org.deeplearning4j.nn.graph.vertex.impl.MergeVertex Maven / Gradle / Ivy
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* * Copyright 2016 Skymind,Inc.
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package org.deeplearning4j.nn.graph.vertex.impl;
import org.deeplearning4j.berkeley.Pair;
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.ndarray.INDArray;
import org.nd4j.linalg.api.ops.impl.transforms.Or;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.indexing.NDArrayIndex;
import java.util.Arrays;
/** A MergeVertex is used to combine the activations of two or more layers/GraphVertex by means of concatenation/merging.
* Exactly how this is done depends on the type of input.
* For 2d (feed forward layer) inputs: MergeVertex([numExamples,layerSize1],[numExamples,layerSize2]) -> [numExamples,layerSize1 + layerSize2]
* For 3d (time series) inputs: MergeVertex([numExamples,layerSize1,timeSeriesLength],[numExamples,layerSize2,timeSeriesLength])
* -> [numExamples,layerSize1 + layerSize2,timeSeriesLength]
* For 4d (convolutional) inputs: MergeVertex([numExamples,depth1,width,height],[numExamples,depth2,width,height])
* -> [numExamples,depth1 + depth2,width,height]
* @author Alex Black
*/
public class MergeVertex extends BaseGraphVertex {
private int[][] forwardPassShapes;
private int fwdPassRank;
public MergeVertex(ComputationGraph graph, String name, int vertexIndex) {
this(graph, name, vertexIndex, null, null);
}
public MergeVertex(ComputationGraph graph, String name, int vertexIndex, VertexIndices[] inputVertices,
VertexIndices[] outputVertices) {
super(graph, name, vertexIndex, inputVertices, outputVertices);
}
@Override
public String toString() {
return "MergeVertex(id=" + this.getVertexIndex() + ",name=\"" + this.getVertexName() + "\")";
}
@Override
public boolean hasLayer() {
return false;
}
@Override
public boolean isOutputVertex() {
return false;
}
@Override
public Layer getLayer() {
return null;
}
@Override
public INDArray doForward(boolean training) {
if (!canDoForward())
throw new IllegalStateException("Cannot do forward pass: inputs not set");
if (inputs.length == 1) {
//No-op case
int[] shape = inputs[0].shape();
forwardPassShapes = new int[][] {Arrays.copyOf(shape, shape.length)};
return inputs[0];
}
forwardPassShapes = new int[inputs.length][0];
int nExamples = inputs[0].size(0);
int nOut = 0;
fwdPassRank = inputs[0].rank();
for (int i = 0; i < inputs.length; i++) {
int[] currShape = inputs[i].shape();
if (fwdPassRank != currShape.length) {
throw new IllegalStateException(
"Cannot merge activations with different ranks: first activations have rank "
+ fwdPassRank + ", activations[" + i + "] have rank " + currShape.length
+ " (shape=" + Arrays.toString(currShape) + ")");
}
forwardPassShapes[i] = Arrays.copyOf(currShape, currShape.length);
if (currShape[0] != nExamples) {
throw new IllegalStateException(
"Cannot merge activations with different number of examples (activations[0] shape: "
+ Arrays.toString(inputs[0].shape()) + ", activations[" + i
+ "] shape: " + Arrays.toString(inputs[i].shape()));
}
nOut += currShape[1]; //Same dimension for all of CNNs, FF, RNNs
}
int nOutCumulative = 0;
INDArray out;
switch (inputs[0].rank()) {
case 2:
//Standard feedforward inputs...
out = Nd4j.create(nExamples, nOut);
for (INDArray activation : inputs) {
int[] currShape = activation.shape();
out.get(NDArrayIndex.all(), NDArrayIndex.interval(nOutCumulative, nOutCumulative + currShape[1]))
.assign(activation);
nOutCumulative += currShape[1];
}
break;
case 3:
//Time series inputs...
