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

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Here is the source code for com.simiacryptus.mindseye.layers.java.TensorConcatLayer.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.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 TensorConcatLayer extends LayerBase {

    @SuppressWarnings("unused")
    private static final Logger log = LoggerFactory.getLogger(TensorConcatLayer.class);
    private int maxBands;

    /**
     * Instantiates a new Img eval key.
     */
    public TensorConcatLayer() {
        setMaxBands(0);
    }

    /**
     * Instantiates a new Img eval key.
     *
     * @param json the json
     */
    protected TensorConcatLayer(@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 TensorConcatLayer fromJson(@Nonnull final JsonObject json, Map<CharSequence, byte[]> rs) {
        return new TensorConcatLayer(json);
    }

    @Nullable
    @Override
    public Result eval(@Nonnull final Result... inObj) {
        Arrays.stream(inObj).forEach(nnResult -> nnResult.addRef());
        final int numBatches = inObj[0].getData().length();
        assert Arrays.stream(inObj)
                .allMatch(x -> x.getData().length() == numBatches) : "All inputs must use same batch size";
        int[] outputDims = new int[] {
                Arrays.stream(inObj).mapToInt(x -> Tensor.length(x.getData().getDimensions())).sum() };

        @Nonnull
        final List<Tensor> 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<UUID> buffer, @Nonnull final TensorList data) -> {
                    assert numBatches == data.length();

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

    @Nonnull
    @Override
    public List<double[]> 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 TensorConcatLayer setMaxBands(int maxBands) {
        this.maxBands = maxBands;
        return this;
    }
}