telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2017 Arm Ltd. All rights reserved. |
David Beck | ecb56cd | 2018-09-05 12:52:57 +0100 | [diff] [blame] | 3 | // SPDX-License-Identifier: MIT |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4 | // |
| 5 | #include "LayerTests.hpp" |
| 6 | |
| 7 | #include "test/TensorHelpers.hpp" |
| 8 | #include "TensorCopyUtils.hpp" |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 9 | #include "Permute.hpp" |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 10 | |
| 11 | #include <boost/test/unit_test.hpp> |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 12 | #include <boost/assert.hpp> |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 13 | |
David Beck | 711fa31 | 2018-09-24 10:46:38 +0100 | [diff] [blame] | 14 | #include <armnn/LayerSupport.hpp> |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 15 | |
David Beck | 711fa31 | 2018-09-24 10:46:38 +0100 | [diff] [blame] | 16 | #include <backends/CpuTensorHandle.hpp> |
| 17 | #include <backends/WorkloadFactory.hpp> |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 18 | |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 19 | #include <algorithm> |
| 20 | #include <boost/cast.hpp> |
| 21 | |
| 22 | #include "WorkloadTestUtils.hpp" |
| 23 | #include "Conv2dTestImpl.hpp" |
| 24 | #include "BatchNormTestImpl.hpp" |
| 25 | #include "ActivationTestImpl.hpp" |
| 26 | #include "Pooling2dTestImpl.hpp" |
| 27 | #include "ReshapeTestImpl.hpp" |
| 28 | #include "FullyConnectedTestImpl.hpp" |
| 29 | #include "SplitterTestImpl.hpp" |
| 30 | #include "SoftmaxTestImpl.hpp" |
| 31 | #include "NormTestImpl.hpp" |
| 32 | #include "PermuteTestImpl.hpp" |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 33 | #include "LstmTestImpl.hpp" |
| 34 | #include "ConvertFp16ToFp32TestImpl.hpp" |
| 35 | #include "ConvertFp32ToFp16TestImpl.hpp" |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 36 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 37 | // 3-channel 16x8 image used as common input data for a number of Conv2d tests. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 38 | static std::vector<float> ConvInput3x8x16({ |
| 39 | 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, |
| 40 | 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, |
| 41 | 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, |
| 42 | 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, |
| 43 | 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, |
| 44 | 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, |
| 45 | 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, |
| 46 | 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, |
| 47 | 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 48 | 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 49 | 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 50 | 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 51 | 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 52 | 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 53 | 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 54 | 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 55 | -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, |
| 56 | -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, |
| 57 | -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, |
| 58 | -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, |
| 59 | -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, |
| 60 | -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, |
| 61 | -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, |
| 62 | -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 |
| 63 | }); |
| 64 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 65 | // 2-channel bias used by a number of Conv2d tests. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 66 | static std::vector<float> Bias2({0, 2}); |
| 67 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 68 | // Helper function that returns either Bias2 or an empty vector depending on whether bias is enabled. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 69 | template<typename T> |
| 70 | boost::multi_array<T, 1> GetBias2(bool biasEnabled, float qScale, int32_t qOffset) |
| 71 | { |
| 72 | if(biasEnabled) |
| 73 | { |
| 74 | armnn::TensorInfo biasDesc({static_cast<unsigned int>(Bias2.size())}, armnn::GetDataType<T>()); |
| 75 | boost::multi_array<T, 1> bias = MakeTensor<T, 1>(biasDesc, QuantizedVector<T>(qScale, qOffset, Bias2)); |
| 76 | return bias; |
| 77 | } |
| 78 | else |
| 79 | { |
| 80 | return boost::multi_array<T, 1>(); |
| 81 | } |
| 82 | } |
| 83 | |
| 84 | template<typename T> |
| 85 | LayerTestResult<T, 4> SimpleConvolution2d3x5TestCommon(armnn::IWorkloadFactory& workloadFactory, |
| 86 | float qScale, |
| 87 | int32_t qOffset, |
jimfly01 | 0a088a6 | 2018-10-25 17:05:05 +0100 | [diff] [blame] | 88 | bool biasEnabled, |
| 89 | const armnn::DataLayoutIndexed& layout) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 90 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 91 | // Use common single-batch 3-channel 16x8 image. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 92 | armnn::TensorInfo inputDesc({1, 3, 8, 16}, armnn::GetDataType<T>()); |
| 93 | boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputDesc, QuantizedVector<T>(qScale, qOffset, ConvInput3x8x16)); |
| 94 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 95 | // Use a 2-element batch with 3-channel 3x5 kernels. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 96 | armnn::TensorInfo kernelDesc({2, 3, 5, 3}, armnn::GetDataType<T>()); |
| 97 | boost::multi_array<T, 4> kernel = MakeTensor<T, 4>(kernelDesc, std::vector<T>( |
| 98 | QuantizedVector<T>(qScale, qOffset, { |
| 99 | 1, 1, 1, |
| 100 | 1, -1, 1, |
| 101 | 1, 1, 1, |
| 102 | 1, 1, 1, |
| 103 | 1, 1, 1, |
| 104 | |
| 105 | 0, 0, 0, |
| 106 | 0, 0, 0, |
| 107 | 0, 0, 0, |
| 108 | 0, 0, 0, |
| 109 | 0, 0, 0, |
| 110 | |
| 111 | 2, 2, 2, |
| 112 | 2, 2, 2, |
| 113 | 2, 2, 2, |
| 114 | 2, 2, 2, |
| 115 | 2, 2, 2, |
| 116 | |
| 117 | |
| 118 | 0, 0, 0, |
| 119 | 0, 0, 0, |
| 120 | 0, 0, 0, |
| 121 | 0, 0, 0, |
| 122 | 0, 0, 0, |
| 123 | |
| 124 | 1, 1, 1, |
| 125 | 1, 1, 1, |
| 126 | 1, 1, 1, |
| 127 | 1, 1, 1, |
| 128 | 1, 1, 1, |
| 129 | |
| 130 | 0, 0, 0, |
| 131 | 0, 0, 0, |
| 132 | 0, 0, 0, |
| 133 | 0, 0, 0, |
| 134 | 0, 0, 0 |
| 135 | }))); |
| 136 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 137 | // Expected output is 2 batch elements of a 1-channel 14x4 image. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 138 | armnn::TensorInfo outputDesc({1, 2, 4, 14}, armnn::GetDataType<T>()); |
| 139 | boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputDesc, std::vector<T>( |
| 140 | QuantizedVector<T>(qScale, qOffset, { |
| 141 | -24, -24, -24, -24, -24, -24, -24, -24, -24, -24, -24, -24, -24, -24, |
| 142 | -25, -25, -25, -25, -25, -25, -25, -25, -25, -25, -25, -25, -25, -25, |
| 143 | -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, |
| 144 | -23.5f, -23.5f, -23.5f, |
| 145 | -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, -23.5f, |
| 146 | -23.5f, -23.5f, -23.5f, |
| 147 | |
| 148 | 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 149 | 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 150 | 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 151 | 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 |
| 152 | }))); |
| 153 | |
| 154 | return SimpleConvolution2dTestImpl<T>(workloadFactory, |
| 155 | input, |
| 156 | kernel, |
| 157 | GetBias2<typename FullyConnectedBiasTypeForInputType<T>::Type>(biasEnabled, qScale, qOffset), |
| 158 | expectedOutput, |
| 159 | qScale, |
jimfly01 | 0a088a6 | 2018-10-25 17:05:05 +0100 | [diff] [blame] | 160 | qOffset, |
| 161 | layout); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 162 | } |
| 163 | |
| 164 | template<typename T> |
| 165 | LayerTestResult<T, 4> SimpleConvolution2d3x3TestCommon(armnn::IWorkloadFactory& workloadFactory, |
| 166 | float qScale, |
| 167 | int32_t qOffset, |
| 168 | bool biasEnabled) |
| 169 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 170 | // Use a 3x3 kernel, which exercises ArmCompute's direct convolution path. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 171 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 172 | // Use common single-batch 3-channel 16x8 image. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 173 | armnn::TensorInfo inputDesc({1, 3, 8, 16}, armnn::GetDataType<T>()); |
| 174 | boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputDesc, QuantizedVector<T>(qScale, qOffset, ConvInput3x8x16)); |
| 175 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 176 | // Use a 2-element batch of 3-channel 3x3 kernels. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 177 | armnn::TensorInfo kernelDesc({2, 3, 3, 3}, armnn::GetDataType<T>()); |
| 178 | boost::multi_array<T, 4> kernel = MakeTensor<T, 4>(kernelDesc, std::vector<T>( |
| 179 | QuantizedVector<T>(qScale, qOffset, { |
| 180 | 1, 1, 1, |
| 181 | 1, -1, 1, |
| 182 | 1, 1, 1, |
| 183 | |
| 184 | 0, 0, 0, |
| 185 | 0, 0, 0, |
| 186 | 0, 0, 0, |
| 187 | |
| 188 | 2, 2, 2, |
| 189 | 2, 2, 2, |
| 190 | 2, 2, 2, |
| 191 | |
| 192 | |
| 193 | 0, 0, 0, |
| 194 | 0, 0, 0, |
| 195 | 0, 0, 0, |
| 196 | |
| 197 | 1, 1, 1, |
| 198 | 1, 1, 1, |
| 199 | 1, 1, 1, |
| 200 | |
| 201 | 0, 0, 0, |
| 202 | 0, 0, 0, |
| 203 | 0, 0, 0 |
| 204 | }))); |
| 205 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 206 | // Expected output is 1 batch of a 2-channel 14x6 image. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 207 | armnn::TensorInfo outputDesc({1, 2, 6, 14}, armnn::GetDataType<T>()); |
| 208 | boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputDesc, std::vector<T>( |
| 209 | QuantizedVector<T>(qScale, qOffset, { |
| 210 | -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, -15, |
| 211 | -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, -16, |
| 212 | -14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f, |
| 213 | -14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f, |
| 214 | -14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f, |
| 215 | -14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f,-14.5f, |
| 216 | |
| 217 | 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 218 | 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 219 | 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 220 | 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 221 | 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, |
| 222 | 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 |
| 223 | }))); |
| 224 | |
| 225 | return SimpleConvolution2dTestImpl<T>(workloadFactory, |
| 226 | input, |
| 227 | kernel, |
| 228 | GetBias2<typename FullyConnectedBiasTypeForInputType<T>::Type>(biasEnabled, qScale, qOffset), |
| 229 | expectedOutput, |
| 230 | qScale, |
| 231 | qOffset); |
| 232 | } |
| 233 | |
Francis Murtagh | d59116e | 2018-10-04 16:03:07 +0100 | [diff] [blame] | 234 | template<typename T> |
| 235 | LayerTestResult<T, 4> SimpleConvolution2d3x3NhwcTestCommon(armnn::IWorkloadFactory& workloadFactory, |
| 236 | float qScale, |
| 237 | int32_t qOffset, |
| 238 | bool biasEnabled, |
| 239 | armnn::DataLayout dataLayout) |
| 240 | { |
| 241 | // Use common single-batch 5x5 image. |
| 242 | |
| 243 | armnn::TensorInfo inputDesc({1, 3, 4, 1}, armnn::GetDataType<T>()); |
| 244 | boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputDesc, |
| 245 | { |
| 246 | 1, 5, 2, 3, |
| 247 | 8, 7, 3, 6, |
| 248 | 3, 3, 9, 1 |
| 249 | }); |
| 250 | |
| 251 | |
| 252 | // Use a 2-element batch of 3-channel 3x3 kernels. |
| 253 | armnn::TensorInfo kernelDesc({1, 3, 3, 1}, armnn::GetDataType<T>()); |
| 254 | boost::multi_array<T, 4> kernel = MakeTensor<T, 4>(kernelDesc, { |
| 255 | 4, 5, 6, |
| 256 | 0, 0, 0, |
| 257 | 3, 2, 1 |
| 258 | }); |
| 259 | |
| 260 | // Expected output is 1 batch of a 5x5 image. |
| 261 | armnn::TensorInfo outputDesc({1, 3, 4, 1}, armnn::GetDataType<T>()); |
| 262 | |
| 263 | const std::vector<float> outputData = |
| 264 | { |
| 265 | 23, 41, 33, 21, |
| 266 | 44, 65, 76, 52, |
| 267 | 82, 85, 79, 42 |
| 268 | }; |
| 269 | |
| 270 | boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputDesc, outputData); |
| 271 | |
| 272 | return SimpleConvolution2dNhwcTestImpl<T>(workloadFactory, |
| 273 | input, |
| 274 | kernel, |
| 275 | boost::multi_array<T, 1>(), |
| 276 | expectedOutput, |
| 277 | dataLayout, |
| 278 | qScale, |
| 279 | qOffset); |
| 280 | } |
| 281 | |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 282 | LayerTestResult<float, 4> SimpleConvolution2d3x5Test(armnn::IWorkloadFactory& workloadFactory, |
jimfly01 | 0a088a6 | 2018-10-25 17:05:05 +0100 | [diff] [blame] | 283 | bool biasEnabled, |
| 284 | const armnn::DataLayoutIndexed& layout) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 285 | { |
jimfly01 | 0a088a6 | 2018-10-25 17:05:05 +0100 | [diff] [blame] | 286 | return SimpleConvolution2d3x5TestCommon<float>(workloadFactory, 0.f, 0, biasEnabled, layout); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 287 | } |
| 288 | |
| 289 | LayerTestResult<uint8_t, 4> SimpleConvolution2d3x5Uint8Test(armnn::IWorkloadFactory& workloadFactory, |
jimfly01 | 0a088a6 | 2018-10-25 17:05:05 +0100 | [diff] [blame] | 290 | bool biasEnabled, |
| 291 | const armnn::DataLayoutIndexed& layout) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 292 | { |
jimfly01 | 0a088a6 | 2018-10-25 17:05:05 +0100 | [diff] [blame] | 293 | return SimpleConvolution2d3x5TestCommon<uint8_t>(workloadFactory, 0.5f, 50, biasEnabled, layout); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 294 | } |
| 295 | |
| 296 | LayerTestResult<float, 4> SimpleConvolution2d3x3Test(armnn::IWorkloadFactory& workloadFactory, |
| 297 | bool biasEnabled) |
| 298 | { |
| 299 | return SimpleConvolution2d3x3TestCommon<float>(workloadFactory, 0.f, 0, biasEnabled); |
| 300 | } |
| 301 | |
Francis Murtagh | d59116e | 2018-10-04 16:03:07 +0100 | [diff] [blame] | 302 | LayerTestResult<float, 4> SimpleConvolution2d3x3NhwcTest(armnn::IWorkloadFactory& workloadFactory, |
| 303 | bool biasEnabled) |
| 304 | { |
| 305 | return SimpleConvolution2d3x3NhwcTestCommon<float>(workloadFactory, 0.f, 0, biasEnabled, armnn::DataLayout::NHWC); |
| 306 | } |
| 307 | |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 308 | LayerTestResult<uint8_t, 4> SimpleConvolution2d3x3Uint8Test(armnn::IWorkloadFactory& workloadFactory, |
| 309 | bool biasEnabled) |
| 310 | { |
| 311 | return SimpleConvolution2d3x3TestCommon<uint8_t>(workloadFactory, 0.5f, 50, biasEnabled); |
| 312 | } |
| 313 | |
| 314 | template<typename T> |
| 315 | LayerTestResult<T, 4> Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTestCommon( |
| 316 | armnn::IWorkloadFactory& workloadFactory, |
| 317 | float qScale, |
| 318 | int32_t qOffset) |
| 319 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 320 | // Use a single-batch 1-channel 3x3 image as input. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 321 | armnn::TensorInfo inputDesc({1, 1, 3, 3}, armnn::GetDataType<T>()); |
| 322 | boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputDesc, std::vector<T>( |
| 323 | QuantizedVector<T>(qScale, qOffset, { |
| 324 | 11,21,31, |
| 325 | 12,22,32, |
| 326 | 13,23,33 |
| 327 | }))); |
| 328 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 329 | // Use 1 batch of a 1-channel 2x2 kernel. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 330 | armnn::TensorInfo kernelDesc({1, 1, 2, 2}, armnn::GetDataType<T>()); |
| 331 | boost::multi_array<T, 4> kernel = MakeTensor<T, 4>(kernelDesc, std::vector<T>( |
| 332 | QuantizedVector<T>(qScale, qOffset, { |
| 333 | -11,-21, |
| 334 | -12,-22, |
| 335 | }))); |
| 336 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 337 | // Expected output is 1 batch of a 1-channel 6x8 image. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 338 | // Manually calculated like this: |
| 339 | //[-11*0 -21*0 -12*0 -22*0 ; -11*0 -21*0 -12*0 -22*0 ; -11*0 -21*0 -12*0 -22*0 ; -11*0 -21*0 -12*0 -22*0 ..] |
| 340 | //[-11*0 -21*0 -12*0 -22*11 ; -11*0 -21*0 -12*11 -22*21 ; -11*0 -21*0 -12*21 -22*31 ; -11*0 -21*0 -12*31 -22*0 ..] |
| 341 | //[-11*0 -21*11 -12*0 -22*12 ; -11*11 -21*21 -12*12 -22*22 ; -11*21 -21*31 -12*22 -22*32 ; -11*31 -21*0 -12*32 -22*0 ..] |
| 342 | //[-11*0 -21*12 -12*0 -22*13 ; -11*12 -21*22 -12*13 -22*23 ; -11*22 -21*32 -12*23 -22*33 ; -11*32 -21*0 -12*33 -22*0 ..] |
| 343 | //[-11*0 -21*13 -12*0 -22*0 ; -11*13 -21*23 -12*0 -22*0 ; -11*23 -21*33 -12*0 -22*0 ; -11*33 -21*0 -12*0 -22*0 ..] |
| 344 | //[-11*0 -21*0 -12*0 -22*0 ; -11*0 -21*0 -12*0 -22*0 ; -11*0 -21*0 -12*0 -22*0 ; -11*0 -21*0 -12*0 -22*0 ..] |
| 345 | //[..... ..... ..... ..... ; ..... ..... ..... ..... ; ..... ..... ..... ..... ; ..... ..... ..... ..... ..] |
| 346 | armnn::TensorInfo outputDesc({1, 1, 8, 6}, armnn::GetDataType<T>()); |
| 347 | boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputDesc, std::vector<T>( |
| 348 | QuantizedVector<T>(qScale, qOffset, { |
| 349 | 0, 0, 0, 0, 0, 0, |
| 350 | -242, -594, -934, -372, 0, 0, |
| 351 | -495, -1190, -1850, -725, 0, 0, |
| 352 | -538, -1256, -1916, -748, 0, 0, |
| 353 | -273, -626, -946, -363, 0, 0, |
| 354 | 0, 0, 0, 0, 0, 0, |
| 355 | 0, 0, 0, 0, 0, 0, |
| 356 | 0, 0, 0, 0, 0, 0 |
| 357 | }))); |
| 358 | |
| 359 | return SimpleConvolution2dTestImpl<T>(workloadFactory, |
| 360 | input, |
| 361 | kernel, |
| 362 | GetBias2<typename FullyConnectedBiasTypeForInputType<T>::Type>(false, qScale, qOffset), |
| 363 | expectedOutput, |
| 364 | qScale, |
| 365 | qOffset, |
jimfly01 | 0a088a6 | 2018-10-25 17:05:05 +0100 | [diff] [blame] | 366 | armnn::DataLayout::NCHW, |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 367 | 1, // Padding left. |
| 368 | 2, // Padding top. |
| 369 | 3, // Padding right. |
| 370 | 4); // Padding bottom. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 371 | } |
| 372 | |
| 373 | template<typename T> |
| 374 | LayerTestResult<T, 4> SimpleConvolution2dAsymmetricPaddingTestCommon(armnn::IWorkloadFactory& workloadFactory, |
| 375 | float qScale, |
| 376 | int32_t qOffset) |
| 377 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 378 | // Use a single-batch 1-channel 5x5 image as input. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 379 | armnn::TensorInfo inputDesc({ 1, 1, 5, 5 }, armnn::GetDataType<T>()); |
| 380 | boost::multi_array<T, 4> input = MakeTensor<T, 4>(inputDesc, std::vector<T>( |
| 381 | QuantizedVector<T>(qScale, qOffset, { |
| 382 | 11,21,31,41,51, |
| 383 | 12,22,32,42,52, |
| 384 | 13,23,33,43,53, |
| 385 | 14,24,34,44,54, |
| 386 | 15,25,35,45,55, |
| 387 | }))); |
| 388 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 389 | // Use 1 batch of a 1-channel 4x4 kernel. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 390 | armnn::TensorInfo kernelDesc({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); |
| 391 | boost::multi_array<T, 4> kernel = MakeTensor<T, 4>(kernelDesc, std::vector<T>( |
| 392 | QuantizedVector<T>(qScale, qOffset, { |
| 393 | -11,-21,-31,-41, |
| 394 | -12,-22,-32,-42, |
| 395 | -13,-23,-33,-43, |
| 396 | -14,-24,-34,-44, |
| 397 | }))); |
| 398 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 399 | // Expected output is 1 batch of a 1-channel 5x5 image. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 400 | armnn::TensorInfo outputDesc({ 1, 1, 5, 5 }, armnn::GetDataType<T>()); |
| 401 | std::vector<T> myVec(outputDesc.GetNumElements(), 0); |
| 402 | boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputDesc, std::vector<T>( |
| 403 | QuantizedVector<T>(qScale, qOffset, { |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 404 | -7140, -10580, -13940, -9300, -5230, |
| 405 | -9590, -14120, -18520, -12290, -6860, |
| 406 | -9980, -14560, -18960, -12560, -7000, |
| 407 | -7518, -10904, -14144, -9318, -5152, |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 408 | -5032, -7256, -9376, -6142, -3368, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 409 | }))); |
| 410 | |
| 411 | return SimpleConvolution2dTestImpl<T>(workloadFactory, |
| 412 | input, |
| 413 | kernel, |
| 414 | GetBias2<typename FullyConnectedBiasTypeForInputType<T>::Type>(false, qScale, qOffset), |
| 415 | expectedOutput, |
| 416 | qScale, |
| 417 | qOffset, |
jimfly01 | 0a088a6 | 2018-10-25 17:05:05 +0100 | [diff] [blame] | 418 | armnn::DataLayout::NCHW, |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 419 | 1, // Padding left. |
| 420 | 1, // Padding top. |
| 421 | 2, // Padding right. |
| 422 | 2); // Padding bottom. |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 423 | } |
| 424 | |
| 425 | template<typename T> |
| 426 | LayerTestResult<T, 4> DepthwiseConvolution2dAsymmetricTestCommon(armnn::IWorkloadFactory& workloadFactory, |
| 427 | float qScale, |
| 428 | int32_t qOffset, |
| 429 | bool biasEnabled) |
| 430 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 431 | // Use a single-batch 2-channel 5x5 image as input. |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 432 | armnn::TensorInfo inputTensorInfo({ 1, 2, 5, 5 }, armnn::GetDataType<T>()); |
| 433 | auto input = MakeTensor<T, 4>(inputTensorInfo, std::vector<T>( |
| 434 | QuantizedVector<T>(inputTensorInfo.GetQuantizationScale(), inputTensorInfo.GetQuantizationOffset(), { |
| 435 | 0, 1, 2, 3, 4, |
| 436 | 5, 6, 7, 8, 9, |
| 437 | 10, 11, 12, 13, 14, |
| 438 | 15, 16, 17, 18, 19, |
| 439 | 20, 21, 22, 23, 24, |
| 440 | |
| 441 | 25, 26, 27, 28, 29, |
| 442 | 30, 31, 32, 33, 34, |
| 443 | 35, 36, 37, 38, 39, |
| 444 | 40, 41, 42, 43, 44, |
| 445 | 45, 46, 47, 48, 49 |
| 446 | }))); |
| 447 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 448 | // Use a depth multiplier of 1 on a 2-channel 4x4 kernel. |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 449 | armnn::TensorInfo kernelTensorInfo({ 1, 2, 4, 4 }, armnn::GetDataType<T>()); |
| 450 | auto kernel = MakeTensor<T, 4>(kernelTensorInfo, std::vector<T>( |
| 451 | QuantizedVector<T>(kernelTensorInfo.GetQuantizationScale(), kernelTensorInfo.GetQuantizationOffset(), { |
| 452 | 32, 31, 30, 29, |
| 453 | 28, 27, 26, 25, |
| 454 | 24, 23, 22, 21, |
| 455 | 20, 19, 18, 17, |
| 456 | |
| 457 | 16, 15, 14, 13, |
| 458 | 12, 11, 10, 9, |
| 459 | 8, 7, 6, 5, |
| 460 | 4, 3, 2, 1 |
| 461 | }))); |
| 462 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 463 | // Expected output is 1 batch of a 2-channel 5x5 image. |
| 464 | // Calculated using the python tensorflow library with strideX=1, strideY=1. |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 465 | armnn::TensorInfo outputTensorInfo({ 1, 2, 5, 5 }, armnn::GetDataType<T>()); |
| 466 | boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputTensorInfo, std::vector<T>( |
| 467 | QuantizedVector<T>(outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(), { |
| 468 | 1062, 1580, 1850, 1530, 1117, |
| 469 | 2140, 3108, 3500, 2842, 2042, |
| 470 | 3580, 5068, 5460, 4342, 3062, |
| 471 | 3618, 5072, 5390, 4248, 2971, |
| 472 | 3074, 4282, 4510, 3533, 2457, |
| 473 | 1550, 2284, 2362, 1955, 1428, |
| 474 | 2910, 4206, 4342, 3528, 2536, |
| 475 | 3390, 4886, 5022, 4068, 2916, |
| 476 | 3566, 5056, 5182, 4133, 2922, |
| 477 | 3100, 4352, 4452, 3517, 2465 |
| 478 | }))); |
| 479 | |
| 480 | return DepthwiseConvolution2dAsymmetricTestImpl<T>(workloadFactory, |
| 481 | input, |
| 482 | kernel, |
| 483 | GetBias2<typename FullyConnectedBiasTypeForInputType<T>::Type>(biasEnabled, qScale, qOffset), |
| 484 | expectedOutput, |
| 485 | qScale, |
| 486 | qOffset, |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 487 | 1, // Padding left. |
| 488 | 1, // Padding top. |
| 489 | 2, // Padding right. |
| 490 | 2, // Padding bottom. |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 491 | 1, // strideX |
| 492 | 1); // strideY |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 493 | } |
| 494 | |
Nikhil Raj | cec6b65 | 2018-10-12 13:51:57 +0100 | [diff] [blame] | 495 | template<typename T> |
| 496 | LayerTestResult<T, 4> DepthwiseConvolution2dNhwcTestCommon(armnn::IWorkloadFactory& workloadFactory, |
| 497 | float qScale, |
| 498 | int32_t qOffset, |
| 499 | bool biasEnabled) |
| 500 | { |
| 501 | armnn::TensorInfo inputTensorInfo({ 1, 5, 5, 2}, armnn::GetDataType<T>()); |
| 502 | auto input = MakeTensor<T, 4>(inputTensorInfo, std::vector<T>( |
| 503 | QuantizedVector<T>(inputTensorInfo.GetQuantizationScale(), inputTensorInfo.GetQuantizationOffset(), { |
| 504 | 0, 25, |
| 505 | 1, 26, |
| 506 | 2, 27, |
| 507 | 3, 28, |
| 508 | 4, 29, |
| 509 | |
| 510 | 5, 30, |
| 511 | 6, 31, |
| 512 | 7, 32, |
| 513 | 8, 33, |
| 514 | 9, 34, |
| 515 | |
| 516 | 10, 35, |
| 517 | 11, 36, |
| 518 | 12, 37, |
| 519 | 13, 38, |
| 520 | 14, 39, |
| 521 | |
| 522 | 15, 40, |
| 523 | 16, 41, |
| 524 | 17, 42, |
| 525 | 18, 43, |
| 526 | 19, 44, |
| 527 | |
| 528 | 20, 45, |
| 529 | 21, 46, |
| 530 | 22, 47, |
| 531 | 23, 48, |
| 532 | 24, 49 |
| 533 | }))); |
| 534 | |
| 535 | armnn::TensorInfo kernelTensorInfo({ 1, 4, 4, 2}, armnn::GetDataType<T>()); |
| 536 | auto kernel = MakeTensor<T, 4>(kernelTensorInfo, std::vector<T>( |
| 537 | QuantizedVector<T>(kernelTensorInfo.GetQuantizationScale(), kernelTensorInfo.GetQuantizationOffset(), { |
| 538 | 32, 16, |
| 539 | 31, 15, |
| 540 | 30, 14, |
| 541 | 29, 13, |
| 542 | |
| 543 | 28, 12, |
| 544 | 27, 11, |
| 545 | 26, 10, |
| 546 | 25, 9, |
| 547 | |
| 548 | 24, 8, |
| 549 | 23, 7, |
| 550 | 22, 6, |
| 551 | 21, 5, |
| 552 | |
| 553 | 20, 4, |
| 554 | 19, 3, |
| 555 | 18, 2, |
| 556 | 17, 1 |
| 557 | }))); |
| 558 | |
| 559 | armnn::TensorInfo outputTensorInfo({ 1, 5, 5, 2}, armnn::GetDataType<T>()); |
| 560 | boost::multi_array<T, 4> expectedOutput = MakeTensor<T, 4>(outputTensorInfo, std::vector<T>( |
| 561 | QuantizedVector<T>(outputTensorInfo.GetQuantizationScale(), outputTensorInfo.GetQuantizationOffset(), { |
| 562 | 1062, 1550, |
| 563 | 1580, 2284, |
| 564 | 1850, 2362, |
| 565 | 1530, 1955, |
| 566 | 1117, 1428, |
| 567 | |
| 568 | 2140, 2910, |
| 569 | 3108, 4206, |
| 570 | 3500, 4342, |
| 571 | 2842, 3528, |
| 572 | 2042, 2536, |
| 573 | |
| 574 | 3580, 3390, |
| 575 | 5068, 4886, |
| 576 | 5460, 5022, |
| 577 | 4342, 4068, |
| 578 | 3062, 2916, |
| 579 | |
| 580 | 3618, 3566, |
| 581 | 5072, 5056, |
| 582 | 5390, 5182, |
| 583 | 4248, 4133, |
| 584 | 2971, 2922, |
| 585 | |
| 586 | 3074, 3100, |
| 587 | 4282, 4352, |
| 588 | 4510, 4452, |
| 589 | 3533, 3517, |
| 590 | 2457, 2465 |
| 591 | }))); |
| 592 | |
| 593 | return DepthwiseConvolution2dNhwcTestImpl<T>(workloadFactory, |
| 594 | input, |
| 595 | kernel, |
| 596 | GetBias2<typename FullyConnectedBiasTypeForInputType<T>::Type>(biasEnabled, qScale, qOffset), |
| 597 | expectedOutput, |
| 598 | qScale, |
| 599 | qOffset, |
| 600 | 1, // Padding left. |
| 601 | 1, // Padding top. |
| 602 | 2, // Padding right. |
| 603 | 2, // Padding bottom. |
| 604 | 1, // strideX |
| 605 | 1); // strideY |
| 606 | } |
| 607 | |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 608 | LayerTestResult<float, 4> |
| 609 | Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTest(armnn::IWorkloadFactory& workloadFactory) |
| 610 | { |
| 611 | return Convolution2dAsymmetricPaddingLargerThanHalfKernelSizeTestCommon<float>(workloadFactory, 0.0f, 0); |
| 612 | } |
| 613 | |
| 614 | LayerTestResult<float, 4> Convolution2dAsymmetricPaddingTest(armnn::IWorkloadFactory& workloadFactory) |
| 615 | { |
| 616 | return SimpleConvolution2dAsymmetricPaddingTestCommon<float>(workloadFactory, 0.0f, 0); |
| 617 | } |
| 618 | |
| 619 | LayerTestResult<float, 4> DepthwiseConvolution2dTest(armnn::IWorkloadFactory& workloadFactory, |
| 620 | bool biasEnabled) |
| 621 | { |
| 622 | return DepthwiseConvolution2dTestImpl<float, float>(workloadFactory, 0.0f, 0, biasEnabled); |
| 623 | } |
| 624 | |
Nikhil Raj | cec6b65 | 2018-10-12 13:51:57 +0100 | [diff] [blame] | 625 | LayerTestResult<float, 4> DepthwiseConvolution2dDepthNhwcTest(armnn::IWorkloadFactory& workloadFactory, |
| 626 | bool biasEnabled) |
| 627 | { |
| 628 | return DepthwiseConvolution2dNhwcTestCommon<float>(workloadFactory, 0.0f, 0, biasEnabled); |
| 629 | } |
| 630 | |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 631 | LayerTestResult<float, 4> DepthwiseConvolution2dDepthMul1Test(armnn::IWorkloadFactory& workloadFactory, |
| 632 | bool biasEnabled) |
| 633 | { |
| 634 | return DepthwiseConvolution2dDepthMul1TestImpl<float, float>(workloadFactory, 0.0f, 0, biasEnabled); |
| 635 | } |
| 636 | |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 637 | LayerTestResult<float, 4> DepthwiseConvolution2dAsymmetricTest(armnn::IWorkloadFactory& workloadFactory, |
| 638 | bool biasEnabled) |
| 639 | { |
| 640 | return DepthwiseConvolution2dAsymmetricTestCommon<float>(workloadFactory, 0.0f, 0, biasEnabled); |
| 641 | } |
| 642 | |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 643 | LayerTestResult<uint8_t, 4> DepthwiseConvolution2dUint8Test(armnn::IWorkloadFactory& workloadFactory, |
| 644 | bool biasEnabled) |
| 645 | { |
| 646 | return DepthwiseConvolution2dTestImpl<uint8_t, int32_t>(workloadFactory, 0.5f, 50, biasEnabled); |
| 647 | } |
| 648 | |
| 649 | LayerTestResult<uint8_t, 4> DepthwiseConvolution2dDepthMul1Uint8Test(armnn::IWorkloadFactory& workloadFactory, |
| 650 | bool biasEnabled) |
| 651 | { |
| 652 | return DepthwiseConvolution2dDepthMul1TestImpl<uint8_t, int32_t>(workloadFactory, 0.5f, 50, biasEnabled); |
| 653 | } |
| 654 | |
| 655 | LayerTestResult<float, 4> Convolution1dTest(armnn::IWorkloadFactory& workloadFactory, bool biasEnabled) |
| 656 | { |
| 657 | return Convolution1dTestImpl<float>(workloadFactory, 0.0f, 0, biasEnabled); |
| 658 | } |
| 659 | |
| 660 | LayerTestResult<uint8_t, 4> Convolution1dUint8Test(armnn::IWorkloadFactory& workloadFactory, bool biasEnabled) |
| 661 | { |
| 662 | return Convolution1dTestImpl<uint8_t>(workloadFactory, 0.1f, 128, biasEnabled); |
| 663 | } |
| 664 | |
| 665 | LayerTestResult<float,4> CompareConvolution2dTest(armnn::IWorkloadFactory& workloadFactory, |
| 666 | armnn::IWorkloadFactory& refWorkloadFactory) |
| 667 | { |
| 668 | return CompareConvolution2dTestImpl<float>(workloadFactory, refWorkloadFactory); |
| 669 | } |
| 670 | |
| 671 | template<typename T> |
| 672 | LayerTestResult<T,4> CompareDepthwiseConvolution2dTest(armnn::IWorkloadFactory& workloadFactory, |
| 673 | armnn::IWorkloadFactory& refWorkloadFactory) |
| 674 | { |
| 675 | return CompareDepthwiseConvolution2dTestImpl<T>(workloadFactory, refWorkloadFactory); |
| 676 | } |
| 677 | |
| 678 | template LayerTestResult<float, 4> CompareDepthwiseConvolution2dTest<float>( |
| 679 | armnn::IWorkloadFactory&, armnn::IWorkloadFactory&); |
| 680 | template LayerTestResult<uint8_t, 4> CompareDepthwiseConvolution2dTest<uint8_t>( |
| 681 | armnn::IWorkloadFactory&, armnn::IWorkloadFactory&); |
| 682 | |
| 683 | LayerTestResult<float,4> SimpleNormalizationAcrossTest(armnn::IWorkloadFactory& workloadFactory) |
| 684 | { |
| 685 | auto normMethod = armnn::NormalizationAlgorithmMethod::LocalBrightness; |
| 686 | auto normChannel = armnn::NormalizationAlgorithmChannel::Across; |
| 687 | return SimpleNormalizationTestImpl(workloadFactory, normChannel, normMethod); |
| 688 | } |
| 689 | |
| 690 | LayerTestResult<float,4> SimpleNormalizationWithinTest(armnn::IWorkloadFactory& workloadFactory) |
| 691 | { |
| 692 | auto normMethod = armnn::NormalizationAlgorithmMethod::LocalBrightness; |
| 693 | auto normChannel = armnn::NormalizationAlgorithmChannel::Within; |
| 694 | return SimpleNormalizationTestImpl(workloadFactory, normChannel, normMethod); |
| 695 | } |
| 696 | |
narpra01 | 55a97bc | 2018-10-02 14:35:53 +0100 | [diff] [blame] | 697 | LayerTestResult<float,4> SimpleNormalizationAcrossNhwcTest(armnn::IWorkloadFactory& workloadFactory) |
| 698 | { |
| 699 | auto normMethod = armnn::NormalizationAlgorithmMethod::LocalBrightness; |
| 700 | auto normChannel = armnn::NormalizationAlgorithmChannel::Across; |
Matteo Martincigh | 8e6f92d | 2018-10-18 08:45:39 +0100 | [diff] [blame] | 701 | return SimpleNormalizationNhwcTestImpl(workloadFactory, normChannel, normMethod); |
narpra01 | 55a97bc | 2018-10-02 14:35:53 +0100 | [diff] [blame] | 702 | } |
| 703 | |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 704 | LayerTestResult<float,2> SimpleSoftmaxTest(armnn::IWorkloadFactory& workloadFactory, float beta) |
| 705 | { |
| 706 | return SimpleSoftmaxTestImpl<float>(workloadFactory, beta); |
| 707 | } |
| 708 | |
| 709 | LayerTestResult<uint8_t,2> SimpleSoftmaxUint8Test(armnn::IWorkloadFactory& workloadFactory, float beta) |
| 710 | { |
| 711 | return SimpleSoftmaxTestImpl<uint8_t>(workloadFactory, beta); |
| 712 | } |
| 713 | |
| 714 | LayerTestResult<float,4> CompareNormalizationTest(armnn::IWorkloadFactory& workloadFactory, |
| 715 | armnn::IWorkloadFactory& refWorkloadFactory, |
| 716 | armnn::NormalizationAlgorithmChannel normChannel, |
| 717 | armnn::NormalizationAlgorithmMethod normMethod) |
| 718 | { |
| 719 | return CompareNormalizationTestImpl(workloadFactory, refWorkloadFactory, normChannel, normMethod); |
| 720 | } |
| 721 | |
| 722 | LayerTestResult<float,2> CompareSoftmaxTest(armnn::IWorkloadFactory& workloadFactory, |
| 723 | armnn::IWorkloadFactory& refWorkloadFactory, |
| 724 | float beta) |
| 725 | { |
| 726 | return CompareSoftmaxTestImpl<float>(workloadFactory, refWorkloadFactory, beta); |
| 727 | } |
| 728 | |
| 729 | LayerTestResult<uint8_t,2> CompareSoftmaxUint8Test(armnn::IWorkloadFactory& workloadFactory, |
| 730 | armnn::IWorkloadFactory& refWorkloadFactory, |
| 731 | float beta) |
| 732 | { |
| 733 | return CompareSoftmaxTestImpl<uint8_t>(workloadFactory, refWorkloadFactory, beta); |
| 734 | } |
| 735 | |
| 736 | std::vector<LayerTestResult<float,3>> SplitterTest(armnn::IWorkloadFactory& workloadFactory) |
| 737 | { |
| 738 | return SplitterTestCommon<float>(workloadFactory); |
| 739 | } |
| 740 | |
| 741 | std::vector<LayerTestResult<uint8_t,3>> SplitterUint8Test(armnn::IWorkloadFactory& workloadFactory) |
| 742 | { |
| 743 | return SplitterTestCommon<uint8_t>(workloadFactory, 1.0f, 0); |
| 744 | } |
| 745 | |
| 746 | LayerTestResult<float, 3> CopyViaSplitterTest(armnn::IWorkloadFactory& workloadFactory) |
| 747 | { |
| 748 | return CopyViaSplitterTestImpl<float>(workloadFactory, 0.0f, 0); |
| 749 | } |
| 750 | |
| 751 | LayerTestResult<uint8_t, 3> CopyViaSplitterUint8Test(armnn::IWorkloadFactory& workloadFactory) |
| 752 | { |
| 753 | return CopyViaSplitterTestImpl<uint8_t>(workloadFactory, 1.0f, 0); |
| 754 | } |
| 755 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 756 | LayerTestResult<float, 2> LstmLayerFloat32WithCifgWithPeepholeNoProjectionTest( |
| 757 | armnn::IWorkloadFactory& workloadFactory) |
| 758 | { |
| 759 | armnn::TensorInfo inputDesc({ 2, 2 }, armnn::GetDataType<float>()); |
| 760 | boost::multi_array<float, 2> input = MakeTensor<float, 2>(inputDesc, std::vector<float>( |
| 761 | { 2., 3., 3., 4. })); |
| 762 | |
| 763 | armnn::TensorInfo outputDesc({ 2, 4 }, armnn::GetDataType<float>()); |
