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/*
 *  ******************************************************************************
 *  *
 *  *
 *  * 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.
 *  *
 *  *  See the NOTICE file distributed with this work for additional
 *  *  information regarding copyright ownership.
 *  * 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 lombok.Getter;
import lombok.NoArgsConstructor;
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.ComputationGraphConfiguration.GraphBuilder;
import org.deeplearning4j.nn.conf.graph.MergeVertex;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.ConvolutionLayer;
import org.deeplearning4j.nn.conf.layers.SpaceToDepthLayer;
import org.deeplearning4j.nn.conf.layers.objdetect.Yolo2OutputLayer;
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.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.learning.config.Adam;
import org.nd4j.linalg.learning.config.IUpdater;

import static org.deeplearning4j.zoo.model.helper.DarknetHelper.addLayers;

@AllArgsConstructor
@Builder
public class YOLO2 extends ZooModel {

    /**
     * Default prior boxes for the model
     */
    public static final double[][] DEFAULT_PRIOR_BOXES = {{0.57273, 0.677385}, {1.87446, 2.06253}, {3.33843, 5.47434}, {7.88282, 3.52778}, {9.77052, 9.16828}};

    @Builder.Default @Getter private int nBoxes = 5;
    @Builder.Default @Getter private double[][] priorBoxes = DEFAULT_PRIOR_BOXES;

    @Builder.Default private long seed = 1234;
    @Builder.Default private int[] inputShape = {3, 608, 608};
    @Builder.Default private int numClasses = 0;
    @Builder.Default private IUpdater updater = new Adam(1e-3);
    @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 YOLO2() {}

    @Override
    public String pretrainedUrl(PretrainedType pretrainedType) {
        if (pretrainedType == PretrainedType.IMAGENET)
            return DL4JResources.getURLString("models/yolo2_dl4j_inference.v3.zip");
        else
            return null;
    }

    @Override
    public long pretrainedChecksum(PretrainedType pretrainedType) {
        if (pretrainedType == PretrainedType.IMAGENET)
            return 3658373840L;
        else
            return 0L;
    }

    @Override
    public Class modelType() {
        return ComputationGraph.class;
    }

    public ComputationGraphConfiguration conf() {
        INDArray priors = Nd4j.create(priorBoxes);

        GraphBuilder graphBuilder = new NeuralNetConfiguration.Builder()
                .seed(seed)
                .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
                .gradientNormalization(GradientNormalization.RenormalizeL2PerLayer)
                .gradientNormalizationThreshold(1.0)
                .updater(updater)
                .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);

        // #######

        addLayers(graphBuilder, 19, 3, 1024, 1024, 0);
        addLayers(graphBuilder, 20, 3, 1024, 1024, 0);

        // route
        addLayers(graphBuilder, 21, "activation_13", 1, 512, 64, 0, 0);

        // reorg
        graphBuilder.addLayer("rearrange_21",new SpaceToDepthLayer.Builder(2).build(), "activation_21")
        // route
                .addVertex("concatenate_21", new MergeVertex(),
                        "rearrange_21", "activation_20");

        addLayers(graphBuilder, 22, "concatenate_21", 3, 1024 + 256, 1024, 0, 0);

        graphBuilder
                .addLayer("convolution2d_23",
                        new ConvolutionLayer.Builder(1,1)
                                .nIn(1024)
                                .nOut(nBoxes * (5 + numClasses))
                                .weightInit(WeightInit.XAVIER)
                                .stride(1,1)
                                .convolutionMode(ConvolutionMode.Same)
                                .weightInit(WeightInit.RELU)
                                .activation(Activation.IDENTITY)
                                .cudnnAlgoMode(cudnnAlgoMode)
                                .build(),
                        "activation_22")
                .addLayer("outputs",
                        new Yolo2OutputLayer.Builder()
                                .boundingBoxPriors(priors)
                                .build(),
                        "convolution2d_23")
                .setOutputs("outputs");

        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];
    }
}