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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2018/07/18 16:20:39 UTC
[GitHub] nswamy closed pull request #11401: [MXNET-531] Custom Operator
Example for Scala
nswamy closed pull request #11401: [MXNET-531] Custom Operator Example for Scala
URL: https://github.com/apache/incubator-mxnet/pull/11401
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diff --git a/scala-package/core/src/main/scala/org/apache/mxnet/NDArrayAPI.scala b/scala-package/core/src/main/scala/org/apache/mxnet/NDArrayAPI.scala
index 6136db29d1e..1d8551c1b1e 100644
--- a/scala-package/core/src/main/scala/org/apache/mxnet/NDArrayAPI.scala
+++ b/scala-package/core/src/main/scala/org/apache/mxnet/NDArrayAPI.scala
@@ -21,4 +21,5 @@ package org.apache.mxnet
* Main code will be generated during compile time through Macros
*/
object NDArrayAPI extends NDArrayAPIBase {
+ // TODO: Implement CustomOp for NDArray
}
diff --git a/scala-package/core/src/main/scala/org/apache/mxnet/SymbolAPI.scala b/scala-package/core/src/main/scala/org/apache/mxnet/SymbolAPI.scala
index 56da4fa64cf..1bfb0559cf9 100644
--- a/scala-package/core/src/main/scala/org/apache/mxnet/SymbolAPI.scala
+++ b/scala-package/core/src/main/scala/org/apache/mxnet/SymbolAPI.scala
@@ -16,6 +16,8 @@
*/
package org.apache.mxnet
+import scala.collection.mutable
+
@AddSymbolAPIs(false)
/**
@@ -23,4 +25,10 @@ package org.apache.mxnet
* Main code will be generated during compile time through Macros
*/
object SymbolAPI extends SymbolAPIBase {
+ def Custom (op_type : String, kwargs : mutable.Map[String, Any],
+ name : String = null, attr : Map[String, String] = null) : Symbol = {
+ val map = kwargs
+ map.put("op_type", op_type)
+ Symbol.createSymbolGeneral("Custom", name, attr, Seq(), map.toMap)
+ }
}
diff --git a/scala-package/examples/src/main/scala/org/apache/mxnetexamples/customop/Data.scala b/scala-package/examples/src/main/scala/org/apache/mxnetexamples/customop/Data.scala
index 7cab3c0bb46..d61269c131f 100644
--- a/scala-package/examples/src/main/scala/org/apache/mxnetexamples/customop/Data.scala
+++ b/scala-package/examples/src/main/scala/org/apache/mxnetexamples/customop/Data.scala
@@ -17,13 +17,8 @@
package org.apache.mxnetexamples.customop
-import org.apache.mxnet.Shape
-import org.apache.mxnet.IO
-import org.apache.mxnet.DataIter
+import org.apache.mxnet.{DataIter, IO, Shape}
-/**
- * @author Depeng Liang
- */
object Data {
// return train and val iterators for mnist
def mnistIterator(dataPath: String, batchSize: Int, inputShape: Shape): (DataIter, DataIter) = {
diff --git a/scala-package/examples/src/main/scala/org/apache/mxnetexamples/customop/ExampleCustomOp.scala b/scala-package/examples/src/main/scala/org/apache/mxnetexamples/customop/ExampleCustomOp.scala
index d033d6525e5..a4b347959bf 100644
--- a/scala-package/examples/src/main/scala/org/apache/mxnetexamples/customop/ExampleCustomOp.scala
+++ b/scala-package/examples/src/main/scala/org/apache/mxnetexamples/customop/ExampleCustomOp.scala
@@ -17,37 +17,26 @@
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, Operator, Shape, Symbol, Xavier}
+import org.apache.mxnet.optimizer.RMSProp
import org.kohsuke.args4j.{CmdLineParser, Option}
import org.slf4j.LoggerFactory
+
import scala.collection.JavaConverters._
-import org.apache.mxnet.Symbol
-import org.apache.mxnet.DType.DType
-import org.apache.mxnet.DataIter
-import org.apache.mxnet.DataBatch
-import org.apache.mxnet.NDArray
-import org.apache.mxnet.Shape
-import org.apache.mxnet.EvalMetric
-import org.apache.mxnet.Context
-import org.apache.mxnet.Xavier
-import org.apache.mxnet.optimizer.RMSProp
-import org.apache.mxnet.CustomOp
-import org.apache.mxnet.CustomOpProp
-import org.apache.mxnet.Operator
-import org.apache.mxnet.optimizer.SGD
