cavis/deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/util/CrashReportingUtilTest.java

/*******************************************************************************
 * 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.util;

import org.apache.commons.io.FileUtils;
import org.deeplearning4j.BaseDL4JTest;
import org.deeplearning4j.datasets.iterator.EarlyTerminationDataSetIterator;
import org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator;
import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.distribution.NormalDistribution;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.ConvolutionLayer;
import org.deeplearning4j.nn.conf.layers.OutputLayer;
import org.deeplearning4j.nn.conf.layers.PoolingType;
import org.deeplearning4j.nn.conf.layers.SubsamplingLayer;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.optimize.listeners.ScoreIterationListener;
import org.junit.After;
import org.junit.Rule;
import org.junit.Test;
import org.junit.rules.TemporaryFolder;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
import org.nd4j.linalg.learning.config.NoOp;
import org.nd4j.linalg.lossfunctions.LossFunctions;

import java.io.File;

import static org.junit.Assert.*;

public class CrashReportingUtilTest extends BaseDL4JTest {

    @Override
    public long getTimeoutMilliseconds() {
        return 120000;
    }

    @Rule
    public TemporaryFolder testDir = new TemporaryFolder();

    @Override
    public DataType getDataType(){
        return DataType.FLOAT;
    }

    @After
    public void after(){
        //Reset dir
        CrashReportingUtil.crashDumpOutputDirectory(null);
    }

    @Test
    public void test() throws Exception {
        File dir = testDir.newFolder();
        CrashReportingUtil.crashDumpOutputDirectory(dir);

        int kernel = 2;
        int stride = 1;
        int padding = 0;
        PoolingType poolingType = PoolingType.MAX;
        int inputDepth = 1;
        int height = 28;
        int width = 28;


        MultiLayerConfiguration conf =
                new NeuralNetConfiguration.Builder().updater(new NoOp())

                        .dist(new NormalDistribution(0, 1))
                        .list().layer(0,
                        new ConvolutionLayer.Builder()
                                .kernelSize(kernel, kernel)
                                .stride(stride, stride)
                                .padding(padding, padding)
                                .nIn(inputDepth)
                                .nOut(3).build())
                        .layer(1, new SubsamplingLayer.Builder(poolingType)
                                .kernelSize(kernel, kernel)
                                .stride(stride, stride)
                                .padding(padding, padding)
                                .build())
                        .layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
                                .activation(Activation.SOFTMAX)
                                .nOut(10).build())
                        .setInputType(InputType.convolutionalFlat(height, width,
                                inputDepth))
                        .build();

        MultiLayerNetwork net = new MultiLayerNetwork(conf);
        net.init();
        net.addListeners(new ScoreIterationListener(1));

        //Test net that hasn't been trained yet
        Exception e = new Exception();
        CrashReportingUtil.writeMemoryCrashDump(net, e);

        File[] list = dir.listFiles();
        assertNotNull(list);
        assertEquals(1, list.length);
        String str = FileUtils.readFileToString(list[0]);
//        System.out.println(str);
        assertTrue(str.contains("Network Information"));
        assertTrue(str.contains("Layer Helpers"));
        assertTrue(str.contains("JavaCPP"));
        assertTrue(str.contains("ScoreIterationListener"));


        //Train:
        DataSetIterator iter = new EarlyTerminationDataSetIterator(new MnistDataSetIterator(32, true, 12345), 5);
        net.fit(iter);
        dir = testDir.newFolder();
        CrashReportingUtil.crashDumpOutputDirectory(dir);
        CrashReportingUtil.writeMemoryCrashDump(net, e);

        list = dir.listFiles();
        assertNotNull(list);
        assertEquals(1, list.length);
        str = FileUtils.readFileToString(list[0]);
        assertTrue(str.contains("Network Information"));
        assertTrue(str.contains("Layer Helpers"));
        assertTrue(str.contains("JavaCPP"));
        assertTrue(str.contains("ScoreIterationListener(1)"));

//        System.out.println("///////////////////////////////////////////////////////////");
//        System.out.println(str);
//        System.out.println("///////////////////////////////////////////////////////////");


        //Also test manual memory info
        String mlnMemoryInfo = net.memoryInfo(32, InputType.convolutionalFlat(28, 28, 1));
//        System.out.println("///////////////////////////////////////////////////////////");
//        System.out.println(mlnMemoryInfo);
//        System.out.println("///////////////////////////////////////////////////////////");

        assertTrue(mlnMemoryInfo.contains("Network Information"));
        assertTrue(mlnMemoryInfo.contains("Layer Helpers"));
        assertTrue(mlnMemoryInfo.contains("JavaCPP"));
        assertTrue(mlnMemoryInfo.contains("ScoreIterationListener(1)"));



        ////////////////////////////////////////
        //Same thing on ComputationGraph:
        dir = testDir.newFolder();
        CrashReportingUtil.crashDumpOutputDirectory(dir);

        ComputationGraph cg = net.toComputationGraph();
        cg.setListeners(new ScoreIterationListener(1));

        //Test net that hasn't been trained yet
        CrashReportingUtil.writeMemoryCrashDump(cg, e);

        list = dir.listFiles();
        assertNotNull(list);
        assertEquals(1, list.length);
        str = FileUtils.readFileToString(list[0]);
        assertTrue(str.contains("Network Information"));
        assertTrue(str.contains("Layer Helpers"));
        assertTrue(str.contains("JavaCPP"));
        assertTrue(str.contains("ScoreIterationListener(1)"));

        //Train:
        cg.fit(iter);
        dir = testDir.newFolder();
        CrashReportingUtil.crashDumpOutputDirectory(dir);
        CrashReportingUtil.writeMemoryCrashDump(cg, e);

        list = dir.listFiles();
        assertNotNull(list);
        assertEquals(1, list.length);
        str = FileUtils.readFileToString(list[0]);
        assertTrue(str.contains("Network Information"));
        assertTrue(str.contains("Layer Helpers"));
        assertTrue(str.contains("JavaCPP"));
        assertTrue(str.contains("ScoreIterationListener(1)"));

//        System.out.println("///////////////////////////////////////////////////////////");
//        System.out.println(str);
//        System.out.println("///////////////////////////////////////////////////////////");


        //Also test manual memory info
        String cgMemoryInfo = cg.memoryInfo(32, InputType.convolutionalFlat(28, 28, 1));
//        System.out.println("///////////////////////////////////////////////////////////");
//        System.out.println(cgMemoryInfo);
//        System.out.println("///////////////////////////////////////////////////////////");

        assertTrue(cgMemoryInfo.contains("Network Information"));
        assertTrue(cgMemoryInfo.contains("Layer Helpers"));
        assertTrue(cgMemoryInfo.contains("JavaCPP"));
        assertTrue(cgMemoryInfo.contains("ScoreIterationListener(1)"));

    }


}