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/*
* 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
import com.intel.analytics.bigdl.nn.abstractnn.{AbstractModule, Activity, TensorModule}
import com.intel.analytics.bigdl.tensor.Tensor
import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
import com.intel.analytics.bigdl.utils.T
import com.intel.analytics.bigdl.utils.serializer._
import com.intel.analytics.bigdl.utils.serializer.converters.DataConverter
import com.intel.analytics.bigdl.serialization.Bigdl.{AttrValue, BigDLModule}
import scala.reflect.ClassTag
/**
* Applies a spatial division operation on a series of 2D inputs using kernel for
* computing the weighted average in a neighborhood. The neighborhood is defined for
* a local spatial region that is the size as kernel and across all features. For
* an input image, since there is only one feature, the region is only spatial. For
* an RGB image, the weighted average is taken over RGB channels and a spatial region.
*
* If the kernel is 1D, then it will be used for constructing and separable 2D kernel.
* The operations will be much more efficient in this case.
*
* The kernel is generally chosen as a gaussian when it is believed that the correlation
* of two pixel locations decrease with increasing distance. On the feature dimension,
* a uniform average is used since the weighting across features is not known.
*
* @param nInputPlane number of input plane, default is 1.
* @param kernel kernel tensor, default is a 9 x 9 tensor.
* @param threshold threshold
* @param thresval threshhold value to replace with
* if data is smaller than theshold
*/
@SerialVersionUID(6036047576084619110L)
class SpatialDivisiveNormalization[T: ClassTag](
val nInputPlane: Int = 1,
var kernel: Tensor[T] = null,
val threshold: Double = 1e-4,
val thresval: Double = 1e-4
)(implicit ev: TensorNumeric[T]) extends TensorModule[T] {
if (kernel == null) kernel = Tensor.ones[T](9, 9)
private val kdim = kernel.nDimension()
require(kdim == 1 || kdim == 2, "averaging kernel must be 2D or 1D" +
s"averaging kernel dimension $kdim")
require(kernel.size(1) % 2 != 0, "averaging kernel must have ODD dimensions" +
s"averaging kernel dimension ${kernel.size(1)}")
if (kdim == 2) {
require(kernel.size(2) % 2 != 0, "averaging kernel must have ODD dimensions" +
s"averaging kernel dimension ${kernel.size(2)}")
}
val padH = math.floor(kernel.size(1).toFloat/2).toInt
val padW = if (kdim == 2) {
math.floor(kernel.size(2).toFloat/2).toInt
} else {
padH
}
// create convolutional mean estimator
private var meanestimator = new Sequential[T]()
meanestimator.add(new SpatialZeroPadding(padW, padW, padH, padH))
if (kdim == 2) {
meanestimator.add(new SpatialConvolution(nInputPlane, 1, kernel.size(2), kernel.size(1)))
} else {
meanestimator.add(new SpatialConvolutionMap[T](
SpatialConvolutionMap.oneToOne[T](nInputPlane), kernel.size(1), 1))
meanestimator.add(new SpatialConvolution(nInputPlane, 1, 1, kernel.size(1)))
}
meanestimator.add(new Replicate(nInputPlane, 1, 3))
// create convolutional std estimator
private var stdestimator = new Sequential[T]()
stdestimator.add(new Square())
stdestimator.add(new SpatialZeroPadding(padW, padW, padH, padH))
if (kdim == 2) {
stdestimator.add(new SpatialConvolution(nInputPlane, 1, kernel.size(2), kernel.size(1)))
} else {
stdestimator.add(new SpatialConvolutionMap[T](
SpatialConvolutionMap.oneToOne[T](nInputPlane), kernel.size(1), 1))
stdestimator.add(new SpatialConvolution(nInputPlane, 1, 1, kernel.size(1)))
}
stdestimator.add(new Replicate(nInputPlane, 1, 3))
stdestimator.add(new Sqrt())
// set kernel(parameters._1(0)) and bias(parameters._1(1))
if (kdim == 2) {
kernel.div(ev.times(kernel.sum(), ev.fromType[Int](nInputPlane)))
for (i <- 1 to nInputPlane) {
meanestimator.modules(1).parameters()._1(0)(1)(1)(i).copy(kernel)
stdestimator.modules(2).parameters()._1(0)(1)(1)(i).copy(kernel)
}
meanestimator.modules(1).parameters()._1(1).zero()
stdestimator.modules(2).parameters()._1(1).zero()
} else {
kernel.div(ev.times(kernel.sum(), ev.sqrt(ev.fromType[Int](nInputPlane))))