| 764 | boost::multi_array<float, 2> expectedOutput = MakeTensor<float, 2>(outputDesc, std::vector<float>( |
| 765 | {-0.36444446f, -0.00352185f, 0.12886585f, -0.05163646f, |
| 766 | -0.42734814f, -0.00478661f, 0.13455015f, -0.03560682f})); |
| 767 | return LstmLayerWithCifgWithPeepholeNoProjectionTestImpl(workloadFactory, input, expectedOutput); |
| 768 | } |
| 769 | |
| 770 | LayerTestResult<float, 2> LstmLayerFloat32NoCifgWithPeepholeWithProjectionTest( |
| 771 | armnn::IWorkloadFactory& workloadFactory) |
| 772 | { |
| 773 | armnn::TensorInfo inputDesc({ 2, 5 }, armnn::GetDataType<float>()); |
| 774 | boost::multi_array<float, 2> input = MakeTensor<float, 2>(inputDesc, std::vector<float>( |
| 775 | {0.787926f, 0.151646f, 0.071352f, 0.118426f, 0.458058f, |
| 776 | 0.295743f, 0.544053f, 0.690064f, 0.858138f, 0.497181f})); |
| 777 | |
| 778 | armnn::TensorInfo outputDesc({ 2, 16 }, armnn::GetDataType<float>()); |
| 779 | boost::multi_array<float, 2> expectedOutput = MakeTensor<float, 2>(outputDesc, std::vector<float>( |
| 780 | {-0.00396806f, 0.029352f, -0.00279226f, 0.0159977f, -0.00835576f, |
| 781 | -0.0211779f, 0.0283512f, -0.0114597f, 0.00907307f, -0.0244004f, |
| 782 | -0.0152191f, -0.0259063f, 0.00914318f, 0.00415118f, 0.017147f, |
| 783 | 0.0134203f, -0.013869f, 0.0287268f, -0.00334693f, 0.00733398f, -0.0287926f, |
| 784 | -0.0186926f, 0.0193662f, -0.0115437f, 0.00422612f, -0.0345232f, |
| 785 | 0.00223253f, -0.00957321f, 0.0210624f, 0.013331f, 0.0150954f, |
| 786 | 0.02168f})); |
| 787 | return LstmLayerFloat32NoCifgWithPeepholeWithProjectionTestImpl(workloadFactory, input, expectedOutput); |
| 788 | } |
| 789 | |
| 790 | LayerTestResult<float, 2> LstmLayerFloat32NoCifgNoPeepholeNoProjectionTest(armnn::IWorkloadFactory& workloadFactory) |
| 791 | { |
| 792 | armnn::TensorInfo inputDesc({2, 2}, armnn::GetDataType<float>()); |
| 793 | boost::multi_array<float, 2> input = MakeTensor<float, 2>(inputDesc, std::vector<float>( |
| 794 | {2., 3., 3., 4.})); |
| 795 | |
| 796 | |
| 797 | armnn::TensorInfo outputDesc({2, 4}, armnn::GetDataType<float>()); |
| 798 | boost::multi_array<float, 2> expectedOutput = MakeTensor<float, 2>(outputDesc, std::vector<float>( |
| 799 | {{-0.02973187f, 0.1229473f, 0.20885126f, -0.15358765f, |
| 800 | -0.0185422f, 0.11281417f, 0.24466537f, -0.1826292f}})); |
| 801 | |
| 802 | return LstmNoCifgNoPeepholeNoProjectionTestImpl(workloadFactory, input, expectedOutput); |
| 803 | } |
| 804 | |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 805 | LayerTestResult<float,3> MergerTest(armnn::IWorkloadFactory& workloadFactory) |
| 806 | { |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 807 | unsigned int outputWidth = 3; |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 808 | unsigned int outputHeight = 6; |
| 809 | unsigned int outputChannels = 3; |
| 810 | |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 811 | unsigned int inputWidth1 = 3; |
| 812 | unsigned int inputHeight1 = 6; |
| 813 | unsigned int inputChannels1 = 2; |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 814 | |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 815 | unsigned int inputWidth2 = 3; |
| 816 | unsigned int inputHeight2 = 6; |
| 817 | unsigned int inputChannels2 = 1; |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 818 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 819 | // Define the tensor descriptors. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 820 | armnn::TensorInfo outputTensorInfo({ outputChannels, outputHeight, outputWidth }, armnn::DataType::Float32); |
| 821 | armnn::TensorInfo inputTensorInfo1({ inputChannels1, inputHeight1, inputWidth1 }, armnn::DataType::Float32); |
| 822 | armnn::TensorInfo inputTensorInfo2({ inputChannels2, inputHeight2, inputWidth2 }, armnn::DataType::Float32); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 823 | |
| 824 | LayerTestResult<float,3> ret(outputTensorInfo); |
| 825 | |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 826 | ret.outputExpected = MakeTensor<float, 3>(outputTensorInfo, std::vector<float>( |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 827 | { |
| 828 | 1.0f, 2.0f, 3.0f, |
| 829 | 4.0f, 5.0f, 6.0f, |
| 830 | 7.0f, 8.0f, 9.0f, |
| 831 | 10.0f, 11.0f, 12.0f, |
| 832 | 13.0f, 14.0f, 15.0f, |
| 833 | 16.0f, 17.0f, 18.0f, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 834 | |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 835 | 19.0f, 20.0f, 21.0f, |
| 836 | 22.0f, 23.0f, 24.0f, |
| 837 | 25.0f, 26.0f, 27.0f, |
| 838 | 28.0f, 29.0f, 30.0f, |
| 839 | 31.0f, 32.0f, 33.0f, |
| 840 | 34.0f, 35.0f, 36.0f, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 841 | |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 842 | 37.0f, 38.0f, 39.0f, |
| 843 | 40.0f, 41.0f, 42.0f, |
| 844 | 43.0f, 44.0f, 45.0f, |
| 845 | 46.0f, 47.0f, 48.0f, |
| 846 | 49.0f, 50.0f, 51.0f, |
| 847 | 52.0f, 53.0f, 54.0f, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 848 | }) |
| 849 | ); |
| 850 | |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 851 | auto input1 = MakeTensor<float, 3>(inputTensorInfo1, std::vector<float>( |
| 852 | { |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 853 | 1.0f, 2.0f, 3.0f, |
| 854 | 4.0f, 5.0f, 6.0f, |
| 855 | 7.0f, 8.0f, 9.0f, |
| 856 | 10.0f, 11.0f, 12.0f, |
| 857 | 13.0f, 14.0f, 15.0f, |
| 858 | 16.0f, 17.0f, 18.0f, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 859 | |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 860 | 19.0f, 20.0f, 21.0f, |
| 861 | 22.0f, 23.0f, 24.0f, |
| 862 | 25.0f, 26.0f, 27.0f, |
| 863 | 28.0f, 29.0f, 30.0f, |
| 864 | 31.0f, 32.0f, 33.0f, |
| 865 | 34.0f, 35.0f, 36.0f, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 866 | }) |
| 867 | ); |
| 868 | |
| 869 | auto input2 = MakeTensor<float, 3>(inputTensorInfo2, std::vector<float>( |
| 870 | { |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 871 | 37.0f, 38.0f, 39.0f, |
| 872 | 40.0f, 41.0f, 42.0f, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 873 | 43.0f, 44.0f, 45.0f, |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 874 | 46.0f, 47.0f, 48.0f, |
| 875 | 49.0f, 50.0f, 51.0f, |
| 876 | 52.0f, 53.0f, 54.0f, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 877 | }) |
| 878 | ); |
| 879 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 880 | std::vector<unsigned int> wOrigin1 = {0, 0, 0}; //Extent of the window is defined by size of input[0]. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 881 | armnn::MergerQueueDescriptor::ViewOrigin window1(wOrigin1); |
| 882 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 883 | std::vector<unsigned int> wOrigin2 = {2, 0, 0}; //Extent of the window is defined by size of input[1]. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 884 | armnn::MergerQueueDescriptor::ViewOrigin window2(wOrigin2); |
| 885 | |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 886 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 887 | |
| 888 | bool subTensorsSupported = workloadFactory.SupportsSubTensors(); |
| 889 | |
| 890 | std::unique_ptr<armnn::ITensorHandle> inputHandle1 = |
| 891 | subTensorsSupported ? |
| 892 | workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo1.GetShape(), wOrigin1.data()) : |
| 893 | workloadFactory.CreateTensorHandle(inputTensorInfo1); |
| 894 | |
| 895 | std::unique_ptr<armnn::ITensorHandle> inputHandle2 = |
| 896 | subTensorsSupported ? |
| 897 | workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo2.GetShape(), wOrigin2.data()) : |
| 898 | workloadFactory.CreateTensorHandle(inputTensorInfo2); |
| 899 | |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 900 | armnn::MergerQueueDescriptor data; |
| 901 | armnn::WorkloadInfo info; |
| 902 | AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); |
| 903 | AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get()); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 904 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 905 | |
| 906 | data.m_ViewOrigins.push_back(window1); |
| 907 | data.m_ViewOrigins.push_back(window2); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 908 | |
| 909 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateMerger(data, info); |
| 910 | |
| 911 | inputHandle1->Allocate(); |
| 912 | inputHandle2->Allocate(); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 913 | outputHandle->Allocate(); |
| 914 | |
| 915 | CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0]); |
| 916 | CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0]); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 917 | |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 918 | workloadFactory.Finalize(); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 919 | workload->Execute(); |
| 920 | |
| 921 | CopyDataFromITensorHandle(&ret.output[0][0][0], outputHandle.get()); |
| 922 | |
| 923 | return ret; |
| 924 | } |
| 925 | |
| 926 | LayerTestResult<float,4> AdditionTest(armnn::IWorkloadFactory& workloadFactory) |
| 927 | { |
| 928 | unsigned int batchSize = 2; |
| 929 | unsigned int channels = 2; |
| 930 | unsigned int height = 2; |
| 931 | unsigned int width = 3; |
| 932 | |
| 933 | armnn::TensorInfo inputTensorInfo1, inputTensorInfo2; |
| 934 | armnn::TensorInfo outputTensorInfo; |
| 935 | |
| 936 | unsigned int shape[] = {batchSize, channels, height, width}; |
| 937 | |
| 938 | inputTensorInfo1 = armnn::TensorInfo(4, shape, armnn::DataType::Float32); |
| 939 | inputTensorInfo2 = armnn::TensorInfo(4, shape, armnn::DataType::Float32); |
| 940 | outputTensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32); |
| 941 | |
| 942 | |
| 943 | auto input1 = MakeTensor<float, 4>(inputTensorInfo1, std::vector<float>( |
| 944 | { |
| 945 | 0.0f, 2.0f, 1.0f, |
| 946 | 0.2f, 1.0f, 2.0f, |
| 947 | |
| 948 | 1.0f, 2.0f, 1.0f, |
| 949 | 0.2f, 1.0f, 2.0f, |
| 950 | |
| 951 | 0.0f, 2.0f, 1.0f, |
| 952 | 4.2f, 1.0f, 2.0f, |
| 953 | |
| 954 | 0.0f, 0.0f, 1.0f, |
| 955 | 0.2f, 1.0f, 2.0f, |
| 956 | })); |
| 957 | |
| 958 | auto input2 = MakeTensor<float, 4>(inputTensorInfo2, std::vector<float>( |
| 959 | { |
| 960 | 1.0f, 2.0f, 1.0f, |
| 961 | 0.0f, 1.0f, 2.0f, |
| 962 | |
| 963 | 1.0f, 2.0f, -2.0f, |
| 964 | 0.2f, 1.0f, 2.0f, |
| 965 | |
| 966 | 0.0f, 2.0f, 1.0f, |
| 967 | 4.2f, 0.0f, -3.0f, |
| 968 | |
| 969 | 0.0f, 0.0f, 1.0f, |
| 970 | 0.7f, 1.0f, 5.0f, |
| 971 | })); |
| 972 | |
| 973 | LayerTestResult<float,4> ret(outputTensorInfo); |
| 974 | ret.outputExpected = MakeTensor<float, 4>(outputTensorInfo, std::vector<float>( |
| 975 | { |
| 976 | 1.0f, 4.0f, 2.0f, |
| 977 | 0.2f, 2.0f, 4.0f, |
| 978 | |
| 979 | 2.0f, 4.0f, -1.0f, |
| 980 | 0.4f, 2.0f, 4.0f, |
| 981 | |
| 982 | 0.0f, 4.0f, 2.0f, |
| 983 | 8.4f, 1.0f, -1.0f, |
| 984 | |
| 985 | 0.0f, 0.0f, 2.0f, |
| 986 | 0.9f, 2.0f, 7.0f, |
| 987 | })); |
| 988 | |
| 989 | std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); |
| 990 | std::unique_ptr<armnn::ITensorHandle> inputHandle2 = workloadFactory.CreateTensorHandle(inputTensorInfo2); |
| 991 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 992 | |
| 993 | armnn::AdditionQueueDescriptor data; |
| 994 | armnn::WorkloadInfo info; |
| 995 | AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); |
| 996 | AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get()); |
| 997 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 998 | |
| 999 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateAddition(data, info); |
| 1000 | |
| 1001 | inputHandle1->Allocate(); |
| 1002 | inputHandle2->Allocate(); |
| 1003 | outputHandle->Allocate(); |
| 1004 | |
| 1005 | CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]); |
| 1006 | CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0][0]); |
| 1007 | |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 1008 | workloadFactory.Finalize(); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1009 | workload->Execute(); |
| 1010 | |
| 1011 | CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); |
| 1012 | |
| 1013 | return ret; |
| 1014 | } |
| 1015 | |
| 1016 | template <typename T> |
| 1017 | LayerTestResult<T, 4> AdditionBroadcastTestImpl(armnn::IWorkloadFactory& workloadFactory, |
| 1018 | float qScale, |
| 1019 | int32_t qOffset) |
| 1020 | { |
| 1021 | armnn::TensorInfo inputTensorInfo1 = armnn::TensorInfo({1, 3, 2, 1}, armnn::GetDataType<T>()); |
| 1022 | armnn::TensorInfo inputTensorInfo2 = armnn::TensorInfo({1, 1, 2, 3}, armnn::GetDataType<T>()); |
| 1023 | armnn::TensorInfo outputTensorInfo = armnn::TensorInfo({1, 3, 2, 3}, armnn::GetDataType<T>()); |
| 1024 | |
| 1025 | if (armnn::IsQuantizedType<T>()) |
| 1026 | { |
| 1027 | inputTensorInfo1.SetQuantizationScale(qScale); |
| 1028 | inputTensorInfo1.SetQuantizationOffset(qOffset); |
| 1029 | inputTensorInfo2.SetQuantizationScale(qScale); |
| 1030 | inputTensorInfo2.SetQuantizationOffset(qOffset); |
| 1031 | outputTensorInfo.SetQuantizationScale(qScale); |
| 1032 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 1033 | } |
| 1034 | |
| 1035 | auto input1 = MakeTensor<T, 4>(inputTensorInfo1, QuantizedVector<T>(qScale, qOffset, |
| 1036 | { |
| 1037 | 0.0f, |
| 1038 | 1.0f, |
| 1039 | |
| 1040 | 2.0f, |
| 1041 | 3.0f, |
| 1042 | |
| 1043 | 4.0f, |
| 1044 | 5.0f, |
| 1045 | })); |
| 1046 | |
| 1047 | auto input2 = MakeTensor<T, 4>(inputTensorInfo2, QuantizedVector<T>(qScale, qOffset, |
| 1048 | { |
| 1049 | 0.5f, 1.5f, 2.5f, |
| 1050 | 3.5f, 4.5f, 5.5f, |
| 1051 | })); |
| 1052 | |
| 1053 | LayerTestResult<T,4> ret(outputTensorInfo); |
| 1054 | ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, |
| 1055 | { |
| 1056 | 0.5f, 1.5f, 2.5f, |
| 1057 | 4.5f, 5.5f, 6.5f, |
| 1058 | |
| 1059 | 2.5f, 3.5f, 4.5f, |
| 1060 | 6.5f, 7.5f, 8.5f, |
| 1061 | |
| 1062 | 4.5f, 5.5f, 6.5f, |
| 1063 | 8.5f, 9.5f, 10.5f, |
| 1064 | })); |
| 1065 | |
| 1066 | std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); |
| 1067 | std::unique_ptr<armnn::ITensorHandle> inputHandle2 = workloadFactory.CreateTensorHandle(inputTensorInfo2); |
| 1068 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 1069 | |
| 1070 | armnn::AdditionQueueDescriptor data; |
| 1071 | armnn::WorkloadInfo info; |
| 1072 | AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); |
| 1073 | AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get()); |
| 1074 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 1075 | |
| 1076 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateAddition(data, info); |
| 1077 | |
| 1078 | inputHandle1->Allocate(); |
| 1079 | inputHandle2->Allocate(); |
| 1080 | outputHandle->Allocate(); |
| 1081 | |
| 1082 | CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]); |
| 1083 | CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0][0]); |
| 1084 | |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 1085 | workloadFactory.Finalize(); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1086 | workload->Execute(); |
| 1087 | |
| 1088 | CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); |
| 1089 | |
| 1090 | return ret; |
| 1091 | } |
| 1092 | |
| 1093 | template <typename T> |
| 1094 | LayerTestResult<T, 4> AdditionBroadcast1ElementTestImpl(armnn::IWorkloadFactory& workloadFactory, |
| 1095 | float qScale, |
| 1096 | int32_t qOffset) |
| 1097 | { |
| 1098 | armnn::TensorInfo inputTensorInfo1 = armnn::TensorInfo({1, 3, 2, 3}, armnn::GetDataType<T>()); |
| 1099 | armnn::TensorInfo inputTensorInfo2 = armnn::TensorInfo({1, 1, 1, 1}, armnn::GetDataType<T>()); |
| 1100 | armnn::TensorInfo outputTensorInfo = armnn::TensorInfo({1, 3, 2, 3}, armnn::GetDataType<T>()); |
| 1101 | |
| 1102 | if (armnn::IsQuantizedType<T>()) |
| 1103 | { |
| 1104 | inputTensorInfo1.SetQuantizationScale(qScale); |
| 1105 | inputTensorInfo1.SetQuantizationOffset(qOffset); |
| 1106 | inputTensorInfo2.SetQuantizationScale(qScale); |
| 1107 | inputTensorInfo2.SetQuantizationOffset(qOffset); |
| 1108 | outputTensorInfo.SetQuantizationScale(qScale); |
| 1109 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 1110 | } |
| 1111 | |
| 1112 | auto input1 = MakeTensor<T, 4>(inputTensorInfo1, QuantizedVector<T>(qScale, qOffset, |
| 1113 | { |
| 1114 | 0.0f, 1.0f, 2.0f, |
| 1115 | 3.0f, 4.0f, 5.0f, |
| 1116 | 6.0f, 7.0f, 8.0f, |
| 1117 | 9.0f, 10.0f, 11.0f, |
| 1118 | 12.0f, 13.0f, 14.0f, |
| 1119 | 15.0f, 16.0f, 17.0f, |
| 1120 | })); |
| 1121 | |
| 1122 | auto input2 = MakeTensor<T, 4>(inputTensorInfo2, QuantizedVector<T>(qScale, qOffset, |
| 1123 | { |
| 1124 | 0.5f, |
| 1125 | })); |
| 1126 | |
| 1127 | LayerTestResult<T,4> ret(outputTensorInfo); |
| 1128 | ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, |
| 1129 | { |
| 1130 | 0.5f, 1.5f, 2.5f, |
| 1131 | 3.5f, 4.5f, 5.5f, |
| 1132 | 6.5f, 7.5f, 8.5f, |
| 1133 | 9.5f, 10.5f, 11.5f, |
| 1134 | 12.5f, 13.5f, 14.5f, |
| 1135 | 15.5f, 16.5f, 17.5f, |
| 1136 | })); |
| 1137 | |
| 1138 | std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); |
| 1139 | std::unique_ptr<armnn::ITensorHandle> inputHandle2 = workloadFactory.CreateTensorHandle(inputTensorInfo2); |
| 1140 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 1141 | |
| 1142 | armnn::AdditionQueueDescriptor data; |
| 1143 | armnn::WorkloadInfo info; |
| 1144 | AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); |
| 1145 | AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get()); |
| 1146 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 1147 | |
| 1148 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateAddition(data, info); |
| 1149 | |
| 1150 | inputHandle1->Allocate(); |
| 1151 | inputHandle2->Allocate(); |
| 1152 | outputHandle->Allocate(); |
| 1153 | |
| 1154 | CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]); |
| 1155 | CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0][0]); |
| 1156 | |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 1157 | workloadFactory.Finalize(); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1158 | workload->Execute(); |
| 1159 | |
| 1160 | CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); |
| 1161 | |
| 1162 | return ret; |
| 1163 | } |
| 1164 | |
| 1165 | LayerTestResult<float, 4> AdditionBroadcastTest(armnn::IWorkloadFactory& workloadFactory) |
| 1166 | { |
| 1167 | return AdditionBroadcastTestImpl<float>(workloadFactory, 0.0f, 0); |
| 1168 | } |
| 1169 | |
| 1170 | LayerTestResult<uint8_t, 4> AdditionBroadcastUint8Test(armnn::IWorkloadFactory& workloadFactory) |
| 1171 | { |
| 1172 | return AdditionBroadcastTestImpl<uint8_t>(workloadFactory, 2.f, 0); |
| 1173 | } |
| 1174 | |
| 1175 | LayerTestResult<float, 4> AdditionBroadcast1ElementTest(armnn::IWorkloadFactory& workloadFactory) |
| 1176 | { |
| 1177 | return AdditionBroadcast1ElementTestImpl<float>(workloadFactory, 0.0f, 0); |
| 1178 | } |
| 1179 | |
| 1180 | LayerTestResult<uint8_t, 4> AdditionBroadcast1ElementUint8Test(armnn::IWorkloadFactory& workloadFactory) |
| 1181 | { |
| 1182 | return AdditionBroadcast1ElementTestImpl<uint8_t>(workloadFactory, 0.1333333f, 128); |
| 1183 | } |
| 1184 | |
| 1185 | LayerTestResult<float,4> CompareAdditionTest(armnn::IWorkloadFactory& workloadFactory, |
David Beck | f195f03 | 2018-09-06 16:46:34 +0100 | [diff] [blame] | 1186 | armnn::IWorkloadFactory& refWorkloadFactory) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1187 | { |
| 1188 | unsigned int batchSize = 4; |
| 1189 | unsigned int channels = 1; |
| 1190 | unsigned int height = 2; |
| 1191 | unsigned int width = 3; |
| 1192 | |
| 1193 | armnn::TensorInfo inputTensorInfo1, inputTensorInfo2; |
| 1194 | armnn::TensorInfo outputTensorInfo; |
| 1195 | |
| 1196 | unsigned int shape[] = {batchSize, channels, height, width}; |
| 1197 | |
| 1198 | inputTensorInfo1 = armnn::TensorInfo(4, shape, armnn::DataType::Float32); |
| 1199 | inputTensorInfo2 = armnn::TensorInfo(4, shape, armnn::DataType::Float32); |
| 1200 | outputTensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32); |
| 1201 | |
| 1202 | auto input1 = MakeRandomTensor<float, 4>(inputTensorInfo1, 1232); |
| 1203 | auto input2 = MakeRandomTensor<float, 4>(inputTensorInfo2, 456); |
| 1204 | |
| 1205 | LayerTestResult<float,4> ret(outputTensorInfo); |
| 1206 | |
| 1207 | std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); |
| 1208 | std::unique_ptr<armnn::ITensorHandle> inputHandle2 = workloadFactory.CreateTensorHandle(inputTensorInfo2); |
| 1209 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 1210 | |
| 1211 | std::unique_ptr<armnn::ITensorHandle> inputHandle1Ref = refWorkloadFactory.CreateTensorHandle(inputTensorInfo1); |
| 1212 | std::unique_ptr<armnn::ITensorHandle> inputHandle2Ref = refWorkloadFactory.CreateTensorHandle(inputTensorInfo2); |
| 1213 | std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo); |
| 1214 | |
| 1215 | armnn::AdditionQueueDescriptor data; |
| 1216 | armnn::WorkloadInfo info; |
| 1217 | AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); |
| 1218 | AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get()); |
| 1219 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 1220 | |
| 1221 | armnn::AdditionQueueDescriptor refData = data; |
| 1222 | armnn::WorkloadInfo refInfo = info; |
| 1223 | SetWorkloadInput(refData, refInfo, 0, inputTensorInfo1, inputHandle1Ref.get()); |
| 1224 | SetWorkloadInput(refData, refInfo, 1, inputTensorInfo2, inputHandle2Ref.get()); |
| 1225 | SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get()); |
| 1226 | |
| 1227 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateAddition(data, info); |
| 1228 | std::unique_ptr<armnn::IWorkload> workloadRef = refWorkloadFactory.CreateAddition(refData, refInfo); |
| 1229 | |
| 1230 | inputHandle1->Allocate(); |
| 1231 | inputHandle2->Allocate(); |
| 1232 | outputHandle->Allocate(); |
| 1233 | inputHandle1Ref->Allocate(); |
| 1234 | inputHandle2Ref->Allocate(); |
| 1235 | outputHandleRef->Allocate(); |
| 1236 | |
| 1237 | CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]); |
| 1238 | CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0][0]); |
| 1239 | CopyDataToITensorHandle(inputHandle1Ref.get(), &input1[0][0][0][0]); |
| 1240 | CopyDataToITensorHandle(inputHandle2Ref.get(), &input2[0][0][0][0]); |
| 1241 | |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 1242 | workloadFactory.Finalize(); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1243 | workload->Execute(); |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 1244 | refWorkloadFactory.Finalize(); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1245 | workloadRef->Execute(); |
| 1246 | |
| 1247 | CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); |
| 1248 | CopyDataFromITensorHandle(&ret.outputExpected[0][0][0][0], outputHandleRef.get()); |
| 1249 | |
| 1250 | return ret; |
| 1251 | } |
| 1252 | |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1253 | namespace { |
David Beck | 5cd01f3 | 2018-09-12 16:00:08 +0100 | [diff] [blame] | 1254 | template <typename T> |
| 1255 | LayerTestResult<T, 4> DivisionTestHelper(armnn::IWorkloadFactory& workloadFactory, |
| 1256 | const unsigned int shape0[4], |
| 1257 | const std::vector<T>& values0, |
| 1258 | float scale0, |
| 1259 | int32_t offset0, |
| 1260 | const unsigned int shape1[4], |
| 1261 | const std::vector<T> & values1, |
| 1262 | float scale1, |
| 1263 | int32_t offset1, |
| 1264 | const unsigned int outShape[4], |
| 1265 | const std::vector<T> & outValues, |
| 1266 | float outScale, |
| 1267 | int32_t outOffset) |
| 1268 | { |
| 1269 | auto dataType = (std::is_same<T, uint8_t>::value ? |
| 1270 | armnn::DataType::QuantisedAsymm8 : |
| 1271 | armnn::DataType::Float32); |
Francis Murtagh | e7a86a4 | 2018-08-29 12:42:10 +0100 | [diff] [blame] | 1272 | |
David Beck | 5cd01f3 | 2018-09-12 16:00:08 +0100 | [diff] [blame] | 1273 | armnn::TensorInfo inputTensorInfo0(4, shape0, dataType); |
| 1274 | armnn::TensorInfo inputTensorInfo1(4, shape1, dataType); |
| 1275 | armnn::TensorInfo outputTensorInfo(4, outShape, dataType); |
Francis Murtagh | e7a86a4 | 2018-08-29 12:42:10 +0100 | [diff] [blame] | 1276 | |
David Beck | 5cd01f3 | 2018-09-12 16:00:08 +0100 | [diff] [blame] | 1277 | inputTensorInfo0.SetQuantizationScale(scale0); |
| 1278 | inputTensorInfo0.SetQuantizationOffset(offset0); |
Francis Murtagh | e7a86a4 | 2018-08-29 12:42:10 +0100 | [diff] [blame] | 1279 | |
David Beck | 5cd01f3 | 2018-09-12 16:00:08 +0100 | [diff] [blame] | 1280 | inputTensorInfo1.SetQuantizationScale(scale1); |
| 1281 | inputTensorInfo1.SetQuantizationOffset(offset1); |
Francis Murtagh | e7a86a4 | 2018-08-29 12:42:10 +0100 | [diff] [blame] | 1282 | |
David Beck | 5cd01f3 | 2018-09-12 16:00:08 +0100 | [diff] [blame] | 1283 | outputTensorInfo.SetQuantizationScale(outScale); |
| 1284 | outputTensorInfo.SetQuantizationOffset(outOffset); |
Francis Murtagh | e7a86a4 | 2018-08-29 12:42:10 +0100 | [diff] [blame] | 1285 | |
David Beck | 5cd01f3 | 2018-09-12 16:00:08 +0100 | [diff] [blame] | 1286 | auto input0 = MakeTensor<T, 4>(inputTensorInfo0, values0); |
| 1287 | auto input1 = MakeTensor<T, 4>(inputTensorInfo1, values1); |
Francis Murtagh | e7a86a4 | 2018-08-29 12:42:10 +0100 | [diff] [blame] | 1288 | |
David Beck | 5cd01f3 | 2018-09-12 16:00:08 +0100 | [diff] [blame] | 1289 | LayerTestResult<T, 4> result(outputTensorInfo); |
| 1290 | result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outValues); |
Francis Murtagh | e7a86a4 | 2018-08-29 12:42:10 +0100 | [diff] [blame] | 1291 | |
David Beck | 5cd01f3 | 2018-09-12 16:00:08 +0100 | [diff] [blame] | 1292 | std::unique_ptr<armnn::ITensorHandle> inputHandle0 = workloadFactory.CreateTensorHandle(inputTensorInfo0); |
| 1293 | std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); |
| 1294 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
Francis Murtagh | e7a86a4 | 2018-08-29 12:42:10 +0100 | [diff] [blame] | 1295 | |
David Beck | 5cd01f3 | 2018-09-12 16:00:08 +0100 | [diff] [blame] | 1296 | armnn::DivisionQueueDescriptor data; |
| 1297 | armnn::WorkloadInfo info; |
| 1298 | AddInputToWorkload(data, info, inputTensorInfo0, inputHandle0.get()); |
| 1299 | AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); |
| 1300 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
Francis Murtagh | e7a86a4 | 2018-08-29 12:42:10 +0100 | [diff] [blame] | 1301 | |
David Beck | 5cd01f3 | 2018-09-12 16:00:08 +0100 | [diff] [blame] | 1302 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateDivision(data, info); |
Francis Murtagh | e7a86a4 | 2018-08-29 12:42:10 +0100 | [diff] [blame] | 1303 | |
David Beck | 5cd01f3 | 2018-09-12 16:00:08 +0100 | [diff] [blame] | 1304 | inputHandle0->Allocate(); |
| 1305 | inputHandle1->Allocate(); |
| 1306 | outputHandle->Allocate(); |
| 1307 | |
| 1308 | CopyDataToITensorHandle(inputHandle0.get(), &input0[0][0][0][0]); |
| 1309 | CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]); |
| 1310 | |
| 1311 | workloadFactory.Finalize(); |
| 1312 | workload->Execute(); |
| 1313 | |
| 1314 | CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); |
| 1315 | |
| 1316 | return result; |
| 1317 | } |
Francis Murtagh | e7a86a4 | 2018-08-29 12:42:10 +0100 | [diff] [blame] | 1318 | } // anonymous namespace |
| 1319 | |
Francis Murtagh | 8c5e3dc | 2018-08-30 17:18:37 +0100 | [diff] [blame] | 1320 | LayerTestResult<float,4> DivisionByZeroTest(armnn::IWorkloadFactory& workloadFactory) |
| 1321 | { |
| 1322 | const unsigned int width = 2; |
| 1323 | const unsigned int height = 2; |
| 1324 | const unsigned int channelCount = 2; |
| 1325 | const unsigned int batchSize = 2; |
| 1326 | |
| 1327 | unsigned int shape[] = { batchSize, channelCount, height, width }; |
| 1328 | |
| 1329 | std::vector<float> input0({ |
| 1330 | 1.f, 1.f, 1.f, 1.f, 0.f, 0.f, 0.f, 0.f, |
| 1331 | -1.f, -1.f, -1.f, -1.f, 5.f, 5.f, 5.f, 5.f }); |
| 1332 | |
| 1333 | std::vector<float> input1({ |
| 1334 | 0.f, 0.f, -0.f, -0.f, 0.f, 0.f, -0.f, -0.f, |
| 1335 | 0.f, 0.f, -0.f, -0.f, 5.f, 5.f, 5.f, 5.f }); |
| 1336 | |
| 1337 | std::vector<float> output({ |
| 1338 | INFINITY, INFINITY, -INFINITY, -INFINITY, NAN, NAN, -NAN, -NAN, |
| 1339 | -INFINITY, -INFINITY, INFINITY, INFINITY, 1, 1, 1, 1 }); |
| 1340 | |
David Beck | 5cd01f3 | 2018-09-12 16:00:08 +0100 | [diff] [blame] | 1341 | return DivisionTestHelper<float>(workloadFactory, |
| 1342 | shape, input0, 1.0f, 0, |
| 1343 | shape, input1, 1.0f, 0, |
| 1344 | shape, output, 1.0f, 0); |
Francis Murtagh | 8c5e3dc | 2018-08-30 17:18:37 +0100 | [diff] [blame] | 1345 | } |
| 1346 | |
Francis Murtagh | e7a86a4 | 2018-08-29 12:42:10 +0100 | [diff] [blame] | 1347 | LayerTestResult<float,4> DivisionTest(armnn::IWorkloadFactory& workloadFactory) |
| 1348 | { |
| 1349 | const unsigned int width = 2; |
| 1350 | const unsigned int height = 2; |
| 1351 | const unsigned int channelCount = 2; |
| 1352 | const unsigned int batchSize = 2; |
| 1353 | |
| 1354 | unsigned int shape[] = { batchSize, channelCount, height, width }; |
| 1355 | |
| 1356 | std::vector<float> input0({ |
| 1357 | 2, 2, 2, 2, 3, 3, 3, 3, |
| 1358 | 4, 4, 4, 4, 5, 5, 5, 5 }); |
| 1359 | |
| 1360 | std::vector<float> input1({ |
| 1361 | 1, 1, 1, 1, 2, 2, 2, 2, |
| 1362 | 4, 4, 4, 4, 4, 4, 4, 4 }); |
| 1363 | |
| 1364 | std::vector<float> output({ |
| 1365 | 2, 2, 2, 2, 1.5, 1.5, 1.5, 1.5, |
| 1366 | 1, 1, 1, 1, 1.25, 1.25, 1.25, 1.25 }); |
| 1367 | |
David Beck | 5cd01f3 | 2018-09-12 16:00:08 +0100 | [diff] [blame] | 1368 | |
| 1369 | return DivisionTestHelper<float>(workloadFactory, |
| 1370 | shape, input0, 1.0f, 0, |
| 1371 | shape, input1, 1.0f, 0, |
| 1372 | shape, output, 1.0f, 0); |
Francis Murtagh | e7a86a4 | 2018-08-29 12:42:10 +0100 | [diff] [blame] | 1373 | } |
| 1374 | |
| 1375 | LayerTestResult<float, 4> DivisionBroadcast1ElementTest(armnn::IWorkloadFactory& workloadFactory) |
| 1376 | { |
| 1377 | unsigned int shape0[] = { 1, 2, 2, 2 }; |
| 1378 | std::vector<float> input0({ 2, 4, 6, 8, 10, 12, 14, 16}); |
| 1379 | |
| 1380 | unsigned int shape1[] = { 1, 1, 1, 1 }; |
| 1381 | std::vector<float> input1({ 2 }); |
| 1382 | |
| 1383 | std::vector<float> output({ 1, 2, 3, 4, 5, 6, 7, 8}); |
| 1384 | |
David Beck | 5cd01f3 | 2018-09-12 16:00:08 +0100 | [diff] [blame] | 1385 | |
| 1386 | return DivisionTestHelper<float>(workloadFactory, |
| 1387 | shape0, input0, 1.0f, 0, |
| 1388 | shape1, input1, 1.0f, 0, |
| 1389 | shape0, output, 1.0f, 0); |
Francis Murtagh | e7a86a4 | 2018-08-29 12:42:10 +0100 | [diff] [blame] | 1390 | } |
| 1391 | |
| 1392 | LayerTestResult<float, 4> DivisionBroadcast1DVectorTest(armnn::IWorkloadFactory& workloadFactory) |
| 1393 | { |
| 1394 | unsigned int shape0[] = { 1, 3, 3, 2 }; |
| 1395 | std::vector<float> input0({ |
| 1396 | 1, 4, 3, 8, 5, 12, |
| 1397 | 7, 16, 9, 20, 11, 24, |
| 1398 | 13, 28, 15, 32, 17, 36}); |
| 1399 | |
| 1400 | unsigned int shape1[] = { 1, 1, 1, 2 }; |
| 1401 | std::vector<float> input1({ 1, 2 }); |
| 1402 | |
| 1403 | std::vector<float> output({ |
| 1404 | 1, 2, 3, 4, 5, 6, |
| 1405 | 7, 8, 9, 10, 11, 12, |
| 1406 | 13, 14, 15, 16, 17, 18}); |
| 1407 | |
David Beck | 5cd01f3 | 2018-09-12 16:00:08 +0100 | [diff] [blame] | 1408 | return DivisionTestHelper<float>(workloadFactory, |
| 1409 | shape0, input0, 1.0f, 0, |
| 1410 | shape1, input1, 1.0f, 0, |
| 1411 | shape0, output, 1.0f, 0); |
| 1412 | } |
| 1413 | |
| 1414 | |
| 1415 | LayerTestResult<uint8_t,4> DivisionUint8Test(armnn::IWorkloadFactory& workloadFactory) |
| 1416 | { |
| 1417 | const unsigned int width = 2; |
| 1418 | const unsigned int height = 2; |
| 1419 | const unsigned int channelCount = 2; |
| 1420 | const unsigned int batchSize = 2; |
| 1421 | |
| 1422 | unsigned int shape[] = { batchSize, channelCount, height, width }; |
| 1423 | |
| 1424 | std::vector<uint8_t> input0({2, 2, 2, 2, 3, 3, 3, 3, |
| 1425 | 4, 4, 4, 4, 5, 5, 5, 5 }); |
| 1426 | |
| 1427 | std::vector<uint8_t> input1({1, 1, 1, 1, 2, 2, 2, 2, |
| 1428 | 4, 4, 4, 4, 4, 4, 4, 4 }); |
| 1429 | |
| 1430 | std::vector<uint8_t> output({8, 8, 8, 8, 6, 6, 6, 6, |
| 1431 | 4, 4, 4, 4, 5, 5, 5, 5}); |
| 1432 | |
| 1433 | |
| 1434 | return DivisionTestHelper<uint8_t>(workloadFactory, |
| 1435 | shape, input0, 1.0f, 0, |
| 1436 | shape, input1, 1.0f, 0, |
| 1437 | shape, output, 0.25f, 0); |
| 1438 | } |
| 1439 | |
| 1440 | LayerTestResult<uint8_t, 4> DivisionBroadcast1ElementUint8Test(armnn::IWorkloadFactory& workloadFactory) |
| 1441 | { |
| 1442 | unsigned int shape0[] = { 1, 2, 2, 2 }; |
| 1443 | std::vector<uint8_t> input0({ 2, 4, 6, 8, 10, 12, 14, 16}); |
| 1444 | |
| 1445 | unsigned int shape1[] = { 1, 1, 1, 1 }; |
| 1446 | std::vector<uint8_t> input1({ 2 }); |
| 1447 | |
| 1448 | std::vector<uint8_t> output({ 1, 2, 3, 4, 5, 6, 7, 8}); |
| 1449 | |
| 1450 | return DivisionTestHelper<uint8_t>(workloadFactory, |
| 1451 | shape0, input0, 1.0f, 0, |
| 1452 | shape1, input1, 1.0f, 0, |
| 1453 | shape0, output, 1.0f, 0); |
| 1454 | } |
| 1455 | |
| 1456 | LayerTestResult<uint8_t, 4> DivisionBroadcast1DVectorUint8Test(armnn::IWorkloadFactory& workloadFactory) |
| 1457 | { |
| 1458 | unsigned int shape0[] = { 1, 3, 3, 2 }; |
| 1459 | std::vector<uint8_t> input0({1, 4, 3, 8, 5, 12, |
| 1460 | 7, 16, 9, 20, 11, 24, |
| 1461 | 13, 28, 15, 32, 17, 36}); |
| 1462 | |
| 1463 | unsigned int shape1[] = { 1, 1, 1, 2 }; |
| 1464 | std::vector<uint8_t> input1({ 1, 2 }); |
| 1465 | |
| 1466 | std::vector<uint8_t> output({1, 2, 3, 4, 5, 6, |
| 1467 | 7, 8, 9, 10, 11, 12, |
| 1468 | 13, 14, 15, 16, 17, 18}); |
| 1469 | |
| 1470 | return DivisionTestHelper<uint8_t>(workloadFactory, |
| 1471 | shape0, input0, 1.0f, 0, |
| 1472 | shape1, input1, 1.0f, 0, |
| 1473 | shape0, output, 1.0f, 0); |
Francis Murtagh | e7a86a4 | 2018-08-29 12:42:10 +0100 | [diff] [blame] | 1474 | } |
| 1475 | |
| 1476 | namespace { |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1477 | LayerTestResult<float,4> MultiplicationTestHelper(armnn::IWorkloadFactory& workloadFactory, |
| 1478 | const unsigned int shape0[4], |
| 1479 | const std::vector<float> & values0, |
| 1480 | const unsigned int shape1[4], |
| 1481 | const std::vector<float> & values1, |
| 1482 | const unsigned int outShape[4], |
| 1483 | const std::vector<float> & outValues) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1484 | { |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1485 | const size_t dimensionCount = 4; |
| 1486 | armnn::TensorInfo inputTensorInfo0{dimensionCount, shape0, armnn::DataType::Float32}; |
| 1487 | armnn::TensorInfo inputTensorInfo1{dimensionCount, shape1, armnn::DataType::Float32}; |
| 1488 | armnn::TensorInfo outputTensorInfo{dimensionCount, outShape, armnn::DataType::Float32}; |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1489 | |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1490 | auto input0 = MakeTensor<float, 4>(inputTensorInfo0, values0); |
| 1491 | auto input1 = MakeTensor<float, 4>(inputTensorInfo1, values1); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1492 | |
| 1493 | LayerTestResult<float,4> ret(outputTensorInfo); |
| 1494 | |
| 1495 | std::unique_ptr<armnn::ITensorHandle> inputHandle0 = workloadFactory.CreateTensorHandle(inputTensorInfo0); |
| 1496 | std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); |
| 1497 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 1498 | |
