getxMin(), W, H, width, height); | |
| double[] brPost = ImageUtils.translateCoordsScaleImage(io.getFaultCoordinates().getyMax(), | |
| io.getFaultCoordinates().getxMax(), W, H, width, height); | |
| // Get grid position for image | |
| int imgGridX = (int) (cxyPostScaling[0] / width * gridW); | |
| int imgGridY = (int) (cxyPostScaling[1] / height * gridH); | |
| // Convert pixels to grid position, for TL and BR X/Y | |
| tlPost[0] = tlPost[0] / width * gridW; | |
| tlPost[1] = tlPost[1] / height * gridH; | |
| brPost[0] = brPost[0] / width * gridW; | |
| brPost[1] = brPost[1] / height * gridH; | |
| // Put TL, BR into label array: | |
| outLabel.putScalar(exampleNum, 0, imgGridY, imgGridX, tlPost[0]); | |
| outLabel.putScalar(exampleNum, 1, imgGridY, imgGridX, tlPost[1]); | |
| outLabel.putScalar(exampleNum, 2, imgGridY, imgGridX, brPost[0]); | |
| outLabel.putScalar(exampleNum, 3, imgGridY, imgGridX, brPost[1]); | |
| // Put label class into label array: (one-hot representation) | |
| int labelIdx = labels.indexOf(io.getSubFaultName().getSaveName()); | |
| outLabel.putScalar(exampleNum, 4 + labelIdx, imgGridY, imgGridX, 1.0); | |
| } | |
| } | |
| @Override | |
| public Record nextRecord() { | |
| List<Writable> list = next(); | |
| URI uri = URI.create("FaultFinderAI"); | |
| // return new org.datavec.api.records.impl.Record(list, metaData) | |
| return new org.datavec.api.records.impl.Record(list, | |
| new RecordMetaDataURI(null, FaultObjectDetectionRecordReader.class)); | |
| } | |
| // the rest below here are not needed, but kept for as need | |
| @Override | |
| public void close() throws IOException { | |
| // TODO Auto-generated method stub | |
| } | |
| @Override | |
| public Configuration getConf() { | |
| // TODO Auto-generated method stub | |
| return null; | |
| } | |
| @Override | |
| public void setConf(Configuration arg0) { | |
| // TODO Auto-generated method stub | |
| } | |
| @Override | |
| public List<String> getLabels() { | |
| // TODO Auto-generated method stub | |
| return null; | |
| } | |
| @Override | |
| public List<RecordListener> getListeners() { | |
| // TODO Auto-generated method stub | |
| return null; | |
| } | |
| @Override | |
| public void initialize(InputSplit arg0) throws IOException, InterruptedException { | |
| // TODO Auto-generated method stub | |
| } | |
| @Override | |
| public void initialize(Configuration arg0, InputSplit arg1) throws IOException, InterruptedException { | |
| // TODO Auto-generated method stub | |
| } | |
| @Override | |
| public Record loadFromMetaData(RecordMetaData arg0) throws IOException { | |
| // TODO Auto-generated method stub | |
| return null; | |
| } | |
| @Override | |
| public List<Record> loadFromMetaData(List<RecordMetaData> arg0) throws IOException { | |
| // TODO Auto-generated method stub | |
| return null; | |
| } | |
| @Override | |
| public List<Writable> record(URI arg0, DataInputStream arg1) throws IOException { | |
| // TODO Auto-generated method stub | |
| return null; | |
| } | |
| @Override | |
| public void setListeners(RecordListener... arg0) { | |
| // TODO Auto-generated method stub | |
| } | |
| @Override | |
| public void setListeners(Collection<RecordListener> arg0) { | |
| // TODO Auto-generated method stub | |
| } | |
| } |
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| public static ComputationGraph computationGraphModelYolo(int numLabels) { | |
| int nBoxes = 5; | |
| int numClasses = 14; | |
| double[][] priorBoxes = { { 1.08, 1.19 }, { 3.42, 4.41 }, { 6.63, 11.38 }, { 9.42, 5.11 }, { 16.62, 10.52 } }; | |
| INDArray priors = Nd4j.create(priorBoxes); | |
| double lambdaNoObj = 0.5; | |
| double lambdaCoord = 1.0; | |
| GraphBuilder graphBuilder = new NeuralNetConfiguration.Builder().weightInit(WeightInit.XAVIER) | |
| .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).updater(new Adam()).graphBuilder() | |
| .addInputs("input").setInputTypes(InputType.convolutional(112, 6, 1)) | |
