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 <algorithm> |
| 10 | #include <cfloat> |
| 11 | #include <cmath> |
| 12 | #include <functional> |
Marat Dukhan | 5ce30d9 | 2020-04-14 03:31:26 -0700 | [diff] [blame] | 13 | #include <limits> |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 14 | #include <ostream> |
| 15 | #include <random> |
| 16 | #include <string> |
| 17 | #include <vector> |
| 18 | |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 19 | #include <xnnpack.h> |
| 20 | |
Frank Barchard | bb4c18b | 2019-09-30 11:05:52 -0700 | [diff] [blame] | 21 | #ifdef BENCHMARK_ARM_COMPUTE_LIBRARY |
| 22 | #include "arm_compute/core/Types.h" |
| 23 | #include "arm_compute/runtime/Tensor.h" |
| 24 | #include "arm_compute/runtime/CPP/CPPScheduler.h" |
| 25 | #include "arm_compute/runtime/NEON/functions/NEDepthwiseConvolutionLayer.h" |
| 26 | #include "arm_compute/runtime/NEON/functions/NEConvolutionLayer.h" |
| 27 | #endif // BENCHMARK_ARM_COMPUTE_LIBRARY |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 28 | #include <benchmark/benchmark.h> |
Frank Barchard | 49b4dcc | 2020-06-26 14:07:19 -0700 | [diff] [blame] | 29 | #include <fp16.h> |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 30 | #ifdef BENCHMARK_TENSORFLOW_LITE |
| 31 | #include "flatbuffers/include/flatbuffers/flatbuffers.h" |
| 32 | #include "tensorflow/lite/interpreter.h" |
| 33 | #include "tensorflow/lite/kernels/register.h" |
| 34 | #include "tensorflow/lite/model.h" |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 35 | #include "tensorflow/lite/schema/schema_generated.h" |
| 36 | #include "tensorflow/lite/version.h" |
| 37 | #endif // BENCHMARK_TENSORFLOW_LITE |
Frank Barchard | bb4c18b | 2019-09-30 11:05:52 -0700 | [diff] [blame] | 38 | #include "bench/utils.h" |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 39 | |
Chao Mei | c664027 | 2020-07-23 09:35:11 -0700 | [diff] [blame] | 40 | #ifndef XNN_NO_QU8_OPERATORS |
Marat Dukhan | 08b7a97 | 2020-07-14 18:17:29 -0700 | [diff] [blame] | 41 | void xnnpack_convolution_qu8(benchmark::State& state, const char* net) { |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 42 | const size_t batch_size = state.range(0); |
| 43 | const size_t input_height = state.range(1); |
| 44 | const size_t input_width = state.range(2); |
| 45 | const size_t kernel_height = state.range(3); |
| 46 | const size_t kernel_width = state.range(4); |
| 47 | const size_t padding_height = state.range(5); |
| 48 | const size_t padding_width = state.range(6); |
| 49 | const size_t subsampling = state.range(7); |
| 50 | const size_t dilation = state.range(8); |
| 51 | const size_t groups = state.range(9); |
| 52 | const size_t group_input_channels = state.range(10); |
| 53 | const size_t group_output_channels = state.range(11); |
| 54 | |
| 55 | std::random_device random_device; |
| 56 | auto rng = std::mt19937(random_device()); |
Marat Dukhan | ecd8311 | 2020-08-03 21:50:28 -0700 | [diff] [blame] | 57 | auto i32rng = std::bind(std::uniform_int_distribution<int32_t>(-10000, 10000), std::ref(rng)); |
Marat Dukhan | 44f0ca7 | 2020-08-02 21:46:58 -0700 | [diff] [blame] | 58 | auto u8rng = std::bind(std::uniform_int_distribution<uint32_t>(0, std::numeric_limits<uint8_t>::max()), std::ref(rng)); |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 59 | |
| 60 | const size_t output_pixel_stride = groups * group_output_channels; |
| 61 | const size_t input_pixel_stride = groups * group_input_channels; |
| 62 | const size_t effective_kernel_height = (kernel_height - 1) * dilation + 1; |
| 63 | const size_t effective_kernel_width = (kernel_width - 1) * dilation + 1; |
| 64 | const size_t padding_left = padding_width / 2; |
| 65 | const size_t padding_top = padding_height / 2; |
| 66 | const size_t padding_right = padding_width - padding_left; |
| 67 | const size_t padding_bottom = padding_height - padding_top; |
| 68 | const size_t output_height = (input_height + padding_height - effective_kernel_height) / subsampling + 1; |
| 69 | const size_t output_width = (input_width + padding_width - effective_kernel_width) / subsampling + 1; |
| 70 | |
| 71 | std::vector<uint8_t> input(batch_size * input_height * input_width * input_pixel_stride); |
| 72 | std::generate(input.begin(), input.end(), std::ref(u8rng)); |
| 73 | std::vector<uint8_t> kernel(groups * group_output_channels * kernel_height * kernel_width * group_input_channels); |
| 74 | std::generate(kernel.begin(), kernel.end(), std::ref(u8rng)); |
| 75 | std::vector<int32_t> bias(groups * group_output_channels); |
Marat Dukhan | ecd8311 | 2020-08-03 21:50:28 -0700 | [diff] [blame] | 76 | std::generate(bias.begin(), bias.end(), std::ref(i32rng)); |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 77 | const size_t output_elements = batch_size * output_height * output_width * output_pixel_stride; |
| 78 | |
Marat Dukhan | 04f03be | 2019-11-19 12:36:47 -0800 | [diff] [blame] | 79 | xnn_status status = xnn_initialize(nullptr /* allocator */); |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 80 | if (status != xnn_status_success) { |
| 81 | state.SkipWithError("failed to initialize XNNPACK"); |
| 82 | return; |
| 83 | } |
| 84 | |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 85 | const size_t num_buffers = 1 + |
Marat Dukhan | 4232323 | 2019-10-23 02:09:02 -0700 | [diff] [blame] | 86 | benchmark::utils::DivideRoundUp<size_t>(benchmark::utils::GetMaxCacheSize(), |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 87 | sizeof(uint8_t) * kernel.size() + sizeof(int32_t) * bias.size() + sizeof(uint8_t) * output_elements); |
| 88 | std::vector<uint8_t> output(output_elements * num_buffers); |
| 89 | |
| 90 | std::vector<xnn_operator_t> convolution_operators(num_buffers); |
| 91 | for (xnn_operator_t& convolution_op : convolution_operators) { |
Marat Dukhan | 08b7a97 | 2020-07-14 18:17:29 -0700 | [diff] [blame] | 92 | status = xnn_create_convolution2d_nhwc_qu8( |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 93 | padding_top, padding_right, padding_bottom, padding_left, |
| 94 | kernel_height, kernel_width, |
| 95 | subsampling, subsampling, |
| 96 | dilation, dilation, |
| 97 | groups, group_input_channels, group_output_channels, |
| 98 | input_pixel_stride, output_pixel_stride, |
| 99 | 127, 0.5f, |
| 100 | 127, 0.5f, |
| 101 | kernel.data(), bias.data(), |
| 102 | 127, 0.5f, 0, 255, |
| 103 | 0 /* flags */, &convolution_op); |
| 104 | if (status != xnn_status_success) { |
Marat Dukhan | 16f1e1a | 2020-08-04 16:38:22 -0700 | [diff] [blame] | 105 | state.SkipWithError("failed to create QUINT8 Convolution operator"); |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 106 | return; |
| 107 | } |
| 108 | } |
| 109 | |
| 110 | for (size_t i = 0; i < convolution_operators.size(); i++) { |
Marat Dukhan | 08b7a97 | 2020-07-14 18:17:29 -0700 | [diff] [blame] | 111 | status = xnn_setup_convolution2d_nhwc_qu8( |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 112 | convolution_operators[i], |
| 113 | batch_size, input_height, input_width, |
| 114 | input.data(), output.data() + i * output_elements, |
| 115 | nullptr /* thread pool */); |
| 116 | if (status != xnn_status_success) { |
Marat Dukhan | 16f1e1a | 2020-08-04 16:38:22 -0700 | [diff] [blame] | 117 | state.SkipWithError("failed to setup QUINT8 Convolution operator"); |
| 118 | return; |
| 119 | } |
| 120 | } |
| 121 | |
| 122 | size_t buffer_index = 0; |
| 123 | for (auto _ : state) { |
| 124 | state.PauseTiming(); |
| 125 | benchmark::utils::PrefetchToL1(input.data(), input.size() * sizeof(uint8_t)); |
| 126 | buffer_index = (buffer_index + 1) % num_buffers; |
| 127 | state.ResumeTiming(); |
| 128 | |
| 129 | status = xnn_run_operator(convolution_operators[buffer_index], |
| 130 | nullptr /* thread pool */); |
| 131 | if (status != xnn_status_success) { |
| 132 | state.SkipWithError("failed to run QUINT8 Convolution operator"); |
| 133 | return; |
| 134 | } |
| 135 | } |
| 136 | |
| 137 | for (xnn_operator_t& convolution_op : convolution_operators) { |
| 138 | status = xnn_delete_operator(convolution_op); |
| 139 | if (status != xnn_status_success) { |
| 140 | state.SkipWithError("failed to delete QUINT8 Convolution operator"); |
| 141 | return; |
| 142 | } |
| 143 | convolution_op = nullptr; |
| 144 | } |
| 145 | |
Marat Dukhan | d713e8a | 2020-12-04 14:23:12 -0800 | [diff] [blame] | 146 | const uint64_t cpu_frequency = benchmark::utils::GetCurrentCpuFrequency(); |
| 147 | if (cpu_frequency != 0) { |
| 148 | state.counters["cpufreq"] = cpu_frequency; |
| 149 | } |
| 150 | |
Marat Dukhan | 16f1e1a | 2020-08-04 16:38:22 -0700 | [diff] [blame] | 151 | state.counters["OPS"] = benchmark::Counter( |
| 152 | uint64_t(state.iterations()) * 2 * |
| 153 | batch_size * output_height * output_width * |
| 154 | groups * group_input_channels * group_output_channels * |
| 155 | kernel_height * kernel_width, |
| 156 | benchmark::Counter::kIsRate); |
| 157 | } |
| 158 | #endif // XNN_NO_QU8_OPERATORS |
| 159 | |
| 160 | #ifndef XNN_NO_QS8_OPERATORS |
| 161 | void xnnpack_convolution_qs8(benchmark::State& state, const char* net) { |
| 162 | const size_t batch_size = state.range(0); |
| 163 | const size_t input_height = state.range(1); |
| 164 | const size_t input_width = state.range(2); |
| 165 | const size_t kernel_height = state.range(3); |
| 166 | const size_t kernel_width = state.range(4); |
| 167 | const size_t padding_height = state.range(5); |
| 168 | const size_t padding_width = state.range(6); |
| 169 | const size_t subsampling = state.range(7); |
| 170 | const size_t dilation = state.range(8); |
| 171 | const size_t groups = state.range(9); |
| 172 | const size_t group_input_channels = state.range(10); |
| 173 | const size_t group_output_channels = state.range(11); |
| 174 | |
| 175 | std::random_device random_device; |
| 176 | auto rng = std::mt19937(random_device()); |
| 177 | auto i32rng = std::bind(std::uniform_int_distribution<int32_t>(-10000, 10000), std::ref(rng)); |
| 178 | auto i8rng = std::bind( |
| 179 | std::uniform_int_distribution<int32_t>(std::numeric_limits<int8_t>::min(), std::numeric_limits<int8_t>::max()), std::ref(rng)); |
| 180 | |
| 181 | const size_t output_pixel_stride = groups * group_output_channels; |
| 182 | const size_t input_pixel_stride = groups * group_input_channels; |
| 183 | const size_t effective_kernel_height = (kernel_height - 1) * dilation + 1; |
| 184 | const size_t effective_kernel_width = (kernel_width - 1) * dilation + 1; |
| 185 | const size_t padding_left = padding_width / 2; |
| 186 | const size_t padding_top = padding_height / 2; |
| 187 | const size_t padding_right = padding_width - padding_left; |
| 188 | const size_t padding_bottom = padding_height - padding_top; |
| 189 | const size_t output_height = (input_height + padding_height - effective_kernel_height) / subsampling + 1; |
| 190 | const size_t output_width = (input_width + padding_width - effective_kernel_width) / subsampling + 1; |
| 191 | |
| 192 | std::vector<int8_t> input(batch_size * input_height * input_width * input_pixel_stride); |
| 193 | std::generate(input.begin(), input.end(), std::ref(i8rng)); |
| 194 | std::vector<int8_t> kernel(groups * group_output_channels * kernel_height * kernel_width * group_input_channels); |
| 195 | std::generate(kernel.begin(), kernel.end(), std::ref(i8rng)); |
| 196 | std::vector<int32_t> bias(groups * group_output_channels); |
| 197 | std::generate(bias.begin(), bias.end(), std::ref(i32rng)); |
| 198 | const size_t output_elements = batch_size * output_height * output_width * output_pixel_stride; |
| 199 | |
| 200 | xnn_status status = xnn_initialize(nullptr /* allocator */); |
| 201 | if (status != xnn_status_success) { |
| 202 | state.SkipWithError("failed to initialize XNNPACK"); |
| 203 | return; |
| 204 | } |
| 205 | |
| 206 | const size_t num_buffers = 1 + |
| 207 | benchmark::utils::DivideRoundUp<size_t>(benchmark::utils::GetMaxCacheSize(), |
| 208 | sizeof(int8_t) * kernel.size() + sizeof(int32_t) * bias.size() + sizeof(int8_t) * output_elements); |
| 209 | std::vector<int8_t> output(output_elements * num_buffers); |
| 210 | |
| 211 | std::vector<xnn_operator_t> convolution_operators(num_buffers); |
| 212 | for (xnn_operator_t& convolution_op : convolution_operators) { |
| 213 | status = xnn_create_convolution2d_nhwc_qs8( |
| 214 | padding_top, padding_right, padding_bottom, padding_left, |
| 215 | kernel_height, kernel_width, |
| 216 | subsampling, subsampling, |
| 217 | dilation, dilation, |
| 218 | groups, group_input_channels, group_output_channels, |
| 219 | input_pixel_stride, output_pixel_stride, |
| 220 | 127, 0.5f, 0.5f, |
| 221 | kernel.data(), bias.data(), |
| 222 | 127, 0.5f, -128, 127, |
| 223 | 0 /* flags */, &convolution_op); |
| 224 | if (status != xnn_status_success) { |
| 225 | state.SkipWithError("failed to create QINT8 Convolution operator"); |
| 226 | return; |
| 227 | } |
| 228 | } |
| 229 | |
| 230 | for (size_t i = 0; i < convolution_operators.size(); i++) { |
| 231 | status = xnn_setup_convolution2d_nhwc_qs8( |
| 232 | convolution_operators[i], |
| 233 | batch_size, input_height, input_width, |
| 234 | input.data(), output.data() + i * output_elements, |
| 235 | nullptr /* thread pool */); |
| 236 | if (status != xnn_status_success) { |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 237 | state.SkipWithError("failed to setup QINT8 Convolution operator"); |
| 238 | return; |
| 239 | } |
| 240 | } |
| 241 | |
| 242 | size_t buffer_index = 0; |
| 243 | for (auto _ : state) { |
| 244 | state.PauseTiming(); |
Marat Dukhan | 4232323 | 2019-10-23 02:09:02 -0700 | [diff] [blame] | 245 | benchmark::utils::PrefetchToL1(input.data(), input.size() * sizeof(uint8_t)); |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 246 | buffer_index = (buffer_index + 1) % num_buffers; |
| 247 | state.ResumeTiming(); |
| 248 | |
| 249 | status = xnn_run_operator(convolution_operators[buffer_index], |
| 250 | nullptr /* thread pool */); |
| 251 | if (status != xnn_status_success) { |
| 252 | state.SkipWithError("failed to run QINT8 Convolution operator"); |
| 253 | return; |
| 254 | } |
| 255 | } |
| 256 | |
| 257 | for (xnn_operator_t& convolution_op : convolution_operators) { |
| 258 | status = xnn_delete_operator(convolution_op); |
| 259 | if (status != xnn_status_success) { |
| 260 | state.SkipWithError("failed to delete QINT8 Convolution operator"); |
| 261 | return; |
| 262 | } |
| 263 | convolution_op = nullptr; |
| 264 | } |
| 265 | |
Marat Dukhan | d713e8a | 2020-12-04 14:23:12 -0800 | [diff] [blame] | 266 | const uint64_t cpu_frequency = benchmark::utils::GetCurrentCpuFrequency(); |
| 267 | if (cpu_frequency != 0) { |
| 268 | state.counters["cpufreq"] = cpu_frequency; |
| 269 | } |
| 270 | |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 271 | state.counters["OPS"] = benchmark::Counter( |
| 272 | uint64_t(state.iterations()) * 2 * |
| 273 | batch_size * output_height * output_width * |
| 274 | groups * group_input_channels * group_output_channels * |
| 275 | kernel_height * kernel_width, |
| 276 | benchmark::Counter::kIsRate); |
| 277 | } |
Marat Dukhan | 16f1e1a | 2020-08-04 16:38:22 -0700 | [diff] [blame] | 278 | #endif // XNN_NO_QS8_OPERATORS |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 279 | |
Chao Mei | c664027 | 2020-07-23 09:35:11 -0700 | [diff] [blame] | 280 | #ifndef XNN_NO_F16_OPERATORS |
Frank Barchard | 49b4dcc | 2020-06-26 14:07:19 -0700 | [diff] [blame] | 281 | void xnnpack_convolution_f16(benchmark::State& state, const char* net) { |
| 282 | if (!benchmark::utils::CheckNEONFP16ARITH(state)) { |
| 283 | return; |
| 284 | } |
| 285 | const size_t batch_size = state.range(0); |
| 286 | const size_t input_height = state.range(1); |
| 287 | const size_t input_width = state.range(2); |
| 288 | const size_t kernel_height = state.range(3); |
| 289 | const size_t kernel_width = state.range(4); |
| 290 | const size_t padding_height = state.range(5); |
| 291 | const size_t padding_width = state.range(6); |
| 292 | const size_t subsampling = state.range(7); |
| 293 | const size_t dilation = state.range(8); |
| 294 | const size_t groups = state.range(9); |
| 295 | const size_t group_input_channels = state.range(10); |
| 296 | const size_t group_output_channels = state.range(11); |