| 1499 | armnn::MultiplicationQueueDescriptor data; |
| 1500 | armnn::WorkloadInfo info; |
| 1501 | AddInputToWorkload(data, info, inputTensorInfo0, inputHandle0.get()); |
| 1502 | AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); |
| 1503 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 1504 | |
| 1505 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateMultiplication(data, info); |
| 1506 | |
| 1507 | inputHandle0->Allocate(); |
| 1508 | inputHandle1->Allocate(); |
| 1509 | outputHandle->Allocate(); |
| 1510 | |
| 1511 | CopyDataToITensorHandle(inputHandle0.get(), &input0[0][0][0][0]); |
| 1512 | CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]); |
| 1513 | |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 1514 | workloadFactory.Finalize(); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1515 | workload->Execute(); |
| 1516 | |
| 1517 | CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); |
| 1518 | |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1519 | ret.outputExpected = MakeTensor<float, 4>(outputTensorInfo, outValues); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1520 | return ret; |
| 1521 | } |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 1522 | } // anonymous namespace |
| 1523 | |
| 1524 | |
| 1525 | LayerTestResult<float,4> MultiplicationTest(armnn::IWorkloadFactory& workloadFactory) |
| 1526 | { |
| 1527 | const unsigned int width = 2; |
| 1528 | const unsigned int height = 2; |
| 1529 | const unsigned int channelCount = 2; |
| 1530 | const unsigned int batchSize = 2; |
| 1531 | |
| 1532 | unsigned int shape[] = { batchSize, channelCount, height, width }; |
| 1533 | |
| 1534 | std::vector<float> input0({ |
| 1535 | 1, 1, 1, 1, 2, 2, 2, 2, |
| 1536 | 3, 3, 3, 3, 4, 4, 4, 4 }); |
| 1537 | |
| 1538 | std::vector<float> input1({ |
| 1539 | 2, 2, 2, 2, 3, 3, 3, 3, |
| 1540 | 4, 4, 4, 4, 5, 5, 5, 5 }); |
| 1541 | |
| 1542 | std::vector<float> output({ |
| 1543 | 2, 2, 2, 2, 6, 6, 6, 6, |
| 1544 | 12, 12, 12, 12, 20, 20, 20, 20 }); |
| 1545 | |
| 1546 | return MultiplicationTestHelper(workloadFactory, |
| 1547 | shape, |
| 1548 | input0, |
| 1549 | shape, |
| 1550 | input1, |
| 1551 | shape, |
| 1552 | output); |
| 1553 | } |
| 1554 | |
| 1555 | LayerTestResult<float, 4> MultiplicationBroadcast1ElementTest(armnn::IWorkloadFactory& workloadFactory) |
| 1556 | { |
| 1557 | unsigned int shape0[] = { 1, 2, 2, 2 }; |
| 1558 | std::vector<float> input0({ 1, 2, 3, 4, 5, 6, 7, 8}); |
| 1559 | |
| 1560 | unsigned int shape1[] = { 1, 1, 1, 1 }; |
| 1561 | std::vector<float> input1({ 2 }); |
| 1562 | |
| 1563 | std::vector<float> output({ 2, 4, 6, 8, 10, 12, 14, 16}); |
| 1564 | |
| 1565 | return MultiplicationTestHelper(workloadFactory, |
| 1566 | shape0, |
| 1567 | input0, |
| 1568 | shape1, |
| 1569 | input1, |
| 1570 | shape0, |
| 1571 | output); |
| 1572 | } |
| 1573 | |
| 1574 | LayerTestResult<float, 4> MultiplicationBroadcast1DVectorTest(armnn::IWorkloadFactory& workloadFactory) |
| 1575 | { |
| 1576 | unsigned int shape0[] = { 1, 3, 3, 2 }; |
| 1577 | std::vector<float> input0({ |
| 1578 | 1, 2, 3, 4, 5, 6, |
| 1579 | 7, 8, 9, 10, 11, 12, |
| 1580 | 13, 14, 15, 16, 17, 18}); |
| 1581 | |
| 1582 | unsigned int shape1[] = { 1, 1, 1, 2 }; |
| 1583 | std::vector<float> input1({ 1, 2 }); |
| 1584 | |
| 1585 | std::vector<float> output({ |
| 1586 | 1, 4, 3, 8, 5, 12, |
| 1587 | 7, 16, 9, 20, 11, 24, |
| 1588 | 13, 28, 15, 32, 17, 36}); |
| 1589 | |
| 1590 | return MultiplicationTestHelper(workloadFactory, |
| 1591 | shape0, |
| 1592 | input0, |
| 1593 | shape1, |
| 1594 | input1, |
| 1595 | shape0, |
| 1596 | output); |
| 1597 | } |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1598 | |
| 1599 | LayerTestResult<float,4> CompareMultiplicationTest(armnn::IWorkloadFactory& workloadFactory, |
| 1600 | armnn::IWorkloadFactory& refWorkloadFactory) |
| 1601 | { |
| 1602 | const unsigned int width = 16; |
| 1603 | const unsigned int height = 32; |
| 1604 | const unsigned int channelCount = 2; |
| 1605 | const unsigned int batchSize = 5; |
| 1606 | |
| 1607 | armnn::TensorInfo inputTensorInfo0; |
| 1608 | armnn::TensorInfo inputTensorInfo1; |
| 1609 | armnn::TensorInfo outputTensorInfo; |
| 1610 | |
| 1611 | constexpr unsigned int shape[] = { batchSize, channelCount, height, width }; |
| 1612 | |
| 1613 | inputTensorInfo0 = armnn::TensorInfo(4, shape, armnn::DataType::Float32); |
| 1614 | inputTensorInfo1 = armnn::TensorInfo(4, shape, armnn::DataType::Float32); |
| 1615 | outputTensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32); |
| 1616 | |
| 1617 | LayerTestResult<float,4> comparisonResult(outputTensorInfo); |
| 1618 | |
| 1619 | auto input0 = MakeRandomTensor<float, 4>(inputTensorInfo0, 803506992); |
| 1620 | auto input1 = MakeRandomTensor<float, 4>(inputTensorInfo1, 54902257); |
| 1621 | |
| 1622 | std::unique_ptr<armnn::ITensorHandle> inputHandle0 = workloadFactory.CreateTensorHandle(inputTensorInfo0); |
| 1623 | std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); |
| 1624 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 1625 | |
| 1626 | std::unique_ptr<armnn::ITensorHandle> inputHandle0Ref = refWorkloadFactory.CreateTensorHandle(inputTensorInfo0); |
| 1627 | std::unique_ptr<armnn::ITensorHandle> inputHandle1Ref = refWorkloadFactory.CreateTensorHandle(inputTensorInfo1); |
| 1628 | std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo); |
| 1629 | |
| 1630 | armnn::MultiplicationQueueDescriptor data; |
| 1631 | armnn::WorkloadInfo info; |
| 1632 | AddInputToWorkload(data, info, inputTensorInfo0, inputHandle0.get()); |
| 1633 | AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); |
| 1634 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 1635 | |
| 1636 | armnn::MultiplicationQueueDescriptor refData = data; |
| 1637 | armnn::WorkloadInfo refInfo = info; |
| 1638 | SetWorkloadInput(refData, refInfo, 0, inputTensorInfo0, inputHandle0Ref.get()); |
| 1639 | SetWorkloadInput(refData, refInfo, 1, inputTensorInfo1, inputHandle1Ref.get()); |
| 1640 | SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get()); |
| 1641 | |
| 1642 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateMultiplication(data, info); |
| 1643 | std::unique_ptr<armnn::IWorkload> workloadRef = refWorkloadFactory.CreateMultiplication(refData, refInfo); |
| 1644 | |
| 1645 | inputHandle0->Allocate(); |
| 1646 | inputHandle1->Allocate(); |
| 1647 | outputHandle->Allocate(); |
| 1648 | inputHandle0Ref->Allocate(); |
| 1649 | inputHandle1Ref->Allocate(); |
| 1650 | outputHandleRef->Allocate(); |
| 1651 | |
| 1652 | CopyDataToITensorHandle(inputHandle0.get(), &input0[0][0][0][0]); |
| 1653 | CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]); |
| 1654 | CopyDataToITensorHandle(inputHandle0Ref.get(), &input0[0][0][0][0]); |
| 1655 | CopyDataToITensorHandle(inputHandle1Ref.get(), &input1[0][0][0][0]); |
| 1656 | |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 1657 | workloadFactory.Finalize(); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1658 | workload->Execute(); |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 1659 | refWorkloadFactory.Finalize(); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1660 | workloadRef->Execute(); |
| 1661 | |
| 1662 | CopyDataFromITensorHandle(&comparisonResult.output[0][0][0][0], outputHandle.get()); |
| 1663 | CopyDataFromITensorHandle(&comparisonResult.outputExpected[0][0][0][0], outputHandleRef.get()); |
| 1664 | |
| 1665 | return comparisonResult; |
| 1666 | } |
| 1667 | |
| 1668 | LayerTestResult<float,4> CompareBatchNormTest(armnn::IWorkloadFactory& workloadFactory, |
| 1669 | armnn::IWorkloadFactory& refWorkloadFactory) |
| 1670 | { |
| 1671 | const unsigned int width = 2; |
| 1672 | const unsigned int height = 3; |
| 1673 | const unsigned int channels = 5; |
| 1674 | const unsigned int batchSize = 3; |
| 1675 | |
| 1676 | armnn::TensorInfo inputTensorInfo; |
| 1677 | armnn::TensorInfo outputTensorInfo; |
| 1678 | armnn::TensorInfo tensorInfo; |
| 1679 | |
| 1680 | constexpr unsigned int shape[] = {batchSize, channels, height, width}; |
| 1681 | constexpr unsigned int tensorShape[] = {channels}; |
| 1682 | |
| 1683 | inputTensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32); |
| 1684 | outputTensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32); |
| 1685 | tensorInfo = armnn::TensorInfo(1, tensorShape, armnn::DataType::Float32); |
| 1686 | |
| 1687 | auto input = MakeRandomTensor<float, 4>(inputTensorInfo, 21312); |
| 1688 | |
| 1689 | auto mean = MakeRandomTensor<float, 1>(tensorInfo, 123); |
| 1690 | auto variance = MakeRandomTensor<float, 1>(tensorInfo, 234, 0.0f); |
| 1691 | auto beta = MakeRandomTensor<float, 1>(tensorInfo, 123); |
| 1692 | auto gamma = MakeRandomTensor<float, 1>(tensorInfo, 345); |
| 1693 | |
| 1694 | LayerTestResult<float,4> ret(outputTensorInfo); |
| 1695 | |
| 1696 | std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| 1697 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 1698 | |
| 1699 | std::unique_ptr<armnn::ITensorHandle> inputHandleRef = refWorkloadFactory.CreateTensorHandle(inputTensorInfo); |
| 1700 | std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo); |
| 1701 | |
| 1702 | armnn::BatchNormalizationQueueDescriptor data; |
| 1703 | armnn::WorkloadInfo info; |
| 1704 | armnn::ScopedCpuTensorHandle meanTensor(tensorInfo); |
| 1705 | armnn::ScopedCpuTensorHandle varianceTensor(tensorInfo); |
| 1706 | armnn::ScopedCpuTensorHandle betaTensor(tensorInfo); |
| 1707 | armnn::ScopedCpuTensorHandle gammaTensor(tensorInfo); |
| 1708 | |
| 1709 | AllocateAndCopyDataToITensorHandle(&meanTensor, &mean[0]); |
| 1710 | AllocateAndCopyDataToITensorHandle(&varianceTensor, &variance[0]); |
| 1711 | AllocateAndCopyDataToITensorHandle(&betaTensor, &beta[0]); |
| 1712 | AllocateAndCopyDataToITensorHandle(&gammaTensor, &gamma[0]); |
| 1713 | |
| 1714 | AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); |
| 1715 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 1716 | data.m_Mean = &meanTensor; |
| 1717 | data.m_Variance = &varianceTensor; |
| 1718 | data.m_Beta = &betaTensor; |
| 1719 | data.m_Gamma = &gammaTensor; |
| 1720 | data.m_Parameters.m_Eps = 0.01f; |
| 1721 | |
| 1722 | armnn::BatchNormalizationQueueDescriptor refData = data; |
| 1723 | armnn::WorkloadInfo refInfo = info; |
| 1724 | SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get()); |
| 1725 | SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get()); |
| 1726 | |
| 1727 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateBatchNormalization(data, info); |
| 1728 | std::unique_ptr<armnn::IWorkload> workloadRef = refWorkloadFactory.CreateBatchNormalization(refData, refInfo); |
| 1729 | |
| 1730 | inputHandle->Allocate(); |
| 1731 | outputHandle->Allocate(); |
| 1732 | inputHandleRef->Allocate(); |
| 1733 | outputHandleRef->Allocate(); |
| 1734 | |
| 1735 | CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); |
| 1736 | CopyDataToITensorHandle(inputHandleRef.get(), &input[0][0][0][0]); |
| 1737 | |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 1738 | workloadFactory.Finalize(); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1739 | workload->Execute(); |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 1740 | refWorkloadFactory.Finalize(); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1741 | workloadRef->Execute(); |
| 1742 | |
| 1743 | CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); |
| 1744 | CopyDataFromITensorHandle(&ret.outputExpected[0][0][0][0], outputHandleRef.get()); |
| 1745 | |
| 1746 | return ret; |
| 1747 | } |
| 1748 | |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 1749 | template<typename T> |
| 1750 | void PermuteTensorData( |
| 1751 | armnn::IWorkloadFactory& workloadFactory, |
| 1752 | const armnn::PermutationVector& mappings, |
| 1753 | armnn::TensorInfo & inputTensorInfo, |
| 1754 | const T * inputData, |
| 1755 | std::vector<T>& outputData) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1756 | { |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 1757 | BOOST_ASSERT_MSG(inputData != nullptr, "inputData must not be null"); |
| 1758 | if (inputData == nullptr) |
| 1759 | { |
| 1760 | // Nullptr is an error in the test. By returning without doing the concatenation |
| 1761 | // I expect the caller to fail the test. It still makes sense to report this as |
| 1762 | // an assert for Debug builds. |
| 1763 | return; |
| 1764 | } |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1765 | |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 1766 | armnn::TensorInfo outputTensorInfo = armnnUtils::Permuted(inputTensorInfo, mappings); |
| 1767 | |
| 1768 | std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| 1769 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 1770 | |
| 1771 | armnn::PermuteQueueDescriptor queueDescriptor; |
| 1772 | queueDescriptor.m_Parameters = armnn::PermuteDescriptor{mappings}; |
| 1773 | armnn::WorkloadInfo workloadInfo; |
| 1774 | AddInputToWorkload(queueDescriptor, workloadInfo, inputTensorInfo, inputHandle.get()); |
| 1775 | AddOutputToWorkload(queueDescriptor, workloadInfo, outputTensorInfo, outputHandle.get()); |
| 1776 | |
| 1777 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreatePermute(queueDescriptor, workloadInfo); |
| 1778 | |
| 1779 | inputHandle->Allocate(); |
| 1780 | outputHandle->Allocate(); |
| 1781 | |
| 1782 | CopyDataToITensorHandle(inputHandle.get(), inputData); |
| 1783 | |
| 1784 | workload->Execute(); |
| 1785 | |
| 1786 | outputData.resize(outputTensorInfo.GetNumElements()); |
| 1787 | CopyDataFromITensorHandle(&outputData[0], outputHandle.get()); |
| 1788 | inputTensorInfo = outputTensorInfo; |
| 1789 | } |
| 1790 | |
| 1791 | armnn::OriginsDescriptor CreateMergerDescriptorForConcatenation( |
| 1792 | const std::vector<armnn::TensorInfo> & inputTensorInfos, |
| 1793 | unsigned int concatDim) |
| 1794 | { |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 1795 | std::vector<armnn::TensorShape> shapes; |
| 1796 | shapes.reserve(inputTensorInfos.size()); |
| 1797 | for (const armnn::TensorInfo& it: inputTensorInfos) |
| 1798 | { |
| 1799 | shapes.push_back(it.GetShape()); |
| 1800 | } |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 1801 | |
| 1802 | return armnn::CreateMergerDescriptorForConcatenation(shapes.begin(), |
| 1803 | shapes.end(), |
| 1804 | concatDim); |
| 1805 | } |
| 1806 | |
| 1807 | // |
| 1808 | // Concatenation is only supported for N and C dimensions for NCHW. In case of |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1809 | // <4 dimensions we need to make sure that the concat dimensions are at least |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 1810 | // the 3rd slowest iterating one. |
| 1811 | // |
| 1812 | |
| 1813 | bool NeedPermuteForConcat( |
| 1814 | const std::vector<armnn::TensorInfo> & inputTensorInfos, |
| 1815 | unsigned int concatDim) |
| 1816 | { |
| 1817 | // See note above. Additionally we expect the input shapes to have the |
| 1818 | // same number of dimensions. |
| 1819 | unsigned int nDimensions = 0; |
| 1820 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1821 | // Determine the number of dimensions as well as sanity check them |
| 1822 | // agains test implementation issues. |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 1823 | for (auto && tensorInfo : inputTensorInfos) |
| 1824 | { |
| 1825 | if (!nDimensions) |
| 1826 | { |
| 1827 | nDimensions = tensorInfo.GetShape().GetNumDimensions(); |
| 1828 | } |
| 1829 | else |
| 1830 | { |
| 1831 | BOOST_ASSERT_MSG(nDimensions == tensorInfo.GetShape().GetNumDimensions(), |
| 1832 | "Input shapes must have the same number of dimensions"); |
| 1833 | } |
| 1834 | } |
| 1835 | |
| 1836 | return (nDimensions-concatDim) < 3; |
| 1837 | } |
| 1838 | |
| 1839 | armnn::TensorShape ExpandTensorShapeTo3dForPermute(const armnn::TensorShape & inputShape) |
| 1840 | { |
| 1841 | unsigned int numDims = inputShape.GetNumDimensions(); |
| 1842 | if (numDims >= 3) |
| 1843 | { |
| 1844 | // Nothing to do if the inputShape has at least 3 dimensions. |
| 1845 | return inputShape; |
| 1846 | } |
| 1847 | |
| 1848 | std::vector<unsigned int> newDims(size_t(3), 1u); |
| 1849 | unsigned int expandedBy = 3 - numDims; |
| 1850 | for (unsigned int i=0; i<numDims; ++i) |
| 1851 | { |
| 1852 | newDims[expandedBy+i] = inputShape[i]; |
| 1853 | } |
| 1854 | return armnn::TensorShape(3u, &newDims[0]); |
| 1855 | } |
| 1856 | |
| 1857 | void Generate3dPermuteVectorForConcat( |
| 1858 | unsigned int numDimensions, |
| 1859 | unsigned int & concatDim, |
| 1860 | std::pair<armnn::PermutationVector, armnn::PermutationVector> & permutations) |
| 1861 | { |
| 1862 | BOOST_ASSERT_MSG(numDimensions <= 3, |
| 1863 | "Only dimensions 1,2 and 3 are supported by this helper"); |
| 1864 | |
| 1865 | unsigned int expandedBy = 3 - numDimensions; |
| 1866 | unsigned int expandedConcatAxis = concatDim + expandedBy; |
| 1867 | |
| 1868 | if (expandedConcatAxis == 2) |
| 1869 | { |
| 1870 | concatDim = 0; |
| 1871 | armnn::PermutationVector forwardPermutation({1, 2, 0}); |
| 1872 | armnn::PermutationVector reversePermutation({2, 0, 1}); |
| 1873 | permutations = std::make_pair(forwardPermutation, reversePermutation); |
| 1874 | } |
| 1875 | else if (expandedConcatAxis == 1) |
| 1876 | { |
| 1877 | concatDim = 0; |
| 1878 | armnn::PermutationVector forwardPermutation({2, 0, 1}); |
| 1879 | armnn::PermutationVector reversePermutation({1, 2, 0}); |
| 1880 | permutations = std::make_pair(forwardPermutation, reversePermutation); |
| 1881 | } |
| 1882 | else |
| 1883 | { |
| 1884 | BOOST_ASSERT(expandedConcatAxis == 0); |
| 1885 | concatDim = 0; |
| 1886 | } |
| 1887 | } |
| 1888 | |
| 1889 | // |
| 1890 | // Permute the input tensors so we can do a supported concatenation. |
| 1891 | // Also treat lower than 3d tensors as 3d by adding dummy 1 dimensions |
| 1892 | // at the front. Finally this function tells what the output shape |
| 1893 | // of the permuted concatenated tensor is going to be. |
| 1894 | // |
| 1895 | template <typename T> |
| 1896 | void PermuteInputsForConcat( |
| 1897 | armnn::IWorkloadFactory& workloadFactory, |
| 1898 | std::vector<armnn::TensorInfo> & inputTensorInfos, |
| 1899 | std::vector<T *> & inputData, |
| 1900 | std::vector<std::vector<T>> & inputDataStorage, |
| 1901 | armnn::PermutationVector & permuteVector, |
| 1902 | unsigned int & concatDim, |
| 1903 | armnn::TensorInfo & outputTensorInfo) |
| 1904 | { |
| 1905 | BOOST_ASSERT_MSG(inputTensorInfos.size() > 1, |
| 1906 | "Expecting more than one tensor to be concatenated here"); |
| 1907 | |
| 1908 | unsigned int numDims = 0; |
| 1909 | unsigned int nthInput = 0; |
| 1910 | const armnn::PermutationVector identity({0, 1, 2}); |
| 1911 | |
| 1912 | std::pair<armnn::PermutationVector, armnn::PermutationVector> permutations = |
| 1913 | std::make_pair(identity, identity); |
| 1914 | |
| 1915 | inputDataStorage.resize(inputData.size()); |
| 1916 | |
| 1917 | for (auto && tensorInfo : inputTensorInfos) |
| 1918 | { |
| 1919 | if (numDims == 0) |
| 1920 | { |
| 1921 | numDims = tensorInfo.GetShape().GetNumDimensions(); |
| 1922 | Generate3dPermuteVectorForConcat(numDims, concatDim, permutations); |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1923 | // Store the reverese permutation. |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 1924 | permuteVector = permutations.second; |
| 1925 | BOOST_ASSERT_MSG(!permuteVector.IsEqual(identity), |
| 1926 | "Test logic error, we don't need permutation, so we shouldn't arrive here"); |
| 1927 | } |
| 1928 | else |
| 1929 | { |
| 1930 | BOOST_ASSERT_MSG(numDims == tensorInfo.GetShape().GetNumDimensions(), |
| 1931 | "All inputs must have the same number of dimensions"); |
| 1932 | } |
| 1933 | |
| 1934 | armnn::TensorInfo newTensorInfo = tensorInfo; |
| 1935 | newTensorInfo.SetShape(ExpandTensorShapeTo3dForPermute(tensorInfo.GetShape())); |
| 1936 | |
| 1937 | PermuteTensorData<T>(workloadFactory, |
| 1938 | permutations.first, |
| 1939 | newTensorInfo, |
| 1940 | inputData[nthInput], |
| 1941 | inputDataStorage[nthInput]); |
| 1942 | |
| 1943 | inputData[nthInput] = inputDataStorage[nthInput].data(); |
| 1944 | inputTensorInfos[nthInput] = newTensorInfo; |
| 1945 | |
| 1946 | ++nthInput; |
| 1947 | } |
| 1948 | |
| 1949 | outputTensorInfo.SetShape( |
| 1950 | armnnUtils::Permuted( |
| 1951 | ExpandTensorShapeTo3dForPermute(outputTensorInfo.GetShape()), |
| 1952 | permutations.first)); |
| 1953 | } |
| 1954 | |
| 1955 | |
| 1956 | // |
| 1957 | // This is the pair of PermuteInputsForConcat(...) which permutes back |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1958 | // the output of the concatenation so we can check it against an expected |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 1959 | // output. |
| 1960 | // |
| 1961 | template <typename T> |
| 1962 | void PermuteOutputForConcat( |
| 1963 | armnn::IWorkloadFactory& workloadFactory, |
| 1964 | const armnn::TensorInfo & tensorInfo, |
| 1965 | const armnn::PermutationVector & permuteVector, |
| 1966 | std::unique_ptr<armnn::ITensorHandle> && inputDataHandle, |
| 1967 | T * data) |
| 1968 | { |
| 1969 | BOOST_ASSERT_MSG(data != nullptr, "data must not be null"); |
| 1970 | if (data == nullptr) |
| 1971 | { |
| 1972 | // Nullptr is an error in the test. By returning without doing the permutation |
| 1973 | // I expect the caller to fail the test. It still makes sense to report this as |
| 1974 | // an assert for Debug builds. |
| 1975 | return; |
| 1976 | } |
| 1977 | |
| 1978 | armnn::TensorInfo resultTensorInfo = tensorInfo; |
| 1979 | std::vector<T> inputData(tensorInfo.GetNumElements()); |
| 1980 | std::vector<T> outputData; |
| 1981 | |
| 1982 | CopyDataFromITensorHandle(&inputData[0], inputDataHandle.get()); |
| 1983 | |
| 1984 | PermuteTensorData<T>(workloadFactory, |
| 1985 | permuteVector, |
| 1986 | resultTensorInfo, |
| 1987 | &inputData[0], |
| 1988 | outputData); |
| 1989 | |
| 1990 | ::memcpy(data, &outputData[0], sizeof(T)*outputData.size()); |
| 1991 | } |
| 1992 | |
| 1993 | template <typename T> |
| 1994 | void Concatenate(armnn::IWorkloadFactory& workloadFactory, |
| 1995 | std::initializer_list<const armnn::TensorInfo> inputTensorInfosOrig, |
| 1996 | std::initializer_list<T *> inputsOrig, |
| 1997 | const armnn::TensorInfo& outputTensorInfoOrig, |
| 1998 | T * output, |
| 1999 | unsigned int concatDim) |
| 2000 | { |
| 2001 | BOOST_ASSERT_MSG(output != nullptr, "output must not be null"); |
| 2002 | if (output == nullptr) |
| 2003 | { |
| 2004 | // Nullptr is an error in the test. By returning without doing the permutation |
| 2005 | // I expect the caller to fail the test. It still makes sense to report this as |
| 2006 | // an assert for Debug builds. |
| 2007 | return; |
| 2008 | } |
| 2009 | |
| 2010 | armnn::MergerQueueDescriptor queueDescriptor; |
| 2011 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2012 | // Saves a copy of the parameters which we might need to change. |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 2013 | std::vector<armnn::TensorInfo> inputTensorInfos(inputTensorInfosOrig.begin(), inputTensorInfosOrig.end()); |
| 2014 | std::vector<T *> inputs = inputsOrig; |
| 2015 | armnn::TensorInfo outputTensorInfo = outputTensorInfoOrig; |
| 2016 | |
| 2017 | armnn::PermutationVector permuteVector{0, 1, 2}; |
| 2018 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2019 | // Holds and automatically releases memory for the reshaped input data. |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 2020 | std::vector<std::vector<T>> tmpInputDataStorage; |
| 2021 | |
| 2022 | const size_t inputCount = inputTensorInfos.size(); |
| 2023 | |
| 2024 | bool needPermuteForConcat = NeedPermuteForConcat(inputTensorInfos, concatDim); |
| 2025 | |
| 2026 | if (needPermuteForConcat) |
| 2027 | { |
| 2028 | // |
| 2029 | // We need to permute the inputs, because concatenation along |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 2030 | // the requested axis is not supported. |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 2031 | // |
| 2032 | PermuteInputsForConcat<T>(workloadFactory, |
| 2033 | inputTensorInfos, |
| 2034 | inputs, |
| 2035 | tmpInputDataStorage, |
| 2036 | permuteVector, |
| 2037 | concatDim, |
| 2038 | outputTensorInfo); |
| 2039 | } |
| 2040 | |
| 2041 | armnn::OriginsDescriptor viewsDescriptor = CreateMergerDescriptorForConcatenation(inputTensorInfos, concatDim); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 2042 | |
| 2043 | queueDescriptor.m_ViewOrigins.reserve(viewsDescriptor.GetNumViews()); |
| 2044 | for (unsigned int i = 0; i < viewsDescriptor.GetNumViews(); ++i) |
| 2045 | { |
| 2046 | queueDescriptor.m_ViewOrigins.emplace_back(std::vector<unsigned int>(viewsDescriptor.GetViewOrigin(i), |
| 2047 | viewsDescriptor.GetViewOrigin(i) + viewsDescriptor.GetNumDimensions())); |
| 2048 | } |
| 2049 | |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 2050 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 2051 | |
| 2052 | std::vector<std::unique_ptr<armnn::ITensorHandle>> inputHandles; |
| 2053 | inputHandles.reserve(inputCount); |
| 2054 | |
| 2055 | const bool subTensorsSupported = workloadFactory.SupportsSubTensors(); |
| 2056 | for (unsigned int i = 0; i < inputCount; ++i) |
| 2057 | { |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 2058 | const armnn::TensorInfo& inputTensorInfo = inputTensorInfos[i]; |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 2059 | |
| 2060 | std::unique_ptr<armnn::ITensorHandle> inputHandle = subTensorsSupported ? |
| 2061 | workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo.GetShape(), |
| 2062 | queueDescriptor.m_ViewOrigins[i].m_Origin.data()) |
| 2063 | : workloadFactory.CreateTensorHandle(inputTensorInfo); |
| 2064 | |
| 2065 | inputHandles.emplace_back(std::move(inputHandle)); |
| 2066 | } |
| 2067 | |
| 2068 | armnn::WorkloadInfo workloadInfo; |
| 2069 | |
| 2070 | for (unsigned int i = 0; i < inputCount; ++i) |
| 2071 | { |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 2072 | AddInputToWorkload(queueDescriptor, workloadInfo, inputTensorInfos[i], inputHandles[i].get()); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 2073 | } |
| 2074 | |
| 2075 | AddOutputToWorkload(queueDescriptor, workloadInfo, outputTensorInfo, outputHandle.get()); |
| 2076 | |
| 2077 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateMerger(queueDescriptor, workloadInfo); |
| 2078 | |
| 2079 | for (auto& inputHandle : inputHandles) |
| 2080 | { |
| 2081 | inputHandle->Allocate(); |
| 2082 | } |
| 2083 | |
| 2084 | outputHandle->Allocate(); |
| 2085 | |
| 2086 | unsigned int nextInputId = 0; |
| 2087 | for (auto& inputHandle : inputHandles) |
| 2088 | { |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 2089 | CopyDataToITensorHandle(inputHandle.get(), inputs[nextInputId]); |
| 2090 | ++nextInputId; |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 2091 | } |
| 2092 | |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 2093 | workloadFactory.Finalize(); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 2094 | workload->Execute(); |
| 2095 | |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 2096 | if (needPermuteForConcat) |
| 2097 | { |
| 2098 | PermuteOutputForConcat<T>(workloadFactory, |
| 2099 | outputTensorInfo, |
| 2100 | permuteVector, |
| 2101 | std::move(outputHandle), |
| 2102 | output); |
| 2103 | } |
| 2104 | else |
| 2105 | { |
| 2106 | CopyDataFromITensorHandle(output, outputHandle.get()); |
| 2107 | } |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 2108 | } |
| 2109 | |
| 2110 | template <typename T> |
| 2111 | LayerTestResult<T, 1> Concatenation1dTestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale, int32_t qOffset) |
| 2112 | { |
| 2113 | armnn::TensorInfo inputTensorInfo({ 3 }, armnn::GetDataType<T>()); |
| 2114 | |
| 2115 | auto input0 = MakeTensor<T, 1>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, { 1.0f, 2.0f, 3.0f })); |
| 2116 | auto input1 = MakeTensor<T, 1>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, { 4.0f, 5.0f, 6.0f })); |
| 2117 | auto input2 = MakeTensor<T, 1>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, { 7.0f, 8.0f, 9.0f })); |
| 2118 | |
| 2119 | armnn::TensorInfo outputTensorInfo({ 9 }, armnn::GetDataType<T>()); |
| 2120 | |
| 2121 | LayerTestResult<T, 1> result(outputTensorInfo); |
| 2122 | |
| 2123 | std::vector<T> output; |
| 2124 | output.resize(outputTensorInfo.GetNumElements()); |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 2125 | Concatenate<T>(workloadFactory, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 2126 | { inputTensorInfo, inputTensorInfo, inputTensorInfo }, |
| 2127 | { input0.data(), input1.data(), input2.data() }, |
| 2128 | outputTensorInfo, |
| 2129 | output.data(), |
| 2130 | 0); |
| 2131 | |
| 2132 | result.output = MakeTensor<T, 1>(outputTensorInfo, output); |
| 2133 | result.outputExpected = MakeTensor<T, 1>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 2134 | 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f |
| 2135 | })); |
| 2136 | |
| 2137 | return result; |
| 2138 | } |
| 2139 | |
| 2140 | LayerTestResult<float, 1> Concatenation1dTest(armnn::IWorkloadFactory& workloadFactory) |
| 2141 | { |
| 2142 | return Concatenation1dTestImpl<float>(workloadFactory, 0.0f, 0); |
| 2143 | } |
| 2144 | |
| 2145 | template <typename T> |
| 2146 | LayerTestResult<T, 2> Concatenation2dTestImpl(armnn::IWorkloadFactory& workloadFactory, |
| 2147 | const armnn::TensorInfo& outputTensorInfo, |
| 2148 | unsigned int dimension, |
| 2149 | const float qScale, |
| 2150 | const int32_t qOffset) |
| 2151 | { |
| 2152 | armnn::TensorInfo inputTensorInfo({ 2, 3 }, armnn::GetDataType<T>()); |
| 2153 | |
| 2154 | auto input0 = MakeTensor<T, 2>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 2155 | // Batch 0 |
| 2156 | 1.0f, 2.0f, 3.0f, |
| 2157 | |
| 2158 | // Batch 1 |
| 2159 | 10.0f, 11.0f, 12.0f, |
| 2160 | })); |
| 2161 | |
| 2162 | auto input1 = MakeTensor<T, 2>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 2163 | // Batch 0 |
| 2164 | 4.0f, 5.0f, 6.0f, |
| 2165 | |
| 2166 | // Batch 1 |
| 2167 | 13.0f, 14.0f, 15.0f, |
| 2168 | })); |
| 2169 | |
| 2170 | auto input2 = MakeTensor<T, 2>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 2171 | // Batch 0 |
| 2172 | 7.0f, 8.0f, 9.0f, |
| 2173 | |
| 2174 | // Batch 1 |
| 2175 | 16.0f, 17.0f, 18.0f, |
| 2176 | })); |
| 2177 | |
| 2178 | LayerTestResult<T, 2> result(outputTensorInfo); |
| 2179 | |
| 2180 | std::vector<T> output; |
| 2181 | output.resize(outputTensorInfo.GetNumElements()); |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 2182 | Concatenate<T>(workloadFactory, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 2183 | { inputTensorInfo, inputTensorInfo, inputTensorInfo }, |
| 2184 | { input0.data(), input1.data(), input2.data() }, |
| 2185 | outputTensorInfo, |
| 2186 | output.data(), |
| 2187 | dimension); |
| 2188 | |
| 2189 | result.output = MakeTensor<T, 2>(outputTensorInfo, output); |
| 2190 | return result; |
| 2191 | } |
| 2192 | |
| 2193 | template <typename T> |
| 2194 | LayerTestResult<T, 2> Concatenation2dDim0TestImpl(armnn::IWorkloadFactory& workloadFactory, |
| 2195 | float qScale, int32_t qOffset) |
| 2196 | { |
| 2197 | armnn::TensorInfo outputTensorInfo({ 6, 3 }, armnn::GetDataType<T>()); |
| 2198 | |
| 2199 | LayerTestResult<T, 2> result = Concatenation2dTestImpl<T>(workloadFactory, outputTensorInfo, 0, qScale, qOffset); |
| 2200 | result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 2201 | // Batch 0 |
| 2202 | 1.0f, 2.0f, 3.0f, |
| 2203 | |
| 2204 | // Batch 1 |
| 2205 | 10.0f, 11.0f, 12.0f, |
| 2206 | |
| 2207 | // Batch 2 |
| 2208 | 4.0f, 5.0f, 6.0f, |
| 2209 | |
| 2210 | // Batch 3 |
| 2211 | 13.0f, 14.0f, 15.0f, |
| 2212 | |
| 2213 | // Batch 4 |
| 2214 | 7.0f, 8.0f, 9.0f, |
| 2215 | |
| 2216 | // Batch 5 |
| 2217 | 16.0f, 17.0f, 18.0f, |
| 2218 | })); |
| 2219 | |
| 2220 | return result; |
| 2221 | } |
| 2222 | |
| 2223 | LayerTestResult<float, 2> Concatenation2dDim0Test(armnn::IWorkloadFactory& workloadFactory) |
| 2224 | { |
| 2225 | return Concatenation2dDim0TestImpl<float>(workloadFactory, 0.0f, 0); |
| 2226 | } |
| 2227 | |
| 2228 | template <typename T> |
| 2229 | LayerTestResult<T, 2> Concatenation2dDim1TestImpl(armnn::IWorkloadFactory& workloadFactory, |
| 2230 | float qScale, int32_t qOffset) |
| 2231 | { |
| 2232 | armnn::TensorInfo outputTensorInfo({ 2, 9 }, armnn::GetDataType<T>()); |
| 2233 | |
| 2234 | LayerTestResult<T, 2> result = Concatenation2dTestImpl<T>(workloadFactory, outputTensorInfo, 1, qScale, qOffset); |
| 2235 | result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 2236 | // Batch 0 |
| 2237 | 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, |
| 2238 | |
| 2239 | // Batch 1 |
| 2240 | 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f |
| 2241 | })); |
| 2242 | |
| 2243 | return result; |
| 2244 | } |
| 2245 | |
| 2246 | LayerTestResult<float, 2> Concatenation2dDim1Test(armnn::IWorkloadFactory& workloadFactory) |
| 2247 | { |
| 2248 | return Concatenation2dDim1TestImpl<float>(workloadFactory, 0.0f, 0); |
| 2249 | } |
| 2250 | |
| 2251 | template <typename T> |
| 2252 | LayerTestResult<T, 2> Concatenation2dDim0DiffInputDimsTestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale, |
| 2253 | int32_t qOffset) |
| 2254 | { |
| 2255 | armnn::TensorInfo input0TensorInfo({ 2, 3 }, armnn::GetDataType<T>()); |
| 2256 | auto input0 = MakeTensor<T, 2>(input0TensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 2257 | // Batch 0 |
| 2258 | 1.0f, 2.0f, 3.0f, |
| 2259 | |
| 2260 | // Batch 1 |
| 2261 | 10.0f, 11.0f, 12.0f, |
| 2262 | })); |
| 2263 | |
| 2264 | armnn::TensorInfo input1TensorInfo({ 3, 3 }, armnn::GetDataType<T>()); |
| 2265 | auto input1 = MakeTensor<T, 2>(input1TensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 2266 | // Batch 0 |
| 2267 | 4.0f, 5.0f, 6.0f, |
| 2268 | |
| 2269 | // Batch 1 |
| 2270 | 13.0f, 14.0f, 15.0f, |
| 2271 | |
| 2272 | // Batch 0 |
| 2273 | 7.0f, 8.0f, 9.0f, |
| 2274 | })); |
| 2275 | |
| 2276 | armnn::TensorInfo input2TensorInfo({ 1, 3 }, armnn::GetDataType<T>()); |
| 2277 | auto input2 = MakeTensor<T, 2>(input2TensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 2278 | // Batch 1 |
| 2279 | 16.0f, 17.0f, 18.0f, |
| 2280 | })); |
| 2281 | |
| 2282 | armnn::TensorInfo outputTensorInfo({ 6, 3 }, armnn::GetDataType<T>()); |
| 2283 | LayerTestResult<T, 2> result(outputTensorInfo); |
| 2284 | |
| 2285 | std::vector<T> output; |
| 2286 | output.resize(outputTensorInfo.GetNumElements()); |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 2287 | Concatenate<T>(workloadFactory, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 2288 | { input0TensorInfo, input1TensorInfo, input2TensorInfo }, |
| 2289 | { input0.data(), input1.data(), input2.data() }, |
| 2290 | outputTensorInfo, |
| 2291 | output.data(), |
| 2292 | 0); |
| 2293 | |
| 2294 | result.output = MakeTensor<T, 2>(outputTensorInfo, output); |
