org.deeplearning4j.zoo.model.FaceNetNN4Small2 Maven / Gradle / Ivy
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* * terms of the Apache License, Version 2.0 which is available at
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* * information regarding copyright ownership.
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package org.deeplearning4j.zoo.model;
import lombok.AllArgsConstructor;
import lombok.Builder;
import lombok.NoArgsConstructor;
import org.deeplearning4j.nn.api.Model;
import org.deeplearning4j.nn.api.OptimizationAlgorithm;
import org.deeplearning4j.nn.conf.*;
import org.deeplearning4j.nn.conf.graph.L2NormalizeVertex;
import org.deeplearning4j.nn.conf.graph.MergeVertex;
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.deeplearning4j.zoo.model.helper.FaceNetHelper;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.learning.config.Adam;
import org.nd4j.linalg.learning.config.IUpdater;
import org.nd4j.linalg.lossfunctions.LossFunctions;
@AllArgsConstructor
@Builder
public class FaceNetNN4Small2 extends ZooModel {
@Builder.Default private long seed = 1234;
@Builder.Default private int[] inputShape = new int[] {3, 96, 96};
@Builder.Default private int numClasses = 0;
@Builder.Default private IUpdater updater = new Adam(0.1, 0.9, 0.999, 0.01);
@Builder.Default private Activation transferFunction = Activation.RELU;
@Builder.Default CacheMode cacheMode = CacheMode.NONE;
@Builder.Default private WorkspaceMode workspaceMode = WorkspaceMode.ENABLED;
@Builder.Default private ConvolutionLayer.AlgoMode cudnnAlgoMode = ConvolutionLayer.AlgoMode.PREFER_FASTEST;
@Builder.Default private int embeddingSize = 128;
private FaceNetNN4Small2() {}
@Override
public String pretrainedUrl(PretrainedType pretrainedType) {
return null;
}
@Override
public long pretrainedChecksum(PretrainedType pretrainedType) {
return 0L;
}
@Override
public Class modelType() {
return ComputationGraph.class;
}
public ComputationGraphConfiguration conf() {
ComputationGraphConfiguration.GraphBuilder graph = new NeuralNetConfiguration.Builder().seed(seed)
.activation(Activation.IDENTITY)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.updater(updater)
.weightInit(WeightInit.RELU)
.l2(5e-5)
.miniBatch(true)
.cacheMode(cacheMode)
.trainingWorkspaceMode(workspaceMode)
.inferenceWorkspaceMode(workspaceMode)
.cudnnAlgoMode(cudnnAlgoMode)
.convolutionMode(ConvolutionMode.Same)
.graphBuilder();
graph.addInputs("input1")
.addLayer("stem-cnn1",
new ConvolutionLayer.Builder(new int[] {7, 7}, new int[] {2, 2},
new int[] {3, 3}).nIn(inputShape[0]).nOut(64)
.cudnnAlgoMode(ConvolutionLayer.AlgoMode.NO_WORKSPACE)
.build(),
"input1")
.addLayer("stem-batch1", new BatchNormalization.Builder(false).nIn(64).nOut(64).build(),
"stem-cnn1")
.addLayer("stem-activation1", new ActivationLayer.Builder().activation(Activation.RELU).build(),
"stem-batch1")
// pool -> norm
.addLayer("stem-pool1",
new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX, new int[] {3, 3},
new int[] {2, 2}, new int[] {1, 1}).build(),
"stem-activation1")
.addLayer("stem-lrn1", new LocalResponseNormalization.Builder(1, 5, 1e-4, 0.75).build(),
"stem-pool1")
// Inception 2
.addLayer("inception-2-cnn1",
new ConvolutionLayer.Builder(new int[] {1, 1}).nIn(64).nOut(64)
.cudnnAlgoMode(ConvolutionLayer.AlgoMode.NO_WORKSPACE).build(),
"stem-lrn1")
.addLayer("inception-2-batch1", new BatchNormalization.Builder(false).nIn(64).nOut(64).build(),
