com.intel.analytics.bigdl.nn.keras.Pooling2D.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.nn.abstractnn.DataFormat
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 2D layers.
* Do not create a new instance of it or use it in a model.
* Please use its child classes, 'AveragePooling2D' and 'MaxPooling2D' instead.
*/
abstract class Pooling2D[T: ClassTag](
val poolSize: Array[Int] = Array(2, 2),
val strides: Array[Int] = null,
val borderMode: String = "valid",
val dimOrdering: DataFormat = DataFormat.NCHW,
val inputShape: Shape = null)(implicit ev: TensorNumeric[T])
extends KerasLayer[Tensor[T], Tensor[T], T](KerasLayer.addBatch(inputShape)) {
require(poolSize.length == 2,
s"For Pooling2D, poolSize should be of length 2 but got length ${poolSize.length}")
require(borderMode == "valid" || borderMode == "same", s"Invalid border mode for " +
s"Pooling2D: $borderMode")
val strideValues: Array[Int] = if (strides == null) poolSize else strides
require(strideValues.length == 2,
s"For Pooling2D, strides should be of length 2 but got length ${strideValues.length}")
override def computeOutputShape(inputShape: Shape): Shape = {
val input = inputShape.toSingle().toArray
require(input.length == 4,
s"Pooling2D requires 4D input, but got input dim ${input.length}")
val (dimH, dimW, dimC) = dimOrdering.getHWCDims(4)
val rows = KerasUtils.computeConvOutputLength(input(dimH -1), poolSize(0),
borderMode, strideValues(0))
val cols = KerasUtils.computeConvOutputLength(input(dimW -1), poolSize(1),
borderMode, strideValues(1))
dimOrdering match {
case DataFormat.NCHW => Shape(input(0), input(1), rows, cols)
case DataFormat.NHWC => Shape(input(0), rows, cols, input(3))
}
}
}