Giorgio Arena | 1f9ca1d | 2018-03-01 11:13:45 +0000 | [diff] [blame] | 1 | /* |
| 2 | * Copyright (c) 2018 ARM Limited. |
| 3 | * |
| 4 | * SPDX-License-Identifier: MIT |
| 5 | * |
| 6 | * Permission is hereby granted, free of charge, to any person obtaining a copy |
| 7 | * of this software and associated documentation files (the "Software"), to |
| 8 | * deal in the Software without restriction, including without limitation the |
| 9 | * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or |
| 10 | * sell copies of the Software, and to permit persons to whom the Software is |
| 11 | * furnished to do so, subject to the following conditions: |
| 12 | * |
| 13 | * The above copyright notice and this permission notice shall be included in all |
| 14 | * copies or substantial portions of the Software. |
| 15 | * |
| 16 | * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
| 17 | * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, |
| 18 | * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE |
| 19 | * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER |
| 20 | * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, |
| 21 | * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE |
| 22 | * SOFTWARE. |
| 23 | */ |
| 24 | #include "Winograd.h" |
| 25 | |
| 26 | #include "tests/validation/Helpers.h" |
| 27 | #include "tests/validation/reference/Utils.h" |
| 28 | |
Gian Marco Iodice | 7e4b239 | 2018-02-22 16:17:20 +0000 | [diff] [blame] | 29 | #include "arm_compute/core/Types.h" |
| 30 | |
Giorgio Arena | 1f9ca1d | 2018-03-01 11:13:45 +0000 | [diff] [blame] | 31 | namespace arm_compute |
| 32 | { |
| 33 | namespace test |
| 34 | { |
| 35 | namespace validation |
| 36 | { |
| 37 | namespace reference |
| 38 | { |
| 39 | namespace |
| 40 | { |
| 41 | template <typename T> |
Giorgio Arena | 2d9de0a | 2018-03-15 17:58:20 +0000 | [diff] [blame^] | 42 | void winograd_filter_transform3x3(const SimpleTensor<T> &in, SimpleTensor<T> &out, const Size2D &output_tile) |
Gian Marco Iodice | 7e4b239 | 2018-02-22 16:17:20 +0000 | [diff] [blame] | 43 | { |
Giorgio Arena | 2d9de0a | 2018-03-15 17:58:20 +0000 | [diff] [blame^] | 44 | const bool is_2x2 = (output_tile.width == 2); |
| 45 | const unsigned int transf_side = is_2x2 ? 4u : 6u; |
| 46 | |
Gian Marco Iodice | 7e4b239 | 2018-02-22 16:17:20 +0000 | [diff] [blame] | 47 | // Simple tensor for the 3x3 input tile |
| 48 | SimpleTensor<T> input_tile{ TensorShape(3u, 3u), in.data_type(), 1 }; |
| 49 | |
| 50 | // Simple tensor for the transformation matrix |
Giorgio Arena | 2d9de0a | 2018-03-15 17:58:20 +0000 | [diff] [blame^] | 51 | SimpleTensor<T> trans_matrix{ TensorShape(3u, transf_side), in.data_type(), 1 }; |
Gian Marco Iodice | 7e4b239 | 2018-02-22 16:17:20 +0000 | [diff] [blame] | 52 | |
| 53 | // Simple tensor for the transformation matrix transpose |
Giorgio Arena | 2d9de0a | 2018-03-15 17:58:20 +0000 | [diff] [blame^] | 54 | SimpleTensor<T> trans_matrix_transposed{ TensorShape(transf_side, 3u), in.data_type(), 1 }; |
