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[pytorch] Delete TorchScript based Android demo app and point user to ExecuTorch (#153767)
Summary: A retry of #153656. This time start from co-dev to make sure we capture internal signals. Test Plan: Rely on CI jobs. Differential Revision: D74911818 Pull Request resolved: https://github.com/pytorch/pytorch/pull/153767 Approved by: https://github.com/kirklandsign, https://github.com/cyyever, https://github.com/Skylion007
This commit is contained in:
Mengwei Liu
committed by
PyTorch MergeBot
PyTorch MergeBot
parent
6487ea30b3
commit
be36bacdaa
@ -2,7 +2,9 @@
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## Demo applications and tutorials
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Demo applications with code walk-through can be find in [this github repo](https://github.com/pytorch/android-demo-app).
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Please refer to [pytorch-labs/executorch-examples](https://github.com/pytorch-labs/executorch-examples/tree/main/dl3/android/DeepLabV3Demo) for the Android demo app based on [ExecuTorch](https://github.com/pytorch/executorch).
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Please join our [Discord](https://discord.com/channels/1334270993966825602/1349854760299270284) for any questions.
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## Publishing
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@ -119,8 +121,6 @@ We also have to add all transitive dependencies of our aars.
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As `pytorch_android` [depends](https://github.com/pytorch/pytorch/blob/master/android/pytorch_android/build.gradle#L76-L77) on `'com.facebook.soloader:nativeloader:0.10.5'` and `'com.facebook.fbjni:fbjni-java-only:0.2.2'`, we need to add them.
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(In case of using maven dependencies they are added automatically from `pom.xml`).
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You can check out [test app example](https://github.com/pytorch/pytorch/blob/master/android/test_app/app/build.gradle) that uses aars directly.
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## Linking to prebuilt libtorch library from gradle dependency
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In some cases, you may want to use libtorch from your android native build.
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@ -202,7 +202,7 @@ find_library(FBJNI_LIBRARY fbjni
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NO_CMAKE_FIND_ROOT_PATH)
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target_link_libraries(${PROJECT_NAME}
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${PYTORCH_LIBRARY})
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${PYTORCH_LIBRARY}
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${FBJNI_LIBRARY})
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```
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@ -233,8 +233,6 @@ void loadAndForwardModel(const std::string& modelPath) {
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To load torchscript model for mobile we need some special setup which is placed in `struct JITCallGuard` in this example. It may change in future, you can track the latest changes keeping an eye in our [pytorch android jni code]([https://github.com/pytorch/pytorch/blob/master/android/pytorch_android/src/main/cpp/pytorch_jni_jit.cpp#L28)
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[Example of linking to libtorch from aar](https://github.com/pytorch/pytorch/tree/master/android/test_app)
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## PyTorch Android API Javadoc
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You can find more details about the PyTorch Android API in the [Javadoc](https://pytorch.org/javadoc/).
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@ -1,30 +0,0 @@
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#!/bin/bash
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set -eux
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PYTORCH_DIR="$(cd $(dirname $0)/..; pwd -P)"
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PYTORCH_ANDROID_DIR=$PYTORCH_DIR/android
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echo "PYTORCH_DIR:$PYTORCH_DIR"
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source "$PYTORCH_ANDROID_DIR/common.sh"
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check_android_sdk
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check_gradle
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parse_abis_list "$@"
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build_android
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# To set proxy for gradle add following lines to ./gradle/gradle.properties:
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# systemProp.http.proxyHost=...
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# systemProp.http.proxyPort=8080
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# systemProp.https.proxyHost=...
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# systemProp.https.proxyPort=8080
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if [ "$CUSTOM_ABIS_LIST" = true ]; then
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NDK_DEBUG=1 $GRADLE_PATH -PnativeLibsDoNotStrip=true -PABI_FILTERS=$ABIS_LIST -p $PYTORCH_ANDROID_DIR clean test_app:assembleDebug
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else
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NDK_DEBUG=1 $GRADLE_PATH -PnativeLibsDoNotStrip=true -p $PYTORCH_ANDROID_DIR clean test_app:assembleDebug
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fi
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find $PYTORCH_ANDROID_DIR -type f -name *apk
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find $PYTORCH_ANDROID_DIR -type f -name *apk | xargs echo "To install apk run: $ANDROID_HOME/platform-tools/adb install -r "
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@ -1,32 +0,0 @@
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#!/bin/bash
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###############################################################################
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# This script tests the custom selective build flow for PyTorch Android, which
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# optimizes library size by only including ops used by a specific model.
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###############################################################################
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set -eux
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PYTORCH_DIR="$(cd $(dirname $0)/..; pwd -P)"
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PYTORCH_ANDROID_DIR="${PYTORCH_DIR}/android"
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BUILD_ROOT="${PYTORCH_DIR}/build_pytorch_android_custom"
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source "${PYTORCH_ANDROID_DIR}/common.sh"
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prepare_model_and_dump_root_ops() {
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cd "${BUILD_ROOT}"
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MODEL="${BUILD_ROOT}/MobileNetV2.pt"
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ROOT_OPS="${BUILD_ROOT}/MobileNetV2.yaml"
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python "${PYTORCH_ANDROID_DIR}/test_app/make_assets_custom.py"
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cp "${MODEL}" "${PYTORCH_ANDROID_DIR}/test_app/app/src/main/assets/mobilenet2.pt"
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}
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# Start building
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mkdir -p "${BUILD_ROOT}"
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check_android_sdk
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check_gradle
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parse_abis_list "$@"
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prepare_model_and_dump_root_ops
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SELECTED_OP_LIST="${ROOT_OPS}" build_android
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# TODO: change this to build test_app instead
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$GRADLE_PATH -PABI_FILTERS=$ABIS_LIST -p $PYTORCH_ANDROID_DIR clean assembleRelease
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@ -3,4 +3,3 @@ include ':app', ':pytorch_android', ':pytorch_android_torchvision', ':pytorch_ho
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project(':pytorch_android_torchvision').projectDir = file('pytorch_android_torchvision')
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project(':pytorch_host').projectDir = file('pytorch_android/host')
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project(':test_app').projectDir = file('test_app/app')
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9
android/test_app/.gitignore
vendored
9
android/test_app/.gitignore
vendored
@ -1,9 +0,0 @@
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local.properties
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**/*.iml
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.gradle
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gradlew*
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gradle/wrapper
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.idea/*
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.DS_Store
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build
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.externalNativeBuild
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@ -1,38 +0,0 @@
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cmake_minimum_required(VERSION 3.5)
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set(PROJECT_NAME pytorch_testapp_jni)
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project(${PROJECT_NAME} CXX)
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set(CMAKE_CXX_STANDARD 17 CACHE STRING "The C++ standard whose features are requested to build this target.")
