org.deeplearning4j.zoo.model.NASNet Maven / Gradle / Ivy
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package org.deeplearning4j.zoo.model;
import lombok.AllArgsConstructor;
import lombok.Builder;
import org.deeplearning4j.common.resources.DL4JResources;
import org.deeplearning4j.nn.api.Model;
import org.deeplearning4j.nn.api.OptimizationAlgorithm;
import org.deeplearning4j.nn.conf.*;
import org.deeplearning4j.nn.conf.distribution.Distribution;
import org.deeplearning4j.nn.conf.distribution.NormalDistribution;
import org.deeplearning4j.nn.conf.distribution.TruncatedNormalDistribution;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.*;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.nn.weights.WeightInit;
import org.deeplearning4j.zoo.ModelMetaData;
import org.deeplearning4j.zoo.PretrainedType;
import org.deeplearning4j.zoo.ZooModel;
import org.deeplearning4j.zoo.ZooType;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.learning.config.AdaDelta;
import org.nd4j.linalg.learning.config.IUpdater;
import org.nd4j.linalg.lossfunctions.LossFunctions;
import org.nd4j.linalg.primitives.Pair;
import static org.deeplearning4j.zoo.model.helper.NASNetHelper.normalA;
import static org.deeplearning4j.zoo.model.helper.NASNetHelper.reductionA;
/**
* U-Net
*
* Implementation of NASNet-A in Deeplearning4j. NASNet refers to Neural Architecture Search Network, a family of models
* that were designed automatically by learning the model architectures directly on the dataset of interest.
*
* This implementation uses 1056 penultimate filters and an input shape of (3, 224, 224). You can change this.
*
* Paper: https://arxiv.org/abs/1707.07012
* ImageNet weights for this model are available and have been converted from https://keras.io/applications/.
*
* @note If using the IMAGENETLARGE weights, the input shape is (3, 331, 331).
* @author Justin Long (crockpotveggies)
*
*/
@AllArgsConstructor
@Builder
public class NASNet extends ZooModel {
@Builder.Default private long seed = 1234;
@Builder.Default private int[] inputShape = new int[] {3, 224, 224};
@Builder.Default private int numClasses = 0;
@Builder.Default private WeightInit weightInit = WeightInit.RELU;
@Builder.Default private IUpdater updater = new AdaDelta();
@Builder.Default private CacheMode cacheMode = CacheMode.DEVICE;
@Builder.Default private WorkspaceMode workspaceMode = WorkspaceMode.ENABLED;
@Builder.Default private ConvolutionLayer.AlgoMode cudnnAlgoMode = ConvolutionLayer.AlgoMode.PREFER_FASTEST;
// NASNet specific
@Builder.Default private int numBlocks = 6;
@Builder.Default private int penultimateFilters = 1056;
@Builder.Default private int stemFilters = 96;
@Builder.Default private int filterMultiplier = 2;
@Builder.Default private boolean skipReduction = true;
private NASNet() {}
@Override
public String pretrainedUrl(PretrainedType pretrainedType) {
if (pretrainedType == PretrainedType.IMAGENET)
return DL4JResources.getURLString("models/nasnetmobile_dl4j_inference.v1.zip");
else if (pretrainedType == PretrainedType.IMAGENETLARGE)
return DL4JResources.getURLString("models/nasnetlarge_dl4j_inference.v1.zip");
else
return null;
}
@Override
public long pretrainedChecksum(PretrainedType pretrainedType) {
if (pretrainedType == PretrainedType.IMAGENET)
return 3082463801L;
else if (pretrainedType == PretrainedType.IMAGENETLARGE)
return 321395591L;
else
return 0L;
}
@Override
public Class modelType() {
return ComputationGraph.class;
}
@Override
