com.intel.analytics.bigdl.nn.keras.GlobalAveragePooling3D.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._
import com.intel.analytics.bigdl.nn._
import com.intel.analytics.bigdl.nn.VolumetricAveragePooling
import com.intel.analytics.bigdl.nn.{Sequential => TSequential}
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 global average pooling operation for 3D data.
* Data format currently supported for this layer is 'CHANNEL_FIRST' (dimOrdering='th').
* Border mode currently supported for this layer is 'valid'.
* The input of this layer should be 5D.
*
* 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 dimOrdering Format of input data. Please use 'CHANNEL_FIRST' (dimOrdering='th').
* @tparam T The numeric type of parameter(e.g. weight, bias). Only support float/double now.
*/
class GlobalAveragePooling3D[T: ClassTag](
dimOrdering: String = "CHANNEL_FIRST",
inputShape: Shape = null)(implicit ev: TensorNumeric[T])
extends GlobalPooling3D[T](dimOrdering, inputShape) {
override def doBuild(inputShape: Shape): AbstractModule[Tensor[T], Tensor[T], T] = {
val input = inputShape.toSingle().toArray
val model = TSequential[T]()
val layer = VolumetricAveragePooling(
kT = input(2),
kW = input(4),
kH = input(3),
dT = 1,
dW = 1,
dH = 1,
countIncludePad = false)
model.add(layer)
model.add(Squeeze(5))
model.add(Squeeze(4))
model.add(Squeeze(3))
model.asInstanceOf[AbstractModule[Tensor[T], Tensor[T], T]]
}
}
object GlobalAveragePooling3D {
def apply[@specialized(Float, Double) T: ClassTag](
dimOrdering: String = "th",
inputShape: Shape = null)(implicit ev: TensorNumeric[T]) : GlobalAveragePooling3D[T] = {
new GlobalAveragePooling3D[T](KerasUtils.toBigDLFormat5D(dimOrdering), inputShape)
}
}