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.
/*******************************************************************************
* 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 lombok.Data;
import lombok.Getter;
import lombok.Setter;
import lombok.val;
import onnx.Onnx;
import org.nd4j.autodiff.functions.DifferentialFunction;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.common.base.Preconditions;
import org.nd4j.imports.NoOpNameFoundException;
import org.nd4j.linalg.api.buffer.DataBuffer;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.memory.MemoryWorkspace;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.shape.Shape;
import org.nd4j.linalg.exception.ND4JIllegalStateException;
import org.nd4j.linalg.factory.Nd4j;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
import java.nio.Buffer;
import java.util.Arrays;
import java.util.Map;
/**
* Base op. An op involves iterating over 2 buffers (x,y) up to n elements
* and applying a transform or accumulating a result.
*
* @author Adam Gibson
*/
@Data
public abstract class BaseOp extends DifferentialFunction implements Op {
protected INDArray x, y, z;
@Getter @Setter
protected String xVertexId,yVertexId,zVertexId;
// cached instance, for dataType checks
protected DataBuffer extraArgz;
protected INDArray dimensionz;
public BaseOp() {
}
public BaseOp(SameDiff sameDiff, boolean inPlace, Object[] extraArgs) {
super(sameDiff, inPlace, extraArgs);
}
public BaseOp(SameDiff sameDiff, Object[] extraArgs) {
super(sameDiff, extraArgs);
}
/**
* Specify an alternative result array
*
* @param x the input
* @param z the output array
*/
public BaseOp(INDArray x, INDArray z) {
this(x, null, z);
}
public BaseOp(INDArray x, INDArray y, INDArray z) {
super(false);
this.x = x;
this.y = y;
this.z = z;
}
/**
* An op for one ndarray
*
* @param x the ndarray
*/
public BaseOp(INDArray x) {
this(x, null, x);
}
public static Type getOpType(Op op) {
Type type = null;
if (op instanceof CustomOp) {
return Type.CUSTOM;
} else if (op instanceof TransformOp) {
if (op.y() == null) {
type = Type.TRANSFORM_FLOAT;
} else {
type = Op.Type.PAIRWISE;
}
} else if (op instanceof ReduceOp) {
if (op.y() == null)
type = ((ReduceOp) op).getOpType();
else
type = Op.Type.REDUCE3;
} else if (op instanceof ScalarOp) {
type = Op.Type.SCALAR;
} else if (op instanceof BroadcastOp) {
type = Op.Type.BROADCAST;
} else if (op instanceof IndexAccumulation) {
type = Op.Type.INDEXREDUCE;
} else if (op instanceof MetaOp) {
type = Type.META;
} else if (op instanceof GridOp) {
type = Type.GRID;
}
return type;
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
}
@Override
public void initFromOnnx(Onnx.NodeProto node, SameDiff initWith, Map attributesForNode, Onnx.GraphProto graph) {
}
@Override
public DataBuffer extraArgsDataBuff(DataType dtype) {
if (extraArgz != null)
return extraArgz;
if (extraArgs != null) {
if (Shape.isZ(dtype) || Shape.isB(dtype)) {
long extraz[] = new long[extraArgs.length];
for (int i = 0; i < extraArgs.length; i++) {
if (extraArgs[i] instanceof Number) {
Number arg = (Number) extraArgs[i];
long val = arg.longValue();
extraz[i] = val;
}
}
extraArgz = Nd4j.getConstantHandler().getConstantBuffer(extraz, dtype);
return extraArgz;
} else if (Shape.isR(dtype)) {
double extraz[] = new double[extraArgs.length];
for (int i = 0; i < extraArgs.length; i++) {
if (!(extraArgs[i] instanceof Number))
continue;
Number arg = (Number) extraArgs[i];
if (arg == null)
arg = 0.0;
double val = arg.doubleValue();
extraz[i] = val;
}
extraArgz = Nd4j.getConstantHandler().getConstantBuffer(extraz, dtype);
return extraArgz;
}
}
return null;
}
@Override
public Buffer extraArgsBuff() {
if (extraArgs != null) {
DataBuffer retBuff;
if (x.data().dataType() == DataType.FLOAT) {
retBuff = Nd4j.createBuffer(new float[extraArgs.length]);
for (int i = 0; i < extraArgs.length; i++) {
Number val = (Number) extraArgs[i];
retBuff.put(i, val.floatValue());
}
return retBuff.asNioFloat();
} else {
retBuff = Nd4j.createBuffer(new double[extraArgs.length]);
for (int i = 0; i < extraArgs.length; i++) {
Number val = (Number) extraArgs[i];
retBuff.put(i, val.doubleValue());
}
return retBuff.asNioDouble();
}
}
return null;
}
@Override
public void setX(INDArray x) {
this.x = x;
}
@Override