| 2295 | result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 2296 | // Batch 0 |
| 2297 | 1.0f, 2.0f, 3.0f, |
| 2298 | |
| 2299 | // Batch 1 |
| 2300 | 10.0f, 11.0f, 12.0f, |
| 2301 | |
| 2302 | // Batch 2 |
| 2303 | 4.0f, 5.0f, 6.0f, |
| 2304 | |
| 2305 | // Batch 3 |
| 2306 | 13.0f, 14.0f, 15.0f, |
| 2307 | |
| 2308 | // Batch 4 |
| 2309 | 7.0f, 8.0f, 9.0f, |
| 2310 | |
| 2311 | // Batch 5 |
| 2312 | 16.0f, 17.0f, 18.0f, |
| 2313 | })); |
| 2314 | |
| 2315 | return result; |
| 2316 | } |
| 2317 | |
| 2318 | LayerTestResult<float, 2> Concatenation2dDim0DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory) |
| 2319 | { |
| 2320 | return Concatenation2dDim0DiffInputDimsTestImpl<float>(workloadFactory, 0.0f, 0); |
| 2321 | } |
| 2322 | |
| 2323 | template <typename T> |
| 2324 | LayerTestResult<T, 2> Concatenation2dDim1DiffInputDimsTestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale, |
| 2325 | int32_t qOffset) |
| 2326 | { |
| 2327 | armnn::TensorInfo input0TensorInfo({ 2, 3 }, armnn::GetDataType<T>()); |
| 2328 | auto input0 = MakeTensor<T, 2>(input0TensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 2329 | // Batch 0 |
| 2330 | 1.0f, 2.0f, 3.0f, |
| 2331 | |
| 2332 | // Batch 1 |
| 2333 | 10.0f, 11.0f, 12.0f, |
| 2334 | })); |
| 2335 | |
| 2336 | armnn::TensorInfo input1TensorInfo({ 2, 5 }, armnn::GetDataType<T>()); |
| 2337 | auto input1 = MakeTensor<T, 2>(input1TensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 2338 | // Batch 0 |
| 2339 | 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, |
| 2340 | |
| 2341 | // Batch 1 |
| 2342 | 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, |
| 2343 | })); |
| 2344 | |
| 2345 | armnn::TensorInfo input2TensorInfo({ 2, 1 }, armnn::GetDataType<T>()); |
| 2346 | auto input2 = MakeTensor<T, 2>(input2TensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 2347 | // Batch 0 |
| 2348 | 9.0f, |
| 2349 | |
| 2350 | // Batch 1 |
| 2351 | 18.0f |
| 2352 | })); |
| 2353 | |
| 2354 | armnn::TensorInfo outputTensorInfo({ 2, 9 }, armnn::GetDataType<T>()); |
| 2355 | LayerTestResult<T, 2> result(outputTensorInfo); |
| 2356 | |
| 2357 | std::vector<T> output; |
| 2358 | output.resize(outputTensorInfo.GetNumElements()); |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 2359 | Concatenate<T>(workloadFactory, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 2360 | { input0TensorInfo, input1TensorInfo, input2TensorInfo }, |
| 2361 | { input0.data(), input1.data(), input2.data() }, |
| 2362 | outputTensorInfo, |
| 2363 | output.data(), |
| 2364 | 1); |
| 2365 | |
| 2366 | result.output = MakeTensor<T, 2>(outputTensorInfo, output); |
| 2367 | result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 2368 | // Batch 0 |
| 2369 | 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, |
| 2370 | |
| 2371 | // Batch 1 |
| 2372 | 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f, |
| 2373 | })); |
| 2374 | |
| 2375 | return result; |
| 2376 | } |
| 2377 | |
| 2378 | LayerTestResult<float, 2> Concatenation2dDim1DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory) |
| 2379 | { |
| 2380 | return Concatenation2dDim1DiffInputDimsTestImpl<float>(workloadFactory, 0.0f, 0); |
| 2381 | } |
| 2382 | |
| 2383 | template <typename T> |
| 2384 | LayerTestResult<T, 3> Concatenation3dTestImpl(armnn::IWorkloadFactory& workloadFactory, |
| 2385 | const armnn::TensorInfo& outputTensorInfo, |
| 2386 | unsigned int dimension, |
| 2387 | float qScale, |
| 2388 | int32_t qOffset) |
| 2389 | { |
| 2390 | armnn::TensorInfo inputTensorInfo({ 2, 3, 2 }, armnn::GetDataType<T>()); |
| 2391 | |
| 2392 | auto input0 = MakeTensor<T, 3>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 2393 | // Batch 0, Channel 0 |
| 2394 | 1.0f, 2.0f, |
| 2395 | |
| 2396 | // Batch 0, Channel 1 |
| 2397 | 3.0f, 4.0f, |
| 2398 | |
| 2399 | // Batch 0, Channel 2 |
| 2400 | 5.0f, 6.0f, |
| 2401 | |
| 2402 | // Batch 1, Channel 0 |
| 2403 | 19.0f, 20.0f, |
| 2404 | |
| 2405 | // Batch 1, Channel 1 |
| 2406 | 21.0f, 22.0f, |
| 2407 | |
| 2408 | // Batch 1, Channel 2 |
| 2409 | 23.0f, 24.0f |
| 2410 | })); |
| 2411 | |
| 2412 | auto input1 = MakeTensor<T, 3>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 2413 | // Batch 0, Channel 0 |
| 2414 | 7.0f, 8.0f, |
| 2415 | |
| 2416 | // Batch 0, Channel 1 |
| 2417 | 9.0f, 10.0f, |
| 2418 | |
| 2419 | // Batch 0, Channel 2 |
| 2420 | 11.0f, 12.0f, |
| 2421 | |
| 2422 | // Batch 1, Channel 0 |
| 2423 | 25.0f, 26.0f, |
| 2424 | |
| 2425 | // Batch 1, Channel 1 |
| 2426 | 27.0f, 28.0f, |
| 2427 | |
| 2428 | // Batch 1, Channel 2 |
| 2429 | 29.0f, 30.0f |
| 2430 | })); |
| 2431 | |
| 2432 | auto input2 = MakeTensor<T, 3>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 2433 | // Batch 0, Channel 0 |
| 2434 | 13.0f, 14.0f, |
| 2435 | |
| 2436 | // Batch 0, Channel 1 |
| 2437 | 15.0f, 16.0f, |
| 2438 | |
| 2439 | // Batch 0, Channel 2 |
| 2440 | 17.0f, 18.0f, |
| 2441 | |
| 2442 | // Batch 1, Channel 0 |
| 2443 | 31.0f, 32.0f, |
| 2444 | |
| 2445 | // Batch 1, Channel 1 |
| 2446 | 33.0f, 34.0f, |
| 2447 | |
| 2448 | // Batch 1, Channel 2 |
| 2449 | 35.0f, 36.0f |
| 2450 | })); |
| 2451 | |
| 2452 | LayerTestResult<T, 3> result(outputTensorInfo); |
| 2453 | |
| 2454 | std::vector<T> output; |
| 2455 | output.resize(outputTensorInfo.GetNumElements()); |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 2456 | Concatenate<T>(workloadFactory, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 2457 | { inputTensorInfo, inputTensorInfo, inputTensorInfo }, |
| 2458 | { input0.data(), input1.data(), input2.data() }, |
| 2459 | outputTensorInfo, |
| 2460 | output.data(), |
| 2461 | dimension); |
| 2462 | |
| 2463 | result.output = MakeTensor<T, 3>(outputTensorInfo, output); |
| 2464 | return result; |
| 2465 | } |
| 2466 | |
| 2467 | template <typename T> |
| 2468 | LayerTestResult<T, 3> Concatenation3dDim0TestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale, |
| 2469 | int32_t qOffset) |
| 2470 | { |
| 2471 | armnn::TensorInfo outputTensorInfo({ 6, 3, 2 }, armnn::GetDataType<T>()); |
| 2472 | |
| 2473 | LayerTestResult<T, 3> result = Concatenation3dTestImpl<T>(workloadFactory, outputTensorInfo, 0, |
| 2474 | qScale, qOffset); |
| 2475 | result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 2476 | // Batch 0, Channel 0 |
| 2477 | 1.0f, 2.0f, |
| 2478 | |
| 2479 | // Batch 0, Channel 1 |
| 2480 | 3.0f, 4.0f, |
| 2481 | |
| 2482 | // Batch 0, Channel 2 |
| 2483 | 5.0f, 6.0f, |
| 2484 | |
| 2485 | // Batch 1, Channel 0 |
| 2486 | 19.0f, 20.0f, |
| 2487 | |
| 2488 | // Batch 1, Channel 1 |
| 2489 | 21.0f, 22.0f, |
| 2490 | |
| 2491 | // Batch 1, Channel 2 |
| 2492 | 23.0f, 24.0f, |
| 2493 | |
| 2494 | // Batch 2, Channel 0 |
| 2495 | 7.0f, 8.0f, |
| 2496 | |
| 2497 | // Batch 2, Channel 1 |
| 2498 | 9.0f, 10.0f, |
| 2499 | |
| 2500 | // Batch 2, Channel 2 |
| 2501 | 11.0f, 12.0f, |
| 2502 | |
| 2503 | // Batch 3, Channel 0 |
| 2504 | 25.0f, 26.0f, |
| 2505 | |
| 2506 | // Batch 3, Channel 1 |
| 2507 | 27.0f, 28.0f, |
| 2508 | |
| 2509 | // Batch 3, Channel 2 |
| 2510 | 29.0f, 30.0f, |
| 2511 | |
| 2512 | // Batch 4, Channel 0 |
| 2513 | 13.0f, 14.0f, |
| 2514 | |
| 2515 | // Batch 4, Channel 1 |
| 2516 | 15.0f, 16.0f, |
| 2517 | |
| 2518 | // Batch 4, Channel 2 |
| 2519 | 17.0f, 18.0f, |
| 2520 | |
| 2521 | // Batch 5, Channel 0 |
| 2522 | 31.0f, 32.0f, |
| 2523 | |
| 2524 | // Batch 5, Channel 1 |
| 2525 | 33.0f, 34.0f, |
| 2526 | |
| 2527 | // Batch 5, Channel 2 |
| 2528 | 35.0f, 36.0f |
| 2529 | })); |
| 2530 | return result; |
| 2531 | } |
| 2532 | |
| 2533 | LayerTestResult<float, 3> Concatenation3dDim0Test(armnn::IWorkloadFactory& workloadFactory) |
| 2534 | { |
| 2535 | return Concatenation3dDim0TestImpl<float>(workloadFactory, 0.0f, 0); |
| 2536 | } |
| 2537 | |
| 2538 | template <typename T> |
| 2539 | LayerTestResult<T, 3> Concatenation3dDim1TestImpl(armnn::IWorkloadFactory& workloadFactory, |
| 2540 | float qScale, int32_t qOffset) |
| 2541 | { |
| 2542 | armnn::TensorInfo outputTensorInfo({ 2, 9, 2 }, armnn::GetDataType<T>()); |
| 2543 | |
| 2544 | LayerTestResult<T, 3> result = Concatenation3dTestImpl<T>(workloadFactory, outputTensorInfo, 1, qScale, qOffset); |
| 2545 | result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 2546 | // Batch 0, Channel 0 |
| 2547 | 1.0f, 2.0f, |
| 2548 | |
| 2549 | // Batch 0, Channel 1 |
| 2550 | 3.0f, 4.0f, |
| 2551 | |
| 2552 | // Batch 0, Channel 2 |
| 2553 | 5.0f, 6.0f, |
| 2554 | |
| 2555 | // Batch 0, Channel 3 |
| 2556 | 7.0f, 8.0f, |
| 2557 | |
| 2558 | // Batch 0, Channel 4 |
| 2559 | 9.0f, 10.0f, |
| 2560 | |
| 2561 | // Batch 0, Channel 5 |
| 2562 | 11.0f, 12.0f, |
| 2563 | |
| 2564 | // Batch 0, Channel 6 |
| 2565 | 13.0f, 14.0f, |
| 2566 | |
| 2567 | // Batch 0, Channel 7 |
| 2568 | 15.0f, 16.0f, |
| 2569 | |
| 2570 | // Batch 0, Channel 8 |
| 2571 | 17.0f, 18.0f, |
| 2572 | |
| 2573 | // Batch 1, Channel 0 |
| 2574 | 19.0f, 20.0f, |
| 2575 | |
| 2576 | // Batch 1, Channel 1 |
| 2577 | 21.0f, 22.0f, |
| 2578 | |
| 2579 | // Batch 1, Channel 2 |
| 2580 | 23.0f, 24.0f, |
| 2581 | |
| 2582 | // Batch 1, Channel 3 |
| 2583 | 25.0f, 26.0f, |
| 2584 | |
| 2585 | // Batch 1, Channel 4 |
| 2586 | 27.0f, 28.0f, |
| 2587 | |
| 2588 | // Batch 1, Channel 5 |
| 2589 | 29.0f, 30.0f, |
| 2590 | |
| 2591 | // Batch 1, Channel 6 |
| 2592 | 31.0f, 32.0f, |
| 2593 | |
| 2594 | // Batch 1, Channel 7 |
| 2595 | 33.0f, 34.0f, |
| 2596 | |
| 2597 | // Batch 1, Channel 8 |
| 2598 | 35.0f, 36.0f |
| 2599 | })); |
| 2600 | |
| 2601 | return result; |
| 2602 | } |
| 2603 | |
| 2604 | LayerTestResult<float, 3> Concatenation3dDim1Test(armnn::IWorkloadFactory& workloadFactory) |
| 2605 | { |
| 2606 | return Concatenation3dDim1TestImpl<float>(workloadFactory, 0.0f, 0); |
| 2607 | } |
| 2608 | |
| 2609 | template <typename T> |
| 2610 | LayerTestResult<T, 3> Concatenation3dDim2TestImpl(armnn::IWorkloadFactory& workloadFactory, |
| 2611 | float qScale, int32_t qOffset) |
| 2612 | { |
| 2613 | armnn::TensorInfo outputTensorInfo({ 2, 3, 6 }, armnn::GetDataType<T>()); |
| 2614 | |
| 2615 | LayerTestResult<T, 3> result = Concatenation3dTestImpl<T>(workloadFactory, outputTensorInfo, 2, qScale, qOffset); |
| 2616 | result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 2617 | // Batch 0, Channel 0 |
| 2618 | 1.0f, 2.0f, 7.0f, 8.0f, 13.0f, 14.0f, |
| 2619 | |
| 2620 | // Batch 0, Channel 1 |
| 2621 | 3.0f, 4.0f, 9.0f, 10.0f, 15.0f, 16.0f, |
| 2622 | |
| 2623 | // Batch 0, Channel 2 |
| 2624 | 5.0f, 6.0f, 11.0f, 12.0f, 17.0f, 18.0f, |
| 2625 | |
| 2626 | // Batch 1, Channel 0 |
| 2627 | 19.0f, 20.0f, 25.0f, 26.0f, 31.0f, 32.0f, |
| 2628 | |
| 2629 | // Batch 1, Channel 1 |
| 2630 | 21.0f, 22.0f, 27.0f, 28.0f, 33.0f, 34.0f, |
| 2631 | |
| 2632 | // Batch 1, Channel 2 |
| 2633 | 23.0f, 24.0f, 29.0f, 30.0f, 35.0f, 36.0f, |
| 2634 | })); |
| 2635 | |
| 2636 | return result; |
| 2637 | } |
| 2638 | |
| 2639 | LayerTestResult<float, 3> Concatenation3dDim2Test(armnn::IWorkloadFactory& workloadFactory) |
| 2640 | { |
| 2641 | return Concatenation3dDim2TestImpl<float>(workloadFactory, 0.0f, 0); |
| 2642 | } |
| 2643 | |
| 2644 | template <typename T> |
| 2645 | LayerTestResult<T, 3> Concatenation3dDim0DiffInputDimsTestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale, |
| 2646 | int32_t qOffset) |
| 2647 | { |
| 2648 | armnn::TensorInfo input0TensorInfo({ 2, 3, 2 }, armnn::GetDataType<T>()); |
| 2649 | auto input0 = MakeTensor<T, 3>(input0TensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 2650 | // Batch 0, Channel 0 |
| 2651 | 1.0f, 2.0f, |
| 2652 | |
| 2653 | // Batch 0, Channel 1 |
| 2654 | 3.0f, 4.0f, |
| 2655 | |
| 2656 | // Batch 0, Channel 2 |
| 2657 | 5.0f, 6.0f, |
| 2658 | |
| 2659 | // Batch 1, Channel 0 |
| 2660 | 19.0f, 20.0f, |
| 2661 | |
| 2662 | // Batch 1, Channel 1 |
| 2663 | 21.0f, 22.0f, |
| 2664 | |
| 2665 | // Batch 1, Channel 2 |
| 2666 | 23.0f, 24.0f |
| 2667 | })); |
| 2668 | |
| 2669 | armnn::TensorInfo input1TensorInfo({ 1, 3, 2 }, armnn::GetDataType<T>()); |
| 2670 | auto input1 = MakeTensor<T, 3>(input1TensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 2671 | // Batch 0, Channel 0 |
| 2672 | 7.0f, 8.0f, |
| 2673 | |
| 2674 | // Batch 0, Channel 1 |
| 2675 | 9.0f, 10.0f, |
| 2676 | |
| 2677 | // Batch 0, Channel 2 |
| 2678 | 11.0f, 12.0f, |
| 2679 | })); |
| 2680 | |
| 2681 | armnn::TensorInfo input2TensorInfo({ 3, 3, 2 }, armnn::GetDataType<T>()); |
| 2682 | auto input2 = MakeTensor<T, 3>(input2TensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 2683 | // Batch 0, Channel 0 |
| 2684 | 25.0f, 26.0f, |
| 2685 | |
| 2686 | // Batch 0, Channel 1 |
| 2687 | 27.0f, 28.0f, |
| 2688 | |
| 2689 | // Batch 0, Channel 2 |
| 2690 | 29.0f, 30.0f, |
| 2691 | |
| 2692 | // Batch 1, Channel 0 |
| 2693 | 13.0f, 14.0f, |
| 2694 | |
| 2695 | // Batch 1, Channel 1 |
| 2696 | 15.0f, 16.0f, |
| 2697 | |
| 2698 | // Batch 1, Channel 2 |
| 2699 | 17.0f, 18.0f, |
| 2700 | |
| 2701 | // Batch 2, Channel 0 |
| 2702 | 31.0f, 32.0f, |
| 2703 | |
| 2704 | // Batch 2, Channel 1 |
| 2705 | 33.0f, 34.0f, |
| 2706 | |
| 2707 | // Batch 2, Channel 2 |
| 2708 | 35.0f, 36.0f |
| 2709 | })); |
| 2710 | |
| 2711 | armnn::TensorInfo outputTensorInfo({ 6, 3, 2 }, armnn::GetDataType<T>()); |
| 2712 | LayerTestResult<T, 3> result(outputTensorInfo); |
| 2713 | |
| 2714 | std::vector<T> output; |
| 2715 | output.resize(outputTensorInfo.GetNumElements()); |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 2716 | Concatenate<T>(workloadFactory, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 2717 | { input0TensorInfo, input1TensorInfo, input2TensorInfo }, |
| 2718 | { input0.data(), input1.data(), input2.data() }, |
| 2719 | outputTensorInfo, |
| 2720 | output.data(), |
| 2721 | 0); |
| 2722 | |
| 2723 | result.output = MakeTensor<T, 3>(outputTensorInfo, output); |
| 2724 | result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 2725 | // Batch 0, Channel 0 |
| 2726 | 1.0f, 2.0f, |
| 2727 | |
| 2728 | // Batch 0, Channel 1 |
| 2729 | 3.0f, 4.0f, |
| 2730 | |
| 2731 | // Batch 0, Channel 2 |
| 2732 | 5.0f, 6.0f, |
| 2733 | |
| 2734 | // Batch 1, Channel 0 |
| 2735 | 19.0f, 20.0f, |
| 2736 | |
| 2737 | // Batch 1, Channel 1 |
| 2738 | 21.0f, 22.0f, |
| 2739 | |
| 2740 | // Batch 1, Channel 2 |
| 2741 | 23.0f, 24.0f, |
| 2742 | |
| 2743 | // Batch 2, Channel 0 |
| 2744 | 7.0f, 8.0f, |
| 2745 | |
| 2746 | // Batch 2, Channel 1 |
| 2747 | 9.0f, 10.0f, |
| 2748 | |
| 2749 | // Batch 2, Channel 2 |
| 2750 | 11.0f, 12.0f, |
| 2751 | |
| 2752 | // Batch 3, Channel 0 |
| 2753 | 25.0f, 26.0f, |
| 2754 | |
| 2755 | // Batch 3, Channel 1 |
| 2756 | 27.0f, 28.0f, |
| 2757 | |
| 2758 | // Batch 3, Channel 2 |
| 2759 | 29.0f, 30.0f, |
| 2760 | |
| 2761 | // Batch 4, Channel 0 |
| 2762 | 13.0f, 14.0f, |
| 2763 | |
| 2764 | // Batch 4, Channel 1 |
| 2765 | 15.0f, 16.0f, |
| 2766 | |
| 2767 | // Batch 4, Channel 2 |
| 2768 | 17.0f, 18.0f, |
| 2769 | |
| 2770 | // Batch 5, Channel 0 |
| 2771 | 31.0f, 32.0f, |
| 2772 | |
| 2773 | // Batch 5, Channel 1 |
| 2774 | 33.0f, 34.0f, |
| 2775 | |
| 2776 | // Batch 5, Channel 2 |
| 2777 | 35.0f, 36.0f |
| 2778 | })); |
| 2779 | |
| 2780 | return result; |
| 2781 | } |
| 2782 | |
| 2783 | LayerTestResult<float, 3> Concatenation3dDim0DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory) |
| 2784 | { |
| 2785 | return Concatenation3dDim0DiffInputDimsTestImpl<float>(workloadFactory, 0.0f, 0); |
| 2786 | } |
| 2787 | |
| 2788 | template <typename T> |
| 2789 | LayerTestResult<T, 3> Concatenation3dDim1DiffInputDimsTestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale, |
| 2790 | int32_t qOffset) |
| 2791 | { |
| 2792 | armnn::TensorInfo input0TensorInfo({ 2, 3, 2 }, armnn::GetDataType<T>()); |
| 2793 | auto input0 = MakeTensor<T, 3>(input0TensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 2794 | // Batch 0, Channel 0 |
| 2795 | 1.0f, 2.0f, |
| 2796 | |
| 2797 | // Batch 0, Channel 1 |
| 2798 | 3.0f, 4.0f, |
| 2799 | |
| 2800 | // Batch 0, Channel 2 |
| 2801 | 5.0f, 6.0f, |
| 2802 | |
| 2803 | // Batch 1, Channel 0 |
| 2804 | 19.0f, 20.0f, |
| 2805 | |
| 2806 | // Batch 1, Channel 1 |
| 2807 | 21.0f, 22.0f, |
| 2808 | |
| 2809 | // Batch 1, Channel 2 |
| 2810 | 23.0f, 24.0f |
| 2811 | })); |
| 2812 | |
| 2813 | armnn::TensorInfo input1TensorInfo({ 2, 4, 2 }, armnn::GetDataType<T>()); |
| 2814 | auto input1 = MakeTensor<T, 3>(input1TensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 2815 | // Batch 0, Channel 0 |
| 2816 | 7.0f, 8.0f, |
| 2817 | |
| 2818 | // Batch 0, Channel 1 |
| 2819 | 9.0f, 10.0f, |
| 2820 | |
| 2821 | // Batch 0, Channel 2 |
| 2822 | 11.0f, 12.0f, |
| 2823 | |
| 2824 | // Batch 0, Channel 3 |
| 2825 | 25.0f, 26.0f, |
| 2826 | |
| 2827 | // Batch 1, Channel 0 |
| 2828 | 27.0f, 28.0f, |
| 2829 | |
| 2830 | // Batch 1, Channel 1 |
| 2831 | 29.0f, 30.0f, |
| 2832 | |
| 2833 | // Batch 1, Channel 2 |
| 2834 | 13.0f, 14.0f, |
| 2835 | |
| 2836 | // Batch 1, Channel 3 |
| 2837 | 15.0f, 16.0f, |
| 2838 | })); |
| 2839 | |
| 2840 | armnn::TensorInfo input2TensorInfo({ 2, 1, 2 }, armnn::GetDataType<T>()); |
| 2841 | auto input2 = MakeTensor<T, 3>(input2TensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 2842 | // Batch 0, Channel 0 |
| 2843 | 17.0f, 18.0f, |
| 2844 | |
| 2845 | // Batch 1, Channel 0 |
| 2846 | 31.0f, 32.0f, |
| 2847 | })); |
| 2848 | |
| 2849 | armnn::TensorInfo outputTensorInfo({ 2, 8, 2 }, armnn::GetDataType<T>()); |
| 2850 | LayerTestResult<T, 3> result(outputTensorInfo); |
| 2851 | |
| 2852 | std::vector<T> output; |
| 2853 | output.resize(outputTensorInfo.GetNumElements()); |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 2854 | Concatenate<T>(workloadFactory, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 2855 | { input0TensorInfo, input1TensorInfo, input2TensorInfo }, |
| 2856 | { input0.data(), input1.data(), input2.data() }, |
| 2857 | outputTensorInfo, |
| 2858 | output.data(), |
| 2859 | 1); |
| 2860 | |
| 2861 | result.output = MakeTensor<T, 3>(outputTensorInfo, output); |
| 2862 | result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 2863 | // Batch 0, Channel 0 |
| 2864 | 1.0f, 2.0f, |
| 2865 | |
| 2866 | // Batch 0, Channel 1 |
| 2867 | 3.0f, 4.0f, |
| 2868 | |
| 2869 | // Batch 0, Channel 2 |
| 2870 | 5.0f, 6.0f, |
| 2871 | |
| 2872 | // Batch 0, Channel 3 |
| 2873 | 7.0f, 8.0f, |
| 2874 | |
| 2875 | // Batch 0, Channel 4 |
| 2876 | 9.0f, 10.0f, |
| 2877 | |
| 2878 | // Batch 0, Channel 5 |
| 2879 | 11.0f, 12.0f, |
| 2880 | |
| 2881 | // Batch 0, Channel 6 |
| 2882 | 25.0f, 26.0f, |
| 2883 | |
| 2884 | // Batch 0, Channel 7 |
| 2885 | 17.0f, 18.0f, |
| 2886 | |
| 2887 | // Batch 1, Channel 0 |
| 2888 | 19.0f, 20.0f, |
| 2889 | |
| 2890 | // Batch 1, Channel 1 |
| 2891 | 21.0f, 22.0f, |
| 2892 | |
| 2893 | // Batch 1, Channel 2 |
| 2894 | 23.0f, 24.0f, |
| 2895 | |
| 2896 | // Batch 1, Channel 3 |
| 2897 | 27.0f, 28.0f, |
| 2898 | |
| 2899 | // Batch 1, Channel 4 |
| 2900 | 29.0f, 30.0f, |
| 2901 | |
| 2902 | // Batch 1, Channel 5 |
| 2903 | 13.0f, 14.0f, |
| 2904 | |
| 2905 | // Batch 1, Channel 6 |
| 2906 | 15.0f, 16.0f, |
| 2907 | |
| 2908 | // Batch 1, Channel 7 |
| 2909 | 31.0f, 32.0f, |
| 2910 | })); |
| 2911 | |
| 2912 | return result; |
| 2913 | } |
| 2914 | |
| 2915 | LayerTestResult<float, 3> Concatenation3dDim1DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory) |
| 2916 | { |
| 2917 | return Concatenation3dDim1DiffInputDimsTestImpl<float>(workloadFactory, 0.0f, 0); |
| 2918 | } |
| 2919 | |
| 2920 | template <typename T> |
| 2921 | LayerTestResult<T, 3> Concatenation3dDim2DiffInputDimsTestImpl(armnn::IWorkloadFactory& workloadFactory, float qScale, |
| 2922 | int32_t qOffset) |
| 2923 | { |
| 2924 | armnn::TensorInfo input0TensorInfo({ 2, 3, 2 }, armnn::GetDataType<T>()); |
| 2925 | auto input0 = MakeTensor<T, 3>(input0TensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 2926 | // Batch 0, Channel 0 |
| 2927 | 1.0f, 2.0f, |
| 2928 | |
| 2929 | // Batch 0, Channel 1 |
| 2930 | 3.0f, 4.0f, |
| 2931 | |
| 2932 | // Batch 0, Channel 2 |
| 2933 | 5.0f, 6.0f, |
| 2934 | |
| 2935 | // Batch 1, Channel 0 |
| 2936 | 19.0f, 20.0f, |
| 2937 | |
| 2938 | // Batch 1, Channel 1 |
| 2939 | 21.0f, 22.0f, |
| 2940 | |
| 2941 | // Batch 1, Channel 2 |
| 2942 | 23.0f, 24.0f |
| 2943 | })); |
| 2944 | |
| 2945 | armnn::TensorInfo input1TensorInfo({ 2, 3, 1 }, armnn::GetDataType<T>()); |
| 2946 | auto input1 = MakeTensor<T, 3>(input1TensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 2947 | // Batch 0, Channel 0 |
| 2948 | 7.0f, |
| 2949 | |
| 2950 | // Batch 0, Channel 1 |
| 2951 | 9.0f, |
| 2952 | |
| 2953 | // Batch 0, Channel 2 |
| 2954 | 11.0f, |
| 2955 | |
| 2956 | // Batch 1, Channel 0 |
| 2957 | 25.0f, |
| 2958 | |
| 2959 | // Batch 1, Channel 1 |
| 2960 | 27.0f, |
| 2961 | |
| 2962 | // Batch 1, Channel 2 |
| 2963 | 29.0f |
| 2964 | })); |
| 2965 | |
| 2966 | armnn::TensorInfo input2TensorInfo({ 2, 3, 3 }, armnn::GetDataType<T>()); |
| 2967 | auto input2 = MakeTensor<T, 3>(input2TensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 2968 | // Batch 0, Channel 0 |
| 2969 | 13.0f, 14.0f, 50.0f, |
| 2970 | |
| 2971 | // Batch 0, Channel 1 |
| 2972 | 15.0f, 16.0f, 51.0f, |
| 2973 | |
| 2974 | // Batch 0, Channel 2 |
| 2975 | 17.0f, 18.0f, 52.0f, |
| 2976 | |
| 2977 | // Batch 1, Channel 0 |
| 2978 | 31.0f, 32.0f, 53.0f, |
| 2979 | |
| 2980 | // Batch 1, Channel 1 |
| 2981 | 33.0f, 34.0f, 54.0f, |
| 2982 | |
| 2983 | // Batch 1, Channel 2 |
| 2984 | 35.0f, 36.0f, 55.0f, |
| 2985 | })); |
| 2986 | |
| 2987 | armnn::TensorInfo outputTensorInfo({ 2, 3, 6 }, armnn::GetDataType<T>()); |
| 2988 | LayerTestResult<T, 3> result(outputTensorInfo); |
| 2989 | |
| 2990 | std::vector<T> output; |
| 2991 | output.resize(outputTensorInfo.GetNumElements()); |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 2992 | Concatenate<T>(workloadFactory, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 2993 | { input0TensorInfo, input1TensorInfo, input2TensorInfo }, |
| 2994 | { input0.data(), input1.data(), input2.data() }, |
| 2995 | outputTensorInfo, |
| 2996 | output.data(), |
| 2997 | 2); |
| 2998 | |
| 2999 | result.output = MakeTensor<T, 3>(outputTensorInfo, output); |
| 3000 | result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, { |
| 3001 | // Batch 0, Channel 0 |
| 3002 | 1.0f, 2.0f, 7.0f, 13.0f, 14.0f, 50.0f, |
| 3003 | |
| 3004 | // Batch 0, Channel 1 |
| 3005 | 3.0f, 4.0f, 9.0f, 15.0f, 16.0f, 51.0f, |
| 3006 | |
| 3007 | // Batch 0, Channel 2 |
| 3008 | 5.0f, 6.0f, 11.0f, 17.0f, 18.0f, 52.0f, |
| 3009 | |
| 3010 | // Batch 1, Channel 0 |
| 3011 | 19.0f, 20.0f, 25.0f, 31.0f, 32.0f, 53.0f, |
| 3012 | |
| 3013 | // Batch 1, Channel 1 |
| 3014 | 21.0f, 22.0f, 27.0f, 33.0f, 34.0f, 54.0f, |
| 3015 | |
| 3016 | // Batch 1, Channel 2 |
| 3017 | 23.0f, 24.0f, 29.0f, 35.0f, 36.0f, 55.0f, |
| 3018 | })); |
| 3019 | |
| 3020 | return result; |
| 3021 | } |
| 3022 | |
| 3023 | LayerTestResult<float, 3> Concatenation3dDim2DiffInputDimsTest(armnn::IWorkloadFactory& workloadFactory) |
| 3024 | { |
| 3025 | return Concatenation3dDim2DiffInputDimsTestImpl<float>(workloadFactory, 0.0f, 0); |
| 3026 | } |
| 3027 | |
James Conroy | 074f371 | 2018-10-03 09:32:03 +0100 | [diff] [blame] | 3028 | LayerTestResult<float, 4> ResizeBilinearNopTestImpl(armnn::IWorkloadFactory& workloadFactory, |
| 3029 | const armnn::TensorShape& inputOutputTensorShape, |
| 3030 | armnn::DataLayout dataLayout) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 3031 | { |
James Conroy | 074f371 | 2018-10-03 09:32:03 +0100 | [diff] [blame] | 3032 | const armnn::TensorInfo inputTensorInfo(inputOutputTensorShape, armnn::DataType::Float32); |
| 3033 | const armnn::TensorInfo outputTensorInfo(inputOutputTensorShape, armnn::DataType::Float32); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 3034 | |
| 3035 | auto input = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>({ |
| 3036 | 1.0f, 2.0f, 3.0f, 4.0f, |
| 3037 | 2.0f, 3.0f, 4.0f, 5.0f, |
| 3038 | 3.0f, 4.0f, 5.0f, 6.0f, |
| 3039 | 4.0f, 5.0f, 6.0f, 7.0f |
| 3040 | })); |
| 3041 | |
| 3042 | LayerTestResult<float, 4> result(outputTensorInfo); |
| 3043 | result.outputExpected = input; |
| 3044 | |
| 3045 | std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| 3046 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 3047 | |
| 3048 | armnn::ResizeBilinearQueueDescriptor descriptor; |
James Conroy | 074f371 | 2018-10-03 09:32:03 +0100 | [diff] [blame] | 3049 | descriptor.m_Parameters.m_DataLayout = dataLayout; |
| 3050 | armnn::WorkloadInfo info; |
| 3051 | AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); |
| 3052 | AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); |
| 3053 | |
| 3054 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResizeBilinear(descriptor, info); |
| 3055 | |
| 3056 | inputHandle->Allocate(); |
| 3057 | outputHandle->Allocate(); |
| 3058 | CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); |
| 3059 | |
| 3060 | workloadFactory.Finalize(); |
| 3061 | workload->Execute(); |
| 3062 | |
| 3063 | CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); |
| 3064 | return result; |
| 3065 | } |
| 3066 | |
| 3067 | LayerTestResult<float, 4> ResizeBilinearNopTest(armnn::IWorkloadFactory& workloadFactory) |
| 3068 | { |
| 3069 | // BatchSize = 1, Channels = 1, Height = 4, Width = 4 |
| 3070 | const armnn::TensorShape inputOutputShape{ 1, 1, 4, 4 }; |
| 3071 | |
| 3072 | return ResizeBilinearNopTestImpl(workloadFactory, inputOutputShape, armnn::DataLayout::NCHW); |
| 3073 | } |
| 3074 | |
| 3075 | LayerTestResult<float, 4> ResizeBilinearNopNhwcTest(armnn::IWorkloadFactory& workloadFactory) |
| 3076 | { |
| 3077 | // BatchSize = 1, Height = 4, Width = 4, Channels = 1 |
| 3078 | const armnn::TensorShape inputOutputShape{ 1, 4, 4, 1 }; |
| 3079 | |
| 3080 | return ResizeBilinearNopTestImpl(workloadFactory, inputOutputShape, armnn::DataLayout::NHWC); |
| 3081 | } |
| 3082 | |
| 3083 | LayerTestResult<float, 4> SimpleResizeBilinearTestImpl(armnn::IWorkloadFactory& workloadFactory, |
| 3084 | const armnn::TensorShape& inputTensorShape, |
| 3085 | const armnn::TensorShape& outputTensorShape, |
| 3086 | armnn::DataLayout dataLayout) |
| 3087 | { |
| 3088 | const armnn::TensorInfo inputTensorInfo(inputTensorShape, armnn::DataType::Float32); |
| 3089 | const armnn::TensorInfo outputTensorInfo(outputTensorShape, armnn::DataType::Float32); |
| 3090 | |
| 3091 | auto input = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>({ |
| 3092 | 1.0f, 255.0f, |
| 3093 | 200.0f, 250.0f |
| 3094 | })); |
| 3095 | |
| 3096 | // The 'resize bilinear' operation projects the top-left corner of output texels into the input image, |
| 3097 | // then figures out the interpolants and weights. Note this is different to projecting the centre of the |
| 3098 | // output texel - and thus we'll expect the output 1x1 matrix to contain, as its single element, the value |
| 3099 | // that was at position (0,0) of the input matrix (rather than an average, which we would expect if projecting |
| 3100 | // the centre). |
| 3101 | LayerTestResult<float, 4> result(outputTensorInfo); |
| 3102 | result.outputExpected = MakeTensor<float, 4>(outputTensorInfo, std::vector<float>({ |
| 3103 | 1.0f |
| 3104 | })); |
| 3105 | |
| 3106 | std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| 3107 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 3108 | |
| 3109 | armnn::ResizeBilinearQueueDescriptor descriptor; |
| 3110 | descriptor.m_Parameters.m_DataLayout = dataLayout; |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 3111 | armnn::WorkloadInfo info; |
| 3112 | AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); |
| 3113 | AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); |
| 3114 | |
| 3115 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResizeBilinear(descriptor, info); |
| 3116 | |
| 3117 | inputHandle->Allocate(); |
| 3118 | outputHandle->Allocate(); |
| 3119 | CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); |
| 3120 | |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 3121 | workloadFactory.Finalize(); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 3122 | workload->Execute(); |
| 3123 | |
| 3124 | CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); |
| 3125 | return result; |
| 3126 | } |
| 3127 | |
| 3128 | LayerTestResult<float, 4> SimpleResizeBilinearTest(armnn::IWorkloadFactory& workloadFactory) |
| 3129 | { |
James Conroy | 074f371 | 2018-10-03 09:32:03 +0100 | [diff] [blame] | 3130 | // inputShape: BatchSize = 1, Channels = 1, Height = 2, Width = 2 |
| 3131 | const armnn::TensorShape inputShape{ 1, 1, 2, 2 }; |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 3132 | |
James Conroy | 074f371 | 2018-10-03 09:32:03 +0100 | [diff] [blame] | 3133 | // outputShape: BatchSize = 1, Channels = 1, Height = 1, Width = 1 |
| 3134 | const armnn::TensorShape outputShape{ 1, 1, 1, 1 }; |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 3135 | |
James Conroy | 074f371 | 2018-10-03 09:32:03 +0100 | [diff] [blame] | 3136 | return SimpleResizeBilinearTestImpl(workloadFactory, inputShape, outputShape, armnn::DataLayout::NCHW); |
| 3137 | } |
| 3138 | |
| 3139 | LayerTestResult<float, 4> SimpleResizeBilinearNhwcTest(armnn::IWorkloadFactory& workloadFactory) |
| 3140 | { |
| 3141 | // inputShape: BatchSize = 1, Height = 2, Width = 2, Channels = 1 |
| 3142 | const armnn::TensorShape inputShape{ 1, 2, 2, 1 }; |
| 3143 | |
| 3144 | // outputShape: BatchSize = 1, Height = 1, Width = 1, Channels = 1 |
| 3145 | const armnn::TensorShape outputShape{ 1, 1, 1, 1 }; |
| 3146 | |
| 3147 | return SimpleResizeBilinearTestImpl(workloadFactory, inputShape, outputShape, armnn::DataLayout::NHWC); |
| 3148 | } |
| 3149 | |
| 3150 | LayerTestResult<float, 4> ResizeBilinearSqMinTestImpl(armnn::IWorkloadFactory& workloadFactory, |
| 3151 | const armnn::TensorShape& inputTensorShape, |
| 3152 | const armnn::TensorShape& outputTensorShape, |
| 3153 | armnn::DataLayout dataLayout) |
| 3154 | { |
| 3155 | const armnn::TensorInfo inputTensorInfo(inputTensorShape, armnn::DataType::Float32); |
| 3156 | const armnn::TensorInfo outputTensorInfo(outputTensorShape, armnn::DataType::Float32); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 3157 | |
| 3158 | auto input = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>({ |
James Conroy | 074f371 | 2018-10-03 09:32:03 +0100 | [diff] [blame] | 3159 | 1.0f, 2.0f, 3.0f, 4.0f, |
| 3160 | 2.0f, 3.0f, 4.0f, 5.0f, |
| 3161 | 3.0f, 4.0f, 5.0f, 6.0f, |
| 3162 | 4.0f, 5.0f, 6.0f, 7.0f |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 3163 | })); |
| 3164 | |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 3165 | LayerTestResult<float, 4> result(outputTensorInfo); |
| 3166 | result.outputExpected = MakeTensor<float, 4>(outputTensorInfo, std::vector<float>({ |
James Conroy | 074f371 | 2018-10-03 09:32:03 +0100 | [diff] [blame] | 3167 | 1.0f, 3.0f, |
| 3168 | 3.0f, 5.0f |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 3169 | })); |
| 3170 | |
| 3171 | std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| 3172 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 3173 | |
| 3174 | armnn::ResizeBilinearQueueDescriptor descriptor; |
James Conroy | 074f371 | 2018-10-03 09:32:03 +0100 | [diff] [blame] | 3175 | descriptor.m_Parameters.m_DataLayout = dataLayout; |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 3176 | armnn::WorkloadInfo info; |
| 3177 | AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); |
| 3178 | AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); |
| 3179 | |
| 3180 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResizeBilinear(descriptor, info); |
| 3181 | |
| 3182 | inputHandle->Allocate(); |
| 3183 | outputHandle->Allocate(); |
| 3184 | CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); |
| 3185 | |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 3186 | workloadFactory.Finalize(); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 3187 | workload->Execute(); |
| 3188 | |
| 3189 | CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); |
| 3190 | return result; |
| 3191 | } |
| 3192 | |
| 3193 | LayerTestResult<float, 4> ResizeBilinearSqMinTest(armnn::IWorkloadFactory& workloadFactory) |
| 3194 | { |
James Conroy | 074f371 | 2018-10-03 09:32:03 +0100 | [diff] [blame] | 3195 | // inputShape: BatchSize = 1, Channels = 1, Height = 4, Width = 4 |
| 3196 | const armnn::TensorShape inputShape{ 1, 1, 4, 4 }; |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 3197 | |
James Conroy | 074f371 | 2018-10-03 09:32:03 +0100 | [diff] [blame] | 3198 | // outputShape: BatchSize = 1, Channels = 1, Height = 2, Width = 2 |
| 3199 | const armnn::TensorShape outputShape{ 1, 1, 2, 2 }; |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 3200 | |
James Conroy | 074f371 | 2018-10-03 09:32:03 +0100 | [diff] [blame] | 3201 | return ResizeBilinearSqMinTestImpl(workloadFactory, inputShape, outputShape, armnn::DataLayout::NCHW); |
| 3202 | } |
| 3203 | |
| 3204 | LayerTestResult<float, 4> ResizeBilinearSqMinNhwcTest(armnn::IWorkloadFactory& workloadFactory) |
| 3205 | { |
| 3206 | // inputShape: BatchSize = 1, Height = 4, Width = 4, Channels = 1 |
| 3207 | const armnn::TensorShape inputShape{ 1, 4, 4, 1 }; |
| 3208 | |
| 3209 | // outputShape: BatchSize = 1, Height = 2, Width = 2, Channels = 1 |
| 3210 | const armnn::TensorShape outputShape{ 1, 2, 2, 1 }; |
| 3211 | |
| 3212 | return ResizeBilinearSqMinTestImpl(workloadFactory, inputShape, outputShape, armnn::DataLayout::NHWC); |
| 3213 | } |
| 3214 | |
| 3215 | LayerTestResult<float, 4> ResizeBilinearMinTestImpl(armnn::IWorkloadFactory& workloadFactory, |
| 3216 | const armnn::TensorShape& inputTensorShape, |
| 3217 | const armnn::TensorShape& outputTensorShape, |
| 3218 | armnn::DataLayout dataLayout) |
| 3219 | { |
| 3220 | const armnn::TensorInfo inputTensorInfo(inputTensorShape, armnn::DataType::Float32); |
| 3221 | const armnn::TensorInfo outputTensorInfo(outputTensorShape, armnn::DataType::Float32); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 3222 | |
| 3223 | auto input = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>({ |
James Conroy | 074f371 | 2018-10-03 09:32:03 +0100 | [diff] [blame] | 3224 | 1.0f, 2.0f, 3.0f, 5.0f, 8.0f, |
| 3225 | 13.0f, 21.0f, 34.0f, 55.0f, 89.0f, |
| 3226 | 144.0f, 233.0f, 377.0f, 610.0f, 987.0f |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 3227 | })); |
| 3228 | |
| 3229 | LayerTestResult<float, 4> result(outputTensorInfo); |
| 3230 | result.outputExpected = MakeTensor<float, 4>(outputTensorInfo, std::vector<float>({ |
James Conroy | 074f371 | 2018-10-03 09:32:03 +0100 | [diff] [blame] | 3231 | 1.0f, 2.6666f, 6.0f, |
| 3232 | 78.5f, 179.3333f, 401.0f |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 3233 | })); |
| 3234 | |
| 3235 | std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| 3236 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 3237 | |
| 3238 | armnn::ResizeBilinearQueueDescriptor descriptor; |