-import org.apache.mxnet.Accuracy
-import org.apache.mxnet.Callback.Speedometer
+import scala.collection.mutable
/**
- * Example of CustomOp
- * @author Depeng Liang
- */
+ * Example of CustomOp
+ */
object ExampleCustomOp {
private val logger = LoggerFactory.getLogger(classOf[ExampleCustomOp])
class Softmax(_param: Map[String, String]) extends CustomOp {
- override def forward(sTrain: Boolean, req: Array[String],
- inData: Array[NDArray], outData: Array[NDArray], aux: Array[NDArray]): Unit = {
+ override def forward(sTrain: Boolean, req: Array[String], inData: Array[NDArray],
+ outData: Array[NDArray], aux: Array[NDArray]): Unit = {
val xShape = inData(0).shape
val x = inData(0).toArray.grouped(xShape(1)).toArray
val yArr = x.map { it =>
@@ -63,8 +52,8 @@ object ExampleCustomOp {
}
override def backward(req: Array[String], outGrad: Array[NDArray],
- inData: Array[NDArray], outData: Array[NDArray],
- inGrad: Array[NDArray], aux: Array[NDArray]): Unit = {
+ inData: Array[NDArray], outData: Array[NDArray],
+ inGrad: Array[NDArray], aux: Array[NDArray]): Unit = {
val l = inData(1).toArray.map(_.toInt)
val oShape = outData(0).shape
val yArr = outData(0).toArray.grouped(oShape(1)).toArray
@@ -86,7 +75,7 @@ object ExampleCustomOp {
override def listOutputs(): Array[String] = Array("output")
override def inferShape(inShape: Array[Shape]):
- (Array[Shape], Array[Shape], Array[Shape]) = {
+ (Array[Shape], Array[Shape], Array[Shape]) = {
val dataShape = inShape(0)
val labelShape = Shape(dataShape(0))
val outputShape = dataShape
@@ -94,16 +83,113 @@ object ExampleCustomOp {
}
override def inferType(inType: Array[DType]):
- (Array[DType], Array[DType], Array[DType]) = {
+ (Array[DType], Array[DType], Array[DType]) = {
(inType, inType.take(1), null)
}
override def createOperator(ctx: String, inShapes: Array[Array[Int]],
- inDtypes: Array[Int]): CustomOp = new Softmax(this.kwargs)
+ inDtypes: Array[Int]): CustomOp = new Softmax(this.kwargs)
}
Operator.register("softmax", new SoftmaxProp)
+ def test(dataPath : String, ctx : Context) : Float = {
+ val data = Symbol.Variable("data")
+ val label = Symbol.Variable("label")
+ val fc1 = Symbol.api.FullyConnected(data = Some(data), num_hidden = 128, name = "fc1")
+ val act1 = Symbol.api.Activation (data = Some(fc1), "relu", name = "relu")
+ val fc2 = Symbol.api.FullyConnected(Some(act1), None, None, 64, name = "fc2")
+ val act2 = Symbol.api.Activation(data = Some(fc2), "relu", name = "relu2")
+ val fc3 = Symbol.api.FullyConnected(Some(act2), None, None, 10, name = "fc3")
+ val kwargs = mutable.Map[String, Any]("label" -> label, "data" -> fc3)
+ val mlp = Symbol.api.Custom(op_type = "softmax", name = "softmax", kwargs = kwargs)
+
+ val (trainIter, testIter) =
+ Data.mnistIterator(dataPath, batchSize = 100, inputShape = Shape(784))
+
+ val datasAndLabels = trainIter.provideData ++ trainIter.provideLabel
+ val (argShapes, outputShapes, auxShapes) = mlp.inferShape(datasAndLabels)
+
+ val initializer = new Xavier(factorType = "in", magnitude = 2.34f)
+ val argNames = mlp.listArguments()
+ val argDict = argNames.zip(argShapes.map(s => NDArray.empty(s, ctx))).toMap
+
+ val gradDict = argNames.zip(argShapes).filter { case (name, shape) =>
+ !datasAndLabels.contains(name)
+ }.map(x => x._1 -> NDArray.empty(x._2, ctx) ).toMap
+
+ argDict.foreach { case (name, ndArray) =>
+ if (!datasAndLabels.contains(name)) {
+ initializer.initWeight(name, ndArray)
+ }
+ }
+
+ val executor = mlp.bind(ctx, argDict, gradDict)
+ val lr = 0.001f
+ val opt = new RMSProp(learningRate = lr, wd = 0.00001f)
+ val paramsGrads = gradDict.toList.zipWithIndex.map { case ((name, grad), idx) =>
+ (idx, name, grad, opt.createState(idx, argDict(name)))
+ }
+
+ val evalMetric = new Accuracy
+ val batchEndCallback = new Speedometer(100, 100)