for (i <- 1 to nInputPlane) {
meanestimator.modules(1).parameters()._1(0)(i).copy(kernel)
meanestimator.modules(2).parameters()._1(0)(1)(1)(i).copy(kernel)
stdestimator.modules(2).parameters()._1(0)(i).copy(kernel)
stdestimator.modules(3).parameters()._1(0)(1)(1)(i).copy(kernel)
}
meanestimator.modules(1).parameters()._1(1).zero()
meanestimator.modules(2).parameters()._1(1).zero()
stdestimator.modules(2).parameters()._1(1).zero()
stdestimator.modules(3).parameters()._1(1).zero()
}
// other operation
private var normalizer = new CDivTable()
private var divider = new CDivTable()
private var thresholder = new Threshold(threshold, thresval)
// coefficient array, to adjust side effects
private var coef: Tensor[T] = Tensor(1, 1, 1)
private val ones: Tensor[T] = Tensor[T]()
private var adjustedstds: Tensor[T] = _
private var thresholdedstds: Tensor[T] = _
private var localstds: Tensor[T] = _
override def updateOutput(input: Tensor[T]): Tensor[T] = {
localstds = stdestimator.updateOutput(input).toTensor[T]
// compute side coefficients
val dim = input.dim()
if (localstds.dim() != coef.dim() || (input.size(dim) != coef.size(dim)) ||
(input.size(dim-1) != coef.size(dim-1)) ) {
if (dim == 4) {
// batch mode
ones.resizeAs(input(1)).fill(ev.fromType[Int](1))
val _coef = meanestimator.updateOutput(ones).toTensor[T]
coef = coef.resizeAs(_coef).copy(_coef).view(Array(1) ++ _coef.size()).expandAs(localstds)
} else {
ones.resizeAs(input).fill(ev.fromType[Int](1))
coef = meanestimator.updateOutput(ones).toTensor[T]
}
}
// normalize std dev
adjustedstds = divider.updateOutput(T(localstds, coef)).asInstanceOf[Tensor[T]]
thresholdedstds = thresholder.updateOutput(adjustedstds)
output = normalizer.updateOutput(T(input, thresholdedstds)).asInstanceOf[Tensor[T]]
output
}
override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = {
// resize grad
gradInput.resizeAs(input).zero()
// backprop through all modules
val gradnorm = normalizer.updateGradInput(T(input, thresholdedstds), gradOutput)
val gradadj = thresholder.updateGradInput(adjustedstds, gradnorm(2))
val graddiv = divider.updateGradInput(T(localstds, coef), gradadj)
gradInput.add(stdestimator.updateGradInput(input, graddiv(1)).toTensor[T])
gradInput.add(gradnorm[Tensor[T]](1))
gradInput
}
override def toString(): String = {
s"${getPrintName}($nInputPlane, kernelTensor, $threshold, $thresval)"
}
override def canEqual(other: Any): Boolean = {
other.isInstanceOf[SpatialDivisiveNormalization[T]]
}
override def equals(other: Any): Boolean = other match {
case that: SpatialDivisiveNormalization[T] =>
super.equals(that) &&
(that canEqual this) &&
kdim == that.kdim &&
padH == that.padH &&
padW == that.padW &&
meanestimator == that.meanestimator &&
stdestimator == that.stdestimator &&
normalizer == that.normalizer &&
divider == that.divider &&
thresholder == that.thresholder &&
nInputPlane == that.nInputPlane &&
kernel == that.kernel &&
threshold == that.threshold &&
thresval == that.thresval
case _ => false
}
override def hashCode(): Int = {
val state = Seq(super.hashCode(), kdim, padH, padW, meanestimator, stdestimator,
normalizer, divider, thresholder, nInputPlane, kernel, threshold, thresval)
state.map(_.hashCode()).foldLeft(0)((a, b) => 31 * a + b)
}
override def clearState() : this.type = {
super.clearState()
meanestimator.clearState()
stdestimator.clearState()
normalizer.clearState()
divider.clearState()
coef = Tensor(1, 1, 1)
ones.set()
adjustedstds = null
thresholdedstds = null
localstds = null
this
}
}
object SpatialDivisiveNormalization extends ModuleSerializable {
def apply[@specialized(Float, Double) T: ClassTag](
nInputPlane: Int = 1,
kernel: Tensor[T] = null,
threshold: Double = 1e-4,
thresval: Double = 1e-4)(
implicit ev: TensorNumeric[T]) : SpatialDivisiveNormalization[T] = {
new SpatialDivisiveNormalization[T](nInputPlane, kernel, threshold, thresval)
}
override def doLoadModule[T: ClassTag](context: DeserializeContext)
(implicit ev: TensorNumeric[T]) : AbstractModule[Activity, Activity, T] = {
val spatialDivisiveNormModule = super.doLoadModule(context).
