Sadik Armagan | 32ca144 | 2020-11-13 17:51:56 +0000 | [diff] [blame] | 1 | // |
Teresa Charlin | ad1b3d7 | 2023-03-14 12:10:28 +0000 | [diff] [blame] | 2 | // Copyright © 2020, 2023 Arm Ltd and Contributors. All rights reserved. |
Sadik Armagan | 32ca144 | 2020-11-13 17:51:56 +0000 | [diff] [blame] | 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
| 5 | |
| 6 | #include "ConvolutionTestHelper.hpp" |
| 7 | |
| 8 | #include <armnn_delegate.hpp> |
| 9 | |
| 10 | #include <flatbuffers/flatbuffers.h> |
| 11 | #include <tensorflow/lite/interpreter.h> |
| 12 | #include <tensorflow/lite/kernels/register.h> |
| 13 | #include <tensorflow/lite/model.h> |
Sadik Armagan | 32ca144 | 2020-11-13 17:51:56 +0000 | [diff] [blame] | 14 | #include <tensorflow/lite/version.h> |
| 15 | |
| 16 | #include <doctest/doctest.h> |
| 17 | |
| 18 | namespace armnnDelegate |
| 19 | { |
| 20 | |
Colm Donelan | eff204a | 2023-11-28 15:46:09 +0000 | [diff] [blame] | 21 | void Conv2DWithBiasesFp32Test() |
Sadik Armagan | 32ca144 | 2020-11-13 17:51:56 +0000 | [diff] [blame] | 22 | { |
| 23 | // Set input data |
| 24 | std::vector<int32_t> inputShape { 1, 5, 5, 1 }; |
| 25 | std::vector<int32_t> filterShape { 1, 3, 3, 1 }; |
| 26 | std::vector<int32_t> biasShape { 1 }; |
| 27 | std::vector<int32_t> outputShape { 1, 3, 3, 1 }; |
| 28 | |
| 29 | static std::vector<float> inputValues = |
| 30 | { |
| 31 | 1, 5, 2, 3, 5, |
| 32 | 8, 7, 3, 6, 3, |
| 33 | 3, 3, 9, 1, 9, |
| 34 | 4, 1, 8, 1, 3, |
| 35 | 6, 8, 1, 9, 2 |
| 36 | }; |
| 37 | |
| 38 | std::vector<float> filterValues = |
| 39 | { |
| 40 | 4, 5, 6, |
| 41 | 0, 0, 0, |
| 42 | 3, 2, 1 |
| 43 | }; |
| 44 | |
| 45 | std::vector<float> biasValues = { 0 }; |
| 46 | |
| 47 | std::vector<float> expectedOutputValues = |
| 48 | { |
| 49 | 23, 33, 24, |
| 50 | 91, 99, 48, |
| 51 | 26, 50, 19 |
| 52 | }; |
| 53 | |
| 54 | tflite::Padding padding = tflite::Padding_SAME; |
| 55 | |
| 56 | ConvolutionTest<float>(tflite::BuiltinOperator_CONV_2D, |
Matthew Sloyan | 080ffd8 | 2023-04-24 12:53:04 +0100 | [diff] [blame] | 57 | ::tflite::TensorType_FLOAT32, |
| 58 | 2, // strideX |
| 59 | 2, // strideY |
| 60 | 1, // dilationX |
| 61 | 1, // dilationY |
| 62 | padding, |
| 63 | tflite::ActivationFunctionType_NONE, |
Matthew Sloyan | 080ffd8 | 2023-04-24 12:53:04 +0100 | [diff] [blame] | 64 | inputShape, |
| 65 | filterShape, |
| 66 | outputShape, |
| 67 | inputValues, |
| 68 | filterValues, |
| 69 | expectedOutputValues, |
| 70 | biasShape, |
| 71 | biasValues); |
Sadik Armagan | 32ca144 | 2020-11-13 17:51:56 +0000 | [diff] [blame] | 72 | } |
| 73 | |
Colm Donelan | eff204a | 2023-11-28 15:46:09 +0000 | [diff] [blame] | 74 | void Conv2DWithBiasesInt8Test() |
Sadik Armagan | 32ca144 | 2020-11-13 17:51:56 +0000 | [diff] [blame] | 75 | { |
| 76 | // Set input data |