| 297 | |
| 298 | std::random_device random_device; |
| 299 | auto rng = std::mt19937(random_device()); |
Marat Dukhan | 44f0ca7 | 2020-08-02 21:46:58 -0700 | [diff] [blame] | 300 | auto f32rng = std::bind(std::uniform_real_distribution<float>(0.1f, 1.0f), std::ref(rng)); |
Frank Barchard | 49b4dcc | 2020-06-26 14:07:19 -0700 | [diff] [blame] | 301 | auto f16rng = std::bind(fp16_ieee_from_fp32_value, f32rng); |
| 302 | |
| 303 | const size_t output_pixel_stride = groups * group_output_channels; |
| 304 | const size_t input_pixel_stride = groups * group_input_channels; |
| 305 | const size_t effective_kernel_height = (kernel_height - 1) * dilation + 1; |
| 306 | const size_t effective_kernel_width = (kernel_width - 1) * dilation + 1; |
| 307 | const size_t padding_left = padding_width / 2; |
| 308 | const size_t padding_top = padding_height / 2; |
| 309 | const size_t padding_right = padding_width - padding_left; |
| 310 | const size_t padding_bottom = padding_height - padding_top; |
| 311 | const size_t output_height = (input_height + padding_height - effective_kernel_height) / subsampling + 1; |
| 312 | const size_t output_width = (input_width + padding_width - effective_kernel_width) / subsampling + 1; |
| 313 | |
| 314 | std::vector<uint16_t> input(batch_size * input_height * input_width * input_pixel_stride + XNN_EXTRA_BYTES / sizeof(uint16_t)); |
| 315 | std::generate(input.begin(), input.end(), std::ref(f16rng)); |
| 316 | std::vector<uint16_t> kernel(groups * group_output_channels * kernel_height * kernel_width * group_input_channels); |
| 317 | std::generate(kernel.begin(), kernel.end(), std::ref(f16rng)); |
| 318 | std::vector<uint16_t> bias(groups * group_output_channels); |
| 319 | std::generate(bias.begin(), bias.end(), std::ref(f16rng)); |
| 320 | const size_t output_elements = batch_size * output_height * output_width * output_pixel_stride; |
| 321 | |
| 322 | xnn_status status = xnn_initialize(nullptr /* allocator */); |
| 323 | if (status != xnn_status_success) { |
| 324 | state.SkipWithError("failed to initialize XNNPACK"); |
| 325 | return; |
| 326 | } |
| 327 | |
| 328 | const size_t num_buffers = 1 + |
| 329 | benchmark::utils::DivideRoundUp<size_t>(benchmark::utils::GetMaxCacheSize(), |
| 330 | sizeof(uint16_t) * (kernel.size() + bias.size() + output_elements)); |
| 331 | std::vector<uint16_t> output(output_elements * num_buffers); |
| 332 | |
| 333 | std::vector<xnn_operator_t> convolution_operators(num_buffers); |
| 334 | for (xnn_operator_t& convolution_op : convolution_operators) { |
| 335 | status = xnn_create_convolution2d_nhwc_f16( |
| 336 | padding_top, padding_right, padding_bottom, padding_left, |
| 337 | kernel_height, kernel_width, |
| 338 | subsampling, subsampling, |
| 339 | dilation, dilation, |
| 340 | groups, group_input_channels, group_output_channels, |
| 341 | input_pixel_stride, output_pixel_stride, |
| 342 | kernel.data(), bias.data(), |
| 343 | -std::numeric_limits<float>::infinity(), +std::numeric_limits<float>::infinity(), |
| 344 | 0 /* flags */, &convolution_op); |
| 345 | if (status != xnn_status_success) { |
| 346 | state.SkipWithError("failed to create FP16 Convolution operator"); |
| 347 | return; |
| 348 | } |
| 349 | } |
| 350 | |
| 351 | for (size_t i = 0; i < convolution_operators.size(); i++) { |
| 352 | status = xnn_setup_convolution2d_nhwc_f16( |
| 353 | convolution_operators[i], |
| 354 | batch_size, input_height, input_width, |
| 355 | input.data(), output.data() + i * output_elements, |
| 356 | nullptr /* thread pool */); |
| 357 | if (status != xnn_status_success) { |
| 358 | state.SkipWithError("failed to setup FP16 Convolution operator"); |
| 359 | return; |
| 360 | } |
| 361 | } |
| 362 | |
| 363 | size_t buffer_index = 0; |
| 364 | for (auto _ : state) { |
| 365 | state.PauseTiming(); |
| 366 | benchmark::utils::PrefetchToL1(input.data(), input.size() * sizeof(uint16_t)); |
| 367 | buffer_index = (buffer_index + 1) % num_buffers; |
| 368 | state.ResumeTiming(); |
| 369 | |
| 370 | status = xnn_run_operator(convolution_operators[buffer_index], nullptr /* thread pool */); |
| 371 | if (status != xnn_status_success) { |
| 372 | state.SkipWithError("failed to run FP16 Convolution operator"); |
| 373 | return; |
| 374 | } |
| 375 | } |
| 376 | |
| 377 | for (xnn_operator_t& convolution_op : convolution_operators) { |
| 378 | status = xnn_delete_operator(convolution_op); |
| 379 | if (status != xnn_status_success) { |
| 380 | state.SkipWithError("failed to delete FP16 Convolution operator"); |
| 381 | return; |
| 382 | } |
| 383 | convolution_op = nullptr; |
| 384 | } |
| 385 | |
Marat Dukhan | d713e8a | 2020-12-04 14:23:12 -0800 | [diff] [blame] | 386 | const uint64_t cpu_frequency = benchmark::utils::GetCurrentCpuFrequency(); |
| 387 | if (cpu_frequency != 0) { |
| 388 | state.counters["cpufreq"] = cpu_frequency; |
| 389 | } |
| 390 | |
Frank Barchard | 49b4dcc | 2020-06-26 14:07:19 -0700 | [diff] [blame] | 391 | state.counters["FLOPS"] = benchmark::Counter( |
| 392 | uint64_t(state.iterations()) * 2 * |
| 393 | batch_size * output_height * output_width * |
| 394 | groups * group_input_channels * group_output_channels * |
| 395 | kernel_height * kernel_width, |
| 396 | benchmark::Counter::kIsRate); |
| 397 | } |
Chao Mei | c664027 | 2020-07-23 09:35:11 -0700 | [diff] [blame] | 398 | #endif // XNN_NO_F16_OPERATORS |
Frank Barchard | 49b4dcc | 2020-06-26 14:07:19 -0700 | [diff] [blame] | 399 | |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 400 | void xnnpack_convolution_f32(benchmark::State& state, const char* net) { |
| 401 | const size_t batch_size = state.range(0); |
| 402 | const size_t input_height = state.range(1); |
| 403 | const size_t input_width = state.range(2); |
| 404 | const size_t kernel_height = state.range(3); |
| 405 | const size_t kernel_width = state.range(4); |
| 406 | const size_t padding_height = state.range(5); |
| 407 | const size_t padding_width = state.range(6); |
| 408 | const size_t subsampling = state.range(7); |
| 409 | const size_t dilation = state.range(8); |
| 410 | const size_t groups = state.range(9); |
| 411 | const size_t group_input_channels = state.range(10); |
| 412 | const size_t group_output_channels = state.range(11); |
| 413 | |
| 414 | std::random_device random_device; |
| 415 | auto rng = std::mt19937(random_device()); |
Marat Dukhan | 44f0ca7 | 2020-08-02 21:46:58 -0700 | [diff] [blame] | 416 | auto f32rng = std::bind(std::uniform_real_distribution<float>(0.0f, 1.0f), std::ref(rng)); |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 417 | |
| 418 | const size_t output_pixel_stride = groups * group_output_channels; |
| 419 | const size_t input_pixel_stride = groups * group_input_channels; |
| 420 | const size_t effective_kernel_height = (kernel_height - 1) * dilation + 1; |
| 421 | const size_t effective_kernel_width = (kernel_width - 1) * dilation + 1; |
| 422 | const size_t padding_left = padding_width / 2; |
| 423 | const size_t padding_top = padding_height / 2; |
| 424 | const size_t padding_right = padding_width - padding_left; |
| 425 | const size_t padding_bottom = padding_height - padding_top; |
| 426 | const size_t output_height = (input_height + padding_height - effective_kernel_height) / subsampling + 1; |
| 427 | const size_t output_width = (input_width + padding_width - effective_kernel_width) / subsampling + 1; |
| 428 | |
| 429 | std::vector<float> input(batch_size * input_height * input_width * input_pixel_stride + XNN_EXTRA_BYTES / sizeof(float)); |
| 430 | std::generate(input.begin(), input.end(), std::ref(f32rng)); |
| 431 | std::vector<float> kernel(groups * group_output_channels * kernel_height * kernel_width * group_input_channels); |
| 432 | std::generate(kernel.begin(), kernel.end(), std::ref(f32rng)); |
| 433 | std::vector<float> bias(groups * group_output_channels); |
| 434 | std::generate(bias.begin(), bias.end(), std::ref(f32rng)); |
| 435 | const size_t output_elements = batch_size * output_height * output_width * output_pixel_stride; |
| 436 | |
Marat Dukhan | 04f03be | 2019-11-19 12:36:47 -0800 | [diff] [blame] | 437 | xnn_status status = xnn_initialize(nullptr /* allocator */); |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 438 | if (status != xnn_status_success) { |
| 439 | state.SkipWithError("failed to initialize XNNPACK"); |
| 440 | return; |
| 441 | } |
| 442 | |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 443 | const size_t num_buffers = 1 + |
Marat Dukhan | 4232323 | 2019-10-23 02:09:02 -0700 | [diff] [blame] | 444 | benchmark::utils::DivideRoundUp<size_t>(benchmark::utils::GetMaxCacheSize(), |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 445 | sizeof(float) * (kernel.size() + bias.size() + output_elements)); |
| 446 | std::vector<float> output(output_elements * num_buffers); |
| 447 | |
| 448 | std::vector<xnn_operator_t> convolution_operators(num_buffers); |
| 449 | for (xnn_operator_t& convolution_op : convolution_operators) { |
| 450 | status = xnn_create_convolution2d_nhwc_f32( |
| 451 | padding_top, padding_right, padding_bottom, padding_left, |
| 452 | kernel_height, kernel_width, |
| 453 | subsampling, subsampling, |
| 454 | dilation, dilation, |
| 455 | groups, group_input_channels, group_output_channels, |
| 456 | input_pixel_stride, output_pixel_stride, |
| 457 | kernel.data(), bias.data(), |
| 458 | -std::numeric_limits<float>::infinity(), +std::numeric_limits<float>::infinity(), |
| 459 | 0 /* flags */, &convolution_op); |
| 460 | if (status != xnn_status_success) { |
| 461 | state.SkipWithError("failed to create FP32 Convolution operator"); |
| 462 | return; |
| 463 | } |
| 464 | } |
| 465 | |
| 466 | for (size_t i = 0; i < convolution_operators.size(); i++) { |
| 467 | status = xnn_setup_convolution2d_nhwc_f32( |
| 468 | convolution_operators[i], |
| 469 | batch_size, input_height, input_width, |
| 470 | input.data(), output.data() + i * output_elements, |
| 471 | nullptr /* thread pool */); |
| 472 | if (status != xnn_status_success) { |
| 473 | state.SkipWithError("failed to setup FP32 Convolution operator"); |
| 474 | return; |
| 475 | } |
| 476 | } |
| 477 | |
| 478 | size_t buffer_index = 0; |
| 479 | for (auto _ : state) { |
| 480 | state.PauseTiming(); |
Marat Dukhan | 4232323 | 2019-10-23 02:09:02 -0700 | [diff] [blame] | 481 | benchmark::utils::PrefetchToL1(input.data(), input.size() * sizeof(float)); |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 482 | buffer_index = (buffer_index + 1) % num_buffers; |
| 483 | state.ResumeTiming(); |
| 484 | |
| 485 | status = xnn_run_operator(convolution_operators[buffer_index], nullptr /* thread pool */); |
| 486 | if (status != xnn_status_success) { |
| 487 | state.SkipWithError("failed to run FP32 Convolution operator"); |
| 488 | return; |
| 489 | } |
| 490 | } |
| 491 | |
| 492 | for (xnn_operator_t& convolution_op : convolution_operators) { |
| 493 | status = xnn_delete_operator(convolution_op); |
| 494 | if (status != xnn_status_success) { |
| 495 | state.SkipWithError("failed to delete FP32 Convolution operator"); |
| 496 | return; |
| 497 | } |
| 498 | convolution_op = nullptr; |
| 499 | } |
| 500 | |
Marat Dukhan | d713e8a | 2020-12-04 14:23:12 -0800 | [diff] [blame] | 501 | const uint64_t cpu_frequency = benchmark::utils::GetCurrentCpuFrequency(); |
| 502 | if (cpu_frequency != 0) { |
| 503 | state.counters["cpufreq"] = cpu_frequency; |
| 504 | } |
| 505 | |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 506 | state.counters["FLOPS"] = benchmark::Counter( |
| 507 | uint64_t(state.iterations()) * 2 * |
| 508 | batch_size * output_height * output_width * |
| 509 | groups * group_input_channels * group_output_channels * |
| 510 | kernel_height * kernel_width, |
| 511 | benchmark::Counter::kIsRate); |
| 512 | } |
| 513 | |
| 514 | #ifdef BENCHMARK_TENSORFLOW_LITE |
| 515 | void tflite_convolution_f32(benchmark::State& state, const char* net) { |
| 516 | const size_t batch_size = state.range(0); |
| 517 | const size_t input_height = state.range(1); |
| 518 | const size_t input_width = state.range(2); |
| 519 | const size_t kernel_height = state.range(3); |
| 520 | const size_t kernel_width = state.range(4); |
| 521 | const size_t padding_height = state.range(5); |
| 522 | const size_t padding_width = state.range(6); |
| 523 | const size_t subsampling = state.range(7); |
| 524 | const size_t dilation = state.range(8); |
| 525 | const size_t groups = state.range(9); |
| 526 | const size_t group_input_channels = state.range(10); |
| 527 | const size_t group_output_channels = state.range(11); |
| 528 | |
| 529 | bool is_depthwise = false; |
| 530 | if (groups != 1) { |
| 531 | if (group_input_channels == 1) { |
| 532 | is_depthwise = true; |
| 533 | } else { |
| 534 | state.SkipWithError("grouped convolution is not supported"); |
| 535 | return; |
| 536 | } |
| 537 | } |
| 538 | |
| 539 | std::random_device random_device; |
| 540 | auto rng = std::mt19937(random_device()); |
Marat Dukhan | 44f0ca7 | 2020-08-02 21:46:58 -0700 | [diff] [blame] | 541 | auto f32rng = std::bind(std::uniform_real_distribution<float>(0.0f, 1.0f), std::ref(rng)); |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 542 | |
| 543 | const size_t effective_kernel_height = (kernel_height - 1) * dilation + 1; |
| 544 | const size_t effective_kernel_width = (kernel_width - 1) * dilation + 1; |
| 545 | |
| 546 | tflite::Padding padding = tflite::Padding_VALID; |
| 547 | if (padding_width == (effective_kernel_width - 1) && padding_height == (effective_kernel_height - 1)) { |
| 548 | padding = tflite::Padding_SAME; |
| 549 | } else if (padding_width == 0 && padding_height == 0) { |
| 550 | padding = tflite::Padding_VALID; |
| 551 | } else { |
| 552 | state.SkipWithError("unsupported padding"); |
| 553 | return; |
| 554 | } |
| 555 | |
| 556 | const size_t output_height = (input_height + padding_height - effective_kernel_height) / subsampling + 1; |
| 557 | const size_t output_width = (input_width + padding_width - effective_kernel_width) / subsampling + 1; |
| 558 | |
| 559 | std::vector<float> kernel(groups * group_output_channels * kernel_height * kernel_width * group_input_channels); |
| 560 | std::generate(kernel.begin(), kernel.end(), std::ref(f32rng)); |
| 561 | std::vector<float> bias(groups * group_output_channels); |
| 562 | std::generate(bias.begin(), bias.end(), std::ref(f32rng)); |
| 563 | |
| 564 | flatbuffers::FlatBufferBuilder builder; |
| 565 | flatbuffers::Offset<tflite::OperatorCode> operator_code = |
| 566 | CreateOperatorCode( |
| 567 | builder, |
| 568 | is_depthwise ? tflite::BuiltinOperator_DEPTHWISE_CONV_2D : tflite::BuiltinOperator_CONV_2D, |
| 569 | 0); |
| 570 | |
| 571 | flatbuffers::Offset<tflite::Conv2DOptions> conv2d_options = CreateConv2DOptions( |
| 572 | builder, |
| 573 | padding, |
| 574 | static_cast<int32_t>(subsampling), static_cast<int32_t>(subsampling), |
| 575 | tflite::ActivationFunctionType_NONE, |
| 576 | static_cast<int32_t>(dilation), static_cast<int32_t>(dilation)); |
| 577 | |
| 578 | flatbuffers::Offset<tflite::DepthwiseConv2DOptions> dwconv2d_options = CreateDepthwiseConv2DOptions( |
| 579 | builder, |
| 580 | padding, |
| 581 | static_cast<int32_t>(subsampling), static_cast<int32_t>(subsampling), |
| 582 | static_cast<int32_t>(group_output_channels), |
| 583 | tflite::ActivationFunctionType_NONE, |
| 584 | static_cast<int32_t>(dilation), static_cast<int32_t>(dilation)); |
| 585 | |
| 586 | flatbuffers::Offset<tflite::Buffer> buffers[3] = { |
| 587 | tflite::CreateBuffer(builder, builder.CreateVector({})), |
| 588 | tflite::CreateBuffer(builder, builder.CreateVector( |
| 589 | reinterpret_cast<const uint8_t*>(kernel.data()), |
| 590 | sizeof(float) * kernel.size())), |
| 591 | tflite::CreateBuffer(builder, builder.CreateVector( |
| 592 | reinterpret_cast<const uint8_t*>(bias.data()), |
| 593 | sizeof(float) * bias.size())), |
| 594 | }; |
| 595 | |
| 596 | const int32_t input_shape[4] = { |
| 597 | static_cast<int32_t>(batch_size), |
| 598 | static_cast<int32_t>(input_height), |
| 599 | static_cast<int32_t>(input_width), |
| 600 | static_cast<int32_t>(groups * group_input_channels) |
| 601 | }; |
| 602 | const int32_t output_shape[4] = { |
| 603 | static_cast<int32_t>(batch_size), |
| 604 | static_cast<int32_t>(output_height), |
