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org.nd4j.linalg.api.ops.BaseReduceOp Maven / Gradle / Ivy
/*
* ******************************************************************************
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* *
* * 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.
* *
* * See the NOTICE file distributed with this work for additional
* * information regarding copyright ownership.
* * 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;
import lombok.Getter;
import lombok.Setter;
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.autodiff.util.SameDiffUtils;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.shape.LongShapeDescriptor;
import org.nd4j.linalg.api.shape.Shape;
import org.nd4j.common.util.ArrayUtil;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
import java.util.List;
import java.util.Map;
@Slf4j
public abstract class BaseReduceOp extends BaseOp implements ReduceOp {
@Setter @Getter
protected boolean keepDims = false;
protected boolean isComplex = false;
@Setter @Getter
protected boolean isEmptyReduce = false;
public BaseReduceOp(SameDiff sameDiff,
SDVariable i_v,
int[] dimensions, boolean keepDims) {
super(sameDiff, null);
if (i_v != null) {
if(dimensions == null || dimensions.length < 1)
dimensions = new int[] {Integer.MAX_VALUE};
this.dimensions = dimensions;
SameDiffUtils.validateDifferentialFunctionSameDiff(sameDiff, i_v, this);
this.keepDims = keepDims;
this.xVertexId = i_v.name();
sameDiff.addArgsFor(new String[]{xVertexId},this);
} else {
throw new IllegalArgumentException("Input not null variable.");
}
defineDimensions(dimensions);
}
public BaseReduceOp(SameDiff sameDiff,
SDVariable i_v,
SDVariable i_v2,
int[] dimensions, boolean keepDims) {
super(sameDiff,null);
if (i_v != null) {
if(dimensions == null || dimensions.length < 1)
dimensions = new int[] {Integer.MAX_VALUE};
this.dimensions = dimensions;
this.xVertexId = i_v.name();
this.yVertexId = i_v2.name();
SameDiffUtils.validateDifferentialFunctionSameDiff(sameDiff, i_v, this);
SameDiffUtils.validateDifferentialFunctionSameDiff(sameDiff, i_v2, this);
this.keepDims = keepDims;
sameDiff.addArgsFor(new String[]{xVertexId,yVertexId},this);
} else {
throw new IllegalArgumentException("Input not null variable.");
}
defineDimensions(dimensions);
}
public BaseReduceOp(SameDiff sameDiff,
SDVariable i_v) {
this(sameDiff, i_v, null, false);
}
public BaseReduceOp(SameDiff sameDiff,
SDVariable i_v,
int[] dimensions) {
this(sameDiff,i_v,dimensions,false);
}
public BaseReduceOp(SameDiff sameDiff,
SDVariable i_v,
SDVariable i_v2,
int[] dimensions) {
this(sameDiff,i_v,i_v2,dimensions,false);
}
public BaseReduceOp() {}
public BaseReduceOp(INDArray x, INDArray y, INDArray z, boolean keepDims, int[] dimensions) {
super(x, y, z);
this.keepDims = keepDims;
this.dimensions = dimensions;
defineDimensions(dimensions);
}
public BaseReduceOp(INDArray x, int... dimensions) {
this(x, null, dimensions);
}
public BaseReduceOp(INDArray x, boolean keepDims, int... dimensions) {
this(x, null, dimensions);
this.keepDims = keepDims;
}
public BaseReduceOp(INDArray x, INDArray y, int... dimensions) {
this(x, y, null, dimensions);
}
public BaseReduceOp(INDArray x, INDArray y, INDArray z, int... dimensions) {
this(x, y, z, false, dimensions);
}
public BaseReduceOp(SameDiff sameDiff) {
this.sameDiff = sameDiff;
}
@Override
public INDArray noOp() {
if (z != null && x != z)
return z().assign(x);
else {
//Need to take into account shapes: for example, [1,3].sum(0) -> [3]
//Or [1,1,1,1].sum(0,2,3) -> [1]
if(keepDims){
return x().dup(x().ordering());
} else {
long[] shape = x.shape();
if(dimensions == null || Shape.isWholeArray(shape, dimensions)){
//Return scalar
return x.reshape().dup();
} else {
//Strip out size 1 dimensions
long[] outShape = ArrayUtil.removeIndex(shape, dimensions);
return x.dup('c').reshape('c', outShape);
}
}
}
}
@Override
public boolean isKeepDims() {
return keepDims;
}
public abstract List calculateOutputShape();
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
if (!attributesForNode.containsKey("axis") && !hasReductionIndices(nodeDef)) {
this.dimensions = new int[] { Integer.MAX_VALUE };
} //Otherwise: dimensions are dynamically set during execution in InferenceSession
if(attributesForNode.containsKey("keep_dims")) {
val keepDims = attributesForNode.get("keep_dims").getB();
this.keepDims = keepDims;
}
defineDimensions(this.dimensions);
}
protected boolean hasReductionIndices(NodeDef nodeDef) {
for(int i = 0; i < nodeDef.getInputCount(); i++) {
if(nodeDef.getInput(i).contains("reduction_indices")) {
return true;
}
}
return false;
}
@Override
public void initFromOnnx(Onnx.NodeProto node, SameDiff initWith, Map attributesForNode, Onnx.GraphProto graph) {
}
@Override
public boolean isComplexAccumulation() {
return isComplex;
}
@Override
public void setDimensions(int... dimensions) {
this.dimensions = dimensions;
defineDimensions(dimensions);
}
}