+ val numEpoch = 10
+ var validationAcc = 0.0f
+
+ for (epoch <- 0 until numEpoch) {
+ val tic = System.currentTimeMillis
+ evalMetric.reset()
+ var nBatch = 0
+ var epochDone = false
+
+ trainIter.reset()
+ while (!epochDone) {
+ var doReset = true
+ while (doReset && trainIter.hasNext) {
+ val dataBatch = trainIter.next()
+ argDict("data").set(dataBatch.data(0))
+ argDict("label").set(dataBatch.label(0))
+ executor.forward(isTrain = true)
+ executor.backward()
+ paramsGrads.foreach { case (idx, name, grad, optimState) =>
+ opt.update(idx, argDict(name), grad, optimState)
+ }
+ evalMetric.update(dataBatch.label, executor.outputs)
+ nBatch += 1
+ batchEndCallback.invoke(epoch, nBatch, evalMetric)
+ }
+ if (doReset) {
+ trainIter.reset()
+ }
+ epochDone = true
+ }
+ val (name, value) = evalMetric.get
+ name.zip(value).foreach { case (n, v) =>
+ logger.info(s"Epoch[$epoch] Train-accuracy=$v")
+ }
+ val toc = System.currentTimeMillis
+ logger.info(s"Epoch[$epoch] Time cost=${toc - tic}")
+
+ evalMetric.reset()
+ testIter.reset()
+ while (testIter.hasNext) {
+ val evalBatch = testIter.next()
+ argDict("data").set(evalBatch.data(0))
+ argDict("label").set(evalBatch.label(0))
+ executor.forward(isTrain = true)
+ evalMetric.update(evalBatch.label, executor.outputs)
+ evalBatch.dispose()
+ }
+ val (names, values) = evalMetric.get
+ names.zip(values).foreach { case (n, v) =>
+ logger.info(s"Epoch[$epoch] Validation-accuracy=$v")
+ validationAcc = Math.max(validationAcc, v)
+ }
+ }
+ executor.dispose()
+ validationAcc
+ }
+
def main(args: Array[String]): Unit = {
val leop = new ExampleCustomOp
val parser: CmdLineParser = new CmdLineParser(leop)
@@ -115,98 +201,8 @@ object ExampleCustomOp {
val dataName = Array("data")
val labelName = Array("softmax_label")
+ test(leop.dataPath, ctx)
- val data = Symbol.Variable("data")
- val label = Symbol.Variable("label")
- val fc1 = Symbol.FullyConnected("fc1")()(Map("data" -> data, "num_hidden" -> 128))
- val act1 = Symbol.Activation("relu1")()(Map("data" -> fc1, "act_type" -> "relu"))
- val fc2 = Symbol.FullyConnected("fc2")()(Map("data" -> act1, "num_hidden" -> 64))
- val act2 = Symbol.Activation("relu2")()(Map("data" -> fc2, "act_type" -> "relu"))
- val fc3 = Symbol.FullyConnected("fc3")()(Map("data" -> act2, "num_hidden" -> 10))
- val mlp = Symbol.Custom("softmax")()(Map("data" -> fc3,
- "label" -> label, "op_type" -> "softmax"))
-
- val (trainIter, testIter) =
- Data.mnistIterator(leop.dataPath, batchSize = 100, inputShape = Shape(784))
-
- val datasAndLabels = trainIter.provideData ++ trainIter.provideLabel
- val (argShapes, outputShapes, auxShapes) = mlp.inferShape(datasAndLabels)
-
- val initializer = new Xavier(factorType = "in", magnitude = 2.34f)
- val argNames = mlp.listArguments()
- val argDict = argNames.zip(argShapes.map(s => NDArray.empty(s, ctx))).toMap
-
- val gradDict = argNames.zip(argShapes).filter { case (name, shape) =>
- !datasAndLabels.contains(name)
- }.map(x => x._1 -> NDArray.empty(x._2, ctx) ).toMap
-
- argDict.foreach { case (name, ndArray) =>
- if (!datasAndLabels.contains(name)) {
- initializer.initWeight(name, ndArray)
- }
- }
-
- val executor = mlp.bind(ctx, argDict, gradDict)
- val lr = 0.001f
- val opt = new RMSProp(learningRate = lr, wd = 0.00001f)
- val paramsGrads = gradDict.toList.zipWithIndex.map { case ((name, grad), idx) =>
- (idx, name, grad, opt.createState(idx, argDict(name)))
- }
-
- val evalMetric = new Accuracy
- val batchEndCallback = new Speedometer(100, 100)
- val numEpoch = 20
-
- for (epoch <- 0 until numEpoch) {
- val tic = System.currentTimeMillis
- evalMetric.reset()
- var nBatch = 0
- var epochDone = false
-
- trainIter.reset()
- while (!epochDone) {
- var doReset = true
- while (doReset && trainIter.hasNext) {