public void setZ(INDArray z) {
this.z = z;
}
@Override
public void setY(INDArray y) {
this.y = y;
}
@Override
public Object[] extraArgs() {
return extraArgs;
}
@Override
public INDArray x() {
return x;
}
@Override
public INDArray y() {
return y;
}
@Override
public INDArray z() {
return z;
}
@Override
public INDArray getInputArgument(int index){
Preconditions.checkState(index >= 0 && index < 2, "Input argument index must be 0 or 1, got %s", index);
return index == 0 ? x : y;
}
@Override
public SDVariable[] outputVariables(String baseName) {
if(zVertexId == null) {
val outputNames = sameDiff.getOutputsForOp(this);
//no need to dynamically create if already exists
if(outputNames != null) {
zVertexId = sameDiff.getVariable(outputNames[0]).name();
return new SDVariable[]{sameDiff.getVariable(outputNames[0])};
}
if(isInPlace()) {
val newVars = sameDiff.generateOutputVariableForOp(this,null,false);
val inputArr = x();
//in place op
if(inputArr == null) {
return newVars;
}
sameDiff.setArrayForVariable(newVars[0].name(),inputArr);
z = inputArr;
if(sameDiff.getOutputsForOp(this) == null)
sameDiff.addOutgoingFor(newVars,this);
return newVars;
}
SDVariable[] newVars = sameDiff.generateOutputVariableForOp(this, baseName, false);
if (sameDiff.getOutputsForOp(this) == null)
sameDiff.addOutgoingFor(newVars, this);
return newVars;
}
return new SDVariable[]{sameDiff.getVariable(zVertexId)};
}
@Override
public String toString() {
return opName();
}
@Override
public CustomOp toCustomOp() {
DynamicCustomOp.DynamicCustomOpsBuilder customOpBuilder = DynamicCustomOp.builder(opName());
customOpBuilder.callInplace(x() == z());
if (y() != null)
customOpBuilder.addInputs(x(), y());
else
customOpBuilder.addInputs(x());
customOpBuilder.addOutputs(z());
if (extraArgs != null) {
for (int i = 0; i < extraArgs.length; i++) {
if (extraArgs[i] instanceof Integer) {
customOpBuilder.addIntegerArguments((Integer) extraArgs[i]);
} else if (extraArgs[i] instanceof Double || extraArgs[i] instanceof Float) {
Double num = (Double) extraArgs[i];
customOpBuilder.addFloatingPointArguments(num);
}
}
}
return customOpBuilder.build();
}
@Override
public boolean equals(Object o) {
if (this == o) return true;
if (o == null || getClass() != o.getClass()) return false;
BaseOp baseOp = (BaseOp) o;
if (x != null ? !x.equals(baseOp.x) : baseOp.x != null) return false;
if (y != null ? !y.equals(baseOp.y) : baseOp.y != null) return false;
if (z != null ? !z.equals(baseOp.z) : baseOp.z != null) return false;
// Probably incorrect - comparing Object[] arrays with Arrays.equals
if (!Arrays.equals(extraArgs, baseOp.extraArgs)) return false;
return extraArgz != null ? extraArgz.equals(baseOp.extraArgz) : baseOp.extraArgz == null;
}
@Override
public int hashCode() {
int result = super.hashCode();
result = 31 * result + (x != null ? x.hashCode() : 0);
result = 31 * result + (y != null ? y.hashCode() : 0);
result = 31 * result + (z != null ? z.hashCode() : 0);
result = 31 * result + Arrays.hashCode(extraArgs);
result = 31 * result + (extraArgz != null ? extraArgz.hashCode() : 0);
return result;
}
protected void defineDimensions(int... dimensions){
if (dimensions != null && dimensions.length > 0) {
if(x != null) {
dimensions = Shape.normalizeAxis(x.rank(), dimensions);
}
}
if (dimensions == null || dimensions.length == 0)
dimensions = new int[]{Integer.MAX_VALUE};
try(MemoryWorkspace ws = Nd4j.getWorkspaceManager().scopeOutOfWorkspaces()) {
this.dimensionz = Shape.ndArrayDimFromInt(dimensions);
}
}
public INDArray dimensions() {
return dimensionz;
}
public Number getFinalResult() {
if (this.z == null)
throw new ND4JIllegalStateException("Op.Z is null. Op wasn't executed yet?");
if (z.isEmpty())
throw new ND4JIllegalStateException("Can't get number from empty array");
if (!z.isScalar())
throw new ND4JIllegalStateException("Can't get final result scalar out of N-dim tensor");
if (z.isR())
return new Double(z.getDouble(0));
else if (z.isZ())
return new Long(z.getInt(0));
else if (z.isB())
return new Integer(z.getInt(0));
throw new ND4JIllegalStateException("???");
}
@Override
public int getNumOutputs(){
//Always 1 for legacy/base ops
return 1;
}
@Override
public void clearArrays(){
x = null;
y = null;
z = null;
}
@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());
}
}
