//
// Copyright © 2017 Arm Ltd. All rights reserved.
// See LICENSE file in the project root for full license information.
//
#include "DriverTestHelpers.hpp"
#include <boost/test/unit_test.hpp>
#include <log/log.h>

#include <OperationsUtils.h>

BOOST_AUTO_TEST_SUITE(Convolution2DTests)

using namespace android::hardware;
using namespace driverTestHelpers;
using namespace armnn_driver;

namespace
{

void PaddingTestImpl(android::nn::PaddingScheme paddingScheme)
{
    auto driver = std::make_unique<ArmnnDriver>(DriverOptions(armnn::Compute::CpuRef));
    neuralnetworks::V1_0::Model model  = {};

    uint32_t outSize = paddingScheme == android::nn::kPaddingSame ? 2 : 1;

    // add operands
    float weightValue[] = {1, -1, 0, 1};
    float biasValue[]   = {0};

    AddInputOperand(model, hidl_vec<uint32_t>{1, 2, 3, 1});
    AddTensorOperand(model, hidl_vec<uint32_t>{1, 2, 2, 1}, weightValue);
    AddTensorOperand(model, hidl_vec<uint32_t>{1}, biasValue);
    AddIntOperand(model, (int32_t)paddingScheme); // padding
    AddIntOperand(model, 2); // stride x
    AddIntOperand(model, 2); // stride y
    AddIntOperand(model, 0); // no activation
    AddOutputOperand(model, hidl_vec<uint32_t>{1, 1, outSize, 1});

    // make the convolution operation
    model.operations.resize(1);
    model.operations[0].type = neuralnetworks::V1_0::OperationType::CONV_2D;
    model.operations[0].inputs  = hidl_vec<uint32_t>{0, 1, 2, 3, 4, 5, 6};
    model.operations[0].outputs = hidl_vec<uint32_t>{7};

    // make the prepared model
    android::sp<IPreparedModel> preparedModel = PrepareModel(model, *driver);

    // construct the request
    DataLocation inloc    = {};
    inloc.poolIndex       = 0;
    inloc.offset          = 0;
    inloc.length          = 6 * sizeof(float);
    RequestArgument input = {};
    input.location        = inloc;
    input.dimensions      = hidl_vec<uint32_t>{};

    DataLocation outloc    = {};
    outloc.poolIndex       = 1;
    outloc.offset          = 0;
    outloc.length          = outSize * sizeof(float);
    RequestArgument output = {};
    output.location        = outloc;
    output.dimensions      = hidl_vec<uint32_t>{};

    Request request = {};
    request.inputs  = hidl_vec<RequestArgument>{input};
    request.outputs = hidl_vec<RequestArgument>{output};


    // set the input data (matching source test)
    float indata[] = {4, 1, 0, 3, -1, 2};
    AddPoolAndSetData(6, request, indata);

    // add memory for the output
    android::sp<IMemory> outMemory = AddPoolAndGetData(outSize, request);
    float*               outdata   = static_cast<float*>(static_cast<void*>(outMemory->getPointer()));

    // run the execution
    Execute(preparedModel, request);

    // check the result
    if (paddingScheme == android::nn::kPaddingValid)
    {
        BOOST_TEST(outdata[0] == 2);
    }
    else if (paddingScheme == android::nn::kPaddingSame)
    {
        BOOST_TEST(outdata[0] == 2);
        BOOST_TEST(outdata[1] == 0);
    }
    else
    {
        BOOST_TEST(false);
    }
}

} // namespace <anonymous>

BOOST_AUTO_TEST_CASE(ConvValidPadding)
{
    PaddingTestImpl(android::nn::kPaddingValid);
}

BOOST_AUTO_TEST_CASE(ConvSamePadding)
{
    PaddingTestImpl(android::nn::kPaddingSame);
}

BOOST_AUTO_TEST_SUITE_END()