- val dataBatch = trainIter.next()
- argDict("data").set(dataBatch.data(0))
- argDict("label").set(dataBatch.label(0))
- executor.forward(isTrain = true)
- executor.backward()
- paramsGrads.foreach { case (idx, name, grad, optimState) =>
- opt.update(idx, argDict(name), grad, optimState)
- }
- evalMetric.update(dataBatch.label, executor.outputs)
- nBatch += 1
- batchEndCallback.invoke(epoch, nBatch, evalMetric)
- }
- if (doReset) {
- trainIter.reset()
- }
- epochDone = true
- }
- val (name, value) = evalMetric.get
- name.zip(value).foreach { case (n, v) =>
- logger.info(s"Epoch[$epoch] Train-accuracy=$v")
- }
- val toc = System.currentTimeMillis
- logger.info(s"Epoch[$epoch] Time cost=${toc - tic}")
-
- evalMetric.reset()
- testIter.reset()
- while (testIter.hasNext) {
- val evalBatch = testIter.next()
- argDict("data").set(evalBatch.data(0))
- argDict("label").set(evalBatch.label(0))
- executor.forward(isTrain = true)
- evalMetric.update(evalBatch.label, executor.outputs)
- evalBatch.dispose()
- }
- val (names, values) = evalMetric.get
- names.zip(values).foreach { case (n, v) =>
- logger.info(s"Epoch[$epoch] Validation-accuracy=$v")
- }
- }
- executor.dispose()
} catch {
case ex: Exception => {
logger.error(ex.getMessage, ex)
diff --git a/scala-package/examples/src/main/scala/org/apache/mxnetexamples/customop/ExampleCustomOpWithRtc.scala b/scala-package/examples/src/main/scala/org/apache/mxnetexamples/customop/ExampleCustomOpWithRtc.scala
index 030670caca1..c3ac347353d 100644
--- a/scala-package/examples/src/main/scala/org/apache/mxnetexamples/customop/ExampleCustomOpWithRtc.scala
+++ b/scala-package/examples/src/main/scala/org/apache/mxnetexamples/customop/ExampleCustomOpWithRtc.scala
@@ -17,31 +17,19 @@
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, Operator, Rtc, Shape, Symbol, Xavier}
+import org.apache.mxnet.optimizer.RMSProp
import org.kohsuke.args4j.{CmdLineParser, Option}
import org.slf4j.LoggerFactory
+
import scala.collection.JavaConverters._
-import org.apache.mxnet.Symbol
-import org.apache.mxnet.DType.DType
-import org.apache.mxnet.DataIter
-import org.apache.mxnet.DataBatch
-import org.apache.mxnet.NDArray
-import org.apache.mxnet.Shape
-import org.apache.mxnet.EvalMetric
-import org.apache.mxnet.Context
-import org.apache.mxnet.Xavier
-import org.apache.mxnet.optimizer.RMSProp
-import org.apache.mxnet.CustomOp
-import org.apache.mxnet.CustomOpProp
-import org.apache.mxnet.Operator
-import org.apache.mxnet.optimizer.SGD
-import org.apache.mxnet.Accuracy
-import org.apache.mxnet.Callback.Speedometer
-import org.apache.mxnet.Rtc
+import scala.collection.mutable
/**
- * Example of CustomOp with Rtc
- * @author Depeng Liang
- */
+ * Example of CustomOp with Rtc
+ */
object ExampleCustomOpWithRtc {
private val logger = LoggerFactory.getLogger(classOf[ExampleCustomOpWithRtc])
@@ -79,8 +67,8 @@ object ExampleCustomOpWithRtc {
}
override def backward(req: Array[String], outGrad: Array[NDArray],
- inData: Array[NDArray], outData: Array[NDArray],
- inGrad: Array[NDArray], aux: Array[NDArray]): Unit = {
+ inData: Array[NDArray], outData: Array[NDArray],
+ inGrad: Array[NDArray], aux: Array[NDArray]): Unit = {
val l = inData(1)
val y = outData(0)
val dx = inGrad(0)
@@ -108,7 +96,7 @@ object ExampleCustomOpWithRtc {
override def listOutputs(): Array[String] = Array("output")
override def inferShape(inShape: Array[Shape]):
- (Array[Shape], Array[Shape], Array[Shape]) = {
+ (Array[Shape], Array[Shape], Array[Shape]) = {
val dataShape = inShape(0)
val labelShape = Shape(dataShape(0))
val outputShape = dataShape
@@ -116,16 +104,111 @@ object ExampleCustomOpWithRtc {
}
override def inferType(inType: Array[DType]):
- (Array[DType], Array[DType], Array[DType]) = {
+ (Array[DType], Array[DType], Array[DType]) = {
(inType, inType.take(1), null)
