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Fixing tests 

Signed-off-by: brian <brian@brutex.de>
enhance-build-infrastructure
Brian Rosenberger 2023-05-08 12:45:48 +02:00
parent ea504bff41
commit 1c39dbee52
11 changed files with 52 additions and 33 deletions
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14
.gitignore vendored
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@ -36,7 +36,8 @@ pom.xml.versionsBackup
pom.xml.next
release.properties
*dependency-reduced-pom.xml
*/build/*
**/build/*
.gradle/*
# Specific for Nd4j
*.md5
@ -84,3 +85,14 @@ bruai4j-native-common/cmake*
/bruai4j-native/bruai4j-native-common/blasbuild/
/bruai4j-native/bruai4j-native-common/build/
/cavis-native/cavis-native-lib/blasbuild/
/cavis-dnn/cavis-dnn-core/build/reports/tests/cudaTest/classes/org.deeplearning4j.gradientcheck.AttentionLayerTest.html
/cavis-dnn/cavis-dnn-core/build/reports/tests/cudaTest/css/base-style.css
/cavis-dnn/cavis-dnn-core/build/reports/tests/cudaTest/css/style.css
/cavis-dnn/cavis-dnn-core/build/reports/tests/cudaTest/js/report.js
/cavis-dnn/cavis-dnn-core/build/reports/tests/cudaTest/packages/org.deeplearning4j.gradientcheck.html
/cavis-dnn/cavis-dnn-core/build/reports/tests/cudaTest/index.html
/cavis-dnn/cavis-dnn-core/build/resources/main/iris.dat
/cavis-dnn/cavis-dnn-core/build/resources/test/junit-platform.properties
/cavis-dnn/cavis-dnn-core/build/resources/test/logback-test.xml
/cavis-dnn/cavis-dnn-core/build/test-results/cudaTest/TEST-org.deeplearning4j.gradientcheck.AttentionLayerTest.xml
/cavis-dnn/cavis-dnn-core/build/tmp/jar/MANIFEST.MF

2
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/exceptions/TestInvalidConfigurations.java
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@ -309,7 +309,7 @@ public class TestInvalidConfigurations extends BaseDL4JTest {
try {
NeuralNetConfiguration conf = NeuralNetConfiguration.builder().convolutionMode(ConvolutionMode.Strict)
.list()
.layer(0, ConvolutionLayer.builder().kernelSize(2, 3).stride(2, 2).padding(0, 0).nOut(5)
.build())
.layer(1, OutputLayer.builder().nOut(10).build())

1
cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/gradientcheck/CNN3DGradientCheckTest.java
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@ -114,7 +114,6 @@ public class CNN3DGradientCheckTest extends BaseDL4JTest {
.dataType(DataType.DOUBLE)
.updater(new NoOp()).weightInit(WeightInit.LECUN_NORMAL)
.dist(new NormalDistribution(0, 1))
.list()
.layer(0, Convolution3D.builder().activation(afn).kernelSize(kernel)
.stride(stride).nIn(convNIn).nOut(convNOut1).hasBias(false)
.convolutionMode(mode).dataFormat(df)

