Examples with MalformedModelException ai.djl.MalformedModelException used on opensource projects

Example 6 with MalformedModelException

use of ai.djl.MalformedModelException in project djl by deepjavalibrary.

the class OrtModel method load.

/**
 * {@inheritDoc}
 */
@Override
public void load(Path modelPath, String prefix, Map<String, ?> options) throws IOException, MalformedModelException {
    setModelDir(modelPath);
    if (block != null) {
        throw new UnsupportedOperationException("ONNX Runtime does not support dynamic blocks");
    }
    Path modelFile = findModelFile(prefix);
    if (modelFile == null) {
        modelFile = findModelFile(modelDir.toFile().getName());
        if (modelFile == null) {
            throw new FileNotFoundException(".onnx file not found in: " + modelPath);
        }
    }
    try {
        SessionOptions ortOptions = getSessionOptions(options);
        OrtSession session = env.createSession(modelFile.toString(), ortOptions);
        block = new OrtSymbolBlock(session, (OrtNDManager) manager);
    } catch (OrtException e) {
        throw new MalformedModelException("ONNX Model cannot be loaded", e);
    }
}

Example 7 with MalformedModelException

use of ai.djl.MalformedModelException in project djl by deepjavalibrary.

the class OrtModel method load.

/**
 * {@inheritDoc}
 */
@Override
public void load(InputStream is, Map<String, ?> options) throws IOException, MalformedModelException {
    if (block != null) {
        throw new UnsupportedOperationException("ONNX Runtime does not support dynamic blocks");
    }
    modelDir = Files.createTempDirectory("ort-model");
    modelDir.toFile().deleteOnExit();
    try {
        byte[] buf = Utils.toByteArray(is);
        SessionOptions ortOptions = getSessionOptions(options);
        OrtSession session = env.createSession(buf, ortOptions);
        block = new OrtSymbolBlock(session, (OrtNDManager) manager);
    } catch (OrtException e) {
        throw new MalformedModelException("ONNX Model cannot be loaded", e);
    }
}

Example 8 with MalformedModelException

use of ai.djl.MalformedModelException in project djl by deepjavalibrary.

the class TfModel method load.

/**
 * {@inheritDoc}
 */
@Override
public void load(Path modelPath, String prefix, Map<String, ?> options) throws FileNotFoundException, MalformedModelException {
    setModelDir(modelPath);
    if (prefix == null) {
        prefix = modelName;
    }
    Path exportDir = findModelDir(prefix);
    if (exportDir == null) {
        exportDir = findModelDir("saved_model.pb");
        if (exportDir == null) {
            throw new FileNotFoundException("No TensorFlow model found in: " + modelDir);
        }
    }
    String[] tags = null;
    ConfigProto configProto = null;
    RunOptions runOptions = null;
    String signatureDefKey = DEFAULT_SERVING_SIGNATURE_DEF_KEY;
    if (options != null) {
        Object tagOption = options.get("Tags");
        if (tagOption instanceof String[]) {
            tags = (String[]) tagOption;
        } else if (tagOption instanceof String) {
            if (((String) tagOption).isEmpty()) {
                tags = new String[0];
            } else {
                tags = ((String) tagOption).split(",");
            }
        }
        Object config = options.get("ConfigProto");
        if (config instanceof ConfigProto) {
            configProto = (ConfigProto) config;
        } else if (config instanceof String) {
            try {
                byte[] buf = Base64.getDecoder().decode((String) config);
                configProto = ConfigProto.parseFrom(buf);
            } catch (InvalidProtocolBufferException e) {
                throw new MalformedModelException("Invalid ConfigProto: " + config, e);
            }
        }
        Object run = options.get("RunOptions");
        if (run instanceof RunOptions) {
            runOptions = (RunOptions) run;
        } else if (run instanceof String) {
            try {
                byte[] buf = Base64.getDecoder().decode((String) run);
                runOptions = RunOptions.parseFrom(buf);
            } catch (InvalidProtocolBufferException e) {
                throw new MalformedModelException("Invalid RunOptions: " + run, e);
            }
        }
        if (options.containsKey("SignatureDefKey")) {
            signatureDefKey = (String) options.get("SignatureDefKey");
        }
    }
    if (tags == null) {
        tags = new String[] { "serve" };
    }
    if (configProto == null) {
        // default one
        configProto = JavacppUtils.getSessionConfig();
    }
    SavedModelBundle bundle = JavacppUtils.loadSavedModelBundle(exportDir.toString(), tags, configProto, runOptions);
    block = new TfSymbolBlock(bundle, signatureDefKey);
}

Example 9 with MalformedModelException

use of ai.djl.MalformedModelException in project djl by deepjavalibrary.

the class FtModel method load.

