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 *  *  information regarding copyright ownership.
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 *  * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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package org.eclipse.deeplearning4j.dl4jcore.nn.layers;

import org.deeplearning4j.BaseDL4JTest;
import org.eclipse.deeplearning4j.dl4jcore.TestUtils;
import org.deeplearning4j.nn.conf.*;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.*;
import org.deeplearning4j.nn.conf.layers.OutputLayer;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.junit.jupiter.api.Test;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.DataSet;
import org.nd4j.linalg.factory.Nd4j;
import static org.junit.jupiter.api.Assertions.assertEquals;
import org.junit.jupiter.api.DisplayName;

@DisplayName("Cache Mode Test")
class CacheModeTest extends BaseDL4JTest {

    @Test
    @DisplayName("Test Conv Cache Mode Simple")
    void testConvCacheModeSimple() {
        MultiLayerConfiguration conf1 = getConf(CacheMode.NONE);
        MultiLayerConfiguration conf2 = getConf(CacheMode.DEVICE);
        MultiLayerNetwork net1 = new MultiLayerNetwork(conf1);
        net1.init();
        MultiLayerNetwork net2 = new MultiLayerNetwork(conf2);
        net2.init();
        INDArray in = Nd4j.rand(3, 28 * 28);
        INDArray labels = TestUtils.randomOneHot(3, 10);
        INDArray out1 = net1.output(in);
        INDArray out2 = net2.output(in);
        assertEquals(out1, out2);
        assertEquals(net1.params(), net2.params());
        net1.fit(in, labels);
        net2.fit(in, labels);
        assertEquals(net1.params(), net2.params());
    }

    private static MultiLayerConfiguration getConf(CacheMode cacheMode) {
        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().activation(Activation.TANH).inferenceWorkspaceMode(WorkspaceMode.ENABLED).trainingWorkspaceMode(WorkspaceMode.ENABLED).seed(12345).cacheMode(cacheMode).list().layer(new ConvolutionLayer.Builder().nOut(3).build()).layer(new ConvolutionLayer.Builder().nOut(3).build()).layer(new OutputLayer.Builder().nOut(10).activation(Activation.SOFTMAX).build()).setInputType(InputType.convolutionalFlat(28, 28, 1)).build();
        return conf;
    }

    @Test
    @DisplayName("Test LSTM Cache Mode Simple")
    void testLSTMCacheModeSimple() {
        for (boolean graves : new boolean[] { true, false }) {
            MultiLayerConfiguration conf1 = getConfLSTM(CacheMode.NONE, graves);
            MultiLayerConfiguration conf2 = getConfLSTM(CacheMode.DEVICE, graves);
            MultiLayerNetwork net1 = new MultiLayerNetwork(conf1);
            net1.init();
            MultiLayerNetwork net2 = new MultiLayerNetwork(conf2);
            net2.init();
            INDArray in = Nd4j.rand(new int[] { 3, 3, 10 });
            INDArray labels = TestUtils.randomOneHotTimeSeries(3, 10, 10);
            INDArray out1 = net1.output(in);
            INDArray out2 = net2.output(in);
            assertEquals(out1, out2);
            assertEquals(net1.params(), net2.params());
            net1.fit(in, labels);
            net2.fit(in, labels);
            assertEquals(net1.params(), net2.params());
        }
    }

    private static MultiLayerConfiguration getConfLSTM(CacheMode cacheMode, boolean graves) {
        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().activation(Activation.TANH).inferenceWorkspaceMode(WorkspaceMode.ENABLED).trainingWorkspaceMode(WorkspaceMode.ENABLED).seed(12345).cacheMode(cacheMode).list().layer(graves ? new GravesLSTM.Builder().nIn(3).nOut(3).build() : new LSTM.Builder().nIn(3).nOut(3).build()).layer(graves ? new GravesLSTM.Builder().nIn(3).nOut(3).build() : new LSTM.Builder().nIn(3).nOut(3).build()).layer(new RnnOutputLayer.Builder().nOut(10).activation(Activation.SOFTMAX).build()).build();
        return conf;
    }

    @Test
    @DisplayName("Test Conv Cache Mode Simple CG")
    void testConvCacheModeSimpleCG() {
        ComputationGraphConfiguration conf1 = getConfCG(CacheMode.NONE);
        ComputationGraphConfiguration conf2 = getConfCG(CacheMode.DEVICE);
        ComputationGraph net1 = new ComputationGraph(conf1);
        net1.init();
        ComputationGraph net2 = new ComputationGraph(conf2);
        net2.init();
        INDArray in = Nd4j.rand(3, 28 * 28);
        INDArray labels = TestUtils.randomOneHot(3, 10);
        INDArray out1 = net1.outputSingle(in);
        INDArray out2 = net2.outputSingle(in);
        assertEquals(out1, out2);
        assertEquals(net1.params(), net2.params());
        net1.fit(new DataSet(in, labels));
        net2.fit(new DataSet(in, labels));
        assertEquals(net1.params(), net2.params());
    }

    private static ComputationGraphConfiguration getConfCG(CacheMode cacheMode) {
        ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().activation(Activation.TANH).inferenceWorkspaceMode(WorkspaceMode.ENABLED).trainingWorkspaceMode(WorkspaceMode.ENABLED).seed(12345).cacheMode(cacheMode).graphBuilder().addInputs("in").layer("0", new ConvolutionLayer.Builder().nOut(3).build(), "in").layer("1", new ConvolutionLayer.Builder().nOut(3).build(), "0").layer("2", new OutputLayer.Builder().nOut(10).activation(Activation.SOFTMAX).build(), "1").setOutputs("2").setInputTypes(InputType.convolutionalFlat(28, 28, 1)).build();
        return conf;
    }
}
