Deprecate NDArrayCollector and instead use ResourceScope

scala-package/core/src/main/scala/org/apache/mxnet/NDArray.scala

@@ -741,10 +741,16 @@ object NDArray extends NDArrayBase {
   * </b>
   */
 class NDArray private[mxnet](private[mxnet] val handle: NDArrayHandle,
-                             val writable: Boolean = true,
-                             addToCollector: Boolean = true) extends NativeResource {
-  if (addToCollector) {
-    NDArrayCollector.collect(this)
+                             val writable: Boolean) extends NativeResource {
+
+  @deprecated("Please use ResourceScope instead", "1.5.0")
+  def this(handle: NDArrayHandle,
+           writable: Boolean = true,
+           addToCollector: Boolean = true) {
+    this(handle, writable)
+    if (addToCollector) {
+      NDArrayCollector.collect(this)
+    }
   }
 
   override def nativeAddress: CPtrAddress = handle

scala-package/core/src/main/scala/org/apache/mxnet/NDArrayCollector.scala

@@ -64,6 +64,7 @@ import scala.collection.mutable
  *  });
  *  </pre>
  */
+@deprecated("Please use ResourceScope instead", "1.5.0")
 object NDArrayCollector {
   private val logger = LoggerFactory.getLogger(classOf[NDArrayCollector])
 
@@ -75,24 +76,28 @@ object NDArrayCollector {
    * Create a collector which will dispose the collected NDArrays automatically.
    * @return an auto-disposable collector.
    */
+  @deprecated("Please use ResourceScope instead", "1.5.0")
   def auto(): NDArrayCollector = new NDArrayCollector(true)
 
   /**
    * Create a collector allows users to later dispose the collected NDArray manually.
    * @return a manually-disposable collector.
    */
+  @deprecated("Please use ResourceScope instead", "1.5.0")
   @Experimental
   def manual(): NDArrayCollector = new NDArrayCollector(false)
 
   /**
    * Collect the NDArrays into the collector of the current thread.
    * @param ndArray NDArrays need to be collected.
    */
+  @deprecated("Please use ResourceScope instead", "1.5.0")
   @varargs def collect(ndArray: NDArray*): Unit = {
     currCollector.get().add(ndArray: _*)
   }
 }
 
+@deprecated("Please use ResourceScope instead", "1.5.0")
 class NDArrayCollector private(private val autoDispose: Boolean = true,
                                private val doCollect: Boolean = true) {
   // native ptr (handle) of the NDArray -> NDArray
@@ -142,6 +147,7 @@ class NDArrayCollector private(private val autoDispose: Boolean = true,
    * @return The result of function <em>codeBlock</em>.
    */
   @Experimental
+  @deprecated("Please use ResourceScope instead", "1.5.0")
   def withScope[T](codeBlock: => T): T = {
     val old = NDArrayCollector.currCollector.get()
     NDArrayCollector.currCollector.set(this)

scala-package/core/src/main/scala/org/apache/mxnet/ResourceScope.scala

@@ -63,6 +63,14 @@ class ResourceScope extends AutoCloseable {
     resource.scope = Some(this)
   }
 
+  /**
+    * Check if a NativeResource is in the scope
+    * @param resource
+    */
+  def contains(resource: NativeResource): Boolean = {
+    resourceQ.contains(resource)
+  }
+
   /**
     * Remove NativeResource from the Scope, this uses
     * object equality to find the resource in the stack.
@@ -80,8 +88,10 @@ class ResourceScope extends AutoCloseable {
   def moveToOuterScope(resource: NativeResource): Unit = {
     val prevScope: Option[ResourceScope] = ResourceScope.getPrevScope()
     if (prevScope.isDefined) {
-      this.remove(resource)
-      prevScope.get.add(resource)
+      if (contains(resource)) {
+        this.remove(resource)
+        prevScope.get.add(resource)
+      }
     } else this.remove(resource)
   }
 
@@ -109,20 +119,16 @@ object ResourceScope {
 
     val curScope = if (scope != null) scope else new ResourceScope()
 