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set(CMAKE_VERBOSE_MAKEFILE ON)
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set(build_DIR ${CMAKE_SOURCE_DIR}/build)
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set(pytorch_testapp_cpp_DIR ${CMAKE_CURRENT_LIST_DIR}/src/main/cpp)
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message(STATUS "ANDROID_STL:${ANDROID_STL}")
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file(GLOB pytorch_testapp_SOURCES
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${pytorch_testapp_cpp_DIR}/pytorch_testapp_jni.cpp
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)
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add_library(${PROJECT_NAME} SHARED
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${pytorch_testapp_SOURCES}
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)
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file(GLOB PYTORCH_INCLUDE_DIRS "${build_DIR}/pytorch_android*.aar/headers")
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file(GLOB PYTORCH_LINK_DIRS "${build_DIR}/pytorch_android*.aar/jni/${ANDROID_ABI}")
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target_compile_options(${PROJECT_NAME} PRIVATE
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-fexceptions
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)
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set(BUILD_SUBDIR ${ANDROID_ABI})
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target_include_directories(${PROJECT_NAME} PRIVATE
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${PYTORCH_INCLUDE_DIRS}
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)
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find_library(PYTORCH_LIBRARY pytorch_jni
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PATHS ${PYTORCH_LINK_DIRS}
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NO_CMAKE_FIND_ROOT_PATH)
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target_link_libraries(${PROJECT_NAME}
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${PYTORCH_LIBRARY}
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log)
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@ -1,190 +0,0 @@
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apply plugin: 'com.android.application'
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repositories {
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jcenter()
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maven {
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url "https://oss.sonatype.org/content/repositories/snapshots"
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}
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flatDir {
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dirs 'aars'
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}
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}
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android {
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configurations {
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extractForNativeBuild
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}
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compileOptions {
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sourceCompatibility 1.8
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targetCompatibility 1.8
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}
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compileSdkVersion rootProject.compileSdkVersion
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buildToolsVersion rootProject.buildToolsVersion
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defaultConfig {
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applicationId "org.pytorch.testapp"
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minSdkVersion rootProject.minSdkVersion
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targetSdkVersion rootProject.targetSdkVersion
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versionCode 1
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versionName "1.0"
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ndk {
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abiFilters ABI_FILTERS.split(",")
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}
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// Commented due to dependency on local copy of pytorch_android aar to aars folder
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//externalNativeBuild {
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// cmake {
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// abiFilters ABI_FILTERS.split(",")
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// arguments "-DANDROID_STL=c++_shared"
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// }
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//}
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buildConfigField("String", "MODULE_ASSET_NAME", "\"mobilenet2q.pt\"")
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buildConfigField("String", "LOGCAT_TAG", "@string/app_name")
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buildConfigField("long[]", "INPUT_TENSOR_SHAPE", "new long[]{1, 3, 224, 224}")
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buildConfigField("boolean", "NATIVE_BUILD", 'false')
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buildConfigField("boolean", "USE_VULKAN_DEVICE", 'false')
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buildConfigField(
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"int",
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"BUILD_LITE_INTERPRETER",
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System.env.BUILD_LITE_INTERPRETER != null ? System.env.BUILD_LITE_INTERPRETER : "1"
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)
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addManifestPlaceholders([APP_NAME: "@string/app_name", MAIN_ACTIVITY: "org.pytorch.testapp.MainActivity"])
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}
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buildTypes {
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debug {
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minifyEnabled false
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debuggable true
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}
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release {
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minifyEnabled false
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}
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}
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// Commented due to dependency on local copy of pytorch_android aar to aars folder
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//externalNativeBuild {
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// cmake {
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// path "CMakeLists.txt"
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// }
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//}
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flavorDimensions "model", "build", "activity"
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productFlavors {
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mnet {
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dimension "model"
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applicationIdSuffix ".mnet"
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buildConfigField("String", "MODULE_ASSET_NAME", "\"mobilenet_v2.ptl\"")
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addManifestPlaceholders([APP_NAME: "MNET"])
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buildConfigField("String", "LOGCAT_TAG", "\"pytorch-mnet\"")
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}
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// NB: This is not working atm https://github.com/pytorch/pytorch/issues/102966
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mnetVulkan {
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dimension "model"
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applicationIdSuffix ".mnet_vulkan"
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buildConfigField("String", "MODULE_ASSET_NAME", "\"mobilenet_v2_vulkan.ptl\"")
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buildConfigField("boolean", "USE_VULKAN_DEVICE", 'true')
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addManifestPlaceholders([APP_NAME: "MNET_VULKAN"])
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buildConfigField("String", "LOGCAT_TAG", "\"pytorch-mnet-vulkan\"")
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}
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resnet18 {
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dimension "model"
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applicationIdSuffix ".resnet18"
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buildConfigField("String", "MODULE_ASSET_NAME", "\"resnet18.ptl\"")
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addManifestPlaceholders([APP_NAME: "RN18"])