"inception-2-cnn1")
.addLayer("inception-2-activation1",
new ActivationLayer.Builder().activation(Activation.RELU).build(),
"inception-2-batch1")
.addLayer("inception-2-cnn2",
new ConvolutionLayer.Builder(new int[] {3, 3}, new int[] {1, 1},
new int[] {1, 1}).nIn(64).nOut(192)
.cudnnAlgoMode(ConvolutionLayer.AlgoMode.NO_WORKSPACE)
.build(),
"inception-2-activation1")
.addLayer("inception-2-batch2",
new BatchNormalization.Builder(false).nIn(192).nOut(192).build(),
"inception-2-cnn2")
.addLayer("inception-2-activation2",
new ActivationLayer.Builder().activation(Activation.RELU).build(),
"inception-2-batch2")
// norm -> pool
.addLayer("inception-2-lrn1", new LocalResponseNormalization.Builder(1, 5, 1e-4, 0.75).build(),
"inception-2-activation2")
.addLayer("inception-2-pool1",
new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX, new int[] {3, 3},
new int[] {2, 2}, new int[] {1, 1}).build(),
"inception-2-lrn1");
// Inception 3a
FaceNetHelper.appendGraph(graph, "3a", 192, new int[] {3, 5}, new int[] {1, 1}, new int[] {128, 32},
new int[] {96, 16, 32, 64}, SubsamplingLayer.PoolingType.MAX, transferFunction,
"inception-2-pool1");
// Inception 3b
FaceNetHelper.appendGraph(graph, "3b", 256, new int[] {3, 5}, new int[] {1, 1}, new int[] {128, 64},
new int[] {96, 32, 64, 64}, SubsamplingLayer.PoolingType.PNORM, 2, transferFunction,
"inception-3a");
// Inception 3c
// Inception.appendGraph(graph, "3c", 320,
// new int[]{3,5}, new int[]{1,1}, new int[]{256,64}, new int[]{128,64},
// SubsamplingLayer.PoolingType.PNORM, 2, true, "inception-3b");
graph.addLayer("3c-1x1",
new ConvolutionLayer.Builder(new int[] {1, 1}, new int[] {1, 1}).nIn(320).nOut(128)
.cudnnAlgoMode(ConvolutionLayer.AlgoMode.NO_WORKSPACE).build(),
"inception-3b")
.addLayer("3c-1x1-norm", FaceNetHelper.batchNorm(128, 128), "3c-1x1")
.addLayer("3c-transfer1", new ActivationLayer.Builder().activation(transferFunction).build(),
"3c-1x1-norm")
.addLayer("3c-3x3",
new ConvolutionLayer.Builder(new int[] {3, 3}, new int[] {2, 2}).nIn(128)
.nOut(256).cudnnAlgoMode(ConvolutionLayer.AlgoMode.NO_WORKSPACE)
.build(),
"3c-transfer1")
.addLayer("3c-3x3-norm", FaceNetHelper.batchNorm(256, 256), "3c-3x3")
.addLayer("3c-transfer2", new ActivationLayer.Builder().activation(transferFunction).build(),
"3c-3x3-norm")
.addLayer("3c-2-1x1",
new ConvolutionLayer.Builder(new int[] {1, 1}, new int[] {1, 1}).nIn(320)
.nOut(32).cudnnAlgoMode(ConvolutionLayer.AlgoMode.NO_WORKSPACE)
.build(),
"inception-3b")
.addLayer("3c-2-1x1-norm", FaceNetHelper.batchNorm(32, 32), "3c-2-1x1")
.addLayer("3c-2-transfer3", new ActivationLayer.Builder().activation(transferFunction).build(),
"3c-2-1x1-norm")
.addLayer("3c-2-5x5",
new ConvolutionLayer.Builder(new int[] {3, 3}, new int[] {2, 2}).nIn(32)
.nOut(64).cudnnAlgoMode(ConvolutionLayer.AlgoMode.NO_WORKSPACE)
.build(),
"3c-2-transfer3")
.addLayer("3c-2-5x5-norm", FaceNetHelper.batchNorm(64, 64), "3c-2-5x5")
.addLayer("3c-2-transfer4", new ActivationLayer.Builder().activation(transferFunction).build(),
"3c-2-5x5-norm")
.addLayer("3c-pool", new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX,
new int[] {3, 3}, new int[] {2, 2}, new int[] {1, 1}).build(), "inception-3b")
.addVertex("inception-3c", new MergeVertex(), "3c-transfer2", "3c-2-transfer4", "3c-pool");
// Inception 4a
FaceNetHelper.appendGraph(graph, "4a", 640, new int[] {3, 5}, new int[] {1, 1}, new int[] {192, 64},
new int[] {96, 32, 128, 256}, SubsamplingLayer.PoolingType.PNORM, 2, transferFunction,
