| XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 1 | // Copyright (c) Facebook, Inc. and its affiliates. |
| 2 | // All rights reserved. |
| 3 | // |
| 4 | // Copyright 2019 Google LLC |
| 5 | // |
| 6 | // This source code is licensed under the BSD-style license found in the |
| 7 | // LICENSE file in the root directory of this source tree. |
| 8 | |
| 9 | #include <gtest/gtest.h> |
| 10 | |
| 11 | #include <xnnpack/params.h> |
| 12 | |
| 13 | #include "average-pooling-operator-tester.h" |
| 14 | |
| 15 | |
| 16 | TEST(AVERAGE_POOLING_OP_Q8, unit_batch_small_1xM_pool) { |
| 17 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 18 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 19 | for (size_t pool_size = 2; pool_size <= xnn_params.q8.avgpool.mr; pool_size++) { |
| 20 | AveragePoolingOperatorTester() |
| 21 | .batch_size(1) |
| 22 | .input_height(2) |
| 23 | .input_width(pool_size + 2) |
| 24 | .pooling_height(1) |
| 25 | .pooling_width(pool_size) |
| 26 | .channels(channels) |
| 27 | .TestQ8(); |
| 28 | } |
| 29 | } |
| 30 | } |
| 31 | |
| 32 | TEST(AVERAGE_POOLING_OP_Q8, unit_batch_small_1xM_pool_with_padding) { |
| 33 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 34 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 35 | for (size_t pool_size = 3; pool_size <= xnn_params.q8.avgpool.mr; pool_size++) { |
| 36 | for (size_t padding_left = 0; padding_left <= 1; padding_left++) { |
| 37 | for (size_t padding_right = 0; padding_right <= 1; padding_right++) { |
| 38 | AveragePoolingOperatorTester() |
| 39 | .batch_size(1) |
| 40 | .input_height(2) |
| 41 | .input_width(pool_size + 2) |
| 42 | .padding_left(padding_left) |
| 43 | .padding_right(padding_right) |
| 44 | .pooling_height(1) |
| 45 | .pooling_width(pool_size) |
| 46 | .channels(channels) |
| 47 | .TestQ8(); |
| 48 | } |
| 49 | } |
| 50 | } |
| 51 | } |
| 52 | } |
| 53 | |
| 54 | TEST(AVERAGE_POOLING_OP_Q8, unit_batch_small_1xM_pool_with_stride) { |
| 55 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 56 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 57 | for (size_t pool_size = 2; pool_size <= xnn_params.q8.avgpool.mr; pool_size++) { |
| 58 | AveragePoolingOperatorTester() |
| 59 | .batch_size(1) |
| 60 | .input_height(2) |
| 61 | .input_width(pool_size + 4) |
| 62 | .pooling_height(1) |
| 63 | .pooling_width(pool_size) |
| 64 | .stride_width(2) |
| 65 | .channels(channels) |
| 66 | .TestQ8(); |
| 67 | } |
| 68 | } |
| 69 | } |
| 70 | |
| 71 | TEST(AVERAGE_POOLING_OP_Q8, unit_batch_small_Mx1_pool) { |
| 72 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 73 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 74 | for (size_t pool_size = 2; pool_size <= xnn_params.q8.avgpool.mr; pool_size++) { |
| 75 | AveragePoolingOperatorTester() |
| 76 | .batch_size(1) |
| 77 | .input_height(pool_size + 1) |
| 78 | .input_width(3) |
| 79 | .pooling_height(pool_size) |
| 80 | .pooling_width(1) |
| 81 | .channels(channels) |
| 82 | .TestQ8(); |
| 83 | } |
| 84 | } |
| 85 | } |
| 86 | |
| 87 | TEST(AVERAGE_POOLING_OP_Q8, unit_batch_small_Mx1_pool_with_padding) { |
| 88 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 89 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 90 | for (size_t pool_size = 2; pool_size <= xnn_params.q8.avgpool.mr; pool_size++) { |
| 91 | for (size_t padding_top = 0; padding_top <= 1; padding_top++) { |
| 92 | for (size_t padding_bottom = 0; padding_bottom <= 1; padding_bottom++) { |
| 93 | AveragePoolingOperatorTester() |
| 94 | .batch_size(1) |
| 95 | .input_height(pool_size + 1) |
| 96 | .input_width(3) |
| 97 | .padding_top(padding_top) |
| 98 | .padding_bottom(padding_bottom) |
| 99 | .pooling_height(pool_size) |
| 100 | .pooling_width(1) |
| 101 | .channels(channels) |
| 102 | .TestQ8(); |
| 103 | } |
| 104 | } |
| 105 | } |
| 106 | } |
| 107 | } |
| 108 | |
| 109 | TEST(AVERAGE_POOLING_OP_Q8, unit_batch_small_Mx1_pool_with_stride) { |
| 110 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 111 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 112 | for (size_t pool_size = 2; pool_size <= xnn_params.q8.avgpool.mr; pool_size++) { |
| 113 | AveragePoolingOperatorTester() |
| 114 | .batch_size(1) |
| 115 | .input_height(pool_size + 3) |
| 116 | .input_width(3) |
| 117 | .pooling_height(pool_size) |
| 118 | .pooling_width(1) |
| 119 | .stride_height(2) |
| 120 | .channels(channels) |
| 121 | .TestQ8(); |
| 122 | } |
| 123 | } |
| 124 | } |
| 125 | |
| 126 | TEST(AVERAGE_POOLING_OP_Q8, unit_batch_small_pool_with_input_stride) { |
| 127 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 128 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 129 | for (size_t pool_size = 2; pool_size <= xnn_params.q8.avgpool.mr; pool_size++) { |
| 130 | AveragePoolingOperatorTester() |
| 131 | .batch_size(1) |
| 132 | .input_height(pool_size + 1) |
| 133 | .input_width(3) |
| 134 | .pooling_height(pool_size) |
| 135 | .pooling_width(1) |
| 136 | .channels(channels) |
| 137 | .input_pixel_stride(5 * channels) |
| 138 | .TestQ8(); |
| 139 | AveragePoolingOperatorTester() |
| 140 | .batch_size(1) |
| 141 | .input_height(2) |
| 142 | .input_width(pool_size + 2) |
| 143 | .pooling_height(1) |
| 144 | .pooling_width(pool_size) |
| 145 | .channels(channels) |
| 146 | .input_pixel_stride(5 * channels) |
| 147 | .TestQ8(); |
| 148 | } |
| 149 | } |
| 150 | } |
| 151 | |
| 152 | TEST(AVERAGE_POOLING_OP_Q8, unit_batch_small_pool_with_output_stride) { |
| 153 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 154 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 155 | for (size_t pool_size = 2; pool_size <= xnn_params.q8.avgpool.mr; pool_size++) { |
| 156 | AveragePoolingOperatorTester() |
| 157 | .batch_size(1) |
| 158 | .input_height(pool_size + 1) |
| 159 | .input_width(3) |
| 160 | .pooling_height(pool_size) |
| 161 | .pooling_width(1) |
| 162 | .channels(channels) |
| 163 | .output_pixel_stride(5 * channels) |
| 164 | .TestQ8(); |
| 165 | AveragePoolingOperatorTester() |
| 166 | .batch_size(1) |
| 167 | .input_height(2) |
| 168 | .input_width(pool_size + 2) |
| 169 | .pooling_height(1) |
| 170 | .pooling_width(pool_size) |
| 171 | .channels(channels) |
| 172 | .output_pixel_stride(5 * channels) |
| 173 | .TestQ8(); |
| 174 | } |
| 175 | } |
| 176 | } |
| 177 | |
| 178 | TEST(AVERAGE_POOLING_OP_Q8, unit_batch_small_pool_with_input_scale) { |
| 179 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 180 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 181 | for (size_t pool_size = 2; pool_size <= xnn_params.q8.avgpool.mr; pool_size++) { |
| 182 | for (float input_scale = 0.01f; input_scale < 100.0f; input_scale *= 3.14159265f) { |
| 183 | AveragePoolingOperatorTester() |
| 184 | .batch_size(1) |
| 185 | .input_height(pool_size + 1) |
| 186 | .input_width(3) |
| 187 | .pooling_height(pool_size) |
| 188 | .pooling_width(1) |
| 189 | .channels(channels) |
| 190 | .input_scale(input_scale) |
| 191 | .TestQ8(); |
| 192 | AveragePoolingOperatorTester() |
| 193 | .batch_size(1) |
| 194 | .input_height(2) |
| 195 | .input_width(pool_size + 2) |
| 196 | .pooling_height(1) |
| 197 | .pooling_width(pool_size) |
| 198 | .channels(channels) |