Gian Marco Iodice | 7e4b239 | 2018-02-22 16:17:20 +0000 | [diff] [blame] | 55 | |
Giorgio Arena | 2d9de0a | 2018-03-15 17:58:20 +0000 | [diff] [blame^] | 56 | // Simple tensor for the 3xSide temporary tile |
| 57 | SimpleTensor<T> tmp_tile{ TensorShape(3u, transf_side), in.data_type(), 1 }; |
Gian Marco Iodice | 7e4b239 | 2018-02-22 16:17:20 +0000 | [diff] [blame] | 58 | |
Giorgio Arena | 2d9de0a | 2018-03-15 17:58:20 +0000 | [diff] [blame^] | 59 | // Simple tensor for the SidexSide output tile |
| 60 | SimpleTensor<T> transf_tile{ TensorShape(transf_side, transf_side), in.data_type(), 1 }; |
Gian Marco Iodice | 7e4b239 | 2018-02-22 16:17:20 +0000 | [diff] [blame] | 61 | |
Giorgio Arena | 2d9de0a | 2018-03-15 17:58:20 +0000 | [diff] [blame^] | 62 | if(is_2x2) |
| 63 | { |
| 64 | // Initialize 3x4 transformation matrix |
| 65 | // 1 | 0 | 0 |
| 66 | // 0.5 | 0.5 | 0.5 |
| 67 | // 0.5 |-0.5 | 0.5 |
| 68 | // 0 | 0 | 1 |
| 69 | trans_matrix[0 + 0 * 3] = 1.0f; |
| 70 | trans_matrix[1 + 0 * 3] = 0.0f; |
| 71 | trans_matrix[2 + 0 * 3] = 0.0f; |
| 72 | trans_matrix[0 + 1 * 3] = 0.5f; |
| 73 | trans_matrix[1 + 1 * 3] = 0.5f; |
| 74 | trans_matrix[2 + 1 * 3] = 0.5f; |
| 75 | trans_matrix[0 + 2 * 3] = 0.5f; |
| 76 | trans_matrix[1 + 2 * 3] = -0.5f; |
| 77 | trans_matrix[2 + 2 * 3] = 0.5f; |
| 78 | trans_matrix[0 + 3 * 3] = 0.0f; |
| 79 | trans_matrix[1 + 3 * 3] = 0.0f; |
| 80 | trans_matrix[2 + 3 * 3] = 1.0f; |
| 81 | } |
| 82 | else |
| 83 | { |
| 84 | // Initialize 3x6 transformation matrix |
| 85 | // 1/4 | 0 | 0 |
| 86 | // -1/6 | -1/6 | -1/6 |
| 87 | // -1/6 | 1/6 | -1/6 |
| 88 | // 1/24 | 1/12 | 1/6 |
| 89 | // 1/24 | -1/12 | 1/6 |
| 90 | // 0 | 0 | 1 |
| 91 | trans_matrix[0 + 0 * 3] = 1.0f / 4.0f; |
| 92 | trans_matrix[1 + 0 * 3] = 0.0f; |
| 93 | trans_matrix[2 + 0 * 3] = 0.0f; |
| 94 | trans_matrix[0 + 1 * 3] = -1.0f / 6.0f; |
| 95 | trans_matrix[1 + 1 * 3] = -1.0f / 6.0f; |
| 96 | trans_matrix[2 + 1 * 3] = -1.0f / 6.0f; |
| 97 | trans_matrix[0 + 2 * 3] = -1.0f / 6.0f; |
| 98 | trans_matrix[1 + 2 * 3] = 1.0f / 6.0f; |
| 99 | trans_matrix[2 + 2 * 3] = -1.0f / 6.0f; |
| 100 | trans_matrix[0 + 3 * 3] = 1.0f / 24.0f; |
| 101 | trans_matrix[1 + 3 * 3] = 1.0f / 12.0f; |
| 102 | trans_matrix[2 + 3 * 3] = 1.0f / 6.0f; |
| 103 | trans_matrix[0 + 4 * 3] = 1.0f / 24.0f; |
| 104 | trans_matrix[1 + 4 * 3] = -1.0f / 12.0f; |
| 105 | trans_matrix[2 + 4 * 3] = 1.0f / 6.0f; |
| 106 | trans_matrix[0 + 5 * 3] = 0.0f; |
| 107 | trans_matrix[1 + 5 * 3] = 0.0f; |
| 108 | trans_matrix[2 + 5 * 3] = 1.0f; |
| 109 | } |
Gian Marco Iodice | 7e4b239 | 2018-02-22 16:17:20 +0000 | [diff] [blame] | 110 | |
| 111 | // Transpose the transformation matrix |
| 112 | transpose_matrix(trans_matrix, trans_matrix_transposed); |
| 113 | |
| 114 | const int num_channels = in.shape()[2]; |
| 115 | const int num_filters = in.shape()[3]; |
| 116 | const int num_batches = in.shape().total_size() / (9 * num_channels * num_filters); |
