mirror of
https://github.com/pytorch/pytorch.git
synced 2025-10-20 12:54:11 +08:00
[Reland] Add a workflow to release Android binaries (#110976)
This adds 2 jobs to build PyTorch Android with and without lite interpreter: * Keep the list of currently supported ABI armeabi-v7a, arm64-v8a, x86, x86_64 * Pass all the test on emulator * Run an the test app on emulator and my Android phone `arm64-v8a` without any issue  * Run on AWS https://us-west-2.console.aws.amazon.com/devicefarm/home#/mobile/projects/b531574a-fb82-40ae-b687-8f0b81341ae0/runs/5fce6818-628a-4099-9aab-23e91a212076 Pull Request resolved: https://github.com/pytorch/pytorch/pull/110976 Approved by: https://github.com/atalman
This commit is contained in:
20
.github/workflows/_run_android_tests.yml
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20
.github/workflows/_run_android_tests.yml
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@ -41,9 +41,17 @@ jobs:
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strategy:
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matrix: ${{ fromJSON(needs.filter.outputs.test-matrix) }}
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fail-fast: false
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# NB: This job can only run on GitHub Linux runner atm. This is an ok thing though
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# because that runner is ephemeral and could access upload secrets
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runs-on: ${{ matrix.runner }}
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env:
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# GitHub runner installs Android SDK on this path
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ANDROID_ROOT: /usr/local/lib/android
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ANDROID_NDK_VERSION: '21.4.7075529'
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BUILD_LITE_INTERPRETER: ${{ matrix.use_lite_interpreter }}
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# 4 of them are supported atm: armeabi-v7a, arm64-v8a, x86, x86_64
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SUPPORT_ABI: '${{ matrix.support_abi }}'
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steps:
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# [see note: pytorch repo ref]
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- name: Checkout PyTorch
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uses: pytorch/pytorch/.github/actions/checkout-pytorch@main
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@ -51,7 +59,7 @@ jobs:
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uses: pytorch/test-infra/.github/actions/setup-miniconda@main
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with:
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python-version: 3.8
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environment-file: .github/requirements/conda-env-${{ runner.os }}-${{ runner.arch }}
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environment-file: .github/requirements/conda-env-${{ runner.os }}-${{ runner.arch }}.txt
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- name: Install NDK
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uses: nick-fields/retry@v2.8.2
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@ -60,12 +68,12 @@ jobs:
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max_attempts: 3
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retry_wait_seconds: 90
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command: |
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set -eux
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# Install NDK 21 after GitHub update
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# https://github.com/actions/virtual-environments/issues/5595
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ANDROID_ROOT="/usr/local/lib/android"
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ANDROID_SDK_ROOT="${ANDROID_ROOT}/sdk"
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ANDROID_NDK="${ANDROID_SDK_ROOT}/ndk-bundle"
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ANDROID_NDK_VERSION="21.4.7075529"
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SDKMANAGER="${ANDROID_SDK_ROOT}/cmdline-tools/latest/bin/sdkmanager"
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# NB: This step downloads and installs NDK, thus it could be flaky.
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@ -86,8 +94,10 @@ jobs:
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- name: Build PyTorch Android
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run: |
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set -eux
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echo "CMAKE_PREFIX_PATH=${CONDA_PREFIX:-"$(dirname "$(which conda)")/../"}" >> "${GITHUB_ENV}"
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${CONDA_RUN} ./scripts/build_pytorch_android.sh x86
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${CONDA_RUN} ./scripts/build_pytorch_android.sh "${SUPPORT_ABI}"
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- name: Run tests
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uses: reactivecircus/android-emulator-runner@v2
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48
.github/workflows/build-android-binaries.yml
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Normal file
48
.github/workflows/build-android-binaries.yml
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@ -0,0 +1,48 @@
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name: Build Android binaries
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on:
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push:
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branches:
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- nightly
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tags:
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# NOTE: Binary build pipelines should only get triggered on release candidate builds
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# Release candidate tags look like: v1.11.0-rc1
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- v[0-9]+.[0-9]+.[0-9]+-rc[0-9]+
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paths:
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- .github/workflows/build-android-binaries.yml
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- .github/workflows/_run_android_tests.yml
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- android/**
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pull_request:
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paths:
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- .github/workflows/build-android-binaries.yml
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- .github/workflows/_run_android_tests.yml
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- android/**
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# NB: We can use this workflow dispatch to test and build the binaries manually
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workflow_dispatch:
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concurrency:
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group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.sha }}-${{ github.event_name == 'workflow_dispatch' }}
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cancel-in-progress: true
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jobs:
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android-build-test:
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name: android-build-test
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uses: ./.github/workflows/_run_android_tests.yml
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with:
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test-matrix: |
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{ include: [
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{ config: 'default',
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shard: 1,
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num_shards: 1,
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runner: 'ubuntu-20.04-16x',
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use_lite_interpreter: 1,
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support_abi: 'armeabi-v7a,arm64-v8a,x86,x86_64',
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},
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{ config: 'default',
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shard: 1,
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num_shards: 1,
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runner: 'ubuntu-20.04-16x',
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use_lite_interpreter: 0,
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support_abi: 'armeabi-v7a,arm64-v8a,x86,x86_64',
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},
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]}
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9
.github/workflows/periodic.yml
