Copy VTS tests from v1.2 to v1.3 am: d0e339e1da (b5f893c1) · Commits · e / os / android_hardware_interfaces

neuralnetworks/1.3/vts/OWNERS

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		# Neuralnetworks team
		butlermichael@google.com
		dgross@google.com
		jeanluc@google.com
		levp@google.com
		miaowang@google.com
		mikie@google.com
		mks@google.com
		pszczepaniak@google.com
		slavash@google.com
		vddang@google.com
		xusongw@google.com

		# VTS team
		yim@google.com
		yuexima@google.com

neuralnetworks/1.3/vts/functional/BasicTests.cpp

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		/*
		* Copyright (C) 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.
		*/

		#define LOG_TAG "neuralnetworks_hidl_hal_test"

		#include "VtsHalNeuralnetworks.h"

		namespace android::hardware::neuralnetworks::V1_2::vts::functional {

		using V1_0::DeviceStatus;
		using V1_0::ErrorStatus;
		using V1_0::PerformanceInfo;

		// create device test
		TEST_P(NeuralnetworksHidlTest, CreateDevice) {}

		// status test
		TEST_P(NeuralnetworksHidlTest, StatusTest) {
		Return<DeviceStatus> status = kDevice->getStatus();
		ASSERT_TRUE(status.isOk());
		EXPECT_EQ(DeviceStatus::AVAILABLE, static_cast<DeviceStatus>(status));
		}

		// initialization
		TEST_P(NeuralnetworksHidlTest, GetCapabilitiesTest) {
		using OperandPerformance = Capabilities::OperandPerformance;
		Return<void> ret = kDevice->getCapabilities_1_2([](ErrorStatus status,
		const Capabilities& capabilities) {
		EXPECT_EQ(ErrorStatus::NONE, status);

		auto isPositive = [](const PerformanceInfo& perf) {
		return perf.execTime > 0.0f && perf.powerUsage > 0.0f;
		};

		EXPECT_TRUE(isPositive(capabilities.relaxedFloat32toFloat16PerformanceScalar));
		EXPECT_TRUE(isPositive(capabilities.relaxedFloat32toFloat16PerformanceTensor));
		const auto& opPerf = capabilities.operandPerformance;
		EXPECT_TRUE(std::all_of(
		opPerf.begin(), opPerf.end(),
		[isPositive](const OperandPerformance& a) { return isPositive(a.info); }));
		EXPECT_TRUE(std::is_sorted(opPerf.begin(), opPerf.end(),
		[](const OperandPerformance& a, const OperandPerformance& b) {
		return a.type < b.type;
		}));
		});
		EXPECT_TRUE(ret.isOk());
		}

		// device version test
		TEST_P(NeuralnetworksHidlTest, GetDeviceVersionStringTest) {
		Return<void> ret =
		kDevice->getVersionString([](ErrorStatus status, const hidl_string& version) {
		EXPECT_EQ(ErrorStatus::NONE, status);
		EXPECT_LT(0, version.size());
		});
		EXPECT_TRUE(ret.isOk());
		}

		// device type test
		TEST_P(NeuralnetworksHidlTest, GetDeviceTypeTest) {
		Return<void> ret = kDevice->getType([](ErrorStatus status, DeviceType type) {
		EXPECT_EQ(ErrorStatus::NONE, status);
		EXPECT_TRUE(type == DeviceType::OTHER \|\| type == DeviceType::CPU \|\|
		type == DeviceType::GPU \|\| type == DeviceType::ACCELERATOR);
		});
		EXPECT_TRUE(ret.isOk());
		}

		// device supported extensions test
		TEST_P(NeuralnetworksHidlTest, GetDeviceSupportedExtensionsTest) {
		Return<void> ret = kDevice->getSupportedExtensions(
		[](ErrorStatus status, const hidl_vec<Extension>& extensions) {
		EXPECT_EQ(ErrorStatus::NONE, status);
		for (auto& extension : extensions) {
		std::string extensionName = extension.name;
		EXPECT_FALSE(extensionName.empty());
		for (char c : extensionName) {
		EXPECT_TRUE(('a' <= c && c <= 'z') \|\| ('0' <= c && c <= '9') \|\| c == '_' \|\|
		c == '.')
		<< "Extension name contains an illegal character: " << c;
		}
		EXPECT_NE(extensionName.find('.'), std::string::npos)
		<< "Extension name must start with the reverse domain name of the "
		"vendor";
		}
		});
		EXPECT_TRUE(ret.isOk());
		}

