org.deeplearning4j.scalnet.layers.pooling.AvgPooling3D.scala Maven / Gradle / Ivy

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A Scala wrapper for Deeplearning4j, inspired by Keras. Scala + DL + Spark + GPUs

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/*******************************************************************************
  * Copyright (c) 2015-2018 Skymind, Inc.
  *
  * This program and the accompanying materials are made available under the
  * terms of the Apache License, Version 2.0 which is available at
  * https://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.
  *
  * SPDX-License-Identifier: Apache-2.0
 ******************************************************************************/
package org.deeplearning4j.scalnet.layers.pooling

import org.deeplearning4j.nn.conf.layers.Subsampling3DLayer
import org.deeplearning4j.scalnet.layers.convolutional.Convolution
import org.deeplearning4j.scalnet.layers.core.Layer

/**
  * 3D average pooling layer in neural net architectures.
  *
  * @author Max Pumperla
  */
class AvgPooling3D(kernelSize: List[Int],
                   stride: List[Int] = List(1, 1, 1),
                   padding: List[Int] = List(0, 0, 0),
                   dilation: List[Int] = List(1, 1, 1),
                   nIn: Option[List[Int]] = None,
                   override val name: String = "")
    extends Convolution(dimension = 3, kernelSize, stride, padding, dilation, 0, nIn, 0)
    with Layer {
  if (kernelSize.length != 3 || stride.length != 3 || padding.length != 3 || dilation.length != 3) {
    throw new IllegalArgumentException("Kernel, stride, padding and dilation lists must all be length 3.")
  }

  override def reshapeInput(nIn: List[Int]): AvgPooling3D =
    new AvgPooling3D(kernelSize, stride, padding, dilation, Some(nIn), name)

  override def compile: org.deeplearning4j.nn.conf.layers.Layer =
    new Subsampling3DLayer.Builder()
      .poolingType(Subsampling3DLayer.PoolingType.AVG)
      .kernelSize(kernelSize.head, kernelSize(1), kernelSize(2))
      .stride(stride.head, stride(1), stride(2))
      .name(name)
      .build()
}

object AvgPooling3D {
  def apply(kernelSize: List[Int],
            stride: List[Int] = List(1, 1, 1),
            padding: List[Int] = List(0, 0, 0),
            dilation: List[Int] = List(1, 1, 1),
            nIn: Option[List[Int]] = None,
            name: String = null): AvgPooling3D =
    new AvgPooling3D(kernelSize, stride, padding, dilation, nIn, name)
}