chore: import upstream snapshot with attribution

tensorflow/lite/java/AndroidManifest.xml

@@ -0,0 +1,11 @@
+<?xml version="1.0" encoding="utf-8"?>
+<manifest xmlns:android="http://schemas.android.com/apk/res/android"
+    package="org.tensorflow.lite">
+
+    <!-- TFLite Java Library is built against NDK API 21. -->
+    <uses-sdk
+        android:minSdkVersion="21" />
+
+    <application />
+
+</manifest>

tensorflow/lite/java/AndroidManifestApi.xml

@@ -0,0 +1,11 @@
+<?xml version="1.0" encoding="utf-8"?>
+<manifest xmlns:android="http://schemas.android.com/apk/res/android"
+    package="org.tensorflow.lite.api">
+
+    <!-- TFLite Java Library is built against NDK API 21. -->
+    <uses-sdk
+        android:minSdkVersion="21" />
+
+    <application />
+
+</manifest>

tensorflow/lite/java/AndroidManifestGpu.xml

@@ -0,0 +1,11 @@
+<?xml version="1.0" encoding="utf-8"?>
+<manifest xmlns:android="http://schemas.android.com/apk/res/android"
+    package="org.tensorflow.lite.gpu">
+
+    <!-- TFLite Java Library is built against NDK API 21. -->
+    <uses-sdk
+        android:minSdkVersion="21" />
+
+    <application />
+
+</manifest>

tensorflow/lite/java/AndroidManifestGpuApi.xml

@@ -0,0 +1,24 @@
+<?xml version="1.0" encoding="utf-8"?>
+<manifest xmlns:android="http://schemas.android.com/apk/res/android"
+    package="org.tensorflow.lite.gpu.api">
+
+    <uses-sdk
+        android:minSdkVersion="21" />
+
+    <application>
+        <!-- Applications that target Android S+ require explicit declaration of
+             any referenced vendor-provided libraries. -->
+        <uses-native-library
+            android:name="libOpenCL.so"
+            android:required="false" />
+
+        <uses-native-library
+            android:name="libOpenCL-car.so"
+            android:required="false" />
+
+        <uses-native-library
+            android:name="libOpenCL-pixel.so"
+            android:required="false" />
+    </application>
+
+</manifest>

tensorflow/lite/java/aar_with_jni.bzl

@@ -0,0 +1,154 @@
+"""Generate zipped aar file including different variants of .so in jni folder."""
+
+load("@build_bazel_rules_android//android:rules.bzl", "android_binary")
+
+def aar_with_jni(
+        name,
+        android_library,
+        headers = None,
+        flatten_headers = False,
+        strip_headers_prefix = "",
+        license_file = "//:LICENSE",
+        third_party_notice = None):
+    """Generates an Android AAR with repo root license given an Android library target.
+
+    Args:
+      name: Name of the generated .aar file.
+      android_library: The `android_library` target to package. Note that the
+          AAR will contain *only that library's .jar` sources. It does not
+          package the transitive closure of all Java source dependencies.
+      headers: Optional list of headers that will be included in the
+          generated .aar file. This is useful for distributing self-contained
+          .aars with native libs that can be used directly by native clients.
+      flatten_headers: Whether to flatten the output paths of included headers.
+      strip_headers_prefix: The prefix to strip from the output paths of included headers.
+      license_file: Optional. The main LICENSE file to include in the AAR.
+          Defaults to //third_party/tensorflow:LICENSE.
+      third_party_notice: Optional. The third party dependency licenses as THIRD_PARTY_NOTICE.txt.
+    """
+
+    # Generate dummy AndroidManifest.xml for dummy apk usage
+    # (dummy apk is generated by <name>_dummy_app_for_so target below)
+    native.genrule(
+        name = name + "_binary_manifest_generator",
+        outs = [name + "_generated_AndroidManifest.xml"],
+        cmd = """
+cat > $(OUTS) <<EOF
+<manifest
+  xmlns:android="http://schemas.android.com/apk/res/android"
+  package="dummy.package.for.so">
+  <uses-sdk android:minSdkVersion="34"/>
+</manifest>
+EOF
+""",
+    )
+
+    # Generate dummy apk including .so files and later we extract out
+    # .so files and throw away the apk.
+    android_binary(
+        name = name + "_dummy_app_for_so",
+        manifest = name + "_generated_AndroidManifest.xml",
+        custom_package = "dummy.package.for.so",
+        deps = [android_library],
+        # In some platforms we don't have an Android SDK/NDK and this target
+        # can't be built. We need to prevent the build system from trying to
+        # use the target in that case.
+        tags = [
+            "manual",
+            "no_cuda_on_cpu_tap",
+        ],
+    )
+
+    srcs = [
+        android_library + ".aar",
+        name + "_dummy_app_for_so_unsigned.apk",
+        license_file,
+    ]
+
+    cmd = """
+cp $(location {0}.aar) $(location :{1}.aar)
+chmod +w $(location :{1}.aar)
+origdir=$$PWD
+cd $$(mktemp -d)
+unzip $$origdir/$(location :{1}_dummy_app_for_so_unsigned.apk) "lib/*"
+cp -r lib jni
+zip -r $$origdir/$(location :{1}.aar) jni/*/*.so
+cp $$origdir/$(location {2}) ./LICENSE
+zip $$origdir/$(location :{1}.aar) LICENSE
+""".format(android_library, name, license_file)
+
+    if headers:
+        srcs += headers
+        cmd += """
+        mkdir headers
+        """
+        for src in headers:
+            if flatten_headers:
+                cmd += """
+                    cp -RL $$origdir/$(location {0}) headers/$$(basename $(location {0}))
+                """.format(src)
+            else:
+                cmd += """
+                    default_dir=$$(dirname $(rootpath {0}))
+                    modified_dir=$$(echo $$default_dir | sed -e 's/^{1}//g')
+                    mkdir -p headers/$$modified_dir
+                    cp -RL $$origdir/$(location {0}) headers/$$modified_dir
+                    if [ -n "{1}" ]; then
+                      sed -i -e 's/^#include \"{1}/#include \"/g' headers/$$modified_dir/$$(basename $(location {0}))
+                    fi
+                """.format(src, strip_headers_prefix.replace("/", "\\/"))
+        cmd += "zip -r $$origdir/$(location :{0}.aar) headers".format(name)
+
+    if third_party_notice:
+        srcs.append(third_party_notice)
+        cmd += """
+            cp $$origdir/$(location {0}) ./THIRD_PARTY_NOTICE.txt
+            zip $$origdir/$(location :{1}.aar) THIRD_PARTY_NOTICE.txt
+        """.format(third_party_notice, name)
+
+    native.genrule(
+        name = name,
+        srcs = srcs,
+        outs = [name + ".aar"],
+        # In some platforms we don't have an Android SDK/NDK and this target
+        # can't be built. We need to prevent the build system from trying to
+        # use the target in that case.
+        tags = ["manual"],
+        cmd = cmd,
+    )
+
+def aar_without_jni(
+        name,
+        android_library,
+        license_file = "//:LICENSE"):
+    """Generates an Android AAR with repo root license given a pure Java Android library target.
+
+    Args:
+      name: Name of the generated .aar file.
+      android_library: The `android_library` target to package. Note that the
+          AAR will contain *only that library's .jar` sources. It does not
+          package the transitive closure of all Java source dependencies.
+      license_file: Optional. The main LICENSE file to include in the AAR.
+          Defaults to //third_party/tensorflow:LICENSE.
+    """
+
+    srcs = [
+        android_library + ".aar",
+        license_file,
+    ]
+
+    cmd = """
+cp $(location {0}.aar) $(location :{1}.aar)
+chmod +w $(location :{1}.aar)
+origdir=$$PWD
+cd $$(mktemp -d)
+cp $$origdir/$(location {2}) ./LICENSE
+zip $$origdir/$(location :{1}.aar) LICENSE
+""".format(android_library, name, license_file)
+
+    native.genrule(
+        name = name,
+        srcs = srcs,
+        outs = [name + ".aar"],
+        cmd = cmd,
+    )

tensorflow/lite/java/build_aar_for_release.sh

@@ -0,0 +1,66 @@
+#!/bin/bash
+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed 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.
+# ==============================================================================
+set -e
+set -x
+
+TMPDIR=`mktemp -d`
+trap "rm -rf $TMPDIR" EXIT
+
+VERSION=1.0
+
+BUILDER=bazel
+BASEDIR=tensorflow/lite
+CROSSTOOL="//external:android/crosstool"
+HOST_CROSSTOOL="@bazel_tools//tools/cpp:toolchain"
+
+BUILD_OPTS="-c opt"
+CROSSTOOL_OPTS="--crosstool_top=$CROSSTOOL --host_crosstool_top=$HOST_CROSSTOOL"
+
+test -d $BASEDIR || (echo "Aborting: not at top-level build directory"; exit 1)
+
+function build_basic_aar() {
+  local OUTDIR=$1
+  $BUILDER build $BUILD_OPTS $BASEDIR/java:tensorflowlite.aar
+  unzip -d $OUTDIR $BUILDER-bin/$BASEDIR/java/tensorflowlite.aar
+  # targetSdkVersion is here to prevent the app from requesting spurious
+  # permissions, such as permission to make phone calls. It worked for v1.0,
+  # but minSdkVersion might be the preferred way to handle this.
+  sed -i -e 's/<application>/<uses-sdk android:targetSdkVersion="25"\/><application>/' $OUTDIR/AndroidManifest.xml
+}
+
+function build_arch() {
+  local ARCH=$1
+  local CONFIG=$2
+  local OUTDIR=$3
+  mkdir -p $OUTDIR/jni/$ARCH/
+  $BUILDER build $BUILD_OPTS $CROSSTOOL_OPTS --cpu=$CONFIG \
+    $BASEDIR/java:libtensorflowlite_jni.so
+  cp $BUILDER-bin/$BASEDIR/java/libtensorflowlite_jni.so $OUTDIR/jni/$ARCH/
+}
+
+rm -rf $TMPDIR
+mkdir -p $TMPDIR/jni
+
+build_basic_aar $TMPDIR
+build_arch arm64-v8a arm64-v8a $TMPDIR
+build_arch armeabi-v7a armeabi-v7a $TMPDIR
+build_arch x86 x86 $TMPDIR
+build_arch x86_64 x86_64 $TMPDIR
+
+AAR_FILE=`realpath tflite-${VERSION}.aar`
+(cd $TMPDIR && zip $AAR_FILE -r *)
+echo "New AAR file is $AAR_FILE"
+

tensorflow/lite/java/demo/.gitignore

@@ -0,0 +1,29 @@
+# This file is based on https://github.com/github/gitignore/blob/master/Android.gitignore
+*.iml
+.idea/compiler.xml
+.idea/copyright
+.idea/dictionaries
+.idea/gradle.xml
+.idea/libraries
+.idea/inspectionProfiles
+.idea/misc.xml
+.idea/modules.xml
+.idea/runConfigurations.xml
+.idea/tasks.xml
+.idea/workspace.xml
+.gradle
+local.properties
+.DS_Store
+build/
+gradleBuild/
+*.apk
+*.ap_
+*.dex
+*.class
+bin/
+gen/
+out/
+*.log
+.navigation/
+/captures
+.externalNativeBuild

tensorflow/lite/java/demo/README.md

@@ -0,0 +1,52 @@
+# TF Lite Android Image Classifier App Example
+
+A simple Android example that demonstrates image classification using the camera.
+
+## Building in Android Studio with TensorFlow Lite AAR from MavenCentral.
+The build.gradle is configured to use TensorFlow Lite's nightly build.
+
+If you see a build error related to compatibility with Tensorflow Lite's Java API (example: method X is
+undefined for type Interpreter), there has likely been a backwards compatible
+change to the API. You will need to pull new app code that's compatible with the
+nightly build and may need to first wait a few days for our external and internal
+code to merge.
+
+## Building from Source with Bazel
+
+1. Follow the [Bazel steps for the TF Demo App](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/android#bazel):
+
+  1. [Install Bazel and Android Prerequisites](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/android#install-bazel-and-android-prerequisites).
+     It's easiest with Android Studio.
+
+      - You'll need at least SDK version 23.
+      - Make sure to install the latest version of Bazel. Some distributions
+        ship with Bazel 0.5.4, which is too old.
+      - Bazel requires Android Build Tools `28.0.0` or higher.
+      - You also need to install the Android Support Repository, available
+        through Android Studio under `Android SDK Manager -> SDK Tools ->
+        Android Support Repository`.
+
+  2. [Edit your `WORKSPACE`](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/android#edit-workspace)
+     to add SDK and NDK targets.
+
+     NOTE: As long as you have the SDK and NDK installed, the `./configure`
+     script will create these rules for you. Answer "Yes" when the script asks
+     to automatically configure the `./WORKSPACE`.
+
+      - Make sure the `api_level` in `WORKSPACE` is set to an SDK version that
+        you have installed.
+      - By default, Android Studio will install the SDK to `~/Android/Sdk` and
+        the NDK to `~/Android/Sdk/ndk-bundle`.
+
+2. Build the app with Bazel. The demo needs C++11:
+
+  ```shell
+  bazel build -c opt //tensorflow/lite/java/demo/app/src/main:TfLiteCameraDemo
+  ```
+
+3. Install the demo on a
+   [debug-enabled device](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/android#install):
+
+  ```shell
+  adb install bazel-bin/tensorflow/lite/java/demo/app/src/main/TfLiteCameraDemo.apk
+  ```

tensorflow/lite/java/demo/app/build.gradle

@@ -0,0 +1,135 @@
+apply plugin: 'com.android.application'
+
+android {
+    compileSdkVersion 26
+    buildToolsVersion "27.0.3"
+    defaultConfig {
+        applicationId "android.example.com.tflitecamerademo"
+        // Required by Camera2 API.
+        minSdkVersion 21
+        targetSdkVersion 26
+        versionCode 1
+        versionName "1.0"
+    }
+    lintOptions {
+        abortOnError false
+    }
+    buildTypes {
+        release {
+            minifyEnabled false
+            proguardFiles getDefaultProguardFile('proguard-android.txt'), 'proguard-rules.pro'
+        }
+    }
+    aaptOptions {
+        noCompress "tflite"
+    }
+
+    compileOptions {
+        sourceCompatibility JavaVersion.VERSION_1_8
+        targetCompatibility JavaVersion.VERSION_1_8
+    }
+}
+
+repositories {
+    mavenCentral()
+    maven {  // Only for snapshot artifacts
+        name 'ossrh-snapshot'
+        url 'https://oss.sonatype.org/content/repositories/snapshots'
+    }
+}
+
+allprojects {
+    repositories {
+        // Uncomment if you want to use a local repo.
+        // mavenLocal()
+        mavenCentral()
+        maven {  // Only for snapshot artifacts
+            name 'ossrh-snapshot'
+            url 'http://oss.sonatype.org/content/repositories/snapshots'
+        }
+    }
+}
+
+dependencies {
+    implementation fileTree(dir: 'libs', include: ['*.jar'])
+    implementation 'com.android.support:appcompat-v7:25.2.0'
+    implementation 'com.android.support.constraint:constraint-layout:1.0.2'
+    implementation 'com.android.support:design:25.2.0'
+    implementation 'com.android.support:support-annotations:25.3.1'
+    implementation 'com.android.support:support-v13:25.2.0'
+
+    // Build off of nightly TensorFlow Lite
+    implementation 'org.tensorflow:tensorflow-lite:0.0.0-nightly-SNAPSHOT'
+    implementation 'org.tensorflow:tensorflow-lite-gpu:0.0.0-nightly-SNAPSHOT'
+    // Use local TensorFlow library
+    // implementation 'org.tensorflow:tensorflow-lite-local:0.0.0'
+}
+
+def targetFolder = "src/main/assets"
+def modelFloatDownloadUrl = "https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_02_22/mobilenet_v1_1.0_224.tgz"
+def modelQuantDownloadUrl = "https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224_quant.tgz"
+def localCacheFloat = "build/intermediates/mobilenet_v1_1.0_224.tgz"
+def localCacheQuant = "build/intermediates/mmobilenet_v1_1.0_224_quant.tgz"
+
+
+task downloadModelFloat(type: DownloadUrlTask) {
+    doFirst {
+        println "Downloading ${modelFloatDownloadUrl}"
+    }
+    sourceUrl = "${modelFloatDownloadUrl}"
+    target = file("${localCacheFloat}")
+}
+
+task downloadModelQuant(type: DownloadUrlTask) {
+    doFirst {
+        println "Downloading ${modelQuantDownloadUrl}"
+    }
+    sourceUrl = "${modelQuantDownloadUrl}"
+    target = file("${localCacheQuant}")
+}
+
+task unzipModelFloat(type: Copy, dependsOn: 'downloadModelFloat') {
+    doFirst {
+        println "Unzipping ${localCacheFloat}"
+    }
+    from tarTree("${localCacheFloat}")
+    into "${targetFolder}"
+}
+
+task unzipModelQuant(type: Copy, dependsOn: 'downloadModelQuant') {
+    doFirst {
+        println "Unzipping ${localCacheQuant}"
+    }
+    from tarTree("${localCacheQuant}")
+    into "${targetFolder}"
+}
+
+task cleanUnusedFiles(type: Delete, dependsOn: ['unzipModelFloat', 'unzipModelQuant']) {
+    delete fileTree("${targetFolder}").matching {
+        include "*.pb"
+        include "*.ckpt.*"
+        include "*.pbtxt.*"
+        include "*.quant_info.*"
+        include "*.meta"
+    }
+}
+
+
+// Ensure the model file is downloaded and extracted before every build
+preBuild.dependsOn unzipModelFloat
+preBuild.dependsOn unzipModelQuant
+preBuild.dependsOn cleanUnusedFiles
+
+class DownloadUrlTask extends DefaultTask {
+    @Input
+    String sourceUrl
+
+    @OutputFile
+    File target
+
+    @TaskAction
+    void download() {
+        ant.get(src: sourceUrl, dest: target)
+    }
+}
+

tensorflow/lite/java/demo/app/src/main/AndroidManifest.xml

@@ -0,0 +1,43 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!--
+ Copyright 2014 The Android Open Source Project
+
+ Licensed 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.
+-->
+
+<manifest xmlns:android="http://schemas.android.com/apk/res/android"
+    package="com.example.android.tflitecamerademo">
+
+    <uses-permission android:name="android.permission.CAMERA" />
+
+    <uses-feature android:name="android.hardware.camera" />
+    <uses-feature android:name="android.hardware.camera.autofocus" />
+
+    <uses-sdk android:minSdkVersion="21" />
+
+    <application android:allowBackup="true"
+        android:label="@string/app_name"
+        android:icon="@drawable/ic_launcher"
+        android:theme="@style/MaterialTheme">
+
+        <activity android:name="com.example.android.tflitecamerademo.CameraActivity"
+                  android:screenOrientation="portrait"
+                  android:label="@string/app_name">
+            <intent-filter>
+                <action android:name="android.intent.action.MAIN" />
+                <category android:name="android.intent.category.LAUNCHER" />
+            </intent-filter>
+        </activity>
+    </application>
+
+</manifest>

tensorflow/lite/java/demo/app/src/main/BUILD

@@ -0,0 +1,33 @@
+load("@build_bazel_rules_android//android:rules.bzl", "android_binary")
+
+package(
+    # copybara:uncomment default_applicable_licenses = ["//tensorflow:LICENSE"],
+    default_visibility = ["//visibility:private"],
+    licenses = ["notice"],
+)
+
+android_binary(
+    name = "TfLiteCameraDemo",
+    srcs = glob(["java/**/*.java"]),
+    assets = [
+        "//tensorflow/lite/java/demo/app/src/main/assets:labels_mobilenet_quant_v1_224.txt",
+        "@tflite_mobilenet_quant//:mobilenet_v1_1.0_224_quant.tflite",
+        "@tflite_mobilenet_float//:mobilenet_v1_1.0_224.tflite",
+    ],
+    assets_dir = "",
+    custom_package = "com.example.android.tflitecamerademo",
+    manifest = "AndroidManifest.xml",
+    nocompress_extensions = [
+        ".tflite",
+    ],
+    resource_files = glob(["res/**"]),
+    # In some platforms we don't have an Android SDK/NDK and this target
+    # can't be built. We need to prevent the build system from trying to
+    # use the target in that case.
+    tags = ["manual"],
+    deps = [
+        "//tensorflow/lite/java:tensorflowlite",
+        "//tensorflow/lite/java:tensorflowlite_gpu",
+        "//tensorflow/lite/java/src/testhelper/java/org/tensorflow/lite:testhelper",
+    ],
+)

tensorflow/lite/java/demo/app/src/main/assets/BUILD

@@ -0,0 +1,14 @@
+package(
+    # copybara:uncomment default_applicable_licenses = ["//tensorflow:LICENSE"],
+    default_visibility = ["//visibility:private"],
+    licenses = ["notice"],
+)
+
+exports_files(
+    glob(
+        ["**/*"],
+        exclude = [
+            "BUILD",
+        ],
+    ),
+)

tensorflow/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/AutoFitTextureView.java

@@ -0,0 +1,72 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed 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 com.example.android.tflitecamerademo;
+
+import android.content.Context;
+import android.util.AttributeSet;
+import android.view.TextureView;
+
+/** A {@link TextureView} that can be adjusted to a specified aspect ratio. */
+public class AutoFitTextureView extends TextureView {
+
+  private int mRatioWidth = 0;
+  private int mRatioHeight = 0;
+
+  public AutoFitTextureView(Context context) {
+    this(context, null);
+  }
+
+  public AutoFitTextureView(Context context, AttributeSet attrs) {
+    this(context, attrs, 0);
+  }
+
+  public AutoFitTextureView(Context context, AttributeSet attrs, int defStyle) {
+    super(context, attrs, defStyle);
+  }
+
+  /**
+   * Sets the aspect ratio for this view. The size of the view will be measured based on the ratio
+   * calculated from the parameters. Note that the actual sizes of parameters don't matter, that is,
+   * calling setAspectRatio(2, 3) and setAspectRatio(4, 6) make the same result.
+   *
+   * @param width Relative horizontal size
+   * @param height Relative vertical size
+   */
+  public void setAspectRatio(int width, int height) {
+    if (width < 0 || height < 0) {
+      throw new IllegalArgumentException("Size cannot be negative.");
+    }
+    mRatioWidth = width;
+    mRatioHeight = height;
+    requestLayout();
+  }
+
+  @Override
+  protected void onMeasure(int widthMeasureSpec, int heightMeasureSpec) {
+    super.onMeasure(widthMeasureSpec, heightMeasureSpec);
+    int width = MeasureSpec.getSize(widthMeasureSpec);
+    int height = MeasureSpec.getSize(heightMeasureSpec);
+    if (0 == mRatioWidth || 0 == mRatioHeight) {
+      setMeasuredDimension(width, height);
+    } else {
+      if (width < height * mRatioWidth / mRatioHeight) {
+        setMeasuredDimension(width, width * mRatioHeight / mRatioWidth);
+      } else {
+        setMeasuredDimension(height * mRatioWidth / mRatioHeight, height);
+      }
+    }
+  }
+}

tensorflow/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/Camera2BasicFragment.java