public ComputationGraph init() {
ComputationGraphConfiguration.GraphBuilder graph = graphBuilder();
graph.addInputs("input").setInputTypes(InputType.convolutional(inputShape[2], inputShape[1], inputShape[0]));
ComputationGraphConfiguration conf = graph.build();
ComputationGraph model = new ComputationGraph(conf);
model.init();
return model;
}
public ComputationGraphConfiguration.GraphBuilder graphBuilder() {
if(penultimateFilters % 24 != 0) {
throw new IllegalArgumentException("For NASNet-A models penultimate filters must be divisible by 24. Current value is "+penultimateFilters);
}
int filters = (int) Math.floor(penultimateFilters / 24);
ComputationGraphConfiguration.GraphBuilder graph = new NeuralNetConfiguration.Builder().seed(seed)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.updater(updater)
.weightInit(weightInit)
.l2(5e-5)
.miniBatch(true)
.cacheMode(cacheMode)
.trainingWorkspaceMode(workspaceMode)
.inferenceWorkspaceMode(workspaceMode)
.cudnnAlgoMode(cudnnAlgoMode)
.convolutionMode(ConvolutionMode.Truncate)
.graphBuilder();
if(!skipReduction) {
graph.addLayer("stem_conv1", new ConvolutionLayer.Builder(3, 3).stride(2, 2).nOut(stemFilters).hasBias(false)
.cudnnAlgoMode(cudnnAlgoMode).build(), "input");
} else {
graph.addLayer("stem_conv1", new ConvolutionLayer.Builder(3, 3).stride(1, 1).nOut(stemFilters).hasBias(false)
.cudnnAlgoMode(cudnnAlgoMode).build(), "input");
}
graph.addLayer("stem_bn1", new BatchNormalization.Builder().eps(1e-3).gamma(0.9997).build(), "stem_conv1");
String inputX = "stem_bn1";
String inputP = null;
if(!skipReduction) {
Pair stem1 = reductionA(graph, (int) Math.floor(stemFilters / Math.pow(filterMultiplier,2)), "stem1", "stem_conv1", inputP);
Pair stem2 = reductionA(graph, (int) Math.floor(stemFilters / (filterMultiplier)), "stem2", stem1.getFirst(), stem1.getSecond());
inputX = stem2.getFirst();
inputP = stem2.getSecond();
}
for(int i = 0; i < numBlocks; i++){
Pair block = normalA(graph, filters, String.valueOf(i), inputX, inputP);
inputX = block.getFirst();
inputP = block.getSecond();
}
String inputP0;
Pair reduce = reductionA(graph, filters * filterMultiplier, "reduce"+numBlocks, inputX, inputP);
inputX = reduce.getFirst();
inputP0 = reduce.getSecond();
if(!skipReduction) inputP = inputP0;
for(int i = 0; i < numBlocks; i++){
Pair block = normalA(graph, filters * filterMultiplier, String.valueOf(i+numBlocks+1), inputX, inputP);
inputX = block.getFirst();
inputP = block.getSecond();
}
reduce = reductionA(graph, filters * (int)Math.pow(filterMultiplier, 2), "reduce"+(2*numBlocks), inputX, inputP);
inputX = reduce.getFirst();
inputP0 = reduce.getSecond();
if(!skipReduction) inputP = inputP0;
for(int i = 0; i < numBlocks; i++){
Pair block = normalA(graph, filters * (int) Math.pow(filterMultiplier, 2), String.valueOf(i+(2*numBlocks)+1), inputX, inputP);
inputX = block.getFirst();
inputP = block.getSecond();
}
// output
graph
.addLayer("act", new ActivationLayer(Activation.RELU), inputX)
.addLayer("avg_pool", new GlobalPoolingLayer.Builder(PoolingType.AVG).build(), "act")
.addLayer("output", new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
.activation(Activation.SOFTMAX).build(), "avg_pool")
.setOutputs("output")
;
return graph;
}
@Override
public ModelMetaData metaData() {
return new ModelMetaData(new int[][] {inputShape}, 1, ZooType.CNN);
}
@Override
public void setInputShape(int[][] inputShape) {
this.inputShape = inputShape[0];
}
}
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