James Conroy | 074f371 | 2018-10-03 09:32:03 +0100 | [diff] [blame] | 3239 | descriptor.m_Parameters.m_DataLayout = dataLayout; |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 3240 | armnn::WorkloadInfo info; |
| 3241 | AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); |
| 3242 | AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); |
| 3243 | |
| 3244 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResizeBilinear(descriptor, info); |
| 3245 | |
| 3246 | inputHandle->Allocate(); |
| 3247 | outputHandle->Allocate(); |
| 3248 | CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); |
| 3249 | |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 3250 | workloadFactory.Finalize(); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 3251 | workload->Execute(); |
| 3252 | |
| 3253 | CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); |
| 3254 | return result; |
| 3255 | } |
| 3256 | |
| 3257 | LayerTestResult<float, 4> ResizeBilinearMinTest(armnn::IWorkloadFactory& workloadFactory) |
| 3258 | { |
James Conroy | 074f371 | 2018-10-03 09:32:03 +0100 | [diff] [blame] | 3259 | // inputShape: BatchSize = 1, Channels = 1, Height = 3, Width = 5 |
| 3260 | const armnn::TensorShape inputShape{ 1, 1, 3, 5 }; |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 3261 | |
James Conroy | 074f371 | 2018-10-03 09:32:03 +0100 | [diff] [blame] | 3262 | // outputShape: BatchSize = 1, Channels = 1, Height = 2, Width = 3 |
| 3263 | const armnn::TensorShape outputShape{ 1, 1, 2, 3 }; |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 3264 | |
James Conroy | 074f371 | 2018-10-03 09:32:03 +0100 | [diff] [blame] | 3265 | return ResizeBilinearMinTestImpl(workloadFactory, inputShape, outputShape, armnn::DataLayout::NCHW); |
| 3266 | } |
| 3267 | |
| 3268 | LayerTestResult<float, 4> ResizeBilinearMinNhwcTest(armnn::IWorkloadFactory& workloadFactory) |
| 3269 | { |
| 3270 | // inputShape: BatchSize = 1, Height = 3, Width = 5, Channels = 1 |
| 3271 | const armnn::TensorShape inputShape{ 1, 3, 5, 1 }; |
| 3272 | |
| 3273 | // outputShape: BatchSize = 1, Height = 2, Width = 3, Channels = 1 |
| 3274 | const armnn::TensorShape outputShape{ 1, 2, 3, 1 }; |
| 3275 | |
| 3276 | return ResizeBilinearMinTestImpl(workloadFactory, inputShape, outputShape, armnn::DataLayout::NHWC); |
| 3277 | } |
| 3278 | |
| 3279 | LayerTestResult<float, 4> ResizeBilinearMagTestImpl(armnn::IWorkloadFactory& workloadFactory, |
| 3280 | const armnn::TensorShape& inputTensorShape, |
| 3281 | const armnn::TensorShape& outputTensorShape, |
| 3282 | armnn::DataLayout dataLayout) |
| 3283 | { |
| 3284 | const armnn::TensorInfo inputTensorInfo(inputTensorShape, armnn::DataType::Float32); |
| 3285 | const armnn::TensorInfo outputTensorInfo(outputTensorShape, armnn::DataType::Float32); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 3286 | |
| 3287 | auto input = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>({ |
James Conroy | 074f371 | 2018-10-03 09:32:03 +0100 | [diff] [blame] | 3288 | 1.0f, 2.0f, |
| 3289 | 13.0f, 21.0f, |
| 3290 | 144.0f, 233.0f |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 3291 | })); |
| 3292 | |
| 3293 | LayerTestResult<float, 4> result(outputTensorInfo); |
| 3294 | result.outputExpected = MakeTensor<float, 4>(outputTensorInfo, std::vector<float>({ |
James Conroy | 074f371 | 2018-10-03 09:32:03 +0100 | [diff] [blame] | 3295 | 1.0f, 1.4f, 1.8f, 2.0f, 2.0f, |
| 3296 | 13.0f, 16.2f, 19.4f, 21.0f, 21.0f, |
| 3297 | 144.0f, 179.6f, 215.2f, 233.0f, 233.0f |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 3298 | })); |
| 3299 | |
| 3300 | std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| 3301 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 3302 | |
| 3303 | armnn::ResizeBilinearQueueDescriptor descriptor; |
James Conroy | 074f371 | 2018-10-03 09:32:03 +0100 | [diff] [blame] | 3304 | descriptor.m_Parameters.m_DataLayout = dataLayout; |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 3305 | armnn::WorkloadInfo info; |
| 3306 | AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); |
| 3307 | AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); |
| 3308 | |
| 3309 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResizeBilinear(descriptor, info); |
| 3310 | |
| 3311 | inputHandle->Allocate(); |
| 3312 | outputHandle->Allocate(); |
| 3313 | CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); |
| 3314 | |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 3315 | workloadFactory.Finalize(); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 3316 | workload->Execute(); |
| 3317 | |
| 3318 | CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); |
| 3319 | return result; |
| 3320 | } |
| 3321 | |
| 3322 | LayerTestResult<float, 4> ResizeBilinearMagTest(armnn::IWorkloadFactory& workloadFactory) |
| 3323 | { |
James Conroy | 074f371 | 2018-10-03 09:32:03 +0100 | [diff] [blame] | 3324 | // inputShape: BatchSize = 1, Channels = 1, Height = 3, Width = 2 |
| 3325 | const armnn::TensorShape inputShape{ 1, 1, 3, 2 }; |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 3326 | |
James Conroy | 074f371 | 2018-10-03 09:32:03 +0100 | [diff] [blame] | 3327 | // outputShape: BatchSize = 1, Channels = 1, Height = 3, Width = 5 |
| 3328 | const armnn::TensorShape outputShape{ 1, 1, 3, 5 }; |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 3329 | |
James Conroy | 074f371 | 2018-10-03 09:32:03 +0100 | [diff] [blame] | 3330 | return ResizeBilinearMagTestImpl(workloadFactory, inputShape, outputShape, armnn::DataLayout::NCHW); |
| 3331 | } |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 3332 | |
James Conroy | 074f371 | 2018-10-03 09:32:03 +0100 | [diff] [blame] | 3333 | LayerTestResult<float, 4> ResizeBilinearMagNhwcTest(armnn::IWorkloadFactory& workloadFactory) |
| 3334 | { |
| 3335 | // inputShape: BatchSize = 1, Height = 3, Width = 2, Channels = 1 |
| 3336 | const armnn::TensorShape inputShape{ 1, 3, 2, 1 }; |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 3337 | |
James Conroy | 074f371 | 2018-10-03 09:32:03 +0100 | [diff] [blame] | 3338 | // outputShape: BatchSize = 1, Height = 3, Width = 5, Channels = 1 |
| 3339 | const armnn::TensorShape outputShape{ 1, 3, 5, 1 }; |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 3340 | |
James Conroy | 074f371 | 2018-10-03 09:32:03 +0100 | [diff] [blame] | 3341 | return ResizeBilinearMagTestImpl(workloadFactory, inputShape, outputShape, armnn::DataLayout::NHWC); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 3342 | } |
| 3343 | |
| 3344 | LayerTestResult<float, 2> FakeQuantizationTest(armnn::IWorkloadFactory& workloadFactory) |
| 3345 | { |
| 3346 | constexpr unsigned int width = 2; |
| 3347 | constexpr unsigned int height = 3; |
| 3348 | |
| 3349 | const armnn::TensorInfo tensorInfo({height, width }, |
| 3350 | armnn::DataType::Float32); |
| 3351 | auto input = MakeTensor<float, 2>(tensorInfo, std::vector<float>({ |
| 3352 | -10.0f, -5.0f, |
| 3353 | 0.0f, 5.0f, |
| 3354 | 10.0f, 10.0f |
| 3355 | })); |
| 3356 | |
| 3357 | LayerTestResult<float, 2> ret(tensorInfo); |
| 3358 | |
| 3359 | std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(tensorInfo); |
| 3360 | |
| 3361 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(tensorInfo); |
| 3362 | |
| 3363 | armnn::FakeQuantizationQueueDescriptor data; |
| 3364 | armnn::WorkloadInfo info; |
| 3365 | |
| 3366 | AddInputToWorkload(data, info, tensorInfo, inputHandle.get()); |
| 3367 | AddOutputToWorkload(data, info, tensorInfo, outputHandle.get()); |
| 3368 | float min = -10.f; |
| 3369 | float max = 10.f; |
| 3370 | |
| 3371 | data.m_Parameters.m_Min = min; |
| 3372 | data.m_Parameters.m_Max = max; |
| 3373 | |
| 3374 | armnn::PassthroughCpuTensorHandle refHandle(tensorInfo, &ret.outputExpected[0][0]); |
| 3375 | armnn::FakeQuantizationQueueDescriptor refData = data; |
| 3376 | armnn::WorkloadInfo refInfo = info; |
| 3377 | SetWorkloadOutput(refData, refInfo, 0, tensorInfo, &refHandle); |
| 3378 | |
| 3379 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateFakeQuantization(data, info); |
| 3380 | |
| 3381 | inputHandle->Allocate(); |
| 3382 | outputHandle->Allocate(); |
| 3383 | |
| 3384 | CopyDataToITensorHandle(inputHandle.get(), &input[0][0]); |
| 3385 | |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 3386 | workloadFactory.Finalize(); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 3387 | workload->Execute(); |
| 3388 | |
| 3389 | CopyDataFromITensorHandle(&ret.output[0][0], outputHandle.get()); |
| 3390 | |
| 3391 | ret.outputExpected = MakeTensor<float, 2>(tensorInfo, std::vector<float>({ |
| 3392 | 0.0f, 63.0f, |
| 3393 | 128.0f, 191.0f, |
| 3394 | 255.0f, 255.0f |
| 3395 | })); |
| 3396 | return ret; |
| 3397 | } |
| 3398 | |
Matteo Martincigh | 539b44d | 2018-10-01 09:26:39 +0100 | [diff] [blame] | 3399 | namespace |
| 3400 | { |
| 3401 | |
| 3402 | LayerTestResult<float, 4> L2NormalizationTestImpl(armnn::IWorkloadFactory& workloadFactory, |
| 3403 | const armnn::TensorShape& inputOutputTensorShape, |
| 3404 | const std::vector<float>& inputValues, |
| 3405 | const std::vector<float>& expectedOutputValues, |
| 3406 | armnn::DataLayout dataLayout) |
| 3407 | { |
| 3408 | const armnn::TensorInfo inputTensorInfo(inputOutputTensorShape, armnn::DataType::Float32); |
| 3409 | const armnn::TensorInfo outputTensorInfo(inputOutputTensorShape, armnn::DataType::Float32); |
| 3410 | |
| 3411 | auto inputTensor = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>(inputValues)); |
| 3412 | |
| 3413 | LayerTestResult<float, 4> result(outputTensorInfo); |
| 3414 | result.outputExpected = MakeTensor<float, 4>(inputTensorInfo, std::vector<float>(expectedOutputValues)); |
| 3415 | |
| 3416 | std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| 3417 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 3418 | |
| 3419 | armnn::L2NormalizationQueueDescriptor descriptor; |
| 3420 | descriptor.m_Parameters.m_DataLayout = dataLayout; |
| 3421 | armnn::WorkloadInfo info; |
| 3422 | |
| 3423 | AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); |
| 3424 | AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); |
| 3425 | |
| 3426 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateL2Normalization(descriptor, info); |
| 3427 | |
| 3428 | inputHandle->Allocate(); |
| 3429 | outputHandle->Allocate(); |
| 3430 | |
| 3431 | CopyDataToITensorHandle(inputHandle.get(), &inputTensor[0][0][0][0]); |
| 3432 | |
| 3433 | workloadFactory.Finalize(); |
| 3434 | workload->Execute(); |
| 3435 | |
| 3436 | CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); |
| 3437 | |
| 3438 | return result; |
| 3439 | } |
| 3440 | |
| 3441 | float CalcInvL2Norm(std::initializer_list<float> elements) |
| 3442 | { |
| 3443 | const float reduction = std::accumulate(elements.begin(), elements.end(), 0.0f, |
| 3444 | [](float acc, float element) { return acc + element * element; }); |
| 3445 | return 1.0f / sqrtf(reduction); |
| 3446 | } |
| 3447 | |
| 3448 | } // anonymous namespace |
| 3449 | |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3450 | template<typename T> |
| 3451 | LayerTestResult<T, 2> Pad2dTestCommon(armnn::IWorkloadFactory& workloadFactory, float qScale, int32_t qOffset) |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3452 | { |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3453 | const armnn::TensorShape inputShape{ 3, 3 }; |
| 3454 | const armnn::TensorShape outputShape{ 7, 7 }; |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3455 | |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3456 | const armnn::TensorInfo inputTensorInfo(inputShape, armnn::GetDataType<T>()); |
| 3457 | const armnn::TensorInfo outputTensorInfo(outputShape, armnn::GetDataType<T>()); |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3458 | |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3459 | std::vector<T> inputValues( |
| 3460 | QuantizedVector<T>(qScale, qOffset, |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3461 | { |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3462 | // Height (3) x Width (3) |
| 3463 | 4, 8, 6, |
| 3464 | 7, 4, 4, |
| 3465 | 3, 2, 4 |
| 3466 | })); |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3467 | |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3468 | std::vector<T> expectedOutputValues( |
| 3469 | QuantizedVector<T>(qScale, qOffset, |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3470 | { |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3471 | 0, 0, 0, 0, 0, 0, 0, |
| 3472 | 0, 0, 0, 0, 0, 0, 0, |
| 3473 | 0, 0, 4, 8, 6, 0, 0, |
| 3474 | 0, 0, 7, 4, 4, 0, 0, |
| 3475 | 0, 0, 3, 2, 4, 0, 0, |
| 3476 | 0, 0, 0, 0, 0, 0, 0, |
| 3477 | 0, 0, 0, 0, 0, 0, 0 |
| 3478 | })); |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3479 | |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3480 | auto inputTensor = MakeTensor<T, 2>(inputTensorInfo, std::vector<T>(inputValues)); |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3481 | |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3482 | LayerTestResult<T, 2> result(outputTensorInfo); |
| 3483 | result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, std::vector<T>(expectedOutputValues)); |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3484 | |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3485 | std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| 3486 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3487 | |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3488 | armnn::PadQueueDescriptor descriptor; |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3489 | |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3490 | std::vector<std::pair<unsigned int, unsigned int>> PadList; |
| 3491 | PadList.push_back(std::pair<unsigned int, unsigned int>(2,2)); |
| 3492 | PadList.push_back(std::pair<unsigned int, unsigned int>(2,2)); |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3493 | |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3494 | descriptor.m_Parameters.m_PadList = PadList; |
| 3495 | armnn::WorkloadInfo info; |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3496 | |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3497 | AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); |
| 3498 | AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3499 | |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3500 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreatePad(descriptor, info); |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3501 | |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3502 | inputHandle->Allocate(); |
| 3503 | outputHandle->Allocate(); |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3504 | |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3505 | CopyDataToITensorHandle(inputHandle.get(), &inputTensor[0][0]); |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3506 | |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3507 | workloadFactory.Finalize(); |
| 3508 | workload->Execute(); |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3509 | |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3510 | CopyDataFromITensorHandle(&result.output[0][0], outputHandle.get()); |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3511 | |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3512 | return result; |
| 3513 | } |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3514 | |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3515 | template <typename T> |
| 3516 | LayerTestResult<T, 3> Pad3dTestCommon(armnn::IWorkloadFactory& workloadFactory, float qScale, int32_t qOffset) |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3517 | { |
| 3518 | const armnn::TensorShape inputShape{ 2, 2, 2 }; |
| 3519 | const armnn::TensorShape outputShape{ 3, 5, 6 }; |
| 3520 | |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3521 | const armnn::TensorInfo inputTensorInfo(inputShape, armnn::GetDataType<T>()); |
| 3522 | const armnn::TensorInfo outputTensorInfo(outputShape, armnn::GetDataType<T>()); |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3523 | |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3524 | std::vector<T> inputValues( |
| 3525 | QuantizedVector<T>(qScale,qOffset, |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3526 | { |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3527 | // Channel 0, Height (2) x Width (2) |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3528 | 0, 4, |
| 3529 | 2, 5, |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3530 | |
| 3531 | // Channel 1, Height (2) x Width (2) |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3532 | 6, 1, |
| 3533 | 5, 2 |
| 3534 | })); |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3535 | |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3536 | std::vector<T> expectedOutputValues( |
| 3537 | QuantizedVector<T>(qScale,qOffset, |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3538 | { |
| 3539 | |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3540 | 0, 0, 0, 0, 0, 0, |
| 3541 | 0, 0, 0, 0, 0, 0, |
| 3542 | 0, 0, 0, 4, 0, 0, |
| 3543 | 0, 0, 2, 5, 0, 0, |
| 3544 | 0, 0, 0, 0, 0, 0, |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3545 | |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3546 | 0, 0, 0, 0, 0, 0, |
| 3547 | 0, 0, 0, 0, 0, 0, |
| 3548 | 0, 0, 6, 1, 0, 0, |
| 3549 | 0, 0, 5, 2, 0, 0, |
| 3550 | 0, 0, 0, 0, 0, 0, |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3551 | |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3552 | 0, 0, 0, 0, 0, 0, |
| 3553 | 0, 0, 0, 0, 0, 0, |
| 3554 | 0, 0, 0, 0, 0, 0, |
| 3555 | 0, 0, 0, 0, 0, 0, |
| 3556 | 0, 0, 0, 0, 0, 0 |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3557 | |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3558 | })); |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3559 | |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3560 | auto inputTensor = MakeTensor<T, 3>(inputTensorInfo, std::vector<T>(inputValues)); |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3561 | |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3562 | LayerTestResult<T, 3> result(outputTensorInfo); |
| 3563 | result.outputExpected = MakeTensor<T, 3>(outputTensorInfo, std::vector<T>(expectedOutputValues)); |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3564 | |
| 3565 | std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| 3566 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 3567 | |
| 3568 | armnn::PadQueueDescriptor descriptor; |
| 3569 | |
| 3570 | std::vector<std::pair<unsigned int, unsigned int>> PadList; |
| 3571 | PadList.push_back(std::pair<unsigned int, unsigned int>(0,1)); |
| 3572 | PadList.push_back(std::pair<unsigned int, unsigned int>(2,1)); |
| 3573 | PadList.push_back(std::pair<unsigned int, unsigned int>(2,2)); |
| 3574 | |
| 3575 | descriptor.m_Parameters.m_PadList = PadList; |
| 3576 | armnn::WorkloadInfo info; |
| 3577 | |
| 3578 | AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); |
| 3579 | AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); |
| 3580 | |
| 3581 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreatePad(descriptor, info); |
| 3582 | |
| 3583 | inputHandle->Allocate(); |
| 3584 | outputHandle->Allocate(); |
| 3585 | |
| 3586 | CopyDataToITensorHandle(inputHandle.get(), &inputTensor[0][0][0]); |
| 3587 | |
| 3588 | workloadFactory.Finalize(); |
| 3589 | workload->Execute(); |
| 3590 | |
| 3591 | CopyDataFromITensorHandle(&result.output[0][0][0], outputHandle.get()); |
| 3592 | |
| 3593 | return result; |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3594 | } |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3595 | |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3596 | template <typename T> |
| 3597 | LayerTestResult<T, 4> Pad4dTestCommon(armnn::IWorkloadFactory& workloadFactory, float qScale, int32_t qOffset) |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3598 | { |
| 3599 | const armnn::TensorShape inputShape{ 2, 2, 3, 2 }; |
| 3600 | const armnn::TensorShape outputShape{ 4, 5, 7, 4 }; |
| 3601 | |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3602 | const armnn::TensorInfo inputTensorInfo(inputShape, armnn::GetDataType<T>()); |
| 3603 | const armnn::TensorInfo outputTensorInfo(outputShape, armnn::GetDataType<T>()); |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3604 | |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3605 | std::vector<T> inputValues( |
| 3606 | QuantizedVector<T>(qScale,qOffset, |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3607 | { |
| 3608 | // Batch 0, Channel 0, Height (3) x Width (2) |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3609 | 0, 1, |
| 3610 | 2, 3, |
| 3611 | 4, 5, |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3612 | |
| 3613 | // Batch 0, Channel 1, Height (3) x Width (2) |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3614 | 6, 7, |
| 3615 | 8, 9, |
| 3616 | 10, 11, |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3617 | |
| 3618 | // Batch 1, Channel 0, Height (3) x Width (2) |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3619 | 12, 13, |
| 3620 | 14, 15, |
| 3621 | 16, 17, |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3622 | |
| 3623 | // Batch 1, Channel 1, Height (3) x Width (2) |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3624 | 18, 19, |
| 3625 | 20, 21, |
| 3626 | 22, 23 |
| 3627 | })); |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3628 | |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3629 | std::vector<T> expectedOutputValues( |
| 3630 | QuantizedVector<T>(qScale,qOffset, |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3631 | { |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3632 | 0, 0, 0, 0, |
| 3633 | 0, 0, 0, 0, |
| 3634 | 0, 0, 0, 0, |
| 3635 | 0, 0, 0, 0, |
| 3636 | 0, 0, 0, 0, |
| 3637 | 0, 0, 0, 0, |
| 3638 | 0, 0, 0, 0, |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3639 | |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3640 | 0, 0, 0, 0, |
| 3641 | 0, 0, 0, 0, |
| 3642 | 0, 0, 0, 0, |
| 3643 | 0, 0, 0, 0, |
| 3644 | 0, 0, 0, 0, |
| 3645 | 0, 0, 0, 0, |
| 3646 | 0, 0, 0, 0, |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3647 | |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3648 | 0, 0, 0, 0, |
| 3649 | 0, 0, 0, 0, |
| 3650 | 0, 0, 0, 0, |
| 3651 | 0, 0, 0, 0, |
| 3652 | 0, 0, 0, 0, |
| 3653 | 0, 0, 0, 0, |
| 3654 | 0, 0, 0, 0, |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3655 | |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3656 | 0, 0, 0, 0, |
| 3657 | 0, 0, 0, 0, |
| 3658 | 0, 0, 0, 0, |
| 3659 | 0, 0, 0, 0, |
| 3660 | 0, 0, 0, 0, |
| 3661 | 0, 0, 0, 0, |
| 3662 | 0, 0, 0, 0, |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3663 | |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3664 | 0, 0, 0, 0, |
| 3665 | 0, 0, 0, 0, |
| 3666 | 0, 0, 0, 0, |
| 3667 | 0, 0, 0, 0, |
| 3668 | 0, 0, 0, 0, |
| 3669 | 0, 0, 0, 0, |
| 3670 | 0, 0, 0, 0, |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3671 | |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3672 | 0, 0, 0, 0, |
| 3673 | 0, 0, 0, 0, |
| 3674 | 0, 0, 0, 0, |
| 3675 | 0, 0, 0, 0, |
| 3676 | 0, 0, 0, 0, |
| 3677 | 0, 0, 0, 0, |
| 3678 | 0, 0, 0, 0, |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3679 | |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3680 | 0, 0, 0, 0, |
| 3681 | 0, 0, 0, 0, |
| 3682 | 0, 0, 0, 0, |
| 3683 | 0, 0, 0, 0, |
| 3684 | 0, 0, 0, 0, |
| 3685 | 0, 0, 0, 0, |
| 3686 | 0, 0, 0, 0, |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3687 | |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3688 | 0, 0, 0, 0, |
| 3689 | 0, 0, 0, 0, |
| 3690 | 0, 0, 0, 0, |
| 3691 | 0, 0, 1, 0, |
| 3692 | 0, 2, 3, 0, |
| 3693 | 0, 4, 5, 0, |
| 3694 | 0, 0, 0, 0, |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3695 | |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3696 | 0, 0, 0, 0, |
| 3697 | 0, 0, 0, 0, |
| 3698 | 0, 0, 0, 0, |
| 3699 | 0, 6, 7, 0, |
| 3700 | 0, 8, 9, 0, |
| 3701 | 0, 10, 11, 0, |
| 3702 | 0, 0, 0, 0, |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3703 | |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3704 | 0, 0, 0, 0, |
| 3705 | 0, 0, 0, 0, |
| 3706 | 0, 0, 0, 0, |
| 3707 | 0, 0, 0, 0, |
| 3708 | 0, 0, 0, 0, |
| 3709 | 0, 0, 0, 0, |
| 3710 | 0, 0, 0, 0, |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3711 | |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3712 | 0, 0, 0, 0, |
| 3713 | 0, 0, 0, 0, |
| 3714 | 0, 0, 0, 0, |
| 3715 | 0, 0, 0, 0, |
| 3716 | 0, 0, 0, 0, |
| 3717 | 0, 0, 0, 0, |
| 3718 | 0, 0, 0, 0, |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3719 | |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3720 | 0, 0, 0, 0, |
| 3721 | 0, 0, 0, 0, |
| 3722 | 0, 0, 0, 0, |
| 3723 | 0, 0, 0, 0, |
| 3724 | 0, 0, 0, 0, |
| 3725 | 0, 0, 0, 0, |
| 3726 | 0, 0, 0, 0, |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3727 | |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3728 | 0, 0, 0, 0, |
| 3729 | 0, 0, 0, 0, |
| 3730 | 0, 0, 0, 0, |
| 3731 | 0, 12, 13, 0, |
| 3732 | 0, 14, 15, 0, |
| 3733 | 0, 16, 17, 0, |
| 3734 | 0, 0, 0, 0, |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3735 | |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3736 | 0, 0, 0, 0, |
| 3737 | 0, 0, 0, 0, |
| 3738 | 0, 0, 0, 0, |
| 3739 | 0, 18, 19, 0, |
| 3740 | 0, 20, 21, 0, |
| 3741 | 0, 22, 23, 0, |
| 3742 | 0, 0, 0, 0, |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3743 | |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3744 | 0, 0, 0, 0, |
| 3745 | 0, 0, 0, 0, |
| 3746 | 0, 0, 0, 0, |
| 3747 | 0, 0, 0, 0, |
| 3748 | 0, 0, 0, 0, |
| 3749 | 0, 0, 0, 0, |
| 3750 | 0, 0, 0, 0, |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3751 | |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3752 | 0, 0, 0, 0, |
| 3753 | 0, 0, 0, 0, |
| 3754 | 0, 0, 0, 0, |
| 3755 | 0, 0, 0, 0, |
| 3756 | 0, 0, 0, 0, |
| 3757 | 0, 0, 0, 0, |
| 3758 | 0, 0, 0, 0, |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3759 | |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3760 | 0, 0, 0, 0, |
| 3761 | 0, 0, 0, 0, |
| 3762 | 0, 0, 0, 0, |
| 3763 | 0, 0, 0, 0, |
| 3764 | 0, 0, 0, 0, |
| 3765 | 0, 0, 0, 0, |
| 3766 | 0, 0, 0, 0, |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3767 | |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3768 | 0, 0, 0, 0, |
| 3769 | 0, 0, 0, 0, |
| 3770 | 0, 0, 0, 0, |
| 3771 | 0, 0, 0, 0, |
| 3772 | 0, 0, 0, 0, |
| 3773 | 0, 0, 0, 0, |
| 3774 | 0, 0, 0, 0, |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3775 | |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3776 | 0, 0, 0, 0, |
| 3777 | 0, 0, 0, 0, |
| 3778 | 0, 0, 0, 0, |
| 3779 | 0, 0, 0, 0, |
| 3780 | 0, 0, 0, 0, |
| 3781 | 0, 0, 0, 0, |
| 3782 | 0, 0, 0, 0, |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3783 | |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3784 | 0, 0, 0, 0, |
| 3785 | 0, 0, 0, 0, |
| 3786 | 0, 0, 0, 0, |
| 3787 | 0, 0, 0, 0, |
| 3788 | 0, 0, 0, 0, |
| 3789 | 0, 0, 0, 0, |
| 3790 | 0, 0, 0, 0 |
| 3791 | })); |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3792 | |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3793 | auto inputTensor = MakeTensor<T, 4>(inputTensorInfo, std::vector<T>(inputValues)); |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3794 | |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3795 | LayerTestResult<T, 4> result(outputTensorInfo); |
| 3796 | result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, std::vector<T>(expectedOutputValues)); |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3797 | |
| 3798 | std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| 3799 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 3800 | |
| 3801 | armnn::PadQueueDescriptor descriptor; |
| 3802 | |
| 3803 | std::vector<std::pair<unsigned int, unsigned int>> PadList; |
| 3804 | PadList.push_back(std::pair<unsigned int, unsigned int>(1,1)); |
| 3805 | PadList.push_back(std::pair<unsigned int, unsigned int>(2,1)); |
| 3806 | PadList.push_back(std::pair<unsigned int, unsigned int>(3,1)); |
| 3807 | PadList.push_back(std::pair<unsigned int, unsigned int>(1,1)); |
| 3808 | |
| 3809 | descriptor.m_Parameters.m_PadList = PadList; |
| 3810 | armnn::WorkloadInfo info; |
| 3811 | |
| 3812 | AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); |
| 3813 | AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); |
| 3814 | |
| 3815 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreatePad(descriptor, info); |
| 3816 | |
| 3817 | inputHandle->Allocate(); |
| 3818 | outputHandle->Allocate(); |
| 3819 | |
| 3820 | CopyDataToITensorHandle(inputHandle.get(), &inputTensor[0][0][0][0]); |
| 3821 | |
| 3822 | workloadFactory.Finalize(); |
| 3823 | |
| 3824 | workload->Execute(); |
| 3825 | |
| 3826 | CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); |
| 3827 | |
| 3828 | return result; |
Mohamed Nour Abouelseoud | dd6acea | 2018-10-18 12:26:19 +0100 | [diff] [blame] | 3829 | } |
| 3830 | |
| 3831 | LayerTestResult<uint8_t, 2> PadUint82dTest(armnn::IWorkloadFactory& workloadFactory) |
| 3832 | { |
| 3833 | return Pad2dTestCommon<uint8_t>(workloadFactory, 1.0f, 0); |
| 3834 | } |
| 3835 | |
| 3836 | LayerTestResult<uint8_t, 3> PadUint83dTest(armnn::IWorkloadFactory& workloadFactory) |
| 3837 | { |
| 3838 | return Pad3dTestCommon<uint8_t>(workloadFactory, 1.0f, 0); |
| 3839 | } |
| 3840 | |
| 3841 | LayerTestResult<uint8_t, 4> PadUint84dTest(armnn::IWorkloadFactory& workloadFactory) |
| 3842 | { |
| 3843 | return Pad4dTestCommon<uint8_t>(workloadFactory, 1.0f, 0); |
| 3844 | } |
| 3845 | |
| 3846 | LayerTestResult<float, 2> PadFloat322dTest(armnn::IWorkloadFactory& workloadFactory) |
| 3847 | { |
| 3848 | return Pad2dTestCommon<float>(workloadFactory, 0.0f, 0); |
| 3849 | } |
| 3850 | |
| 3851 | LayerTestResult<float, 3> PadFloat323dTest(armnn::IWorkloadFactory& workloadFactory) |
| 3852 | { |
| 3853 | return Pad3dTestCommon<float>(workloadFactory, 0.0f, 0); |
| 3854 | } |
| 3855 | |
| 3856 | LayerTestResult<float, 4> PadFloat324dTest(armnn::IWorkloadFactory& workloadFactory) |
| 3857 | { |
| 3858 | return Pad4dTestCommon<float>(workloadFactory, 0.0f, 0); |
| 3859 | } |
Mohamed Nour Abouelseoud | 7420e55 | 2018-10-12 12:26:24 +0100 | [diff] [blame] | 3860 | |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 3861 | LayerTestResult<float, 4> L2Normalization1dTest(armnn::IWorkloadFactory& workloadFactory) |
| 3862 | { |
Matteo Martincigh | 539b44d | 2018-10-01 09:26:39 +0100 | [diff] [blame] | 3863 | // Width: 1 |
| 3864 | // Height: 1 |
| 3865 | // Channels: 10 |
| 3866 | // BatchSize: 1 |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 3867 | |
Matteo Martincigh | 539b44d | 2018-10-01 09:26:39 +0100 | [diff] [blame] | 3868 | const armnn::TensorShape inputOutputShape{ 1, 10, 1, 1 }; |
| 3869 | std::vector<float> inputValues |
| 3870 | { |
| 3871 | // Batch 0, Channel 0, Height (1) x Width (1) |
| 3872 | 1.0f, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 3873 | |
Matteo Martincigh | 539b44d | 2018-10-01 09:26:39 +0100 | [diff] [blame] | 3874 | // Batch 0, Channel 1, Height (1) x Width (1) |
| 3875 | 2.0f, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 3876 | |
Matteo Martincigh | 539b44d | 2018-10-01 09:26:39 +0100 | [diff] [blame] | 3877 | // Batch 0, Channel 2, Height (1) x Width (1) |
| 3878 | 3.0f, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 3879 | |
Matteo Martincigh | 539b44d | 2018-10-01 09:26:39 +0100 | [diff] [blame] | 3880 | // Batch 0, Channel 3, Height (1) x Width (1) |
| 3881 | 4.0f, |
| 3882 | |
| 3883 | // Batch 0, Channel 4, Height (1) x Width (1) |
| 3884 | 5.0f, |
| 3885 | |
| 3886 | // Batch 0, Channel 5, Height (1) x Width (1) |
| 3887 | 6.0f, |
| 3888 | |
| 3889 | // Batch 0, Channel 6, Height (1) x Width (1) |
| 3890 | 7.0f, |
| 3891 | |
| 3892 | // Batch 0, Channel 7, Height (1) x Width (1) |
| 3893 | 8.0f, |
| 3894 | |
| 3895 | // Batch 0, Channel 8, Height (1) x Width (1) |
| 3896 | 9.0f, |
| 3897 | |
| 3898 | // Batch 0, Channel 9, Height (1) x Width (1) |
| 3899 | 10.0f |
| 3900 | }; |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 3901 | const float approxInvL2Norm = 0.050964719f; |
Matteo Martincigh | 539b44d | 2018-10-01 09:26:39 +0100 | [diff] [blame] | 3902 | std::vector<float> expectedOutputValues |
| 3903 | { |
| 3904 | // Batch 0, Channel 0, Height (1) x Width (1) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 3905 | 1.0f * approxInvL2Norm, |
| 3906 | 2.0f * approxInvL2Norm, |
| 3907 | 3.0f * approxInvL2Norm, |
| 3908 | 4.0f * approxInvL2Norm, |
| 3909 | 5.0f * approxInvL2Norm, |
| 3910 | 6.0f * approxInvL2Norm, |
| 3911 | 7.0f * approxInvL2Norm, |
| 3912 | 8.0f * approxInvL2Norm, |
| 3913 | 9.0f * approxInvL2Norm, |
| 3914 | 10.0f * approxInvL2Norm |
Matteo Martincigh | 539b44d | 2018-10-01 09:26:39 +0100 | [diff] [blame] | 3915 | }; |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 3916 | |
Matteo Martincigh | 539b44d | 2018-10-01 09:26:39 +0100 | [diff] [blame] | 3917 | return L2NormalizationTestImpl(workloadFactory, inputOutputShape, |
| 3918 | inputValues, expectedOutputValues, armnn::DataLayout::NCHW); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 3919 | } |
| 3920 | |
Matteo Martincigh | 539b44d | 2018-10-01 09:26:39 +0100 | [diff] [blame] | 3921 | LayerTestResult<float, 4> L2Normalization1dNhwcTest(armnn::IWorkloadFactory& workloadFactory) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 3922 | { |
Matteo Martincigh | 539b44d | 2018-10-01 09:26:39 +0100 | [diff] [blame] | 3923 | // Width: 1 |
| 3924 | // Height: 1 |
| 3925 | // Channels: 10 |
| 3926 | // BatchSize: 1 |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 3927 | |
Matteo Martincigh | 539b44d | 2018-10-01 09:26:39 +0100 | [diff] [blame] | 3928 | const armnn::TensorShape inputOutputShape{ 1, 1, 1, 10 }; |
| 3929 | std::vector<float> inputValues |
| 3930 | { |
| 3931 | // Batch 0, Height 0, Width (1) x Channel (10) |
| 3932 | 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f |
| 3933 | }; |
| 3934 | const float approxInvL2Norm = 0.050964719f; |
| 3935 | std::vector<float> expectedOutputValues |
| 3936 | { |
| 3937 | // Batch 0, Height 0, Width (1) x Channel (10) |
| 3938 | 1.0f * approxInvL2Norm, |
| 3939 | 2.0f * approxInvL2Norm, |
| 3940 | 3.0f * approxInvL2Norm, |
| 3941 | 4.0f * approxInvL2Norm, |
| 3942 | 5.0f * approxInvL2Norm, |
| 3943 | 6.0f * approxInvL2Norm, |
| 3944 | 7.0f * approxInvL2Norm, |
| 3945 | 8.0f * approxInvL2Norm, |
| 3946 | 9.0f * approxInvL2Norm, |
| 3947 | 10.0f * approxInvL2Norm |
| 3948 | }; |
| 3949 | |
| 3950 | return L2NormalizationTestImpl(workloadFactory, inputOutputShape, |
| 3951 | inputValues, expectedOutputValues, armnn::DataLayout::NHWC); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 3952 | } |
| 3953 | |
| 3954 | LayerTestResult<float, 4> L2Normalization2dTest(armnn::IWorkloadFactory& workloadFactory) |
| 3955 | { |
Matteo Martincigh | 539b44d | 2018-10-01 09:26:39 +0100 | [diff] [blame] | 3956 | // Width: 5 |
| 3957 | // Height: 1 |
| 3958 | // Channels: 2 |
| 3959 | // BatchSize: 1 |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 3960 | |
