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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.TensorModule
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
/**
* Cropping layer for 3D data (e.g. spatial or spatio-temporal).
*
* # Input shape
* 5D tensor with shape:
* (batchSize, channels, first_axis_to_crop, second_axis_to_crop, third_axis_to_crop)
* # Output shape
* 5D tensor with shape:
* (batchSize, channels, first_cropped_axis, second_cropped_axis, third_cropped_axis)
*
* @param dim1Crop How many units should be trimmed off at the beginning and end of
first cropping dimensions.
* @param dim2Crop How many units should be trimmed off at the beginning and end of
* the second dimension
* @param dim3Crop How many units should be trimmed off at the beginning and end of
* the third dimension
* @param dataFormat: Cropping3D.CHANNEL_FIRST or Cropping3D.CHANNEL_LAST
*/
class Cropping3D[T: ClassTag](
val dim1Crop: Array[Int],
val dim2Crop: Array[Int],
val dim3Crop: Array[Int],
val dataFormat: String = Cropping3D.CHANNEL_FIRST
)(implicit ev: TensorNumeric[T]) extends TensorModule[T] {
require(dim1Crop.length == 2, "dim1Crop should be an array of length 2")
require(dim2Crop.length == 2, "dim2Crop should be an array of length 2")
require(dim3Crop.length == 2, "dim3Crop should be an array of length 2")
override def computeOutputShape(inputShape: Shape): Shape = {
val input = inputShape.toSingle().toArray
require(input.length == 5,
s"Cropping3D requires 5D input, but got input dim ${input.length}")
val outputShape = dataFormat match {
case Cropping3D.CHANNEL_FIRST =>
Array(input(0), input(1), input(2)-dim1Crop(0)-dim1Crop(1),
input(3)-dim2Crop(0)-dim2Crop(1), input(4)-dim3Crop(0)-dim3Crop(1))
case Cropping3D.CHANNEL_LAST =>
Array(input(0), input(1)-dim1Crop(0)-dim1Crop(1),
input(2)-dim2Crop(0)-dim2Crop(1), input(3)-dim3Crop(0)-dim3Crop(1), input(4))
}
Shape(outputShape)
}
override def updateOutput(input: Tensor[T]): Tensor[T] = {
require(input.dim() == 5, "input dimensions should be 5." +
" (batchSize, channels, first_axis_to_crop, second_axis_to_crop, third_axis_to_crop)")
val (dim1, dim2, dim3, dim1Start, dim1Cropped, dim2Start, dim2Cropped, dim3Start, dim3Cropped) =
calculateStartAndLength(input)
require(dim1Cropped > 0, s"dim1Crop: ${dim1Crop.mkString(", ")} is too large. dim1" +
s" dimension length: ${input.size(dim1)}")
require(dim2Cropped > 0, s"dim2Crop: ${dim2Crop.mkString(", ")} is too large. dim2" +
s" dimension length: ${input.size(dim2)}")
require(dim3Cropped > 0, s"dim3Crop: ${dim3Crop.mkString(", ")} is too large. dim3" +
s" dimension length: ${input.size(dim3)}")
val cropped = input
.narrow(dim1, dim1Start, dim1Cropped)
.narrow(dim2, dim2Start, dim2Cropped)
.narrow(dim3, dim3Start, dim3Cropped)
output.resizeAs(cropped).copy(cropped)
}
override def updateGradInput(input: Tensor[T], gradOutput: Tensor[T]): Tensor[T] = {
val (dim1, dim2, dim3, dim1Start, dim1Cropped, dim2Start, dim2Cropped, dim3Start, dim3Cropped) =
calculateStartAndLength(input)
gradInput.resizeAs(input).zero()
.narrow(dim1, dim1Start, dim1Cropped)
.narrow(dim2, dim2Start, dim2Cropped)
.narrow(dim3, dim3Start, dim3Cropped)
.copy(gradOutput)
gradInput
}
/**
* Calculate the start position and length after cropping
*/
private def calculateStartAndLength(input: Tensor[T]):
(Int, Int, Int, Int, Int, Int, Int, Int, Int) = {
val (dim1, dim2, dim3) = dataFormat match {
case Cropping3D.CHANNEL_FIRST => (3, 4, 5)
case Cropping3D.CHANNEL_LAST => (2, 3, 4)
case _ => throw new IllegalArgumentException(s"$dataFormat is not a supported format")
}
val dim1Start = dim1Crop(0) + 1
val dim1Cropped = input.size(dim1) - dim1Crop(0) - dim1Crop(1)
val dim2Start = dim2Crop(0) + 1
val dim2Cropped = input.size(dim2) - dim2Crop(0) - dim2Crop(1)
val dim3Start = dim3Crop(0) + 1
val dim3Cropped = input.size(dim3) - dim3Crop(0) - dim3Crop(1)
(dim1, dim2, dim3, dim1Start, dim1Cropped, dim2Start, dim2Cropped, dim3Start, dim3Cropped)
}
override def clearState(): this.type = {
super.clearState()
this
}
override def toString(): String = {
s"$getPrintName(dim1: ${dim1Crop.mkString(", ")};" +
s" dim2Crop: ${dim2Crop.mkString(", ")};" +
s" dim3Crop: ${dim3Crop.mkString(", ")})"
}
override def canEqual(other: Any): Boolean = other.isInstanceOf[Cropping3D[T]]
override def equals(other: Any): Boolean = other match {
case that: Cropping3D[T] =>
super.equals(that) &&
(that canEqual this) &&
dim1Crop.sameElements(that.dim1Crop) &&
dim2Crop.sameElements(that.dim2Crop) &&
dim3Crop.sameElements(that.dim3Crop) &&
dataFormat == that.dataFormat
case _ => false
}
override def hashCode(): Int = {
def getHashCode(a: Any): Int = if (a == null) 0 else a.hashCode()
val state = Seq(super.hashCode(), dim1Crop, dim2Crop, dim3Crop, dataFormat)
state.map(getHashCode).foldLeft(0)((a, b) => 37 * a + b)
}
}
object Cropping3D {
val CHANNEL_FIRST = "channel_first"
val CHANNEL_LAST = "channel_last"
def apply[T: ClassTag](
dim1Crop: Array[Int],
dim2Crop: Array[Int],
dim3Crop: Array[Int],
format: String = Cropping3D.CHANNEL_FIRST)(implicit ev: TensorNumeric[T]): Cropping3D[T] = {
new Cropping3D[T](dim1Crop, dim2Crop, dim3Crop, format)
}
}
