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[MXNET-1249] Fix Object Detector Performance with GPU #13522

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Dec 5, 2018
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[MXNET-1249] Fix Object Detector Performance with GPU #13522

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b3bf916
Reduce post processing time
lanking520 Dec 4, 2018
c41057c
fix ssd
lanking520 Dec 4, 2018
5ef1ee0
fix the CI
lanking520 Dec 4, 2018
a6673b5
add comments
lanking520 Dec 5, 2018
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18 changes: 12 additions & 6 deletions scala-package/infer/src/main/scala/org/apache/mxnet/infer/Classifier.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -24,6 +24,7 @@ import org.slf4j.LoggerFactory

import scala.io
import scala.collection.mutable.ListBuffer
import scala.collection.parallel.mutable.ParArray

trait ClassifierBase {

Expand Down Expand Up @@ -110,16 +111,21 @@ class Classifier(modelPathPrefix: String,
: IndexedSeq[IndexedSeq[(String, Float)]] = {

// considering only the first output
val predictResultND: NDArray = predictor.predictWithNDArray(input)(0)

val predictResult: ListBuffer[Array[Float]] = ListBuffer[Array[Float]]()
// Copy NDArray to CPU to avoid frequent GPU to CPU copying
val predictResultND: NDArray =
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predictor.predictWithNDArray(input)(0).asInContext(Context.cpu())
// Parallel Execution with ParArray for better performance
val predictResultPar: ParArray[Array[Float]] =
new ParArray[Array[Float]](predictResultND.shape(0))

// iterating over the individual items(batch size is in axis 0)
for (i <- 0 until predictResultND.shape(0)) {
(0 until predictResultND.shape(0)).toVector.par.foreach( i => {
val r = predictResultND.at(i)
predictResult += r.toArray
predictResultPar(i) = r.toArray
r.dispose()
}
})

val predictResult = predictResultPar.toArray

var result: ListBuffer[IndexedSeq[(String, Float)]] =
ListBuffer.empty[IndexedSeq[(String, Float)]]
Expand Down
31 changes: 16 additions & 15 deletions scala-package/infer/src/main/scala/org/apache/mxnet/infer/ObjectDetector.scala
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Expand Up @@ -19,6 +19,8 @@ package org.apache.mxnet.infer

// scalastyle:off
import java.awt.image.BufferedImage

import scala.collection.parallel.mutable.ParArray
// scalastyle:on
import org.apache.mxnet.NDArray
import org.apache.mxnet.DataDesc
Expand Down Expand Up @@ -94,39 +96,39 @@ class ObjectDetector(modelPathPrefix: String,
def objectDetectWithNDArray(input: IndexedSeq[NDArray], topK: Option[Int])
: IndexedSeq[IndexedSeq[(String, Array[Float])]] = {

val predictResult = predictor.predictWithNDArray(input)(0)
var batchResult = ListBuffer[IndexedSeq[(String, Array[Float])]]()
for (i <- 0 until predictResult.shape(0)) {
// Copy NDArray to CPU to avoid frequent GPU to CPU copying
val predictResult = predictor.predictWithNDArray(input)(0).asInContext(Context.cpu())
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If possible, can we apply this to ImageClassifier class as well ?
Maybe this can provide a speed boost there as well ?

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Same nitpick I had on imageclassifier. A comment explaining this wouldn't hurt since it's not obvious why we're doing this.

// Parallel Execution with ParArray for better performance
var batchResult = new ParArray[IndexedSeq[(String, Array[Float])]](predictResult.shape(0))
(0 until predictResult.shape(0)).toArray.par.foreach( i => {
val r = predictResult.at(i)
batchResult += sortAndReformat(r, topK)
batchResult(i) = sortAndReformat(r, topK)
handler.execute(r.dispose())
}
})
handler.execute(predictResult.dispose())
batchResult.toIndexedSeq
}

private[infer] def sortAndReformat(predictResultND: NDArray, topK: Option[Int])
: IndexedSeq[(String, Array[Float])] = {
val predictResult: ListBuffer[Array[Float]] = ListBuffer[Array[Float]]()
val accuracy: ListBuffer[Float] = ListBuffer[Float]()

// iterating over the all the predictions
val length = predictResultND.shape(0)

for (i <- 0 until length) {
val predictResult = (0 until length).toArray.par.flatMap( i => {
val r = predictResultND.at(i)
val tempArr = r.toArray
if (tempArr(0) != -1.0) {
predictResult += tempArr
accuracy += tempArr(1)
val res = if (tempArr(0) != -1.0) {
Array[Array[Float]](tempArr)
} else {
// Ignore the minus 1 part
Array[Array[Float]]()
}
handler.execute(r.dispose())
}
res
}).toArray
var result = IndexedSeq[(String, Array[Float])]()
if (topK.isDefined) {
var sortedIndices = accuracy.zipWithIndex.sortBy(-_._1).map(_._2)
var sortedIndices = predictResult.zipWithIndex.sortBy(-_._1(1)).map(_._2)
sortedIndices = sortedIndices.take(topK.get)
// takeRight(5) would provide the output as Array[Accuracy, Xmin, Ymin, Xmax, Ymax
result = sortedIndices.map(idx
Expand All @@ -136,7 +138,6 @@ class ObjectDetector(modelPathPrefix: String,
result = predictResult.map(ele
=> (synset(ele(0).toInt), ele.takeRight(5))).toIndexedSeq
}

result
}

Expand Down