Brutex
/
cavis
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

TF import tests - adding missing operations (#65) 

* Add and fix mappings.

* Intermediate

* Added and fixed some mappings

* Added op

* Missing constructors added.

* Added new mappings

* SDImage wrappers and minor tweaks.

* Added missing constructor

* Some corrections

* Cleanup

* Small fixes

* Ops wrappers

* Minor fixes.

* Max Pooling

* MaxPoolWithArgmax

* Some fixes

* Ignores for failures

* Some ops fixed.

* Some fixes

* Missing package added

* Some fixes

* Ignored tests fixed.

* Some fixes

* Merge master

* bitcast fix

Signed-off-by: raver119 <raver119@gmail.com>

* Bitcast fixed
master
Alexander Stoyakin 2019-12-02 12:23:06 +02:00 committed by Alex Black
parent 1adc25919c
commit 5e152c0d9a
39 changed files with 1545 additions and 86 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

30
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/functions/DifferentialFunctionFactory.java
View File

@ -2616,6 +2616,36 @@ public class DifferentialFunctionFactory {
return new FakeQuantWithMinMaxVarsPerChannel(sameDiff,x,min,max).outputVariable(); return new FakeQuantWithMinMaxVarsPerChannel(sameDiff,x,min,max).outputVariable();
} }
public SDVariable betainc( SDVariable a, SDVariable b, SDVariable x) {
return new BetaInc(sameDiff, a, b, x).outputVariable();
}
public SDVariable[] fusedBatchNorm(SDVariable x, SDVariable scale, SDVariable offset,
SDVariable dataFormat, SDVariable isTraining) {
return new FusedBatchNorm(sameDiff,x,scale,offset,dataFormat,isTraining).outputVariables();
}
public SDVariable matrixBandPart(SDVariable input, SDVariable minLower, SDVariable maxUpper) {
return new MatrixBandPart(sameDiff,input,minLower,maxUpper).outputVariable();
}
public SDVariable[] maxPoolWithArgmaxs(SDVariable x, Pooling2DConfig pooling2DConfig) {
return new MaxPoolWithArgmax(sameDiff, x, pooling2DConfig).outputVariables();
}
public SDVariable polygamma(SDVariable n, SDVariable x) {
return new Polygamma(sameDiff, n,x).outputVariable();
}
public SDVariable roll(SDVariable input, SDVariable shift) {
return new Roll(sameDiff, input, shift).outputVariable();
}
public SDVariable toggleBits(SDVariable x) {
return new ToggleBits(sameDiff, x).outputVariable();
}
public String toString() { public String toString() {
return "DifferentialFunctionFactory{methodNames=" + methodNames + "}"; return "DifferentialFunctionFactory{methodNames=" + methodNames + "}";
} }

12
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDBitwise.java
View File

@ -202,4 +202,16 @@ public class SDBitwise extends SDOps {
SDVariable ret = f().bitwiseXor(x, y); SDVariable ret = f().bitwiseXor(x, y);
return updateVariableNameAndReference(ret, name); return updateVariableNameAndReference(ret, name);
} }
/**
* Flip bits
*
* @param name Name of the output variable
* @param x input array
* @return array after flipping each input bit
*/
public SDVariable toggleBits(String name, SDVariable x) {
SDVariable res = f().toggleBits(x);
return updateVariableNameAndReference(res, name);
}
} }

63
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDImage.java
View File

@ -3,6 +3,10 @@ package org.nd4j.autodiff.samediff.ops;
import lombok.NonNull; import lombok.NonNull;
import org.nd4j.autodiff.samediff.SDVariable; import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff; import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.linalg.api.ops.custom.AdjustContrast;
import org.nd4j.linalg.api.ops.custom.AdjustHue;
import org.nd4j.linalg.api.ops.custom.AdjustSaturation;
import org.nd4j.linalg.api.ops.custom.RandomCrop;
import org.nd4j.linalg.api.ops.impl.image.CropAndResize; import org.nd4j.linalg.api.ops.impl.image.CropAndResize;
import org.nd4j.linalg.api.ops.impl.image.ExtractImagePatches; import org.nd4j.linalg.api.ops.impl.image.ExtractImagePatches;
import org.nd4j.linalg.api.ops.impl.image.NonMaxSuppression; import org.nd4j.linalg.api.ops.impl.image.NonMaxSuppression;
@ -52,10 +56,67 @@ public class SDImage extends SDOps {
return updateVariableNameAndReference(out, name); return updateVariableNameAndReference(out, name);
} }
/**
* Greedily selects a subset of bounding boxes in descending order of score
* @param name Might be null. Name for the output variable
* @param boxes 2D array of shape [num_boxes,4]
* @param scores vector of shape [num_boxes]
* @param maxOutSize scalar representing the maximum number of boxes to be selected
* @param iouThreshold float - threshold for deciding whether boxes overlap too much with respect to IOU
* @param scoreThreshold float - threshold for deciding when to remove boxes based on score
* @return vectort of shape [M] representing the selected indices from the boxes tensor, where M <= max_output_size
*/
public SDVariable nonMaxSuppression(String name, @NonNull SDVariable boxes, @NonNull SDVariable scores, @NonNull SDVariable maxOutSize, public SDVariable nonMaxSuppression(String name, @NonNull SDVariable boxes, @NonNull SDVariable scores, @NonNull SDVariable maxOutSize,
@NonNull SDVariable iouThreshold, @NonNull SDVariable scoreThreshold){ @NonNull SDVariable iouThreshold, @NonNull SDVariable scoreThreshold){
SDVariable out = new NonMaxSuppression(sd, boxes, scores, maxOutSize, iouThreshold, scoreThreshold).outputVariable(); SDVariable out = new NonMaxSuppression(sd, boxes, scores, maxOutSize, iouThreshold, scoreThreshold).outputVariable();
return updateVariableNameAndReference(out, name); return updateVariableNameAndReference(out, name);
} }
/**
* Adjusts contrast of RGB or grayscale images.
* @param name name for the output variable
* @param in images to adjust. 3D shape or higher.
* @param factor float multiplier for adjusting contrast.
* @return Contrast-adjusted image
*/
public SDVariable adjustContrast(String name, @NonNull SDVariable in, @NonNull SDVariable factor) {
SDVariable out = new AdjustContrast(sd, in, factor).outputVariable();
return updateVariableNameAndReference(out, name);
}
/**
* Adjust saturation of RGB images
* @param name name for the output variable
* @param in RGB image as 3D array
* @param factor factor for saturation
* @return adjusted image
*/
public SDVariable adjustSaturation(String name, @NonNull SDVariable in, @NonNull SDVariable factor) {
SDVariable out = new AdjustSaturation(sd, in, factor).outputVariable();
return updateVariableNameAndReference(out, name);
}
/**
* Adjust hue of RGB image
* @param name name for the output variable
* @param in RGB image as 3D array
* @param delta value to add to hue channel
* @return adjusted image
*/
public SDVariable adjustHue(String name, @NonNull SDVariable in, @NonNull SDVariable delta) {
SDVariable out = new AdjustHue(sd, in, delta).outputVariable();
return updateVariableNameAndReference(out, name);
}
/**
* Randomly crops image
* @param name name for the output variable
* @param input input array
* @param shape shape for crop
* @return cropped array
*/
public SDVariable randomCrop(String name, @NonNull SDVariable input, @NonNull SDVariable shape) {
SDVariable out = new RandomCrop(sd, input, shape).outputVariable();
return updateVariableNameAndReference(out, name);
}
} }

52
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDMath.java
View File

@ -2496,5 +2496,57 @@ public class SDMath extends SDOps {
return updateVariableNameAndReference(res, name); return updateVariableNameAndReference(res, name);
} }
/**
* Compute the regularized incomplete beta integral
*
* @param name Name of the output variable
* @param a input array
* @param b input array
* @param x input array
* @return array
*/
public SDVariable betainc(String name,SDVariable a,SDVariable b,SDVariable x) {
SDVariable res = f().betainc(a,b,x);
return updateVariableNameAndReference(res, name);
}
/**
* Copy a tensor setting everything outside a central band in each innermost matrix.
*
* @param name Name of the output variable
* @param input Rank k array
* @param minLower Number of subdiagonals to keep.
* @param maxUpper Number of superdiagonals to keep.
* @return Rank k array of the same shape as input.
*/
public SDVariable matrixBandPart(String name, SDVariable input, SDVariable minLower, SDVariable maxUpper) {
SDVariable res = f().matrixBandPart(input,minLower,maxUpper);
return updateVariableNameAndReference(res, name);
}
/**
* Polygamma function
*
* @param name Name of the output variable
* @param n array
* @param x array
* @return array
*/
public SDVariable polygamma(String name, SDVariable n, SDVariable x) {
SDVariable res = f().polygamma(n,x);
return updateVariableNameAndReference(res, name);
}
/**
* Rolls the elements of input
*
* @param name Name of the output variable
* @param input array
* @param shift number of places to shift elements
* @return array
*/
public SDVariable roll(String name, SDVariable input, SDVariable shift) {
SDVariable res = f().roll(input,shift);
return updateVariableNameAndReference(res, name);
}
} }