| 199 | .input_scale(input_scale) |
| 200 | .TestQ8(); |
| 201 | } |
| 202 | } |
| 203 | } |
| 204 | } |
| 205 | |
| 206 | TEST(AVERAGE_POOLING_OP_Q8, unit_batch_small_pool_with_input_zero_point) { |
| 207 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 208 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 209 | for (size_t pool_size = 2; pool_size <= xnn_params.q8.avgpool.mr; pool_size++) { |
| 210 | for (int32_t input_zero_point = 0; input_zero_point <= 255; input_zero_point += 51) { |
| 211 | AveragePoolingOperatorTester() |
| 212 | .batch_size(1) |
| 213 | .input_height(pool_size + 1) |
| 214 | .input_width(3) |
| 215 | .pooling_height(pool_size) |
| 216 | .pooling_width(1) |
| 217 | .channels(channels) |
| 218 | .input_zero_point(uint8_t(input_zero_point)) |
| 219 | .TestQ8(); |
| 220 | AveragePoolingOperatorTester() |
| 221 | .batch_size(1) |
| 222 | .input_height(2) |
| 223 | .input_width(pool_size + 2) |
| 224 | .pooling_height(1) |
| 225 | .pooling_width(pool_size) |
| 226 | .channels(channels) |
| 227 | .input_zero_point(uint8_t(input_zero_point)) |
| 228 | .TestQ8(); |
| 229 | } |
| 230 | } |
| 231 | } |
| 232 | } |
| 233 | |
| 234 | TEST(AVERAGE_POOLING_OP_Q8, unit_batch_small_pool_with_output_scale) { |
| 235 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 236 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 237 | for (size_t pool_size = 2; pool_size <= xnn_params.q8.avgpool.mr; pool_size++) { |
| 238 | for (float output_scale = 0.01f; output_scale < 100.0f; output_scale *= 3.14159265f) { |
| 239 | AveragePoolingOperatorTester() |
| 240 | .batch_size(1) |
| 241 | .input_height(pool_size + 1) |
| 242 | .input_width(3) |
| 243 | .pooling_height(pool_size) |
| 244 | .pooling_width(1) |
| 245 | .channels(channels) |
| 246 | .output_scale(output_scale) |
| 247 | .TestQ8(); |
| 248 | AveragePoolingOperatorTester() |
| 249 | .batch_size(1) |
| 250 | .input_height(2) |
| 251 | .input_width(pool_size + 2) |
| 252 | .pooling_height(1) |
| 253 | .pooling_width(pool_size) |
| 254 | .channels(channels) |
| 255 | .output_scale(output_scale) |
| 256 | .TestQ8(); |
| 257 | } |
| 258 | } |
| 259 | } |
| 260 | } |
| 261 | |
| 262 | TEST(AVERAGE_POOLING_OP_Q8, unit_batch_small_pool_with_output_zero_point) { |
| 263 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 264 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 265 | for (size_t pool_size = 2; pool_size <= xnn_params.q8.avgpool.mr; pool_size++) { |
| 266 | for (int32_t output_zero_point = 0; output_zero_point <= 255; output_zero_point += 51) { |
| 267 | AveragePoolingOperatorTester() |
| 268 | .batch_size(1) |
| 269 | .input_height(pool_size + 1) |
| 270 | .input_width(3) |
| 271 | .pooling_height(pool_size) |
| 272 | .pooling_width(1) |
| 273 | .channels(channels) |
| 274 | .output_zero_point(uint8_t(output_zero_point)) |
| 275 | .TestQ8(); |
| 276 | AveragePoolingOperatorTester() |
| 277 | .batch_size(1) |
| 278 | .input_height(2) |
| 279 | .input_width(pool_size + 2) |
| 280 | .pooling_height(1) |
| 281 | .pooling_width(pool_size) |
| 282 | .channels(channels) |
| 283 | .output_zero_point(uint8_t(output_zero_point)) |
| 284 | .TestQ8(); |
| 285 | } |
| 286 | } |
| 287 | } |
| 288 | } |
| 289 | |
| 290 | TEST(AVERAGE_POOLING_OP_Q8, unit_batch_small_pool_with_qmin) { |
| 291 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 292 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 293 | for (size_t pool_size = 2; pool_size <= xnn_params.q8.avgpool.mr; pool_size++) { |
| 294 | AveragePoolingOperatorTester() |
| 295 | .batch_size(1) |
| 296 | .input_height(pool_size + 1) |
| 297 | .input_width(3) |
| 298 | .pooling_height(pool_size) |
| 299 | .pooling_width(1) |
| 300 | .channels(channels) |
| 301 | .qmin(128) |
| 302 | .TestQ8(); |
| 303 | AveragePoolingOperatorTester() |
| 304 | .batch_size(1) |
| 305 | .input_height(2) |
| 306 | .input_width(pool_size + 2) |
| 307 | .pooling_height(1) |
| 308 | .pooling_width(pool_size) |
| 309 | .channels(channels) |
| 310 | .qmin(128) |
| 311 | .TestQ8(); |
| 312 | } |
| 313 | } |
| 314 | } |
| 315 | |
| 316 | TEST(AVERAGE_POOLING_OP_Q8, unit_batch_small_pool_with_qmax) { |
| 317 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 318 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 319 | for (size_t pool_size = 2; pool_size <= xnn_params.q8.avgpool.mr; pool_size++) { |
| 320 | AveragePoolingOperatorTester() |
| 321 | .batch_size(1) |
| 322 | .input_height(pool_size + 1) |
| 323 | .input_width(3) |
| 324 | .pooling_height(pool_size) |
| 325 | .pooling_width(1) |
| 326 | .channels(channels) |
| 327 | .qmax(128) |
| 328 | .TestQ8(); |
| 329 | AveragePoolingOperatorTester() |
| 330 | .batch_size(1) |
| 331 | .input_height(2) |
| 332 | .input_width(pool_size + 2) |
| 333 | .pooling_height(1) |
| 334 | .pooling_width(pool_size) |
| 335 | .channels(channels) |
| 336 | .qmax(128) |
| 337 | .TestQ8(); |
| 338 | } |
| 339 | } |
| 340 | } |
| 341 | |
| 342 | TEST(AVERAGE_POOLING_OP_Q8, unit_batch_large_1xM_pool) { |
| 343 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 344 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 345 | for (size_t pool_size = xnn_params.q8.avgpool.mr + 1; pool_size <= xnn_params.q8.avgpool.mr + xnn_params.q8.avgpool.qr; pool_size++) { |
| 346 | AveragePoolingOperatorTester() |
| 347 | .batch_size(1) |
| 348 | .input_height(2) |
| 349 | .input_width(pool_size + 2) |
| 350 | .pooling_height(1) |
| 351 | .pooling_width(pool_size) |
| 352 | .channels(channels) |
| 353 | .TestQ8(); |
| 354 | } |
| 355 | } |
| 356 | } |
| 357 | |
| 358 | TEST(AVERAGE_POOLING_OP_Q8, unit_batch_large_1xM_pool_with_padding) { |
| 359 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 360 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 361 | for (size_t pool_size = 3; pool_size <= xnn_params.q8.avgpool.mr; pool_size++) { |
| 362 | for (size_t padding_left = 0; padding_left <= 1; padding_left++) { |
| 363 | for (size_t padding_right = 0; padding_right <= 1; padding_right++) { |
| 364 | AveragePoolingOperatorTester() |
| 365 | .batch_size(1) |
| 366 | .input_height(2) |
| 367 | .input_width(pool_size + 2) |
| 368 | .padding_left(padding_left) |
| 369 | .padding_right(padding_right) |
| 370 | .pooling_height(1) |
| 371 | .pooling_width(pool_size) |
| 372 | .channels(channels) |
| 373 | .TestQ8(); |
| 374 | } |
| 375 | } |
| 376 | } |
| 377 | } |
| 378 | } |
| 379 | |
| 380 | TEST(AVERAGE_POOLING_OP_Q8, unit_batch_large_1xM_pool_with_stride) { |
| 381 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 382 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 383 | for (size_t pool_size = xnn_params.q8.avgpool.mr + 1; pool_size <= xnn_params.q8.avgpool.mr + xnn_params.q8.avgpool.qr; pool_size++) { |
| 384 | AveragePoolingOperatorTester() |
| 385 | .batch_size(1) |
| 386 | .input_height(2) |
| 387 | .input_width(pool_size + 4) |
| 388 | .pooling_height(1) |
| 389 | .pooling_width(pool_size) |
| 390 | .stride_width(2) |
| 391 | .channels(channels) |
| 392 | .TestQ8(); |
| 393 | } |
| 394 | } |
| 395 | } |
| 396 | |
| 397 | TEST(AVERAGE_POOLING_OP_Q8, unit_batch_large_Mx1_pool) { |