| 77 | std::vector<int32_t> inputShape { 1, 2, 2, 1 }; |
| 78 | std::vector<int32_t> filterShape { 1, 2, 2, 1 }; |
| 79 | std::vector<int32_t> biasShape { 1 }; |
| 80 | std::vector<int32_t> outputShape { 1, 2, 2, 1 }; |
| 81 | |
Jan Eilers | eb61612 | 2020-11-20 11:59:40 +0000 | [diff] [blame] | 82 | static std::vector<int8_t> inputValues = { 1, 2, 3, 4 }; |
Sadik Armagan | 32ca144 | 2020-11-13 17:51:56 +0000 | [diff] [blame] | 83 | |
Jan Eilers | eb61612 | 2020-11-20 11:59:40 +0000 | [diff] [blame] | 84 | std::vector<int8_t> filterValues = { 2, 1, 0, 6 }; |
Sadik Armagan | 32ca144 | 2020-11-13 17:51:56 +0000 | [diff] [blame] | 85 | |
| 86 | std::vector<int32_t> biasValues = { 10 }; |
| 87 | |
Jan Eilers | eb61612 | 2020-11-20 11:59:40 +0000 | [diff] [blame] | 88 | std::vector<int8_t> expectedOutputValues = |
Sadik Armagan | 32ca144 | 2020-11-13 17:51:56 +0000 | [diff] [blame] | 89 | { |
| 90 | (1 * 2 + 2 * 1 + 3 * 0 + 4 * 6 + 10) / 2, // 19 |
| 91 | (2 * 2 + 0 * 1 + 4 * 0 + 0 * 6 + 10) / 2, // 7 |
| 92 | (3 * 2 + 4 * 1 + 0 * 0 + 0 * 6 + 10) / 2, // 10 |
| 93 | (4 * 2 + 0 * 1 + 0 * 0 + 0 * 6 + 10) / 2, // 9 |
| 94 | }; |
| 95 | |
| 96 | tflite::Padding padding = tflite::Padding_SAME; |
| 97 | |
Jan Eilers | eb61612 | 2020-11-20 11:59:40 +0000 | [diff] [blame] | 98 | ConvolutionTest<int8_t, int32_t>(tflite::BuiltinOperator_CONV_2D, |
Matthew Sloyan | 080ffd8 | 2023-04-24 12:53:04 +0100 | [diff] [blame] | 99 | ::tflite::TensorType_INT8, |
| 100 | 1, // strideX |
| 101 | 1, // strideY |
| 102 | 1, // dilationX |
| 103 | 1, // dilationY |
| 104 | padding, |
| 105 | tflite::ActivationFunctionType_NONE, |
Matthew Sloyan | 080ffd8 | 2023-04-24 12:53:04 +0100 | [diff] [blame] | 106 | inputShape, |
| 107 | filterShape, |
| 108 | outputShape, |
| 109 | inputValues, |
| 110 | filterValues, |
| 111 | expectedOutputValues, |
| 112 | biasShape, |
| 113 | biasValues); |
Sadik Armagan | 32ca144 | 2020-11-13 17:51:56 +0000 | [diff] [blame] | 114 | } |
| 115 | |
Colm Donelan | eff204a | 2023-11-28 15:46:09 +0000 | [diff] [blame] | 116 | void Conv2DWithBiasesReluUint8Test() |
Sadik Armagan | 32ca144 | 2020-11-13 17:51:56 +0000 | [diff] [blame] | 117 | { |
| 118 | // Set input data |
| 119 | std::vector<int32_t> inputShape { 1, 2, 2, 1 }; |
| 120 | std::vector<int32_t> filterShape { 1, 2, 2, 1 }; |
| 121 | std::vector<int32_t> biasShape { 1 }; |
| 122 | std::vector<int32_t> outputShape { 1, 2, 2, 1 }; |
| 123 | |
| 124 | static std::vector<uint8_t> inputValues = { 1, 2, 4, 8 }; |
| 125 | |
| 126 | std::vector<uint8_t> filterValues = { 2, 1, 0, 6 }; |
| 127 | |
| 128 | std::vector<int32_t> biasValues = { 16 }; |
| 129 | |
| 130 | // factors to consider: |
| 131 | // - the filter zero point is non zero, hence the (x-fz) |
| 132 | // - the output scale is 2 hence the /2 |
