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[MXNET-1198] MXNet Java API (#13162)
* [MXNET-984] Add Java NDArray and introduce Java Operator Builder class (#12816) * clean history and add commit * add lint header * bypass the java unittest when make the package * clean up redundant test * clean spacing issue * revert the change * clean up * cleanup the JMacros * adding line escape * revert some changes and fix scala style * fixes regarding to Naveen's comment * Java Inference api and SSD example (#12830) * New Java inference API and SSD example * Adding license to java files and fixing SSD example * Fixing SSD example to point to ObjectDetector instead of ImageClassifier * Make scripts for object detector independent to os and hw cpu/gpu * Added API Docs to Java Inference API. Small fixes for PR * Cosmetic updates for API DOCS requested during PR * Attempt to fix the CI Javafx compiler issue * Migrate from Javafx to apache commons for Pair implementation * Removing javafx from pom file * Fixes to appease the ScalaStyle deity * Minor fix in SSD script and Readme * Added ObjectDetectorOutput which is a POJO for Object Detector to simplify the return type * Removing Apache Commons Immutable Pair * Adding license to new file * Minor style fixes * minor style fix * Updating to be in scala style and not explicitly declare some unnecessary variables * NativeResource Management in Scala (#12647) (#12883) * add Generic MXNetHandle trait and MXNetHandlePhantomRef class that will be used by all MXNetObjects * Generic Handle with AutoCloseable * add NativeResource and NativeResourceManager with Periodic GC calling * use NativeResource trait in NDArray, Symbol and Executor * add run train mnist script * create a Generic ResourceScope that can collect all NativeResources to dispose at the end * modify NativeResource and ResourceScope, extend NativeResource in NDArray, Symbol and Executor * remove GCExecutor * deRegister PhantomReferences by when calling dispose() * add Finalizer(temporary) to NativeResource * refactor NativeResource.dispose() method * update NativeResource/add Unit Test for NativeResource * updates to NativeResource/NativeResourceRef and unit tests to NativeResource * remove redundant code added because of the object equality that was needed * add ResourceScope * Fix NativeResource to not remove from Scope, add Unit Tests to ResourceScope * cleanup log/print debug statements * use TreeSet inplace of ArrayBuffer to speedup removal of resources from ResourceScope Fix Executor dispose and make KVStore a NativeResource * fix segfault that was happening because of NDArray creation on the fly in Optimizer * Add comments for dispose(param:Boolean) * Added unit tests for Resource Scope in Java (#12955) * Bumping down minimum java support from 8 to 7 (#12965) * [MXNET-984] Java NDArray Documentation Generation (#12835) * cherry pick javaDoc changes * update NDArray changes * refactoring change and merge all docGen in a single place * clean the scalastyle * take on Piyush nit * drop the comments * First pass at adding JavaDocs for new java api classes (#12963) * First pass at adding JavaDocs for new java api classes * Fix a scalastyle issue * Updating JavaDoc based on feedback * [MXNET-1160] add Java build/run example (#12969) * add example * clean up nit * find the pain point * add java tut into whitelist * Trigger CI * add java demo and split scala demo * address the comments * change the examples * fix the wrong configuration * Maven Surefire bug workaround (#13097) * use ResourceScope in Model/Trainer/FeedForward.scala (#12882) (#13164) * use ResourceScope in Model/Trainer/FeedForward.scala * add moveToOuterScope public method to move resources to a outerScope if it exists * fix memory leak in FeedForward.scala by making it a native resource and disposing argparams, auxParams in dispose() method * [MXNET-1187] Added Tutorial for Java under mxnet.io/docs/tutorials (#13183) * Added tutorial for Java installation on IntelliJ for mxnet.io website * Added correct image resources * Removed spurious quotes * Added java tutorial to whitelisting * Added community download edition link to intelliJ section * [MXNET-1202] Change Builder class into a better way (#13159) * applying changes for Builder functions * simplify the code structure * update docgen * follow Naveen's suggestion * apply comments to Param * clean up param build * change on the comments * add one description line * [MXNET-1041] Add Java benchmark (#13095) * add java benchmark * applied changes based on Piyush comments * applies Andrew's change * fix clojure test issue * update the statistic names * follow Naveen's instruction * [MXNET-918] [Introduce Random module / Refact code generation (#13038)][Cherry pick] (#13242) * [MXNET-918] Introduce Random module / Refact code generation (#13038) * refactor code gen * remove xxxAPIMacroBase (overkill) * CI errors / scala-style * PR review comments * clean up the duplicated code * add comments * Fixed missing break statement (#13257) * Java Benchmark failure (#13258) * patch fix * update ignore * rename getContext to bindToDevice * Update JavaBenchmark.java * Addressing PR feedback for merging Java API into master (#13277) * Addressing PR feedback for merging Java API into master * Changed constructors to package private instead of private * clean up the NDArray follow the comments (#13281) * [MXNET-1181] Added command line alternative to IntelliJ in install instructions (#13267) * Added command line alternative to IntelliJ * Removed the duplicate file * Fixed typos * Fixed minor command issue * add defaults and clean up the tests (#13295) * [MXNET-1187] Added Java SSD Inference Tutorial for website (#13201) * Added Java SSD Inference Tutorial for website * Added whitelisting to SSD tutorial * Address PR feedback * Marking intelliJ as optional * [MXNET-1182] Predictor example (#13237) * add initial commit * push back predictor * name fix and bug fix * update readme and script to run * minor fix * minor fix * fix on doc * update predictor * Reducing the length of setup tutorial (#13306)