| 605 | static_cast<int32_t>(output_width), |
| 606 | static_cast<int32_t>(groups * group_output_channels) |
| 607 | }; |
| 608 | const int32_t filter_shape[4] = { |
| 609 | static_cast<int32_t>(group_output_channels), |
| 610 | static_cast<int32_t>(kernel_height), |
| 611 | static_cast<int32_t>(kernel_width), |
| 612 | static_cast<int32_t>(groups * group_input_channels) |
| 613 | }; |
| 614 | const int32_t bias_shape[1] = { |
| 615 | static_cast<int32_t>(groups * group_output_channels) |
| 616 | }; |
| 617 | |
| 618 | flatbuffers::Offset<tflite::Tensor> tensors[4] = { |
| 619 | tflite::CreateTensor(builder, |
| 620 | builder.CreateVector<int32_t>(input_shape, 4), |
| 621 | tflite::TensorType_FLOAT32, |
| 622 | 0 /* buffer id */, |
| 623 | builder.CreateString("input")), |
| 624 | tflite::CreateTensor(builder, |
| 625 | builder.CreateVector<int32_t>(filter_shape, 4), |
| 626 | tflite::TensorType_FLOAT32, |
| 627 | 1 /* buffer id */, |
| 628 | builder.CreateString("filter")), |
| 629 | tflite::CreateTensor(builder, |
| 630 | builder.CreateVector<int32_t>(bias_shape, 1), |
| 631 | tflite::TensorType_FLOAT32, |
| 632 | 2 /* buffer id */, |
| 633 | builder.CreateString("bias")), |
| 634 | tflite::CreateTensor(builder, |
| 635 | builder.CreateVector<int32_t>(output_shape, 4), |
| 636 | tflite::TensorType_FLOAT32, |
| 637 | 0 /* buffer id */, |
| 638 | builder.CreateString("output")), |
| 639 | }; |
| 640 | |
| 641 | const int32_t op_inputs[3] = { 0, 1, 2 }; |
| 642 | const int32_t op_outputs[1] = { 3 }; |
| 643 | flatbuffers::Offset<tflite::Operator> op = CreateOperator( |
| 644 | builder, |
| 645 | 0 /* opcode_index */, |
| 646 | builder.CreateVector<int32_t>(op_inputs, 3), |
| 647 | builder.CreateVector<int32_t>(op_outputs, 1), |
| 648 | is_depthwise ? tflite::BuiltinOptions_DepthwiseConv2DOptions : tflite::BuiltinOptions_Conv2DOptions, |
| 649 | is_depthwise ? dwconv2d_options.Union() : conv2d_options.Union(), |
| 650 | /*custom_options */ 0, |
| 651 | tflite::CustomOptionsFormat_FLEXBUFFERS); |
| 652 | |
| 653 | const int32_t graph_inputs[1] = { 0 }; |
| 654 | const int32_t graph_outputs[1] = { 3 }; |
| 655 | flatbuffers::Offset<tflite::SubGraph> subgraph = CreateSubGraph( |
| 656 | builder, |
| 657 | builder.CreateVector(tensors, 4), |
| 658 | builder.CreateVector<int32_t>(graph_inputs, 1), |
| 659 | builder.CreateVector<int32_t>(graph_outputs, 1), |
| 660 | builder.CreateVector(&op, 1), |
| 661 | builder.CreateString("Conv2D subgraph")); |
| 662 | |
| 663 | flatbuffers::Offset<flatbuffers::String> description = builder.CreateString("Conv2D model"); |
| 664 | |
| 665 | flatbuffers::Offset<tflite::Model> model_buffer = tflite::CreateModel(builder, |
| 666 | TFLITE_SCHEMA_VERSION, |
| 667 | builder.CreateVector(&operator_code, 1), |
| 668 | builder.CreateVector(&subgraph, 1), |
| 669 | description, |
| 670 | builder.CreateVector(buffers, 3)); |
| 671 | |
| 672 | builder.Finish(model_buffer); |
| 673 | |
| 674 | const tflite::Model* model = tflite::GetModel(builder.GetBufferPointer()); |
Chao Mei | f9fdaa7 | 2021-05-18 23:04:34 -0700 | [diff] [blame] | 675 | tflite::ops::builtin::BuiltinOpResolverWithoutDefaultDelegates resolver; |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 676 | tflite::InterpreterBuilder interpreterBuilder(model, resolver); |
| 677 | std::unique_ptr<tflite::Interpreter> interpreter; |
| 678 | if (interpreterBuilder(&interpreter) != kTfLiteOk) { |
| 679 | state.SkipWithError("failed to create TFLite interpreter"); |
| 680 | return; |
| 681 | } |
| 682 | if (interpreter == nullptr) { |
| 683 | state.SkipWithError("TFLite interpreter is null"); |
| 684 | return; |
| 685 | } |
| 686 | interpreter->SetNumThreads(1); |
| 687 | |
| 688 | if (interpreter->AllocateTensors() != kTfLiteOk) { |
| 689 | state.SkipWithError("failed to allocate tensors"); |
| 690 | return; |
| 691 | } |
| 692 | |
| 693 | std::generate( |
| 694 | interpreter->typed_tensor<float>(0), |
| 695 | interpreter->typed_tensor<float>(0) + batch_size * groups * group_input_channels * input_height * input_width, |
| 696 | std::ref(f32rng)); |
| 697 | |
| 698 | for (auto _ : state) { |
| 699 | state.PauseTiming(); |
Marat Dukhan | 4232323 | 2019-10-23 02:09:02 -0700 | [diff] [blame] | 700 | benchmark::utils::WipeCache(); |
| 701 | benchmark::utils::PrefetchToL1( |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 702 | interpreter->typed_tensor<float>(0), |
| 703 | batch_size * groups * group_input_channels * input_height * input_width * sizeof(float)); |
| 704 | state.ResumeTiming(); |
| 705 | |
| 706 | if (interpreter->Invoke() != kTfLiteOk) { |
| 707 | state.SkipWithError("failed to invoke TFLite interpreter"); |
| 708 | return; |
| 709 | } |
| 710 | } |
| 711 | |
Marat Dukhan | d713e8a | 2020-12-04 14:23:12 -0800 | [diff] [blame] | 712 | const uint64_t cpu_frequency = benchmark::utils::GetCurrentCpuFrequency(); |
| 713 | if (cpu_frequency != 0) { |
| 714 | state.counters["cpufreq"] = cpu_frequency; |
| 715 | } |
| 716 | |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 717 | state.counters["FLOPS"] = benchmark::Counter( |
| 718 | uint64_t(state.iterations()) * 2 * |
| 719 | batch_size * output_height * output_width * |
| 720 | groups * group_input_channels * group_output_channels * |
| 721 | kernel_height * kernel_width, |
| 722 | benchmark::Counter::kIsRate); |
| 723 | |
| 724 | interpreter.reset(); |
| 725 | } |
| 726 | #endif // BENCHMARK_TENSORFLOW_LITE |
| 727 | |
| 728 | #ifdef BENCHMARK_ARM_COMPUTE_LIBRARY |
| 729 | static std::string compare_with_convolution_f32_reference_output( |
| 730 | const benchmark::State& state, const float* input, size_t input_size, |
| 731 | const float* kernel, size_t kernel_size, const float* bias, size_t bias_size, |
| 732 | const float* output, size_t output_size) |
| 733 | { |
| 734 | const size_t batch_size = state.range(0); |
| 735 | const size_t input_height = state.range(1); |
| 736 | const size_t input_width = state.range(2); |
| 737 | const size_t kernel_height = state.range(3); |
| 738 | const size_t kernel_width = state.range(4); |
| 739 | const size_t padding_height = state.range(5); |
| 740 | const size_t padding_width = state.range(6); |
| 741 | const size_t subsampling = state.range(7); |
| 742 | const size_t dilation = state.range(8); |
| 743 | const size_t groups = state.range(9); |
| 744 | const size_t group_input_channels = state.range(10); |
| 745 | const size_t group_output_channels = state.range(11); |
| 746 | |
| 747 | const size_t effective_kernel_height = (kernel_height - 1) * dilation + 1; |
| 748 | const size_t effective_kernel_width = (kernel_width - 1) * dilation + 1; |
| 749 | const size_t output_height = (input_height + padding_height - effective_kernel_height) / subsampling + 1; |
| 750 | const size_t output_width = (input_width + padding_width - effective_kernel_width) / subsampling + 1; |
| 751 | const size_t input_pixel_stride = groups * group_input_channels; |
| 752 | const size_t padding_left = padding_width / 2; |
| 753 | const size_t padding_top = padding_height / 2; |
| 754 | |
| 755 | assert(input_size == batch_size * input_height * input_width * groups * group_input_channels); |
| 756 | |
| 757 | assert(kernel_size == group_output_channels * kernel_height * kernel_width * groups * group_input_channels); |
| 758 | |
| 759 | assert(bias_size == groups * group_output_channels); |
| 760 | |
| 761 | assert(output_size == batch_size * output_height * output_width * groups * group_output_channels); |
| 762 | |
| 763 | std::vector<float> output_ref(output_size); |
| 764 | for (size_t i = 0; i < batch_size; i++) { |
| 765 | for (size_t oy = 0; oy < output_height; oy++) { |
| 766 | for (size_t ox = 0; ox < output_width; ox++) { |
| 767 | for (size_t g = 0; g < groups; g++) { |
| 768 | for (size_t oc = 0; oc < group_output_channels; oc++) { |
| 769 | output_ref[(((i * output_height + oy) * output_width + ox) * groups + g) * group_output_channels + oc] = |
| 770 | bias[g * group_output_channels + oc]; |
| 771 | } |
| 772 | } |
| 773 | } |
| 774 | } |
| 775 | } |
| 776 | for (size_t i = 0; i < batch_size; i++) { |
| 777 | for (size_t oy = 0; oy < output_height; oy++) { |
| 778 | for (size_t ox = 0; ox < output_width; ox++) { |
| 779 | for (size_t ky = 0; ky < kernel_height; ky++) { |
| 780 | const size_t iy = oy * subsampling + ky * dilation - padding_top; |
| 781 | if (iy < input_height) { |
| 782 | for (size_t kx = 0; kx < kernel_width; kx++) { |
| 783 | const size_t ix = ox * subsampling + kx * dilation - padding_left; |
| 784 | if (ix < input_width) { |
| 785 | for (size_t g = 0; g < groups; g++) { |
| 786 | for (size_t oc = 0; oc < group_output_channels; oc++) { |
| 787 | for (size_t ic = 0; ic < group_input_channels; ic++) { |
| 788 | output_ref[(((i * output_height + oy) * output_width + ox) * groups + g) * group_output_channels + oc] += |
| 789 | input[((i * input_height + iy) * input_width + ix) * input_pixel_stride + g * group_input_channels + ic] * |
| 790 | kernel[(((oc * kernel_height + ky) * kernel_width + kx) * groups + g) * group_input_channels + ic]; |
| 791 | } // group_input_channels loop |
| 792 | } // group_output_channels loop |
| 793 | } // groups loop |
| 794 | } |
| 795 | } // kernel_width loop |
| 796 | } |
| 797 | } // kernel_height loop |
| 798 | } // output_width loop |
| 799 | } // output_height loop |
| 800 | } // batch_size loop |
| 801 | |
| 802 | const float relative_error_tolerance = 1e-4; |
| 803 | for (size_t i = 0; i < batch_size; i++) { |
| 804 | for (size_t y = 0; y < output_height; y++) { |
| 805 | for (size_t x = 0; x < output_width; x++) { |
| 806 | for (size_t g = 0; g < groups; g++) { |
| 807 | for (size_t c = 0; c < group_output_channels; c++) { |
| 808 | const size_t idx = (((i * output_height + y) * output_width + x) * groups + g) * group_output_channels + c; |
| 809 | const float value_ref = output_ref[idx]; |
| 810 | const float value = output[idx]; |
| 811 | if (std::abs(value - value_ref) > std::max(std::abs(value_ref) * relative_error_tolerance, std::numeric_limits<float>::epsilon())) { |
| 812 | std::ostringstream error_stream; |
| 813 | error_stream << "(x, y) = (" << x << ", " << y << "), group = " << g |
| 814 | << ", channel = " << c << ", refValue = " << value_ref |
| 815 | << ", actualValue = " << value |
| 816 | << ", absDiff=" << std::abs(value - value_ref); |
| 817 | return error_stream.str(); |
| 818 | } |
| 819 | } |
| 820 | } |
| 821 | } |
| 822 | } |
| 823 | } |
| 824 | return ""; |
| 825 | } |
| 826 | |
| 827 | void armcl_convolution_f32(benchmark::State& state, const char* net) { |
| 828 | const size_t batch_size = state.range(0); |
| 829 | const size_t input_height = state.range(1); |
| 830 | const size_t input_width = state.range(2); |
| 831 | const size_t kernel_height = state.range(3); |
| 832 | const size_t kernel_width = state.range(4); |
| 833 | const size_t padding_height = state.range(5); |
| 834 | const size_t padding_width = state.range(6); |
| 835 | const size_t subsampling = state.range(7); |
| 836 | const size_t dilation = state.range(8); |
| 837 | const size_t groups = state.range(9); |
| 838 | const size_t group_input_channels = state.range(10); |
| 839 | const size_t group_output_channels = state.range(11); |
| 840 | |
| 841 | const size_t effective_kernel_height = (kernel_height - 1) * dilation + 1; |
| 842 | const size_t effective_kernel_width = (kernel_width - 1) * dilation + 1; |
| 843 | const size_t padding_left = padding_width / 2; |
| 844 | const size_t padding_top = padding_height / 2; |
| 845 | const size_t padding_right = padding_width - padding_left; |
| 846 | const size_t padding_bottom = padding_height - padding_top; |
| 847 | const size_t output_height = (input_height + padding_height - effective_kernel_height) / subsampling + 1; |
| 848 | const size_t output_width = (input_width + padding_width - effective_kernel_width) / subsampling + 1; |
| 849 | |
| 850 | arm_compute::PadStrideInfo pad_stride_info( |
| 851 | subsampling /* stride height */, |
| 852 | subsampling /* stride width */, |
| 853 | padding_left, padding_right, padding_top, padding_bottom, |
| 854 | arm_compute::DimensionRoundingType::FLOOR); |
| 855 | arm_compute::Size2D dilation_info(dilation, dilation); |
| 856 | // Note: activation is disabled by default. |
| 857 | arm_compute::ActivationLayerInfo activation_info; |
| 858 | |
| 859 | // Note: no batch size and reverse order of dimensions, i.e. CWHN for NHWC. |
| 860 | arm_compute::TensorShape input_shape( |
| 861 | /* C */ groups * group_input_channels, |
| 862 | /* W */ input_width, |
| 863 | /* H */ input_height, |
| 864 | /* N */ batch_size); |
| 865 | arm_compute::TensorInfo input_info( |
| 866 | input_shape, |
| 867 | 1 /* number of channels per element (!) */, |
| 868 | arm_compute::DataType::F32); |
| 869 | input_info.set_data_layout(arm_compute::DataLayout::NHWC); |
| 870 | arm_compute::Tensor input_tensor; |
| 871 | input_tensor.allocator()->init(input_info); |
| 872 | input_tensor.allocator()->allocate(); |
| 873 | |
| 874 | // Note: reverse order of dimensions, i.e. for IWHO for OHWI. |
| 875 | arm_compute::TensorShape kernel_shape( |
| 876 | /* I */ groups * group_input_channels, |
| 877 | /* W */ kernel_width, |
| 878 | /* H */ kernel_height, |
| 879 | /* O */ group_output_channels); |
| 880 | arm_compute::TensorInfo kernel_info( |
| 881 | kernel_shape, |
| 882 | 1 /* number of channels per element (!) */, |
| 883 | arm_compute::DataType::F32); |
| 884 | kernel_info.set_data_layout(arm_compute::DataLayout::NHWC); |
| 885 | arm_compute::Tensor kernelTensor; |
| 886 | kernelTensor.allocator()->init(kernel_info); |
| 887 | kernelTensor.allocator()->allocate(); |
| 888 | |
| 889 | arm_compute::TensorShape bias_shape(groups * group_output_channels); |
| 890 | arm_compute::TensorInfo bias_info( |
| 891 | bias_shape, |
| 892 | 1 /* number of channels per element (!) */, |
| 893 | arm_compute::DataType::F32); |
| 894 | bias_info.set_data_layout(arm_compute::DataLayout::NHWC); |
| 895 | arm_compute::Tensor bias_tensor; |
| 896 | bias_tensor.allocator()->init(bias_info); |
| 897 | bias_tensor.allocator()->allocate(); |
| 898 | |
| 899 | // Note: no batch size and reverse order of dimensions, i.e. CWHN for NHWC. |
| 900 | arm_compute::TensorShape output_shape( |
| 901 | /* C */ groups * group_output_channels, |
| 902 | /* W */ output_width, |
| 903 | /* H */ output_height, |
| 904 | /* N */ batch_size); |
| 905 | arm_compute::TensorInfo output_info( |
| 906 | output_shape, |
| 907 | 1 /* number of channels per element (!) */, |
| 908 | arm_compute::DataType::F32); |
| 909 | output_info.set_data_layout(arm_compute::DataLayout::NHWC); |
| 910 | arm_compute::Tensor output_tensor; |
| 911 | output_tensor.allocator()->init(output_info); |
| 912 | output_tensor.allocator()->allocate(); |
| 913 | |
| 914 | std::random_device random_device; |
| 915 | auto rng = std::mt19937(random_device()); |
Marat Dukhan | 44f0ca7 | 2020-08-02 21:46:58 -0700 | [diff] [blame] | 916 | auto f32rng = std::bind(std::uniform_real_distribution<float>(0.0f, 1.0f), std::ref(rng)); |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 917 | |
| 918 | std::generate( |
| 919 | reinterpret_cast<float*>(input_tensor.buffer()), |
| 920 | reinterpret_cast<float*>(input_tensor.buffer()) + input_shape.total_size(), |
| 921 | std::ref(f32rng)); |
| 922 | std::generate( |
| 923 | reinterpret_cast<float*>(kernelTensor.buffer()), |
| 924 | reinterpret_cast<float*>(kernelTensor.buffer()) + kernel_shape.total_size(), |
| 925 | std::ref(f32rng)); |
| 926 | std::generate( |
| 927 | reinterpret_cast<float*>(bias_tensor.buffer()), |
| 928 | reinterpret_cast<float*>(bias_tensor.buffer()) + bias_shape.total_size(), |
| 929 | std::ref(f32rng)); |
| 930 | std::generate( |
| 931 | reinterpret_cast<float*>(output_tensor.buffer()), |
| 932 | reinterpret_cast<float*>(output_tensor.buffer()) + output_shape.total_size(), |
| 933 | std::ref(f32rng)); |
| 934 | |
| 935 | bool is_depthwise = false; |
| 936 | if (groups != 1) { |
| 937 | // NEConvolutionLayer uses NEGEMMConvolutionLayer by default, which doesn't support grouped convolution. |
| 938 | // However, depthwise convolution is supported via NEDepthwiseConvolutionLayer. |
| 939 | if (group_input_channels == 1) { |