| 398 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 399 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 400 | for (size_t pool_size = xnn_params.q8.avgpool.mr + 1; pool_size <= xnn_params.q8.avgpool.mr + xnn_params.q8.avgpool.qr; pool_size++) { |
| 401 | AveragePoolingOperatorTester() |
| 402 | .batch_size(1) |
| 403 | .input_height(pool_size + 1) |
| 404 | .input_width(3) |
| 405 | .pooling_height(pool_size) |
| 406 | .pooling_width(1) |
| 407 | .channels(channels) |
| 408 | .TestQ8(); |
| 409 | } |
| 410 | } |
| 411 | } |
| 412 | |
| 413 | TEST(AVERAGE_POOLING_OP_Q8, unit_batch_large_Mx1_pool_with_padding) { |
| 414 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 415 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 416 | for (size_t pool_size = xnn_params.q8.avgpool.mr + 1; pool_size <= xnn_params.q8.avgpool.mr + xnn_params.q8.avgpool.qr; pool_size++) { |
| 417 | for (size_t padding_top = 0; padding_top <= 1; padding_top++) { |
| 418 | for (size_t padding_bottom = 0; padding_bottom <= 1; padding_bottom++) { |
| 419 | AveragePoolingOperatorTester() |
| 420 | .batch_size(1) |
| 421 | .input_height(pool_size + 1) |
| 422 | .input_width(3) |
| 423 | .padding_top(padding_top) |
| 424 | .padding_bottom(padding_bottom) |
| 425 | .pooling_height(pool_size) |
| 426 | .pooling_width(1) |
| 427 | .channels(channels) |
| 428 | .TestQ8(); |
| 429 | } |
| 430 | } |
| 431 | } |
| 432 | } |
| 433 | } |
| 434 | |
| 435 | TEST(AVERAGE_POOLING_OP_Q8, unit_batch_large_Mx1_pool_with_stride) { |
| 436 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 437 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 438 | for (size_t pool_size = xnn_params.q8.avgpool.mr + 1; pool_size <= xnn_params.q8.avgpool.mr + xnn_params.q8.avgpool.qr; pool_size++) { |
| 439 | for (size_t padding_top = 0; padding_top <= 1; padding_top++) { |
| 440 | for (size_t padding_bottom = 0; padding_bottom <= 1; padding_bottom++) { |
| 441 | AveragePoolingOperatorTester() |
| 442 | .batch_size(1) |
| 443 | .input_height(pool_size + 1) |
| 444 | .input_width(3) |
| 445 | .padding_top(padding_top) |
| 446 | .padding_bottom(padding_bottom) |
| 447 | .pooling_height(pool_size) |
| 448 | .pooling_width(1) |
| 449 | .channels(channels) |
| 450 | .TestQ8(); |
| 451 | } |
| 452 | } |
| 453 | } |
| 454 | } |
| 455 | } |
| 456 | |
| 457 | TEST(AVERAGE_POOLING_OP_Q8, unit_batch_large_pool_with_input_stride) { |
| 458 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 459 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 460 | for (size_t pool_size = xnn_params.q8.avgpool.mr + 1; pool_size <= xnn_params.q8.avgpool.mr + xnn_params.q8.avgpool.qr; pool_size++) { |
| 461 | AveragePoolingOperatorTester() |
| 462 | .batch_size(1) |
| 463 | .input_height(pool_size + 1) |
| 464 | .input_width(3) |
| 465 | .pooling_height(pool_size) |
| 466 | .pooling_width(1) |
| 467 | .channels(channels) |
| 468 | .input_pixel_stride(5 * channels) |
| 469 | .TestQ8(); |
| 470 | AveragePoolingOperatorTester() |
| 471 | .batch_size(1) |
| 472 | .input_height(2) |
| 473 | .input_width(pool_size + 2) |
| 474 | .pooling_height(1) |
| 475 | .pooling_width(pool_size) |
| 476 | .channels(channels) |
| 477 | .input_pixel_stride(5 * channels) |
| 478 | .TestQ8(); |
| 479 | } |
| 480 | } |
| 481 | } |
| 482 | |
| 483 | TEST(AVERAGE_POOLING_OP_Q8, unit_batch_large_pool_with_input_scale) { |
| 484 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 485 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 486 | for (size_t pool_size = xnn_params.q8.avgpool.mr + 1; pool_size <= xnn_params.q8.avgpool.mr + xnn_params.q8.avgpool.qr; pool_size++) { |
| 487 | for (float input_scale = 0.01f; input_scale < 100.0f; input_scale *= 3.14159265f) { |
| 488 | AveragePoolingOperatorTester() |
| 489 | .batch_size(1) |
| 490 | .input_height(pool_size + 1) |
| 491 | .input_width(3) |
| 492 | .pooling_height(pool_size) |
| 493 | .pooling_width(1) |
| 494 | .channels(channels) |
| 495 | .input_scale(input_scale) |
| 496 | .TestQ8(); |
| 497 | AveragePoolingOperatorTester() |
| 498 | .batch_size(1) |
| 499 | .input_height(2) |
| 500 | .input_width(pool_size + 2) |
| 501 | .pooling_height(1) |
| 502 | .pooling_width(pool_size) |
| 503 | .channels(channels) |
| 504 | .input_scale(input_scale) |
| 505 | .TestQ8(); |
| 506 | } |
| 507 | } |
| 508 | } |
| 509 | } |
| 510 | |
| 511 | TEST(AVERAGE_POOLING_OP_Q8, unit_batch_large_pool_with_input_zero_point) { |
| 512 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 513 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 514 | for (size_t pool_size = xnn_params.q8.avgpool.mr + 1; pool_size <= xnn_params.q8.avgpool.mr + xnn_params.q8.avgpool.qr; pool_size++) { |
| 515 | for (int32_t input_zero_point = 0; input_zero_point <= 255; input_zero_point += 51) { |
| 516 | AveragePoolingOperatorTester() |
| 517 | .batch_size(1) |
| 518 | .input_height(pool_size + 1) |
| 519 | .input_width(3) |
| 520 | .pooling_height(pool_size) |
| 521 | .pooling_width(1) |
| 522 | .channels(channels) |
| 523 | .input_zero_point(uint8_t(input_zero_point)) |
| 524 | .TestQ8(); |
| 525 | AveragePoolingOperatorTester() |
| 526 | .batch_size(1) |
| 527 | .input_height(2) |
| 528 | .input_width(pool_size + 2) |
| 529 | .pooling_height(1) |
| 530 | .pooling_width(pool_size) |
| 531 | .channels(channels) |
| 532 | .input_zero_point(uint8_t(input_zero_point)) |
| 533 | .TestQ8(); |
| 534 | } |
| 535 | } |
| 536 | } |
| 537 | } |
| 538 | |
| 539 | TEST(AVERAGE_POOLING_OP_Q8, unit_batch_large_pool_with_output_stride) { |
| 540 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 541 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 542 | for (size_t pool_size = xnn_params.q8.avgpool.mr + 1; pool_size <= xnn_params.q8.avgpool.mr + xnn_params.q8.avgpool.qr; pool_size++) { |
| 543 | AveragePoolingOperatorTester() |
| 544 | .batch_size(1) |
| 545 | .input_height(pool_size + 1) |
| 546 | .input_width(3) |
| 547 | .pooling_height(pool_size) |
| 548 | .pooling_width(1) |
| 549 | .channels(channels) |
| 550 | .output_pixel_stride(5 * channels) |
| 551 | .TestQ8(); |
| 552 | AveragePoolingOperatorTester() |
| 553 | .batch_size(1) |
| 554 | .input_height(2) |
| 555 | .input_width(pool_size + 2) |
| 556 | .pooling_height(1) |
| 557 | .pooling_width(pool_size) |
| 558 | .channels(channels) |
| 559 | .output_pixel_stride(5 * channels) |
| 560 | .TestQ8(); |
| 561 | } |
| 562 | } |
| 563 | } |
| 564 | |
| 565 | TEST(AVERAGE_POOLING_OP_Q8, unit_batch_large_pool_with_output_scale) { |
| 566 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 567 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 568 | for (size_t pool_size = xnn_params.q8.avgpool.mr + 1; pool_size <= xnn_params.q8.avgpool.mr + xnn_params.q8.avgpool.qr; pool_size++) { |
| 569 | for (float output_scale = 0.01f; output_scale < 100.0f; output_scale *= 3.14159265f) { |
| 570 | AveragePoolingOperatorTester() |
| 571 | .batch_size(1) |
| 572 | .input_height(pool_size + 1) |
| 573 | .input_width(3) |
| 574 | .pooling_height(pool_size) |
| 575 | .pooling_width(1) |
| 576 | .channels(channels) |
| 577 | .output_scale(output_scale) |
| 578 | .TestQ8(); |
| 579 | AveragePoolingOperatorTester() |