@@ -0,0 +1,862 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed 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 com.example.android.tflitecamerademo;
+
+import android.app.Activity;
+import android.app.AlertDialog;
+import android.app.Dialog;
+import android.app.DialogFragment;
+import android.app.Fragment;
+import android.content.Context;
+import android.content.DialogInterface;
+import android.content.pm.PackageInfo;
+import android.content.pm.PackageManager;
+import android.content.res.Configuration;
+import android.graphics.Bitmap;
+import android.graphics.ImageFormat;
+import android.graphics.Matrix;
+import android.graphics.Point;
+import android.graphics.RectF;
+import android.graphics.SurfaceTexture;
+import android.hardware.camera2.CameraAccessException;
+import android.hardware.camera2.CameraCaptureSession;
+import android.hardware.camera2.CameraCharacteristics;
+import android.hardware.camera2.CameraDevice;
+import android.hardware.camera2.CameraManager;
+import android.hardware.camera2.CaptureRequest;
+import android.hardware.camera2.CaptureResult;
+import android.hardware.camera2.TotalCaptureResult;
+import android.hardware.camera2.params.StreamConfigurationMap;
+import android.media.ImageReader;
+import android.os.Bundle;
+import android.os.Handler;
+import android.os.HandlerThread;
+import android.os.Process;
+import android.text.SpannableString;
+import android.text.SpannableStringBuilder;
+import android.util.Log;
+import android.util.Size;
+import android.view.LayoutInflater;
+import android.view.Surface;
+import android.view.TextureView;
+import android.view.View;
+import android.view.ViewGroup;
+import android.widget.AdapterView;
+import android.widget.ArrayAdapter;
+import android.widget.ListView;
+import android.widget.NumberPicker;
+import android.widget.TextView;
+import android.widget.Toast;
+import androidx.annotation.NonNull;
+import android.support.v13.app.FragmentCompat;
+import java.io.IOException;
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.Collections;
+import java.util.Comparator;
+import java.util.List;
+import java.util.concurrent.Semaphore;
+import java.util.concurrent.TimeUnit;
+
+/** Basic fragments for the Camera. */
+public class Camera2BasicFragment extends Fragment
+    implements FragmentCompat.OnRequestPermissionsResultCallback {
+
+  /** Tag for the {@link Log}. */
+  private static final String TAG = "TfLiteCameraDemo";
+
+  private static final String FRAGMENT_DIALOG = "dialog";
+
+  private static final String HANDLE_THREAD_NAME = "CameraBackground";
+
+  private static final int PERMISSIONS_REQUEST_CODE = 1;
+
+  private final Object lock = new Object();
+  private boolean runClassifier = false;
+  private boolean checkedPermissions = false;
+  private TextView textView;
+  private NumberPicker np;
+  private ImageClassifier classifier;
+  private ListView deviceView;
+  private ListView modelView;
+
+
+  /** Max preview width that is guaranteed by Camera2 API */
+  private static final int MAX_PREVIEW_WIDTH = 1920;
+
+  /** Max preview height that is guaranteed by Camera2 API */
+  private static final int MAX_PREVIEW_HEIGHT = 1080;
+
+  /**
+   * {@link TextureView.SurfaceTextureListener} handles several lifecycle events on a {@link
+   * TextureView}.
+   */
+  private final TextureView.SurfaceTextureListener surfaceTextureListener =
+      new TextureView.SurfaceTextureListener() {
+
+        @Override
+        public void onSurfaceTextureAvailable(SurfaceTexture texture, int width, int height) {
+          openCamera(width, height);
+        }
+
+        @Override
+        public void onSurfaceTextureSizeChanged(SurfaceTexture texture, int width, int height) {
+          configureTransform(width, height);
+        }
+
+        @Override
+        public boolean onSurfaceTextureDestroyed(SurfaceTexture texture) {
+          return true;
+        }
+
+        @Override
+        public void onSurfaceTextureUpdated(SurfaceTexture texture) {}
+      };
+
+  // Model parameter constants.
+  private String gpu;
+  private String cpu;
+  private String nnApi;
+  private String mobilenetV1Quant;
+  private String mobilenetV1Float;
+
+
+
+  /** ID of the current {@link CameraDevice}. */
+  private String cameraId;
+
+  /** An {@link AutoFitTextureView} for camera preview. */
+  private AutoFitTextureView textureView;
+
+  /** A {@link CameraCaptureSession } for camera preview. */
+  private CameraCaptureSession captureSession;
+
+  /** A reference to the opened {@link CameraDevice}. */
+  private CameraDevice cameraDevice;
+
+  /** The {@link android.util.Size} of camera preview. */
+  private Size previewSize;
+
+  /** {@link CameraDevice.StateCallback} is called when {@link CameraDevice} changes its state. */
+  private final CameraDevice.StateCallback stateCallback =
+      new CameraDevice.StateCallback() {
+
+        @Override
+        public void onOpened(@NonNull CameraDevice currentCameraDevice) {
+          // This method is called when the camera is opened.  We start camera preview here.
+          cameraOpenCloseLock.release();
+          cameraDevice = currentCameraDevice;
+          createCameraPreviewSession();
+        }
+
+        @Override
+        public void onDisconnected(@NonNull CameraDevice currentCameraDevice) {
+          cameraOpenCloseLock.release();
+          currentCameraDevice.close();
+          cameraDevice = null;
+        }
+
+        @Override
+        public void onError(@NonNull CameraDevice currentCameraDevice, int error) {
+          cameraOpenCloseLock.release();
+          currentCameraDevice.close();
+          cameraDevice = null;
+          Activity activity = getActivity();
+          if (null != activity) {
+            activity.finish();
+          }
+        }
+      };
+
+  private ArrayList<String> deviceStrings = new ArrayList<String>();
+  private ArrayList<String> modelStrings = new ArrayList<String>();
+
+  /** Current indices of device and model. */
+  int currentDevice = -1;
+
+  int currentModel = -1;
+
+  int currentNumThreads = -1;
+
+  /** An additional thread for running tasks that shouldn't block the UI. */
+  private HandlerThread backgroundThread;
+
+  /** A {@link Handler} for running tasks in the background. */
+  private Handler backgroundHandler;
+
+  /** An {@link ImageReader} that handles image capture. */
+  private ImageReader imageReader;
+
+  /** {@link CaptureRequest.Builder} for the camera preview */
+  private CaptureRequest.Builder previewRequestBuilder;
+
+  /** {@link CaptureRequest} generated by {@link #previewRequestBuilder} */
+  private CaptureRequest previewRequest;
+
+  /** A {@link Semaphore} to prevent the app from exiting before closing the camera. */
+  private Semaphore cameraOpenCloseLock = new Semaphore(1);
+
+  /** A {@link CameraCaptureSession.CaptureCallback} that handles events related to capture. */
+  private CameraCaptureSession.CaptureCallback captureCallback =
+      new CameraCaptureSession.CaptureCallback() {
+
+        @Override
+        public void onCaptureProgressed(
+            @NonNull CameraCaptureSession session,
+            @NonNull CaptureRequest request,
+            @NonNull CaptureResult partialResult) {}
+
+        @Override
+        public void onCaptureCompleted(
+            @NonNull CameraCaptureSession session,
+            @NonNull CaptureRequest request,
+            @NonNull TotalCaptureResult result) {}
+      };
+
+  /**
+   * Shows a {@link Toast} on the UI thread for the classification results.
+   *
+   * @param text The message to show
+   */
+  private void showToast(String s) {
+    SpannableStringBuilder builder = new SpannableStringBuilder();
+    SpannableString str1 = new SpannableString(s);
+    builder.append(str1);
+    showToast(builder);
+  }
+
+  private void showToast(SpannableStringBuilder builder) {
+    final Activity activity = getActivity();
+    if (activity != null) {
+      activity.runOnUiThread(
+          new Runnable() {
+            @Override
+            public void run() {
+              textView.setText(builder, TextView.BufferType.SPANNABLE);
+            }
+          });
+    }
+  }
+
+  /**
+   * Resizes image.
+   *
+   * Attempting to use too large a preview size could  exceed the camera bus' bandwidth limitation,
+   * resulting in gorgeous previews but the storage of garbage capture data.
+   *
+   * Given {@code choices} of {@code Size}s supported by a camera, choose the smallest one that is
+   * at least as large as the respective texture view size, and that is at most as large as the
+   * respective max size, and whose aspect ratio matches with the specified value. If such size
+   * doesn't exist, choose the largest one that is at most as large as the respective max size, and
+   * whose aspect ratio matches with the specified value.
+   *
+   * @param choices The list of sizes that the camera supports for the intended output class
+   * @param textureViewWidth The width of the texture view relative to sensor coordinate
+   * @param textureViewHeight The height of the texture view relative to sensor coordinate
+   * @param maxWidth The maximum width that can be chosen
+   * @param maxHeight The maximum height that can be chosen
+   * @param aspectRatio The aspect ratio
+   * @return The optimal {@code Size}, or an arbitrary one if none were big enough
+   */
+  private static Size chooseOptimalSize(
+      Size[] choices,
+      int textureViewWidth,
+      int textureViewHeight,
+      int maxWidth,
+      int maxHeight,
+      Size aspectRatio) {
+
+    // Collect the supported resolutions that are at least as big as the preview Surface
+    List<Size> bigEnough = new ArrayList<>();
+    // Collect the supported resolutions that are smaller than the preview Surface
+    List<Size> notBigEnough = new ArrayList<>();
+    int w = aspectRatio.getWidth();
+    int h = aspectRatio.getHeight();
+    for (Size option : choices) {
+      if (option.getWidth() <= maxWidth
+          && option.getHeight() <= maxHeight
+          && option.getHeight() == option.getWidth() * h / w) {
+        if (option.getWidth() >= textureViewWidth && option.getHeight() >= textureViewHeight) {
+          bigEnough.add(option);
+        } else {
+          notBigEnough.add(option);
+        }
+      }
+    }
+
+    // Pick the smallest of those big enough. If there is no one big enough, pick the
+    // largest of those not big enough.
+    if (bigEnough.size() > 0) {
+      return Collections.min(bigEnough, new CompareSizesByArea());
+    } else if (notBigEnough.size() > 0) {
+      return Collections.max(notBigEnough, new CompareSizesByArea());
+    } else {
+      Log.e(TAG, "Couldn't find any suitable preview size");
+      return choices[0];
+    }
+  }
+
+  public static Camera2BasicFragment newInstance() {
+    return new Camera2BasicFragment();
+  }
+
+  /** Layout the preview and buttons. */
+  @Override
+  public View onCreateView(
+      LayoutInflater inflater, ViewGroup container, Bundle savedInstanceState) {
+    return inflater.inflate(R.layout.fragment_camera2_basic, container, false);
+  }
+
+  private void updateActiveModel() {
+    // Get UI information before delegating to background
+    final int modelIndex = modelView.getCheckedItemPosition();
+    final int deviceIndex = deviceView.getCheckedItemPosition();
+    final int numThreads = np.getValue();
+
+    backgroundHandler.post(
+        () -> {
+          if (modelIndex == currentModel
+              && deviceIndex == currentDevice
+              && numThreads == currentNumThreads) {
+            return;
+          }
+          currentModel = modelIndex;
+          currentDevice = deviceIndex;
+          currentNumThreads = numThreads;
+
+          // Disable classifier while updating
+          if (classifier != null) {
+            classifier.close();
+            classifier = null;
+          }
+
+          // Lookup names of parameters.
+          String model = modelStrings.get(modelIndex);
+          String device = deviceStrings.get(deviceIndex);
+
+          Log.i(TAG, "Changing model to " + model + " device " + device);
+
+          // Try to load model.
+          try {
+            if (model.equals(mobilenetV1Quant)) {
+              classifier = new ImageClassifierQuantizedMobileNet(getActivity());
+            } else if (model.equals(mobilenetV1Float)) {
+              classifier = new ImageClassifierFloatMobileNet(getActivity());
+            } else {
+              showToast("Failed to load model");
+            }
+          } catch (IOException e) {
+            Log.d(TAG, "Failed to load", e);
+            classifier = null;
+          }
+
+          // Customize the interpreter to the type of device we want to use.
+          if (classifier == null) {
+            return;
+          }
+          classifier.setNumThreads(numThreads);
+          if (device.equals(cpu)) {
+          } else if (device.equals(gpu)) {
+            classifier.useGpu();
+          } else if (device.equals(nnApi)) {
+            classifier.useNNAPI();
+          }
+        });
+  }
+
+  /** Connect the buttons to their event handler. */
+  @Override
+  public void onViewCreated(final View view, Bundle savedInstanceState) {
+    gpu = getString(R.string.gpu);
+    cpu = getString(R.string.cpu);
+    nnApi = getString(R.string.nnapi);
+    mobilenetV1Quant = getString(R.string.mobilenetV1Quant);
+    mobilenetV1Float = getString(R.string.mobilenetV1Float);
+
+    // Get references to widgets.
+    textureView = (AutoFitTextureView) view.findViewById(R.id.texture);
+    textView = (TextView) view.findViewById(R.id.text);
+    deviceView = (ListView) view.findViewById(R.id.device);
+    modelView = (ListView) view.findViewById(R.id.model);
+
+    // Build list of models
+    modelStrings.add(mobilenetV1Quant);
+    modelStrings.add(mobilenetV1Float);
+
+    // Build list of devices
+    int defaultModelIndex = 0;
+    deviceStrings.add(cpu);
+    deviceStrings.add(gpu);
+    deviceStrings.add(nnApi);
+
+    deviceView.setAdapter(
+        new ArrayAdapter<String>(
+            getContext(), R.layout.listview_row, R.id.listview_row_text, deviceStrings));
+    deviceView.setChoiceMode(ListView.CHOICE_MODE_SINGLE);
+    deviceView.setOnItemClickListener(
+        new AdapterView.OnItemClickListener() {
+          @Override
+          public void onItemClick(AdapterView<?> parent, View view, int position, long id) {
+            updateActiveModel();
+          }
+        });
+    deviceView.setItemChecked(0, true);
+
+    modelView.setChoiceMode(ListView.CHOICE_MODE_SINGLE);
+    ArrayAdapter<String> modelAdapter =
+        new ArrayAdapter<>(
+            getContext(), R.layout.listview_row, R.id.listview_row_text, modelStrings);
+    modelView.setAdapter(modelAdapter);
+    modelView.setItemChecked(defaultModelIndex, true);
+    modelView.setOnItemClickListener(
+        new AdapterView.OnItemClickListener() {
+          @Override
+          public void onItemClick(AdapterView<?> parent, View view, int position, long id) {
+            updateActiveModel();
+          }
+        });
+
+    np = (NumberPicker) view.findViewById(R.id.np);
+    np.setMinValue(1);
+    np.setMaxValue(10);
+    np.setWrapSelectorWheel(true);
+    np.setOnValueChangedListener(
+        new NumberPicker.OnValueChangeListener() {
+          @Override
+          public void onValueChange(NumberPicker picker, int oldVal, int newVal) {
+            updateActiveModel();
+          }
+        });
+
+    // Start initial model.
+  }
+
+  /** Load the model and labels. */
+  @Override
+  public void onActivityCreated(Bundle savedInstanceState) {
+    super.onActivityCreated(savedInstanceState);
+    startBackgroundThread();
+  }
+
+  @Override
+  public void onResume() {
+    super.onResume();
+    startBackgroundThread();
+
+    // When the screen is turned off and turned back on, the SurfaceTexture is already
+    // available, and "onSurfaceTextureAvailable" will not be called. In that case, we can open
+    // a camera and start preview from here (otherwise, we wait until the surface is ready in
+    // the SurfaceTextureListener).
+    if (textureView.isAvailable()) {
+      openCamera(textureView.getWidth(), textureView.getHeight());
+    } else {
+      textureView.setSurfaceTextureListener(surfaceTextureListener);
+    }
+  }
+
+  @Override
+  public void onPause() {
+    closeCamera();
+    stopBackgroundThread();
+    super.onPause();
+  }
+
+  @Override
+  public void onDestroy() {
+    if (classifier != null) {
+      classifier.close();
+    }
+    super.onDestroy();
+  }
+
+  /**
+   * Sets up member variables related to camera.
+   *
+   * @param width The width of available size for camera preview
+   * @param height The height of available size for camera preview
+   */
+  private void setUpCameraOutputs(int width, int height) {
+    Activity activity = getActivity();
+    CameraManager manager = (CameraManager) activity.getSystemService(Context.CAMERA_SERVICE);
+    try {
+      for (String cameraId : manager.getCameraIdList()) {
+        CameraCharacteristics characteristics = manager.getCameraCharacteristics(cameraId);
+
+        // We don't use a front facing camera in this sample.
+        Integer facing = characteristics.get(CameraCharacteristics.LENS_FACING);
+        if (facing != null && facing == CameraCharacteristics.LENS_FACING_FRONT) {
+          continue;
+        }
+
+        StreamConfigurationMap map =
+            characteristics.get(CameraCharacteristics.SCALER_STREAM_CONFIGURATION_MAP);
+        if (map == null) {
+          continue;
+        }
+
+        // // For still image captures, we use the largest available size.
+        Size largest =
+            Collections.max(
+                Arrays.asList(map.getOutputSizes(ImageFormat.JPEG)), new CompareSizesByArea());
+        imageReader =
+            ImageReader.newInstance(
+                largest.getWidth(), largest.getHeight(), ImageFormat.JPEG, /*maxImages*/ 2);
+
+        // Find out if we need to swap dimension to get the preview size relative to sensor
+        // coordinate.
+        int displayRotation = activity.getWindowManager().getDefaultDisplay().getRotation();
+        // noinspection ConstantConditions
+        /* Orientation of the camera sensor */
+        int sensorOrientation = characteristics.get(CameraCharacteristics.SENSOR_ORIENTATION);
+        boolean swappedDimensions = false;
+        switch (displayRotation) {
+          case Surface.ROTATION_0:
+          case Surface.ROTATION_180:
+            if (sensorOrientation == 90 || sensorOrientation == 270) {
+              swappedDimensions = true;
+            }
+            break;
+          case Surface.ROTATION_90:
+          case Surface.ROTATION_270:
+            if (sensorOrientation == 0 || sensorOrientation == 180) {
+              swappedDimensions = true;
+            }
+            break;
+          default:
+            Log.e(TAG, "Display rotation is invalid: " + displayRotation);
+        }
+
+        Point displaySize = new Point();
+        activity.getWindowManager().getDefaultDisplay().getSize(displaySize);
+        int rotatedPreviewWidth = width;
+        int rotatedPreviewHeight = height;
+        int maxPreviewWidth = displaySize.x;
+        int maxPreviewHeight = displaySize.y;
+
+        if (swappedDimensions) {
+          rotatedPreviewWidth = height;
+          rotatedPreviewHeight = width;
+          maxPreviewWidth = displaySize.y;
+          maxPreviewHeight = displaySize.x;
+        }
+
+        if (maxPreviewWidth > MAX_PREVIEW_WIDTH) {
+          maxPreviewWidth = MAX_PREVIEW_WIDTH;
+        }
+
+        if (maxPreviewHeight > MAX_PREVIEW_HEIGHT) {
+          maxPreviewHeight = MAX_PREVIEW_HEIGHT;
+        }
+
+        previewSize =
+            chooseOptimalSize(
+                map.getOutputSizes(SurfaceTexture.class),
+                rotatedPreviewWidth,
+                rotatedPreviewHeight,
+                maxPreviewWidth,
+                maxPreviewHeight,
+                largest);
+
+        // We fit the aspect ratio of TextureView to the size of preview we picked.
+        int orientation = getResources().getConfiguration().orientation;
+        if (orientation == Configuration.ORIENTATION_LANDSCAPE) {
+          textureView.setAspectRatio(previewSize.getWidth(), previewSize.getHeight());
+        } else {
+          textureView.setAspectRatio(previewSize.getHeight(), previewSize.getWidth());
+        }
+
+        this.cameraId = cameraId;
+        return;
+      }
+    } catch (CameraAccessException e) {
+      Log.e(TAG, "Failed to access Camera", e);
+    } catch (NullPointerException e) {
+      // Currently an NPE is thrown when the Camera2API is used but not supported on the
+      // device this code runs.
+      ErrorDialog.newInstance(getString(R.string.camera_error))
+          .show(getChildFragmentManager(), FRAGMENT_DIALOG);
+    }
+  }
+
+  private String[] getRequiredPermissions() {
+    Activity activity = getActivity();
+    try {
+      PackageInfo info =
+          activity
+              .getPackageManager()
+              .getPackageInfo(activity.getPackageName(), PackageManager.GET_PERMISSIONS);
+      String[] ps = info.requestedPermissions;
+      if (ps != null && ps.length > 0) {
+        return ps;
+      } else {
+        return new String[0];
+      }
+    } catch (Exception e) {
+      return new String[0];
+    }
+  }
+
+  /** Opens the camera specified by {@link Camera2BasicFragment#cameraId}. */
+  private void openCamera(int width, int height) {
+    if (!checkedPermissions && !allPermissionsGranted()) {
+      FragmentCompat.requestPermissions(this, getRequiredPermissions(), PERMISSIONS_REQUEST_CODE);
+      return;
+    } else {
+      checkedPermissions = true;
+    }
+    setUpCameraOutputs(width, height);
+    configureTransform(width, height);
+    Activity activity = getActivity();
+    CameraManager manager = (CameraManager) activity.getSystemService(Context.CAMERA_SERVICE);
+    try {
+      if (!cameraOpenCloseLock.tryAcquire(2500, TimeUnit.MILLISECONDS)) {
+        throw new RuntimeException("Time out waiting to lock camera opening.");
+      }
+      manager.openCamera(cameraId, stateCallback, backgroundHandler);
+    } catch (CameraAccessException e) {
+      Log.e(TAG, "Failed to open Camera", e);
+    } catch (InterruptedException e) {
+      throw new RuntimeException("Interrupted while trying to lock camera opening.", e);
+    }
+  }
+
+  private boolean allPermissionsGranted() {
+    for (String permission : getRequiredPermissions()) {
+      if (getActivity().checkPermission(permission, Process.myPid(), Process.myUid())
+          != PackageManager.PERMISSION_GRANTED) {
+        return false;
+      }
+    }
+    return true;
+  }
+
+  @Override
+  public void onRequestPermissionsResult(
+      int requestCode, @NonNull String[] permissions, @NonNull int[] grantResults) {
+    super.onRequestPermissionsResult(requestCode, permissions, grantResults);
+  }
+
+  /** Closes the current {@link CameraDevice}. */
+  private void closeCamera() {
+    try {
+      cameraOpenCloseLock.acquire();
+      if (null != captureSession) {
+        captureSession.close();
+        captureSession = null;
+      }
+      if (null != cameraDevice) {
+        cameraDevice.close();
+        cameraDevice = null;
+      }
+      if (null != imageReader) {
+        imageReader.close();
+        imageReader = null;
+      }
+    } catch (InterruptedException e) {
+      throw new RuntimeException("Interrupted while trying to lock camera closing.", e);
+    } finally {
+      cameraOpenCloseLock.release();
+    }
+  }
+
+  /** Starts a background thread and its {@link Handler}. */
+  private void startBackgroundThread() {
+    backgroundThread = new HandlerThread(HANDLE_THREAD_NAME);
+    backgroundThread.start();
+    backgroundHandler = new Handler(backgroundThread.getLooper());
+    // Start the classification train & load an initial model.
+    synchronized (lock) {
+      runClassifier = true;
+    }
+    backgroundHandler.post(periodicClassify);
+    updateActiveModel();
+  }
+
+  /** Stops the background thread and its {@link Handler}. */
+  private void stopBackgroundThread() {
+    backgroundThread.quitSafely();
+    try {
+      backgroundThread.join();
+      backgroundThread = null;
+      backgroundHandler = null;
+      synchronized (lock) {
+        runClassifier = false;
+      }
+    } catch (InterruptedException e) {
+      Log.e(TAG, "Interrupted when stopping background thread", e);
+    }
+  }
+
+  /** Takes photos and classify them periodically. */
+  private Runnable periodicClassify =
+      new Runnable() {
+        @Override
+        public void run() {
+          synchronized (lock) {
+            if (runClassifier) {
+              classifyFrame();
+            }
+          }
+          backgroundHandler.post(periodicClassify);
+        }
+      };
+
+  /** Creates a new {@link CameraCaptureSession} for camera preview. */
+  private void createCameraPreviewSession() {
+    try {
+      SurfaceTexture texture = textureView.getSurfaceTexture();
+      assert texture != null;
+
+      // We configure the size of default buffer to be the size of camera preview we want.
+      texture.setDefaultBufferSize(previewSize.getWidth(), previewSize.getHeight());
+
+      // This is the output Surface we need to start preview.
+      Surface surface = new Surface(texture);
+
+      // We set up a CaptureRequest.Builder with the output Surface.
+      previewRequestBuilder = cameraDevice.createCaptureRequest(CameraDevice.TEMPLATE_PREVIEW);
+      previewRequestBuilder.addTarget(surface);
+
+      // Here, we create a CameraCaptureSession for camera preview.
+      cameraDevice.createCaptureSession(
+          Arrays.asList(surface),
+          new CameraCaptureSession.StateCallback() {
+
+            @Override
+            public void onConfigured(@NonNull CameraCaptureSession cameraCaptureSession) {
+              // The camera is already closed
+              if (null == cameraDevice) {
+                return;
+              }
+
+              // When the session is ready, we start displaying the preview.
+              captureSession = cameraCaptureSession;
+              try {
+                // Auto focus should be continuous for camera preview.
+                previewRequestBuilder.set(
+                    CaptureRequest.CONTROL_AF_MODE,
+                    CaptureRequest.CONTROL_AF_MODE_CONTINUOUS_PICTURE);
+
+                // Finally, we start displaying the camera preview.
+                previewRequest = previewRequestBuilder.build();
+                captureSession.setRepeatingRequest(
+                    previewRequest, captureCallback, backgroundHandler);
+              } catch (CameraAccessException e) {
+                Log.e(TAG, "Failed to set up config to capture Camera", e);
+              }
+            }
+
+            @Override
+            public void onConfigureFailed(@NonNull CameraCaptureSession cameraCaptureSession) {
+              showToast("Failed");
+            }
+          },
+          null);
+    } catch (CameraAccessException e) {
+      Log.e(TAG, "Failed to preview Camera", e);
+    }
+  }
+
+  /**
+   * Configures the necessary {@link android.graphics.Matrix} transformation to `textureView`. This
+   * method should be called after the camera preview size is determined in setUpCameraOutputs and
+   * also the size of `textureView` is fixed.
+   *
+   * @param viewWidth The width of `textureView`
+   * @param viewHeight The height of `textureView`
+   */
+  private void configureTransform(int viewWidth, int viewHeight) {
+    Activity activity = getActivity();
+    if (null == textureView || null == previewSize || null == activity) {
+      return;
+    }
+    int rotation = activity.getWindowManager().getDefaultDisplay().getRotation();
+    Matrix matrix = new Matrix();
+    RectF viewRect = new RectF(0, 0, viewWidth, viewHeight);
+    RectF bufferRect = new RectF(0, 0, previewSize.getHeight(), previewSize.getWidth());
+    float centerX = viewRect.centerX();
+    float centerY = viewRect.centerY();
+    if (Surface.ROTATION_90 == rotation || Surface.ROTATION_270 == rotation) {
+      bufferRect.offset(centerX - bufferRect.centerX(), centerY - bufferRect.centerY());
+      matrix.setRectToRect(viewRect, bufferRect, Matrix.ScaleToFit.FILL);
+      float scale =
+          Math.max(
+              (float) viewHeight / previewSize.getHeight(),
+              (float) viewWidth / previewSize.getWidth());
+      matrix.postScale(scale, scale, centerX, centerY);
+      matrix.postRotate(90 * (rotation - 2), centerX, centerY);
+    } else if (Surface.ROTATION_180 == rotation) {
+      matrix.postRotate(180, centerX, centerY);
+    }
+    textureView.setTransform(matrix);
+  }
+
+  /** Classifies a frame from the preview stream. */
+  private void classifyFrame() {
+    if (classifier == null || getActivity() == null || cameraDevice == null) {
+      // It's important to not call showToast every frame, or else the app will starve and
+      // hang. updateActiveModel() already puts an error message up with showToast.
+      // showToast("Uninitialized Classifier or invalid context.");
+      return;
+    }
+    SpannableStringBuilder textToShow = new SpannableStringBuilder();
+    Bitmap bitmap = textureView.getBitmap(classifier.getImageSizeX(), classifier.getImageSizeY());
+    classifier.classifyFrame(bitmap, textToShow);
+    bitmap.recycle();
+    showToast(textToShow);
+  }
+
+  /** Compares two {@code Size}s based on their areas. */
+  private static class CompareSizesByArea implements Comparator<Size> {
+
+    @Override
+    public int compare(Size lhs, Size rhs) {
+      // We cast here to ensure the multiplications won't overflow
+      return Long.signum(
+          (long) lhs.getWidth() * lhs.getHeight() - (long) rhs.getWidth() * rhs.getHeight());
+    }
+  }
+
+  /** Shows an error message dialog. */
+  public static class ErrorDialog extends DialogFragment {
+
+    private static final String ARG_MESSAGE = "message";
+
+    public static ErrorDialog newInstance(String message) {
+      ErrorDialog dialog = new ErrorDialog();
+      Bundle args = new Bundle();
+      args.putString(ARG_MESSAGE, message);
+      dialog.setArguments(args);
+      return dialog;
+    }
+
+    @Override
+    public Dialog onCreateDialog(Bundle savedInstanceState) {
+      final Activity activity = getActivity();
+      return new AlertDialog.Builder(activity)
+          .setMessage(getArguments().getString(ARG_MESSAGE))
+          .setPositiveButton(
+              android.R.string.ok,
+              new DialogInterface.OnClickListener() {
+                @Override
+                public void onClick(DialogInterface dialogInterface, int i) {
+                  activity.finish();
+                }
+              })
+          .create();
+    }
+  }
+}

tensorflow/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/CameraActivity.java

@@ -0,0 +1,35 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed 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 com.example.android.tflitecamerademo;
+
+import android.app.Activity;
+import android.os.Bundle;
+
+/** Main {@code Activity} class for the Camera app. */
+public class CameraActivity extends Activity {
+
+  @Override
+  protected void onCreate(Bundle savedInstanceState) {
+    super.onCreate(savedInstanceState);
+    setContentView(R.layout.activity_camera);
+    if (null == savedInstanceState) {
+      getFragmentManager()
+          .beginTransaction()
+          .replace(R.id.container, Camera2BasicFragment.newInstance())
+          .commit();
+    }
+  }
+}

tensorflow/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/ImageClassifier.java

@@ -0,0 +1,373 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed 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 com.example.android.tflitecamerademo;
+
+import android.app.Activity;
+import android.content.res.AssetFileDescriptor;
+import android.graphics.Bitmap;
+import android.os.SystemClock;
+import android.text.SpannableString;
+import android.text.SpannableStringBuilder;
+import android.text.style.ForegroundColorSpan;
+import android.text.style.RelativeSizeSpan;
+import android.util.Log;
+import java.io.BufferedReader;
+import java.io.FileInputStream;
+import java.io.IOException;
+import java.io.InputStreamReader;
+import java.nio.ByteBuffer;
+import java.nio.ByteOrder;
+import java.nio.MappedByteBuffer;
+import java.nio.channels.FileChannel;
+import java.util.AbstractMap;
+import java.util.ArrayList;
+import java.util.Comparator;
+import java.util.List;
+import java.util.Map;
+import java.util.PriorityQueue;
+import org.tensorflow.lite.Interpreter;
+import org.tensorflow.lite.gpu.GpuDelegate;
+import org.tensorflow.lite.nnapi.NnApiDelegate;
+
+/**
+ * Classifies images with Tensorflow Lite.
+ */
+public abstract class ImageClassifier {
+  // Display preferences
+  private static final float GOOD_PROB_THRESHOLD = 0.3f;
+  private static final int SMALL_COLOR = 0xffddaa88;
+
+  /** Tag for the {@link Log}. */
+  private static final String TAG = "TfLiteCameraDemo";
+
+  /** Number of results to show in the UI. */
+  private static final int RESULTS_TO_SHOW = 3;
+
+  /** Dimensions of inputs. */
+  private static final int DIM_BATCH_SIZE = 1;
+
+  private static final int DIM_PIXEL_SIZE = 3;
+
+  /** Preallocated buffers for storing image data in. */
+  private int[] intValues = new int[getImageSizeX() * getImageSizeY()];
+
+  /** Options for configuring the Interpreter. */
+  private final Interpreter.Options tfliteOptions = new Interpreter.Options();
+
+  /** The loaded TensorFlow Lite model. */
+  private MappedByteBuffer tfliteModel;
+
+  /** An instance of the driver class to run model inference with Tensorflow Lite. */
+  protected Interpreter tflite;
+
+  /** Labels corresponding to the output of the vision model. */
+  private List<String> labelList;
+
+  /** A ByteBuffer to hold image data, to be feed into Tensorflow Lite as inputs. */
+  protected ByteBuffer imgData = null;
+
+  /** multi-stage low pass filter * */
+  private float[][] filterLabelProbArray = null;
+
+  private static final int FILTER_STAGES = 3;
+  private static final float FILTER_FACTOR = 0.4f;
+
+  private PriorityQueue<Map.Entry<String, Float>> sortedLabels =
+      new PriorityQueue<>(
+          RESULTS_TO_SHOW,
+          new Comparator<Map.Entry<String, Float>>() {
+            @Override
+            public int compare(Map.Entry<String, Float> o1, Map.Entry<String, Float> o2) {
+              return (o1.getValue()).compareTo(o2.getValue());
+            }
+          });
+
+  /** holds a gpu delegate */
+  GpuDelegate gpuDelegate = null;
+  /** holds an nnapi delegate */
+  NnApiDelegate nnapiDelegate = null;
+
+  /** Initializes an {@code ImageClassifier}. */
+  ImageClassifier(Activity activity) throws IOException {
+    tfliteModel = loadModelFile(activity);
+    tflite = new Interpreter(tfliteModel, tfliteOptions);
+    labelList = loadLabelList(activity);
+    imgData =
+        ByteBuffer.allocateDirect(
+            DIM_BATCH_SIZE
+                * getImageSizeX()
+                * getImageSizeY()
+                * DIM_PIXEL_SIZE
+                * getNumBytesPerChannel());
+    imgData.order(ByteOrder.nativeOrder());
+    filterLabelProbArray = new float[FILTER_STAGES][getNumLabels()];
+    Log.d(TAG, "Created a Tensorflow Lite Image Classifier.");
+  }
+
+  /** Classifies a frame from the preview stream. */
+  void classifyFrame(Bitmap bitmap, SpannableStringBuilder builder) {
+    if (tflite == null) {
+      Log.e(TAG, "Image classifier has not been initialized; Skipped.");
+      builder.append(new SpannableString("Uninitialized Classifier."));
+    }
+    convertBitmapToByteBuffer(bitmap);
+    // Here's where the magic happens!!!
+    long startTime = SystemClock.uptimeMillis();
+    runInference();
+    long endTime = SystemClock.uptimeMillis();
+    Log.d(TAG, "Timecost to run model inference: " + Long.toString(endTime - startTime));
+
+    // Smooth the results across frames.
+    applyFilter();
+
+    // Print the results.
+    printTopKLabels(builder);
+    long duration = endTime - startTime;
+    SpannableString span = new SpannableString(duration + " ms");
+    span.setSpan(new ForegroundColorSpan(android.graphics.Color.LTGRAY), 0, span.length(), 0);
+    builder.append(span);
+  }
+
+  void applyFilter() {
+    int numLabels = getNumLabels();
+
+    // Low pass filter `labelProbArray` into the first stage of the filter.
+    for (int j = 0; j < numLabels; ++j) {
+      filterLabelProbArray[0][j] +=
+          FILTER_FACTOR * (getProbability(j) - filterLabelProbArray[0][j]);
+    }
+    // Low pass filter each stage into the next.
+    for (int i = 1; i < FILTER_STAGES; ++i) {
+      for (int j = 0; j < numLabels; ++j) {
+        filterLabelProbArray[i][j] +=
+            FILTER_FACTOR * (filterLabelProbArray[i - 1][j] - filterLabelProbArray[i][j]);
+      }
+    }
+
+    // Copy the last stage filter output back to `labelProbArray`.
+    for (int j = 0; j < numLabels; ++j) {
+      setProbability(j, filterLabelProbArray[FILTER_STAGES - 1][j]);
+    }
+  }
+
+  private void recreateInterpreter() {
+    if (tflite != null) {
+      tflite.close();
+      tflite = new Interpreter(tfliteModel, tfliteOptions);
+    }
+  }
+
+  public void useGpu() {
+    if (gpuDelegate == null) {
+      GpuDelegate.Options options = new GpuDelegate.Options();
+      options.setQuantizedModelsAllowed(true);
+
+      gpuDelegate = new GpuDelegate(options);
+      tfliteOptions.addDelegate(gpuDelegate);
+      recreateInterpreter();
+    }
+  }
+
+  public void useCPU() {
+    recreateInterpreter();
+  }
+
+  public void useNNAPI() {
+    nnapiDelegate = new NnApiDelegate();
+    tfliteOptions.addDelegate(nnapiDelegate);
+    recreateInterpreter();
+  }
+
+  public void setNumThreads(int numThreads) {
+    tfliteOptions.setNumThreads(numThreads);
+    recreateInterpreter();
+  }
+
+  /** Closes tflite to release resources. */
+  public void close() {
+    tflite.close();
+    tflite = null;
+    if (gpuDelegate != null) {
+      gpuDelegate.close();
+      gpuDelegate = null;
+    }
+    if (nnapiDelegate != null) {
+      nnapiDelegate.close();
+      nnapiDelegate = null;
+    }
+    tfliteModel = null;
+  }
+
+  /** Reads label list from Assets. */
+  private List<String> loadLabelList(Activity activity) throws IOException {
+    List<String> labelList = new ArrayList<String>();
+    BufferedReader reader =
+        new BufferedReader(new InputStreamReader(activity.getAssets().open(getLabelPath())));
+    String line;
+    while ((line = reader.readLine()) != null) {
+      labelList.add(line);
+    }
+    reader.close();
+    return labelList;
+  }
+
+  /** Memory-map the model file in Assets. */
+  private MappedByteBuffer loadModelFile(Activity activity) throws IOException {
+    AssetFileDescriptor fileDescriptor = activity.getAssets().openFd(getModelPath());
+    FileInputStream inputStream = new FileInputStream(fileDescriptor.getFileDescriptor());
+    FileChannel fileChannel = inputStream.getChannel();
+    long startOffset = fileDescriptor.getStartOffset();
+    long declaredLength = fileDescriptor.getDeclaredLength();
+    return fileChannel.map(FileChannel.MapMode.READ_ONLY, startOffset, declaredLength);
+  }
+
+  /** Writes Image data into a {@code ByteBuffer}. */
+  private void convertBitmapToByteBuffer(Bitmap bitmap) {
+    if (imgData == null) {
+      return;
+    }
+    imgData.rewind();
+    bitmap.getPixels(intValues, 0, bitmap.getWidth(), 0, 0, bitmap.getWidth(), bitmap.getHeight());
+    // Convert the image to floating point.
+    int pixel = 0;
+    long startTime = SystemClock.uptimeMillis();
+    for (int i = 0; i < getImageSizeX(); ++i) {
+      for (int j = 0; j < getImageSizeY(); ++j) {
+        final int val = intValues[pixel++];
+        addPixelValue(val);
+      }
+    }
+    long endTime = SystemClock.uptimeMillis();
+    Log.d(TAG, "Timecost to put values into ByteBuffer: " + Long.toString(endTime - startTime));
+  }
+
+  /** Prints top-K labels, to be shown in UI as the results. */
+  private void printTopKLabels(SpannableStringBuilder builder) {
+    for (int i = 0; i < getNumLabels(); ++i) {
+      sortedLabels.add(
+          new AbstractMap.SimpleEntry<>(labelList.get(i), getNormalizedProbability(i)));
+      if (sortedLabels.size() > RESULTS_TO_SHOW) {
+        sortedLabels.poll();
+      }
+    }
+
+    final int size = sortedLabels.size();
+    for (int i = 0; i < size; i++) {
+      Map.Entry<String, Float> label = sortedLabels.poll();
+      SpannableString span =
+          new SpannableString(String.format("%s: %4.2f\n", label.getKey(), label.getValue()));
+      int color;
+      // Make it white when probability larger than threshold.
+      if (label.getValue() > GOOD_PROB_THRESHOLD) {
+        color = android.graphics.Color.WHITE;
+      } else {
+        color = SMALL_COLOR;
+      }
+      // Make first item bigger.
+      if (i == size - 1) {
+        float sizeScale = (i == size - 1) ? 1.25f : 0.8f;
+        span.setSpan(new RelativeSizeSpan(sizeScale), 0, span.length(), 0);
+      }
+      span.setSpan(new ForegroundColorSpan(color), 0, span.length(), 0);
+      builder.insert(0, span);
+    }
+  }
+
+  /**
+   * Get the name of the model file stored in Assets.
+   *
+   * @return
+   */
+  protected abstract String getModelPath();
+
+  /**
+   * Get the name of the label file stored in Assets.
+   *
+   * @return
+   */
+  protected abstract String getLabelPath();
+
+  /**
+   * Get the image size along the x axis.
+   *
+   * @return
+   */
+  protected abstract int getImageSizeX();
+
+  /**
+   * Get the image size along the y axis.
+   *
+   * @return
+   */
+  protected abstract int getImageSizeY();
+
+  /**
+   * Get the number of bytes that is used to store a single color channel value.
+   *
+   * @return
+   */
+  protected abstract int getNumBytesPerChannel();
+
+  /**
+   * Add pixelValue to byteBuffer.
+   *
+   * @param pixelValue
+   */
+  protected abstract void addPixelValue(int pixelValue);
+
+  /**
+   * Read the probability value for the specified label This is either the original value as it was
+   * read from the net's output or the updated value after the filter was applied.
+   *
+   * @param labelIndex
+   * @return
+   */
+  protected abstract float getProbability(int labelIndex);
+
+  /**
+   * Set the probability value for the specified label.
+   *
+   * @param labelIndex
+   * @param value
+   */
+  protected abstract void setProbability(int labelIndex, Number value);
+
+  /**
+   * Get the normalized probability value for the specified label. This is the final value as it
+   * will be shown to the user.
+   *
+   * @return
+   */
+  protected abstract float getNormalizedProbability(int labelIndex);
+
+  /**
+   * Run inference using the prepared input in {@link #imgData}. Afterwards, the result will be
+   * provided by getProbability().
+   *
+   * <p>This additional method is necessary, because we don't have a common base for different
+   * primitive data types.
+   */
+  protected abstract void runInference();
+
+  /**
+   * Get the total number of labels.
+   *
+   * @return
+   */
+  protected int getNumLabels() {
+    return labelList.size();
+  }
+}

tensorflow/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/ImageClassifierFloatInception.java