| 940 | is_depthwise = true; |
| 941 | } else { |
| 942 | state.SkipWithError("grouped convolution is not supported"); |
| 943 | return; |
| 944 | } |
| 945 | } |
| 946 | |
| 947 | std::shared_ptr<arm_compute::IFunction> layer; |
| 948 | if (is_depthwise) { |
| 949 | if (dilation != 1) { |
| 950 | state.SkipWithError("dilated depthwise convolution is not supported"); |
| 951 | return; |
| 952 | } |
| 953 | |
| 954 | // Avoid NEDepthwiseConvolutionLayer3x3 when stride isn't 2 in order to pass the output verification. |
| 955 | // TODO(b/130206370) This looks like a bug and needs further investigation. |
| 956 | if (kernel_height == 3 && kernel_width == 3 && subsampling == 2) { |
| 957 | auto* depthwise_3x3_convolution_layer = new arm_compute::NEDepthwiseConvolutionLayer3x3(); |
| 958 | layer.reset(depthwise_3x3_convolution_layer); |
| 959 | depthwise_3x3_convolution_layer->configure( |
| 960 | &input_tensor, &kernelTensor, &bias_tensor, &output_tensor, |
| 961 | pad_stride_info, group_output_channels, activation_info); |
| 962 | |
| 963 | if (!depthwise_3x3_convolution_layer->validate( |
| 964 | &input_info, &kernel_info, &bias_info, &output_info, |
| 965 | pad_stride_info, group_output_channels, activation_info)) |
| 966 | { |
| 967 | state.SkipWithError("validation failed"); |
| 968 | return; |
| 969 | } |
| 970 | } else { |
| 971 | auto* depthwise_convolution_layer = new arm_compute::NEDepthwiseConvolutionLayer(); |
| 972 | layer.reset(depthwise_convolution_layer); |
| 973 | depthwise_convolution_layer->configure( |
| 974 | &input_tensor, &kernelTensor, &bias_tensor, &output_tensor, |
| 975 | pad_stride_info, group_output_channels, activation_info); |
| 976 | |
| 977 | if (!depthwise_convolution_layer->validate( |
| 978 | &input_info, &kernel_info, &bias_info, &output_info, |
| 979 | pad_stride_info, group_output_channels, activation_info)) |
| 980 | { |
| 981 | state.SkipWithError("validation failed"); |
| 982 | return; |
| 983 | } |
| 984 | } |
| 985 | } else { |
| 986 | auto* convolution_layer = new arm_compute::NEConvolutionLayer(); |
| 987 | layer.reset(convolution_layer); |
| 988 | convolution_layer->configure( |
| 989 | &input_tensor, &kernelTensor, &bias_tensor, &output_tensor, |
| 990 | pad_stride_info, arm_compute::WeightsInfo(), dilation_info, activation_info, |
| 991 | true /* enable fast math */, groups); |
| 992 | |
| 993 | if (!convolution_layer->validate( |
| 994 | &input_info, &kernel_info, &bias_info, &output_info, |
| 995 | pad_stride_info, arm_compute::WeightsInfo(), dilation_info, activation_info, |
| 996 | true /* enable fast math */, groups)) |
| 997 | { |
| 998 | state.SkipWithError("validation failed"); |
| 999 | return; |
| 1000 | } |
| 1001 | } |
| 1002 | |
| 1003 | // Dry run to let ACL do one-time initializations. |
| 1004 | arm_compute::CPPScheduler::get().set_num_threads(1); |
| 1005 | layer->run(); |
| 1006 | |
| 1007 | for (auto _ : state) { |
| 1008 | state.PauseTiming(); |
Marat Dukhan | 4232323 | 2019-10-23 02:09:02 -0700 | [diff] [blame] | 1009 | benchmark::utils::WipeCache(); |
| 1010 | benchmark::utils::PrefetchToL1( |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 1011 | input_tensor.buffer(), |
| 1012 | batch_size * groups * group_input_channels * input_height * input_width * sizeof(float)); |
| 1013 | state.ResumeTiming(); |
| 1014 | |
| 1015 | layer->run(); |
| 1016 | } |
| 1017 | |
| 1018 | // Validate outputs. |
| 1019 | const std::string error_string = compare_with_convolution_f32_reference_output( |
| 1020 | state, reinterpret_cast<const float*>(input_tensor.buffer()), |
| 1021 | input_shape.total_size(), |
| 1022 | reinterpret_cast<const float*>(kernelTensor.buffer()), |
| 1023 | kernel_shape.total_size(), |
| 1024 | reinterpret_cast<const float*>(bias_tensor.buffer()), |
| 1025 | bias_shape.total_size(), |
| 1026 | reinterpret_cast<const float*>(output_tensor.buffer()), |
| 1027 | output_shape.total_size()); |
| 1028 | |
| 1029 | if (!error_string.empty()) { |
| 1030 | state.SkipWithError(("validation failed: " + error_string).c_str()); |
| 1031 | return; |
| 1032 | } |
| 1033 | |
| 1034 | input_tensor.allocator()->free(); |
| 1035 | kernelTensor.allocator()->free(); |
| 1036 | bias_tensor.allocator()->free(); |
| 1037 | output_tensor.allocator()->free(); |
| 1038 | |
Marat Dukhan | d713e8a | 2020-12-04 14:23:12 -0800 | [diff] [blame] | 1039 | const uint64_t cpu_frequency = benchmark::utils::GetCurrentCpuFrequency(); |
| 1040 | if (cpu_frequency != 0) { |
| 1041 | state.counters["cpufreq"] = cpu_frequency; |
| 1042 | } |
| 1043 | |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 1044 | state.counters["FLOPS"] = benchmark::Counter( |
| 1045 | uint64_t(state.iterations()) * 2 * |
| 1046 | batch_size * output_height * output_width * |
| 1047 | groups * group_input_channels * group_output_channels * |
| 1048 | kernel_height * kernel_width, |
| 1049 | benchmark::Counter::kIsRate); |
| 1050 | } |
| 1051 | #endif // BENCHMARK_ARM_COMPUTE_LIBRARY |
| 1052 | |
| 1053 | // ShuffleNet v1 with 1 group. |
| 1054 | static void ShuffleNetV1G1(benchmark::internal::Benchmark* b) { |
| 1055 | b->ArgNames({"N", "H", "W", "KH", "KW", "PH", "PW", "S", "D", "G", "GCin", "GCout"}); |
| 1056 | |
| 1057 | /*************************** Conv 1 **************************/ |
| 1058 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1059 | b->Args({1, 224, 224, 3, 3, 2, 2, 2, 1, 1, 3, 24}); |
| 1060 | /******************* Stage 2: stride-2 unit ******************/ |
| 1061 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1062 | b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 24, 36}); |
| 1063 | b->Args({1, 56, 56, 3, 3, 2, 2, 2, 1, 36, 1, 1}); |
| 1064 | b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 36, 120}); |
| 1065 | /******************* Stage 2: stride-1 units *****************/ |
| 1066 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1067 | b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 144, 36}); |
| 1068 | b->Args({1, 28, 28, 3, 3, 2, 2, 2, 1, 36, 1, 1}); |
| 1069 | b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 36, 144}); |
| 1070 | /******************* Stage 3: stride-2 unit ******************/ |
| 1071 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1072 | b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 144, 72}); |
| 1073 | b->Args({1, 28, 28, 3, 3, 2, 2, 2, 1, 72, 1, 1}); |
| 1074 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 72, 144}); |
| 1075 | /******************* Stage 3: stride-1 units *****************/ |
| 1076 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1077 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 288, 72}); |
| 1078 | b->Args({1, 14, 14, 3, 3, 2, 2, 2, 1, 72, 1, 1}); |
| 1079 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 72, 288}); |
| 1080 | /******************* Stage 4: stride-2 unit ******************/ |
| 1081 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1082 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 288, 144}); |
| 1083 | b->Args({1, 14, 14, 3, 3, 2, 2, 2, 1, 144, 1, 1}); |
| 1084 | b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 144, 288}); |
| 1085 | /******************* Stage 4: stride-1 units *****************/ |
| 1086 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1087 | b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 576, 144}); |
| 1088 | b->Args({1, 7, 7, 3, 3, 2, 2, 2, 1, 144, 1, 1}); |
| 1089 | b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 144, 576}); |
| 1090 | } |
| 1091 | |
| 1092 | // ShuffleNet v1 with 2 groups. |
| 1093 | static void ShuffleNetV1G2(benchmark::internal::Benchmark* b) { |
| 1094 | b->ArgNames({"N", "H", "W", "KH", "KW", "PH", "PW", "S", "D", "G", "GCin", "GCout"}); |
| 1095 | |
| 1096 | /*************************** Conv 1 **************************/ |
| 1097 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1098 | b->Args({1, 224, 224, 3, 3, 2, 2, 2, 1, 1, 3, 24}); |
| 1099 | /******************* Stage 2: stride-2 unit ******************/ |
| 1100 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1101 | b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 24, 50}); |
| 1102 | b->Args({1, 56, 56, 3, 3, 2, 2, 2, 1, 50, 1, 1}); |
| 1103 | b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 2, 25, 88}); |
| 1104 | /******************* Stage 2: stride-1 units *****************/ |
| 1105 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1106 | b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 2, 100, 25}); |
| 1107 | b->Args({1, 28, 28, 3, 3, 2, 2, 2, 1, 50, 1, 1}); |
| 1108 | b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 2, 25, 100}); |
| 1109 | /******************* Stage 3: stride-2 unit ******************/ |
| 1110 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1111 | b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 2, 100, 50}); |
| 1112 | b->Args({1, 28, 28, 3, 3, 2, 2, 2, 1, 100, 1, 1}); |
| 1113 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 2, 50, 100}); |
| 1114 | /******************* Stage 3: stride-1 units *****************/ |
| 1115 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1116 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 2, 200, 50}); |
| 1117 | b->Args({1, 14, 14, 3, 3, 2, 2, 2, 1, 100, 1, 1}); |
| 1118 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 2, 50, 200}); |
| 1119 | /******************* Stage 4: stride-2 unit ******************/ |
| 1120 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1121 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 2, 200, 100}); |
| 1122 | b->Args({1, 14, 14, 3, 3, 2, 2, 2, 1, 200, 1, 1}); |
| 1123 | b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 2, 100, 200}); |
| 1124 | /******************* Stage 4: stride-1 units *****************/ |
| 1125 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1126 | b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 2, 400, 100}); |
| 1127 | b->Args({1, 7, 7, 3, 3, 2, 2, 2, 1, 200, 1, 1}); |
| 1128 | b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 2, 100, 400}); |
| 1129 | } |
| 1130 | |
| 1131 | // ShuffleNet v1 with 3 groups. |
| 1132 | static void ShuffleNetV1G3(benchmark::internal::Benchmark* b) { |
| 1133 | b->ArgNames({"N", "H", "W", "KH", "KW", "PH", "PW", "S", "D", "G", "GCin", "GCout"}); |
| 1134 | |
| 1135 | /*************************** Conv 1 **************************/ |
| 1136 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1137 | b->Args({1, 224, 224, 3, 3, 2, 2, 2, 1, 1, 3, 24}); |
| 1138 | /******************* Stage 2: stride-2 unit ******************/ |
| 1139 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1140 | b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 24, 60}); |
| 1141 | b->Args({1, 56, 56, 3, 3, 2, 2, 2, 1, 60, 1, 1}); |
| 1142 | b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 3, 20, 72}); |
| 1143 | /******************* Stage 2: stride-1 units *****************/ |
| 1144 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1145 | b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 3, 80, 20}); |
| 1146 | b->Args({1, 28, 28, 3, 3, 2, 2, 2, 1, 60, 1, 1}); |
| 1147 | b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 3, 20, 80}); |
| 1148 | /******************* Stage 3: stride-2 unit ******************/ |
| 1149 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1150 | b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 3, 80, 40}); |
| 1151 | b->Args({1, 28, 28, 3, 3, 2, 2, 2, 1, 120, 1, 1}); |
| 1152 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 3, 40, 80}); |
| 1153 | /******************* Stage 3: stride-1 units *****************/ |
| 1154 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1155 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 3, 160, 40}); |
| 1156 | b->Args({1, 14, 14, 3, 3, 2, 2, 2, 1, 120, 1, 1}); |
| 1157 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 3, 40, 160}); |
| 1158 | /******************* Stage 4: stride-2 unit ******************/ |
| 1159 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1160 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 3, 160, 80}); |
| 1161 | b->Args({1, 14, 14, 3, 3, 2, 2, 2, 1, 240, 1, 1}); |
| 1162 | b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 3, 80, 160}); |
| 1163 | /******************* Stage 4: stride-1 units *****************/ |
| 1164 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1165 | b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 3, 320, 80}); |
| 1166 | b->Args({1, 7, 7, 3, 3, 2, 2, 2, 1, 240, 1, 1}); |
| 1167 | b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 3, 80, 320}); |
| 1168 | } |
| 1169 | |
| 1170 | // ShuffleNet v1 with 4 groups. |
| 1171 | static void ShuffleNetV1G4(benchmark::internal::Benchmark* b) { |
| 1172 | b->ArgNames({"N", "H", "W", "KH", "KW", "PH", "PW", "S", "D", "G", "GCin", "GCout"}); |
| 1173 | |
| 1174 | /*************************** Conv 1 **************************/ |
| 1175 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1176 | b->Args({1, 224, 224, 3, 3, 2, 2, 2, 1, 1, 3, 24}); |
| 1177 | /******************* Stage 2: stride-2 unit ******************/ |
| 1178 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1179 | b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 24, 68}); |
| 1180 | b->Args({1, 56, 56, 3, 3, 2, 2, 2, 1, 68, 1, 1}); |
| 1181 | b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 4, 17, 62}); |
| 1182 | /******************* Stage 2: stride-1 units *****************/ |
| 1183 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1184 | b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 4, 68, 17}); |
| 1185 | b->Args({1, 28, 28, 3, 3, 2, 2, 2, 1, 68, 1, 1}); |
| 1186 | b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 4, 17, 68}); |
| 1187 | /******************* Stage 3: stride-2 unit ******************/ |
| 1188 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1189 | b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 4, 68, 34}); |
| 1190 | b->Args({1, 28, 28, 3, 3, 2, 2, 2, 1, 136, 1, 1}); |
| 1191 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 4, 34, 68}); |
| 1192 | /******************* Stage 3: stride-1 units *****************/ |
| 1193 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1194 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 4, 136, 34}); |
| 1195 | b->Args({1, 14, 14, 3, 3, 2, 2, 2, 1, 136, 1, 1}); |
| 1196 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 4, 34, 136}); |
| 1197 | /******************* Stage 4: stride-2 unit ******************/ |
| 1198 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1199 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 4, 136, 68}); |
| 1200 | b->Args({1, 14, 14, 3, 3, 2, 2, 2, 1, 272, 1, 1}); |
| 1201 | b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 4, 68, 136}); |
| 1202 | /******************* Stage 4: stride-1 units *****************/ |
| 1203 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1204 | b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 4, 272, 68}); |
| 1205 | b->Args({1, 7, 7, 3, 3, 2, 2, 2, 1, 272, 1, 1}); |
| 1206 | b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 4, 68, 272}); |
| 1207 | } |
| 1208 | |
| 1209 | // ShuffleNet v1 with 8 groups. |
| 1210 | static void ShuffleNetV1G8(benchmark::internal::Benchmark* b) { |
| 1211 | b->ArgNames({"N", "H", "W", "KH", "KW", "PH", "PW", "S", "D", "G", "GCin", "GCout"}); |
| 1212 | |
| 1213 | /*************************** Conv 1 **************************/ |
| 1214 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1215 | b->Args({1, 224, 224, 3, 3, 2, 2, 2, 1, 1, 3, 24}); |
| 1216 | /******************* Stage 2: stride-2 unit ******************/ |
| 1217 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1218 | b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 24, 96}); |
| 1219 | b->Args({1, 56, 56, 3, 3, 2, 2, 2, 1, 96, 1, 1}); |
| 1220 | b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 8, 12, 45}); |
| 1221 | /******************* Stage 2: stride-1 units *****************/ |
| 1222 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1223 | b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 8, 48, 12}); |
| 1224 | b->Args({1, 28, 28, 3, 3, 2, 2, 2, 1, 96, 1, 1}); |
| 1225 | b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 8, 12, 48}); |