/**
 * {@inheritDoc}
 */
@Override
public void load(Path modelPath, String prefix, Map<String, ?> options) throws IOException, MalformedModelException {
    if (Files.notExists(modelPath)) {
        throw new FileNotFoundException("Model directory doesn't exist: " + modelPath.toAbsolutePath());
    }
    modelDir = modelPath.toAbsolutePath();
    Path modelFile = findModelFile(prefix);
    if (modelFile == null) {
        modelFile = findModelFile(modelDir.toFile().getName());
        if (modelFile == null) {
            throw new FileNotFoundException("No .ftz or .bin file found in : " + modelPath);
        }
    }
    String modelFilePath = modelFile.toString();
    FtWrapper fta = FtWrapper.newInstance();
    if (!fta.checkModel(modelFilePath)) {
        throw new MalformedModelException("Malformed FastText model file:" + modelFilePath);
    }
    fta.loadModel(modelFilePath);
    if (options != null) {
        for (Map.Entry<String, ?> entry : options.entrySet()) {
            properties.put(entry.getKey(), entry.getValue().toString());
        }
    }
    String modelType = fta.getModelType();
    properties.put("model-type", modelType);
    if ("sup".equals(modelType)) {
        String labelPrefix = properties.getOrDefault("label-prefix", FtTextClassification.DEFAULT_LABEL_PREFIX);
        block = new FtTextClassification(fta, labelPrefix);
        modelDir = block.getModelFile();
    } else if ("cbow".equals(modelType) || "sg".equals(modelType)) {
        block = new FtWordEmbeddingBlock(fta);
        modelDir = block.getModelFile();
    } else {
        throw new MalformedModelException("Unexpected FastText model type: " + modelType);
    }
}

Example 10 with MalformedModelException

use of ai.djl.MalformedModelException in project build-your-own-social-media-analytics-with-apache-kafka by scholzj.

the class TopologyProducer method buildTopology.

@Produces
public Topology buildTopology() {
    final TweetSerde tweetSerde = new TweetSerde();
    try {
        Criteria<String, Classifications> criteria = Criteria.builder().optApplication(Application.NLP.SENTIMENT_ANALYSIS).setTypes(String.class, Classifications.class).build();
        predictor = ModelZoo.loadModel(criteria).newPredictor();
    } catch (IOException | ModelNotFoundException | MalformedModelException e) {
        LOG.error("Failed to load model", e);
        throw new RuntimeException("Failed to load model", e);
    }
    final StreamsBuilder builder = new StreamsBuilder();
    builder.stream(SOURCE_TOPIC, Consumed.with(Serdes.ByteArray(), tweetSerde)).flatMapValues(value -> {
        if (value.getRetweetedStatus() != null) {
            // We ignore retweets => we do not want alert for every retweet
            return List.of();
        } else {
            String tweet = value.getText();
            try {
                Classifications classifications = predictor.predict(tweet);
                String statusUrl = "https://twitter.com/" + value.getUser().getScreenName() + "/status/" + value.getId();
                String alert = String.format("The following tweet was classified as %s with %2.2f%% probability: %s", classifications.best().getClassName().toLowerCase(Locale.ENGLISH), classifications.best().getProbability() * 100, statusUrl);
                // We care nly about strong results where probability is > 50%
                if (classifications.best().getProbability() > 0.50) {
                    LOG.infov("Tweeting: {0}", alert);
                    return List.of(alert);
                } else {
                    LOG.infov("Not tweeting: {0}", alert);
                    return List.of();
                }
            } catch (TranslateException e) {
                LOG.errorv("Failed to classify the tweet {0}", value);
                return List.of();
            }
        }
    }).peek((key, value) -> LOG.infov("{0}", value)).to(TARGET_TOPIC, Produced.with(Serdes.ByteArray(), Serdes.String()));
    return builder.build();
}