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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include "QuantizeHelper.hpp"
#include <armnnUtils/Permute.hpp>
#include <QuantizeHelper.hpp>
#include <ResolveType.hpp>
#include <CommonTestUtils.hpp>
#include <map>
#include <vector>
namespace
{
INetworkPtr CreateTransposeConvolution2dNetwork(const armnn::TransposeConvolution2dDescriptor& descriptor,
const armnn::TensorInfo& inputInfo,
const armnn::TensorInfo& outputInfo,
const armnn::ConstTensor& weights,
const armnn::Optional<armnn::ConstTensor>& biases)
{
using namespace armnn;
INetworkPtr network(INetwork::Create());
IConnectableLayer* input = network->AddInputLayer(0, "input");
IConnectableLayer* transposeConvolution2d =
network->AddTransposeConvolution2dLayer(descriptor, weights, biases, "transposeConvolution2d");
IConnectableLayer* output = network->AddOutputLayer(0, "output");
Connect(input, transposeConvolution2d, inputInfo, 0, 0);
Connect(transposeConvolution2d, output, outputInfo, 0, 0);
return network;
}
} // anonymous namespace
template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType>
void TransposeConvolution2dEndToEnd(const std::vector<armnn::BackendId>& backends,
armnn::DataLayout dataLayout)
{
using namespace armnn;
using T = ResolveType<ArmnnType>;
constexpr unsigned int batches = 1u;
constexpr unsigned int channels = 1u;
constexpr unsigned int wInput = 3u;
constexpr unsigned int hInput = wInput;
constexpr unsigned int wOutput = 5u;
constexpr unsigned int hOutput = wOutput;
constexpr unsigned int wWeights = 3u;
constexpr unsigned int hWeights = wWeights;
TensorShape inputShape = MakeTensorShape(batches, channels, hInput, wInput, dataLayout);
TensorShape outputShape = MakeTensorShape(batches, channels, hOutput, wOutput, dataLayout);
TensorShape weightsShape = MakeTensorShape(batches, channels, hWeights, wWeights, dataLayout);
const float qScale = IsQuantizedType<T>() ? 0.25f : 1.0f;
const int32_t qOffset = IsQuantizedType<T>() ? 50 : 0;
TensorInfo inputInfo(inputShape, ArmnnType, qScale, qOffset, true);
TensorInfo outputInfo(outputShape, ArmnnType, qScale, qOffset);
TensorInfo weightsInfo(weightsShape, ArmnnType, qScale, qOffset, true);
TensorInfo biasesInfo({ channels }, ArmnnBType, qScale * qScale, 0, true);
std::vector<float> inputData =
{
1.f, 1.f, 1.f,
1.f, 1.f, 1.f,
1.f, 1.f, 1.f
};
std::vector<float> weightsData =
{
1.f, 2.f, 3.f,
4.f, 5.f, 6.f,
7.f, 8.f, 9.f
};
std::vector<float> biasesData = { 1.f };
std::vector<float> expectedOutputData =
{
6.f, 11.f, 6.f, 11.f, 6.f,
11.f, 21.f, 11.f, 21.f, 11.f,
6.f, 11.f, 6.f, 11.f, 6.f,
11.f, 21.f, 11.f, 21.f, 11.f,
6.f, 11.f, 6.f, 11.f, 6.f
};
TransposeConvolution2dDescriptor descriptor;
descriptor.m_PadLeft = 1;
descriptor.m_PadRight = 1;
descriptor.m_PadTop = 1;
descriptor.m_PadBottom = 1;
descriptor.m_StrideX = 2;
descriptor.m_StrideY = 2;
descriptor.m_BiasEnabled = true;
descriptor.m_DataLayout = dataLayout;
// swizzle data if needed
if (dataLayout == armnn::DataLayout::NHWC)
{
constexpr size_t dataTypeSize = sizeof(float);
const armnn::PermutationVector nchwToNhwc = { 0, 3, 1, 2 };
std::vector<float> tmp(inputData.size());
armnnUtils::Permute(inputInfo.GetShape(), nchwToNhwc, inputData.data(), tmp.data(), dataTypeSize);
inputData = tmp;
tmp.resize(weightsData.size());
armnnUtils::Permute(weightsInfo.GetShape(), nchwToNhwc, weightsData.data(), tmp.data(), dataTypeSize);
weightsData = tmp;
tmp.resize(expectedOutputData.size());
armnnUtils::Permute(outputInfo.GetShape(), nchwToNhwc, expectedOutputData.data(), tmp.data(), dataTypeSize);
expectedOutputData = tmp;
}
// quantize data
std::vector<T> qInputData = armnnUtils::QuantizedVector<T>(inputData, qScale, qOffset);
std::vector<T> qWeightsData = armnnUtils::QuantizedVector<T>(weightsData, qScale, qOffset);
std::vector<T> qExpectedOutputData = armnnUtils::QuantizedVector<T>(expectedOutputData, qScale, qOffset);
using BT = ResolveType<ArmnnBType>;
std::vector<BT> qBiasesData = armnnUtils::QuantizedVector<BT>(biasesData, qScale * qScale, 0);
ConstTensor weights(weightsInfo, qWeightsData);
ConstTensor biases(biasesInfo, qBiasesData);
INetworkPtr network = CreateTransposeConvolution2dNetwork(descriptor,
inputInfo,
outputInfo,
weights,
Optional<ConstTensor>(biases));
EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(network),
{ { 0, qInputData } },
{ { 0, qExpectedOutputData } },
backends);
}
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