org.deeplearning4j.zoo.model.Darknet19 Maven / Gradle / Ivy
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* 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
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* under the License.
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* SPDX-License-Identifier: Apache-2.0
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package org.deeplearning4j.zoo.model;
import lombok.AllArgsConstructor;
import lombok.Builder;
import lombok.NoArgsConstructor;
import org.deeplearning4j.common.resources.DL4JResources;
import org.deeplearning4j.nn.api.Model;
import org.deeplearning4j.nn.conf.*;
import org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder;
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.IUpdater;
import org.nd4j.linalg.learning.config.Nesterovs;
import org.nd4j.linalg.lossfunctions.LossFunctions;
import static org.deeplearning4j.zoo.model.helper.DarknetHelper.addLayers;
/**
* Darknet19
* Reference: https://arxiv.org/pdf/1612.08242.pdf
*
* ImageNet weights for this model are available and have been converted from https://pjreddie.com/darknet/imagenet/
* using https://github.com/allanzelener/YAD2K .
*
* There are 2 pretrained models, one for 224x224 images and one fine-tuned for 448x448 images.
* Call setInputShape() with either {3, 224, 224} or {3, 448, 448} before initialization.
* The channels of the input images need to be in RGB order (not BGR), with values normalized within [0, 1].
* The output labels are as per
* https://github.com/pjreddie/darknet/blob/master/data/imagenet.shortnames.list .
*
* @author saudet
*/
@AllArgsConstructor
@Builder
public class Darknet19 extends ZooModel {
@Builder.Default private long seed = 1234;
@Builder.Default private int[] inputShape = {3, 224, 224};
@Builder.Default private int numClasses = 0;
@Builder.Default private WeightInit weightInit = WeightInit.RELU;
@Builder.Default private IUpdater updater = new Nesterovs(1e-3, 0.9);
@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 Darknet19() {}
@Override
public String pretrainedUrl(PretrainedType pretrainedType) {
if (pretrainedType == PretrainedType.IMAGENET)
if (inputShape[1] == 448 && inputShape[2] == 448)
return DL4JResources.getURLString("models/darknet19_448_dl4j_inference.v2.zip");
else
return DL4JResources.getURLString("models/darknet19_dl4j_inference.v2.zip");
else
return null;
}
@Override
public long pretrainedChecksum(PretrainedType pretrainedType) {
if (pretrainedType == PretrainedType.IMAGENET)
if (inputShape[1] == 448 && inputShape[2] == 448)
return 1054319943L;
else
return 691100891L;
else
return 0L;
}
@Override
public Class modelType() {
return ComputationGraph.class;
}
public ComputationGraphConfiguration conf() {
GraphBuilder graphBuilder = new NeuralNetConfiguration.Builder()
.seed(seed)
.updater(updater)
.weightInit(weightInit)
.l2(0.00001)
.activation(Activation.IDENTITY)
.cacheMode(cacheMode)
.trainingWorkspaceMode(workspaceMode)
.inferenceWorkspaceMode(workspaceMode)
.cudnnAlgoMode(cudnnAlgoMode)
.graphBuilder()
.addInputs("input")
.setInputTypes(InputType.convolutional(inputShape[2], inputShape[1], inputShape[0]));
addLayers(graphBuilder, 1, 3, inputShape[0], 32, 2);
addLayers(graphBuilder, 2, 3, 32, 64, 2);
addLayers(graphBuilder, 3, 3, 64, 128, 0);
addLayers(graphBuilder, 4, 1, 128, 64, 0);
addLayers(graphBuilder, 5, 3, 64, 128, 2);
addLayers(graphBuilder, 6, 3, 128, 256, 0);
addLayers(graphBuilder, 7, 1, 256, 128, 0);
addLayers(graphBuilder, 8, 3, 128, 256, 2);
addLayers(graphBuilder, 9, 3, 256, 512, 0);
addLayers(graphBuilder, 10, 1, 512, 256, 0);
addLayers(graphBuilder, 11, 3, 256, 512, 0);
addLayers(graphBuilder, 12, 1, 512, 256, 0);
addLayers(graphBuilder, 13, 3, 256, 512, 2);
addLayers(graphBuilder, 14, 3, 512, 1024, 0);
addLayers(graphBuilder, 15, 1, 1024, 512, 0);
addLayers(graphBuilder, 16, 3, 512, 1024, 0);
addLayers(graphBuilder, 17, 1, 1024, 512, 0);
addLayers(graphBuilder, 18, 3, 512, 1024, 0);
int layerNumber = 19;
graphBuilder
.addLayer("convolution2d_" + layerNumber,
new ConvolutionLayer.Builder(1,1)
.nIn(1024)
.nOut(numClasses)
.weightInit(WeightInit.XAVIER)
.stride(1,1)
.convolutionMode(ConvolutionMode.Same)
.weightInit(WeightInit.RELU)
.activation(Activation.IDENTITY)
.build(),
"activation_" + (layerNumber - 1))
.addLayer("globalpooling", new GlobalPoolingLayer.Builder(PoolingType.AVG)
.build(), "convolution2d_" + layerNumber)
.addLayer("softmax", new ActivationLayer.Builder()
.activation(Activation.SOFTMAX)
.build(), "globalpooling")
.addLayer("loss", new LossLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
.build(), "softmax")
.setOutputs("loss");
return graphBuilder.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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