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AdaptedVGG16.java
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AdaptedVGG16.java
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package org.genericsystem.cv;
import java.io.File;
import java.io.IOException;
import java.nio.file.Paths;
import java.util.List;
import java.util.Random;
import org.apache.commons.io.FilenameUtils;
import org.datavec.api.io.filters.BalancedPathFilter;
import org.datavec.api.io.labels.ParentPathLabelGenerator;
import org.datavec.api.split.FileSplit;
import org.datavec.api.split.InputSplit;
import org.datavec.image.loader.BaseImageLoader;
import org.datavec.image.recordreader.ImageRecordReader;
import org.datavec.image.transform.ImageTransform;
import org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator;
import org.deeplearning4j.datasets.iterator.AsyncDataSetIterator;
import org.deeplearning4j.earlystopping.EarlyStoppingConfiguration;
import org.deeplearning4j.earlystopping.saver.LocalFileGraphSaver;
import org.deeplearning4j.earlystopping.scorecalc.DataSetLossCalculatorCG;
import org.deeplearning4j.earlystopping.termination.MaxEpochsTerminationCondition;
import org.deeplearning4j.earlystopping.termination.ScoreImprovementEpochTerminationCondition;
import org.deeplearning4j.earlystopping.trainer.EarlyStoppingGraphTrainer;
import org.deeplearning4j.eval.Evaluation;
import org.deeplearning4j.nn.conf.GradientNormalization;
import org.deeplearning4j.nn.conf.layers.OutputLayer;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.nn.transferlearning.FineTuneConfiguration;
import org.deeplearning4j.nn.transferlearning.TransferLearning;
import org.deeplearning4j.nn.transferlearning.TransferLearningHelper;
import org.deeplearning4j.nn.weights.WeightInit;
import org.deeplearning4j.optimize.listeners.ScoreIterationListener;
import org.deeplearning4j.util.ModelSerializer;
import org.deeplearning4j.zoo.PretrainedType;
import org.deeplearning4j.zoo.ZooModel;
import org.deeplearning4j.zoo.model.VGG16;
import org.nd4j.jita.conf.CudaEnvironment;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.dataset.ExistingMiniBatchDataSetIterator;
import org.nd4j.linalg.dataset.api.DataSet;
import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
import org.nd4j.linalg.dataset.api.preprocessor.DataNormalization;
import org.nd4j.linalg.dataset.api.preprocessor.ImagePreProcessingScaler;
import org.nd4j.linalg.lossfunctions.LossFunctions;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
public class AdaptedVGG16 {
private static final Logger log = LoggerFactory.getLogger(AdaptedVGG16.class);
private static String featurizedLayer = "block5_pool";
public static void main(String[] args) throws Exception {
System.setProperty("org.bytedeco.javacpp.maxphysicalbytes", "10G");
CudaEnvironment.getInstance().getConfiguration()
.setMaximumDeviceCacheableLength(1024 * 1024 * 1024L)
.setMaximumDeviceCache(6L * 1024 * 1024 * 1024L)
.setMaximumHostCacheableLength(1024 * 1024 * 1024L)
.setMaximumHostCache(6L * 1024 * 1024 * 1024L);
double learningRate = 0.1;
int batchSize = 2;
int nEpochs = 30;
int height = 224;
int width = 224;
int channels = 3;
int seed = 123;
String[] allowedExtensions = BaseImageLoader.ALLOWED_FORMATS;
Random randNumGen = new Random(seed);
File parentDir = new File(System.getProperty("user.dir"), "training");
FileSplit filesInDir = new FileSplit(parentDir, allowedExtensions, randNumGen);
ParentPathLabelGenerator labelMaker = new ParentPathLabelGenerator();
BalancedPathFilter pathFilter = new BalancedPathFilter(randNumGen, allowedExtensions, labelMaker, 0, 0, 10, 0);
InputSplit[] filesInDirSplit = filesInDir.sample(pathFilter, 70, 15, 15);
InputSplit trainData = filesInDirSplit[0];
InputSplit validData = filesInDirSplit[1];
InputSplit testData = filesInDirSplit[2];
ImageRecordReader recordReader = new ImageRecordReader(height, width, channels, labelMaker);
recordReader.initialize(trainData, null);
List<String> labels = recordReader.getLabels();
int outputNum = recordReader.numLabels();
// Until version 0.8.0 of deeplearning4j
// TrainedModelHelper modelImportHelper = new TrainedModelHelper(TrainedModels.VGG16);
// ComputationGraph vgg16 = modelImportHelper.loadModel();
// From version 0.8.1-SNAPSHOT
ZooModel zooModel = new VGG16();
ComputationGraph vgg16 = (ComputationGraph) zooModel.initPretrained(PretrainedType.IMAGENET);
FineTuneConfiguration fineTuneConfig = new FineTuneConfiguration.Builder()
.gradientNormalization(GradientNormalization.RenormalizeL2PerLayer)
.learningRate(learningRate)
.build();
ComputationGraph net = new TransferLearning.GraphBuilder(vgg16)
.fineTuneConfiguration(fineTuneConfig)
.setFeatureExtractor(featurizedLayer)
.removeVertexKeepConnections("predictions")
// .addLayer("fc3", new DenseLayer.Builder()
// .activation(Activation.RELU)
// .weightInit(WeightInit.RELU)
// .nIn(4096).nOut(2048).build(), "fc2")
.addLayer("predictions",
new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.nIn(4096).nOut(outputNum)
.weightInit(WeightInit.RELU)
.activation(Activation.SOFTMAX).build(), "fc2")
.build();
String tempDir = System.getProperty("java.io.tmpdir");
String saveDirectory = FilenameUtils.concat(tempDir, "EarlyStoppingIntermediaryResults/");
Paths.get(saveDirectory).toFile().mkdirs();
TransferLearningHelper transferLearningHelper = new TransferLearningHelper(net, featurizedLayer);
ComputationGraph graph = transferLearningHelper.unfrozenGraph();
// Stats visualisation on http://localhost:9000/train, and print score every 10th iteration.
