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Timeouts and scala 2.12 for deeplearning4j-nlp-korean workaround (#380) 

* Increase default timeout on Spark tests

Signed-off-by: Alex Black <blacka101@gmail.com>

* #8840 disable deeplearning4j-nlp-korean module for scala 2.12

Signed-off-by: Alex Black <blacka101@gmail.com>

* Fix for change-scala-versions.sh

Signed-off-by: Alex Black <blacka101@gmail.com>

* CUDA test fixes + more timeout issues

Signed-off-by: Alex Black <blacka101@gmail.com>

* More CUDA

Signed-off-by: Alex Black <blacka101@gmail.com>

* Small fix for cuDNN subsampling + same mode

Signed-off-by: Alex Black <blacka101@gmail.com>

* Flaky test fix

Signed-off-by: Alex Black <blacka101@gmail.com>

* Reduce memory requirements for ValidateCuDNN BN test

Signed-off-by: Alex Black <blacka101@gmail.com>

* Fix slow/ineffirient scalnet tests

Signed-off-by: Alex Black <blacka101@gmail.com>

* Increase timeouts to avoid failures if CI machines are slower than expected

Signed-off-by: Alex Black <blacka101@gmail.com>

* Ignore flaky test (issue #8849) and increase timeout for slow CI downloads

Signed-off-by: Alex Black <blacka101@gmail.com>
master
Alex Black 2020-04-20 10:26:00 +10:00 committed by GitHub
parent 163222e853
commit 73aa760c0f
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41 changed files with 247 additions and 61 deletions
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10
change-scala-versions.sh
View File

@ -88,5 +88,15 @@ find "$BASEDIR" -name 'pom.xml' -not -path '*target*' \
#Scala maven plugin, <scalaVersion>2.11</scalaVersion>
find "$BASEDIR" -name 'pom.xml' -not -path '*target*' \
-exec bash -c "sed_i 's/\(scalaVersion>\)'$FROM_VERSION'<\/scalaVersion>/\1'$TO_VERSION'<\/scalaVersion>/g' {}" \;
# Disable deeplearning4j-nlp-korean for scala 2.12 - see https://github.com/eclipse/deeplearning4j/issues/8840
if [ $TO_VERSION = $SCALA_211_VERSION ]; then
#Enable
sed -i 's/ <!--<module>deeplearning4j-nlp-korean<\/module>-->/ <module>deeplearning4j-nlp-korean<\/module>/g' deeplearning4j/deeplearning4j-nlp-parent/pom.xml
else
#Disable
sed -i 's/ <module>deeplearning4j-nlp-korean<\/module>/ <!--<module>deeplearning4j-nlp-korean<\/module>-->/g' deeplearning4j/deeplearning4j-nlp-parent/pom.xml
fi
echo "Done updating Scala versions.";

13
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/TestUtils.java
View File

@ -31,6 +31,7 @@ import org.deeplearning4j.nn.layers.recurrent.LSTM;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.util.ModelSerializer;
import org.nd4j.base.Preconditions;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.random.impl.BernoulliDistribution;
import org.nd4j.linalg.factory.Nd4j;
@ -124,12 +125,20 @@ public class TestUtils {
return randomOneHot(examples, nOut, new Random(12345));
}
public static INDArray randomOneHot(DataType dataType, long examples, long nOut){
return randomOneHot(dataType, examples, nOut, new Random(12345));
}
public static INDArray randomOneHot(long examples, long nOut, long rngSeed){
return randomOneHot(examples, nOut, new Random(rngSeed));
}
public static INDArray randomOneHot(long examples, long nOut, Random rng){
INDArray arr = Nd4j.create(examples, nOut);
public static INDArray randomOneHot(long examples, long nOut, Random rng) {
return randomOneHot(Nd4j.defaultFloatingPointType(), examples,nOut, rng);
}
public static INDArray randomOneHot(DataType dataType, long examples, long nOut, Random rng){
INDArray arr = Nd4j.create(dataType, examples, nOut);
for( int i=0; i<examples; i++ ){
arr.putScalar(i, rng.nextInt((int) nOut), 1.0);
}

2
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/datasets/iterator/DataSetIteratorTest.java
View File

@ -59,7 +59,7 @@ public class DataSetIteratorTest extends BaseDL4JTest {
@Override
public long getTimeoutMilliseconds() {
return 90000;
return 360000; //Should run quickly; increased to large timeout due to occasonal slow CI downloads
}
@Test

5
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/gradientcheck/AttentionLayerTest.java
View File