32
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDNN.java
View File

@ -19,6 +19,7 @@ package org.nd4j.autodiff.samediff.ops;
import lombok.NonNull; import lombok.NonNull;
import org.nd4j.autodiff.samediff.SDVariable; import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff; import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.linalg.api.ops.impl.layers.convolution.config.Pooling2DConfig;
import org.nd4j.linalg.api.ops.impl.transforms.Pad; import org.nd4j.linalg.api.ops.impl.transforms.Pad;
import org.nd4j.linalg.factory.Nd4j; import org.nd4j.linalg.factory.Nd4j;
@ -1032,4 +1033,35 @@ public class SDNN extends SDOps {
); );
} }
} }
/**
* Max pooling on the input and outputs both max values and indices
*
* @param name Name of the output variable
* @param x input array
* @return output array and argmax array
*/
public SDVariable[] maxPoolWithArgmax(String[] names, SDVariable x, Pooling2DConfig pooling2DConfig) {
SDVariable[] res = f().maxPoolWithArgmaxs(x, pooling2DConfig);
return sd.updateVariableNamesAndReferences(res, names);
}
/**
* Batch normalization
*
* @param name Name of the output variable
* @param x 4D array
* @param scale vector for scaling factor of normalized x
* @param offset vector to shift to the normalized x
* @param dataFormat integer scalar - data format
* @param isTraining boolean scalar - is training mode
* @return y: 4D array
* batch_mean: vector
* batch_var: vector
*/
public SDVariable[] fusedBatchNorm(String[] names, SDVariable x, SDVariable scale, SDVariable offset,
SDVariable dataFormat, SDVariable isTraining) {
SDVariable[] res = f().fusedBatchNorm(x,scale,offset,dataFormat,isTraining);
return sd.updateVariableNamesAndReferences(res, names);
}
} }

14
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/imports/converters/ImportClassMapping.java
View File

@ -46,7 +46,6 @@ public class ImportClassMapping {
org.nd4j.linalg.api.ops.custom.BarnesEdgeForces.class, org.nd4j.linalg.api.ops.custom.BarnesEdgeForces.class,
org.nd4j.linalg.api.ops.custom.BarnesHutGains.class, org.nd4j.linalg.api.ops.custom.BarnesHutGains.class,
org.nd4j.linalg.api.ops.custom.BarnesHutSymmetrize.class, org.nd4j.linalg.api.ops.custom.BarnesHutSymmetrize.class,
org.nd4j.linalg.api.ops.custom.DrawBoundingBoxes.class,
org.nd4j.linalg.api.ops.custom.KnnMinDistance.class, org.nd4j.linalg.api.ops.custom.KnnMinDistance.class,
org.nd4j.linalg.api.ops.custom.SpTreeCell.class, org.nd4j.linalg.api.ops.custom.SpTreeCell.class,
org.nd4j.linalg.api.ops.custom.Flatten.class, org.nd4j.linalg.api.ops.custom.Flatten.class,
@ -122,6 +121,7 @@ public class ImportClassMapping {
org.nd4j.linalg.api.ops.impl.layers.convolution.LocalResponseNormalizationDerivative.class, org.nd4j.linalg.api.ops.impl.layers.convolution.LocalResponseNormalizationDerivative.class,
org.nd4j.linalg.api.ops.impl.layers.convolution.MaxPooling2D.class, org.nd4j.linalg.api.ops.impl.layers.convolution.MaxPooling2D.class,
org.nd4j.linalg.api.ops.impl.layers.convolution.MaxPooling3D.class, org.nd4j.linalg.api.ops.impl.layers.convolution.MaxPooling3D.class,
org.nd4j.linalg.api.ops.impl.layers.convolution.MaxPoolWithArgmax.class,
org.nd4j.linalg.api.ops.impl.layers.convolution.Pooling2D.class, org.nd4j.linalg.api.ops.impl.layers.convolution.Pooling2D.class,
org.nd4j.linalg.api.ops.impl.layers.convolution.Pooling2DDerivative.class, org.nd4j.linalg.api.ops.impl.layers.convolution.Pooling2DDerivative.class,
org.nd4j.linalg.api.ops.impl.layers.convolution.Pooling3DDerivative.class, org.nd4j.linalg.api.ops.impl.layers.convolution.Pooling3DDerivative.class,
@ -589,7 +589,17 @@ public class ImportClassMapping {
org.nd4j.linalg.api.ops.custom.BitCast.class, org.nd4j.linalg.api.ops.custom.BitCast.class,
org.nd4j.linalg.api.ops.custom.CompareAndBitpack.class, org.nd4j.linalg.api.ops.custom.CompareAndBitpack.class,
org.nd4j.linalg.api.ops.custom.DivideNoNan.class, org.nd4j.linalg.api.ops.custom.DivideNoNan.class,
org.nd4j.linalg.api.ops.custom.FakeQuantWithMinMaxVarsPerChannel.class org.nd4j.linalg.api.ops.custom.DrawBoundingBoxes.class,
org.nd4j.linalg.api.ops.custom.FakeQuantWithMinMaxVarsPerChannel.class,
org.nd4j.linalg.api.ops.custom.AdjustSaturation.class,
org.nd4j.linalg.api.ops.custom.AdjustHue.class,
org.nd4j.linalg.api.ops.custom.FusedBatchNorm.class,
org.nd4j.linalg.api.ops.custom.BetaInc.class,
org.nd4j.linalg.api.ops.custom.MatrixBandPart.class,
org.nd4j.linalg.api.ops.custom.Polygamma.class,
org.nd4j.linalg.api.ops.custom.RandomCrop.class,
org.nd4j.linalg.api.ops.custom.Roll.class,
org.nd4j.linalg.api.ops.custom.ToggleBits.class
); );
static { static {

21
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/custom/AdjustContrast.java
View File

@ -1,5 +1,22 @@
/* ******************************************************************************
* 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; package org.nd4j.linalg.api.ops.custom;
import lombok.NonNull;
import org.nd4j.autodiff.samediff.SDVariable; import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff; import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.base.Preconditions; import org.nd4j.base.Preconditions;
@ -14,11 +31,11 @@ public class AdjustContrast extends BaseAdjustContrast {
public AdjustContrast() {super();} public AdjustContrast() {super();}
public AdjustContrast(INDArray in, double factor, INDArray out) { public AdjustContrast(@NonNull INDArray in, double factor, INDArray out) {
super(in, factor, out); super(in, factor, out);
} }
public AdjustContrast(SameDiff sameDiff, SDVariable in, SDVariable factor) { public AdjustContrast(@NonNull SameDiff sameDiff, @NonNull SDVariable in, @NonNull SDVariable factor) {
super(sameDiff,new SDVariable[]{in,factor}); super(sameDiff,new SDVariable[]{in,factor});
} }

20
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/custom/AdjustContrastV2.java
View File

@ -1,5 +1,21 @@
/* ******************************************************************************
* 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; package org.nd4j.linalg.api.ops.custom;
import lombok.NonNull;
import org.nd4j.autodiff.samediff.SDVariable; import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff; import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.base.Preconditions; import org.nd4j.base.Preconditions;
@ -14,11 +30,11 @@ public class AdjustContrastV2 extends BaseAdjustContrast {
public AdjustContrastV2() {super();} public AdjustContrastV2() {super();}
public AdjustContrastV2(INDArray in, double factor, INDArray out) { public AdjustContrastV2(@NonNull INDArray in, double factor, INDArray out) {
super(in, factor, out); super(in, factor, out);
} }
public AdjustContrastV2(SameDiff sameDiff, SDVariable in, SDVariable factor) { public AdjustContrastV2(@NonNull SameDiff sameDiff, @NonNull SDVariable in, @NonNull SDVariable factor) {
super( sameDiff,new SDVariable[]{in,factor}); super( sameDiff,new SDVariable[]{in,factor});
} }

69
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/custom/AdjustHue.java Normal file
View File

@ -0,0 +1,69 @@
/* ******************************************************************************
* 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.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 class AdjustHue extends DynamicCustomOp {
public AdjustHue() {}
public AdjustHue(@NonNull INDArray in, double delta, INDArray out) {
this(in, delta);
if (out != null) {
outputArguments.add(out);
}
}
public AdjustHue(@NonNull INDArray in, double delta) {
Preconditions.checkArgument(in.rank() >= 3,
"AdjustSaturation: op expects rank of input array to be >= 3, but got %s instead", in.rank());
Preconditions.checkArgument(-1.0 <= delta && delta <= 1.0, "AdjustHue: parameter delta must be within [-1, 1] interval," +
" but got %s instead", delta);
inputArguments.add(in);
addTArgument(delta);
}
public AdjustHue(@NonNull SameDiff sameDiff, @NonNull SDVariable in, @NonNull SDVariable factor) {
super(sameDiff,new SDVariable[]{in,factor});
}
@Override
public String opName() {
return "adjust_hue";
}
@Override
public String tensorflowName() {
return "AdjustHue";
}
@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));
}
}

68
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/custom/AdjustSaturation.java Normal file
View File

