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Tensorflow import tests and fixes (#435)

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* ignored ops checked

Signed-off-by: Andrii Tuzhykov <andrewtuzhykov@gmail.com>

* reconfigured AdjustContrast + commented primitive_gru

Signed-off-by: Andrii Tuzhykov <andrewtuzhykov@gmail.com>

* minor changes + exception ops commented

Signed-off-by: Andrii Tuzhykov <andrewtuzhykov@gmail.com>

* figured out non existent tf ops and random ops check

Signed-off-by: Andrii Tuzhykov <andrewtuzhykov@gmail.com>

* minor changes to tensorflowop and randomness cheks

Signed-off-by: Andrii Tuzhykov <andrewtuzhykov@gmail.com>

* deconv2d tensorfloname removed

* Fix Flatbuffers ser/de with character fields

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

* TFGraphTestAllSameDiff tests passed except NonMaxSuppression

Signed-off-by: Andrii Tuzhykov <andrewtuzhykov@gmail.com>

* minor changes

Signed-off-by: Andrii Tuzhykov <andrewtuzhykov@gmail.com>

* temporary ignored section added

Signed-off-by: Andrii Tuzhykov <andrewtuzhykov@gmail.com>

* ignores removed

Signed-off-by: Andrii Tuzhykov <andrewtuzhykov@gmail.com>

* org.nd4j.base.Preconditions -> org.nd4j.common.base.Preconditions

Signed-off-by: Andrii Tuzhykov <andrewtuzhykov@gmail.com>

* temsorflownames reverts and replace CopyHost

* ignored mod op tests due to known issue

Signed-off-by: Andrii Tuzhykov <andrewtuzhykov@gmail.com>

* rsestored mod after fixing in cpp level

Signed-off-by: Andrii Tuzhykov <andrewtuzhykov@gmail.com>

* ignored random_shuffle op test due to known issue

Signed-off-by: Andrii Tuzhykov <andrewtuzhykov@gmail.com>

* increased random_uniform mean/std comparator sensitivity

Signed-off-by: Andrii Tuzhykov <andrewtuzhykov@gmail.com>

* igmored random tests due to SameDiff RNG seed is not set.

Signed-off-by: Andrii Tuzhykov <andrewtuzhykov@gmail.com>

Co-authored-by: Alex Black <blacka101@gmail.com>
This commit is contained in:
Andrii T 2020-05-19 17:18:52 +03:00 committed by GitHub
parent 6e9c849e4a
commit ec757f654d
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26 changed files with 158 additions and 238 deletions
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4
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/functions/DifferentialFunction.java
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@ -261,6 +261,10 @@ public abstract class DifferentialFunction {
if(target.getType() == float.class && value instanceof Double){
value = ((Double) value).floatValue();
}
//Edge case: we store char fields as integers, rather than introduce an extra property
if(target.getType() == char.class && value instanceof Integer){
value = (char)((Integer)value).intValue();
}
target.set(this,value);
} catch (IllegalAccessException e) {

2
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/serde/FlatBuffersMapper.java
View File

@ -483,6 +483,8 @@ public class FlatBuffersMapper {
//No op
} else if (v instanceof Boolean) {
b = new boolean[]{(Boolean) v};
} else if(v instanceof Character){
i = new int[]{(Character)v};
} else if (v instanceof Number) {
if (v instanceof Double) {
d = new double[]{(Double) v};

7
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/validation/OpValidation.java
View File

@ -1220,7 +1220,12 @@ public class OpValidation {
"absargmax",
"absargmin",
"entropy_shannon", //This is a thing, but quite different from our op: https://www.tensorflow.org/versions/r1.2/api_docs/python/tf/contrib/bayesflow/entropy/entropy_shannon
"count_zero"
"count_zero",
"SaveV2",
"LoadV2",
"RestoreV2",
"RandomCrop" // NotImplementedError: Op RandomCrop is not available in GraphDef version 134. It has been removed in version 8. Random crop is now pure Python.
);
return new HashSet<>(list);