| 1226 | /******************* Stage 3: stride-2 unit ******************/ |
| 1227 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1228 | b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 8, 48, 24}); |
| 1229 | b->Args({1, 28, 28, 3, 3, 2, 2, 2, 1, 192, 1, 1}); |
| 1230 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 8, 24, 48}); |
| 1231 | /******************* Stage 3: stride-1 units *****************/ |
| 1232 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1233 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 8, 96, 24}); |
| 1234 | b->Args({1, 14, 14, 3, 3, 2, 2, 2, 1, 192, 1, 1}); |
| 1235 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 8, 24, 96}); |
| 1236 | /******************* Stage 4: stride-2 unit ******************/ |
| 1237 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1238 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 8, 96, 48}); |
| 1239 | b->Args({1, 14, 14, 3, 3, 2, 2, 2, 1, 384, 1, 1}); |
| 1240 | b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 8, 48, 96}); |
| 1241 | /******************* Stage 4: stride-1 units *****************/ |
| 1242 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1243 | b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 8, 192, 48}); |
| 1244 | b->Args({1, 7, 7, 3, 3, 2, 2, 2, 1, 384, 1, 1}); |
| 1245 | b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 8, 48, 192}); |
| 1246 | } |
| 1247 | |
| 1248 | // ShuffleNet v2 (0.5X scale) |
| 1249 | static void ShuffleNetV2X05(benchmark::internal::Benchmark* b) { |
| 1250 | b->ArgNames({"N", "H", "W", "KH", "KW", "PH", "PW", "S", "D", "G", "GCin", "GCout"}); |
| 1251 | |
| 1252 | /*************************** Conv 1 **************************/ |
| 1253 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1254 | b->Args({1, 224, 224, 3, 3, 2, 2, 2, 1, 1, 3, 24}); |
| 1255 | /************************** Stage 2 **************************/ |
| 1256 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1257 | b->Args({1, 56, 56, 3, 3, 2, 2, 2, 1, 24, 1, 1}); |
| 1258 | b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 24, 24}); |
| 1259 | b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 24, 24}); |
| 1260 | b->Args({1, 28, 28, 3, 3, 2, 2, 1, 1, 24, 1, 1}); |
| 1261 | /************************** Stage 3 **************************/ |
| 1262 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1263 | b->Args({1, 28, 28, 3, 3, 2, 2, 2, 1, 48, 1, 1}); |
| 1264 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 48, 48}); |
| 1265 | b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 48, 48}); |
| 1266 | b->Args({1, 14, 14, 3, 3, 2, 2, 1, 1, 48, 1, 1}); |
| 1267 | /************************** Stage 4 **************************/ |
| 1268 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1269 | b->Args({1, 14, 14, 3, 3, 2, 2, 2, 1, 96, 1, 1}); |
| 1270 | b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 96, 96}); |
| 1271 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 96, 96}); |
| 1272 | b->Args({1, 7, 7, 3, 3, 2, 2, 1, 1, 96, 1, 1}); |
| 1273 | /*************************** Conv 5 **************************/ |
| 1274 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1275 | b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 192, 1024}); |
| 1276 | } |
| 1277 | |
| 1278 | // ShuffleNet v2 (1.0X scale) |
| 1279 | static void ShuffleNetV2X10(benchmark::internal::Benchmark* b) { |
| 1280 | b->ArgNames({"N", "H", "W", "KH", "KW", "PH", "PW", "S", "D", "G", "GCin", "GCout"}); |
| 1281 | |
| 1282 | /*************************** Conv 1 **************************/ |
| 1283 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1284 | b->Args({1, 224, 224, 3, 3, 2, 2, 2, 1, 1, 3, 24}); |
| 1285 | /************************** Stage 2 **************************/ |
| 1286 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1287 | b->Args({1, 56, 56, 3, 3, 2, 2, 2, 1, 24, 1, 1}); |
| 1288 | b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 24, 58}); |
| 1289 | b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 24, 58}); |
| 1290 | b->Args({1, 56, 56, 3, 3, 2, 2, 2, 1, 58, 1, 1}); |
| 1291 | b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 58, 58}); |
| 1292 | b->Args({1, 28, 28, 3, 3, 2, 2, 1, 1, 58, 1, 1}); |
| 1293 | /************************** Stage 3 **************************/ |
| 1294 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1295 | b->Args({1, 28, 28, 3, 3, 2, 2, 2, 1, 116, 1, 1}); |
| 1296 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 116, 116}); |
| 1297 | b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 116, 116}); |
| 1298 | b->Args({1, 14, 14, 3, 3, 2, 2, 1, 1, 116, 1, 1}); |
| 1299 | /************************** Stage 4 **************************/ |
| 1300 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1301 | b->Args({1, 14, 14, 3, 3, 2, 2, 2, 1, 232, 1, 1}); |
| 1302 | b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 232, 232}); |
| 1303 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 232, 232}); |
| 1304 | b->Args({1, 7, 7, 3, 3, 2, 2, 1, 1, 232, 1, 1}); |
| 1305 | /*************************** Conv 5 **************************/ |
| 1306 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1307 | b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 464, 1024}); |
| 1308 | } |
| 1309 | |
| 1310 | // ShuffleNet v2 (1.5X scale) |
| 1311 | static void ShuffleNetV2X15(benchmark::internal::Benchmark* b) { |
| 1312 | b->ArgNames({"N", "H", "W", "KH", "KW", "PH", "PW", "S", "D", "G", "GCin", "GCout"}); |
| 1313 | |
| 1314 | /*************************** Conv 1 **************************/ |
| 1315 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1316 | b->Args({1, 224, 224, 3, 3, 2, 2, 2, 1, 1, 3, 24}); |
| 1317 | /************************** Stage 2 **************************/ |
| 1318 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1319 | b->Args({1, 56, 56, 3, 3, 2, 2, 2, 1, 24, 1, 1}); |
| 1320 | b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 24, 88}); |
| 1321 | b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 24, 88}); |
| 1322 | b->Args({1, 56, 56, 3, 3, 2, 2, 2, 1, 88, 1, 1}); |
| 1323 | b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 88, 88}); |
| 1324 | b->Args({1, 28, 28, 3, 3, 2, 2, 1, 1, 88, 1, 1}); |
| 1325 | /************************** Stage 3 **************************/ |
| 1326 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1327 | b->Args({1, 28, 28, 3, 3, 2, 2, 2, 1, 176, 1, 1}); |
| 1328 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 176, 176}); |
| 1329 | b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 176, 176}); |
| 1330 | b->Args({1, 14, 14, 3, 3, 2, 2, 1, 1, 176, 1, 1}); |
| 1331 | /************************** Stage 4 **************************/ |
| 1332 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1333 | b->Args({1, 14, 14, 3, 3, 2, 2, 2, 1, 352, 1, 1}); |
| 1334 | b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 352, 352}); |
| 1335 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 352, 352}); |
| 1336 | b->Args({1, 7, 7, 3, 3, 2, 2, 1, 1, 352, 1, 1}); |
| 1337 | /*************************** Conv 5 **************************/ |
| 1338 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1339 | b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 704, 1024}); |
| 1340 | } |
| 1341 | |
| 1342 | // ShuffleNet v2 (2.0X scale) |
| 1343 | static void ShuffleNetV2X20(benchmark::internal::Benchmark* b) { |
| 1344 | b->ArgNames({"N", "H", "W", "KH", "KW", "PH", "PW", "S", "D", "G", "GCin", "GCout"}); |
| 1345 | |
| 1346 | /*************************** Conv 1 **************************/ |
| 1347 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1348 | b->Args({1, 224, 224, 3, 3, 2, 2, 2, 1, 1, 3, 24}); |
| 1349 | /************************** Stage 2 **************************/ |
| 1350 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1351 | b->Args({1, 56, 56, 3, 3, 2, 2, 2, 1, 24, 1, 1}); |
| 1352 | b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 24, 122}); |
| 1353 | b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 24, 122}); |
| 1354 | b->Args({1, 56, 56, 3, 3, 2, 2, 2, 1, 122, 1, 1}); |
| 1355 | b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 122, 122}); |
| 1356 | b->Args({1, 28, 28, 3, 3, 2, 2, 1, 1, 122, 1, 1}); |
| 1357 | /************************** Stage 3 **************************/ |
| 1358 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1359 | b->Args({1, 28, 28, 3, 3, 2, 2, 2, 1, 244, 1, 1}); |
| 1360 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 244, 244}); |
| 1361 | b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 244, 244}); |
| 1362 | b->Args({1, 14, 14, 3, 3, 2, 2, 1, 1, 244, 1, 1}); |
| 1363 | /************************** Stage 4 **************************/ |
| 1364 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1365 | b->Args({1, 14, 14, 3, 3, 2, 2, 2, 1, 488, 1, 1}); |
| 1366 | b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 488, 488}); |
| 1367 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 488, 488}); |
| 1368 | b->Args({1, 7, 7, 3, 3, 2, 2, 1, 1, 488, 1, 1}); |
| 1369 | /*************************** Conv 5 **************************/ |
| 1370 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1371 | b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 976, 2048}); |
| 1372 | } |
| 1373 | |
| 1374 | static void MobileNetV1(benchmark::internal::Benchmark* b) { |
| 1375 | b->ArgNames({"N", "H", "W", "KH", "KW", "PH", "PW", "S", "D", "G", "GCin", "GCout"}); |
| 1376 | |
| 1377 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1378 | b->Args({1, 224, 224, 3, 3, 2, 2, 2, 1, 1, 3, 32}); |
| 1379 | b->Args({1, 112, 112, 3, 3, 2, 2, 1, 1, 32, 1, 1}); |
| 1380 | b->Args({1, 112, 112, 1, 1, 0, 0, 1, 1, 1, 32, 64}); |
| 1381 | b->Args({1, 112, 112, 3, 3, 2, 2, 2, 1, 64, 1, 1}); |
| 1382 | b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 64, 128}); |
| 1383 | b->Args({1, 56, 56, 3, 3, 2, 2, 1, 1, 128, 1, 1}); |
| 1384 | b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 128, 128}); |
| 1385 | b->Args({1, 56, 56, 3, 3, 2, 2, 2, 1, 128, 1, 1}); |
| 1386 | b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 128, 256}); |
| 1387 | b->Args({1, 28, 28, 3, 3, 2, 2, 1, 1, 256, 1, 1}); |
| 1388 | b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 256, 256}); |
| 1389 | b->Args({1, 28, 28, 3, 3, 2, 2, 2, 1, 256, 1, 1}); |
| 1390 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 256, 512}); |
| 1391 | b->Args({1, 14, 14, 3, 3, 2, 2, 1, 1, 512, 1, 1}); |
| 1392 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 512, 512}); |
| 1393 | b->Args({1, 14, 14, 3, 3, 2, 2, 2, 1, 512, 1, 1}); |
| 1394 | b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 512, 1024}); |
| 1395 | b->Args({1, 7, 7, 3, 3, 2, 2, 1, 1, 1024, 1, 1}); |
| 1396 | b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 1024, 1024}); |
| 1397 | } |
| 1398 | |
| 1399 | static void MobileNetV2(benchmark::internal::Benchmark* b) { |
| 1400 | b->ArgNames({"N", "H", "W", "KH", "KW", "PH", "PW", "S", "D", "G", "GCin", "GCout"}); |
| 1401 | |
| 1402 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1403 | b->Args({1, 224, 224, 3, 3, 2, 2, 2, 1, 1, 3, 32}); |
| 1404 | |
| 1405 | /************************ Bottleneck 1 ***********************/ |
| 1406 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1407 | b->Args({1, 112, 112, 3, 3, 2, 2, 1, 1, 32, 1, 1}); |
| 1408 | b->Args({1, 112, 112, 1, 1, 0, 0, 1, 1, 1, 32, 16}); |
| 1409 | |
| 1410 | /************************ Bottleneck 2 ***********************/ |
| 1411 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1412 | b->Args({1, 112, 112, 1, 1, 0, 0, 1, 1, 1, 16, 96}); |
| 1413 | b->Args({1, 112, 112, 3, 3, 2, 2, 2, 1, 96, 1, 1}); |
| 1414 | b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 96, 24}); |
| 1415 | b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 24, 144}); |
| 1416 | b->Args({1, 56, 56, 3, 3, 2, 2, 1, 1, 144, 1, 1}); |
| 1417 | b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 144, 24}); |
| 1418 | |
| 1419 | /************************ Bottleneck 3 ***********************/ |
| 1420 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1421 | //b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 24, 144}); |
| 1422 | b->Args({1, 56, 56, 3, 3, 2, 2, 2, 1, 144, 1, 1}); |
| 1423 | b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 144, 32}); |
| 1424 | b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 32, 192}); |
| 1425 | b->Args({1, 28, 28, 3, 3, 2, 2, 1, 1, 192, 1, 1}); |
| 1426 | b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 192, 32}); |
| 1427 | //b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 32, 192}); |
| 1428 | //b->Args({1, 28, 28, 3, 3, 2, 2, 1, 1, 192, 1, 1}); |
| 1429 | //b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 192, 32}); |
| 1430 | |
| 1431 | /************************ Bottleneck 4 ***********************/ |
| 1432 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1433 | //b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 32, 192}); |
| 1434 | b->Args({1, 28, 28, 3, 3, 2, 2, 2, 1, 192, 1, 1}); |
| 1435 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 192, 64}); |
| 1436 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 64, 384}); |
| 1437 | b->Args({1, 14, 14, 3, 3, 2, 2, 1, 1, 384, 1, 1}); |
| 1438 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 384, 64}); |
| 1439 | //b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 64, 384}); |
| 1440 | //b->Args({1, 14, 14, 3, 3, 2, 2, 1, 1, 384, 1, 1}); |
| 1441 | //b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 384, 64}); |
| 1442 | //b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 64, 384}); |
| 1443 | //b->Args({1, 14, 14, 3, 3, 2, 2, 1, 1, 384, 1, 1}); |
| 1444 | //b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 384, 64}); |
| 1445 | |
| 1446 | /************************ Bottleneck 5 ***********************/ |
| 1447 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1448 | //b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 64, 384}); |
| 1449 | //b->Args({1, 14, 14, 3, 3, 2, 2, 1, 1, 384, 1, 1}); |
| 1450 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 384, 96}); |
| 1451 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 96, 576}); |
| 1452 | b->Args({1, 14, 14, 3, 3, 2, 2, 1, 1, 576, 1, 1}); |
| 1453 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 576, 96}); |
| 1454 | //b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 96, 576}); |
| 1455 | //b->Args({1, 14, 14, 3, 3, 2, 2, 1, 1, 576, 1, 1}); |
| 1456 | //b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 576, 96}); |
| 1457 | |
| 1458 | /************************ Bottleneck 6 ***********************/ |
| 1459 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1460 | //b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 96, 576}); |
| 1461 | b->Args({1, 14, 14, 3, 3, 2, 2, 2, 1, 576, 1, 1}); |
| 1462 | b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 576, 160}); |
| 1463 | b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 160, 960}); |
| 1464 | b->Args({1, 7, 7, 3, 3, 2, 2, 1, 1, 960, 1, 1}); |
| 1465 | b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 960, 160}); |
| 1466 | //b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 160, 960}); |
| 1467 | //b->Args({1, 7, 7, 3, 3, 2, 2, 1, 1, 960, 1, 1}); |
| 1468 | //b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 960, 160}); |
| 1469 | |
| 1470 | /************************ Bottleneck 7 ***********************/ |
| 1471 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1472 | //b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 160, 960}); |
| 1473 | //b->Args({1, 7, 7, 3, 3, 2, 2, 1, 1, 960, 1, 1}); |
| 1474 | b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 960, 320}); |
| 1475 | |
| 1476 | /******************** Pre-pooling Conv2D *********************/ |
| 1477 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1478 | b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 320, 1280}); |
| 1479 | /******************** Post-pooling Conv2D ********************/ |
| 1480 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1481 | b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1280, 1000}); |
| 1482 | } |
| 1483 | |
| 1484 | static void MobileNetV3Small(benchmark::internal::Benchmark* b) { |