| 133 | // - output zero point is non zero, hence the +outZero |
| 134 | // - RELU cuts negative values and then we add the output zero point |
| 135 | uint8_t bias = 16; |
| 136 | uint8_t outZero = 20; |
| 137 | uint8_t fz = 4; // filter zero point |
| 138 | |
| 139 | std::vector<uint8_t> expectedOutputValues = |
| 140 | { |
| 141 | std::max(outZero, static_cast<uint8_t>((1*(2-fz) + 2*(1-fz) + 4*(0-fz) + 8*(6-fz) + bias)/2 + outZero)), |
| 142 | std::max(outZero, static_cast<uint8_t>((2*(2-fz) + 0*(1-fz) + 8*(0-fz) + 0*(6-fz) + bias)/2 + outZero)), |
| 143 | std::max(outZero, static_cast<uint8_t>((4*(2-fz) + 8*(1-fz) + 0*(0-fz) + 0*(6-fz) + bias)/2 + outZero)), |
| 144 | std::max(outZero, static_cast<uint8_t>((8*(2-fz) + 0*(1-fz) + 0*(0-fz) + 0*(6-fz) + bias)/2 + outZero)) |
| 145 | }; |
| 146 | |
| 147 | tflite::Padding padding = tflite::Padding_SAME; |
| 148 | |
| 149 | ConvolutionTest<uint8_t, int32_t>(tflite::BuiltinOperator_CONV_2D, |
Matthew Sloyan | 080ffd8 | 2023-04-24 12:53:04 +0100 | [diff] [blame] | 150 | ::tflite::TensorType_UINT8, |
| 151 | 1, // strideX |
| 152 | 1, // strideY |
| 153 | 1, // dilationX |
| 154 | 1, // dilationY |
| 155 | padding, |
| 156 | tflite::ActivationFunctionType_RELU, |
Matthew Sloyan | 080ffd8 | 2023-04-24 12:53:04 +0100 | [diff] [blame] | 157 | inputShape, |
| 158 | filterShape, |
| 159 | outputShape, |
| 160 | inputValues, |
| 161 | filterValues, |
| 162 | expectedOutputValues, |
| 163 | biasShape, |
| 164 | biasValues, |
| 165 | {1.0f}, // biasScale |
| 166 | {0}, // biasOffset |
| 167 | {1.0f}, // filterScale |
| 168 | {4}, // filterOffsets |
| 169 | 2, // output scale |
| 170 | 20); // output offset |
Sadik Armagan | 32ca144 | 2020-11-13 17:51:56 +0000 | [diff] [blame] | 171 | } |
| 172 | |
Colm Donelan | eff204a | 2023-11-28 15:46:09 +0000 | [diff] [blame] | 173 | void Conv2DWithBiasesRelu6Uint8Test() |
Sadik Armagan | 32ca144 | 2020-11-13 17:51:56 +0000 | [diff] [blame] | 174 | { |
| 175 | // Set input data |
| 176 | std::vector<int32_t> inputShape { 1, 2, 2, 1 }; |
| 177 | std::vector<int32_t> filterShape { 1, 2, 2, 1 }; |
| 178 | std::vector<int32_t> biasShape { 1 }; |
| 179 | std::vector<int32_t> outputShape { 1, 2, 2, 1 }; |
| 180 | |
| 181 | static std::vector<uint8_t> inputValues = { 1, 2, 4, 1 }; |
| 182 | |
| 183 | std::vector<uint8_t> filterValues = { 2, 1, 0, 6 }; |
| 184 | |
| 185 | std::vector<int32_t> biasValues = { 0 }; |
| 186 | |
| 187 | // factors to consider: |
| 188 | // - the output scale is 2 hence the /2 |
| 189 | // - RELU6 cuts output values at +6 |
| 190 | uint8_t relu6Min = 6 / 2; // divide by output scale |
| 191 | |
| 192 | std::vector<uint8_t> expectedOutputValues = |
| 193 | { |
| 194 | std::min(relu6Min, static_cast<uint8_t>((1 * 2 + 2 * 1 + 4 * 0 + 1 * 6) / 2)), |