		// getNumberOfCacheFilesNeeded test
		TEST_P(NeuralnetworksHidlTest, getNumberOfCacheFilesNeeded) {
		Return<void> ret = kDevice->getNumberOfCacheFilesNeeded(
		[](ErrorStatus status, uint32_t numModelCache, uint32_t numDataCache) {
		EXPECT_EQ(ErrorStatus::NONE, status);
		EXPECT_LE(numModelCache,
		static_cast<uint32_t>(Constant::MAX_NUMBER_OF_CACHE_FILES));
		EXPECT_LE(numDataCache, static_cast<uint32_t>(Constant::MAX_NUMBER_OF_CACHE_FILES));
		});
		EXPECT_TRUE(ret.isOk());
		}
		} // namespace android::hardware::neuralnetworks::V1_2::vts::functional

neuralnetworks/1.3/vts/functional/Callbacks.cpp

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		/*
		* Copyright (C) 2019 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.
		*/

		#define LOG_TAG "Callbacks"

		#include "1.2/Callbacks.h"

		#include <android-base/logging.h>

		#include <limits>

		namespace android::hardware::neuralnetworks::V1_2::implementation {

		using V1_0::ErrorStatus;

		constexpr Timing kNoTiming = {.timeOnDevice = std::numeric_limits<uint64_t>::max(),
		.timeInDriver = std::numeric_limits<uint64_t>::max()};

		// PreparedModelCallback methods begin here

		Return<void> PreparedModelCallback::notify(ErrorStatus errorStatus,
		const sp<V1_0::IPreparedModel>& preparedModel) {
		{
		std::lock_guard<std::mutex> hold(mMutex);

		// quick-return if object has already been notified
		if (mNotified) {
		return Void();
		}

		// store results and mark as notified
		mErrorStatus = errorStatus;
		mPreparedModel = preparedModel;
		mNotified = true;
		}

		mCondition.notify_all();
		return Void();
		}

		Return<void> PreparedModelCallback::notify_1_2(ErrorStatus errorStatus,
		const sp<V1_2::IPreparedModel>& preparedModel) {
		return notify(errorStatus, preparedModel);
		}

		void PreparedModelCallback::wait() const {
		std::unique_lock<std::mutex> lock(mMutex);
		mCondition.wait(lock, [this] { return mNotified; });
		}

		ErrorStatus PreparedModelCallback::getStatus() const {
		wait();
		return mErrorStatus;
		}

		sp<V1_0::IPreparedModel> PreparedModelCallback::getPreparedModel() const {
		wait();
		return mPreparedModel;
		}

		// ExecutionCallback methods begin here

		Return<void> ExecutionCallback::notify(ErrorStatus errorStatus) {
		notifyInternal(errorStatus, {}, kNoTiming);
		return Void();
		}

		Return<void> ExecutionCallback::notify_1_2(ErrorStatus errorStatus,
		const hidl_vec<OutputShape>& outputShapes,
		const Timing& timing) {
		if (errorStatus == ErrorStatus::OUTPUT_INSUFFICIENT_SIZE) {
		// outputShapes must not be empty if OUTPUT_INSUFFICIENT_SIZE.
		if (outputShapes.size() == 0) {
		LOG(ERROR) << "Notified with empty output shape vector when OUTPUT_INSUFFICIENT_SIZE";
		notifyInternal(ErrorStatus::GENERAL_FAILURE, {}, kNoTiming);
		return Void();
		}
		} else if (errorStatus != ErrorStatus::NONE) {
		// outputShapes must be empty if errorStatus is neither NONE nor OUTPUT_INSUFFICIENT_SIZE.
		if (outputShapes.size() != 0) {
		LOG(ERROR) << "Notified with non-empty output shape vector when error status is "
		"neither NONE nor OUTPUT_INSUFFICIENT_SIZE";
		notifyInternal(ErrorStatus::GENERAL_FAILURE, {}, kNoTiming);
		return Void();
		}
		}
		notifyInternal(errorStatus, outputShapes, timing);
		return Void();
		}

		void ExecutionCallback::wait() const {
		std::unique_lock<std::mutex> lock(mMutex);
		mCondition.wait(lock, [this] { return mNotified; });
		}

		ErrorStatus ExecutionCallback::getStatus() const {
		wait();
		return mErrorStatus;
		}

		const std::vector<OutputShape>& ExecutionCallback::getOutputShapes() const {
		wait();
		return mOutputShapes;
		}

		Timing ExecutionCallback::getTiming() const {
		wait();
		return mTiming;
		}

		void ExecutionCallback::notifyInternal(ErrorStatus errorStatus,
		const hidl_vec<OutputShape>& outputShapes,
		const Timing& timing) {
		{
		std::lock_guard<std::mutex> hold(mMutex);

		// quick-return if object has already been notified
		if (mNotified) {
		return;
		}

		mErrorStatus = errorStatus;
		mOutputShapes = outputShapes;
		mTiming = timing;
		mNotified = true;
		}
		mCondition.notify_all();
		}

		} // namespace android::hardware::neuralnetworks::V1_2::implementation