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/test/mlts/models/
DREADME.txt22 MobileNet tensorflow lite model based on:
34 8bit quantized MobileNet tensorflow lite model based on:
46 MobileNet v2 tensorflow lite model based on:
58 8bit quantized MobileNet v2 tensorflow lite model based on:
70 Float version of MobileNet SSD tensorflow model based on:
83 8bit quantized MobileNet SSD tensorflow lite model based on:
96 TTS tensorflow lite model based on:
102 Note that the tensorflow lite model is the acoustic model in the paper. It is used because it is
103 much heavier than the duration model.
106 ASR tensorflow lite model based on the ASR acoustic model in:
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/test/mlts/benchmark/jni/
Drandom_graph_test_jni.cpp190 GeneratedModel model; in runRandomModel() local
191 createModel(*testModel, &model); in runRandomModel()
192 if (!model.isValid()) { in runRandomModel()
196 auto modelFinishResult = model.finish(); in runRandomModel()
212 model.getHandle(), &mDevice, 1, opsSupportedFlags.get()); in runRandomModel()
247 auto [compilationResult, compilation] = CreateCompilation(model, devices); in runRandomModel()
404 android::nn::generated_tests::GeneratedModel model; in OperationsSupportedByDevice() local
405 createModel(*testModel, &model); in OperationsSupportedByDevice()
406 if (!model.isValid()) { in OperationsSupportedByDevice()
410 auto modelFinishResult = model.finish(); in OperationsSupportedByDevice()
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Dbenchmark_jni.cpp113 BenchmarkModel* model = (BenchmarkModel *) _modelHandle; in Java_com_android_nn_benchmark_core_NNTestBase_destroyModel() local
114 delete(model); in Java_com_android_nn_benchmark_core_NNTestBase_destroyModel()
125 BenchmarkModel* model = (BenchmarkModel *) _modelHandle; in Java_com_android_nn_benchmark_core_NNTestBase_resizeInputTensors() local
130 return model->resizeInputTensors(std::move(shape)); in Java_com_android_nn_benchmark_core_NNTestBase_resizeInputTensors()
322 BenchmarkModel* model = reinterpret_cast<BenchmarkModel*>(_modelHandle); in Java_com_android_nn_benchmark_core_NNTestBase_runBenchmark() local
343 bool success = model->benchmark(data.data(), inferencesSeqMaxCount, timeoutSec, flags, &result); in Java_com_android_nn_benchmark_core_NNTestBase_runBenchmark()
425 BenchmarkModel* model = reinterpret_cast<BenchmarkModel*>(_modelHandle); in Java_com_android_nn_benchmark_core_NNTestBase_dumpAllLayers() local
433 model->dumpAllLayers(dumpPathStr, data.data()); in Java_com_android_nn_benchmark_core_NNTestBase_dumpAllLayers()
506 BenchmarkModel* model = reinterpret_cast<BenchmarkModel*>(_modelHandle); in Java_com_android_nn_benchmark_core_NNTestBase_runCompilationBenchmark() local
515 model->benchmarkCompilation(maxNumIterations, warmupTimeoutSec, in Java_com_android_nn_benchmark_core_NNTestBase_runCompilationBenchmark()
Drun_tflite.cpp106 BenchmarkModel* model = new BenchmarkModel(); in create() local
107 if (!model->init(modelfile, tfliteBackend, enable_intermediate_tensors_dump, nnapiErrno, in create()
110 delete model; in create()
113 return model; in create()
/test/mlts/benchmark/src/com/android/nn/benchmark/app/
DAcceleratorSpecificTestSupport.java47 for (TestModels.TestModelEntry model : TestModels.modelsList()) { in findTestModelRunningOnAccelerator()
48 if (Processor.isTestModelSupportedByAccelerator(context, model, acceleratorName)) { in findTestModelRunningOnAccelerator()
49 return Optional.of(model); in findTestModelRunningOnAccelerator()
58 for (TestModels.TestModelEntry model : TestModels.modelsList()) { in findAllTestModelsRunningOnAccelerator()
59 if (Processor.isTestModelSupportedByAccelerator(context, model, acceleratorName)) { in findAllTestModelsRunningOnAccelerator()
60 result.add(model); in findAllTestModelsRunningOnAccelerator()
DNNTest.java41 public NNTest(TestModels.TestModelEntry model) { in NNTest() argument