@@ -0,0 +1,105 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed 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 com.example.android.tflitecamerademo;
+
+import android.app.Activity;
+
+import java.io.IOException;
+
+/**
+ * This classifier works with the Inception-v3 slim model.
+ * It applies floating point inference rather than using a quantized model.
+ */
+public class ImageClassifierFloatInception extends ImageClassifier {
+
+  /**
+   * The inception net requires additional normalization of the used input.
+   */
+  private static final int IMAGE_MEAN = 128;
+  private static final float IMAGE_STD = 128.0f;
+
+  /**
+   * An array to hold inference results, to be feed into Tensorflow Lite as outputs.
+   * This isn't part of the super class, because we need a primitive array here.
+   */
+  private float[][] labelProbArray = null;
+
+  /**
+   * Initializes an {@code ImageClassifier}.
+   *
+   * @param activity
+   */
+  ImageClassifierFloatInception(Activity activity) throws IOException {
+    super(activity);
+    labelProbArray = new float[1][getNumLabels()];
+  }
+
+  @Override
+  protected String getModelPath() {
+    // you can download this file from
+    // https://storage.googleapis.com/download.tensorflow.org/models/tflite/inception_v3_slim_2016_android_2017_11_10.zip
+    return "inceptionv3_slim_2016.tflite";
+  }
+
+  @Override
+  protected String getLabelPath() {
+    return "labels_imagenet_slim.txt";
+  }
+
+  @Override
+  protected int getImageSizeX() {
+    return 299;
+  }
+
+  @Override
+  protected int getImageSizeY() {
+    return 299;
+  }
+
+  @Override
+  protected int getNumBytesPerChannel() {
+    // a 32bit float value requires 4 bytes
+    return 4;
+  }
+
+  @Override
+  protected void addPixelValue(int pixelValue) {
+    imgData.putFloat((((pixelValue >> 16) & 0xFF) - IMAGE_MEAN) / IMAGE_STD);
+    imgData.putFloat((((pixelValue >> 8) & 0xFF) - IMAGE_MEAN) / IMAGE_STD);
+    imgData.putFloat(((pixelValue & 0xFF) - IMAGE_MEAN) / IMAGE_STD);
+  }
+
+  @Override
+  protected float getProbability(int labelIndex) {
+    return labelProbArray[0][labelIndex];
+  }
+
+  @Override
+  protected void setProbability(int labelIndex, Number value) {
+    labelProbArray[0][labelIndex] = value.floatValue();
+  }
+
+  @Override
+  protected float getNormalizedProbability(int labelIndex) {
+    // TODO the following value isn't in [0,1] yet, but may be greater. Why?
+    return getProbability(labelIndex);
+  }
+
+  @Override
+  protected void runInference() {
+    tflite.run(imgData, labelProbArray);
+  }
+}

tensorflow/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/ImageClassifierFloatMobileNet.java

@@ -0,0 +1,99 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed 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 com.example.android.tflitecamerademo;
+
+import android.app.Activity;
+import java.io.IOException;
+
+/** This classifier works with the float MobileNet model. */
+public class ImageClassifierFloatMobileNet extends ImageClassifier {
+
+  /** The mobile net requires additional normalization of the used input. */
+  private static final float IMAGE_MEAN = 127.5f;
+
+  private static final float IMAGE_STD = 127.5f;
+
+  /**
+   * An array to hold inference results, to be feed into Tensorflow Lite as outputs. This isn't part
+   * of the super class, because we need a primitive array here.
+   */
+  private float[][] labelProbArray = null;
+
+  /**
+   * Initializes an {@code ImageClassifierFloatMobileNet}.
+   *
+   * @param activity
+   */
+  ImageClassifierFloatMobileNet(Activity activity) throws IOException {
+    super(activity);
+    labelProbArray = new float[1][getNumLabels()];
+  }
+
+  @Override
+  protected String getModelPath() {
+    // you can download this file from
+    // see build.gradle for where to obtain this file. It should be auto
+    // downloaded into assets.
+    return "mobilenet_v1_1.0_224.tflite";
+  }
+
+  @Override
+  protected String getLabelPath() {
+    return "labels_mobilenet_quant_v1_224.txt";
+  }
+
+  @Override
+  protected int getImageSizeX() {
+    return 224;
+  }
+
+  @Override
+  protected int getImageSizeY() {
+    return 224;
+  }
+
+  @Override
+  protected int getNumBytesPerChannel() {
+    return 4; // Float.SIZE / Byte.SIZE;
+  }
+
+  @Override
+  protected void addPixelValue(int pixelValue) {
+    imgData.putFloat((((pixelValue >> 16) & 0xFF) - IMAGE_MEAN) / IMAGE_STD);
+    imgData.putFloat((((pixelValue >> 8) & 0xFF) - IMAGE_MEAN) / IMAGE_STD);
+    imgData.putFloat(((pixelValue & 0xFF) - IMAGE_MEAN) / IMAGE_STD);
+  }
+
+  @Override
+  protected float getProbability(int labelIndex) {
+    return labelProbArray[0][labelIndex];
+  }
+
+  @Override
+  protected void setProbability(int labelIndex, Number value) {
+    labelProbArray[0][labelIndex] = value.floatValue();
+  }
+
+  @Override
+  protected float getNormalizedProbability(int labelIndex) {
+    return labelProbArray[0][labelIndex];
+  }
+
+  @Override
+  protected void runInference() {
+    tflite.run(imgData, labelProbArray);
+  }
+}

tensorflow/lite/java/demo/app/src/main/java/com/example/android/tflitecamerademo/ImageClassifierQuantizedMobileNet.java

@@ -0,0 +1,97 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed 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 com.example.android.tflitecamerademo;
+
+import android.app.Activity;
+import java.io.IOException;
+
+/**
+ * This classifier works with the quantized MobileNet model.
+ */
+public class ImageClassifierQuantizedMobileNet extends ImageClassifier {
+
+  /**
+   * An array to hold inference results, to be feed into Tensorflow Lite as outputs.
+   * This isn't part of the super class, because we need a primitive array here.
+   */
+  private byte[][] labelProbArray = null;
+
+  /**
+   * Initializes an {@code ImageClassifier}.
+   *
+   * @param activity
+   */
+  ImageClassifierQuantizedMobileNet(Activity activity) throws IOException {
+    super(activity);
+    labelProbArray = new byte[1][getNumLabels()];
+  }
+
+  @Override
+  protected String getModelPath() {
+    // you can download this file from
+    // see build.gradle for where to obtain this file. It should be auto
+    // downloaded into assets.
+    return "mobilenet_v1_1.0_224_quant.tflite";
+  }
+
+  @Override
+  protected String getLabelPath() {
+    return "labels_mobilenet_quant_v1_224.txt";
+  }
+
+  @Override
+  protected int getImageSizeX() {
+    return 224;
+  }
+
+  @Override
+  protected int getImageSizeY() {
+    return 224;
+  }
+
+  @Override
+  protected int getNumBytesPerChannel() {
+    // the quantized model uses a single byte only
+    return 1;
+  }
+
+  @Override
+  protected void addPixelValue(int pixelValue) {
+    imgData.put((byte) ((pixelValue >> 16) & 0xFF));
+    imgData.put((byte) ((pixelValue >> 8) & 0xFF));
+    imgData.put((byte) (pixelValue & 0xFF));
+  }
+
+  @Override
+  protected float getProbability(int labelIndex) {
+    return labelProbArray[0][labelIndex];
+  }
+
+  @Override
+  protected void setProbability(int labelIndex, Number value) {
+    labelProbArray[0][labelIndex] = value.byteValue();
+  }
+
+  @Override
+  protected float getNormalizedProbability(int labelIndex) {
+    return (labelProbArray[0][labelIndex] & 0xff) / 255.0f;
+  }
+
+  @Override
+  protected void runInference() {
+    tflite.run(imgData, labelProbArray);
+  }
+}

tensorflow/lite/java/demo/app/src/main/res/drawable/item_selector.xml

@@ -0,0 +1,11 @@
+<?xml version="1.0" encoding="utf-8"?>
+<selector xmlns:android="http://schemas.android.com/apk/res/android">
+
+    <!-- pressed -->
+    <item android:drawable="@color/selection_highlight" android:state_pressed="true" />
+    <!-- focused -->
+    <item android:drawable="@color/selection_focus" android:state_activated="true" />
+    <!-- default -->
+    <item android:drawable="@color/item_normal" />
+
+</selector>

tensorflow/lite/java/demo/app/src/main/res/layout-land/fragment_camera2_basic.xml

@@ -0,0 +1,118 @@
+<?xml version="1.0" encoding="utf-8"?><!--
+ Copyright 2014 The Android Open Source Project
+
+ Licensed 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.
+-->
+
+<LinearLayout
+    xmlns:android="http://schemas.android.com/apk/res/android"
+    xmlns:tools="http://schemas.android.com/tools" android:layout_width="match_parent"
+    android:layout_height="match_parent"
+    android:background="#bb7700"
+    android:orientation="horizontal">
+
+    <com.example.android.tflitecamerademo.AutoFitTextureView
+      android:id="@+id/texture"
+      android:layout_width="0dp"
+      android:layout_height="match_parent"
+      android:layout_weight=".8"/>
+
+    <LinearLayout
+      android:layout_width="0dp"
+      android:layout_height="match_parent"
+      android:layout_weight=".2"
+      android:orientation="vertical">
+
+        <ImageView
+            android:id="@+id/logoview"
+            android:layout_width="wrap_content"
+            android:layout_height="47dp"
+            android:scaleType="centerInside"
+            android:src="@drawable/logo"/>
+
+        <TextView
+            android:id="@+id/text"
+            android:layout_width="match_parent"
+            android:layout_height="160dp"
+            android:paddingTop="20dp"
+            android:textColor="#FFF"
+            android:textSize="20sp"
+            android:textStyle="bold"/>
+        <LinearLayout
+            android:id="@+id/modelLayout"
+            android:layout_width="match_parent"
+            android:layout_height="150dp"
+            android:orientation="vertical">
+
+            <TextView
+                android:id="@+id/textView"
+                android:layout_width="match_parent"
+                android:layout_height="20dp"
+                android:text="@string/modelLabel"
+                android:textAlignment="center"
+                android:textColor="@android:color/white"/>
+
+            <ListView
+                android:id="@+id/model"
+                android:layout_width="match_parent"
+                android:layout_height="wrap_content">
+
+            </ListView>
+        </LinearLayout>
+        <LinearLayout
+            android:id="@+id/deviceLayout"
+            android:layout_width="match_parent"
+            android:layout_height="150dp"
+            android:orientation="vertical">
+
+            <TextView
+                android:id="@+id/textView2"
+                android:layout_width="match_parent"
+                android:layout_height="20dp"
+                android:text="@string/deviceLabel"
+                android:textAlignment="center"
+                android:textColor="@android:color/white"/>
+
+            <ListView
+                android:id="@+id/device"
+                android:layout_width="match_parent"
+                android:layout_height="wrap_content"/>
+
+        </LinearLayout>
+
+        <LinearLayout
+            android:layout_width="match_parent"
+            android:layout_height="wrap_content"
+        >
+
+            <TextView
+                android:layout_width="wrap_content"
+                android:layout_height="wrap_content"
+                android:gravity="center"
+                android:text="Threads"
+                android:textAlignment="center"
+                android:textColor="@android:color/white"/>
+
+            <NumberPicker
+                android:id="@+id/np"
+                android:layout_width="match_parent"
+                android:layout_height="wrap_content"
+                android:layout_marginLeft="10dp"
+                android:theme="@style/AppTheme.Picker"
+                android:visibility="visible"/>
+
+        </LinearLayout>
+
+    </LinearLayout>
+</LinearLayout>
+

tensorflow/lite/java/demo/app/src/main/res/layout-v26/fragment_camera2_basic.xml

@@ -0,0 +1,140 @@
+<?xml version="1.0" encoding="utf-8"?><!--
+ Copyright 2014 The Android Open Source Project
+
+ Licensed 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.
+-->
+<RelativeLayout xmlns:android="http://schemas.android.com/apk/res/android"
+    xmlns:app="http://schemas.android.com/apk/res-auto"
+    android:layout_width="match_parent"
+    android:layout_height="match_parent"
+    android:background="#bb7700">
+
+    <com.example.android.tflitecamerademo.AutoFitTextureView
+        android:id="@+id/texture"
+        android:layout_width="match_parent"
+        android:layout_height="match_parent"
+        android:layout_weight="1" />
+
+    <LinearLayout
+        android:layout_width="wrap_content"
+        android:layout_height="wrap_content"
+        android:layout_above="@+id/bottom_info_view"
+        android:layout_alignParentEnd="false"
+        android:layout_alignParentStart="true"
+        android:layout_alignParentTop="false"
+        android:background="#bb7700"
+        android:orientation="vertical"
+        android:weightSum="100">
+
+        <ImageView
+            android:id="@+id/logoview2"
+            android:layout_width="wrap_content"
+            android:layout_height="wrap_content"
+            android:layout_weight="30"
+            android:scaleType="fitStart"
+            android:src="@drawable/logo" />
+
+        <TextView
+            android:id="@+id/text"
+            android:layout_width="match_parent"
+            android:layout_height="wrap_content"
+            android:layout_alignParentBottom="true"
+            android:layout_alignParentEnd="true"
+            android:layout_alignParentRight="true"
+            android:layout_weight="30"
+            android:textColor="#FFF"
+            android:textSize="20sp"
+            android:textStyle="bold" />
+
+    </LinearLayout>
+
+    <LinearLayout
+        android:id="@+id/bottom_info_view"
+        android:layout_width="match_parent"
+        android:layout_height="200dp"
+
+        android:layout_alignParentBottom="true"
+        android:layout_marginBottom="10dp"
+        android:background="#513400"
+        android:orientation="horizontal">
+
+        <LinearLayout
+            android:layout_width="wrap_content"
+            android:layout_height="match_parent"
+            android:orientation="vertical">
+
+            <TextView
+                android:layout_width="wrap_content"
+                android:layout_height="wrap_content"
+                android:gravity="center"
+                android:text="Threads"
+                android:textAlignment="center"
+                android:textColor="@android:color/white" />
+
+            <NumberPicker
+                android:id="@+id/np"
+                android:layout_width="wrap_content"
+                android:layout_height="wrap_content"
+                android:layout_marginLeft="10dp"
+                android:theme="@style/AppTheme.Picker"
+                android:visibility="visible" />
+
+        </LinearLayout>
+
+        <LinearLayout
+            android:id="@+id/modelLayout"
+            android:layout_width="150dp"
+            android:layout_height="match_parent"
+            android:orientation="vertical">
+
+            <TextView
+                android:id="@+id/textView"
+                android:layout_width="match_parent"
+                android:layout_height="20dp"
+                android:text="@string/modelLabel"
+                android:textAlignment="center"
+                android:textColor="@android:color/white" />
+
+            <ListView
+                android:id="@+id/model"
+                android:layout_width="match_parent"
+                android:layout_height="180dp">
+
+            </ListView>
+        </LinearLayout>
+
+        <LinearLayout
+            android:id="@+id/deviceLayout"
+            android:layout_width="140dp"
+            android:layout_height="match_parent"
+            android:orientation="vertical">
+
+            <TextView
+                android:id="@+id/textView2"
+                android:layout_width="match_parent"
+                android:layout_height="20dp"
+                android:text="@string/deviceLabel"
+                android:textAlignment="center"
+                android:textColor="@android:color/white" />
+
+            <ListView
+                android:id="@+id/device"
+                android:layout_width="match_parent"
+                android:layout_height="180dp" />
+
+        </LinearLayout>
+
+    </LinearLayout>
+
+
+</RelativeLayout>

tensorflow/lite/java/demo/app/src/main/res/layout/activity_camera.xml

@@ -0,0 +1,22 @@
+<?xml version="1.0" encoding="utf-8"?><!--
+ Copyright 2014 The Android Open Source Project
+
+ Licensed 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.
+-->
+<FrameLayout xmlns:android="http://schemas.android.com/apk/res/android"
+    xmlns:tools="http://schemas.android.com/tools"
+    android:id="@+id/container"
+    android:layout_width="match_parent"
+    android:layout_height="match_parent"
+    android:background="#000"
+    tools:context="com.example.android.tflitecamerademo.CameraActivity" />

tensorflow/lite/java/demo/app/src/main/res/layout/fragment_camera2_basic.xml

@@ -0,0 +1,138 @@
+<?xml version="1.0" encoding="utf-8"?><!--
+ Copyright 2014 The Android Open Source Project
+
+ Licensed 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.
+-->
+<RelativeLayout xmlns:android="http://schemas.android.com/apk/res/android"
+    xmlns:app="http://schemas.android.com/apk/res-auto"
+    android:layout_width="match_parent"
+    android:layout_height="match_parent"
+    android:background="#bb7700">
+
+    <com.example.android.tflitecamerademo.AutoFitTextureView
+        android:id="@+id/texture"
+        android:layout_width="match_parent"
+        android:layout_height="match_parent"
+        android:layout_weight="1" />
+
+    <LinearLayout
+        android:layout_width="wrap_content"
+        android:layout_height="wrap_content"
+        android:layout_above="@+id/bottom_info_view"
+        android:layout_alignParentEnd="false"
+        android:layout_alignParentStart="true"
+        android:layout_alignParentTop="false"
+        android:background="#bb7700"
+        android:orientation="vertical"
+        android:weightSum="100">
+
+        <ImageView
+            android:id="@+id/logoview2"
+            android:layout_width="wrap_content"
+            android:layout_height="wrap_content"
+            android:layout_weight="30"
+            android:scaleType="fitStart"
+            android:src="@drawable/logo" />
+
+        <TextView
+            android:id="@+id/text"
+            android:layout_width="match_parent"
+            android:layout_height="wrap_content"
+            android:layout_alignParentBottom="true"
+            android:layout_alignParentEnd="true"
+            android:layout_alignParentRight="true"
+            android:layout_weight="30"
+            android:textColor="#FFF"
+            android:textSize="20sp"
+            android:textStyle="bold" />
+
+    </LinearLayout>
+
+    <LinearLayout
+        android:id="@+id/bottom_info_view"
+        android:layout_width="match_parent"
+        android:layout_height="200dp"
+
+        android:layout_alignParentBottom="true"
+        android:layout_marginBottom="10dp"
+        android:background="#513400"
+        android:orientation="horizontal">
+
+      <LinearLayout
+            android:layout_width="wrap_content"
+            android:layout_height="match_parent"
+            android:orientation="vertical">
+
+        <TextView
+            android:layout_width="wrap_content"
+                android:layout_height="wrap_content"
+            android:gravity="center"
+                android:text="Threads"
+            android:textAlignment="center"
+            android:textColor="@android:color/white" />
+
+        <NumberPicker
+            android:id="@+id/np"
+            android:layout_width="wrap_content"
+            android:layout_height="wrap_content"
+            android:layout_marginLeft="10dp"
+            android:theme="@style/AppTheme.Picker"
+            android:visibility="visible" />
+
+        </LinearLayout>
+
+        <LinearLayout
+            android:id="@+id/modelLayout"
+            android:layout_width="150dp"
+            android:layout_height="match_parent"
+            android:orientation="vertical">
+
+            <TextView
+                android:id="@+id/textView"
+                android:layout_width="match_parent"
+                android:layout_height="20dp"
+                android:text="@string/modelLabel"
+                android:textAlignment="center"
+                android:textColor="@android:color/white" />
+
+            <ListView
+                android:id="@+id/model"
+                android:layout_width="match_parent"
+                android:layout_height="180dp">
+
+            </ListView>
+        </LinearLayout>
+
+        <LinearLayout
+            android:id="@+id/deviceLayout"
+            android:layout_width="140dp"
+            android:layout_height="match_parent"
+            android:orientation="vertical">
+
+            <TextView
+                android:id="@+id/textView2"
+                android:layout_width="match_parent"
+                android:layout_height="20dp"
+                android:text="@string/deviceLabel"
+                android:textAlignment="center"
+                android:textColor="@android:color/white" />
+
+            <ListView
+                android:id="@+id/device"
+                android:layout_width="match_parent"
+                android:layout_height="180dp" />
+
+        </LinearLayout>
+
+    </LinearLayout>
+</RelativeLayout>

tensorflow/lite/java/demo/app/src/main/res/layout/listview_row.xml

@@ -0,0 +1,17 @@
+<?xml version="1.0" encoding="utf-8"?>
+<LinearLayout xmlns:android="http://schemas.android.com/apk/res/android"
+    android:layout_width="match_parent"
+    android:layout_height="match_parent">
+
+
+    <TextView
+        android:id="@+id/listview_row_text"
+        android:layout_width="match_parent"
+        android:layout_height="match_parent"
+        android:layout_marginRight="2dp"
+        android:background="@drawable/item_selector"
+        android:padding="10dp"
+        android:textSize="18sp"
+        android:textStyle="bold" />
+
+</LinearLayout>

tensorflow/lite/java/demo/app/src/main/res/values-sw600dp/template-dimens.xml

@@ -0,0 +1,24 @@
+<!--
+  Copyright 2013 The Android Open Source Project
+
+  Licensed 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.
+  -->
+
+<resources>
+
+    <!-- Semantic definitions -->
+
+    <dimen name="horizontal_page_margin">@dimen/margin_huge</dimen>
+    <dimen name="vertical_page_margin">@dimen/margin_medium</dimen>
+
+</resources>

tensorflow/lite/java/demo/app/src/main/res/values-sw600dp/template-styles.xml

@@ -0,0 +1,25 @@
+<!--
+  Copyright 2013 The Android Open Source Project
+
+  Licensed 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.
+  -->
+
+<resources>
+
+    <style name="Widget.SampleMessage">
+        <item name="android:textAppearance">?android:textAppearanceLarge</item>
+        <item name="android:lineSpacingMultiplier">1.2</item>
+        <item name="android:shadowDy">-6.5</item>
+    </style>
+
+</resources>

tensorflow/lite/java/demo/app/src/main/res/values-v11/template-styles.xml

@@ -0,0 +1,22 @@
+<!--
+  Copyright 2013 The Android Open Source Project
+
+  Licensed 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.
+  -->
+
+<resources>
+
+    <!-- Activity themes -->
+    <style name="Theme.Base" parent="android:Theme.Holo.Light" />
+
+</resources>

tensorflow/lite/java/demo/app/src/main/res/values-v21/base-colors.xml

@@ -0,0 +1,21 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!--
+ Copyright 2013 The Android Open Source Project
+
+ Licensed 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.
+-->
+
+<resources>
+
+
+</resources>

tensorflow/lite/java/demo/app/src/main/res/values-v21/base-template-styles.xml

@@ -0,0 +1,24 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!--
+ Copyright 2013 The Android Open Source Project
+
+ Licensed 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.
+-->
+
+<resources>
+
+    <!-- Activity themes -->
+    <style name="Theme.Base" parent="android:Theme.Material.Light">
+    </style>
+
+</resources>

tensorflow/lite/java/demo/app/src/main/res/values/base-strings.xml

@@ -0,0 +1,31 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!--
+ Copyright 2013 The Android Open Source Project
+
+ Licensed 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.
+-->
+
+<resources>
+    <string name="app_name">TfLite Camera Demo</string>
+    <string name="intro_message">
+        <![CDATA[
+
+
+            This sample demonstrates the basic use of TfLite API. Check the source code to see how
+            you can use TfLite for efficient, on-device inference with trained TensorFlow models.
+
+
+        ]]>
+    </string>
+    <string name="threads">Threads:</string>
+</resources>

tensorflow/lite/java/demo/app/src/main/res/values/colors.xml

@@ -0,0 +1,22 @@
+<?xml version="1.0" encoding="utf-8"?>
+<!--
+ Copyright 2015 The Android Open Source Project
+
+ Licensed 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.
+-->
+<resources>
+    <color name="control_background">#cc4285f4</color>
+    <color name="selection_highlight">#aaaaaa</color>
+    <color name="selection_focus">#eeaa55</color>
+    <color name="item_normal">#eeeeee</color>
+</resources>

tensorflow/lite/java/demo/app/src/main/res/values/strings.xml

@@ -0,0 +1,33 @@
+<?xml version="1.0" encoding="utf-8"?><!--
+ Copyright 2014 The Android Open Source Project
+
+ Licensed 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.
+-->
+<resources>
+    <string name="picture">Picture</string>
+    <string name="description_info">Info</string>
+    <string name="request_permission">This sample needs camera permission.</string>
+    <string name="camera_error">This device doesn\'t support Camera2 API.</string>
+    <string name="toggle_turn_on">NN:On</string>
+    <string name="toggle_turn_off">NN:Off</string>
+    <string name="toggle">Use NNAPI</string>
+    <string name="tflite">tflite</string>
+    <string name="nnapi">NNAPI</string>
+    <string name="gpu">GPU</string>
+    <string name="cpu">CPU</string>
+    <string name="modelLabel">Model</string>
+    <string name="deviceLabel">Device</string>
+    <string name="mobilenetV1Quant">mobilenet v1 quant</string>
+    <string name="mobilenetV1Float">mobilenet v1 float</string>
+
+</resources>

tensorflow/lite/java/demo/app/src/main/res/values/styles.xml

@@ -0,0 +1,23 @@
+<?xml version="1.0" encoding="utf-8"?><!--
+ Copyright 2014 The Android Open Source Project
+
+ Licensed 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.
+-->
+<resources>
+  <style name="MaterialTheme" parent="android:Theme.Material.Light.NoActionBar.Fullscreen" />
+   <style name="AppTheme.Picker" parent="android:Theme.Material.Light.NoActionBar.Fullscreen" >
+    <item name="android:textColorPrimary">@android:color/white</item>
+
+</style>
+
+</resources>

tensorflow/lite/java/demo/app/src/main/res/values/template-dimens.xml

@@ -0,0 +1,32 @@
+<!--
+  Copyright 2013 The Android Open Source Project
+
+  Licensed 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.
+  -->
+
+<resources>
+
+    <!-- Define standard dimensions to comply with Holo-style grids and rhythm. -->
+
+    <dimen name="margin_tiny">4dp</dimen>
+    <dimen name="margin_small">8dp</dimen>
+    <dimen name="margin_medium">16dp</dimen>
+    <dimen name="margin_large">32dp</dimen>
+    <dimen name="margin_huge">64dp</dimen>
+
+    <!-- Semantic definitions -->
+
+    <dimen name="horizontal_page_margin">@dimen/margin_medium</dimen>
+    <dimen name="vertical_page_margin">@dimen/margin_medium</dimen>
+
+</resources>

tensorflow/lite/java/demo/app/src/main/res/values/template-styles.xml

@@ -0,0 +1,42 @@
+<!--
+  Copyright 2013 The Android Open Source Project
+
+  Licensed 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.
+  -->
+
+<resources>
+
+    <!-- Activity themes -->
+
+    <style name="Theme.Base" parent="android:Theme.Light" />
+
+    <style name="Theme.Sample" parent="Theme.Base" />
+
+    <style name="AppTheme" parent="Theme.Sample" />
+    <!-- Widget styling -->
+
+    <style name="Widget" />
+
+    <style name="Widget.SampleMessage">
+        <item name="android:textAppearance">?android:textAppearanceMedium</item>
+        <item name="android:lineSpacingMultiplier">1.1</item>
+    </style>
+
+    <style name="Widget.SampleMessageTile">
+        <item name="android:background">@drawable/tile</item>
+        <item name="android:shadowColor">#7F000000</item>
+        <item name="android:shadowDy">-3.5</item>
+        <item name="android:shadowRadius">2</item>
+    </style>
+
+</resources>

tensorflow/lite/java/demo/build.gradle

@@ -0,0 +1,29 @@
+// Top-level build file where you can add configuration options common to all sub-projects/modules.
+
+buildscript {
+    repositories {
+        google()
+        mavenCentral()
+    }
+    dependencies {
+        classpath 'com.android.tools.build:gradle:3.1.4'
+
+        // NOTE: Do not place your application dependencies here; they belong
+        // in the individual module build.gradle files
+    }
+}
+
+allprojects {
+    repositories {
+        google()
+        mavenCentral()
+        maven {  // Only for snapshot artifacts
+            name 'ossrh-snapshot'
+            url 'https://oss.sonatype.org/content/repositories/snapshots'
+        }
+    }
+}
+
+task clean(type: Delete) {
+    delete rootProject.buildDir
+}

tensorflow/lite/java/demo/gradle.properties

@@ -0,0 +1,17 @@
+# Project-wide Gradle settings.
+
+# IDE (e.g. Android Studio) users:
+# Gradle settings configured through the IDE *will override*
+# any settings specified in this file.
+
+# For more details on how to configure your build environment visit
+# http://www.gradle.org/docs/current/userguide/build_environment.html
+
+# Specifies the JVM arguments used for the daemon process.
+# The setting is particularly useful for tweaking memory settings.
+org.gradle.jvmargs=-Xmx1536m
+
+# When configured, Gradle will run in incubating parallel mode.
+# This option should only be used with decoupled projects. More details, visit
+# http://www.gradle.org/docs/current/userguide/multi_project_builds.html#sec:decoupled_projects
+# org.gradle.parallel=true

tensorflow/lite/java/demo/gradle/wrapper/gradle-wrapper.properties

@@ -0,0 +1,6 @@
+#Thu Sep 28 09:01:41 PDT 2017
+distributionBase=GRADLE_USER_HOME
+distributionPath=wrapper/dists
+zipStoreBase=GRADLE_USER_HOME
+zipStorePath=wrapper/dists
+distributionUrl=https\://services.gradle.org/distributions/gradle-4.4-all.zip