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‎docs/tutorials/index.md‎

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* [MXNet-Scala Examples](https://github.com/apache/incubator-mxnet/tree/master/scala-package/examples/src/main/scala/org/apache/mxnetexamples)
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<hr>
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## Java Tutorials
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* Getting Started
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* [Developer Environment Setup on IntelliJ IDE](/tutorials/java/mxnet_java_on_intellij.html)
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* [Multi Object Detection using pre-trained Single Shot Detector (SSD) Model](/tutorials/java/ssd_inference.html)
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* [MXNet-Java Examples](https://github.com/apache/incubator-mxnet/tree/master/scala-package/examples/src/main/java/org/apache/mxnetexamples)
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<hr>
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## C++ Tutorials
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* Models

‎docs/tutorials/java/mxnet_java_on_intellij.md‎

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# Run MXNet Java Examples Using the IntelliJ IDE (macOS)
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This tutorial guides you through setting up a simple Java project in IntelliJ IDE on macOS and demonstrates usage of the MXNet Java APIs.
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## Prerequisites:
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To use this tutorial you need the following pre-requisites:
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- [Java 8 JDK](http://www.oracle.com/technetwork/java/javase/downloads/index.html)
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- [Maven](https://maven.apache.org/install.html)
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- [OpenCV](https://opencv.org/)
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- [IntelliJ IDEA](https://www.jetbrains.com/idea/) (One can download the community edition from [here](https://www.jetbrains.com/idea/download))
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### MacOS Prerequisites
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You can run the following commands to install the prerequisites.
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```
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/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
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brew update
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brew tap caskroom/versions
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brew cask install java8
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brew install maven
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brew install opencv
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```
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You can also run this tutorial on an Ubuntu machine after installing the following prerequisites.
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### Ubuntu Prerequisites
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Run the following commands to install the prerequisites.
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```bash
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wget https://github.com/apache/incubator-mxnet/blob/master/ci/docker/install/ubuntu_core.sh
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sudo ./ubuntu_core.sh
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wget https://github.com/apache/incubator-mxnet/blob/master/ci/docker/install/ubuntu_scala.sh
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sudo ./ubuntu_scala.sh
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```
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Note : You might need to run `chmod u+x ubuntu_core.sh` and `chmod u+x ubuntu_scala` before running the scripts.
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The `ubuntu_scala.sh` installs the common dependencies required for both MXNet Scala and MXNet Java packages.
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## Set Up Your Project
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**Step 1.** Install and setup [IntelliJ IDEA](https://www.jetbrains.com/idea/)
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**Step 2.** Create a new Project:
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![intellij welcome](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/scala/intellij-welcome.png)
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From the IntelliJ welcome screen, select "Create New Project".
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Choose the Maven project type.
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Select the checkbox for `Create from archetype`, then choose `org.apache.maven.archetypes:maven-archetype-quickstart` from the list below. More on this can be found on a Maven tutorial : [Maven in 5 Minutes](https://maven.apache.org/guides/getting-started/maven-in-five-minutes.html).
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![maven project type - archetype](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/java/project-archetype.png)
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click `Next`.