Matteo Martincigh | 539b44d | 2018-10-01 09:26:39 +0100 | [diff] [blame] | 3961 | const armnn::TensorShape inputOutputShape{ 1, 2, 1, 5 }; |
| 3962 | std::vector<float> inputValues |
| 3963 | { |
| 3964 | // Batch 0, Channel 0, Height (1) x Width (5) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 3965 | 1.0f, 3.0f, 5.0f, 7.0f, 9.0f, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 3966 | |
Matteo Martincigh | 539b44d | 2018-10-01 09:26:39 +0100 | [diff] [blame] | 3967 | // Batch 0, Channel 1, Height (1) x Width (5) |
| 3968 | 2.0f, 4.0f, 6.0f, 8.0f, 10.0f |
| 3969 | }; |
| 3970 | std::vector<float> expectedOutputValues |
| 3971 | { |
| 3972 | // Batch 0, Channel 0, Height (1) x Width (5) |
| 3973 | 1.0f * CalcInvL2Norm({ 1.0f, 2.0f }), |
| 3974 | 3.0f * CalcInvL2Norm({ 3.0f, 4.0f }), |
| 3975 | 5.0f * CalcInvL2Norm({ 5.0f, 6.0f }), |
| 3976 | 7.0f * CalcInvL2Norm({ 7.0f, 8.0f }), |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 3977 | 9.0f * CalcInvL2Norm({ 9.0f, 10.0f }), |
| 3978 | |
Matteo Martincigh | 539b44d | 2018-10-01 09:26:39 +0100 | [diff] [blame] | 3979 | // Batch 0, Channel 1, Height (1) x Width (5) |
| 3980 | 2.0f * CalcInvL2Norm({ 1.0f, 2.0f }), |
| 3981 | 4.0f * CalcInvL2Norm({ 3.0f, 4.0f }), |
| 3982 | 6.0f * CalcInvL2Norm({ 5.0f, 6.0f }), |
| 3983 | 8.0f * CalcInvL2Norm({ 7.0f, 8.0f }), |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 3984 | 10.0f * CalcInvL2Norm({ 9.0f, 10.0f }) |
Matteo Martincigh | 539b44d | 2018-10-01 09:26:39 +0100 | [diff] [blame] | 3985 | }; |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 3986 | |
Matteo Martincigh | 539b44d | 2018-10-01 09:26:39 +0100 | [diff] [blame] | 3987 | return L2NormalizationTestImpl(workloadFactory, inputOutputShape, |
| 3988 | inputValues, expectedOutputValues, armnn::DataLayout::NCHW); |
| 3989 | } |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 3990 | |
Matteo Martincigh | 539b44d | 2018-10-01 09:26:39 +0100 | [diff] [blame] | 3991 | LayerTestResult<float, 4> L2Normalization2dNhwcTest(armnn::IWorkloadFactory& workloadFactory) |
| 3992 | { |
| 3993 | // Width: 5 |
| 3994 | // Height: 1 |
| 3995 | // Channels: 2 |
| 3996 | // BatchSize: 1 |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 3997 | |
Matteo Martincigh | 539b44d | 2018-10-01 09:26:39 +0100 | [diff] [blame] | 3998 | const armnn::TensorShape inputOutputShape{ 1, 1, 5, 2 }; |
| 3999 | std::vector<float> inputValues |
| 4000 | { |
| 4001 | // Batch 0, Height 0, Width (5) x Channel (2) |
| 4002 | 1.0f, 2.0f, |
| 4003 | 3.0f, 4.0f, |
| 4004 | 5.0f, 6.0f, |
| 4005 | 7.0f, 8.0f, |
| 4006 | 9.0f, 10.0f |
| 4007 | }; |
| 4008 | std::vector<float> expectedOutputValues |
| 4009 | { |
| 4010 | // Batch 0, Height 0, Width (5) x Channel (2) |
| 4011 | 1.0f * CalcInvL2Norm({ 1.0f, 2.0f }), |
| 4012 | 2.0f * CalcInvL2Norm({ 1.0f, 2.0f }), |
| 4013 | 3.0f * CalcInvL2Norm({ 3.0f, 4.0f }), |
| 4014 | 4.0f * CalcInvL2Norm({ 3.0f, 4.0f }), |
| 4015 | 5.0f * CalcInvL2Norm({ 5.0f, 6.0f }), |
| 4016 | 6.0f * CalcInvL2Norm({ 5.0f, 6.0f }), |
| 4017 | 7.0f * CalcInvL2Norm({ 7.0f, 8.0f }), |
| 4018 | 8.0f * CalcInvL2Norm({ 7.0f, 8.0f }), |
| 4019 | 9.0f * CalcInvL2Norm({ 9.0f, 10.0f }), |
| 4020 | 10.0f * CalcInvL2Norm({ 9.0f, 10.0f }) |
| 4021 | }; |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4022 | |
Matteo Martincigh | 539b44d | 2018-10-01 09:26:39 +0100 | [diff] [blame] | 4023 | return L2NormalizationTestImpl(workloadFactory, inputOutputShape, |
| 4024 | inputValues, expectedOutputValues, armnn::DataLayout::NHWC); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4025 | } |
| 4026 | |
| 4027 | LayerTestResult<float, 4> L2Normalization3dTest(armnn::IWorkloadFactory& workloadFactory) |
| 4028 | { |
Matteo Martincigh | 539b44d | 2018-10-01 09:26:39 +0100 | [diff] [blame] | 4029 | // Width: 3 |
| 4030 | // Height: 4 |
| 4031 | // Channels: 2 |
| 4032 | // BatchSize: 1 |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4033 | |
Matteo Martincigh | 539b44d | 2018-10-01 09:26:39 +0100 | [diff] [blame] | 4034 | const armnn::TensorShape inputOutputShape{ 1, 2, 4, 3 }; |
| 4035 | std::vector<float> inputValues |
| 4036 | { |
| 4037 | // Batch 0, Channel 0, Height (4) x Width (3) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4038 | 119.0f, 21.0f, 150.0f, |
| 4039 | 149.0f, 32.0f, 179.0f, |
| 4040 | 15.0f, 227.0f, 141.0f, |
| 4041 | 147.0f, 199.0f, 220.0f, |
| 4042 | |
Matteo Martincigh | 539b44d | 2018-10-01 09:26:39 +0100 | [diff] [blame] | 4043 | // Batch 0, Channel 1, Height (4) x Width (3) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4044 | 110.0f, 140.0f, 73.0f, |
| 4045 | 211.0f, 212.0f, 89.0f, |
| 4046 | 24.0f, 138.0f, 188.0f, |
Matteo Martincigh | 539b44d | 2018-10-01 09:26:39 +0100 | [diff] [blame] | 4047 | 162.0f, 12.0f, 161.0f |
| 4048 | }; |
| 4049 | std::vector<float> expectedOutputValues |
| 4050 | { |
| 4051 | // Batch 0, Channel 0, Height (4) x Width (3) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4052 | 119.0f * CalcInvL2Norm({ 119.0f, 110.0f }), |
| 4053 | 21.0f * CalcInvL2Norm({ 21.0f, 140.0f }), |
| 4054 | 150.0f * CalcInvL2Norm({ 150.0f, 73.0f }), |
| 4055 | 149.0f * CalcInvL2Norm({ 149.0f, 211.0f }), |
| 4056 | 32.0f * CalcInvL2Norm({ 32.0f, 212.0f }), |
| 4057 | 179.0f * CalcInvL2Norm({ 179.0f, 89.0f }), |
| 4058 | 15.0f * CalcInvL2Norm({ 15.0f, 24.0f }), |
| 4059 | 227.0f * CalcInvL2Norm({ 227.0f, 138.0f }), |
| 4060 | 141.0f * CalcInvL2Norm({ 141.0f, 188.0f }), |
| 4061 | 147.0f * CalcInvL2Norm({ 147.0f, 162.0f }), |
| 4062 | 199.0f * CalcInvL2Norm({ 199.0f, 12.0f }), |
| 4063 | 220.0f * CalcInvL2Norm({ 220.0f, 161.0f }), |
| 4064 | |
Matteo Martincigh | 539b44d | 2018-10-01 09:26:39 +0100 | [diff] [blame] | 4065 | // Batch 0, Channel 1, Height (4) x Width (3) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4066 | 110.0f * CalcInvL2Norm({ 119.0f, 110.0f }), |
| 4067 | 140.0f * CalcInvL2Norm({ 21.0f, 140.0f }), |
| 4068 | 73.0f * CalcInvL2Norm({ 150.0f, 73.0f }), |
| 4069 | 211.0f * CalcInvL2Norm({ 149.0f, 211.0f }), |
| 4070 | 212.0f * CalcInvL2Norm({ 32.0f, 212.0f }), |
| 4071 | 89.0f * CalcInvL2Norm({ 179.0f, 89.0f }), |
| 4072 | 24.0f * CalcInvL2Norm({ 15.0f, 24.0f }), |
| 4073 | 138.0f * CalcInvL2Norm({ 227.0f, 138.0f }), |
| 4074 | 188.0f * CalcInvL2Norm({ 141.0f, 188.0f }), |
| 4075 | 162.0f * CalcInvL2Norm({ 147.0f, 162.0f }), |
| 4076 | 12.0f * CalcInvL2Norm({ 199.0f, 12.0f }), |
Matteo Martincigh | 539b44d | 2018-10-01 09:26:39 +0100 | [diff] [blame] | 4077 | 161.0f * CalcInvL2Norm({ 220.0f, 161.0f }) |
| 4078 | }; |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4079 | |
Matteo Martincigh | 539b44d | 2018-10-01 09:26:39 +0100 | [diff] [blame] | 4080 | return L2NormalizationTestImpl(workloadFactory, inputOutputShape, |
| 4081 | inputValues, expectedOutputValues, armnn::DataLayout::NCHW); |
| 4082 | } |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4083 | |
Matteo Martincigh | 539b44d | 2018-10-01 09:26:39 +0100 | [diff] [blame] | 4084 | LayerTestResult<float, 4> L2Normalization3dNhwcTest(armnn::IWorkloadFactory& workloadFactory) |
| 4085 | { |
| 4086 | // Width: 3 |
| 4087 | // Height: 4 |
| 4088 | // Channels: 2 |
| 4089 | // BatchSize: 1 |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4090 | |
Matteo Martincigh | 539b44d | 2018-10-01 09:26:39 +0100 | [diff] [blame] | 4091 | const armnn::TensorShape inputOutputShape{ 1, 4, 3, 2 }; |
| 4092 | std::vector<float> inputValues |
| 4093 | { |
| 4094 | // Batch 0, Height 0, Width (3) x Channel (2) |
| 4095 | 119.0f, 110.0f, |
| 4096 | 21.0f, 140.0f, |
| 4097 | 150.0f, 73.0f, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4098 | |
Matteo Martincigh | 539b44d | 2018-10-01 09:26:39 +0100 | [diff] [blame] | 4099 | // Batch 0, Height 1, Width (3) x Channel (2) |
| 4100 | 149.0f, 211.0f, |
| 4101 | 32.0f, 212.0f, |
| 4102 | 179.0f, 89.0f, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4103 | |
Matteo Martincigh | 539b44d | 2018-10-01 09:26:39 +0100 | [diff] [blame] | 4104 | // Batch 0, Height 2, Width (3) x Channel (2) |
| 4105 | 15.0f, 24.0f, |
| 4106 | 227.0f, 138.0f, |
| 4107 | 141.0f, 188.0f, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4108 | |
Matteo Martincigh | 539b44d | 2018-10-01 09:26:39 +0100 | [diff] [blame] | 4109 | // Batch 0, Height 3, Width (3) x Channel (2) |
| 4110 | 147.0f, 162.0f, |
| 4111 | 199.0f, 12.0f, |
| 4112 | 220.0f, 161.0f |
| 4113 | }; |
| 4114 | std::vector<float> expectedOutputValues |
| 4115 | { |
| 4116 | // Batch 0, Height 0, Width (3) x Channel (2) |
| 4117 | 119.0f * CalcInvL2Norm({ 119.0f, 110.0f }), |
| 4118 | 110.0f * CalcInvL2Norm({ 119.0f, 110.0f }), |
| 4119 | 21.0f * CalcInvL2Norm({ 21.0f, 140.0f }), |
| 4120 | 140.0f * CalcInvL2Norm({ 21.0f, 140.0f }), |
| 4121 | 150.0f * CalcInvL2Norm({ 150.0f, 73.0f }), |
| 4122 | 73.0f * CalcInvL2Norm({ 150.0f, 73.0f }), |
| 4123 | |
| 4124 | // Batch 0, Height 1, Width (3) x Channel (2) |
| 4125 | 149.0f * CalcInvL2Norm({ 149.0f, 211.0f }), |
| 4126 | 211.0f * CalcInvL2Norm({ 149.0f, 211.0f }), |
| 4127 | 32.0f * CalcInvL2Norm({ 32.0f, 212.0f }), |
| 4128 | 212.0f * CalcInvL2Norm({ 32.0f, 212.0f }), |
| 4129 | 179.0f * CalcInvL2Norm({ 179.0f, 89.0f }), |
| 4130 | 89.0f * CalcInvL2Norm({ 179.0f, 89.0f }), |
| 4131 | |
| 4132 | // Batch 0, Height 2, Width (3) x Channel (2) |
| 4133 | 15.0f * CalcInvL2Norm({ 15.0f, 24.0f }), |
| 4134 | 24.0f * CalcInvL2Norm({ 15.0f, 24.0f }), |
| 4135 | 227.0f * CalcInvL2Norm({ 227.0f, 138.0f }), |
| 4136 | 138.0f * CalcInvL2Norm({ 227.0f, 138.0f }), |
| 4137 | 141.0f * CalcInvL2Norm({ 141.0f, 188.0f }), |
| 4138 | 188.0f * CalcInvL2Norm({ 141.0f, 188.0f }), |
| 4139 | |
| 4140 | // Batch 0, Height 3, Width (3) x Channel (2) |
| 4141 | 147.0f * CalcInvL2Norm({ 147.0f, 162.0f }), |
| 4142 | 162.0f * CalcInvL2Norm({ 147.0f, 162.0f }), |
| 4143 | 199.0f * CalcInvL2Norm({ 199.0f, 12.0f }), |
| 4144 | 12.0f * CalcInvL2Norm({ 199.0f, 12.0f }), |
| 4145 | 220.0f * CalcInvL2Norm({ 220.0f, 161.0f }), |
| 4146 | 161.0f * CalcInvL2Norm({ 220.0f, 161.0f }) |
| 4147 | }; |
| 4148 | |
| 4149 | return L2NormalizationTestImpl(workloadFactory, inputOutputShape, |
| 4150 | inputValues, expectedOutputValues, armnn::DataLayout::NHWC); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4151 | } |
| 4152 | |
| 4153 | LayerTestResult<float, 4> L2Normalization4dTest(armnn::IWorkloadFactory& workloadFactory) |
| 4154 | { |
Matteo Martincigh | 539b44d | 2018-10-01 09:26:39 +0100 | [diff] [blame] | 4155 | // Width: 3 |
| 4156 | // Height: 4 |
| 4157 | // Channels: 3 |
| 4158 | // BatchSize: 2 |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4159 | |
Matteo Martincigh | 539b44d | 2018-10-01 09:26:39 +0100 | [diff] [blame] | 4160 | const armnn::TensorShape inputOutputShape{ 2, 3, 4, 3 }; |
| 4161 | std::vector<float> inputValues |
| 4162 | { |
| 4163 | // Batch 0, Channel 0, Height (4) x Width (3) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4164 | 235.0f, 46.0f, 178.0f, |
| 4165 | 100.0f, 123.0f, 19.0f, |
| 4166 | 172.0f, 74.0f, 250.0f, |
| 4167 | 6.0f, 195.0f, 80.0f, |
| 4168 | |
Matteo Martincigh | 539b44d | 2018-10-01 09:26:39 +0100 | [diff] [blame] | 4169 | // Batch 0, Channel 1, Height (4) x Width (3) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4170 | 113.0f, 95.0f, 202.0f, |
| 4171 | 77.0f, 114.0f, 71.0f, |
| 4172 | 122.0f, 246.0f, 166.0f, |
| 4173 | 82.0f, 28.0f, 37.0f, |
| 4174 | |
Matteo Martincigh | 539b44d | 2018-10-01 09:26:39 +0100 | [diff] [blame] | 4175 | // Batch 0, Channel 2, Height (4) x Width (3) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4176 | 56.0f, 170.0f, 162.0f, |
| 4177 | 194.0f, 89.0f, 254.0f, |
| 4178 | 12.0f, 209.0f, 200.0f, |
| 4179 | 1.0f, 64.0f, 54.0f, |
| 4180 | |
Matteo Martincigh | 539b44d | 2018-10-01 09:26:39 +0100 | [diff] [blame] | 4181 | // Batch 1, Channel 0, Height (4) x Width (3) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4182 | 67.0f, 90.0f, 49.0f, |
| 4183 | 7.0f, 163.0f, 18.0f, |
| 4184 | 25.0f, 117.0f, 103.0f, |
| 4185 | 247.0f, 59.0f, 189.0f, |
| 4186 | |
Matteo Martincigh | 539b44d | 2018-10-01 09:26:39 +0100 | [diff] [blame] | 4187 | // Batch 1, Channel 1, Height (4) x Width (3) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4188 | 239.0f, 104.0f, 199.0f, |
| 4189 | 17.0f, 124.0f, 153.0f, |
| 4190 | 222.0f, 217.0f, 75.0f, |
| 4191 | 32.0f, 126.0f, 21.0f, |
| 4192 | |
Matteo Martincigh | 539b44d | 2018-10-01 09:26:39 +0100 | [diff] [blame] | 4193 | // Batch 1, Channel 2, Height (4) x Width (3) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4194 | 97.0f, 145.0f, 215.0f, |
| 4195 | 115.0f, 116.0f, 238.0f, |
| 4196 | 226.0f, 16.0f, 132.0f, |
Matteo Martincigh | 539b44d | 2018-10-01 09:26:39 +0100 | [diff] [blame] | 4197 | 92.0f, 125.0f, 88.0f |
| 4198 | }; |
| 4199 | std::vector<float> expectedOutputValues |
| 4200 | { |
| 4201 | // Batch 0, Channel 0, Height (4) x Width (3) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4202 | 235.0f * CalcInvL2Norm({ 235.0f, 113.0f, 56.0f }), |
| 4203 | 46.0f * CalcInvL2Norm({ 46.0f, 95.0f, 170.0f }), |
| 4204 | 178.0f * CalcInvL2Norm({ 178.0f, 202.0F, 162.0f }), |
| 4205 | 100.0f * CalcInvL2Norm({ 100.0f, 77.0f, 194.0f }), |
| 4206 | 123.0f * CalcInvL2Norm({ 123.0f, 114.0f, 89.0f }), |
| 4207 | 19.0f * CalcInvL2Norm({ 19.0f, 71.0f, 254.0f }), |
| 4208 | 172.0f * CalcInvL2Norm({ 172.0f, 122.0f, 12.0f }), |
| 4209 | 74.0f * CalcInvL2Norm({ 74.0f, 246.0f, 209.0f }), |
| 4210 | 250.0f * CalcInvL2Norm({ 250.0f, 166.0f, 200.0f }), |
| 4211 | 6.0f * CalcInvL2Norm({ 6.0f, 82.0f, 1.0f }), |
| 4212 | 195.0f * CalcInvL2Norm({ 195.0f, 28.0f, 64.0f }), |
| 4213 | 80.0f * CalcInvL2Norm({ 80.0f, 37.0f, 54.0f }), |
| 4214 | |
Matteo Martincigh | 539b44d | 2018-10-01 09:26:39 +0100 | [diff] [blame] | 4215 | // Batch 0, Channel 1, Height (4) x Width (3) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4216 | 113.0f * CalcInvL2Norm({ 235.0f, 113.0f, 56.0f }), |
| 4217 | 95.0f * CalcInvL2Norm({ 46.0f, 95.0f, 170.0f }), |
| 4218 | 202.0f * CalcInvL2Norm({ 178.0f, 202.0F, 162.0f }), |
| 4219 | 77.0f * CalcInvL2Norm({ 100.0f, 77.0f, 194.0f }), |
| 4220 | 114.0f * CalcInvL2Norm({ 123.0f, 114.0f, 89.0f }), |
| 4221 | 71.0f * CalcInvL2Norm({ 19.0f, 71.0f, 254.0f }), |
| 4222 | 122.0f * CalcInvL2Norm({ 172.0f, 122.0f, 12.0f }), |
| 4223 | 246.0f * CalcInvL2Norm({ 74.0f, 246.0f, 209.0f }), |
| 4224 | 166.0f * CalcInvL2Norm({ 250.0f, 166.0f, 200.0f }), |
| 4225 | 82.0f * CalcInvL2Norm({ 6.0f, 82.0f, 1.0f }), |
| 4226 | 28.0f * CalcInvL2Norm({ 195.0f, 28.0f, 64.0f }), |
| 4227 | 37.0f * CalcInvL2Norm({ 80.0f, 37.0f, 54.0f }), |
| 4228 | |
Matteo Martincigh | 539b44d | 2018-10-01 09:26:39 +0100 | [diff] [blame] | 4229 | // Batch 0, Channel 2, Height (4) x Width (3) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4230 | 56.0f * CalcInvL2Norm({ 235.0f, 113.0f, 56.0f }), |
| 4231 | 170.0f * CalcInvL2Norm({ 46.0f, 95.0f, 170.0f }), |
| 4232 | 162.0f * CalcInvL2Norm({ 178.0f, 202.0F, 162.0f }), |
| 4233 | 194.0f * CalcInvL2Norm({ 100.0f, 77.0f, 194.0f }), |
| 4234 | 89.0f * CalcInvL2Norm({ 123.0f, 114.0f, 89.0f }), |
| 4235 | 254.0f * CalcInvL2Norm({ 19.0f, 71.0f, 254.0f }), |
| 4236 | 12.0f * CalcInvL2Norm({ 172.0f, 122.0f, 12.0f }), |
| 4237 | 209.0f * CalcInvL2Norm({ 74.0f, 246.0f, 209.0f }), |
| 4238 | 200.0f * CalcInvL2Norm({ 250.0f, 166.0f, 200.0f }), |
| 4239 | 1.0f * CalcInvL2Norm({ 6.0f, 82.0f, 1.0f }), |
| 4240 | 64.0f * CalcInvL2Norm({ 195.0f, 28.0f, 64.0f }), |
| 4241 | 54.0f * CalcInvL2Norm({ 80.0f, 37.0f, 54.0f }), |
| 4242 | |
Matteo Martincigh | 539b44d | 2018-10-01 09:26:39 +0100 | [diff] [blame] | 4243 | // Batch 1, Channel 0, Height (4) x Width (3) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4244 | 67.0f * CalcInvL2Norm({ 67.0f, 239.0f, 97.0f }), |
| 4245 | 90.0f * CalcInvL2Norm({ 90.0f, 104.0f, 145.0f }), |
| 4246 | 49.0f * CalcInvL2Norm({ 49.0f, 199.0f, 215.0f }), |
| 4247 | 7.0f * CalcInvL2Norm({ 7.0f, 17.0f, 115.0f }), |
| 4248 | 163.0f * CalcInvL2Norm({ 163.0f, 124.0f, 116.0f }), |
| 4249 | 18.0f * CalcInvL2Norm({ 18.0f, 153.0f, 238.0f }), |
| 4250 | 25.0f * CalcInvL2Norm({ 25.0f, 222.0f, 226.0f }), |
| 4251 | 117.0f * CalcInvL2Norm({ 117.0f, 217.0f, 16.0f }), |
| 4252 | 103.0f * CalcInvL2Norm({ 103.0f, 75.0f, 132.0f }), |
| 4253 | 247.0f * CalcInvL2Norm({ 247.0f, 32.0f, 92.0f }), |
| 4254 | 59.0f * CalcInvL2Norm({ 59.0f, 126.0f, 125.0f }), |
| 4255 | 189.0f * CalcInvL2Norm({ 189.0f, 21.0f, 88.0f }), |
| 4256 | |
Matteo Martincigh | 539b44d | 2018-10-01 09:26:39 +0100 | [diff] [blame] | 4257 | // Batch 1, Channel 1, Height (4) x Width (3) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4258 | 239.0f * CalcInvL2Norm({ 67.0f, 239.0f, 97.0f }), |
| 4259 | 104.0f * CalcInvL2Norm({ 90.0f, 104.0f, 145.0f }), |
| 4260 | 199.0f * CalcInvL2Norm({ 49.0f, 199.0f, 215.0f }), |
| 4261 | 17.0f * CalcInvL2Norm({ 7.0f, 17.0f, 115.0f }), |
| 4262 | 124.0f * CalcInvL2Norm({ 163.0f, 124.0f, 116.0f }), |
| 4263 | 153.0f * CalcInvL2Norm({ 18.0f, 153.0f, 238.0f }), |
| 4264 | 222.0f * CalcInvL2Norm({ 25.0f, 222.0f, 226.0f }), |
| 4265 | 217.0f * CalcInvL2Norm({ 117.0f, 217.0f, 16.0f }), |
| 4266 | 75.0f * CalcInvL2Norm({ 103.0f, 75.0f, 132.0f }), |
| 4267 | 32.0f * CalcInvL2Norm({ 247.0f, 32.0f, 92.0f }), |
| 4268 | 126.0f * CalcInvL2Norm({ 59.0f, 126.0f, 125.0f }), |
| 4269 | 21.0f * CalcInvL2Norm({ 189.0f, 21.0f, 88.0f }), |
| 4270 | |
Matteo Martincigh | 539b44d | 2018-10-01 09:26:39 +0100 | [diff] [blame] | 4271 | // Batch 1, Channel 2, Height (4) x Width (3) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4272 | 97.0f * CalcInvL2Norm({ 67.0f, 239.0f, 97.0f }), |
| 4273 | 145.0f * CalcInvL2Norm({ 90.0f, 104.0f, 145.0f }), |
| 4274 | 215.0f * CalcInvL2Norm({ 49.0f, 199.0f, 215.0f }), |
| 4275 | 115.0f * CalcInvL2Norm({ 7.0f, 17.0f, 115.0f }), |
| 4276 | 116.0f * CalcInvL2Norm({ 163.0f, 124.0f, 116.0f }), |
| 4277 | 238.0f * CalcInvL2Norm({ 18.0f, 153.0f, 238.0f }), |
| 4278 | 226.0f * CalcInvL2Norm({ 25.0f, 222.0f, 226.0f }), |
| 4279 | 16.0f * CalcInvL2Norm({ 117.0f, 217.0f, 16.0f }), |
| 4280 | 132.0f * CalcInvL2Norm({ 103.0f, 75.0f, 132.0f }), |
| 4281 | 92.0f * CalcInvL2Norm({ 247.0f, 32.0f, 92.0f }), |
| 4282 | 125.0f * CalcInvL2Norm({ 59.0f, 126.0f, 125.0f }), |
Matteo Martincigh | 539b44d | 2018-10-01 09:26:39 +0100 | [diff] [blame] | 4283 | 88.0f * CalcInvL2Norm({ 189.0f, 21.0f, 88.0f }) |
| 4284 | }; |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4285 | |
Matteo Martincigh | 539b44d | 2018-10-01 09:26:39 +0100 | [diff] [blame] | 4286 | return L2NormalizationTestImpl(workloadFactory, inputOutputShape, |
| 4287 | inputValues, expectedOutputValues, armnn::DataLayout::NCHW); |
| 4288 | } |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4289 | |
Matteo Martincigh | 539b44d | 2018-10-01 09:26:39 +0100 | [diff] [blame] | 4290 | LayerTestResult<float, 4> L2Normalization4dNhwcTest(armnn::IWorkloadFactory& workloadFactory) |
| 4291 | { |
| 4292 | // Width: 3 |
| 4293 | // Height: 4 |
| 4294 | // Channels: 3 |
| 4295 | // BatchSize: 2 |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4296 | |
Matteo Martincigh | 539b44d | 2018-10-01 09:26:39 +0100 | [diff] [blame] | 4297 | const armnn::TensorShape inputOutputShape{ 2, 4, 3, 3 }; |
| 4298 | std::vector<float> inputValues |
| 4299 | { |
| 4300 | // Batch 0, Height 0, Width (3) x Channel (3) |
| 4301 | 235.0f, 113.0f, 56.0f, |
| 4302 | 46.0f, 95.0f, 170.0f, |
| 4303 | 178.0f, 202.0f, 162.0f, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4304 | |
Matteo Martincigh | 539b44d | 2018-10-01 09:26:39 +0100 | [diff] [blame] | 4305 | // Batch 0, Height 1, Width (3) x Channel (3) |
| 4306 | 100.0f, 77.0f, 194.0f, |
| 4307 | 123.0f, 114.0f, 89.0f, |
| 4308 | 19.0f, 71.0f, 254.0f, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4309 | |
Matteo Martincigh | 539b44d | 2018-10-01 09:26:39 +0100 | [diff] [blame] | 4310 | // Batch 0, Height 2, Width (3) x Channel (3) |
| 4311 | 172.0f, 122.0f, 12.0f, |
| 4312 | 74.0f, 246.0f, 209.0f, |
| 4313 | 250.0f, 166.0f, 200.0f, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4314 | |
Matteo Martincigh | 539b44d | 2018-10-01 09:26:39 +0100 | [diff] [blame] | 4315 | // Batch 0, Height 3, Width (3) x Channel (3) |
| 4316 | 6.0f, 82.0f, 1.0f, |
| 4317 | 195.0f, 28.0f, 64.0f, |
| 4318 | 80.0f, 37.0f, 54.0f, |
| 4319 | |
| 4320 | // Batch 1, Height 0, Width (3) x Channel (3) |
| 4321 | 67.0f, 239.0f, 97.0f, |
| 4322 | 90.0f, 104.0f, 145.0f, |
| 4323 | 49.0f, 199.0f, 215.0f, |
| 4324 | |
| 4325 | // Batch 1, Height 1, Width (3) x Channel (3) |
| 4326 | 7.0f, 17.0f, 115.0f, |
| 4327 | 163.0f, 124.0f, 116.0f, |
| 4328 | 18.0f, 153.0f, 238.0f, |
| 4329 | |
| 4330 | // Batch 1, Height 2, Width (3) x Channel (3) |
| 4331 | 25.0f, 222.0f, 226.0f, |
| 4332 | 117.0f, 217.0f, 16.0f, |
| 4333 | 103.0f, 75.0f, 132.0f, |
| 4334 | |
| 4335 | // Batch 1, Height 3, Width (3) x Channel (3) |
| 4336 | 247.0f, 32.0f, 92.0f, |
| 4337 | 59.0f, 126.0f, 125.0f, |
| 4338 | 189.0f, 21.0f, 88.0f |
| 4339 | }; |
| 4340 | std::vector<float> expectedOutputValues |
| 4341 | { |
| 4342 | // Batch 0, Height 0, Width (3) x Channel (3) |
| 4343 | 235.0f * CalcInvL2Norm({ 235.0f, 113.0f, 56.0f }), |
| 4344 | 113.0f * CalcInvL2Norm({ 235.0f, 113.0f, 56.0f }), |
| 4345 | 56.0f * CalcInvL2Norm({ 235.0f, 113.0f, 56.0f }), |
| 4346 | 46.0f * CalcInvL2Norm({ 46.0f, 95.0f, 170.0f }), |
| 4347 | 95.0f * CalcInvL2Norm({ 46.0f, 95.0f, 170.0f }), |
| 4348 | 170.0f * CalcInvL2Norm({ 46.0f, 95.0f, 170.0f }), |
| 4349 | 178.0f * CalcInvL2Norm({ 178.0f, 202.0F, 162.0f }), |
| 4350 | 202.0f * CalcInvL2Norm({ 178.0f, 202.0F, 162.0f }), |
| 4351 | 162.0f * CalcInvL2Norm({ 178.0f, 202.0F, 162.0f }), |
| 4352 | |
| 4353 | // Batch 0, Height 1, Width (3) x Channel (3) |
| 4354 | 100.0f * CalcInvL2Norm({ 100.0f, 77.0f, 194.0f }), |
| 4355 | 77.0f * CalcInvL2Norm({ 100.0f, 77.0f, 194.0f }), |
| 4356 | 194.0f * CalcInvL2Norm({ 100.0f, 77.0f, 194.0f }), |
| 4357 | 123.0f * CalcInvL2Norm({ 123.0f, 114.0f, 89.0f }), |
| 4358 | 114.0f * CalcInvL2Norm({ 123.0f, 114.0f, 89.0f }), |
| 4359 | 89.0f * CalcInvL2Norm({ 123.0f, 114.0f, 89.0f }), |
| 4360 | 19.0f * CalcInvL2Norm({ 19.0f, 71.0f, 254.0f }), |
| 4361 | 71.0f * CalcInvL2Norm({ 19.0f, 71.0f, 254.0f }), |
| 4362 | 254.0f * CalcInvL2Norm({ 19.0f, 71.0f, 254.0f }), |
| 4363 | |
| 4364 | // Batch 0, Height 2, Width (3) x Channel (3) |
| 4365 | 172.0f * CalcInvL2Norm({ 172.0f, 122.0f, 12.0f }), |
| 4366 | 122.0f * CalcInvL2Norm({ 172.0f, 122.0f, 12.0f }), |
| 4367 | 12.0f * CalcInvL2Norm({ 172.0f, 122.0f, 12.0f }), |
| 4368 | 74.0f * CalcInvL2Norm({ 74.0f, 246.0f, 209.0f }), |
| 4369 | 246.0f * CalcInvL2Norm({ 74.0f, 246.0f, 209.0f }), |
| 4370 | 209.0f * CalcInvL2Norm({ 74.0f, 246.0f, 209.0f }), |
| 4371 | 250.0f * CalcInvL2Norm({ 250.0f, 166.0f, 200.0f }), |
| 4372 | 166.0f * CalcInvL2Norm({ 250.0f, 166.0f, 200.0f }), |
| 4373 | 200.0f * CalcInvL2Norm({ 250.0f, 166.0f, 200.0f }), |
| 4374 | |
| 4375 | // Batch 0, Height 3, Width (3) x Channel (3) |
| 4376 | 6.0f * CalcInvL2Norm({ 6.0f, 82.0f, 1.0f }), |
| 4377 | 82.0f * CalcInvL2Norm({ 6.0f, 82.0f, 1.0f }), |
| 4378 | 1.0f * CalcInvL2Norm({ 6.0f, 82.0f, 1.0f }), |
| 4379 | 195.0f * CalcInvL2Norm({ 195.0f, 28.0f, 64.0f }), |
| 4380 | 28.0f * CalcInvL2Norm({ 195.0f, 28.0f, 64.0f }), |
| 4381 | 64.0f * CalcInvL2Norm({ 195.0f, 28.0f, 64.0f }), |
| 4382 | 80.0f * CalcInvL2Norm({ 80.0f, 37.0f, 54.0f }), |
| 4383 | 37.0f * CalcInvL2Norm({ 80.0f, 37.0f, 54.0f }), |
| 4384 | 54.0f * CalcInvL2Norm({ 80.0f, 37.0f, 54.0f }), |
| 4385 | |
| 4386 | // Batch 1, Height 0, Width (3) x Channel (3) |
| 4387 | 67.0f * CalcInvL2Norm({ 67.0f, 239.0f, 97.0f }), |
| 4388 | 239.0f * CalcInvL2Norm({ 67.0f, 239.0f, 97.0f }), |
| 4389 | 97.0f * CalcInvL2Norm({ 67.0f, 239.0f, 97.0f }), |
| 4390 | 90.0f * CalcInvL2Norm({ 90.0f, 104.0f, 145.0f }), |
| 4391 | 104.0f * CalcInvL2Norm({ 90.0f, 104.0f, 145.0f }), |
| 4392 | 145.0f * CalcInvL2Norm({ 90.0f, 104.0f, 145.0f }), |
| 4393 | 49.0f * CalcInvL2Norm({ 49.0f, 199.0f, 215.0f }), |
| 4394 | 199.0f * CalcInvL2Norm({ 49.0f, 199.0f, 215.0f }), |
| 4395 | 215.0f * CalcInvL2Norm({ 49.0f, 199.0f, 215.0f }), |
| 4396 | |
| 4397 | // Batch 1, Height 1, Width (3) x Channel (3) |
| 4398 | 7.0f * CalcInvL2Norm({ 7.0f, 17.0f, 115.0f }), |
| 4399 | 17.0f * CalcInvL2Norm({ 7.0f, 17.0f, 115.0f }), |
| 4400 | 115.0f * CalcInvL2Norm({ 7.0f, 17.0f, 115.0f }), |
| 4401 | 163.0f * CalcInvL2Norm({ 163.0f, 124.0f, 116.0f }), |
| 4402 | 124.0f * CalcInvL2Norm({ 163.0f, 124.0f, 116.0f }), |
| 4403 | 116.0f * CalcInvL2Norm({ 163.0f, 124.0f, 116.0f }), |
| 4404 | 18.0f * CalcInvL2Norm({ 18.0f, 153.0f, 238.0f }), |
| 4405 | 153.0f * CalcInvL2Norm({ 18.0f, 153.0f, 238.0f }), |
| 4406 | 238.0f * CalcInvL2Norm({ 18.0f, 153.0f, 238.0f }), |
| 4407 | |
| 4408 | // Batch 1, Height 2, Width (3) x Channel (3) |
| 4409 | 25.0f * CalcInvL2Norm({ 25.0f, 222.0f, 226.0f }), |
| 4410 | 222.0f * CalcInvL2Norm({ 25.0f, 222.0f, 226.0f }), |
| 4411 | 226.0f * CalcInvL2Norm({ 25.0f, 222.0f, 226.0f }), |
| 4412 | 117.0f * CalcInvL2Norm({ 117.0f, 217.0f, 16.0f }), |
| 4413 | 217.0f * CalcInvL2Norm({ 117.0f, 217.0f, 16.0f }), |
| 4414 | 16.0f * CalcInvL2Norm({ 117.0f, 217.0f, 16.0f }), |
| 4415 | 103.0f * CalcInvL2Norm({ 103.0f, 75.0f, 132.0f }), |
| 4416 | 75.0f * CalcInvL2Norm({ 103.0f, 75.0f, 132.0f }), |
| 4417 | 132.0f * CalcInvL2Norm({ 103.0f, 75.0f, 132.0f }), |
| 4418 | |
| 4419 | // Batch 1, Height 3, Width (3) x Channel (3) |
| 4420 | 247.0f * CalcInvL2Norm({ 247.0f, 32.0f, 92.0f }), |
| 4421 | 32.0f * CalcInvL2Norm({ 247.0f, 32.0f, 92.0f }), |
| 4422 | 92.0f * CalcInvL2Norm({ 247.0f, 32.0f, 92.0f }), |
| 4423 | 59.0f * CalcInvL2Norm({ 59.0f, 126.0f, 125.0f }), |
| 4424 | 126.0f * CalcInvL2Norm({ 59.0f, 126.0f, 125.0f }), |
| 4425 | 125.0f * CalcInvL2Norm({ 59.0f, 126.0f, 125.0f }), |
| 4426 | 189.0f * CalcInvL2Norm({ 189.0f, 21.0f, 88.0f }), |
| 4427 | 21.0f * CalcInvL2Norm({ 189.0f, 21.0f, 88.0f }), |
| 4428 | 88.0f * CalcInvL2Norm({ 189.0f, 21.0f, 88.0f }) |
| 4429 | }; |
| 4430 | |
| 4431 | return L2NormalizationTestImpl(workloadFactory, inputOutputShape, |
| 4432 | inputValues, expectedOutputValues, armnn::DataLayout::NHWC); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4433 | } |
| 4434 | |
| 4435 | template <typename T> |
| 4436 | LayerTestResult<T, 4> ConstantTestImpl(armnn::IWorkloadFactory& workloadFactory, |
| 4437 | float qScale, |
| 4438 | int32_t qOffset) |
| 4439 | { |
| 4440 | constexpr unsigned int inputWidth = 3; |
| 4441 | constexpr unsigned int inputHeight = 4; |
| 4442 | constexpr unsigned int inputChannels = 3; |
| 4443 | constexpr unsigned int inputBatchSize = 2; |
| 4444 | |
| 4445 | constexpr unsigned int outputWidth = inputWidth; |
| 4446 | constexpr unsigned int outputHeight = inputHeight; |
| 4447 | constexpr unsigned int outputChannels = inputChannels; |
| 4448 | constexpr unsigned int outputBatchSize = inputBatchSize; |
| 4449 | |
| 4450 | armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, |
| 4451 | armnn::GetDataType<T>()); |
| 4452 | |
| 4453 | armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, |
| 4454 | armnn::GetDataType<T>()); |
| 4455 | |
| 4456 | // Set quantization parameters if the requested type is a quantized type. |
| 4457 | if(armnn::IsQuantizedType<T>()) |
| 4458 | { |
| 4459 | inputTensorInfo.SetQuantizationScale(qScale); |
| 4460 | inputTensorInfo.SetQuantizationOffset(qOffset); |
| 4461 | outputTensorInfo.SetQuantizationScale(qScale); |
| 4462 | outputTensorInfo.SetQuantizationOffset(qOffset); |
| 4463 | } |
| 4464 | |
| 4465 | auto input = MakeTensor<T, 4>(inputTensorInfo, std::vector<T>( |
| 4466 | QuantizedVector<T>(qScale, qOffset, { |
| 4467 | // Batch 0, Channel 0 |
| 4468 | 235.0f, 46.0f, 178.0f, |
| 4469 | 100.0f, 123.0f, 19.0f, |
| 4470 | 172.0f, 74.0f, 250.0f, |
| 4471 | 6.0f, 195.0f, 80.0f, |
| 4472 | |
| 4473 | // Batch 0, Channel 1 |
| 4474 | 113.0f, 95.0f, 202.0f, |
| 4475 | 77.0f, 114.0f, 71.0f, |
| 4476 | 122.0f, 246.0f, 166.0f, |
| 4477 | 82.0f, 28.0f, 37.0f, |
| 4478 | |
| 4479 | // Batch 0, Channel 2 |
| 4480 | 56.0f, 170.0f, 162.0f, |
| 4481 | 194.0f, 89.0f, 254.0f, |
| 4482 | 12.0f, 209.0f, 200.0f, |
| 4483 | 1.0f, 64.0f, 54.0f, |
| 4484 | |
| 4485 | // Batch 1, Channel 0 |
| 4486 | 67.0f, 90.0f, 49.0f, |
| 4487 | 7.0f, 163.0f, 18.0f, |
| 4488 | 25.0f, 117.0f, 103.0f, |
| 4489 | 247.0f, 59.0f, 189.0f, |
| 4490 | |
| 4491 | // Batch 1, Channel 1 |
| 4492 | 239.0f, 104.0f, 199.0f, |
| 4493 | 17.0f, 124.0f, 153.0f, |
| 4494 | 222.0f, 217.0f, 75.0f, |
| 4495 | 32.0f, 126.0f, 21.0f, |
| 4496 | |
| 4497 | // Batch 1, Channel 2 |
| 4498 | 97.0f, 145.0f, 215.0f, |
| 4499 | 115.0f, 116.0f, 238.0f, |
| 4500 | 226.0f, 16.0f, 132.0f, |
| 4501 | 92.0f, 125.0f, 88.0f, |
| 4502 | }))); |
| 4503 | |
| 4504 | LayerTestResult<T, 4> result(outputTensorInfo); |
| 4505 | result.outputExpected = input; |
| 4506 | |
| 4507 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 4508 | |
| 4509 | armnn::ScopedCpuTensorHandle constantTensor(inputTensorInfo); |
| 4510 | AllocateAndCopyDataToITensorHandle(&constantTensor, &input[0][0][0][0]); |
| 4511 | |
| 4512 | armnn::ConstantQueueDescriptor descriptor; |
| 4513 | descriptor.m_LayerOutput = &constantTensor; |
| 4514 | |
| 4515 | armnn::WorkloadInfo info; |
| 4516 | AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); |
| 4517 | |
| 4518 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateConstant(descriptor, info); |
| 4519 | |
| 4520 | outputHandle->Allocate(); |
| 4521 | |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 4522 | workloadFactory.Finalize(); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4523 | workload->Execute(); |
| 4524 | |
| 4525 | CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); |
| 4526 | return result; |
| 4527 | } |
| 4528 | |
| 4529 | LayerTestResult<float, 4> ConstantTest(armnn::IWorkloadFactory& workloadFactory) |
| 4530 | { |
| 4531 | return ConstantTestImpl<float>(workloadFactory, 0.0f, 0); |
| 4532 | } |
| 4533 | |
| 4534 | LayerTestResult<uint8_t, 4> ConstantTestUint8(armnn::IWorkloadFactory& workloadFactory) |
| 4535 | { |
| 4536 | return ConstantTestImpl<uint8_t>(workloadFactory, 1.0f, 0); |
| 4537 | } |
| 4538 | |
| 4539 | LayerTestResult<uint8_t, 3> MergerUint8Test(armnn::IWorkloadFactory& workloadFactory) |
| 4540 | { |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 4541 | unsigned int outputWidth = 3; |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4542 | unsigned int outputHeight = 6; |
| 4543 | unsigned int outputChannels = 3; |
| 4544 | |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 4545 | unsigned int inputWidth1 = 3; |
| 4546 | unsigned int inputHeight1 = 6; |
| 4547 | unsigned int inputChannels1 = 2; |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4548 | |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 4549 | unsigned int inputWidth2 = 3; |
| 4550 | unsigned int inputHeight2 = 6; |
| 4551 | unsigned int inputChannels2 = 1; |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4552 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 4553 | // Defines the tensor descriptors. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4554 | armnn::TensorInfo outputTensorInfo({ outputChannels, outputHeight, outputWidth }, armnn::DataType::QuantisedAsymm8); |
| 4555 | armnn::TensorInfo inputTensorInfo1({ inputChannels1, inputHeight1, inputWidth1 }, armnn::DataType::QuantisedAsymm8); |
| 4556 | armnn::TensorInfo inputTensorInfo2({ inputChannels2, inputHeight2, inputWidth2 }, armnn::DataType::QuantisedAsymm8); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4557 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 4558 | // Arbitrary scale and offsets. They don't really matter as the merger operator doesn't dequantize/quantize them. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4559 | const float scale = 0.13497836f; |
| 4560 | const int32_t offset = -7; |
| 4561 | |
| 4562 | outputTensorInfo.SetQuantizationScale(scale); |
| 4563 | outputTensorInfo.SetQuantizationOffset(offset); |
| 4564 | inputTensorInfo1.SetQuantizationScale(scale); |
| 4565 | inputTensorInfo1.SetQuantizationOffset(offset); |
| 4566 | inputTensorInfo2.SetQuantizationScale(scale); |
| 4567 | inputTensorInfo2.SetQuantizationOffset(offset); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4568 | |
| 4569 | LayerTestResult<uint8_t, 3> ret(outputTensorInfo); |
| 4570 | |
| 4571 | ret.outputExpected = MakeTensor<uint8_t, 3>(outputTensorInfo, std::vector<uint8_t>( |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 4572 | { |
| 4573 | 1, 2, 3, |
| 4574 | 4, 5, 6, |
| 4575 | 7, 8, 9, |
| 4576 | 10, 11, 12, |
| 4577 | 13, 14, 15, |
| 4578 | 16, 17, 18, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4579 | |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 4580 | 19, 20, 21, |
| 4581 | 22, 23, 24, |
| 4582 | 25, 26, 27, |
| 4583 | 28, 29, 30, |
| 4584 | 31, 32, 33, |
| 4585 | 34, 35, 36, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4586 | |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 4587 | 37, 38, 39, |
| 4588 | 40, 41, 42, |
| 4589 | 43, 44, 45, |
| 4590 | 46, 47, 48, |
| 4591 | 49, 50, 51, |
| 4592 | 52, 53, 54, |
| 4593 | }) |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4594 | ); |
| 4595 | |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4596 | auto input1 = MakeTensor<uint8_t, 3>(inputTensorInfo1, std::vector<uint8_t>( |
| 4597 | { |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 4598 | 1, 2, 3, |
| 4599 | 4, 5, 6, |
| 4600 | 7, 8, 9, |
| 4601 | 10, 11, 12, |
| 4602 | 13, 14, 15, |
| 4603 | 16, 17, 18, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4604 | |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 4605 | 19, 20, 21, |
| 4606 | 22, 23, 24, |
| 4607 | 25, 26, 27, |
| 4608 | 28, 29, 30, |
| 4609 | 31, 32, 33, |
| 4610 | 34, 35, 36, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4611 | }) |
| 4612 | ); |
| 4613 | |
| 4614 | auto input2 = MakeTensor<uint8_t, 3>(inputTensorInfo2, std::vector<uint8_t>( |
| 4615 | { |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 4616 | 37, 38, 39, |
| 4617 | 40, 41, 42, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4618 | 43, 44, 45, |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 4619 | 46, 47, 48, |
| 4620 | 49, 50, 51, |
| 4621 | 52, 53, 54, |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4622 | }) |
| 4623 | ); |
| 4624 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 4625 | std::vector<unsigned int> wOrigin1 = { 0, 0, 0 }; //Extent of the window is defined by size of input[0]. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4626 | armnn::MergerQueueDescriptor::ViewOrigin window1(wOrigin1); |
| 4627 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 4628 | std::vector<unsigned int> wOrigin2 = { 2, 0, 0 }; //Extent of the window is defined by size of input[1]. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4629 | armnn::MergerQueueDescriptor::ViewOrigin window2(wOrigin2); |
| 4630 | |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4631 | |
| 4632 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 4633 | |
| 4634 | bool subTensorsSupported = workloadFactory.SupportsSubTensors(); |