| 580 | .batch_size(1) |
| 581 | .input_height(2) |
| 582 | .input_width(pool_size + 2) |
| 583 | .pooling_height(1) |
| 584 | .pooling_width(pool_size) |
| 585 | .channels(channels) |
| 586 | .output_scale(output_scale) |
| 587 | .TestQ8(); |
| 588 | } |
| 589 | } |
| 590 | } |
| 591 | } |
| 592 | |
| 593 | TEST(AVERAGE_POOLING_OP_Q8, unit_batch_large_pool_with_output_zero_point) { |
| 594 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 595 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 596 | for (size_t pool_size = xnn_params.q8.avgpool.mr + 1; pool_size <= xnn_params.q8.avgpool.mr + xnn_params.q8.avgpool.qr; pool_size++) { |
| 597 | for (int32_t output_zero_point = 0; output_zero_point <= 255; output_zero_point += 51) { |
| 598 | AveragePoolingOperatorTester() |
| 599 | .batch_size(1) |
| 600 | .input_height(pool_size + 1) |
| 601 | .input_width(3) |
| 602 | .pooling_height(pool_size) |
| 603 | .pooling_width(1) |
| 604 | .channels(channels) |
| 605 | .output_zero_point(uint8_t(output_zero_point)) |
| 606 | .TestQ8(); |
| 607 | AveragePoolingOperatorTester() |
| 608 | .batch_size(1) |
| 609 | .input_height(2) |
| 610 | .input_width(pool_size + 2) |
| 611 | .pooling_height(1) |
| 612 | .pooling_width(pool_size) |
| 613 | .channels(channels) |
| 614 | .output_zero_point(uint8_t(output_zero_point)) |
| 615 | .TestQ8(); |
| 616 | } |
| 617 | } |
| 618 | } |
| 619 | } |
| 620 | |
| 621 | TEST(AVERAGE_POOLING_OP_Q8, unit_batch_large_pool_with_qmin) { |
| 622 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 623 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 624 | for (size_t pool_size = xnn_params.q8.avgpool.mr + 1; pool_size <= xnn_params.q8.avgpool.mr + xnn_params.q8.avgpool.qr; pool_size++) { |
| 625 | AveragePoolingOperatorTester() |
| 626 | .batch_size(1) |
| 627 | .input_height(pool_size + 1) |
| 628 | .input_width(3) |
| 629 | .pooling_height(pool_size) |
| 630 | .pooling_width(1) |
| 631 | .channels(channels) |
| 632 | .qmin(128) |
| 633 | .TestQ8(); |
| 634 | AveragePoolingOperatorTester() |
| 635 | .batch_size(1) |
| 636 | .input_height(2) |
| 637 | .input_width(pool_size + 2) |
| 638 | .pooling_height(1) |
| 639 | .pooling_width(pool_size) |
| 640 | .channels(channels) |
| 641 | .qmin(128) |
| 642 | .TestQ8(); |
| 643 | } |
| 644 | } |
| 645 | } |
| 646 | |
| 647 | TEST(AVERAGE_POOLING_OP_Q8, unit_batch_large_pool_with_qmax) { |
| 648 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 649 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 650 | for (size_t pool_size = xnn_params.q8.avgpool.mr + 1; pool_size <= xnn_params.q8.avgpool.mr + xnn_params.q8.avgpool.qr; pool_size++) { |
| 651 | AveragePoolingOperatorTester() |
| 652 | .batch_size(1) |
| 653 | .input_height(pool_size + 1) |
| 654 | .input_width(3) |
| 655 | .pooling_height(pool_size) |
| 656 | .pooling_width(1) |
| 657 | .channels(channels) |
| 658 | .qmax(128) |
| 659 | .TestQ8(); |
| 660 | AveragePoolingOperatorTester() |
| 661 | .batch_size(1) |
| 662 | .input_height(2) |
| 663 | .input_width(pool_size + 2) |
| 664 | .pooling_height(1) |
| 665 | .pooling_width(pool_size) |
| 666 | .channels(channels) |
| 667 | .qmax(128) |
| 668 | .TestQ8(); |
| 669 | } |
| 670 | } |
| 671 | } |
| 672 | |
| 673 | TEST(AVERAGE_POOLING_OP_Q8, small_batch_small_pool) { |
| 674 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 675 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 676 | for (size_t pool_size = 2; pool_size <= xnn_params.q8.avgpool.mr; pool_size++) { |
| 677 | AveragePoolingOperatorTester() |
| 678 | .batch_size(3) |
| 679 | .input_height(pool_size + 1) |
| 680 | .input_width(3) |
| 681 | .pooling_height(pool_size) |
| 682 | .pooling_width(1) |
| 683 | .channels(channels) |
| 684 | .TestQ8(); |
| 685 | AveragePoolingOperatorTester() |
| 686 | .batch_size(3) |
| 687 | .input_height(2) |
| 688 | .input_width(pool_size + 2) |
| 689 | .pooling_height(1) |
| 690 | .pooling_width(pool_size) |
| 691 | .channels(channels) |
| 692 | .TestQ8(); |
| 693 | } |
| 694 | } |
| 695 | } |
| 696 | |
| 697 | TEST(AVERAGE_POOLING_OP_Q8, small_batch_small_pool_with_input_stride) { |
| 698 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 699 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 700 | for (size_t pool_size = 2; pool_size <= xnn_params.q8.avgpool.mr; pool_size++) { |
| 701 | AveragePoolingOperatorTester() |
| 702 | .batch_size(3) |
| 703 | .input_height(pool_size + 1) |
| 704 | .input_width(3) |
| 705 | .pooling_height(pool_size) |
| 706 | .pooling_width(1) |
| 707 | .channels(channels) |
| 708 | .input_pixel_stride(5 * channels) |
| 709 | .TestQ8(); |
| 710 | AveragePoolingOperatorTester() |
| 711 | .batch_size(3) |
| 712 | .input_height(2) |
| 713 | .input_width(pool_size + 1) |
| 714 | .pooling_height(1) |
| 715 | .pooling_width(pool_size) |
| 716 | .channels(channels) |
| 717 | .input_pixel_stride(5 * channels) |
| 718 | .TestQ8(); |
| 719 | } |
| 720 | } |
| 721 | } |
| 722 | |
| 723 | TEST(AVERAGE_POOLING_OP_Q8, small_batch_small_pool_with_output_stride) { |
| 724 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 725 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 726 | for (size_t pool_size = 2; pool_size <= xnn_params.q8.avgpool.mr; pool_size++) { |
| 727 | AveragePoolingOperatorTester() |
| 728 | .batch_size(3) |
| 729 | .input_height(pool_size + 1) |
| 730 | .input_width(3) |
| 731 | .pooling_height(pool_size) |
| 732 | .pooling_width(1) |
| 733 | .channels(channels) |
| 734 | .output_pixel_stride(5 * channels) |
| 735 | .TestQ8(); |
| 736 | AveragePoolingOperatorTester() |
| 737 | .batch_size(3) |
| 738 | .input_height(2) |
| 739 | .input_width(pool_size + 1) |
| 740 | .pooling_height(1) |
| 741 | .pooling_width(pool_size) |
| 742 | .channels(channels) |
| 743 | .output_pixel_stride(5 * channels) |
| 744 | .TestQ8(); |
| 745 | } |
| 746 | } |
| 747 | } |
| 748 | |
| 749 | TEST(AVERAGE_POOLING_OP_Q8, small_batch_large_pool) { |
| 750 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 751 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 752 | for (size_t pool_size = xnn_params.q8.avgpool.mr + 1; pool_size <= xnn_params.q8.avgpool.mr + xnn_params.q8.avgpool.qr; pool_size++) { |
| 753 | AveragePoolingOperatorTester() |
| 754 | .batch_size(3) |
| 755 | .input_height(pool_size + 1) |
| 756 | .input_width(3) |
| 757 | .pooling_height(pool_size) |
| 758 | .pooling_width(1) |
| 759 | .channels(channels) |
| 760 | .TestQ8(); |
| 761 | AveragePoolingOperatorTester() |
| 762 | .batch_size(3) |
| 763 | .input_height(2) |
| 764 | .input_width(pool_size + 2) |
| 765 | .pooling_height(1) |
| 766 | .pooling_width(pool_size) |
| 767 | .channels(channels) |
| 768 | .TestQ8(); |
| 769 | } |
| 770 | } |
| 771 | } |
| 772 | |
| 773 | TEST(AVERAGE_POOLING_OP_Q8, small_batch_large_pool_with_input_stride) { |
| 774 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 775 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 776 | for (size_t pool_size = xnn_params.q8.avgpool.mr + 1; pool_size <= xnn_params.q8.avgpool.mr + xnn_params.q8.avgpool.qr; pool_size++) { |
| 777 | AveragePoolingOperatorTester() |
| 778 | .batch_size(3) |
