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com.intel.analytics.bigdl.nn.ops.DepthwiseConv2D.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.ops
import com.intel.analytics.bigdl.nn.{SpatialConvolution, SpatialSeparableConvolution}
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.Table
import com.intel.analytics.bigdl.utils.tf.loaders.Adapter
import scala.reflect.ClassTag
class DepthwiseConv2D[T: ClassTag](
strideW: Int, strideH: Int,
padW: Int, padH: Int,
dataFormat: DataFormat
)(implicit ev: TensorNumeric[T]) extends Operation[Table, Tensor[T], T] {
private var conv: SpatialConvolution[T] = _
private var channelMultiplier = 0
override def updateOutput(inputs: Table): Tensor[T] = {
val input: Tensor[T] = inputs[Tensor[T]](1)
val filter: Tensor[T] = inputs[Tensor[T]](2)
val channelDim = if (dataFormat == DataFormat.NHWC) 4 else 2
val kHDim = if (dataFormat == DataFormat.NHWC) 1 else 3
val kWDim = if (dataFormat == DataFormat.NHWC) 2 else 4
if (conv == null) {
channelMultiplier = filter.size(channelDim)
conv = SpatialConvolution(
nInputPlane = input.size(channelDim),
nOutputPlane = channelMultiplier * input.size(channelDim),
kernelH = filter.size(kHDim),
kernelW = filter.size(kWDim),
strideH = strideH,
strideW = strideW,
padH = padH,
padW = padW,
withBias = false,
format = dataFormat
)
conv.weight.zero()
}
SpatialSeparableConvolution.copyWeight(conv.weight, input.size(channelDim), channelMultiplier,
filter, dataFormat)
output = conv.forward(input)
output
}
}
object DepthwiseConv2D {
def apply[T: ClassTag](
strideW: Int, strideH: Int,
padW: Int, padH: Int,
dataFormat: DataFormat = DataFormat.NHWC
)(implicit ev: TensorNumeric[T]): DepthwiseConv2D[T] =
new DepthwiseConv2D(strideW, strideH, padW, padH, dataFormat)
}
private[bigdl] class DepthwiseConv2DBackpropInput[T: ClassTag](
strideW: Int, strideH: Int,
padW: Int, padH: Int,
dataFormat: DataFormat
)(implicit ev: TensorNumeric[T]) extends Operation[Table, Tensor[T], T] {
private var conv: SpatialConvolution[T] = _
private var channelMultiplier = 0
private val dummyInput = Tensor[T]()
override def updateOutput(inputs: Table): Tensor[T] = {
val inputSize: Tensor[Int] = inputs[Tensor[Int]](1)
val filter: Tensor[T] = inputs[Tensor[T]](2)
val gradOutput: Tensor[T] = inputs[Tensor[T]](3)
val channelDim = if (dataFormat == DataFormat.NHWC) 4 else 2
val kHDim = if (dataFormat == DataFormat.NHWC) 1 else 3
val kWDim = if (dataFormat == DataFormat.NHWC) 2 else 4
dummyInput.resize(inputSize.toArray())
if (conv == null) {
channelMultiplier = filter.size(4)
conv = SpatialConvolution(
nInputPlane = inputSize.valueAt(channelDim),
nOutputPlane = channelMultiplier * inputSize.valueAt(channelDim),
kernelH = filter.size(kHDim),
kernelW = filter.size(kWDim),
strideH = strideH,
strideW = strideW,
padH = padH,
padW = padW,
withBias = false,
format = dataFormat
)
conv.weight.zero()
conv.forward(dummyInput)
}
SpatialSeparableConvolution.copyWeight(conv.weight, inputSize.valueAt(channelDim),
channelMultiplier, filter, dataFormat)
output = conv.updateGradInput(dummyInput, gradOutput)
output
}
}
private[bigdl] object DepthwiseConv2DBackpropInput {
def apply[T: ClassTag](
strideW: Int, strideH: Int,
padW: Int, padH: Int,
dataFormat: DataFormat
)(implicit ev: TensorNumeric[T]): DepthwiseConv2DBackpropInput[T] =
new DepthwiseConv2DBackpropInput(strideW, strideH, padW, padH, dataFormat)
}
private[bigdl] class DepthwiseConv2DBackpropFilter[T: ClassTag](
strideW: Int, strideH: Int,
padW: Int, padH: Int,
dataFormat: DataFormat
)(implicit ev: TensorNumeric[T]) extends Operation[Table, Tensor[T], T] {
private var conv: SpatialConvolution[T] = _
private var channelMultiplier = 0
override def updateOutput(inputs: Table): Tensor[T] = {
val input: Tensor[T] = inputs[Tensor[T]](1)
val filterSize: Tensor[Int] = inputs[Tensor[Int]](2)
val gradOutput: Tensor[T] = inputs[Tensor[T]](3)
val channelDim = if (dataFormat == DataFormat.NHWC) 4 else 2
val kHDim = if (dataFormat == DataFormat.NHWC) 1 else 3
val kWDim = if (dataFormat == DataFormat.NHWC) 2 else 4
if (conv == null) {
channelMultiplier = filterSize.valueAt(4)
conv = SpatialConvolution(
nInputPlane = input.size(channelDim),
nOutputPlane = channelMultiplier * input.size(channelDim),
kernelH = filterSize.valueAt(kHDim),
kernelW = filterSize.valueAt(kWDim),
strideH = strideH,
strideW = strideW,
padH = padH,
padW = padW,
withBias = false,
format = dataFormat
)
}
conv.forward(input)
conv.zeroGradParameters()
conv.accGradParameters(input, gradOutput)
output.resize(filterSize.toArray())
SpatialSeparableConvolution.copyDepthGradWeight(input.size(channelDim), channelMultiplier,
conv.gradWeight, output, dataFormat)
output
}
}
private[bigdl] object DepthwiseConv2DBackpropFilter {
def apply[T: ClassTag](
strideW: Int, strideH: Int,
padW: Int, padH: Int,
dataFormat: DataFormat
)(implicit ev: TensorNumeric[T]): DepthwiseConv2DBackpropFilter[T] =
new DepthwiseConv2DBackpropFilter(strideW, strideH, padW, padH, dataFormat)
}