@ -46,6 +46,11 @@ public class AttentionLayerTest extends BaseDL4JTest {
@Rule
public ExpectedException exceptionRule = ExpectedException.none();
@Override
public long getTimeoutMilliseconds() {
return 90000L;
}
@Test
public void testSelfAttentionLayer() {
int nIn = 3;

5
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/gradientcheck/BNGradientCheckTest.java
View File

@ -61,6 +61,11 @@ public class BNGradientCheckTest extends BaseDL4JTest {
Nd4j.setDataType(DataType.DOUBLE);
}
@Override
public long getTimeoutMilliseconds() {
return 90000L;
}
@Test
public void testGradient2dSimple() {
DataNormalization scaler = new NormalizerMinMaxScaler();

5
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/gradientcheck/CNN1DGradientCheckTest.java
View File

@ -54,6 +54,11 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
Nd4j.setDataType(DataType.DOUBLE);
}
@Override
public long getTimeoutMilliseconds() {
return 90000L;
}
@Test
public void testCnn1DWithLocallyConnected1D() {
Nd4j.getRandom().setSeed(1337);

5
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/gradientcheck/CNN3DGradientCheckTest.java
View File

@ -55,6 +55,11 @@ public class CNN3DGradientCheckTest extends BaseDL4JTest {
Nd4j.setDataType(DataType.DOUBLE);
}
@Override
public long getTimeoutMilliseconds() {
return 90000L;
}
@Test
public void testCnn3DPlain() {
Nd4j.getRandom().setSeed(1337);

5
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/gradientcheck/CNNGradientCheckTest.java
View File

@ -62,6 +62,11 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
Nd4j.setDataType(DataType.DOUBLE);
}
@Override
public long getTimeoutMilliseconds() {
return 90000L;
}
@Test
public void testGradientCNNMLN() {
//Parameterized test, testing combinations of:

5
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/gradientcheck/CapsnetGradientCheckTest.java
View File

@ -43,6 +43,11 @@ import java.util.Random;
public class CapsnetGradientCheckTest extends BaseDL4JTest {
@Override
public long getTimeoutMilliseconds() {
return 90000L;
}
@Test
public void testCapsNet() {

5
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/gradientcheck/DropoutGradientCheck.java
View File

@ -58,6 +58,11 @@ public class DropoutGradientCheck extends BaseDL4JTest {
Nd4j.setDataType(DataType.DOUBLE);
}
@Override
public long getTimeoutMilliseconds() {
return 90000L;
}
@Test
public void testDropoutGradient() {
int minibatch = 3;

5
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/gradientcheck/GlobalPoolingGradientCheckTests.java
View File

@ -53,6 +53,11 @@ public class GlobalPoolingGradientCheckTests extends BaseDL4JTest {
private static final double DEFAULT_MAX_REL_ERROR = 1e-3;
private static final double DEFAULT_MIN_ABS_ERROR = 1e-8;
@Override
public long getTimeoutMilliseconds() {
return 90000L;
}
@Test
public void testRNNGlobalPoolingBasicMultiLayer() {
//Basic test of global pooling w/ LSTM

5
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/gradientcheck/GradientCheckTests.java
View File

@ -68,6 +68,11 @@ public class GradientCheckTests extends BaseDL4JTest {
Nd4j.setDataType(DataType.DOUBLE);
}
@Override
public long getTimeoutMilliseconds() {
return 90000L;
}
@Test
public void testMinibatchApplication() {
IrisDataSetIterator iter = new IrisDataSetIterator(30, 150);

5
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/gradientcheck/GradientCheckTestsComputationGraph.java
View File

@ -69,6 +69,11 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
Nd4j.setDataType(DataType.DOUBLE);
}
@Override
public long getTimeoutMilliseconds() {
return 90000L;
}
@Test
public void testBasicIris() {
Nd4j.getRandom().setSeed(12345);

5
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/gradientcheck/LRNGradientCheckTests.java
View File

@ -53,6 +53,11 @@ public class LRNGradientCheckTests extends BaseDL4JTest {
Nd4j.setDataType(DataType.DOUBLE);
}
@Override
public long getTimeoutMilliseconds() {
return 90000L;
}
@Test
public void testGradientLRNSimple() {

5
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/gradientcheck/LSTMGradientCheckTests.java
View File

@ -54,6 +54,11 @@ public class LSTMGradientCheckTests extends BaseDL4JTest {
Nd4j.setDataType(DataType.DOUBLE);
}
@Override
public long getTimeoutMilliseconds() {
return 90000L;
}
@Test
public void testLSTMBasicMultiLayer() {
//Basic test of GravesLSTM layer

