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src/main/java/org/dromara/easyai/unet/UNetEncoder.java
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181
src/main/java/org/dromara/easyai/unet/UNetEncoder.java
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package org.dromara.easyai.unet;
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import org.dromara.easyai.conv.ConvCount;
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import org.dromara.easyai.entity.ThreeChannelMatrix;
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import org.dromara.easyai.i.ActiveFunction;
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import org.dromara.easyai.i.OutBack;
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import org.dromara.easyai.matrixTools.Matrix;
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import org.dromara.easyai.matrixTools.MatrixOperation;
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import org.dromara.easyai.nerveEntity.ConvParameter;
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import org.dromara.easyai.nerveEntity.ConvSize;
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import java.util.*;
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/**
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* @author lidapeng
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* @time 2025/3/2 07:51
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* @des unet编码器
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*/
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public class UNetEncoder extends ConvCount {
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private final ConvParameter convParameter = new ConvParameter();//内存中卷积层模型及临时数据
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private final MatrixOperation matrixOperation = new MatrixOperation();
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private final int kerSize;
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private final float studyRate;//学习率
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private final int deep;//当前深度
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private final int channelNo;//卷积层数
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private List<Matrix> decodeErrorMatrix;//从解码器传来的误差矩阵
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private final ActiveFunction activeFunction;
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private UNetEncoder afterEncoder;//下一个编码器
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private UNetEncoder beforeEncoder;//上一个编码器
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private UNetDecoder decoder;//下一个解码器
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private final int xSize;
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private final int ySize;
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private final float oneStudyRate;
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private final float gaMa;
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private final float gMaxTh;
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private final boolean aoTu;
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public UNetEncoder(int kerSize, int channelNo, int deep, ActiveFunction activeFunction
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, float studyRate, int xSize, int ySize, float oneStudyRate, float gaMa, float gMaxTh, boolean aoTu) throws Exception {//核心大小
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Random random = new Random();
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this.xSize = xSize;
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this.aoTu = aoTu;
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this.gMaxTh = gMaxTh;
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this.gaMa = gaMa;
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this.ySize = ySize;
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this.oneStudyRate = oneStudyRate;
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this.studyRate = studyRate;
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this.kerSize = kerSize;
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this.activeFunction = activeFunction;
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this.deep = deep;
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this.channelNo = channelNo;
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List<Matrix> nerveMatrixList = convParameter.getNerveMatrixList();
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List<ConvSize> convSizeList = convParameter.getConvSizeList();
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List<Matrix> dymStudyRateList = convParameter.getDymStudyRateList();
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for (int i = 0; i < channelNo; i++) {
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initNervePowerMatrix(random, nerveMatrixList, dymStudyRateList);
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convSizeList.add(new ConvSize());
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}
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if (deep == 1) {
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List<List<Float>> oneConvPowers = new ArrayList<>();
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List<List<Float>> oneDymStudyRateList = new ArrayList<>();
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for (int k = 0; k < channelNo; k++) {
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List<Float> oneConvPower = new ArrayList<>();
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List<Float> oneDymStudyRate = new ArrayList<>();
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oneConvPowers.add(oneConvPower);
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oneDymStudyRateList.add(oneDymStudyRate);
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//通道数
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int channelNum = 3;
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for (int i = 0; i < channelNum; i++) {
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oneConvPower.add(random.nextFloat() / channelNum);
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oneDymStudyRate.add(0f);
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}
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}
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convParameter.setOneDymStudyRateList(oneDymStudyRateList);
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convParameter.setOneConvPower(oneConvPowers);
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}
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}
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public ConvParameter getConvParameter() {
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return convParameter;
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}
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protected void setDecodeErrorMatrix(List<Matrix> decodeErrorMatrix) {
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this.decodeErrorMatrix = decodeErrorMatrix;
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}
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protected List<Matrix> getAfterConvMatrix(long eventID) {//卷积后的矩阵
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List<Matrix> outMatrixList = convParameter.getFeatureMap().get(eventID);
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convParameter.getFeatureMap().remove(eventID);
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return outMatrixList;