}
override def createOperator(ctx: String, inShapes: Array[Array[Int]],
- inDtypes: Array[Int]): CustomOp = new Softmax(this.kwargs)
+ inDtypes: Array[Int]): CustomOp = new Softmax(this.kwargs)
}
Operator.register("softmax", new SoftmaxProp)
+ def test(dataPath : String, ctx : Context) : Float = {
+ val data = Symbol.Variable("data")
+ val label = Symbol.Variable("label")
+ val fc1 = Symbol.api.FullyConnected(data = Some(data), num_hidden = 128, name = "fc1")
+ val act1 = Symbol.api.Activation (data = Some(fc1), "relu", name = "relu")
+ val fc2 = Symbol.api.FullyConnected(Some(act1), None, None, 64, name = "fc2")
+ val act2 = Symbol.api.Activation(data = Some(fc2), "relu", name = "relu2")
+ val fc3 = Symbol.api.FullyConnected(Some(act2), None, None, 10, name = "fc3")
+ val kwargs = mutable.Map[String, Any]("label" -> label, "data" -> fc3,
+ "forwardBlockDim" -> new Shape(1, 1, 1))
+ val mlp = Symbol.api.Custom(op_type = "softmax", name = "softmax", kwargs = kwargs)
+
+ val (trainIter, testIter) =
+ Data.mnistIterator(dataPath, batchSize = 100, inputShape = Shape(784))
+ val datasAndLabels = trainIter.provideData ++ trainIter.provideLabel
+ val (argShapes, outputShapes, auxShapes) = mlp.inferShape(datasAndLabels)
+
+ val initializer = new Xavier(factorType = "in", magnitude = 2.34f)
+
+ val argNames = mlp.listArguments()
+ val argDict = argNames.zip(argShapes.map(s => NDArray.empty(s, ctx))).toMap
+ val gradDict = argNames.zip(argShapes).filter { case (name, shape) =>
+ !datasAndLabels.contains(name)
+ }.map(x => x._1 -> NDArray.empty(x._2, ctx) ).toMap
+ argDict.foreach { case (name, ndArray) =>
+ if (!datasAndLabels.contains(name)) {
+ initializer.initWeight(name, ndArray)
+ }
+ }
+
+ val executor = mlp.bind(ctx, argDict, gradDict)
+ val lr = 0.001f
+ val opt = new RMSProp(learningRate = lr, wd = 0.00001f)
+ val paramsGrads = gradDict.toList.zipWithIndex.map { case ((name, grad), idx) =>
+ (idx, name, grad, opt.createState(idx, argDict(name)))
+ }
+ val evalMetric = new Accuracy
+ val batchEndCallback = new Speedometer(100, 100)
+ val numEpoch = 10
+ var validationAcc = 0.0f
+
+ for (epoch <- 0 until numEpoch) {
+ val tic = System.currentTimeMillis
+ evalMetric.reset()
+ var nBatch = 0
+ var epochDone = false
+
+ trainIter.reset()
+ while (!epochDone) {
+ var doReset = true
+ while (doReset && trainIter.hasNext) {
+ val dataBatch = trainIter.next()
+ argDict("data").set(dataBatch.data(0))
+ argDict("label").set(dataBatch.label(0))
+ executor.forward(isTrain = true)
+ executor.backward()
+ paramsGrads.foreach { case (idx, name, grad, optimState) =>
+ opt.update(idx, argDict(name), grad, optimState)
+ }
+ evalMetric.update(dataBatch.label, executor.outputs)
+ nBatch += 1
+ batchEndCallback.invoke(epoch, nBatch, evalMetric)
+ }
+ if (doReset) {
+ trainIter.reset()
+ }
+ epochDone = true
+ }
+ val (name, value) = evalMetric.get
+ name.zip(value).foreach { case (n, v) =>
+ logger.info(s"Epoch[$epoch] Train-accuracy=$v")
+ }
+ val toc = System.currentTimeMillis
+ logger.info(s"Epoch[$epoch] Time cost=${toc - tic}")
+
+ evalMetric.reset()
+ testIter.reset()
+ while (testIter.hasNext) {
+ val evalBatch = testIter.next()
+ argDict("data").set(evalBatch.data(0))
+ argDict("label").set(evalBatch.label(0))
+ executor.forward(isTrain = true)
+ evalMetric.update(evalBatch.label, executor.outputs)
+ evalBatch.dispose()
+ }
+ val (names, values) = evalMetric.get
+ names.zip(values).foreach { case (n, v) =>
+ logger.info(s"Epoch[$epoch] Validation-accuracy=$v")
+ validationAcc = Math.max(validationAcc, v)
+ }
+ }
+ executor.dispose()
+ validationAcc
+ }
+
def main(args: Array[String]): Unit = {
val leop = new ExampleCustomOpWithRtc
val parser: CmdLineParser = new CmdLineParser(leop)
@@ -137,95 +220,8 @@ object ExampleCustomOpWithRtc {