42 super(model, /*acceleratorName=*/null); in NNTest()
DTFLiteTest.java31 public TFLiteTest(TestModelEntry model) { in TFLiteTest() argument
32 super(model, /*acceleratorName=*/null); in TFLiteTest()
DNNInferenceStressTest.java44 public NNInferenceStressTest(TestModels.TestModelEntry model, String acceleratorName) { in NNInferenceStressTest() argument
45 super(model, acceleratorName); in NNInferenceStressTest()
DNNCrystalBallTest.java49 public NNCrystalBallTest(TestModels.TestModelEntry model, String acceleratorName) { in NNCrystalBallTest() argument
50 super(model, acceleratorName); in NNCrystalBallTest()
DNNModelLoadingStressTest.java47 public NNModelLoadingStressTest(TestModels.TestModelEntry model, String acceleratorName) { in NNModelLoadingStressTest() argument
48 super(model, acceleratorName); in NNModelLoadingStressTest()
DNNScoringTest.java53 public NNScoringTest(TestModels.TestModelEntry model, String acceleratorName) { in NNScoringTest() argument
54 super(model, acceleratorName); in NNScoringTest()
DBenchmarkTestBase.java87 public BenchmarkTestBase(TestModelEntry model, String acceleratorName) { in BenchmarkTestBase() argument
89 mModel = model; in BenchmarkTestBase()
304 … TestModels.modelsList().stream().map(model -> new Object[] {model}).collect(Collectors.toList()) in modelsOnAccelerators()
/test/mlts/benchmark/
Dbuild_and_run_benchmark.sh60 -m|--filter-model)
105 model-loading-stress)
125 multi-process-model-load-stress)
130 memory-mapped-model-load-stress)
134 model-load-random-stress)
DREADME.txt44 partition the model and assign the best available one(s) by using the
62 on a single model in multiple processes and threads with different probabilities in client process
65 * multi-process-model-load-stress: this extends the `parallel-inference-stress` running inference
66 on a single model in multiple processes and threads with different probabilities in client process
69 * memory-mapped-model-load-stress: runs a series of parallel model compilation with memory mapped
72 * model-load-random-stress: test compiling a large set of randomly generated models
/test/mlts/benchmark/native/
Dmulti_process_test.cpp115 std::unique_ptr<BenchmarkModel> model(BenchmarkModel::create( in runModel() local
122 if (!model) { in runModel()
137 std::unique_ptr<BenchmarkModel> model(BenchmarkModel::create( in runModel() local
144 if (!model) { in runModel()
152 return model->benchmark(data, std::numeric_limits<int>::max(), in runModel()
/test/app_compat/csuite/harness/src/main/java/com/android/csuite/core/
DDeviceJUnit4ClassRunner.java21 import org.junit.runners.model.InitializationError;
/test/mlts/benchmark/src/com/android/nn/benchmark/core/
DTestModels.java130 static public void registerModel(TestModelEntry model) { in registerModel() argument
134 sTestModelEntryList.add(model); in registerModel()
/test/mlts/benchmark/tools/
Dtensor_utils.py106 for model in topk_aosp['models']:
107 self.nnapi_to_tflite_name[model['name']] = model['modelFile']
108 self.tflite_to_nnapi_name[model['modelFile']] = model['name']
118 model = self.ModelMetaData(self.__get_model_json_path(tflite_model_name))
120 self.models[nnapi_model_name] = model
/test/mlts/benchmark/crashtest/src/com/android/nn/crashtest/
DMainActivity.java176 model -> { in modelsForAccelerator()
180 model, acceleratorName); in modelsForAccelerator()
186 acceleratorName, model.mModelName)); in modelsForAccelerator()
/test/cts-root/hostsidetests/packageinstaller/src/com/android/cts_root/packageinstaller/host/
DSessionCleanUpHostTest.java34 import org.junit.runners.model.Statement;
/test/vts-testcase/nbu/src/
DREADME.md39 The two devices should be of the same model and build (identical fingerprint).
/test/mlts/benchmark/results/
DChart.bundle.min.js10 …model:t._model,deltaK:0,mK:0}}),c=h.length;for(e=0;e<c;++e)if(!(n=h[e]).model.skip){if(i=e>0?h[e-1…
/test/mlts/models/assets/image_classification/
Dlabels.txt663 model t