5
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/gradientcheck/LossFunctionGradientCheck.java
View File

@ -73,6 +73,11 @@ public class LossFunctionGradientCheck extends BaseDL4JTest {
private static final double DEFAULT_MAX_REL_ERROR = 1e-5;
private static final double DEFAULT_MIN_ABS_ERROR = 1e-8;
@Override
public long getTimeoutMilliseconds() {
return 90000L;
}
@Test
public void lossFunctionGradientCheck() {
ILossFunction[] lossFunctions = new ILossFunction[] {new LossBinaryXENT(), new LossBinaryXENT(),

5
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/gradientcheck/NoBiasGradientCheckTests.java
View File

@ -48,6 +48,11 @@ public class NoBiasGradientCheckTests extends BaseDL4JTest {
Nd4j.setDataType(DataType.DOUBLE);
}
@Override
public long getTimeoutMilliseconds() {
return 90000L;
}
@Test
public void testGradientNoBiasDenseOutput() {

5
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/gradientcheck/OutputLayerGradientChecks.java
View File

@ -48,6 +48,11 @@ public class OutputLayerGradientChecks extends BaseDL4JTest {
Nd4j.setDataType(DataType.DOUBLE);
}
@Override
public long getTimeoutMilliseconds() {
return 90000L;
}
@Test
public void testRnnLossLayer() {
Nd4j.getRandom().setSeed(12345L);

5
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/gradientcheck/RnnGradientChecks.java
View File

@ -52,6 +52,11 @@ public class RnnGradientChecks extends BaseDL4JTest {
Nd4j.setDataType(DataType.DOUBLE);
}
@Override
public long getTimeoutMilliseconds() {
return 90000L;
}
@Test
@Ignore("AB 2019/06/24 - Ignored to get to all passing baseline to prevent regressions via CI - see issue #7912")
public void testBidirectionalWrapper() {

5
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/gradientcheck/UtilLayerGradientChecks.java
View File

@ -52,6 +52,11 @@ public class UtilLayerGradientChecks extends BaseDL4JTest {
Nd4j.setDataType(DataType.DOUBLE);
}
@Override
public long getTimeoutMilliseconds() {
return 90000L;
}
@Test
public void testMaskLayer() {
Nd4j.getRandom().setSeed(12345);

5
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/gradientcheck/VaeGradientCheckTests.java
View File

@ -56,6 +56,11 @@ public class VaeGradientCheckTests extends BaseDL4JTest {
Nd4j.setDataType(DataType.DOUBLE);
}
@Override
public long getTimeoutMilliseconds() {
return 90000L;
}
@Test
public void testVaeAsMLP() {
//Post pre-training: a VAE can be used as a MLP, by taking the mean value from p(z|x) as the output

5
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/gradientcheck/YoloGradientCheckTests.java
View File

@ -64,6 +64,11 @@ public class YoloGradientCheckTests extends BaseDL4JTest {
@Rule
public TemporaryFolder testDir = new TemporaryFolder();
@Override
public long getTimeoutMilliseconds() {
return 90000L;
}
@Test
public void testYoloOutputLayer() {
int depthIn = 2;

15
deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/transferlearning/TransferLearningMLNTest.java
View File