asInstanceOf[SpatialDivisiveNormalization[T]]
val attrMap = context.bigdlModule.getAttrMap
spatialDivisiveNormModule.meanestimator = DataConverter.
getAttributeValue(context, attrMap.get("meanestimator")).
asInstanceOf[Sequential[T]]
spatialDivisiveNormModule.stdestimator = DataConverter.
getAttributeValue(context, attrMap.get("stdestimator")).
asInstanceOf[Sequential[T]]
spatialDivisiveNormModule.normalizer = DataConverter.
getAttributeValue(context, attrMap.get("normalizer")).
asInstanceOf[CDivTable[T]]
spatialDivisiveNormModule.divider = DataConverter.
getAttributeValue(context, attrMap.get("divider")).
asInstanceOf[CDivTable[T]]
spatialDivisiveNormModule.thresholder = DataConverter.
getAttributeValue(context, attrMap.get("thresholder")).
asInstanceOf[Threshold[T]]
spatialDivisiveNormModule
}
override def doSerializeModule[T: ClassTag](context: SerializeContext[T],
spatialDivisiveNormBuilder : BigDLModule.Builder)
(implicit ev: TensorNumeric[T]) : Unit = {
val spatialDivisiveNormModule = context.moduleData
.module.asInstanceOf[SpatialDivisiveNormalization[T]]
super.doSerializeModule(context, spatialDivisiveNormBuilder)
val meanestimatorBuilder = AttrValue.newBuilder
DataConverter.setAttributeValue(context, meanestimatorBuilder,
spatialDivisiveNormModule.meanestimator, ModuleSerializer.tensorModuleType)
spatialDivisiveNormBuilder.putAttr("meanestimator", meanestimatorBuilder.build)
val stdestimatorBuilder = AttrValue.newBuilder
DataConverter.setAttributeValue(context, stdestimatorBuilder,
spatialDivisiveNormModule.stdestimator, ModuleSerializer.tensorModuleType)
spatialDivisiveNormBuilder.putAttr("stdestimator", stdestimatorBuilder.build)
val normalizerBuilder = AttrValue.newBuilder
DataConverter.setAttributeValue(context, normalizerBuilder,
spatialDivisiveNormModule.normalizer, ModuleSerializer.tensorModuleType)
spatialDivisiveNormBuilder.putAttr("normalizer", normalizerBuilder.build)
val dividerBuilder = AttrValue.newBuilder
DataConverter.setAttributeValue(context, dividerBuilder,
spatialDivisiveNormModule.divider, ModuleSerializer.tensorModuleType)
spatialDivisiveNormBuilder.putAttr("divider", dividerBuilder.build)
val thresholderBuilder = AttrValue.newBuilder
DataConverter.setAttributeValue(context, thresholderBuilder,
spatialDivisiveNormModule.thresholder, ModuleSerializer.tensorModuleType)
spatialDivisiveNormBuilder.putAttr("thresholder", thresholderBuilder.build)
}
}