| 779 | .input_height(pool_size + 1) |
| 780 | .input_width(3) |
| 781 | .pooling_height(pool_size) |
| 782 | .pooling_width(1) |
| 783 | .channels(channels) |
| 784 | .input_pixel_stride(5 * channels) |
| 785 | .TestQ8(); |
| 786 | AveragePoolingOperatorTester() |
| 787 | .batch_size(3) |
| 788 | .input_height(2) |
| 789 | .input_width(pool_size + 1) |
| 790 | .pooling_height(1) |
| 791 | .pooling_width(pool_size) |
| 792 | .channels(channels) |
| 793 | .input_pixel_stride(5 * channels) |
| 794 | .TestQ8(); |
| 795 | } |
| 796 | } |
| 797 | } |
| 798 | |
| 799 | TEST(AVERAGE_POOLING_OP_Q8, small_batch_large_pool_with_output_stride) { |
| 800 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 801 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 802 | for (size_t pool_size = xnn_params.q8.avgpool.mr + 1; pool_size <= xnn_params.q8.avgpool.mr + xnn_params.q8.avgpool.qr; pool_size++) { |
| 803 | AveragePoolingOperatorTester() |
| 804 | .batch_size(3) |
| 805 | .input_height(pool_size + 1) |
| 806 | .input_width(3) |
| 807 | .pooling_height(pool_size) |
| 808 | .pooling_width(1) |
| 809 | .channels(channels) |
| 810 | .output_pixel_stride(5 * channels) |
| 811 | .TestQ8(); |
| 812 | AveragePoolingOperatorTester() |
| 813 | .batch_size(3) |
| 814 | .input_height(2) |
| 815 | .input_width(pool_size + 1) |
| 816 | .pooling_height(1) |
| 817 | .pooling_width(pool_size) |
| 818 | .channels(channels) |
| 819 | .output_pixel_stride(5 * channels) |
| 820 | .TestQ8(); |
| 821 | } |
| 822 | } |
| 823 | } |
| 824 | |
| 825 | TEST(AVERAGE_POOLING_OP_Q8, setup_increasing_batch) { |
| 826 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 827 | AveragePoolingOperatorTester() |
| 828 | .batch_size(3) |
| 829 | .next_batch_size(5) |
| 830 | .input_height(8) |
| 831 | .input_width(8) |
| 832 | .pooling_height(5) |
| 833 | .pooling_width(3) |
| 834 | .channels(24) |
| 835 | .TestSetupQ8(); |
| 836 | } |
| 837 | |
| 838 | TEST(AVERAGE_POOLING_OP_Q8, setup_decreasing_batch) { |
| 839 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 840 | AveragePoolingOperatorTester() |
| 841 | .batch_size(5) |
| 842 | .next_batch_size(3) |
| 843 | .input_height(8) |
| 844 | .input_width(8) |
| 845 | .pooling_height(5) |
| 846 | .pooling_width(3) |
| 847 | .channels(24) |
| 848 | .TestSetupQ8(); |
| 849 | } |
| 850 | |
| 851 | TEST(AVERAGE_POOLING_OP_Q8, setup_changing_height) { |
| 852 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 853 | AveragePoolingOperatorTester() |
| 854 | .batch_size(3) |
| 855 | .input_height(8) |
| 856 | .input_width(8) |
| 857 | .next_input_height(9) |
| 858 | .pooling_height(5) |
| 859 | .pooling_width(3) |
| 860 | .channels(24) |
| 861 | .TestSetupQ8(); |
| 862 | AveragePoolingOperatorTester() |
| 863 | .batch_size(3) |
| 864 | .input_height(8) |
| 865 | .input_width(8) |
| 866 | .next_input_height(7) |
| 867 | .pooling_height(5) |
| 868 | .pooling_width(3) |
| 869 | .channels(24) |
| 870 | .TestSetupQ8(); |
| 871 | } |
| 872 | |
| 873 | TEST(AVERAGE_POOLING_OP_Q8, setup_changing_width) { |
| 874 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 875 | AveragePoolingOperatorTester() |
| 876 | .batch_size(3) |
| 877 | .input_height(8) |
| 878 | .input_width(8) |
| 879 | .next_input_width(9) |
| 880 | .pooling_height(5) |
| 881 | .pooling_width(3) |
| 882 | .channels(24) |
| 883 | .TestSetupQ8(); |
| 884 | AveragePoolingOperatorTester() |
| 885 | .batch_size(3) |
| 886 | .input_height(8) |
| 887 | .input_width(8) |
| 888 | .next_input_width(7) |
| 889 | .pooling_height(5) |
| 890 | .pooling_width(3) |
| 891 | .channels(24) |
| 892 | .TestSetupQ8(); |
| 893 | } |
| 894 | |
| 895 | TEST(AVERAGE_POOLING_OP_Q8, setup_swap_height_and_width) { |
| 896 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 897 | AveragePoolingOperatorTester() |
| 898 | .batch_size(3) |
| 899 | .input_height(9) |
| 900 | .input_width(8) |
| 901 | .next_input_height(8) |
| 902 | .next_input_width(9) |
| 903 | .pooling_height(5) |
| 904 | .pooling_width(3) |
| 905 | .channels(24) |
| 906 | .TestSetupQ8(); |
| 907 | } |
| 908 | |
| 909 | TEST(AVERAGE_POOLING_OP_F32, unit_batch_small_1xM_pool) { |
| 910 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 911 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 912 | for (size_t pool_size = 2; pool_size <= xnn_params.f32.avgpool.mr; pool_size++) { |
| 913 | AveragePoolingOperatorTester() |
| 914 | .batch_size(1) |
| 915 | .input_height(2) |
| 916 | .input_width(pool_size + 2) |
| 917 | .pooling_height(1) |
| 918 | .pooling_width(pool_size) |
| 919 | .channels(channels) |
| 920 | .TestF32(); |
| 921 | } |
| 922 | } |
| 923 | } |
| 924 | |
| 925 | TEST(AVERAGE_POOLING_OP_F32, unit_batch_small_1xM_pool_with_padding) { |
| 926 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 927 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 928 | for (size_t pool_size = 3; pool_size <= xnn_params.f32.avgpool.mr; pool_size++) { |
| 929 | for (size_t padding_left = 0; padding_left <= 1; padding_left++) { |
| 930 | for (size_t padding_right = 0; padding_right <= 1; padding_right++) { |
| 931 | AveragePoolingOperatorTester() |
| 932 | .batch_size(1) |
| 933 | .input_height(2) |
| 934 | .input_width(pool_size + 2) |
| 935 | .padding_left(padding_left) |
| 936 | .padding_right(padding_right) |
| 937 | .pooling_height(1) |
| 938 | .pooling_width(pool_size) |
| 939 | .channels(channels) |
| 940 | .TestF32(); |
| 941 | } |
| 942 | } |
| 943 | } |
| 944 | } |
| 945 | } |
| 946 | |
| 947 | TEST(AVERAGE_POOLING_OP_F32, unit_batch_small_1xM_pool_with_stride) { |
| 948 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 949 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 950 | for (size_t pool_size = 2; pool_size <= xnn_params.f32.avgpool.mr; pool_size++) { |
| 951 | AveragePoolingOperatorTester() |
| 952 | .batch_size(1) |
| 953 | .input_height(2) |
| 954 | .input_width(pool_size + 4) |
| 955 | .pooling_height(1) |
| 956 | .pooling_width(pool_size) |
| 957 | .stride_width(2) |
| 958 | .channels(channels) |
| 959 | .TestF32(); |
| 960 | } |
| 961 | } |
| 962 | } |
| 963 | |
| 964 | TEST(AVERAGE_POOLING_OP_F32, unit_batch_small_Mx1_pool) { |
| 965 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 966 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 967 | for (size_t pool_size = 2; pool_size <= xnn_params.f32.avgpool.mr; pool_size++) { |
| 968 | AveragePoolingOperatorTester() |
| 969 | .batch_size(1) |
| 970 | .input_height(pool_size + 1) |
| 971 | .input_width(3) |
| 972 | .pooling_height(pool_size) |
| 973 | .pooling_width(1) |
| 974 | .channels(channels) |
| 975 | .TestF32(); |
| 976 | } |
| 977 | } |
| 978 | } |
| 979 | |
| 980 | TEST(AVERAGE_POOLING_OP_F32, unit_batch_small_Mx1_pool_with_padding) { |
| 981 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 982 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 983 | for (size_t pool_size = 2; pool_size <= xnn_params.f32.avgpool.mr; pool_size++) { |
| 984 | for (size_t padding_top = 0; padding_top <= 1; padding_top++) { |
| 985 | for (size_t padding_bottom = 0; padding_bottom <= 1; padding_bottom++) { |
| 986 | AveragePoolingOperatorTester() |