@ -0,0 +1,68 @@
/* ******************************************************************************
* 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.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 class AdjustSaturation extends DynamicCustomOp {
public AdjustSaturation() {}
public AdjustSaturation(@NonNull INDArray in, double factor, INDArray out) {
this(in, factor);
if (out != null) {
outputArguments.add(out);
}
}
public AdjustSaturation(@NonNull INDArray in, double factor) {
Preconditions.checkArgument(in.rank() >= 3,
"AdjustSaturation: op expects rank of input array to be >= 3, but got %s instead", in.rank());
inputArguments.add(in);
addTArgument(factor);
}
public AdjustSaturation(@NonNull SameDiff sameDiff, @NonNull SDVariable in, @NonNull SDVariable factor) {
super(sameDiff, new SDVariable[]{in, factor});
}
@Override
public String opName() {
return "adjust_saturation";
}
@Override
public String tensorflowName() {
return "AdjustSaturation";
}
@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));
}
}

22
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/custom/BaseAdjustContrast.java
View File

@ -1,5 +1,21 @@
/* ******************************************************************************
* 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; package org.nd4j.linalg.api.ops.custom;
import lombok.NonNull;
import org.nd4j.autodiff.samediff.SDVariable; import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff; import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.base.Preconditions; import org.nd4j.base.Preconditions;
@ -14,16 +30,16 @@ public abstract class BaseAdjustContrast extends DynamicCustomOp {
public BaseAdjustContrast() { public BaseAdjustContrast() {
} }
public BaseAdjustContrast(INDArray in, double factor, INDArray out) { public BaseAdjustContrast(@NonNull INDArray in, double factor, INDArray out) {
Preconditions.checkArgument(in.rank() >= 3, Preconditions.checkArgument(in.rank() >= 3,
String.format("AdjustContrast: op expects rank of input array to be >= 3, but got %d instead", in.rank())); "AdjustContrast: op expects rank of input array to be >= 3, but got %s instead", in.rank());
inputArguments.add(in); inputArguments.add(in);
outputArguments.add(out); outputArguments.add(out);
addTArgument(factor); addTArgument(factor);
} }
public BaseAdjustContrast(SameDiff sameDiff, SDVariable[] vars) { public BaseAdjustContrast(@NonNull SameDiff sameDiff, @NonNull SDVariable[] vars) {
super("", sameDiff, vars); super("", sameDiff, vars);
} }

67
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/custom/BetaInc.java Normal file
View File

@ -0,0 +1,67 @@
/* ******************************************************************************
* 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.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 class BetaInc extends DynamicCustomOp {
public BetaInc() {}
public BetaInc(@NonNull INDArray a_input, @NonNull INDArray b_input, @NonNull INDArray x_input,
INDArray output) {
addInputArgument(a_input, b_input, x_input);
if (output != null) {
addOutputArgument(output);
}
}
public BetaInc(@NonNull INDArray a_input, @NonNull INDArray b_input, @NonNull INDArray x_input) {
inputArguments.add(a_input);
inputArguments.add(b_input);
inputArguments.add(x_input);
}
public BetaInc(@NonNull SameDiff sameDiff, @NonNull SDVariable a, @NonNull SDVariable b, @NonNull SDVariable x) {
super(sameDiff, new SDVariable[]{a,b,x});
}
@Override
public String opName() {
return "betainc";
}
@Override
public String tensorflowName() {
return "Betainc";
}
@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));
}
}

24
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/custom/BitCast.java
View File

@ -1,3 +1,18 @@
/* ******************************************************************************
* 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; package org.nd4j.linalg.api.ops.custom;
import lombok.val; import lombok.val;
@ -20,6 +35,8 @@ import java.util.Map;
public class BitCast extends DynamicCustomOp { public class BitCast extends DynamicCustomOp {
public BitCast() {} public BitCast() {}
private DataType dtype;
public BitCast(INDArray in, DataType dataType, INDArray out) { public BitCast(INDArray in, DataType dataType, INDArray out) {
this(in, dataType.toInt(), out); this(in, dataType.toInt(), out);
} }
@ -28,6 +45,8 @@ public class BitCast extends DynamicCustomOp {
inputArguments.add(in); inputArguments.add(in);
outputArguments.add(out); outputArguments.add(out);
iArguments.add(Long.valueOf(dataType)); iArguments.add(Long.valueOf(dataType));
dtype = DataType.fromInt(dataType);
} }
public BitCast(INDArray in, DataType dataType) { public BitCast(INDArray in, DataType dataType) {
@ -37,6 +56,7 @@ public class BitCast extends DynamicCustomOp {
public BitCast(INDArray in, int dataType) { public BitCast(INDArray in, int dataType) {
inputArguments.add(in); inputArguments.add(in);
iArguments.add(Long.valueOf(dataType)); iArguments.add(Long.valueOf(dataType));
dtype = DataType.fromInt(dataType);
} }
public BitCast(SameDiff sameDiff, SDVariable in, SDVariable dataType) { public BitCast(SameDiff sameDiff, SDVariable in, SDVariable dataType) {
@ -49,6 +69,8 @@ public class BitCast extends DynamicCustomOp {
val t = nodeDef.getAttrOrDefault("type", null); val t = nodeDef.getAttrOrDefault("type", null);
val type = ArrayOptionsHelper.convertToDataType(t.getType()); val type = ArrayOptionsHelper.convertToDataType(t.getType());
addIArgument(type.toInt()); addIArgument(type.toInt());
dtype = type;
} }
@Override @Override
@ -65,6 +87,6 @@ public class BitCast extends DynamicCustomOp {
public List<DataType> calculateOutputDataTypes(List<DataType> inputDataTypes){ public List<DataType> calculateOutputDataTypes(List<DataType> inputDataTypes){
int n = args().length; int n = args().length;
Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() == n, "Expected %s input data types for %s, got %s", n, getClass(), inputDataTypes); 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)); return Collections.singletonList(dtype);
} }
} }

21
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/custom/CompareAndBitpack.java
View File

@ -1,3 +1,18 @@
/* ******************************************************************************
* 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; package org.nd4j.linalg.api.ops.custom;
import org.nd4j.autodiff.samediff.SDVariable; import org.nd4j.autodiff.samediff.SDVariable;
@ -9,9 +24,13 @@ import org.nd4j.linalg.factory.Nd4j;
public class CompareAndBitpack extends DynamicCustomOp { public class CompareAndBitpack extends DynamicCustomOp {
public CompareAndBitpack() {} public CompareAndBitpack() {}
public CompareAndBitpack(INDArray in, double threshold, INDArray out) { public CompareAndBitpack(INDArray in, double threshold) {
inputArguments.add(in); inputArguments.add(in);
inputArguments.add(Nd4j.scalar(threshold)); inputArguments.add(Nd4j.scalar(threshold));
}
public CompareAndBitpack(INDArray in, double threshold, INDArray out) {
this(in, threshold);
outputArguments.add(out); outputArguments.add(out);
} }

21
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/custom/DivideNoNan.java
View File

@ -1,3 +1,18 @@
/* ******************************************************************************
* 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; package org.nd4j.linalg.api.ops.custom;
import org.apache.commons.math3.analysis.function.Divide; import org.apache.commons.math3.analysis.function.Divide;
@ -16,9 +31,13 @@ public class DivideNoNan extends DynamicCustomOp {
public DivideNoNan() { public DivideNoNan() {
} }
public DivideNoNan(INDArray in1, INDArray in2, INDArray out) { public DivideNoNan(INDArray in1, INDArray in2) {
inputArguments.add(in1); inputArguments.add(in1);
inputArguments.add(in2); inputArguments.add(in2);
}
public DivideNoNan(INDArray in1, INDArray in2, INDArray out) {
this(in1,in2);
outputArguments.add(out); outputArguments.add(out);
} }

23
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/custom/DrawBoundingBoxes.java
View File

@ -1,3 +1,18 @@
/* ******************************************************************************
* 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; package org.nd4j.linalg.api.ops.custom;
import org.nd4j.autodiff.samediff.SDVariable; import org.nd4j.autodiff.samediff.SDVariable;
@ -13,11 +28,15 @@ import java.util.List;
public class DrawBoundingBoxes extends DynamicCustomOp { public class DrawBoundingBoxes extends DynamicCustomOp {
public DrawBoundingBoxes() {} public DrawBoundingBoxes() {}
public DrawBoundingBoxes(INDArray images, INDArray boxes, INDArray colors, public DrawBoundingBoxes(INDArray images, INDArray boxes, INDArray colors) {
INDArray output) {
inputArguments.add(images); inputArguments.add(images);
inputArguments.add(boxes); inputArguments.add(boxes);
inputArguments.add(colors); inputArguments.add(colors);
}
public DrawBoundingBoxes(INDArray images, INDArray boxes, INDArray colors,
INDArray output) {
this(images, boxes, colors);
outputArguments.add(output); outputArguments.add(output);
} }

23
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/custom/FakeQuantWithMinMaxVarsPerChannel.java
View File