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9
.github/workflows/periodic.yml
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@ -189,7 +189,14 @@ jobs:
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with:
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test-matrix: |
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{ include: [
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{ config: "default", shard: 1, num_shards: 1, runner: "ubuntu-20.04-16x" },
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{ config: 'default',
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shard: 1,
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num_shards: 1,
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runner: 'ubuntu-20.04-16x',
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use_lite_interpreter: 1,
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# Just set x86 for testing here
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support_abi: 'x86',
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},
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]}
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linux-vulkan-focal-py3_11-clang10-build:
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4
.gitignore
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4
.gitignore
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@ -365,3 +365,7 @@ venv/
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# Log files
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*.log
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sweep/
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# Android build artifacts
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android/pytorch_android/.cxx
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android/pytorch_android_torchvision/.cxx
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@ -10,6 +10,7 @@ import java.io.IOException;
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import java.util.HashMap;
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import java.util.Map;
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import org.junit.Test;
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import org.junit.Ignore;
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public abstract class PytorchTestBase {
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private static final String TEST_MODULE_ASSET_NAME = "android_api_module.ptl";
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@ -413,7 +414,10 @@ public abstract class PytorchTestBase {
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}
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@Test
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@Ignore
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public void testSpectralOps() throws IOException {
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// NB: This model fails without lite interpreter. The error is as follows:
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// RuntimeError: stft requires the return_complex parameter be given for real inputs
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runModel("spectral_ops");
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}
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@ -41,6 +41,11 @@ android {
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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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@ -63,14 +68,15 @@ android {
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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", "\"mnet.pt\"")
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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", "\"mnet_vulkan.pt\"")
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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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@ -78,7 +84,7 @@ android {
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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.pt\"")
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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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@ -149,8 +155,8 @@ dependencies {
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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:1.12.0-SNAPSHOT'
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nightlyImplementation 'org.pytorch:pytorch_android_torchvision:1.12.0-SNAPSHOT'
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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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@ -47,7 +47,11 @@ from tensor_ops import (
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TensorViewOpsModule,
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)
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from torch.jit.mobile import _load_for_lite_interpreter
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from torchvision_models import MobileNetV2Module
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from torchvision_models import (
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MobileNetV2Module,
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MobileNetV2VulkanModule,
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Resnet18Module,
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)
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test_path_ios = "ios/TestApp/models/"
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test_path_android = "android/pytorch_android/src/androidTest/assets/"
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@ -98,6 +102,8 @@ all_modules = {
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"torchscript_collection_ops": TSCollectionOpsModule(),
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# vision
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"mobilenet_v2": MobileNetV2Module(),
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"mobilenet_v2_vulkan": MobileNetV2VulkanModule(),
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"resnet18": Resnet18Module(),
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# android api module
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"android_api_module": AndroidAPIModule(),
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}
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@ -19,3 +19,37 @@ class MobileNetV2Module:
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)
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optimized_module(example)
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return optimized_module
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class MobileNetV2VulkanModule:
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def getModule(self):
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model = torchvision.models.mobilenet_v2(pretrained=True)
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model.eval()
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example = torch.zeros(1, 3, 224, 224)
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traced_script_module = torch.jit.trace(model, example)
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optimized_module = optimize_for_mobile(traced_script_module, backend="vulkan")
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augment_model_with_bundled_inputs(
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optimized_module,
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[
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(example, ),
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],
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)
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optimized_module(example)
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return optimized_module
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class Resnet18Module:
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def getModule(self):
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model = torchvision.models.resnet18(pretrained=True)
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model.eval()
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example = torch.zeros(1, 3, 224, 224)
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traced_script_module = torch.jit.trace(model, example)
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optimized_module = optimize_for_mobile(traced_script_module)
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augment_model_with_bundled_inputs(
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optimized_module,
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[
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(example, ),
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],
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)
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optimized_module(example)
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return optimized_module
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