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Commit b098d21e authored by Colin Cross's avatar Colin Cross
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Revert "Revert "Copy VTS tests from v1.2 to v1.3" am: 9613b464 am: 432f6d16 am: fe2cd911"

This reverts commit 1ea542bd.

Reason for revert: revert of a cherry-pick broke master

Change-Id: I09797f5f3898501a008186a22dd411b00e9e2c67
parent bcc123b2
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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
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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
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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
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