com.simiacryptus.mindseye.layers.java.ImgCropLayer.java Source code

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Here is the source code for com.simiacryptus.mindseye.layers.java.ImgCropLayer.java

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/*
 * Copyright (c) 2018 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.*;

import javax.annotation.Nonnull;
import javax.annotation.Nullable;
import java.util.*;
import java.util.stream.IntStream;

/**
 * Reduces the resolution of the input by selecting a centered window. The output png will have the same number of
 * color bands.
 */
@SuppressWarnings("serial")
public class ImgCropLayer extends LayerBase {

    private final int sizeX;
    private final int sizeY;

    /**
     * Instantiates a new Img crop key.
     *
     * @param sizeX the size x
     * @param sizeY the size y
     */
    public ImgCropLayer(final int sizeX, final int sizeY) {
        super();
        this.sizeX = sizeX;
        this.sizeY = sizeY;
    }

    /**
     * Instantiates a new Img crop key.
     *
     * @param json the json
     */
    protected ImgCropLayer(@Nonnull final JsonObject json) {
        super(json);
        sizeX = json.getAsJsonPrimitive("sizeX").getAsInt();
        sizeY = json.getAsJsonPrimitive("sizeY").getAsInt();
    }

    /**
     * Copy condense tensor.
     *
     * @param inputData  the input data
     * @param outputData the output data
     * @return the tensor
     */
    @Nonnull
    public static Tensor copy(@Nonnull final Tensor inputData, @Nonnull final Tensor outputData) {
        @Nonnull
        final int[] inDim = inputData.getDimensions();
        @Nonnull
        final int[] outDim = outputData.getDimensions();
        assert 3 == inDim.length;
        assert 3 == outDim.length;
        assert inDim[2] == outDim[2] : Arrays.toString(inDim) + "; " + Arrays.toString(outDim);
        double fx = (inDim[0] - outDim[0]) / 2.0;
        double fy = (inDim[1] - outDim[1]) / 2.0;
        final int paddingX = (int) (fx < 0 ? Math.ceil(fx) : Math.floor(fx));
        final int paddingY = (int) (fy < 0 ? Math.ceil(fy) : Math.floor(fy));
        outputData.coordStream(true).forEach((c) -> {
            int x = c.getCoords()[0] + paddingX;
            int y = c.getCoords()[1] + paddingY;
            int z = c.getCoords()[2];
            int width = inputData.getDimensions()[0];
            int height = inputData.getDimensions()[1];
            double value;
            if (x < 0) {
                value = 0.0;
            } else if (x >= width) {
                value = 0.0;
            } else if (y < 0) {
                value = 0.0;
            } else if (y >= height) {
                value = 0.0;
            } else {
                value = inputData.get(x, y, z);
            }
            outputData.set(c, value);
        });
        return outputData;
    }

    /**
     * From json img crop key.
     *
     * @param json the json
     * @param rs   the rs
     * @return the img crop key
     */
    public static ImgCropLayer fromJson(@Nonnull final JsonObject json, Map<CharSequence, byte[]> rs) {
        return new ImgCropLayer(json);
    }

    @Nonnull
    @Override
    public Result eval(@Nonnull final Result... inObj) {
        Arrays.stream(inObj).forEach(nnResult -> nnResult.addRef());
        final Result input = inObj[0];
        final TensorList batch = input.getData();
        @Nonnull
        final int[] inputDims = batch.getDimensions();
        assert 3 == inputDims.length;
        return new Result(TensorArray.wrap(IntStream.range(0, batch.length()).parallel().mapToObj(dataIndex -> {
            @Nonnull
            final Tensor outputData = new Tensor(sizeX, sizeY, inputDims[2]);
            Tensor inputData = batch.get(dataIndex);
            ImgCropLayer.copy(inputData, outputData);
            inputData.freeRef();
            return outputData;
        }).toArray(i -> new Tensor[i])),
                (@Nonnull final DeltaSet<UUID> buffer, @Nonnull final TensorList error) -> {
                    if (input.isAlive()) {
                        @Nonnull
                        TensorArray tensorArray = TensorArray
                                .wrap(IntStream.range(0, error.length()).parallel().mapToObj(dataIndex -> {
                                    @Nullable
                                    final Tensor err = error.get(dataIndex);
                                    @Nonnull
                                    final Tensor passback = new Tensor(inputDims);
                                    copy(err, passback);
                                    err.freeRef();
                                    return passback;
                                }).toArray(i -> new Tensor[i]));
                        input.accumulate(buffer, tensorArray);
                    }
                }) {

            @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<CharSequence, byte[]> resources, DataSerializer dataSerializer) {
        @Nonnull
        final JsonObject json = super.getJsonStub();
        json.addProperty("sizeX", sizeX);
        json.addProperty("sizeY", sizeY);
        return json;
    }

    @Nonnull
    @Override
    public List<double[]> state() {
        return new ArrayList<>();
    }

}