| 1485 | b->ArgNames({"N", "H", "W", "KH", "KW", "PH", "PW", "S", "D", "G", "GCin", "GCout"}); |
| 1486 | |
| 1487 | /*********************** Initial Stage ***********************/ |
| 1488 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1489 | b->Args({1, 224, 224, 3, 3, 2, 2, 2, 1, 1, 3, 16}); |
| 1490 | /*********************** Bottleneck 1 ************************/ |
| 1491 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1492 | b->Args({1, 112, 112, 3, 3, 2, 2, 2, 1, 16, 1, 1}); |
| 1493 | b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 16, 8}); |
| 1494 | b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 8, 16}); |
| 1495 | b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 16, 16}); |
| 1496 | /*********************** Bottleneck 2 ************************/ |
| 1497 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1498 | b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 16, 72}); |
| 1499 | b->Args({1, 56, 56, 3, 3, 2, 2, 2, 1, 72, 1, 1}); |
| 1500 | b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 72, 24}); |
| 1501 | /*********************** Bottleneck 3 ************************/ |
| 1502 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1503 | b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 24, 88}); |
| 1504 | b->Args({1, 28, 28, 3, 3, 2, 2, 1, 1, 88, 1, 1}); |
| 1505 | b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 88, 24}); |
| 1506 | /*********************** Bottleneck 4 ************************/ |
| 1507 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1508 | b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 24, 96}); |
| 1509 | b->Args({1, 28, 28, 5, 5, 4, 4, 2, 1, 96, 1, 1}); |
| 1510 | b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 96, 24}); |
| 1511 | b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 24, 96}); |
| 1512 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 96, 40}); |
| 1513 | /*********************** Bottleneck 5 ************************/ |
| 1514 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1515 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 40, 240}); |
| 1516 | b->Args({1, 14, 14, 5, 5, 4, 4, 1, 1, 240, 1, 1}); |
| 1517 | b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 240, 64}); |
| 1518 | b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 64, 240}); |
| 1519 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 240, 40}); |
| 1520 | /*********************** Bottleneck 6 ************************/ |
| 1521 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1522 | //b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 40, 240}); |
| 1523 | //b->Args({1, 14, 14, 5, 5, 4, 4, 1, 1, 240, 1, 1}); |
| 1524 | //b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 240, 64}); |
| 1525 | //b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 64, 240}); |
| 1526 | //b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 240, 40}); |
| 1527 | /*********************** Bottleneck 7 ************************/ |
| 1528 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1529 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 40, 120}); |
| 1530 | b->Args({1, 14, 14, 5, 5, 4, 4, 1, 1, 120, 1, 1}); |
| 1531 | b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 120, 32}); |
| 1532 | b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 32, 120}); |
| 1533 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 120, 48}); |
| 1534 | /*********************** Bottleneck 8 ************************/ |
| 1535 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1536 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 48, 144}); |
| 1537 | b->Args({1, 14, 14, 5, 5, 4, 4, 1, 1, 144, 1, 1}); |
| 1538 | b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 144, 40}); |
| 1539 | b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 40, 144}); |
| 1540 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 144, 48}); |
| 1541 | /*********************** Bottleneck 9 ************************/ |
| 1542 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1543 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 48, 288}); |
| 1544 | b->Args({1, 14, 14, 5, 5, 4, 4, 2, 1, 288, 1, 1}); |
| 1545 | b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 288, 72}); |
| 1546 | b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 72, 288}); |
| 1547 | b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 288, 96}); |
| 1548 | /*********************** Bottleneck 10 ***********************/ |
| 1549 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1550 | b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 96, 576}); |
| 1551 | b->Args({1, 7, 7, 5, 5, 4, 4, 1, 1, 576, 1, 1}); |
| 1552 | b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 576, 144}); |
| 1553 | b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 144, 576}); |
| 1554 | b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 576, 96}); |
| 1555 | /*********************** Bottleneck 11 ***********************/ |
| 1556 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1557 | //b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 96, 576}); |
| 1558 | //b->Args({1, 7, 7, 5, 5, 4, 4, 1, 1, 576, 1, 1}); |
| 1559 | //b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 576, 144}); |
| 1560 | //b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 144, 576}); |
| 1561 | //b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 576, 96}); |
| 1562 | /************************ Last Stage ************************/ |
| 1563 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1564 | //b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 96, 576}); |
| 1565 | b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 576, 1024}); |
| 1566 | b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1024, 1001}); |
| 1567 | } |
| 1568 | |
| 1569 | static void MobileNetV3Large(benchmark::internal::Benchmark* b) { |
| 1570 | b->ArgNames({"N", "H", "W", "KH", "KW", "PH", "PW", "S", "D", "G", "GCin", "GCout"}); |
| 1571 | |
| 1572 | /*********************** Initial Stage ***********************/ |
| 1573 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1574 | b->Args({1, 224, 224, 3, 3, 2, 2, 2, 1, 1, 3, 16}); |
| 1575 | /*********************** Bottleneck 1 ************************/ |
| 1576 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1577 | b->Args({1, 112, 112, 3, 3, 2, 2, 1, 1, 16, 1, 1}); |
| 1578 | b->Args({1, 112, 112, 1, 1, 0, 0, 1, 1, 1, 16, 16}); |
| 1579 | /*********************** Bottleneck 2 ************************/ |
| 1580 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1581 | b->Args({1, 112, 112, 1, 1, 0, 0, 1, 1, 1, 16, 64}); |
| 1582 | b->Args({1, 112, 112, 3, 3, 2, 2, 2, 1, 64, 1, 1}); |
| 1583 | b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 64, 24}); |
| 1584 | /*********************** Bottleneck 3 ************************/ |
| 1585 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1586 | b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 24, 72}); |
| 1587 | b->Args({1, 56, 56, 3, 3, 2, 2, 1, 1, 72, 1, 1}); |
| 1588 | b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 72, 24}); |
| 1589 | /*********************** Bottleneck 4 ************************/ |
| 1590 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1591 | //b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 24, 72}); |
| 1592 | b->Args({1, 56, 56, 5, 5, 4, 4, 2, 1, 72, 1, 1}); |
| 1593 | b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 72, 24}); |
| 1594 | b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 24, 72}); |
| 1595 | b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 72, 40}); |
| 1596 | /*********************** Bottleneck 5 ************************/ |
| 1597 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1598 | b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 40, 120}); |
| 1599 | b->Args({1, 28, 28, 5, 5, 4, 4, 1, 1, 120, 1, 1}); |
| 1600 | b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 120, 32}); |
| 1601 | b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 32, 120}); |
| 1602 | b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 120, 40}); |
| 1603 | /*********************** Bottleneck 6 ************************/ |
| 1604 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1605 | //b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 40, 120}); |
| 1606 | //b->Args({1, 28, 28, 5, 5, 4, 4, 1, 1, 120, 1, 1}); |
| 1607 | //b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 120, 32}); |
| 1608 | //b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 32, 120}); |
| 1609 | //b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 120, 40}); |
| 1610 | /*********************** Bottleneck 7 ************************/ |
| 1611 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1612 | b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 40, 240}); |
| 1613 | b->Args({1, 28, 28, 3, 3, 2, 2, 2, 1, 240, 1, 1}); |
| 1614 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 240, 80}); |
| 1615 | /*********************** Bottleneck 8 ************************/ |
| 1616 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1617 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 80, 200}); |
| 1618 | b->Args({1, 14, 14, 3, 3, 2, 2, 1, 1, 200, 1, 1}); |
| 1619 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 200, 80}); |
| 1620 | /*********************** Bottleneck 9 ************************/ |
| 1621 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1622 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 80, 184}); |
| 1623 | b->Args({1, 14, 14, 3, 3, 2, 2, 1, 1, 184, 1, 1}); |
| 1624 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 184, 80}); |
| 1625 | /********************** Bottleneck 10 ***********************/ |
| 1626 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1627 | //b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 80, 184}); |
| 1628 | //b->Args({1, 14, 14, 3, 3, 2, 2, 1, 1, 184, 1, 1}); |
| 1629 | //b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 184, 80}); |
| 1630 | /********************** Bottleneck 11 ***********************/ |
| 1631 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1632 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 80, 480}); |
| 1633 | b->Args({1, 14, 14, 3, 3, 2, 2, 1, 1, 480, 1, 1}); |
| 1634 | b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 480, 120}); |
| 1635 | b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 120, 480}); |
| 1636 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 480, 112}); |
| 1637 | /********************** Bottleneck 12 ***********************/ |
| 1638 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1639 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 112, 672}); |
| 1640 | b->Args({1, 14, 14, 3, 3, 2, 2, 1, 1, 672, 1, 1}); |
| 1641 | b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 672, 168}); |
| 1642 | b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 168, 672}); |
| 1643 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 672, 112}); |
| 1644 | /********************** Bottleneck 13 ***********************/ |
| 1645 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1646 | //b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 112, 672}); |
| 1647 | b->Args({1, 14, 14, 5, 5, 4, 4, 2, 1, 672, 1, 1}); |
| 1648 | b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 672, 160}); |
| 1649 | /********************** Bottleneck 14 ***********************/ |
| 1650 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1651 | b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 160, 960}); |
| 1652 | b->Args({1, 7, 7, 5, 5, 4, 4, 1, 1, 960, 1, 1}); |
| 1653 | b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 960, 240}); |
| 1654 | b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 240, 960}); |
| 1655 | b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 960, 160}); |
| 1656 | /********************** Bottleneck 15 ***********************/ |
| 1657 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1658 | //b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 160, 960}); |
| 1659 | //b->Args({1, 7, 7, 5, 5, 4, 4, 1, 1, 960, 1, 1}); |
| 1660 | //b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 960, 240}); |
| 1661 | //b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 240, 960}); |
| 1662 | //b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 960, 160}); |
| 1663 | /************************ Last Stage ***********************/ |
| 1664 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1665 | //b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 160, 960}); |
| 1666 | b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 960, 1280}); |
| 1667 | b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1280, 1001}); |
| 1668 | } |
| 1669 | |
| 1670 | // SqueezeNet 1.0 |
| 1671 | static void SqueezeNetV10(benchmark::internal::Benchmark* b) { |
| 1672 | b->ArgNames({"N", "H", "W", "KH", "KW", "PH", "PW", "S", "D", "G", "GCin", "GCout"}); |
| 1673 | |
| 1674 | /************************** Conv 1 *************************/ |
| 1675 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1676 | b->Args({1, 224, 224, 7, 7, 6, 6, 2, 1, 1, 3, 96}); |
| 1677 | /************************** Fire 2 *************************/ |
| 1678 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1679 | b->Args({1, 55, 55, 1, 1, 0, 0, 1, 1, 1, 96, 16}); |
| 1680 | b->Args({1, 55, 55, 1, 1, 0, 0, 1, 1, 1, 16, 64}); |
| 1681 | b->Args({1, 55, 55, 3, 3, 2, 2, 1, 1, 1, 16, 64}); |
| 1682 | /************************** Fire 3 *************************/ |
| 1683 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1684 | b->Args({1, 56, 55, 1, 1, 0, 0, 1, 1, 1, 128, 16}); |
| 1685 | //b->Args({1, 55, 55, 1, 1, 0, 0, 1, 1, 1, 16, 64}); |
| 1686 | //b->Args({1, 55, 55, 3, 3, 2, 2, 1, 1, 1, 16, 64}); |
| 1687 | /************************** Fire 4 *************************/ |
| 1688 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1689 | b->Args({1, 55, 55, 1, 1, 0, 0, 1, 1, 1, 128, 32}); |
| 1690 | b->Args({1, 55, 55, 1, 1, 0, 0, 1, 1, 1, 32, 128}); |
| 1691 | b->Args({1, 55, 55, 3, 3, 2, 2, 1, 1, 1, 32, 128}); |
| 1692 | /************************** Fire 5 *************************/ |
| 1693 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1694 | b->Args({1, 27, 27, 1, 1, 0, 0, 1, 1, 1, 256, 32}); |
| 1695 | b->Args({1, 27, 27, 1, 1, 0, 0, 1, 1, 1, 32, 128}); |
| 1696 | b->Args({1, 27, 27, 3, 3, 2, 2, 1, 1, 1, 32, 128}); |
| 1697 | /************************** Fire 6 *************************/ |
| 1698 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1699 | b->Args({1, 27, 27, 1, 1, 0, 0, 1, 1, 1, 256, 48}); |
| 1700 | b->Args({1, 27, 27, 1, 1, 0, 0, 1, 1, 1, 48, 192}); |
| 1701 | b->Args({1, 27, 27, 3, 3, 2, 2, 1, 1, 1, 48, 192}); |
| 1702 | /************************** Fire 7 *************************/ |
| 1703 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1704 | b->Args({1, 27, 27, 1, 1, 0, 0, 1, 1, 1, 384, 48}); |
| 1705 | //b->Args({1, 27, 27, 1, 1, 0, 0, 1, 1, 1, 48, 192}); |
| 1706 | //b->Args({1, 27, 27, 3, 3, 2, 2, 1, 1, 1, 48, 192}); |
| 1707 | /************************** Fire 8 *************************/ |
| 1708 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1709 | b->Args({1, 27, 27, 1, 1, 0, 0, 1, 1, 1, 384, 64}); |
| 1710 | b->Args({1, 27, 27, 1, 1, 0, 0, 1, 1, 1, 64, 256}); |
| 1711 | b->Args({1, 27, 27, 3, 3, 2, 2, 1, 1, 1, 64, 256}); |
| 1712 | /************************** Fire 9 *************************/ |
| 1713 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1714 | b->Args({1, 13, 13, 1, 1, 0, 0, 1, 1, 1, 512, 64}); |
| 1715 | b->Args({1, 13, 13, 1, 1, 0, 0, 1, 1, 1, 64, 256}); |
| 1716 | b->Args({1, 13, 13, 3, 3, 2, 2, 1, 1, 1, 64, 256}); |
| 1717 | /************************* Conv 10 *************************/ |
| 1718 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1719 | b->Args({1, 13, 13, 1, 1, 0, 0, 1, 1, 1, 512, 1000}); |
| 1720 | } |
| 1721 | |
| 1722 | // SqueezeNet 1.1 |
| 1723 | static void SqueezeNetV11(benchmark::internal::Benchmark* b) { |
| 1724 | b->ArgNames({"N", "H", "W", "KH", "KW", "PH", "PW", "S", "D", "G", "GCin", "GCout"}); |
| 1725 | |
| 1726 | /************************** Conv 1 *************************/ |
