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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.deeplearning4j.nn.conf.graph;
import lombok.val;
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.buffer.DataType;
import org.nd4j.linalg.api.ndarray.INDArray;
/**
* Removes the first column and row from an input. This is to fix inconsistencies from ZeroPadding
* layers in imported models from Caffe.
* See https://gist.github.com/joelouismarino/a2ede9ab3928f999575423b9887abd14.
*
* @author Justin Long (crockpotveggies)
*/
public class PoolHelperVertex extends GraphVertex {
@Override
public PoolHelperVertex clone() {
return new PoolHelperVertex();
}
@Override
public boolean equals(Object o) {
return o instanceof PoolHelperVertex;
}
@Override
public int hashCode() {
return 433682566;
}
@Override
public long numParams(boolean backprop) {
return 0;
}
@Override
public int minVertexInputs() {
return 1;
}
@Override
public int maxVertexInputs() {
return 1;
}
@Override
public org.deeplearning4j.nn.graph.vertex.GraphVertex instantiate(ComputationGraph graph, String name, int idx,
INDArray paramsView, boolean initializeParams, DataType networkDatatype) {
return new org.deeplearning4j.nn.graph.vertex.impl.PoolHelperVertex(graph, name, idx, networkDatatype);
}
@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: MergeVertex 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: MergeVertex cannot merge activations of different types:"
+ " first type = " + first.getType() + ", input type " + (i + 1)
+ " = " + vertexInputs[i].getType());
}
long 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 channels, width and heights match:
InputType.InputTypeConvolutional firstConv = (InputType.InputTypeConvolutional) first;
// FIXME: int cast
val fd = (int) firstConv.getChannels();
val fw = (int) firstConv.getWidth();
val fh = (int) 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: MergeVertex 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];
// FIXME: int cast
int od = (int) otherConv.getChannels();
int ow = (int) otherConv.getWidth();
int oh = (int) otherConv.getHeight();
if (fw != ow || fh != oh) {
throw new InvalidInputTypeException(
"Invalid input: MergeVertex cannot merge CNN activations of different width/heights:"
+ "first [channels,width,height] = [" + fd + "," + fw + "," + fh
+ "], input " + i + " = [" + od + "," + ow + "," + oh + "]");
}
depthSum += od;
}
return InputType.convolutional(fh, fw, depthSum);
}
}
@Override
public MemoryReport getMemoryReport(InputType... inputTypes) {
//It's just a get op on the forward pass... no memory use
InputType outputType = getOutputType(-1, inputTypes);
return new LayerMemoryReport.Builder(null, PoolHelperVertex.class, inputTypes[0], outputType)
.standardMemory(0, 0) //No params
.workingMemory(0, 0, 0, 0).cacheMemory(0, 0) //No caching
.build();
}
}