| 4635 | |
| 4636 | std::unique_ptr<armnn::ITensorHandle> inputHandle1 = |
| 4637 | subTensorsSupported ? |
| 4638 | workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo1.GetShape(), wOrigin1.data()) : |
| 4639 | workloadFactory.CreateTensorHandle(inputTensorInfo1); |
| 4640 | |
| 4641 | std::unique_ptr<armnn::ITensorHandle> inputHandle2 = |
| 4642 | subTensorsSupported ? |
| 4643 | workloadFactory.CreateSubTensorHandle(*outputHandle, inputTensorInfo2.GetShape(), wOrigin2.data()) : |
| 4644 | workloadFactory.CreateTensorHandle(inputTensorInfo2); |
| 4645 | |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4646 | |
| 4647 | armnn::MergerQueueDescriptor data; |
| 4648 | armnn::WorkloadInfo info; |
| 4649 | AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); |
| 4650 | AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get()); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4651 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 4652 | |
| 4653 | data.m_ViewOrigins.push_back(window1); |
| 4654 | data.m_ViewOrigins.push_back(window2); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4655 | |
| 4656 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateMerger(data, info); |
| 4657 | |
| 4658 | inputHandle1->Allocate(); |
| 4659 | inputHandle2->Allocate(); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4660 | outputHandle->Allocate(); |
| 4661 | |
| 4662 | CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0]); |
| 4663 | CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0]); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4664 | |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 4665 | workloadFactory.Finalize(); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4666 | workload->Execute(); |
| 4667 | |
| 4668 | CopyDataFromITensorHandle(&ret.output[0][0][0], outputHandle.get()); |
| 4669 | |
| 4670 | return ret; |
| 4671 | } |
| 4672 | |
| 4673 | LayerTestResult<uint8_t, 4> AdditionUint8Test(armnn::IWorkloadFactory& workloadFactory) |
| 4674 | { |
| 4675 | unsigned int batchSize = 1; |
| 4676 | unsigned int channels = 2; |
| 4677 | unsigned int height = 2; |
| 4678 | unsigned int width = 3; |
| 4679 | |
| 4680 | const float scale = 7.0f; |
| 4681 | const int32_t offset = 3; |
| 4682 | |
| 4683 | armnn::TensorInfo inputTensorInfo1, inputTensorInfo2; |
| 4684 | armnn::TensorInfo outputTensorInfo; |
| 4685 | |
| 4686 | const unsigned int shape[] = { batchSize, channels, height, width }; |
| 4687 | inputTensorInfo1 = armnn::TensorInfo(4, shape, armnn::DataType::QuantisedAsymm8); |
| 4688 | inputTensorInfo1.SetQuantizationScale(scale); |
| 4689 | inputTensorInfo1.SetQuantizationOffset(offset); |
| 4690 | |
| 4691 | inputTensorInfo2 = armnn::TensorInfo(4, shape, armnn::DataType::QuantisedAsymm8); |
| 4692 | inputTensorInfo2.SetQuantizationScale(scale); |
| 4693 | inputTensorInfo2.SetQuantizationOffset(offset); |
| 4694 | |
| 4695 | outputTensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::QuantisedAsymm8); |
| 4696 | outputTensorInfo.SetQuantizationScale(scale); |
| 4697 | outputTensorInfo.SetQuantizationOffset(offset); |
| 4698 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 4699 | // See dequantized values to the right. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4700 | auto input1 = MakeTensor<uint8_t, 4>(inputTensorInfo1, std::vector<uint8_t>( |
| 4701 | { |
| 4702 | 63, 35, 77, 70, 56, 112, // 420, 224, 518, 469, 371, 763 |
| 4703 | 203, 28, 252, 168, 245, 91 // 1400, 175, 1743, 1155, 1694, 616 |
| 4704 | })); |
| 4705 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 4706 | // See dequantized values to the right. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4707 | auto input2 = MakeTensor<uint8_t, 4>(inputTensorInfo1, std::vector<uint8_t>( |
| 4708 | { |
| 4709 | 21, 7, 175, 231, 175, 210, // 126, 28, 1204, 1596, 1204, 1449 |
| 4710 | 126, 161, 63, 21, 105, 126 // 861, 1106, 420, 126, 714, 861 |
| 4711 | })); |
| 4712 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 4713 | // See dequantized values to the right. |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4714 | LayerTestResult<uint8_t, 4> result(outputTensorInfo); |
| 4715 | result.outputExpected = MakeTensor<uint8_t, 4>(outputTensorInfo, std::vector<uint8_t>( |
| 4716 | { |
| 4717 | 81, 39, 249, 255, 228, 255, // 546, 252, 1722, 2065(clamped), 1575, 2212(clamped) |
| 4718 | 255, 186, 255, 186, 255, 214, // 2261(clamped), 1281, 2163(clamped), 1281, 2408(clamped), 1477 |
| 4719 | })); |
| 4720 | |
| 4721 | std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); |
| 4722 | std::unique_ptr<armnn::ITensorHandle> inputHandle2 = workloadFactory.CreateTensorHandle(inputTensorInfo2); |
| 4723 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 4724 | |
| 4725 | armnn::AdditionQueueDescriptor data; |
| 4726 | armnn::WorkloadInfo info; |
| 4727 | AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); |
| 4728 | AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get()); |
| 4729 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 4730 | |
| 4731 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateAddition(data, info); |
| 4732 | |
| 4733 | inputHandle1->Allocate(); |
| 4734 | inputHandle2->Allocate(); |
| 4735 | outputHandle->Allocate(); |
| 4736 | |
| 4737 | CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]); |
| 4738 | CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0][0]); |
| 4739 | |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 4740 | workloadFactory.Finalize(); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4741 | workload->Execute(); |
| 4742 | |
| 4743 | CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); |
| 4744 | |
| 4745 | return result; |
| 4746 | } |
| 4747 | |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 4748 | namespace |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4749 | { |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 4750 | LayerTestResult<uint8_t, 4> MultiplicationUint8TestHelper(armnn::IWorkloadFactory& workloadFactory, |
| 4751 | const unsigned int shape0[4], |
| 4752 | const std::vector<uint8_t> & values0, |
| 4753 | float scale0, |
| 4754 | int32_t offset0, |
| 4755 | const unsigned int shape1[4], |
| 4756 | const std::vector<uint8_t> & values1, |
| 4757 | float scale1, |
| 4758 | int32_t offset1, |
| 4759 | const unsigned int outShape[4], |
| 4760 | const std::vector<uint8_t> & outValues, |
| 4761 | float outScale, |
| 4762 | int32_t outOffset) |
| 4763 | { |
| 4764 | armnn::TensorInfo inputTensorInfo0(4, shape0, armnn::DataType::QuantisedAsymm8); |
| 4765 | armnn::TensorInfo inputTensorInfo1(4, shape1, armnn::DataType::QuantisedAsymm8); |
| 4766 | armnn::TensorInfo outputTensorInfo(4, outShape, armnn::DataType::QuantisedAsymm8); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4767 | |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 4768 | inputTensorInfo0.SetQuantizationScale(scale0); |
| 4769 | inputTensorInfo0.SetQuantizationOffset(offset0); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4770 | |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 4771 | inputTensorInfo1.SetQuantizationScale(scale1); |
| 4772 | inputTensorInfo1.SetQuantizationOffset(offset1); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4773 | |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 4774 | outputTensorInfo.SetQuantizationScale(outScale); |
| 4775 | outputTensorInfo.SetQuantizationOffset(outOffset); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4776 | |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 4777 | auto input0 = MakeTensor<uint8_t, 4>(inputTensorInfo0, values0); |
| 4778 | auto input1 = MakeTensor<uint8_t, 4>(inputTensorInfo1, values1); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4779 | |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4780 | LayerTestResult<uint8_t, 4> result(outputTensorInfo); |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 4781 | result.outputExpected = MakeTensor<uint8_t, 4>(outputTensorInfo, outValues); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4782 | |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 4783 | std::unique_ptr<armnn::ITensorHandle> inputHandle0 = workloadFactory.CreateTensorHandle(inputTensorInfo0); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4784 | std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4785 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 4786 | |
| 4787 | armnn::MultiplicationQueueDescriptor data; |
| 4788 | armnn::WorkloadInfo info; |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 4789 | AddInputToWorkload(data, info, inputTensorInfo0, inputHandle0.get()); |
| 4790 | AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4791 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 4792 | |
| 4793 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateMultiplication(data, info); |
| 4794 | |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 4795 | inputHandle0->Allocate(); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4796 | inputHandle1->Allocate(); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4797 | outputHandle->Allocate(); |
| 4798 | |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 4799 | CopyDataToITensorHandle(inputHandle0.get(), &input0[0][0][0][0]); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4800 | CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4801 | |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 4802 | workloadFactory.Finalize(); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4803 | workload->Execute(); |
| 4804 | |
| 4805 | CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); |
| 4806 | |
| 4807 | return result; |
| 4808 | } |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 4809 | } // anonymous namespace |
| 4810 | |
| 4811 | LayerTestResult<uint8_t, 4> MultiplicationUint8Test(armnn::IWorkloadFactory& workloadFactory) |
| 4812 | { |
| 4813 | unsigned int batchSize = 1; |
| 4814 | unsigned int channels = 2; |
| 4815 | unsigned int height = 2; |
| 4816 | unsigned int width = 3; |
| 4817 | const unsigned int shape[] = { batchSize, channels, height, width }; |
| 4818 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 4819 | // See dequantized values to the right. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 4820 | std::vector<uint8_t> input0({ |
| 4821 | 62, 37, 3, 172, 13, 111, // 244, 144, 8, 684, 48, 440, |
| 4822 | 188, 20, 73, 31, 23, 31 // 748, 76, 288, 120, 88, 120 |
| 4823 | }); |
| 4824 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 4825 | // See dequantized values to the right. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 4826 | std::vector<uint8_t> input1({ |
| 4827 | 126, 240, 252, 183, 121, 247, // 384, 726, 762, 555, 369, 747, |
| 4828 | 48, 115, 151, 79, 78, 97 // 150, 351, 459, 243, 240, 297 |
| 4829 | }); |
| 4830 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 4831 | // See dequantized values to the right. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 4832 | std::vector<uint8_t> output( |
| 4833 | { |
| 4834 | 64, 72, 0, 255, 8, 236, // 93696, 104544, 6096(clamped), 379620(clamped), 17712, 328680, |
| 4835 | 77, 15, 92, 16, 10, 21, // 112200, 26676, 132192, 29160, 21120, 35640 |
| 4836 | }); |
| 4837 | |
| 4838 | return MultiplicationUint8TestHelper(workloadFactory, |
| 4839 | shape, |
| 4840 | input0, |
| 4841 | 4.0f, |
| 4842 | 1, |
| 4843 | shape, |
| 4844 | input1, |
| 4845 | 3.0f, |
| 4846 | -2, |
| 4847 | shape, |
| 4848 | output, |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 4849 | 1366.255f, // Scale/offset chosen to have output values out of range. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 4850 | -5); |
| 4851 | } |
| 4852 | |
| 4853 | LayerTestResult<uint8_t, 4> MultiplicationBroadcast1ElementUint8Test(armnn::IWorkloadFactory& workloadFactory) |
| 4854 | { |
| 4855 | const unsigned int shape0[] = { 1, 2, 2, 3 }; |
| 4856 | const unsigned int shape1[] = { 1, 1, 1, 1 }; |
| 4857 | |
| 4858 | std::vector<uint8_t> input0({ |
| 4859 | 1, 2, 3, 4, 5, 6, |
| 4860 | 7, 8, 9, 10, 11, 12 |
| 4861 | }); |
| 4862 | |
| 4863 | std::vector<uint8_t> input1({2}); |
| 4864 | |
| 4865 | std::vector<uint8_t> output({ |
| 4866 | 2, 4, 6, 8, 10, 12, |
| 4867 | 14, 16, 18, 20, 22, 24 |
| 4868 | }); |
| 4869 | |
| 4870 | return MultiplicationUint8TestHelper(workloadFactory, |
| 4871 | shape0, |
| 4872 | input0, |
| 4873 | 1.0f, |
| 4874 | 0, |
| 4875 | shape1, |
| 4876 | input1, |
| 4877 | 1.0f, |
| 4878 | 0, |
| 4879 | shape0, |
| 4880 | output, |
| 4881 | 1.0f, |
| 4882 | 0); |
| 4883 | } |
| 4884 | |
| 4885 | LayerTestResult<uint8_t, 4> MultiplicationBroadcast1DVectorUint8Test(armnn::IWorkloadFactory& workloadFactory) |
| 4886 | { |
| 4887 | const unsigned int shape0[] = { 1, 2, 2, 3 }; |
| 4888 | const unsigned int shape1[] = { 1, 1, 1, 3 }; |
| 4889 | |
| 4890 | std::vector<uint8_t> input0({ |
| 4891 | 1, 2, 3, 4, 5, 6, |
| 4892 | 7, 8, 9, 10, 11, 12 |
| 4893 | }); |
| 4894 | |
| 4895 | std::vector<uint8_t> input1({1, 2, 3}); |
| 4896 | |
| 4897 | std::vector<uint8_t> output({ |
| 4898 | 1, 4, 9, 4, 10, 18, |
| 4899 | 7, 16, 27, 10, 22, 36 |
| 4900 | }); |
| 4901 | |
| 4902 | return MultiplicationUint8TestHelper(workloadFactory, |
| 4903 | shape0, |
| 4904 | input0, |
| 4905 | 1.0f, |
| 4906 | 0, |
| 4907 | shape1, |
| 4908 | input1, |
| 4909 | 1.0f, |
| 4910 | 0, |
| 4911 | shape0, |
| 4912 | output, |
| 4913 | 1.0f, |
| 4914 | 0); |
| 4915 | } |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 4916 | |
David Beck | f195f03 | 2018-09-06 16:46:34 +0100 | [diff] [blame] | 4917 | namespace |
| 4918 | { |
| 4919 | template <typename T> |
| 4920 | LayerTestResult<T, 4> SubtractionTestHelper(armnn::IWorkloadFactory& workloadFactory, |
| 4921 | const unsigned int shape0[4], |
| 4922 | const std::vector<T>& values0, |
| 4923 | float scale0, |
| 4924 | int32_t offset0, |
| 4925 | const unsigned int shape1[4], |
| 4926 | const std::vector<T> & values1, |
| 4927 | float scale1, |
| 4928 | int32_t offset1, |
| 4929 | const unsigned int outShape[4], |
| 4930 | const std::vector<T> & outValues, |
| 4931 | float outScale, |
| 4932 | int32_t outOffset) |
| 4933 | { |
| 4934 | auto dataType = (std::is_same<T, uint8_t>::value ? |
| 4935 | armnn::DataType::QuantisedAsymm8 : |
| 4936 | armnn::DataType::Float32); |
| 4937 | |
| 4938 | armnn::TensorInfo inputTensorInfo0(4, shape0, dataType); |
| 4939 | armnn::TensorInfo inputTensorInfo1(4, shape1, dataType); |
| 4940 | armnn::TensorInfo outputTensorInfo(4, outShape, dataType); |
| 4941 | |
| 4942 | inputTensorInfo0.SetQuantizationScale(scale0); |
| 4943 | inputTensorInfo0.SetQuantizationOffset(offset0); |
| 4944 | |
| 4945 | inputTensorInfo1.SetQuantizationScale(scale1); |
| 4946 | inputTensorInfo1.SetQuantizationOffset(offset1); |
| 4947 | |
| 4948 | outputTensorInfo.SetQuantizationScale(outScale); |
| 4949 | outputTensorInfo.SetQuantizationOffset(outOffset); |
| 4950 | |
| 4951 | auto input0 = MakeTensor<T, 4>(inputTensorInfo0, values0); |
| 4952 | auto input1 = MakeTensor<T, 4>(inputTensorInfo1, values1); |
| 4953 | |
| 4954 | LayerTestResult<T, 4> result(outputTensorInfo); |
| 4955 | result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outValues); |
| 4956 | |
| 4957 | std::unique_ptr<armnn::ITensorHandle> inputHandle0 = workloadFactory.CreateTensorHandle(inputTensorInfo0); |
| 4958 | std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); |
| 4959 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 4960 | |
| 4961 | armnn::SubtractionQueueDescriptor data; |
| 4962 | armnn::WorkloadInfo info; |
| 4963 | AddInputToWorkload(data, info, inputTensorInfo0, inputHandle0.get()); |
| 4964 | AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); |
| 4965 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 4966 | |
| 4967 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateSubtraction(data, info); |
| 4968 | |
| 4969 | inputHandle0->Allocate(); |
| 4970 | inputHandle1->Allocate(); |
| 4971 | outputHandle->Allocate(); |
| 4972 | |
| 4973 | CopyDataToITensorHandle(inputHandle0.get(), &input0[0][0][0][0]); |
| 4974 | CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]); |
| 4975 | |
| 4976 | workloadFactory.Finalize(); |
| 4977 | workload->Execute(); |
| 4978 | |
| 4979 | CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); |
| 4980 | |
| 4981 | return result; |
| 4982 | } |
| 4983 | } // anonymous namespace |
| 4984 | |
| 4985 | LayerTestResult<uint8_t, 4> SubtractionUint8Test(armnn::IWorkloadFactory& workloadFactory) |
| 4986 | { |
| 4987 | const unsigned int shape0[] = { 1, 1, 2, 2 }; |
| 4988 | const unsigned int shape1[] = { 1, 1, 2, 2 }; |
| 4989 | |
| 4990 | std::vector<uint8_t> input0({ 10, 12, 14, 16 }); |
| 4991 | std::vector<uint8_t> input1({ 1, 2, 1, 2 }); |
| 4992 | std::vector<uint8_t> output({ 3, 3, 5, 5 }); |
| 4993 | |
| 4994 | return SubtractionTestHelper(workloadFactory, |
| 4995 | shape0, input0, 0.5f, 2, |
| 4996 | shape1, input1, 1.0f, 0, |
| 4997 | shape0, output, 1.0f, 0); |
| 4998 | } |
| 4999 | |
| 5000 | LayerTestResult<uint8_t, 4> SubtractionBroadcast1ElementUint8Test(armnn::IWorkloadFactory& workloadFactory) |
| 5001 | { |
| 5002 | const unsigned int shape0[] = { 1, 1, 2, 2 }; |
| 5003 | const unsigned int shape1[] = { 1, 1, 1, 1 }; |
| 5004 | |
| 5005 | std::vector<uint8_t> input0({ 10, 12, 14, 16 }); |
| 5006 | std::vector<uint8_t> input1({ 2 }); |
| 5007 | std::vector<uint8_t> output({ 5, 6, 7, 8 }); |
| 5008 | |
| 5009 | return SubtractionTestHelper(workloadFactory, |
| 5010 | shape0, input0, 0.5f, 2, |
| 5011 | shape1, input1, 1.0f, 0, |
| 5012 | shape0, output, 1.0f, 3); |
| 5013 | } |
| 5014 | |
| 5015 | LayerTestResult<uint8_t, 4> SubtractionBroadcastUint8Test(armnn::IWorkloadFactory& workloadFactory) |
| 5016 | { |
| 5017 | const unsigned int shape0[] = { 1, 1, 2, 2 }; |
| 5018 | const unsigned int shape1[] = { 1, 1, 2, 1 }; |
| 5019 | |
| 5020 | std::vector<uint8_t> input0({ 10, 12, 14, 16 }); |
| 5021 | std::vector<uint8_t> input1({ 2, 1 }); |
| 5022 | std::vector<uint8_t> output({ 8, 11, 12, 15 }); |
| 5023 | |
| 5024 | return SubtractionTestHelper(workloadFactory, |
| 5025 | shape0, input0, 1.0f, 0, |
| 5026 | shape1, input1, 1.0f, 0, |
| 5027 | shape0, output, 1.0f, 0); |
| 5028 | } |
| 5029 | |
| 5030 | LayerTestResult<float, 4> SubtractionTest(armnn::IWorkloadFactory& workloadFactory) |
| 5031 | { |
| 5032 | const unsigned int shape0[] = { 1, 1, 2, 2 }; |
| 5033 | const unsigned int shape1[] = { 1, 1, 2, 2 }; |
| 5034 | |
| 5035 | std::vector<float> input0({ 1, 2, 3, 4 }); |
| 5036 | std::vector<float> input1({ 1, -1, 0, 2 }); |
| 5037 | std::vector<float> output({ 0, 3, 3, 2 }); |
| 5038 | |
| 5039 | return SubtractionTestHelper(workloadFactory, |
| 5040 | shape0, input0, 1.0f, 0, |
| 5041 | shape1, input1, 1.0f, 0, |
| 5042 | shape0, output, 1.0f, 0); |
| 5043 | } |
| 5044 | |
| 5045 | LayerTestResult<float, 4> SubtractionBroadcast1ElementTest(armnn::IWorkloadFactory& workloadFactory) |
| 5046 | { |
| 5047 | const unsigned int shape0[] = { 1, 1, 2, 2 }; |
| 5048 | const unsigned int shape1[] = { 1, 1, 1, 1 }; |
| 5049 | |
| 5050 | std::vector<float> input0({ 1, 2, 3, 4 }); |
| 5051 | std::vector<float> input1({ 10 }); |
| 5052 | std::vector<float> output({ -9, -8, -7, -6 }); |
| 5053 | |
| 5054 | return SubtractionTestHelper(workloadFactory, |
| 5055 | shape0, input0, 1.0f, 0, |
| 5056 | shape1, input1, 1.0f, 0, |
| 5057 | shape0, output, 1.0f, 0); |
| 5058 | } |
| 5059 | |
| 5060 | LayerTestResult<float, 4> SubtractionBroadcastTest(armnn::IWorkloadFactory& workloadFactory) |
| 5061 | { |
| 5062 | const unsigned int shape0[] = { 1, 1, 2, 2 }; |
| 5063 | const unsigned int shape1[] = { 1, 1, 1, 2 }; |
| 5064 | |
| 5065 | std::vector<float> input0({ 1, 2, 3, 4 }); |
| 5066 | std::vector<float> input1({ 10, -5 }); |
| 5067 | std::vector<float> output({ -9, 7, -7, 9 }); |
| 5068 | |
| 5069 | return SubtractionTestHelper(workloadFactory, |
| 5070 | shape0, input0, 1.0f, 0, |
| 5071 | shape1, input1, 1.0f, 0, |
| 5072 | shape0, output, 1.0f, 0); |
| 5073 | } |
| 5074 | |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 5075 | LayerTestResult<uint8_t, 4> ResizeBilinearNopUint8Test(armnn::IWorkloadFactory& workloadFactory) |
| 5076 | { |
| 5077 | constexpr unsigned int inputWidth = 4; |
| 5078 | constexpr unsigned int inputHeight = 4; |
| 5079 | constexpr unsigned int inputChannels = 1; |
| 5080 | constexpr unsigned int inputBatchSize = 1; |
| 5081 | |
| 5082 | constexpr unsigned int outputWidth = inputWidth; |
| 5083 | constexpr unsigned int outputHeight = inputHeight; |
| 5084 | constexpr unsigned int outputChannels = inputChannels; |
| 5085 | constexpr unsigned int outputBatchSize = inputBatchSize; |
| 5086 | |
| 5087 | armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, |
| 5088 | armnn::DataType::QuantisedAsymm8); |
| 5089 | inputTensorInfo.SetQuantizationScale(1.5f); |
| 5090 | inputTensorInfo.SetQuantizationOffset(-3); |
| 5091 | |
| 5092 | armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, |
| 5093 | armnn::DataType::QuantisedAsymm8); |
| 5094 | outputTensorInfo.SetQuantizationScale(1.5f); |
| 5095 | outputTensorInfo.SetQuantizationOffset(-3); |
| 5096 | |
| 5097 | auto input = MakeTensor<uint8_t, 4>(inputTensorInfo, std::vector<uint8_t>({ |
| 5098 | 1, 2, 3, 4, |
| 5099 | 2, 3, 4, 5, |
| 5100 | 3, 4, 5, 6, |
| 5101 | 4, 5, 6, 7 |
| 5102 | })); |
| 5103 | |
| 5104 | LayerTestResult<uint8_t, 4> result(outputTensorInfo); |
| 5105 | result.outputExpected = input; |
| 5106 | |
| 5107 | std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| 5108 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 5109 | |
| 5110 | armnn::ResizeBilinearQueueDescriptor descriptor; |
| 5111 | armnn::WorkloadInfo info; |
| 5112 | AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); |
| 5113 | AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); |
| 5114 | |
| 5115 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResizeBilinear(descriptor, info); |
| 5116 | |
| 5117 | inputHandle->Allocate(); |
| 5118 | outputHandle->Allocate(); |
| 5119 | CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); |
| 5120 | |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 5121 | workloadFactory.Finalize(); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 5122 | workload->Execute(); |
| 5123 | |
| 5124 | CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); |
| 5125 | return result; |
| 5126 | } |
| 5127 | |
| 5128 | LayerTestResult<uint8_t, 4> SimpleResizeBilinearUint8Test(armnn::IWorkloadFactory& workloadFactory) |
| 5129 | { |
| 5130 | constexpr unsigned int inputWidth = 2; |
| 5131 | constexpr unsigned int inputHeight = 2; |
| 5132 | constexpr unsigned int inputChannels = 1; |
| 5133 | constexpr unsigned int inputBatchSize = 1; |
| 5134 | |
| 5135 | constexpr unsigned int outputWidth = inputWidth / 2; |
| 5136 | constexpr unsigned int outputHeight = inputHeight / 2; |
| 5137 | constexpr unsigned int outputChannels = inputChannels; |
| 5138 | constexpr unsigned int outputBatchSize = inputBatchSize; |
| 5139 | |
| 5140 | armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, |
| 5141 | armnn::DataType::QuantisedAsymm8); |
| 5142 | inputTensorInfo.SetQuantizationScale(0.1567f); |
| 5143 | inputTensorInfo.SetQuantizationOffset(1); |
| 5144 | |
| 5145 | armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, |
| 5146 | armnn::DataType::QuantisedAsymm8); |
| 5147 | outputTensorInfo.SetQuantizationScale(0.1567f); |
| 5148 | outputTensorInfo.SetQuantizationOffset(1); |
| 5149 | |
| 5150 | auto input = MakeTensor<uint8_t, 4>(inputTensorInfo, std::vector<uint8_t>({ |
| 5151 | 1, 255, |
| 5152 | 200, 250 |
| 5153 | })); |
| 5154 | |
| 5155 | // The 'resize bilinear' operation projects the top-left corner of output texels into the input image, |
| 5156 | // then figures out the interpolants and weights. Note this is different to projecting the centre of the |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 5157 | // output texel - and thus we'll expect the output 1x1 matrix to contain, as its single element, the value |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 5158 | // that was at position (0,0) of the input matrix (rather than an average, which we would expect if projecting |
| 5159 | // the centre). |
| 5160 | LayerTestResult<uint8_t, 4> result(outputTensorInfo); |
| 5161 | result.outputExpected = MakeTensor<uint8_t, 4>(outputTensorInfo, std::vector<uint8_t>({ |
| 5162 | 1 |
| 5163 | })); |
| 5164 | |
| 5165 | std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| 5166 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 5167 | |
| 5168 | armnn::ResizeBilinearQueueDescriptor descriptor; |
| 5169 | armnn::WorkloadInfo info; |
| 5170 | AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); |
| 5171 | AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); |
| 5172 | |
| 5173 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResizeBilinear(descriptor, info); |
| 5174 | |
| 5175 | inputHandle->Allocate(); |
| 5176 | outputHandle->Allocate(); |
| 5177 | CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); |
| 5178 | |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 5179 | workloadFactory.Finalize(); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 5180 | workload->Execute(); |
| 5181 | |
| 5182 | CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); |
| 5183 | return result; |
| 5184 | } |
| 5185 | |
| 5186 | LayerTestResult<uint8_t, 4> ResizeBilinearSqMinUint8Test(armnn::IWorkloadFactory& workloadFactory) |
| 5187 | { |
| 5188 | constexpr unsigned int inputWidth = 4; |
| 5189 | constexpr unsigned int inputHeight = 4; |
| 5190 | constexpr unsigned int inputChannels = 1; |
| 5191 | constexpr unsigned int inputBatchSize = 1; |
| 5192 | |
| 5193 | constexpr unsigned int outputWidth = inputWidth / 2; |
| 5194 | constexpr unsigned int outputHeight = inputHeight / 2; |
| 5195 | constexpr unsigned int outputChannels = inputChannels; |
| 5196 | constexpr unsigned int outputBatchSize = inputBatchSize; |
| 5197 | |
| 5198 | armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, |
| 5199 | armnn::DataType::QuantisedAsymm8); |
| 5200 | inputTensorInfo.SetQuantizationScale(3.141592f); |
| 5201 | inputTensorInfo.SetQuantizationOffset(3); |
| 5202 | |
| 5203 | armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, |
| 5204 | armnn::DataType::QuantisedAsymm8); |
| 5205 | outputTensorInfo.SetQuantizationScale(3.141592f); |
| 5206 | outputTensorInfo.SetQuantizationOffset(3); |
| 5207 | |
| 5208 | auto input = MakeTensor<uint8_t, 4>(inputTensorInfo, std::vector<uint8_t>({ |
| 5209 | 1, 2, 3, 4, |
| 5210 | 2, 3, 4, 5, |
| 5211 | 3, 4, 5, 6, |
| 5212 | 4, 5, 6, 7 |
| 5213 | })); |
| 5214 | |
| 5215 | LayerTestResult<uint8_t, 4> result(outputTensorInfo); |
| 5216 | result.outputExpected = MakeTensor<uint8_t, 4>(outputTensorInfo, std::vector<uint8_t>({ |
| 5217 | 1, 3, |
| 5218 | 3, 5 |
| 5219 | })); |
| 5220 | |
| 5221 | std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| 5222 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 5223 | |
| 5224 | armnn::ResizeBilinearQueueDescriptor descriptor; |
| 5225 | armnn::WorkloadInfo info; |
| 5226 | AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); |
| 5227 | AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); |
| 5228 | |
| 5229 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResizeBilinear(descriptor, info); |
| 5230 | |
| 5231 | inputHandle->Allocate(); |
| 5232 | outputHandle->Allocate(); |
| 5233 | CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); |
| 5234 | |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 5235 | workloadFactory.Finalize(); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 5236 | workload->Execute(); |
| 5237 | |
| 5238 | CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); |
| 5239 | return result; |
| 5240 | } |
| 5241 | |
| 5242 | LayerTestResult<uint8_t, 4> ResizeBilinearMinUint8Test(armnn::IWorkloadFactory& workloadFactory) |
| 5243 | { |
| 5244 | constexpr unsigned int inputWidth = 3; |
| 5245 | constexpr unsigned int inputHeight = 2; |
| 5246 | constexpr unsigned int inputChannels = 1; |
| 5247 | constexpr unsigned int inputBatchSize = 1; |
| 5248 | |
| 5249 | constexpr unsigned int outputWidth = 2; |
| 5250 | constexpr unsigned int outputHeight = 1; |
| 5251 | constexpr unsigned int outputChannels = inputChannels; |
| 5252 | constexpr unsigned int outputBatchSize = inputBatchSize; |
| 5253 | |
| 5254 | armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, |
| 5255 | armnn::DataType::QuantisedAsymm8); |
| 5256 | inputTensorInfo.SetQuantizationScale(1.5f); |
| 5257 | inputTensorInfo.SetQuantizationOffset(-1); |
| 5258 | |
| 5259 | armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, |
| 5260 | armnn::DataType::QuantisedAsymm8); |
| 5261 | outputTensorInfo.SetQuantizationScale(1.5f); |
| 5262 | outputTensorInfo.SetQuantizationOffset(-1); |
| 5263 | |
| 5264 | auto input = MakeTensor<uint8_t, 4>(inputTensorInfo, std::vector<uint8_t>({ |
| 5265 | 1, 2, 3, // 3.0, 4.5, 6.0 |
| 5266 | 5, 8, 13 // 9.0, 13.5, 21.0 |
| 5267 | })); |
| 5268 | |
| 5269 | LayerTestResult<uint8_t, 4> result(outputTensorInfo); |
| 5270 | result.outputExpected = MakeTensor<uint8_t, 4>(outputTensorInfo, std::vector<uint8_t>({ |
| 5271 | 1, 3 // 3.0, 5.25 |
| 5272 | })); |
| 5273 | |
| 5274 | std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| 5275 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 5276 | |
| 5277 | armnn::ResizeBilinearQueueDescriptor descriptor; |
| 5278 | armnn::WorkloadInfo info; |
| 5279 | AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); |
| 5280 | AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); |
| 5281 | |
| 5282 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResizeBilinear(descriptor, info); |
| 5283 | |
| 5284 | inputHandle->Allocate(); |
| 5285 | outputHandle->Allocate(); |
| 5286 | |
| 5287 | CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); |
| 5288 | |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 5289 | workloadFactory.Finalize(); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 5290 | workload->Execute(); |
| 5291 | |
| 5292 | CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); |
| 5293 | return result; |
| 5294 | } |
| 5295 | |
| 5296 | LayerTestResult<uint8_t, 4> ResizeBilinearMagUint8Test(armnn::IWorkloadFactory& workloadFactory) |
| 5297 | { |
| 5298 | constexpr unsigned int inputWidth = 2; |
| 5299 | constexpr unsigned int inputHeight = 3; |
| 5300 | constexpr unsigned int inputChannels = 1; |
| 5301 | constexpr unsigned int inputBatchSize = 1; |
| 5302 | |
| 5303 | constexpr unsigned int outputWidth = 5; |
| 5304 | constexpr unsigned int outputHeight = 3; |
| 5305 | constexpr unsigned int outputChannels = inputChannels; |
| 5306 | constexpr unsigned int outputBatchSize = inputBatchSize; |
| 5307 | |
| 5308 | armnn::TensorInfo inputTensorInfo({ inputBatchSize, inputChannels, inputHeight, inputWidth }, |
| 5309 | armnn::DataType::QuantisedAsymm8); |
| 5310 | inputTensorInfo.SetQuantizationScale(0.010765f); |
| 5311 | inputTensorInfo.SetQuantizationOffset(7); |
| 5312 | |
| 5313 | armnn::TensorInfo outputTensorInfo({ outputBatchSize, outputChannels, outputHeight, outputWidth }, |
| 5314 | armnn::DataType::QuantisedAsymm8); |
| 5315 | outputTensorInfo.SetQuantizationScale(0.010132f); |
| 5316 | outputTensorInfo.SetQuantizationOffset(-18); |
| 5317 | |
| 5318 | auto input = MakeTensor<uint8_t, 4>(inputTensorInfo, std::vector<uint8_t>({ |
| 5319 | 24, 228, // 0.183005, 2.379065, |
| 5320 | 105, 128, // 1.05497, 1.302565 |
| 5321 | 230, 71 // 2.400595, 0.68896 |
| 5322 | })); |
| 5323 | |
| 5324 | LayerTestResult<uint8_t, 4> result(outputTensorInfo); |
| 5325 | result.outputExpected = MakeTensor<uint8_t, 4>(outputTensorInfo, std::vector<uint8_t>({ |
| 5326 | 0, 87, 173, 217, 217, // 0.18300501, 1.06142902, 1.93985295, 2.37906504, 2.37906504 |
| 5327 | 86, 96, 106, 111, 111, // 1.05497003, 1.15400803, 1.25304604, 1.30256498, 1.30256498 |
| 5328 | 219, 151, 84, 50, 50 // 2.40059495, 1.71594095, 1.03128707, 0.68896002, 0.68896002 |
| 5329 | })); |
| 5330 | |
| 5331 | std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| 5332 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 5333 | |
| 5334 | armnn::ResizeBilinearQueueDescriptor descriptor; |
| 5335 | armnn::WorkloadInfo info; |
| 5336 | AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); |
| 5337 | AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); |
| 5338 | |
| 5339 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResizeBilinear(descriptor, info); |
| 5340 | |
| 5341 | inputHandle->Allocate(); |
| 5342 | outputHandle->Allocate(); |
| 5343 | CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); |
| 5344 | |
surmeh01 | 3537c2c | 2018-05-18 16:31:43 +0100 | [diff] [blame] | 5345 | workloadFactory.Finalize(); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 5346 | workload->Execute(); |
| 5347 | |
| 5348 | CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); |
| 5349 | return result; |
| 5350 | } |
| 5351 | |
| 5352 | LayerTestResult<float, 4> BatchNormTest(armnn::IWorkloadFactory& workloadFactory) |
| 5353 | { |
Matteo Martincigh | 8eb675e | 2018-10-17 14:43:29 +0100 | [diff] [blame] | 5354 | // BatchSize: 1 |
| 5355 | // Channels: 2 |
| 5356 | // Height: 3 |
| 5357 | // Width: 2 |
| 5358 | |
| 5359 | const armnn::TensorShape inputOutputShape{ 1, 2, 3, 2 }; |
| 5360 | std::vector<float> inputValues |
| 5361 | { |
| 5362 | // Batch 0, Channel 0, Height (3) x Width (2) |
| 5363 | 1.f, 4.f, |
| 5364 | 4.f, 2.f, |
| 5365 | 1.f, 6.f, |
| 5366 | |
| 5367 | // Batch 0, Channel 1, Height (3) x Width (2) |
| 5368 | 1.f, 1.f, |
| 5369 | 4.f, 1.f, |
| 5370 | -2.f, 4.f |
| 5371 | }; |
| 5372 | std::vector<float> expectedOutputValues |
| 5373 | { |
| 5374 | // Batch 0, Channel 0, Height (3) x Width (2) |
| 5375 | 1.f, 4.f, |
| 5376 | 4.f, 2.f, |
| 5377 | 1.f, 6.f, |
| 5378 | |
| 5379 | // Batch 0, Channel 1, Height (3) x Width (2) |
| 5380 | 3.f, 3.f, |
| 5381 | 4.f, 3.f, |
| 5382 | 2.f, 4.f |
| 5383 | }; |
| 5384 | |
| 5385 | return BatchNormTestImpl<float>(workloadFactory, inputOutputShape, inputValues, expectedOutputValues, |
| 5386 | 0.f, 0, armnn::DataLayout::NCHW); |
| 5387 | } |
| 5388 | |
| 5389 | LayerTestResult<float, 4> BatchNormNhwcTest(armnn::IWorkloadFactory& workloadFactory) |
| 5390 | { |
| 5391 | // BatchSize: 1 |
| 5392 | // Height: 3 |
| 5393 | // Width: 2 |
| 5394 | // Channels: 2 |
| 5395 | |
| 5396 | const armnn::TensorShape inputOutputShape{ 1, 3, 2, 2 }; |
| 5397 | std::vector<float> inputValues |
| 5398 | { |
| 5399 | // Batch 0, Height 0, Width (2) x Channel (2) |
| 5400 | 1.f, 1.f, |
| 5401 | 4.f, 1.f, |
| 5402 | |
| 5403 | // Batch 0, Height 1, Width (2) x Channel (2) |
| 5404 | 4.f, 4.f, |
| 5405 | 2.f, 1.f, |
| 5406 | |
| 5407 | // Batch 0, Height 2, Width (2) x Channel (2) |
| 5408 | 1.f, -2.f, |
| 5409 | 6.f, 4.f |
| 5410 | }; |
| 5411 | std::vector<float> expectedOutputValues |
| 5412 | { |