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buildConfigField("String", "LOGCAT_TAG", "\"pytorch-resnet18\"")
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}
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local {
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dimension "build"
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}
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nightly {
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dimension "build"
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}
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aar {
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dimension "build"
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}
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// Commented due to dependency on local copy of pytorch_android aar to aars folder
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//nativeBuild {
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// dimension "build"
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// buildConfigField("boolean", "NATIVE_BUILD", "true")
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//}
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camera {
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dimension "activity"
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addManifestPlaceholders([MAIN_ACTIVITY: "org.pytorch.testapp.CameraActivity"])
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}
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base {
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dimension "activity"
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sourceSets {
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main {
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java {
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exclude 'org/pytorch/testapp/CameraActivity.java'
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}
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}
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}
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}
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}
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packagingOptions {
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doNotStrip '**.so'
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}
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// Filtering for CI
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if (!testAppAllVariantsEnabled.toBoolean()) {
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variantFilter { variant ->
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def names = variant.flavors*.name
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if (names.contains("nightly")
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|| names.contains("camera")
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|| names.contains("aar")
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|| names.contains("nativeBuild")) {
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setIgnore(true)
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}
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}
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}
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}
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tasks.all { task ->
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// Disable externalNativeBuild for all but nativeBuild variant
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if (task.name.startsWith('externalNativeBuild')
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&& !task.name.contains('NativeBuild')) {
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task.enabled = false
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}
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}
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dependencies {
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implementation 'com.android.support:appcompat-v7:28.0.0'
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implementation 'com.facebook.soloader:nativeloader:0.10.5'
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localImplementation project(':pytorch_android')
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localImplementation project(':pytorch_android_torchvision')
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// Commented due to dependency on local copy of pytorch_android aar to aars folder
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//nativeBuildImplementation(name: 'pytorch_android-release', ext: 'aar')
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//nativeBuildImplementation(name: 'pytorch_android_torchvision-release', ext: 'aar')
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//extractForNativeBuild(name: 'pytorch_android-release', ext: 'aar')
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nightlyImplementation 'org.pytorch:pytorch_android:2.2.0-SNAPSHOT'
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nightlyImplementation 'org.pytorch:pytorch_android_torchvision:2.2.0-SNAPSHOT'
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aarImplementation(name:'pytorch_android', ext:'aar')
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aarImplementation(name:'pytorch_android_torchvision', ext:'aar')
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aarImplementation 'com.facebook.soloader:nativeloader:0.10.5'
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aarImplementation 'com.facebook.fbjni:fbjni-java-only:0.2.2'
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def camerax_version = "1.0.0-alpha05"
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cameraImplementation "androidx.camera:camera-core:$camerax_version"
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cameraImplementation "androidx.camera:camera-camera2:$camerax_version"
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cameraImplementation 'com.google.android.material:material:1.0.0-beta01'
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}
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task extractAARForNativeBuild {
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doLast {
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configurations.extractForNativeBuild.files.each {
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def file = it.absoluteFile
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copy {
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from zipTree(file)
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into "$buildDir/$file.name"
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include "headers/**"
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include "jni/**"
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}
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}
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}
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}
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tasks.whenTaskAdded { task ->
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if (task.name.contains('externalNativeBuild')) {
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task.dependsOn(extractAARForNativeBuild)
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}
|
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}
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@ -1,27 +0,0 @@
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<?xml version="1.0" encoding="utf-8"?>
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<manifest xmlns:android="http://schemas.android.com/apk/res/android"
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package="org.pytorch.testapp">
|
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|
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<application
|
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android:allowBackup="true"
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android:label="${APP_NAME}"
|
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android:supportsRtl="true"
|
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android:theme="@style/AppTheme">
|
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|
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<activity android:name="${MAIN_ACTIVITY}">
|
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<intent-filter>
|
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<action android:name="android.intent.action.MAIN" />
|
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|
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<category android:name="android.intent.category.LAUNCHER" />
|
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</intent-filter>
|
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</activity>
|
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</application>
|
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|
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<uses-permission android:name="android.permission.CAMERA" />
|
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|
||||
<!--
|
||||
Permissions required by the Snapdragon Profiler to collect GPU metrics.