-    @inline def resourceInGeneric(g: scala.collection.Iterable[_]) = {
-      g.foreach( n =>
-        n match {
-          case nRes: NativeResource => {
-            curScope.moveToOuterScope(nRes)
-          }
-          case kv: scala.Tuple2[_, _] => {
-            if (kv._1.isInstanceOf[NativeResource]) curScope.moveToOuterScope(
-              kv._1.asInstanceOf[NativeResource])
-            if (kv._2.isInstanceOf[NativeResource]) curScope.moveToOuterScope(
-              kv._2.asInstanceOf[NativeResource])
-          }
-        }
-      )
+    def recursiveMoveToOuterScope(resource: Any): Unit = {
+      resource match {
+        case nRes: NativeResource => curScope.moveToOuterScope(nRes)
+        case ndRet: NDArrayFuncReturn => ndRet.arr.foreach( nd => curScope.moveToOuterScope(nd) )
+        case resInGeneric: scala.collection.Traversable[_] =>
+          resInGeneric.foreach(recursiveMoveToOuterScope)
+        case resProduct: scala.Product =>
+          resProduct.productIterator.foreach(recursiveMoveToOuterScope)
+        case _ => // do nothing
+      }
     }
 
     @inline def safeAddSuppressed(t: Throwable, suppressed: Throwable): Unit = {
@@ -133,13 +139,7 @@ object ResourceScope {
 
     try {
       val ret = body
-       ret match {
-          // don't de-allocate if returning any collection that contains NativeResource.
-        case resInGeneric: scala.collection.Iterable[_] => resourceInGeneric(resInGeneric)
-        case nRes: NativeResource => curScope.moveToOuterScope(nRes)
-        case ndRet: NDArrayFuncReturn => ndRet.arr.foreach( nd => curScope.moveToOuterScope(nd) )
-        case _ => // do nothing
-      }
+      recursiveMoveToOuterScope(ret)
       ret
     } catch {
       case t: Throwable =>

scala-package/core/src/main/scala/org/apache/mxnet/io/NDArrayIter.scala

@@ -177,7 +177,7 @@ class NDArrayIter(data: IndexedSeq[(DataDesc, NDArray)],
     val shape = Shape(dataBatchSize) ++ ndArray.shape.slice(1, ndArray.shape.size)
     // The new NDArray  has to be created such that it inherits dtype from the passed in array
     val newArray = NDArray.zeros(shape, dtype = ndArray.dtype)
-    NDArrayCollector.auto().withScope {
+    ResourceScope.using() {
       val batch = ndArray.slice(cursor, numData)
       val padding = ndArray.slice(0, padNum)
       newArray.slice(0, dataBatchSize - padNum).set(batch)

scala-package/examples/src/main/scala/org/apache/mxnetexamples/benchmark/ScalaInferenceBenchmark.scala

@@ -50,7 +50,7 @@ object ScalaInferenceBenchmark {
   List[Long] = {
     var inferenceTimes: List[Long] = List()
     for (i <- 1 to totalRuns) {
-      NDArrayCollector.auto().withScope {
+      ResourceScope.using() {
         val startTimeSingle = System.currentTimeMillis()
         objectToRun.runSingleInference(loadedModel, dataSet)
         val estimatedTimeSingle = System.currentTimeMillis() - startTimeSingle
@@ -67,7 +67,7 @@ object ScalaInferenceBenchmark {
 
     var inferenceTimes: List[Long] = List()
     for (batch <- dataSetBatches) {
-      NDArrayCollector.auto().withScope {
+      ResourceScope.using() {
         val loadedBatch = objecToRun.loadInputBatch(batch)
         val startTimeSingle = System.currentTimeMillis()
         objecToRun.runBatchInference(loadedModel, loadedBatch)
@@ -133,7 +133,7 @@ object ScalaInferenceBenchmark {
 