49
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/NeuralNetBaseBuilderConfiguration.java
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@ -565,6 +565,7 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
this.activation = activation;
return self();
}
@JsonIgnore
public B activation(IActivation activation) {
this.activation = activation;
return self();
@ -583,7 +584,7 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
public B constrainWeights(LayerConstraint... constraints) {
constrainWeights$value = Arrays.asList(constraints);
constrainWeights$set = true;
return (B) this;
return self();
}
/**
@ -618,7 +619,7 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
public B constrainAllParameters(LayerConstraint... constraints) {
allParamConstraints$value = Arrays.asList(constraints);
allParamConstraints$set = true;
return (B) this;
return self();
}
/**
@ -635,7 +636,7 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
public B constrainBias(LayerConstraint... constraints) {
biasConstraints$value = Arrays.asList(constraints);
biasConstraints$set = true;
return (B) this;
return self();
}
/**
@ -645,10 +646,11 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
* @param processor what to use to preProcess the data.
* @return builder pattern
*/
public B inputPreProcessor(Integer layer, InputPreProcessor processor) {
public B inputPreProcessor(@NonNull Integer layer, @NonNull InputPreProcessor processor) {
if(inputPreProcessors$value==null) inputPreProcessors$value=new LinkedHashMap<>();
inputPreProcessors$value.put(layer, processor);
inputPreProcessors$set = true;
return (B) this;
return self();
}
/**
@ -658,7 +660,7 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
* @param layer the layer
* @return builder
*/
public B layer(Integer index, @NonNull LayerConfiguration layer) {
public B layer(@NonNull Integer index, @NonNull LayerConfiguration layer) {
innerConfigurations$value.add(index, layer);
innerConfigurations$set = true;
return self();
@ -680,10 +682,11 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
* @param layer the layer
* @return builder
*/
@JsonIgnore
public B layer(@NonNull LayerConfiguration layer) {
innerConfigurations$value.add(layer);
innerConfigurations$set = true;
return (B) this;
return self();
}
public B layer(@NonNull LayerConfiguration.LayerConfigurationBuilder<?, ?> layer) {
return this.layer(layer.build());
@ -699,7 +702,7 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
public B layersFromArray(@NonNull LayerConfiguration[] arrLayers) {
innerConfigurations$value.addAll(List.of(arrLayers));
innerConfigurations$set = true;
return (B) this;
return self();
}
/** Specify additional layer configurations */
@ -707,7 +710,7 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
public B layersFromList(@NonNull List<LayerConfiguration> listLayers) {
innerConfigurations$value.addAll(listLayers);
innerConfigurations$set = true;
return (B) this;
return self();
}
/**
@ -723,7 +726,7 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
regularization$value.add(new L1Regularization(l1));
}
regularization$set = true;
return (B) this;
return self();
}
/**
@ -751,7 +754,7 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
regularization$value.add(new L2Regularization(l2));
}
regularization$set = true;
return (B) this;
return self();
}
/**
@ -766,7 +769,7 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
regularizationBias$value.add(new L1Regularization(l1Bias));
}
regularizationBias$set = true;
return (B) this;
return self();
}
/**
@ -791,7 +794,7 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
"L2 bias regularization removed: incompatible with added WeightDecay regularization");
regularizationBias$value.add(new L2Regularization(l2Bias));
}
return (B) this;
return self();
}
/**
@ -833,7 +836,7 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
regularization$value.add(new WeightDecay(coefficient, applyLR));
}
regularization$set = true;
return (B) this;
return self();
}
/**
@ -870,7 +873,7 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
regularizationBias$value.add(new WeightDecay(coefficient, applyLR));
}
regularization$set = true;
return (B) this;
return self();
}
@ -881,7 +884,7 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
*/
@Deprecated
public B list() {
return (B) this;
return self();
}
/**
@ -897,19 +900,19 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
public B weightInit(Distribution distribution) {
this.weightInit$value = new WeightInitDistribution(distribution);
this.weightInit$set = true;
return (B) this;
return self();
}
@JsonIgnore
public B weightInit(WeightInit weightInit) {
this.weightInit$value = weightInit.getWeightInitFunction();
this.weightInit$set = true;
return (B) this;
return self();
}
public B weightInit(IWeightInit iWeightInit) {
this.weightInit$value = iWeightInit;
this.weightInit$set = true;
return (B) this;
return self();
}
/**
@ -919,11 +922,11 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
* @return
*/
public B dist(@NonNull Distribution distribution) {
return (B) weightInit(distribution);
return weightInit(distribution);
}
public B dropOut(@NonNull IDropout dropout) {
return (B) idropOut(dropout);
return idropOut(dropout);
}
/**
@ -933,7 +936,7 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
* @return builder
*/
public B dropOut(double dropout) {
return (B) idropOut(new Dropout(dropout));
return idropOut(new Dropout(dropout));
}
/**
@ -946,7 +949,7 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
public B confs(@NonNull List<NeuralNetConfiguration> confs) {
innerConfigurations$value.addAll(confs);
innerConfigurations$set = true;
return (B) this;
return self();
}
}
}

1
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/Convolution3D.java
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@ -38,6 +38,7 @@ import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
@Data
@NoArgsConstructor
@ToString(callSuper = true)
@EqualsAndHashCode(callSuper = true)
@SuperBuilder(builderMethodName = "innerBuilder")