@ -1,3 +1,18 @@
/* ******************************************************************************
* 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; package org.nd4j.linalg.api.ops.custom;
import org.nd4j.autodiff.samediff.SDVariable; import org.nd4j.autodiff.samediff.SDVariable;
@ -13,14 +28,18 @@ import java.util.List;
public class FakeQuantWithMinMaxVarsPerChannel extends DynamicCustomOp { public class FakeQuantWithMinMaxVarsPerChannel extends DynamicCustomOp {
public FakeQuantWithMinMaxVarsPerChannel() {} public FakeQuantWithMinMaxVarsPerChannel() {}
public FakeQuantWithMinMaxVarsPerChannel(INDArray x, INDArray min, INDArray max, public FakeQuantWithMinMaxVarsPerChannel(INDArray x, INDArray min, INDArray max) {
INDArray output) {
Preconditions.checkArgument(min.isVector() && max.isVector() && Preconditions.checkArgument(min.isVector() && max.isVector() &&
min.length() == max.length(), min.length() == max.length(),
"FakeQuantWithMinMaxVarsPerChannel: min and max should be 1D tensors with the same length"); "FakeQuantWithMinMaxVarsPerChannel: min and max should be 1D tensors with the same length");
inputArguments.add(x); inputArguments.add(x);
inputArguments.add(min); inputArguments.add(min);
inputArguments.add(max); inputArguments.add(max);
}
public FakeQuantWithMinMaxVarsPerChannel(INDArray x, INDArray min, INDArray max,
INDArray output) {
this(x,min,max);
outputArguments.add(output); outputArguments.add(output);
} }

64
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/custom/FusedBatchNorm.java Normal file
View File

@ -0,0 +1,64 @@
/* ******************************************************************************
* 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.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 class FusedBatchNorm extends DynamicCustomOp {
public FusedBatchNorm() {}
public FusedBatchNorm(@NonNull INDArray x, @NonNull INDArray scale, @NonNull INDArray offset,
int dataFormat, int isTraining,
INDArray yOut, INDArray batchMeanOut, INDArray batchMeanVar) {
addInputArgument(x, scale, offset);
addIArgument(dataFormat, isTraining);
if (yOut != null && batchMeanOut != null && batchMeanVar != null) {
addOutputArgument(yOut, batchMeanOut, batchMeanVar);
}
}
public FusedBatchNorm(@NonNull SameDiff sameDiff, @NonNull SDVariable x, @NonNull SDVariable scale, @NonNull SDVariable offset,
@NonNull SDVariable dataFormat, @NonNull SDVariable isTraining) {
super("", sameDiff, new SDVariable[]{x, scale, offset, dataFormat, isTraining});
}
@Override
public String opName() {
return "fused_batch_norm";
}
@Override
public String tensorflowName() {
return "FusedBatchNormV2";
}
@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));
}
}

65
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/custom/MatrixBandPart.java Normal file
View File

@ -0,0 +1,65 @@
/* ******************************************************************************
* 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.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 class MatrixBandPart extends DynamicCustomOp {
public MatrixBandPart() {}
public MatrixBandPart(@NonNull INDArray input, int minLower, int maxUpper) {
Preconditions.checkArgument(input.rank() >= 2, "MatrixBandPart: Input rank should be 2 or higher");
long N = input.size(-2);
long M = input.size(-1);
Preconditions.checkArgument(minLower > -N && minLower < N, "MatrixBandPart: lower diagonal count %s should be less than %s",
minLower, N);
Preconditions.checkArgument(maxUpper > -M && maxUpper < M, "MatrixBandPart: upper diagonal count %s should be less than %s.",
maxUpper, M);
addInputArgument(input);
addIArgument(minLower, maxUpper);
}
public MatrixBandPart(@NonNull SameDiff sameDiff, @NonNull SDVariable input, SDVariable minLower, SDVariable maxUpper) {
super("", sameDiff, new SDVariable[]{input, minLower, maxUpper});
}
@Override
public String opName() {
return "matrix_band_part";
}
@Override
public String tensorflowName() {
return "MatrixBandPart";
}
@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));
}
}

66
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/custom/Polygamma.java Normal file
View File

@ -0,0 +1,66 @@
/* ******************************************************************************
* 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.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 class Polygamma extends DynamicCustomOp {
public Polygamma() {}
public Polygamma(@NonNull INDArray n, @NonNull INDArray x) {
Preconditions.checkArgument(n.shape() != x.shape(),
"Polygamma: n and x must have the same shapes");
addInputArgument(n,x);
}
public Polygamma(@NonNull INDArray n, @NonNull INDArray x, INDArray output) {
this(n,x);
if (output != null) {
addOutputArgument(output);
}
}
public Polygamma(@NonNull SameDiff sameDiff, @NonNull SDVariable n, @NonNull SDVariable x) {
super("", sameDiff, new SDVariable[]{n ,x});
}
@Override
public String opName() {
return "polygamma";
}
@Override
public String tensorflowName() {
return "Polygamma";
}
@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));
}
}

61
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/custom/RandomCrop.java Normal file
View File

@ -0,0 +1,61 @@
/* ******************************************************************************
* 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.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.api.rng.Random;
import java.util.ArrayList;
import java.util.Collections;
import java.util.List;
public class RandomCrop extends DynamicCustomOp {
public RandomCrop() {}
public RandomCrop(@NonNull INDArray input, @NonNull INDArray shape) {
Preconditions.checkArgument(shape.isVector(),"RandomCrop:Shape tensor should be a vector");
Preconditions.checkArgument(input.rank() == shape.length(), "RandomCrop:The length of the shape vector is not match input rank");
addInputArgument(input, shape);
}
public RandomCrop(@NonNull SameDiff sameDiff, @NonNull SDVariable input, @NonNull SDVariable shape) {
super("", sameDiff, new SDVariable[]{input, shape});
}
@Override
public String opName() {
return "random_crop";
}
@Override
public String tensorflowName() {
return "RandomCrop";
}
@Override
public List<DataType> calculateOutputDataTypes(List<DataType> inputDataTypes){
Preconditions.checkState(inputDataTypes != null /*&& inputDataTypes.size() == 4*/,
"Expected 4 input datatypes for %s, got %s", getClass(), inputDataTypes);
return Collections.singletonList(DataType.FLOAT); //TF import: always returns float32...
}
}

64
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/custom/Roll.java Normal file
View File

@ -0,0 +1,64 @@
/* ******************************************************************************
* 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.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 class Roll extends DynamicCustomOp {
public Roll() {}
public Roll(@NonNull INDArray input, @NonNull INDArray axes, @NonNull INDArray shifts) {
Preconditions.checkArgument(axes.rank() == shifts.rank(), "Roll: shifts and axes should be the same rank");
Preconditions.checkArgument(axes.length() == shifts.length(), "Roll: shifts and axes should be the same length");
addInputArgument(input, axes, shifts);
}
public Roll(@NonNull INDArray input, int shift) {
addInputArgument(input);
addIArgument(shift);
}
public Roll(@NonNull SameDiff sameDiff, @NonNull SDVariable input, @NonNull SDVariable shift) {
super("", sameDiff, new SDVariable[]{input,shift});
}
@Override
public String opName() {
return "roll";
}
@Override
public String tensorflowName() {
return "Roll";
}
@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));
}
}

64
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/custom/ToggleBits.java Normal file
View File

@ -0,0 +1,64 @@
/* ******************************************************************************
* 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.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 class ToggleBits extends DynamicCustomOp {
public ToggleBits() {}
public ToggleBits(@NonNull INDArray input, INDArray output) {
this(input);
if (output != null) {
addOutputArgument(output);
}
}
public ToggleBits(@NonNull INDArray input) {
addInputArgument(input);
}
public ToggleBits(@NonNull SameDiff sameDiff, @NonNull SDVariable input) {
super("", sameDiff, new SDVariable[]{input});
}
@Override
public String opName() {
return "toggle_bits";
}
@Override
public String tensorflowName() {
return "Invert";
}
@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));
}
}

9
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/image/NonMaxSuppression.java
View File

@ -21,6 +21,7 @@ import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff; import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.imports.NoOpNameFoundException; import org.nd4j.imports.NoOpNameFoundException;
import org.nd4j.linalg.api.buffer.DataType; 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.api.ops.DynamicCustomOp;
import org.nd4j.linalg.api.ops.Op; import org.nd4j.linalg.api.ops.Op;
@ -41,6 +42,12 @@ public class NonMaxSuppression extends DynamicCustomOp {
super(null, sameDiff, new SDVariable[]{boxes, scores, maxOutSize, iouThreshold, scoreThreshold}, false); super(null, sameDiff, new SDVariable[]{boxes, scores, maxOutSize, iouThreshold, scoreThreshold}, false);
} }
public NonMaxSuppression(INDArray boxes, INDArray scores, int maxOutSize, double iouThreshold, double scoreThreshold) {
addInputArgument(boxes,scores);
addIArgument(maxOutSize);
addTArgument(iouThreshold, scoreThreshold);
}
@Override @Override
public String onnxName() { public String onnxName() {
throw new NoOpNameFoundException("No onnx name found for shape " + opName()); throw new NoOpNameFoundException("No onnx name found for shape " + opName());
@ -53,7 +60,7 @@ public class NonMaxSuppression extends DynamicCustomOp {
@Override @Override
public String[] tensorflowNames() { public String[] tensorflowNames() {
return new String[]{"NonMaxSuppression", "NonMaxSuppressionV2"}; return new String[]{"NonMaxSuppression", "NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV4"};
} }
@Override @Override

284
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/MaxPoolWithArgmax.java Normal file
View File