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![project metadata](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/java/intellij-project-metadata.png)
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Set the project's metadata. For this tutorial, use the following:
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**GroupId**
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```
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mxnet
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```
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**ArtifactId**
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```
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ArtifactId: javaMXNet
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```
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**Version**
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```
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1.0-SNAPSHOT
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```
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![project properties](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/java/intellij-project-properties.png)
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Review the project's properties. The settings can be left as their default.
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![project location](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/java/intellij-project-location.png)
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Set the project's location. The rest of the settings can be left as their default.
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![project 1](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/java/intellij-project-pom.png)
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After clicking Finish, you will be presented with the project's first view.
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The project's `pom.xml` will be open for editing.
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**Step 3.** Add the following Maven dependency to your `pom.xml` file under the `dependencies` tag:
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```html
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<dependency>
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<groupId>org.apache.mxnet</groupId>
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<artifactId>mxnet-full_2.11-osx-x86_64-cpu</artifactId>
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<version>1.4.0</version>
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</dependency>
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```
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To view the latest MXNet Maven packages, you can check [MXNet Maven package repository](https://search.maven.org/#search%7Cga%7C1%7Cg%3A%22org.apache.mxnet%22)
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Note :
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- Change the osx-x86_64 to linux-x86_64 if your platform is linux.
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- Change cpu into gpu if you have a gpu backed machine and want to use gpu.
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**Step 4.** Import dependencies with Maven:
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- Note the prompt in the lower right corner that states "Maven projects need to be imported". If this is not visible, click on the little greed balloon that appears in the lower right corner.
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![import_dependencies](https://raw.githubusercontent.com/dmlc/web-data/master/mxnet/java/project-import-changes.png)
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Click "Import Changes" in this prompt.
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**Step 5.** Build the project:
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- To build the project, from the menu choose Build, and then choose Build Project.
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**Step 6.** Navigate to the App.java class in the project and paste the code from HelloWorld.java from [Java Demo project](https://github.com/apache/incubator-mxnet/blob/java-api/scala-package/mxnet-demo/java-demo/src/main/java/sample/HelloWorld.java) on MXNet repository, overwriting the original hello world code.
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You can also grab the entire [Java Demo project](https://github.com/apache/incubator-mxnet/tree/java-api/scala-package/mxnet-demo/java-demo) and run it by following the instructions on the [README](https://github.com/apache/incubator-mxnet/blob/java-api/scala-package/mxnet-demo/java-demo/README.md)
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**Step 7.** Now run the App.java.
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The result should be something similar to this:
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```
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Hello World!
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(1,2)
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Process finished with exit code 0
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```
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### Troubleshooting
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If you get an error, check the dependencies at the beginning of this tutorial. For example, you might see the following in the middle of the error messages, where `x.x` would the version it's looking for.
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```
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...
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Library not loaded: /usr/local/opt/opencv/lib/libopencv_calib3d.x.x.dylib
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...
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```
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This can be resolved be installing OpenCV.
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### Command Line Build Option
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- You can also compile the project by using the following command at the command line. Change directories to this project's root folder then run the following:
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```bash
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mvn clean install dependency:copy-dependencies
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```
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If the command succeeds, you should see a lot of info and some warning messages, followed by:
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```bash
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[INFO] ------------------------------------------------------------------------
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[INFO] BUILD SUCCESS
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[INFO] ------------------------------------------------------------------------
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[INFO] Total time: 3.475 s
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[INFO] Finished at: 2018-11-08T05:06:31-08:00
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[INFO] ------------------------------------------------------------------------
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```
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The build generates a new jar file in the `target` folder called `javaMXNet-1.0-SNAPSHOT.jar`.
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To run the App.java use the following command from the project's root folder and you should see the same output as we got when the project was run from IntelliJ.
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```bash
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java -cp target/javaMXNet-1.0-SNAPSHOT.jar:target/dependency/* mxnet.App
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```
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## Next Steps
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For more information about MXNet Java resources, see the following:
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* [Java Inference API](https://mxnet.incubator.apache.org/api/java/infer.html)
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* [Java Inference Examples](https://github.com/apache/incubator-mxnet/tree/java-api/scala-package/examples/src/main/java/org/apache/mxnetexamples/infer/)
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* [MXNet Tutorials Index](http://mxnet.io/tutorials/index.html)

‎docs/tutorials/java/ssd_inference.md‎

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# Multi Object Detection using pre-trained SSD Model via Java Inference APIs
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This tutorial shows how to use MXNet Java Inference APIs to run inference on a pre-trained Single Shot Detector (SSD) Model.
