update readme and script to run

scala-package/examples/scripts/infer/predictor/run_predictor_java_example.sh

@@ -0,0 +1,44 @@
+#!/bin/bash
+
+# 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.
+
+hw_type=cpu
+if [[ $3 = gpu ]]
+then
+    hw_type=gpu
+fi
+
+platform=linux-x86_64
+
+if [[ $OSTYPE = [darwin]* ]]
+then
+    platform=osx-x86_64
+fi
+
+MXNET_ROOT=$(cd "$(dirname $0)/../../../../../"; pwd)
+CLASS_PATH=$MXNET_ROOT/scala-package/assembly/$platform-$hw_type/target/*:$MXNET_ROOT/scala-package/examples/target/*:$MXNET_ROOT/scala-package/examples/target/classes/lib/*:$MXNET_ROOT/scala-package/infer/target/*
+
+# model dir and prefix
+MODEL_DIR=$1
+# input image
+INPUT_IMG=$2
+
+java -Xmx8G -cp $CLASS_PATH \
+	org.apache.mxnetexamples.javaapi.infer.predictor.PredictorExample \
+	--model-path-prefix $MODEL_DIR \
+	--input-image $INPUT_IMG

scala-package/examples/src/main/java/org/apache/mxnetexamples/javaapi/infer/predictor/PredictorExample.java

@@ -18,10 +18,7 @@
 package org.apache.mxnetexamples.javaapi.infer.predictor;
 
 import org.apache.mxnet.infer.javaapi.Predictor;
-import org.apache.mxnet.javaapi.Context;
-import org.apache.mxnet.javaapi.DType;
-import org.apache.mxnet.javaapi.DataDesc;
-import org.apache.mxnet.javaapi.Shape;
+import org.apache.mxnet.javaapi.*;
 import org.kohsuke.args4j.CmdLineParser;
 import org.kohsuke.args4j.Option;
 import org.slf4j.Logger;
@@ -42,8 +39,6 @@ public class PredictorExample {
     private String modelPathPrefix = "/model/ssd_resnet50_512";
     @Option(name = "--input-image", usage = "the input image")
     private String inputImagePath = "/images/dog.jpg";
-    @Option(name = "--input-dir", usage = "the input batch of images directory")
-    private String inputImageDir = "/images/";
 
     final static Logger logger = LoggerFactory.getLogger(PredictorExample.class);
 
@@ -97,9 +92,12 @@ private static float[] imagePreprocess(BufferedImage buf) {
         return result;
     }
 
-    private static String printMaximumClass(float[] probabilities) throws IOException {
-        BufferedReader reader = new BufferedReader(new FileReader("/tmp/resnet18/synset.txt"));
-        ArrayList<String> list = new ArrayList<String>();
+    private static String printMaximumClass(float[] probabilities,
+                                            String modelPathPrefix) throws IOException {
+        String synsetFilePath = modelPathPrefix.substring(0,
+                1 + modelPathPrefix.lastIndexOf(File.separator)) + "/synset.txt";
+        BufferedReader reader = new BufferedReader(new FileReader(synsetFilePath));
+        ArrayList<String> list = new ArrayList<>();
         String line = reader.readLine();
 
         while (line != null){
@@ -147,7 +145,22 @@ public static void main(String[] args) {
         // predict
         float[][] result = predictor.predict(new float[][]{imagePreprocess(img)});
         try {
-            System.out.println(printMaximumClass(result[0]));
+            System.out.println("Predict with Float input");
+            System.out.println(printMaximumClass(result[0], inst.modelPathPrefix));
+        } catch (Exception e) {
+            System.err.println(e);
+        }
+        // predict with NDArray
+        NDArray nd = new NDArray(
+                imagePreprocess(img),
+                new Shape(new int[]{1, 3, 224, 224}),
+                Context.cpu());
+        List<NDArray> ndList = new ArrayList<>();
+        ndList.add(nd);
+        List<NDArray> ndResult = predictor.predictWithNDArray(ndList);
+        try {
+            System.out.println("Predict with NDArray");
+            System.out.println(printMaximumClass(ndResult.get(0).toArray(), inst.modelPathPrefix));
         } catch (Exception e) {
             System.err.println(e);
         }

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

@@ -0,0 +1,60 @@
+# Image Classification using Java Predictor
+
+In this example, you will learn how to use Java Inference API to 
+build and run pre-trained Resnet 18 model.
+
+## Contents
+
+1. [Prerequisites](#prerequisites)
+2. [Download artifacts](#download-artifacts)
+3. [Setup datapath and parameters](#setup-datapath-and-parameters)
+4. [Run the image inference example](#run-the-image-inference-example)
+
+## Prerequisites
+
+1. MXNet
+2. MXNet Scala Package
+3. [IntelliJ IDE (or alternative IDE) project setup](https://github.com/apache/incubator-mxnet/blob/master/docs/tutorials/java/mxnet_java_on_intellij.md) with the MXNet Java Package
+4. wget
+
+## Download Artifacts
+
+For this tutorial, you can get the model and sample input image by running following bash file. This script will use `wget` to download these artifacts from AWS S3.
+
+From the `scala-package/examples/scripts/infer/imageclassifier/` folder run:
+
+```bash
+./get_resnet_18_data.sh
+```
+
+**Note**: You may need to run `chmod +x get_resnet_18_data.sh` before running this script.
+
+### Setup Datapath and Parameters
+
+The available arguments are as follows:
+
+| Argument                      | Comments                                 |
+| ----------------------------- | ---------------------------------------- |
+| `model-dir`                   | Folder path with prefix to the model (including json, params, and any synset file). |
+| `input-image`                 | The image to run inference on. |
+
+## Run the image inference example
+
+After the previous steps, you should be able to run the code using the following script that will pass all of the required parameters to the Infer API.
+
+From the `scala-package/examples/scripts/infer/predictor/` folder run:
+
+```bash
+bash run_predictor_java_example.sh ../models/resnet-18/resnet-18 ../images/kitten.jpg
+```
+
+**Notes**:
+* These are relative paths to this script.
+* You may need to run `chmod +x run_predictor_java_example.sh` before running this script.
+
+The example should give expected output as shown below:
+```
+Probability : 0.30337515 Class : n02123159 tiger cat
+Probability : 0.30337515 Class : n02123159 tiger cat
+```
+the outputs come from the the input image, with top1 predictions picked.