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com.intel.analytics.bigdl.nn.SpatialZeroPadding.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
import com.intel.analytics.bigdl.nn.abstractnn.TensorModule
import com.intel.analytics.bigdl.tensor.Tensor
import com.intel.analytics.bigdl.tensor.TensorNumericMath.TensorNumeric
import scala.reflect.ClassTag
/**
* Each feature map of a given input is padded with specified number of zeros.
* If padding values are negative, then input is cropped.
* @param padLeft pad left position
* @param padRight pad right position
* @param padTop pad top position
* @param padBottom pad bottom position
*/
@SerialVersionUID(- 5144173515559923276L)
class SpatialZeroPadding[T: ClassTag](
padLeft: Int, padRight: Int, padTop: Int, padBottom: Int)(
implicit ev: TensorNumeric[T]) extends TensorModule[T] {
def this(padLeft: Int)(implicit ev: TensorNumeric[T]) = this(padLeft, padLeft, padLeft, padLeft)
override def updateOutput(input: Tensor[T]): Tensor[T] = {
if (input.dim() == 3) {
// sizes
val h = input.size(2) + this.padTop + this.padBottom
val w = input.size(3) + this.padLeft + this.padRight
if (w < 1 || h < 1) {
throw new IllegalArgumentException("input is too small")
}
this.output.resize(Array(input.size(1), h, w))
this.output.zero()
// crop input if necessary
var cInput = input
if (this.padTop < 0) cInput =
cInput.narrow(2, 1 - this.padTop, cInput.size(2) + this.padTop)
if (this.padBottom < 0) cInput =
cInput.narrow(2, 1, cInput.size(2) + this.padBottom)
if (this.padLeft < 0) cInput =
cInput.narrow(3, 1 - this.padLeft, cInput.size(3) + this.padLeft)
if (this.padRight < 0) cInput = cInput.narrow(3, 1, cInput.size(3) + this.padRight)
// crop output if necessary
var cOutput = output
if (this.padTop > 0) cOutput =
cOutput.narrow(2, 1 + this.padTop, cOutput.size(2) - this.padTop)
if (this.padBottom > 0) cOutput =
cOutput.narrow(2, 1, cOutput.size(2) - this.padBottom)
if (this.padLeft > 0) cOutput =
cOutput.narrow(3, 1 + this.padLeft, cOutput.size(3) - this.padLeft)
if (this.padRight > 0) cOutput =
cOutput.narrow(3, 1, cOutput.size(3) - this.padRight)
cOutput.copy(cInput)
} else if (input.dim() == 4) {
// sizes
val h = input.size(3) + this.padTop + this.padBottom
val w = input.size(4) + this.padLeft + this.padRight
if (w < 1 || h < 1) {
throw new IllegalArgumentException("input is too small")
}
this.output.resize(Array(input.size(1), input.size(2), h, w))
this.output.zero()
// crop input if necessary
var cInput = input
if (this.padTop < 0) cInput =
cInput.narrow(3, 1 - this.padTop, cInput.size(3) + this.padTop)
if (this.padBottom < 0) cInput =
cInput.narrow(3, 1, cInput.size(3) + this.padBottom)
if (this.padLeft < 0) cInput =
cInput.narrow(4, 1 - this.padLeft, cInput.size(4) + this.padLeft)
if (this.padRight < 0) cInput =
cInput.narrow(4, 1, cInput.size(4) + this.padRight)
// crop output if necessary
var cOutput = output
if (this.padTop > 0) cOutput =
cOutput.narrow(3, 1 + this.padTop, cOutput.size(3) - this.padTop)
if (this.padBottom > 0) cOutput =
cOutput.narrow(3, 1, cOutput.size(3) - this.padBottom)
if (this.padLeft > 0) cOutput =
cOutput.narrow(4, 1 + this.padLeft, cOutput.size(4) - this.padLeft)
if (this.padRight > 0) cOutput =
cOutput.narrow(4, 1, cOutput.size(4) - this.padRight)
cOutput.copy(cInput)
} else {
throw new IllegalArgumentException("input must be 3 or 4-dimensional")
}
this.output
}
override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = {
if (input.dim() == 3) {
this.gradInput.resizeAs(input).zero()
// crop gradInput if necessary
var cgInput = gradInput
if (this.padTop < 0) cgInput =
cgInput.narrow(2, 1 - this.padTop, cgInput.size(2) + this.padTop)
if (this.padBottom < 0) cgInput =
cgInput.narrow(2, 1, cgInput.size(2) + this.padBottom)
if (this.padLeft < 0) cgInput =
cgInput.narrow(3, 1 - this.padLeft, cgInput.size(3) + this.padLeft)
if (this.padRight < 0) cgInput =
cgInput.narrow(3, 1, cgInput.size(3) + this.padRight)
// crop output if necessary
var cgOutput = gradOutput
if (this.padTop > 0) cgOutput =
cgOutput.narrow(2, 1 + this.padTop, cgOutput.size(2) - this.padTop)
if (this.padBottom > 0) cgOutput =
cgOutput.narrow(2, 1, cgOutput.size(2) - this.padBottom)
if (this.padLeft > 0) cgOutput =
cgOutput.narrow(3, 1 + this.padLeft, cgOutput.size(3) - this.padLeft)
if (this.padRight > 0) cgOutput =
cgOutput.narrow(3, 1, cgOutput.size(3) - this.padRight)
cgInput.copy(cgOutput)
} else if (input.dim() == 4) {
this.gradInput.resizeAs(input).zero()
// crop gradInput if necessary
var cgInput = gradInput
if (this.padTop < 0) cgInput =
cgInput.narrow(3, 1 - this.padTop, cgInput.size(3) + this.padTop)
if (this.padBottom < 0) cgInput =
cgInput.narrow(3, 1, cgInput.size(3) + this.padBottom)
if (this.padLeft < 0) cgInput =
cgInput.narrow(4, 1 - this.padLeft, cgInput.size(4) + this.padLeft)
if (this.padRight < 0) cgInput =
cgInput.narrow(4, 1, cgInput.size(4) + this.padRight)
// crop output if necessary
var cgOutput = gradOutput
if (this.padTop > 0) cgOutput =
cgOutput.narrow(3, 1 + this.padTop, cgOutput.size(3) - this.padTop)
if (this.padBottom > 0) cgOutput =
cgOutput.narrow(3, 1, cgOutput.size(3) - this.padBottom)
if (this.padLeft > 0) cgOutput =
cgOutput.narrow(4, 1 + this.padLeft, cgOutput.size(4) - this.padLeft)
if (this.padRight > 0) cgOutput =
cgOutput.narrow(4, 1, cgOutput.size(4) - this.padRight)
cgInput.copy(cgOutput)
} else {
throw new IllegalArgumentException("input must be 3 or 4-dimensional")
}
this.gradInput
}
override def toString(): String = {
s"${getPrintName}(l=$padLeft, r=$padRight, t=$padTop, b=$padBottom)"
}
}
object SpatialZeroPadding {
def apply[@specialized(Float, Double) T: ClassTag](
padLeft: Int,
padRight: Int,
padTop: Int,
padBottom: Int)(implicit ev: TensorNumeric[T]) : SpatialZeroPadding[T] = {
new SpatialZeroPadding[T](padLeft, padRight, padTop, padBottom)
}
}