|
||||
-->
|
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<uses-permission android:name="android.permission.INTERNET" />
|
||||
<uses-permission android:name="android.permission.WRITE_EXTERNAL_STORAGE" />
|
||||
</manifest>
|
||||
@ -1,3 +0,0 @@
|
||||
*
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||||
*/
|
||||
!.gitignore
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||||
@ -1,77 +0,0 @@
|
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#include <android/log.h>
|
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#include <pthread.h>
|
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#include <unistd.h>
|
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#include <cassert>
|
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#include <cmath>
|
||||
#include <vector>
|
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#define ALOGI(...) \
|
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__android_log_print(ANDROID_LOG_INFO, "PyTorchTestAppJni", __VA_ARGS__)
|
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#define ALOGE(...) \
|
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__android_log_print(ANDROID_LOG_ERROR, "PyTorchTestAppJni", __VA_ARGS__)
|
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|
||||
#include "jni.h"
|
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|
||||
#include <torch/script.h>
|
||||
|
||||
namespace pytorch_testapp_jni {
|
||||
namespace {
|
||||
|
||||
template <typename T>
|
||||
void log(const char* m, T t) {
|
||||
std::ostringstream os;
|
||||
os << t << std::endl;
|
||||
ALOGI("%s %s", m, os.str().c_str());
|
||||
}
|
||||
|
||||
struct JITCallGuard {
|
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c10::InferenceMode guard;
|
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torch::jit::GraphOptimizerEnabledGuard no_optimizer_guard{false};
|
||||
};
|
||||
} // namespace
|
||||
|
||||
static void loadAndForwardModel(JNIEnv* env, jclass, jstring jModelPath) {
|
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const char* modelPath = env->GetStringUTFChars(jModelPath, 0);
|
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assert(modelPath);
|
||||
|
||||
// To load torchscript model for mobile we need set these guards,
|
||||
// because mobile build doesn't support features like autograd for smaller
|
||||
// build size which is placed in `struct JITCallGuard` in this example. It may
|
||||
// change in future, you can track the latest changes keeping an eye in
|
||||
// android/pytorch_android/src/main/cpp/pytorch_jni_jit.cpp
|
||||
JITCallGuard guard;
|
||||
torch::jit::Module module = torch::jit::load(modelPath);
|
||||
module.eval();
|
||||
torch::Tensor t = torch::randn({1, 3, 224, 224});
|
||||
log("input tensor:", t);
|
||||
c10::IValue t_out = module.forward({t});
|
||||
log("output tensor:", t_out);
|
||||
env->ReleaseStringUTFChars(jModelPath, modelPath);
|
||||
}
|
||||
} // namespace pytorch_testapp_jni
|
||||
|
||||
JNIEXPORT jint JNI_OnLoad(JavaVM* vm, void*) {
|
||||
JNIEnv* env;
|
||||
if (vm->GetEnv(reinterpret_cast<void**>(&env), JNI_VERSION_1_6) != JNI_OK) {
|
||||
return JNI_ERR;
|
||||
}
|
||||
|
||||
jclass c =
|
||||
env->FindClass("org/pytorch/testapp/LibtorchNativeClient$NativePeer");
|
||||
if (c == nullptr) {
|
||||
return JNI_ERR;
|
||||
}
|
||||
|
||||
static const JNINativeMethod methods[] = {
|
||||
{"loadAndForwardModel",
|
||||
"(Ljava/lang/String;)V",
|
||||
(void*)pytorch_testapp_jni::loadAndForwardModel},
|
||||
};
|
||||
int rc = env->RegisterNatives(
|
||||
c, methods, sizeof(methods) / sizeof(JNINativeMethod));
|
||||
|
||||
if (rc != JNI_OK) {
|
||||
return rc;
|
||||
}
|
||||
|
||||
return JNI_VERSION_1_6;
|
||||
}
|
||||
@ -1,214 +0,0 @@
|
||||
package org.pytorch.testapp;
|
||||
|
||||
import android.Manifest;
|
||||
import android.content.pm.PackageManager;
|
||||
import android.os.Bundle;
|
||||
import android.os.Handler;
|
||||
import android.os.HandlerThread;
|
||||
import android.os.SystemClock;
|
||||
import android.util.Log;
|
||||
import android.util.Size;
|
||||
import android.view.TextureView;
|
||||
import android.view.ViewStub;
|
||||
import android.widget.TextView;
|
||||
import android.widget.Toast;
|
||||
import androidx.annotation.Nullable;
|
||||
import androidx.annotation.UiThread;
|
||||
import androidx.annotation.WorkerThread;
|
||||
import androidx.appcompat.app.AppCompatActivity;
|
||||
import androidx.camera.core.CameraX;
|
||||
import androidx.camera.core.ImageAnalysis;
|
||||
import androidx.camera.core.ImageAnalysisConfig;
|
||||
import androidx.camera.core.ImageProxy;
|
||||
import androidx.camera.core.Preview;
|
||||
import androidx.camera.core.PreviewConfig;
|
||||
import androidx.core.app.ActivityCompat;
|
||||
import java.nio.FloatBuffer;
|
||||
import org.pytorch.IValue;
|
||||