tensorflow/lite/java/demo/gradlew

@@ -0,0 +1,160 @@
+#!/usr/bin/env bash
+
+##############################################################################
+##
+##  Gradle start up script for UN*X
+##
+##############################################################################
+
+# Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script.
+DEFAULT_JVM_OPTS=""
+
+APP_NAME="Gradle"
+APP_BASE_NAME=`basename "$0"`
+
+# Use the maximum available, or set MAX_FD != -1 to use that value.
+MAX_FD="maximum"
+
+warn ( ) {
+    echo "$*"
+}
+
+die ( ) {
+    echo
+    echo "$*"
+    echo
+    exit 1
+}
+
+# OS specific support (must be 'true' or 'false').
+cygwin=false
+msys=false
+darwin=false
+case "`uname`" in
+  CYGWIN* )
+    cygwin=true
+    ;;
+  Darwin* )
+    darwin=true
+    ;;
+  MINGW* )
+    msys=true
+    ;;
+esac
+
+# Attempt to set APP_HOME
+# Resolve links: $0 may be a link
+PRG="$0"
+# Need this for relative symlinks.
+while [ -h "$PRG" ] ; do
+    ls=`ls -ld "$PRG"`
+    link=`expr "$ls" : '.*-> \(.*\)$'`
+    if expr "$link" : '/.*' > /dev/null; then
+        PRG="$link"
+    else
+        PRG=`dirname "$PRG"`"/$link"
+    fi
+done
+SAVED="`pwd`"
+cd "`dirname \"$PRG\"`/" >/dev/null
+APP_HOME="`pwd -P`"
+cd "$SAVED" >/dev/null
+
+CLASSPATH=$APP_HOME/gradle/wrapper/gradle-wrapper.jar
+
+# Determine the Java command to use to start the JVM.
+if [ -n "$JAVA_HOME" ] ; then
+    if [ -x "$JAVA_HOME/jre/sh/java" ] ; then
+        # IBM's JDK on AIX uses strange locations for the executables
+        JAVACMD="$JAVA_HOME/jre/sh/java"
+    else
+        JAVACMD="$JAVA_HOME/bin/java"
+    fi
+    if [ ! -x "$JAVACMD" ] ; then
+        die "ERROR: JAVA_HOME is set to an invalid directory: $JAVA_HOME
+
+Please set the JAVA_HOME variable in your environment to match the
+location of your Java installation."
+    fi
+else
+    JAVACMD="java"
+    which java >/dev/null 2>&1 || die "ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH.
+
+Please set the JAVA_HOME variable in your environment to match the
+location of your Java installation."
+fi
+
+# Increase the maximum file descriptors if we can.
+if [ "$cygwin" = "false" -a "$darwin" = "false" ] ; then
+    MAX_FD_LIMIT=`ulimit -H -n`
+    if [ $? -eq 0 ] ; then
+        if [ "$MAX_FD" = "maximum" -o "$MAX_FD" = "max" ] ; then
+            MAX_FD="$MAX_FD_LIMIT"
+        fi
+        ulimit -n $MAX_FD
+        if [ $? -ne 0 ] ; then
+            warn "Could not set maximum file descriptor limit: $MAX_FD"
+        fi
+    else
+        warn "Could not query maximum file descriptor limit: $MAX_FD_LIMIT"
+    fi
+fi
+
+# For Darwin, add options to specify how the application appears in the dock
+if $darwin; then
+    GRADLE_OPTS="$GRADLE_OPTS \"-Xdock:name=$APP_NAME\" \"-Xdock:icon=$APP_HOME/media/gradle.icns\""
+fi
+
+# For Cygwin, switch paths to Windows format before running java
+if $cygwin ; then
+    APP_HOME=`cygpath --path --mixed "$APP_HOME"`
+    CLASSPATH=`cygpath --path --mixed "$CLASSPATH"`
+    JAVACMD=`cygpath --unix "$JAVACMD"`
+
+    # We build the pattern for arguments to be converted via cygpath
+    ROOTDIRSRAW=`find -L / -maxdepth 1 -mindepth 1 -type d 2>/dev/null`
+    SEP=""
+    for dir in $ROOTDIRSRAW ; do
+        ROOTDIRS="$ROOTDIRS$SEP$dir"
+        SEP="|"
+    done
+    OURCYGPATTERN="(^($ROOTDIRS))"
+    # Add a user-defined pattern to the cygpath arguments
+    if [ "$GRADLE_CYGPATTERN" != "" ] ; then
+        OURCYGPATTERN="$OURCYGPATTERN|($GRADLE_CYGPATTERN)"
+    fi
+    # Now convert the arguments - kludge to limit ourselves to /bin/sh
+    i=0
+    for arg in "$@" ; do
+        CHECK=`echo "$arg"|egrep -c "$OURCYGPATTERN" -`
+        CHECK2=`echo "$arg"|egrep -c "^-"`                                 ### Determine if an option
+
+        if [ $CHECK -ne 0 ] && [ $CHECK2 -eq 0 ] ; then                    ### Added a condition
+            eval `echo args$i`=`cygpath --path --ignore --mixed "$arg"`
+        else
+            eval `echo args$i`="\"$arg\""
+        fi
+        i=$((i+1))
+    done
+    case $i in
+        (0) set -- ;;
+        (1) set -- "$args0" ;;
+        (2) set -- "$args0" "$args1" ;;
+        (3) set -- "$args0" "$args1" "$args2" ;;
+        (4) set -- "$args0" "$args1" "$args2" "$args3" ;;
+        (5) set -- "$args0" "$args1" "$args2" "$args3" "$args4" ;;
+        (6) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" ;;
+        (7) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" ;;
+        (8) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" "$args7" ;;
+        (9) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" "$args7" "$args8" ;;
+    esac
+fi
+
+# Split up the JVM_OPTS And GRADLE_OPTS values into an array, following the shell quoting and substitution rules
+function splitJvmOpts() {
+    JVM_OPTS=("$@")
+}
+eval splitJvmOpts $DEFAULT_JVM_OPTS $JAVA_OPTS $GRADLE_OPTS
+JVM_OPTS[${#JVM_OPTS[*]}]="-Dorg.gradle.appname=$APP_BASE_NAME"
+
+exec "$JAVACMD" "${JVM_OPTS[@]}" -classpath "$CLASSPATH" org.gradle.wrapper.GradleWrapperMain "$@"

tensorflow/lite/java/demo/gradlew.bat

@@ -0,0 +1,105 @@
+:: Copyright 2026 The TensorFlow Authors. All Rights Reserved.
+::
+:: Licensed 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.
+:: ==============================================================================
+
+@if "%DEBUG%" == "" @echo off
+@rem ##########################################################################
+@rem
+@rem  Gradle startup script for Windows
+@rem
+@rem ##########################################################################
+
+@rem Set local scope for the variables with windows NT shell
+if "%OS%"=="Windows_NT" setlocal
+
+@rem Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script.
+set DEFAULT_JVM_OPTS=
+
+set DIRNAME=%~dp0
+if "%DIRNAME%" == "" set DIRNAME=.
+set APP_BASE_NAME=%~n0
+set APP_HOME=%DIRNAME%
+
+@rem Find java.exe
+if defined JAVA_HOME goto findJavaFromJavaHome
+
+set JAVA_EXE=java.exe
+%JAVA_EXE% -version >NUL 2>&1
+if "%ERRORLEVEL%" == "0" goto init
+
+echo.
+echo ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH.
+echo.
+echo Please set the JAVA_HOME variable in your environment to match the
+echo location of your Java installation.
+
+goto fail
+
+:findJavaFromJavaHome
+set JAVA_HOME=%JAVA_HOME:"=%
+set JAVA_EXE=%JAVA_HOME%/bin/java.exe
+
+if exist "%JAVA_EXE%" goto init
+
+echo.
+echo ERROR: JAVA_HOME is set to an invalid directory: %JAVA_HOME%
+echo.
+echo Please set the JAVA_HOME variable in your environment to match the
+echo location of your Java installation.
+
+goto fail
+
+:init
+@rem Get command-line arguments, handling Windowz variants
+
+if not "%OS%" == "Windows_NT" goto win9xME_args
+if "%@eval[2+2]" == "4" goto 4NT_args
+
+:win9xME_args
+@rem Slurp the command line arguments.
+set CMD_LINE_ARGS=
+set _SKIP=2
+
+:win9xME_args_slurp
+if "x%~1" == "x" goto execute
+
+set CMD_LINE_ARGS=%*
+goto execute
+
+:4NT_args
+@rem Get arguments from the 4NT Shell from JP Software
+set CMD_LINE_ARGS=%$
+
+:execute
+@rem Setup the command line
+
+set CLASSPATH=%APP_HOME%\gradle\wrapper\gradle-wrapper.jar
+
+@rem Execute Gradle
+"%JAVA_EXE%" %DEFAULT_JVM_OPTS% %JAVA_OPTS% %GRADLE_OPTS% "-Dorg.gradle.appname=%APP_BASE_NAME%" -classpath "%CLASSPATH%" org.gradle.wrapper.GradleWrapperMain %CMD_LINE_ARGS%
+
+:end
+@rem End local scope for the variables with windows NT shell
+if "%ERRORLEVEL%"=="0" goto mainEnd
+
+:fail
+rem Set variable GRADLE_EXIT_CONSOLE if you need the _script_ return code instead of
+rem the _cmd.exe /c_ return code!
+if  not "" == "%GRADLE_EXIT_CONSOLE%" exit 1
+exit /b 1
+
+:mainEnd
+if "%OS%"=="Windows_NT" endlocal
+
+:omega

tensorflow/lite/java/demo/settings.gradle

@@ -0,0 +1 @@
+include ':app'

tensorflow/lite/java/gpu_version_script.lds

@@ -0,0 +1,12 @@
+VERS_1.0 {
+  # Export JNI and native C symbols.
+  global:
+    Java_*;
+    JNI_OnLoad;
+    JNI_OnUnload;
+    TfLiteGpu*;
+
+  # Hide everything else.
+  local:
+    *;
+};

tensorflow/lite/java/jni/BUILD

@@ -0,0 +1,33 @@
+load("@rules_cc//cc:cc_library.bzl", "cc_library")
+load("//tensorflow:tensorflow.default.bzl", "get_compatible_with_portable")
+
+package(
+    # copybara:uncomment default_applicable_licenses = ["//tensorflow:LICENSE"],
+    licenses = ["notice"],
+)
+
+# We need special handling for JNI inclusion for Android. Rather than duplicating this logic
+# for every target that uses JNI, we use a single proxy target that
+# encapsulates it.
+alias(
+    name = "jni",
+    actual = select({
+        # The Android toolchain makes <jni.h> available in the system include
+        # path.
+        # Aliases need to resolve to a single target however, so alias to an
+        # empty library instead.
+        # (Making this target a cc_library with empty deps for the Android case
+        # doesn't work, because go/cpp-features#layering-check requires targets
+        # to _directly_ depend on libraries they include, and cc_library doesn't
+        # have any direct equivalent to java_library's 'export' attribute).
+        "//tensorflow:android": ":empty",
+        # For non-Android toolchains, depend on the JDK JNI headers.
+        "//conditions:default": "@bazel_tools//tools/jdk:jni",
+    }),
+    visibility = ["//visibility:public"],
+)
+
+cc_library(
+    name = "empty",
+    compatible_with = get_compatible_with_portable(),
+)

tensorflow/lite/java/ovic/BUILD

@@ -0,0 +1,132 @@
+# Description:
+# OVIC Benchmarker Java API.
+
+load("@build_bazel_rules_android//android:rules.bzl", "android_library")
+load("@rules_java//java:defs.bzl", "java_binary", "java_library", "java_test")
+load("//tensorflow/java:build_defs.bzl", "JAVACOPTS")
+
+package(
+    # copybara:uncomment default_applicable_licenses = ["//tensorflow:LICENSE"],
+    default_visibility = ["//visibility:public"],
+    licenses = ["notice"],
+)
+
+# Build targets for OVIC classification.
+java_test(
+    name = "OvicClassifierTest",
+    size = "medium",
+    srcs = ["src/test/java/org/tensorflow/ovic/OvicClassifierTest.java"],
+    data = [
+        "//tensorflow/lite/java/ovic/src/testdata:labels.txt",
+        "//tensorflow/lite/java/ovic/src/testdata:ovic_testdata",
+    ],
+    javacopts = JAVACOPTS,
+    tags = [
+        "no_oss",
+    ],
+    test_class = "org.tensorflow.ovic.OvicClassifierTest",
+    visibility = ["//visibility:public"],
+    deps = [
+        "//tensorflow/lite/java/ovic:ovicbenchmarkerlib_java",
+        "@com_google_truth",
+        "@junit",
+    ],
+)
+
+java_binary(
+    name = "ovic_validator",
+    srcs = ["src/main/java/org/tensorflow/ovic/OvicValidator.java"],
+    data = [
+        "//tensorflow/lite/java/ovic/src/testdata:labels.txt",
+    ],
+    main_class = "org.tensorflow.ovic.OvicValidator",
+    tags = ["no_oss"],
+    deps = [
+        "//tensorflow/lite/java/ovic:ovicbenchmarkerlib_java",
+        "//tensorflow/lite/java/ovic:ovicdetectionbenchmarkerlib_java",
+    ],
+)
+
+android_library(
+    name = "ovicbenchmarkerlib",
+    srcs = [
+        "src/main/java/org/tensorflow/ovic/OvicBenchmarker.java",
+        "src/main/java/org/tensorflow/ovic/OvicClassificationResult.java",
+        "src/main/java/org/tensorflow/ovic/OvicClassifier.java",
+        "src/main/java/org/tensorflow/ovic/OvicClassifierBenchmarker.java",
+    ],
+    tags = ["no_oss"],
+    deps = [
+        "//tensorflow/lite/java:tensorflowlite",
+        "//tensorflow/lite/java/src/testhelper/java/org/tensorflow/lite:testhelper",
+    ],
+)
+
+java_library(
+    name = "ovicbenchmarkerlib_java",
+    srcs = [
+        "src/main/java/org/tensorflow/ovic/OvicClassificationResult.java",
+        "src/main/java/org/tensorflow/ovic/OvicClassifier.java",
+    ],
+    javacopts = JAVACOPTS,
+    tags = ["no_oss"],
+    deps = [
+        "//tensorflow/lite/java:libtensorflowlite_jni.so",
+        "//tensorflow/lite/java:tensorflowlite_javalib",
+        "//tensorflow/lite/java/src/main/native",
+        "//tensorflow/lite/java/src/testhelper/java/org/tensorflow/lite:testhelper_javalib",
+    ],
+)
+
+# Build targets for OVIC detection.
+java_test(
+    name = "OvicDetectorTest",
+    size = "medium",
+    srcs = ["src/test/java/org/tensorflow/ovic/OvicDetectorTest.java"],
+    data = [
+        "//tensorflow/lite/java/ovic/src/testdata:coco_labels.txt",
+        "//tensorflow/lite/java/ovic/src/testdata:ovic_testdata",
+    ],
+    javacopts = JAVACOPTS,
+    tags = [
+        "no_oss",
+    ],
+    test_class = "org.tensorflow.ovic.OvicDetectorTest",
+    visibility = ["//visibility:public"],
+    deps = [
+        "//tensorflow/lite/java/ovic:ovicdetectionbenchmarkerlib_java",
+        "@com_google_truth",
+        "@junit",
+    ],
+)
+
+android_library(
+    name = "ovicdetectionbenchmarkerlib",
+    srcs = [
+        "src/main/java/org/tensorflow/ovic/BoundingBox.java",
+        "src/main/java/org/tensorflow/ovic/OvicBenchmarker.java",
+        "src/main/java/org/tensorflow/ovic/OvicDetectionResult.java",
+        "src/main/java/org/tensorflow/ovic/OvicDetector.java",
+        "src/main/java/org/tensorflow/ovic/OvicDetectorBenchmarker.java",
+    ],
+    deps = [
+        "//tensorflow/lite/java:tensorflowlite",
+        "//tensorflow/lite/java/src/testhelper/java/org/tensorflow/lite:testhelper",
+    ],
+)
+
+java_library(
+    name = "ovicdetectionbenchmarkerlib_java",
+    srcs = [
+        "src/main/java/org/tensorflow/ovic/BoundingBox.java",
+        "src/main/java/org/tensorflow/ovic/OvicDetectionResult.java",
+        "src/main/java/org/tensorflow/ovic/OvicDetector.java",
+    ],
+    javacopts = JAVACOPTS,
+    deps = [
+        "//tensorflow/lite/java:libtensorflowlite_jni.so",
+        "//tensorflow/lite/java:tensorflowlite_javalib",
+        "//tensorflow/lite/java/src/main/native",
+        "//tensorflow/lite/java/src/testhelper/java/org/tensorflow/lite:testhelper_javalib",
+    ],
+)

tensorflow/lite/java/ovic/README.md

@@ -0,0 +1,321 @@
+# OVIC Benchmarker for LPCV 2020
+
+This folder contains the SDK for track one of the
+[Low Power Computer Vision workshop at CVPR 2020.](https://lpcv.ai/2020CVPR/ovic-track)
+
+## Pre-requisite
+
+Follow the steps [here](https://www.tensorflow.org/lite/demo_android) to install
+Tensorflow, Bazel, and the Android NDK and SDK.
+
+## Test the benchmarker:
+
+The testing utilities helps the developers (you) to make sure that your
+submissions in TfLite format will be processed as expected in the competition's
+benchmarking system.
+
+Note: for now the tests only provides correctness checks, i.e. classifier
+predicts the correct category on the test image, but no on-device latency
+measurements. To test the latency measurement functionality, the tests will
+print the latency running on a desktop computer, which is not indicative of the
+on-device run-time. We are releasing an benchmarker Apk that would allow
+developers to measure latency on their own devices.
+
+### Obtain the sample models
+
+The test data (models and images) should be downloaded automatically for you by
+Bazel. In case they are not, you can manually install them as below.
+
+Note: all commands should be called from your tensorflow installation folder
+(under this folder you should find `tensorflow/lite`).
+
+*   Download the
+    [testdata package](https://storage.googleapis.com/download.tensorflow.org/data/ovic_2019_04_30.zip):
+
+```sh
+curl -L https://storage.googleapis.com/download.tensorflow.org/data/ovic_2019_04_30.zip -o /tmp/ovic.zip
+```
+
+*   Unzip the package into the testdata folder:
+
+```sh
+unzip -j /tmp/ovic.zip -d tensorflow/lite/java/ovic/src/testdata/
+```
+
+### Run tests
+
+You can run test with Bazel as below. This helps to ensure that the installation
+is correct.
+
+```sh
+bazel test //tensorflow/lite/java/ovic:OvicClassifierTest --cxxopt=-Wno-all --test_output=all
+
+bazel test //tensorflow/lite/java/ovic:OvicDetectorTest --cxxopt=-Wno-all --test_output=all
+```
+
+### Test your submissions
+
+Once you have a submission that follows the instructions from the
+[competition site](https://lpcv.ai/2020CVPR/ovic-track), you can verify it in
+two ways:
+
+#### Validate using randomly generated images
+
+You can call the validator binary below to verify that your model fits the
+format requirements. This often helps you to catch size mismatches (e.g. output
+for classification should be [1, 1001] instead of [1,1,1,1001]). Let say the
+submission file is located at `/path/to/my_model.lite`, then call:
+
+```sh
+bazel build //tensorflow/lite/java/ovic:ovic_validator --cxxopt=-Wno-all
+bazel-bin/tensorflow/lite/java/ovic/ovic_validator /path/to/my_model.lite classify
+```
+
+Successful validation should print the following message to terminal:
+
+```
+Successfully validated /path/to/my_model.lite.
+
+```
+
+To validate detection models, use the same command but provide "detect" as the
+second argument instead of "classify".
+
+#### Test that the model produces sensible outcomes
+
+You can go a step further to verify that the model produces results as expected.
+This helps you catch bugs during TFLite conversion (e.g. using the wrong mean
+and std values).
+
+*   Move your submission to the testdata folder:
+
+```sh
+cp /path/to/my_model.lite tensorflow/lite/java/ovic/src/testdata/
+```
+
+*   Resize the test image to the resolutions that are expected by your
+    submission:
+
+The test images can be found at
+`tensorflow/lite/java/ovic/src/testdata/test_image_*.jpg`. You may reuse these
+images if your image resolutions are 128x128 or 224x224.
+
+*   Add your model and test image to the BUILD rule at
+    `tensorflow/lite/java/ovic/src/testdata/BUILD`:
+
+```JSON
+filegroup(
+    name = "ovic_testdata",
+    srcs = [
+        "@tflite_ovic_testdata//:detect.lite",
+        "@tflite_ovic_testdata//:float_model.lite",
+        "@tflite_ovic_testdata//:low_res_model.lite",
+        "@tflite_ovic_testdata//:quantized_model.lite",
+        "@tflite_ovic_testdata//:test_image_128.jpg",
+        "@tflite_ovic_testdata//:test_image_224.jpg"
+        "my_model.lite",        # <--- Your submission.
+        "my_test_image.jpg",    # <--- Your test image.
+    ],
+    ...
+```
+
+*   For classification models, modify `OvicClassifierTest.java`:
+
+    *   change `TEST_IMAGE_PATH` to `my_test_image.jpg`.
+
+    *   change either `FLOAT_MODEL_PATH` or `QUANTIZED_MODEL_PATH` to
+        `my_model.lite` depending on whether your model runs inference in float
+        or
+        [8-bit](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/quantize).
+
+    *   change `TEST_IMAGE_GROUNDTRUTH` (ImageNet class ID) to be consistent
+        with your test image.
+
+*   For detection models, modify `OvicDetectorTest.java`:
+
+    *   change `TEST_IMAGE_PATH` to `my_test_image.jpg`.
+    *   change `MODEL_PATH` to `my_model.lite`.
+    *   change `GROUNDTRUTH` (COCO class ID) to be consistent with your test
+        image.
+
+Now you can run the bazel tests to catch any runtime issues with the submission.
+
+Note: Please make sure that your submission passes the test. If a submission
+fails to pass the test it will not be processed by the submission server.
+
+## Measure on-device latency
+
+We provide two ways to measure the on-device latency of your submission. The
+first is through our competition server, which is reliable and repeatable, but
+is limited to a few trials per day. The second is through the benchmarker Apk,
+which requires a device and may not be as accurate as the server, but has a fast
+turn-around and no access limitations. We recommend that the participants use
+the benchmarker apk for early development, and reserve the competition server
+for evaluating promising submissions.
+
+### Running the benchmarker app
+
+Make sure that you have followed instructions in
+[Test your submissions](#test-your-submissions) to add your model to the
+testdata folder and to the corresponding build rules.
+
+Modify `tensorflow/lite/java/ovic/demo/app/OvicBenchmarkerActivity.java`:
+
+*   Add your model to the benchmarker apk by changing `modelPath` and
+    `testImagePath` to your submission and test image.
+
+```
+  if (benchmarkClassification) {
+    ...
+    testImagePath = "my_test_image.jpg";
+    modelPath = "my_model.lite";
+  } else {  // Benchmarking detection.
+  ...
+```
+
+If you are adding a detection model, simply modify `modelPath` and
+`testImagePath` in the else block above.
+
+*   Adjust the benchmark parameters when needed:
+
+You can change the length of each experiment, and the processor affinity below.
+`BIG_CORE_MASK` is an integer whose binary encoding represents the set of used
+cores. This number is phone-specific. For example, Pixel 4 has 8 cores: the 4
+little cores are represented by the 4 less significant bits, and the 4 big cores
+by the 4 more significant bits. Therefore a mask value of 16, or in binary
+`00010000`, represents using only the first big core. The mask 32, or in binary
+`00100000` uses the second big core and should deliver identical results as the
+mask 16 because the big cores are interchangeable.
+
+```
+  /** Wall time for each benchmarking experiment. */
+  private static final double WALL_TIME = 3000;
+  /** Maximum number of iterations in each benchmarking experiment. */
+  private static final int MAX_ITERATIONS = 100;
+  /** Mask for binding to a single big core. Pixel 1 (4), Pixel 4 (16). */
+  private static final int BIG_CORE_MASK = 16;
+```
+
+Note: You'll need ROOT access to the phone to change processor affinity.
+
+*   Build and install the app.
+
+```
+bazel build -c opt --cxxopt=-Wno-all //tensorflow/lite/java/ovic/demo/app:ovic_benchmarker_binary
+adb install -r bazel-bin/tensorflow/lite/java/ovic/demo/app/ovic_benchmarker_binary.apk
+```
+
+Start the app and pick a task by clicking either the `CLF` button for
+classification or the `DET` button for detection. The button should turn bright
+green, signaling that the experiment is running. The benchmarking results will
+be displayed after about the `WALL_TIME` you specified above. For example:
+
+```
+my_model.lite: Average latency=158.6ms after 20 runs.
+```
+
+### Sample latencies
+
+Note: the benchmarking results can be quite different depending on the
+background processes running on the phone. A few things that help stabilize the
+app's readings are placing the phone on a cooling plate, restarting the phone,
+and shutting down internet access.
+
+Classification Model | Pixel 1 | Pixel 2 | Pixel 4
+-------------------- | :-----: | ------: | :-----:
+float_model.lite     | 97      | 113     | 37
+quantized_model.lite | 73      | 61      | 13
+low_res_model.lite   | 3       | 3       | 1
+
+Detection Model        | Pixel 2 | Pixel 4
+---------------------- | :-----: | :-----:
+detect.lite            | 248     | 82
+quantized_detect.lite  | 59      | 17
+quantized_fpnlite.lite | 96      | 29
+
+All latency numbers are in milliseconds. The Pixel 1 and Pixel 2 latency numbers
+are measured on `Oct 17 2019` (Github commit hash
+[I05def66f58fa8f2161522f318e00c1b520cf0606](https://github.com/tensorflow/tensorflow/commit/4b02bc0e0ff7a0bc02264bc87528253291b7c949#diff-4e94df4d2961961ba5f69bbd666e0552))
+
+The Pixel 4 latency numbers are measured on `Apr 14 2020` (Github commit hash
+[4b2cb67756009dda843c6b56a8b320c8a54373e0](https://github.com/tensorflow/tensorflow/commit/4b2cb67756009dda843c6b56a8b320c8a54373e0)).
+
+Since Pixel 4 has excellent support for 8-bit quantized models, we strongly
+recommend you to check out the
+[Post-Training Quantization tutorial](https://www.tensorflow.org/lite/performance/post_training_quantization).
+
+The detection models above are both single-shot models (i.e. no object proposal
+generation) using TfLite's *fast* version of Non-Max-Suppression (NMS). The fast
+NMS is significant faster than the regular NMS (used by the ObjectDetectionAPI
+in training) at the expense of about 1% mAP for the listed models.
+
+### Latency table
+
+We have compiled a latency table for common neural network operators such as
+convolutions, separable convolutions, and matrix multiplications. The table of
+results is available here:
+
+*   https://storage.cloud.google.com/ovic-data/
+
+The results were generated by creating a small network containing a single
+operation, and running the op under the test harness. For more details see the
+NetAdapt paper<sup>1</sup>. We plan to expand table regularly as we test with
+newer OS releases and updates to Tensorflow Lite.
+
+### Sample benchmarks
+
+Below are the baseline models (MobileNetV2, MnasNet, and MobileNetV3) used to
+compute the reference accuracy for ImageNet classification. The naming
+convention of the models are `[precision]_[model
+class]_[resolution]_[multiplier]`. Pixel 2 Latency (ms) is measured on a single
+Pixel 2 big core using the competition server on `Oct 17 2019`, while Pixel 4
+latency (ms) is measured on a single Pixel 4 big core using the competition
+server on `Apr 14 2020`. You can find these models on TFLite's
+[hosted model page](https://www.tensorflow.org/lite/guide/hosted_models#image_classification).
+
+Model                               | Pixel 2 | Pixel 4 | Top-1 Accuracy
+:---------------------------------: | :-----: | :-----: | :------------:
+quant_mobilenetv2_96_35             | 4       | 1       | 0.420
+quant_mobilenetv2_96_50             | 5       | 1       | 0.478
+quant_mobilenetv2_128_35            | 6       | 2       | 0.474
+quant_mobilenetv2_128_50            | 8       | 2       | 0.546
+quant_mobilenetv2_160_35            | 9       | 2       | 0.534
+quant_mobilenetv2_96_75             | 8       | 2       | 0.560
+quant_mobilenetv2_96_100            | 10      | 3       | 0.579
+quant_mobilenetv2_160_50            | 12      | 3       | 0.583
+quant_mobilenetv2_192_35            | 12      | 3       | 0.557
+quant_mobilenetv2_128_75            | 13      | 3       | 0.611
+quant_mobilenetv2_192_50            | 16      | 4       | 0.616
+quant_mobilenetv2_128_100           | 16      | 4       | 0.629
+quant_mobilenetv2_224_35            | 17      | 5       | 0.581
+quant_mobilenetv2_160_75            | 20      | 5       | 0.646
+float_mnasnet_96_100                | 21      | 7       | 0.625
+quant_mobilenetv2_224_50            | 22      | 6       | 0.637
+quant_mobilenetv2_160_100           | 25      | 6       | 0.674
+quant_mobilenetv2_192_75            | 29      | 7       | 0.674
+quant_mobilenetv2_192_100           | 35      | 9       | 0.695
+float_mnasnet_224_50                | 35      | 12      | 0.679
+quant_mobilenetv2_224_75            | 39      | 10      | 0.684
+float_mnasnet_160_100               | 45      | 15      | 0.706
+quant_mobilenetv2_224_100           | 48      | 12      | 0.704
+float_mnasnet_224_75                | 55      | 18      | 0.718
+float_mnasnet_192_100               | 62      | 20      | 0.724
+float_mnasnet_224_100               | 84      | 27      | 0.742
+float_mnasnet_224_130               | 126     | 40      | 0.758
+float_v3-small-minimalistic_224_100 | -       | 5       | 0.620
+quant_v3-small_224_100              | -       | 5       | 0.641
+float_v3-small_224_75               | -       | 5       | 0.656
+float_v3-small_224_100              | -       | 7       | 0.677
+quant_v3-large_224_100              | -       | 12      | 0.728
+float_v3-large_224_75               | -       | 15      | 0.735
+float_v3-large-minimalistic_224_100 | -       | 17      | 0.722
+float_v3-large_224_100              | -       | 20      | 0.753
+
+### References
+
+1.  **NetAdapt: Platform-Aware Neural Network Adaptation for Mobile
+    Applications**<br />
+    Yang, Tien-Ju, Andrew Howard, Bo Chen, Xiao Zhang, Alec Go, Mark Sandler,
+    Vivienne Sze, and Hartwig Adam. In Proceedings of the European Conference
+    on Computer Vision (ECCV), pp. 285-300. 2018<br />
+    [[link]](https://arxiv.org/abs/1804.03230) arXiv:1804.03230, 2018.

tensorflow/lite/java/ovic/Winner_OSS_Template.md

@@ -0,0 +1,121 @@
+<!--
+• This is a README.md template we encourage you to use when you release your model.
+• There are general sections we added to this template for various ML models.
+• You may need to add or remove sections depending on your needs.
+-->
+
+# Project Name
+
+## Authors
+The **1st place winner** of the **4th On-device Visual Intelligence Competition** ([OVIC](https://docs.google.com/document/d/1Rxm_N7dGRyPXjyPIdRwdhZNRye52L56FozDnfYuCi0k/edit#)) of Low-Power Computer Vision Challenge ([LPCVC](https://lpcv.ai/))
+
+* Last name, First name ([@GitHubUsername](https://github.com/username))
+* Last name, First name ([@GitHubUsername](https://github.com/username))
+* Last name, First name ([@GitHubUsername](https://github.com/username))
+
+## Description
+<!-- Provide description of the model -->
+The model submitted for the OVIC and full implementation code for training, evaluation, and inference
+
+* OVIC track: Image Classification, Object Detection
+
+## Algorithm
+<!-- Provide details of the algorithms used -->
+
+## Requirements
+<!--
+• Provide description of the model 
+• Provide brief information of the algorithms used
+-->
+
+To install requirements:
+
+```setup
+pip install -r requirements.txt
+```
+
+## Pre-trained Models
+
+| Model | Download | MD5 checksum |
+|-------|----------|--------------|
+| Model Name | Download Link (Size: KB) | MD5 checksum |
+
+The model tar file contains the followings:
+* Trained model checkpoint
+* Frozen trained model
+* TensorFlow Lite model
+
+## Results
+
+### [4th OVIC Public Ranked Leaderboard](https://lpcvc.ecn.purdue.edu/score_board_r4/?contest=round4)
+
+#### Image Classification (from the Leaderboard)
+
+| Rank | Username | Latency | Accuracy on Classified | # Classified | Accuracy/Time | Metric | Reference Accuracy |
+|------|----------|---------|------------------------|--------------|---------------|--------|--------------------|
+| 1 | Username | xx.x | 0.xxxx | 20000.0 | xxx | 0.xxxxx | 0.xxxxx |
+
+ * **Metric**: Accuracy improvement over the reference accuracy from the Pareto optimal curve
+ * **Accuracy on Classified**: The accuracy in [0, 1] computed based only on the images classified within the wall-time
+ * **\# Classified**: The number of images classified within the wall-time
+ * **Accuracy/Time**: The accuracy divided by either the total inference time or the wall-time, whichever is longer
+ * **Reference accuracy**: The reference accuracy of models from the Pareto optimal curve that have the same latency as the submission
+
+#### Object Detection
+
+| Rank | Username | Metric | Runtime | mAP over time | mAP of processed |
+|------|----------|--------|---------|---------------|------------------|
+| 1 | Username | 0.xxxxx | xxx.x | xxx | xxx |
+
+* **Metric**: COCO mAP computed on the entire minival dataset
+* **mAP over time**: COCO mAP on the minival dataset divided by latency per image
+* **mAP of processed**: COCO mAP computed only on the processed images
+
+## Dataset
+<!--
+• Provide detailed information of the dataset used
+-->
+
+## Training
+<!--
+• Provide detailed training information (preprocessing, hyperparameters, random seeds, and environment) 
+• Provide a command line example for training.
+-->
+
+Please run this command line for training.
+
+```shell
+python3 ...
+```
+
+## Evaluation
+<!--
+• Provide evaluation script with details of how to reproduce results.
+• Describe data preprocessing / postprocessing steps
+• Provide a command line example for evaluation.
+-->
+
+Please run this command line for evaluation.
+
+```shell
+python3 ...
+```
+
+## References
+<!-- Link to references -->
+
+## License
+<!--
+• Place your license text in a file named LICENSE.txt (or LICENSE.md) in the root of the repository.
+• Please also include information about your license in this README.md file.
+e.g., [Adding a license to a repository](https://help.github.com/en/github/building-a-strong-community/adding-a-license-to-a-repository)
+-->
+
+This project is licensed under the terms of the **Apache License 2.0**.
+
+## Citation
+<!--
+If you want to make your repository citable, please follow the instructions at [Making Your Code Citable](https://guides.github.com/activities/citable-code/)
+-->
+
+If you want to cite this repository in your research paper, please use the following information.