       logger.info("Running single inference call")
       // Benchmarking single inference call
-      NDArrayCollector.auto().withScope {
+      ResourceScope.using() {
         val loadedModel = loadModel(exampleToBenchmark, context, false)
         val dataSet = loadDataSet(exampleToBenchmark)
         val inferenceTimes = runInference(exampleToBenchmark, loadedModel, dataSet, baseCLI.count)
@@ -143,7 +143,7 @@ object ScalaInferenceBenchmark {
       if (baseCLI.batchSize != 0) {
         logger.info("Running for batch inference call")
         // Benchmarking batch inference call
-        NDArrayCollector.auto().withScope {
+        ResourceScope.using() {
           val loadedModel = loadModel(exampleToBenchmark, context, true)
           val batchDataSet = loadBatchDataSet(exampleToBenchmark, baseCLI.batchSize)
           val inferenceTimes = runBatchInference(exampleToBenchmark, loadedModel, batchDataSet)

scala-package/examples/src/main/scala/org/apache/mxnetexamples/cnntextclassification/CNNTextClassification.scala

@@ -18,7 +18,7 @@
 package org.apache.mxnetexamples.cnntextclassification
 
 import org.apache.mxnet.optimizer.RMSProp
-import org.apache.mxnet.{Context, Executor, Model, NDArray, NDArrayCollector, Optimizer, Shape, Symbol, Uniform}
+import org.apache.mxnet.{Context, Executor, Model, NDArray, Optimizer, ResourceScope, Shape, Symbol, Uniform}
 import org.kohsuke.args4j.{CmdLineParser, Option}
 import org.slf4j.LoggerFactory
 
@@ -131,7 +131,7 @@ object CNNTextClassification {
       numTotal = 0f
       updateRate = 0
 
-      NDArrayCollector.auto().withScope {
+      ResourceScope.using() {
         for (begin <- 0 until trainBatches.length by batchSize) {
           val (batchD, batchL) = {
             if (begin + batchSize <= trainBatches.length) {
@@ -239,7 +239,7 @@ object CNNTextClassification {
 
   def test(w2vFilePath : String, mrDatasetPath: String,
            ctx : Context, saveModelPath: String) : Float = {
-    val output = NDArrayCollector.auto().withScope {
+    val output = ResourceScope.using() {
       val (numEmbed, word2vec) = DataHelper.loadGoogleModel(w2vFilePath)
       val (datas, labels) = DataHelper.loadMSDataWithWord2vec(
         mrDatasetPath, numEmbed, word2vec)

scala-package/examples/src/main/scala/org/apache/mxnetexamples/customop/ExampleCustomOp.scala

@@ -19,7 +19,7 @@ package org.apache.mxnetexamples.customop
 
 import org.apache.mxnet.Callback.Speedometer
 import org.apache.mxnet.DType.DType
-import org.apache.mxnet.{Accuracy, Context, CustomOp, CustomOpProp, NDArray, NDArrayCollector, Operator, Shape, Symbol, Xavier}
+import org.apache.mxnet.{Accuracy, Context, CustomOp, CustomOpProp, NDArray, Operator, ResourceScope, Shape, Symbol, Xavier}
 import org.apache.mxnet.optimizer.RMSProp
 import org.kohsuke.args4j.{CmdLineParser, Option}
 import org.slf4j.LoggerFactory
@@ -141,7 +141,7 @@ object ExampleCustomOp {
       evalMetric.reset()
       var nBatch = 0
       var epochDone = false
-      NDArrayCollector.auto().withScope {
+      ResourceScope.using() {
         trainIter.reset()
         while (!epochDone) {
           var doReset = true

scala-package/examples/src/main/scala/org/apache/mxnetexamples/gan/GanMnist.scala

@@ -17,7 +17,7 @@
 
 package org.apache.mxnetexamples.gan
 
-import org.apache.mxnet.{Context, CustomMetric, DataBatch, IO, NDArray, NDArrayCollector, Shape, Symbol, Xavier}
+import org.apache.mxnet.{Context, CustomMetric, DataBatch, IO, NDArray, ResourceScope, Shape, Symbol, Xavier}
 import org.apache.mxnet.optimizer.Adam
 import org.kohsuke.args4j.{CmdLineParser, Option}
 import org.slf4j.LoggerFactory
@@ -104,7 +104,7 @@ object GanMnist {
 
   def runTraining(dataPath : String, context : Context,
                   outputPath : String, numEpoch : Int): Float = {
-    val output = NDArrayCollector.auto().withScope {
+    val output = ResourceScope.using() {
       val lr = 0.0005f
       val beta1 = 0.5f
       val batchSize = 100
@@ -147,7 +147,7 @@ object GanMnist {
         t = 0
         while (mnistIter.hasNext) {
           dataBatch = mnistIter.next()
-          NDArrayCollector.auto().withScope {
+          ResourceScope.using() {
             gMod.update(dataBatch)
             gMod.dLabel.set(0f)
             metricAcc.update(Array(gMod.dLabel), gMod.outputsFake)

scala-package/examples/src/main/scala/org/apache/mxnetexamples/infer/imageclassifier/ImageClassifierExample.scala