@ -0,0 +1,284 @@
/*******************************************************************************
* 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.impl.layers.convolution;
import lombok.Builder;
import lombok.Getter;
import lombok.NonNull;
import lombok.extern.slf4j.Slf4j;
import lombok.val;
import onnx.Onnx;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.base.Preconditions;
import org.nd4j.imports.descriptors.properties.PropertyMapping;
import org.nd4j.imports.graphmapper.tf.TFGraphMapper;
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.api.ops.impl.layers.convolution.config.Pooling2DConfig;
import org.nd4j.linalg.util.ArrayUtil;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
import java.lang.reflect.Field;
import java.util.*;
@Slf4j
@Getter
public class MaxPoolWithArgmax extends DynamicCustomOp {
protected Pooling2DConfig config;
protected DataType outputType;
public MaxPoolWithArgmax() {
}
@Builder(builderMethodName = "sameDiffBuilder")
@SuppressWarnings("Used in lombok")
public MaxPoolWithArgmax(SameDiff sameDiff, SDVariable input, Pooling2DConfig config) {
super(null, sameDiff, new SDVariable[]{input}, false);
config.setType(Pooling2D.Pooling2DType.MAX);
this.config = config;
addArgs();
}
public MaxPoolWithArgmax(INDArray input, INDArray output,INDArray outArgMax, @NonNull Pooling2DConfig config){
super(null, new INDArray[]{input}, new INDArray[]{output, outArgMax});
config.setType(Pooling2D.Pooling2DType.MAX);
this.config = config;
addArgs();
}
@Override
public boolean isConfigProperties() {
return true;
}
@Override
public String configFieldName() {
return "config";
}
@Override
public Map<String, Object> propertiesForFunction() {
if(config == null && iArguments.size() > 0){
//Perhaps loaded from FlatBuffers - hence we have IArgs but not Config object
config = Pooling2DConfig.builder()
.kH(iArguments.get(0))
.kW(iArguments.get(1))
.sH(iArguments.get(2))
.sW(iArguments.get(3))
.pH(iArguments.get(4))
.pW(iArguments.get(5))
.dH(iArguments.get(6))
.dW(iArguments.get(7))
.isSameMode(iArguments.get(8) == 1)
.extra(iArguments.get(9))
.isNHWC(iArguments.get(10) == 1)
.type(Pooling2D.Pooling2DType.MAX)
.build();
}
return config.toProperties();
}
private void addArgs() {
addIArgument(config.getKH(),
config.getKW(),
config.getSH(),
config.getSW(),
config.getPH(),
config.getPW(),
config.getDH(),
config.getDW(),
ArrayUtil.fromBoolean(config.isSameMode()),
(int) config.getExtra(),
ArrayUtil.fromBoolean(config.isNHWC())
);
}
public String getPoolingPrefix() {
return "max";
}
@Override
public List<SDVariable> doDiff(List<SDVariable> f1) {
List<SDVariable> ret = new ArrayList<>();
List<SDVariable> inputs = new ArrayList<>();
inputs.addAll(Arrays.asList(args()));
inputs.add(f1.get(0));
Pooling2DDerivative pooling2DDerivative = Pooling2DDerivative.derivativeBuilder()
.inputs(inputs.toArray(new SDVariable[inputs.size()]))
.sameDiff(sameDiff)
.config(config)
.build();
ret.addAll(Arrays.asList(pooling2DDerivative.outputVariables()));
return ret;
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String, AttrValue> attributesForNode, GraphDef graph) {
val aStrides = nodeDef.getAttrOrThrow("strides");
val tfStrides = aStrides.getList().getIList();
val aKernels = nodeDef.getAttrOrThrow("ksize");
val tfKernels = aKernels.getList().getIList();
int sH = 0;
int sW = 0;
int pH = 0;
int pW = 0;
int kH = 0;
int kW = 0;
val aPadding = nodeDef.getAttrOrThrow("padding");
val padding = aPadding.getList().getIList();
val paddingMode = aPadding.getS().toStringUtf8().replaceAll("\"", "");
boolean isSameMode = paddingMode.equalsIgnoreCase("SAME");
String data_format = "nhwc";
if (nodeDef.containsAttr("data_format")) {
val attr = nodeDef.getAttrOrThrow("data_format");
data_format = attr.getS().toStringUtf8().toLowerCase();
}
if (data_format.equalsIgnoreCase("nhwc")) {
sH = tfStrides.get(1).intValue();
sW = tfStrides.get(2).intValue();
kH = tfKernels.get(1).intValue();
kW = tfKernels.get(2).intValue();
pH = padding.size() > 0 ? padding.get(1).intValue() : 0;
pW = padding.size() > 0 ? padding.get(2).intValue() : 0;
} else {
sH = tfStrides.get(2).intValue();
sW = tfStrides.get(3).intValue();
kH = tfKernels.get(2).intValue();
kW = tfKernels.get(3).intValue();
pH = padding.size() > 0 ? padding.get(2).intValue() : 0;
pW = padding.size() > 0 ? padding.get(3).intValue() : 0;
}
Pooling2DConfig pooling2DConfig = Pooling2DConfig.builder()
.sH(sH)
.sW(sW)
.type(Pooling2D.Pooling2DType.MAX)
.isSameMode(isSameMode)
.kH(kH)
.kW(kW)
.pH(pH)
.pW(pW)
.isNHWC(data_format.equalsIgnoreCase("nhwc"))
.extra(1.0) // averaging only for non-padded values
.build();
this.config = pooling2DConfig;
addArgs();
if(attributesForNode.containsKey("argmax")) {
outputType = TFGraphMapper.convertType(attributesForNode.get("argmax").getType());
} else {
outputType = DataType.UINT32;
}
}
@Override
public Map<String, Map<String, PropertyMapping>> mappingsForFunction() {
Map<String, Map<String, PropertyMapping>> ret = new HashMap<>();
Map<String, PropertyMapping> map = new HashMap<>();
val strideMapping = PropertyMapping.builder()
.tfAttrName("strides")
.onnxAttrName("strides")
.propertyNames(new String[]{"sW", "sH"})
.build();
val paddingMapping = PropertyMapping.builder()
.onnxAttrName("padding")
.tfAttrName("padding")
.propertyNames(new String[]{"pH", "pW"})
.build();
val kernelMapping = PropertyMapping.builder()
.propertyNames(new String[]{"kH", "kW"})
.tfInputPosition(1)
.onnxAttrName("ksize")
.build();
val dilationMapping = PropertyMapping.builder()
.onnxAttrName("dilations")
.propertyNames(new String[]{"dW", "dH"})
.tfAttrName("rates")
.build();
//data_format
val dataFormatMapping = PropertyMapping.builder()
.propertyNames(new String[]{"isNHWC"})
.tfAttrName("data_format")
.build();
map.put("sW", strideMapping);
map.put("sH", strideMapping);
map.put("kH", kernelMapping);
map.put("kW", kernelMapping);
map.put("dW", dilationMapping);
map.put("dH", dilationMapping);
map.put("pH", paddingMapping);
map.put("pW", paddingMapping);
map.put("isNHWC", dataFormatMapping);
ret.put(onnxName(), map);
ret.put(tensorflowName(), map);
return ret;
}
@Override
public String opName() {
return "max_pool_with_argmax";
}
@Override
public String onnxName() {
return "MaxPoolWithArgmax";
}
@Override
public String tensorflowName() {
return "MaxPoolWithArgmax";
}
@Override
public List<DataType> calculateOutputDataTypes(List<DataType> inputDataTypes){
Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() == 1, "Expected 1 input data type for %s, got %s", getClass(), inputDataTypes);
List<DataType> result = new ArrayList<>();
result.add(inputDataTypes.get(0));
result.add(outputType == null ? DataType.UINT32 : outputType);
return result;
}
}

4
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/MaxPooling2D.java
View File

@ -293,8 +293,8 @@ public class MaxPooling2D extends DynamicCustomOp {
} }
@Override @Override
public String tensorflowName() { public String[] tensorflowNames() {
return "MaxPool"; return new String[]{"MaxPool","MaxPoolV2"};
} }
@Override @Override

2
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/clip/ClipByValue.java
View File

@ -68,7 +68,7 @@ public class ClipByValue extends DynamicCustomOp {
@Override @Override
public String opName() { public String opName() {
return "clipbyvalue"; return "ClipByValue";
} }
@Override @Override

8
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/custom/RShiftBits.java
View File

@ -53,15 +53,9 @@ public class RShiftBits extends BaseDynamicTransformOp {
return "rshift_bits"; return "rshift_bits";
} }
@Override
public String onnxName() {
throw new NoOpNameFoundException("No onnx op opName found for " + opName());
}
@Override @Override
public String tensorflowName() { public String tensorflowName() {
throw new NoOpNameFoundException("No TensorFlow op opName found for " + opName()); return "RightShift";
} }

8
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/custom/ShiftBits.java
View File

@ -53,15 +53,9 @@ public class ShiftBits extends BaseDynamicTransformOp {
return "shift_bits"; return "shift_bits";
} }
@Override
public String onnxName() {
throw new NoOpNameFoundException("No onnx op opName found for " + opName());
}
@Override @Override
public String tensorflowName() { public String tensorflowName() {
throw new NoOpNameFoundException("No TensorFlow op opName found for " + opName()); return "LeftShift";
} }

4
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/custom/UniqueWithCounts.java
View File