1
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/imports/converters/ImportClassMapping.java
View File

@ -625,7 +625,6 @@ public class ImportClassMapping {
org.nd4j.linalg.api.ops.compat.CompatSparseToDense.class,
org.nd4j.linalg.api.ops.compat.CompatStringSplit.class,
org.nd4j.linalg.api.ops.custom.AdjustContrast.class,
org.nd4j.linalg.api.ops.custom.AdjustContrastV2.class,
org.nd4j.linalg.api.ops.custom.HsvToRgb.class,
org.nd4j.linalg.api.ops.custom.RgbToHsv.class,
org.nd4j.linalg.api.ops.custom.RgbToYiq.class,

37
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/custom/AdjustContrast.java
View File

@ -1,4 +1,3 @@
/* ******************************************************************************
* Copyright (c) 2019 Konduit K.K.
*
@ -19,14 +18,27 @@ package org.nd4j.linalg.api.ops.custom;
import lombok.NonNull;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.common.base.Preconditions;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
public class AdjustContrast extends BaseAdjustContrast {
import java.util.Collections;
import java.util.List;
public AdjustContrast() {super();}
public class AdjustContrast extends DynamicCustomOp {
public AdjustContrast() {
super();
}
public AdjustContrast(@NonNull INDArray in, double factor, INDArray out) {
super(in, factor, out);
Preconditions.checkArgument(in.rank() >= 3,
"AdjustContrast: op expects rank of input array to be >= 3, but got %s instead", in.rank());
inputArguments.add(in);
outputArguments.add(out);
addTArgument(factor);
}
public AdjustContrast(@NonNull INDArray in, double factor) {
@ -34,21 +46,28 @@ public class AdjustContrast extends BaseAdjustContrast {
}
public AdjustContrast(@NonNull SameDiff sameDiff, @NonNull SDVariable in, @NonNull SDVariable factor) {
super(sameDiff,new SDVariable[]{in,factor});
super(sameDiff, new SDVariable[]{in, factor});
}
public AdjustContrast(@NonNull SameDiff sameDiff, @NonNull SDVariable in, double factor) {
super(sameDiff,new SDVariable[]{in});
super(sameDiff, new SDVariable[]{in});
addTArgument(factor);
}
@Override
public String opName() {
return "adjust_contrast";
return "adjust_contrast_v2";
}
@Override
public String tensorflowName() {
return "AdjustContrast";
public String[] tensorflowNames() {
return new String[]{"AdjustContrast", "AdjustContrastv2"};
}
@Override
public List<DataType> calculateOutputDataTypes(List<DataType> inputDataTypes) {
int n = args().length;
Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() == n, "Expected %s input data types for %s, got %s", n, getClass(), inputDataTypes);
return Collections.singletonList(inputDataTypes.get(0));
}
}

44
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/custom/AdjustContrastV2.java
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@ -1,44 +0,0 @@
/* ******************************************************************************
* Copyright (c) 2019 Konduit K.K.
*
* 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.nd4j.linalg.api.ops.custom;
import lombok.NonNull;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.linalg.api.ndarray.INDArray;
public class AdjustContrastV2 extends BaseAdjustContrast {
public AdjustContrastV2() {super();}
public AdjustContrastV2(@NonNull INDArray in, double factor, INDArray out) {
super(in, factor, out);
}
public AdjustContrastV2(@NonNull SameDiff sameDiff, @NonNull SDVariable in, @NonNull SDVariable factor) {
super( sameDiff,new SDVariable[]{in,factor});
}
@Override
public String opName() {
return "adjust_contrast_v2";
}
@Override
public String tensorflowName() {
return "AdjustContrastv2";
}
}

52
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/custom/BaseAdjustContrast.java
View File