// UIServer uiServer = UIServer.getInstance();
// StatsStorage statsStorage = new InMemoryStatsStorage();
// uiServer.attach(statsStorage);
graph.setListeners(/*new StatsListener(statsStorage), */new ScoreIterationListener(10));
saveFeaturized(getDataSetIterator(recordReader, trainData, null, batchSize, outputNum), transferLearningHelper, "train");
saveFeaturized(getDataSetIterator(recordReader, validData, null, batchSize, outputNum), transferLearningHelper, "validation");
saveFeaturized(getDataSetIterator(recordReader, testData, null, batchSize, outputNum), transferLearningHelper, "test");
EarlyStoppingConfiguration<ComputationGraph> esConf = new EarlyStoppingConfiguration.Builder<ComputationGraph>()
.epochTerminationConditions(new MaxEpochsTerminationCondition(nEpochs))
.evaluateEveryNEpochs(1)
.epochTerminationConditions(new ScoreImprovementEpochTerminationCondition(20))
.scoreCalculator(new DataSetLossCalculatorCG(getPresavedIterator("validation"), true))
.modelSaver(new LocalFileGraphSaver(saveDirectory))
.build();
EarlyStoppingGraphTrainer trainer = new EarlyStoppingGraphTrainer(esConf, graph, getPresavedIterator("train"));
trainer.fit();
// ImageTransform flipTransform1 = new FlipImageTransform(rng);
// ImageTransform rotateTransform = new RotateImageTransform(rng, 20, 20, 100, 5);
// ImageTransform warpTransform = new WarpImageTransform(rng, 42);
// List<ImageTransform> transforms = Arrays.asList(new ImageTransform[] { warpTransform/* , rotateTransform */ });
// for (ImageTransform transform : transforms) {
// System.out.print("\nTraining on transformation: " + transform.getClass().toString() + "\n\n");
// dataIter = getDataSetIterator(recordReader, trainData, transform, batchSize, outputNum);
// trainer = new EarlyStoppingGraphTrainer(esConf, result.getBestModel(), dataIter);
// result = trainer.fit();
// }
log.info("Model evaluation:");
Evaluation eval = graph.evaluate(getPresavedIterator("test"), labels);
log.info(eval.stats(true));
File modelFile = new File("AdaptedVGG16-" + System.currentTimeMillis() + ".zip");
ModelSerializer.writeModel(graph, modelFile, true);
log.info("Model saved to " + modelFile);
}
public static DataSetIterator getPresavedIterator(String name) {
System.out.println("format : " + "images-" + featurizedLayer + "-" + name + "-%d.bin");
DataSetIterator existingTestData = new ExistingMiniBatchDataSetIterator(new File(name + "Folder"), "images-" + featurizedLayer + "-" + name + "-%d.bin");
DataSetIterator asyncTestIter = new AsyncDataSetIterator(existingTestData);
return asyncTestIter;
}
private static void saveFeaturized(DataSetIterator dataIter, TransferLearningHelper transferLearningHelper, String name) {
int dataSaved = 0;
while(dataIter.hasNext()) {
DataSet currentFeaturized = transferLearningHelper.featurize(dataIter.next());
saveToDisk(currentFeaturized, dataSaved, name);
dataSaved++;
}
}
public static void saveToDisk(DataSet currentFeaturized, int iterNum, String name) {
File fileFolder = new File(name + "Folder");
if (iterNum == 0) {
fileFolder.mkdirs();
}
String fileName = "images-" + featurizedLayer + "-" + name + "-" + iterNum + ".bin";
currentFeaturized.save(new File(fileFolder, fileName));
log.info("Saved " + name + "dataset #" + iterNum);
}
private static DataSetIterator getDataSetIterator(ImageRecordReader recordReader, InputSplit data, ImageTransform transform, int batchSize, int outputNum) {
try {
recordReader.initialize(data, transform);
} catch (IOException e) {
log.warn("Impossible to initialize recordReader", e);
}
DataSetIterator dataIter = new RecordReaderDataSetIterator(recordReader, batchSize, 1, outputNum);
DataNormalization scaler = new ImagePreProcessingScaler(-1, 1);
scaler.fit(dataIter);
dataIter.setPreProcessor(scaler);
return dataIter;
}
}