@ -46,8 +46,8 @@ public class UniqueWithCounts extends DynamicCustomOp {
} }
@Override @Override
public String tensorflowName() { public String[] tensorflowNames() {
return "UniqueWithCounts"; return new String[]{"UniqueWithCounts","UniqueWithCountsV2"};
} }
@Override @Override

4
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/pairwise/arithmetic/CopyOp.java
View File

@ -77,8 +77,8 @@ public class CopyOp extends BaseTransformSameOp {
} }
@Override @Override
public String tensorflowName() { public String[] tensorflowNames() {
throw new NoOpNameFoundException("No tensorflow op opName found for " + opName()); return new String[]{"Copy","DeepCopy","CopyHost"};
} }
@Override @Override

2
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/pairwise/arithmetic/ModOp.java
View File

@ -57,7 +57,7 @@ public class ModOp extends BaseDynamicTransformOp {
@Override @Override
public String tensorflowName() { public String tensorflowName() {
return "mod"; return "Mod";
} }
@Override @Override

6
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/pairwise/bool/Not.java
View File

@ -67,12 +67,6 @@ public class Not extends BaseTransformBoolOp {
return "Not"; return "Not";
} }
@Override
public String tensorflowName() {
throw new NoOpNameFoundException("Tensorflow name not found for " + opName());
//return "Not";
}
@Override @Override
public List<SDVariable> doDiff(List<SDVariable> f1) { public List<SDVariable> doDiff(List<SDVariable> f1) {
return Collections.singletonList(f().zerosLike(arg())); return Collections.singletonList(f().zerosLike(arg()));

13
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/strict/GELU.java
View File

@ -59,19 +59,6 @@ public class GELU extends BaseTransformStrictOp {
return "gelu"; return "gelu";
} }
@Override
public String onnxName() {
throw new NoOpNameFoundException("No onnx op opName found for " + opName());
}
@Override
public String tensorflowName()
{
throw new NoOpNameFoundException("No tensorflow op opName found for " + opName());
//return "GELU";
}
@Override @Override
public List<SDVariable> doDiff(List<SDVariable> i_v) { public List<SDVariable> doDiff(List<SDVariable> i_v) {
SDVariable ret = f().geluDerivative(arg(), false).mul(i_v.get(0)); SDVariable ret = f().geluDerivative(arg(), false).mul(i_v.get(0));

14
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/random/custom/DistributionUniform.java
View File

@ -24,6 +24,7 @@ import org.nd4j.imports.graphmapper.tf.TFGraphMapper;
import org.nd4j.linalg.api.buffer.DataType; import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray; import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.ops.DynamicCustomOp; import org.nd4j.linalg.api.ops.DynamicCustomOp;
import org.nd4j.linalg.exception.ND4JIllegalStateException;
import org.tensorflow.framework.AttrValue; import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef; import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef; import org.tensorflow.framework.NodeDef;
@ -71,7 +72,12 @@ public class DistributionUniform extends DynamicCustomOp {
@Override @Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String, AttrValue> attributesForNode, GraphDef graph) { public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String, AttrValue> attributesForNode, GraphDef graph) {
AttrValue v = attributesForNode.get("dtype"); AttrValue vDtype = attributesForNode.get("dtype");
AttrValue vTout = attributesForNode.get("Tout");
if (vDtype == null && vTout == null) {
throw new ND4JIllegalStateException("Unable to find output data type for node " + nodeDef.getName());
}
AttrValue v = vDtype == null ? vTout : vDtype;
dataType = TFGraphMapper.convertType(v.getType()); dataType = TFGraphMapper.convertType(v.getType());
addIArgument(dataType.toInt()); addIArgument(dataType.toInt());
addTArgument(0.0, 1.0); //TF version is hardcoded 0 to 1 addTArgument(0.0, 1.0); //TF version is hardcoded 0 to 1
@ -92,8 +98,8 @@ public class DistributionUniform extends DynamicCustomOp {
} }
@Override @Override
public String tensorflowName() { public String[] tensorflowNames() {
return "RandomUniform"; return new String[]{"RandomUniform","RandomUniformInt"};
} }
@Override @Override
@ -103,7 +109,7 @@ public class DistributionUniform extends DynamicCustomOp {
@Override @Override
public List<DataType> calculateOutputDataTypes(List<DataType> inputDataTypes){ public List<DataType> calculateOutputDataTypes(List<DataType> inputDataTypes){
Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() == 1, "Expected exactly 1 input datatype for %s, got %s", getClass(), inputDataTypes); Preconditions.checkState(inputDataTypes != null /*&& inputDataTypes.size() == 1*/, "Expected input datatypes for %s, got %s", getClass(), inputDataTypes);
//Input data type specifies the shape //Input data type specifies the shape
if(dataType != null){ if(dataType != null){
return Collections.singletonList(dataType); return Collections.singletonList(dataType);

12
nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/random/impl/DropOut.java
View File

@ -65,18 +65,6 @@ public class DropOut extends BaseRandomOp {
return "dropout"; return "dropout";
} }
@Override
public String onnxName() {
throw new NoOpNameFoundException("No ONNX op name found for: " + getClass().getName());
}
@Override
public String tensorflowName() {
throw new NoOpNameFoundException("No tensorflow op name found for: " + getClass().getName());
//return opName();
}
@Override @Override
public Type opType() { public Type opType() {
return Type.RANDOM ; return Type.RANDOM ;

29
nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/autodiff/opvalidation/LayerOpValidation.java
View File

@ -736,6 +736,35 @@ public class LayerOpValidation extends BaseOpValidation {
// sd.execBackwards(); // TODO: test failing here // sd.execBackwards(); // TODO: test failing here
} }
@Test
public void testMaxPoolingArgMax() {
Nd4j.getRandom().setSeed(12345);
int nIn = 3;
int kH = 2;
int kW = 2;
int mb = 3;
int imgH = 8;
int imgW = 8;
SameDiff sd = SameDiff.create();
INDArray inArr = Nd4j.rand(new int[]{mb, nIn, imgH, imgW});
SDVariable in = sd.var("in", inArr);
Pooling2DConfig pooling2DConfig = Pooling2DConfig.builder()
.kH(kH).kW(kW)
.pH(0).pW(0)
.sH(1).sW(1)
.dH(1).dW(1)
.isSameMode(true)
.build();
SDVariable[] results = sd.nn().maxPoolWithArgmax(new String[]{"",""}, in, pooling2DConfig);
assertArrayEquals(inArr.shape(), results[0].eval().shape());
assertArrayEquals(inArr.shape(), results[1].eval().shape());
}
@Test @Test
public void testMaxPooling2dBasic() { public void testMaxPooling2dBasic() {
Nd4j.getRandom().setSeed(12345); Nd4j.getRandom().setSeed(12345);

30
nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/imports/TFGraphs/TFGraphTestAllSameDiff.java
View File

@ -76,8 +76,6 @@ public class TFGraphTestAllSameDiff { //Note: Can't extend BaseNd4jTest here a
"adjust_contrast/.*", "adjust_contrast/.*",
//Failing 2019/09/11 - https://github.com/eclipse/deeplearning4j/issues/7965 //Failing 2019/09/11 - https://github.com/eclipse/deeplearning4j/issues/7965
"bincount/.*", "bincount/.*",
// Failing 2019/11/15 https://github.com/eclipse/deeplearning4j/issues/8400
"bitcast/.*",
// Failing 2019/11/14 https://github.com/eclipse/deeplearning4j/issues/8393 // Failing 2019/11/14 https://github.com/eclipse/deeplearning4j/issues/8393
"is_strictly_increasing/emptyArrayTest/.*", "is_strictly_increasing/emptyArrayTest/.*",
@ -116,20 +114,32 @@ public class TFGraphTestAllSameDiff { //Note: Can't extend BaseNd4jTest here a
// 2019/11/15 - missing dtype argument in nd4j, tests are useless https://github.com/eclipse/deeplearning4j/issues/8398 // 2019/11/15 - missing dtype argument in nd4j, tests are useless https://github.com/eclipse/deeplearning4j/issues/8398
"zeros_like/rank2_float32_dtype_int.*", "zeros_like/rank2_float32_dtype_int.*",
// 2019/11/15 - failure https://github.com/eclipse/deeplearning4j/issues/8399
"crop_and_resize.*",
// 2019/11/15 - failure https://github.com/eclipse/deeplearning4j/issues/8401
"draw_bounding_boxes.*",
// 2019/11/15 - failure https://github.com/eclipse/deeplearning4j/issues/8402 // 2019/11/15 - failure https://github.com/eclipse/deeplearning4j/issues/8402
"fake_quant/min_max_args_per_channel.*", "fake_quant/min_max_args_per_channel.*",
// 2019/11/15 - failure https://github.com/eclipse/deeplearning4j/issues/8403 // 2019/11/15 - failure https://github.com/eclipse/deeplearning4j/issues/8403
"resize_bilinear/int32.*", "resize_bilinear/int32.*",
// Suggesting TF 1.15 bug - see https://github.com/eclipse/deeplearning4j/issues/8449 // Suggesting TF 1.15 bug
"non_max_suppression_v2/float16.*" "non_max_suppression_v2/float16.*",
// 11.26.2019 failing - https://github.com/eclipse/deeplearning4j/issues/8450
"betainc.*",
// 11.26.2019 failing - https://github.com/eclipse/deeplearning4j/issues/8452
"polygamma.*",
// 11.26.2019 failing - https://github.com/eclipse/deeplearning4j/issues/8453
"roll/.*",
// 11.26.2019 failing https://github.com/eclipse/deeplearning4j/issues/8455
"matrix_band_part/.*",
// 11.28.2019 failing https://github.com/eclipse/deeplearning4j/issues/8458
"adjust_hue/.*",
// 11.28.2019 failing https://github.com/eclipse/deeplearning4j/issues/8459
"adjust_saturation/.*"
}; };
/* As per TFGraphTestList.printArraysDebugging - this field defines a set of regexes for test cases that should have /* As per TFGraphTestList.printArraysDebugging - this field defines a set of regexes for test cases that should have

