com.simiacryptus.mindseye.layers.java.ImgTileSubnetLayer Maven / Gradle / Ivy
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Pure Java Neural Networks Components
/*
* 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.mindseye.lang.DataSerializer;
import com.simiacryptus.mindseye.lang.Layer;
import com.simiacryptus.mindseye.lang.Result;
import com.simiacryptus.mindseye.lang.TensorList;
import com.simiacryptus.mindseye.layers.WrapperLayer;
import javax.annotation.Nonnull;
import javax.annotation.Nullable;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
import java.util.Map;
import java.util.concurrent.atomic.AtomicInteger;
/**
* This key works as a scaling function, similar to a father wavelet. Allows convolutional and pooling layers to work
* across larger png regions.
*/
@SuppressWarnings("serial")
public class ImgTileSubnetLayer extends WrapperLayer {
private final int height;
private final int width;
private final int strideX;
private final int strideY;
/**
* Instantiates a new Rescaled subnet key.
*
* @param subnetwork the subnetwork
* @param width the width
* @param height the scale
* @param strideX the stride x
* @param strideY the stride y
*/
public ImgTileSubnetLayer(final Layer subnetwork, final int width, final int height, final int strideX, final int strideY) {
super(subnetwork);
this.height = height;
this.width = width;
this.strideX = strideX;
this.strideY = strideY;
}
/**
* Instantiates a new Img tile subnet key.
*
* @param subnetwork the subnetwork
* @param width the width
* @param height the height
*/
public ImgTileSubnetLayer(final Layer subnetwork, final int width, final int height) {
this(subnetwork, width, height, width, height);
}
/**
* Instantiates a new Rescaled subnet key.
*
* @param json the json
* @param rs the rs
*/
protected ImgTileSubnetLayer(@Nonnull final JsonObject json, Map rs) {
super(json, rs);
height = json.getAsJsonPrimitive("height").getAsInt();
width = json.getAsJsonPrimitive("width").getAsInt();
strideX = json.getAsJsonPrimitive("strideX").getAsInt();
strideY = json.getAsJsonPrimitive("strideY").getAsInt();
JsonObject subnetwork = json.getAsJsonObject("subnetwork");
}
/**
* From json rescaled subnet key.
*
* @param json the json
* @param rs the rs
* @return the rescaled subnet key
*/
public static ImgTileSubnetLayer fromJson(@Nonnull final JsonObject json, Map rs) {
return new ImgTileSubnetLayer(json, rs);
}
@Nullable
@Override
public Result evalAndFree(@Nonnull final Result... inObj) {
assert 1 == inObj.length;
Result input = inObj[0];
final TensorList inputData = input.getData();
@Nonnull final int[] inputDims = inputData.getDimensions();
assert 3 == inputDims.length;
int cols = (int) (Math.ceil((inputDims[0] - width) * 1.0 / strideX) + 1);
int rows = (int) (Math.ceil((inputDims[1] - height) * 1.0 / strideY) + 1);
if (cols == 1 && rows == 1) return getInner().evalAndFree(inObj);
Result[] results = new Result[rows * cols];
TensorList[] passback = new TensorList[rows * cols];
int index = 0;
AtomicInteger passbacks = new AtomicInteger(0);
for (int row = 0; row < rows; row++) {
for (int col = 0; col < cols; col++) {
input.addRef();
inputData.addRef();
int positionX = col * strideX;
int positionY = row * strideY;
assert positionX >= 0;
assert positionY >= 0;
assert positionX < inputDims[0];
assert positionY < inputDims[1];
final int finalIndex = index;
ImgTileSelectLayer tileSelectLayer = new ImgTileSelectLayer(width, height, positionX, positionY);
Result selectedTile = tileSelectLayer.evalAndFree(new Result(inputData, (ctx, delta) -> {
passback[finalIndex] = delta;
if (passbacks.incrementAndGet() == rows * cols) {
passbacks.set(0);
ImgTileAssemblyLayer imgTileAssemblyLayer = new ImgTileAssemblyLayer(cols, rows);
TensorList reassembled = imgTileAssemblyLayer.evalAndFree(Arrays.stream(passback).map(t -> new Result(t, (c2, d2) -> {
})).toArray(i -> new Result[i])).getDataAndFree();
imgTileAssemblyLayer.freeRef();
input.accumulate(ctx, reassembled);
}
}) {
@Override
protected void _free() {
input.freeRef();
super._free();
}
});
tileSelectLayer.freeRef();
results[index] = getInner().evalAndFree(selectedTile);
index = index + 1;
}
}
input.freeRef();
inputData.freeRef();
ImgTileAssemblyLayer imgTileAssemblyLayer = new ImgTileAssemblyLayer(cols, rows);
Result result = imgTileAssemblyLayer.evalAndFree(results);
imgTileAssemblyLayer.freeRef();
return result;
}
@Nonnull
@Override
public JsonObject getJson(Map resources, DataSerializer dataSerializer) {
@Nonnull final JsonObject json = super.getJson(resources, dataSerializer);
json.addProperty("height", height);
json.addProperty("width", width);
json.addProperty("strideX", strideX);
json.addProperty("strideY", strideY);
return json;
}
@Nonnull
@Override
public List state() {
return new ArrayList<>();
}
}