| .addLayer("cnn1", | |
| new ConvolutionLayer.Builder(3, 2).nIn(1).stride(1, 1).convolutionMode(ConvolutionMode.Same) | |
| .nOut(40).activation(new ActivationReLU()).build(), | |
| "input") | |
| .addLayer("cnn2", | |
| new ConvolutionLayer.Builder(2, 2).nIn(40).stride(1, 1).convolutionMode(ConvolutionMode.Same) | |
| .nOut(30).activation(new ActivationReLU()).build(), | |
| "cnn1") | |
| .addLayer("pool1", | |
| new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX).kernelSize(2, 2).stride(2, 2) | |
| .build(), | |
| "cnn2") | |
| .addLayer("cnn3", | |
| new ConvolutionLayer.Builder(2, 2).nIn(30).stride(1, 1).convolutionMode(ConvolutionMode.Same) | |
| .nOut(20).activation(new ActivationReLU()).build(), | |
| "pool1") | |
| .addLayer("cnn4", | |
| new ConvolutionLayer.Builder(1, 1).nIn(20).nOut(nBoxes * (5 + numClasses)) | |
| .weightInit(WeightInit.XAVIER).stride(1, 1).convolutionMode(ConvolutionMode.Same) | |
| .weightInit(WeightInit.RELU).activation(Activation.IDENTITY).build(), | |
| "cnn3") | |
| .addLayer("outputs", | |
| new Yolo2OutputLayer.Builder().lambbaNoObj(lambdaNoObj).lambdaCoord(lambdaCoord) | |
| .boundingBoxPriors(priors).build(), | |
| "cnn4") | |
| .setOutputs("outputs").backprop(true).pretrain(false); | |
| ComputationGraph neuralNetwork = new ComputationGraph(graphBuilder.build()); | |
| // initialize the network | |
| neuralNetwork.init(); | |
| System.out.println(neuralNetwork.summary(InputType.convolutional(112, 6, 1))); | |
| return neuralNetwork; | |
| } |
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| SLF4J: Class path contains multiple SLF4J bindings. | |
| SLF4J: Found binding in [jar:file:/Users/Mike/.m2/repository/org/slf4j/slf4j-simple/1.7.25/slf4j-simple-1.7.25.jar!/org/slf4j/impl/StaticLoggerBinder.class] | |
| SLF4J: Found binding in [jar:file:/Users/Mike/.m2/repository/ch/qos/logback/logback-classic/1.1.3/logback-classic-1.1.3.jar!/org/slf4j/impl/StaticLoggerBinder.class] | |
| SLF4J: Found binding in [jar:file:/Users/Mike/.m2/repository/org/jlab/coat/coat-libs/5.1-SNAPSHOT/coat-libs-5.1-SNAPSHOT.jar!/org/slf4j/impl/StaticLoggerBinder.class] | |
| SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation. | |
| SLF4J: Actual binding is of type [org.slf4j.impl.SimpleLoggerFactory] | |
| [main] INFO org.nd4j.linalg.factory.Nd4jBackend - Loaded [CpuBackend] backend | |
| [main] WARN org.nd4j.versioncheck.VersionCheck - *** ND4J VERSION CHECK FAILED - INCOMPATIBLE VERSIONS FOUND *** | |
| [main] WARN org.nd4j.versioncheck.VersionCheck - Incompatible versions (different version number) of DL4J, ND4J, RL4J, DataVec, Arbiter are unlikely to function correctly | |
| [main] INFO org.nd4j.versioncheck.VersionCheck - Versions of artifacts found on classpath: | |
| [main] INFO org.nd4j.versioncheck.VersionCheck - org.datavec : datavec-api : 1.0.0-beta | |
| [main] INFO org.nd4j.versioncheck.VersionCheck - org.datavec : datavec-data-codec : 0.9.1 | |
| [main] INFO org.nd4j.versioncheck.VersionCheck - org.datavec : datavec-data-image : 1.0.0-beta | |
| [main] INFO org.nd4j.versioncheck.VersionCheck - org.deeplearning4j : deeplearning4j-core : 1.0.0-beta | |
| [main] INFO org.nd4j.versioncheck.VersionCheck - org.deeplearning4j : deeplearning4j-datasets : 1.0.0-beta | |
| [main] INFO org.nd4j.versioncheck.VersionCheck - org.deeplearning4j : deeplearning4j-datavec-iterators : 1.0.0-beta | |
| [main] INFO org.nd4j.versioncheck.VersionCheck - org.deeplearning4j : deeplearning4j-modelimport : 1.0.0-beta | |
| [main] INFO org.nd4j.versioncheck.VersionCheck - org.deeplearning4j : deeplearning4j-nlp : 1.0.0-beta | |
| [main] INFO org.nd4j.versioncheck.VersionCheck - org.deeplearning4j : deeplearning4j-nn : 1.0.0-beta | |
| [main] INFO org.nd4j.versioncheck.VersionCheck - org.deeplearning4j : deeplearning4j-play_2.11 : 1.0.0-beta | |
| [main] INFO org.nd4j.versioncheck.VersionCheck - org.deeplearning4j : deeplearning4j-tsne : 1.0.0-beta | |
