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//
// Copyright © 2017 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include "TestUtils.hpp"
#include <Graph.hpp>
#include <ResolveType.hpp>
#include <SubgraphViewSelector.hpp>
#include <armnn/BackendRegistry.hpp>
#include <armnn/Types.hpp>
#include <armnn/backends/SubgraphView.hpp>
#include <armnn/backends/TensorHandle.hpp>
#include <algorithm>
#include <random>
#include <vector>
// Checks that two collections have the exact same contents (in any order)
// The given collections do not have to contain duplicates
// Cannot use std::sort here because std lists have their own std::list::sort method
template <typename CollectionType>
bool AreEqual(const CollectionType& lhs, const CollectionType& rhs)
{
if (lhs.size() != rhs.size())
{
return false;
}
auto lhs_it = std::find_if(lhs.begin(), lhs.end(), [&rhs](auto& item)
{
return std::find(rhs.begin(), rhs.end(), item) == rhs.end();
});
return lhs_it == lhs.end();
}
// Checks that the given collection contains the specified item
template <typename CollectionType>
bool Contains(const CollectionType& collection, const typename CollectionType::value_type& item)
{
return std::find(collection.begin(), collection.end(), item) != collection.end();
}
// Checks that the given map contains the specified key
template <typename MapType>
bool Contains(const MapType& map, const typename MapType::key_type& key)
{
return map.find(key) != map.end();
}
// Utility template for comparing tensor elements
template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
inline bool Compare(T a, T b, float tolerance = 0.000001f)
{
if (ArmnnType == armnn::DataType::Boolean)
{
// NOTE: Boolean is represented as uint8_t (with zero equals
// false and everything else equals true), therefore values
// need to be casted to bool before comparing them
return static_cast<bool>(a) == static_cast<bool>(b);
}
// NOTE: All other types can be cast to float and compared with
// a certain level of tolerance
return std::fabs(static_cast<float>(a) - static_cast<float>(b)) <= tolerance;
}
template <typename ConvolutionLayer>
void SetWeightAndBias(ConvolutionLayer* layer, const armnn::TensorInfo& weightInfo, const armnn::TensorInfo& biasInfo)
{
layer->m_Weight = std::make_unique<armnn::ScopedTensorHandle>(weightInfo);
layer->m_Bias = std::make_unique<armnn::ScopedTensorHandle>(biasInfo);
layer->m_Weight->Allocate();
layer->m_Bias->Allocate();
}
armnn::SubgraphView::InputSlots CreateInputsFrom(const std::vector<armnn::Layer*>& layers);
armnn::SubgraphView::OutputSlots CreateOutputsFrom(const std::vector<armnn::Layer*>& layers);
armnn::SubgraphView::SubgraphViewPtr CreateSubgraphViewFrom(armnn::SubgraphView::InputSlots&& inputs,
armnn::SubgraphView::OutputSlots&& outputs,
armnn::SubgraphView::Layers&& layers);
armnn::IBackendInternalUniquePtr CreateBackendObject(const armnn::BackendId& backendId);
armnn::TensorShape MakeTensorShape(unsigned int batches,
unsigned int channels,
unsigned int height,
unsigned int width,
armnn::DataLayout layout);
template<typename DataType>
static std::vector<DataType> GenerateRandomData(size_t size)
{
constexpr bool isIntegerType = std::is_integral<DataType>::value;
using Distribution =
typename std::conditional<isIntegerType,
std::uniform_int_distribution<DataType>,
std::uniform_real_distribution<DataType>>::type;
static constexpr DataType lowerLimit = std::numeric_limits<DataType>::min();
static constexpr DataType upperLimit = std::numeric_limits<DataType>::max();
static Distribution distribution(lowerLimit, upperLimit);
static std::default_random_engine generator;
std::vector<DataType> randomData(size);
generate(randomData.begin(), randomData.end(), []() { return distribution(generator); });
return randomData;
}
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