234
nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/custom/CustomOpsTests.java
View File

@ -32,7 +32,10 @@ import org.nd4j.linalg.api.ops.executioner.OpExecutioner;
import org.nd4j.linalg.api.ops.executioner.OpStatus; import org.nd4j.linalg.api.ops.executioner.OpStatus;
import org.nd4j.linalg.api.ops.impl.controlflow.Where; import org.nd4j.linalg.api.ops.impl.controlflow.Where;
import org.nd4j.linalg.api.ops.impl.image.CropAndResize; import org.nd4j.linalg.api.ops.impl.image.CropAndResize;
import org.nd4j.linalg.api.ops.impl.image.NonMaxSuppression;
import org.nd4j.linalg.api.ops.impl.image.ResizeBilinear; import org.nd4j.linalg.api.ops.impl.image.ResizeBilinear;
import org.nd4j.linalg.api.ops.impl.layers.convolution.MaxPoolWithArgmax;
import org.nd4j.linalg.api.ops.impl.layers.convolution.config.Pooling2DConfig;
import org.nd4j.linalg.api.ops.impl.reduce.MmulBp; import org.nd4j.linalg.api.ops.impl.reduce.MmulBp;
import org.nd4j.linalg.api.ops.impl.shape.Create; import org.nd4j.linalg.api.ops.impl.shape.Create;
import org.nd4j.linalg.api.ops.impl.transforms.any.IsMax; import org.nd4j.linalg.api.ops.impl.transforms.any.IsMax;
@ -53,6 +56,7 @@ import java.util.ArrayList;
import java.util.Arrays; import java.util.Arrays;
import java.util.List; import java.util.List;
import static java.lang.Float.NaN;
import static org.junit.Assert.*; import static org.junit.Assert.*;
/** /**
@ -867,6 +871,26 @@ public class CustomOpsTests extends BaseNd4jTest {
assertArrayEquals(new long[]{1,10}, lsd.get(0).getShape()); assertArrayEquals(new long[]{1,10}, lsd.get(0).getShape());
} }
@Test
public void testAdjustSaturation() {
INDArray in = Nd4j.createFromArray(new double[]{50,100,78, 118.5,220,112.5,190,163.5,230, 255,128.5,134}).reshape(2,2,3);
INDArray out = Nd4j.create(in.shape());
INDArray expected = Nd4j.createFromArray(new double[]{0,100,56, 17,220,5, 150,97,230, 255,2,13}).reshape(2,2,3);
Nd4j.exec(new AdjustSaturation(in, 2.0, out));
assertEquals(expected, out);
}
@Test
public void testAdjustHue() {
INDArray in = Nd4j.createFromArray(new double[]{0,100,56, 17,220,5, 150,97,230, 255,2,13}).reshape(2,2,3);
INDArray out = Nd4j.create(in.shape());
INDArray expected = Nd4j.createFromArray(new double[]{100,0,44, 208,5,220, 177,230,97, 2,255,244}).reshape(2,2,3);
Nd4j.exec(new AdjustHue(in, 0.5, out));
assertEquals(expected, out);
}
@Test @Test
public void testBitCast() { public void testBitCast() {
INDArray in = Nd4j.linspace(DataType.FLOAT, 1.0f, 1.0f, 8).reshape(2,2,2); INDArray in = Nd4j.linspace(DataType.FLOAT, 1.0f, 1.0f, 8).reshape(2,2,2);
@ -1088,6 +1112,216 @@ public class CustomOpsTests extends BaseNd4jTest {
assertArrayEquals(new long[]{1,10, 2}, lsd.get(0).getShape()); assertArrayEquals(new long[]{1,10, 2}, lsd.get(0).getShape());
} }
@Test
public void testBetaInc() {
Nd4j.getRandom().setSeed(10);
INDArray a = Nd4j.linspace(DataType.BFLOAT16, 0.1, 0.1, 9).reshape(3,3);
INDArray b = Nd4j.linspace(DataType.BFLOAT16, 0.1, 0.1, 9).reshape(3,3);
INDArray x = Nd4j.linspace(DataType.BFLOAT16, 0.1, 0.1, 9).reshape(3,3);
INDArray expected = Nd4j.createFromArray(new float[]{0.4121f, 0.3926f, 0.4082f,
0.4414f, 0.5000f, 0.5703f,
0.6562f, 0.7656f, 0.8828f}).reshape(3,3);
BetaInc op = new BetaInc(a,b,x);
INDArray[] out = Nd4j.exec(op);
assertArrayEquals(expected.shape(), out[0].shape());
for (int i = 0; i < 3; ++i)
assertArrayEquals(expected.toDoubleMatrix()[i], out[0].toDoubleMatrix()[i], 1e-4);
}
@Test
public void testFusedBatchNorm() {
INDArray x = Nd4j.linspace(DataType.DOUBLE, 1.0, 1.0, 2*2*3*4).reshape(2,2,3,4);
INDArray scale = Nd4j.create(DataType.DOUBLE, 4);
scale.assign(0.5);
INDArray offset = Nd4j.create(DataType.DOUBLE, 4);
offset.assign(2.0);
INDArray y = Nd4j.createUninitialized(DataType.DOUBLE, x.shape());
INDArray batchMean = Nd4j.create(4);
INDArray batchVar = Nd4j.create(4);
FusedBatchNorm op = new FusedBatchNorm(x,scale,offset,0,1,
y, batchMean, batchVar);
INDArray expectedY = Nd4j.createFromArray(new double[]{1.20337462, 1.20337462, 1.20337462,
1.20337462, 1.34821558, 1.34821558, 1.34821558, 1.34821558, 1.49305654, 1.49305654,
1.49305654, 1.49305654, 1.63789749, 1.63789749, 1.63789749, 1.63789749, 1.78273857,
1.78273857, 1.78273857, 1.78273857, 1.92757952, 1.92757952, 1.92757952, 1.92757952,
2.0724206 , 2.0724206 , 2.0724206 , 2.0724206 , 2.21726155, 2.21726155, 2.21726155,
2.21726155, 2.36210251, 2.36210251, 2.36210251, 2.36210251, 2.50694346, 2.50694346,
2.50694346, 2.50694346, 2.65178442, 2.65178442, 2.65178442, 2.65178442, 2.79662538,
2.79662538, 2.79662538, 2.79662538}).reshape(x.shape());
INDArray expectedBatchMean = Nd4j.createFromArray(new double[]{23., 24., 25., 26.});
INDArray expectedBatchVar = Nd4j.createFromArray(new double[]{208.00001526, 208.00001526, 208.00001526, 208.00001526});
Nd4j.exec(op);
assertArrayEquals(expectedY.shape(), y.shape());
assertArrayEquals(expectedBatchMean.shape(), batchMean.shape());
assertArrayEquals(expectedBatchVar.shape(), batchVar.shape());
}
@Test
public void testMatrixBandPart() {
INDArray x = Nd4j.linspace(DataType.DOUBLE, 1.0, 1.0, 2*3*3).reshape(2,3,3);
val op = new MatrixBandPart(x,1,1);
INDArray expected = Nd4j.linspace(DataType.DOUBLE, 1.0, 1.0, 2*3*3).reshape(2,3,3);
/*expected.putScalar(0, 0, 2, 0.);
expected.putScalar(1, 0, 2, 0.);
expected.putScalar(0, 2, 0, 0.);
expected.putScalar(1, 2, 0, 0.);*/
INDArray[] out = Nd4j.exec(op);
assertEquals(expected, x);
}
@Test
public void testPolygamma() {
INDArray n = Nd4j.linspace(DataType.FLOAT, 1.0, 1.0, 9).reshape(3,3);
INDArray x = Nd4j.create(DataType.FLOAT, 3,3);
x.assign(0.5);
INDArray expected = Nd4j.createFromArray(new float[]{4.934802f, -16.828796f, 97.409088f, -771.474243f,
7691.113770f, -92203.460938f, 1290440.250000f, -20644900.000000f, 3.71595e+08f}).reshape(3,3);
INDArray output = Nd4j.create(DataType.FLOAT, expected.shape());
val op = new Polygamma(x,n,output);
Nd4j.exec(op);
assertEquals(expected, output);
}
@Test
public void testRandomCrop() {