@@ -49,7 +49,7 @@ object ImageClassifierExample {
   def runInferenceOnSingleImage(modelPathPrefix: String, inputImagePath: String,
                                 context: Array[Context]):
   IndexedSeq[IndexedSeq[(String, Float)]] = {
-    NDArrayCollector.auto().withScope {
+    ResourceScope.using() {
       val dType = DType.Float32
       val inputShape = Shape(1, 3, 224, 224)
 
@@ -71,7 +71,7 @@ object ImageClassifierExample {
   def runInferenceOnBatchOfImage(modelPathPrefix: String, inputImageDir: String,
                                  context: Array[Context]):
   IndexedSeq[IndexedSeq[(String, Float)]] = {
-    NDArrayCollector.auto().withScope {
+    ResourceScope.using() {
       val dType = DType.Float32
       val inputShape = Shape(1, 3, 224, 224)
 

scala-package/examples/src/main/scala/org/apache/mxnetexamples/infer/objectdetector/SSDClassifierExample.scala

@@ -51,7 +51,7 @@ object SSDClassifierExample {
   def runObjectDetectionSingle(modelPathPrefix: String, inputImagePath: String,
                                context: Array[Context]):
   IndexedSeq[IndexedSeq[(String, Array[Float])]] = {
-    NDArrayCollector.auto().withScope {
+    ResourceScope.using() {
       val dType = DType.Float32
       val inputShape = Shape(1, 3, 512, 512)
       // ssd detections, numpy.array([[id, score, x1, y1, x2, y2]...])
@@ -68,7 +68,7 @@ object SSDClassifierExample {
   def runObjectDetectionBatch(modelPathPrefix: String, inputImageDir: String,
                               context: Array[Context]):
   IndexedSeq[IndexedSeq[(String, Array[Float])]] = {
-    NDArrayCollector.auto().withScope {
+    ResourceScope.using() {
       val dType = DType.Float32
       val inputShape = Shape(1, 3, 512, 512)
       // ssd detections, numpy.array([[id, score, x1, y1, x2, y2]...])

scala-package/examples/src/main/scala/org/apache/mxnetexamples/multitask/ExampleMultiTask.scala

@@ -25,7 +25,7 @@ import org.slf4j.LoggerFactory
 
 import scala.collection.JavaConverters._
 import org.apache.commons.io.FileUtils
-import org.apache.mxnet.{Context, DataBatch, DataDesc, DataIter, EvalMetric, Executor, NDArray, NDArrayCollector, Shape, Symbol, Xavier}
+import org.apache.mxnet.{Context, DataBatch, DataDesc, DataIter, EvalMetric, Executor, NDArray, ResourceScope, Shape, Symbol, Xavier}
 import org.apache.mxnet.DType.DType
 import org.apache.mxnet.optimizer.RMSProp
 import org.apache.mxnetexamples.Util
@@ -222,7 +222,7 @@ object ExampleMultiTask {
 
   def train(batchSize: Int, numEpoch: Int, ctx: Context, modelDirPath: String):
   (Executor, MultiAccuracy) = {
-    NDArrayCollector.auto().withScope {
+    ResourceScope.using() {
       val lr = 0.001f
       val network = ExampleMultiTask.buildNetwork()
       val (trainIter, valIter) =

scala-package/examples/src/main/scala/org/apache/mxnetexamples/neuralstyle/NeuralStyle.scala

