com.simiacryptus.mindseye.layers.java.ProductInputsLayer Maven / Gradle / Ivy

Go to download
Show more of this group Show more artifacts with this name
Show all versions of mindseye-java Show documentation
Pure Java Neural Networks Components
The newest version!
/*
 * 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.JsonObject;
import com.simiacryptus.mindseye.lang.*;
import com.simiacryptus.ref.lang.RefUtil;
import com.simiacryptus.ref.wrappers.RefArrays;
import com.simiacryptus.ref.wrappers.RefIntStream;
import com.simiacryptus.ref.wrappers.RefList;
import org.jetbrains.annotations.NotNull;

import javax.annotation.Nonnull;
import javax.annotation.Nullable;
import java.util.Map;
import java.util.UUID;
import java.util.function.Function;
import java.util.function.IntFunction;

import static com.simiacryptus.mindseye.lang.Result.anyAlive;

/**
 * The type Product inputs layer.
 */
@SuppressWarnings("serial")
public class ProductInputsLayer extends LayerBase {

  /**
   * Instantiates a new Product inputs layer.
   */
  public ProductInputsLayer() {
  }

  /**
   * Instantiates a new Product inputs layer.
   *
   * @param id the id
   */
  protected ProductInputsLayer(@Nonnull final JsonObject id) {
    super(id);
  }

  /**
   * From json product inputs layer.
   *
   * @param json the json
   * @param rs   the rs
   * @return the product inputs layer
   */
  @Nonnull
  @SuppressWarnings("unused")
  public static ProductInputsLayer fromJson(@Nonnull final JsonObject json, Map rs) {
    return new ProductInputsLayer(json);
  }

  @Nonnull
  @Override
  public Result eval(@Nonnull final Result... inObj) {
    assert inObj.length > 1;
    for (int i = 1; i < inObj.length; i++) {
      TensorList temp_57_0011 = inObj[0].getData();
      final int dim0 = Tensor.length(temp_57_0011.getDimensions());
      temp_57_0011.freeRef();
      TensorList temp_57_0012 = inObj[i].getData();
      final int dimI = Tensor.length(temp_57_0012.getDimensions());
      temp_57_0012.freeRef();
      if (dim0 != 1 && dimI != 1 && dim0 != dimI) {
        TensorList temp_57_0013 = inObj[0].getData();
        TensorList temp_57_0014 = inObj[i].getData();
        IllegalArgumentException temp_57_0010 = new IllegalArgumentException(
            RefArrays.toString(temp_57_0013.getDimensions()) + " != "
                + RefArrays.toString(temp_57_0014.getDimensions()));
        temp_57_0014.freeRef();
        temp_57_0013.freeRef();
        RefUtil.freeRef(inObj);
        throw temp_57_0010;
      }
    }
    boolean alive = anyAlive(RefUtil.addRef(inObj));
    TensorList data = fwd(RefUtil.addRef(inObj));
    Accumulator accumulator = new Accumulator(inObj);
    return new Result(data, accumulator, alive);
  }

  @Nonnull
  @Override
  public JsonObject getJson(Map resources, DataSerializer dataSerializer) {
    return super.getJsonStub();
  }

  @Nonnull
  @Override
  public RefList state() {
    return RefArrays.asList();
  }

  public @SuppressWarnings("unused")
  void _free() {
    super._free();
  }

  @Nonnull
  public @Override
  @SuppressWarnings("unused")
  ProductInputsLayer addRef() {
    return (ProductInputsLayer) super.addRef();
  }

  @NotNull
  private TensorList fwd(@Nonnull Result[] inObj) {
    return RefUtil.get(RefArrays.stream(inObj).parallel().map(x -> {
      return Result.getData(x);
    }).reduce((l, r) -> {
      return new TensorArray(
          RefIntStream.range(0, Math.max(l.length(), r.length())).parallel()
              .mapToObj(RefUtil.wrapInterface((IntFunction) i1 -> {
                return Tensor.product(
                    l.get(1 == l.length() ? 0 : i1),
                    r.get(1 == r.length() ? 0 : i1));
              }, l, r)).toArray(Tensor[]::new));
    }));
  }

  private static class Accumulator extends Result.Accumulator {

    private final Result[] inObj;

    /**
     * Instantiates a new Accumulator.
     *
     * @param inObj the in obj
     */
    public Accumulator(Result... inObj) {
      this.inObj = inObj;
    }

    @Override
    public void accept(@Nullable DeltaSet buffer, @Nonnull TensorList delta) {
      for (@Nonnull final Result input : inObj) {
        if (input.isAlive()) {
          @Nonnull
          TensorList passback = RefUtil.get(RefArrays.stream(RefUtil.addRef(inObj)).parallel()
              .map(RefUtil.wrapInterface((Function) x -> {
                TensorList temp_57_0004 = x == input ? delta.addRef() : x.getData();
                x.freeRef();
                return temp_57_0004;
              }, delta.addRef(), input.addRef())).reduce((l, r) -> {
                return new TensorArray(RefIntStream.range(0, Math.max(l.length(), r.length()))
                    .parallel().mapToObj(RefUtil.wrapInterface((IntFunction) j -> {
                      @Nullable final Tensor left = l.get(1 == l.length() ? 0 : j);
                      @Nullable final Tensor right = r.get(1 == r.length() ? 0 : j);
                      Tensor temp_57_0006 = Tensor.product(left.addRef(),
                          right.addRef());
                      right.freeRef();
                      left.freeRef();
                      return temp_57_0006;
                    }, l, r)).toArray(Tensor[]::new));
              }));
          final TensorList inputData = input.getData();
          if (1 == inputData.length() && 1 < passback.length()) {
            TensorArray passback1 = new TensorArray(RefUtil.get(passback.stream().reduce((a, b) -> {
              return Tensor.add(a, b);
            })));
            passback.freeRef();
            passback = passback1;
          }
          if (1 == Tensor.length(inputData.getDimensions()) && 1 < Tensor.length(passback.getDimensions())) {
            TensorArray passback1 = new TensorArray(passback.stream().map(a -> {
              Tensor temp_57_0008 = new Tensor(a.sum());
              a.freeRef();
              return temp_57_0008;
            }).toArray(Tensor[]::new));
            passback.freeRef();
            passback = passback1;
          }
          inputData.freeRef();
          DeltaSet buffer1 = buffer == null ? null : buffer.addRef();
          Result.Accumulator accumulator = input.getAccumulator();
          try {
            accumulator.accept(buffer1, passback);
          } finally {
            accumulator.freeRef();
          }
        }
      }
      delta.freeRef();
      if (null != buffer)
        buffer.freeRef();
    }

    public @SuppressWarnings("unused")
    void _free() {
      super._free();
      RefUtil.freeRef(inObj);
    }
  }
}