Georgios Pinitas | be2772a | 2018-08-17 15:33:39 +0100 | [diff] [blame] | 1 | /* |
Sang-Hoon Park | 11fedda | 2020-01-15 14:44:04 +0000 | [diff] [blame] | 2 | * Copyright (c) 2018-2020 ARM Limited. |
Georgios Pinitas | be2772a | 2018-08-17 15:33:39 +0100 | [diff] [blame] | 3 | * |
| 4 | * SPDX-License-Identifier: MIT |
| 5 | * |
| 6 | * Permission is hereby granted, free of charge, to any person obtaining a copy |
| 7 | * of this software and associated documentation files (the "Software"), to |
| 8 | * deal in the Software without restriction, including without limitation the |
| 9 | * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or |
| 10 | * sell copies of the Software, and to permit persons to whom the Software is |
| 11 | * furnished to do so, subject to the following conditions: |
| 12 | * |
| 13 | * The above copyright notice and this permission notice shall be included in all |
| 14 | * copies or substantial portions of the Software. |
| 15 | * |
| 16 | * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
| 17 | * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, |
| 18 | * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE |
| 19 | * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER |
| 20 | * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, |
| 21 | * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE |
| 22 | * SOFTWARE. |
| 23 | */ |
| 24 | #include "arm_compute/graph.h" |
| 25 | #include "support/ToolchainSupport.h" |
| 26 | #include "utils/CommonGraphOptions.h" |
| 27 | #include "utils/GraphUtils.h" |
| 28 | #include "utils/Utils.h" |
| 29 | |
| 30 | using namespace arm_compute::utils; |
| 31 | using namespace arm_compute::graph::frontend; |
| 32 | using namespace arm_compute::graph_utils; |
| 33 | |
Georgios Pinitas | 108ab0b | 2018-09-14 18:35:11 +0100 | [diff] [blame] | 34 | /** Example demonstrating how to implement InceptionV4's network using the Compute Library's graph API */ |
Georgios Pinitas | be2772a | 2018-08-17 15:33:39 +0100 | [diff] [blame] | 35 | class InceptionResNetV2Example final : public Example |
| 36 | { |
| 37 | public: |
| 38 | InceptionResNetV2Example() |
| 39 | : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "InceptionResNetV2") |
| 40 | { |
| 41 | } |
| 42 | bool do_setup(int argc, char **argv) override |
| 43 | { |
| 44 | // Parse arguments |
| 45 | cmd_parser.parse(argc, argv); |
Georgios Pinitas | cd60a5f | 2019-08-21 17:06:54 +0100 | [diff] [blame] | 46 | cmd_parser.validate(); |
Georgios Pinitas | be2772a | 2018-08-17 15:33:39 +0100 | [diff] [blame] | 47 | |
| 48 | // Consume common parameters |
| 49 | common_params = consume_common_graph_parameters(common_opts); |
| 50 | |
| 51 | // Return when help menu is requested |
| 52 | if(common_params.help) |
| 53 | { |
| 54 | cmd_parser.print_help(argv[0]); |
| 55 | return false; |
| 56 | } |
| 57 | |
| 58 | // Set default layout if needed |
| 59 | if(!common_opts.data_layout->is_set() && common_params.target == Target::NEON) |
| 60 | { |
| 61 | common_params.data_layout = DataLayout::NCHW; |
| 62 | } |
| 63 | |
| 64 | // Checks |
Anthony Barbier | cdd68c0 | 2018-08-23 15:03:41 +0100 | [diff] [blame] | 65 | ARM_COMPUTE_EXIT_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type), "QASYMM8 not supported for this graph"); |
Georgios Pinitas | be2772a | 2018-08-17 15:33:39 +0100 | [diff] [blame] | 66 | |
| 67 | // Print parameter values |
| 68 | std::cout << common_params << std::endl; |
| 69 | |
| 70 | // Create model path |
| 71 | std::string data_path = common_params.data_path; |
| 72 | std::string model_path = "/cnn_data/inception_resnet_v2_model/"; |
| 73 | if(!data_path.empty()) |
| 74 | { |
| 75 | data_path += model_path; |
| 76 | } |
| 77 | |
| 78 | // Create a preprocessor object |
| 79 | std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>(0.f, 1.f); |
| 80 | |
| 81 | // Create input descriptor |
Sang-Hoon Park | 11fedda | 2020-01-15 14:44:04 +0000 | [diff] [blame] | 82 | const auto operation_layout = common_params.data_layout; |
| 83 | const TensorShape tensor_shape = permute_shape(TensorShape(299U, 299U, 3U, 1U), DataLayout::NCHW, operation_layout); |
| 84 | TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_layout); |
Georgios Pinitas | be2772a | 2018-08-17 15:33:39 +0100 | [diff] [blame] | 85 | |
| 86 | // Set weights trained layout |
| 87 | const DataLayout weights_layout = DataLayout::NCHW; |
| 88 | |
| 89 | graph << common_params.target |
| 90 | << common_params.fast_math_hint |
