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/*
* Copyright (c) 2019 by Andrew Charneski.
*
* The author licenses this file to you 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 com.simiacryptus.mindseye.layers.java;
import com.google.gson.JsonObject;
import com.simiacryptus.lang.Tuple2;
import com.simiacryptus.mindseye.lang.*;
import com.simiacryptus.ref.lang.RefUtil;
import com.simiacryptus.ref.wrappers.RefArrays;
import com.simiacryptus.ref.wrappers.RefCollectors;
import com.simiacryptus.ref.wrappers.RefIntStream;
import com.simiacryptus.ref.wrappers.RefList;
import com.simiacryptus.util.JsonUtil;
import com.simiacryptus.util.Util;
import org.jetbrains.annotations.NotNull;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import javax.annotation.Nonnull;
import javax.annotation.Nullable;
import java.util.Map;
import java.util.UUID;
import java.util.function.*;
/**
* The type Max pooling layer.
*/
@SuppressWarnings("serial")
public class MaxPoolingLayer extends LayerBase {
private static final Function>> calcRegionsCache = Util
.cache(MaxPoolingLayer::calcRegions);
@SuppressWarnings("unused")
private static final Logger log = LoggerFactory.getLogger(MaxPoolingLayer.class);
private int[] kernelDims;
/**
* Instantiates a new Max pooling layer.
*/
protected MaxPoolingLayer() {
super();
}
/**
* Instantiates a new Max pooling layer.
*
* @param kernelDims the kernel dims
*/
public MaxPoolingLayer(@Nonnull final int... kernelDims) {
this.kernelDims = RefArrays.copyOf(kernelDims, kernelDims.length);
}
/**
* Instantiates a new Max pooling layer.
*
* @param id the id
* @param kernelDims the kernel dims
*/
protected MaxPoolingLayer(@Nonnull final JsonObject id, @Nonnull final int... kernelDims) {
super(id);
this.kernelDims = RefArrays.copyOf(kernelDims, kernelDims.length);
}
/**
* From json max pooling layer.
*
* @param json the json
* @param rs the rs
* @return the max pooling layer
*/
@Nonnull
@SuppressWarnings("unused")
public static MaxPoolingLayer fromJson(@Nonnull final JsonObject json, Map rs) {
return new MaxPoolingLayer(json, JsonUtil.getIntArray(json.getAsJsonArray("heapCopy")));
}
private static RefList> calcRegions(final MaxPoolingLayer.CalcRegionsParameter p) {
@Nonnull final Tensor input = new Tensor(p.inputDims);
final int[] newDims = RefIntStream.range(0, p.inputDims.length).map(i -> {
//assert 0 == p.inputDims[i] % p.kernelDims[i];
return (int) Math.ceil(p.inputDims[i] * 1.0 / p.kernelDims[i]);
}).toArray();
@Nonnull final Tensor output = new Tensor(newDims);
RefList> temp_53_0001 = output.coordStream(true)
.map(RefUtil.wrapInterface((Function>) o -> {
Tensor tensor = new Tensor(p.kernelDims);
final int[] inCoords = tensor.coordStream(true)
.mapToInt(RefUtil.wrapInterface((ToIntFunction) kernelCoord -> {
@Nonnull final int[] result = new int[o.getCoords().length];
for (int index = 0; index < o.getCoords().length; index++) {
final int outputCoordinate = o.getCoords()[index];
final int kernelSize = p.kernelDims[index];
final int baseCoordinate = Math.min(outputCoordinate * kernelSize, p.inputDims[index] - kernelSize);
final int kernelCoordinate = kernelCoord.getCoords()[index];
result[index] = baseCoordinate + kernelCoordinate;
}
return input.index(result);
}, input.addRef())).toArray();
tensor.freeRef();
return new Tuple2<>(o.getIndex(), inCoords);
}, input)).collect(RefCollectors.toList());
output.freeRef();
return temp_53_0001;
}
@Nonnull
@Override
public Result eval(@Nonnull final Result... inObj) {
final Result in = inObj[0].addRef();
RefUtil.freeRef(inObj);
final TensorList inData = in.getData();
@Nonnull final int[] inputDims = inData.getDimensions();
int length = inData.length();
TensorArray data = fwd(inputDims, length);
boolean alive = in.isAlive();
int[][] gradientMap = getGradientMap(in.addRef(), inData, inputDims, length, data.addRef());
Result.Accumulator accumulator = new Accumulator(inputDims, gradientMap, in.getAccumulator(), in.isAlive());
in.freeRef();
return new Result(data, accumulator, alive);
}
@Nonnull
@Override
public JsonObject getJson(Map resources, DataSerializer dataSerializer) {
@Nonnull final JsonObject json = super.getJsonStub();