| 987 | .batch_size(1) |
| 988 | .input_height(pool_size + 1) |
| 989 | .input_width(3) |
| 990 | .padding_top(padding_top) |
| 991 | .padding_bottom(padding_bottom) |
| 992 | .pooling_height(pool_size) |
| 993 | .pooling_width(1) |
| 994 | .channels(channels) |
| 995 | .TestF32(); |
| 996 | } |
| 997 | } |
| 998 | } |
| 999 | } |
| 1000 | } |
| 1001 | |
| 1002 | TEST(AVERAGE_POOLING_OP_F32, unit_batch_small_Mx1_pool_with_stride) { |
| 1003 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 1004 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 1005 | for (size_t pool_size = 2; pool_size <= xnn_params.f32.avgpool.mr; pool_size++) { |
| 1006 | AveragePoolingOperatorTester() |
| 1007 | .batch_size(1) |
| 1008 | .input_height(pool_size + 3) |
| 1009 | .input_width(3) |
| 1010 | .pooling_height(pool_size) |
| 1011 | .pooling_width(1) |
| 1012 | .stride_height(2) |
| 1013 | .channels(channels) |
| 1014 | .TestF32(); |
| 1015 | } |
| 1016 | } |
| 1017 | } |
| 1018 | |
| 1019 | TEST(AVERAGE_POOLING_OP_F32, unit_batch_small_pool_with_input_stride) { |
| 1020 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 1021 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 1022 | for (size_t pool_size = 2; pool_size <= xnn_params.f32.avgpool.mr; pool_size++) { |
| 1023 | AveragePoolingOperatorTester() |
| 1024 | .batch_size(1) |
| 1025 | .input_height(pool_size + 1) |
| 1026 | .input_width(3) |
| 1027 | .pooling_height(pool_size) |
| 1028 | .pooling_width(1) |
| 1029 | .channels(channels) |
| 1030 | .input_pixel_stride(5 * channels) |
| 1031 | .TestF32(); |
| 1032 | AveragePoolingOperatorTester() |
| 1033 | .batch_size(1) |
| 1034 | .input_height(2) |
| 1035 | .input_width(pool_size + 2) |
| 1036 | .pooling_height(1) |
| 1037 | .pooling_width(pool_size) |
| 1038 | .channels(channels) |
| 1039 | .input_pixel_stride(5 * channels) |
| 1040 | .TestF32(); |
| 1041 | } |
| 1042 | } |
| 1043 | } |
| 1044 | |
| 1045 | TEST(AVERAGE_POOLING_OP_F32, unit_batch_small_pool_with_output_stride) { |
| 1046 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 1047 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 1048 | for (size_t pool_size = 2; pool_size <= xnn_params.f32.avgpool.mr; pool_size++) { |
| 1049 | AveragePoolingOperatorTester() |
| 1050 | .batch_size(1) |
| 1051 | .input_height(pool_size + 1) |
| 1052 | .input_width(3) |
| 1053 | .pooling_height(pool_size) |
| 1054 | .pooling_width(1) |
| 1055 | .channels(channels) |
| 1056 | .output_pixel_stride(5 * channels) |
| 1057 | .TestF32(); |
| 1058 | AveragePoolingOperatorTester() |
| 1059 | .batch_size(1) |
| 1060 | .input_height(2) |
| 1061 | .input_width(pool_size + 2) |
| 1062 | .pooling_height(1) |
| 1063 | .pooling_width(pool_size) |
| 1064 | .channels(channels) |
| 1065 | .output_pixel_stride(5 * channels) |
| 1066 | .TestF32(); |
| 1067 | } |
| 1068 | } |
| 1069 | } |
| 1070 | |
| 1071 | TEST(AVERAGE_POOLING_OP_F32, unit_batch_small_pool_with_qmin) { |
| 1072 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 1073 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 1074 | for (size_t pool_size = 2; pool_size <= xnn_params.f32.avgpool.mr; pool_size++) { |
| 1075 | AveragePoolingOperatorTester() |
| 1076 | .batch_size(1) |
| 1077 | .input_height(pool_size + 1) |
| 1078 | .input_width(3) |
| 1079 | .pooling_height(pool_size) |
| 1080 | .pooling_width(1) |
| 1081 | .channels(channels) |
| 1082 | .qmin(128) |
| 1083 | .TestF32(); |
| 1084 | AveragePoolingOperatorTester() |
| 1085 | .batch_size(1) |
| 1086 | .input_height(2) |
| 1087 | .input_width(pool_size + 2) |
| 1088 | .pooling_height(1) |
| 1089 | .pooling_width(pool_size) |
| 1090 | .channels(channels) |
| 1091 | .qmin(128) |
| 1092 | .TestF32(); |
| 1093 | } |
| 1094 | } |
| 1095 | } |
| 1096 | |
| 1097 | TEST(AVERAGE_POOLING_OP_F32, unit_batch_small_pool_with_qmax) { |
| 1098 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 1099 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 1100 | for (size_t pool_size = 2; pool_size <= xnn_params.f32.avgpool.mr; pool_size++) { |
| 1101 | AveragePoolingOperatorTester() |
| 1102 | .batch_size(1) |
| 1103 | .input_height(pool_size + 1) |
| 1104 | .input_width(3) |
| 1105 | .pooling_height(pool_size) |
| 1106 | .pooling_width(1) |
| 1107 | .channels(channels) |
| 1108 | .qmax(128) |
| 1109 | .TestF32(); |
| 1110 | AveragePoolingOperatorTester() |
| 1111 | .batch_size(1) |
| 1112 | .input_height(2) |
| 1113 | .input_width(pool_size + 2) |
| 1114 | .pooling_height(1) |
| 1115 | .pooling_width(pool_size) |
| 1116 | .channels(channels) |
| 1117 | .qmax(128) |
| 1118 | .TestF32(); |
| 1119 | } |
| 1120 | } |
| 1121 | } |
| 1122 | |
| 1123 | TEST(AVERAGE_POOLING_OP_F32, unit_batch_large_1xM_pool) { |
| 1124 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 1125 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 1126 | for (size_t pool_size = xnn_params.f32.avgpool.mr + 1; pool_size <= xnn_params.f32.avgpool.mr + xnn_params.f32.avgpool.qr; pool_size++) { |
| 1127 | AveragePoolingOperatorTester() |
| 1128 | .batch_size(1) |
| 1129 | .input_height(2) |
| 1130 | .input_width(pool_size + 2) |
| 1131 | .pooling_height(1) |
| 1132 | .pooling_width(pool_size) |
| 1133 | .channels(channels) |
| 1134 | .TestF32(); |
| 1135 | } |
| 1136 | } |
| 1137 | } |
| 1138 | |
| 1139 | TEST(AVERAGE_POOLING_OP_F32, unit_batch_large_1xM_pool_with_padding) { |
| 1140 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 1141 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 1142 | for (size_t pool_size = 3; pool_size <= xnn_params.f32.avgpool.mr; pool_size++) { |
| 1143 | for (size_t padding_left = 0; padding_left <= 1; padding_left++) { |
| 1144 | for (size_t padding_right = 0; padding_right <= 1; padding_right++) { |
| 1145 | AveragePoolingOperatorTester() |
| 1146 | .batch_size(1) |
| 1147 | .input_height(2) |
| 1148 | .input_width(pool_size + 2) |
| 1149 | .padding_left(padding_left) |
| 1150 | .padding_right(padding_right) |
| 1151 | .pooling_height(1) |
| 1152 | .pooling_width(pool_size) |
| 1153 | .channels(channels) |
| 1154 | .TestF32(); |
| 1155 | } |
| 1156 | } |
| 1157 | } |
| 1158 | } |
| 1159 | } |
| 1160 | |
| 1161 | TEST(AVERAGE_POOLING_OP_F32, unit_batch_large_1xM_pool_with_stride) { |
| 1162 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 1163 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 1164 | for (size_t pool_size = xnn_params.f32.avgpool.mr + 1; pool_size <= xnn_params.f32.avgpool.mr + xnn_params.f32.avgpool.qr; pool_size++) { |
| 1165 | AveragePoolingOperatorTester() |
| 1166 | .batch_size(1) |
| 1167 | .input_height(2) |
| 1168 | .input_width(pool_size + 4) |
| 1169 | .pooling_height(1) |
| 1170 | .pooling_width(pool_size) |
| 1171 | .stride_width(2) |
| 1172 | .channels(channels) |
| 1173 | .TestF32(); |
| 1174 | } |
| 1175 | } |
| 1176 | } |
| 1177 | |
| 1178 | TEST(AVERAGE_POOLING_OP_F32, unit_batch_large_Mx1_pool) { |
| 1179 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 1180 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 1181 | for (size_t pool_size = xnn_params.f32.avgpool.mr + 1; pool_size <= xnn_params.f32.avgpool.mr + xnn_params.f32.avgpool.qr; pool_size++) { |
