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* * terms of the Apache License, Version 2.0 which is available at
* * https://www.apache.org/licenses/LICENSE-2.0.
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* * information regarding copyright ownership.
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* * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* * SPDX-License-Identifier: Apache-2.0
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package org.deeplearning4j.nn.graph.vertex.impl;
import org.deeplearning4j.nn.api.Layer;
import org.deeplearning4j.nn.api.MaskState;
import org.deeplearning4j.nn.gradient.Gradient;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.deeplearning4j.nn.graph.vertex.BaseGraphVertex;
import org.deeplearning4j.nn.graph.vertex.VertexIndices;
import org.nd4j.linalg.api.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.indexing.INDArrayIndex;
import org.nd4j.linalg.indexing.NDArrayIndex;
import org.nd4j.common.primitives.Pair;
import org.deeplearning4j.nn.workspace.ArrayType;
import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
import java.util.Arrays;
public class SubsetVertex extends BaseGraphVertex {
private int from;
private int to; //inclusive
private long[] forwardShape;
public SubsetVertex(ComputationGraph graph, String name, int vertexIndex, int from, int to, DataType dataType) {
this(graph, name, vertexIndex, null, null, from, to, dataType);
}
public SubsetVertex(ComputationGraph graph, String name, int vertexIndex, VertexIndices[] inputVertices,
VertexIndices[] outputVertices, int from, int to, DataType dataType) {
super(graph, name, vertexIndex, inputVertices, outputVertices, dataType);
this.from = from;
this.to = to;
}
@Override
public boolean hasLayer() {
return false;
}
@Override
public Layer getLayer() {
return null;
}
@Override
public INDArray doForward(boolean training, LayerWorkspaceMgr workspaceMgr) {
if (!canDoForward())
throw new IllegalStateException("Cannot do forward pass: input not set");
forwardShape = Arrays.copyOf(inputs[0].shape(), inputs[0].rank());
INDArray out;
switch (inputs[0].rank()) {
case 2:
out = inputs[0].get(NDArrayIndex.all(), NDArrayIndex.interval(from, to, true));
break;
case 3:
out = inputs[0].get(NDArrayIndex.all(), NDArrayIndex.interval(from, to, true), NDArrayIndex.all());
break;
case 4:
out = inputs[0].get(NDArrayIndex.all(), NDArrayIndex.interval(from, to, true), NDArrayIndex.all(),
NDArrayIndex.all());
break;
default:
throw new UnsupportedOperationException(
"Cannot get subset for activations of rank " + inputs[0].rank());
}
return workspaceMgr.dup(ArrayType.ACTIVATIONS, out);
}
@Override
public Pair doBackward(boolean tbptt, LayerWorkspaceMgr workspaceMgr) {
if (!canDoBackward())
throw new IllegalStateException("Cannot do backward pass: error not set");
INDArray out = workspaceMgr.create(ArrayType.ACTIVATION_GRAD, epsilon.dataType(), forwardShape);
switch (forwardShape.length) {
case 2:
out.put(new INDArrayIndex[] {NDArrayIndex.all(), NDArrayIndex.interval(from, to, true)}, epsilon);
break;
case 3:
out.put(new INDArrayIndex[] {NDArrayIndex.all(), NDArrayIndex.interval(from, to, true),
NDArrayIndex.all()}, epsilon);
break;
case 4:
out.put(new INDArrayIndex[] {NDArrayIndex.all(), NDArrayIndex.interval(from, to, true),
NDArrayIndex.all(), NDArrayIndex.all()}, epsilon);
break;
default:
throw new RuntimeException("Invalid activation rank"); //Should never happen
}
return new Pair<>(null, new INDArray[] {out});
}
@Override
public String toString() {
return "SubsetVertex(id=" + this.getVertexIndex() + ",name=\"" + this.getVertexName() + "\",fromIdx=" + from
+ ",toIdx=" + to + ")";
}
@Override
public void setBackpropGradientsViewArray(INDArray backpropGradientsViewArray) {
if (backpropGradientsViewArray != null)
throw new RuntimeException("Vertex does not have gradients; gradients view array cannot be set here");
}
@Override
public Pair feedForwardMaskArrays(INDArray[] maskArrays, MaskState currentMaskState,
int minibatchSize) {
//No op: subset just provides part of the activations for each example (or time step)
if (maskArrays == null || maskArrays.length == 0) {
return null;
}
return new Pair<>(maskArrays[0], currentMaskState);
}
}