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/*******************************************************************************
* 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.impl.shape;
import lombok.NoArgsConstructor;
import lombok.val;
import onnx.Onnx;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
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.ops.DynamicCustomOp;
import org.tensorflow.framework.AttrValue;
import org.tensorflow.framework.GraphDef;
import org.tensorflow.framework.NodeDef;
import java.util.*;
/**
* Gather op
*/
@NoArgsConstructor
public class Gather extends DynamicCustomOp {
protected int[] indices;
protected int jaxis = 0;
public Gather(SameDiff sameDiff, SDVariable input, int[] indices, int axis, boolean inPlace) {
super(null, sameDiff, new SDVariable[] {input}, inPlace);
addIArgument(axis);
addIArgument(indices);
this.jaxis = axis;
this.indices = indices;
}
public Gather(SameDiff sameDiff, SDVariable input, SDVariable indices, int axis, boolean inPlace) {
super(null, sameDiff, new SDVariable[] {input, indices}, inPlace);
addIArgument(axis);
this.jaxis = axis;
}
@Override
public String onnxName() {
return "Gather";
}
@Override
public String[] tensorflowNames() {
return new String[]{"Gather", "GatherV2"};
}
@Override
public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map attributesForNode, GraphDef graph) {
TFGraphMapper.initFunctionFromProperties(nodeDef.getOp(), this, attributesForNode, nodeDef, graph);
}
@Override
public void initFromOnnx(Onnx.NodeProto node, SameDiff initWith, Map attributesForNode, Onnx.GraphProto graph) {
}
@Override
public Map> mappingsForFunction() {
Map> ret = new HashMap<>();
Map map = new HashMap<>();
val broadcast = PropertyMapping.builder()
.onnxAttrName("indices")
.tfInputPosition(1)
.propertyNames(new String[]{"indices"}).build();
map.put("indices", broadcast);
ret.put(tensorflowNames()[0], map);
ret.put(onnxName(), map);
Map map2 = new HashMap<>();
val broadcast2 = PropertyMapping.builder()
.tfInputPosition(1)
.propertyNames(new String[]{"indices"}).build();
map2.put("indices", broadcast2);
val axis2 = PropertyMapping.builder()
.tfInputPosition(2)
.propertyNames(new String[]{"axis"}).build();
map2.put("axis", axis2);
ret.put("GatherV2", map2);
return ret;
}
@Override
public String opName() {
return "gather";
}
@Override
public List doDiff(List i_v){
//2 args: input and indices. Plus integer dimension arg
//Gather backprop is just scatter add
SDVariable indicesGrad = sameDiff.zerosLike(arg(1));
SDVariable inputGrad = sameDiff.zerosLike(arg(0));
SDVariable[] inputs = args();
SDVariable axis;
SDVariable rank = inputs[0].rank();
if(inputs.length == 2){
axis = sameDiff.constant(jaxis);
if(jaxis < 0)
axis = axis.add(rank);
} else {
axis = inputs[2];
}
//Use scatter add plus permute
SDVariable dimsExAxis = sameDiff.range(null, sameDiff.constant(0), rank, sameDiff.constant(1), DataType.INT);
SDVariable axisRank1 = axis.reshape(1);
dimsExAxis = sameDiff.math().listDiff(dimsExAxis, axisRank1)[0]; //Don't need indices
SDVariable permuteDims = sameDiff.concat(0, axisRank1, dimsExAxis);
SDVariable invertDims = sameDiff.invertPermutation(permuteDims);
//Permute gradients so original axis is at position 0... then scatter add, and reverse
SDVariable gradAtOut = i_v.get(0);
SDVariable permuteGrad = gradAtOut.permute(permuteDims);
SDVariable inputGradPermute = inputGrad.permute(permuteDims);
inputGrad = sameDiff.scatterAdd(inputGradPermute, arg(1), permuteGrad);
//Now, invert the permutation so axis is back where it was
inputGrad = inputGrad.permute(invertDims);
return Arrays.asList(inputGrad, indicesGrad);
}
@Override
public List calculateOutputDataTypes(List dataTypes){
//Output type is same as (first) input type
return Collections.singletonList(dataTypes.get(0));
}
}