org.deeplearning4j.zoo.model.helper.NASNetHelper Maven / Gradle / Ivy
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package org.deeplearning4j.zoo.model.helper;
import lombok.val;
import org.deeplearning4j.nn.conf.ComputationGraphConfiguration;
import org.deeplearning4j.nn.conf.ConvolutionMode;
import org.deeplearning4j.nn.conf.graph.ElementWiseVertex;
import org.deeplearning4j.nn.conf.graph.MergeVertex;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.*;
import org.deeplearning4j.nn.conf.layers.convolutional.Cropping2D;
import org.deeplearning4j.zoo.model.NASNet;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.common.primitives.Pair;
import java.util.Map;
public class NASNetHelper {
public static String sepConvBlock(ComputationGraphConfiguration.GraphBuilder graphBuilder, int filters, int kernelSize, int stride, String blockId, String input) {
String prefix = "sepConvBlock"+blockId;
graphBuilder
.addLayer(prefix+"_act", new ActivationLayer(Activation.RELU), input)
.addLayer(prefix+"_sepconv1", new SeparableConvolution2D.Builder(kernelSize, kernelSize).stride(stride, stride).nOut(filters).hasBias(false)
.convolutionMode(ConvolutionMode.Same).build(), prefix+"_act")
.addLayer(prefix+"_conv1_bn", new BatchNormalization.Builder().eps(1e-3).gamma(0.9997).build(), prefix+"_sepconv1")
.addLayer(prefix+"_act2", new ActivationLayer(Activation.RELU), prefix+"_conv1_bn")
.addLayer(prefix+"_sepconv2", new SeparableConvolution2D.Builder(kernelSize, kernelSize).stride(stride, stride).nOut(filters).hasBias(false)
.convolutionMode(ConvolutionMode.Same).build(), prefix+"_act2")
.addLayer(prefix+"_conv2_bn", new BatchNormalization.Builder().eps(1e-3).gamma(0.9997).build(), prefix+"_sepconv2");
return prefix+"_conv2_bn";
}
public static String adjustBlock(ComputationGraphConfiguration.GraphBuilder graphBuilder, int filters, String blockId, String input) {
return adjustBlock(graphBuilder, filters, blockId, input, null);
}
public static String adjustBlock(ComputationGraphConfiguration.GraphBuilder graphBuilder, int filters, String blockId, String input, String inputToMatch) {
String prefix = "adjustBlock"+blockId;
String outputName = input;
if(inputToMatch == null) {
inputToMatch = input;
}
Map layerActivationTypes = graphBuilder.getLayerActivationTypes();
val shapeToMatch = layerActivationTypes.get(inputToMatch).getShape();
val inputShape = layerActivationTypes.get(input).getShape();
if(shapeToMatch[1] != inputShape[1]) {
graphBuilder
.addLayer(prefix+"_relu1", new ActivationLayer(Activation.RELU), input)
// tower 1
.addLayer(prefix+"_avgpool1", new SubsamplingLayer.Builder(PoolingType.AVG).kernelSize(1,1).stride(2,2)
.convolutionMode(ConvolutionMode.Truncate).build(), prefix+"_relu1")
.addLayer(prefix+"_conv1", new ConvolutionLayer.Builder(1,1).stride(1,1).nOut((int) Math.floor(filters / 2)).hasBias(false)
.convolutionMode(ConvolutionMode.Same).build(), prefix+"_avg_pool_1")
// tower 2
.addLayer(prefix+"_zeropad1", new ZeroPaddingLayer(0,1), prefix+"_relu1")
.addLayer(prefix+"_crop1", new Cropping2D(1,0), prefix+"_zeropad_1")
.addLayer(prefix+"_avgpool2", new SubsamplingLayer.Builder(PoolingType.AVG).kernelSize(1,1).stride(2,2)
.convolutionMode(ConvolutionMode.Truncate).build(), prefix+"_crop1")
.addLayer(prefix+"_conv2", new ConvolutionLayer.Builder(1,1).stride(1,1).nOut((int) Math.floor(filters / 2)).hasBias(false)
.convolutionMode(ConvolutionMode.Same).build(), prefix+"_avgpool2")
.addVertex(prefix+"_concat1", new MergeVertex(), prefix+"_conv1", prefix+"_conv2")
.addLayer(prefix+"_bn1", new BatchNormalization.Builder().eps(1e-3).gamma(0.9997)
.build(), prefix+"_concat1");
outputName = prefix+"_bn1";
}
if(inputShape[3] != filters) {
graphBuilder
.addLayer(prefix+"_projection_relu", new ActivationLayer(Activation.RELU), outputName)
.addLayer(prefix+"_projection_conv", new ConvolutionLayer.Builder(1,1).stride(1,1).nOut(filters).hasBias(false)
.convolutionMode(ConvolutionMode.Same).build(), prefix+"_projection_relu")
.addLayer(prefix+"_projection_bn", new BatchNormalization.Builder().eps(1e-3).gamma(0.9997)
.build(), prefix+"_projection_conv");
outputName = prefix+"_projection_bn";
}
return outputName;
}
public static Pair normalA(ComputationGraphConfiguration.GraphBuilder graphBuilder, int filters, String blockId, String inputX, String inputP) {
String prefix = "normalA"+blockId;
String topAdjust = adjustBlock(graphBuilder, filters, prefix, inputP, inputX);
// top block
graphBuilder
.addLayer(prefix+"_relu1", new ActivationLayer(Activation.RELU), topAdjust)
.addLayer(prefix+"_conv1", new ConvolutionLayer.Builder(1,1).stride(1,1).nOut(filters).hasBias(false)