import org.pytorch.MemoryFormat;
|
||||
import org.pytorch.Module;
|
||||
import org.pytorch.PyTorchAndroid;
|
||||
import org.pytorch.Tensor;
|
||||
import org.pytorch.torchvision.TensorImageUtils;
|
||||
|
||||
public class CameraActivity extends AppCompatActivity {
|
||||
private static final String TAG = BuildConfig.LOGCAT_TAG;
|
||||
private static final int TEXT_TRIM_SIZE = 4096;
|
||||
|
||||
private static final int REQUEST_CODE_CAMERA_PERMISSION = 200;
|
||||
private static final String[] PERMISSIONS = {Manifest.permission.CAMERA};
|
||||
|
||||
private long mLastAnalysisResultTime;
|
||||
|
||||
protected HandlerThread mBackgroundThread;
|
||||
protected Handler mBackgroundHandler;
|
||||
protected Handler mUIHandler;
|
||||
|
||||
private TextView mTextView;
|
||||
private StringBuilder mTextViewStringBuilder = new StringBuilder();
|
||||
|
||||
@Override
|
||||
protected void onCreate(Bundle savedInstanceState) {
|
||||
super.onCreate(savedInstanceState);
|
||||
setContentView(R.layout.activity_camera);
|
||||
mTextView = findViewById(R.id.text);
|
||||
mUIHandler = new Handler(getMainLooper());
|
||||
startBackgroundThread();
|
||||
|
||||
if (ActivityCompat.checkSelfPermission(this, Manifest.permission.CAMERA)
|
||||
!= PackageManager.PERMISSION_GRANTED) {
|
||||
ActivityCompat.requestPermissions(this, PERMISSIONS, REQUEST_CODE_CAMERA_PERMISSION);
|
||||
} else {
|
||||
setupCameraX();
|
||||
}
|
||||
}
|
||||
|
||||
@Override
|
||||
protected void onPostCreate(@Nullable Bundle savedInstanceState) {
|
||||
super.onPostCreate(savedInstanceState);
|
||||
startBackgroundThread();
|
||||
}
|
||||
|
||||
protected void startBackgroundThread() {
|
||||
mBackgroundThread = new HandlerThread("ModuleActivity");
|
||||
mBackgroundThread.start();
|
||||
mBackgroundHandler = new Handler(mBackgroundThread.getLooper());
|
||||
}
|
||||
|
||||
@Override
|
||||
protected void onDestroy() {
|
||||
stopBackgroundThread();
|
||||
super.onDestroy();
|
||||
}
|
||||
|
||||
protected void stopBackgroundThread() {
|
||||
mBackgroundThread.quitSafely();
|
||||
try {
|
||||
mBackgroundThread.join();
|
||||
mBackgroundThread = null;
|
||||
mBackgroundHandler = null;
|
||||
} catch (InterruptedException e) {
|
||||
Log.e(TAG, "Error on stopping background thread", e);
|
||||
}
|
||||
}
|
||||
|
||||
@Override
|
||||
public void onRequestPermissionsResult(
|
||||
int requestCode, String[] permissions, int[] grantResults) {
|
||||
if (requestCode == REQUEST_CODE_CAMERA_PERMISSION) {
|
||||
if (grantResults[0] == PackageManager.PERMISSION_DENIED) {
|
||||
Toast.makeText(
|
||||
this,
|
||||
"You can't use image classification example without granting CAMERA permission",
|
||||
Toast.LENGTH_LONG)
|
||||
.show();
|
||||
finish();
|
||||
} else {
|
||||
setupCameraX();
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
private static final int TENSOR_WIDTH = 224;
|
||||
private static final int TENSOR_HEIGHT = 224;
|
||||
|
||||
private void setupCameraX() {
|
||||
final TextureView textureView =
|
||||
((ViewStub) findViewById(R.id.camera_texture_view_stub))
|
||||
.inflate()
|
||||
.findViewById(R.id.texture_view);
|
||||
final PreviewConfig previewConfig = new PreviewConfig.Builder().build();
|
||||
final Preview preview = new Preview(previewConfig);
|
||||
preview.setOnPreviewOutputUpdateListener(
|
||||
new Preview.OnPreviewOutputUpdateListener() {
|
||||
@Override
|
||||
public void onUpdated(Preview.PreviewOutput output) {
|
||||
textureView.setSurfaceTexture(output.getSurfaceTexture());
|
||||
}
|
||||
});
|
||||
|
||||
final ImageAnalysisConfig imageAnalysisConfig =
|
||||
new ImageAnalysisConfig.Builder()
|
||||
.setTargetResolution(new Size(TENSOR_WIDTH, TENSOR_HEIGHT))
|
||||
.setCallbackHandler(mBackgroundHandler)
|
||||
.setImageReaderMode(ImageAnalysis.ImageReaderMode.ACQUIRE_LATEST_IMAGE)
|
||||
.build();
|
||||
final ImageAnalysis imageAnalysis = new ImageAnalysis(imageAnalysisConfig);
|
||||
imageAnalysis.setAnalyzer(
|
||||
new ImageAnalysis.Analyzer() {
|
||||
@Override
|
||||
public void analyze(ImageProxy image, int rotationDegrees) {
|
||||
if (SystemClock.elapsedRealtime() - mLastAnalysisResultTime < 500) {
|
||||
return;
|
||||
}
|
||||
|
||||
final Result result = CameraActivity.this.analyzeImage(image, rotationDegrees);
|
||||
|
||||
if (result != null) {
|
||||
mLastAnalysisResultTime = SystemClock.elapsedRealtime();
|
||||
CameraActivity.this.runOnUiThread(
|
||||
new Runnable() {
|
||||
@Override
|
||||
public void run() {
|
||||
CameraActivity.this.handleResult(result);
|
||||
}
|
||||
});
|
||||
}
|
||||
}
|
||||
});
|
||||
|
||||
CameraX.bindToLifecycle(this, preview, imageAnalysis);
|
||||
}
|
||||
|
||||
private Module mModule;
|
||||
private FloatBuffer mInputTensorBuffer;
|
||||
private Tensor mInputTensor;
|
||||
|
||||
@WorkerThread
|
||||