| 1182 | AveragePoolingOperatorTester() |
| 1183 | .batch_size(1) |
| 1184 | .input_height(pool_size + 1) |
| 1185 | .input_width(3) |
| 1186 | .pooling_height(pool_size) |
| 1187 | .pooling_width(1) |
| 1188 | .channels(channels) |
| 1189 | .TestF32(); |
| 1190 | } |
| 1191 | } |
| 1192 | } |
| 1193 | |
| 1194 | TEST(AVERAGE_POOLING_OP_F32, unit_batch_large_Mx1_pool_with_padding) { |
| 1195 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 1196 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 1197 | for (size_t pool_size = xnn_params.f32.avgpool.mr + 1; pool_size <= xnn_params.f32.avgpool.mr + xnn_params.f32.avgpool.qr; pool_size++) { |
| 1198 | for (size_t padding_top = 0; padding_top <= 1; padding_top++) { |
| 1199 | for (size_t padding_bottom = 0; padding_bottom <= 1; padding_bottom++) { |
| 1200 | AveragePoolingOperatorTester() |
| 1201 | .batch_size(1) |
| 1202 | .input_height(pool_size + 1) |
| 1203 | .input_width(3) |
| 1204 | .padding_top(padding_top) |
| 1205 | .padding_bottom(padding_bottom) |
| 1206 | .pooling_height(pool_size) |
| 1207 | .pooling_width(1) |
| 1208 | .channels(channels) |
| 1209 | .TestF32(); |
| 1210 | } |
| 1211 | } |
| 1212 | } |
| 1213 | } |
| 1214 | } |
| 1215 | |
| 1216 | TEST(AVERAGE_POOLING_OP_F32, unit_batch_large_Mx1_pool_with_stride) { |
| 1217 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 1218 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 1219 | for (size_t pool_size = xnn_params.f32.avgpool.mr + 1; pool_size <= xnn_params.f32.avgpool.mr + xnn_params.f32.avgpool.qr; pool_size++) { |
| 1220 | for (size_t padding_top = 0; padding_top <= 1; padding_top++) { |
| 1221 | for (size_t padding_bottom = 0; padding_bottom <= 1; padding_bottom++) { |
| 1222 | AveragePoolingOperatorTester() |
| 1223 | .batch_size(1) |
| 1224 | .input_height(pool_size + 1) |
| 1225 | .input_width(3) |
| 1226 | .padding_top(padding_top) |
| 1227 | .padding_bottom(padding_bottom) |
| 1228 | .pooling_height(pool_size) |
| 1229 | .pooling_width(1) |
| 1230 | .channels(channels) |
| 1231 | .TestF32(); |
| 1232 | } |
| 1233 | } |
| 1234 | } |
| 1235 | } |
| 1236 | } |
| 1237 | |
| 1238 | TEST(AVERAGE_POOLING_OP_F32, unit_batch_large_pool_with_input_stride) { |
| 1239 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 1240 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 1241 | for (size_t pool_size = xnn_params.f32.avgpool.mr + 1; pool_size <= xnn_params.f32.avgpool.mr + xnn_params.f32.avgpool.qr; pool_size++) { |
| 1242 | AveragePoolingOperatorTester() |
| 1243 | .batch_size(1) |
| 1244 | .input_height(pool_size + 1) |
| 1245 | .input_width(3) |
| 1246 | .pooling_height(pool_size) |
| 1247 | .pooling_width(1) |
| 1248 | .channels(channels) |
| 1249 | .input_pixel_stride(5 * channels) |
| 1250 | .TestF32(); |
| 1251 | AveragePoolingOperatorTester() |
| 1252 | .batch_size(1) |
| 1253 | .input_height(2) |
| 1254 | .input_width(pool_size + 2) |
| 1255 | .pooling_height(1) |
| 1256 | .pooling_width(pool_size) |
| 1257 | .channels(channels) |
| 1258 | .input_pixel_stride(5 * channels) |
| 1259 | .TestF32(); |
| 1260 | } |
| 1261 | } |
| 1262 | } |
| 1263 | |
| 1264 | TEST(AVERAGE_POOLING_OP_F32, unit_batch_large_pool_with_output_stride) { |
| 1265 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 1266 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 1267 | for (size_t pool_size = xnn_params.f32.avgpool.mr + 1; pool_size <= xnn_params.f32.avgpool.mr + xnn_params.f32.avgpool.qr; pool_size++) { |
| 1268 | AveragePoolingOperatorTester() |
| 1269 | .batch_size(1) |
| 1270 | .input_height(pool_size + 1) |
| 1271 | .input_width(3) |
| 1272 | .pooling_height(pool_size) |
| 1273 | .pooling_width(1) |
| 1274 | .channels(channels) |
| 1275 | .output_pixel_stride(5 * channels) |
| 1276 | .TestF32(); |
| 1277 | AveragePoolingOperatorTester() |
| 1278 | .batch_size(1) |
| 1279 | .input_height(2) |
| 1280 | .input_width(pool_size + 2) |
| 1281 | .pooling_height(1) |
| 1282 | .pooling_width(pool_size) |
| 1283 | .channels(channels) |
| 1284 | .output_pixel_stride(5 * channels) |
| 1285 | .TestF32(); |
| 1286 | } |
| 1287 | } |
| 1288 | } |
| 1289 | |
| 1290 | TEST(AVERAGE_POOLING_OP_F32, unit_batch_large_pool_with_qmin) { |
| 1291 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 1292 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 1293 | for (size_t pool_size = xnn_params.f32.avgpool.mr + 1; pool_size <= xnn_params.f32.avgpool.mr + xnn_params.f32.avgpool.qr; pool_size++) { |
| 1294 | AveragePoolingOperatorTester() |
| 1295 | .batch_size(1) |
| 1296 | .input_height(pool_size + 1) |
| 1297 | .input_width(3) |
| 1298 | .pooling_height(pool_size) |
| 1299 | .pooling_width(1) |
| 1300 | .channels(channels) |
| 1301 | .qmin(128) |
| 1302 | .TestF32(); |
| 1303 | AveragePoolingOperatorTester() |
| 1304 | .batch_size(1) |
| 1305 | .input_height(2) |
| 1306 | .input_width(pool_size + 2) |
| 1307 | .pooling_height(1) |
| 1308 | .pooling_width(pool_size) |
| 1309 | .channels(channels) |
| 1310 | .qmin(128) |
| 1311 | .TestF32(); |
| 1312 | } |
| 1313 | } |
| 1314 | } |
| 1315 | |
| 1316 | TEST(AVERAGE_POOLING_OP_F32, unit_batch_large_pool_with_qmax) { |
| 1317 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 1318 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 1319 | for (size_t pool_size = xnn_params.f32.avgpool.mr + 1; pool_size <= xnn_params.f32.avgpool.mr + xnn_params.f32.avgpool.qr; pool_size++) { |
| 1320 | AveragePoolingOperatorTester() |
| 1321 | .batch_size(1) |
| 1322 | .input_height(pool_size + 1) |
| 1323 | .input_width(3) |
| 1324 | .pooling_height(pool_size) |
| 1325 | .pooling_width(1) |
| 1326 | .channels(channels) |
| 1327 | .qmax(128) |
| 1328 | .TestF32(); |
| 1329 | AveragePoolingOperatorTester() |
| 1330 | .batch_size(1) |
| 1331 | .input_height(2) |
| 1332 | .input_width(pool_size + 2) |
| 1333 | .pooling_height(1) |
| 1334 | .pooling_width(pool_size) |
| 1335 | .channels(channels) |
| 1336 | .qmax(128) |
| 1337 | .TestF32(); |
| 1338 | } |
| 1339 | } |
| 1340 | } |
| 1341 | |
| 1342 | TEST(AVERAGE_POOLING_OP_F32, small_batch_small_pool) { |
| 1343 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 1344 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 1345 | for (size_t pool_size = 2; pool_size <= xnn_params.f32.avgpool.mr; pool_size++) { |
| 1346 | AveragePoolingOperatorTester() |
| 1347 | .batch_size(3) |
| 1348 | .input_height(pool_size + 1) |
| 1349 | .input_width(3) |
| 1350 | .pooling_height(pool_size) |
| 1351 | .pooling_width(1) |
| 1352 | .channels(channels) |
| 1353 | .TestF32(); |
| 1354 | AveragePoolingOperatorTester() |
| 1355 | .batch_size(3) |
| 1356 | .input_height(2) |
| 1357 | .input_width(pool_size + 2) |
| 1358 | .pooling_height(1) |
| 1359 | .pooling_width(pool_size) |
| 1360 | .channels(channels) |
| 1361 | .TestF32(); |
| 1362 | } |
| 1363 | } |
| 1364 | } |
| 1365 | |
| 1366 | TEST(AVERAGE_POOLING_OP_F32, small_batch_small_pool_with_input_stride) { |
| 1367 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 1368 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 1369 | for (size_t pool_size = 2; pool_size <= xnn_params.f32.avgpool.mr; pool_size++) { |