@ -1,52 +0,0 @@
/* ******************************************************************************
* Copyright (c) 2019 Konduit K.K.
*
* 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.nd4j.linalg.api.ops.custom;
import lombok.NonNull;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.common.base.Preconditions;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import java.util.Collections;
import java.util.List;
public abstract class BaseAdjustContrast extends DynamicCustomOp {
public BaseAdjustContrast() {
}
public BaseAdjustContrast(@NonNull INDArray in, double factor, INDArray out) {
Preconditions.checkArgument(in.rank() >= 3,
"AdjustContrast: op expects rank of input array to be >= 3, but got %s instead", in.rank());
inputArguments.add(in);
outputArguments.add(out);
addTArgument(factor);
}
public BaseAdjustContrast(@NonNull SameDiff sameDiff, @NonNull SDVariable[] vars) {
super("", sameDiff, vars);
}
@Override
public List<DataType> calculateOutputDataTypes(List<DataType> inputDataTypes){
int n = args().length;
Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() == n, "Expected %s input data types for %s, got %s", n, getClass(), inputDataTypes);
return Collections.singletonList(inputDataTypes.get(0));
}
}

12
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/custom/CompareAndBitpack.java
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@ -17,10 +17,15 @@ package org.nd4j.linalg.api.ops.custom;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.common.base.Preconditions;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.DynamicCustomOp;
import org.nd4j.linalg.factory.Nd4j;
import java.util.Collections;
import java.util.List;
public class CompareAndBitpack extends DynamicCustomOp {
public CompareAndBitpack() {}
@ -47,4 +52,11 @@ public class CompareAndBitpack extends DynamicCustomOp {
public String tensorflowName() {
return "CompareAndBitpack";
}
@Override
public List<DataType> calculateOutputDataTypes(List<DataType> dataTypes){
Preconditions.checkState(dataTypes != null && dataTypes.size() == 2, "Expected exactly 2 input datatypes for %s, got input %s", getClass(), dataTypes);
Preconditions.checkState(dataTypes.get(0) == dataTypes.get(1), "Input data types must be the same: got %s", dataTypes);
return Collections.singletonList(DataType.UINT8);
}
}

4
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/custom/RgbToGrayscale.java
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@ -37,8 +37,4 @@ public class RgbToGrayscale extends DynamicCustomOp {
return "rgb_to_grs";
}
@Override
public String tensorflowName() {
return "RgbToGrs";
}
}

5
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/custom/RgbToYiq.java
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@ -42,11 +42,6 @@ public class RgbToYiq extends DynamicCustomOp {
return "rgb_to_yiq";
}
@Override
public String tensorflowName() {
return "RgbToYiq";
}
@Override
public List<DataType> calculateOutputDataTypes(List<DataType> inputDataTypes){
int n = args().length;

5
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/custom/RgbToYuv.java
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@ -42,11 +42,6 @@ public class RgbToYuv extends DynamicCustomOp {
return "rgb_to_yuv";
}
@Override
public String tensorflowName() {
return "RgbToYuv";
}
@Override
public List<DataType> calculateOutputDataTypes(List<DataType> inputDataTypes){
int n = args().length;

5
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/custom/YiqToRgb.java
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@ -41,11 +41,6 @@ public class YiqToRgb extends DynamicCustomOp {
return "yiq_to_rgb";
}
@Override
public String tensorflowName() {
return "YiqToRgb";
}
@Override
public List<DataType> calculateOutputDataTypes(List<DataType> inputDataTypes){
int n = args().length;

4
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/custom/YuvToRgb.java
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@ -42,10 +42,6 @@ public class YuvToRgb extends DynamicCustomOp {
return "yuv_to_rgb";
}
@Override
public String tensorflowName() {
return "YuvToRgb";
}
@Override
public List<DataType> calculateOutputDataTypes(List<DataType> inputDataTypes){

2
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/image/NonMaxSuppressionV3.java
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@ -53,7 +53,7 @@ public class NonMaxSuppressionV3 extends DynamicCustomOp {
@Override
public String[] tensorflowNames() {
return new String[]{"NonMaxSuppressionV3","NonMaxSuppressionV4"};
return new String[]{"NonMaxSuppressionV3","NonMaxSuppressionV4","NonMaxSuppressionV5"};
}
@Override