| 1727 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1728 | b->Args({1, 224, 224, 3, 3, 2, 2, 2, 1, 1, 3, 64}); |
| 1729 | /************************** Fire 2 *************************/ |
| 1730 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1731 | b->Args({1, 55, 55, 1, 1, 0, 0, 1, 1, 1, 64, 16}); |
| 1732 | b->Args({1, 55, 55, 1, 1, 0, 0, 1, 1, 1, 16, 64}); |
| 1733 | b->Args({1, 55, 55, 3, 3, 2, 2, 1, 1, 1, 16, 64}); |
| 1734 | /************************** Fire 3 *************************/ |
| 1735 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1736 | b->Args({1, 55, 55, 1, 1, 0, 0, 1, 1, 1, 128, 16}); |
| 1737 | //b->Args({1, 55, 55, 1, 1, 0, 0, 1, 1, 1, 16, 64}); |
| 1738 | //b->Args({1, 55, 55, 3, 3, 2, 2, 1, 1, 1, 16, 64}); |
| 1739 | /************************** Fire 4 *************************/ |
| 1740 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1741 | b->Args({1, 27, 27, 1, 1, 0, 0, 1, 1, 1, 128, 32}); |
| 1742 | b->Args({1, 27, 27, 1, 1, 0, 0, 1, 1, 1, 32, 128}); |
| 1743 | b->Args({1, 27, 27, 3, 3, 2, 2, 1, 1, 1, 32, 128}); |
| 1744 | /************************** Fire 5 *************************/ |
| 1745 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1746 | b->Args({1, 27, 27, 1, 1, 0, 0, 1, 1, 1, 256, 32}); |
| 1747 | //b->Args({1, 27, 27, 1, 1, 0, 0, 1, 1, 1, 32, 128}); |
| 1748 | //b->Args({1, 27, 27, 3, 3, 2, 2, 1, 1, 1, 32, 128}); |
| 1749 | /************************** Fire 6 *************************/ |
| 1750 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1751 | b->Args({1, 13, 13, 1, 1, 0, 0, 1, 1, 1, 256, 48}); |
| 1752 | b->Args({1, 13, 13, 1, 1, 0, 0, 1, 1, 1, 48, 192}); |
| 1753 | b->Args({1, 13, 13, 3, 3, 2, 2, 1, 1, 1, 48, 192}); |
| 1754 | /************************** Fire 7 *************************/ |
| 1755 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1756 | b->Args({1, 13, 13, 1, 1, 0, 0, 1, 1, 1, 384, 48}); |
| 1757 | //b->Args({1, 13, 13, 1, 1, 0, 0, 1, 1, 1, 48, 192}); |
| 1758 | //b->Args({1, 13, 13, 3, 3, 2, 2, 1, 1, 1, 48, 192}); |
| 1759 | /************************** Fire 8 *************************/ |
| 1760 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1761 | b->Args({1, 13, 13, 1, 1, 0, 0, 1, 1, 1, 384, 64}); |
| 1762 | b->Args({1, 13, 13, 1, 1, 0, 0, 1, 1, 1, 64, 256}); |
| 1763 | b->Args({1, 13, 13, 3, 3, 2, 2, 1, 1, 1, 64, 256}); |
| 1764 | /************************** Fire 9 *************************/ |
| 1765 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1766 | b->Args({1, 13, 13, 1, 1, 0, 0, 1, 1, 1, 512, 64}); |
| 1767 | //b->Args({1, 13, 13, 1, 1, 0, 0, 1, 1, 1, 64, 256}); |
| 1768 | //b->Args({1, 13, 13, 3, 3, 2, 2, 1, 1, 1, 64, 256}); |
| 1769 | /************************* Conv 10 *************************/ |
| 1770 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1771 | b->Args({1, 13, 13, 1, 1, 0, 0, 1, 1, 1, 512, 1000}); |
| 1772 | } |
| 1773 | |
| 1774 | static void InceptionV3(benchmark::internal::Benchmark* b) { |
| 1775 | b->ArgNames({"N", "H", "W", "KH", "KW", "PH", "PW", "S", "D", "G", "GCin", "GCout"}); |
| 1776 | |
| 1777 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1778 | b->Args({1, 299, 299, 3, 3, 0, 0, 2, 1, 1, 3, 32}); |
| 1779 | b->Args({1, 149, 149, 3, 3, 0, 0, 1, 1, 1, 32, 32}); |
| 1780 | b->Args({1, 147, 147, 3, 3, 2, 2, 1, 1, 1, 32, 64}); |
| 1781 | b->Args({1, 73, 73, 1, 1, 0, 0, 1, 1, 1, 64, 80}); |
| 1782 | b->Args({1, 73, 73, 3, 3, 0, 0, 1, 1, 1, 80, 192}); |
| 1783 | b->Args({1, 35, 35, 1, 1, 0, 0, 1, 1, 1, 192, 64}); |
| 1784 | b->Args({1, 35, 35, 1, 1, 0, 0, 1, 1, 1, 192, 48}); |
| 1785 | b->Args({1, 35, 35, 5, 5, 4, 4, 1, 1, 1, 48, 64}); |
| 1786 | b->Args({1, 35, 35, 3, 3, 2, 2, 1, 1, 1, 64, 96}); |
| 1787 | b->Args({1, 35, 35, 3, 3, 2, 2, 1, 1, 1, 96, 96}); |
| 1788 | b->Args({1, 35, 35, 1, 1, 0, 0, 1, 1, 1, 192, 32}); |
| 1789 | b->Args({1, 35, 35, 1, 1, 0, 0, 1, 1, 1, 256, 64}); |
| 1790 | b->Args({1, 35, 35, 1, 1, 0, 0, 1, 1, 1, 256, 48}); |
| 1791 | b->Args({1, 35, 35, 1, 1, 0, 0, 1, 1, 1, 288, 64}); |
| 1792 | b->Args({1, 35, 35, 1, 1, 0, 0, 1, 1, 1, 288, 48}); |
| 1793 | b->Args({1, 35, 35, 3, 3, 0, 0, 2, 1, 1, 288, 384}); |
| 1794 | b->Args({1, 35, 35, 3, 3, 0, 0, 2, 1, 1, 96, 96}); |
| 1795 | b->Args({1, 17, 17, 1, 1, 0, 0, 1, 1, 1, 768, 192}); |
| 1796 | b->Args({1, 17, 17, 1, 1, 0, 0, 1, 1, 1, 768, 128}); |
| 1797 | b->Args({1, 17, 17, 1, 7, 0, 6, 1, 1, 1, 128, 128}); |
| 1798 | b->Args({1, 17, 17, 7, 1, 6, 0, 1, 1, 1, 128, 192}); |
| 1799 | b->Args({1, 17, 17, 7, 1, 6, 0, 1, 1, 1, 128, 128}); |
| 1800 | b->Args({1, 17, 17, 1, 7, 0, 6, 1, 1, 1, 128, 192}); |
| 1801 | b->Args({1, 17, 17, 1, 1, 0, 0, 1, 1, 1, 768, 160}); |
| 1802 | b->Args({1, 17, 17, 1, 7, 0, 6, 1, 1, 1, 160, 160}); |
| 1803 | b->Args({1, 17, 17, 7, 1, 6, 0, 1, 1, 1, 160, 192}); |
| 1804 | b->Args({1, 17, 17, 7, 1, 6, 0, 1, 1, 1, 160, 160}); |
| 1805 | b->Args({1, 17, 17, 1, 7, 0, 6, 1, 1, 1, 160, 192}); |
| 1806 | b->Args({1, 17, 17, 1, 7, 0, 6, 1, 1, 1, 192, 192}); |
| 1807 | b->Args({1, 17, 17, 7, 1, 6, 0, 1, 1, 1, 192, 192}); |
| 1808 | b->Args({1, 17, 17, 3, 3, 0, 0, 2, 1, 1, 192, 320}); |
| 1809 | b->Args({1, 17, 17, 3, 3, 0, 0, 2, 1, 1, 192, 192}); |
| 1810 | b->Args({1, 8, 8, 1, 1, 0, 0, 1, 1, 1, 1280, 320}); |
| 1811 | b->Args({1, 8, 8, 1, 1, 0, 0, 1, 1, 1, 1280, 384}); |
| 1812 | b->Args({1, 8, 8, 1, 3, 0, 2, 1, 1, 1, 384, 384}); |
| 1813 | b->Args({1, 8, 8, 3, 1, 2, 0, 1, 1, 1, 384, 384}); |
| 1814 | b->Args({1, 8, 8, 1, 1, 0, 0, 1, 1, 1, 1280, 448}); |
| 1815 | b->Args({1, 8, 8, 3, 3, 2, 2, 1, 1, 1, 448, 384}); |
| 1816 | b->Args({1, 8, 8, 1, 1, 0, 0, 1, 1, 1, 1280, 192}); |
| 1817 | b->Args({1, 8, 8, 1, 1, 0, 0, 1, 1, 1, 2048, 320}); |
| 1818 | b->Args({1, 8, 8, 1, 1, 0, 0, 1, 1, 1, 2048, 384}); |
| 1819 | b->Args({1, 8, 8, 1, 1, 0, 0, 1, 1, 1, 2048, 448}); |
| 1820 | b->Args({1, 8, 8, 1, 1, 0, 0, 1, 1, 1, 2048, 192}); |
| 1821 | b->Args({1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 2048, 1001}); |
| 1822 | } |
| 1823 | |
| 1824 | static void ResNet18(benchmark::internal::Benchmark* b) { |
| 1825 | b->ArgNames({"N", "H", "W", "KH", "KW", "PH", "PW", "S", "D", "G", "GCin", "GCout"}); |
| 1826 | |
| 1827 | /************************* Conv 1 *************************/ |
| 1828 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1829 | b->Args({1, 224, 224, 7, 7, 6, 6, 2, 1, 1, 3, 64}); |
| 1830 | /************************ Conv 2.X ************************/ |
| 1831 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1832 | b->Args({1, 56, 56, 3, 3, 2, 2, 1, 1, 1, 64, 64}); |
| 1833 | /************************ Conv 3.X ************************/ |
| 1834 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1835 | b->Args({1, 56, 56, 3, 3, 2, 2, 2, 1, 1, 64, 128}); |
| 1836 | b->Args({1, 28, 28, 3, 3, 2, 2, 1, 1, 1, 128, 128}); |
| 1837 | b->Args({1, 56, 56, 1, 1, 0, 0, 2, 1, 1, 64, 128}); |
| 1838 | /************************ Conv 4.X ************************/ |
| 1839 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1840 | b->Args({1, 28, 28, 3, 3, 2, 2, 2, 1, 1, 128, 256}); |
| 1841 | b->Args({1, 14, 14, 3, 3, 2, 2, 1, 1, 1, 256, 256}); |
| 1842 | b->Args({1, 28, 28, 1, 1, 0, 0, 2, 1, 1, 128, 256}); |
| 1843 | /************************ Conv 5.X ************************/ |
| 1844 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1845 | b->Args({1, 14, 14, 3, 3, 2, 2, 2, 1, 1, 256, 512}); |
| 1846 | b->Args({1, 7, 7, 3, 3, 2, 2, 1, 1, 1, 512, 512}); |
| 1847 | b->Args({1, 14, 14, 1, 1, 0, 0, 2, 1, 1, 256, 512}); |
| 1848 | } |
| 1849 | |
| 1850 | static void ResNet50(benchmark::internal::Benchmark* b) { |
| 1851 | b->ArgNames({"N", "H", "W", "KH", "KW", "PH", "PW", "S", "D", "G", "GCin", "GCout"}); |
| 1852 | |
| 1853 | /************************* Conv 1 *************************/ |
| 1854 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1855 | b->Args({1, 224, 224, 7, 7, 6, 6, 2, 1, 1, 3, 64}); |
| 1856 | /************************ Conv 2.1 ************************/ |
| 1857 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1858 | b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 64, 64}); |
| 1859 | b->Args({1, 56, 56, 3, 3, 2, 2, 1, 1, 1, 64, 64}); |
| 1860 | b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 64, 256}); |
| 1861 | //b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 64, 256}); |
| 1862 | /************************ Conv 2.X ************************/ |
| 1863 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1864 | b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 256, 64}); |
| 1865 | //b->Args({1, 56, 56, 3, 3, 2, 2, 1, 1, 1, 64, 64}); |
| 1866 | //b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 64, 256}); |
| 1867 | /************************ Conv 3.1 ************************/ |
| 1868 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1869 | b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 256, 128}); |
| 1870 | b->Args({1, 56, 56, 3, 3, 2, 2, 2, 1, 1, 128, 128}); |
| 1871 | b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 128, 512}); |
| 1872 | b->Args({1, 56, 56, 1, 1, 0, 0, 2, 1, 1, 256, 512}); |
| 1873 | /************************ Conv 3.X ************************/ |
| 1874 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1875 | b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 512, 128}); |
| 1876 | b->Args({1, 28, 28, 3, 3, 2, 2, 1, 1, 1, 128, 128}); |
| 1877 | //b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 128, 512}); |
| 1878 | /************************ Conv 4.1 ************************/ |
| 1879 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1880 | b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 512, 256}); |
| 1881 | b->Args({1, 28, 28, 3, 3, 2, 2, 2, 1, 1, 256, 256}); |
| 1882 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 256, 1024}); |
| 1883 | b->Args({1, 28, 28, 1, 1, 0, 0, 2, 1, 1, 512, 1024}); |
| 1884 | /************************ Conv 4.X ************************/ |
| 1885 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1886 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 1024, 256}); |
| 1887 | b->Args({1, 14, 14, 3, 3, 2, 2, 1, 1, 1, 256, 256}); |
| 1888 | //b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 256, 1024}); |
| 1889 | /************************ Conv 5.1 ************************/ |
| 1890 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1891 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 1024, 512}); |
| 1892 | b->Args({1, 14, 14, 3, 3, 2, 2, 2, 1, 1, 512, 512}); |
| 1893 | b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 512, 2048}); |
| 1894 | b->Args({1, 14, 14, 1, 1, 0, 0, 2, 1, 1, 1024, 2048}); |
| 1895 | /************************ Conv 5.X ************************/ |
| 1896 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1897 | b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 2048, 512}); |
| 1898 | b->Args({1, 7, 7, 3, 3, 2, 2, 1, 1, 1, 512, 512}); |
| 1899 | //b->Args({1, 7, 7, 1, 1, 0, 0, 1, 1, 1, 512, 2048}); |
| 1900 | } |
| 1901 | |
| 1902 | static void VGG(benchmark::internal::Benchmark* b) { |
| 1903 | b->ArgNames({"N", "H", "W", "KH", "KW", "PH", "PW", "S", "D", "G", "GCin", "GCout"}); |
| 1904 | |
| 1905 | /************************* Conv 1.1 ************************/ |
| 1906 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1907 | b->Args({1, 224, 224, 3, 3, 2, 2, 1, 1, 1, 3, 64}); |
| 1908 | /************************* Conv 1.2 ************************/ |
| 1909 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1910 | b->Args({1, 224, 224, 3, 3, 2, 2, 1, 1, 1, 64, 64}); |
| 1911 | |
| 1912 | /************************* Conv 2.1 ************************/ |
| 1913 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1914 | b->Args({1, 112, 112, 3, 3, 2, 2, 1, 1, 1, 64, 128}); |
| 1915 | /************************* Conv 2.2 ************************/ |
| 1916 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1917 | b->Args({1, 112, 112, 3, 3, 2, 2, 1, 1, 1, 128, 128}); |
| 1918 | |
| 1919 | /************************* Conv 3.1 ************************/ |
| 1920 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1921 | b->Args({1, 56, 56, 3, 3, 2, 2, 1, 1, 1, 128, 256}); |
| 1922 | /************************* Conv 3.2 ************************/ |
| 1923 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1924 | b->Args({1, 56, 56, 3, 3, 2, 2, 1, 1, 1, 256, 256}); |
| 1925 | /************************* Conv 3.3 ************************/ |
| 1926 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1927 | b->Args({1, 56, 56, 1, 1, 0, 0, 1, 1, 1, 256, 256}); |
| 1928 | |
| 1929 | /************************* Conv 4.1 ************************/ |
| 1930 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1931 | b->Args({1, 28, 28, 3, 3, 2, 2, 1, 1, 1, 256, 512}); |
| 1932 | /************************* Conv 4.2 ************************/ |
| 1933 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1934 | b->Args({1, 28, 28, 3, 3, 2, 2, 1, 1, 1, 512, 512}); |
| 1935 | /************************* Conv 4.3 ************************/ |
| 1936 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1937 | b->Args({1, 28, 28, 1, 1, 0, 0, 1, 1, 1, 512, 512}); |
| 1938 | |
| 1939 | /************************* Conv 5.X ************************/ |
| 1940 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1941 | b->Args({1, 14, 14, 3, 3, 2, 2, 1, 1, 1, 512, 512}); |
| 1942 | /************************* Conv 5.3 ************************/ |
| 1943 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1944 | b->Args({1, 14, 14, 1, 1, 0, 0, 1, 1, 1, 512, 512}); |
| 1945 | } |
| 1946 | |
| 1947 | // SRCNN (9-1-5) |
| 1948 | static void SRCNN915(benchmark::internal::Benchmark* b) { |
| 1949 | b->ArgNames({"N", "H", "W", "KH", "KW", "PH", "PW", "S", "D", "G", "GCin", "GCout"}); |
| 1950 | |
| 1951 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1952 | b->Args({1, 384, 384, 9, 9, 0, 0, 1, 1, 1, 1, 64}); |
| 1953 | b->Args({1, 376, 376, 1, 1, 0, 0, 1, 1, 1, 64, 32}); |
| 1954 | b->Args({1, 376, 376, 5, 5, 0, 0, 1, 1, 1, 32, 1}); |
| 1955 | } |
| 1956 | |
| 1957 | // SRCNN (9-3-5) |
| 1958 | static void SRCNN935(benchmark::internal::Benchmark* b) { |
| 1959 | b->ArgNames({"N", "H", "W", "KH", "KW", "PH", "PW", "S", "D", "G", "GCin", "GCout"}); |
| 1960 | |
| 1961 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1962 | b->Args({1, 384, 384, 9, 9, 0, 0, 1, 1, 1, 1, 64}); |
| 1963 | b->Args({1, 376, 376, 3, 3, 0, 0, 1, 1, 1, 64, 32}); |
| 1964 | b->Args({1, 374, 374, 5, 5, 0, 0, 1, 1, 1, 32, 1}); |
| 1965 | } |
| 1966 | |
| 1967 | // SRCNN (9-5-5) |
| 1968 | static void SRCNN955(benchmark::internal::Benchmark* b) { |
| 1969 | b->ArgNames({"N", "H", "W", "KH", "KW", "PH", "PW", "S", "D", "G", "GCin", "GCout"}); |
| 1970 | |
| 1971 | /* N H W KH KW PH PW S D G GCin GCout */ |
| 1972 | b->Args({1, 384, 384, 9, 9, 0, 0, 1, 1, 1, 1, 64}); |
| 1973 | b->Args({1, 376, 376, 5, 5, 0, 0, 1, 1, 1, 64, 32}); |
| 1974 | b->Args({1, 372, 372, 5, 5, 0, 0, 1, 1, 1, 32, 1}); |
| 1975 | } |
| 1976 | |
Chao Mei | c664027 | 2020-07-23 09:35:11 -0700 | [diff] [blame] | 1977 | #ifndef XNN_NO_F16_OPERATORS |
Marat Dukhan | 16f1e1a | 2020-08-04 16:38:22 -0700 | [diff] [blame] | 1978 | BENCHMARK_CAPTURE(xnnpack_convolution_f16, mobilenet_v1, "MobileNet v1")->Apply(MobileNetV1)->UseRealTime(); |
| 1979 | BENCHMARK_CAPTURE(xnnpack_convolution_f16, mobilenet_v2, "MobileNet v2")->Apply(MobileNetV2)->UseRealTime(); |
| 1980 | BENCHMARK_CAPTURE(xnnpack_convolution_f16, mobilenet_v3_small, "MobileNet v3 Small")->Apply(MobileNetV3Small)->UseRealTime(); |
| 1981 | BENCHMARK_CAPTURE(xnnpack_convolution_f16, mobilenet_v3_large, "MobileNet v3 Large")->Apply(MobileNetV3Large)->UseRealTime(); |
| 1982 | BENCHMARK_CAPTURE(xnnpack_convolution_f16, shufflenet_v1_g1, "ShuffleNet v1 (1 group)")->Apply(ShuffleNetV1G1)->UseRealTime(); |
| 1983 | BENCHMARK_CAPTURE(xnnpack_convolution_f16, shufflenet_v1_g2, "ShuffleNet v1 (2 groups)")->Apply(ShuffleNetV1G2)->UseRealTime(); |
| 1984 | BENCHMARK_CAPTURE(xnnpack_convolution_f16, shufflenet_v1_g3, "ShuffleNet v1 (3 groups)")->Apply(ShuffleNetV1G3)->UseRealTime(); |
| 1985 | BENCHMARK_CAPTURE(xnnpack_convolution_f16, shufflenet_v1_g4, "ShuffleNet v1 (4 groups)")->Apply(ShuffleNetV1G4)->UseRealTime(); |
| 1986 | BENCHMARK_CAPTURE(xnnpack_convolution_f16, shufflenet_v1_g8, "ShuffleNet v1 (8 groups)")->Apply(ShuffleNetV1G8)->UseRealTime(); |
