Many resources are needed to download a project. Please understand that we have to compensate our server costs. Thank you in advance.
Project price only 1 $
You can buy this project and download/modify it how often you want.
org.nd4j.linalg.api.ops.BaseAccumulation Maven / Gradle / Ivy
/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* 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;
import com.google.common.primitives.Ints;
import lombok.Getter;
import lombok.extern.slf4j.Slf4j;
import lombok.val;
import onnx.OnnxProto3;
import org.apache.commons.lang3.ArrayUtils;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.imports.graphmapper.onnx.OnnxGraphMapper;
import org.nd4j.imports.graphmapper.tf.TFGraphMapper;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.shape.Shape;
import org.nd4j.linalg.exception.ND4JIllegalStateException;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
import java.util.ArrayList;
import java.util.Collections;
import java.util.List;
import java.util.Map;
/**
* Base class for accumulation, initiates the initial entry
* with respect to the child class. Also contains baseline fields
* for the over all field with accumulation.
*
* @author Adam Gibson
*/
@Slf4j
public abstract class BaseAccumulation extends BaseOp implements Accumulation {
protected Number finalResult;
protected boolean keepDims = false;
// flag for tf imported ops, shows that there's probably one more value appended in axis
@Getter
protected boolean newFormat = false;
protected boolean isComplex = false;
public BaseAccumulation(SameDiff sameDiff,
SDVariable i_v,
int[] dimensions,boolean keepDims) {
super(sameDiff,new Object[]{dimensions});
if (i_v != null) {
if(dimensions == null || dimensions.length < 1)
dimensions = new int[] {Integer.MAX_VALUE};
this.dimensions = dimensions;
f().validateDifferentialFunctionsameDiff(i_v);
this.keepDims = keepDims;
this.xVertexId = i_v.getVarName();
sameDiff.addArgsFor(new String[]{xVertexId},this);
if(Shape.isPlaceholderShape(i_v.getShape())) {
sameDiff.addPropertyToResolve(this,i_v.getVarName());
}
} else {
throw new IllegalArgumentException("Input not null variable.");
}
this.newFormat = true;
}
public BaseAccumulation(SameDiff sameDiff,
SDVariable i_v,
SDVariable i_v2,
int[] dimensions,boolean keepDims) {
super(sameDiff,new Object[]{dimensions});
if (i_v != null) {
if(dimensions == null || dimensions.length < 1)
dimensions = new int[] {Integer.MAX_VALUE};
this.dimensions = dimensions;
this.xVertexId = i_v.getVarName();
this.yVertexId = i_v2.getVarName();
f().validateDifferentialFunctionsameDiff(i_v);
f().validateDifferentialFunctionsameDiff(i_v2);
this.keepDims = keepDims;
sameDiff.addArgsFor(new String[]{xVertexId,yVertexId},this);
} else {
throw new IllegalArgumentException("Input not null variable.");
}
this.newFormat = true;
}
public BaseAccumulation(SameDiff sameDiff,
SDVariable i_v) {
this(sameDiff, i_v, null, false);
}
public BaseAccumulation(SameDiff sameDiff,
SDVariable i_v,
int[] dimensions) {
this(sameDiff,i_v,dimensions,false);
}
public BaseAccumulation(SameDiff sameDiff,
SDVariable i_v,
SDVariable i_v2,
int[] dimensions) {
this(sameDiff,i_v,i_v2,dimensions,false);
}
public BaseAccumulation() {}
/**
* Initialize with the given
* input, pairwise transform, result, and number
* of elements
*
* @param x the input
* @param y the pairwise transform
* @param z the result
* @param n the number of elements
*/
public BaseAccumulation(INDArray x, INDArray y, INDArray z, long n) {
super(x, y, z, n);
init();
}
public BaseAccumulation(INDArray x, INDArray y, INDArray z, boolean newFormat, boolean keepDims, int[] dimensions) {
super(x, y, z, x.lengthLong());
this.newFormat = newFormat;
this.keepDims = keepDims;
this.dimensions = dimensions;
init();
}
public BaseAccumulation(INDArray x, INDArray y, long n) {
this(x, y, x, n);
}
public BaseAccumulation(INDArray x) {
this(x, null, x, x.lengthLong());
}
public BaseAccumulation(INDArray x, INDArray y) {
this(x, y, x, x.lengthLong());
//if (y != null)
// LinAlgExceptions.assertSameLength(x, y);
}
public BaseAccumulation(SameDiff sameDiff) {
this.sameDiff = sameDiff;
}
private void init() {
if (z == null || x == z)
init(x, y, x, x.lengthLong());
else
init(x, y, z, x.lengthLong());
}
@Override
public INDArray noOp() {
if (z != null && x != z)
return z().assign(x);
else
return x().dup(x().ordering());
}
@Override
public boolean isKeepDims() {
return keepDims;
}
@Override
public List calculateOutputShape() {
if(args().length < 1) {
throw new ND4JIllegalStateException("Unable to compute input shape. No arguments found.");
}
long[] argShape = arg().getShape();
if (argShape == null && x() == null) {
return Collections.emptyList();
}
long[] inputShape = (argShape == null ? x().shape() : argShape);
List ret = new ArrayList<>(1);
val reducedShape = Shape.getReducedShape(inputShape,dimensions, isKeepDims(), newFormat);
ret.add(reducedShape);
return ret;
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
newFormat = true;
if (!attributesForNode.containsKey("axis") && !hasReductionIndices(nodeDef)) {
this.dimensions = new int[] { Integer.MAX_VALUE };
}
else if(hasReductionIndices(nodeDef)) {
NodeDef reductionNode = null;
for(int i = 0; i < graph.getNodeCount(); i++) {
if (graph.getNode(i).getName().equals(nodeDef.getName() + "/reduction_indices")) {
reductionNode = graph.getNode(i);
val arr = TFGraphMapper.getInstance().getNDArrayFromTensor("value", reductionNode, graph);
boolean keepAxis = nodeDef.getAttrOrThrow("keep_dims").getB();
// keepAxis = false by default
//int[] dimensions = ArrayUtils.add(arr.data().asInt(), 0, keepAxis ? 1 : 0);
int[] dimensions = arr.data().asInt();
this.dimensions = dimensions;
break;
}
}
if(reductionNode == null)
throw new ND4JIllegalStateException("No node found!");
}
else {
val dims = TFGraphMapper.getInstance().getNDArrayFromTensor("axis",nodeDef,graph).data().asInt();
this.dimensions = dims;
}
if(attributesForNode.containsKey("keep_dims")) {
val keepDims = attributesForNode.get("keep_dims").getB();
this.keepDims = keepDims;
}
}
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(OnnxProto3.NodeProto node, SameDiff initWith, Map attributesForNode, OnnxProto3.GraphProto graph) {
if (!attributesForNode.containsKey("axes")) {
this.dimensions = new int[] { Integer.MAX_VALUE };
}
else {
val map = OnnxGraphMapper.getInstance().getAttrMap(node);
val dims = Ints.toArray(map.get("axes").getIntsList());
this.dimensions = dims;
}
}
@Override
public void setFinalResult(double value) {
this.finalResult = value;
}
@Override
public Number getFinalResult() {
return finalResult;
}
@Override
public double zeroDouble() {
return 0;
}
@Override
public float zeroFloat() {
return 0;
}
@Override
public float zeroHalf() {
return 0;
}
@Override
public Type opType() {
return Type.REDUCE;
}
@Override
public boolean isComplexAccumulation() {
return isComplex;
}
}