.convolutionMode(ConvolutionMode.Same).build(), prefix+"_relu1")
.addLayer(prefix+"_bn1", new BatchNormalization.Builder().eps(1e-3).gamma(0.9997)
.build(), prefix+"_conv1");
// block 1
String left1 = sepConvBlock(graphBuilder, filters, 5, 1, prefix+"_left1", prefix+"_bn1");
String right1 = sepConvBlock(graphBuilder, filters, 3, 1, prefix+"_right1", topAdjust);
graphBuilder.addVertex(prefix+"_add1", new ElementWiseVertex(ElementWiseVertex.Op.Add), left1, right1);
// block 2
String left2 = sepConvBlock(graphBuilder, filters, 5, 1, prefix+"_left2", topAdjust);
String right2 = sepConvBlock(graphBuilder, filters, 3, 1, prefix+"_right2", topAdjust);
graphBuilder.addVertex(prefix+"_add2", new ElementWiseVertex(ElementWiseVertex.Op.Add), left2, right2);
// block 3
graphBuilder
.addLayer(prefix+"_left3", new SubsamplingLayer.Builder(PoolingType.AVG).kernelSize(3,3).stride(1,1)
.convolutionMode(ConvolutionMode.Same).build(), prefix+"_bn1")
.addVertex(prefix+"_add3", new ElementWiseVertex(ElementWiseVertex.Op.Add), prefix+"_left3", topAdjust);
// block 4
graphBuilder
.addLayer(prefix+"_left4", new SubsamplingLayer.Builder(PoolingType.AVG).kernelSize(3,3).stride(1,1)
.convolutionMode(ConvolutionMode.Same).build(), topAdjust)
.addLayer(prefix+"_right4", new SubsamplingLayer.Builder(PoolingType.AVG).kernelSize(3,3).stride(1,1)
.convolutionMode(ConvolutionMode.Same).build(), topAdjust)
.addVertex(prefix+"_add4", new ElementWiseVertex(ElementWiseVertex.Op.Add), prefix+"_left4", prefix+"_right4");
// block 5
String left5 = sepConvBlock(graphBuilder, filters, 3, 1, prefix+"_left5", topAdjust);
graphBuilder.addVertex(prefix+"_add5", new ElementWiseVertex(ElementWiseVertex.Op.Add), prefix+"_left5", prefix+"_bn1");
// output
graphBuilder.addVertex(prefix, new MergeVertex(),
topAdjust, prefix+"_add1", prefix+"_add2", prefix+"_add3", prefix+"_add4", prefix+"_add5");
return new Pair<>(prefix, inputX);
}
public static Pair reductionA(ComputationGraphConfiguration.GraphBuilder graphBuilder, int filters, String blockId, String inputX, String inputP) {
String prefix = "reductionA"+blockId;
String topAdjust = adjustBlock(graphBuilder, filters, prefix, inputP, inputX);
// top block
graphBuilder
.addLayer(prefix+"_relu1", new ActivationLayer(Activation.RELU), topAdjust)
.addLayer(prefix+"_conv1", new ConvolutionLayer.Builder(1,1).stride(1,1).nOut(filters).hasBias(false)
.convolutionMode(ConvolutionMode.Same).build(), prefix+"_relu1")
.addLayer(prefix+"_bn1", new BatchNormalization.Builder().eps(1e-3).gamma(0.9997)
.build(), prefix+"_conv1");
// block 1
String left1 = sepConvBlock(graphBuilder, filters, 5, 2, prefix+"_left1", prefix+"_bn1");
String right1 = sepConvBlock(graphBuilder, filters, 7, 2, prefix+"_right1", topAdjust);
graphBuilder.addVertex(prefix+"_add1", new ElementWiseVertex(ElementWiseVertex.Op.Add), left1, right1);
// block 2
graphBuilder.addLayer(prefix+"_left2", new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX).kernelSize(3,3).stride(2,2)
.convolutionMode(ConvolutionMode.Same).build(), prefix+"_bn1");
String right2 = sepConvBlock(graphBuilder, filters, 3, 1, prefix+"_right2", topAdjust);
graphBuilder.addVertex(prefix+"_add2", new ElementWiseVertex(ElementWiseVertex.Op.Add), prefix+"_left2", right2);
// block 3
graphBuilder.addLayer(prefix+"_left3", new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.AVG).kernelSize(3,3).stride(2,2)
.convolutionMode(ConvolutionMode.Same).build(), prefix+"_bn1");
String right3 = sepConvBlock(graphBuilder, filters, 5, 2, prefix+"_right3", topAdjust);
graphBuilder.addVertex(prefix+"_add3", new ElementWiseVertex(ElementWiseVertex.Op.Add), prefix+"_left3", right3);
// block 4
graphBuilder
.addLayer(prefix+"_left4", new SubsamplingLayer.Builder(PoolingType.AVG).kernelSize(3,3).stride(1,1)
.convolutionMode(ConvolutionMode.Same).build(), prefix+"_add1")
.addVertex(prefix+"_add4", new ElementWiseVertex(ElementWiseVertex.Op.Add), prefix+"_add2", prefix+"_left4");
// block 5
String left5 = sepConvBlock(graphBuilder, filters, 3, 2, prefix+"_left5", prefix+"_add1");
graphBuilder
.addLayer(prefix+"_right5", new SubsamplingLayer.Builder(PoolingType.MAX).kernelSize(3,3).stride(2,2)
.convolutionMode(ConvolutionMode.Same).build(), prefix+"_bn1")
.addVertex(prefix+"_add5", new ElementWiseVertex(ElementWiseVertex.Op.Add), left5, prefix+"_right5");
// output
graphBuilder.addVertex(prefix, new MergeVertex(),
prefix+"_add2", prefix+"_add3", prefix+"_add4", prefix+"_add5");
return new Pair<>(prefix, inputX);
}
}
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