| 1370 | AveragePoolingOperatorTester() |
| 1371 | .batch_size(3) |
| 1372 | .input_height(pool_size + 1) |
| 1373 | .input_width(3) |
| 1374 | .pooling_height(pool_size) |
| 1375 | .pooling_width(1) |
| 1376 | .channels(channels) |
| 1377 | .input_pixel_stride(5 * channels) |
| 1378 | .TestF32(); |
| 1379 | AveragePoolingOperatorTester() |
| 1380 | .batch_size(3) |
| 1381 | .input_height(2) |
| 1382 | .input_width(pool_size + 1) |
| 1383 | .pooling_height(1) |
| 1384 | .pooling_width(pool_size) |
| 1385 | .channels(channels) |
| 1386 | .input_pixel_stride(5 * channels) |
| 1387 | .TestF32(); |
| 1388 | } |
| 1389 | } |
| 1390 | } |
| 1391 | |
| 1392 | TEST(AVERAGE_POOLING_OP_F32, small_batch_small_pool_with_output_stride) { |
| 1393 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 1394 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 1395 | for (size_t pool_size = 2; pool_size <= xnn_params.f32.avgpool.mr; pool_size++) { |
| 1396 | AveragePoolingOperatorTester() |
| 1397 | .batch_size(3) |
| 1398 | .input_height(pool_size + 1) |
| 1399 | .input_width(3) |
| 1400 | .pooling_height(pool_size) |
| 1401 | .pooling_width(1) |
| 1402 | .channels(channels) |
| 1403 | .output_pixel_stride(5 * channels) |
| 1404 | .TestF32(); |
| 1405 | AveragePoolingOperatorTester() |
| 1406 | .batch_size(3) |
| 1407 | .input_height(2) |
| 1408 | .input_width(pool_size + 1) |
| 1409 | .pooling_height(1) |
| 1410 | .pooling_width(pool_size) |
| 1411 | .channels(channels) |
| 1412 | .output_pixel_stride(5 * channels) |
| 1413 | .TestF32(); |
| 1414 | } |
| 1415 | } |
| 1416 | } |
| 1417 | |
| 1418 | TEST(AVERAGE_POOLING_OP_F32, small_batch_large_pool) { |
| 1419 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 1420 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 1421 | for (size_t pool_size = xnn_params.f32.avgpool.mr + 1; pool_size <= xnn_params.f32.avgpool.mr + xnn_params.f32.avgpool.qr; pool_size++) { |
| 1422 | AveragePoolingOperatorTester() |
| 1423 | .batch_size(3) |
| 1424 | .input_height(pool_size + 1) |
| 1425 | .input_width(3) |
| 1426 | .pooling_height(pool_size) |
| 1427 | .pooling_width(1) |
| 1428 | .channels(channels) |
| 1429 | .TestF32(); |
| 1430 | AveragePoolingOperatorTester() |
| 1431 | .batch_size(3) |
| 1432 | .input_height(2) |
| 1433 | .input_width(pool_size + 2) |
| 1434 | .pooling_height(1) |
| 1435 | .pooling_width(pool_size) |
| 1436 | .channels(channels) |
| 1437 | .TestF32(); |
| 1438 | } |
| 1439 | } |
| 1440 | } |
| 1441 | |
| 1442 | TEST(AVERAGE_POOLING_OP_F32, small_batch_large_pool_with_input_stride) { |
| 1443 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 1444 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 1445 | for (size_t pool_size = xnn_params.f32.avgpool.mr + 1; pool_size <= xnn_params.f32.avgpool.mr + xnn_params.f32.avgpool.qr; pool_size++) { |
| 1446 | AveragePoolingOperatorTester() |
| 1447 | .batch_size(3) |
| 1448 | .input_height(pool_size + 1) |
| 1449 | .input_width(3) |
| 1450 | .pooling_height(pool_size) |
| 1451 | .pooling_width(1) |
| 1452 | .channels(channels) |
| 1453 | .input_pixel_stride(5 * channels) |
| 1454 | .TestF32(); |
| 1455 | AveragePoolingOperatorTester() |
| 1456 | .batch_size(3) |
| 1457 | .input_height(2) |
| 1458 | .input_width(pool_size + 1) |
| 1459 | .pooling_height(1) |
| 1460 | .pooling_width(pool_size) |
| 1461 | .channels(channels) |
| 1462 | .input_pixel_stride(5 * channels) |
| 1463 | .TestF32(); |
| 1464 | } |
| 1465 | } |
| 1466 | } |
| 1467 | |
| 1468 | TEST(AVERAGE_POOLING_OP_F32, small_batch_large_pool_with_output_stride) { |
| 1469 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 1470 | for (size_t channels = 1; channels <= 100; channels += 15) { |
| 1471 | for (size_t pool_size = xnn_params.f32.avgpool.mr + 1; pool_size <= xnn_params.f32.avgpool.mr + xnn_params.f32.avgpool.qr; pool_size++) { |
| 1472 | AveragePoolingOperatorTester() |
| 1473 | .batch_size(3) |
| 1474 | .input_height(pool_size + 1) |
| 1475 | .input_width(3) |
| 1476 | .pooling_height(pool_size) |
| 1477 | .pooling_width(1) |
| 1478 | .channels(channels) |
| 1479 | .output_pixel_stride(5 * channels) |
| 1480 | .TestF32(); |
| 1481 | AveragePoolingOperatorTester() |
| 1482 | .batch_size(3) |
| 1483 | .input_height(2) |
| 1484 | .input_width(pool_size + 1) |
| 1485 | .pooling_height(1) |
| 1486 | .pooling_width(pool_size) |
| 1487 | .channels(channels) |
| 1488 | .output_pixel_stride(5 * channels) |
| 1489 | .TestF32(); |
| 1490 | } |
| 1491 | } |
| 1492 | } |
| 1493 | |
| 1494 | TEST(AVERAGE_POOLING_OP_F32, setup_increasing_batch) { |
| 1495 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 1496 | AveragePoolingOperatorTester() |
| 1497 | .batch_size(3) |
| 1498 | .next_batch_size(5) |
| 1499 | .input_height(8) |
| 1500 | .input_width(8) |
| 1501 | .pooling_height(5) |
| 1502 | .pooling_width(3) |
| 1503 | .channels(24) |
| 1504 | .TestSetupF32(); |
| 1505 | } |
| 1506 | |
| 1507 | TEST(AVERAGE_POOLING_OP_F32, setup_decreasing_batch) { |
| 1508 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 1509 | AveragePoolingOperatorTester() |
| 1510 | .batch_size(5) |
| 1511 | .next_batch_size(3) |
| 1512 | .input_height(8) |
| 1513 | .input_width(8) |
| 1514 | .pooling_height(5) |
| 1515 | .pooling_width(3) |
| 1516 | .channels(24) |
| 1517 | .TestSetupF32(); |
| 1518 | } |
| 1519 | |
| 1520 | TEST(AVERAGE_POOLING_OP_F32, setup_changing_height) { |
| 1521 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 1522 | AveragePoolingOperatorTester() |
| 1523 | .batch_size(3) |
| 1524 | .input_height(8) |
| 1525 | .input_width(8) |
| 1526 | .next_input_height(9) |
| 1527 | .pooling_height(5) |
| 1528 | .pooling_width(3) |
| 1529 | .channels(24) |
| 1530 | .TestSetupF32(); |
| 1531 | AveragePoolingOperatorTester() |
| 1532 | .batch_size(3) |
| 1533 | .input_height(8) |
| 1534 | .input_width(8) |
| 1535 | .next_input_height(7) |
| 1536 | .pooling_height(5) |
| 1537 | .pooling_width(3) |
| 1538 | .channels(24) |
| 1539 | .TestSetupF32(); |
| 1540 | } |
| 1541 | |
| 1542 | TEST(AVERAGE_POOLING_OP_F32, setup_changing_width) { |
| 1543 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 1544 | AveragePoolingOperatorTester() |
| 1545 | .batch_size(3) |
| 1546 | .input_height(8) |
| 1547 | .input_width(8) |
| 1548 | .next_input_width(9) |
| 1549 | .pooling_height(5) |
| 1550 | .pooling_width(3) |
| 1551 | .channels(24) |
| 1552 | .TestSetupF32(); |
| 1553 | AveragePoolingOperatorTester() |
| 1554 | .batch_size(3) |
| 1555 | .input_height(8) |
| 1556 | .input_width(8) |
| 1557 | .next_input_width(7) |
| 1558 | .pooling_height(5) |
| 1559 | .pooling_width(3) |
| 1560 | .channels(24) |
| 1561 | .TestSetupF32(); |
| 1562 | } |
| 1563 | |
| 1564 | TEST(AVERAGE_POOLING_OP_F32, setup_swap_height_and_width) { |
| 1565 | ASSERT_EQ(xnn_status_success, xnn_initialize()); |
| 1566 | AveragePoolingOperatorTester() |
| 1567 | .batch_size(3) |
| 1568 | .input_height(9) |
| 1569 | .input_width(8) |
| 1570 | .next_input_height(8) |
| 1571 | .next_input_width(9) |
| 1572 | .pooling_height(5) |
| 1573 | .pooling_width(3) |
| 1574 | .channels(24) |
| 1575 | .TestSetupF32(); |
| 1576 | } |