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com.intel.analytics.bigdl.nn.mkldnn.MaxPooling.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.mkldnn
import com.intel.analytics.bigdl.mkl._
import com.intel.analytics.bigdl.nn.Utils
import com.intel.analytics.bigdl.nn.abstractnn.Activity
import com.intel.analytics.bigdl.tensor.Tensor
class MaxPooling(
kW: Int,
kH: Int,
dW: Int = 1,
dH: Int = 1,
padW: Int = 0,
padH: Int = 0
) extends MklDnnLayer {
@transient private var workSpaceFormat: MemoryData = _
@transient private var workSpace: Tensor[Float] = _
@transient private var fwdMemPrims: Array[Long] = _
@transient private var bwdMemPrims: Array[Long] = _
@transient private var paddingTL: Array[Int] = _
@transient private var paddingBR: Array[Int] = _
@transient private var fwdPD: Long = _
override private[mkldnn] def initFwdPrimitives(inputs: Array[MemoryData], phase: Phase) = {
_inputFormats = singleNativeData(inputs)
val strides = Array(dW, dH)
val kernel = Array(kH, kW)
val n = _inputFormats(0).shape(0)
val c = _inputFormats(0).shape(1)
val h = _inputFormats(0).shape(2)
val w = _inputFormats(0).shape(3)
val (pt, pb, pl, pr, oh, ow) =
Utils.getPaddingAndOutputSize(h, w, dH, dW, kH, kW, padH, padW)
paddingTL = Array(pt, pl)
paddingBR = Array(pb, pr)
Utils.getSAMEOutSizeAndPadding(h, w, dH, dW, kH, kW)
Utils.getOutSizeAndPaddingForDNN(h, w, dH, dW, kH, kW, padH, padW, true)
val outputMD = MklDnn.MemoryDescInit(4, Array(n, c, oh, ow), DataType.F32, Memory.Format.any)
val description = MklDnn.PoolingForwardDescInit(
PropKind.Forward, AlgKind.PoolingMax,
_inputFormats(0).getMemoryDescription(), outputMD, strides, kernel, paddingTL, paddingBR,
MklDnn.PaddingKind.mkldnnPaddingZero)
fwdPD = MklDnn.PrimitiveDescCreate(description, runtime.engine, 0L)
_outputFormats = Array(MemoryData.primitiveOutput(fwdPD))
output = initTensor(_outputFormats(0))
workSpaceFormat = MemoryData.primitiveWorkSpace(fwdPD)
workSpace = initTensor(workSpaceFormat)
updateOutputPrimitives = Array(MklDnn.PrimitiveCreate2(fwdPD,
_inputFormats.map(_.getPrimitive(runtime)), Array(0), 1,
Array(_outputFormats(0), workSpaceFormat).map(_.getPrimitive(runtime)), 2))
fwdMemPrims = Array(_inputFormats(0), _outputFormats(0), workSpaceFormat)
.map(_.getPrimitive(runtime))
(_inputFormats, _outputFormats)
}
override private[mkldnn] def initBwdPrimitives(grad: Array[MemoryData], phase: Phase) = {
_gradOutputFormats = singleNativeData(grad)
_gradOutputFormatsForWeight = _gradOutputFormats
val strides = Array(dW, dH)
val kernel = Array(kH, kW)
val description = MklDnn.PoolingBackwardDescInit(AlgKind.PoolingMax,
_inputFormats(0).getMemoryDescription(),
_gradOutputFormats(0).getMemoryDescription(),
strides, kernel, paddingTL, paddingBR, MklDnn.PaddingKind.mkldnnPaddingZero)
val pd = MklDnn.PrimitiveDescCreate(description, runtime.engine, fwdPD)
_gradInputFormats = Array(MemoryData.primitiveGradInput(pd))
updateGradInputPrimitives = Array(MklDnn.PrimitiveCreate2(pd,
Array(_gradOutputFormats(0), workSpaceFormat).map(_.getPrimitive(runtime)),
Array(0, 0), 2, _gradInputFormats.map(_.getPrimitive(runtime)), 1))
gradInput = initTensor(_gradInputFormats(0))
bwdMemPrims = Array(_inputFormats(0), _gradOutputFormats(0), workSpaceFormat,
_gradInputFormats(0)).map(_.getPrimitive(runtime))
(_gradOutputFormats, _gradInputFormats)
}
override def updateOutput(input: Activity): Activity = {
val buffer = Array(input.asInstanceOf[Tensor[Float]], output.asInstanceOf[Tensor[Float]],
workSpace)
MklDnnOps.streamSubmit(runtime.stream, 1, updateOutputPrimitives, 1, fwdMemPrims, buffer)
output
}
override def updateGradInput(input: Activity, gradOutput: Activity): Activity = {
val buffer = Array(
input.asInstanceOf[Tensor[Float]], gradOutput.asInstanceOf[Tensor[Float]], workSpace,
gradInput.asInstanceOf[Tensor[Float]])
MklDnnOps.streamSubmit(runtime.stream, 1, updateGradInputPrimitives, 1,
bwdMemPrims, buffer)
gradInput
}
}
object MaxPooling {
def apply(
kW: Int,
kH: Int,
dW: Int = 1,
dH: Int = 1,
padW: Int = 0,
padH: Int = 0
): MaxPooling = new MaxPooling(kW, kH, dW, dH, padW, padH)
}