address comments

scala-package/core/src/main/scala/org/apache/mxnet/javaapi/Layout.scala

@@ -0,0 +1,34 @@
+/*
+ * 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.mxnet.javaapi
+
+/**
+  * Layout definition of DataDesc
+  * N Batch size
+  * C channels
+  * H Height
+  * W Weight
+  * T sequence length
+  * __undefined__ default value of Layout
+  */
+object Layout {
+  val UNDEFINED: String = org.apache.mxnet.Layout.UNDEFINED
+  val NCHW: String = org.apache.mxnet.Layout.NCHW
+  val NTC: String = org.apache.mxnet.Layout.NTC
+  val NT: String = org.apache.mxnet.Layout.NT
+  val N: String = org.apache.mxnet.Layout.N
+}

scala-package/examples/scripts/infer/bert/get_bert_data.sh

@@ -25,9 +25,6 @@ data_path=$MXNET_ROOT/scripts/infer/models/static-bert-qa/
 
 if [ ! -d "$data_path" ]; then
   mkdir -p "$data_path"
-fi
-
-if [ ! -f "$data_path" ]; then
   curl https://s3.us-east-2.amazonaws.com/mxnet-scala/scala-example-ci/BertQA/vocab.json -o $data_path/vocab.json
   curl https://s3.us-east-2.amazonaws.com/mxnet-scala/scala-example-ci/BertQA/static_bert_qa-0002.params -o $data_path/static_bert_qa-0002.params
   curl https://s3.us-east-2.amazonaws.com/mxnet-scala/scala-example-ci/BertQA/static_bert_qa-symbol.json -o $data_path/static_bert_qa-symbol.json

scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer/bert/BertUtil.java → scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer/bert/BertDataParser.java

@@ -25,7 +25,7 @@
 import com.google.gson.JsonElement;
 import com.google.gson.JsonObject;
 
-public class BertUtil {
+public class BertDataParser {
 
     private Map<String, Integer> token2idx;
     private List<String> idx2token;

scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer/bert/BertQA.java

@@ -65,10 +65,8 @@ static List<String> postProcessing(NDArray result, List<String> tokens) {
         NDArray[] output = NDArray.split(
                 NDArray.new splitParam(result, 2).setAxis(2));
         // Get the formatted logits result
-        NDArray startLogits = NDArray.reshape(
-                NDArray.new reshapeParam(output[0]).setShape(new Shape(new int[]{0, -3})))[0];
-        NDArray endLogits = NDArray.reshape(
-                NDArray.new reshapeParam(output[1]).setShape(new Shape(new int[]{0, -3})))[0];
+        NDArray startLogits = output[0].reshape(new int[]{0, -3});
+        NDArray endLogits = output[1].reshape(new int[]{0, -3});
         // Get Probability distribution
         float[] startProb = NDArray.softmax(
                 NDArray.new softmaxParam(startLogits))[0].toArray();
@@ -83,7 +81,7 @@ public static void main(String[] args) throws Exception{
         BertQA inst = new BertQA();
         CmdLineParser parser = new CmdLineParser(inst);
         parser.parseArgument(args);
-        BertUtil util = new BertUtil();
+        BertDataParser util = new BertDataParser();
         Context context = Context.cpu();
         if (System.getenv().containsKey("SCALA_TEST_ON_GPU") &&
                 Integer.valueOf(System.getenv("SCALA_TEST_ON_GPU")) == 1) {
@@ -115,26 +113,25 @@ public static void main(String[] args) throws Exception{
             indexesFloat.add((float) integer);
         }
         // Preparing the input data
-        NDArray inputs = new NDArray(indexesFloat,
-                new Shape(new int[]{1, inst.seqLength}), context);
-        NDArray tokenTypesND = new NDArray(tokenTypes,
-                new Shape(new int[]{1, inst.seqLength}), context);
-        NDArray validLengthND = new NDArray(new float[] {(float) validLength},
-                new Shape(new int[]{1}), context);
-        List<NDArray> inputBatch = new ArrayList<>();
-        inputBatch.add(inputs);
-        inputBatch.add(tokenTypesND);
-        inputBatch.add(validLengthND);
+        List<NDArray> inputBatch = Arrays.asList(
+                new NDArray(indexesFloat,
+                        new Shape(new int[]{1, inst.seqLength}), context),
+                new NDArray(tokenTypes,
+                        new Shape(new int[]{1, inst.seqLength}), context),
+                new NDArray(new float[] { validLength },
+                        new Shape(new int[]{1}), context)
+        );
         // Build the model
-        List<DataDesc> inputDescs = new ArrayList<>();
         List<Context> contexts = new ArrayList<>();
         contexts.add(context);
-        inputDescs.add(new DataDesc("data0",
-                new Shape(new int[]{1, inst.seqLength}), DType.Float32(), "NT"));
-        inputDescs.add(new DataDesc("data1",
-                new Shape(new int[]{1, inst.seqLength}), DType.Float32(), "NT"));
-        inputDescs.add(new DataDesc("data2",
-                new Shape(new int[]{1}), DType.Float32(), "N"));
+        List<DataDesc> inputDescs = Arrays.asList(
+                new DataDesc("data0",
+                        new Shape(new int[]{1, inst.seqLength}), DType.Float32(), Layout.NT()),
+                new DataDesc("data1",
+                        new Shape(new int[]{1, inst.seqLength}), DType.Float32(), Layout.NT()),
+                new DataDesc("data2",
+                        new Shape(new int[]{1}), DType.Float32(), Layout.N())
+        );
         Predictor bertQA = new Predictor(inst.modelPathPrefix, inputDescs, contexts, inst.epoch);
         // Start prediction
         NDArray result = bertQA.predictWithNDArray(inputBatch).get(0);

scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer/bert/README.md

@@ -46,8 +46,6 @@ From the `scala-package/examples/scripts/infer/bert/` folder run:
 ./get_bert_data.sh
 ```
 
-**Note**: You may need to run `chmod +x get_bert_data.sh` before running this script.
-
 ### Step 2: Setup data path of the model
 
 ### Setup Datapath and Parameters
@@ -80,7 +78,7 @@ To learn more about how BERT works in MXNet, please follow this [tutorial](https
 
 The model was extracted from the GluonNLP with static length settings.
 
-[Download link for the scrtipt](https://gluon-nlp.mxnet.io/_downloads/bert.zip)
+[Download link for the script](https://gluon-nlp.mxnet.io/_downloads/bert.zip)
 
 The original description can be found in [here](https://gluon-nlp.mxnet.io/model_zoo/bert/index.html#bert-base-on-squad-1-1).
 ```bash