| 1987 | BENCHMARK_CAPTURE(xnnpack_convolution_f16, shufflenet_v2_x05, "ShuffleNet v2 0.5X")->Apply(ShuffleNetV2X05)->UseRealTime(); |
| 1988 | BENCHMARK_CAPTURE(xnnpack_convolution_f16, shufflenet_v2_x10, "ShuffleNet v2 1.0X")->Apply(ShuffleNetV2X10)->UseRealTime(); |
| 1989 | BENCHMARK_CAPTURE(xnnpack_convolution_f16, shufflenet_v2_x15, "ShuffleNet v2 1.5X")->Apply(ShuffleNetV2X15)->UseRealTime(); |
| 1990 | BENCHMARK_CAPTURE(xnnpack_convolution_f16, shufflenet_v2_x20, "ShuffleNet v2 2.0X")->Apply(ShuffleNetV2X20)->UseRealTime(); |
| 1991 | BENCHMARK_CAPTURE(xnnpack_convolution_f16, squeezenet_v10, "SqueezeNet 1.0")->Apply(SqueezeNetV10)->UseRealTime(); |
| 1992 | BENCHMARK_CAPTURE(xnnpack_convolution_f16, squeezenet_v11, "SqueezeNet 1.1")->Apply(SqueezeNetV11)->UseRealTime(); |
| 1993 | BENCHMARK_CAPTURE(xnnpack_convolution_f16, inception_v3, "Inception v3")->Apply(InceptionV3)->UseRealTime(); |
| 1994 | BENCHMARK_CAPTURE(xnnpack_convolution_f16, resnet18, "ResNet-18")->Apply(ResNet18)->UseRealTime(); |
| 1995 | BENCHMARK_CAPTURE(xnnpack_convolution_f16, resnet50, "ResNet-50")->Apply(ResNet50)->UseRealTime(); |
| 1996 | BENCHMARK_CAPTURE(xnnpack_convolution_f16, vgg, "VGG")->Apply(VGG)->UseRealTime(); |
| 1997 | BENCHMARK_CAPTURE(xnnpack_convolution_f16, srcnn915, "SRCNN (9-1-5)")->Apply(SRCNN915)->UseRealTime(); |
| 1998 | BENCHMARK_CAPTURE(xnnpack_convolution_f16, srcnn935, "SRCNN (9-3-5)")->Apply(SRCNN935)->UseRealTime(); |
| 1999 | BENCHMARK_CAPTURE(xnnpack_convolution_f16, srcnn955, "SRCNN (9-5-5)")->Apply(SRCNN955)->UseRealTime(); |
Chao Mei | c664027 | 2020-07-23 09:35:11 -0700 | [diff] [blame] | 2000 | #endif // XNN_NO_F16_OPERATORS |
Frank Barchard | 49b4dcc | 2020-06-26 14:07:19 -0700 | [diff] [blame] | 2001 | |
Marat Dukhan | 16f1e1a | 2020-08-04 16:38:22 -0700 | [diff] [blame] | 2002 | #ifndef XNN_NO_F32_OPERATORS |
| 2003 | BENCHMARK_CAPTURE(xnnpack_convolution_f32, mobilenet_v1, "MobileNet v1")->Apply(MobileNetV1)->UseRealTime(); |
| 2004 | BENCHMARK_CAPTURE(xnnpack_convolution_f32, mobilenet_v2, "MobileNet v2")->Apply(MobileNetV2)->UseRealTime(); |
| 2005 | BENCHMARK_CAPTURE(xnnpack_convolution_f32, mobilenet_v3_small, "MobileNet v3 Small")->Apply(MobileNetV3Small)->UseRealTime(); |
| 2006 | BENCHMARK_CAPTURE(xnnpack_convolution_f32, mobilenet_v3_large, "MobileNet v3 Large")->Apply(MobileNetV3Large)->UseRealTime(); |
| 2007 | BENCHMARK_CAPTURE(xnnpack_convolution_f32, shufflenet_v1_g1, "ShuffleNet v1 (1 group)")->Apply(ShuffleNetV1G1)->UseRealTime(); |
| 2008 | BENCHMARK_CAPTURE(xnnpack_convolution_f32, shufflenet_v1_g2, "ShuffleNet v1 (2 groups)")->Apply(ShuffleNetV1G2)->UseRealTime(); |
| 2009 | BENCHMARK_CAPTURE(xnnpack_convolution_f32, shufflenet_v1_g3, "ShuffleNet v1 (3 groups)")->Apply(ShuffleNetV1G3)->UseRealTime(); |
| 2010 | BENCHMARK_CAPTURE(xnnpack_convolution_f32, shufflenet_v1_g4, "ShuffleNet v1 (4 groups)")->Apply(ShuffleNetV1G4)->UseRealTime(); |
| 2011 | BENCHMARK_CAPTURE(xnnpack_convolution_f32, shufflenet_v1_g8, "ShuffleNet v1 (8 groups)")->Apply(ShuffleNetV1G8)->UseRealTime(); |
| 2012 | BENCHMARK_CAPTURE(xnnpack_convolution_f32, shufflenet_v2_x05, "ShuffleNet v2 0.5X")->Apply(ShuffleNetV2X05)->UseRealTime(); |
| 2013 | BENCHMARK_CAPTURE(xnnpack_convolution_f32, shufflenet_v2_x10, "ShuffleNet v2 1.0X")->Apply(ShuffleNetV2X10)->UseRealTime(); |
| 2014 | BENCHMARK_CAPTURE(xnnpack_convolution_f32, shufflenet_v2_x15, "ShuffleNet v2 1.5X")->Apply(ShuffleNetV2X15)->UseRealTime(); |
| 2015 | BENCHMARK_CAPTURE(xnnpack_convolution_f32, shufflenet_v2_x20, "ShuffleNet v2 2.0X")->Apply(ShuffleNetV2X20)->UseRealTime(); |
| 2016 | BENCHMARK_CAPTURE(xnnpack_convolution_f32, squeezenet_v10, "SqueezeNet 1.0")->Apply(SqueezeNetV10)->UseRealTime(); |
| 2017 | BENCHMARK_CAPTURE(xnnpack_convolution_f32, squeezenet_v11, "SqueezeNet 1.1")->Apply(SqueezeNetV11)->UseRealTime(); |
| 2018 | BENCHMARK_CAPTURE(xnnpack_convolution_f32, inception_v3, "Inception v3")->Apply(InceptionV3)->UseRealTime(); |
| 2019 | BENCHMARK_CAPTURE(xnnpack_convolution_f32, resnet18, "ResNet-18")->Apply(ResNet18)->UseRealTime(); |
| 2020 | BENCHMARK_CAPTURE(xnnpack_convolution_f32, resnet50, "ResNet-50")->Apply(ResNet50)->UseRealTime(); |
| 2021 | BENCHMARK_CAPTURE(xnnpack_convolution_f32, vgg, "VGG")->Apply(VGG)->UseRealTime(); |
| 2022 | BENCHMARK_CAPTURE(xnnpack_convolution_f32, srcnn915, "SRCNN (9-1-5)")->Apply(SRCNN915)->UseRealTime(); |
| 2023 | BENCHMARK_CAPTURE(xnnpack_convolution_f32, srcnn935, "SRCNN (9-3-5)")->Apply(SRCNN935)->UseRealTime(); |
| 2024 | BENCHMARK_CAPTURE(xnnpack_convolution_f32, srcnn955, "SRCNN (9-5-5)")->Apply(SRCNN955)->UseRealTime(); |
| 2025 | #endif // XNN_NO_F32_OPERATORS |
| 2026 | |
| 2027 | #ifndef XNN_NO_QS8_OPERATORS |
| 2028 | BENCHMARK_CAPTURE(xnnpack_convolution_qs8, mobilenet_v1, "MobileNet v1")->Apply(MobileNetV1)->UseRealTime(); |
| 2029 | BENCHMARK_CAPTURE(xnnpack_convolution_qs8, mobilenet_v2, "MobileNet v2")->Apply(MobileNetV2)->UseRealTime(); |
| 2030 | BENCHMARK_CAPTURE(xnnpack_convolution_qs8, mobilenet_v3_small, "MobileNet v3 Small")->Apply(MobileNetV3Small)->UseRealTime(); |
| 2031 | BENCHMARK_CAPTURE(xnnpack_convolution_qs8, mobilenet_v3_large, "MobileNet v3 Large")->Apply(MobileNetV3Large)->UseRealTime(); |
| 2032 | BENCHMARK_CAPTURE(xnnpack_convolution_qs8, shufflenet_v1_g1, "ShuffleNet v1 (1 group)")->Apply(ShuffleNetV1G1)->UseRealTime(); |
| 2033 | BENCHMARK_CAPTURE(xnnpack_convolution_qs8, shufflenet_v1_g2, "ShuffleNet v1 (2 groups)")->Apply(ShuffleNetV1G2)->UseRealTime(); |
| 2034 | BENCHMARK_CAPTURE(xnnpack_convolution_qs8, shufflenet_v1_g3, "ShuffleNet v1 (3 groups)")->Apply(ShuffleNetV1G3)->UseRealTime(); |
| 2035 | BENCHMARK_CAPTURE(xnnpack_convolution_qs8, shufflenet_v1_g4, "ShuffleNet v1 (4 groups)")->Apply(ShuffleNetV1G4)->UseRealTime(); |
| 2036 | BENCHMARK_CAPTURE(xnnpack_convolution_qs8, shufflenet_v1_g8, "ShuffleNet v1 (8 groups)")->Apply(ShuffleNetV1G8)->UseRealTime(); |
| 2037 | BENCHMARK_CAPTURE(xnnpack_convolution_qs8, shufflenet_v2_x05, "ShuffleNet v2 0.5X")->Apply(ShuffleNetV2X05)->UseRealTime(); |
| 2038 | BENCHMARK_CAPTURE(xnnpack_convolution_qs8, shufflenet_v2_x10, "ShuffleNet v2 1.0X")->Apply(ShuffleNetV2X10)->UseRealTime(); |
| 2039 | BENCHMARK_CAPTURE(xnnpack_convolution_qs8, shufflenet_v2_x15, "ShuffleNet v2 1.5X")->Apply(ShuffleNetV2X15)->UseRealTime(); |
| 2040 | BENCHMARK_CAPTURE(xnnpack_convolution_qs8, shufflenet_v2_x20, "ShuffleNet v2 2.0X")->Apply(ShuffleNetV2X20)->UseRealTime(); |
| 2041 | BENCHMARK_CAPTURE(xnnpack_convolution_qs8, squeezenet_v10, "SqueezeNet 1.0")->Apply(SqueezeNetV10)->UseRealTime(); |
| 2042 | BENCHMARK_CAPTURE(xnnpack_convolution_qs8, squeezenet_v11, "SqueezeNet 1.1")->Apply(SqueezeNetV11)->UseRealTime(); |
| 2043 | BENCHMARK_CAPTURE(xnnpack_convolution_qs8, inception_v3, "Inception v3")->Apply(InceptionV3)->UseRealTime(); |
| 2044 | BENCHMARK_CAPTURE(xnnpack_convolution_qs8, resnet18, "ResNet-18")->Apply(ResNet18)->UseRealTime(); |
| 2045 | BENCHMARK_CAPTURE(xnnpack_convolution_qs8, resnet50, "ResNet-50")->Apply(ResNet50)->UseRealTime(); |
| 2046 | BENCHMARK_CAPTURE(xnnpack_convolution_qs8, vgg, "VGG")->Apply(VGG)->UseRealTime(); |
| 2047 | BENCHMARK_CAPTURE(xnnpack_convolution_qs8, srcnn915, "SRCNN (9-1-5)")->Apply(SRCNN915)->UseRealTime(); |
| 2048 | BENCHMARK_CAPTURE(xnnpack_convolution_qs8, srcnn935, "SRCNN (9-3-5)")->Apply(SRCNN935)->UseRealTime(); |
| 2049 | BENCHMARK_CAPTURE(xnnpack_convolution_qs8, srcnn955, "SRCNN (9-5-5)")->Apply(SRCNN955)->UseRealTime(); |
| 2050 | #endif // XNN_NO_QS8_OPERATORS |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 2051 | |
Chao Mei | c664027 | 2020-07-23 09:35:11 -0700 | [diff] [blame] | 2052 | #ifndef XNN_NO_QU8_OPERATORS |
Marat Dukhan | 16f1e1a | 2020-08-04 16:38:22 -0700 | [diff] [blame] | 2053 | BENCHMARK_CAPTURE(xnnpack_convolution_qu8, mobilenet_v1, "MobileNet v1")->Apply(MobileNetV1)->UseRealTime(); |
| 2054 | BENCHMARK_CAPTURE(xnnpack_convolution_qu8, mobilenet_v2, "MobileNet v2")->Apply(MobileNetV2)->UseRealTime(); |
| 2055 | BENCHMARK_CAPTURE(xnnpack_convolution_qu8, mobilenet_v3_small, "MobileNet v3 Small")->Apply(MobileNetV3Small)->UseRealTime(); |
| 2056 | BENCHMARK_CAPTURE(xnnpack_convolution_qu8, mobilenet_v3_large, "MobileNet v3 Large")->Apply(MobileNetV3Large)->UseRealTime(); |
| 2057 | BENCHMARK_CAPTURE(xnnpack_convolution_qu8, shufflenet_v1_g1, "ShuffleNet v1 (1 group)")->Apply(ShuffleNetV1G1)->UseRealTime(); |
| 2058 | BENCHMARK_CAPTURE(xnnpack_convolution_qu8, shufflenet_v1_g2, "ShuffleNet v1 (2 groups)")->Apply(ShuffleNetV1G2)->UseRealTime(); |
| 2059 | BENCHMARK_CAPTURE(xnnpack_convolution_qu8, shufflenet_v1_g3, "ShuffleNet v1 (3 groups)")->Apply(ShuffleNetV1G3)->UseRealTime(); |
| 2060 | BENCHMARK_CAPTURE(xnnpack_convolution_qu8, shufflenet_v1_g4, "ShuffleNet v1 (4 groups)")->Apply(ShuffleNetV1G4)->UseRealTime(); |
| 2061 | BENCHMARK_CAPTURE(xnnpack_convolution_qu8, shufflenet_v1_g8, "ShuffleNet v1 (8 groups)")->Apply(ShuffleNetV1G8)->UseRealTime(); |
| 2062 | BENCHMARK_CAPTURE(xnnpack_convolution_qu8, shufflenet_v2_x05, "ShuffleNet v2 0.5X")->Apply(ShuffleNetV2X05)->UseRealTime(); |
| 2063 | BENCHMARK_CAPTURE(xnnpack_convolution_qu8, shufflenet_v2_x10, "ShuffleNet v2 1.0X")->Apply(ShuffleNetV2X10)->UseRealTime(); |
| 2064 | BENCHMARK_CAPTURE(xnnpack_convolution_qu8, shufflenet_v2_x15, "ShuffleNet v2 1.5X")->Apply(ShuffleNetV2X15)->UseRealTime(); |
| 2065 | BENCHMARK_CAPTURE(xnnpack_convolution_qu8, shufflenet_v2_x20, "ShuffleNet v2 2.0X")->Apply(ShuffleNetV2X20)->UseRealTime(); |
| 2066 | BENCHMARK_CAPTURE(xnnpack_convolution_qu8, squeezenet_v10, "SqueezeNet 1.0")->Apply(SqueezeNetV10)->UseRealTime(); |
| 2067 | BENCHMARK_CAPTURE(xnnpack_convolution_qu8, squeezenet_v11, "SqueezeNet 1.1")->Apply(SqueezeNetV11)->UseRealTime(); |
| 2068 | BENCHMARK_CAPTURE(xnnpack_convolution_qu8, inception_v3, "Inception v3")->Apply(InceptionV3)->UseRealTime(); |
| 2069 | BENCHMARK_CAPTURE(xnnpack_convolution_qu8, resnet18, "ResNet-18")->Apply(ResNet18)->UseRealTime(); |
| 2070 | BENCHMARK_CAPTURE(xnnpack_convolution_qu8, resnet50, "ResNet-50")->Apply(ResNet50)->UseRealTime(); |
| 2071 | BENCHMARK_CAPTURE(xnnpack_convolution_qu8, vgg, "VGG")->Apply(VGG)->UseRealTime(); |
| 2072 | BENCHMARK_CAPTURE(xnnpack_convolution_qu8, srcnn915, "SRCNN (9-1-5)")->Apply(SRCNN915)->UseRealTime(); |
| 2073 | BENCHMARK_CAPTURE(xnnpack_convolution_qu8, srcnn935, "SRCNN (9-3-5)")->Apply(SRCNN935)->UseRealTime(); |
| 2074 | BENCHMARK_CAPTURE(xnnpack_convolution_qu8, srcnn955, "SRCNN (9-5-5)")->Apply(SRCNN955)->UseRealTime(); |
Chao Mei | c664027 | 2020-07-23 09:35:11 -0700 | [diff] [blame] | 2075 | #endif // XNN_NO_QU8_OPERATORS |
XNNPACK Team | b455b12 | 2019-09-27 18:10:33 -0700 | [diff] [blame] | 2076 | |
| 2077 | #ifdef BENCHMARK_TENSORFLOW_LITE |
| 2078 | BENCHMARK_CAPTURE(tflite_convolution_f32, mobilenet_v1, "MobileNet v1")->Apply(MobileNetV1)->UseRealTime(); |
| 2079 | BENCHMARK_CAPTURE(tflite_convolution_f32, mobilenet_v2, "MobileNet v2")->Apply(MobileNetV2)->UseRealTime(); |
| 2080 | BENCHMARK_CAPTURE(tflite_convolution_f32, mobilenet_v3_small, "MobileNet v3 Small")->Apply(MobileNetV3Small)->UseRealTime(); |
| 2081 | BENCHMARK_CAPTURE(tflite_convolution_f32, mobilenet_v3_large, "MobileNet v3 Large")->Apply(MobileNetV3Large)->UseRealTime(); |
| 2082 | BENCHMARK_CAPTURE(tflite_convolution_f32, shufflenet_v1_g1, "ShuffleNet v1 (1 group)")->Apply(ShuffleNetV1G1)->UseRealTime(); |
| 2083 | BENCHMARK_CAPTURE(tflite_convolution_f32, shufflenet_v1_g2, "ShuffleNet v1 (2 groups)")->Apply(ShuffleNetV1G2)->UseRealTime(); |
| 2084 | BENCHMARK_CAPTURE(tflite_convolution_f32, shufflenet_v1_g3, "ShuffleNet v1 (3 groups)")->Apply(ShuffleNetV1G3)->UseRealTime(); |
| 2085 | BENCHMARK_CAPTURE(tflite_convolution_f32, shufflenet_v1_g4, "ShuffleNet v1 (4 groups)")->Apply(ShuffleNetV1G4)->UseRealTime(); |
| 2086 | BENCHMARK_CAPTURE(tflite_convolution_f32, shufflenet_v1_g8, "ShuffleNet v1 (8 groups)")->Apply(ShuffleNetV1G8)->UseRealTime(); |
| 2087 | BENCHMARK_CAPTURE(tflite_convolution_f32, shufflenet_v2_x05, "ShuffleNet v2 0.5X")->Apply(ShuffleNetV2X05)->UseRealTime(); |
| 2088 | BENCHMARK_CAPTURE(tflite_convolution_f32, shufflenet_v2_x10, "ShuffleNet v2 1.0X")->Apply(ShuffleNetV2X10)->UseRealTime(); |
| 2089 | BENCHMARK_CAPTURE(tflite_convolution_f32, shufflenet_v2_x15, "ShuffleNet v2 1.5X")->Apply(ShuffleNetV2X15)->UseRealTime(); |
| 2090 | BENCHMARK_CAPTURE(tflite_convolution_f32, shufflenet_v2_x20, "ShuffleNet v2 2.0X")->Apply(ShuffleNetV2X20)->UseRealTime(); |
| 2091 | BENCHMARK_CAPTURE(tflite_convolution_f32, squeezenet_v10, "SqueezeNet 1.0")->Apply(SqueezeNetV10)->UseRealTime(); |
| 2092 | BENCHMARK_CAPTURE(tflite_convolution_f32, squeezenet_v11, "SqueezeNet 1.1")->Apply(SqueezeNetV11)->UseRealTime(); |
| 2093 | BENCHMARK_CAPTURE(tflite_convolution_f32, inception_v3, "Inception v3")->Apply(InceptionV3)->UseRealTime(); |
| 2094 | BENCHMARK_CAPTURE(tflite_convolution_f32, resnet18, "ResNet-18")->Apply(ResNet18)->UseRealTime(); |
| 2095 | BENCHMARK_CAPTURE(tflite_convolution_f32, resnet50, "ResNet-50")->Apply(ResNet50)->UseRealTime(); |
| 2096 | BENCHMARK_CAPTURE(tflite_convolution_f32, vgg, "VGG")->Apply(VGG)->UseRealTime(); |
| 2097 | BENCHMARK_CAPTURE(tflite_convolution_f32, srcnn915, "SRCNN (9-1-5)")->Apply(SRCNN915)->UseRealTime(); |
| 2098 | BENCHMARK_CAPTURE(tflite_convolution_f32, srcnn935, "SRCNN (9-3-5)")->Apply(SRCNN935)->UseRealTime(); |
| 2099 | BENCHMARK_CAPTURE(tflite_convolution_f32, srcnn955, "SRCNN (9-5-5)")->Apply(SRCNN955)->UseRealTime(); |
| 2100 | #endif // BENCHMARK_TENSORFLOW_LITE |
| 2101 | |
| 2102 | #ifdef BENCHMARK_ARM_COMPUTE_LIBRARY |
| 2103 | BENCHMARK_CAPTURE(armcl_convolution_f32, mobilenet_v1, "MobileNet v1")->Apply(MobileNetV1)->UseRealTime(); |
| 2104 | BENCHMARK_CAPTURE(armcl_convolution_f32, mobilenet_v2, "MobileNet v2")->Apply(MobileNetV2)->UseRealTime(); |
| 2105 | BENCHMARK_CAPTURE(armcl_convolution_f32, shufflenet_v1_g1, "ShuffleNet v1 (1 group)")->Apply(ShuffleNetV1G1)->UseRealTime(); |
| 2106 | BENCHMARK_CAPTURE(armcl_convolution_f32, shufflenet_v1_g2, "ShuffleNet v1 (2 groups)")->Apply(ShuffleNetV1G2)->UseRealTime(); |
| 2107 | BENCHMARK_CAPTURE(armcl_convolution_f32, shufflenet_v1_g3, "ShuffleNet v1 (3 groups)")->Apply(ShuffleNetV1G3)->UseRealTime(); |
| 2108 | BENCHMARK_CAPTURE(armcl_convolution_f32, shufflenet_v1_g4, "ShuffleNet v1 (4 groups)")->Apply(ShuffleNetV1G4)->UseRealTime(); |
| 2109 | BENCHMARK_CAPTURE(armcl_convolution_f32, shufflenet_v1_g8, "ShuffleNet v1 (8 groups)")->Apply(ShuffleNetV1G8)->UseRealTime(); |
| 2110 | BENCHMARK_CAPTURE(armcl_convolution_f32, shufflenet_v2_x05, "ShuffleNet v2 0.5X")->Apply(ShuffleNetV2X05)->UseRealTime(); |
| 2111 | BENCHMARK_CAPTURE(armcl_convolution_f32, shufflenet_v2_x10, "ShuffleNet v2 1.0X")->Apply(ShuffleNetV2X10)->UseRealTime(); |
| 2112 | BENCHMARK_CAPTURE(armcl_convolution_f32, shufflenet_v2_x15, "ShuffleNet v2 1.5X")->Apply(ShuffleNetV2X15)->UseRealTime(); |
| 2113 | BENCHMARK_CAPTURE(armcl_convolution_f32, shufflenet_v2_x20, "ShuffleNet v2 2.0X")->Apply(ShuffleNetV2X20)->UseRealTime(); |
| 2114 | BENCHMARK_CAPTURE(armcl_convolution_f32, squeezenet_v10, "SqueezeNet 1.0")->Apply(SqueezeNetV10)->UseRealTime(); |
| 2115 | BENCHMARK_CAPTURE(armcl_convolution_f32, squeezenet_v11, "SqueezeNet 1.1")->Apply(SqueezeNetV11)->UseRealTime(); |
| 2116 | BENCHMARK_CAPTURE(armcl_convolution_f32, inception_v3, "Inception v3")->Apply(InceptionV3)->UseRealTime(); |
| 2117 | BENCHMARK_CAPTURE(armcl_convolution_f32, resnet18, "ResNet-18")->Apply(ResNet18)->UseRealTime(); |
| 2118 | BENCHMARK_CAPTURE(armcl_convolution_f32, resnet50, "ResNet-50")->Apply(ResNet50)->UseRealTime(); |
| 2119 | BENCHMARK_CAPTURE(armcl_convolution_f32, vgg, "VGG")->Apply(VGG)->UseRealTime(); |
| 2120 | BENCHMARK_CAPTURE(armcl_convolution_f32, srcnn915, "SRCNN (9-1-5)")->Apply(SRCNN915)->UseRealTime(); |
| 2121 | BENCHMARK_CAPTURE(armcl_convolution_f32, srcnn935, "SRCNN (9-3-5)")->Apply(SRCNN935)->UseRealTime(); |
| 2122 | BENCHMARK_CAPTURE(armcl_convolution_f32, srcnn955, "SRCNN (9-5-5)")->Apply(SRCNN955)->UseRealTime(); |
| 2123 | #endif // BENCHMARK_ARM_COMPUTE_LIBRARY |
| 2124 | |
| 2125 | #ifndef XNNPACK_BENCHMARK_NO_MAIN |
| 2126 | BENCHMARK_MAIN(); |
| 2127 | #endif |