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The SSD model is trained on the Pascal VOC 2012 dataset. The network is a SSD model built on Resnet50 as the base network to extract image features. The model is trained to detect the following entities (classes): ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor']. For more details about the model, you can refer to the [MXNet SSD example](https://github.com/apache/incubator-mxnet/tree/master/example/ssd).
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## Prerequisites
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To complete this tutorial, you need the following:
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* [MXNet Java Setup on IntelliJ IDEA](/java/mxnet_java_on_intellij.html) (Optional)
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* [wget](https://www.gnu.org/software/wget/) To download model artifacts
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* SSD Model artifacts
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* Use the following script to get the SSD Model files :
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```bash
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data_path=/tmp/resnet50_ssd
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mkdir -p "$data_path"
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wget https://s3.amazonaws.com/model-server/models/resnet50_ssd/resnet50_ssd_model-symbol.json -P $data_path
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wget https://s3.amazonaws.com/model-server/models/resnet50_ssd/resnet50_ssd_model-0000.params -P $data_path
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wget https://s3.amazonaws.com/model-server/models/resnet50_ssd/synset.txt -P $data_path
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```
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* Test images : A few sample images to run inference on.
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* Use the following script to download sample images :
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```bash
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image_path=/tmp/resnet50_ssd/images
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mkdir -p "$image_path"
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cd $image_path
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wget https://cloud.githubusercontent.com/assets/3307514/20012567/cbb60336-a27d-11e6-93ff-cbc3f09f5c9e.jpg -O dog.jpg
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wget https://cloud.githubusercontent.com/assets/3307514/20012563/cbb41382-a27d-11e6-92a9-18dab4fd1ad3.jpg -O person.jpg
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```
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Alternately, you can get the entire SSD Model artifacts + images in one single script from the MXNet Repository by running [get_ssd_data.sh script](https://github.com/apache/incubator-mxnet/blob/master/scala-package/examples/scripts/infer/objectdetector/get_ssd_data.sh)
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## Time to code!
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1\. Following the [MXNet Java Setup on IntelliJ IDEA](/java/mxnet_java_on_intellij.html) tutorial, in the same project `JavaMXNet`, create a new empty class called : `ObjectDetectionTutorial.java`.
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2\. In the `main` function of `ObjectDetectionTutorial.java` define the downloaded model path and the image data paths. This is the same path where we downloaded the model artifacts and images in a previous step.
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```java
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String modelPathPrefix = "/tmp/resnet50_ssd/resnet50_ssd_model";
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String inputImagePath = "/tmp/resnet50_ssd/images/dog.jpg";
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```
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3\. We can run the inference code in this example on either CPU or GPU (if you have a GPU backed machine) by choosing the appropriate context.
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```java
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List<Context> context = getContext();
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...
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private static List<Context> getContext() {
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List<Context> ctx = new ArrayList<>();
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ctx.add(Context.cpu()); // Choosing CPU Context here
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return ctx;
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}
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```
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4\. To provide an input to the model, define the input shape to the model and the Input Data Descriptor (DataDesc) as shown below :
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```java
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Shape inputShape = new Shape(new int[] {1, 3, 512, 512});
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List<DataDesc> inputDescriptors = new ArrayList<DataDesc>();
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inputDescriptors.add(new DataDesc("data", inputShape, DType.Float32(), "NCHW"));
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```
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The input shape can be interpreted as follows : The input has a batch size of 1, with 3 RGB channels in the image, and the height and width of the image is 512 each.
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5\. To run an actual inference on the given image, add the following lines to the `ObjectDetectionTutorial.java` class :
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```java
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BufferedImage img = ObjectDetector.loadImageFromFile(inputImagePath);
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ObjectDetector objDet = new ObjectDetector(modelPathPrefix, inputDescriptors, context, 0);
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List<List<ObjectDetectorOutput>> output = objDet.imageObjectDetect(img, 3); // Top 3 objects detected will be returned
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```
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6\. Let's piece all of the above steps together by showing the final contents of the `ObjectDetectionTutorial.java`.
