Merge branch 'master' of https://github.com/horschi/deeplearning4j into horschi-master
Alex Black
commit
2e52f0217f
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@ -43,9 +43,12 @@ import org.nd4j.linalg.primitives.Pair;
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import java.io.*;
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import java.util.ArrayList;
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import java.util.Enumeration;
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import java.util.HashMap;
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import java.util.List;
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import java.util.Map;
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import java.util.zip.ZipEntry;
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import java.util.zip.ZipFile;
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import java.util.zip.ZipInputStream;
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import java.util.zip.ZipOutputStream;
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/**
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@ -215,7 +218,24 @@ public class ModelSerializer {
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*/
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public static MultiLayerNetwork restoreMultiLayerNetwork(@NonNull File file, boolean loadUpdater)
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throws IOException {
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ZipFile zipFile = new ZipFile(file);
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return restoreMultiLayerNetwork(new FileInputStream(file), loadUpdater);
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}
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/**
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* Load a MultiLayerNetwork from InputStream from an input stream<br>
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* Note: the input stream is read fully and closed by this method. Consequently, the input stream cannot be re-used.
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*
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* @param is the inputstream to load from
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* @return the loaded multi layer network
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* @throws IOException
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* @see #restoreMultiLayerNetworkAndNormalizer(InputStream, boolean)
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*/
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public static MultiLayerNetwork restoreMultiLayerNetwork(@NonNull InputStream is, boolean loadUpdater)
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throws IOException {
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checkInputStream(is);
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Map<String, byte[]> zipFile = loadZipData(is);
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boolean gotConfig = false;
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boolean gotCoefficients = false;
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@ -229,11 +249,11 @@ public class ModelSerializer {
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DataSetPreProcessor preProcessor = null;
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ZipEntry config = zipFile.getEntry(CONFIGURATION_JSON);
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byte[] config = zipFile.get(CONFIGURATION_JSON);
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if (config != null) {
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//restoring configuration
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InputStream stream = zipFile.getInputStream(config);
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InputStream stream = new ByteArrayInputStream(config);
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BufferedReader reader = new BufferedReader(new InputStreamReader(stream));
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String line = "";
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StringBuilder js = new StringBuilder();
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@ -248,25 +268,25 @@ public class ModelSerializer {
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}
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ZipEntry coefficients = zipFile.getEntry(COEFFICIENTS_BIN);
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byte[] coefficients = zipFile.get(COEFFICIENTS_BIN);
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if (coefficients != null ) {
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if(coefficients.getSize() > 0) {
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InputStream stream = zipFile.getInputStream(coefficients);
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if(coefficients.length > 0) {
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InputStream stream = new ByteArrayInputStream(coefficients);
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DataInputStream dis = new DataInputStream(new BufferedInputStream(stream));
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params = Nd4j.read(dis);
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dis.close();
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gotCoefficients = true;
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} else {
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ZipEntry noParamsMarker = zipFile.getEntry(NO_PARAMS_MARKER);
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byte[] noParamsMarker = zipFile.get(NO_PARAMS_MARKER);
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gotCoefficients = (noParamsMarker != null);
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}
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}
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if (loadUpdater) {
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ZipEntry updaterStateEntry = zipFile.getEntry(UPDATER_BIN);
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byte[] updaterStateEntry = zipFile.get(UPDATER_BIN);
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if (updaterStateEntry != null) {
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InputStream stream = zipFile.getInputStream(updaterStateEntry);
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InputStream stream = new ByteArrayInputStream(updaterStateEntry);
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DataInputStream dis = new DataInputStream(new BufferedInputStream(stream));
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updaterState = Nd4j.read(dis);
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@ -275,9 +295,9 @@ public class ModelSerializer {
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}
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}
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ZipEntry prep = zipFile.getEntry(PREPROCESSOR_BIN);
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byte[] prep = zipFile.get(PREPROCESSOR_BIN);
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if (prep != null) {
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InputStream stream = zipFile.getInputStream(prep);
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InputStream stream = new ByteArrayInputStream(prep);
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ObjectInputStream ois = new ObjectInputStream(stream);
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try {
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@ -290,7 +310,6 @@ public class ModelSerializer {
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}
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zipFile.close();
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if (gotConfig && gotCoefficients) {
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MultiLayerConfiguration confFromJson;
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@ -328,31 +347,6 @@ public class ModelSerializer {
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+ "], gotCoefficients: [" + gotCoefficients + "], gotUpdater: [" + gotUpdaterState + "]");
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}
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/**
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* Load a MultiLayerNetwork from InputStream from an input stream<br>
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* Note: the input stream is read fully and closed by this method. Consequently, the input stream cannot be re-used.
