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inter
2025-09-04 14:09:40 +08:00
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src/main/java/org/dromara/easyai/unet/UNetManager.java Normal file
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package org.dromara.easyai.unet;
import org.dromara.easyai.config.UNetConfig;
import org.dromara.easyai.conv.ConvCount;
import org.dromara.easyai.function.ReLu;
import org.dromara.easyai.function.Tanh;
import org.dromara.easyai.matrixTools.Matrix;
import org.dromara.easyai.nerveEntity.ConvParameter;
import java.util.ArrayList;
import java.util.List;
/**
* @author lidapeng
* @time 2025/2/25 20:19
* @des UNet网络管理器
*/
public class UNetManager extends ConvCount {
private final List<UNetEncoder> encoderList = new ArrayList<>();
private final List<UNetDecoder> decoderList = new ArrayList<>();
private final int kernLen;
private final int channelNo;
private final int deep;
private final float studyRate;
private final float oneStudyRate;//1v1卷积权重学习率
private UNetInput input;//输入类
private final float gaMa;
private final float gMaxTh;
private final boolean auTo;
public UNetInput getInput() {
return input;
}
public UNetManager(UNetConfig uNetConfig) throws Exception {
int xSize = uNetConfig.getXSize();
int ySize = uNetConfig.getYSize();
gaMa = uNetConfig.getGaMa();
gMaxTh = uNetConfig.getGMaxTh();
auTo = uNetConfig.isAuto();
int minFeatureValue = uNetConfig.getMinFeatureValue();
this.kernLen = uNetConfig.getKerSize();
this.channelNo = uNetConfig.getChannelNo();
this.studyRate = uNetConfig.getStudyRate();
this.oneStudyRate = uNetConfig.getOneStudyRate();
this.deep = getConvMyDep(xSize, ySize, kernLen, minFeatureValue);//编码器深度深度
if (deep > 1) {
initEncoder(xSize, ySize);//初始化编码器
initDecoder(uNetConfig.isCutting(), uNetConfig.getCutTh());
connectionCoder();
} else {
throw new Exception("minFeatureValue 设置的值太大了");
}
}
private float[] getFValue(Float[] values) {
float[] fValue = new float[values.length];
for (int i = 0; i < values.length; i++) {
fValue[i] = values[i];
}
return fValue;
}
private Float[] getValue(float[] values) {
Float[] result = new Float[values.length];
for (int i = 0; i < values.length; i++) {
result[i] = values[i];
}
return result;
}
public void insertModel(UNetModel uNetModel) throws Exception {
List<ConvModel> encoderModel = uNetModel.getEncoderModels();
List<ConvModel> decoderModel = uNetModel.getDecoderModels();
if (encoderModel.size() != deep) {
throw new Exception("模型深度不匹配");
}
for (int i = 0; i < deep; i++) {
ConvParameter convParameter = encoderList.get(i).getConvParameter();
List<Matrix> matrixList = convParameter.getNerveMatrixList();
ConvModel convModel = encoderModel.get(i);
List<Float[]> downPowers = convModel.getDownNervePower();
List<List<Float>> oneNervePower = convModel.getOneNervePowerList();
convParameter.setOneConvPower(oneNervePower);
for (int j = 0; j < matrixList.size(); j++) {
Matrix matrix = matrixList.get(j);
float[] power = getFValue(downPowers.get(j));
matrix.setCudaMatrix(power, matrix.getX(), matrix.getY());
}
}
for (int i = 0; i < deep + 1; i++) {
ConvParameter convParameter = decoderList.get(i).getConvParameter();
List<Matrix> matrixList = convParameter.getNerveMatrixList();
ConvModel convModel = decoderModel.get(i);
List<Float[]> downPowers = convModel.getDownNervePower();
List<Float[]> upNervePowerModel = convModel.getUpNervePower();
convParameter.setUpOneConvPower(convModel.getOneNervePower());
List<Matrix> upNervePowers = convParameter.getUpNerveMatrixList();
for (int j = 0; j < upNervePowerModel.size(); j++) {
float[] upPower = getFValue(upNervePowerModel.get(j));