tensorflow/lite/java/ovic/demo/app/AndroidManifest.xml

@@ -0,0 +1,48 @@
+<?xml version="1.0" encoding="utf-8"?>
+<!--
+ Copyright 2018 The Android Open Source Project
+
+ Licensed 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.
+-->
+
+<manifest xmlns:android="http://schemas.android.com/apk/res/android"
+    package="ovic.demo.app"
+    android:versionCode="1"
+    android:versionName="1.0" >
+
+    <uses-sdk
+        android:minSdkVersion="19"
+        android:targetSdkVersion="21" />
+
+    <uses-permission android:name="android.permission.READ_EXTERNAL_STORAGE" />
+    <uses-permission android:name="android.permission.WRITE_EXTERNAL_STORAGE" />
+    <uses-permission android:name="android.permission.READ_PHONE_STATE" />
+
+    <application
+        android:allowBackup="true"
+        android:icon="@drawable/ic_launcher"
+        android:largeHeap="true"
+        android:label="@string/app_name">
+        <activity
+            android:name="ovic.demo.app.OvicBenchmarkerActivity"
+            android:label="@string/app_name"
+            android:screenOrientation="portrait">
+
+          <intent-filter>
+              <action android:name="android.intent.action.MAIN" />
+              <category android:name="android.intent.category.LAUNCHER" />
+          </intent-filter>
+      </activity>
+  </application>
+
+</manifest>

tensorflow/lite/java/ovic/demo/app/BUILD

@@ -0,0 +1,34 @@
+# Sample app for OVIC benchmarking.
+
+load("@build_bazel_rules_android//android:rules.bzl", "android_binary")
+
+package(
+    # copybara:uncomment default_applicable_licenses = ["//tensorflow:LICENSE"],
+    licenses = ["notice"],
+)
+
+android_binary(
+    name = "ovic_benchmarker_binary",
+    srcs = [
+        "OvicBenchmarkerActivity.java",
+    ],
+    assets = [
+        "//tensorflow/lite/java/ovic/src/testdata:coco_labels.txt",
+        "//tensorflow/lite/java/ovic/src/testdata:labels.txt",
+        "//tensorflow/lite/java/ovic/src/testdata:ovic_testdata",
+    ],
+    assets_dir = "",
+    custom_package = "ovic.demo.app",
+    manifest = "AndroidManifest.xml",
+    nocompress_extensions = [
+        ".lite",
+        ".tflite",
+    ],
+    resource_files = glob(["res/**"]),
+    tags = ["manual"],
+    deps = [
+        "//tensorflow/lite/java:tensorflowlite",
+        "//tensorflow/lite/java/ovic:ovicbenchmarkerlib",
+        "//tensorflow/lite/java/ovic:ovicdetectionbenchmarkerlib",
+    ],
+)

tensorflow/lite/java/ovic/demo/app/OvicBenchmarkerActivity.java

@@ -0,0 +1,279 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed 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 ovic.demo.app;
+
+import android.app.Activity;
+import android.content.res.AssetFileDescriptor;
+import android.content.res.AssetManager;
+import android.graphics.Bitmap;
+import android.graphics.BitmapFactory;
+import android.os.Bundle;
+import android.os.Process;
+import android.os.SystemClock;
+import android.util.Log;
+import android.view.View;
+import android.widget.TextView;
+import java.io.BufferedReader;
+import java.io.File;
+import java.io.FileInputStream;
+import java.io.FileReader;
+import java.io.IOException;
+import java.io.InputStream;
+import java.nio.MappedByteBuffer;
+import java.nio.channels.FileChannel;
+import java.text.DecimalFormat;
+import org.tensorflow.ovic.OvicBenchmarker;
+import org.tensorflow.ovic.OvicClassifierBenchmarker;
+import org.tensorflow.ovic.OvicDetectorBenchmarker;
+
+/** Class that benchmark image classifier models. */
+public class OvicBenchmarkerActivity extends Activity {
+  /** Tag for the {@link Log}. */
+  private static final String TAG = "OvicBenchmarkerActivity";
+
+  /** Name of the task-dependent data files stored in Assets. */
+  private static String labelPath = null;
+
+  private static String testImagePath = null;
+  private static String modelPath = null;
+  /**
+   * Each bottom press will launch a benchmarking experiment. The experiment stops when either the
+   * total native latency reaches WALL_TIME or the number of iterations reaches MAX_ITERATIONS,
+   * whichever comes first.
+   */
+  /** Wall time for each benchmarking experiment. */
+  private static final double WALL_TIME = 3000;
+  /** Maximum number of iterations in each benchmarking experiment. */
+  private static final int MAX_ITERATIONS = 100;
+  /** Mask for binding to a single big core. Pixel 1 (4), Pixel 2 (16). */
+  private static final int BIG_CORE_MASK = 16;
+  /** Amount of time in milliseconds to wait for affinity to set. */
+  private static final int WAIT_TIME_FOR_AFFINITY = 1000;
+
+  /* The model to be benchmarked. */
+  private MappedByteBuffer model = null;
+  private InputStream labelInputStream = null;
+  private OvicBenchmarker benchmarker;
+
+  private TextView textView = null;
+  // private Button startButton = null;
+  private static final DecimalFormat df2 = new DecimalFormat(".##");
+
+  @Override
+  protected void onCreate(Bundle savedInstanceState) {
+    super.onCreate(savedInstanceState);
+    setContentView(R.layout.activity_main);
+
+    // TextView used to display the progress, for information purposes only.
+    textView = (TextView) findViewById(R.id.textView);
+  }
+
+  private Bitmap loadTestBitmap() throws IOException {
+    InputStream imageStream = getAssets().open(testImagePath);
+    return BitmapFactory.decodeStream(imageStream);
+  }
+
+  public void initializeTest(boolean benchmarkClassification) throws IOException {
+    Log.i(TAG, "Initializing benchmarker.");
+    if (benchmarkClassification) {
+      benchmarker = new OvicClassifierBenchmarker(WALL_TIME);
+      labelPath = "labels.txt";
+      testImagePath = "test_image_224.jpg";
+      modelPath = "quantized_model.lite";
+    } else { // Benchmarking detection.
+      benchmarker = new OvicDetectorBenchmarker(WALL_TIME);
+      labelPath = "coco_labels.txt";
+      testImagePath = "test_image_224.jpg";
+      modelPath = "detect.lite";
+    }
+    AssetManager am = getAssets();
+    AssetFileDescriptor fileDescriptor = am.openFd(modelPath);
+    FileInputStream modelInputStream = new FileInputStream(fileDescriptor.getFileDescriptor());
+    FileChannel fileChannel = modelInputStream.getChannel();
+    long startOffset = fileDescriptor.getStartOffset();
+    long declaredLength = fileDescriptor.getDeclaredLength();
+    model = fileChannel.map(FileChannel.MapMode.READ_ONLY, startOffset, declaredLength);
+    labelInputStream = am.open(labelPath);
+  }
+
+  public Boolean doTestIteration() throws IOException, InterruptedException {
+    if (benchmarker == null) {
+      throw new RuntimeException("Benchmarker has not been initialized.");
+    }
+    if (benchmarker.shouldStop()) {
+      return false;
+    }
+    if (!benchmarker.readyToTest()) {
+      Log.i(TAG, "getting ready to test.");
+      benchmarker.getReadyToTest(labelInputStream, model);
+      if (!benchmarker.readyToTest()) {
+        throw new RuntimeException("Failed to get the benchmarker ready.");
+      }
+    }
+    Log.i(TAG, "Going to do test iter.");
+    // Start testing.
+    Bitmap testImageBitmap = loadTestBitmap();
+    try {
+      if (!benchmarker.processBitmap(testImageBitmap)) {
+        throw new RuntimeException("Failed to run test.");
+      }
+    } catch (Exception e) {
+      e.printStackTrace();
+      throw e;
+    } finally {
+      testImageBitmap.recycle();
+    }
+    String iterResultString = benchmarker.getLastResultString();
+    if (iterResultString == null) {
+      throw new RuntimeException("Inference failed to produce a result.");
+    }
+    Log.i(TAG, iterResultString);
+    return true;
+  }
+
+  public void detectPressed(View view) throws IOException {
+    benchmarkSession(false);
+  }
+
+  public void classifyPressed(View view) throws IOException {
+    benchmarkSession(true);
+  }
+
+  private void benchmarkSession(boolean benchmarkClassification) throws IOException {
+    try {
+      initializeTest(benchmarkClassification);
+    } catch (IOException e) {
+      Log.e(TAG, "Can't initialize benchmarker.", e);
+      throw e;
+    }
+    String displayText = "";
+    if (benchmarkClassification) {
+      displayText = "Classification benchmark: ";
+    } else {
+      displayText = "Detection benchmark: ";
+    }
+    try {
+      setProcessorAffinity(BIG_CORE_MASK);
+    } catch (IOException e) {
+      Log.e(TAG, e.getMessage());
+      displayText = e.getMessage() + "\n";
+    }
+    Log.i(TAG, "Successfully initialized benchmarker.");
+    int testIter = 0;
+    Boolean iterSuccess = false;
+    while (testIter < MAX_ITERATIONS) {
+      try {
+        iterSuccess = doTestIteration();
+      } catch (IOException e) {
+        Log.e(TAG, "Error during iteration " + testIter);
+        throw e;
+      } catch (InterruptedException e) {
+        Log.e(TAG, "Interrupted at iteration " + testIter);
+        displayText += e.getMessage() + "\n";
+      }
+      if (!iterSuccess) {
+        break;
+      }
+      testIter++;
+    }
+    Log.i(TAG, "Benchmarking finished");
+
+    if (textView != null) {
+      if (testIter > 0) {
+        textView.setText(
+            displayText
+                + modelPath
+                + ": Average latency="
+                + df2.format(benchmarker.getTotalRuntimeNano() * 1.0e-6 / testIter)
+                + "ms after "
+                + testIter
+                + " runs.");
+      } else {
+        textView.setText("Benchmarker failed to run on more than one images.");
+      }
+    }
+  }
+
+  // TODO(b/153429929) Remove with resolution of issue (see below).
+  @SuppressWarnings("RuntimeExec")
+  private static void setProcessorAffinity(int mask) throws IOException {
+    int myPid = Process.myPid();
+    Log.i(TAG, String.format("Setting processor affinity to 0x%02x", mask));
+
+    String command = String.format("taskset -a -p %x %d", mask, myPid);
+    try {
+      // TODO(b/153429929) This is deprecated, but updating is not safe while verification is hard.
+      Runtime.getRuntime().exec(command).waitFor();
+    } catch (InterruptedException e) {
+      throw new IOException("Interrupted: " + e);
+    }
+
+    // Make sure set took effect - try for a second to confirm the change took.  If not then fail.
+    long startTimeMs = SystemClock.elapsedRealtime();
+    while (true) {
+      int readBackMask = readCpusAllowedMask();
+      if (readBackMask == mask) {
+        Log.i(TAG, String.format("Successfully set affinity to 0x%02x", mask));
+        return;
+      }
+      if (SystemClock.elapsedRealtime() > startTimeMs + WAIT_TIME_FOR_AFFINITY) {
+        throw new IOException(
+            String.format(
+                "Core-binding failed: affinity set to 0x%02x but read back as 0x%02x\n"
+                    + "please root device.",
+                mask, readBackMask));
+      }
+
+      try {
+        Thread.sleep(50);
+      } catch (InterruptedException e) {
+        // Ignore sleep interrupted, will sleep again and compare is final cross-check.
+      }
+    }
+  }
+
+  public static int readCpusAllowedMask() throws IOException {
+    // Determine how many CPUs there are total
+    final String pathname = "/proc/self/status";
+    final String resultPrefix = "Cpus_allowed:";
+    File file = new File(pathname);
+    String line = "<NO LINE READ>";
+    String allowedCPU = "";
+    Integer allowedMask = null;
+    BufferedReader bufReader = null;
+    try {
+      bufReader = new BufferedReader(new FileReader(file));
+      while ((line = bufReader.readLine()) != null) {
+        if (line.startsWith(resultPrefix)) {
+          allowedMask = Integer.valueOf(line.substring(resultPrefix.length()).trim(), 16);
+          allowedCPU = bufReader.readLine();
+          break;
+        }
+      }
+    } catch (RuntimeException e) {
+      throw new IOException(
+          "Invalid number in " + pathname + " line: \"" + line + "\": " + e.getMessage());
+    } finally {
+      if (bufReader != null) {
+        bufReader.close();
+      }
+    }
+    if (allowedMask == null) {
+      throw new IOException(pathname + " missing " + resultPrefix + " line");
+    }
+    Log.i(TAG, allowedCPU);
+    return allowedMask;
+  }
+}

tensorflow/lite/java/ovic/demo/app/build.gradle

@@ -0,0 +1,49 @@
+apply plugin: 'com.android.application'
+
+android {
+    compileSdkVersion 26
+    buildToolsVersion "27.0.3"
+    defaultConfig {
+        applicationId "android.example.com.ovicbenchmarker"
+        minSdkVersion 15
+        targetSdkVersion 26
+        versionCode 1
+        versionName "1.0"
+    }
+    lintOptions {
+        abortOnError false
+    }
+    buildTypes {
+        release {
+            minifyEnabled false
+            proguardFiles getDefaultProguardFile('proguard-android.txt'), 'proguard-rules.pro'
+        }
+    }
+    aaptOptions {
+        noCompress "lite", "tflite"
+    }
+
+    compileOptions {
+        sourceCompatibility JavaVersion.VERSION_1_8
+        targetCompatibility JavaVersion.VERSION_1_8
+    }
+}
+
+repositories {
+    mavenCentral()
+    maven {  // Only for snapshot artifacts
+        name 'ossrh-snapshot'
+        url 'https://oss.sonatype.org/content/repositories/snapshots'
+    }
+}
+
+dependencies {
+    implementation fileTree(dir: 'libs', include: ['*.jar'])
+    implementation 'com.android.support:appcompat-v7:25.2.0'
+    implementation 'com.android.support.constraint:constraint-layout:1.0.2'
+    implementation 'com.android.support:design:25.2.0'
+    implementation 'com.android.support:support-annotations:25.3.1'
+    implementation 'com.android.support:support-v13:25.2.0'
+
+    implementation 'org.tensorflow:tensorflow-lite:+'
+}

tensorflow/lite/java/ovic/demo/app/res/drawable/start_button_color.xml

@@ -0,0 +1,39 @@
+<?xml version="1.0" encoding="utf-8"?>
+<!--
+ Copyright 2018 The Android Open Source Project
+
+ Licensed 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.
+-->
+<selector
+  xmlns:android="http://schemas.android.com/apk/res/android">
+  <item
+    android:state_enabled="false">
+    <shape android:shape="rectangle">
+      <solid android:color="#808080"/>
+    </shape>
+  </item>
+  <item
+    android:state_enabled="true"
+    android:state_pressed="true">
+    <shape android:shape="rectangle">
+      <solid android:color="#44ff44"/>
+    </shape>
+  </item>
+  <item
+    android:state_enabled="true"
+    android:state_pressed="false">
+    <shape android:shape="rectangle"  >
+      <solid android:color="#227f22"/>
+    </shape>
+  </item>
+</selector>

tensorflow/lite/java/ovic/demo/app/res/layout/activity_main.xml

@@ -0,0 +1,73 @@
+<?xml version="1.0" encoding="utf-8"?>
+<!--
+ Copyright 2018 The Android Open Source Project
+
+ Licensed 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.
+-->
+<RelativeLayout
+    xmlns:android="http://schemas.android.com/apk/res/android"
+    xmlns:tools="http://schemas.android.com/tools"
+    android:layout_width="match_parent"
+    android:layout_height="match_parent"
+    android:paddingBottom="@dimen/activity_vertical_margin"
+    android:paddingLeft="@dimen/activity_horizontal_margin"
+    android:paddingRight="@dimen/activity_horizontal_margin"
+    android:paddingTop="@dimen/activity_vertical_margin"
+    tools:context="ovic.demo.app.OvicBenchmarkerActivity">
+
+  <TextView
+    android:layout_width="wrap_content"
+    android:layout_height="wrap_content"
+    android:text="@string/initial_status_msg"
+    android:id="@+id/textView"
+    android:layout_above="@+id/button_clf_start"
+    android:layout_alignParentTop="true"/>
+
+  <Button
+    android:layout_width="wrap_content"
+    android:layout_height="wrap_content"
+    android:text="@string/start_clf_label"
+    android:id="@id/button_clf_start"
+    android:layout_alignParentBottom="true"
+    android:layout_alignParentLeft="true"
+    android:background="@drawable/start_button_color"
+    android:padding="10dp"
+    android:layout_marginRight="30dp"
+    android:layout_marginLeft="100dp"
+    android:layout_marginTop="10dp"
+    android:foreground="#000000"
+    android:textColor="#ffffff"
+    android:enabled="true"
+    style="?android:attr/buttonBarButtonStyle"
+    android:onClick="classifyPressed"/>
+
+  <Button
+    android:layout_width="wrap_content"
+    android:layout_height="wrap_content"
+    android:text="@string/start_det_label"
+    android:id="@+id/button_det_start"
+    android:layout_alignParentBottom="true"
+    android:layout_alignParentRight="true"
+    android:layout_toRightOf="@id/button_clf_start"
+    android:background="@drawable/start_button_color"
+    android:padding="10dp"
+    android:layout_marginRight="100dp"
+    android:layout_marginLeft="30dp"
+    android:layout_marginTop="10dp"
+    android:foreground="#000000"
+    android:textColor="#ffffff"
+    android:enabled="true"
+    style="?android:attr/buttonBarButtonStyle"
+    android:onClick="detectPressed"/>
+
+</RelativeLayout>

tensorflow/lite/java/ovic/demo/app/res/values/dimens.xml

@@ -0,0 +1,20 @@
+<?xml version="1.0" encoding="utf-8"?>
+<!--
+ Copyright 2018 The Android Open Source Project
+
+ Licensed 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.
+-->
+<resources>
+    <dimen name="activity_vertical_margin">20dp</dimen>
+    <dimen name="activity_horizontal_margin">16dp</dimen>
+</resources>

tensorflow/lite/java/ovic/demo/app/res/values/strings.xml

@@ -0,0 +1,23 @@
+<?xml version="1.0" encoding="utf-8"?>
+<!--
+ Copyright 2018 The Android Open Source Project
+
+ Licensed 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.
+-->
+<resources>
+    <string name="app_name" translatable="false">Benchmarker</string>
+
+    <string name="start_clf_label" translatable="false">Clf</string>
+    <string name="start_det_label" translatable="false">Det</string>
+    <string name="initial_status_msg" translatable="false"> Press start to run the benchmarks.</string>
+</resources>

tensorflow/lite/java/ovic/demo/build.gradle

@@ -0,0 +1,29 @@
+// Top-level build file where you can add configuration options common to all sub-projects/modules.
+
+buildscript {
+    repositories {
+        google()
+        mavenCentral()
+    }
+    dependencies {
+        classpath 'com.android.tools.build:gradle:3.1.4'
+
+        // NOTE: Do not place your application dependencies here; they belong
+        // in the individual module build.gradle files
+    }
+}
+
+allprojects {
+    repositories {
+        google()
+        mavenCentral()
+        maven {  // Only for snapshot artifacts
+            name 'ossrh-snapshot'
+            url 'https://oss.sonatype.org/content/repositories/snapshots'
+        }
+    }
+}
+
+task clean(type: Delete) {
+    delete rootProject.buildDir
+}

tensorflow/lite/java/ovic/demo/gradle.properties

@@ -0,0 +1,17 @@
+# Project-wide Gradle settings.
+
+# IDE (e.g. Android Studio) users:
+# Gradle settings configured through the IDE *will override*
+# any settings specified in this file.
+
+# For more details on how to configure your build environment visit
+# http://www.gradle.org/docs/current/userguide/build_environment.html
+
+# Specifies the JVM arguments used for the daemon process.
+# The setting is particularly useful for tweaking memory settings.
+org.gradle.jvmargs=-Xmx1536m
+
+# When configured, Gradle will run in incubating parallel mode.
+# This option should only be used with decoupled projects. More details, visit
+# http://www.gradle.org/docs/current/userguide/multi_project_builds.html#sec:decoupled_projects
+# org.gradle.parallel=true

tensorflow/lite/java/ovic/demo/gradle/wrapper/gradle-wrapper.properties

@@ -0,0 +1,6 @@
+#Thu Sep 28 09:01:41 PDT 2017
+distributionBase=GRADLE_USER_HOME
+distributionPath=wrapper/dists
+zipStoreBase=GRADLE_USER_HOME
+zipStorePath=wrapper/dists
+distributionUrl=https\://services.gradle.org/distributions/gradle-4.4-all.zip

tensorflow/lite/java/ovic/demo/gradlew

@@ -0,0 +1,160 @@
+#!/usr/bin/env bash
+
+##############################################################################
+##
+##  Gradle start up script for UN*X
+##
+##############################################################################
+
+# Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script.
+DEFAULT_JVM_OPTS=""
+
+APP_NAME="Gradle"
+APP_BASE_NAME=`basename "$0"`
+
+# Use the maximum available, or set MAX_FD != -1 to use that value.
+MAX_FD="maximum"
+
+warn ( ) {
+    echo "$*"
+}
+
+die ( ) {
+    echo
+    echo "$*"
+    echo
+    exit 1
+}
+
+# OS specific support (must be 'true' or 'false').
+cygwin=false
+msys=false
+darwin=false
+case "`uname`" in
+  CYGWIN* )
+    cygwin=true
+    ;;
+  Darwin* )
+    darwin=true
+    ;;
+  MINGW* )
+    msys=true
+    ;;
+esac
+
+# Attempt to set APP_HOME
+# Resolve links: $0 may be a link
+PRG="$0"
+# Need this for relative symlinks.
+while [ -h "$PRG" ] ; do
+    ls=`ls -ld "$PRG"`
+    link=`expr "$ls" : '.*-> \(.*\)$'`
+    if expr "$link" : '/.*' > /dev/null; then
+        PRG="$link"
+    else
+        PRG=`dirname "$PRG"`"/$link"
+    fi
+done
+SAVED="`pwd`"
+cd "`dirname \"$PRG\"`/" >/dev/null
+APP_HOME="`pwd -P`"
+cd "$SAVED" >/dev/null
+
+CLASSPATH=$APP_HOME/gradle/wrapper/gradle-wrapper.jar
+
+# Determine the Java command to use to start the JVM.
+if [ -n "$JAVA_HOME" ] ; then
+    if [ -x "$JAVA_HOME/jre/sh/java" ] ; then
+        # IBM's JDK on AIX uses strange locations for the executables
+        JAVACMD="$JAVA_HOME/jre/sh/java"
+    else
+        JAVACMD="$JAVA_HOME/bin/java"
+    fi
+    if [ ! -x "$JAVACMD" ] ; then
+        die "ERROR: JAVA_HOME is set to an invalid directory: $JAVA_HOME
+
+Please set the JAVA_HOME variable in your environment to match the
+location of your Java installation."
+    fi
+else
+    JAVACMD="java"
+    which java >/dev/null 2>&1 || die "ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH.
+
+Please set the JAVA_HOME variable in your environment to match the
+location of your Java installation."
+fi
+
+# Increase the maximum file descriptors if we can.
+if [ "$cygwin" = "false" -a "$darwin" = "false" ] ; then
+    MAX_FD_LIMIT=`ulimit -H -n`
+    if [ $? -eq 0 ] ; then
+        if [ "$MAX_FD" = "maximum" -o "$MAX_FD" = "max" ] ; then
+            MAX_FD="$MAX_FD_LIMIT"
+        fi
+        ulimit -n $MAX_FD
+        if [ $? -ne 0 ] ; then
+            warn "Could not set maximum file descriptor limit: $MAX_FD"
+        fi
+    else
+        warn "Could not query maximum file descriptor limit: $MAX_FD_LIMIT"
+    fi
+fi
+
+# For Darwin, add options to specify how the application appears in the dock
+if $darwin; then
+    GRADLE_OPTS="$GRADLE_OPTS \"-Xdock:name=$APP_NAME\" \"-Xdock:icon=$APP_HOME/media/gradle.icns\""
+fi
+
+# For Cygwin, switch paths to Windows format before running java
+if $cygwin ; then
+    APP_HOME=`cygpath --path --mixed "$APP_HOME"`
+    CLASSPATH=`cygpath --path --mixed "$CLASSPATH"`
+    JAVACMD=`cygpath --unix "$JAVACMD"`
+
+    # We build the pattern for arguments to be converted via cygpath
+    ROOTDIRSRAW=`find -L / -maxdepth 1 -mindepth 1 -type d 2>/dev/null`
+    SEP=""
+    for dir in $ROOTDIRSRAW ; do
+        ROOTDIRS="$ROOTDIRS$SEP$dir"
+        SEP="|"
+    done
+    OURCYGPATTERN="(^($ROOTDIRS))"
+    # Add a user-defined pattern to the cygpath arguments
+    if [ "$GRADLE_CYGPATTERN" != "" ] ; then
+        OURCYGPATTERN="$OURCYGPATTERN|($GRADLE_CYGPATTERN)"
+    fi
+    # Now convert the arguments - kludge to limit ourselves to /bin/sh
+    i=0
+    for arg in "$@" ; do
+        CHECK=`echo "$arg"|egrep -c "$OURCYGPATTERN" -`
+        CHECK2=`echo "$arg"|egrep -c "^-"`                                 ### Determine if an option
+
+        if [ $CHECK -ne 0 ] && [ $CHECK2 -eq 0 ] ; then                    ### Added a condition
+            eval `echo args$i`=`cygpath --path --ignore --mixed "$arg"`
+        else
+            eval `echo args$i`="\"$arg\""
+        fi
+        i=$((i+1))
+    done
+    case $i in
+        (0) set -- ;;
+        (1) set -- "$args0" ;;
+        (2) set -- "$args0" "$args1" ;;
+        (3) set -- "$args0" "$args1" "$args2" ;;
+        (4) set -- "$args0" "$args1" "$args2" "$args3" ;;
+        (5) set -- "$args0" "$args1" "$args2" "$args3" "$args4" ;;
+        (6) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" ;;
+        (7) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" ;;
+        (8) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" "$args7" ;;
+        (9) set -- "$args0" "$args1" "$args2" "$args3" "$args4" "$args5" "$args6" "$args7" "$args8" ;;
+    esac
+fi
+
+# Split up the JVM_OPTS And GRADLE_OPTS values into an array, following the shell quoting and substitution rules
+function splitJvmOpts() {
+    JVM_OPTS=("$@")
+}
+eval splitJvmOpts $DEFAULT_JVM_OPTS $JAVA_OPTS $GRADLE_OPTS
+JVM_OPTS[${#JVM_OPTS[*]}]="-Dorg.gradle.appname=$APP_BASE_NAME"
+
+exec "$JAVACMD" "${JVM_OPTS[@]}" -classpath "$CLASSPATH" org.gradle.wrapper.GradleWrapperMain "$@"

tensorflow/lite/java/ovic/demo/gradlew.bat

@@ -0,0 +1,105 @@
+:: Copyright 2026 The TensorFlow Authors. All Rights Reserved.
+::
+:: Licensed 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.
+:: ==============================================================================
+
+@if "%DEBUG%" == "" @echo off
+@rem ##########################################################################
+@rem
+@rem  Gradle startup script for Windows
+@rem
+@rem ##########################################################################
+
+@rem Set local scope for the variables with windows NT shell
+if "%OS%"=="Windows_NT" setlocal
+
+@rem Add default JVM options here. You can also use JAVA_OPTS and GRADLE_OPTS to pass JVM options to this script.
+set DEFAULT_JVM_OPTS=
+
+set DIRNAME=%~dp0
+if "%DIRNAME%" == "" set DIRNAME=.
+set APP_BASE_NAME=%~n0
+set APP_HOME=%DIRNAME%
+
+@rem Find java.exe
+if defined JAVA_HOME goto findJavaFromJavaHome
+
+set JAVA_EXE=java.exe
+%JAVA_EXE% -version >NUL 2>&1
+if "%ERRORLEVEL%" == "0" goto init
+
+echo.
+echo ERROR: JAVA_HOME is not set and no 'java' command could be found in your PATH.
+echo.
+echo Please set the JAVA_HOME variable in your environment to match the
+echo location of your Java installation.
+
+goto fail
+
+:findJavaFromJavaHome
+set JAVA_HOME=%JAVA_HOME:"=%
+set JAVA_EXE=%JAVA_HOME%/bin/java.exe
+
+if exist "%JAVA_EXE%" goto init
+
+echo.
+echo ERROR: JAVA_HOME is set to an invalid directory: %JAVA_HOME%
+echo.
+echo Please set the JAVA_HOME variable in your environment to match the
+echo location of your Java installation.
+
+goto fail
+
+:init
+@rem Get command-line arguments, handling Windowz variants
+
+if not "%OS%" == "Windows_NT" goto win9xME_args
+if "%@eval[2+2]" == "4" goto 4NT_args
+
+:win9xME_args
+@rem Slurp the command line arguments.
+set CMD_LINE_ARGS=
+set _SKIP=2
+
+:win9xME_args_slurp
+if "x%~1" == "x" goto execute
+
+set CMD_LINE_ARGS=%*
+goto execute
+
+:4NT_args
+@rem Get arguments from the 4NT Shell from JP Software
+set CMD_LINE_ARGS=%$
+
+:execute
+@rem Setup the command line
+
+set CLASSPATH=%APP_HOME%\gradle\wrapper\gradle-wrapper.jar
+
+@rem Execute Gradle
+"%JAVA_EXE%" %DEFAULT_JVM_OPTS% %JAVA_OPTS% %GRADLE_OPTS% "-Dorg.gradle.appname=%APP_BASE_NAME%" -classpath "%CLASSPATH%" org.gradle.wrapper.GradleWrapperMain %CMD_LINE_ARGS%
+
+:end
+@rem End local scope for the variables with windows NT shell
+if "%ERRORLEVEL%"=="0" goto mainEnd
+
+:fail
+rem Set variable GRADLE_EXIT_CONSOLE if you need the _script_ return code instead of
+rem the _cmd.exe /c_ return code!
+if  not "" == "%GRADLE_EXIT_CONSOLE%" exit 1
+exit /b 1
+
+:mainEnd
+if "%OS%"=="Windows_NT" endlocal
+
+:omega

tensorflow/lite/java/ovic/demo/settings.gradle

@@ -0,0 +1 @@
+include ':app'

tensorflow/lite/java/ovic/src/main/java/org/tensorflow/ovic/BoundingBox.java

@@ -0,0 +1,68 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed 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
+
+    https://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.tensorflow.ovic;
+
+/** Class for holding a detection bounding box with category and confidence. */
+public class BoundingBox {
+  // Upper left point.
+  public float x1;
+  public float y1;
+
+  // Lower right point.
+  public float x2;
+  public float y2;
+
+  // The area of the box
+  public float area;
+
+  // The object category
+  public int category;
+
+  // The confidence of the detection
+  public float score;
+
+  public BoundingBox(float x1, float y1, float x2, float y2, int category, float score) {
+    this.x1 = x1;
+    this.y1 = y1;
+    this.x2 = x2;
+    this.y2 = y2;
+    this.category = category;
+    this.score = score;
+    // -1 stands for area not initialized
+    this.area = -1;
+  }
+
+  // The intersection area of two bounding boxes
+  public float intersect(BoundingBox bbx) {
+    return Math.max(0, Math.min(x2, bbx.x2) - Math.max(x1, bbx.x1))
+        * Math.max(0, Math.min(y2, bbx.y2) - Math.max(y1, bbx.y1));
+  }
+
+  // The union area of two bounding boxes
+  public float union(BoundingBox bbx) {
+    return bbx.getArea() + this.getArea() - this.intersect(bbx);
+  }
+
+  public float getArea() {
+    if (area < 0) {
+      area = (x2 - x1) * (y2 - y1);
+    }
+    return area;
+  }
+
+  public float computeIoU(BoundingBox bbx) {
+    return (float) (this.intersect(bbx) * 1.0 / this.union(bbx));
+  }
+}

tensorflow/lite/java/ovic/src/main/java/org/tensorflow/ovic/OvicBenchmarker.java

@@ -0,0 +1,147 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed 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
+
+    https://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.tensorflow.ovic;
+
+import android.graphics.Bitmap;
+import android.os.SystemClock;
+import android.util.Log;
+import java.io.IOException;
+import java.io.InputStream;
+import java.nio.ByteBuffer;
+import java.nio.MappedByteBuffer;
+
+/**
+ * Base class that benchmarks image models.
+ *
+ * <p>===================== General workflow =======================
+ *
+ * <pre>{@code
+ * benchmarker = new OvicBenchmarker();
+ * benchmarker.getReadyToTest(labelInputStream, model);
+ * while (!benchmarker.shouldStop()) {
+ *   Bitmap bitmap = ...
+ *   imgId = ...
+ *   benchmarker.processBitmap(bitmap, imgId);
+ * }
+ * }</pre>
+ */
+public abstract class OvicBenchmarker {
+  /** Tag for the {@link Log}. */
+  private static final String TAG = "OvicBenchmarker";
+
+  /** Dimensions of inputs. */
+  protected static final int DIM_BATCH_SIZE = 1;
+
+  protected static final int DIM_PIXEL_SIZE = 3;
+  protected int imgHeight = 224;
+  protected int imgWidth = 224;
+
+  /* Preallocated buffers for storing image data in. */
+  protected int[] intValues = null;
+
+  /** A ByteBuffer to hold image data, to be feed into classifier as inputs. */
+  protected ByteBuffer imgData = null;
+
+  /** Total runtime in ns. */
+  protected double totalRuntimeNano = 0.0;
+  /** Total allowed runtime in ms. */
+  protected double wallTimeMilli = 20000 * 30.0;
+  /** Record whether benchmark has started (used to skip the first image). */
+  protected boolean benchmarkStarted = false;
+
+  /**
+   * Initializes an {@link OvicBenchmarker}
+   *
+   * @param wallTimeMilli: a double number specifying the total amount of time to benchmark.
+   */
+  protected OvicBenchmarker(double wallTimeMilli) {
+    benchmarkStarted = false;
+    totalRuntimeNano = 0.0;
+    this.wallTimeMilli = wallTimeMilli;
+  }
+
+  /** Return the cumulative latency of all runs so far. */
+  public double getTotalRuntimeNano() {
+    return totalRuntimeNano;
+  }
+
+  /** Check whether the benchmarker should stop. */
+  public Boolean shouldStop() {
+    if ((totalRuntimeNano * 1.0 / 1e6) >= wallTimeMilli) {
+      Log.e(
+          TAG,
+          "Total runtime "
+              + (totalRuntimeNano * 1.0 / 1e6)
+              + " exceeded walltime (ms) "
+              + wallTimeMilli);
+      return true;
+    }
+    return false;
+  }
+
+  /** Abstract class for checking whether the benchmarker is ready to start processing images */
+  public abstract boolean readyToTest();
+
+  /**
+   * Abstract class for getting the benchmarker ready.
+   *
+   * @param labelInputStream: an {@link InputStream} specifying where the list of labels should be
+   *     read from.
+   * @param model: a {@link MappedByteBuffer} model to benchmark.
+   */
+  public abstract void getReadyToTest(InputStream labelInputStream, MappedByteBuffer model);
+
+  /**
+   * Perform test on a single bitmap image.
+   *
+   * @param bitmap: a {@link Bitmap} image to process.
+   * @param imageId: an ID uniquely representing the image.
+   */
+  public abstract boolean processBitmap(Bitmap bitmap, int imageId)
+      throws IOException, InterruptedException;
+
+  /** Perform test on a single bitmap image without an image ID. */
+  public boolean processBitmap(Bitmap bitmap) throws IOException, InterruptedException {
+    return processBitmap(bitmap, /* imageId = */ 0);
+  }
+
+  /** Returns the last inference results as string. */
+  public abstract String getLastResultString();
+
+  /**
+   * Loads input buffer from intValues into ByteBuffer for the interpreter. Input buffer must be
+   * loaded in intValues and output will be placed in imgData.
+   */
+  protected void loadsInputToByteBuffer() {
+    if (imgData == null || intValues == null) {
+      throw new RuntimeException("Benchmarker is not yet ready to test.");
+    }
+    // Convert the image to ByteBuffer.
+    imgData.rewind();
+    int pixel = 0;
+    long startTime = SystemClock.uptimeMillis();
+
+    for (int i = 0; i < imgHeight; ++i) {
+      for (int j = 0; j < imgWidth; ++j) {
+        final int pixelValue = intValues[pixel++];
+        imgData.put((byte) ((pixelValue >> 16) & 0xFF));
+        imgData.put((byte) ((pixelValue >> 8) & 0xFF));
+        imgData.put((byte) (pixelValue & 0xFF));
+      }
+    }
+    long endTime = SystemClock.uptimeMillis();
+    Log.d(TAG, "Timecost to put values into ByteBuffer: " + Long.toString(endTime - startTime));
+  }
+}

tensorflow/lite/java/ovic/src/main/java/org/tensorflow/ovic/OvicClassificationResult.java