| 117 | |
| 118 | for(int n = 0; n < num_batches; ++n) |
| 119 | { |
| 120 | for(int w = 0; w < num_filters; ++w) |
| 121 | { |
| 122 | for(int z = 0; z < num_channels; ++z) |
| 123 | { |
| 124 | // Load the 3x3 tile from the input tensor |
| 125 | get_tile(in, input_tile, Coordinates(0, 0, z, w, n)); |
| 126 | |
| 127 | // First transformation |
| 128 | matrix_multiply(trans_matrix, input_tile, tmp_tile); |
| 129 | |
| 130 | // Second transformation |
Giorgio Arena | 2d9de0a | 2018-03-15 17:58:20 +0000 | [diff] [blame^] | 131 | matrix_multiply(tmp_tile, trans_matrix_transposed, transf_tile); |
Gian Marco Iodice | 7e4b239 | 2018-02-22 16:17:20 +0000 | [diff] [blame] | 132 | |
| 133 | // Store the 4x4 output tile across the 16 channels |
Giorgio Arena | 2d9de0a | 2018-03-15 17:58:20 +0000 | [diff] [blame^] | 134 | const int output_offset = w + z * num_filters; |
| 135 | |
| 136 | for(unsigned int out_h = 0, out_pos = 0; out_h < transf_side; ++out_h) |
| 137 | { |
| 138 | for(unsigned int out_w = 0; out_w < transf_side; ++out_w, ++out_pos) |
| 139 | { |
| 140 | out[output_offset + out_pos * num_filters * num_channels] = transf_tile[out_w + out_h * transf_side]; |
| 141 | } |
| 142 | } |
Gian Marco Iodice | 7e4b239 | 2018-02-22 16:17:20 +0000 | [diff] [blame] | 143 | } |
| 144 | } |
| 145 | } |
| 146 | } |
Gian Marco Iodice | d2fab73 | 2018-03-02 11:18:12 +0000 | [diff] [blame] | 147 | |
| 148 | template <typename T> |
| 149 | void winograd_input_transform3x3(const SimpleTensor<T> &src, SimpleTensor<T> &dst, const PadStrideInfo &conv_info) |
| 150 | { |
| 151 | TensorShape shape4x4(4u, 4u); |
| 152 | |
| 153 | // Simple tensor for the 4x4 input tile |
| 154 | SimpleTensor<T> src_tile{ shape4x4, src.data_type() }; |
| 155 | |
| 156 | // Simple tensor for the 4x4 temporary tile |
| 157 | SimpleTensor<T> tmp_tile{ shape4x4, src.data_type() }; |
| 158 | |
| 159 | // Simple tensor for the 4x4 output tile |
| 160 | SimpleTensor<T> dst_tile{ shape4x4, src.data_type() }; |
| 161 | |
| 162 | // Simple tensor for the transformation matrix |
| 163 | SimpleTensor<T> matrix{ shape4x4, src.data_type() }; |
| 164 | |
| 165 | // Simple tensor for the transformation matrix transposed |
| 166 | SimpleTensor<T> matrix_transposed{ shape4x4, src.data_type() }; |
| 167 | |
| 168 | const float matrix_values[] = { 1.f, 0.f, -1.f, 0.f, |
| 169 | 0.f, 1.f, 1.f, 0.f, |
| 170 | 0.f, -1.f, 1.f, 0.f, |
| 171 | 0.f, 1.f, 0.f, -1.f |
| 172 | }; |
| 173 | |
| 174 | for(int i = 0; i < matrix.num_elements(); ++i) |
| 175 | { |
| 176 | matrix[i] = matrix_values[i]; |
| 177 | } |
| 178 | |
| 179 | transpose_matrix(matrix, matrix_transposed); |
| 180 | |
| 181 | const int in_w = src.shape().x(); |
| 182 | const int in_h = src.shape().y(); |
| 183 | const int in_d = src.shape().z(); |
| 184 | const int num_batches = src.shape().total_size() / (in_w * in_h * in_d); |