Matrix upNervePower = upNervePowers.get(j);
upNervePower.setCudaMatrix(upPower, upNervePower.getX(), upNervePower.getY());
}
for (int j = 0; j < matrixList.size(); j++) {
Matrix matrix = matrixList.get(j);
float[] power = getFValue(downPowers.get(j));
matrix.setCudaMatrix(power, matrix.getX(), matrix.getY());
}
}
}
public UNetModel getModel() {
UNetModel unetModel = new UNetModel();
List<ConvModel> encoderModel = new ArrayList<>();
List<ConvModel> decoderModel = new ArrayList<>();
unetModel.setEncoderModels(encoderModel);
unetModel.setDecoderModels(decoderModel);
for (int i = 0; i < deep; i++) {//遍历每一层
ConvModel convModel = new ConvModel();
encoderModel.add(convModel);
ConvParameter convParameter = encoderList.get(i).getConvParameter();
List<Float[]> downNervePower = new ArrayList<>();
convModel.setDownNervePower(downNervePower);
List<List<Float>> onePowers = convParameter.getOneConvPower();
if (onePowers != null && !onePowers.isEmpty()) {
convModel.setOneNervePowerList(onePowers);
}
List<Matrix> downNerveMatrix = convParameter.getNerveMatrixList();//下采样卷积权重
for (Matrix nerveMatrix : downNerveMatrix) {
Float[] downPower = getValue(nerveMatrix.getCudaMatrix());
downNervePower.add(downPower);
}
}
for (int i = 0; i < deep + 1; i++) {
ConvModel convModel = new ConvModel();
decoderModel.add(convModel);
ConvParameter convParameter = decoderList.get(i).getConvParameter();
convModel.setOneNervePower(convParameter.getUpOneConvPower());
List<Float[]> downNervePower = new ArrayList<>();
convModel.setDownNervePower(downNervePower);
List<Matrix> upNerveMatrix = convParameter.getUpNerveMatrixList();
List<Float[]> upNervePower = new ArrayList<>();
for (Matrix upMatrix : upNerveMatrix) {
upNervePower.add(getValue(upMatrix.getCudaMatrix()));
}
convModel.setUpNervePower(upNervePower);
List<Matrix> downNerveMatrix = convParameter.getNerveMatrixList();
for (Matrix nerveMatrix : downNerveMatrix) {
Float[] downPower = getValue(nerveMatrix.getCudaMatrix());
downNervePower.add(downPower);
}
}
return unetModel;
}
private void connectionCoder() {//链接编解码器
UNetEncoder lastUNetEncoder = encoderList.get(deep - 1);//最后一层编码器
UNetDecoder firstUNetDecoder = decoderList.get(0);//第一层解码器
lastUNetEncoder.setDecoder(firstUNetDecoder);
firstUNetDecoder.setEncoder(lastUNetEncoder);
for (int i = 0; i < deep; i++) {
UNetEncoder uNetEncoder = encoderList.get(i);
UNetDecoder uNetDecoder = decoderList.get(deep - i);
uNetDecoder.setMyUNetEncoder(uNetEncoder);//绑定统计编码器
}
}
private void initDecoder(boolean cutting, float cutTh) throws Exception {
Cutting myCut = null;
if (cutting) {
myCut = new Cutting(cutTh);
}
for (int i = 0; i < deep + 1; i++) {
UNetDecoder uNetDecoder = new UNetDecoder(kernLen, i + 1, channelNo, new Tanh(),
i == deep, studyRate, myCut, oneStudyRate, gaMa, gMaxTh, auTo);
decoderList.add(uNetDecoder);
}
for (int i = 0; i < deep; i++) {
UNetDecoder uNetDecoder = decoderList.get(i);
UNetDecoder nextUNetDecoder = decoderList.get(i + 1);
uNetDecoder.setAfterDecoder(nextUNetDecoder);
nextUNetDecoder.setBeforeDecoder(uNetDecoder);
}
}
private void initEncoder(int xSize, int ySize) throws Exception {
for (int i = 0; i < deep; i++) {
UNetEncoder uNetEncoder = new UNetEncoder(kernLen, channelNo, i + 1, new ReLu(), studyRate
, xSize, ySize, oneStudyRate, gaMa, gMaxTh, auTo);
if (i == 0) {
input = new UNetInput(uNetEncoder);
}
encoderList.add(uNetEncoder);
}
for (int i = 0; i < deep - 1; i++) {
UNetEncoder uNetEncoder = encoderList.get(i);
UNetEncoder nextUNetEncoder = encoderList.get(i + 1);
uNetEncoder.setAfterEncoder(nextUNetEncoder);
nextUNetEncoder.setBeforeEncoder(uNetEncoder);
}
}
}