@@ -0,0 +1,59 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed 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
+
+    https://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.tensorflow.ovic;
+
+import java.util.ArrayList;
+
+/** Result class for inference run on a single image. */
+public class OvicClassificationResult {
+
+  /** Top K classes and probabilities. */
+  public final ArrayList<String> topKClasses;
+
+  public final ArrayList<Float> topKProbs;
+  public final ArrayList<Integer> topKIndices;
+
+  /** Latency (ms). */
+  public Long latencyMilli;
+
+  /** Latency (ns). */
+  public Long latencyNano;
+
+  OvicClassificationResult() {
+    topKClasses = new ArrayList<>();
+    topKProbs = new ArrayList<>();
+    topKIndices = new ArrayList<>();
+    latencyMilli = -1L;
+    latencyNano = -1L;
+  }
+
+  @Override
+  public String toString() {
+    String textToShow = latencyMilli + "ms";
+    textToShow += "\n" + latencyNano + "ns";
+    for (int k = 0; k < topKProbs.size(); ++k) {
+      textToShow +=
+          "\nPrediction ["
+              + k
+              + "] = Class "
+              + topKIndices.get(k)
+              + " ("
+              + topKClasses.get(k)
+              + ") : "
+              + topKProbs.get(k);
+    }
+    return textToShow;
+  }
+}

tensorflow/lite/java/ovic/src/main/java/org/tensorflow/ovic/OvicClassifier.java

@@ -0,0 +1,227 @@
+/*Copyright 2018 Google LLC
+
+Licensed 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
+
+    https://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.tensorflow.ovic;
+
+import static java.nio.charset.StandardCharsets.UTF_8;
+
+import java.io.BufferedReader;
+import java.io.IOException;
+import java.io.InputStream;
+import java.io.InputStreamReader;
+import java.nio.ByteBuffer;
+import java.nio.MappedByteBuffer;
+import java.util.AbstractMap;
+import java.util.ArrayList;
+import java.util.Collections;
+import java.util.Comparator;
+import java.util.List;
+import java.util.Map;
+import java.util.PriorityQueue;
+import org.tensorflow.lite.Interpreter;
+import org.tensorflow.lite.TestHelper;
+
+/** Class for running ImageNet classification with a TfLite model. */
+public class OvicClassifier {
+
+  /** Tag for the {@link Log}. */
+  private static final String TAG = "OvicClassifier";
+
+  /** Number of results to show (i.e. the "K" in top-K predictions). */
+  private static final int RESULTS_TO_SHOW = 5;
+
+  /** An instance of the driver class to run model inference with Tensorflow Lite. */
+  private Interpreter tflite;
+
+  /** Labels corresponding to the output of the vision model. */
+  private final List<String> labelList;
+
+  /** An array to hold inference results, to be feed into Tensorflow Lite as outputs. */
+  private byte[][] inferenceOutputArray = null;
+  /** An array to hold final prediction probabilities. */
+  private float[][] labelProbArray = null;
+
+  /** Input resultion. */
+  private int[] inputDims = null;
+  /** Whether the model runs as float or quantized. */
+  private Boolean outputIsFloat = null;
+
+  private final PriorityQueue<Map.Entry<Integer, Float>> sortedLabels =
+      new PriorityQueue<>(
+          RESULTS_TO_SHOW,
+          new Comparator<Map.Entry<Integer, Float>>() {
+            @Override
+            public int compare(Map.Entry<Integer, Float> o1, Map.Entry<Integer, Float> o2) {
+              return o1.getValue().compareTo(o2.getValue());
+            }
+          });
+
+  /** Initializes an {@code OvicClassifier}. */
+  public OvicClassifier(InputStream labelInputStream, MappedByteBuffer model) throws IOException {
+    if (model == null) {
+      throw new RuntimeException("Input model is empty.");
+    }
+    labelList = loadLabelList(labelInputStream);
+    // OVIC uses one thread for CPU inference.
+    tflite = new Interpreter(model, new Interpreter.Options().setNumThreads(1));
+    inputDims = TestHelper.getInputDims(tflite, 0);
+    if (inputDims.length != 4) {
+      throw new RuntimeException("The model's input dimensions must be 4 (BWHC).");
+    }
+    if (inputDims[0] != 1) {
+      throw new IllegalStateException(
+          "The model must have a batch size of 1, got " + inputDims[0] + " instead.");
+    }
+    if (inputDims[3] != 3) {
+      throw new IllegalStateException(
+          "The model must have three color channels, got " + inputDims[3] + " instead.");
+    }
+    int minSide = Math.min(inputDims[1], inputDims[2]);
+    int maxSide = Math.max(inputDims[1], inputDims[2]);
+    if (minSide <= 0 || maxSide > 1000) {
+      throw new RuntimeException("The model's resolution must be between (0, 1000].");
+    }
+    String outputDataType = TestHelper.getOutputDataType(tflite, 0);
+    switch (outputDataType) {
+      case "float":
+        outputIsFloat = true;
+        break;
+      case "byte":
+        outputIsFloat = false;
+        break;
+      default:
+        throw new IllegalStateException("Cannot process output type: " + outputDataType);
+    }
+    inferenceOutputArray = new byte[1][labelList.size()];
+    labelProbArray = new float[1][labelList.size()];
+  }
+
+  /** Classifies a {@link ByteBuffer} image. */
+  // @throws RuntimeException if model is uninitialized.
+  public OvicClassificationResult classifyByteBuffer(ByteBuffer imgData) {
+    if (tflite == null) {
+      throw new RuntimeException(TAG + ": ImageNet classifier has not been initialized; Failed.");
+    }
+    if (outputIsFloat == null) {
+      throw new RuntimeException(TAG + ": Classifier output type has not been resolved.");
+    }
+    if (outputIsFloat) {
+      tflite.run(imgData, labelProbArray);
+    } else {
+      tflite.run(imgData, inferenceOutputArray);
+      /** Convert results to float */
+      for (int i = 0; i < inferenceOutputArray[0].length; i++) {
+        labelProbArray[0][i] = (inferenceOutputArray[0][i] & 0xff) / 255.0f;
+      }
+    }
+    OvicClassificationResult iterResult = computeTopKLabels();
+    iterResult.latencyMilli = getLastNativeInferenceLatencyMilliseconds();
+    iterResult.latencyNano = getLastNativeInferenceLatencyNanoseconds();
+    return iterResult;
+  }
+
+  /** Return the probability array of all classes. */
+  public float[][] getlabelProbArray() {
+    return labelProbArray;
+  }
+
+  /** Return the number of top labels predicted by the classifier. */
+  public int getNumPredictions() {
+    return RESULTS_TO_SHOW;
+  }
+
+  /** Return the four dimensions of the input image. */
+  public int[] getInputDims() {
+    return inputDims;
+  }
+
+  /**
+   * Get native inference latency of last image classification run.
+   *
+   * @throws RuntimeException if model is uninitialized.
+   */
+  public Long getLastNativeInferenceLatencyMilliseconds() {
+    if (tflite == null) {
+      throw new RuntimeException(TAG + ": ImageNet classifier has not been initialized; Failed.");
+    }
+    Long latency = tflite.getLastNativeInferenceDurationNanoseconds();
+    return (latency == null) ? null : (Long) (latency / 1000000);
+  }
+
+  /**
+   * Get native inference latency of last image classification run.
+   *
+   * @throws RuntimeException if model is uninitialized.
+   */
+  public Long getLastNativeInferenceLatencyNanoseconds() {
+    if (tflite == null) {
+      throw new IllegalStateException(
+          TAG + ": ImageNet classifier has not been initialized; Failed.");
+    }
+    return tflite.getLastNativeInferenceDurationNanoseconds();
+  }
+
+  /** Closes tflite to release resources. */
+  public void close() {
+    tflite.close();
+    tflite = null;
+  }
+
+  /** Reads label list from Assets. */
+  private static List<String> loadLabelList(InputStream labelInputStream) throws IOException {
+    List<String> labelList = new ArrayList<>();
+    try (BufferedReader reader =
+        new BufferedReader(new InputStreamReader(labelInputStream, UTF_8))) {
+      String line;
+      while ((line = reader.readLine()) != null) {
+        labelList.add(line);
+      }
+    }
+    return labelList;
+  }
+
+  /** Computes top-K labels. */
+  private OvicClassificationResult computeTopKLabels() {
+    if (labelList == null) {
+      throw new RuntimeException("Label file has not been loaded.");
+    }
+    for (int i = 0; i < labelList.size(); ++i) {
+      sortedLabels.add(new AbstractMap.SimpleEntry<>(i, labelProbArray[0][i]));
+      if (sortedLabels.size() > RESULTS_TO_SHOW) {
+        sortedLabels.poll();
+      }
+    }
+    OvicClassificationResult singleImageResult = new OvicClassificationResult();
+    if (sortedLabels.size() != RESULTS_TO_SHOW) {
+      throw new RuntimeException(
+          "Number of returned labels does not match requirement: "
+              + sortedLabels.size()
+              + " returned, but "
+              + RESULTS_TO_SHOW
+              + " required.");
+    }
+    for (int i = 0; i < RESULTS_TO_SHOW; ++i) {
+      Map.Entry<Integer, Float> label = sortedLabels.poll();
+      // ImageNet model prediction indices are 0-based.
+      singleImageResult.topKIndices.add(label.getKey());
+      singleImageResult.topKClasses.add(labelList.get(label.getKey()));
+      singleImageResult.topKProbs.add(label.getValue());
+    }
+    // Labels with lowest probability are returned first, hence need to reverse them.
+    Collections.reverse(singleImageResult.topKIndices);
+    Collections.reverse(singleImageResult.topKClasses);
+    Collections.reverse(singleImageResult.topKProbs);
+    return singleImageResult;
+  }
+}

tensorflow/lite/java/ovic/src/main/java/org/tensorflow/ovic/OvicClassifierBenchmarker.java

@@ -0,0 +1,142 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed 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
+
+    https://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.tensorflow.ovic;
+
+import android.graphics.Bitmap;
+import android.util.Log;
+import java.io.IOException;
+import java.io.InputStream;
+import java.nio.ByteBuffer;
+import java.nio.ByteOrder;
+import java.nio.MappedByteBuffer;
+
+/** Class that benchmarks image classifier models. */
+public final class OvicClassifierBenchmarker extends OvicBenchmarker {
+  /** Tag for the {@link Log}. */
+  private static final String TAG = "OvicClassifierBenchmarker";
+
+  /** ImageNet preprocessing parameters. */
+  private static final float CENTRAL_FRACTION = 0.875f;
+  private OvicClassifier classifier;
+  private OvicClassificationResult iterResult = null;
+
+  public OvicClassifierBenchmarker(double wallTime) {
+    super(wallTime);
+  }
+
+  /** Test if the classifier is ready for benchmarking. */
+  @Override
+  public boolean readyToTest() {
+    return (classifier != null);
+  }
+
+  /**
+   * Getting the benchmarker ready for classifying images.
+   *
+   * @param labelInputStream: an {@link InputStream} specifying where the list of labels should be
+   *     read from.
+   * @param model: a {@link MappedByteBuffer} model to benchmark.
+   */
+  @Override
+   public void getReadyToTest(InputStream labelInputStream, MappedByteBuffer model) {
+    try {
+      Log.i(TAG, "Creating classifier.");
+      classifier = new OvicClassifier(labelInputStream, model);
+      int [] inputDims = classifier.getInputDims();
+      imgHeight = inputDims[1];
+      imgWidth = inputDims[2];
+      // Only accept QUANTIZED_UINT8 input.
+      imgData = ByteBuffer.allocateDirect(DIM_BATCH_SIZE * imgHeight * imgWidth * DIM_PIXEL_SIZE);
+      imgData.order(ByteOrder.nativeOrder());
+      intValues = new int[imgHeight * imgWidth];
+    } catch (Exception e) {
+        Log.e(TAG, e.getMessage());
+        Log.e(TAG, "Failed to initialize ImageNet classifier for the benchmarker.");
+    }
+  }
+
+  /**
+   * Perform classification on a single bitmap image.
+   *
+   * @param bitmap: a {@link Bitmap} image to process.
+   * @param imageId: an ID uniquely representing the image.
+   */
+  @Override
+  public boolean processBitmap(Bitmap bitmap, int imageId)
+      throws IOException, InterruptedException {
+    if (shouldStop() || !readyToTest()) {
+      return false;
+    }
+    try {
+      Log.i(TAG, "Converting bitmap.");
+      convertBitmapToInput(bitmap);
+      Log.i(TAG, "Classifying image: " + imageId);
+      iterResult = classifier.classifyByteBuffer(imgData);
+    } catch (RuntimeException e) {
+      Log.e(TAG, e.getMessage());
+      Log.e(TAG, "Failed to classify image.");
+    }
+    if (iterResult == null || iterResult.latencyMilli == null || iterResult.latencyNano == null) {
+      throw new RuntimeException("Classification result or timing is invalid.");
+    }
+    Log.d(TAG, "Native inference latency (ms): " + iterResult.latencyMilli);
+    Log.d(TAG, "Native inference latency (ns): " + iterResult.latencyNano);
+    Log.i(TAG, iterResult.toString());
+
+    if (!benchmarkStarted) {  // Skip the first image to discount warming-up time.
+      benchmarkStarted = true;
+    } else {
+      totalRuntimeNano += ((double) iterResult.latencyNano);
+    }
+    return true;
+  }
+
+  /** Return how many classes are predicted per image. */
+  public int getNumPredictions() {
+    return classifier.getNumPredictions();
+  }
+
+  public OvicClassificationResult getLastClassificationResult() {
+    return iterResult;
+  }
+
+  @Override
+  public String getLastResultString() {
+    if (iterResult == null) {
+      return null;
+    } else {
+      return iterResult.toString();
+    }
+  }
+
+  /**
+   * Preprocess bitmap according to ImageNet protocol then writes result into a {@link ByteBuffer}.
+   *
+   * @param bitmap: a {@link Bitmap} source image.
+   */
+  private void convertBitmapToInput(Bitmap bitmap) {
+    // Perform transformations corresponding to evaluation mode.
+    float width = (float) bitmap.getWidth();
+    float height = (float) bitmap.getHeight();
+    int stWidth = Math.round((width - width * CENTRAL_FRACTION) / 2);
+    int stHeight = Math.round((height - height * CENTRAL_FRACTION) / 2);
+    int newWidth = Math.round(width - stWidth * 2);
+    int newHeight = Math.round(height - stHeight * 2);
+    bitmap = Bitmap.createBitmap(bitmap, stWidth, stHeight, newWidth, newHeight);
+    bitmap = Bitmap.createScaledBitmap(bitmap, imgWidth, imgHeight, true);
+    bitmap.getPixels(intValues, 0, bitmap.getWidth(), 0, 0, bitmap.getWidth(), bitmap.getHeight());
+    loadsInputToByteBuffer();
+  }
+}

tensorflow/lite/java/ovic/src/main/java/org/tensorflow/ovic/OvicDetectionResult.java

@@ -0,0 +1,95 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed 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
+
+    https://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.tensorflow.ovic;
+
+import java.util.ArrayList;
+
+/** Result class for inference run on a single image. */
+public class OvicDetectionResult {
+
+  // Top K classes and probabilities.
+  public final ArrayList<BoundingBox> detections;
+  // Latency (ms).
+  public Long latencyMilli = -1L;
+  // Latency (ns).
+  public Long latencyNano = -1L;
+  // id of the image.
+  public int id = -1;
+  // Number of valid detections (separately maintained, maybe different from detections.size()).
+  public int count = 0;
+
+  // Create OvicDetectionResult object with pre-filled capacity. Note that detections.size() will
+  // be equal to capacity after this call.
+  OvicDetectionResult(int capacity) {
+    detections = new ArrayList<BoundingBox>(capacity);
+    for (int i = 0; i < capacity; i++) {
+      detections.add(new BoundingBox(-1.0f, -1.0f, -1.0f, -1.0f, -1, -1.0f));
+    }
+  }
+
+  public void resetTo(Long latencyMilli, Long latencyNano, int id) {
+    count = 0;
+    this.latencyMilli = latencyMilli;
+    this.latencyNano = latencyNano;
+    this.id = id;
+  }
+
+  public void addBox(float x1, float y1, float x2, float y2, int category, float score) {
+    detections.get(count).x1 = x1;
+    detections.get(count).y1 = y1;
+    detections.get(count).x2 = x2;
+    detections.get(count).y2 = y2;
+    detections.get(count).category = category;
+    detections.get(count).score = score;
+    count += 1;
+  }
+
+  public void scaleUp(double scaleFactorWidth, double scaleFactorHeight) {
+    for (BoundingBox box : detections) {
+      box.x1 = (float) (box.x1 * scaleFactorWidth);
+      box.y1 = (float) (box.y1 * scaleFactorHeight);
+      box.x2 = (float) (box.x2 * scaleFactorWidth);
+      box.y2 = (float) (box.y2 * scaleFactorHeight);
+    }
+  }
+
+  @Override
+  public String toString() {
+    String textToShow = latencyMilli + "ms";
+    textToShow += "\n" + latencyNano + "ns";
+    int k = 0;
+    for (BoundingBox box : detections) {
+      textToShow +=
+          "\nPrediction ["
+              + k
+              + "] = Class "
+              + box.category
+              + " ("
+              + box.x1
+              + ", "
+              + box.y1
+              + ", "
+              + box.x2
+              + ", "
+              + box.y2
+              + ") : "
+              + box.score;
+      k++;
+    }
+
+
+    return textToShow;
+  }
+}

tensorflow/lite/java/ovic/src/main/java/org/tensorflow/ovic/OvicDetector.java

@@ -0,0 +1,198 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed 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
+
+    https://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.tensorflow.ovic;
+
+import static java.nio.charset.StandardCharsets.UTF_8;
+
+import java.io.BufferedReader;
+import java.io.IOException;
+import java.io.InputStream;
+import java.io.InputStreamReader;
+import java.nio.ByteBuffer;
+import java.nio.MappedByteBuffer;
+import java.util.ArrayList;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+import org.tensorflow.lite.Interpreter;
+import org.tensorflow.lite.TestHelper;
+
+/** Class for running COCO detection with a TfLite model. */
+public class OvicDetector implements AutoCloseable {
+
+  /** Tag for the {@link Log}. */
+  private static final String TAG = "OvicDetector";
+
+  /** An instance of the driver class to run model inference with Tensorflow Lite. */
+  private Interpreter tflite;
+
+  /** Labels corresponding to the output of the vision model. */
+  private final List<String> labelList;
+
+  /** Number of detections per image. 10 for demo, 100 for the actual competition. */
+  private static final int NUM_RESULTS = 100;
+
+  /** The output arrays for the mobilenet SSD. */
+  private float[][][] outputLocations;
+  private float[][] outputClasses;
+  private float[][] outputScores;
+  private float[] numDetections;
+  private Map<Integer, Object> outputMap;
+
+  /** Input resolution. */
+  private final int[] inputDims;
+
+  /** Final result. */
+  public OvicDetectionResult result = null;
+
+  OvicDetector(InputStream labelInputStream, MappedByteBuffer model) throws IOException {
+    // Load the label list.
+    labelList = loadLabelList(labelInputStream);
+
+    // Create the TfLite interpreter.
+    tflite = new Interpreter(model, new Interpreter.Options().setNumThreads(1));
+    inputDims = TestHelper.getInputDims(tflite, 0);
+    if (TestHelper.getInputDataType(tflite, 0).equals("float")) {
+      throw new RuntimeException("The model's input must be QUANTIZED_UINT8.");
+    }
+    if (inputDims.length != 4) {
+      throw new RuntimeException("The model's input dimensions must be 4 (BWHC).");
+    }
+    if (inputDims[0] != 1) {
+      throw new RuntimeException(
+          "The model must have a batch size of 1, got " + inputDims[0] + " instead.");
+    }
+    if (inputDims[3] != 3) {
+      throw new RuntimeException(
+          "The model must have three color channels, got " + inputDims[3] + " instead.");
+    }
+    // Check the resolution.
+    int minSide = Math.min(inputDims[1], inputDims[2]);
+    int maxSide = Math.max(inputDims[1], inputDims[2]);
+    if (minSide <= 0 || maxSide > 1000) {
+      throw new RuntimeException("The model's resolution must be between (0, 1000].");
+    }
+
+    // Initialize the input array and result arrays. The input images are stored in a list of
+    // Object. Since this function anaylzed one image per time, there is only 1 item.
+    // The output is fomulated as a map of int -> Object. The output arrays are added to the map.
+    outputLocations = new float[1][NUM_RESULTS][4];
+    outputClasses = new float[1][NUM_RESULTS];
+    outputScores = new float[1][NUM_RESULTS];
+    numDetections = new float[1];
+    outputMap = new HashMap<>();
+    outputMap.put(0, outputLocations);
+    outputMap.put(1, outputClasses);
+    outputMap.put(2, outputScores);
+    outputMap.put(3, numDetections);
+    // Preallocate the result. This will be where inference result is stored after each
+    // detectByteBuffer call.
+    result = new OvicDetectionResult(NUM_RESULTS);
+  }
+
+  /** Reads label list from Assets. */
+  private static List<String> loadLabelList(InputStream labelInputStream) throws IOException {
+    List<String> labelList = new ArrayList<>();
+    try (BufferedReader reader =
+        new BufferedReader(new InputStreamReader(labelInputStream, UTF_8))) {
+      String line;
+      while ((line = reader.readLine()) != null) {
+        labelList.add(line);
+      }
+    }
+    return labelList;
+  }
+
+  /**
+   * The interface to run the detection. This method currently only support float mobilenet_ssd
+   * model. The quantized models will be added in the future.
+   *
+   * @param imgData The image buffer in ByteBuffer format.
+   * @return boolean indicator of whether detection was a success. If success, the detection results
+   *  is available in the result member variable.
+   *     See OvicDetectionResult.java for details.
+   */
+  boolean detectByteBuffer(ByteBuffer imgData, int imageId) {
+    if (tflite == null) {
+      throw new RuntimeException(TAG + ": Detector has not been initialized; Failed.");
+    }
+
+    Object[] inputArray = {imgData};
+    tflite.runForMultipleInputsOutputs(inputArray, outputMap);
+
+    Long latencyMilli = getLastNativeInferenceLatencyMilliseconds();
+    Long latencyNano = getLastNativeInferenceLatencyNanoseconds();
+
+    // Update the results.
+    result.resetTo(latencyMilli, latencyNano, imageId);
+    for (int i = 0; i < NUM_RESULTS; i++) {
+      // The model returns normalized coordinates [start_y, start_x, end_y, end_x].
+      // The boxes expect pixel coordinates [x1, y1, x2, y2].
+      // The height and width of the input are in inputDims[1] and inputDims[2].
+      // The following command converts between model outputs to bounding boxes.
+      result.addBox(
+          outputLocations[0][i][1] * inputDims[2],
+          outputLocations[0][i][0] * inputDims[1],
+          outputLocations[0][i][3] * inputDims[2],
+          outputLocations[0][i][2] * inputDims[1],
+          Math.round(outputClasses[0][i] + 1 /* Label offset */),
+          outputScores[0][i]);
+    }
+    return true;  // Marks that the result is available.
+  }
+
+  /**
+   * Get native inference latency of last image detection run.
+   *
+   * @throws RuntimeException if model is uninitialized.
+   * @return The inference latency in milliseconds.
+   */
+  public Long getLastNativeInferenceLatencyMilliseconds() {
+    if (tflite == null) {
+      throw new RuntimeException(TAG + ": ImageNet classifier has not been initialized; Failed.");
+    }
+    Long latency = tflite.getLastNativeInferenceDurationNanoseconds();
+    return (latency == null) ? null : (Long) (latency / 1000000);
+  }
+
+  /**
+   * Get native inference latency of last image detection run.
+   *
+   * @throws RuntimeException if model is uninitialized.
+   * @return The inference latency in nanoseconds.
+   */
+  public Long getLastNativeInferenceLatencyNanoseconds() {
+    if (tflite == null) {
+      throw new IllegalStateException(
+          TAG + ": ImageNet classifier has not been initialized; Failed.");
+    }
+    return tflite.getLastNativeInferenceDurationNanoseconds();
+  }
+
+  public int[] getInputDims() {
+    return inputDims;
+  }
+
+  public List<String> getLabels() {
+    return labelList;
+  }
+
+  /** Closes tflite to release resources. */
+  @Override
+  public void close() {
+    tflite.close();
+    tflite = null;
+  }
+}

tensorflow/lite/java/ovic/src/main/java/org/tensorflow/ovic/OvicDetectorBenchmarker.java