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*
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* @param is the inputstream to load from
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* @return the loaded multi layer network
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* @throws IOException
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* @see #restoreMultiLayerNetworkAndNormalizer(InputStream, boolean)
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*/
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public static MultiLayerNetwork restoreMultiLayerNetwork(@NonNull InputStream is, boolean loadUpdater)
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throws IOException {
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checkInputStream(is);
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File tmpFile = null;
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try{
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tmpFile = tempFileFromStream(is);
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return restoreMultiLayerNetwork(tmpFile, loadUpdater);
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} finally {
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if(tmpFile != null){
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tmpFile.delete();
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}
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}
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}
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/**
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* Restore a multi layer network from an input stream<br>
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* * Note: the input stream is read fully and closed by this method. Consequently, the input stream cannot be re-used.
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@ -404,15 +398,9 @@ public class ModelSerializer {
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@NonNull InputStream is, boolean loadUpdater) throws IOException {
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checkInputStream(is);
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File tmpFile = null;
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try {
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tmpFile = tempFileFromStream(is);
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return restoreMultiLayerNetworkAndNormalizer(tmpFile, loadUpdater);
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} finally {
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if (tmpFile != null) {
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tmpFile.delete();
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}
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}
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MultiLayerNetwork net = restoreMultiLayerNetwork(is, loadUpdater);
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Normalizer norm = restoreNormalizerFromInputStream(is);
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return new Pair<>(net, norm);
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}
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/**
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@ -425,9 +413,7 @@ public class ModelSerializer {
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*/
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public static Pair<MultiLayerNetwork, Normalizer> restoreMultiLayerNetworkAndNormalizer(@NonNull File file, boolean loadUpdater)
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throws IOException {
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MultiLayerNetwork net = restoreMultiLayerNetwork(file, loadUpdater);
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Normalizer norm = restoreNormalizerFromFile(file);
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return new Pair<>(net, norm);
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return restoreMultiLayerNetworkAndNormalizer(new FileInputStream(file), loadUpdater);
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}
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/**
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@ -465,87 +451,7 @@ public class ModelSerializer {
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throws IOException {
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checkInputStream(is);
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File tmpFile = null;
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try{
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tmpFile = tempFileFromStream(is);
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return restoreComputationGraph(tmpFile, loadUpdater);
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} finally {
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if(tmpFile != null){
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tmpFile.delete();
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}
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}
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}
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/**
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* Load a computation graph from a InputStream
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* @param is the inputstream to get the computation graph from
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* @return the loaded computation graph
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*
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* @throws IOException
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*/
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public static ComputationGraph restoreComputationGraph(@NonNull InputStream is) throws IOException {
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return restoreComputationGraph(is, true);
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}
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/**
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* Load a computation graph from a file
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* @param file the file to get the computation graph from
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* @return the loaded computation graph
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*
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* @throws IOException
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*/
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public static ComputationGraph restoreComputationGraph(@NonNull File file) throws IOException {
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return restoreComputationGraph(file, true);
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}
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/**
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* Restore a ComputationGraph and Normalizer (if present - null if not) from the InputStream.
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* Note: the input stream is read fully and closed by this method. Consequently, the input stream cannot be re-used.
