org.deeplearning4j.nn.conf.serde.MultiLayerConfigurationDeserializer Maven / Gradle / Ivy
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* Copyright (c) 2015-2018 Skymind, Inc.
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* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
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package org.deeplearning4j.nn.conf.serde;
import org.apache.commons.io.IOUtils;
import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.dropout.Dropout;
import org.deeplearning4j.nn.conf.layers.BaseLayer;
import org.deeplearning4j.nn.conf.layers.BatchNormalization;
import org.deeplearning4j.nn.conf.layers.Layer;
import org.deeplearning4j.nn.conf.weightnoise.DropConnect;
import org.deeplearning4j.nn.params.BatchNormalizationParamInitializer;
import org.nd4j.shade.jackson.core.JsonLocation;
import org.nd4j.shade.jackson.core.JsonParser;
import org.nd4j.shade.jackson.databind.DeserializationContext;
import org.nd4j.shade.jackson.databind.JsonDeserializer;
import org.nd4j.shade.jackson.databind.JsonNode;
import org.nd4j.shade.jackson.databind.ObjectMapper;
import org.nd4j.shade.jackson.databind.node.ArrayNode;
import org.nd4j.shade.jackson.databind.node.ObjectNode;
import java.io.IOException;
import java.io.StringReader;
import java.util.List;
public class MultiLayerConfigurationDeserializer extends BaseNetConfigDeserializer {
public MultiLayerConfigurationDeserializer(JsonDeserializer defaultDeserializer) {
super(defaultDeserializer, MultiLayerConfiguration.class);
}
@Override
public MultiLayerConfiguration deserialize(JsonParser jp, DeserializationContext ctxt) throws IOException {
long charOffsetStart = jp.getCurrentLocation().getCharOffset();
MultiLayerConfiguration conf = (MultiLayerConfiguration) defaultDeserializer.deserialize(jp, ctxt);
Layer[] layers = new Layer[conf.getConfs().size()];
for (int i = 0; i < layers.length; i++) {
layers[i] = conf.getConf(i).getLayer();
}
//Now, check if we need to manually handle IUpdater deserialization from legacy format
boolean attemptIUpdaterFromLegacy = requiresIUpdaterFromLegacy(layers);
boolean requiresLegacyRegularizationHandling = requiresRegularizationFromLegacy(layers);
boolean requiresLegacyWeightInitHandling = requiresWeightInitFromLegacy(layers);
if(attemptIUpdaterFromLegacy || requiresLegacyRegularizationHandling || requiresLegacyWeightInitHandling) {
JsonLocation endLocation = jp.getCurrentLocation();
long charOffsetEnd = endLocation.getCharOffset();
Object sourceRef = endLocation.getSourceRef();
String s;
if (sourceRef instanceof StringReader) {
//Workaround: sometimes sourceRef is a String, sometimes a StringReader
((StringReader) sourceRef).reset();
s = IOUtils.toString((StringReader)sourceRef);
} else {
s = sourceRef.toString();
}
String jsonSubString = s.substring((int) charOffsetStart - 1, (int) charOffsetEnd);
ObjectMapper om = NeuralNetConfiguration.mapper();
JsonNode rootNode = om.readTree(jsonSubString);
ArrayNode confsNode = (ArrayNode)rootNode.get("confs");
for( int i=0; i (first/only child) -> updater
if(on.has("layer")){
confNode = on;
on = (ObjectNode) on.get("layer");
} else {
continue;
}
on = (ObjectNode) on.elements().next();
handleUpdaterBackwardCompatibility((BaseLayer)layers[i], on);
}
if(attemptIUpdaterFromLegacy) {
if (layers[i].getIDropout() == null) {
//Check for legacy dropout/dropconnect
if (on.has("dropOut")) {
double d = on.get("dropOut").asDouble();
if (!Double.isNaN(d)) {
//Might be dropout or dropconnect...
if (confNode != null && layers[i] instanceof BaseLayer && confNode.has("useDropConnect")
&& confNode.get("useDropConnect").asBoolean(false)) {
((BaseLayer) layers[i]).setWeightNoise(new DropConnect(d));
} else {
if (d > 0.0) {
layers[i].setIDropout(new Dropout(d));
}
}
}
}
}
}
if(requiresLegacyRegularizationHandling) {
if (layers[i] instanceof BaseLayer && ((BaseLayer) layers[i]).getRegularization() == null) {
if(on.has("layer")){
//Legacy format
ObjectNode layerNode = (ObjectNode)on.get("layer");
if(layerNode.has("@class")){
//Later legacy format: class field for JSON subclass
on = layerNode;
} else {
//Early legacy format: wrapper object for JSON subclass
on = (ObjectNode) on.get("layer").elements().next();
}
}
handleL1L2BackwardCompatibility((BaseLayer) layers[i], on);
}
}
if(requiresLegacyWeightInitHandling){
if (layers[i] instanceof BaseLayer && ((BaseLayer) layers[i]).getWeightInitFn() == null) {
if(on.has("layer")){
//Legacy format
ObjectNode layerNode = (ObjectNode)on.get("layer");
if(layerNode.has("@class")){
//Later legacy format: class field for JSON subclass
on = layerNode;
} else {
//Early legacy format: wrapper object for JSON subclass
on = (ObjectNode) on.get("layer").elements().next();
}
}
handleWeightInitBackwardCompatibility((BaseLayer) layers[i], on);
}
}
}
}
//After 1.0.0-beta3, batchnorm reparameterized to support both variance and log10stdev
//JSON deserialization uses public BatchNormalization() constructor which defaults to log10stdev now
// but, as there is no useLogStdev=false property for legacy batchnorm JSON, the 'real' value (useLogStdev=false)
// is not set to override the default, unless we do it manually here
for(NeuralNetConfiguration nnc : conf.getConfs()){
Layer l = nnc.getLayer();
if(l instanceof BatchNormalization){
BatchNormalization bn = (BatchNormalization)l;
List vars = nnc.getVariables();
boolean isVariance = vars.contains(BatchNormalizationParamInitializer.GLOBAL_VAR);
bn.setUseLogStd(!isVariance);
}
}
return conf;
}
}