json.add("heapCopy", JsonUtil.getJson(kernelDims));
return json;
}
@Nonnull
@Override
public RefList state() {
return RefArrays.asList();
}
public @SuppressWarnings("unused")
void _free() {
super._free();
}
@Nonnull
public @Override
@SuppressWarnings("unused")
MaxPoolingLayer addRef() {
return (MaxPoolingLayer) super.addRef();
}
private int[][] getGradientMap(Result in, TensorList inData, int[] inputDims, int length, TensorArray data) {
final RefList> regions = MaxPoolingLayer.calcRegionsCache
.apply(new CalcRegionsParameter(inputDims, kernelDims));
return RefIntStream.range(0, length).mapToObj(RefUtil.wrapInterface(dataIndex -> {
@Nullable final Tensor input = inData.get(dataIndex);
final Tensor output = ((TensorList) data).get(dataIndex);
@Nonnull final IntToDoubleFunction keyExtractor = RefUtil.wrapInterface(input::get,
input.addRef());
@Nonnull final int[] gradientMap = new int[input.length()];
regions.parallelStream().forEach(RefUtil.wrapInterface((Consumer>) tuple -> {
final Integer from = tuple.getFirst();
final int[] toList = tuple.getSecond();
int toMax = -1;
double bestValue = Double.NEGATIVE_INFINITY;
for (final int c : toList) {
final double value = keyExtractor.applyAsDouble(c);
if (-1 == toMax || bestValue < value) {
bestValue = value;
toMax = c;
}
}
gradientMap[from] = toMax;
output.set(from, input.get(toMax));
}, output, input, keyExtractor));
return gradientMap;
}, in, data, regions, inData)).toArray(l -> new int[l][]);
}
@NotNull
private TensorArray fwd(int[] inputDims, int length) {
return new TensorArray(RefIntStream.range(0, length).mapToObj(dataIndex -> {
return new Tensor(RefIntStream.range(0, inputDims.length).map(i -> {
return (int) Math.ceil(inputDims[i] * 1.0 / kernelDims[i]);
}).toArray());
}).toArray(Tensor[]::new));
}
/**
* The type Calc regions parameter.
*/
public static class CalcRegionsParameter {
/**
* The Input dims.
*/
public final int[] inputDims;
/**
* The Kernel dims.
*/
public final int[] kernelDims;
/**
* Instantiates a new Calc regions parameter.
*
* @param inputDims the input dims
* @param kernelDims the kernel dims
*/
public CalcRegionsParameter(final int[] inputDims, final int[] kernelDims) {
this.inputDims = inputDims;
this.kernelDims = kernelDims;
}
@Override
public boolean equals(@Nullable final Object obj) {
if (this == obj) {
return true;
}
if (obj == null) {
return false;
}
if (getClass() != obj.getClass()) {
return false;
}
final MaxPoolingLayer.CalcRegionsParameter other = (MaxPoolingLayer.CalcRegionsParameter) obj;
if (!RefArrays.equals(inputDims, other.inputDims)) {
return false;
}
return RefArrays.equals(kernelDims, other.kernelDims);
}
@Override
public int hashCode() {
final int prime = 31;
int result = 1;
result = prime * result + RefArrays.hashCode(inputDims);
result = prime * result + RefArrays.hashCode(kernelDims);
return result;
}
}
private static class Accumulator extends Result.Accumulator {
private final int[] inputDims;
private final int[][] gradientMapA;
private Result.Accumulator accumulator;
private boolean alive;
/**
* Instantiates a new Accumulator.
*
* @param inputDims the input dims
* @param gradientMapA the gradient map a
* @param accumulator the accumulator
* @param alive the alive
*/
public Accumulator(int[] inputDims, int[][] gradientMapA, Result.Accumulator accumulator, boolean alive) {
this.inputDims = inputDims;
this.gradientMapA = gradientMapA;
this.accumulator = accumulator;
this.alive = alive;
}
@Override
public void accept(@Nullable DeltaSet buffer, @Nonnull TensorList data) {
if (alive) {
@Nonnull
TensorArray tensorArray = new TensorArray(RefIntStream.range(0, data.length()).parallel()
.mapToObj(RefUtil.wrapInterface((IntFunction) dataIndex -> {
@Nonnull final Tensor backSignal = new Tensor(inputDims);
final int[] ints = gradientMapA[dataIndex];
@Nullable final Tensor datum = data.get(dataIndex);
for (int i = 0; i < datum.length(); i++) {
backSignal.add(ints[i], datum.get(i));
}
datum.freeRef();
return backSignal;
}, data)).toArray(Tensor[]::new));
this.accumulator.accept(buffer, tensorArray);
} else {
if (null != buffer)
buffer.freeRef();
data.freeRef();
}
}
public @SuppressWarnings("unused")
void _free() {
super._free();
accumulator.freeRef();
}
}
}