9
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/ConvolutionLayer.java
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@ -47,6 +47,7 @@ import org.nd4j.linalg.api.ndarray.INDArray;
* size, the stride and padding The pooling layer takes the kernel size
*/
@ToString(callSuper = true)
@NoArgsConstructor
@EqualsAndHashCode(callSuper = true)
@SuperBuilder(builderMethodName = "innerBuilder")
public class ConvolutionLayer extends FeedForwardLayer {
@ -361,7 +362,7 @@ public class ConvolutionLayer extends FeedForwardLayer {
* @param kernelSize kernel size
*/
public B kernelSize(int... kernelSize) {
this.kernelSize$value = ValidationUtils.validate2NonNegative(kernelSize, false, "kernelSize");
this.kernelSize$value = ValidationUtils.validate3NonNegative(kernelSize,"kernelSize");
this.kernelSize$set = true;
return self();
}
@ -371,7 +372,7 @@ public class ConvolutionLayer extends FeedForwardLayer {
* @param stride kernel size
*/
public B stride(int... stride) {
this.stride$value = ValidationUtils.validate2NonNegative(stride, false, "stride");
this.stride$value = ValidationUtils.validate3NonNegative(stride, "stride");
this.stride$set = true;
return self();
}
@ -382,7 +383,7 @@ public class ConvolutionLayer extends FeedForwardLayer {
* @param padding kernel size
*/
public B padding(int... padding) {
this.padding$value = ValidationUtils.validate2NonNegative(padding, false, "padding");
this.padding$value = ValidationUtils.validate3NonNegative(padding, "padding");
this.padding$set = true;
return self();
}
@ -392,7 +393,7 @@ public class ConvolutionLayer extends FeedForwardLayer {
* @param dilation kernel size
*/
public B dilation(int... dilation) {
this.dilation$value = ValidationUtils.validate2NonNegative(dilation, false, "dilation");
this.dilation$value = ValidationUtils.validate3NonNegative(dilation, "dilation");
this.dilation$set = true;
return self();
}

1
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/DenseLayer.java
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@ -38,6 +38,7 @@ import org.nd4j.linalg.api.ndarray.INDArray;
/** Dense Layer Uses WeightInitXavier as default */
@Data
@NoArgsConstructor
@ToString(callSuper = true)
@EqualsAndHashCode(callSuper = true)
@SuperBuilder

3
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/FeedForwardLayer.java
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@ -20,6 +20,7 @@
package org.deeplearning4j.nn.conf.layers;
import com.fasterxml.jackson.annotation.JsonIgnore;
import lombok.*;
import lombok.experimental.SuperBuilder;
import org.deeplearning4j.nn.conf.DataFormat;
@ -44,7 +45,7 @@ public abstract class FeedForwardLayer extends BaseLayerConfiguration {
*/
@Getter
protected long nIn;
@JsonIgnore
public void setNIn(int in) {
this.nIn = in;
}

2
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/LayerConfiguration.java
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@ -326,7 +326,7 @@ public abstract class LayerConfiguration
log.warn("Calling getUpdater() in {} will always return no-Op Updater.", LayerConfiguration.class.getSimpleName());
return Updater.NONE.getIUpdaterWithDefaultConfig();
}
@Deprecated
@Deprecated @JsonIgnore
public void setUpdater(Updater updater) {
setUpdater(updater.getIUpdaterWithDefaultConfig());
}

1
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/OutputLayer.java
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@ -35,6 +35,7 @@ import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.lossfunctions.LossFunctions;
@Data
@NoArgsConstructor
@ToString(callSuper = true)
@EqualsAndHashCode(callSuper = true)
@SuperBuilder(builderMethodName = "innerBuilder")

2
cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/ocnn/OCNNOutputLayer.java
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@ -48,7 +48,7 @@ public class OCNNOutputLayer extends BaseOutputLayer {
* The hidden layer size for the one class neural network. Note this would be nOut on a dense
* layer. NOut in this neural net is always set to 1 though.
*/
@Builder.Default @Getter private int hiddenLayerSize; // embedded hidden layer size aka "K"
@Getter private int hiddenLayerSize; // embedded hidden layer size aka "K"
/** For nu definition see the paper */
@Builder.Default @Getter private double nu = 0.04;
/**