val dataName = Array("data")
val labelName = Array("softmax_label")
+ test(leop.dataPath, ctx)
- val data = Symbol.Variable("data")
- val label = Symbol.Variable("label")
- val fc1 = Symbol.FullyConnected("fc1")()(Map("data" -> data, "num_hidden" -> 128))
- val act1 = Symbol.Activation("relu1")()(Map("data" -> fc1, "act_type" -> "relu"))
- val fc2 = Symbol.FullyConnected("fc2")()(Map("data" -> act1, "num_hidden" -> 64))
- val act2 = Symbol.Activation("relu2")()(Map("data" -> fc2, "act_type" -> "relu"))
- val fc3 = Symbol.FullyConnected("fc3")()(Map("data" -> act2, "num_hidden" -> 10))
- val mlp = Symbol.Custom("softmax")()(Map("data" -> fc3,
- "label" -> label, "op_type" -> "softmax", "forwardBlockDim" -> "(1,1,1)"))
-
- val (trainIter, testIter) =
- Data.mnistIterator(leop.dataPath, batchSize = 100, inputShape = Shape(784))
- val datasAndLabels = trainIter.provideData ++ trainIter.provideLabel
- val (argShapes, outputShapes, auxShapes) = mlp.inferShape(datasAndLabels)
-
- val initializer = new Xavier(factorType = "in", magnitude = 2.34f)
-
- val argNames = mlp.listArguments()
- val argDict = argNames.zip(argShapes.map(s => NDArray.empty(s, ctx))).toMap
- val gradDict = argNames.zip(argShapes).filter { case (name, shape) =>
- !datasAndLabels.contains(name)
- }.map(x => x._1 -> NDArray.empty(x._2, ctx) ).toMap
- argDict.foreach { case (name, ndArray) =>
- if (!datasAndLabels.contains(name)) {
- initializer.initWeight(name, ndArray)
- }
- }
-
- val executor = mlp.bind(ctx, argDict, gradDict)
- val lr = 0.001f
- val opt = new RMSProp(learningRate = lr, wd = 0.00001f)
- val paramsGrads = gradDict.toList.zipWithIndex.map { case ((name, grad), idx) =>
- (idx, name, grad, opt.createState(idx, argDict(name)))
- }
- val evalMetric = new Accuracy
- val batchEndCallback = new Speedometer(100, 100)
- val numEpoch = 20
-
- for (epoch <- 0 until numEpoch) {
- val tic = System.currentTimeMillis
- evalMetric.reset()
- var nBatch = 0
- var epochDone = false
-
- trainIter.reset()
- while (!epochDone) {
- var doReset = true
- while (doReset && trainIter.hasNext) {
- val dataBatch = trainIter.next()
- argDict("data").set(dataBatch.data(0))
- argDict("label").set(dataBatch.label(0))
- executor.forward(isTrain = true)
- executor.backward()
- paramsGrads.foreach { case (idx, name, grad, optimState) =>
- opt.update(idx, argDict(name), grad, optimState)
- }
- evalMetric.update(dataBatch.label, executor.outputs)
- nBatch += 1
- batchEndCallback.invoke(epoch, nBatch, evalMetric)
- }
- if (doReset) {
- trainIter.reset()
- }
- epochDone = true
- }
- val (name, value) = evalMetric.get
- name.zip(value).foreach { case (n, v) =>
- logger.info(s"Epoch[$epoch] Train-accuracy=$v")
- }
- val toc = System.currentTimeMillis
- logger.info(s"Epoch[$epoch] Time cost=${toc - tic}")
-
- evalMetric.reset()
- testIter.reset()
- while (testIter.hasNext) {
- val evalBatch = testIter.next()
- argDict("data").set(evalBatch.data(0))
- argDict("label").set(evalBatch.label(0))
- executor.forward(isTrain = true)
- evalMetric.update(evalBatch.label, executor.outputs)
- evalBatch.dispose()
- }
- val (names, values) = evalMetric.get
- names.zip(values).foreach { case (n, v) =>
- logger.info(s"Epoch[$epoch] Validation-accuracy=$v")
- }
- }
- executor.dispose()
} catch {
case ex: Exception => {
logger.error(ex.getMessage, ex)
diff --git a/scala-package/examples/src/main/scala/org/apache/mxnetexamples/customop/README.md b/scala-package/examples/src/main/scala/org/apache/mxnetexamples/customop/README.md
new file mode 100644
index 00000000000..886fa2cc9d4
--- /dev/null
+++ b/scala-package/examples/src/main/scala/org/apache/mxnetexamples/customop/README.md
@@ -0,0 +1,22 @@
+# Custom Operator Example for Scala
+This is the example using Custom Operator for type-safe api of Scala.