5
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/DeConv2D.java
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@ -306,11 +306,6 @@ public class DeConv2D extends DynamicCustomOp {
return "ConvTranspose";
}
@Override
public String tensorflowName() {
return "Conv2DTranspose";
}
@Override
public List<SDVariable> doDiff(List<SDVariable> f1) {

6
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/custom/IsNonDecreasing.java
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@ -62,12 +62,6 @@ public class IsNonDecreasing extends DynamicCustomOp {
return "is_non_decreasing";
}
@Override
public String tensorflowName() {
return "IsNonDecreasing";
}
@Override
public List<SDVariable> doDiff(List<SDVariable> f1) {
return Collections.singletonList(sameDiff.zerosLike(arg()));

2
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/pairwise/arithmetic/CopyOp.java
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@ -78,7 +78,7 @@ public class CopyOp extends BaseTransformSameOp {
@Override
public String[] tensorflowNames() {
return new String[]{"Copy","DeepCopy","CopyHost"};
return new String[]{"Copy"};
}
@Override

2
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/same/Identity.java
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@ -64,7 +64,7 @@ public class Identity extends BaseDynamicTransformOp {
@Override
public String[] tensorflowNames() {
return new String[]{"Identity"};
return new String[]{"Identity", "DeepCopy", "CopyHost"};
}
@Override

4
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/segment/UnsortedSegmentMean.java
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@ -55,10 +55,6 @@ public class UnsortedSegmentMean extends DynamicCustomOp {
return "unsorted_segment_mean";
}
@Override
public String tensorflowName() {
return "UnsortedSegmentMean";
}
@Override
public List<SDVariable> doDiff(List<SDVariable> gradients){

5
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/segment/UnsortedSegmentSqrtN.java
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@ -55,11 +55,6 @@ public class UnsortedSegmentSqrtN extends DynamicCustomOp {
return "unsorted_segment_sqrt_n";
}
@Override
public String tensorflowName() {
return "UnsortedSegmentSqrtN";
}
@Override
public List<SDVariable> doDiff(List<SDVariable> gradients){
return new UnsortedSegmentSqrtNBp(sameDiff, arg(0), arg(1), gradients.get(0), numSegments).outputs();

4
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/random/custom/RandomGamma.java
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@ -71,9 +71,7 @@ public class RandomGamma extends DynamicCustomOp {
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String, AttrValue> attributesForNode, GraphDef graph) {
if(attributesForNode.containsKey("alpha")) {
outputDataType = DataTypeAdapter.dtypeConv(attributesForNode.get("alpha").getType());
}
outputDataType = DataTypeAdapter.dtypeConv(attributesForNode.get("T").getType());
}
@Override

6
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/random/impl/DropOutInverted.java
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@ -84,12 +84,6 @@ public class DropOutInverted extends BaseRandomOp {
return "Dropout";
}
@Override
public String tensorflowName() {
return "Dropout";
}
@Override
public List<SDVariable> doDiff(List<SDVariable> f1) {
return null;

6
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/random/impl/UniformDistribution.java
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@ -100,12 +100,6 @@ public class UniformDistribution extends BaseRandomOp {
throw new NoOpNameFoundException("No onnx op opName found for " + opName());
}
@Override
public String tensorflowName() {
return "RandomUniformGG";
}
@Override
public List<SDVariable> doDiff(List<SDVariable> f1) {
return Collections.emptyList();