@@ -0,0 +1,154 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed 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
+
+    https://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.tensorflow.ovic;
+
+import android.graphics.Bitmap;
+import android.util.Log;
+import java.io.IOException;
+import java.io.InputStream;
+import java.nio.ByteBuffer;
+import java.nio.ByteOrder;
+import java.nio.MappedByteBuffer;
+
+/**
+ * Class that benchmarks object detection models.
+ */
+public final class OvicDetectorBenchmarker extends OvicBenchmarker {
+  /** Tag for the {@link Log}. */
+  private static final String TAG = "OvicDetectorBenchmarker";
+
+  public double scaleFactorWidth = 1.0f;
+  public double scaleFactorHeight = 1.0f;
+  private Bitmap scaledBitmap = null;  // Preallocate bitmap for scaling.
+
+  private OvicDetector detector;
+
+  /**
+   * Initializes an {@link OvicDetectionBenchmarker}
+   *
+   * @param wallTime: a double number specifying the total amount of time to benchmark.
+   */
+  public OvicDetectorBenchmarker(double wallTime) {
+    super(wallTime);
+  }
+
+  /** Check to see if the detector is ready to test. */
+  @Override
+  public boolean readyToTest() {
+    return (detector != null);
+  }
+
+  /**
+   * Getting the benchmarker ready for detecting images.
+   *
+   * @param labelInputStream: an {@link InputStream} specifying where the list of labels should be
+   *     read from.
+   * @param model: a {@link MappedByteBuffer} model to benchmark.
+   */
+  @Override
+  public void getReadyToTest(InputStream labelInputStream, MappedByteBuffer model) {
+    try {
+      Log.i(TAG, "Creating detector.");
+      detector = new OvicDetector(labelInputStream, model);
+      int[] inputDims = detector.getInputDims();
+      imgHeight = inputDims[1];
+      imgWidth = inputDims[2];
+      imgData = ByteBuffer.allocateDirect(DIM_BATCH_SIZE * imgHeight * imgWidth * DIM_PIXEL_SIZE);
+      imgData.order(ByteOrder.nativeOrder());
+      intValues = new int[imgHeight * imgWidth];
+      benchmarkStarted = false;
+    } catch (Exception e) {
+      Log.e(TAG, e.getMessage());
+      Log.e(TAG, "Failed to initialize COCO detector for the benchmarker.", e);
+    }
+  }
+
+  /**
+   * Perform detection on a single ByteBuffer {@link ByteBuffer} image. The image must have the
+   * same dimension that the model expects.
+   *
+   * @param image: a {@link ByteBuffer} image to process.
+   * @param imageId: an ID uniquely representing the image.
+   */
+  public boolean processBuffer(ByteBuffer image, int imageId) {
+    if (!readyToTest()) {
+      return false;
+    }
+    try {
+      if (!detector.detectByteBuffer(image, imageId)) {
+        return false;
+      }
+    } catch (RuntimeException e) {
+      Log.e(TAG, e.getMessage());
+      return false;
+    }
+
+    if (!benchmarkStarted) { // Skip the first image to discount warming-up time.
+      benchmarkStarted = true;
+    } else {
+      totalRuntimeNano += ((double) detector.result.latencyNano);
+    }
+    return true;  // Indicating that result is ready.
+  }
+
+  /**
+   * Perform detection on a single bitmap image.
+   *
+   * @param bitmap: a {@link Bitmap} image to process.
+   * @param imageId: an ID uniquely representing the image.
+   */
+  @Override
+  public boolean processBitmap(Bitmap bitmap, int imageId)
+      throws IOException, InterruptedException {
+    if (shouldStop() || !readyToTest()) {
+      return false;
+    }
+    convertBitmapToInput(bitmap);  // Scale bitmap if needed, store result in imgData.
+    if (!processBuffer(imgData, imageId)) {
+      return false;
+    }
+    // Scale results back to original image coordinates.
+    detector.result.scaleUp(scaleFactorWidth, scaleFactorHeight);
+    return true;  // Indicating that result is ready.
+  }
+
+  public OvicDetectionResult getLastDetectionResult() {
+    return detector.result;
+  }
+
+  @Override
+  public String getLastResultString() {
+    if (detector.result == null) {
+      return null;
+    }
+    return detector.result.toString();
+  }
+
+  /**
+   * Preprocess bitmap image into {@link ByteBuffer} format for the detector.
+   *
+   * @param bitmap: a {@link Bitmap} source image.
+   */
+  private void convertBitmapToInput(Bitmap bitmap) {
+    int originalWidth = bitmap.getWidth();
+    int originalHeight = bitmap.getHeight();
+    scaledBitmap = Bitmap.createScaledBitmap(bitmap, imgWidth, imgHeight, true);
+    scaleFactorWidth = originalWidth * 1.0 / imgWidth;
+    scaleFactorHeight = originalHeight * 1.0 / imgHeight;
+    scaledBitmap.getPixels(intValues, 0, imgWidth, 0, 0, imgWidth, imgHeight);
+    scaledBitmap.recycle();
+    loadsInputToByteBuffer();
+  }
+}

tensorflow/lite/java/ovic/src/main/java/org/tensorflow/ovic/OvicValidator.java

@@ -0,0 +1,103 @@
+/*Copyright 2018 Google LLC
+
+Licensed 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
+
+    https://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.tensorflow.ovic;
+
+import java.io.File;
+import java.io.FileInputStream;
+import java.io.IOException;
+import java.io.InputStream;
+import java.io.PrintStream;
+import java.nio.ByteBuffer;
+import java.nio.ByteOrder;
+import java.nio.MappedByteBuffer;
+import java.nio.channels.FileChannel;
+import java.util.Random;
+
+/** Validate a submission model. */
+public class OvicValidator {
+  private static void printUsage(PrintStream s) {
+    s.println("Java program that validates a submission model.");
+    s.println();
+    s.println("Usage: ovic_validator <submission file> [<task>]");
+    s.println();
+    s.println("Where:");
+    s.println("<submission file> is the model in TfLite format,");
+    s.println("<task> is the type of the task: \"classify\" (default) or \"detect\";");
+  }
+
+  public static void main(String[] args) {
+    if (args.length != 2) {
+      printUsage(System.err);
+      System.exit(1);
+    }
+    final String modelFile = args[0];
+    final String taskString = args[1];
+    final boolean isDetection = taskString.equals("detect");
+    // Label file for detection is never used, so the same label file is used for both tasks.
+    final String labelPath =
+        "tensorflow/lite/java/ovic/src/testdata/labels.txt";
+
+    try {
+      MappedByteBuffer model = loadModelFile(modelFile);
+      File labelsfile = new File(labelPath);
+      InputStream labelsInputStream = new FileInputStream(labelsfile);
+
+      if (isDetection) {
+        OvicDetector detector = new OvicDetector(labelsInputStream, model);
+        int[] inputDims = detector.getInputDims();
+        ByteBuffer imgData = createByteBuffer(inputDims[1], inputDims[2]);
+        if (!detector.detectByteBuffer(imgData, /*imageId=*/ 0)) {
+          throw new RuntimeException("Failed to return detections.");
+        }
+      } else {
+        OvicClassifier classifier = new OvicClassifier(labelsInputStream, model);
+        int[] inputDims = classifier.getInputDims();
+        ByteBuffer imgData = createByteBuffer(inputDims[1], inputDims[2]);
+        OvicClassificationResult testResult = classifier.classifyByteBuffer(imgData);
+        if (testResult.topKClasses.isEmpty()) {
+          throw new RuntimeException("Failed to return top K predictions.");
+        }
+      }
+      System.out.printf("Successfully validated %s.%n", modelFile);
+    } catch (Exception e) {
+      System.out.println(e.getMessage());
+      System.out.printf("Failed to validate %s.%n", modelFile);
+    }
+  }
+
+  private static ByteBuffer createByteBuffer(int imgWidth, int imgHeight) {
+    ByteBuffer imgData = ByteBuffer.allocateDirect(imgHeight * imgWidth * 3);
+    imgData.order(ByteOrder.nativeOrder());
+    Random rand = new Random();
+    for (int y = 0; y < imgHeight; y++) {
+      for (int x = 0; x < imgWidth; x++) {
+        int val = rand.nextInt();
+        imgData.put((byte) ((val >> 16) & 0xFF));
+        imgData.put((byte) ((val >> 8) & 0xFF));
+        imgData.put((byte) (val & 0xFF));
+      }
+    }
+    return imgData;
+  }
+
+  private static MappedByteBuffer loadModelFile(String modelFilePath) throws IOException {
+    File modelfile = new File(modelFilePath);
+    FileInputStream inputStream = new FileInputStream(modelfile);
+    FileChannel fileChannel = inputStream.getChannel();
+    long startOffset = 0L;
+    long declaredLength = fileChannel.size();
+    return fileChannel.map(FileChannel.MapMode.READ_ONLY, startOffset, declaredLength);
+  }
+}

tensorflow/lite/java/ovic/src/test/java/org/tensorflow/ovic/OvicClassifierTest.java

@@ -0,0 +1,174 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed 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
+
+    https://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.tensorflow.ovic;
+
+import static com.google.common.truth.Truth.assertThat;
+import static org.junit.Assert.fail;
+
+import java.awt.image.BufferedImage;
+import java.io.File;
+import java.io.FileInputStream;
+import java.io.IOException;
+import java.io.InputStream;
+import java.nio.ByteBuffer;
+import java.nio.ByteOrder;
+import java.nio.MappedByteBuffer;
+import java.nio.channels.FileChannel;
+import javax.imageio.ImageIO;
+import org.junit.Before;
+import org.junit.Test;
+import org.junit.runner.RunWith;
+import org.junit.runners.JUnit4;
+
+/** Unit tests for {@link org.tensorflow.ovic.OvicClassifier}. */
+@RunWith(JUnit4.class)
+public final class OvicClassifierTest {
+
+  private OvicClassifier classifier;
+  private InputStream labelsInputStream = null;
+  private MappedByteBuffer quantizedModel = null;
+  private MappedByteBuffer floatModel = null;
+  private MappedByteBuffer lowResModel = null;
+  private ByteBuffer testImage = null;
+  private ByteBuffer lowResTestImage = null;
+  private OvicClassificationResult testResult = null;
+  private static final String LABELS_PATH =
+      "tensorflow/lite/java/ovic/src/testdata/labels.txt";
+  private static final String QUANTIZED_MODEL_PATH =
+      "external/tflite_ovic_testdata/quantized_model.lite";
+  private static final String LOW_RES_MODEL_PATH =
+      "external/tflite_ovic_testdata/low_res_model.lite";
+  private static final String FLOAT_MODEL_PATH =
+      "external/tflite_ovic_testdata/float_model.lite";
+  private static final String TEST_IMAGE_PATH =
+      "external/tflite_ovic_testdata/test_image_224.jpg";
+  private static final String TEST_LOW_RES_IMAGE_PATH =
+      "external/tflite_ovic_testdata/test_image_128.jpg";
+  private static final int TEST_IMAGE_GROUNDTRUTH = 653; // "military uniform"
+
+  @Before
+  public void setUp() {
+    try {
+      File labelsfile = new File(LABELS_PATH);
+      labelsInputStream = new FileInputStream(labelsfile);
+      quantizedModel = loadModelFile(QUANTIZED_MODEL_PATH);
+      floatModel = loadModelFile(FLOAT_MODEL_PATH);
+      lowResModel = loadModelFile(LOW_RES_MODEL_PATH);
+      File imageFile = new File(TEST_IMAGE_PATH);
+      BufferedImage img = ImageIO.read(imageFile);
+      testImage = toByteBuffer(img);
+      // Low res image and models.
+      imageFile = new File(TEST_LOW_RES_IMAGE_PATH);
+      img = ImageIO.read(imageFile);
+      lowResTestImage = toByteBuffer(img);
+    } catch (IOException e) {
+      System.out.print(e.getMessage());
+    }
+    System.out.println("Successful setup");
+  }
+
+  @Test
+  public void ovicClassifier_quantizedModelCreateSuccess() throws Exception {
+    classifier = new OvicClassifier(labelsInputStream, quantizedModel);
+    assertThat(classifier).isNotNull();
+  }
+
+  @Test
+  public void ovicClassifier_floatModelCreateSuccess() throws Exception {
+    classifier = new OvicClassifier(labelsInputStream, floatModel);
+    assertThat(classifier).isNotNull();
+  }
+
+  @Test
+  public void ovicClassifier_quantizedModelClassifySuccess() throws Exception {
+    classifier = new OvicClassifier(labelsInputStream, quantizedModel);
+    testResult = classifier.classifyByteBuffer(testImage);
+    assertCorrectTopK(testResult);
+  }
+
+  @Test
+  public void ovicClassifier_floatModelClassifySuccess() throws Exception {
+    classifier = new OvicClassifier(labelsInputStream, floatModel);
+    testResult = classifier.classifyByteBuffer(testImage);
+    assertCorrectTopK(testResult);
+  }
+
+  @Test
+  public void ovicClassifier_lowResModelClassifySuccess() throws Exception {
+    classifier = new OvicClassifier(labelsInputStream, lowResModel);
+    testResult = classifier.classifyByteBuffer(lowResTestImage);
+    assertCorrectTopK(testResult);
+  }
+
+  @Test
+  public void ovicClassifier_latencyNotNull() throws Exception {
+    classifier = new OvicClassifier(labelsInputStream, floatModel);
+    testResult = classifier.classifyByteBuffer(testImage);
+    assertThat(testResult.latencyNano).isNotNull();
+  }
+
+  @Test
+  public void ovicClassifier_mismatchedInputResolutionFails() throws Exception {
+    classifier = new OvicClassifier(labelsInputStream, lowResModel);
+    int[] inputDims = classifier.getInputDims();
+    assertThat(inputDims[1]).isEqualTo(128);
+    assertThat(inputDims[2]).isEqualTo(128);
+    try {
+      testResult = classifier.classifyByteBuffer(testImage);
+      fail();
+    } catch (IllegalArgumentException e) {
+      // Success.
+    }
+  }
+
+  private static ByteBuffer toByteBuffer(BufferedImage image) {
+    ByteBuffer imgData = ByteBuffer.allocateDirect(
+        image.getHeight() * image.getWidth() * 3);
+    imgData.order(ByteOrder.nativeOrder());
+    for (int y = 0; y < image.getHeight(); y++) {
+      for (int x = 0; x < image.getWidth(); x++) {
+        int val = image.getRGB(x, y);
+        imgData.put((byte) ((val >> 16) & 0xFF));
+        imgData.put((byte) ((val >> 8) & 0xFF));
+        imgData.put((byte) (val & 0xFF));
+      }
+    }
+    return imgData;
+  }
+
+  private static void assertCorrectTopK(OvicClassificationResult testResult) {
+    assertThat(testResult.topKClasses.size()).isGreaterThan(0);
+    Boolean topKAccurate = false;
+    // Assert that the correct class is in the top K.
+    for (int i = 0; i < testResult.topKIndices.size(); i++) {
+      if (testResult.topKIndices.get(i) == TEST_IMAGE_GROUNDTRUTH) {
+        topKAccurate = true;
+        break;
+      }
+    }
+    System.out.println(testResult.toString());
+    System.out.flush();
+    assertThat(topKAccurate).isTrue();
+  }
+
+  private static MappedByteBuffer loadModelFile(String modelFilePath) throws IOException {
+    File modelfile = new File(modelFilePath);
+    FileInputStream inputStream = new FileInputStream(modelfile);
+    FileChannel fileChannel = inputStream.getChannel();
+    long startOffset = 0L;
+    long declaredLength = fileChannel.size();
+    return fileChannel.map(FileChannel.MapMode.READ_ONLY, startOffset, declaredLength);
+  }
+}

tensorflow/lite/java/ovic/src/test/java/org/tensorflow/ovic/OvicDetectorTest.java

@@ -0,0 +1,131 @@
+/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+
+Licensed 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
+
+    https://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.tensorflow.ovic;
+
+import static com.google.common.truth.Truth.assertThat;
+
+import java.awt.Graphics2D;
+import java.awt.image.BufferedImage;
+import java.io.File;
+import java.io.FileInputStream;
+import java.io.IOException;
+import java.io.InputStream;
+import java.nio.ByteBuffer;
+import java.nio.ByteOrder;
+import java.nio.MappedByteBuffer;
+import java.nio.channels.FileChannel;
+import javax.imageio.ImageIO;
+import org.junit.Before;
+import org.junit.Test;
+import org.junit.runner.RunWith;
+import org.junit.runners.JUnit4;
+
+/** Unit test for {@link org.tensorflow.ovic.OvicDetector}. */
+@RunWith(JUnit4.class)
+public final class OvicDetectorTest {
+  private OvicDetector detector = null;
+  private InputStream labelsInputStream = null;
+  private MappedByteBuffer model = null;
+  private ByteBuffer testImage = null;
+
+  private static final String LABELS_PATH =
+      "tensorflow/lite/java/ovic/src/testdata/coco_labels.txt";
+  private static final String MODEL_PATH =
+      "external/tflite_ovic_testdata/quantized_detect.lite";
+  private static final String TEST_IMAGE_PATH =
+      "external/tflite_ovic_testdata/test_image_224.jpg";
+  private static final int GROUNDTRUTH = 1 /* Person */;
+
+  @Before
+  public void setUp() {
+    try {
+      // load models.
+      model = loadModelFile(MODEL_PATH);
+
+      // Load label files;
+      File labelsfile = new File(LABELS_PATH);
+      labelsInputStream = new FileInputStream(labelsfile);
+
+      // Create detector.
+      detector = new OvicDetector(labelsInputStream, model);
+
+      // Load test image and convert into byte buffer.
+      File imageFile = new File(TEST_IMAGE_PATH);
+      BufferedImage rawimg = ImageIO.read(imageFile);
+      int[] inputDims = detector.getInputDims();
+      BufferedImage img = new BufferedImage(inputDims[1], inputDims[2], rawimg.getType());
+      Graphics2D g = img.createGraphics();
+      g.drawImage(rawimg, 0, 0, inputDims[1], inputDims[2], null);
+      g.dispose();
+      testImage = toByteBuffer(img);
+    } catch (IOException e) {
+      System.out.println(e.getMessage());
+    }
+
+    System.out.println("Successfully setup");
+  }
+
+  private static MappedByteBuffer loadModelFile(String modelFilePath) throws IOException {
+    File modelfile = new File(modelFilePath);
+    FileInputStream inputStream = new FileInputStream(modelfile);
+    FileChannel fileChannel = inputStream.getChannel();
+    long startOffset = 0L;
+    long declaredLength = fileChannel.size();
+    return fileChannel.map(FileChannel.MapMode.READ_ONLY, startOffset, declaredLength);
+  }
+
+  private static ByteBuffer toByteBuffer(BufferedImage image) {
+    ByteBuffer imgData = ByteBuffer.allocateDirect(image.getHeight() * image.getWidth() * 3);
+    imgData.order(ByteOrder.nativeOrder());
+    for (int y = 0; y < image.getHeight(); y++) {
+      for (int x = 0; x < image.getWidth(); x++) {
+        int pixelValue = image.getRGB(x, y);
+        imgData.put((byte) ((pixelValue >> 16) & 0xFF));
+        imgData.put((byte) ((pixelValue >> 8) & 0xFF));
+        imgData.put((byte) (pixelValue & 0xFF));
+      }
+    }
+    return imgData;
+  }
+
+  @Test
+  public void ovicDetector_detectSuccess() throws Exception {
+    assertThat(detector.detectByteBuffer(testImage, 1)).isTrue();
+    assertThat(detector.result).isNotNull();
+  }
+
+  @Test
+  public void ovicDetector_simpleBatchTest() throws Exception {
+    final int numRepeats = 5;
+    for (int i = 0; i < numRepeats; i++) {
+      assertThat(detector.detectByteBuffer(testImage, 1)).isTrue();
+      OvicDetectionResult result = detector.result;
+      Boolean detectWithinTop5 = false;
+      for (int j = 0; j < Math.min(5, result.count); j++) {
+        if (result.detections.get(j).category == GROUNDTRUTH) {
+          detectWithinTop5 = true;
+          break;
+        }
+      }
+      if (!detectWithinTop5) {
+        System.out.println("---------------- Image " + i + " ---------------------");
+        System.out.println("Expect category " + GROUNDTRUTH);
+        System.out.println("Detection results: ");
+        System.out.println(result.toString());
+      }
+      assertThat(detectWithinTop5).isTrue();
+    }
+  }
+}

tensorflow/lite/java/ovic/src/testdata/BUILD

@@ -0,0 +1,28 @@
+# Testdata for OVIC benchmarker demo App and tests.
+
+package(
+    # copybara:uncomment default_applicable_licenses = ["//tensorflow:LICENSE"],
+    licenses = ["notice"],
+)
+
+filegroup(
+    name = "ovic_testdata",
+    srcs = [
+        "@tflite_ovic_testdata//:detect.lite",
+        "@tflite_ovic_testdata//:float_model.lite",
+        "@tflite_ovic_testdata//:low_res_model.lite",
+        "@tflite_ovic_testdata//:quantized_detect.lite",
+        "@tflite_ovic_testdata//:quantized_model.lite",
+        "@tflite_ovic_testdata//:test_image_128.jpg",
+        "@tflite_ovic_testdata//:test_image_224.jpg",
+    ],
+    visibility = ["//visibility:public"],
+)
+
+exports_files(
+    [
+        "labels.txt",
+        "coco_labels.txt",
+    ],
+    visibility = ["//visibility:public"],
+)

tensorflow/lite/java/ovic/src/testdata/coco_labels.txt

@@ -0,0 +1,91 @@
+person
+bicycle
+car
+motorcycle
+airplane
+bus
+train
+truck
+boat
+traffic light
+fire hydrant
+empty
+stop sign
+parking meter
+bench
+bird
+cat
+dog
+horse
+sheep
+cow
+elephant
+bear
+zebra
+giraffe
+empty
+backpack
+umbrella
+empty
+empty
+handbag
+tie
+suitcase
+frisbee
+skis
+snowboard
+sports ball
+kite
+baseball bat
+baseball glove
+skateboard
+surfboard
+tennis racket
+bottle
+empty
+wine glasses
+cup
+fork
+knife
+spoon
+bowl
+banana
+apple
+sandwich
+orange
+broccoli
+carrot
+hot dog
+pizza
+donut
+cake
+chair
+couch
+potted plant
+bed
+empty
+dining table
+empty
+empty
+toilet
+empty
+tv
+laptop
+mouse
+remote
+keyboard
+cell phone
+microwave
+oven
+toaster
+sink
+refrigerator
+empty
+book
+clock
+vase
+scissors
+teddy bear
+hair drier
+toothbrush
+empty

tensorflow/lite/java/proguard.flags

@@ -0,0 +1,5 @@
+-keep class org.tensorflow.lite.annotations.UsedByReflection
+-keep @org.tensorflow.lite.annotations.UsedByReflection class *
+-keepclassmembers class * {
+    @org.tensorflow.lite.annotations.UsedByReflection *;
+}

tensorflow/lite/java/src/main/java/org/tensorflow/lite/DataType.java

@@ -0,0 +1,77 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed 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.tensorflow.lite;
+
+/** Represents the type of elements in a TensorFlow Lite {@link Tensor} as an enum. */
+public enum DataType {
+  /** 32-bit single precision floating point. */
+  FLOAT32(1),
+
+  /** 32-bit signed integer. */
+  INT32(2),
+
+  /** 8-bit unsigned integer. */
+  UINT8(3),
+
+  /** 64-bit signed integer. */
+  INT64(4),
+
+  /** Strings. */
+  STRING(5),
+
+  /** Bool. */
+  BOOL(6),
+
+  /** 16-bit signed integer. */
+  INT16(7),
+
+  /** 8-bit signed integer. */
+  INT8(9);
+
+  private final int value;
+
+  DataType(int value) {
+    this.value = value;
+  }
+
+  /** Returns the size of an element of this type, in bytes, or -1 if element size is variable. */
+  public int byteSize() {
+    switch (this) {
+      case FLOAT32:
+      case INT32:
+        return 4;
+      case INT16:
+        return 2;
+      case INT8:
+      case UINT8:
+        return 1;
+      case INT64:
+        return 8;
+      case BOOL:
+        // Boolean size is JVM-dependent.
+        return -1;
+      case STRING:
+        return -1;
+    }
+    throw new IllegalArgumentException(
+        "DataType error: DataType " + this + " is not supported yet");
+  }
+
+  /** Corresponding value of the TfLiteType enum in the TensorFlow Lite C API. */
+  int c() {
+    return value;
+  }
+}

tensorflow/lite/java/src/main/java/org/tensorflow/lite/DataTypeUtils.java

@@ -0,0 +1,72 @@
+/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
+
+Licensed 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.tensorflow.lite;
+
+/**
+ * Utility methods for DataType.
+ */
+class DataTypeUtils {
+
+  private DataTypeUtils() {}
+
+  /** Gets string names of the data type. */
+  static String toStringName(DataType dataType) {
+    switch (dataType) {
+      case FLOAT32:
+        return "float";
+      case INT32:
+        return "int";
+      case INT16:
+        return "short";
+      case INT8:
+      case UINT8:
+        return "byte";
+      case INT64:
+        return "long";
+      case BOOL:
+        return "bool";
+      case STRING:
+        return "string";
+    }
+    throw new IllegalArgumentException(
+        "DataType error: DataType " + dataType + " is not supported yet");
+  }
+
+  /** Converts a C TfLiteType enum value to the corresponding type. */
+  static DataType fromC(int c) {
+    switch (c) {
+      case 1:
+        return DataType.FLOAT32;
+      case 2:
+        return DataType.INT32;
+      case 3:
+        return DataType.UINT8;
+      case 4:
+        return DataType.INT64;
+      case 5:
+        return DataType.STRING;
+      case 6:
+        return DataType.BOOL;
+      case 7:
+        return DataType.INT16;
+      case 9:
+        return DataType.INT8;
+      default: // continue below to handle unsupported C types.
+    }
+    throw new IllegalArgumentException(
+        "DataType error: DataType " + c + " is not recognized in Java.");
+  }
+}

tensorflow/lite/java/src/main/java/org/tensorflow/lite/Delegate.java

@@ -0,0 +1,56 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed 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.tensorflow.lite;
+
+import java.io.Closeable;
+
+/**
+ * Wrapper for a native TensorFlow Lite Delegate.
+ *
+ * <p>If a delegate implementation holds additional resources or memory that should be explicitly
+ * freed, then best practice is to add a {@code close()} method to the implementation and have the
+ * client call that explicitly when the delegate instance is no longer in use. While this approach
+ * technically allows sharing of a single delegate instance across multiple interpreter instances,
+ * the delegate implementation must explicitly support this.
+ */
+public interface Delegate extends Closeable {
+  /**
+   * Returns a native handle to the TensorFlow Lite delegate implementation.
+   *
+   * <p>Note: The Java {@link Delegate} maintains ownership of the native delegate instance, and
+   * must ensure its existence for the duration of usage with any {@link InterpreterApi} instance.
+   *
+   * <p>Note: the native delegate instance may not be created until the delegate has been attached
+   * to an interpreter, so this method should not be called until after an interpreter has been
+   * constructed with this delegate.
+   *
+   * @throws IllegalStateException if called before the native delegate instance has been
+   *     constructed.
+   * @return The native delegate handle. In C/C++, this should be a pointer to
+   *     'TfLiteOpaqueDelegate'.
+   */
+  long getNativeHandle();
+
+  /**
+   * Closes the delegate and releases any resources associated with it.
+   *
+   * <p>In contrast to the method declared in the base {@link Closeable} interface, this method
+   * does not throw checked exceptions.
+   */
+  @SuppressWarnings("StaticOrDefaultInterfaceMethod")
+  @Override
+  default void close() {}
+}

tensorflow/lite/java/src/main/java/org/tensorflow/lite/DelegateFactory.java

@@ -0,0 +1,28 @@
+/* Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+
+Licensed 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.tensorflow.lite;
+
+/** Allows creating delegates for different runtime flavors. */
+public interface DelegateFactory {
+  /**
+   * Create a {@link Delegate} for the given {@link RuntimeFlavor}.
+   *
+   * <p>Note for developers implementing this interface: Currently TF Lite in Google Play Services
+   * does not support external (developer-provided) delegates. Correspondingly, implementations of
+   * this method can expect to be called with {@link RuntimeFlavor#APPLICATION}.
+   */
+  Delegate create(RuntimeFlavor runtimeFlavor);
+}

tensorflow/lite/java/src/main/java/org/tensorflow/lite/Interpreter.java

@@ -0,0 +1,293 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed 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.tensorflow.lite;
+
+import java.io.File;
+import java.nio.ByteBuffer;
+import org.checkerframework.checker.nullness.qual.NonNull;
+
+/**
+ * Driver class to drive model inference with TensorFlow Lite.
+ *
+ * <p>Note: If you don't need access to any of the "experimental" API features below, prefer to use
+ * InterpreterApi and InterpreterFactory rather than using Interpreter directly.
+ *
+ * <p>A {@code Interpreter} encapsulates a pre-trained TensorFlow Lite model, in which operations
+ * are executed for model inference.
+ *
+ * <p>For example, if a model takes only one input and returns only one output:
+ *
+ * <pre>{@code
+ * try (Interpreter interpreter = new Interpreter(file_of_a_tensorflowlite_model)) {
+ *   interpreter.run(input, output);
+ * }
+ * }</pre>
+ *
+ * <p>If a model takes multiple inputs or outputs:
+ *
+ * <pre>{@code
+ * Object[] inputs = {input0, input1, ...};
+ * Map<Integer, Object> map_of_indices_to_outputs = new HashMap<>();
+ * FloatBuffer ith_output = FloatBuffer.allocateDirect(3 * 2 * 4);  // Float tensor, shape 3x2x4.
+ * ith_output.order(ByteOrder.nativeOrder());
+ * map_of_indices_to_outputs.put(i, ith_output);
+ * try (Interpreter interpreter = new Interpreter(file_of_a_tensorflowlite_model)) {
+ *   interpreter.runForMultipleInputsOutputs(inputs, map_of_indices_to_outputs);
+ * }
+ * }</pre>
+ *
+ * <p>If a model takes or produces string tensors:
+ *
+ * <pre>{@code
+ * String[] input = {"foo", "bar"};  // Input tensor shape is [2].
+ * String[][] output = new String[3][2];  // Output tensor shape is [3, 2].
+ * try (Interpreter interpreter = new Interpreter(file_of_a_tensorflowlite_model)) {
+ *   interpreter.runForMultipleInputsOutputs(input, output);
+ * }
+ * }</pre>
+ *
+ * <p>Note that there's a distinction between shape [] and shape[1]. For scalar string tensor
+ * outputs:
+ *
+ * <pre>{@code
+ * String[] input = {"foo"};  // Input tensor shape is [1].
+ * ByteBuffer outputBuffer = ByteBuffer.allocate(OUTPUT_BYTES_SIZE);  // Output tensor shape is [].
+ * try (Interpreter interpreter = new Interpreter(file_of_a_tensorflowlite_model)) {
+ *   interpreter.runForMultipleInputsOutputs(input, outputBuffer);
+ * }
+ * byte[] outputBytes = new byte[outputBuffer.remaining()];
+ * outputBuffer.get(outputBytes);
+ * // Below, the `charset` can be StandardCharsets.UTF_8.
+ * String output = new String(outputBytes, charset);
+ * }</pre>
+ *
+ * <p>Orders of inputs and outputs are determined when converting TensorFlow model to TensorFlowLite
+ * model with Toco, as are the default shapes of the inputs.
+ *
+ * <p>When inputs are provided as (multi-dimensional) arrays, the corresponding input tensor(s) will
+ * be implicitly resized according to that array's shape. When inputs are provided as {@code Buffer}
+ * types, no implicit resizing is done; the caller must ensure that the {@code Buffer} byte size
+ * either matches that of the corresponding tensor, or that they first resize the tensor via {@link
+ * #resizeInput(int, int[])}. Tensor shape and type information can be obtained via the {@link
+ * Tensor} class, available via {@link #getInputTensor(int)} and {@link #getOutputTensor(int)}.
+ *
+ * <p><b>WARNING:</b>{@code Interpreter} instances are <b>not</b> thread-safe. A {@code Interpreter}
+ * owns resources that <b>must</b> be explicitly freed by invoking {@link #close()}
+ *
+ * <p>The TFLite library is built against NDK API 19. It may work for Android API levels below 19,
+ * but is not guaranteed.
+ */
+public final class Interpreter extends InterpreterImpl implements InterpreterApi {
+
+  /** An options class for controlling runtime interpreter behavior. */
+  public static class Options extends InterpreterImpl.Options {
+    public Options() {}
+
+    public Options(InterpreterApi.Options options) {
+      super(options);
+    }
+
+    Options(InterpreterImpl.Options options) {
+      super(options);
+    }
+
+    @Override
+    public Options setUseXNNPACK(boolean useXNNPACK) {
+      super.setUseXNNPACK(useXNNPACK);
+      return this;
+    }
+
+    @Override
+    public Options setNumThreads(int numThreads) {
+      super.setNumThreads(numThreads);
+      return this;
+    }
+
+    @Override
+    public Options setUseNNAPI(boolean useNNAPI) {
+      super.setUseNNAPI(useNNAPI);
+      return this;
+    }
+
+    /**
+     * Sets whether to allow float16 precision for FP32 calculation when possible. Defaults to false
+     * (disallow).
+     *
+     * @deprecated Prefer using <a
+     *     href="https://github.com/tensorflow/tensorflow/blob/5dc7f6981fdaf74c8c5be41f393df705841fb7c5/tensorflow/lite/delegates/nnapi/java/src/main/java/org/tensorflow/lite/nnapi/NnApiDelegate.java#L127">NnApiDelegate.Options#setAllowFp16(boolean
+     *     enable)</a>.
+     */
+    @Deprecated
+    public Options setAllowFp16PrecisionForFp32(boolean allow) {
+      this.allowFp16PrecisionForFp32 = allow;
+      return this;
+    }
+
+    @Override
+    public Options addDelegate(Delegate delegate) {
+      super.addDelegate(delegate);
+      return this;
+    }
+
+    @Override
+    public Options addDelegateFactory(DelegateFactory delegateFactory) {
+      super.addDelegateFactory(delegateFactory);
+      return this;
+    }
+
+    /**
+     * Advanced: Set if buffer handle output is allowed.
+     *
+     * <p>When a {@link Delegate} supports hardware acceleration, the interpreter will make the data
+     * of output tensors available in the CPU-allocated tensor buffers by default. If the client can
+     * consume the buffer handle directly (e.g. reading output from OpenGL texture), it can set this
+     * flag to false, avoiding the copy of data to the CPU buffer. The delegate documentation should
+     * indicate whether this is supported and how it can be used.
+     *
+     * <p>WARNING: This is an experimental interface that is subject to change.
+     */
+    public Options setAllowBufferHandleOutput(boolean allow) {
+      this.allowBufferHandleOutput = allow;
+      return this;
+    }
+
+    /**
+     * Enables or disables compression of identical per-channel quantization zero-points into a
+     * single value to reduce memory usage.
+     *
+     * <p>value whether to enable compression of identical per-channel quantization zero-points.
+     *
+     * <p>WARNING: This is an experimental interface that is subject to change.
+     */
+    public Options setCompressQuantizationZeroPoints(boolean value) {
+      this.compressQuantizationZeroPoints = value;
+      return this;
+    }
+
+    /**
+     * Returns whether compression of identical per-channel quantization zero-points is enabled.
+     *
+     * <p>WARNING: This is an experimental interface that is subject to change.
+     */
+    public boolean getCompressQuantizationZeroPoints() {
+      return compressQuantizationZeroPoints != null && compressQuantizationZeroPoints;
+    }
+
+    @Override
+    public Options setCancellable(boolean allow) {
+      super.setCancellable(allow);
+      return this;
+    }
+
+    @Override
+    public Options setRuntime(InterpreterApi.Options.TfLiteRuntime runtime) {
+      super.setRuntime(runtime);
+      return this;
+    }
+  }
+
+  /**
+   * Initializes an {@code Interpreter}.
+   *
+   * @param modelFile a File of a pre-trained TF Lite model.
+   * @throws IllegalArgumentException if {@code modelFile} does not encode a valid TensorFlow Lite
+   *     model.
+   */
+  public Interpreter(@NonNull File modelFile) {
+    this(modelFile, /* options= */ null);
+  }
+
+  /**
+   * Initializes an {@code Interpreter} and specifies options for customizing interpreter behavior.
+   *
+   * @param modelFile a file of a pre-trained TF Lite model
+   * @param options a set of options for customizing interpreter behavior
+   * @throws IllegalArgumentException if {@code modelFile} does not encode a valid TensorFlow Lite
+   *     model.
+   */
+  public Interpreter(@NonNull File modelFile, Options options) {
+    this(new NativeInterpreterWrapperExperimental(modelFile.getAbsolutePath(), options));
+  }
+
+  /**
+   * Initializes an {@code Interpreter} with a {@code ByteBuffer} of a model file.
+   *
+   * <p>The ByteBuffer should not be modified after the construction of a {@code Interpreter}. The
+   * {@code ByteBuffer} can be either a {@code MappedByteBuffer} that memory-maps a model file, or a
+   * direct {@code ByteBuffer} of nativeOrder() that contains the bytes content of a model.
+   *
+   * @throws IllegalArgumentException if {@code byteBuffer} is not a {@code MappedByteBuffer} nor a
+   *     direct {@code ByteBuffer} of nativeOrder.
+   */
+  public Interpreter(@NonNull ByteBuffer byteBuffer) {
+    this(byteBuffer, /* options= */ null);
+  }
+
+  /**
+   * Initializes an {@code Interpreter} with a {@code ByteBuffer} of a model file and a set of
+   * custom {@link Interpreter.Options}.
+   *
+   * <p>The {@code ByteBuffer} should not be modified after the construction of an {@code
+   * Interpreter}. The {@code ByteBuffer} can be either a {@code MappedByteBuffer} that memory-maps
+   * a model file, or a direct {@code ByteBuffer} of nativeOrder() that contains the bytes content
+   * of a model.
+   *
+   * @throws IllegalArgumentException if {@code byteBuffer} is not a {@code MappedByteBuffer} nor a
+   *     direct {@code ByteBuffer} of nativeOrder.
+   */
+  public Interpreter(@NonNull ByteBuffer byteBuffer, Options options) {
+    this(new NativeInterpreterWrapperExperimental(byteBuffer, options));
+  }
+
+  private Interpreter(NativeInterpreterWrapperExperimental wrapper) {
+    super(wrapper);
+    wrapperExperimental = wrapper;
+  }
+
+  /**
+   * Advanced: Resets all variable tensors to the default value.
+   *
+   * <p>If a variable tensor doesn't have an associated buffer, it will be reset to zero.
+   *
+   * <p>WARNING: This is an experimental API and subject to change.
+   */
+  public void resetVariableTensors() {
+    checkNotClosed();
+    wrapperExperimental.resetVariableTensors();
+  }
+
+  /**
+   * Advanced: Interrupts inference in the middle of a call to {@link Interpreter#run}.
+   *
+   * <p>A cancellation flag will be set to true when this function gets called. The interpreter will
+   * check the flag between Op invocations, and if it's {@code true}, the interpreter will stop
+   * execution. The interpreter will remain a cancelled state until explicitly "uncancelled" by
+   * {@code setCancelled(false)}.
+   *
+   * <p>WARNING: This is an experimental API and subject to change.
+   *
+   * @param cancelled {@code true} to cancel inference in a best-effort way; {@code false} to
+   *     resume.
+   * @throws IllegalStateException if the interpreter is not initialized with the cancellable
+   *     option, which is by default off.
+   * @see Interpreter.Options#setCancellable(boolean).
+   */
+  public void setCancelled(boolean cancelled) {
+    wrapper.setCancelled(cancelled);
+  }
+
+  private final NativeInterpreterWrapperExperimental wrapperExperimental;
+}