| 195 | std::min(relu6Min, static_cast<uint8_t>((2 * 2 + 0 * 1 + 1 * 0 + 0 * 6) / 2)), |
| 196 | std::min(relu6Min, static_cast<uint8_t>((4 * 2 + 1 * 1 + 0 * 0 + 0 * 6) / 2)), |
| 197 | std::min(relu6Min, static_cast<uint8_t>((1 * 2 + 0 * 1 + 0 * 0 + 0 * 6) / 2)) |
| 198 | }; |
| 199 | |
| 200 | tflite::Padding padding = tflite::Padding_SAME; |
| 201 | |
| 202 | ConvolutionTest<uint8_t, int32_t>(tflite::BuiltinOperator_CONV_2D, |
Matthew Sloyan | 080ffd8 | 2023-04-24 12:53:04 +0100 | [diff] [blame] | 203 | ::tflite::TensorType_UINT8, |
| 204 | 1, // strideX |
| 205 | 1, // strideY |
| 206 | 1, // dilationX |
| 207 | 1, // dilationY |
| 208 | padding, |
| 209 | tflite::ActivationFunctionType_RELU6, |
Matthew Sloyan | 080ffd8 | 2023-04-24 12:53:04 +0100 | [diff] [blame] | 210 | inputShape, |
| 211 | filterShape, |
| 212 | outputShape, |
| 213 | inputValues, |
| 214 | filterValues, |
| 215 | expectedOutputValues, |
| 216 | biasShape, |
| 217 | biasValues); |
Sadik Armagan | 32ca144 | 2020-11-13 17:51:56 +0000 | [diff] [blame] | 218 | } |
| 219 | |
Jan Eilers | ea835e7 | 2021-04-21 16:58:28 +0100 | [diff] [blame] | 220 | |
Colm Donelan | eff204a | 2023-11-28 15:46:09 +0000 | [diff] [blame] | 221 | void Conv2DPerChannelInt8Test() |
Jan Eilers | ea835e7 | 2021-04-21 16:58:28 +0100 | [diff] [blame] | 222 | { |
| 223 | // Set input data |
| 224 | std::vector<int32_t> inputShape { 1,4,4,2 }; |
| 225 | std::vector<int32_t> filterShape { 4,2,2,2 }; |
| 226 | std::vector<int32_t> biasShape { 4 }; |
| 227 | std::vector<int32_t> outputShape { 1,4,4,4 }; |
| 228 | |
| 229 | static std::vector<int8_t> inputValues = |
| 230 | { |
| 231 | -11, 40,-26, 11,-28, 8, 0, -8, |
| 232 | -10, 34, 47, 0,-33,-14, 28, 35, |
| 233 | 6,-28,-26, 8, 13, 33,-31,-41, |
| 234 | 31,-20,-31,-16, 8,-18,-44, 0 |
| 235 | }; |
| 236 | |
| 237 | std::vector<float> filterScales = { 1.858268, 2.0, 1.992126, 1.905512 }; |
| 238 | int32_t filterQuantizationDim = 0; |
| 239 | std::vector<int8_t> filterValues = |
| 240 | { |
| 241 | 13,-44, 5,-14, 21,-45, 36,-25, |
| 242 | -42, -2, 24,-30,-31, 35, 43,-30, |
| 243 | -20, -5, 25, 17, 18, 20, 4,-46, |
| 244 | -49, 9, -3,-20, 46, 5, 7,-15 |
| 245 | }; |
| 246 | |
| 247 | std::vector<int32_t> biasValues = { 0,0,0,0 }; |
| 248 | std::vector<float> biasScales = { 0.721445, 0.7764700055, 0.773414, 0.739787 }; |
| 249 | |
| 250 | std::vector<int8_t> expectedOutputValues = |
| 251 | { |
| 252 | -1, 9, 3, 5, 1, -1, 5, 9, |
| 253 | 2, 7, -1, 2, 2, 4, 5, 6, |
| 254 | 1, 1, 4, 4, 2, 0, -4, -3, |
| 255 | 0, 6, 12, 6, 3, 0, -1, -2, |
| 256 | 7, -4, 4, 4, 3, 6, 6, 2, |
| 257 | 0, -3, -1, 4, 4, 8, 3, 1, |
| 258 | 5, 0, 0, 1, 4, 7, 4, 6, |
| 259 | 4, 0, 1, 2, 2, 7, 5, 7 |
| 260 | }; |
| 261 | float outputQuantScale = 401.960785f; |
| 262 | int outputQuantOffset = 3; |
| 263 | float inputQuantScale = 0.388235f; |
