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 *  *  information regarding copyright ownership.
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package org.deeplearning4j.nn.layers.convolution;

import org.deeplearning4j.BaseDL4JTest;
import org.deeplearning4j.TestUtils;
import org.deeplearning4j.nn.api.OptimizationAlgorithm;
import org.deeplearning4j.nn.conf.ComputationGraphConfiguration;
import org.deeplearning4j.nn.conf.ConvolutionMode;
import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.*;
import org.deeplearning4j.nn.conf.layers.ConvolutionLayer;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.nn.weights.WeightInit;
import org.junit.jupiter.api.BeforeEach;
import org.junit.jupiter.api.Test;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.buffer.util.DataTypeUtil;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.MultiDataSet;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.learning.config.Nesterovs;
import org.nd4j.linalg.learning.config.NoOp;
import org.nd4j.linalg.lossfunctions.LossFunctions;

import java.util.Map;

import static org.junit.jupiter.api.Assertions.assertArrayEquals;
import static org.junit.jupiter.api.Assertions.assertEquals;

/**
 * @author Max Pumperla
 */
public class LocallyConnectedLayerTest extends BaseDL4JTest {

    @BeforeEach
    public void before() {
        DataTypeUtil.setDTypeForContext(DataType.DOUBLE);
        Nd4j.factory().setDType(DataType.DOUBLE);
        Nd4j.EPS_THRESHOLD = 1e-4;
    }

    @Test
    public void test2dForward(){
        MultiLayerConfiguration.Builder builder = new NeuralNetConfiguration.Builder().seed(123)
                        .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).l2(2e-4)
                        .updater(new Nesterovs(0.9)).dropOut(0.5)
                        .list()
                        .layer(new LocallyConnected2D.Builder().kernelSize(8, 8).nIn(3)
                                                        .stride(4, 4).nOut(16).dropOut(0.5)
                                                        .convolutionMode(ConvolutionMode.Strict)
                                                        .setInputSize(28, 28)
                                                        .activation(Activation.RELU).weightInit(
                                                                        WeightInit.XAVIER)
                                                        .build())
                        .layer(new OutputLayer.Builder(LossFunctions.LossFunction.SQUARED_LOSS) //output layer
                                        .nOut(10).weightInit(WeightInit.XAVIER).activation(Activation.SOFTMAX).build())
                        .setInputType(InputType.convolutionalFlat(28, 28, 3));

        MultiLayerConfiguration conf = builder.build();
        MultiLayerNetwork network = new MultiLayerNetwork(conf);
        network.init();

        INDArray input = Nd4j.ones(10, 3, 28, 28);
        INDArray output = network.output(input, false);

        assertArrayEquals(new long[] {10, 10}, output.shape());
    }

    @Test
    public void test1dForward(){
        MultiLayerConfiguration.Builder builder = new NeuralNetConfiguration.Builder().seed(123)
                .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).l2(2e-4)
                .updater(new Nesterovs(0.9)).dropOut(0.5)
                .list()
                .layer(new LocallyConnected1D.Builder().kernelSize(4).nIn(3)
                        .stride(1).nOut(16).dropOut(0.5)
                        .convolutionMode(ConvolutionMode.Strict)
                        .setInputSize(28)
                        .activation(Activation.RELU).weightInit(
                                WeightInit.XAVIER)
                        .build())
                .layer(new OutputLayer.Builder(LossFunctions.LossFunction.SQUARED_LOSS) //output layer
                        .nOut(10).weightInit(WeightInit.XAVIER).activation(Activation.SOFTMAX).build())
                .setInputType(InputType.recurrent(3,  8));

        MultiLayerConfiguration conf = builder.build();
        MultiLayerNetwork network = new MultiLayerNetwork(conf);
        network.init();

        INDArray input = Nd4j.ones(10, 3, 8);
        INDArray output = network.output(input, false);;
        for (int i = 0; i < 100; i++) { // TODO: this falls flat for 1000 iterations on my machine
            output = network.output(input, false);
        }

        assertArrayEquals(new long[] {(8 - 4 + 1) * 10, 10}, output.shape());
        network.fit(input, output);

    }

    @Test
    public void testLocallyConnected(){
        for (DataType globalDtype : new DataType[]{DataType.DOUBLE, DataType.FLOAT, DataType.HALF}) {
            Nd4j.setDefaultDataTypes(globalDtype, globalDtype);
            for (DataType networkDtype : new DataType[]{DataType.DOUBLE, DataType.FLOAT, DataType.HALF}) {
                assertEquals(globalDtype, Nd4j.dataType());
                assertEquals(globalDtype, Nd4j.defaultFloatingPointType());

                for (int test = 0; test < 2; test++) {
                    String msg = "Global dtype: " + globalDtype + ", network dtype: " + networkDtype + ", test=" + test;

                    ComputationGraphConfiguration.GraphBuilder b = new NeuralNetConfiguration.Builder()
                            .dataType(networkDtype)
                            .seed(123)
                            .updater(new NoOp())
                            .weightInit(WeightInit.XAVIER)
                            .convolutionMode(ConvolutionMode.Same)
                            .graphBuilder();

                    INDArray[] in;
                    INDArray label;
                    switch (test){
                        case 0:
                            b.addInputs("in")
                                    .addLayer("1", new LSTM.Builder().nOut(5).build(), "in")
                                    .addLayer("2", new LocallyConnected1D.Builder().kernelSize(2).nOut(4).build(), "1")
                                    .addLayer("out", new RnnOutputLayer.Builder().nOut(10).build(), "2")
                                    .setOutputs("out")
                                    .setInputTypes(InputType.recurrent(5, 4));
                            in = new INDArray[]{Nd4j.rand(networkDtype, 2, 5, 4)};
                            label = TestUtils.randomOneHotTimeSeries(2, 10, 4).castTo(networkDtype);
                            break;
                        case 1:
                            b.addInputs("in")
                                    .addLayer("1", new ConvolutionLayer.Builder().kernelSize(2,2).nOut(5).convolutionMode(ConvolutionMode.Same).build(), "in")
                                    .addLayer("2", new LocallyConnected2D.Builder().kernelSize(2,2).nOut(5).build(), "1")
                                    .addLayer("out", new OutputLayer.Builder().nOut(10).build(), "2")
                                    .setOutputs("out")
//                                    .setInputTypes(InputType.convolutional(28, 28, 1));
//                            in = new INDArray[]{Nd4j.rand(networkDtype, 2, 1, 28, 28)};
                                    .setInputTypes(InputType.convolutional(8, 8, 1));
                            in = new INDArray[]{Nd4j.rand(networkDtype, 2, 1, 8, 8)};
                            label = TestUtils.randomOneHot(2, 10).castTo(networkDtype);
                            break;
                        default:
                            throw new RuntimeException();
                    }

                    ComputationGraph net = new ComputationGraph(b.build());
                    net.init();

                    INDArray out = net.outputSingle(in);
                    assertEquals(networkDtype, out.dataType(), msg);
                    Map<String, INDArray> ff = net.feedForward(in, false);
                    for (Map.Entry<String, INDArray> e : ff.entrySet()) {
                        if (e.getKey().equals("in"))
                            continue;
                        String s = msg + " - layer: " + e.getKey();
                        assertEquals(networkDtype, e.getValue().dataType(), s);
                    }

                    net.setInputs(in);
                    net.setLabels(label);
                    net.computeGradientAndScore();

                    net.fit(new MultiDataSet(in, new INDArray[]{label}));
                }
            }
        }
    }
}
