1 //
2 // Copyright © 2022 Arm Ltd and Contributors. All rights reserved.
3 // SPDX-License-Identifier: MIT
4 //
5 #pragma once
6
7 #include "EndToEndTestImpl.hpp"
8 #include <armnnUtils/QuantizeHelper.hpp>
9
10 #include <ResolveType.hpp>
11
12 #include <CommonTestUtils.hpp>
13 #include <armnnTestUtils/DataLayoutUtils.hpp>
14
15 #include <map>
16 #include <vector>
17
18 namespace
19 {
20
CreateConstConvolution2dNetwork(const armnn::Convolution2dDescriptor & 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,bool biasEnabled)21 armnn::INetworkPtr CreateConstConvolution2dNetwork(const armnn::Convolution2dDescriptor& descriptor,
22 const armnn::TensorInfo& inputInfo,
23 const armnn::TensorInfo& weightsInfo,
24 const armnn::TensorInfo& biasInfo,
25 const armnn::TensorInfo& outputInfo,
26 const armnn::ConstTensor& weights,
27 const armnn::ConstTensor& biases,
28 bool biasEnabled)
29 {
30 using namespace armnn;
31
32 INetworkPtr network(INetwork::Create());
33 IConnectableLayer* input = network->AddInputLayer(0, "input");
34 IConnectableLayer* weightsLayer = network->AddConstantLayer(weights, "Weights");
35 IConnectableLayer* convolution2d = network->AddConvolution2dLayer(descriptor, "convolution2d");
36 IConnectableLayer* output = network->AddOutputLayer(0, "output");
37
38 Connect(input, convolution2d, inputInfo, 0, 0);
39 Connect(weightsLayer, convolution2d, weightsInfo, 0, 1);
40
41 if(biasEnabled)
42 {
43 armnn::IConnectableLayer* biasLayer = network->AddConstantLayer(biases, "Bias");
44 Connect(biasLayer, convolution2d, biasInfo, 0, 2);
45 }
46
47 Connect(convolution2d, output, outputInfo, 0, 0);
48
49 return network;
50 }
51
52 template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>>
Convolution2dEndToEnd(const std::vector<armnn::BackendId> & backends,armnn::DataLayout dataLayout,bool biasEnabled=true)53 void Convolution2dEndToEnd(const std::vector<armnn::BackendId>& backends,
54 armnn::DataLayout dataLayout,
55 bool biasEnabled = true)
56 {
57 using namespace armnn;
58
59 const float qScale = IsQuantizedType<T>() ? 0.25f : 1.0f;
60 const int32_t qOffset = IsQuantizedType<T>() ? 50 : 0;
61
62 TensorInfo inputInfo({ 1, 5, 5, 1 }, ArmnnType, qScale, qOffset, true);
63 TensorInfo outputInfo({ 1, 3, 3, 1 }, ArmnnType, qScale, qOffset);
64 TensorInfo weightsInfo({ 1, 3, 3, 1 }, ArmnnType, qScale, qOffset, true);
65 TensorInfo biasesInfo({ 1 }, ArmnnType, qScale * qScale, 0, true);
66
67 std::vector<float> inputData =
68 {
69 1.0f, 5.0f, 2.0f, 3.0f, 5.0f,
70 8.0f, 7.0f, 3.0f, 6.0f, 3.0f,
71 3.0f, 3.0f, 9.0f, 1.0f, 9.0f,
72 4.0f, 1.0f, 8.0f, 1.0f, 3.0f,
73 6.0f, 8.0f, 1.0f, 9.0f, 2.0f
74 };
75
76 std::vector<float> weightsData =
77 {
78 4.0f, 5.0f, 6.0f,
79 0.0f, 0.0f, 0.0f,
80 3.0f, 2.0f, 1.0f
81 };
82
83 std::vector<float> biasesData = { 1.0f };
84
85 float bias = biasEnabled ? biasesData[0] : 0.0f;
86 std::vector<float> expectedOutputData =
87 {
88 65.0f + bias, 76.0f + bias, 91.0f + bias,
89 107.0f + bias, 99.0f + bias, 89.0f + bias,
90 116.0f + bias, 98.0f + bias, 118.0f + bias,
91 };
92
93 Convolution2dDescriptor descriptor;
94 descriptor.m_PadLeft = 0;
95 descriptor.m_PadRight = 0;
96 descriptor.m_PadTop = 0;
97 descriptor.m_PadBottom = 0;
98 descriptor.m_StrideX = 1;
99 descriptor.m_StrideY = 1;
100 descriptor.m_BiasEnabled = biasEnabled;
101 descriptor.m_DataLayout = dataLayout;
102
103 if (dataLayout == DataLayout::NCHW)
104 {
105 PermuteTensorNhwcToNchw(inputInfo, inputData);
106 PermuteTensorNhwcToNchw(weightsInfo, weightsData);
107 PermuteTensorNhwcToNchw(outputInfo, expectedOutputData);
108 }
109
110 // Quantize data
111 std::vector<T> qInputData = armnnUtils::QuantizedVector<T>(inputData, qScale, qOffset);
112 std::vector<T> qWeightsData = armnnUtils::QuantizedVector<T>(weightsData, qScale, qOffset);
113 std::vector<T> qExpectedOutputData = armnnUtils::QuantizedVector<T>(expectedOutputData, qScale, qOffset);
114 std::vector<T> qBiasesData = armnnUtils::QuantizedVector<T>(biasesData, qScale * qScale, 0);
115
116 ConstTensor weights(weightsInfo, qWeightsData);
117 ConstTensor biases(biasesInfo, qBiasesData);
118
119 INetworkPtr network = CreateConstConvolution2dNetwork(descriptor,
120 inputInfo,
121 weightsInfo,
122 biasesInfo,
123 outputInfo,
124 weights,
125 biases,
126 biasEnabled);
127
128 EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(network),
129 {{ 0, qInputData }},
130 {{ 0, qExpectedOutputData }},
131 backends);
132 }
133
134 } // anonymous namespace
135