com.intel.analytics.bigdl.nn.keras.Pooling3D.scala Maven / Gradle / Ivy
/*
* Copyright 2016 The BigDL Authors.
*
* Licensed 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.intel.analytics.bigdl.nn.keras
import com.intel.analytics.bigdl.tensor.Tensor
import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
import com.intel.analytics.bigdl.utils.Shape
import scala.reflect.ClassTag
/**
* Abstract class for different pooling 3D layers.
* Do not create a new instance of it or use it in a model.
* Please use its child classes, 'AveragePooling3D' and 'MaxPooling3D' instead.
*/
abstract class Pooling3D[T: ClassTag](
val poolSize: Array[Int] = Array(2, 2, 2),
val strides: Array[Int] = null,
val dimOrdering: String = "CHANNEL_FIRST",
val inputShape: Shape = null)(implicit ev: TensorNumeric[T])
extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) {
require(dimOrdering.toLowerCase() == "channel_first", s"Pooling3D currently only supports " +
s"format CHANNEL_FIRST, but got format $dimOrdering")
require(poolSize.length == 3,
s"For Pooling3D, poolSize should be of length 3 but got length ${poolSize.length}")
val strideValues: Array[Int] = if (strides == null) poolSize else strides
require(strideValues.length == 3,
s"For Pooling3D, strides should be of length 3 but got length ${strideValues.length}")
override def computeOutputShape(inputShape: Shape): Shape = {
val input = inputShape.toSingle().toArray
require(input.length == 5,
s"Pooling3D requires 5D input, but got input dim ${input.length}")
val dim1Length = KerasUtils.computeConvOutputLength(input(2), poolSize(0),
"valid", strideValues(0))
val dim2Length = KerasUtils.computeConvOutputLength(input(3), poolSize(1),
"valid", strideValues(1))
val dim3Length = KerasUtils.computeConvOutputLength(input(4), poolSize(2),
"valid", strideValues(2))
Shape(input(0), input(1), dim1Length, dim2Length, dim3Length)
}
}