"inception-3c");
// // Inception 4e
// Inception.appendGraph(graph, "4e", 640,
// new int[]{3,5}, new int[]{2,2}, new int[]{256,128}, new int[]{160,64},
// SubsamplingLayer.PoolingType.MAX, 2, 1, true, "inception-4a");
graph.addLayer("4e-1x1",
new ConvolutionLayer.Builder(new int[] {1, 1}, new int[] {1, 1}).nIn(640).nOut(160)
.cudnnAlgoMode(ConvolutionLayer.AlgoMode.NO_WORKSPACE).build(),
"inception-4a")
.addLayer("4e-1x1-norm", FaceNetHelper.batchNorm(160, 160), "4e-1x1")
.addLayer("4e-transfer1", new ActivationLayer.Builder().activation(transferFunction).build(),
"4e-1x1-norm")
.addLayer("4e-3x3",
new ConvolutionLayer.Builder(new int[] {3, 3}, new int[] {2, 2}).nIn(160)
.nOut(256).cudnnAlgoMode(ConvolutionLayer.AlgoMode.NO_WORKSPACE)
.build(),
"4e-transfer1")
.addLayer("4e-3x3-norm", FaceNetHelper.batchNorm(256, 256), "4e-3x3")
.addLayer("4e-transfer2", new ActivationLayer.Builder().activation(transferFunction).build(),
"4e-3x3-norm")
.addLayer("4e-2-1x1",
new ConvolutionLayer.Builder(new int[] {1, 1}, new int[] {1, 1}).nIn(640)
.nOut(64).cudnnAlgoMode(ConvolutionLayer.AlgoMode.NO_WORKSPACE)
.build(),
"inception-4a")
.addLayer("4e-2-1x1-norm", FaceNetHelper.batchNorm(64, 64), "4e-2-1x1")
.addLayer("4e-2-transfer3", new ActivationLayer.Builder().activation(transferFunction).build(),
"4e-2-1x1-norm")
.addLayer("4e-2-5x5",
new ConvolutionLayer.Builder(new int[] {3, 3}, new int[] {2, 2}).nIn(64)
.nOut(128).cudnnAlgoMode(ConvolutionLayer.AlgoMode.NO_WORKSPACE)
.build(),
"4e-2-transfer3")
.addLayer("4e-2-5x5-norm", FaceNetHelper.batchNorm(128, 128), "4e-2-5x5")
.addLayer("4e-2-transfer4", new ActivationLayer.Builder().activation(transferFunction).build(),
"4e-2-5x5-norm")
.addLayer("4e-pool", new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX,
new int[] {3, 3}, new int[] {2, 2}, new int[] {1, 1}).build(), "inception-4a")
.addVertex("inception-4e", new MergeVertex(), "4e-transfer2", "4e-2-transfer4", "4e-pool");
// Inception 5a
// Inception.appendGraph(graph, "5a", 1024,
// new int[]{3}, new int[]{1}, new int[]{384}, new int[]{96,96,256},
// SubsamplingLayer.PoolingType.PNORM, 2, true, "inception-4e");
graph.addLayer("5a-1x1",
new ConvolutionLayer.Builder(new int[] {1, 1}, new int[] {1, 1}).nIn(1024).nOut(256)
.cudnnAlgoMode(ConvolutionLayer.AlgoMode.NO_WORKSPACE).build(),
"inception-4e").addLayer("5a-1x1-norm", FaceNetHelper.batchNorm(256, 256), "5a-1x1")
.addLayer("5a-transfer1", new ActivationLayer.Builder().activation(transferFunction).build(),
"5a-1x1-norm")
.addLayer("5a-2-1x1",
new ConvolutionLayer.Builder(new int[] {1, 1}, new int[] {1, 1}).nIn(1024)
.nOut(96).cudnnAlgoMode(ConvolutionLayer.AlgoMode.NO_WORKSPACE)
.build(),
"inception-4e")
.addLayer("5a-2-1x1-norm", FaceNetHelper.batchNorm(96, 96), "5a-2-1x1")
.addLayer("5a-2-transfer2", new ActivationLayer.Builder().activation(transferFunction).build(),
"5a-2-1x1-norm")
.addLayer("5a-2-3x3",
new ConvolutionLayer.Builder(new int[] {3, 3}, new int[] {1, 1}).nIn(96)
.nOut(384).cudnnAlgoMode(ConvolutionLayer.AlgoMode.NO_WORKSPACE)
.build(),
"5a-2-transfer2")
.addLayer("5a-2-3x3-norm", FaceNetHelper.batchNorm(384, 384), "5a-2-3x3")
.addLayer("5a-transfer3", new ActivationLayer.Builder().activation(transferFunction).build(),
"5a-2-3x3-norm")
.addLayer("5a-3-pool",
new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.PNORM,
new int[] {3, 3}, new int[] {1, 1}).pnorm(2).build(),
"inception-4e")