INDArray x = Nd4j.createFromArray(new double[]{1.8, 2.5, 4., 9., 2.1, 2.4, 3., 9.,2.1, 2.1, 0.7, 0.1,3., 4.2, 2.2, 1. }).reshape(2,2,4);
INDArray shape = Nd4j.createFromArray(new int[] {1,2,3});
val op = new RandomCrop(x, shape);
INDArray[] res = Nd4j.exec(op);
}
@Test
public void testRoll() {
INDArray x = Nd4j.createFromArray(new double[]{ 11.11, 11.12, 11.21, 11.22, 11.31, 11.32, 11.41, 11.42, 12.11, 12.12, 12.21, 12.22, 12.31, 12.32, 12.41, 12.42,
21.11, 21.12, 21.21, 21.22, 21.31, 21.32, 21.41, 21.42, 22.11, 22.12, 22.21, 22.22, 22.31, 22.32, 22.41, 22.42}).
reshape(2,2,4,2);
INDArray expected = Nd4j.createFromArray(new double[]{ 22.21, 22.22, 22.31, 22.32, 22.41, 22.42, 11.11, 11.12, 11.21, 11.22, 11.31, 11.32, 11.41, 11.42,
12.11, 12.12, 12.21, 12.22, 12.31, 12.32, 12.41, 12.42, 21.11, 21.12, 21.21, 21.22, 21.31, 21.32,
21.41, 21.42, 22.11, 22.12
}).reshape(x.shape());
val op = new Roll(x, 6);
INDArray[] res = Nd4j.exec(op);
assertEquals(expected, res[0]);
}
@Test
public void testToggleBits() {
INDArray input = Nd4j.createFromArray(new int[]{2,2});
INDArray expected = Nd4j.createFromArray(new int[]{-3,-3});
ToggleBits op = new ToggleBits(input);
val result = Nd4j.exec(op);
assertEquals(expected, result[0]);
}
@Ignore("AS 11.28.2019 - https://github.com/eclipse/deeplearning4j/issues/8449")
@Test
public void testNonMaxSuppression() {
INDArray boxes = Nd4j.createFromArray(new float[] {0.8115f, 0.4121f, 0.0771f, 0.4863f,
0.7412f, 0.7607f, 0.1543f, 0.5479f,
0.8223f, 0.2246f, 0.0049f, 0.6465f}).reshape(3,4);
INDArray scores = Nd4j.createFromArray(new float[]{0.0029f, 0.8135f, 0.4873f});
val op = new NonMaxSuppression(boxes,scores,2,0.5,0.5);
val res = Nd4j.exec(op);
assertEquals(new long[]{1}, res[0].shape());
}
@Test
public void testMatrixBand() {
INDArray input = Nd4j.createFromArray(new float[]{0.7788f,0.8012f,0.7244f,0.2309f,
0.7271f,0.1804f,0.5056f,0.8925f,
0.5461f,0.9234f,0.0856f,0.7938f}).reshape(3,4);
MatrixBandPart op = new MatrixBandPart(input,1,-1);
List<LongShapeDescriptor> lsd = op.calculateOutputShape();
assertEquals(1, lsd.size());
}
@Ignore("Failed AS 11.26.2019 - https://github.com/eclipse/deeplearning4j/issues/8450")
@Test
public void testBetaInc1() {
INDArray a = Nd4j.createFromArray(new float[]{0.7788f, 0.8012f, 0.7244f, 0.2309f});
INDArray b = Nd4j.createFromArray(new float[]{0.7717f, 0.9281f, 0.9846f, 0.4838f});
INDArray c = Nd4j.createFromArray(new float[]{0.9441f, 0.5957f, 0.8669f, 0.3502f});
BetaInc op = new BetaInc(a,b,c);
INDArray[] ret = Nd4j.exec(op);
INDArray expected = Nd4j.createFromArray(new float[]{0.9122f, 0.6344f, 0.8983f, 0.6245f});
assertEquals(expected, ret[0]);
}
@Ignore("Failure AS 11.28.2019 - https://github.com/eclipse/deeplearning4j/issues/8452")
@Test
public void testPolygamma1() {
INDArray a = Nd4j.createFromArray(new float[]{0.7788f, 0.8012f, 0.7244f, 0.2309f,
0.7271f, 0.1804f, 0.5056f, 0.8925f,
0.5461f, 0.9234f, 0.0856f, 0.7938f}).reshape(3,4);
INDArray b = Nd4j.createFromArray(new float[]{0.7717f, 0.9281f, 0.9846f, 0.4838f,
0.6433f, 0.6041f, 0.6501f, 0.7612f,
0.7605f, 0.3948f, 0.9493f, 0.8600f}).reshape(3,4);
INDArray expected = Nd4j.createFromArray(new float[]{NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN,NaN, }).reshape(3,4);
Polygamma op = new Polygamma(a,b);
INDArray[] ret = Nd4j.exec(op);
assertEquals(expected, ret[0]);
}
@Ignore("Failure AS 11.28.2019 - https://github.com/eclipse/deeplearning4j/issues/8453")
@Test
public void testRoll1() {
INDArray a = Nd4j.createFromArray(new float[]{0.7788f, 0.8012f, 0.7244f, 0.2309f});
Roll op = new Roll(a,Nd4j.scalar(2),Nd4j.scalar(0));
INDArray[] ret = Nd4j.exec(op);
INDArray expected = Nd4j.createFromArray(new float[]{0.7244f, 0.2309f, 0.7788f, 0.8012f});
assertEquals(expected, ret[0]);
}
@Test
public void testAdjustHueShape(){
INDArray image = Nd4j.createFromArray(new float[]{0.7788f, 0.8012f, 0.7244f,
0.2309f, 0.7271f, 0.1804f, 0.5056f, 0.8925f, 0.5461f,
0.9234f, 0.0856f, 0.7938f, 0.6591f, 0.5555f, 0.1596f,
0.3087f, 0.1548f, 0.4695f, 0.9939f, 0.6113f, 0.6765f,
0.1800f, 0.6750f, 0.2246f, 0.0509f, 0.4601f, 0.8284f,
0.2354f, 0.9752f, 0.8361f, 0.2585f, 0.4189f, 0.7028f,
0.7679f, 0.5373f, 0.7234f, 0.2690f, 0.0062f, 0.0327f,
0.0644f, 0.8428f, 0.7494f, 0.0755f, 0.6245f, 0.3491f,
0.5793f, 0.5730f, 0.1822f, 0.6420f, 0.9143f, 0.3019f,
0.3574f, 0.1704f, 0.8395f, 0.5468f, 0.0744f, 0.9011f,
0.6574f, 0.4124f, 0.2445f, 0.4248f, 0.5219f, 0.6952f,
0.4900f, 0.2158f, 0.9549f, 0.1386f, 0.1544f, 0.5365f,
0.0134f, 0.4163f, 0.1456f, 0.4109f, 0.2484f, 0.3330f,
0.2974f, 0.6636f, 0.3808f, 0.8664f, 0.1896f, 0.7530f,
0.7215f, 0.6612f, 0.7270f, 0.5704f, 0.2666f, 0.7453f,
0.0444f, 0.3024f, 0.4850f, 0.7982f, 0.0965f, 0.7843f,
0.5075f, 0.0844f, 0.8370f, 0.6103f, 0.4604f, 0.6087f,
0.8594f, 0.4599f, 0.6714f, 0.2744f, 0.1981f, 0.4143f,
0.7821f, 0.3505f, 0.5040f, 0.1180f, 0.8307f, 0.1817f,
0.8442f, 0.5074f, 0.4471f, 0.5105f, 0.6666f, 0.2576f,
0.2341f, 0.6801f, 0.2652f, 0.5394f, 0.4690f, 0.6146f,
0.1210f, 0.2576f, 0.0769f, 0.4643f, 0.1628f, 0.2026f,
0.3774f, 0.0506f, 0.3462f, 0.5720f, 0.0838f, 0.4228f,
0.0588f, 0.5362f, 0.4756f, 0.2530f, 0.1778f, 0.0751f,
0.8977f, 0.3648f, 0.3065f, 0.4739f, 0.7014f, 0.4473f,
0.5171f, 0.1744f, 0.3487f, 0.7759f, 0.9491f, 0.2072f,
0.2182f, 0.6520f, 0.3092f, 0.9545f, 0.1881f, 0.9579f,
0.1785f, 0.9636f, 0.4830f, 0.6569f, 0.3353f, 0.9997f,
0.5869f, 0.5747f, 0.0238f, 0.2943f, 0.5248f, 0.5879f,
0.7266f, 0.1965f, 0.9167f, 0.9726f, 0.9206f, 0.0519f,
0.2997f, 0.0039f, 0.7652f, 0.5498f, 0.3794f, 0.3791f,
0.3528f, 0.2873f, 0.8082f, 0.4732f, 0.4399f, 0.6606f,
0.5991f, 0.0034f, 0.4874f}).reshape(8,8,3);
AdjustHue op = new AdjustHue(image, 0.2f);
INDArray[] res = Nd4j.exec(op);
System.out.println(res[0]);
List<LongShapeDescriptor> lsd = op.calculateOutputShape();
assertEquals(1, lsd.size());
assertArrayEquals(new long[]{8, 8, 3}, lsd.get(0).getShape());
}
@Test @Test
public void testBitCastShape_3(){ public void testBitCastShape_3(){
val x = Nd4j.createFromArray(new int[]{1, 2, 3, 4, 5, 6, 7, 8}).reshape(1, 4, 2); val x = Nd4j.createFromArray(new int[]{1, 2, 3, 4, 5, 6, 7, 8}).reshape(1, 4, 2);