calumleslie commented on a change in pull request #9678: [First cut] Scala 
Inference APIs
URL: https://github.com/apache/incubator-mxnet/pull/9678#discussion_r167600563
 
 

 ##########
 File path: 
scala-package/infer/src/main/scala/ml/dmlc/mxnet/infer/PredictBase.scala
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 @@ -0,0 +1,200 @@
+/*
+ * 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 ml.dmlc.mxnet.infer
+
+import ml.dmlc.mxnet.io.NDArrayIter
+import ml.dmlc.mxnet.{DataDesc, NDArray, Shape}
+import ml.dmlc.mxnet.module.Module
+
+import scala.collection.mutable.ListBuffer
+import org.slf4j.LoggerFactory
+
+/**
+  * Base Trait for MXNNet Predictor classes.
+  */
+trait PredictBase {
+
+  /**
+    * This method will take input as IndexedSeq one dimensional arrays and 
creates
+    * NDArray needed for inference. The array will be reshaped based on the 
input descriptors.
+    * @param input: A IndexedSequence of Java one-dimensional array, An 
IndexedSequence is
+    *             is needed when the model has more than one input/output
+    * @return IndexedSequence array of outputs.
+    */
+  def predict(input: IndexedSeq[Array[Float]]): IndexedSeq[Array[Float]]
+
+  /**
+    * Predict using NDArray as input. This method is useful when the input is 
a batch of data
+    * or when multiple operations on the input/output have to performed.
+    * Note: User is responsible for managing allocation/deallocation of 
NDArrays.
+    * @param input: IndexedSequence NDArrays.
+    * @return output of Predictions as NDArrays.
+    */
+  def predictWithNDArray(input: IndexedSeq[NDArray]): IndexedSeq[NDArray]
+
+}
+
+/**
+  * Implementation of predict routines.
+  *
+  * @param modelPathPrefix PathPrefix from where to load the model.
+  *                        Example: file://model-dir/resnet-152(containing 
resnet-152-symbol.json,
+  *                        resnet-152-XXXX.params and optionally synset.txt).
+  *                        Supports model loading from various sources like 
local disk,
+  *                        hdfs, https and s3. file://, hdfs://, https://, 
s3://
+  * @param inputDescriptors Descriptors defining the input node names, shape,
+  *                         layout and Type parameters
+  * @param outputDescriptors Descriptors defining the output node names, shape,
+  *                          layout and Type parameters
+  */
+class Predictor(modelPathPrefix: String,
+             protected val inputDescriptors: IndexedSeq[DataDesc],
+             protected var outputDescriptors:
+             Option[IndexedSeq[DataDesc]] = None) extends PredictBase {
+
+  private val logger = LoggerFactory.getLogger(classOf[Predictor])
+
+  protected var batchIndex = inputDescriptors(0).layout.indexOf('N')
+  protected var batchSize = if (batchIndex != -1 ) 
inputDescriptors(0).shape(batchIndex) else 1
+
+  protected var iDescriptors = inputDescriptors
+
+  inputDescriptors.foreach((f: DataDesc) => require(f.layout.indexOf('N') == 
batchIndex,
+    "batch size should be in the same index for all inputs"))
+
+
+  if (batchIndex != -1) {
+    inputDescriptors.foreach((f: DataDesc) => require(f.shape(batchIndex) == 
batchSize,
+      "batch size should be same for all inputs"))
+  } else {
+    // TODO: this is assuming that the input needs a batch
+    iDescriptors = inputDescriptors.map((f : DataDesc) => new DataDesc(f.name,
+    Shape(1 +: f.shape.toVector), f.dtype, 'N' +: f.layout) )
+    batchIndex = 1
+  }
+
+  protected val mxNetHandler = MXNetHandler()
+
+  protected val mod = loadModule()
+
+  /**
+    * This method will take input as IndexedSeq one dimensional arrays and 
creates
+    * NDArray needed for inference. The array will be reshaped based on the 
input descriptors.
+    *
+    * @param input : A IndexedSequence of Java one-dimensional array, An 
IndexedSequence is
+    *              is needed when the model has more than one input/output
+    * @return IndexedSequence array of outputs.
+    */
+  override def predict(input: IndexedSeq[Array[Float]]): 
IndexedSeq[Array[Float]] = {
+
+    require(input.length == inputDescriptors.length, "number of inputs 
provided: %d" +
+      " do not match number of inputs in inputDescriptors: 
%d".format(input.length,
+        inputDescriptors.length))
+
+    for((i, d) <- input.zip(inputDescriptors)) {
+      require (i.length == d.shape.product/batchSize, "number of elements:" +
+        " %d in the input does not match the shape:%s".format( i.length, 
d.shape.toString()))
+    }
+
+    var inputND: ListBuffer[NDArray] = ListBuffer.empty[NDArray]
+
+    for((i, d) <- input.zip(inputDescriptors)) {
+      val shape = d.shape.toVector.patch(from = batchIndex, patch = Vector(1), 
replaced = 1)
+
+      inputND += mxNetHandler.execute(NDArray.array(i, Shape(shape)))
 
 Review comment:
   Calls to MXNet's native library are not safe to call from more than one 
thread per program execution (unless that has been changed). This is just how 
MXNet works at present. We [described this on the discussion site at one 
point](https://discuss.mxnet.io/t/fixing-thread-safety-issues-in-scala-library/236).
   
   It's actually not entirely known if some calls are safe and some are not but 
we've found the best stability from single-threading all calls to MXNet.

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