| 185 | const int num_tiles_x = std::ceil((in_w - 2 + conv_info.pad_left() + conv_info.pad_right()) / 2.0f); |
| 186 | const int num_tiles_y = std::ceil((in_h - 2 + conv_info.pad_top() + conv_info.pad_bottom()) / 2.0f); |
| 187 | |
| 188 | ARM_COMPUTE_ERROR_ON((num_tiles_x * num_tiles_y) != static_cast<int>(dst.shape().y())); |
| 189 | |
| 190 | for(int b = 0; b < num_batches; ++b) |
| 191 | { |
| 192 | for(int z = 0; z < in_d; ++z) |
| 193 | { |
| 194 | for(int y = 0; y < num_tiles_y; ++y) |
| 195 | { |
| 196 | for(int x = 0; x < num_tiles_x; ++x) |
| 197 | { |
| 198 | int xi = x * 2 - conv_info.pad_left(); |
| 199 | int yi = y * 2 - conv_info.pad_top(); |
| 200 | |
| 201 | // Get the 4x4 tile from the input tensor |
| 202 | get_tile(src, src_tile, Coordinates(xi, yi, z, b)); |
| 203 | |
| 204 | // Compute the transformation |
| 205 | matrix_multiply(matrix, src_tile, tmp_tile); |
| 206 | matrix_multiply(tmp_tile, matrix_transposed, dst_tile); |
| 207 | |
| 208 | // Store the 4x4 output tile across the 16 channels |
| 209 | for(int i = 0; i < 16; ++i) |
| 210 | { |
| 211 | int xo = z; |
| 212 | int yo = x + y * num_tiles_x; |
| 213 | dst[coords2index(dst.shape(), Coordinates(xo, yo, i, b))] = dst_tile[i]; |
| 214 | } |
| 215 | } |
| 216 | } |
| 217 | } |
| 218 | } |
| 219 | } |
| 220 | |
| 221 | template <typename T> |
| 222 | void winograd_output_transform3x3(const SimpleTensor<T> &in, SimpleTensor<T> &out, int num_tiles_x) |
| 223 | { |
| 224 | ARM_COMPUTE_ERROR_ON(in.shape()[2] != 16); |
| 225 | ARM_COMPUTE_ERROR_ON(in.shape()[0] != out.shape()[2]); |
| 226 | |
| 227 | // Simple tensor for the 3x3 input tile |
| 228 | SimpleTensor<T> input_tile{ TensorShape(4u, 4u), in.data_type(), 1 }; |
| 229 | |
| 230 | // Simple tensor for the transformation matrix |
| 231 | SimpleTensor<T> trans_matrix{ TensorShape(4u, 2u), in.data_type(), 1 }; |
| 232 | |
| 233 | // Simple tensor for the transformation matrix transpose |
| 234 | SimpleTensor<T> trans_matrix_transposed{ TensorShape(2u, 4u), in.data_type(), 1 }; |
| 235 | |
| 236 | // Simple tensor for the 4x3 temporary tile |
| 237 | SimpleTensor<T> tmp_tile{ TensorShape(4u, 2u), in.data_type(), 1 }; |
| 238 | |
| 239 | // Simple tensor for the 4x4 output tile |
| 240 | SimpleTensor<T> output_tile{ TensorShape(2u, 2u), in.data_type(), 1 }; |
| 241 | |
| 242 | // Initialize transformation matrix |
| 243 | // 1 | 1 | 1 | 1 |
| 244 | // 0 | 1 | -1 | -1 |
| 245 | trans_matrix[0 + 0 * 4] = 1.0f; |
| 246 | trans_matrix[1 + 0 * 4] = 1.0f; |
| 247 | trans_matrix[2 + 0 * 4] = 1.0f; |
| 248 | trans_matrix[3 + 0 * 4] = 0.0f; |
| 249 | trans_matrix[0 + 1 * 4] = 0.0f; |
| 250 | trans_matrix[1 + 1 * 4] = 1.0f; |
| 251 | trans_matrix[2 + 1 * 4] = -1.0f; |
| 252 | trans_matrix[3 + 1 * 4] = -1.0f; |
| 253 | |
| 254 | // Transpose the transformation matrix |
| 255 | transpose_matrix(trans_matrix, trans_matrix_transposed); |
| 256 | |
| 257 | const int w_in = in.shape()[0]; |
| 258 | const int h_in = in.shape()[1]; |