63
nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/imports/TFGraphs/TFGraphTestAllHelper.java
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@ -851,7 +851,68 @@ public class TFGraphTestAllHelper {
return (t, s) -> Nd4j.sort(t, true).equals(Nd4j.sort(s, true));
}
if(modelName.startsWith("alpha_dropout") || modelName.startsWith("layers_dropout") || modelName.equals("dropout"))
if(modelName.startsWith("empty")){
return (t, s) -> {
boolean areEqualShapes = t.equalShapes(s);
boolean areEqualDataTypes = t.dataType() == s.dataType();
return areEqualShapes && areEqualDataTypes;
}; }
// sum of all elements along dimesions before and after shuffle has to be the same
if(modelName.startsWith("random_shuffle")){
return (t, s) -> Nd4j.sort(t, true).equals(Nd4j.sort(s, true));
}
if(modelName.startsWith("random_normal")){
return (t, s) -> {
boolean areEqualShapes = t.equalShapes(s);
double meanS = s.meanNumber().doubleValue();
double meanT = t.meanNumber().doubleValue();
double stdS = s.stdNumber().doubleValue();
double stdT = t.stdNumber().doubleValue();
double eps = 1;
return areEqualShapes && (Math.abs(meanS-meanT) < eps) && (Math.abs(stdS-stdT) < eps);
}; }
if(modelName.startsWith("random_gamma")){
return (t, s) -> {
boolean areEqualShapes = t.equalShapes(s);
boolean nonNegativeValues = (t.minNumber().doubleValue() > 0) && (t.minNumber().doubleValue() > 0);
double meanS = s.meanNumber().doubleValue();
double meanT = t.meanNumber().doubleValue();
double stdS = s.stdNumber().doubleValue();
double stdT = t.stdNumber().doubleValue();
double eps = 1;
return areEqualShapes && nonNegativeValues && (Math.abs(meanS-meanT) < eps) && (Math.abs(stdS-stdT) < eps);
};
}
if(modelName.startsWith("random_poisson") || modelName.startsWith("random_poisson_v2")){
return (t, s) -> {
boolean areEqualShapes = t.equalShapes(s);
boolean nonNegativeValues = (t.minNumber().doubleValue() >= 0) && (t.minNumber().doubleValue() >= 0);
double meanS = s.meanNumber().doubleValue();
double meanT = t.meanNumber().doubleValue();
double stdS = s.stdNumber().doubleValue();
double stdT = t.stdNumber().doubleValue();
double eps = 1;
return areEqualShapes && nonNegativeValues && (Math.abs(meanS-meanT) < eps) && (Math.abs(stdS-stdT) < eps);
};
}
if(modelName.startsWith("random_uniform")|| modelName.startsWith("random_uniform_int")){
return (t, s) -> {
boolean areEqualShapes = t.equalShapes(s);
double meanS = s.meanNumber().doubleValue();
double meanT = t.meanNumber().doubleValue();
double stdS = s.stdNumber().doubleValue();
double stdT = t.stdNumber().doubleValue();
double eps = 1;
return areEqualShapes && (Math.abs(stdS-stdT) < eps) && (Math.abs(meanS-meanT) < eps);
};
}
if(modelName.startsWith("alpha_dropout") || modelName.startsWith("layers_dropout") || modelName.startsWith("dropout"))
//We can't compare dropout using simple equality due to randomness
return (t, s) -> {
double[] tfNums = t.ravel().toDoubleVector();

93
nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/imports/TFGraphs/TFGraphTestAllSameDiff.java
View File

