Many resources are needed to download a project. Please understand that we have to compensate our server costs. Thank you in advance. Project price only 1 $
You can buy this project and download/modify it how often you want.
/*
* 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.SpatialMaxPooling
import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, 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
/**
* Applies max pooling operation for spatial data.
* The input of this layer should be 4D.
*
* When you use this layer as the first layer of a model, you need to provide the argument
* inputShape (a Single Shape, does not include the batch dimension).
*
* @param poolSize Int array of length 2 corresponding to the downscale vertically and
* horizontally. Default is (2, 2), which will halve the image in each dimension.
* @param strides Int array of length 2. Stride values. Default is null, and in this case it will
* be equal to poolSize.
* @param borderMode Either 'valid' or 'same'. Default is 'valid'.
* @param dimOrdering Format of input data. Either DataFormat.NCHW (dimOrdering='th') or
* DataFormat.NHWC (dimOrdering='tf'). Default is NCHW.
* @tparam T Numeric type of parameter(e.g. weight, bias). Only support float/double now.
*/
class MaxPooling2D[T: ClassTag] (
poolSize: Array[Int] = Array(2, 2),
strides: Array[Int] = null,
borderMode: String = "valid",
dimOrdering: DataFormat = DataFormat.NCHW,
inputShape: Shape = null)(implicit ev: TensorNumeric[T])
extends Pooling2D[T](poolSize, strides, borderMode, dimOrdering, inputShape) {
override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = {
val pads = KerasUtils.getPadsFromBorderMode(borderMode)
val layer = SpatialMaxPooling(
kW = poolSize(1),
kH = poolSize(0),
dW = strideValues(1),
dH = strideValues(0),
padW = pads._2,
padH = pads._1,
format = dimOrdering)
layer.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]]
}
}
object MaxPooling2D {
def apply[@specialized(Float, Double) T: ClassTag](
poolSize: (Int, Int) = (2, 2),
strides: (Int, Int) = null,
borderMode: String = "valid",
dimOrdering: String = "th",
inputShape: Shape = null)
(implicit ev: TensorNumeric[T]): MaxPooling2D[T] = {
val strideValues = if (strides != null) Array(strides._1, strides._2) else null
new MaxPooling2D[T](Array(poolSize._1, poolSize._2), strideValues,
borderMode, KerasUtils.toBigDLFormat(dimOrdering), inputShape)
}
}