| 259 | const int c_in = in.shape()[2]; |
| 260 | const int w_out = out.shape()[0]; |
| 261 | const int h_out = out.shape()[1]; |
| 262 | const int c_out = out.shape()[2]; |
| 263 | const int num_batches = in.shape().total_size() / (w_in * h_in * c_in); |
| 264 | |
| 265 | // Input strides |
| 266 | const int stridey_in = w_in; |
| 267 | const int stridez_in = stridey_in * h_in; |
| 268 | const int stridew_in = stridez_in * c_in; |
| 269 | |
| 270 | // Output strides |
| 271 | const int stridey_out = w_out; |
| 272 | const int stridez_out = stridey_out * h_out; |
| 273 | const int stridew_out = stridez_out * c_out; |
| 274 | |
| 275 | for(int n = 0; n < num_batches; ++n) |
| 276 | { |
| 277 | for(int y = 0; y < h_in; ++y) |
| 278 | { |
| 279 | for(int x = 0; x < w_in; ++x) |
| 280 | { |
| 281 | // Load the 4x4 tile across the 16 channels of the input tensor |
| 282 | for(int z = 0; z < c_in; ++z) |
| 283 | { |
| 284 | input_tile[z] = in[x + (y * stridey_in) + (z * stridez_in) + (n * stridew_in)]; |
| 285 | } |
| 286 | |
| 287 | // First transformation |
| 288 | matrix_multiply(trans_matrix, input_tile, tmp_tile); |
| 289 | |
| 290 | // Second transformation |
| 291 | matrix_multiply(tmp_tile, trans_matrix_transposed, output_tile); |
| 292 | |
| 293 | // Store the 2x2 output tile |
| 294 | const int xo = (y % num_tiles_x) * 2; |
| 295 | const int yo = (y / num_tiles_x) * 2; |
| 296 | const int zo = x; |
| 297 | |
| 298 | const int output_offset = xo + (yo * stridey_out) + (zo * stridez_out) + (n * stridew_out); |
| 299 | out[output_offset + 0 * stridey_out + 0] = output_tile[0 + 0 * 2]; |
| 300 | |
| 301 | // Check out-of-bound writes |
| 302 | if(xo + 1 < w_out) |
| 303 | { |
| 304 | out[output_offset + 0 * stridey_out + 1] = output_tile[1 + 0 * 2]; |
| 305 | } |
| 306 | |
| 307 | if(yo + 1 < h_out) |
| 308 | { |
| 309 | out[output_offset + 1 * stridey_out + 0] = output_tile[0 + 1 * 2]; |
| 310 | } |
| 311 | |
| 312 | if((yo + 1 < h_out) && (xo + 1 < w_out)) |
| 313 | { |
| 314 | out[output_offset + 1 * stridey_out + 1] = output_tile[1 + 1 * 2]; |
| 315 | } |
| 316 | } |
| 317 | } |
| 318 | } |
| 319 | } |
Giorgio Arena | 1f9ca1d | 2018-03-01 11:13:45 +0000 | [diff] [blame] | 320 | } // namespace |
| 321 | |
| 322 | template <typename T> |
| 323 | SimpleTensor<T> winograd_input_transform(const SimpleTensor<T> &src, const TensorShape &dst_shape, const PadStrideInfo &conv_info, const Size2D &kernel_dims) |
| 324 | { |
| 325 | ARM_COMPUTE_ERROR_ON(kernel_dims.width != kernel_dims.height); |
| 326 | ARM_COMPUTE_ERROR_ON(src.data_layout() != DataLayout::NCHW); |
| 327 | |
| 328 | SimpleTensor<T> dst{ dst_shape, src.data_type() }; |
| 329 | |
| 330 | switch(kernel_dims.width) |
| 331 | { |
| 332 | case 3: |
| 333 | winograd_input_transform3x3(src, dst, conv_info); |
| 334 | break; |
| 335 | default: |
| 336 | ARM_COMPUTE_ERROR("Only 3x3 kernels are supported"); |
| 337 | } |
| 338 | |
| 339 | return dst; |
| 340 | } |
| 341 | |