@ -63,7 +63,8 @@ public class TransferLearningMLNTest extends BaseDL4JTest {
public void simpleFineTune() {
long rng = 12345L;
DataSet randomData = new DataSet(Nd4j.rand(10, 4), Nd4j.rand(10, 3));
Nd4j.getRandom().setSeed(rng);
DataSet randomData = new DataSet(Nd4j.rand(DataType.FLOAT, 10, 4), TestUtils.randomOneHot(DataType.FLOAT, 10, 3));
//original conf
NeuralNetConfiguration.Builder confToChange =
new NeuralNetConfiguration.Builder().seed(rng).optimizationAlgo(OptimizationAlgorithm.LBFGS)
@ -123,7 +124,8 @@ public class TransferLearningMLNTest extends BaseDL4JTest {
@Test
public void testNoutChanges() {
DataSet randomData = new DataSet(Nd4j.rand(10, 4), Nd4j.rand(10, 2));
Nd4j.getRandom().setSeed(12345);
DataSet randomData = new DataSet(Nd4j.rand(DataType.FLOAT, 10, 4), TestUtils.randomOneHot(DataType.FLOAT,10, 2));
NeuralNetConfiguration.Builder equivalentConf = new NeuralNetConfiguration.Builder().updater(new Sgd(0.1));
FineTuneConfiguration overallConf = new FineTuneConfiguration.Builder().updater(new Sgd(0.1))
@ -185,7 +187,8 @@ public class TransferLearningMLNTest extends BaseDL4JTest {
@Test
public void testRemoveAndAdd() {
DataSet randomData = new DataSet(Nd4j.rand(10, 4), Nd4j.rand(10, 3));
Nd4j.getRandom().setSeed(12345);
DataSet randomData = new DataSet(Nd4j.rand(DataType.FLOAT,10, 4), TestUtils.randomOneHot(DataType.FLOAT, 10, 3));
NeuralNetConfiguration.Builder equivalentConf = new NeuralNetConfiguration.Builder().updater(new Sgd(0.1));
FineTuneConfiguration overallConf = new FineTuneConfiguration.Builder().updater(new Sgd(0.1)).build();
@ -377,8 +380,9 @@ public class TransferLearningMLNTest extends BaseDL4JTest {
@Test
public void testAllWithCNN() {
Nd4j.getRandom().setSeed(12345);
DataSet randomData = new DataSet(Nd4j.rand(10, 28 * 28 * 3).reshape(10, 3, 28, 28), Nd4j.rand(10, 10));
DataSet randomData = new DataSet(Nd4j.rand(DataType.FLOAT, 10, 28 * 28 * 3).reshape(10, 3, 28, 28), TestUtils.randomOneHot(DataType.FLOAT,10, 10));
MultiLayerNetwork modelToFineTune =
new MultiLayerNetwork(
new NeuralNetConfiguration.Builder().seed(123)
@ -528,8 +532,9 @@ public class TransferLearningMLNTest extends BaseDL4JTest {
@Test
public void testAllWithCNNNew() {
Nd4j.getRandom().setSeed(12345);
DataSet randomData = new DataSet(Nd4j.rand(10, 28 * 28 * 3).reshape(10, 3, 28, 28), Nd4j.rand(10, 10));
DataSet randomData = new DataSet(Nd4j.rand(DataType.FLOAT,10, 28 * 28 * 3).reshape(10, 3, 28, 28), TestUtils.randomOneHot(10, 10));
MultiLayerNetwork modelToFineTune =
new MultiLayerNetwork(
new NeuralNetConfiguration.Builder().seed(123)

44
deeplearning4j/deeplearning4j-cuda/src/test/java/org/deeplearning4j/TestDataTypes.java
View File

@ -39,6 +39,8 @@ import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.executioner.OpExecutioner;
import org.nd4j.linalg.dataset.DataSet;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.learning.config.Adam;
import org.nd4j.linalg.learning.config.Sgd;
import org.nd4j.linalg.lossfunctions.LossFunctions;
import java.lang.reflect.Field;
@ -51,27 +53,9 @@ import static org.junit.Assert.*;
@Slf4j
public class TestDataTypes extends BaseDL4JTest {
private static DataType typeBefore;
@BeforeClass
public static void beforeClass(){
typeBefore = Nd4j.dataType();
}
@AfterClass
public static void afterClass(){
Nd4j.setDataType(typeBefore);
}
@Override
public void beforeTest(){
Nd4j.getExecutioner().setProfilingMode(getProfilingMode());
Nd4j.setDataType(DataType.HALF);
}
@Override
public OpExecutioner.ProfilingMode getProfilingMode(){
return OpExecutioner.ProfilingMode.NAN_PANIC;
public long getTimeoutMilliseconds() {
return 180000L;
}
@Test
@ -87,6 +71,7 @@ public class TestDataTypes extends BaseDL4JTest {
assertEquals(globalDtype, Nd4j.defaultFloatingPointType());
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.updater(new Sgd(1e-2))
.dataType(netDType)
.convolutionMode(ConvolutionMode.Same)
.activation(Activation.TANH)
@ -140,11 +125,22 @@ public class TestDataTypes extends BaseDL4JTest {
INDArray fp32Train = outMapTrain.get(DataType.FLOAT).castTo(DataType.DOUBLE);
INDArray fp16Train = outMapTrain.get(DataType.HALF).castTo(DataType.DOUBLE);
assertTrue(fp64Train.equalsWithEps(fp32Train, 1e-3));
assertTrue(fp64Train.equalsWithEps(fp16Train, 1e-2));
boolean eq64_32 = fp64Train.equalsWithEps(fp32Train, 1e-3);
boolean eq64_16 = fp64Train.equalsWithEps(fp16Train, 1e-2);
if(!eq64_32){
System.out.println("FP64/32");
System.out.println("fp64Train:\n" + fp64Train);
System.out.println("fp32Train:\n" + fp32Train);
}
if(!eq64_16){
System.out.println("FP64/16");
System.out.println("fp64Train:\n" + fp64Train);
System.out.println("fp16Train:\n" + fp16Train);
}
assertTrue(eq64_32);
assertTrue(eq64_16);
}
}