.addLayer("5a-3-1x1reduce",
new ConvolutionLayer.Builder(new int[] {1, 1}, new int[] {1, 1}).nIn(1024)
.nOut(96).cudnnAlgoMode(ConvolutionLayer.AlgoMode.NO_WORKSPACE)
.build(),
"5a-3-pool")
.addLayer("5a-3-1x1reduce-norm", FaceNetHelper.batchNorm(96, 96), "5a-3-1x1reduce")
.addLayer("5a-3-transfer4", new ActivationLayer.Builder().activation(Activation.RELU).build(),
"5a-3-1x1reduce-norm")
.addVertex("inception-5a", new MergeVertex(), "5a-transfer1", "5a-transfer3", "5a-3-transfer4");
// Inception 5b
// Inception.appendGraph(graph, "5b", 736,
// new int[]{3}, new int[]{1}, new int[]{384}, new int[]{96,96,256},
// SubsamplingLayer.PoolingType.MAX, 1, 1, true, "inception-5a");
graph.addLayer("5b-1x1",
new ConvolutionLayer.Builder(new int[] {1, 1}, new int[] {1, 1}).nIn(736).nOut(256)
.cudnnAlgoMode(ConvolutionLayer.AlgoMode.NO_WORKSPACE).build(),
"inception-5a").addLayer("5b-1x1-norm", FaceNetHelper.batchNorm(256, 256), "5b-1x1")
.addLayer("5b-transfer1", new ActivationLayer.Builder().activation(transferFunction).build(),
"5b-1x1-norm")
.addLayer("5b-2-1x1",
new ConvolutionLayer.Builder(new int[] {1, 1}, new int[] {1, 1}).nIn(736)
.nOut(96).cudnnAlgoMode(ConvolutionLayer.AlgoMode.NO_WORKSPACE)
.build(),
"inception-5a")
.addLayer("5b-2-1x1-norm", FaceNetHelper.batchNorm(96, 96), "5b-2-1x1")
.addLayer("5b-2-transfer2", new ActivationLayer.Builder().activation(transferFunction).build(),
"5b-2-1x1-norm")
.addLayer("5b-2-3x3",
new ConvolutionLayer.Builder(new int[] {3, 3}, new int[] {1, 1}).nIn(96)
.nOut(384).cudnnAlgoMode(ConvolutionLayer.AlgoMode.NO_WORKSPACE)
.build(),
"5b-2-transfer2")
.addLayer("5b-2-3x3-norm", FaceNetHelper.batchNorm(384, 384), "5b-2-3x3")
.addLayer("5b-2-transfer3", new ActivationLayer.Builder().activation(transferFunction).build(),
"5b-2-3x3-norm")
.addLayer("5b-3-pool",
new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX, new int[] {3, 3},
new int[] {1, 1}, new int[] {1, 1}).build(),
"inception-5a")
.addLayer("5b-3-1x1reduce",
new ConvolutionLayer.Builder(new int[] {1, 1}, new int[] {1, 1}).nIn(736)
.nOut(96).cudnnAlgoMode(ConvolutionLayer.AlgoMode.NO_WORKSPACE)
.build(),
"5b-3-pool")
.addLayer("5b-3-1x1reduce-norm", FaceNetHelper.batchNorm(96, 96), "5b-3-1x1reduce")
.addLayer("5b-3-transfer4", new ActivationLayer.Builder().activation(transferFunction).build(),
"5b-3-1x1reduce-norm")
.addVertex("inception-5b", new MergeVertex(), "5b-transfer1", "5b-2-transfer3",
"5b-3-transfer4");
graph.addLayer("avgpool",
new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.AVG, new int[] {3, 3},
new int[] {3, 3}).build(),
"inception-5b")
.addLayer("bottleneck",new DenseLayer.Builder().nOut(embeddingSize)
.activation(Activation.IDENTITY).build(),"avgpool")
.addVertex("embeddings", new L2NormalizeVertex(new int[] {}, 1e-6), "bottleneck")
.addLayer("lossLayer", new CenterLossOutputLayer.Builder()
.lossFunction(LossFunctions.LossFunction.SQUARED_LOSS)
.activation(Activation.SOFTMAX).nOut(numClasses).lambda(1e-4).alpha(0.9)
.gradientNormalization(GradientNormalization.RenormalizeL2PerLayer).build(),
"embeddings")
.setOutputs("lossLayer")
.setInputTypes(InputType.convolutional(inputShape[2], inputShape[1], inputShape[0]));
return graph.build();
}
@Override
public ComputationGraph init() {
ComputationGraph model = new ComputationGraph(conf());
model.init();
return model;
}
@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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