com.simiacryptus.mindseye.layers.java.ImgConcatLayer 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.JsonElement;
import com.google.gson.JsonObject;
import com.simiacryptus.mindseye.lang.*;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import javax.annotation.Nonnull;
import javax.annotation.Nullable;
import java.util.*;
/**
* Concatenates two or more images apply the same resolution so the output contains all input color bands.
*/
@SuppressWarnings("serial")
public class ImgConcatLayer extends LayerBase {
@SuppressWarnings("unused")
private static final Logger log = LoggerFactory.getLogger(ImgConcatLayer.class);
private int maxBands;
/**
* Instantiates a new Img eval key.
*/
public ImgConcatLayer() {
setMaxBands(0);
}
/**
* Instantiates a new Img eval key.
*
* @param json the json
*/
protected ImgConcatLayer(@Nonnull final JsonObject json) {
super(json);
JsonElement maxBands = json.get("maxBands");
if (null != maxBands) setMaxBands(maxBands.getAsInt());
}
/**
* From json img eval key.
*
* @param json the json
* @param rs the rs
* @return the img eval key
*/
public static ImgConcatLayer fromJson(@Nonnull final JsonObject json, Map rs) {
return new ImgConcatLayer(json);
}
@Nullable
@Override
public Result eval(@Nonnull final Result... inObj) {
Arrays.stream(inObj).forEach(nnResult -> nnResult.addRef());
assert Arrays.stream(inObj).allMatch(x -> x.getData().getDimensions().length == 3) : "This component is for use mapCoords 3d png tensors only";
final int numBatches = inObj[0].getData().length();
assert Arrays.stream(inObj).allMatch(x -> x.getData().length() == numBatches) : "All inputs must use same batch size";
@Nonnull final int[] outputDims = Arrays.copyOf(inObj[0].getData().getDimensions(), 3);
outputDims[2] = Arrays.stream(inObj).mapToInt(x -> x.getData().getDimensions()[2]).sum();
if (maxBands > 0) outputDims[2] = Math.min(maxBands, outputDims[2]);
assert Arrays.stream(inObj).allMatch(x -> x.getData().getDimensions()[0] == outputDims[0]) : "Inputs must be same size";
assert Arrays.stream(inObj).allMatch(x -> x.getData().getDimensions()[1] == outputDims[1]) : "Inputs must be same size";
@Nonnull final List outputTensors = new ArrayList<>();
for (int b = 0; b < numBatches; b++) {
@Nonnull final Tensor outputTensor = new Tensor(outputDims);
int pos = 0;
@Nullable final double[] outputTensorData = outputTensor.getData();
for (int i = 0; i < inObj.length; i++) {
@Nullable Tensor tensor = inObj[i].getData().get(b);
@Nullable final double[] data = tensor.getData();
System.arraycopy(data, 0, outputTensorData, pos, Math.min(data.length, outputTensorData.length - pos));
pos += data.length;
tensor.freeRef();
}
outputTensors.add(outputTensor);
}
return new Result(TensorArray.wrap(outputTensors.toArray(new Tensor[]{})), (@Nonnull final DeltaSet buffer, @Nonnull final TensorList data) -> {
assert numBatches == data.length();
@Nonnull final List splitBatches = new ArrayList<>();
for (int b = 0; b < numBatches; b++) {
@Nullable final Tensor tensor = data.get(b);
@Nonnull final Tensor[] outputTensors2 = new Tensor[inObj.length];
int pos = 0;
for (int i = 0; i < inObj.length; i++) {
@Nonnull final Tensor dest = new Tensor(inObj[i].getData().getDimensions());
@Nullable double[] tensorData = tensor.getData();
System.arraycopy(tensorData, pos, dest.getData(), 0, Math.min(dest.length(), tensorData.length - pos));
pos += dest.length();
outputTensors2[i] = dest;
}
tensor.freeRef();
splitBatches.add(outputTensors2);
}
@Nonnull final Tensor[][] splitData = new Tensor[inObj.length][];
for (int i = 0; i < splitData.length; i++) {
splitData[i] = new Tensor[numBatches];
}
for (int i = 0; i < inObj.length; i++) {
for (int b = 0; b < numBatches; b++) {
splitData[i][b] = splitBatches.get(b)[i];
}
}
for (int i = 0; i < inObj.length; i++) {
@Nonnull TensorArray tensorArray = TensorArray.wrap(splitData[i]);
inObj[i].accumulate(buffer, tensorArray);
}
}) {
@Override
protected void _free() {
Arrays.stream(inObj).forEach(nnResult -> nnResult.freeRef());
}
@Override
public boolean isAlive() {
for (@Nonnull final Result element : inObj)
if (element.isAlive()) {
return true;
}
return false;
}
};
}
@Nonnull
@Override
public JsonObject getJson(Map resources, DataSerializer dataSerializer) {
@Nonnull JsonObject json = super.getJsonStub();
json.addProperty("maxBands", maxBands);
return json;
}
@Nonnull
@Override
public List state() {
return Arrays.asList();
}
/**
* Gets max bands.
*
* @return the max bands
*/
public int getMaxBands() {
return maxBands;
}
/**
* Sets max bands.
*
* @param maxBands the max bands
* @return the max bands
*/
@Nonnull
public ImgConcatLayer setMaxBands(int maxBands) {
this.maxBands = maxBands;
return this;
}
}