com.simiacryptus.mindseye.layers.java.ImgBandSelectLayer 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.JsonArray;
import com.google.gson.JsonObject;
import com.google.gson.JsonPrimitive;
import com.simiacryptus.mindseye.lang.*;
import javax.annotation.Nonnull;
import javax.annotation.Nullable;
import java.util.*;
import java.util.stream.IntStream;
/**
* Selects specific color bands from the input, producing an png apply the same resolution but fewer bands.
*/
@SuppressWarnings("serial")
public class ImgBandSelectLayer extends LayerBase {
private final int[] bands;
/**
* Instantiates a new Img band select key.
*
* @param bands the bands
*/
public ImgBandSelectLayer(final int... bands) {
super();
this.bands = bands;
}
/**
* Instantiates a new Img band select key.
*
* @param json the json
*/
protected ImgBandSelectLayer(@Nonnull final JsonObject json) {
super(json);
final JsonArray jsonArray = json.getAsJsonArray("bands");
bands = new int[jsonArray.size()];
for (int i = 0; i < bands.length; i++) {
bands[i] = jsonArray.get(i).getAsInt();
}
}
/**
* From json img band select key.
*
* @param json the json
* @param rs the rs
* @return the img band select key
*/
public static ImgBandSelectLayer fromJson(@Nonnull final JsonObject json, Map rs) {
return new ImgBandSelectLayer(json);
}
@Nonnull
@Override
public Result eval(@Nonnull final Result... inObj) {
final Result input = inObj[0];
final TensorList batch = input.getData();
@Nonnull final int[] inputDims = batch.getDimensions();
assert 3 == inputDims.length;
@Nonnull final Tensor outputDims = new Tensor(inputDims[0], inputDims[1], bands.length);
Arrays.stream(inObj).forEach(nnResult -> nnResult.addRef());
@Nonnull TensorArray wrap = TensorArray.wrap(IntStream.range(0, batch.length()).parallel()
.mapToObj(dataIndex -> outputDims.mapCoords((c) -> {
int[] coords = c.getCoords();
@Nullable Tensor tensor = batch.get(dataIndex);
double v = tensor.get(coords[0], coords[1], bands[coords[2]]);
tensor.freeRef();
return v;
}))
.toArray(i -> new Tensor[i]));
outputDims.freeRef();
return new Result(wrap, (@Nonnull final DeltaSet buffer, @Nonnull final TensorList error) -> {
if (input.isAlive()) {
@Nonnull TensorArray tensorArray = TensorArray.wrap(IntStream.range(0, error.length()).parallel()
.mapToObj(dataIndex -> {
@Nonnull final Tensor passback = new Tensor(inputDims);
@Nullable final Tensor err = error.get(dataIndex);
err.coordStream(false).forEach(c -> {
int[] coords = c.getCoords();
passback.set(coords[0], coords[1], bands[coords[2]], err.get(c));
});
err.freeRef();
return passback;
}).toArray(i -> new Tensor[i]));
input.accumulate(buffer, tensorArray);
}
error.freeRef();
}) {
@Override
protected void _free() {
Arrays.stream(inObj).forEach(nnResult -> nnResult.freeRef());
}
@Override
public boolean isAlive() {
return input.isAlive() || !isFrozen();
}
};
}
@Nonnull
@Override
public JsonObject getJson(Map resources, DataSerializer dataSerializer) {
@Nonnull final JsonObject json = super.getJsonStub();
@Nonnull final JsonArray array = new JsonArray();
for (final int b : bands) {
array.add(new JsonPrimitive(b));
}
json.add("bands", array);
return json;
}
@Nonnull
@Override
public List state() {
return new ArrayList<>();
}
}