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*
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* @param is Input stream to read from
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* @param loadUpdater Whether to load the updater from the model or not
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* @return Model and normalizer, if present
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* @throws IOException If an error occurs when reading from the stream
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*/
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public static Pair<ComputationGraph, Normalizer> restoreComputationGraphAndNormalizer(
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@NonNull InputStream is, boolean loadUpdater) throws IOException {
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checkInputStream(is);
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File tmpFile = null;
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try {
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tmpFile = tempFileFromStream(is);
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return restoreComputationGraphAndNormalizer(tmpFile, loadUpdater);
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} finally {
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if (tmpFile != null) {
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tmpFile.delete();
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}
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}
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}
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/**
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* Restore a ComputationGraph and Normalizer (if present - null if not) from a File
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*
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* @param file File to read the model and normalizer from
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* @param loadUpdater Whether to load the updater from the model or not
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* @return Model and normalizer, if present
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* @throws IOException If an error occurs when reading from the File
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*/
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public static Pair<ComputationGraph, Normalizer> restoreComputationGraphAndNormalizer(@NonNull File file, boolean loadUpdater)
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throws IOException {
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ComputationGraph net = restoreComputationGraph(file, loadUpdater);
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Normalizer norm = restoreNormalizerFromFile(file);
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return new Pair<>(net, norm);
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}
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/**
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* Load a computation graph from a file
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* @param file the file to get the computation graph from
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* @return the loaded computation graph
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*
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* @throws IOException
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*/
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public static ComputationGraph restoreComputationGraph(@NonNull File file, boolean loadUpdater) throws IOException {
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ZipFile zipFile = new ZipFile(file);
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Map<String, byte[]> files = loadZipData(is);
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boolean gotConfig = false;
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boolean gotCoefficients = false;
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@ -558,11 +464,11 @@ public class ModelSerializer {
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DataSetPreProcessor preProcessor = null;
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ZipEntry config = zipFile.getEntry(CONFIGURATION_JSON);
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byte[] config = files.get(CONFIGURATION_JSON);
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if (config != null) {
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//restoring configuration
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InputStream stream = zipFile.getInputStream(config);
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InputStream stream = new ByteArrayInputStream(config);
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BufferedReader reader = new BufferedReader(new InputStreamReader(stream));
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String line = "";
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StringBuilder js = new StringBuilder();
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@ -577,27 +483,27 @@ public class ModelSerializer {
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}
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ZipEntry coefficients = zipFile.getEntry(COEFFICIENTS_BIN);
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byte[] coefficients = files.get(COEFFICIENTS_BIN);
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if (coefficients != null) {
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if(coefficients.getSize() > 0) {
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InputStream stream = zipFile.getInputStream(coefficients);
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DataInputStream dis = new DataInputStream(new BufferedInputStream(stream));
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if(coefficients.length > 0) {
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InputStream stream = new ByteArrayInputStream(coefficients);
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DataInputStream dis = new DataInputStream(stream);
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params = Nd4j.read(dis);
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dis.close();
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gotCoefficients = true;
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} else {
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ZipEntry noParamsMarker = zipFile.getEntry(NO_PARAMS_MARKER);
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byte[] noParamsMarker = files.get(NO_PARAMS_MARKER);
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gotCoefficients = (noParamsMarker != null);
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}
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}
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if (loadUpdater) {
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ZipEntry updaterStateEntry = zipFile.getEntry(UPDATER_BIN);
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byte[] updaterStateEntry = files.get(UPDATER_BIN);
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if (updaterStateEntry != null) {
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InputStream stream = zipFile.getInputStream(updaterStateEntry);
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DataInputStream dis = new DataInputStream(new BufferedInputStream(stream));
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InputStream stream = new ByteArrayInputStream(updaterStateEntry);
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DataInputStream dis = new DataInputStream(stream);
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updaterState = Nd4j.read(dis);
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dis.close();
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@ -605,9 +511,9 @@ public class ModelSerializer {
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}
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}
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ZipEntry prep = zipFile.getEntry(PREPROCESSOR_BIN);
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byte[] prep = files.get(PREPROCESSOR_BIN);
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if (prep != null) {
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InputStream stream = zipFile.getInputStream(prep);
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InputStream stream = new ByteArrayInputStream(prep);
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ObjectInputStream ois = new ObjectInputStream(stream);
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try {
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@ -620,8 +526,6 @@ public class ModelSerializer {
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}
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zipFile.close();
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if (gotConfig && gotCoefficients) {
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ComputationGraphConfiguration confFromJson;
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try{
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@ -662,6 +566,70 @@ public class ModelSerializer {
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+ "], gotCoefficients: [" + gotCoefficients + "], gotUpdater: [" + gotUpdaterState + "]");
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}
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/**
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* Load a computation graph from a InputStream
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* @param is the inputstream to get the computation graph from
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* @return the loaded computation graph
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*
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* @throws IOException
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*/
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public static ComputationGraph restoreComputationGraph(@NonNull InputStream is) throws IOException {
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return restoreComputationGraph(is, true);
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}
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/**
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* Load a computation graph from a file
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* @param file the file to get the computation graph from
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* @return the loaded computation graph
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*
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* @throws IOException
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*/
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public static ComputationGraph restoreComputationGraph(@NonNull File file) throws IOException {
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return restoreComputationGraph(file, true);
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}
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/**
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* Restore a ComputationGraph and Normalizer (if present - null if not) from the InputStream.
|
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* Note: the input stream is read fully and closed by this method. Consequently, the input stream cannot be re-used.
