Java tutorial
/* * 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 or expands png resolution by rearranging the values in NxN tiles to effectively stripe the small-scale * spacial dimension across N^2 color bands. */ @SuppressWarnings("serial") public class ImgReshapeLayer extends LayerBase { private final boolean expand; private final int kernelSizeX; private final int kernelSizeY; /** * Instantiates a new Img reshapeCast key. * * @param kernelSizeX the kernel size x * @param kernelSizeY the kernel size y * @param expand the expandPlasma */ public ImgReshapeLayer(final int kernelSizeX, final int kernelSizeY, final boolean expand) { super(); this.kernelSizeX = kernelSizeX; this.kernelSizeY = kernelSizeY; this.expand = expand; } /** * Instantiates a new Img reshapeCast key. * * @param json the json */ protected ImgReshapeLayer(@Nonnull final JsonObject json) { super(json); kernelSizeX = json.getAsJsonPrimitive("kernelSizeX").getAsInt(); kernelSizeY = json.getAsJsonPrimitive("kernelSizeY").getAsInt(); expand = json.getAsJsonPrimitive("expandPlasma").getAsBoolean(); } /** * Copy condense tensor. * * @param inputData the input data * @param outputData the output data * @return the tensor */ @Nonnull public static Tensor copyCondense(@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[0] >= outDim[0]; assert inDim[1] >= outDim[1]; assert inDim[2] < outDim[2]; assert 0 == inDim[0] % outDim[0]; assert 0 == inDim[1] % outDim[1]; final int kernelSizeX = inDim[0] / outDim[0]; final int kernelSizeY = inDim[0] / outDim[0]; int index = 0; @Nullable final double[] outputDataData = outputData.getData(); for (int z = 0; z < inDim[2]; z++) { for (int xx = 0; xx < kernelSizeX; xx++) { for (int yy = 0; yy < kernelSizeY; yy++) { for (int y = 0; y < inDim[1]; y += kernelSizeY) { for (int x = 0; x < inDim[0]; x += kernelSizeX) { outputDataData[index++] = inputData.get(x + xx, y + yy, z); } } } } } return outputData; } /** * Copy expandPlasma tensor. * * @param inputData the input data * @param outputData the output data * @return the tensor */ @Nonnull public static Tensor copyExpand(@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[0] <= outDim[0]; assert inDim[1] <= outDim[1]; assert inDim[2] > outDim[2]; assert 0 == outDim[0] % inDim[0]; assert 0 == outDim[1] % inDim[1]; final int kernelSizeX = outDim[0] / inDim[0]; final int kernelSizeY = outDim[0] / inDim[0]; int index = 0; for (int z = 0; z < outDim[2]; z++) { for (int xx = 0; xx < kernelSizeX; xx++) { for (int yy = 0; yy < kernelSizeY; yy++) { for (int y = 0; y < outDim[1]; y += kernelSizeY) { for (int x = 0; x < outDim[0]; x += kernelSizeX) { outputData.set(x + xx, y + yy, z, inputData.getData()[index++]); } } } } } return outputData; } /** * From json img reshapeCast key. * * @param json the json * @param rs the rs * @return the img reshapeCast key */ public static ImgReshapeLayer fromJson(@Nonnull final JsonObject json, Map<CharSequence, byte[]> rs) { return new ImgReshapeLayer(json); } @Nonnull @Override public Result eval(@Nonnull final Result... inObj) { //assert Arrays.stream(inObj).flatMapToDouble(input-> input.getData().stream().flatMapToDouble(x-> Arrays.stream(x.getData()))).allMatch(v->Double.isFinite(v)); 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; assert expand || 0 == inputDims[0] % kernelSizeX : (inputDims[0] + " % " + kernelSizeX); assert expand || 0 == inputDims[1] % kernelSizeY : (inputDims[1] + " % " + kernelSizeY); assert !expand || 0 == inputDims[2] % (kernelSizeX * kernelSizeY); //assert input.getData().stream().flatMapToDouble(x-> Arrays.stream(x.getData())).allMatch(v->Double.isFinite(v)); Tensor outputDims; if (expand) { outputDims = new Tensor(inputDims[0] * kernelSizeX, inputDims[1] * kernelSizeY, inputDims[2] / (kernelSizeX * kernelSizeY)); } else { outputDims = new Tensor(inputDims[0] / kernelSizeX, inputDims[1] / kernelSizeY, inputDims[2] * kernelSizeX * kernelSizeY); } TensorArray data = TensorArray.wrap(IntStream.range(0, batch.length()).parallel().mapToObj(dataIndex -> { Tensor inputData = batch.get(dataIndex); Tensor tensor = expand ? ImgReshapeLayer.copyExpand(inputData, outputDims.copy()) : ImgReshapeLayer.copyCondense(inputData, outputDims.copy()); inputData.freeRef(); return tensor; }).toArray(i -> new Tensor[i])); outputDims.freeRef(); return new Result(data, (@Nonnull final DeltaSet<UUID> buffer, @Nonnull final TensorList error) -> { //assert error.stream().flatMapToDouble(x-> Arrays.stream(x.getData())).allMatch(v->Double.isFinite(v)); 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); Tensor tensor = expand ? ImgReshapeLayer.copyCondense(err, passback) : ImgReshapeLayer.copyExpand(err, passback); err.freeRef(); return tensor; }).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("kernelSizeX", kernelSizeX); json.addProperty("kernelSizeY", kernelSizeX); json.addProperty("expandPlasma", expand); return json; } @Nonnull @Override public List<double[]> state() { return new ArrayList<>(); } }