org.deeplearning4j.zoo.model.UNet Maven / Gradle / Ivy
/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
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.TruncatedNormalDistribution;
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.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.learning.config.AdaDelta;
import org.nd4j.linalg.learning.config.IUpdater;
import org.nd4j.linalg.lossfunctions.LossFunctions;
/**
* U-Net
*
* An implementation of U-Net, a deep learning network for image segmentation in Deeplearning4j.
* The u-net is convolutional network architecture for fast and precise segmentation of images.
* Up to now it has outperformed the prior best method (a sliding-window convolutional network) on the ISBI challenge for
* segmentation of neuronal structures in electron microscopic stacks.
*
* Paper: https://arxiv.org/abs/1505.04597
* Weights are available for image segmentation trained on a synthetic dataset
*
* @author Justin Long (crockpotveggies)
*
*/
@AllArgsConstructor
@Builder
public class UNet extends ZooModel {
@Builder.Default private long seed = 1234;
@Builder.Default private int[] inputShape = new int[] {3, 512, 512};
@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.NONE;
@Builder.Default private WorkspaceMode workspaceMode = WorkspaceMode.ENABLED;
@Builder.Default private ConvolutionLayer.AlgoMode cudnnAlgoMode = ConvolutionLayer.AlgoMode.PREFER_FASTEST;
private UNet() {}
@Override
public String pretrainedUrl(PretrainedType pretrainedType) {
if (pretrainedType == PretrainedType.SEGMENT)
return DL4JResources.getURLString("models/unet_dl4j_segment_inference.v1.zip");
else
return null;
}
@Override
public long pretrainedChecksum(PretrainedType pretrainedType) {
if (pretrainedType == PretrainedType.SEGMENT)
return 712347958L;
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() {
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)
.graphBuilder();
graph
.addLayer("conv1-1", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(64)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "input")
.addLayer("conv1-2", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(64)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "conv1-1")
.addLayer("pool1", new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX).kernelSize(2,2)
.build(), "conv1-2")
.addLayer("conv2-1", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(128)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "pool1")
.addLayer("conv2-2", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(128)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "conv2-1")
.addLayer("pool2", new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX).kernelSize(2,2)
.build(), "conv2-2")
.addLayer("conv3-1", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(256)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "pool2")
.addLayer("conv3-2", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(256)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "conv3-1")
.addLayer("pool3", new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX).kernelSize(2,2)
.build(), "conv3-2")
.addLayer("conv4-1", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(512)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "pool3")
.addLayer("conv4-2", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(512)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "conv4-1")
.addLayer("drop4", new DropoutLayer.Builder(0.5).build(), "conv4-2")
.addLayer("pool4", new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX).kernelSize(2,2)
.build(), "drop4")
.addLayer("conv5-1", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(1024)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "pool4")
.addLayer("conv5-2", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(1024)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "conv5-1")
.addLayer("drop5", new DropoutLayer.Builder(0.5).build(), "conv5-2")
// up6
.addLayer("up6-1", new Upsampling2D.Builder(2).build(), "drop5")
.addLayer("up6-2", new ConvolutionLayer.Builder(2,2).stride(1,1).nOut(512)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "up6-1")
.addVertex("merge6", new MergeVertex(), "drop4", "up6-2")
.addLayer("conv6-1", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(512)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "merge6")
.addLayer("conv6-2", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(512)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "conv6-1")
// up7
.addLayer("up7-1", new Upsampling2D.Builder(2).build(), "conv6-2")
.addLayer("up7-2", new ConvolutionLayer.Builder(2,2).stride(1,1).nOut(256)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "up7-1")
.addVertex("merge7", new MergeVertex(), "conv3-2", "up7-2")
.addLayer("conv7-1", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(256)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "merge7")
.addLayer("conv7-2", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(256)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "conv7-1")
// up8
.addLayer("up8-1", new Upsampling2D.Builder(2).build(), "conv7-2")
.addLayer("up8-2", new ConvolutionLayer.Builder(2,2).stride(1,1).nOut(128)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "up8-1")
.addVertex("merge8", new MergeVertex(), "conv2-2", "up8-2")
.addLayer("conv8-1", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(128)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "merge8")
.addLayer("conv8-2", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(128)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "conv8-1")
// up9
.addLayer("up9-1", new Upsampling2D.Builder(2).build(), "conv8-2")
.addLayer("up9-2", new ConvolutionLayer.Builder(2,2).stride(1,1).nOut(64)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "up9-1")
.addVertex("merge9", new MergeVertex(), "conv1-2", "up9-2")
.addLayer("conv9-1", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(64)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "merge9")
.addLayer("conv9-2", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(64)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "conv9-1")
.addLayer("conv9-3", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(2)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.RELU).build(), "conv9-2")
.addLayer("conv10", new ConvolutionLayer.Builder(3,3).stride(1,1).nOut(1)
.convolutionMode(ConvolutionMode.Same).cudnnAlgoMode(cudnnAlgoMode)
.activation(Activation.IDENTITY).build(), "conv9-3")
.addLayer("output", new CnnLossLayer.Builder(LossFunctions.LossFunction.XENT)
.activation(Activation.SIGMOID).build(), "conv10")
.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];
}
}
© 2015 - 2024 Weber Informatics LLC | Privacy Policy