+In the example, a `Softmax` operator is implemented to run the MNIST example.
+
+There is also an example using RTC. However, the rtc module is depreciated and no longer can be used. Please contribute to use CudaModule operator to replace the rtc.
+
+## Setup
+### Download the source File
+```$xslt
+https://s3.us-east-2.amazonaws.com/mxnet-scala/scala-example-ci/mnist/mnist.zip
+```
+### Unzip the file
+```$xslt
+unzip mnist.zip
+```
+### Arguement Configuration
+Then you need to define the arguments that you would like to pass in the model:
+```$xslt
+--data-path <location of your downloaded file>
+```
+
+you can find more in [here](https://github.com/apache/incubator-mxnet/blob/scala-package/examples/src/main/scala/org/apache/mxnet/examples/customop/ExampleCustomOp.scala#L218-L221)
\ No newline at end of file
diff --git a/scala-package/examples/src/test/scala/org/apache/mxnetexamples/customop/CustomOpExampleSuite.scala b/scala-package/examples/src/test/scala/org/apache/mxnetexamples/customop/CustomOpExampleSuite.scala
new file mode 100644
index 00000000000..4ba0e1bb87c
--- /dev/null
+++ b/scala-package/examples/src/test/scala/org/apache/mxnetexamples/customop/CustomOpExampleSuite.scala
@@ -0,0 +1,86 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You 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 org.apache.mxnetexamples.customop
+
+import java.io.File
+import java.net.URL
+
+import org.apache.commons.io.FileUtils
+import org.apache.mxnet.Context
+import org.scalatest.{BeforeAndAfterAll, FunSuite}
+import org.slf4j.LoggerFactory
+
+import scala.sys.process.Process
+
+class CustomOpExampleSuite extends FunSuite with BeforeAndAfterAll {
+ private val logger = LoggerFactory.getLogger(classOf[CustomOpExampleSuite])
+
+ test("Example CI: Test Customop MNIST") {
+ // This test is CPU only
+ if (System.getenv().containsKey("SCALA_TEST_ON_GPU") &&
+ System.getenv("SCALA_TEST_ON_GPU").toInt == 1) {
+ logger.info("CPU test only, skipped...")
+ } else {
+ logger.info("Downloading mnist model")
+ val baseUrl = "https://s3.us-east-2.amazonaws.com/mxnet-scala/scala-example-ci"
+ val tempDirPath = System.getProperty("java.io.tmpdir")
+ val modelDirPath = tempDirPath + File.separator + "mnist/"
+ val tmpFile = new File(tempDirPath + "/mnist/mnist.zip")
+ if (!tmpFile.exists()) {
+ FileUtils.copyURLToFile(new URL(baseUrl + "/mnist/mnist.zip"),
+ tmpFile)
+ }
+ // TODO: Need to confirm with Windows
+ Process("unzip " + tempDirPath + "/mnist/mnist.zip -d "
+ + tempDirPath + "/mnist/") !
+ val context = Context.cpu()
+ val output = ExampleCustomOp.test(modelDirPath, context)
+ assert(output >= 0.95f)
+ }
+ }
+
+ test("Example CI: Test CustomopRtc MNIST") {
+ // This test is GPU only
+ // TODO: RTC is depreciated, need to change to CUDA Module
+ val RTC_fixed = false
+ if (RTC_fixed) {
+ if (System.getenv().containsKey("SCALA_TEST_ON_GPU") &&
+ System.getenv("SCALA_TEST_ON_GPU").toInt == 1) {
+ logger.info("Downloading mnist model")
+ val baseUrl = "https://s3.us-east-2.amazonaws.com/mxnet-scala/scala-example-ci"
+ val tempDirPath = System.getProperty("java.io.tmpdir")
+ val modelDirPath = tempDirPath + File.separator + "mnist/"
+ val tmpFile = new File(tempDirPath + "/mnist/mnist.zip")
+ if (!tmpFile.exists()) {
+ FileUtils.copyURLToFile(new URL(baseUrl + "/mnist/mnist.zip"),
+ tmpFile)
+ }
+ // TODO: Need to confirm with Windows
+ Process("unzip " + tempDirPath + "/mnist/mnist.zip -d "
+ + tempDirPath + "/mnist/") !
+ val context = Context.gpu()
+ val output = ExampleCustomOpWithRtc.test(modelDirPath, context)
+ assert(output >= 0.95f)
+ } else {
+ logger.info("GPU test only, skipped...")