25
deeplearning4j/deeplearning4j-cuda/src/test/java/org/deeplearning4j/ValidateCuDNN.java
View File

@ -52,6 +52,11 @@ import java.util.List;
@Slf4j
public class ValidateCuDNN extends BaseDL4JTest {
@Override
public long getTimeoutMilliseconds() {
return 360000L;
}
@Test
public void validateConvLayers() {
Nd4j.getRandom().setSeed(12345);
@ -124,15 +129,16 @@ public class ValidateCuDNN extends BaseDL4JTest {
validateLayers(net, classesToTest, true, fShape, lShape, CuDNNValidationUtil.MAX_REL_ERROR, CuDNNValidationUtil.MIN_ABS_ERROR);
}
@Test @Ignore //AB 2019/05/21 - https://github.com/deeplearning4j/deeplearning4j/issues/7766
@Test
public void validateConvLayersSimpleBN() {
//Test ONLY BN - no other CuDNN functionality (i.e., DL4J impls for everything else)
Nd4j.getRandom().setSeed(12345);
int minibatch = 8;
int numClasses = 10;
//imageHeight,imageWidth,channels
int imageHeight = 240;
int imageWidth = 240;
int imageHeight = 48;
int imageWidth = 48;
int channels = 3;
IActivation activation = new ActivationIdentity();
MultiLayerConfiguration multiLayerConfiguration = new NeuralNetConfiguration.Builder()
@ -171,8 +177,8 @@ public class ValidateCuDNN extends BaseDL4JTest {
MultiLayerNetwork net = new MultiLayerNetwork(multiLayerConfiguration);
net.init();
int[] fShape = new int[]{32, channels, imageHeight, imageWidth};
int[] lShape = new int[]{32, numClasses};
int[] fShape = new int[]{minibatch, channels, imageHeight, imageWidth};
int[] lShape = new int[]{minibatch, numClasses};
List<Class<?>> classesToTest = new ArrayList<>();
classesToTest.add(org.deeplearning4j.nn.layers.normalization.BatchNormalization.class);
@ -185,10 +191,11 @@ public class ValidateCuDNN extends BaseDL4JTest {
//Test ONLY LRN - no other CuDNN functionality (i.e., DL4J impls for everything else)
Nd4j.getRandom().setSeed(12345);
int minibatch = 8;
int numClasses = 10;
//imageHeight,imageWidth,channels
int imageHeight = 240;
int imageWidth = 240;
int imageHeight = 48;
int imageWidth = 48;
int channels = 3;
IActivation activation = new ActivationIdentity();
MultiLayerConfiguration multiLayerConfiguration = new NeuralNetConfiguration.Builder()
@ -229,8 +236,8 @@ public class ValidateCuDNN extends BaseDL4JTest {
MultiLayerNetwork net = new MultiLayerNetwork(multiLayerConfiguration);
net.init();
int[] fShape = new int[]{32, channels, imageHeight, imageWidth};
int[] lShape = new int[]{32, numClasses};
int[] fShape = new int[]{minibatch, channels, imageHeight, imageWidth};
int[] lShape = new int[]{minibatch, numClasses};
List<Class<?>> classesToTest = new ArrayList<>();
classesToTest.add(org.deeplearning4j.nn.layers.normalization.LocalResponseNormalization.class);

31
deeplearning4j/deeplearning4j-cuda/src/test/java/org/deeplearning4j/convolution/TestConvolution.java
View File

