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Commit 13fdfcd4 authored by Lev Proleev's avatar Lev Proleev
Browse files

Copy VTS tests from v1.2 to v1.3 

So that it's easier to see what actually has changed in VTS tests for
version 1.3

Bug: 139120468
Test: m
Change-Id: I09797f5f3898501a008186a22dd411b00e9e2c67
Merged-In: I09797f5f3898501a008186a22dd411b00e9e2c67
(cherry picked from commit 3b13b55a)
parent ce8c632a

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