@ -66,23 +66,29 @@ public class TFGraphTestAllSameDiff { //Note: Can't extend BaseNd4jTest here a
public static final String[] IGNORE_REGEXES = new String[]{
//Failing 2019/07/01 - Issue 10, https://github.com/deeplearning4j/deeplearning4j/issues/6958
//Still failing 2019/09/11
//Still failing 2020/04/27
//java.lang.IllegalStateException: Requested output variable LogMatrixDeterminant:1 does not exist in SameDiff instance
"slogdet/.*",
//Failing 2019/09/11 - https://github.com/eclipse/deeplearning4j/issues/7965
// Still failing 2020/04/27 java.lang.IllegalStateException: Requested output variable Bincount does not exist in SameDiff instance
"bincount/.*",
// Failing 2019/11/14 https://github.com/eclipse/deeplearning4j/issues/8393
"is_strictly_increasing/emptyArrayTest/.*",
//TODO floormod and truncatemod behave differently - i.e., "c" vs. "python" semantics. Need to check implementations too
// Still failing 2020/04/27 java.lang.IllegalStateException: Could not find class for TF Ops: TruncateMod
"truncatemod/.*",
//Still failing as of 2019/09/11 - https://github.com/deeplearning4j/deeplearning4j/issues/6464 - not sure if related to: https://github.com/deeplearning4j/deeplearning4j/issues/6447
"cnn2d_nn/nhwc_b1_k12_s12_d12_SAME",
//2019/09/11 - No tensorflow op found for SparseTensorDenseAdd
// 2020/04/27 java.lang.IllegalStateException: Could not find class for TF Ops: SparseTensorDenseAdd
"confusion/.*",
//2019/09/11 - Couple of tests failing (InferenceSession issues)
// Still failing 2020/04/27 Requested output variable concat does not exist in SameDiff instance
"rnn/bstack/d_.*",
//2019/05/21 - Failing on AVX2/512 intermittently (Linux, OSX), passing elsewhere
@ -97,87 +103,68 @@ public class TFGraphTestAllSameDiff { //Note: Can't extend BaseNd4jTest here a
"g_11",
//2019/07/09 - Need "Multinomial" op - https://github.com/eclipse/deeplearning4j/issues/7913
// Still failing 2020/04/27 java.lang.IllegalStateException: Could not find class for TF Ops: Multinomial
"multinomial/.*",
//2019/11/04 AB - disabled, pending libnd4j deconv3d_tf implementation
// Still failing 2020/04/27 java.lang.IllegalStateException: Could not find descriptor for op: deconv3d_tf - class: org.nd4j.linalg.api.ops.impl.layers.convolution.DeConv3DTF
"conv3d_transpose.*",
//2019/11/15 - mapping is not present yet https://github.com/eclipse/deeplearning4j/issues/8397
// Still failing 2020/04/27 java.lang.AssertionError: Predictions do not match on ragged/reduce_mean/2d_a1, node RaggedReduceMean/truediv
"ragged/reduce_mean/.*",
// 2019/11/15 - missing dtype argument in nd4j, tests are useless https://github.com/eclipse/deeplearning4j/issues/8398
"zeros_like/rank2_float32_dtype_int.*",
// Still failing 2020/04/27 java.lang.IndexOutOfBoundsException: 1
"zeros_like/rank2_float32_dtype_int.*",
// 11.26.2019 failing - https://github.com/eclipse/deeplearning4j/issues/8453
// Still failing 2020/04/27 java.lang.AssertionError: Predictions do not match on roll/rank2_float32_zeroshift, node Roll
"roll/.*",
// 11.26.2019 failing https://github.com/eclipse/deeplearning4j/issues/8455
// still failing 2020/04/27
// java.lang.IllegalStateException: Failed to calculate output shapes for op matrix_band_part (MatrixBandPart) - no shapes were returned by calculateOutputShape()
"matrix_band_part/.*",
// 12.20.2019 - https://github.com/eclipse/deeplearning4j/issues/8559
// Still failing 2020/27/04 java.lang.AssertionError: Predictions do not match on fused_batch_norm/float32_nhcw, node FusedBatchNormV3
"fused_batch_norm/.*",
// AB 2020/01/04 - https://github.com/eclipse/deeplearning4j/issues/8592
"emptyArrayTests/reshape/rank2_shape2-0_2-0--1",
// 01.05.2020 - https://github.com/eclipse/deeplearning4j/issues/8898
"primitive_gru",
//AB 2020/01/07 - Known issues
"bitcast/from_float64_to_int64",
"bitcast/from_rank2_float64_to_int64",
"bitcast/from_float64_to_uint64",
// 05.05.2020 - https://github.com/eclipse/deeplearning4j/issues/8921
"random_poisson/rank1_float16", "random_poisson/rank1_float32", "random_poisson/rank1_float16", "random_poisson/rank1_half",
"random_poisson_v2/rank1_float64", "random_poisson_v2/rank1_float16", "random_poisson_v2/rank1_half",
//NEWLY ADDED TESTCASES from 27/04/2020
"non_max_suppression_v2/.*", "non_max_suppression/.*",
//08.05.2020 - https://github.com/eclipse/deeplearning4j/issues/8927
"random_gamma/.*",
"non_max_suppression_v5/.*",
"non_max_suppression_v4/.*",
"non_max_suppression_v3/.*",
"dropout/.*",
"max_pool_with_argmax/.*",
"conv2d_transpose/.*",
//08.05.2020 - https://github.com/eclipse/deeplearning4j/issues/8928
"Conv3DBackpropInputV2/.*",
"Conv3DBackpropInput/.*",
"mod/.*",
"leaky_relu/.*",
"DeepCopy/.*",
"empty/.*",
"ones_like/.*",
"is_non_decreasing/.*",
"div/.*",
"lgamma/.*",
//12.05.2020 - https://github.com/eclipse/deeplearning4j/issues/8940
"compare_and_bitpack/.*",
//12.05.2020 - https://github.com/eclipse/deeplearning4j/issues/8943
"max_pool_with_argmax/int64_int64_padding_SAME", "max_pool_with_argmax/int32_int64_padding_SAME",
//12.05.2020 - https://github.com/eclipse/deeplearning4j/issues/8946
"non_max_suppression_v4/.*","non_max_suppression_v5/.*",
// 18.05.2020 - https://github.com/eclipse/deeplearning4j/issues/8960
"random_shuffle/.*",
// 18.05.2020 - https://github.com/eclipse/deeplearning4j/issues/8963
"random_uniform/.*",
"random_uniform_int/.*",
"resize_area/.*",
"zeros_like_tf1/.*",
"Conv2DTranspose/.*",
"rgb_to_yuv/.*",
"rgb_to_grayscale/.*",
"rgb_to_yiq/.*",
"losses/.*",
"yiq_to_rgb/.*",
"yuv_to_rgb/.*",
"emptyArrayTests/.*",
"random_normal/.*",
"random_shuffle/.*",
"random_poisson_v2/.*",
"random_gamma/.*",
"random_poisson/.*",
"random_crop/.*",
"compare_and_bitpack/.*",
"adjust_contrast/.*",
"confusion/.*",
"bitcast/.*",
"roll/.*",
"matrix_band_part/.*",
"conv3d_transpose_layers/.*",
"multinomial/.*",
"unsorted_segment/.*",
"cnn2d_nn/.*",
"truncatemod/.*",
"bincount/.*",
"slogdet/.*",
"adjust_contrast_v2/.*"
"random_poisson/.*",
"random_poisson_v2/.*",
};
};
/* As per TFGraphTestList.printArraysDebugging - this field defines a set of regexes for test cases that should have
all arrays printed during execution.

