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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.numeric.NumericFloat
import com.intel.analytics.bigdl.tensor.{Storage, Tensor}
import scala.collection.mutable.ArrayBuffer
/**
* Generates a regular grid of multi-scale, multi-aspect anchor boxes.
*/
class Anchor(ratios: Array[Float], scales: Array[Float]) extends Serializable {
private var baseSize = 16
private var basicAnchors: Tensor[Float] = generateBasicAnchors(ratios, scales, baseSize)
val anchorNum = ratios.length * scales.length
/**
* first generate shiftX and shiftY over the whole feature map
* then apply shifts for each basic anchors
* @param width feature map width
* @param height feature map height
* @param featStride stride to move
* @return all anchors over the feature map
*/
def generateAnchors(width: Int, height: Int, featStride: Float = 16): Tensor[Float] = {
val (shiftX, shiftY) = generateShifts(width, height, featStride)
if (featStride != baseSize) {
basicAnchors = generateBasicAnchors(ratios, scales, featStride)
baseSize = featStride.toInt
}
getAllAnchors(shiftX, shiftY, basicAnchors)
}
/**
* Here, we generate anchors without change area.
* @param ratios
* @param scales
* @param baseSize stride to move
* @return anchors with shape (ratios number * scales number, 4).
* And element order is (-width / 2, -height / 2, width / 2, height / 2)
*/
private def generateBasicAnchors(ratios: Array[Float], scales: Array[Float],
baseSize: Float = 16): Tensor[Float] = {
val anchors = new ArrayBuffer[Float]
for (i <- 0 until scales.length) {
val area = math.pow(scales(i) * baseSize, 2)
for (j <- 0 until ratios.length) {
val w = math.sqrt(area / ratios(j)).toFloat
val h = ratios(j) * w.toFloat
val halfW = w / 2.0f
val halfH = h / 2.0f
anchors.append(-halfW)
anchors.append(-halfH)
anchors.append(halfW)
anchors.append(halfH)
}
}
Tensor[Float](data = anchors.toArray, shape = Array[Int](ratios.length * scales.length, 4))
}
@transient private var shiftX: Tensor[Float] = _
@transient private var shiftY: Tensor[Float] = _
/**
* generate shifts wrt width, height and featStride
* in order to generate anchors over the whole feature map
* @param width feature map width
* @param height feature map height
* @param featStride stride to move
* @return shiftX and shiftY
*/
private[nn] def generateShifts(width: Int, height: Int, featStride: Float):
(Tensor[Float], Tensor[Float]) = {
if (shiftX == null) {
shiftX = Tensor[Float]
shiftY = Tensor[Float]
}
var i = -1
shiftX.resize(width).apply1 { x => i += 1; i * featStride } // 0, f, 2f, ..., wf
i = -1
shiftY.resize(height).apply1 { x => i += 1; i * featStride } // 0, f, 2f, ..., hf
(shiftX, shiftY)
}
@transient private var allAnchors: Tensor[Float] = _
/**
* each anchor add with shiftX and shiftY
* @param shiftX a list of shift in X direction
* @param shiftY a list of shift in Y direction
* @param anchors basic anchors that will apply shifts
* @return anchors with all shifts
*/
private def getAllAnchors(shiftX: Tensor[Float], shiftY: Tensor[Float],
anchors: Tensor[Float]): Tensor[Float] = {
if (allAnchors == null) {
allAnchors = Tensor[Float]
}
val S = shiftX.nElement() * shiftY.nElement()
val A = anchors.size(1)
allAnchors.resize(S * A, 4)
val xsArr = shiftX.storage().array()
val ysArr = shiftY.storage().array()
val allAnchorArr = allAnchors.storage().array()
var aOffset = allAnchors.storageOffset() - 1
val anchorArr = anchors.storage().array()
var ysOffset = shiftY.storageOffset() - 1
var ys = 0
while (ys < shiftY.nElement()) {
var xs = 0
var xsOffset = shiftX.storageOffset() - 1
while (xs < shiftX.nElement()) {
var a = 0
var anchorOffset = anchors.storageOffset() - 1
while (a < A) {
allAnchorArr(aOffset) = anchorArr(anchorOffset) + xsArr(xsOffset)
allAnchorArr(aOffset + 1) = anchorArr(anchorOffset + 1) + ysArr(ysOffset)
allAnchorArr(aOffset + 2) = anchorArr(anchorOffset + 2) + xsArr(xsOffset)
allAnchorArr(aOffset + 3) = anchorArr(anchorOffset + 3) + ysArr(ysOffset)
aOffset += 4
anchorOffset += 4
a += 1
}
xs += 1
xsOffset += 1
}
ys += 1
ysOffset += 1
}
allAnchors
}
/**
* Given a vector of widths (ws) and heights (hs) around a center
* (x_ctr, y_ctr), output a set of anchors (windows).