@ -70,6 +70,11 @@ public class TestConvolution extends BaseDL4JTest {
@Rule
public TemporaryFolder testDir = new TemporaryFolder();
@Override
public long getTimeoutMilliseconds() {
return 240000L;
}
@Test
public void testSameModeActivationSizes() {
int inH = 3;
@ -117,6 +122,8 @@ public class TestConvolution extends BaseDL4JTest {
for (ConvolutionMode c : cm) {
for (ConvolutionLayer.AlgoMode a : new ConvolutionLayer.AlgoMode[]{ConvolutionLayer.AlgoMode.NO_WORKSPACE, ConvolutionLayer.AlgoMode.PREFER_FASTEST}) {
for (boolean conv : new boolean[]{true, false}) {
String msg = c + " - " + a + " - " + (conv ? "conv" : "subsampling");
System.out.println(msg);
org.deeplearning4j.nn.conf.layers.Layer l;
if (conv) {
@ -125,7 +132,9 @@ public class TestConvolution extends BaseDL4JTest {
l = new SubsamplingLayer.Builder().kernelSize(4, 4).stride(2, 2).build();
}
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().seed(12345)
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.dataType(DataType.DOUBLE)
.seed(12345)
.l2(0.0005).updater(new Sgd(0.01)).weightInit(WeightInit.XAVIER).convolutionMode(c).cudnnAlgoMode(a).list()
.layer(0, l)
.layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
@ -159,32 +168,32 @@ public class TestConvolution extends BaseDL4JTest {
throw new RuntimeException();
INDArray in = Nd4j.rand(new int[]{1, 1, 20, 20}); //(20-4+0)/2 +1 = 9
INDArray in = Nd4j.rand(DataType.DOUBLE, new int[]{1, 1, 20, 20}); //(20-4+0)/2 +1 = 9
INDArray outCudnn = layerCudnn.activate(in, false, LayerWorkspaceMgr.noWorkspaces());
INDArray outStd = layerStandard.activate(in, false, LayerWorkspaceMgr.noWorkspaces());
assertEquals(outStd, outCudnn);
assertEquals(msg, outStd, outCudnn);
//Check backprop:
INDArray epsilon = Nd4j.rand(outStd.shape());
Pair<Gradient, INDArray> pCudnn = layerCudnn.backpropGradient(epsilon, LayerWorkspaceMgr.noWorkspaces());
Pair<Gradient, INDArray> pStd = layerStandard.backpropGradient(epsilon, LayerWorkspaceMgr.noWorkspaces());
INDArray epsilon = Nd4j.rand(DataType.DOUBLE, outStd.shape());
Pair<Gradient, INDArray> pCudnn = layerCudnn.backpropGradient(epsilon.dup(), LayerWorkspaceMgr.noWorkspaces());
Pair<Gradient, INDArray> pStd = layerStandard.backpropGradient(epsilon.dup(), LayerWorkspaceMgr.noWorkspaces());
System.out.println(Arrays.toString(pStd.getSecond().data().asFloat()));
System.out.println(Arrays.toString(pCudnn.getSecond().data().asFloat()));
// System.out.println(Arrays.toString(pStd.getSecond().data().asFloat()));
// System.out.println(Arrays.toString(pCudnn.getSecond().data().asFloat()));
INDArray epsOutStd = pStd.getSecond();
INDArray epsOutCudnn = pCudnn.getSecond();
assertTrue(epsOutStd.equalsWithEps(epsOutCudnn, 1e-4));
assertTrue(msg, epsOutStd.equalsWithEps(epsOutCudnn, 1e-4));
if (conv) {
INDArray gradStd = pStd.getFirst().gradient();
INDArray gradCudnn = pCudnn.getFirst().gradient();
assertTrue(gradStd.equalsWithEps(gradCudnn, 1e-4));
assertTrue(msg, gradStd.equalsWithEps(gradCudnn, 1e-4));
}
}
}
@ -192,7 +201,7 @@ public class TestConvolution extends BaseDL4JTest {
}
@Test @Ignore //AB 2019/05/21 - Ignored to get master passing - issue logged here: https://github.com/deeplearning4j/deeplearning4j/issues/7766
@Test
public void validateXceptionImport() throws Exception {
File dir = testDir.newFolder();
File fSource = Resources.asFile("modelimport/keras/examples/xception/xception_tf_keras_2.h5");

5
deeplearning4j/deeplearning4j-cuda/src/test/java/org/deeplearning4j/gradientcheck/CNNGradientCheckTest.java
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@ -61,6 +61,11 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
Nd4j.setDataType(DataType.DOUBLE);
}
@Override
public long getTimeoutMilliseconds() {
return 180000L;
}
@Test
public void testGradientCNNMLN() {
//Parameterized test, testing combinations of:

4
deeplearning4j/deeplearning4j-cuda/src/test/java/org/deeplearning4j/gradientcheck/CuDNNGradientChecks.java
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@ -77,6 +77,10 @@ public class CuDNNGradientChecks extends BaseDL4JTest {
DataTypeUtil.setDTypeForContext(DataType.DOUBLE);
}
@Override
public long getTimeoutMilliseconds() {
return 180000L;
}
@Test
public void testConvolutional() throws Exception {