int tsLength = inputs[0].size(2);
out = Nd4j.create(nExamples, nOut, tsLength);
for (INDArray activation : inputs) {
int[] currShape = activation.shape();
out.get(NDArrayIndex.all(), NDArrayIndex.interval(nOutCumulative, nOutCumulative + currShape[1]),
NDArrayIndex.all()).assign(activation);
nOutCumulative += currShape[1];
}
break;
case 4:
fwdPassRank = 4;
int[] outShape = Arrays.copyOf(inputs[0].shape(), 4);
outShape[1] = nOut;
out = Nd4j.create(outShape);
//Input activations: [minibatch,depth,width,height]
for (INDArray activation : inputs) {
out.get(NDArrayIndex.all(),
NDArrayIndex.interval(nOutCumulative, nOutCumulative + activation.size(1)),
NDArrayIndex.all(), NDArrayIndex.all()).assign(activation);
nOutCumulative += activation.size(1);
}
break;
default:
throw new UnsupportedOperationException("Cannot merge activations with rank 4 or more");
}
return out;
}
@Override
public Pair doBackward(boolean tbptt) {
if (!canDoBackward())
throw new IllegalStateException("Cannot do backward pass: errors not set");
if (forwardPassShapes.length == 1) {
//No op case
return new Pair<>(null, new INDArray[] {epsilon});
}
//Split the epsilons in the opposite way that the activations were merged
INDArray[] out = new INDArray[forwardPassShapes.length];
for (int i = 0; i < out.length; i++)
out[i] = Nd4j.create(forwardPassShapes[i]);
int cumulative = 0;
switch (fwdPassRank) {
case 2:
//Standard
for (int i = 0; i < forwardPassShapes.length; i++) {
out[i].assign(epsilon.get(NDArrayIndex.all(), //All rows
NDArrayIndex.interval(cumulative, cumulative + forwardPassShapes[i][1]))); //subset of columns
cumulative += forwardPassShapes[i][1];
}
break;
case 3:
for (int i = 0; i < forwardPassShapes.length; i++) {
out[i].assign(epsilon.get(NDArrayIndex.all(), //All rows
NDArrayIndex.interval(cumulative, cumulative + forwardPassShapes[i][1]), //subset of columns
NDArrayIndex.all())); //All time steps
cumulative += forwardPassShapes[i][1];
}
break;
case 4:
for (int i = 0; i < forwardPassShapes.length; i++) {
out[i].assign(epsilon.get(NDArrayIndex.all(),
NDArrayIndex.interval(cumulative, cumulative + forwardPassShapes[i][1]), //Subset of depth
NDArrayIndex.all(), //Width
NDArrayIndex.all())); //height
cumulative += forwardPassShapes[i][1];
}
break;
default:
throw new RuntimeException("Invalid rank during forward pass (not 2, 3, 4)"); //Should never happen
}
return new Pair<>(null, out);
}
@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) {
if (maskArrays == null) {
return new Pair<>(null, currentMaskState);
}
//Most common case: all or none.
//If there's only *some* mask arrays: assume the others (missing) are equivalent to all 1s
//And for handling multiple masks: best strategy seems to be an OR operation
//i.e., output is 1 if any of the input are 1s
//Which means: if any masks are missing, output null (equivalent to no mask)
//Otherwise do an element-wise OR operation
for (INDArray arr : maskArrays) {
if (arr == null) {
return new Pair<>(null, currentMaskState);
}
}
//At this point: all present. Do OR operation
if (maskArrays.length == 1) {
return new Pair<>(maskArrays[0], currentMaskState);
} else {
INDArray ret = maskArrays[0].dup(maskArrays[0].ordering());
Nd4j.getExecutioner().exec(new Or(maskArrays[0], maskArrays[1], ret));
for (int i = 2; i < maskArrays.length; i++) {
Nd4j.getExecutioner().exec(new Or(maskArrays[i], ret, ret));
}
return new Pair<>(ret, currentMaskState);
}
}
}
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