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```java
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package mxnet;
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import org.apache.mxnet.infer.javaapi.ObjectDetector;
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import org.apache.mxnet.infer.javaapi.ObjectDetectorOutput;
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import org.apache.mxnet.javaapi.Context;
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import org.apache.mxnet.javaapi.DType;
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import org.apache.mxnet.javaapi.DataDesc;
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import org.apache.mxnet.javaapi.Shape;
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import java.awt.image.BufferedImage;
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import java.util.ArrayList;
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import java.util.Arrays;
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import java.util.List;
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public class ObjectDetectionTutorial {
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public static void main(String[] args) {
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String modelPathPrefix = "/tmp/resnet50_ssd/resnet50_ssd_model";
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String inputImagePath = "/tmp/resnet50_ssd/images/dog.jpg";
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List<Context> context = getContext();
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Shape inputShape = new Shape(new int[] {1, 3, 512, 512});
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List<DataDesc> inputDescriptors = new ArrayList<DataDesc>();
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inputDescriptors.add(new DataDesc("data", inputShape, DType.Float32(), "NCHW"));
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BufferedImage img = ObjectDetector.loadImageFromFile(inputImagePath);
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ObjectDetector objDet = new ObjectDetector(modelPathPrefix, inputDescriptors, context, 0);
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List<List<ObjectDetectorOutput>> output = objDet.imageObjectDetect(img, 3);
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printOutput(output, inputShape);
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}
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private static List<Context> getContext() {
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List<Context> ctx = new ArrayList<>();
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ctx.add(Context.cpu());
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return ctx;
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}
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private static void printOutput(List<List<ObjectDetectorOutput>> output, Shape inputShape) {
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StringBuilder outputStr = new StringBuilder();
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int width = inputShape.get(3);
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int height = inputShape.get(2);
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for (List<ObjectDetectorOutput> ele : output) {
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for (ObjectDetectorOutput i : ele) {
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outputStr.append("Class: " + i.getClassName() + "\n");
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outputStr.append("Probabilties: " + i.getProbability() + "\n");
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List<Float> coord = Arrays.asList(i.getXMin() * width,
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i.getXMax() * height, i.getYMin() * width, i.getYMax() * height);
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StringBuilder sb = new StringBuilder();
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for (float c: coord) {
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sb.append(", ").append(c);
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}
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outputStr.append("Coord:" + sb.substring(2)+ "\n");
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}
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}
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System.out.println(outputStr);
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}
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}
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```
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7\. To compile and run this code, change directories to this project's root folder, then run the following:
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```bash
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mvn clean install dependency:copy-dependencies
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```
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The build generates a new jar file in the `target` folder called `javaMXNet-1.0-SNAPSHOT.jar`.
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To run the ObjectDetectionTutorial.java use the following command from the project's root folder.
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```bash
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java -cp target/javaMXNet-1.0-SNAPSHOT.jar:target/dependency/* mxnet.ObjectDetectionTutorial
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```
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You should see a similar output being generated for the dog image that we used:
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```bash
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Class: car
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Probabilties: 0.99847263
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Coord:312.21335, 72.02908, 456.01443, 150.66176
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Class: bicycle
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Probabilties: 0.9047381
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Coord:155.9581, 149.96365, 383.83694, 418.94516
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Class: dog
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Probabilties: 0.82268167
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Coord:83.82356, 179.14001, 206.63783, 476.78754
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```
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![dog_1](https://cloud.githubusercontent.com/assets/3307514/20012567/cbb60336-a27d-11e6-93ff-cbc3f09f5c9e.jpg)
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The results returned by the inference call translate into the regions in the image where the model detected objects.
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![dog_2](https://cloud.githubusercontent.com/assets/3307514/19171063/91ec2792-8be0-11e6-983c-773bd6868fa8.png)
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## Next Steps
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For more information about MXNet Java resources, see the following:
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* [Java Inference API](/api/java/infer.html)
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* [Java Inference Examples](https://github.com/apache/incubator-mxnet/tree/java-api/scala-package/examples/src/main/java/org/apache/mxnetexamples/infer/)
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* [MXNet Tutorials Index](/tutorials/index.html)

‎scala-package/.gitignore‎

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.flattened-pom.xml
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core/src/main/scala/org/apache/mxnet/NDArrayAPIBase.scala
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core/src/main/scala/org/apache/mxnet/NDArrayBase.scala
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core/src/main/scala/org/apache/mxnet/javaapi/NDArrayBase.scala
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core/src/main/scala/org/apache/mxnet/SymbolAPIBase.scala
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core/src/main/scala/org/apache/mxnet/SymbolBase.scala
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examples/scripts/infer/images/
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examples/scripts/infer/models/

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