@Nullable
|
||||
protected Result analyzeImage(ImageProxy image, int rotationDegrees) {
|
||||
Log.i(TAG, String.format("analyzeImage(%s, %d)", image, rotationDegrees));
|
||||
if (mModule == null) {
|
||||
Log.i(TAG, "Loading module from asset '" + BuildConfig.MODULE_ASSET_NAME + "'");
|
||||
mModule = PyTorchAndroid.loadModuleFromAsset(getAssets(), BuildConfig.MODULE_ASSET_NAME);
|
||||
mInputTensorBuffer = Tensor.allocateFloatBuffer(3 * TENSOR_WIDTH * TENSOR_HEIGHT);
|
||||
mInputTensor =
|
||||
Tensor.fromBlob(mInputTensorBuffer, new long[] {1, 3, TENSOR_WIDTH, TENSOR_HEIGHT});
|
||||
}
|
||||
|
||||
final long startTime = SystemClock.elapsedRealtime();
|
||||
TensorImageUtils.imageYUV420CenterCropToFloatBuffer(
|
||||
image.getImage(),
|
||||
rotationDegrees,
|
||||
TENSOR_WIDTH,
|
||||
TENSOR_HEIGHT,
|
||||
TensorImageUtils.TORCHVISION_NORM_MEAN_RGB,
|
||||
TensorImageUtils.TORCHVISION_NORM_STD_RGB,
|
||||
mInputTensorBuffer,
|
||||
0,
|
||||
MemoryFormat.CHANNELS_LAST);
|
||||
final long moduleForwardStartTime = SystemClock.elapsedRealtime();
|
||||
final Tensor outputTensor = mModule.forward(IValue.from(mInputTensor)).toTensor();
|
||||
final long moduleForwardDuration = SystemClock.elapsedRealtime() - moduleForwardStartTime;
|
||||
|
||||
final float[] scores = outputTensor.getDataAsFloatArray();
|
||||
final long analysisDuration = SystemClock.elapsedRealtime() - startTime;
|
||||
|
||||
return new Result(scores, moduleForwardDuration, analysisDuration);
|
||||
}
|
||||
|
||||
@UiThread
|
||||
protected void handleResult(Result result) {
|
||||
int ixs[] = Utils.topK(result.scores, 1);
|
||||
String message =
|
||||
String.format(
|
||||
"forwardDuration:%d class:%s",
|
||||
result.moduleForwardDuration, Constants.IMAGENET_CLASSES[ixs[0]]);
|
||||
Log.i(TAG, message);
|
||||
mTextViewStringBuilder.insert(0, '\n').insert(0, message);
|
||||
if (mTextViewStringBuilder.length() > TEXT_TRIM_SIZE) {
|
||||
mTextViewStringBuilder.delete(TEXT_TRIM_SIZE, mTextViewStringBuilder.length());
|
||||
}
|
||||
mTextView.setText(mTextViewStringBuilder.toString());
|
||||
}
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
@ -1,22 +0,0 @@
|
||||
package org.pytorch.testapp;
|
||||
|
||||
import com.facebook.soloader.nativeloader.NativeLoader;
|
||||
import com.facebook.soloader.nativeloader.SystemDelegate;
|
||||
|
||||
public final class LibtorchNativeClient {
|
||||
|
||||
public static void loadAndForwardModel(final String modelPath) {
|
||||
NativePeer.loadAndForwardModel(modelPath);
|
||||
}
|
||||
|
||||
private static class NativePeer {
|
||||
static {
|
||||
if (!NativeLoader.isInitialized()) {
|
||||
NativeLoader.init(new SystemDelegate());
|
||||
}
|
||||
NativeLoader.loadLibrary("pytorch_testapp_jni");
|
||||
}
|
||||
|
||||
private static native void loadAndForwardModel(final String modelPath);
|
||||
}
|
||||
}
|
||||
@ -1,171 +0,0 @@
|
||||
package org.pytorch.testapp;
|
||||
|
||||
import android.content.Context;
|
||||
import android.os.Bundle;
|
||||
import android.os.Handler;
|
||||
import android.os.HandlerThread;
|
||||
import android.os.SystemClock;
|
||||
import android.util.Log;
|
||||
import android.widget.TextView;
|
||||
import androidx.annotation.Nullable;
|
||||
import androidx.annotation.UiThread;
|
||||
import androidx.annotation.WorkerThread;
|
||||
import androidx.appcompat.app.AppCompatActivity;
|
||||
import java.io.File;
|
||||
import java.io.FileOutputStream;
|
||||
import java.io.IOException;
|
||||
import java.io.InputStream;
|
||||
import java.io.OutputStream;
|
||||
import java.nio.FloatBuffer;
|
||||
import org.pytorch.Device;
|
||||
import org.pytorch.IValue;
|
||||
import org.pytorch.MemoryFormat;
|
||||
import org.pytorch.Module;
|
||||
import org.pytorch.PyTorchAndroid;
|
||||
import org.pytorch.Tensor;
|
||||
|
||||
public class MainActivity extends AppCompatActivity {
|
||||
|
||||
private static final String TAG = BuildConfig.LOGCAT_TAG;
|
||||
private static final int TEXT_TRIM_SIZE = 4096;
|
||||
|
||||
private TextView mTextView;
|
||||
|
||||
protected HandlerThread mBackgroundThread;
|
||||
protected Handler mBackgroundHandler;
|
||||
private Module mModule;
|
||||
private FloatBuffer mInputTensorBuffer;
|
||||
private Tensor mInputTensor;
|
||||
private StringBuilder mTextViewStringBuilder = new StringBuilder();
|
||||
|
||||
private final Runnable mModuleForwardRunnable =
|
||||
new Runnable() {
|
||||
@Override
|
||||
public void run() {
|
||||
final Result result = doModuleForward();
|
||||
runOnUiThread(
|
||||
new Runnable() {
|
||||
@Override
|
||||
public void run() {
|
||||
handleResult(result);
|
||||
if (mBackgroundHandler != null) {
|
||||
mBackgroundHandler.post(mModuleForwardRunnable);
|
||||
}
|
||||
}
|
||||
});
|
||||
}
|
||||
};
|
||||
|
||||
public static String assetFilePath(Context context, String assetName) {
