29 using namespace armnn;
35 IConnectableLayer* convolution3d = network->AddConvolution3dLayer(descriptor,
"convolution3d");
38 Connect(input, convolution3d, inputInfo, 0, 0);
39 Connect(weightsLayer, convolution3d, weightsInfo, 0, 1);
40 Connect(biasLayer, convolution3d, biasInfo, 0, 2);
41 Connect(convolution3d, output, outputInfo, 0, 0);
48 template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType>
52 using namespace armnn;
56 const float qScale = IsQuantizedType<T>() ? 0.25f : 1.0f;
57 const int32_t qOffset = IsQuantizedType<T>() ? 50 : 0;
59 TensorInfo inputInfo({ 1, 5, 5, 5, 1 }, ArmnnType, qScale, qOffset,
true);
60 TensorInfo outputInfo({ 1, 2, 2, 2, 1 }, ArmnnType, qScale, qOffset);
61 TensorInfo weightsInfo({ 3, 3, 3, 1, 1 }, ArmnnType, qScale, qOffset,
true);
62 TensorInfo biasesInfo({ 1 }, ArmnnBType, qScale * qScale, 0,
true);
64 std::vector<float> inputData =
66 0.0f, 1.0f, 2.0f, 3.0f, 4.0f,
67 5.0f, 6.0f, 7.0f, 8.0f, 9.0f,
68 10.0f, 11.0f, 12.0f, 13.0f, 14.0f,
69 15.0f, 16.0f, 17.0f, 18.0f, 19.0f,
71 20.0f, 21.0f, 22.0f, 23.0f, 24.0f,
72 25.0f, 26.0f, 27.0f, 28.0f, 29.0f,
73 30.0f, 31.0f, 32.0f, 33.0f, 34.0f,
74 35.0f, 36.0f, 37.0f, 38.0f, 39.0f,
75 40.0f, 41.0f, 42.0f, 43.0f, 44.0f,
77 45.0f, 46.0f, 47.0f, 48.0f, 49.0f,
78 50.0f, 51.0f, 52.0f, 53.0f, 54.0f,
79 55.0f, 56.0f, 57.0f, 58.0f, 59.0f,
80 60.0f, 61.0f, 62.0f, 63.0f, 64.0f,
81 65.0f, 66.0f, 67.0f, 68.0f, 69.0f,
83 70.0f, 71.0f, 72.0f, 73.0f, 74.0f,
84 75.0f, 76.0f, 77.0f, 78.0f, 79.0f,
85 80.0f, 81.0f, 82.0f, 83.0f, 84.0f,
86 85.0f, 86.0f, 87.0f, 88.0f, 89.0f,
87 90.0f, 91.0f, 92.0f, 93.0f, 94.0f,
88 95.0f, 96.0f, 97.0f, 98.0f, 99.0f,
90 100.0f, 101.0f, 102.0f, 103.0f, 104.0f,
91 105.0f, 106.0f, 107.0f, 108.0f, 109.0f,
92 110.0f, 111.0f, 112.0f, 113.0f, 114.0f,
93 115.0f, 116.0f, 117.0f, 118.0f, 119.0f,
94 120.0f, 121.0f, 122.0f, 123.0f, 124.0f
97 std::vector<float> weightsData =
112 std::vector<float> biasesData = { 1.f };
114 std::vector<float> expectedOutputData =
146 std::vector<T> qInputData = armnnUtils::QuantizedVector<T>(inputData, qScale, qOffset);
147 std::vector<T> qWeightsData = armnnUtils::QuantizedVector<T>(weightsData, qScale, qOffset);
148 std::vector<T> qExpectedOutputData = armnnUtils::QuantizedVector<T>(expectedOutputData, qScale, qOffset);
150 std::vector<BT> qBiasesData = armnnUtils::QuantizedVector<BT>(biasesData, qScale * qScale, 0);
155 INetworkPtr network = CreateConvolution3dNetwork(descriptor,
163 EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(network),
164 { { 0, qInputData } },
165 { { 0, qExpectedOutputData } },
Interface for a layer that is connectable to other layers via InputSlots and OutputSlots.
uint32_t m_PadBack
Padding back value in the depth dimension.
void PermuteTensorNdhwcToNcdhw(armnn::TensorInfo &tensorInfo, std::vector< T > &tensorData)
uint32_t m_StrideX
Stride value when proceeding through input for the width dimension.
void Convolution3dEndToEnd(const std::vector< armnn::BackendId > &backends, armnn::DataLayout dataLayout)
typename ResolveTypeImpl< DT >::Type ResolveType
uint32_t m_PadBottom
Padding bottom value in the height dimension.
bool m_BiasEnabled
Enable/disable bias.
Copyright (c) 2021 ARM Limited and Contributors.
uint32_t m_StrideY
Stride value when proceeding through input for the height dimension.
A tensor defined by a TensorInfo (shape and data type) and an immutable backing store.
uint32_t m_PadFront
Padding front value in the depth dimension.
uint32_t m_PadLeft
Padding left value in the width dimension.
A Convolution3dDescriptor for the Convolution3dLayer.
uint32_t m_PadRight
Padding right value in the width dimension.
DataLayout m_DataLayout
The data layout to be used (NDHWC, NCDHW).
uint32_t m_PadTop
Padding top value in the height dimension.
void Connect(armnn::IConnectableLayer *from, armnn::IConnectableLayer *to, const armnn::TensorInfo &tensorInfo, unsigned int fromIndex, unsigned int toIndex)
std::unique_ptr< INetwork, void(*)(INetwork *network)> INetworkPtr
static INetworkPtr Create(NetworkOptions networkOptions={})
uint32_t m_StrideZ
Stride value when proceeding through input for the depth dimension.