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*
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* @param is Input stream to read from
|
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* @param loadUpdater Whether to load the updater from the model or not
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* @return Model and normalizer, if present
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* @throws IOException If an error occurs when reading from the stream
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*/
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public static Pair<ComputationGraph, Normalizer> restoreComputationGraphAndNormalizer(
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@NonNull InputStream is, boolean loadUpdater) throws IOException {
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checkInputStream(is);
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ComputationGraph net = restoreComputationGraph(is, loadUpdater);
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Normalizer norm = restoreNormalizerFromInputStream(is);
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return new Pair<>(net, norm);
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}
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/**
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* Restore a ComputationGraph and Normalizer (if present - null if not) from a File
|
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*
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* @param file File to read the model and normalizer from
|
||||
* @param loadUpdater Whether to load the updater from the model or not
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* @return Model and normalizer, if present
|
||||
* @throws IOException If an error occurs when reading from the File
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*/
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public static Pair<ComputationGraph, Normalizer> restoreComputationGraphAndNormalizer(@NonNull File file, boolean loadUpdater)
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throws IOException {
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return restoreComputationGraphAndNormalizer(new FileInputStream(file), loadUpdater);
|
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}
|
||||
|
||||
/**
|
||||
* Load a computation graph from a file
|
||||
* @param file the file to get the computation graph from
|
||||
* @return the loaded computation graph
|
||||
*
|
||||
* @throws IOException
|
||||
*/
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public static ComputationGraph restoreComputationGraph(@NonNull File file, boolean loadUpdater) throws IOException {
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return restoreComputationGraph(new FileInputStream(file), loadUpdater);
|
||||
}
|
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|
||||
/**
|
||||
*
|
||||
* @param model
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|
|
@ -811,15 +779,16 @@ public class ModelSerializer {
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|||
}
|
||||
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//Add new object:
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||||
ZipEntry entry = new ZipEntry("objects/" + key);
|
||||
writeFile.putNextEntry(entry);
|
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|
||||
try(ByteArrayOutputStream baos = new ByteArrayOutputStream(); ObjectOutputStream oos = new ObjectOutputStream(baos)){
|
||||
oos.writeObject(o);
|
||||
byte[] bytes = baos.toByteArray();
|
||||
ZipEntry entry = new ZipEntry("objects/" + key);
|
||||
entry.setSize(bytes.length);
|
||||
writeFile.putNextEntry(entry);
|
||||
writeFile.write(bytes);
|
||||
}
|
||||
writeFile.closeEntry();
|
||||
}
|
||||
|
||||
writeFile.close();
|
||||
zipFile.close();
|
||||
|
|
@ -904,18 +873,12 @@ public class ModelSerializer {
|
|||
* @param file
|
||||
* @return
|
||||
*/
|
||||
public static <T extends Normalizer> T restoreNormalizerFromFile(File file) {
|
||||
try (ZipFile zipFile = new ZipFile(file)) {
|
||||
ZipEntry norm = zipFile.getEntry(NORMALIZER_BIN);
|
||||
|
||||
// checking for file existence
|
||||
if (norm == null)
|
||||
return null;
|
||||
|
||||
return NormalizerSerializer.getDefault().restore(zipFile.getInputStream(norm));
|
||||
public static <T extends Normalizer> T restoreNormalizerFromFile(File file) throws IOException {