| 5413 | // Batch 0, Height 0, Width (2) x Channel (2) |
| 5414 | 1.f, 3.f, |
| 5415 | 4.f, 3.f, |
| 5416 | |
| 5417 | // Batch 0, Height 1, Width (2) x Channel (2) |
| 5418 | 4.f, 4.f, |
| 5419 | 2.f, 3.f, |
| 5420 | |
| 5421 | // Batch 0, Height 2, Width (2) x Channel (2) |
| 5422 | 1.f, 2.f, |
| 5423 | 6.f, 4.f |
| 5424 | }; |
| 5425 | |
| 5426 | return BatchNormTestImpl<float>(workloadFactory, inputOutputShape, inputValues, expectedOutputValues, |
| 5427 | 0.f, 0, armnn::DataLayout::NHWC); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 5428 | } |
| 5429 | |
| 5430 | LayerTestResult<uint8_t, 4> BatchNormUint8Test(armnn::IWorkloadFactory& workloadFactory) |
| 5431 | { |
Matteo Martincigh | 8eb675e | 2018-10-17 14:43:29 +0100 | [diff] [blame] | 5432 | // BatchSize: 1 |
| 5433 | // Channels: 2 |
| 5434 | // Height: 3 |
| 5435 | // Width: 2 |
| 5436 | |
| 5437 | const armnn::TensorShape inputOutputShape{ 1, 2, 3, 2 }; |
| 5438 | std::vector<float> inputValues |
| 5439 | { |
| 5440 | // Batch 0, Channel 0, Height (3) x Width (2) |
| 5441 | 1.f, 4.f, |
| 5442 | 4.f, 2.f, |
| 5443 | 1.f, 6.f, |
| 5444 | |
| 5445 | // Batch 0, Channel 1, Height (3) x Width (2) |
| 5446 | 1.f, 1.f, |
| 5447 | 4.f, 1.f, |
| 5448 | -2.f, 4.f |
| 5449 | }; |
| 5450 | std::vector<float> expectedOutputValues |
| 5451 | { |
| 5452 | // Batch 0, Channel 0, Height (3) x Width (2) |
| 5453 | 1.f, 4.f, |
| 5454 | 4.f, 2.f, |
| 5455 | 1.f, 6.f, |
| 5456 | |
| 5457 | // Batch 0, Channel 1, Height (3) x Width (2) |
| 5458 | 3.f, 3.f, |
| 5459 | 4.f, 3.f, |
| 5460 | 2.f, 4.f |
| 5461 | }; |
| 5462 | |
| 5463 | return BatchNormTestImpl<uint8_t>(workloadFactory, inputOutputShape, inputValues, expectedOutputValues, |
| 5464 | 1.f/20.f, 50, armnn::DataLayout::NCHW); |
| 5465 | } |
| 5466 | |
| 5467 | LayerTestResult<uint8_t, 4> BatchNormUint8NhwcTest(armnn::IWorkloadFactory& workloadFactory) |
| 5468 | { |
| 5469 | // BatchSize: 1 |
| 5470 | // Height: 3 |
| 5471 | // Width: 2 |
| 5472 | // Channels: 2 |
| 5473 | |
| 5474 | const armnn::TensorShape inputOutputShape{ 1, 3, 2, 2 }; |
| 5475 | std::vector<float> inputValues |
| 5476 | { |
| 5477 | // Batch 0, Height 0, Width (2) x Channel (2) |
| 5478 | 1.f, 1.f, |
| 5479 | 4.f, 1.f, |
| 5480 | |
| 5481 | // Batch 0, Height 1, Width (2) x Channel (2) |
| 5482 | 4.f, 4.f, |
| 5483 | 2.f, 1.f, |
| 5484 | |
| 5485 | // Batch 0, Height 2, Width (2) x Channel (2) |
| 5486 | 1.f, -2.f, |
| 5487 | 6.f, 4.f |
| 5488 | }; |
| 5489 | std::vector<float> expectedOutputValues |
| 5490 | { |
| 5491 | // Batch 0, Height 0, Width (2) x Channel (2) |
| 5492 | 1.f, 3.f, |
| 5493 | 4.f, 3.f, |
| 5494 | |
| 5495 | // Batch 0, Height 1, Width (2) x Channel (2) |
| 5496 | 4.f, 4.f, |
| 5497 | 2.f, 3.f, |
| 5498 | |
| 5499 | // Batch 0, Height 2, Width (2) x Channel (2) |
| 5500 | 1.f, 2.f, |
| 5501 | 6.f, 4.f |
| 5502 | }; |
| 5503 | |
| 5504 | return BatchNormTestImpl<uint8_t>(workloadFactory, inputOutputShape, inputValues, expectedOutputValues, |
| 5505 | 1.f/20.f, 50, armnn::DataLayout::NHWC); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 5506 | } |
| 5507 | |
| 5508 | LayerTestResult<uint8_t, 4> ConstantUint8Test(armnn::IWorkloadFactory& workloadFactory) |
| 5509 | { |
| 5510 | return ConstantTestImpl<uint8_t>(workloadFactory, 2e-6f, 1); |
| 5511 | } |
| 5512 | |
| 5513 | LayerTestResult<uint8_t, 1> Concatenation1dUint8Test(armnn::IWorkloadFactory& workloadFactory) |
| 5514 | { |
| 5515 | return Concatenation1dTestImpl<uint8_t>(workloadFactory, 0.5f, -1); |
| 5516 | } |
| 5517 | |
| 5518 | LayerTestResult<uint8_t, 2> Concatenation2dDim0Uint8Test(armnn::IWorkloadFactory& workloadFactory) |
| 5519 | { |
| 5520 | return Concatenation2dDim0TestImpl<uint8_t>(workloadFactory, 0.5f, -1); |
| 5521 | } |
| 5522 | |
| 5523 | LayerTestResult<uint8_t, 2> Concatenation2dDim1Uint8Test(armnn::IWorkloadFactory& workloadFactory) |
| 5524 | { |
| 5525 | return Concatenation2dDim1TestImpl<uint8_t>(workloadFactory, 0.5f, -1); |
| 5526 | } |
| 5527 | |
| 5528 | LayerTestResult<uint8_t, 2> Concatenation2dDim0DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory) |
| 5529 | { |
| 5530 | return Concatenation2dDim0DiffInputDimsTestImpl<uint8_t>(workloadFactory, 0.5f, -1); |
| 5531 | } |
| 5532 | |
| 5533 | LayerTestResult<uint8_t, 2> Concatenation2dDim1DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory) |
| 5534 | { |
| 5535 | return Concatenation2dDim1DiffInputDimsTestImpl<uint8_t>(workloadFactory, 0.5f, -1); |
| 5536 | } |
| 5537 | |
| 5538 | LayerTestResult<uint8_t, 3> Concatenation3dDim0Uint8Test(armnn::IWorkloadFactory& workloadFactory) |
| 5539 | { |
| 5540 | return Concatenation3dDim0TestImpl<uint8_t>(workloadFactory, 0.5f, -1); |
| 5541 | } |
| 5542 | |
| 5543 | LayerTestResult<uint8_t, 3> Concatenation3dDim1Uint8Test(armnn::IWorkloadFactory& workloadFactory) |
| 5544 | { |
| 5545 | return Concatenation3dDim1TestImpl<uint8_t>(workloadFactory, 0.5f, -1); |
| 5546 | } |
| 5547 | |
| 5548 | LayerTestResult<uint8_t, 3> Concatenation3dDim2Uint8Test(armnn::IWorkloadFactory& workloadFactory) |
| 5549 | { |
| 5550 | return Concatenation3dDim2TestImpl<uint8_t>(workloadFactory, 0.5f, -1); |
| 5551 | } |
| 5552 | |
| 5553 | LayerTestResult<uint8_t, 3> Concatenation3dDim0DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory) |
| 5554 | { |
| 5555 | return Concatenation3dDim0TestImpl<uint8_t>(workloadFactory, 0.5f, -1); |
| 5556 | } |
| 5557 | |
| 5558 | LayerTestResult<uint8_t, 3> Concatenation3dDim1DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory) |
| 5559 | { |
| 5560 | return Concatenation3dDim1DiffInputDimsTestImpl<uint8_t>(workloadFactory, 0.5f, -1); |
| 5561 | } |
| 5562 | |
| 5563 | LayerTestResult<uint8_t, 3> Concatenation3dDim2DiffInputDimsUint8Test(armnn::IWorkloadFactory& workloadFactory) |
| 5564 | { |
| 5565 | return Concatenation3dDim2DiffInputDimsTestImpl<uint8_t>(workloadFactory, 0.5f, -1); |
| 5566 | } |
| 5567 | |
| 5568 | LayerTestResult<float, 4> SimpleMaxPooling2dSize2x2Stride2x2Test(armnn::IWorkloadFactory& workloadFactory, |
| 5569 | bool forceNoPadding) |
| 5570 | { |
| 5571 | return SimpleMaxPooling2dSize2x2Stride2x2TestCommon<float>(workloadFactory, forceNoPadding); |
| 5572 | } |
| 5573 | |
| 5574 | LayerTestResult<uint8_t, 4> SimpleMaxPooling2dSize2x2Stride2x2Uint8Test(armnn::IWorkloadFactory& workloadFactory, |
| 5575 | bool forceNoPadding) |
| 5576 | { |
| 5577 | return SimpleMaxPooling2dSize2x2Stride2x2TestCommon<uint8_t>(workloadFactory, forceNoPadding, 3.0f, -5); |
| 5578 | } |
| 5579 | |
| 5580 | LayerTestResult<float, 4> SimpleMaxPooling2dSize3x3Stride2x4Test(armnn::IWorkloadFactory& workloadFactory, |
| 5581 | bool forceNoPadding) |
| 5582 | { |
| 5583 | return SimpleMaxPooling2dSize3x3Stride2x4TestCommon<float>(workloadFactory, forceNoPadding); |
| 5584 | } |
| 5585 | |
| 5586 | LayerTestResult<uint8_t, 4> SimpleMaxPooling2dSize3x3Stride2x4Uint8Test(armnn::IWorkloadFactory& workloadFactory, |
| 5587 | bool forceNoPadding) |
| 5588 | { |
| 5589 | return SimpleMaxPooling2dSize3x3Stride2x4TestCommon<uint8_t>(workloadFactory, forceNoPadding, 0.1f, 128); |
| 5590 | } |
| 5591 | |
| 5592 | LayerTestResult<float, 4> SimpleAveragePooling2dTest(armnn::IWorkloadFactory& workloadFactory) |
| 5593 | { |
James Conroy | 6948227 | 2018-10-19 10:41:35 +0100 | [diff] [blame] | 5594 | return SimpleAveragePooling2dTest<float>(workloadFactory); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 5595 | } |
| 5596 | |
Francis Murtagh | 043d0d0 | 2018-10-05 14:08:48 +0100 | [diff] [blame] | 5597 | LayerTestResult<float, 4> SimpleAveragePooling2dNhwcTest(armnn::IWorkloadFactory& workloadFactory) |
| 5598 | { |
James Conroy | 6948227 | 2018-10-19 10:41:35 +0100 | [diff] [blame] | 5599 | return SimpleAveragePooling2dNhwcTest<float>(workloadFactory); |
Francis Murtagh | 043d0d0 | 2018-10-05 14:08:48 +0100 | [diff] [blame] | 5600 | } |
| 5601 | |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 5602 | LayerTestResult<uint8_t, 4> SimpleAveragePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory) |
| 5603 | { |
James Conroy | 6948227 | 2018-10-19 10:41:35 +0100 | [diff] [blame] | 5604 | return SimpleAveragePooling2dTest<uint8_t>(workloadFactory, 0.5, -1); |
| 5605 | } |
| 5606 | |
| 5607 | LayerTestResult<uint8_t, 4> SimpleAveragePooling2dUint8NhwcTest(armnn::IWorkloadFactory& workloadFactory) |
| 5608 | { |
| 5609 | return SimpleAveragePooling2dNhwcTest<uint8_t>(workloadFactory, 0.5, -1); |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 5610 | } |
| 5611 | |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 5612 | LayerTestResult<float, 4> IgnorePaddingAveragePooling2dSize3x2Stride2x2Test(armnn::IWorkloadFactory& workloadFactory, |
| 5613 | bool forceNoPadding) |
| 5614 | { |
| 5615 | return IgnorePaddingAveragePooling2dSize3x2Stride2x2TestCommon<float>(workloadFactory, forceNoPadding); |
| 5616 | } |
| 5617 | |
telsoa01 | 4fcda01 | 2018-03-09 14:13:49 +0000 | [diff] [blame] | 5618 | LayerTestResult<float, 4> LargeTensorsAveragePooling2dTest(armnn::IWorkloadFactory& workloadFactory) |
| 5619 | { |
| 5620 | return LargeTensorsAveragePooling2dTestCommon<float>(workloadFactory); |
| 5621 | } |
| 5622 | |
| 5623 | LayerTestResult<uint8_t, 4> LargeTensorsAveragePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory) |
| 5624 | { |
| 5625 | return LargeTensorsAveragePooling2dTestCommon<uint8_t>(workloadFactory, 0.5, -1); |
| 5626 | } |
| 5627 | |
| 5628 | LayerTestResult<float, 4> SimpleL2Pooling2dTest(armnn::IWorkloadFactory& workloadFactory) |
| 5629 | { |
| 5630 | return SimpleL2Pooling2dTestCommon<float>(workloadFactory); |
| 5631 | } |
| 5632 | |
| 5633 | LayerTestResult<uint8_t, 4> SimpleL2Pooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory) |
| 5634 | { |
| 5635 | return SimpleL2Pooling2dTestCommon<uint8_t>(workloadFactory); |
| 5636 | } |
| 5637 | |
| 5638 | LayerTestResult<float, 4> L2Pooling2dSize3Stride1Test(armnn::IWorkloadFactory& workloadFactory) |
| 5639 | { |
| 5640 | return L2Pooling2dSize3Stride1TestCommon<float>(workloadFactory); |
| 5641 | } |
| 5642 | |
| 5643 | LayerTestResult<uint8_t, 4> L2Pooling2dSize3Stride1Uint8Test(armnn::IWorkloadFactory& workloadFactory) |
| 5644 | { |
| 5645 | return L2Pooling2dSize3Stride1TestCommon<uint8_t>(workloadFactory); |
| 5646 | } |
| 5647 | |
| 5648 | LayerTestResult<float, 4> L2Pooling2dSize3Stride3Test(armnn::IWorkloadFactory& workloadFactory) |
| 5649 | { |
| 5650 | return L2Pooling2dSize3Stride3TestCommon<float>(workloadFactory); |
| 5651 | } |
| 5652 | |
| 5653 | LayerTestResult<uint8_t, 4> L2Pooling2dSize3Stride3Uint8Test(armnn::IWorkloadFactory& workloadFactory) |
| 5654 | { |
| 5655 | return L2Pooling2dSize3Stride3TestCommon<uint8_t>(workloadFactory); |
| 5656 | } |
| 5657 | |
| 5658 | LayerTestResult<float, 4> L2Pooling2dSize3Stride4Test(armnn::IWorkloadFactory& workloadFactory) |
| 5659 | { |
| 5660 | return L2Pooling2dSize3Stride4TestCommon<float>(workloadFactory); |
| 5661 | } |
| 5662 | |
| 5663 | LayerTestResult<uint8_t, 4> L2Pooling2dSize3Stride4Uint8Test(armnn::IWorkloadFactory& workloadFactory) |
| 5664 | { |
| 5665 | return L2Pooling2dSize3Stride4TestCommon<uint8_t>(workloadFactory); |
| 5666 | } |
| 5667 | |
| 5668 | LayerTestResult<float, 4> L2Pooling2dSize7Test(armnn::IWorkloadFactory& workloadFactory) |
| 5669 | { |
| 5670 | return L2Pooling2dSize7TestCommon<float>(workloadFactory); |
| 5671 | } |
| 5672 | |
| 5673 | LayerTestResult<uint8_t, 4> L2Pooling2dSize7Uint8Test(armnn::IWorkloadFactory& workloadFactory) |
| 5674 | { |
| 5675 | return L2Pooling2dSize7TestCommon<uint8_t>(workloadFactory); |
| 5676 | } |
| 5677 | |
| 5678 | LayerTestResult<float, 4> L2Pooling2dSize9Test(armnn::IWorkloadFactory& workloadFactory) |
| 5679 | { |
| 5680 | return L2Pooling2dSize9TestCommon<float>(workloadFactory); |
| 5681 | } |
| 5682 | |
| 5683 | LayerTestResult<uint8_t, 4> L2Pooling2dSize9Uint8Test(armnn::IWorkloadFactory& workloadFactory) |
| 5684 | { |
| 5685 | return L2Pooling2dSize9TestCommon<uint8_t>(workloadFactory); |
| 5686 | } |
| 5687 | |
| 5688 | LayerTestResult<float, 4> AsymmetricNonSquarePooling2dTest(armnn::IWorkloadFactory& workloadFactory) |
| 5689 | { |
| 5690 | return AsymmetricNonSquarePooling2dTestCommon<float>(workloadFactory); |
| 5691 | } |
| 5692 | |
| 5693 | LayerTestResult<uint8_t, 4> AsymmetricNonSquarePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory) |
| 5694 | { |
| 5695 | return AsymmetricNonSquarePooling2dTestCommon<uint8_t>(workloadFactory); |
| 5696 | } |
| 5697 | |
| 5698 | LayerTestResult<float, 4> ComparePooling2dTest(armnn::IWorkloadFactory& workloadFactory, |
| 5699 | armnn::IWorkloadFactory& refWorkloadFactory, |
| 5700 | armnn::PoolingAlgorithm poolingType) |
| 5701 | { |
| 5702 | return ComparePooling2dTestCommon<float>(workloadFactory, refWorkloadFactory, poolingType); |
| 5703 | } |
| 5704 | |
| 5705 | LayerTestResult<uint8_t, 4> ComparePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory, |
| 5706 | armnn::IWorkloadFactory& refWorkloadFactory, |
| 5707 | armnn::PoolingAlgorithm poolingType) |
| 5708 | { |
| 5709 | return ComparePooling2dTestCommon<uint8_t>(workloadFactory, refWorkloadFactory, poolingType, 0.1f, 128); |
| 5710 | } |
| 5711 | |
| 5712 | LayerTestResult<float, 2> FullyConnectedLargeTest(armnn::IWorkloadFactory& workloadFactory, |
| 5713 | bool transposeWeights) |
| 5714 | { |
| 5715 | return FullyConnectedLargeTestCommon<float>(workloadFactory, transposeWeights); |
| 5716 | } |
| 5717 | |
| 5718 | LayerTestResult<float, 4> IgnorePaddingSimpleMaxPooling2dTest(armnn::IWorkloadFactory& workloadFactory) |
| 5719 | { |
| 5720 | return IgnorePaddingSimpleMaxPooling2dTestCommon<float>(workloadFactory); |
| 5721 | } |
| 5722 | |
| 5723 | LayerTestResult<uint8_t, 4> IgnorePaddingSimpleMaxPooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory) |
| 5724 | { |
| 5725 | return IgnorePaddingSimpleMaxPooling2dTestCommon<uint8_t>(workloadFactory, 1.0f, -5); |
| 5726 | } |
| 5727 | |
| 5728 | LayerTestResult<float, 4> IgnorePaddingMaxPooling2dSize3Test(armnn::IWorkloadFactory& workloadFactory) |
| 5729 | { |
| 5730 | return IgnorePaddingMaxPooling2dSize3TestCommon<float>(workloadFactory); |
| 5731 | } |
| 5732 | |
| 5733 | LayerTestResult<uint8_t, 4> IgnorePaddingMaxPooling2dSize3Uint8Test(armnn::IWorkloadFactory& workloadFactory) |
| 5734 | { |
| 5735 | return IgnorePaddingMaxPooling2dSize3TestCommon<uint8_t>(workloadFactory, 1.0f, -5); |
| 5736 | } |
| 5737 | |
| 5738 | LayerTestResult<float, 4> IgnorePaddingSimpleAveragePooling2dTest(armnn::IWorkloadFactory& workloadFactory) |
| 5739 | { |
| 5740 | return IgnorePaddingSimpleAveragePooling2dTestCommon<float>(workloadFactory); |
| 5741 | } |
| 5742 | |
| 5743 | LayerTestResult<uint8_t, 4> IgnorePaddingSimpleAveragePooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory) |
| 5744 | { |
| 5745 | return IgnorePaddingSimpleAveragePooling2dTestCommon<uint8_t>(workloadFactory); |
| 5746 | } |
| 5747 | |
| 5748 | LayerTestResult<float, 4> IgnorePaddingSimpleAveragePooling2dNoPaddingTest(armnn::IWorkloadFactory& workloadFactory) |
| 5749 | { |
| 5750 | return IgnorePaddingSimpleAveragePooling2dNoPaddingTestCommon<float>(workloadFactory); |
| 5751 | } |
| 5752 | |
| 5753 | LayerTestResult<uint8_t, 4> IgnorePaddingSimpleAveragePooling2dNoPaddingUint8Test( |
| 5754 | armnn::IWorkloadFactory& workloadFactory) |
| 5755 | { |
| 5756 | return IgnorePaddingSimpleAveragePooling2dNoPaddingTestCommon<uint8_t>(workloadFactory); |
| 5757 | } |
| 5758 | |
| 5759 | LayerTestResult<float, 4> IgnorePaddingAveragePooling2dSize3Test(armnn::IWorkloadFactory& workloadFactory) |
| 5760 | { |
| 5761 | return IgnorePaddingAveragePooling2dSize3TestCommon<float>(workloadFactory); |
| 5762 | } |
| 5763 | |
| 5764 | LayerTestResult<uint8_t, 4> IgnorePaddingAveragePooling2dSize3Uint8Test(armnn::IWorkloadFactory& workloadFactory) |
| 5765 | { |
| 5766 | return IgnorePaddingAveragePooling2dSize3TestCommon<uint8_t>(workloadFactory); |
| 5767 | } |
| 5768 | |
| 5769 | LayerTestResult<float, 4> IgnorePaddingSimpleL2Pooling2dTest(armnn::IWorkloadFactory& workloadFactory) |
| 5770 | { |
| 5771 | return IgnorePaddingSimpleL2Pooling2dTestCommon<float>(workloadFactory); |
| 5772 | } |
| 5773 | |
| 5774 | LayerTestResult<uint8_t, 4> IgnorePaddingSimpleL2Pooling2dUint8Test(armnn::IWorkloadFactory& workloadFactory) |
| 5775 | { |
| 5776 | return IgnorePaddingSimpleL2Pooling2dTestCommon<uint8_t>(workloadFactory); |
| 5777 | } |
| 5778 | |
| 5779 | LayerTestResult<float, 4> IgnorePaddingL2Pooling2dSize3Test(armnn::IWorkloadFactory& workloadFactory) |
| 5780 | { |
| 5781 | return IgnorePaddingL2Pooling2dSize3TestCommon<float>(workloadFactory); |
| 5782 | } |
| 5783 | |
| 5784 | LayerTestResult<uint8_t, 4> IgnorePaddingL2Pooling2dSize3Uint8Test(armnn::IWorkloadFactory& workloadFactory) |
| 5785 | { |
| 5786 | return IgnorePaddingL2Pooling2dSize3TestCommon<uint8_t>(workloadFactory); |
| 5787 | } |
| 5788 | |
| 5789 | LayerTestResult<float, 4> SimplePermuteFloat32Test(armnn::IWorkloadFactory& workloadFactory) |
| 5790 | { |
| 5791 | return SimplePermuteFloat32TestCommon(workloadFactory); |
| 5792 | }; |
| 5793 | |
| 5794 | LayerTestResult<uint8_t, 4> SimplePermuteUint8Test(armnn::IWorkloadFactory& workloadFactory) |
| 5795 | { |
| 5796 | return SimplePermuteUint8TestCommon(workloadFactory); |
| 5797 | }; |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 5798 | |
| 5799 | LayerTestResult<float, 4> PermuteFloat32ValueSet1Test(armnn::IWorkloadFactory& workloadFactory) |
| 5800 | { |
| 5801 | return PermuteFloat32ValueSet1TestCommon(workloadFactory); |
| 5802 | }; |
| 5803 | |
| 5804 | LayerTestResult<float, 4> PermuteFloat32ValueSet2Test(armnn::IWorkloadFactory& workloadFactory) |
| 5805 | { |
| 5806 | return PermuteFloat32ValueSet2TestCommon(workloadFactory); |
| 5807 | }; |
| 5808 | |
| 5809 | LayerTestResult<float, 4> PermuteFloat32ValueSet3Test(armnn::IWorkloadFactory& workloadFactory) |
| 5810 | { |
| 5811 | return PermuteFloat32ValueSet3TestCommon(workloadFactory); |
narpra01 | 1e4c31d | 2018-09-28 11:07:51 +0100 | [diff] [blame] | 5812 | }; |
| 5813 | |
| 5814 | namespace |
| 5815 | { |
Matteo Martincigh | 28dcab6 | 2018-10-19 16:40:03 +0100 | [diff] [blame] | 5816 | |
narpra01 | 1e4c31d | 2018-09-28 11:07:51 +0100 | [diff] [blame] | 5817 | template <typename T, std::size_t InputDim, std::size_t OutputDim> |
| 5818 | LayerTestResult<T, OutputDim> MeanTestHelper(armnn::IWorkloadFactory& workloadFactory, |
Matteo Martincigh | 28dcab6 | 2018-10-19 16:40:03 +0100 | [diff] [blame] | 5819 | const unsigned int* inputShape, |
| 5820 | const std::vector<T>& inputData, |
| 5821 | const std::vector<unsigned int>& axis, |
| 5822 | bool keepDims, |
| 5823 | const unsigned int* outputShape, |
| 5824 | const std::vector<T>& outputData, |
| 5825 | float scale = 1.0f, |
| 5826 | int32_t offset = 0) |
narpra01 | 1e4c31d | 2018-09-28 11:07:51 +0100 | [diff] [blame] | 5827 | { |
Matteo Martincigh | 28dcab6 | 2018-10-19 16:40:03 +0100 | [diff] [blame] | 5828 | auto dataType = (std::is_same<T, uint8_t>::value ? armnn::DataType::QuantisedAsymm8 : armnn::DataType::Float32); |
narpra01 | 1e4c31d | 2018-09-28 11:07:51 +0100 | [diff] [blame] | 5829 | |
| 5830 | armnn::TensorInfo inputTensorInfo(InputDim, inputShape, dataType); |
| 5831 | armnn::TensorInfo outputTensorInfo(OutputDim, outputShape, dataType); |
| 5832 | |
| 5833 | inputTensorInfo.SetQuantizationScale(scale); |
| 5834 | inputTensorInfo.SetQuantizationOffset(offset); |
| 5835 | |
| 5836 | outputTensorInfo.SetQuantizationScale(scale); |
| 5837 | outputTensorInfo.SetQuantizationOffset(offset); |
| 5838 | |
| 5839 | auto input = MakeTensor<T, InputDim>(inputTensorInfo, inputData); |
| 5840 | |
| 5841 | LayerTestResult<T, OutputDim> result(outputTensorInfo); |
| 5842 | result.outputExpected = MakeTensor<T, OutputDim>(outputTensorInfo, outputData); |
| 5843 | |
| 5844 | std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); |
| 5845 | std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); |
| 5846 | |
| 5847 | armnn::MeanQueueDescriptor data; |
| 5848 | data.m_Parameters.m_Axis = axis; |
| 5849 | data.m_Parameters.m_KeepDims = keepDims; |
| 5850 | armnn::WorkloadInfo info; |
| 5851 | AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); |
| 5852 | AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); |
| 5853 | |
| 5854 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateMean(data, info); |
| 5855 | |
| 5856 | inputHandle->Allocate(); |
| 5857 | outputHandle->Allocate(); |
| 5858 | |
| 5859 | CopyDataToITensorHandle(inputHandle.get(), input.origin()); |
| 5860 | |
| 5861 | workloadFactory.Finalize(); |
| 5862 | workload->Execute(); |
| 5863 | |
| 5864 | CopyDataFromITensorHandle(result.output.origin(), outputHandle.get()); |
| 5865 | |
| 5866 | return result; |
| 5867 | } |
Matteo Martincigh | 28dcab6 | 2018-10-19 16:40:03 +0100 | [diff] [blame] | 5868 | |
narpra01 | 1e4c31d | 2018-09-28 11:07:51 +0100 | [diff] [blame] | 5869 | } // anonymous namespace |
| 5870 | |
| 5871 | LayerTestResult<uint8_t, 1> MeanUint8SimpleTest(armnn::IWorkloadFactory& workloadFactory) |
| 5872 | { |
| 5873 | const unsigned int inputShape[] = { 3, 2 }; |
| 5874 | const unsigned int outputShape[] = { 1 }; |
| 5875 | |
| 5876 | std::vector<uint8_t> input({ 1, 1, 2, 2, 3, 3 }); |
| 5877 | std::vector<uint8_t> output({ 2 }); |
| 5878 | |
| 5879 | return MeanTestHelper<uint8_t, 2, 1>(workloadFactory, inputShape, input, {}, false, outputShape, output); |
| 5880 | } |
| 5881 | |
| 5882 | LayerTestResult<uint8_t, 3> MeanUint8SimpleAxisTest(armnn::IWorkloadFactory& workloadFactory) |
| 5883 | { |
| 5884 | const unsigned int inputShape[] = { 1, 1, 3, 2 }; |
| 5885 | const unsigned int outputShape[] = { 1, 1, 2 }; |
| 5886 | |
| 5887 | std::vector<uint8_t> input({ 1, 1, 2, 2, 3, 3 }); |
| 5888 | std::vector<uint8_t> output({ 2, 2 }); |
| 5889 | |
Matteo Martincigh | 28dcab6 | 2018-10-19 16:40:03 +0100 | [diff] [blame] | 5890 | return MeanTestHelper<uint8_t, 4, 3>(workloadFactory, inputShape, input, { 2 }, false, outputShape, output); |
narpra01 | 1e4c31d | 2018-09-28 11:07:51 +0100 | [diff] [blame] | 5891 | } |
| 5892 | |
| 5893 | LayerTestResult<uint8_t, 4> MeanUint8KeepDimsTest(armnn::IWorkloadFactory& workloadFactory) |
| 5894 | { |
| 5895 | const unsigned int inputShape[] = { 1, 1, 3, 2 }; |
| 5896 | const unsigned int outputShape[] = { 1, 1, 1, 2 }; |
| 5897 | |
| 5898 | std::vector<uint8_t> input({ 1, 1, 2, 2, 3, 3 }); |
| 5899 | std::vector<uint8_t> output({ 2, 2 }); |
| 5900 | |
Matteo Martincigh | 28dcab6 | 2018-10-19 16:40:03 +0100 | [diff] [blame] | 5901 | return MeanTestHelper<uint8_t, 4, 4>(workloadFactory, inputShape, input, { 2 }, true, outputShape, output); |
narpra01 | 1e4c31d | 2018-09-28 11:07:51 +0100 | [diff] [blame] | 5902 | } |
| 5903 | |
| 5904 | LayerTestResult<uint8_t, 4> MeanUint8MultipleDimsTest(armnn::IWorkloadFactory& workloadFactory) |
| 5905 | { |
| 5906 | const unsigned int inputShape[] = { 2, 3, 1, 2 }; |
| 5907 | const unsigned int outputShape[] = { 1, 3, 1, 1 }; |
| 5908 | |
Matteo Martincigh | 28dcab6 | 2018-10-19 16:40:03 +0100 | [diff] [blame] | 5909 | std::vector<uint8_t> input({ 1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6 }); |
narpra01 | 1e4c31d | 2018-09-28 11:07:51 +0100 | [diff] [blame] | 5910 | std::vector<uint8_t> output({ 1, 3, 5 }); |
| 5911 | |
Matteo Martincigh | 28dcab6 | 2018-10-19 16:40:03 +0100 | [diff] [blame] | 5912 | return MeanTestHelper<uint8_t, 4, 4>(workloadFactory, inputShape, input, { 0, 3 }, true, outputShape, output); |
narpra01 | 1e4c31d | 2018-09-28 11:07:51 +0100 | [diff] [blame] | 5913 | } |
| 5914 | |
| 5915 | LayerTestResult<uint8_t, 1> MeanVtsUint8Test(armnn::IWorkloadFactory& workloadFactory) |
| 5916 | { |
Matteo Martincigh | 28dcab6 | 2018-10-19 16:40:03 +0100 | [diff] [blame] | 5917 | const unsigned int inputShape[] = { 4, 3, 2 }; |
narpra01 | 1e4c31d | 2018-09-28 11:07:51 +0100 | [diff] [blame] | 5918 | const unsigned int outputShape[] = { 2 }; |
| 5919 | |
Matteo Martincigh | 28dcab6 | 2018-10-19 16:40:03 +0100 | [diff] [blame] | 5920 | std::vector<uint8_t> input({ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, |
| 5921 | 24 }); |
| 5922 | std::vector<uint8_t> output({ 12, 13 }); |
narpra01 | 1e4c31d | 2018-09-28 11:07:51 +0100 | [diff] [blame] | 5923 | |
Matteo Martincigh | 28dcab6 | 2018-10-19 16:40:03 +0100 | [diff] [blame] | 5924 | return MeanTestHelper<uint8_t, 3, 1>(workloadFactory, inputShape, input, { 0, 1 }, false, outputShape, |
| 5925 | output, 0.8f, 5); |
narpra01 | 1e4c31d | 2018-09-28 11:07:51 +0100 | [diff] [blame] | 5926 | } |
| 5927 | |
| 5928 | LayerTestResult<float, 1> MeanFloatSimpleTest(armnn::IWorkloadFactory& workloadFactory) |
| 5929 | { |
| 5930 | const unsigned int inputShape[] = { 3, 2 }; |
| 5931 | const unsigned int outputShape[] = { 1 }; |
| 5932 | |
Matteo Martincigh | 28dcab6 | 2018-10-19 16:40:03 +0100 | [diff] [blame] | 5933 | std::vector<float> input({ 1.0f, 1.0f, 2.0f, 2.0f, 3.0f, 3.0f }); |
| 5934 | std::vector<float> output({ 2.0f }); |
narpra01 | 1e4c31d | 2018-09-28 11:07:51 +0100 | [diff] [blame] | 5935 | |
| 5936 | return MeanTestHelper<float, 2, 1>(workloadFactory, inputShape, input, {}, false, outputShape, output); |
| 5937 | } |
| 5938 | |
| 5939 | LayerTestResult<float, 3> MeanFloatSimpleAxisTest(armnn::IWorkloadFactory& workloadFactory) |
| 5940 | { |
| 5941 | const unsigned int inputShape[] = { 2, 3, 1, 2 }; |
| 5942 | const unsigned int outputShape[] = { 3, 1, 2 }; |
| 5943 | |
Matteo Martincigh | 28dcab6 | 2018-10-19 16:40:03 +0100 | [diff] [blame] | 5944 | std::vector<float> input({ 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f }); |
| 5945 | std::vector<float> output({ 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f }); |
narpra01 | 1e4c31d | 2018-09-28 11:07:51 +0100 | [diff] [blame] | 5946 | |
Matteo Martincigh | 28dcab6 | 2018-10-19 16:40:03 +0100 | [diff] [blame] | 5947 | return MeanTestHelper<float, 4, 3>(workloadFactory, inputShape, input, { 0 }, false, outputShape, output); |
narpra01 | 1e4c31d | 2018-09-28 11:07:51 +0100 | [diff] [blame] | 5948 | } |
| 5949 | |
| 5950 | LayerTestResult<float, 4> MeanFloatKeepDimsTest(armnn::IWorkloadFactory& workloadFactory) |
| 5951 | { |
| 5952 | const unsigned int inputShape[] = { 1, 1, 3, 2 }; |
| 5953 | const unsigned int outputShape[] = { 1, 1, 1, 2 }; |
| 5954 | |
Matteo Martincigh | 28dcab6 | 2018-10-19 16:40:03 +0100 | [diff] [blame] | 5955 | std::vector<float> input({ 1.0f, 1.0f, 2.0f, 2.0f, 3.0f, 3.0f }); |
| 5956 | std::vector<float> output({ 2.0f, 2.0f }); |
narpra01 | 1e4c31d | 2018-09-28 11:07:51 +0100 | [diff] [blame] | 5957 | |
Matteo Martincigh | 28dcab6 | 2018-10-19 16:40:03 +0100 | [diff] [blame] | 5958 | return MeanTestHelper<float, 4, 4>(workloadFactory, inputShape, input, { 2 }, true, outputShape, output); |
narpra01 | 1e4c31d | 2018-09-28 11:07:51 +0100 | [diff] [blame] | 5959 | } |
| 5960 | |
| 5961 | LayerTestResult<float, 4> MeanFloatMultipleDimsTest(armnn::IWorkloadFactory& workloadFactory) |
| 5962 | { |
| 5963 | const unsigned int inputShape[] = { 2, 3, 1, 2 }; |
| 5964 | const unsigned int outputShape[] = { 1, 3, 1, 1 }; |
| 5965 | |
Matteo Martincigh | 28dcab6 | 2018-10-19 16:40:03 +0100 | [diff] [blame] | 5966 | std::vector<float> input({ 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f }); |
| 5967 | std::vector<float> output({ 1.5f, 3.5f, 5.5f }); |
narpra01 | 1e4c31d | 2018-09-28 11:07:51 +0100 | [diff] [blame] | 5968 | |
Matteo Martincigh | 28dcab6 | 2018-10-19 16:40:03 +0100 | [diff] [blame] | 5969 | return MeanTestHelper<float, 4, 4>(workloadFactory, inputShape, input, { 0, 3 }, true, outputShape, output); |
narpra01 | 1e4c31d | 2018-09-28 11:07:51 +0100 | [diff] [blame] | 5970 | } |
| 5971 | |
| 5972 | LayerTestResult<float, 1> MeanVtsFloat1Test(armnn::IWorkloadFactory& workloadFactory) |
| 5973 | { |
Matteo Martincigh | 28dcab6 | 2018-10-19 16:40:03 +0100 | [diff] [blame] | 5974 | const unsigned int inputShape[] = { 4, 3, 2 }; |
narpra01 | 1e4c31d | 2018-09-28 11:07:51 +0100 | [diff] [blame] | 5975 | const unsigned int outputShape[] = { 2 }; |
| 5976 | |
Matteo Martincigh | 28dcab6 | 2018-10-19 16:40:03 +0100 | [diff] [blame] | 5977 | std::vector<float> input({ 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, |
| 5978 | 15.0f, 16.0f, 17.0f, 18.0f, 19.0f, 20.0f, 21.0f, 22.0f, 23.0f, 24.0f }); |
| 5979 | std::vector<float> output({ 12.0f, 13.0f }); |
narpra01 | 1e4c31d | 2018-09-28 11:07:51 +0100 | [diff] [blame] | 5980 | |
Matteo Martincigh | 28dcab6 | 2018-10-19 16:40:03 +0100 | [diff] [blame] | 5981 | return MeanTestHelper<float, 3, 1>(workloadFactory, inputShape, input, { 0, 1 }, false, outputShape, output); |
narpra01 | 1e4c31d | 2018-09-28 11:07:51 +0100 | [diff] [blame] | 5982 | } |
| 5983 | |
| 5984 | LayerTestResult<float, 3> MeanVtsFloat2Test(armnn::IWorkloadFactory& workloadFactory) |
| 5985 | { |
Matteo Martincigh | 28dcab6 | 2018-10-19 16:40:03 +0100 | [diff] [blame] | 5986 | const unsigned int inputShape[] = { 4, 3, 2 }; |
| 5987 | const unsigned int outputShape[] = { 1, 3, 1 }; |
narpra01 | 1e4c31d | 2018-09-28 11:07:51 +0100 | [diff] [blame] | 5988 | |
Matteo Martincigh | 28dcab6 | 2018-10-19 16:40:03 +0100 | [diff] [blame] | 5989 | std::vector<float> input({ 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, |
| 5990 | 15.0f, 16.0f, 17.0f, 18.0f, 19.0f, 20.0f, 21.0f, 22.0f, 23.0f, 24.0f }); |
| 5991 | std::vector<float> output({ 10.5f, 12.5f, 14.5f }); |
narpra01 | 1e4c31d | 2018-09-28 11:07:51 +0100 | [diff] [blame] | 5992 | |
Matteo Martincigh | 28dcab6 | 2018-10-19 16:40:03 +0100 | [diff] [blame] | 5993 | return MeanTestHelper<float, 3, 3>(workloadFactory, inputShape, input, { 0, 2 }, true, outputShape, output); |
| 5994 | } |
| 5995 | |
| 5996 | LayerTestResult<float, 3> MeanVtsFloat3Test(armnn::IWorkloadFactory& workloadFactory) |
| 5997 | { |
| 5998 | const unsigned int inputShape[] = { 1, 2, 2, 1 }; |
| 5999 | const unsigned int outputShape[] = { 1, 2, 1 }; |
| 6000 | |
| 6001 | std::vector<float> input({ 1.0f, 2.0f, 3.0f, 4.0f }); |
| 6002 | std::vector<float> output({ 1.5f, 3.5f }); |
| 6003 | |
| 6004 | return MeanTestHelper<float, 4, 3>(workloadFactory, inputShape, input, { 2 }, false, outputShape, output); |
narpra01 | 1e4c31d | 2018-09-28 11:07:51 +0100 | [diff] [blame] | 6005 | } |
Éanna Ó Catháin | 47c1ddb | 2018-10-12 14:24:13 +0100 | [diff] [blame] | 6006 | |
| 6007 | LayerTestResult<float, 4> AdditionAfterMaxPoolTest(armnn::IWorkloadFactory& workloadFactory) |
| 6008 | { |
| 6009 | // Create Initial Tensor |
| 6010 | // 1, 2, 3 |
| 6011 | // 4, 5, 6 |
| 6012 | // 7, 8, 9 |
| 6013 | |
| 6014 | armnn::TensorInfo poolingInputTensorInfo({ 1, 1, 3, 3}, armnn::GetDataType<float>()); |
| 6015 | armnn::TensorInfo poolingOutputTensorInfo({ 1, 1, 2, 2}, armnn::GetDataType<float>()); |
| 6016 | |
| 6017 | boost::multi_array<float, 4> poolingInput = MakeTensor<float,4>(poolingInputTensorInfo, |
| 6018 | {1, 2, 3, |
| 6019 | 4, 5, 6, |
| 6020 | 7, 8, 9 |
| 6021 | }); |
| 6022 | |
| 6023 | std::unique_ptr<armnn::ITensorHandle> poolingInputHandle = |
| 6024 | workloadFactory.CreateTensorHandle(poolingInputTensorInfo); |
| 6025 | std::unique_ptr<armnn::ITensorHandle> poolingOutputHandle = |
| 6026 | workloadFactory.CreateTensorHandle(poolingOutputTensorInfo); |
| 6027 | |
| 6028 | // Apply MaxPool poolSize = 1x1, stride=2x2 |
| 6029 | // Result = |
| 6030 | // 1, 3 |
| 6031 | // 7, 9 |
| 6032 | armnn::Pooling2dDescriptor descriptor; |
| 6033 | descriptor.m_PoolHeight = 1; |
| 6034 | descriptor.m_PoolWidth = 1; |
| 6035 | descriptor.m_StrideX = 2; |
| 6036 | descriptor.m_StrideY = 2; |
| 6037 | descriptor.m_PoolType = armnn::PoolingAlgorithm::Max; |
| 6038 | |
| 6039 | armnn::Pooling2dQueueDescriptor queueDescriptor; |
| 6040 | queueDescriptor.m_Parameters = descriptor; |
| 6041 | armnn::WorkloadInfo workloadInfo; |
| 6042 | AddInputToWorkload(queueDescriptor, workloadInfo, poolingInputTensorInfo, poolingInputHandle.get()); |
| 6043 | AddOutputToWorkload(queueDescriptor, workloadInfo, poolingOutputTensorInfo, poolingOutputHandle.get()); |
| 6044 | |
| 6045 | // Create the MaxPool |
| 6046 | std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreatePooling2d(queueDescriptor, workloadInfo); |
| 6047 | |
| 6048 | //LayerTestResult<float, 4> result(poolingOutputTensorInfo); |
| 6049 | auto shape( GetTensorShapeAsArray<4>(poolingOutputTensorInfo)); |
| 6050 | boost::multi_array<float, 4> resultMaxPool; |
| 6051 | resultMaxPool.resize(shape); |
| 6052 | |
| 6053 | |
| 6054 | // Create addition with another tensor the same size |
| 6055 | // This would be the result to apply a Conv2d with kernel ones(2) and stride 1x1 |
| 6056 | // with the initial tensor. |
| 6057 | // 12, 16 |
| 6058 | // 24, 28 |
| 6059 | |
| 6060 | armnn::TensorInfo addInputTensorInfo({ 1,1,2,2}, armnn::GetDataType<float>()); |
| 6061 | armnn::TensorInfo addOutputTensorInfo({ 1,1,2,2}, armnn::GetDataType<float>()); |
| 6062 | |
| 6063 | boost::multi_array<float, 4> addInput = MakeTensor<float,4>(addInputTensorInfo, |
| 6064 | {12, 16, |
| 6065 | 24, 28, |
| 6066 | }); |
| 6067 | |
| 6068 | // Expected output tensor after MaxPool and Addition. |
| 6069 | LayerTestResult<float,4> addRet(addOutputTensorInfo); |
| 6070 | addRet.outputExpected = MakeTensor<float, 4>(addOutputTensorInfo, std::vector<float>( |
| 6071 | { |
| 6072 | 13, 19, |
| 6073 | 31, 37 |
| 6074 | })); |
| 6075 | |
| 6076 | std::unique_ptr<armnn::ITensorHandle> addInputHandle = workloadFactory.CreateTensorHandle(addInputTensorInfo); |
| 6077 | std::unique_ptr<armnn::ITensorHandle> addOutputHandle = workloadFactory.CreateTensorHandle(addOutputTensorInfo); |
| 6078 | |
| 6079 | armnn::AdditionQueueDescriptor data; |
| 6080 | armnn::WorkloadInfo info; |
| 6081 | |
| 6082 | // Add the output of the MaxPool and the new tensor |
| 6083 | AddInputToWorkload(data, info, poolingOutputTensorInfo, poolingOutputHandle.get()); |
| 6084 | AddInputToWorkload(data, info, addInputTensorInfo, addInputHandle.get()); |
| 6085 | AddOutputToWorkload(data, info, addOutputTensorInfo, addOutputHandle.get()); |
| 6086 | |
| 6087 | std::unique_ptr<armnn::IWorkload> addWorkload = workloadFactory.CreateAddition(data, info); |
| 6088 | |
| 6089 | poolingInputHandle->Allocate(); |
| 6090 | poolingOutputHandle->Allocate(); |
| 6091 | addInputHandle->Allocate(); |
| 6092 | addOutputHandle->Allocate(); |
| 6093 | |
| 6094 | CopyDataToITensorHandle(poolingInputHandle.get(), &poolingInput[0][0][0][0]); |
| 6095 | CopyDataFromITensorHandle(&resultMaxPool[0][0][0][0], poolingOutputHandle.get()); |
| 6096 | |
| 6097 | CopyDataToITensorHandle(poolingOutputHandle.get(), &resultMaxPool[0][0][0][0]); |
| 6098 | CopyDataToITensorHandle(addInputHandle.get(), &addInput[0][0][0][0]); |
| 6099 | |
| 6100 | workload->Execute(); |
| 6101 | addWorkload->Execute(); |
| 6102 | |
| 6103 | CopyDataFromITensorHandle(&addRet.output[0][0][0][0], addOutputHandle.get()); |
| 6104 | |
| 6105 | workloadFactory.Finalize(); |
| 6106 | |
| 6107 | return addRet; |
| 6108 | } |