|
||||
File file = new File(context.getFilesDir(), assetName);
|
||||
if (file.exists() && file.length() > 0) {
|
||||
return file.getAbsolutePath();
|
||||
}
|
||||
|
||||
try (InputStream is = context.getAssets().open(assetName)) {
|
||||
try (OutputStream os = new FileOutputStream(file)) {
|
||||
byte[] buffer = new byte[4 * 1024];
|
||||
int read;
|
||||
while ((read = is.read(buffer)) != -1) {
|
||||
os.write(buffer, 0, read);
|
||||
}
|
||||
os.flush();
|
||||
}
|
||||
return file.getAbsolutePath();
|
||||
} catch (IOException e) {
|
||||
Log.e(TAG, "Error process asset " + assetName + " to file path");
|
||||
}
|
||||
return null;
|
||||
}
|
||||
|
||||
@Override
|
||||
protected void onCreate(Bundle savedInstanceState) {
|
||||
super.onCreate(savedInstanceState);
|
||||
if (BuildConfig.NATIVE_BUILD) {
|
||||
final String modelFileAbsoluteFilePath =
|
||||
new File(assetFilePath(this, BuildConfig.MODULE_ASSET_NAME)).getAbsolutePath();
|
||||
LibtorchNativeClient.loadAndForwardModel(modelFileAbsoluteFilePath);
|
||||
return;
|
||||
}
|
||||
setContentView(R.layout.activity_main);
|
||||
mTextView = findViewById(R.id.text);
|
||||
startBackgroundThread();
|
||||
mBackgroundHandler.post(mModuleForwardRunnable);
|
||||
}
|
||||
|
||||
protected void startBackgroundThread() {
|
||||
mBackgroundThread = new HandlerThread(TAG + "_bg");
|
||||
mBackgroundThread.start();
|
||||
mBackgroundHandler = new Handler(mBackgroundThread.getLooper());
|
||||
}
|
||||
|
||||
@Override
|
||||
protected void onDestroy() {
|
||||
stopBackgroundThread();
|
||||
super.onDestroy();
|
||||
}
|
||||
|
||||
protected void stopBackgroundThread() {
|
||||
mBackgroundThread.quitSafely();
|
||||
try {
|
||||
mBackgroundThread.join();
|
||||
mBackgroundThread = null;
|
||||
mBackgroundHandler = null;
|
||||
} catch (InterruptedException e) {
|
||||
Log.e(TAG, "Error stopping background thread", e);
|
||||
}
|
||||
}
|
||||
|
||||
@WorkerThread
|
||||
@Nullable
|
||||
protected Result doModuleForward() {
|
||||
if (mModule == null) {
|
||||
final long[] shape = BuildConfig.INPUT_TENSOR_SHAPE;
|
||||
long numElements = 1;
|
||||
for (int i = 0; i < shape.length; i++) {
|
||||
numElements *= shape[i];
|
||||
}
|
||||
mInputTensorBuffer = Tensor.allocateFloatBuffer((int) numElements);
|
||||
mInputTensor =
|
||||
Tensor.fromBlob(
|
||||
mInputTensorBuffer, BuildConfig.INPUT_TENSOR_SHAPE, MemoryFormat.CHANNELS_LAST);
|
||||
PyTorchAndroid.setNumThreads(1);
|
||||
mModule =
|
||||
BuildConfig.USE_VULKAN_DEVICE
|
||||
? PyTorchAndroid.loadModuleFromAsset(
|
||||
getAssets(), BuildConfig.MODULE_ASSET_NAME, Device.VULKAN)
|
||||
: PyTorchAndroid.loadModuleFromAsset(getAssets(), BuildConfig.MODULE_ASSET_NAME);
|
||||
}
|
||||
|
||||
final long startTime = SystemClock.elapsedRealtime();
|
||||
final long moduleForwardStartTime = SystemClock.elapsedRealtime();
|
||||
final Tensor outputTensor = mModule.forward(IValue.from(mInputTensor)).toTensor();
|
||||
final long moduleForwardDuration = SystemClock.elapsedRealtime() - moduleForwardStartTime;
|
||||
final float[] scores = outputTensor.getDataAsFloatArray();
|
||||
final long analysisDuration = SystemClock.elapsedRealtime() - startTime;
|
||||
return new Result(scores, moduleForwardDuration, analysisDuration);
|
||||
}
|
||||
|
||||
static class Result {
|
||||
|
||||
private final float[] scores;
|
||||
private final long totalDuration;
|
||||
private final long moduleForwardDuration;
|
||||
|
||||
public Result(float[] scores, long moduleForwardDuration, long totalDuration) {
|
||||
this.scores = scores;
|
||||
this.moduleForwardDuration = moduleForwardDuration;
|
||||
this.totalDuration = totalDuration;
|
||||
}
|
||||
}
|
||||
|
||||
@UiThread
|
||||
protected void handleResult(Result result) {
|
||||
String message = String.format("forwardDuration:%d", result.moduleForwardDuration);
|
||||
mTextViewStringBuilder.insert(0, '\n').insert(0, message);
|
||||
if (mTextViewStringBuilder.length() > TEXT_TRIM_SIZE) {
|
||||
mTextViewStringBuilder.delete(TEXT_TRIM_SIZE, mTextViewStringBuilder.length());
|
||||
}
|
||||
mTextView.setText(mTextViewStringBuilder.toString());
|
||||
}
|
||||
}
|
||||
@ -1,14 +0,0 @@
|
||||
package org.pytorch.testapp;
|
||||
|
||||
class Result {
|
||||
|
||||
public final float[] scores;
|
||||
public final long totalDuration;
|
||||
public final long moduleForwardDuration;
|
||||
|
||||
public Result(float[] scores, long moduleForwardDuration, long totalDuration) {
|
||||
this.scores = scores;
|
||||
this.moduleForwardDuration = moduleForwardDuration;
|
||||
this.totalDuration = totalDuration;
|
||||
}
|
||||
}
|
||||
@ -1,28 +0,0 @@
|
||||
package org.pytorch.testapp;
|
||||
|
||||
import java.util.Arrays;
|
||||
|
||||
public class Utils {
|
||||
|
||||
public static int[] topK(float[] a, final int topk) {
|
||||