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}
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//发送特征三通道矩阵
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public void sendThreeChannel(long eventID, OutBack outBack, ThreeChannelMatrix feature, ThreeChannelMatrix featureE,
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boolean study) throws Exception {
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if (study && featureE == null) {
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throw new Exception("训练时期望矩阵不能为空");
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}
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if (feature.getX() != xSize && feature.getY() != ySize) {
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throw new Exception("输入图片尺寸与初始化参数不一致");
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}
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List<Matrix> matrixList = new ArrayList<>();
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matrixList.add(feature.getMatrixR());
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matrixList.add(feature.getMatrixG());
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matrixList.add(feature.getMatrixB());
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if (study) {
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convParameter.setFeatureMatrixList(matrixList);
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}
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sendMatrixList(eventID, outBack, featureE, matrixList, study, feature);
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}
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protected void sendFeature(long eventID, OutBack outBack, ThreeChannelMatrix featureE,
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List<Matrix> myFeatures, boolean study, ThreeChannelMatrix backGround) throws Exception {
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List<Matrix> convMatrixList = downConvAndPooling(myFeatures, convParameter, channelNo, activeFunction, kerSize, true, eventID);
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if (afterEncoder != null) {//后面还有编码器，继续向后传递
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afterEncoder.sendFeature(eventID, outBack, featureE, convMatrixList, study, backGround);
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} else {//向解码器传递
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decoder.sendFeature(eventID, outBack, featureE, convMatrixList, study, backGround);
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}
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}
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protected void backError(List<Matrix> errorMatrix) throws Exception {//接收误差
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List<Matrix> errorList = backDownPoolingByList(errorMatrix, convParameter.getOutX(), convParameter.getOutY());//池化误差返回
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List<Matrix> errorMatrixList = matrixOperation.addMatrixList(errorList, decodeErrorMatrix);
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List<Matrix> myErrorMatrix = backAllDownConv(convParameter, errorMatrixList, studyRate, activeFunction, channelNo, kerSize,
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gaMa, gMaxTh, aoTu);
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if (beforeEncoder != null) {
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beforeEncoder.backError(myErrorMatrix);
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} else {//最后一层 调整1v1卷积
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backOneConvByList(myErrorMatrix, convParameter.getFeatureMatrixList(), convParameter.getOneConvPower(), oneStudyRate
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, convParameter.getOneDymStudyRateList(), gaMa, gMaxTh, aoTu);
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}
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}
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public void sendMatrixList(long eventID, OutBack outBack, ThreeChannelMatrix featureE, List<Matrix> feature,
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boolean study, ThreeChannelMatrix backGround) throws Exception {
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List<Matrix> myFeatures = manyOneConv(feature, convParameter.getOneConvPower());//矩阵重新调整维度
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List<Matrix> convMatrixList = downConvAndPooling(myFeatures, convParameter, channelNo, activeFunction, kerSize, true, eventID);
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if (afterEncoder != null) {//后面还有编码器，继续向后传递
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afterEncoder.sendFeature(eventID, outBack, featureE, convMatrixList, study, backGround);
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} else {//向解码器传递
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decoder.sendFeature(eventID, outBack, featureE, convMatrixList, study, backGround);
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}
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}
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private void initNervePowerMatrix(Random random, List<Matrix> nervePowerMatrixList, List<Matrix> dymStudyRageList) throws Exception {
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int convSize = kerSize * kerSize;
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Matrix nervePowerMatrix = new Matrix(convSize, 1);
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for (int i = 0; i < convSize; i++) {
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float power = random.nextFloat() / kerSize;
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nervePowerMatrix.setNub(i, 0, power);
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}
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dymStudyRageList.add(new Matrix(convSize, 1));
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nervePowerMatrixList.add(nervePowerMatrix);
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}
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public UNetEncoder getAfterEncoder() {
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return afterEncoder;
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}
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public void setAfterEncoder(UNetEncoder afterEncoder) {
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this.afterEncoder = afterEncoder;
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}
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public UNetEncoder getBeforeEncoder() {
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return beforeEncoder;
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}
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public void setBeforeEncoder(UNetEncoder beforeEncoder) {
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this.beforeEncoder = beforeEncoder;
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}
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public UNetDecoder getDecoder() {
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return decoder;
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}
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public void setDecoder(UNetDecoder decoder) {
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this.decoder = decoder;
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}
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}
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