org.deeplearning4j.nn.conf.graph.UnstackVertex Maven / Gradle / Ivy
/*-
*
* * Copyright 2016 Skymind,Inc.
* *
* * Licensed under the Apache License, Version 2.0 (the "License");
* * you may not use this file except in compliance with the License.
* * You may obtain a copy of the License at
* *
* * http://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.
*
*/
package org.deeplearning4j.nn.conf.graph;
import lombok.Getter;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.inputs.InvalidInputTypeException;
import org.deeplearning4j.nn.conf.memory.LayerMemoryReport;
import org.deeplearning4j.nn.conf.memory.MemoryReport;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.shade.jackson.annotation.JsonProperty;
/**
* UnstackVertex allows for unstacking of inputs so that they may be forwarded through
* a network. This is useful for cases such as Triplet Embedding, where embeddings can
* be separated and run through subsequent layers.
*
* Works similarly to SubsetVertex, except on dimension 0 of the input.
*
* @author Justin Long (crockpotveggies)
*/
@Getter
public class UnstackVertex extends GraphVertex {
protected int from;
protected int stackSize;
/**
* @param from The first column index of the stacked inputs.
* @param stackSize The total number of stacked inputs. An interval is automatically
* calculated according to the size of the first dimension.
*/
public UnstackVertex(@JsonProperty("from") int from, @JsonProperty("stackSize") int stackSize) {
this.from = from;
this.stackSize = stackSize;
}
@Override
public org.deeplearning4j.nn.graph.vertex.GraphVertex instantiate(ComputationGraph graph, String name, int idx,
INDArray paramsView, boolean initializeParams) {
return new org.deeplearning4j.nn.graph.vertex.impl.UnstackVertex(graph, name, idx, null, null, from, stackSize);
}
@Override
public UnstackVertex clone() {
return new UnstackVertex(from, stackSize);
}
@Override
public boolean equals(Object o) {
if (!(o instanceof UnstackVertex))
return false;
return ((UnstackVertex) o).from == from && ((UnstackVertex) o).stackSize == stackSize;
}
@Override
public int numParams(boolean backprop) {
return 0;
}
@Override
public int minVertexInputs() {
return 1;
}
@Override
public int maxVertexInputs() {
return 1;
}
@Override
public int hashCode() {
return 433682566;
}
@Override
public InputType getOutputType(int layerIndex, InputType... vertexInputs) throws InvalidInputTypeException {
if (vertexInputs.length == 1)
return vertexInputs[0];
InputType first = vertexInputs[0];
if (first.getType() == InputType.Type.CNNFlat) {
//TODO
//Merging flattened CNN format data could be messy?
throw new InvalidInputTypeException(
"Invalid input: UnstackVertex cannot currently merge CNN data in flattened format. Got: "
+ vertexInputs);
} else if (first.getType() != InputType.Type.CNN) {
//FF or RNN data inputs
int size = 0;
InputType.Type type = null;
for (int i = 0; i < vertexInputs.length; i++) {
if (vertexInputs[i].getType() != first.getType()) {
throw new InvalidInputTypeException(
"Invalid input: UnstackVertex cannot merge activations of different types:"
+ " first type = " + first.getType() + ", input type " + (i + 1)
+ " = " + vertexInputs[i].getType());
}
int thisSize;
switch (vertexInputs[i].getType()) {
case FF:
thisSize = ((InputType.InputTypeFeedForward) vertexInputs[i]).getSize();
type = InputType.Type.FF;
break;
case RNN:
thisSize = ((InputType.InputTypeRecurrent) vertexInputs[i]).getSize();
type = InputType.Type.RNN;
break;
default:
throw new IllegalStateException("Unknown input type: " + vertexInputs[i]); //Should never happen
}
if (thisSize <= 0) {//Size is not defined
size = -1;
} else {
size += thisSize;
}
}
if (size > 0) {
//Size is specified
if (type == InputType.Type.FF)
return InputType.feedForward(size);
else
return InputType.recurrent(size);
} else {
//size is unknown
if (type == InputType.Type.FF)
return InputType.feedForward(-1);
else
return InputType.recurrent(-1);
}
} else {
//CNN inputs... also check that the depth, width and heights match:
InputType.InputTypeConvolutional firstConv = (InputType.InputTypeConvolutional) first;
int fd = firstConv.getDepth();
int fw = firstConv.getWidth();
int fh = firstConv.getHeight();
int depthSum = fd;
for (int i = 1; i < vertexInputs.length; i++) {
if (vertexInputs[i].getType() != InputType.Type.CNN) {
throw new InvalidInputTypeException(
"Invalid input: UnstackVertex cannot process activations of different types:"
+ " first type = " + InputType.Type.CNN + ", input type " + (i + 1)
+ " = " + vertexInputs[i].getType());
}
InputType.InputTypeConvolutional otherConv = (InputType.InputTypeConvolutional) vertexInputs[i];
int od = otherConv.getDepth();
int ow = otherConv.getWidth();
int oh = otherConv.getHeight();
if (fw != ow || fh != oh) {
throw new InvalidInputTypeException(
"Invalid input: UnstackVertex cannot merge CNN activations of different width/heights:"
+ "first [depth,width,height] = [" + fd + "," + fw + "," + fh
+ "], input " + i + " = [" + od + "," + ow + "," + oh + "]");
}
depthSum += od;
}
return InputType.convolutional(fh, fw, depthSum);
}
}
@Override
public MemoryReport getMemoryReport(InputType... inputTypes) {
//Get op with dup - accounted for in activations size (no working memory)
//Do one dup on the forward pass (output activations). Accounted for in output activations.
InputType outputType = getOutputType(-1, inputTypes);
return new LayerMemoryReport.Builder(null, UnstackVertex.class, inputTypes[0], outputType).standardMemory(0, 0) //No params
.workingMemory(0, 0, 0, 0).cacheMemory(0, 0) //No caching
.build();
}
}
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