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new 6cbc273 Scala/Java Predict API fix #14756 (#14804)
6cbc273 is described below
commit 6cbc273454d0e6c24307183eeedc5c3e33e2ccc1
Author: Lanking <lanking...@live.com>
AuthorDate: Fri Apr 26 20:40:02 2019 -0700
Scala/Java Predict API fix #14756 (#14804)
* add fix in the code
* add unit test
* update comments
---
.../scala/org/apache/mxnet/module/BaseModule.scala | 17 +++++++++++--
.../test/scala/org/apache/mxnet/ModuleSuite.scala | 28 ++++++++++++++++++++++
2 files changed, 43 insertions(+), 2 deletions(-)
diff --git
a/scala-package/core/src/main/scala/org/apache/mxnet/module/BaseModule.scala
b/scala-package/core/src/main/scala/org/apache/mxnet/module/BaseModule.scala
index 3be8e06..7fbdae5 100644
--- a/scala-package/core/src/main/scala/org/apache/mxnet/module/BaseModule.scala
+++ b/scala-package/core/src/main/scala/org/apache/mxnet/module/BaseModule.scala
@@ -247,11 +247,23 @@ abstract class BaseModule {
/**
* Run prediction and collect the outputs.
- * @param evalData
+ * @param evalData dataIter to do the Inference
* @param numBatch Default is -1, indicating running all the batches in the
data iterator.
* @param reset Default is `True`, indicating whether we should reset the
data iter before start
* doing prediction.
* @return The return value will be a list `[out1, out2, out3]`.
+ * The concatenation process will be like
+ * {{{
+ * outputBatches = [
+ * [a1, a2, a3], // batch a
+ * [b1, b2, b3] // batch b
+ * ]
+ * result = [
+ * NDArray, // [a1, b1]
+ * NDArray, // [a2, b2]
+ * NDArray, // [a3, b3]
+ * ]
+ * }}}
* Where each element is concatenation of the outputs for all the
mini-batches.
*/
def predict(evalData: DataIter, numBatch: Int = -1, reset: Boolean = true)
@@ -264,7 +276,8 @@ abstract class BaseModule {
s"in mini-batches (${out.size})." +
"Maybe bucketing is used?")
)
- val concatenatedOutput = outputBatches.map(out => NDArray.concatenate(out))
+ val oBT = outputBatches.transpose
+ val concatenatedOutput = oBT.map(out => NDArray.concatenate(out))
outputBatches.foreach(_.foreach(_.dispose()))
concatenatedOutput
}
diff --git
a/scala-package/core/src/test/scala/org/apache/mxnet/ModuleSuite.scala
b/scala-package/core/src/test/scala/org/apache/mxnet/ModuleSuite.scala
index 3e753a1..5aed01b 100644
--- a/scala-package/core/src/test/scala/org/apache/mxnet/ModuleSuite.scala
+++ b/scala-package/core/src/test/scala/org/apache/mxnet/ModuleSuite.scala
@@ -23,6 +23,34 @@ import org.apache.mxnet.optimizer._
import org.apache.mxnet.io._
class ModuleSuite extends FunSuite with BeforeAndAfterAll {
+
+ class myModule(symbol : Symbol) extends Module (symbol) {
+ override def predictEveryBatch(evalData: DataIter,
+ numBatch: Int = 1, reset: Boolean = true):
+ IndexedSeq[IndexedSeq[NDArray]] = {
+ val data = IndexedSeq(
+ NDArray.ones(Shape(1, 10, 1)),
+ NDArray.ones(Shape(1, 10, 1)),
+ NDArray.ones(Shape(1, 10, 4))
+ )
+ List.fill(numBatch)(data).toIndexedSeq
+ }
+ }
+
+ test("predict") {
+ val sym = Symbol.Variable("data")
+ val mod = new myModule(sym)
+ val dummyIter = new NDArrayIter(IndexedSeq(NDArray.ones(1)))
+ var output = mod.predict(dummyIter, 1)
+ require(output(0).shape == Shape(1, 10, 1))
+ require(output(1).shape == Shape(1, 10, 1))
+ require(output(2).shape == Shape(1, 10, 4))
+ output = mod.predict(dummyIter, 2)
+ require(output(0).shape == Shape(2, 10, 1))
+ require(output(1).shape == Shape(2, 10, 1))
+ require(output(2).shape == Shape(2, 10, 4))
+ }
+
test ("model dtype") {
val dType = DType.Float32
val dShape = Shape(3, 8, 7)