16
nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/custom/CustomOpsTests.java
View File

@ -847,22 +847,6 @@ public class CustomOpsTests extends BaseNd4jTest {
assertArrayEquals(new long[]{256, 256, 3}, lsd.get(0).getShape());
}
@Test
public void testAdjustContrastV2() {
INDArray in = Nd4j.linspace(DataType.DOUBLE,1.0,1.0, 4*4*3).reshape(4,4,3);
INDArray out = Nd4j.createUninitialized(4,4,3);
INDArray expected = Nd4j.createFromArray(new double[]{-21.5, -20.5, -19.5, -15.5, -14.5, -13.5, -9.5, -8.5, -7.5, -3.5, -2.5, -1.5,
2.5, 3.5, 4.5, 8.5, 9.5, 10.5, 14.5, 15.5, 16.5, 20.5, 21.5, 22.5,
26.5, 27.5, 28.5, 32.5, 33.5, 34.5, 38.5, 39.5, 40.5, 44.5, 45.5, 46.5,
50.5, 51.5, 52.5, 56.5, 57.5, 58.5, 62.5, 63.5, 64.5, 68.5, 69.5, 70.5
}).reshape(4,4,3);
Nd4j.exec(new AdjustContrastV2(in, 2.0, out));
assertArrayEquals(out.shape(), in.shape());
assertEquals(expected, out);
}
@Ignore("AS 11/13/2019 https://github.com/eclipse/deeplearning4j/issues/8374")
@Test