|
||||
try {
|
||||
return restoreNormalizerFromInputStream(new FileInputStream(file));
|
||||
} catch (Exception e) {
|
||||
log.warn("Error while restoring normalizer, trying to restore assuming deprecated format...");
|
||||
DataNormalization restoredDeprecated = restoreNormalizerFromFileDeprecated(file);
|
||||
DataNormalization restoredDeprecated = restoreNormalizerFromInputStreamDeprecated(new FileInputStream(file));
|
||||
|
||||
log.warn("Recovered using deprecated method. Will now re-save the normalizer to fix this issue.");
|
||||
addNormalizerToModel(file, restoredDeprecated);
|
||||
|
|
@ -934,14 +897,17 @@ public class ModelSerializer {
|
|||
public static <T extends Normalizer> T restoreNormalizerFromInputStream(InputStream is) throws IOException {
|
||||
checkInputStream(is);
|
||||
|
||||
File tmpFile = null;
|
||||
Map<String, byte[]> files = loadZipData(is);
|
||||
byte[] norm = files.get(NORMALIZER_BIN);
|
||||
|
||||
// checking for file existence
|
||||
if (norm == null)
|
||||
return null;
|
||||
try {
|
||||
tmpFile = tempFileFromStream(is);
|
||||
return restoreNormalizerFromFile(tmpFile);
|
||||
} finally {
|
||||
if(tmpFile != null){
|
||||
tmpFile.delete();
|
||||
return NormalizerSerializer.getDefault().restore(new ByteArrayInputStream(norm));
|
||||
}
|
||||
catch (Exception e) {
|
||||
throw new IOException("Error loading normalizer", e);
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -953,17 +919,9 @@ public class ModelSerializer {
|
|||
* @param file
|
||||
* @return
|
||||
*/
|
||||
private static DataNormalization restoreNormalizerFromFileDeprecated(File file) {
|
||||
try (ZipFile zipFile = new ZipFile(file)) {
|
||||
ZipEntry norm = zipFile.getEntry(NORMALIZER_BIN);
|
||||
|
||||
// checking for file existence
|
||||
if (norm == null)
|
||||
return null;
|
||||
|
||||
InputStream stream = zipFile.getInputStream(norm);
|
||||
private static DataNormalization restoreNormalizerFromInputStreamDeprecated(InputStream stream) {
|
||||
try {
|
||||
ObjectInputStream ois = new ObjectInputStream(stream);
|
||||
|
||||
try {
|
||||
DataNormalization normalizer = (DataNormalization) ois.readObject();
|
||||
return normalizer;
|
||||
|
|
@ -996,31 +954,30 @@ public class ModelSerializer {
|
|||
*/
|
||||
}
|
||||
|
||||
private static void checkTempFileFromInputStream(File f) throws IOException {
|
||||
if (f.length() <= 0) {
|
||||
throw new IOException("Error reading from input stream: temporary file is empty after copying entire stream." +
|
||||
" Stream may have been closed before reading, is attempting to be used multiple times, or does not" +
|
||||
" point to a model file?");
|
||||
private static Map<String, byte[]> loadZipData(InputStream is) throws IOException {
|
||||
Map<String, byte[]> result = new HashMap<>();
|
||||
try (final ZipInputStream zis = new ZipInputStream(is)) {
|
||||
while (true) {
|
||||
final ZipEntry zipEntry = zis.getNextEntry();
|
||||
if (zipEntry == null)
|
||||
break;
|
||||
if(zipEntry.isDirectory() || zipEntry.getSize() > Integer.MAX_VALUE)
|
||||
throw new IllegalArgumentException();
|
||||
|
||||
final int size = (int) (zipEntry.getSize());
|
||||
final byte[] data;
|
||||
if (size >= 0) { // known size
|
||||
data = IOUtils.readFully(zis, size);
|
||||
}
|
||||
else { // unknown size
|
||||
final ByteArrayOutputStream bout = new ByteArrayOutputStream();
|
||||
IOUtils.copy(zis, bout);
|
||||
data = bout.toByteArray();
|
||||
}
|
||||
result.put(zipEntry.getName(), data);
|
||||
}
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
private static File tempFileFromStream(InputStream is) throws IOException{
|
||||
checkInputStream(is);
|
||||
String p = System.getProperty(DL4JSystemProperties.DL4J_TEMP_DIR_PROPERTY);
|
||||
File tmpFile = DL4JFileUtils.createTempFile("dl4jModelSerializer", "bin");
|
||||
try {
|
||||
tmpFile.deleteOnExit();
|
||||
BufferedOutputStream bufferedOutputStream = new BufferedOutputStream(new FileOutputStream(tmpFile));
|
||||
IOUtils.copy(is, bufferedOutputStream);
|
||||
bufferedOutputStream.flush();
|
||||
IOUtils.closeQuietly(bufferedOutputStream);
|
||||
checkTempFileFromInputStream(tmpFile);
|
||||
return tmpFile;
|
||||
} catch (IOException e){
|
||||
if(tmpFile != null){
|
||||
tmpFile.delete();
|
||||
}
|
||||
throw e;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
|
|||
Loading…
Reference in New Issue