5
deeplearning4j/deeplearning4j-cuda/src/test/java/org/deeplearning4j/lstm/ValidateCudnnDropout.java
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@ -30,6 +30,11 @@ import static org.junit.Assert.assertTrue;
public class ValidateCudnnDropout extends BaseDL4JTest {
@Override
public long getTimeoutMilliseconds() {
return 180000L;
}
@Test
public void testCudnnDropoutSimple() {
for (int[] shape : new int[][]{{10, 10}, {5, 2, 5, 2}}) {

7
deeplearning4j/deeplearning4j-cuda/src/test/java/org/deeplearning4j/lstm/ValidateCudnnLSTM.java
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@ -46,6 +46,11 @@ import static org.junit.Assert.*;
*/
public class ValidateCudnnLSTM extends BaseDL4JTest {
@Override
public long getTimeoutMilliseconds() {
return 180000L;
}
@Test
public void validateImplSimple() throws Exception {
@ -109,7 +114,7 @@ public class ValidateCudnnLSTM extends BaseDL4JTest {
mln1.computeGradientAndScore();
mln2.computeGradientAndScore();
assertEquals(mln1.score(), mln2.score(), 1e-8);
assertEquals(mln1.score(), mln2.score(), 1e-5);
Gradient g1 = mln1.gradient();
Gradient g2 = mln2.gradient();

2
deeplearning4j/deeplearning4j-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/e2e/KerasModelEndToEndTest.java
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@ -98,7 +98,7 @@ public class KerasModelEndToEndTest extends BaseDL4JTest {
@Override
public long getTimeoutMilliseconds() {
return 90000L;
return 180000L; //Most benchmarks should run very quickly; large timeout is to avoid issues with unusually slow download of test resources
}
@Test(expected = IllegalStateException.class)

4
deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/convolution/subsampling/SubsamplingLayer.java
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@ -116,10 +116,10 @@ public class SubsamplingLayer extends AbstractLayer<org.deeplearning4j.nn.conf.l
int[] dilation = layerConf().getDilation();
int[] pad;
int[] outSize = new int[]{(int)input.size(2), (int)input.size(3)}; //NCHW
int[] outSizeFwd = new int[]{(int)epsilon.size(2), (int)epsilon.size(3)}; //NCHW
boolean same = convolutionMode == ConvolutionMode.Same;
if (same) {
pad = ConvolutionUtils.getSameModeTopLeftPadding(outSize, new int[] {inH, inW}, kernel, strides, dilation);
pad = ConvolutionUtils.getSameModeTopLeftPadding(outSizeFwd, new int[] {inH, inW}, kernel, strides, dilation);
} else {
pad = layerConf().getPadding();
}

6
deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark-nlp-java8/src/test/java/org/deeplearning4j/spark/models/sequencevectors/SparkSequenceVectorsTest.java
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@ -43,6 +43,12 @@ import static org.junit.Assert.assertNotEquals;
* @author raver119@gmail.com
*/
public class SparkSequenceVectorsTest extends BaseDL4JTest {
@Override
public long getTimeoutMilliseconds() {
return 120000L;
}
protected static List<Sequence<VocabWord>> sequencesCyclic;
private JavaSparkContext sc;

6
deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark-nlp-java8/src/test/java/org/deeplearning4j/spark/models/word2vec/SparkWord2VecTest.java
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@ -48,6 +48,12 @@ import static org.junit.Assert.*;
* @author raver119@gmail.com
*/
public class SparkWord2VecTest extends BaseDL4JTest {
@Override
public long getTimeoutMilliseconds() {
return 120000L;
}
private static List<String> sentences;
private JavaSparkContext sc;

5
deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark-nlp/src/test/java/org/deeplearning4j/spark/text/BaseSparkTest.java
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@ -34,6 +34,11 @@ import java.util.Map;
public abstract class BaseSparkTest extends BaseDL4JTest implements Serializable {
protected transient JavaSparkContext sc;
@Override
public long getTimeoutMilliseconds() {
return 120000L;
}
@Before
public void before() throws Exception {
sc = getContext();