Gian Marco Iodice | 7e4b239 | 2018-02-22 16:17:20 +0000 | [diff] [blame] | 342 | template <typename T> |
Giorgio Arena | 2d9de0a | 2018-03-15 17:58:20 +0000 | [diff] [blame^] | 343 | SimpleTensor<T> winograd_filter_transform(const SimpleTensor<T> &in, const TensorShape &output_shape, const Size2D &output_tile) |
Gian Marco Iodice | 7e4b239 | 2018-02-22 16:17:20 +0000 | [diff] [blame] | 344 | { |
| 345 | ARM_COMPUTE_ERROR_ON_MSG(in.data_layout() != DataLayout::NCHW, "Only supported NCHW data format"); |
| 346 | |
| 347 | // Create reference |
| 348 | SimpleTensor<T> out{ output_shape, in.data_type(), 1 }; |
| 349 | |
| 350 | switch(in.shape()[0]) |
| 351 | { |
| 352 | case 3: |
Giorgio Arena | 2d9de0a | 2018-03-15 17:58:20 +0000 | [diff] [blame^] | 353 | winograd_filter_transform3x3(in, out, output_tile); |
Gian Marco Iodice | 7e4b239 | 2018-02-22 16:17:20 +0000 | [diff] [blame] | 354 | break; |
| 355 | default: |
| 356 | ARM_COMPUTE_ERROR("Only supported 3x3 kernel"); |
| 357 | break; |
| 358 | } |
| 359 | |
| 360 | return out; |
| 361 | } |
| 362 | |
Gian Marco Iodice | d2fab73 | 2018-03-02 11:18:12 +0000 | [diff] [blame] | 363 | template <typename T> |
| 364 | SimpleTensor<T> winograd_output_transform(const SimpleTensor<T> &in, const TensorShape &output_shape, const Size2D &kernel_dims, const Size2D &num_tiles) |
| 365 | { |
| 366 | ARM_COMPUTE_ERROR_ON_MSG(in.data_layout() != DataLayout::NCHW, "Only supported NCHW data format"); |
| 367 | ARM_COMPUTE_ERROR_ON(kernel_dims.width != kernel_dims.height); |
| 368 | ARM_COMPUTE_ERROR_ON(in.shape()[1] != num_tiles.area()); |
| 369 | |
| 370 | // Create reference |
| 371 | SimpleTensor<T> out{ output_shape, in.data_type(), 1 }; |
| 372 | |
| 373 | switch(kernel_dims.width) |
| 374 | { |
| 375 | case 3: |
| 376 | winograd_output_transform3x3(in, out, num_tiles.width); |
| 377 | break; |
| 378 | default: |
| 379 | ARM_COMPUTE_ERROR("Only supported 3x3 kernel"); |
| 380 | break; |
| 381 | } |
| 382 | |
| 383 | return out; |
| 384 | } |
| 385 | |
Giorgio Arena | 1f9ca1d | 2018-03-01 11:13:45 +0000 | [diff] [blame] | 386 | template SimpleTensor<float> winograd_input_transform(const SimpleTensor<float> &src, const TensorShape &dst_shape, const PadStrideInfo &conv_info, const Size2D &kernel_dims); |
Giorgio Arena | 2d9de0a | 2018-03-15 17:58:20 +0000 | [diff] [blame^] | 387 | template SimpleTensor<float> winograd_filter_transform(const SimpleTensor<float> &in, const TensorShape &output_shape, const Size2D &output_tile); |
Gian Marco Iodice | d2fab73 | 2018-03-02 11:18:12 +0000 | [diff] [blame] | 388 | template SimpleTensor<float> winograd_output_transform(const SimpleTensor<float> &in, const TensorShape &output_shape, const Size2D &kernel_dims, const Size2D &num_tiles); |
Giorgio Arena | 1f9ca1d | 2018-03-01 11:13:45 +0000 | [diff] [blame] | 389 | } // namespace reference |
| 390 | } // namespace validation |
| 391 | } // namespace test |
| 392 | } // namespace arm_compute |