float values[] = new float[topk];
|
||||
Arrays.fill(values, -Float.MAX_VALUE);
|
||||
int ixs[] = new int[topk];
|
||||
Arrays.fill(ixs, -1);
|
||||
|
||||
for (int i = 0; i < a.length; i++) {
|
||||
for (int j = 0; j < topk; j++) {
|
||||
if (a[i] > values[j]) {
|
||||
for (int k = topk - 1; k >= j + 1; k--) {
|
||||
values[k] = values[k - 1];
|
||||
ixs[k] = ixs[k - 1];
|
||||
}
|
||||
values[j] = a[i];
|
||||
ixs[j] = i;
|
||||
break;
|
||||
}
|
||||
}
|
||||
}
|
||||
return ixs;
|
||||
}
|
||||
}
|
||||
@ -1,23 +0,0 @@
|
||||
<?xml version="1.0" encoding="utf-8"?>
|
||||
<FrameLayout
|
||||
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"
|
||||
tools:context=".CameraActivity">
|
||||
|
||||
<ViewStub
|
||||
android:id="@+id/camera_texture_view_stub"
|
||||
android:layout_width="match_parent"
|
||||
android:layout_height="match_parent"
|
||||
android:layout="@layout/texture_view"/>
|
||||
|
||||
<TextView
|
||||
android:id="@+id/text"
|
||||
android:layout_width="match_parent"
|
||||
android:layout_height="match_parent"
|
||||
android:layout_gravity="top"
|
||||
android:textSize="16sp"
|
||||
android:textStyle="bold"
|
||||
android:textColor="#ff0000"/>
|
||||
</FrameLayout>
|
||||
@ -1,17 +0,0 @@
|
||||
<?xml version="1.0" encoding="utf-8"?>
|
||||
<FrameLayout 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"
|
||||
tools:context=".MainActivity">
|
||||
|
||||
<TextView
|
||||
android:id="@+id/text"
|
||||
android:layout_width="match_parent"
|
||||
android:layout_height="match_parent"
|
||||
android:layout_gravity="top"
|
||||
android:textSize="14sp"
|
||||
android:background="@android:color/black"
|
||||
android:textColor="@android:color/white" />
|
||||
|
||||
</FrameLayout>
|
||||
@ -1,5 +0,0 @@
|
||||
<?xml version="1.0" encoding="utf-8"?>
|
||||
<TextureView xmlns:android="http://schemas.android.com/apk/res/android"
|
||||
android:id="@+id/texture_view"
|
||||
android:layout_width="match_parent"
|
||||
android:layout_height="0dp" />
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 2.0 KiB |
Binary file not shown.
|
Before Width: | Height: | Size: 2.7 KiB |
@ -1,6 +0,0 @@
|
||||
<?xml version="1.0" encoding="utf-8"?>
|
||||
<resources>
|
||||
<color name="colorPrimary">#008577</color>
|
||||
<color name="colorPrimaryDark">#00574B</color>
|
||||
<color name="colorAccent">#D81B60</color>
|
||||
</resources>
|
||||
@ -1,3 +0,0 @@
|
||||
<resources>
|
||||
<string name="app_name">PyTest</string>
|
||||
</resources>
|
||||
@ -1,11 +0,0 @@
|
||||
<resources>
|
||||
|
||||
<!-- Base application theme. -->
|
||||
<style name="AppTheme" parent="Theme.AppCompat.Light.DarkActionBar">
|
||||
<!-- Customize your theme here. -->
|
||||
<item name="colorPrimary">@color/colorPrimary</item>
|
||||
<item name="colorPrimaryDark">@color/colorPrimaryDark</item>
|
||||
<item name="colorAccent">@color/colorAccent</item>
|
||||
</style>
|
||||
|
||||
</resources>
|
||||
@ -1,24 +0,0 @@
|
||||
from torchvision import models
|
||||
|
||||
import torch
|
||||
|
||||
|
||||
print(torch.version.__version__)
|
||||
|
||||
resnet18 = models.resnet18(weights=models.ResNet18_Weights.IMAGENET1K_V1)
|
||||
resnet18.eval()
|
||||
resnet18_traced = torch.jit.trace(resnet18, torch.rand(1, 3, 224, 224)).save(
|
||||
"app/src/main/assets/resnet18.pt"
|
||||
)
|
||||
|
||||
resnet50 = models.resnet50(weights=models.ResNet50_Weights.IMAGENET1K_V1)
|
||||
resnet50.eval()
|
||||
torch.jit.trace(resnet50, torch.rand(1, 3, 224, 224)).save(
|
||||
"app/src/main/assets/resnet50.pt"
|
||||
)
|
||||
|
||||
mobilenet2q = models.quantization.mobilenet_v2(pretrained=True, quantize=True)
|
||||
mobilenet2q.eval()
|
||||
torch.jit.trace(mobilenet2q, torch.rand(1, 3, 224, 224)).save(
|
||||
"app/src/main/assets/mobilenet2q.pt"
|
||||
)
|
||||
@ -1,27 +0,0 @@
|
||||
"""
|
||||
This is a script for PyTorch Android custom selective build test. It prepares
|
||||
MobileNetV2 TorchScript model, and dumps root ops used by the model for custom
|
||||
build script to create a tailored build which only contains these used ops.
|
||||
"""
|
||||
|
||||
import yaml
|
||||
from torchvision import models
|
||||
|
||||
import torch
|
||||
|
||||
|
||||
# Download and trace the model.
|
||||
model = models.mobilenet_v2(weights=models.MobileNet_V2_Weights.IMAGENET1K_V1)
|
||||
model.eval()
|
||||
example = torch.rand(1, 3, 224, 224)
|
||||
# TODO: create script model with `torch.jit.script`
|
||||
traced_script_module = torch.jit.trace(model, example)
|
||||
|
||||
# Save traced TorchScript model.
|
||||
traced_script_module.save("MobileNetV2.pt")
|
||||
|
||||
# Dump root ops used by the model (for custom build optimization).
|
||||
ops = torch.jit.export_opnames(traced_script_module)
|
||||
|
||||
with open("MobileNetV2.yaml", "w") as output:
|
||||
yaml.dump(ops, output)
|
||||
Reference in New Issue
Block a user