| [main] INFO org.nd4j.versioncheck.VersionCheck - org.deeplearning4j : deeplearning4j-ui-components : 1.0.0-beta | |
| [main] INFO org.nd4j.versioncheck.VersionCheck - org.deeplearning4j : deeplearning4j-ui-model : 1.0.0-beta | |
| [main] INFO org.nd4j.versioncheck.VersionCheck - org.deeplearning4j : deeplearning4j-ui-resources : 1.0.0-beta | |
| [main] INFO org.nd4j.versioncheck.VersionCheck - org.deeplearning4j : deeplearning4j-ui_2.11 : 1.0.0-beta | |
| [main] INFO org.nd4j.versioncheck.VersionCheck - org.deeplearning4j : deeplearning4j-util : 1.0.0-beta | |
| [main] INFO org.nd4j.versioncheck.VersionCheck - org.deeplearning4j : deeplearning4j-utility-iterators : 1.0.0-beta | |
| [main] INFO org.nd4j.versioncheck.VersionCheck - org.deeplearning4j : deeplearning4j-zoo : 1.0.0-beta | |
| [main] INFO org.nd4j.versioncheck.VersionCheck - org.deeplearning4j : nearestneighbor-core : 1.0.0-beta | |
| [main] INFO org.nd4j.versioncheck.VersionCheck - org.nd4j : jackson : 1.0.0-beta | |
| [main] INFO org.nd4j.versioncheck.VersionCheck - org.nd4j : nd4j-api : 1.0.0-beta | |
| [main] INFO org.nd4j.versioncheck.VersionCheck - org.nd4j : nd4j-base64 : 1.0.0-beta | |
| [main] INFO org.nd4j.versioncheck.VersionCheck - org.nd4j : nd4j-buffer : 1.0.0-beta | |
| [main] INFO org.nd4j.versioncheck.VersionCheck - org.nd4j : nd4j-common : 1.0.0-beta | |
| [main] INFO org.nd4j.versioncheck.VersionCheck - org.nd4j : nd4j-context : 1.0.0-beta | |
| [main] INFO org.nd4j.versioncheck.VersionCheck - org.nd4j : nd4j-jackson : 1.0.0-beta | |
| [main] INFO org.nd4j.versioncheck.VersionCheck - org.nd4j : nd4j-native-api : 1.0.0-beta | |
| [main] INFO org.nd4j.versioncheck.VersionCheck - org.nd4j : nd4j-native-platform : 1.0.0-beta | |
| [main] INFO org.nd4j.versioncheck.VersionCheck - org.nd4j : nd4j-native : 1.0.0-beta | |
| [main] INFO org.nd4j.nativeblas.NativeOpsHolder - Number of threads used for NativeOps: 2 | |
| [main] INFO org.nd4j.nativeblas.Nd4jBlas - Number of threads used for BLAS: 2 | |
| [main] INFO org.nd4j.linalg.api.ops.executioner.DefaultOpExecutioner - Backend used: [CPU]; OS: [Mac OS X] | |
| [main] INFO org.nd4j.linalg.api.ops.executioner.DefaultOpExecutioner - Cores: [4]; Memory: [1.8GB]; | |
| [main] INFO org.nd4j.linalg.api.ops.executioner.DefaultOpExecutioner - Blas vendor: [OPENBLAS] | |
| [main] INFO org.deeplearning4j.nn.graph.ComputationGraph - Starting ComputationGraph with WorkspaceModes set to [training: ENABLED; inference: ENABLED], cacheMode set to [NONE] | |
| ========================================================================================================================================================================================================================================================== | |
| VertexName (VertexType) nIn,nOut TotalParams ParamsShape Vertex Inputs InputShape OutputShape | |
| ========================================================================================================================================================================================================================================================== | |
| input (InputVertex) -,- - - - - - | |
| cnn1 (ConvolutionLayer) 1,40 280 W:{40,1,3,2}, b:{1,40} [input] InputTypeConvolutional(h=112,w=6,c=1) InputTypeConvolutional(h=112,w=6,c=40) | |
| cnn2 (ConvolutionLayer) 40,30 4830 W:{30,40,2,2}, b:{1,30} [cnn1] InputTypeConvolutional(h=112,w=6,c=40) InputTypeConvolutional(h=112,w=6,c=30) | |
| pool1 (SubsamplingLayer) -,- 0 - [cnn2] InputTypeConvolutional(h=112,w=6,c=30) InputTypeConvolutional(h=56,w=3,c=30) | |
| cnn3 (ConvolutionLayer) 30,20 2420 W:{20,30,2,2}, b:{1,20} [pool1] InputTypeConvolutional(h=56,w=3,c=30) InputTypeConvolutional(h=56,w=3,c=20) | |
| cnn4 (ConvolutionLayer) 20,95 1995 W:{95,20,1,1}, b:{1,95} [cnn3] InputTypeConvolutional(h=56,w=3,c=20) InputTypeConvolutional(h=56,w=3,c=95) | |
| outputs (Yolo2OutputLayer) -,- 0 - [cnn4] InputTypeConvolutional(h=56,w=3,c=95) InputTypeConvolutional(h=56,w=3,c=95) | |
| ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | |
| Total Parameters: 9525 | |
| Trainable Parameters: 9525 | |
| Frozen Parameters: 0 | |
| ========================================================================================================================================================================================================================================================== | |
| [main] INFO play.api.Play - Application started (Prod) | |
| [main] INFO play.core.server.NettyServer - Listening for HTTP on /0:0:0:0:0:0:0:0:9000 | |
| [main] INFO org.deeplearning4j.ui.play.PlayUIServer - DL4J UI Server started at http://localhost:9000 | |
| [main] INFO org.deeplearning4j.ui.play.PlayUIServer - StatsStorage instance attached to UI: InMemoryStatsStorage(uid=b9d9382e) | |
| [main] INFO org.deeplearning4j.optimize.listeners.ScoreIterationListener - Score at iteration 0 is 109.92279276251793 | |
| Exception in thread "AMDSI prefetch thread" java.lang.RuntimeException: java.lang.IllegalArgumentException: Invalid indices: cannot get [0,0,56,0] from a [1, 18, 56, 3] NDArray | |
| at org.deeplearning4j.datasets.iterator.AsyncMultiDataSetIterator$AsyncPrefetchThread.run(AsyncMultiDataSetIterator.java:374) | |
| Caused by: java.lang.IllegalArgumentException: Invalid indices: cannot get [0,0,56,0] from a [1, 18, 56, 3] NDArray | |
| at org.nd4j.linalg.api.shape.Shape.getOffsetUnsafe(Shape.java:946) | |
| at org.nd4j.linalg.api.ndarray.BaseNDArray.putScalar(BaseNDArray.java:1426) | |
| at faultrecordreader.FaultObjectDetectionRecordReader.label(FaultObjectDetectionRecordReader.java:216) | |
| at faultrecordreader.FaultObjectDetectionRecordReader.next(FaultObjectDetectionRecordReader.java:171) | |
| at org.deeplearning4j.datasets.datavec.RecordReaderMultiDataSetIterator.next(RecordReaderMultiDataSetIterator.java:144) | |
| at org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator.next(RecordReaderDataSetIterator.java:366) | |
| at org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator.next(RecordReaderDataSetIterator.java:457) | |
| at org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator.next(RecordReaderDataSetIterator.java:86) | |
| at org.deeplearning4j.datasets.iterator.impl.MultiDataSetIteratorAdapter.next(MultiDataSetIteratorAdapter.java:62) | |
| at org.deeplearning4j.datasets.iterator.impl.MultiDataSetIteratorAdapter.next(MultiDataSetIteratorAdapter.java:13) | |
| at org.deeplearning4j.datasets.iterator.AsyncMultiDataSetIterator$AsyncPrefetchThread.run(AsyncMultiDataSetIterator.java:346) | |
| Exception in thread "main" java.lang.IllegalArgumentException: Invalid indices: cannot get [0,0,56,0] from a [1, 18, 56, 3] NDArray | |
| at org.nd4j.linalg.api.shape.Shape.getOffsetUnsafe(Shape.java:946) | |
| at org.nd4j.linalg.api.ndarray.BaseNDArray.putScalar(BaseNDArray.java:1426) | |
| at faultrecordreader.FaultObjectDetectionRecordReader.label(FaultObjectDetectionRecordReader.java:216) | |
| at faultrecordreader.FaultObjectDetectionRecordReader.next(FaultObjectDetectionRecordReader.java:171) | |
| at org.deeplearning4j.datasets.datavec.RecordReaderMultiDataSetIterator.next(RecordReaderMultiDataSetIterator.java:144) | |
| at org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator.next(RecordReaderDataSetIterator.java:366) | |
| at org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator.next(RecordReaderDataSetIterator.java:457) | |
| at org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator.next(RecordReaderDataSetIterator.java:86) | |
| at org.deeplearning4j.datasets.iterator.impl.MultiDataSetIteratorAdapter.next(MultiDataSetIteratorAdapter.java:62) | |
| at org.deeplearning4j.datasets.iterator.impl.MultiDataSetIteratorAdapter.next(MultiDataSetIteratorAdapter.java:13) | |
| at org.deeplearning4j.datasets.iterator.AsyncMultiDataSetIterator$AsyncPrefetchThread.run(AsyncMultiDataSetIterator.java:346) |
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