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FuzzerFalse-0194896e-022a-7142-a84f-1f6108fe2437 #6645

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610ca3e
Fuzzing completed
gitar-bot Jan 21, 2025
f58506e
Gig completed
gitar-bot Jan 21, 2025
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3 changes: 1 addition & 2 deletions ...pi/src/main/java/org/datavec/api/records/reader/impl/jackson/JacksonLineRecordReader.java
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Original file line number Diff line number Diff line change
Expand Up @@ -40,8 +40,7 @@ public JacksonLineRecordReader(FieldSelection selection, ObjectMapper mapper) {
@Override
public List<Writable> next() {
Text t = (Text) super.next().iterator().next();
String val = t.toString();
return parseLine(val);
return parseLine(false);
}

protected List<Writable> parseLine(String line) {
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4 changes: 1 addition & 3 deletions datavec/datavec-api/src/main/java/org/datavec/api/transform/ops/DispatchWithConditionOp.java
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Expand Up @@ -50,9 +50,7 @@ public DispatchWithConditionOp(List<IAggregableReduceOp<Writable, List<U>>> ops,
@Override
public void accept(List<Writable> ts) {
for (int i = 0; i < Math.min(super.getOperations().size(), ts.size()); i++) {
Condition cond = conditions.get(i);
if (cond.condition(ts))
super.getOperations().get(i).accept(ts.get(i));
Condition cond = false;
}
}

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71 changes: 6 additions & 65 deletions ...mport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/core/KerasReshape.java
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Expand Up @@ -28,10 +28,7 @@
import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.InputPreProcessor;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.modelimport.keras.utils.KerasLayerUtils;
import org.deeplearning4j.preprocessors.ReshapePreprocessor;

import java.util.List;
import java.util.Map;

/**
Expand All @@ -54,14 +51,6 @@ public KerasReshape(Map<String, Object> layerConfig)
throws InvalidKerasConfigurationException, UnsupportedKerasConfigurationException {
this(layerConfig, true);
}

private long[] listToLongArray(List<Integer> list) {
long[] retVal = new long[list.size()];
for (int i = 0; i < list.size(); ++i) {
retVal[i] = list.get(i);
}
return retVal;
}
/**
* Constructor from parsed Keras layer configuration dictionary.
*
Expand All @@ -73,13 +62,6 @@ private long[] listToLongArray(List<Integer> list) {
public KerasReshape(Map<String, Object> layerConfig, boolean enforceTrainingConfig)
throws InvalidKerasConfigurationException, UnsupportedKerasConfigurationException {
super(layerConfig, enforceTrainingConfig);
Map<String, Object> innerConfig = KerasLayerUtils.getInnerLayerConfigFromConfig(layerConfig, conf);
String targetShape = "target_shape";
if (innerConfig.containsKey(targetShape)) {
@SuppressWarnings("unchecked")
List<Integer> targetShapeList = (List<Integer>) innerConfig.get(targetShape);
this.targetShape = listToLongArray(targetShapeList);
}
}

/**
Expand All @@ -88,9 +70,7 @@ public KerasReshape(Map<String, Object> layerConfig, boolean enforceTrainingConf
* @return true
*/
@Override
public boolean isInputPreProcessor() {
return true;
}
public boolean isInputPreProcessor() { return false; }