| 264 | int inputQuantOffset = 1; |
| 265 | |
| 266 | tflite::Padding padding = tflite::Padding_SAME; |
| 267 | |
| 268 | ConvolutionTest<int8_t, int32_t>(tflite::BuiltinOperator_CONV_2D, |
Matthew Sloyan | 080ffd8 | 2023-04-24 12:53:04 +0100 | [diff] [blame] | 269 | ::tflite::TensorType_INT8, |
| 270 | 1, // strideX |
| 271 | 1, // strideY |
| 272 | 1, // dilationX |
| 273 | 1, // dilationY |
| 274 | padding, |
| 275 | tflite::ActivationFunctionType_NONE, |
Matthew Sloyan | 080ffd8 | 2023-04-24 12:53:04 +0100 | [diff] [blame] | 276 | inputShape, |
| 277 | filterShape, |
| 278 | outputShape, |
| 279 | inputValues, |
| 280 | filterValues, |
| 281 | expectedOutputValues, |
| 282 | biasShape, |
| 283 | biasValues, |
| 284 | biasScales, |
| 285 | {0,0,0,0}, |
| 286 | filterScales, |
| 287 | {0,0,0,0}, |
| 288 | outputQuantScale, |
| 289 | outputQuantOffset, |
| 290 | inputQuantScale, |
| 291 | inputQuantOffset, |
| 292 | 1, // depth_multiplier is ignored for conv2d value doesn't matter |
| 293 | filterQuantizationDim); |
Jan Eilers | ea835e7 | 2021-04-21 16:58:28 +0100 | [diff] [blame] | 294 | } |
| 295 | |
Colm Donelan | eff204a | 2023-11-28 15:46:09 +0000 | [diff] [blame] | 296 | TEST_SUITE("Convolution2dTest_Tests") |
Sadik Armagan | 32ca144 | 2020-11-13 17:51:56 +0000 | [diff] [blame] | 297 | { |
| 298 | |
Colm Donelan | eff204a | 2023-11-28 15:46:09 +0000 | [diff] [blame] | 299 | TEST_CASE ("Conv2DWithBiases_Fp32_Test") |
Sadik Armagan | 32ca144 | 2020-11-13 17:51:56 +0000 | [diff] [blame] | 300 | { |
Colm Donelan | eff204a | 2023-11-28 15:46:09 +0000 | [diff] [blame] | 301 | Conv2DWithBiasesFp32Test(); |
Sadik Armagan | 32ca144 | 2020-11-13 17:51:56 +0000 | [diff] [blame] | 302 | } |
| 303 | |
Colm Donelan | eff204a | 2023-11-28 15:46:09 +0000 | [diff] [blame] | 304 | TEST_CASE ("Conv2DWithBiases_Int8_Test") |
Sadik Armagan | 32ca144 | 2020-11-13 17:51:56 +0000 | [diff] [blame] | 305 | { |
Colm Donelan | eff204a | 2023-11-28 15:46:09 +0000 | [diff] [blame] | 306 | Conv2DWithBiasesInt8Test(); |
Sadik Armagan | 32ca144 | 2020-11-13 17:51:56 +0000 | [diff] [blame] | 307 | } |
| 308 | |
Colm Donelan | eff204a | 2023-11-28 15:46:09 +0000 | [diff] [blame] | 309 | TEST_CASE ("Conv2DPerChannel_Int8_Test") |
Jan Eilers | ea835e7 | 2021-04-21 16:58:28 +0100 | [diff] [blame] | 310 | { |
Colm Donelan | eff204a | 2023-11-28 15:46:09 +0000 | [diff] [blame] | 311 | Conv2DPerChannelInt8Test(); |
Jan Eilers | ea835e7 | 2021-04-21 16:58:28 +0100 | [diff] [blame] | 312 | } |
| 313 | |
Colm Donelan | eff204a | 2023-11-28 15:46:09 +0000 | [diff] [blame] | 314 | } //End of TEST_SUITE("Convolution2dTest_Tests") |
Sadik Armagan | 32ca144 | 2020-11-13 17:51:56 +0000 | [diff] [blame] | 315 | |
Sadik Armagan | 32ca144 | 2020-11-13 17:51:56 +0000 | [diff] [blame] | 316 | } // namespace armnnDelegate |