tensorflow/lite/java/src/main/java/org/tensorflow/lite/InterpreterApi.java

@@ -0,0 +1,625 @@
+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed 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.tensorflow.lite;
+
+import java.io.File;
+import java.nio.ByteBuffer;
+import java.util.ArrayList;
+import java.util.Collections;
+import java.util.List;
+import java.util.Map;
+import org.checkerframework.checker.nullness.qual.NonNull;
+import org.tensorflow.lite.InterpreterApi.Options.TfLiteRuntime;
+import org.tensorflow.lite.acceleration.ValidatedAccelerationConfig;
+import org.tensorflow.lite.nnapi.NnApiDelegate;
+
+/**
+ * Interface to TensorFlow Lite model interpreter, excluding experimental methods.
+ *
+ * <p>An {@code InterpreterApi} instance encapsulates a pre-trained TensorFlow Lite model, in which
+ * operations are executed for model inference.
+ *
+ * <p>For example, if a model takes only one input and returns only one output:
+ *
+ * <pre>{@code
+ * try (InterpreterApi interpreter =
+ *     new InterpreterApi.create(file_of_a_tensorflowlite_model)) {
+ *   interpreter.run(input, output);
+ * }
+ * }</pre>
+ *
+ * <p>If a model takes multiple inputs or outputs:
+ *
+ * <pre>{@code
+ * Object[] inputs = {input0, input1, ...};
+ * Map<Integer, Object> map_of_indices_to_outputs = new HashMap<>();
+ * FloatBuffer ith_output = FloatBuffer.allocateDirect(3 * 2 * 4);  // Float tensor, shape 3x2x4.
+ * ith_output.order(ByteOrder.nativeOrder());
+ * map_of_indices_to_outputs.put(i, ith_output);
+ * try (InterpreterApi interpreter =
+ *     new InterpreterApi.create(file_of_a_tensorflowlite_model)) {
+ *   interpreter.runForMultipleInputsOutputs(inputs, map_of_indices_to_outputs);
+ * }
+ * }</pre>
+ *
+ * <p>If a model takes or produces string tensors:
+ *
+ * <pre>{@code
+ * String[] input = {"foo", "bar"};  // Input tensor shape is [2].
+ * String[][] output = new String[3][2];  // Output tensor shape is [3, 2].
+ * try (InterpreterApi interpreter =
+ *     new InterpreterApi.create(file_of_a_tensorflowlite_model)) {
+ *   interpreter.runForMultipleInputsOutputs(input, output);
+ * }
+ * }</pre>
+ *
+ * <p>Note that there's a distinction between shape [] and shape[1]. For scalar string tensor
+ * outputs:
+ *
+ * <pre>{@code
+ * String[] input = {"foo"};  // Input tensor shape is [1].
+ * ByteBuffer outputBuffer = ByteBuffer.allocate(OUTPUT_BYTES_SIZE);  // Output tensor shape is [].
+ * try (Interpreter interpreter = new Interpreter(file_of_a_tensorflowlite_model)) {
+ *   interpreter.runForMultipleInputsOutputs(input, outputBuffer);
+ * }
+ * byte[] outputBytes = new byte[outputBuffer.remaining()];
+ * outputBuffer.get(outputBytes);
+ * // Below, the `charset` can be StandardCharsets.UTF_8.
+ * String output = new String(outputBytes, charset);
+ * }</pre>
+ *
+ * <p>Orders of inputs and outputs are determined when converting TensorFlow model to TensorFlowLite
+ * model with Toco, as are the default shapes of the inputs.
+ *
+ * <p>When inputs are provided as (multi-dimensional) arrays, the corresponding input tensor(s) will
+ * be implicitly resized according to that array's shape. When inputs are provided as {@link
+ * java.nio.Buffer} types, no implicit resizing is done; the caller must ensure that the {@link
+ * java.nio.Buffer} byte size either matches that of the corresponding tensor, or that they first
+ * resize the tensor via {@link #resizeInput(int, int[])}. Tensor shape and type information can be
+ * obtained via the {@link Tensor} class, available via {@link #getInputTensor(int)} and {@link
+ * #getOutputTensor(int)}.
+ *
+ * <p><b>WARNING:</b>{@code InterpreterApi} instances are <b>not</b> thread-safe.
+ *
+ * <p><b>WARNING:</b>An {@code InterpreterApi} instance owns resources that <b>must</b> be
+ * explicitly freed by invoking {@link #close()}
+ *
+ * <p>The TFLite library is built against NDK API 19. It may work for Android API levels below 19,
+ * but is not guaranteed.
+ */
+public interface InterpreterApi extends AutoCloseable {
+
+  /** An options class for controlling runtime interpreter behavior. */
+  class Options {
+
+    public Options() {
+      this.delegates = new ArrayList<>();
+      this.delegateFactories = new ArrayList<>();
+    }
+
+    public Options(Options other) {
+      this.numThreads = other.numThreads;
+      this.useNNAPI = other.useNNAPI;
+      this.allowCancellation = other.allowCancellation;
+      this.delegates = new ArrayList<>(other.delegates);
+      this.delegateFactories = new ArrayList<>(other.delegateFactories);
+      this.runtime = other.runtime;
+      this.validatedAccelerationConfig = other.validatedAccelerationConfig;
+      this.useXNNPACK = other.useXNNPACK;
+    }
+
+    /**
+     * Sets the number of threads to be used for ops that support multi-threading.
+     *
+     * <p>{@code numThreads} should be {@code >= -1}. Setting {@code numThreads} to 0 has the effect
+     * of disabling multithreading, which is equivalent to setting {@code numThreads} to 1. If
+     * unspecified, or set to the value -1, the number of threads used will be
+     * implementation-defined and platform-dependent.
+     */
+    public Options setNumThreads(int numThreads) {
+      this.numThreads = numThreads;
+      return this;
+    }
+
+    /**
+     * Returns the number of threads to be used for ops that support multi-threading.
+     *
+     * <p>{@code numThreads} should be {@code >= -1}. Values of 0 (or 1) disable multithreading.
+     * Default value is -1: the number of threads used will be implementation-defined and
+     * platform-dependent.
+     */
+    public int getNumThreads() {
+      return numThreads;
+    }
+
+    /** Sets whether to use NN API (if available) for op execution. Defaults to false (disabled). */
+    public Options setUseNNAPI(boolean useNNAPI) {
+      this.useNNAPI = useNNAPI;
+      return this;
+    }
+
+    /**
+     * Returns whether to use NN API (if available) for op execution. Default value is false
+     * (disabled).
+     */
+    public boolean getUseNNAPI() {
+      return useNNAPI != null && useNNAPI;
+    }
+
+    /**
+     * Advanced: Set if the interpreter is able to be cancelled.
+     *
+     * <p>Interpreters may have an experimental API <a
+     * href="https://www.tensorflow.org/lite/api_docs/java/org/tensorflow/lite/Interpreter#setCancelled(boolean)">setCancelled(boolean)</a>.
+     * If this interpreter is cancellable and such a method is invoked, a cancellation flag will be
+     * set to true. The interpreter will check the flag between Op invocations, and if it's {@code
+     * true}, the interpreter will stop execution. The interpreter will remain a cancelled state
+     * until explicitly "uncancelled" by {@code setCancelled(false)}.
+     */
+    public Options setCancellable(boolean allow) {
+      this.allowCancellation = allow;
+      return this;
+    }
+
+    /**
+     * Advanced: Returns whether the interpreter is able to be cancelled.
+     *
+     * <p>Interpreters may have an experimental API <a
+     * href="https://www.tensorflow.org/lite/api_docs/java/org/tensorflow/lite/Interpreter#setCancelled(boolean)">setCancelled(boolean)</a>.
+     * If this interpreter is cancellable and such a method is invoked, a cancellation flag will be
+     * set to true. The interpreter will check the flag between Op invocations, and if it's {@code
+     * true}, the interpreter will stop execution. The interpreter will remain a cancelled state
+     * until explicitly "uncancelled" by {@code setCancelled(false)}.
+     */
+    public boolean isCancellable() {
+      return allowCancellation != null && allowCancellation;
+    }
+
+    /**
+     * Adds a {@link Delegate} to be applied during interpreter creation.
+     *
+     * <p>Delegates added here are applied before any delegates created from a {@link
+     * DelegateFactory} that was added with {@link #addDelegateFactory}.
+     *
+     * <p>Note that TF Lite in Google Play Services (see {@link #setRuntime}) does not support
+     * external (developer-provided) delegates, and adding a {@link Delegate} other than {@link
+     * NnApiDelegate} here is not allowed when using TF Lite in Google Play Services.
+     */
+    public Options addDelegate(Delegate delegate) {
+      delegates.add(delegate);
+      return this;
+    }
+
+    /**
+     * Returns the list of delegates intended to be applied during interpreter creation that have
+     * been registered via {@code addDelegate}.
+     */
+    public List<Delegate> getDelegates() {
+      return Collections.unmodifiableList(delegates);
+    }
+
+    /**
+     * Adds a {@link DelegateFactory} which will be invoked to apply its created {@link Delegate}
+     * during interpreter creation.
+     *
+     * <p>Delegates from a delegated factory that was added here are applied after any delegates
+     * added with {@link #addDelegate}.
+     */
+    public Options addDelegateFactory(DelegateFactory delegateFactory) {
+      delegateFactories.add(delegateFactory);
+      return this;
+    }
+
+    /**
+     * Returns the list of delegate factories that have been registered via {@code
+     * addDelegateFactory}).
+     */
+    public List<DelegateFactory> getDelegateFactories() {
+      return Collections.unmodifiableList(delegateFactories);
+    }
+
+    /**
+     * Enum to represent where to get the TensorFlow Lite runtime implementation from.
+     *
+     * <p>The difference between this class and the RuntimeFlavor class: This class specifies a
+     * <em>preference</em> which runtime to use, whereas {@link RuntimeFlavor} specifies which exact
+     * runtime <em>is</em> being used.
+     */
+    public enum TfLiteRuntime {
+      /**
+       * Use a TF Lite runtime implementation that is linked into the application. If there is no
+       * suitable TF Lite runtime implementation linked into the application, then attempting to
+       * create an InterpreterApi instance with this TfLiteRuntime setting will throw an
+       * IllegalStateException exception (even if the OS or system services could provide a TF Lite
+       * runtime implementation).
+       *
+       * <p>This is the default setting. This setting is also appropriate for apps that must run on
+       * systems that don't provide a TF Lite runtime implementation.
+       */
+      FROM_APPLICATION_ONLY,
+
+      /**
+       * Use a TF Lite runtime implementation provided by the OS or system services. This will be
+       * obtained from a system library / shared object / service, such as Google Play Services. It
+       * may be newer than the version linked into the application (if any). If there is no suitable
+       * TF Lite runtime implementation provided by the system, then attempting to create an
+       * InterpreterApi instance with this TfLiteRuntime setting will throw an IllegalStateException
+       * exception (even if there is a TF Lite runtime implementation linked into the application).
+       *
+       * <p>This setting is appropriate for code that will use a system-provided TF Lite runtime,
+       * which can reduce app binary size and can be updated more frequently.
+       */
+      FROM_SYSTEM_ONLY,
+
+      /**
+       * Use a system-provided TF Lite runtime implementation, if any, otherwise use the TF Lite
+       * runtime implementation linked into the application, if any. If no suitable TF Lite runtime
+       * can be found in any location, then attempting to create an InterpreterApi instance with
+       * this TFLiteRuntime setting will throw an IllegalStateException. If there is both a suitable
+       * TF Lite runtime linked into the application and also a suitable TF Lite runtime provided by
+       * the system, the one provided by the system will be used.
+       *
+       * <p>This setting is suitable for use in code that doesn't care where the TF Lite runtime is
+       * coming from (e.g. middleware layers).
+       */
+      PREFER_SYSTEM_OVER_APPLICATION,
+    }
+
+    /** Specify where to get the TF Lite runtime implementation from. */
+    public Options setRuntime(TfLiteRuntime runtime) {
+      this.runtime = runtime;
+      return this;
+    }
+
+    /** Return where to get the TF Lite runtime implementation from. */
+    public TfLiteRuntime getRuntime() {
+      return runtime;
+    }
+
+    /** Specify the acceleration configuration. */
+    public Options setAccelerationConfig(ValidatedAccelerationConfig config) {
+      this.validatedAccelerationConfig = config;
+      return this;
+    }
+
+    /** Return the acceleration configuration. */
+    public ValidatedAccelerationConfig getAccelerationConfig() {
+      return this.validatedAccelerationConfig;
+    }
+
+    /**
+     * Enable or disable an optimized set of CPU kernels (provided by XNNPACK). Enabled by default.
+     */
+    public Options setUseXNNPACK(boolean useXNNPACK) {
+      this.useXNNPACK = useXNNPACK;
+      return this;
+    }
+
+    public boolean getUseXNNPACK() {
+      // A null value indicates the default behavior, which is currently to apply the delegate.
+      return useXNNPACK == null || useXNNPACK.booleanValue();
+    }
+
+    TfLiteRuntime runtime = TfLiteRuntime.FROM_APPLICATION_ONLY;
+    int numThreads = -1;
+    Boolean useNNAPI;
+
+    /**
+     * Note: the initial "null" value indicates default behavior (XNNPACK delegate will be applied
+     * by default whenever possible).
+     *
+     * <p>Disabling this flag will disable use of a highly optimized set of CPU kernels provided via
+     * the XNNPACK delegate. Currently, this is restricted to a subset of floating point operations.
+     * See
+     * https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/delegates/xnnpack/README.md
+     * for more details.
+     */
+    Boolean useXNNPACK;
+
+    Boolean allowCancellation;
+    ValidatedAccelerationConfig validatedAccelerationConfig;
+
+    // See InterpreterApi.Options#addDelegate.
+    final List<Delegate> delegates;
+    // See InterpreterApi.Options#addDelegateFactory.
+    private final List<DelegateFactory> delegateFactories;
+  }
+
+  /**
+   * Constructs an {@link InterpreterApi} instance, using the specified model and options. The model
+   * will be loaded from a file.
+   *
+   * @param modelFile A file containing a pre-trained TF Lite model.
+   * @param options A set of options for customizing interpreter behavior.
+   * @throws IllegalArgumentException if {@code modelFile} does not encode a valid TensorFlow Lite
+   *     model.
+   */
+  @SuppressWarnings("StaticOrDefaultInterfaceMethod")
+  static InterpreterApi create(@NonNull File modelFile, InterpreterApi.Options options) {
+    TfLiteRuntime runtime = (options == null ? null : options.getRuntime());
+    InterpreterFactoryApi factory = TensorFlowLite.getFactory(runtime);
+    return factory.create(modelFile, options);
+  }
+
+  /**
+   * Constructs an {@link InterpreterApi} instance, using the specified model and options. The model
+   * will be read from a {@code ByteBuffer}.
+   *
+   * @param byteBuffer A pre-trained TF Lite model, in binary serialized form. The ByteBuffer should
+   *     not be modified after the construction of an {@link InterpreterApi} instance. The {@code
+   *     ByteBuffer} can be either a {@code MappedByteBuffer} that memory-maps a model file, or a
+   *     direct {@code ByteBuffer} of nativeOrder() that contains the bytes content of a model.
+   * @param options A set of options for customizing interpreter behavior.
+   * @throws IllegalArgumentException if {@code byteBuffer} is not a {@code MappedByteBuffer} nor a
+   *     direct {@code ByteBuffer} of nativeOrder.
+   */
+  @SuppressWarnings("StaticOrDefaultInterfaceMethod")
+  static InterpreterApi create(@NonNull ByteBuffer byteBuffer, InterpreterApi.Options options) {
+    TfLiteRuntime runtime = (options == null ? null : options.getRuntime());
+    InterpreterFactoryApi factory = TensorFlowLite.getFactory(runtime);
+    return factory.create(byteBuffer, options);
+  }
+
+  /**
+   * Runs model inference if the model takes only one input, and provides only one output.
+   *
+   * <p>Warning: The API is more efficient if a {@code Buffer} (preferably direct, but not required)
+   * is used as the input/output data type. Please consider using {@code Buffer} to feed and fetch
+   * primitive data for better performance. The following concrete {@code Buffer} types are
+   * supported:
+   *
+   * <ul>
+   *   <li>{@code ByteBuffer} - compatible with any underlying primitive Tensor type.
+   *   <li>{@code FloatBuffer} - compatible with float Tensors.
+   *   <li>{@code IntBuffer} - compatible with int32 Tensors.
+   *   <li>{@code LongBuffer} - compatible with int64 Tensors.
+   * </ul>
+   *
+   * Note that boolean types are only supported as arrays, not {@code Buffer}s, or as scalar inputs.
+   *
+   * @param input an array or multidimensional array, or a {@code Buffer} of primitive types
+   *     including int, float, long, and byte. {@code Buffer} is the preferred way to pass large
+   *     input data for primitive types, whereas string types require using the (multi-dimensional)
+   *     array input path. When a {@code Buffer} is used, its content should remain unchanged until
+   *     model inference is done, and the caller must ensure that the {@code Buffer} is at the
+   *     appropriate read position. A {@code null} value is allowed only if the caller is using a
+   *     {@link Delegate} that allows buffer handle interop, and such a buffer has been bound to the
+   *     input {@link Tensor}.
+   * @param output a multidimensional array of output data, or a {@code Buffer} of primitive types
+   *     including int, float, long, and byte. When a {@code Buffer} is used, the caller must ensure
+   *     that it is set the appropriate write position. A null value is allowed, and is useful for
+   *     certain cases, e.g., if the caller is using a {@link Delegate} that allows buffer handle
+   *     interop, and such a buffer has been bound to the output {@link Tensor} (see also <a
+   *     href="https://www.tensorflow.org/lite/api_docs/java/org/tensorflow/lite/Interpreter.Options#setAllowBufferHandleOutput(boolean)">Interpreter.Options#setAllowBufferHandleOutput(boolean)</a>),
+   *     or if the graph has dynamically shaped outputs and the caller must query the output {@link
+   *     Tensor} shape after inference has been invoked, fetching the data directly from the output
+   *     tensor (via {@link Tensor#asReadOnlyBuffer()}).
+   * @throws IllegalArgumentException if {@code input} is null or empty, or if an error occurs when
+   *     running inference.
+   * @throws IllegalArgumentException (EXPERIMENTAL, subject to change) if the inference is
+   *     interrupted by {@code setCancelled(true)}.
+   */
+  void run(Object input, Object output);
+
+  /**
+   * Runs model inference if the model takes multiple inputs, or returns multiple outputs.
+   *
+   * <p>Warning: The API is more efficient if {@code Buffer}s (preferably direct, but not required)
+   * are used as the input/output data types. Please consider using {@code Buffer} to feed and fetch
+   * primitive data for better performance. The following concrete {@code Buffer} types are
+   * supported:
+   *
+   * <ul>
+   *   <li>{@code ByteBuffer} - compatible with any underlying primitive Tensor type.
+   *   <li>{@code FloatBuffer} - compatible with float Tensors.
+   *   <li>{@code IntBuffer} - compatible with int32 Tensors.
+   *   <li>{@code LongBuffer} - compatible with int64 Tensors.
+   * </ul>
+   *
+   * Note that boolean types are only supported as arrays, not {@code Buffer}s, or as scalar inputs.
+   *
+   * <p>Note: {@code null} values for individual elements of {@code inputs} and {@code outputs} is
+   * allowed only if the caller is using a {@link Delegate} that allows buffer handle interop, and
+   * such a buffer has been bound to the corresponding input or output {@link Tensor}(s).
+   *
+   * @param inputs an array of input data. The inputs should be in the same order as inputs of the
+   *     model. Each input can be an array or multidimensional array, or a {@code Buffer} of
+   *     primitive types including int, float, long, and byte. {@code Buffer} is the preferred way
+   *     to pass large input data, whereas string types require using the (multi-dimensional) array
+   *     input path. When {@code Buffer} is used, its content should remain unchanged until model
+   *     inference is done, and the caller must ensure that the {@code Buffer} is at the appropriate
+   *     read position.
+   * @param outputs a map mapping output indices to multidimensional arrays of output data or {@code
+   *     Buffer}s of primitive types including int, float, long, and byte. It only needs to keep
+   *     entries for the outputs to be used. When a {@code Buffer} is used, the caller must ensure
+   *     that it is set the appropriate write position. The map may be empty for cases where either
+   *     buffer handles are used for output tensor data, or cases where the outputs are dynamically
+   *     shaped and the caller must query the output {@link Tensor} shape after inference has been
+   *     invoked, fetching the data directly from the output tensor (via {@link
+   *     Tensor#asReadOnlyBuffer()}).
+   * @throws IllegalArgumentException if {@code inputs} is null or empty, if {@code outputs} is
+   *     null, or if an error occurs when running inference.
+   */
+  void runForMultipleInputsOutputs(
+      Object @NonNull [] inputs, @NonNull Map<Integer, Object> outputs);
+
+  /**
+   * Explicitly updates allocations for all tensors, if necessary.
+   *
+   * <p>This will propagate shapes and memory allocations for dependent tensors using the input
+   * tensor shape(s) as given.
+   *
+   * <p>Note: This call is *purely optional*. Tensor allocation will occur automatically during
+   * execution if any input tensors have been resized. This call is most useful in determining the
+   * shapes for any output tensors before executing the graph, e.g.,
+   *
+   * <pre> {@code
+   * interpreter.resizeInput(0, new int[]{1, 4, 4, 3}));
+   * interpreter.allocateTensors();
+   * FloatBuffer input = FloatBuffer.allocate(interpreter.getInputTensor(0).numElements());
+   * // Populate inputs...
+   * FloatBuffer output = FloatBuffer.allocate(interpreter.getOutputTensor(0).numElements());
+   * interpreter.run(input, output)
+   * // Process outputs...}</pre>
+   *
+   * <p>Note: Some graphs have dynamically shaped outputs, in which case the output shape may not
+   * fully propagate until inference is executed.
+   *
+   * @throws IllegalStateException if the graph's tensors could not be successfully allocated.
+   */
+  void allocateTensors();
+
+  /**
+   * Resizes idx-th input of the native model to the given dims.
+   *
+   * @throws IllegalArgumentException if {@code idx} is negative or is not smaller than the number
+   *     of model inputs; or if error occurs when resizing the idx-th input.
+   */
+  void resizeInput(int idx, int @NonNull [] dims);
+
+  /**
+   * Resizes idx-th input of the native model to the given dims.
+   *
+   * <p>When `strict` is True, only unknown dimensions can be resized. Unknown dimensions are
+   * indicated as `-1` in the array returned by `Tensor.shapeSignature()`.
+   *
+   * @throws IllegalArgumentException if {@code idx} is negative or is not smaller than the number
+   *     of model inputs; or if error occurs when resizing the idx-th input. Additionally, the error
+   *     occurs when attempting to resize a tensor with fixed dimensions when `strict` is True.
+   */
+  void resizeInput(int idx, int @NonNull [] dims, boolean strict);
+
+  /** Gets the number of input tensors. */
+  int getInputTensorCount();
+
+  /**
+   * Gets index of an input given the op name of the input.
+   *
+   * @throws IllegalArgumentException if {@code opName} does not match any input in the model used
+   *     to initialize the interpreter.
+   */
+  int getInputIndex(String opName);
+
+  /**
+   * Gets the Tensor associated with the provided input index.
+   *
+   * @throws IllegalArgumentException if {@code inputIndex} is negative or is not smaller than the
+   *     number of model inputs.
+   */
+  Tensor getInputTensor(int inputIndex);
+
+  /** Gets the number of output Tensors. */
+  int getOutputTensorCount();
+
+  /**
+   * Gets index of an output given the op name of the output.
+   *
+   * @throws IllegalArgumentException if {@code opName} does not match any output in the model used
+   *     to initialize the interpreter.
+   */
+  int getOutputIndex(String opName);
+
+  /**
+   * Gets the Tensor associated with the provided output index.
+   *
+   * <p>Note: Output tensor details (e.g., shape) may not be fully populated until after inference
+   * is executed. If you need updated details *before* running inference (e.g., after resizing an
+   * input tensor, which may invalidate output tensor shapes), use {@link #allocateTensors()} to
+   * explicitly trigger allocation and shape propagation. Note that, for graphs with output shapes
+   * that are dependent on input *values*, the output shape may not be fully determined until
+   * running inference.
+   *
+   * @throws IllegalArgumentException if {@code outputIndex} is negative or is not smaller than the
+   *     number of model outputs.
+   */
+  Tensor getOutputTensor(int outputIndex);
+
+  /**
+   * Runs model inference based on SignatureDef provided through {@code signatureKey}.
+   *
+   * <p>See {@link Interpreter#run(Object, Object)} for more details on the allowed input and output
+   * data types.
+   *
+   * @param inputs A map from input name in the SignatureDef to an input object.
+   * @param outputs A map from output name in SignatureDef to output data. This may be empty if the
+   *     caller wishes to query the {@link Tensor} data directly after inference (e.g., if the
+   *     output shape is dynamic, or output buffer handles are used).
+   * @param signatureKey Signature key identifying the SignatureDef.
+   * @throws IllegalArgumentException if {@code inputs} is null or empty, if {@code outputs} or
+   *     {@code signatureKey} is null, or if an error occurs when running inference.
+   */
+  public void runSignature(
+      @NonNull Map<String, Object> inputs,
+      @NonNull Map<String, Object> outputs,
+      String signatureKey);
+
+  /**
+   * Same as {@link #runSignature(Map, Map, String)} but doesn't require passing a signatureKey,
+   * assuming the model has one SignatureDef. If the model has more than one SignatureDef it will
+   * throw an exception.
+   */
+  public void runSignature(
+      @NonNull Map<String, Object> inputs, @NonNull Map<String, Object> outputs);
+
+  /**
+   * Gets the Tensor associated with the provided input name and signature method name.
+   *
+   * @param inputName Input name in the signature.
+   * @param signatureKey Signature key identifying the SignatureDef, can be null if the model has
+   *     one signature.
+   * @throws IllegalArgumentException if {@code inputName} or {@code signatureKey} is null or empty,
+   *     or invalid name provided.
+   */
+  public Tensor getInputTensorFromSignature(String inputName, String signatureKey);
+
+  /** Gets the list of SignatureDef exported method names available in the model. */
+  public String[] getSignatureKeys();
+
+  /** Gets the list of SignatureDefs inputs for method {@code signatureKey}. */
+  public String[] getSignatureInputs(String signatureKey);
+
+  /** Gets the list of SignatureDefs outputs for method {@code signatureKey}. */
+  public String[] getSignatureOutputs(String signatureKey);
+
+  /**
+   * Gets the Tensor associated with the provided output name in specific signature method.
+   *
+   * <p>Note: Output tensor details (e.g., shape) may not be fully populated until after inference
+   * is executed. If you need updated details *before* running inference (e.g., after resizing an
+   * input tensor, which may invalidate output tensor shapes), use {@link #allocateTensors()} to
+   * explicitly trigger allocation and shape propagation. Note that, for graphs with output shapes
+   * that are dependent on input *values*, the output shape may not be fully determined until
+   * running inference.
+   *
+   * @param outputName Output name in the signature.
+   * @param signatureKey Signature key identifying the SignatureDef, can be null if the model has
+   *     one signature.
+   * @throws IllegalArgumentException if {@code outputName} or {@code signatureKey} is null or
+   *     empty, or invalid name provided.
+   */
+  public Tensor getOutputTensorFromSignature(String outputName, String signatureKey);
+
+  /**
+   * Returns native inference timing.
+   *
+   * @throws IllegalArgumentException if the model is not initialized by the interpreter.
+   */
+  Long getLastNativeInferenceDurationNanoseconds();
+
+  /** Release resources associated with the {@code InterpreterApi} instance. */
+  @Override
+  void close();
+}

tensorflow/lite/java/src/main/java/org/tensorflow/lite/InterpreterFactory.java

@@ -0,0 +1,59 @@
+/* Copyright 2021 The TensorFlow Authors. All Rights Reserved.
+
+Licensed 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.tensorflow.lite;
+
+import java.io.File;
+import java.nio.ByteBuffer;
+import org.checkerframework.checker.nullness.qual.NonNull;
+
+/**
+ * Factory for constructing InterpreterApi instances.
+ *
+ * <p>Deprecated; please use the InterpreterApi.create method instead.
+ */
+@Deprecated
+public class InterpreterFactory {
+  public InterpreterFactory() {}
+
+  /**
+   * Constructs an {@link InterpreterApi} instance, using the specified model and options. The model
+   * will be loaded from a file.
+   *
+   * @param modelFile A file containing a pre-trained TF Lite model.
+   * @param options A set of options for customizing interpreter behavior.
+   * @throws IllegalArgumentException if {@code modelFile} does not encode a valid TensorFlow Lite
+   *     model.
+   */
+  public InterpreterApi create(@NonNull File modelFile, InterpreterApi.Options options) {
+    return InterpreterApi.create(modelFile, options);
+  }
+
+  /**
+   * Constructs an {@link InterpreterApi} instance, using the specified model and options. The model
+   * will be read from a {@code ByteBuffer}.
+   *
+   * @param byteBuffer A pre-trained TF Lite model, in binary serialized form. The ByteBuffer should
+   *     not be modified after the construction of an {@link InterpreterApi} instance. The {@code
+   *     ByteBuffer} can be either a {@code MappedByteBuffer} that memory-maps a model file, or a
+   *     direct {@code ByteBuffer} of nativeOrder() that contains the bytes content of a model.
+   * @param options A set of options for customizing interpreter behavior.
+   * @throws IllegalArgumentException if {@code byteBuffer} is not a {@code MappedByteBuffer} nor a
+   *     direct {@code ByteBuffer} of nativeOrder.
+   */
+  public InterpreterApi create(@NonNull ByteBuffer byteBuffer, InterpreterApi.Options options) {
+    return InterpreterApi.create(byteBuffer, options);
+  }
+}