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//
// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#pragma once
#include "EndToEndTestImpl.hpp"
#include "QuantizeHelper.hpp"
#include <ResolveType.hpp>
#include <CommonTestUtils.hpp>
#include <armnnTestUtils/DataLayoutUtils.hpp>
#include <map>
#include <vector>
namespace
{
armnn::INetworkPtr CreateConvolution3dNetwork(const armnn::Convolution3dDescriptor& descriptor,
const armnn::TensorInfo& inputInfo,
const armnn::TensorInfo& weightsInfo,
const armnn::TensorInfo& biasInfo,
const armnn::TensorInfo& outputInfo,
const armnn::ConstTensor& weights,
const armnn::ConstTensor& biases)
{
using namespace armnn;
INetworkPtr network(INetwork::Create());
IConnectableLayer* input = network->AddInputLayer(0, "input");
armnn::IConnectableLayer* weightsLayer = network->AddConstantLayer(weights, "Weights");
armnn::IConnectableLayer* biasLayer = network->AddConstantLayer(biases, "Bias");
IConnectableLayer* convolution3d = network->AddConvolution3dLayer(descriptor, "convolution3d");
IConnectableLayer* output = network->AddOutputLayer(0, "output");
Connect(input, convolution3d, inputInfo, 0, 0);
Connect(weightsLayer, convolution3d, weightsInfo, 0, 1);
Connect(biasLayer, convolution3d, biasInfo, 0, 2);
Connect(convolution3d, output, outputInfo, 0, 0);
return network;
}
} // anonymous namespace
template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType>
void Convolution3dEndToEnd(const std::vector<armnn::BackendId>& backends,
armnn::DataLayout dataLayout)
{
using namespace armnn;
using T = ResolveType<ArmnnType>;
using BT = ResolveType<ArmnnBType>;
const float qScale = IsQuantizedType<T>() ? 0.25f : 1.0f;
const int32_t qOffset = IsQuantizedType<T>() ? 50 : 0;
TensorInfo inputInfo({ 1, 5, 5, 5, 1 }, ArmnnType, qScale, qOffset, true);
TensorInfo outputInfo({ 1, 2, 2, 2, 1 }, ArmnnType, qScale, qOffset);
TensorInfo weightsInfo({ 3, 3, 3, 1, 1 }, ArmnnType, qScale, qOffset, true);
TensorInfo biasesInfo({ 1 }, ArmnnBType, qScale * qScale, 0, true);
std::vector<float> inputData =
{
0.0f, 1.0f, 2.0f, 3.0f, 4.0f,
5.0f, 6.0f, 7.0f, 8.0f, 9.0f,
10.0f, 11.0f, 12.0f, 13.0f, 14.0f,
15.0f, 16.0f, 17.0f, 18.0f, 19.0f,
20.0f, 21.0f, 22.0f, 23.0f, 24.0f,
25.0f, 26.0f, 27.0f, 28.0f, 29.0f,
30.0f, 31.0f, 32.0f, 33.0f, 34.0f,
35.0f, 36.0f, 37.0f, 38.0f, 39.0f,
40.0f, 41.0f, 42.0f, 43.0f, 44.0f,
45.0f, 46.0f, 47.0f, 48.0f, 49.0f,
50.0f, 51.0f, 52.0f, 53.0f, 54.0f,
55.0f, 56.0f, 57.0f, 58.0f, 59.0f,
60.0f, 61.0f, 62.0f, 63.0f, 64.0f,
65.0f, 66.0f, 67.0f, 68.0f, 69.0f,
70.0f, 71.0f, 72.0f, 73.0f, 74.0f,
75.0f, 76.0f, 77.0f, 78.0f, 79.0f,
80.0f, 81.0f, 82.0f, 83.0f, 84.0f,
85.0f, 86.0f, 87.0f, 88.0f, 89.0f,
90.0f, 91.0f, 92.0f, 93.0f, 94.0f,
95.0f, 96.0f, 97.0f, 98.0f, 99.0f,
100.0f, 101.0f, 102.0f, 103.0f, 104.0f,
105.0f, 106.0f, 107.0f, 108.0f, 109.0f,
110.0f, 111.0f, 112.0f, 113.0f, 114.0f,
115.0f, 116.0f, 117.0f, 118.0f, 119.0f,
120.0f, 121.0f, 122.0f, 123.0f, 124.0f
};
std::vector<float> weightsData =
{
1.0f, 1.0f, 1.0f,
1.0f, 1.0f, 1.0f,
1.0f, 1.0f, 1.0f,
0.0f, 0.0f, 0.0f,
0.0f, 0.0f, 0.0f,
0.0f, 0.0f, 0.0f,
1.0f, 1.0f, 1.0f,
1.0f, 1.0f, 1.0f,
1.0f, 1.0f, 1.0f,
};
std::vector<float> biasesData = { 1.f };
std::vector<float> expectedOutputData =
{
559.0f, 595.0f,
739.0f, 775.0f,
1459.0f, 1495.0f,
1639.0f, 1675.0f,
};
Convolution3dDescriptor descriptor;
descriptor.m_PadLeft = 0;
descriptor.m_PadRight = 0;
descriptor.m_PadTop = 0;
descriptor.m_PadBottom = 0;
descriptor.m_PadFront = 0;
descriptor.m_PadBack = 0;
descriptor.m_StrideX = 2;
descriptor.m_StrideY = 2;
descriptor.m_StrideZ = 2;
descriptor.m_BiasEnabled = true;
descriptor.m_DataLayout = dataLayout;
// Permute input and output if NCDHW.
if (dataLayout == DataLayout::NCDHW)
{
PermuteTensorNdhwcToNcdhw(inputInfo, inputData);
PermuteTensorNdhwcToNcdhw(outputInfo, expectedOutputData);
}
// 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);
std::vector<BT> qBiasesData = armnnUtils::QuantizedVector<BT>(biasesData, qScale * qScale, 0);
ConstTensor weights(weightsInfo, qWeightsData);
ConstTensor biases(biasesInfo, qBiasesData);
INetworkPtr network = CreateConvolution3dNetwork(descriptor,
inputInfo,
weightsInfo,
biasesInfo,
outputInfo,
weights,
biases);
EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(network),
{ { 0, qInputData } },
{ { 0, qExpectedOutputData } },
backends);
}
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