5
deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark-nlp/src/test/java/org/deeplearning4j/spark/text/TextPipelineTest.java
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@ -31,6 +31,7 @@ import org.deeplearning4j.spark.text.functions.CountCumSum;
import org.deeplearning4j.spark.text.functions.TextPipeline;
import org.deeplearning4j.text.stopwords.StopWords;
import org.junit.Before;
import org.junit.Ignore;
import org.junit.Test;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.primitives.Counter;
@ -350,7 +351,7 @@ public class TextPipelineTest extends BaseSparkTest {
*
* @throws Exception
*/
@Test
@Test @Ignore //AB 2020/04/19 https://github.com/eclipse/deeplearning4j/issues/8849
public void testZipFunction1() throws Exception {
JavaSparkContext sc = getContext();
JavaRDD<String> corpusRDD = getCorpusRDD(sc);
@ -388,7 +389,7 @@ public class TextPipelineTest extends BaseSparkTest {
sc.stop();
}
@Test
@Test @Ignore //AB 2020/04/19 https://github.com/eclipse/deeplearning4j/issues/8849
public void testZipFunction2() throws Exception {
JavaSparkContext sc = getContext();
JavaRDD<String> corpusRDD = getCorpusRDD(sc);

6
deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark-parameterserver/src/test/java/org/deeplearning4j/spark/parameterserver/BaseSparkTest.java
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@ -53,6 +53,12 @@ public abstract class BaseSparkTest extends BaseDL4JTest implements Serializable
protected transient DataSet data;
protected transient JavaRDD<DataSet> sparkData;
@Override
public long getTimeoutMilliseconds() {
return 120000L;
}
@Before
public void before() {

5
deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark/src/test/java/org/deeplearning4j/spark/BaseSparkKryoTest.java
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@ -28,6 +28,11 @@ import java.util.Map;
*/
public class BaseSparkKryoTest extends BaseSparkTest {
@Override
public long getTimeoutMilliseconds() {
return 120000L;
}
@Override
public JavaSparkContext getContext() {
if (sc != null) {

5
deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark/src/test/java/org/deeplearning4j/spark/BaseSparkTest.java
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@ -55,6 +55,11 @@ public abstract class BaseSparkTest extends BaseDL4JTest implements Serializable
protected transient DataSet data;
protected transient JavaRDD<DataSet> sparkData;
@Override
public long getTimeoutMilliseconds() {
return 120000L;
}
@Before
public void before() {

6
scalnet/src/test/scala/org/deeplearning4j/scalnet/examples/dl4j/feedforward/IrisCSVExample.scala
View File

@ -21,6 +21,7 @@ import org.datavec.api.records.reader.impl.csv.CSVRecordReader
import org.datavec.api.split.FileSplit
import org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator
import org.deeplearning4j.datasets.iterator.impl.ListDataSetIterator
import org.deeplearning4j.nn.conf.Updater
import org.deeplearning4j.optimize.listeners.ScoreIterationListener
import org.deeplearning4j.scalnet.layers.core.Dense
import org.deeplearning4j.scalnet.logging.Logging
@ -29,6 +30,7 @@ import org.nd4j.linalg.activations.Activation
import org.nd4j.linalg.dataset.api.iterator.DataSetIterator
import org.nd4j.linalg.dataset.{ DataSet, SplitTestAndTrain }
import org.nd4j.linalg.io.ClassPathResource
import org.nd4j.linalg.learning.config.Adam
import org.nd4j.linalg.lossfunctions.LossFunctions.LossFunction
object IrisCSVExample extends App with Logging {
@ -41,7 +43,7 @@ object IrisCSVExample extends App with Logging {
val hiddenSize = 128
val inputSize = 4
val outputSize = 3
val epochs = 1000
val epochs = 20
val scoreFrequency = 5
val seed = 1234
@ -64,7 +66,7 @@ object IrisCSVExample extends App with Logging {
model.add(Dense(nOut = hiddenSize, activation = Activation.RELU))
model.add(Dense(nOut = hiddenSize, activation = Activation.RELU))
model.add(Dense(outputSize, activation = Activation.SOFTMAX))
model.compile(LossFunction.MCXENT)
model.compile(LossFunction.MCXENT, updater = Updater.ADAM)
logger.info("Train model...")
model.fit(training_data, epochs, List(new ScoreIterationListener(scoreFrequency)))

2
scalnet/src/test/scala/org/deeplearning4j/scalnet/examples/dl4j/recurrent/BasicRNNExample.scala
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@ -34,7 +34,7 @@ object BasicRNNExample extends App with Logging {
val learningString = "*Der Cottbuser Postkutscher putzt den Cottbuser Postkutschkasten.".toVector
val learningChars = learningString.distinct
val hiddenSize = 64
val epochs = 200
val epochs = 20
val seed = 1234
val rand = new Random(seed)