@@ -170,7 +170,7 @@ object NeuralStyle {
                   contentWeight : Float, tvWeight : Float, gaussianRadius : Int,
                   lr: Float, maxNumEpochs: Int, maxLongEdge: Int,
                   saveEpochs : Int, stopEps: Float) : Unit = {
-    NDArrayCollector.auto().withScope {
+    ResourceScope.using() {
       val contentNp = preprocessContentImage(contentImage, maxLongEdge, dev)
       val styleNp = preprocessStyleImage(styleImage, contentNp.shape, dev)
       val size = (contentNp.shape(2), contentNp.shape(3))

scala-package/examples/src/main/scala/org/apache/mxnetexamples/neuralstyle/end2end/BoostInference.scala

@@ -17,7 +17,7 @@
 
 package org.apache.mxnetexamples.neuralstyle.end2end
 
-import org.apache.mxnet.{Context, NDArrayCollector, Shape}
+import org.apache.mxnet.{Context, ResourceScope, Shape}
 import org.kohsuke.args4j.{CmdLineParser, Option}
 import org.slf4j.LoggerFactory
 
@@ -29,7 +29,7 @@ object BoostInference {
 
   def runInference(modelPath: String, outputPath: String, guassianRadius : Int,
                    inputImage : String, ctx : Context): Unit = {
-    NDArrayCollector.auto().withScope {
+    ResourceScope.using() {
       val dShape = Shape(1, 3, 480, 640)
       val clipNorm = 1.0f * dShape.product
       // generator
@@ -47,7 +47,7 @@ object BoostInference {
         DataProcessing.preprocessContentImage(s"$inputImage", dShape, ctx)
       var data = Array(contentNp)
       for (i <- 0 until gens.length) {
-        NDArrayCollector.auto().withScope {
+        ResourceScope.using() {
           gens(i).forward(data.takeRight(1))
           val newImg = gens(i).getOutputs()(0)
           data :+= newImg

scala-package/examples/src/main/scala/org/apache/mxnetexamples/neuralstyle/end2end/BoostTrain.scala

@@ -19,7 +19,7 @@ package org.apache.mxnetexamples.neuralstyle.end2end
 
 import java.io.File
 
-import org.apache.mxnet.{Context, Executor, NDArray, NDArrayCollector, Shape, Symbol}
+import org.apache.mxnet.{Context, Executor, NDArray, ResourceScope, Shape, Symbol}
 import org.apache.mxnet.optimizer.SGD
 import org.kohsuke.args4j.{CmdLineParser, Option}
 import org.slf4j.LoggerFactory
@@ -56,7 +56,7 @@ object BoostTrain {
 
   def runTraining(dataPath : String, vggModelPath: String, ctx : Context,
                   styleImage : String, saveModelPath : String) : Unit = {
-    NDArrayCollector.auto().withScope {
+    ResourceScope.using() {
       // params
       val vggParams = NDArray.load2Map(vggModelPath)
       val styleWeight = 1.2f
@@ -117,7 +117,7 @@ object BoostTrain {
 
       // train
       for (i <- startEpoch until endEpoch) {
-        NDArrayCollector.auto().withScope {
+        ResourceScope.using() {
           filelist = Random.shuffle(filelist)
           for (idx <- filelist.indices) {
             var dataArray = Array[NDArray]()

scala-package/examples/src/main/scala/org/apache/mxnetexamples/rnn/LstmBucketing.scala

@@ -62,7 +62,7 @@ object LstmBucketing {
 
   def runTraining(trainData : String, validationData : String,
                   ctx : Array[Context], numEpoch : Int): Unit = {
-    NDArrayCollector.auto().withScope {
+    ResourceScope.using() {
       val batchSize = 32
       val buckets = Array(10, 20, 30, 40, 50, 60)
       val numHidden = 200

scala-package/examples/src/main/scala/org/apache/mxnetexamples/rnn/TestCharRnn.scala

@@ -34,7 +34,7 @@ object TestCharRnn {
   private val logger = LoggerFactory.getLogger(classOf[TrainCharRnn])
 
   def runInferenceCharRNN(dataPath: String, modelPrefix: String, starterSentence : String): Unit = {
-    NDArrayCollector.auto().withScope {
+    ResourceScope.using() {
       // The batch size for training
       val batchSize = 32
       // We can support various length input