Example usage for org.deeplearning4j.util ModelSerializer restoreComputationGraph

Introduction

In this page you can find the example usage for org.deeplearning4j.util ModelSerializer restoreComputationGraph.

Prototype

public static ComputationGraph restoreComputationGraph(@NonNull File file) throws IOException 

Source Link

Document

Load a computation graph from a file

Usage

From source file:org.apache.tika.dl.imagerec.DL4JVGG16Net.java

License:Apache License

@Override
public void initialize(Map<String, Param> params) throws TikaConfigException {
    try {/*from  w w w . j a  v  a 2 s  .  c  o m*/
        if (serialize) {
            if (cacheDir.exists()) {
                model = ModelSerializer.restoreComputationGraph(cacheDir);
                LOG.info("Preprocessed Model Loaded from {}", cacheDir);
            } else {
                LOG.warn("Preprocessed Model doesn't exist at {}", cacheDir);
                cacheDir.getParentFile().mkdirs();
                ZooModel zooModel = VGG16.builder().build();
                model = (ComputationGraph) zooModel.initPretrained(PretrainedType.IMAGENET);
                LOG.info(
                        "Saving the Loaded model for future use. Saved models are more optimised to consume less resources.");
                ModelSerializer.writeModel(model, cacheDir, true);
            }
        } else {
            LOG.info("Weight graph model loaded via dl4j Helper functions");
            ZooModel zooModel = VGG16.builder().build();
            model = (ComputationGraph) zooModel.initPretrained(PretrainedType.IMAGENET);
        }
        imageNetLabels = new ImageNetLabels();
        available = true;
    } catch (Exception e) {
        available = false;
        LOG.warn(e.getMessage(), e);
        throw new TikaConfigException(e.getMessage(), e);
    }
}

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From source file:org.apache.tika.parser.recognition.dl4j.DL4JImageRecogniser.java

License:Apache License

@Override
public void initialize(Map<String, Param> params) throws TikaConfigException {
    try {// ww w.j a v  a  2s  . com
        TrainedModelHelper helper;
        switch (modelType) {
        case "VGG16NOTOP":
            throw new TikaConfigException("VGG16NOTOP is not supported right now");
            /*# TODO hookup VGGNOTOP by uncommenting following code once the issue is resolved by dl4j team
            modelFile = new File(MODEL_DIR_PREPROCESSED+File.separator+"vgg16_notop.zip");
            locationToSave= new File(MODEL_DIR+File.separator+"tikaPreprocessed"+File.separator+"vgg16.zip");
            helper = new TrainedModelHelper(TrainedModels.VGG16NOTOP);
            break;*/
        case "VGG16":
            helper = new TrainedModelHelper(TrainedModels.VGG16);
            modelFile = new File(MODEL_DIR_PREPROCESSED + File.separator + "vgg16.zip");
            locationToSave = new File(
                    MODEL_DIR + File.separator + "tikaPreprocessed" + File.separator + "vgg16.zip");
            break;
        default:
            throw new TikaConfigException("Unknown or unsupported model");
        }
        if (serialize.trim().toLowerCase(Locale.ROOT).equals("yes")) {
            if (!modelFile.exists()) {
                LOG.warn("Preprocessed Model doesn't exist at {}", modelFile);
                modelFile.getParentFile().mkdirs();
                model = helper.loadModel();
                LOG.info(
                        "Saving the Loaded model for future use. Saved models are more optimised to consume less resources.");
                ModelSerializer.writeModel(model, locationToSave, true);
                available = true;
            } else {
                model = ModelSerializer.restoreComputationGraph(locationToSave);
                LOG.info("Preprocessed Model Loaded from {}", locationToSave);
                available = true;
            }

        } else if (serialize.trim().toLowerCase(Locale.ROOT).equals("no")) {
            LOG.info("Weight graph model loaded via dl4j Helper functions");
            model = helper.loadModel();
            available = true;
        } else {
            throw new TikaConfigException("Configuration Error. serialization can be either yes or no.");
        }

        if (!available) {
            return;
        }
        HashMap<Pattern, String> patterns = new HashMap<>();
        patterns.put(Pattern.compile(outPattern), null);
        setMetadataExtractionPatterns(patterns);
        setIgnoredLineConsumer(IGNORED_LINE_LOGGER);
    } catch (Exception e) {
        LOG.warn("exception occured");
        throw new TikaConfigException(e.getMessage(), e);
    }
}

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