* note that the value of ws and hs is changed after mkAnchors (half)
* x1 = xCtr - (ws-1)/2 = xCtr - ws/2 + 0.5
* y1 = yCtr - (hs-1)/2 = yCtr - hs/2 + 0.5
* x2 = xCtr + (ws-1)/2 = xCtr + ws/2 - 0.5
* y2 = yCtr + (hs-1)/2 = yCtr + hs/2 - 0.5
* @param ws widths
* @param hs heights
* @param xCtr center x
* @param yCtr center y
* @return anchors around this center, with shape (4, N)
*/
private def mkAnchors(ws: Tensor[Float], hs: Tensor[Float],
xCtr: Float, yCtr: Float): Tensor[Float] = {
require(ws.size(1) == hs.size(1))
val anchors = Tensor(ws.size(1), 4)
var i = 1
while (i <= ws.size(1)) {
val w = ws.valueAt(i) / 2 - 0.5f
val h = hs.valueAt(i) / 2 - 0.5f
anchors.setValue(i, 1, xCtr - w)
anchors.setValue(i, 2, yCtr - h)
anchors.setValue(i, 3, xCtr + w)
anchors.setValue(i, 4, yCtr + h)
i += 1
}
anchors
}
/**
* Return width, height, x center, and y center for an anchor (window).
*/
private def getBasicAchorInfo(anchor: Tensor[Float]): (Float, Float, Float, Float) = {
val w = anchor.valueAt(3) - anchor.valueAt(1) + 1
val h = anchor.valueAt(4) - anchor.valueAt(2) + 1
val xCtr = anchor.valueAt(1) + 0.5f * (w - 1)
val yCtr = anchor.valueAt(2) + 0.5f * (h - 1)
(w, h, xCtr, yCtr)
}
@transient var ws: Tensor[Float] = _
@transient var hs: Tensor[Float] = _
/**
* Enumerate a set of anchors for each aspect ratio with respect to an anchor.
* ratio = height / width
*/
private def ratioEnum(anchor: Tensor[Float], ratios: Tensor[Float]): Tensor[Float] = {
val (width, height, xCtr, yCtr) = getBasicAchorInfo(anchor)
val area = width * height
if (ws == null) {
ws = Tensor()
hs = Tensor()
}
// get a set of widths
ws.resizeAs(ratios).map(ratios, (w, ratio) => Math.sqrt(area / ratio).round)
// get corresponding heights
hs.resizeAs(ws).cmul(ws, ratios).apply1(Math.round)
mkAnchors(ws, hs, xCtr, yCtr)
}
/**
* Enumerate a set of anchors for each scale wrt an anchor.
*/
private def scaleEnum(anchor: Tensor[Float], scales: Tensor[Float]): Tensor[Float] = {
if (ws == null) {
ws = Tensor()
hs = Tensor()
}
val (width, height, xCtr, yCtr) = getBasicAchorInfo(anchor)
ws.resizeAs(scales).mul(scales, width)
hs.resizeAs(scales).mul(scales, height)
mkAnchors(ws, hs, xCtr, yCtr)
}
}
object Anchor {
def apply(ratios: Array[Float], scales: Array[Float]): Anchor = new Anchor(ratios, scales)
}
