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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include "CommonTestUtils.hpp"
#include <ResolveType.hpp>
#include <armnn/INetwork.hpp>
#include <armnn/utility/NumericCast.hpp>
#include <doctest/doctest.h>
#include <vector>
namespace
{
template<armnn::DataType ArmnnTypeInput>
INetworkPtr CreateComparisonNetwork(const std::vector<TensorShape>& inputShapes,
const TensorShape& outputShape,
ComparisonOperation operation,
const float qScale = 1.0f,
const int32_t qOffset = 0)
{
using namespace armnn;
INetworkPtr net(INetwork::Create());
ComparisonDescriptor descriptor(operation);
IConnectableLayer* comparisonLayer = net->AddComparisonLayer(descriptor, "comparison");
for (unsigned int i = 0; i < inputShapes.size(); ++i)
{
TensorInfo inputTensorInfo(inputShapes[i], ArmnnTypeInput, qScale, qOffset);
IConnectableLayer* input = net->AddInputLayer(armnn::numeric_cast<LayerBindingId>(i));
Connect(input, comparisonLayer, inputTensorInfo, 0, i);
}
TensorInfo outputTensorInfo(outputShape, DataType::Boolean, qScale, qOffset);
IConnectableLayer* output = net->AddOutputLayer(0, "output");
Connect(comparisonLayer, output, outputTensorInfo, 0, 0);
return net;
}
template<armnn::DataType ArmnnInType,
typename TInput = armnn::ResolveType<ArmnnInType>>
void ComparisonSimpleEndToEnd(const std::vector<BackendId>& backends,
ComparisonOperation operation,
const std::vector<uint8_t> expectedOutput)
{
using namespace armnn;
const std::vector<TensorShape> inputShapes{{ 2, 2, 2, 2 }, { 2, 2, 2, 2 }};
const TensorShape& outputShape = { 2, 2, 2, 2 };
// Builds up the structure of the network
INetworkPtr net = CreateComparisonNetwork<ArmnnInType>(inputShapes, outputShape, operation);
CHECK(net);
const std::vector<TInput> input0({ 1, 1, 1, 1, 5, 5, 5, 5,
3, 3, 3, 3, 4, 4, 4, 4 });
const std::vector<TInput> input1({ 1, 1, 1, 1, 3, 3, 3, 3,
5, 5, 5, 5, 4, 4, 4, 4 });
std::map<int, std::vector<TInput>> inputTensorData = {{ 0, input0 }, { 1, input1 }};
std::map<int, std::vector<uint8_t>> expectedOutputData = {{ 0, expectedOutput }};
EndToEndLayerTestImpl<ArmnnInType, DataType::Boolean>(move(net), inputTensorData, expectedOutputData, backends);
}
template<armnn::DataType ArmnnInType,
typename TInput = armnn::ResolveType<ArmnnInType>>
void ComparisonBroadcastEndToEnd(const std::vector<BackendId>& backends,
ComparisonOperation operation,
const std::vector<uint8_t> expectedOutput)
{
using namespace armnn;
const std::vector<TensorShape> inputShapes{{ 1, 2, 2, 3 }, { 1, 1, 1, 3 }};
const TensorShape& outputShape = { 1, 2, 2, 3 };
// Builds up the structure of the network
INetworkPtr net = CreateComparisonNetwork<ArmnnInType>(inputShapes, outputShape, operation);
const std::vector<TInput> input0({ 1, 2, 3, 1, 0, 6,
7, 8, 9, 10, 11, 12 });
const std::vector<TInput> input1({ 1, 1, 3 });
std::map<int, std::vector<TInput>> inputTensorData = {{ 0, input0 }, { 1, input1 }};
std::map<int, std::vector<uint8_t>> expectedOutputData = {{ 0, expectedOutput }};
EndToEndLayerTestImpl<ArmnnInType, DataType::Boolean>(move(net), inputTensorData, expectedOutputData, backends);
}
} // anonymous namespace
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