+ }
+ } else {
+ logger.warn("RTC module is not up to date, please don't use this" +
+ "\nCreate CudaModule for this")
+ }
+ }
+}
diff --git a/scala-package/macros/src/main/scala/org/apache/mxnet/APIDocGenerator.scala b/scala-package/macros/src/main/scala/org/apache/mxnet/APIDocGenerator.scala
index 9a8ec645f27..b446fb3d5b4 100644
--- a/scala-package/macros/src/main/scala/org/apache/mxnet/APIDocGenerator.scala
+++ b/scala-package/macros/src/main/scala/org/apache/mxnet/APIDocGenerator.scala
@@ -56,7 +56,12 @@ private[mxnet] object APIDocGenerator{
def absClassGen(FILE_PATH : String, isSymbol : Boolean) : String = {
// scalastyle:off
val absClassFunctions = getSymbolNDArrayMethods(isSymbol)
- val absFuncs = absClassFunctions.map(absClassFunction => {
+ // Defines Operators that should not generated
+ val notGenerated = Set("Custom")
+ // TODO: Add Filter to the same location in case of refactor
+ val absFuncs = absClassFunctions.filterNot(_.name.startsWith("_"))
+ .filterNot(ele => notGenerated.contains(ele.name))
+ .map(absClassFunction => {
val scalaDoc = generateAPIDocFromBackend(absClassFunction)
val defBody = generateAPISignature(absClassFunction, isSymbol)
s"$scalaDoc\n$defBody"
@@ -178,9 +183,6 @@ private[mxnet] object APIDocGenerator{
_LIB.mxSymbolGetAtomicSymbolInfo(
handle, name, desc, numArgs, argNames, argTypes, argDescs, keyVarNumArgs)
-
- val realName = if (aliasName == name.value) "" else s"(a.k.a., ${name.value})"
-
val argList = argNames zip argTypes zip argDescs map { case ((argName, argType), argDesc) =>
val typeAndOption = CToScalaUtils.argumentCleaner(argName, argType, returnType)
new absClassArg(argName, typeAndOption._1, argDesc, typeAndOption._2)
diff --git a/scala-package/macros/src/main/scala/org/apache/mxnet/NDArrayMacro.scala b/scala-package/macros/src/main/scala/org/apache/mxnet/NDArrayMacro.scala
index 644bc5c4489..2d3a1c7ec5a 100644
--- a/scala-package/macros/src/main/scala/org/apache/mxnet/NDArrayMacro.scala
+++ b/scala-package/macros/src/main/scala/org/apache/mxnet/NDArrayMacro.scala
@@ -85,14 +85,16 @@ private[mxnet] object NDArrayMacro {
val isContrib: Boolean = c.prefix.tree match {
case q"new AddNDArrayAPIs($b)" => c.eval[Boolean](c.Expr(b))
}
+ // Defines Operators that should not generated
+ val notGenerated = Set("Custom")
val newNDArrayFunctions = {
if (isContrib) ndarrayFunctions.filter(
func => func.name.startsWith("_contrib_") || !func.name.startsWith("_"))
else ndarrayFunctions.filterNot(_.name.startsWith("_"))
- }
+ }.filterNot(ele => notGenerated.contains(ele.name))
- val functionDefs = newNDArrayFunctions map { ndarrayfunction =>
+ val functionDefs = newNDArrayFunctions.map { ndarrayfunction =>
// Construct argument field
var argDef = ListBuffer[String]()
diff --git a/scala-package/macros/src/main/scala/org/apache/mxnet/SymbolMacro.scala b/scala-package/macros/src/main/scala/org/apache/mxnet/SymbolMacro.scala
index 3e790ef4126..42aa11781d8 100644
--- a/scala-package/macros/src/main/scala/org/apache/mxnet/SymbolMacro.scala
+++ b/scala-package/macros/src/main/scala/org/apache/mxnet/SymbolMacro.scala
@@ -89,13 +89,16 @@ private[mxnet] object SymbolImplMacros {
case q"new AddSymbolAPIs($b)" => c.eval[Boolean](c.Expr(b))
}
+ // Defines Operators that should not generated
+ val notGenerated = Set("Custom")
+
// TODO: Put Symbol.api.foo --> Stable APIs
// Symbol.contrib.bar--> Contrib APIs
val newSymbolFunctions = {
if (isContrib) symbolFunctions.filter(
func => func.name.startsWith("_contrib_") || !func.name.startsWith("_"))
else symbolFunctions.filter(!_.name.startsWith("_"))
- }
+ }.filterNot(ele => notGenerated.contains(ele.name))
val functionDefs = newSymbolFunctions map { symbolfunction =>
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