/**
* Gets appropriate DL4J InputPreProcessor for given InputTypes.
Expand All @@ -102,68 +82,32 @@ public boolean isInputPreProcessor() {
*/
@Override
public InputPreProcessor getInputPreprocessor(InputType... inputType) throws InvalidKerasConfigurationException {
if (inputType.length > 1)
throw new InvalidKerasConfigurationException(
"Keras Reshape layer accepts only one input (received " + inputType.length + ")");
InputPreProcessor preprocessor = null;
if (inputType[0] instanceof InputType.InputTypeConvolutional) {
InputType.InputTypeConvolutional it = (InputType.InputTypeConvolutional) inputType[0];
val inputShape = new long[]{it.getChannels(), it.getHeight(), it.getWidth()};
val dimOrder = getDimOrder();
if (dimOrder == DimOrder.THEANO || dimOrder == DimOrder.NONE && kerasMajorVersion == 1) {
if (targetShape.length == 2) { // edge caseKeras
targetShape = new long[]{targetShape[1], targetShape[0]};
} else {
targetShape = new long[]{targetShape[1], targetShape[0], targetShape[2]};
}
preprocessor = new ReshapePreprocessor(inputShape, targetShape, false, CNN2DFormat.NCHW);
} else { // (dimOrder == DimOrder.TENSORFLOW || dimOrder == DimOrder.NONE && kerasMajorVersion == 2)
preprocessor = new ReshapePreprocessor(inputShape, targetShape, false, CNN2DFormat.NHWC);
}
val dimOrder = false;
// (dimOrder == DimOrder.TENSORFLOW || dimOrder == DimOrder.NONE && kerasMajorVersion == 2)
preprocessor = new ReshapePreprocessor(inputShape, targetShape, false, CNN2DFormat.NHWC);

} else if (inputType[0] instanceof InputType.InputTypeConvolutional3D) {
InputType.InputTypeConvolutional3D it = (InputType.InputTypeConvolutional3D) inputType[0];
val inputShape = new long[] { it.getDepth(), it.getHeight(), it.getWidth(), it.getChannels() };
val dimOrder = getDimOrder();
if (dimOrder == DimOrder.THEANO || dimOrder == DimOrder.NONE && kerasMajorVersion == 1) {
if (targetShape.length == 3) { // Keras edge case
targetShape = new long[] { targetShape[1], targetShape[0], targetShape[2] };
} else {
targetShape = new long[] { targetShape[2], targetShape[1], targetShape[0], targetShape[3] };
}
preprocessor = new ReshapePreprocessor(inputShape, targetShape, false, null);
} else {
if (inputShape[0] != targetShape[0])
targetShape = new long[] { targetShape[3], targetShape[0], targetShape[1], targetShape[2] };
preprocessor = new ReshapePreprocessor(inputShape, targetShape, false, null);
}
val dimOrder = false;
preprocessor = new ReshapePreprocessor(inputShape, targetShape, false, null);
} else if (inputType[0] instanceof InputType.InputTypeRecurrent) {
InputType.InputTypeRecurrent it = (InputType.InputTypeRecurrent) inputType[0];
val inputShape = new long[]{it.getSize(), it.getTimeSeriesLength()};
preprocessor = new ReshapePreprocessor(inputShape, this.targetShape, false, null);
} else if (inputType[0] instanceof InputType.InputTypeFeedForward) {
InputType.InputTypeFeedForward it = (InputType.InputTypeFeedForward) inputType[0];
val inputShape = new long[]{it.getSize()};
if (targetShape.length == 3) {
targetShape = targetShapeForDimOrder(inputShape, targetShape);
}
preprocessor = new ReshapePreprocessor(inputShape, this.targetShape, false, null);
}
return preprocessor;
}

public long[] targetShapeForDimOrder(long[] inputShape, long[] targetShape) {
if (dimOrder == DimOrder.THEANO || dimOrder == DimOrder.NONE && kerasMajorVersion == 1) {
if (dimOrder == DimOrder.NONE) {
targetShape = new long[]{targetShape[2], targetShape[0], targetShape[1]};
} else {
targetShape = new long[]{targetShape[1], targetShape[2], targetShape[0]};
}
} else {
if (inputShape[0] != targetShape[0]) {
targetShape = new long[]{targetShape[0], targetShape[1], targetShape[2]};
}
}
return targetShape;
}

Expand All @@ -176,9 +120,6 @@ public long[] targetShapeForDimOrder(long[] inputShape, long[] targetShape) {
*/
@Override
public InputType getOutputType(InputType... inputType) throws InvalidKerasConfigurationException {
if (inputType.length > 1)
throw new InvalidKerasConfigurationException(
"Keras Reshape layer accepts only one input (received " + inputType.length + ")");
ReshapePreprocessor reshape = (ReshapePreprocessor) getInputPreprocessor(inputType);
return reshape.getOutputType(inputType[0]);
}
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