| 91 | << InputLayer(input_descriptor, get_input_accessor(common_params, std::move(preprocessor), false)) |
| 92 | // Conv2d_1a_3x3 |
| 93 | << ConvolutionLayer(3U, 3U, 32U, |
| 94 | get_weights_accessor(data_path, "Conv2d_1a_3x3_weights.npy", weights_layout), |
| 95 | std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| 96 | PadStrideInfo(2, 2, 0, 0)) |
| 97 | .set_name("Conv2d_1a_3x3/convolution") |
| 98 | << BatchNormalizationLayer(get_weights_accessor(data_path, "Conv2d_1a_3x3_BatchNorm_moving_mean.npy"), |
| 99 | get_weights_accessor(data_path, "Conv2d_1a_3x3_BatchNorm_moving_variance.npy"), |
| 100 | get_random_accessor(1.f, 1.f), |
| 101 | get_weights_accessor(data_path, "Conv2d_1a_3x3_BatchNorm_beta.npy"), |
| 102 | 0.0010000000474974513f) |
| 103 | .set_name("Conv2d_1a_3x3/BatchNorm") |
| 104 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_1a_3x3/Relu") |
| 105 | // Conv2d_2a_3x3 |
| 106 | << ConvolutionLayer(3U, 3U, 32U, |
| 107 | get_weights_accessor(data_path, "Conv2d_2a_3x3_weights.npy", weights_layout), |
| 108 | std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| 109 | PadStrideInfo(1, 1, 0, 0)) |
| 110 | .set_name("Conv2d_2a_3x3/convolution") |
| 111 | << BatchNormalizationLayer(get_weights_accessor(data_path, "Conv2d_2a_3x3_BatchNorm_moving_mean.npy"), |
| 112 | get_weights_accessor(data_path, "Conv2d_2a_3x3_BatchNorm_moving_variance.npy"), |
| 113 | get_random_accessor(1.f, 1.f), |
| 114 | get_weights_accessor(data_path, "Conv2d_2a_3x3_BatchNorm_beta.npy"), |
| 115 | 0.0010000000474974513f) |
| 116 | .set_name("Conv2d_2a_3x3/BatchNorm") |
| 117 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_2a_3x3/Relu") |
| 118 | // Conv2d_2b_3x3 |
| 119 | << ConvolutionLayer(3U, 3U, 64U, |
| 120 | get_weights_accessor(data_path, "Conv2d_2b_3x3_weights.npy", weights_layout), |
| 121 | std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| 122 | PadStrideInfo(1, 1, 1, 1)) |
| 123 | .set_name("Conv2d_2b_3x3/convolution") |
| 124 | << BatchNormalizationLayer(get_weights_accessor(data_path, "Conv2d_2b_3x3_BatchNorm_moving_mean.npy"), |
| 125 | get_weights_accessor(data_path, "Conv2d_2b_3x3_BatchNorm_moving_variance.npy"), |
| 126 | get_random_accessor(1.f, 1.f), |
| 127 | get_weights_accessor(data_path, "Conv2d_2b_3x3_BatchNorm_beta.npy"), |
| 128 | 0.0010000000474974513f) |
| 129 | .set_name("Conv2d_2b_3x3/BatchNorm") |
| 130 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_2b_3x3/Relu") |
| 131 | // MaxPool_3a_3x3 |
Sang-Hoon Park | 11fedda | 2020-01-15 14:44:04 +0000 | [diff] [blame] | 132 | << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL), true)).set_name("MaxPool_3a_3x3/MaxPool") |
Georgios Pinitas | be2772a | 2018-08-17 15:33:39 +0100 | [diff] [blame] | 133 | // Conv2d_3b_1x1 |
| 134 | << ConvolutionLayer(1U, 1U, 80U, |
| 135 | get_weights_accessor(data_path, "Conv2d_3b_1x1_weights.npy", weights_layout), |
| 136 | std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| 137 | PadStrideInfo(1, 1, 0, 0)) |
| 138 | .set_name("Conv2d_3b_1x1/convolution") |
| 139 | << BatchNormalizationLayer(get_weights_accessor(data_path, "Conv2d_3b_1x1_BatchNorm_moving_mean.npy"), |
| 140 | get_weights_accessor(data_path, "Conv2d_3b_1x1_BatchNorm_moving_variance.npy"), |
| 141 | get_random_accessor(1.f, 1.f), |
| 142 | get_weights_accessor(data_path, "Conv2d_3b_1x1_BatchNorm_beta.npy"), |
| 143 | 0.0010000000474974513f) |
| 144 | .set_name("Conv2d_3b_1x1/BatchNorm") |
| 145 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_3b_1x1/Relu") |
| 146 | // Conv2d_4a_3x3 |
| 147 | << ConvolutionLayer(3U, 3U, 192U, |
| 148 | get_weights_accessor(data_path, "Conv2d_4a_3x3_weights.npy", weights_layout), |
| 149 | std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| 150 | PadStrideInfo(1, 1, 0, 0)) |
| 151 | .set_name("Conv2d_4a_3x3/convolution") |
| 152 | << BatchNormalizationLayer(get_weights_accessor(data_path, "Conv2d_4a_3x3_BatchNorm_moving_mean.npy"), |
| 153 | get_weights_accessor(data_path, "Conv2d_4a_3x3_BatchNorm_moving_variance.npy"), |
| 154 | get_random_accessor(1.f, 1.f), |
| 155 | get_weights_accessor(data_path, "Conv2d_4a_3x3_BatchNorm_beta.npy"), |
| 156 | 0.0010000000474974513f) |
| 157 | .set_name("Conv2d_4a_3x3/BatchNorm") |
| 158 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_4a_3x3/Relu") |
| 159 | // MaxPool_5a_3x3 |
Sang-Hoon Park | 11fedda | 2020-01-15 14:44:04 +0000 | [diff] [blame] | 160 | << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0), true)).set_name("MaxPool_5a_3x3/MaxPool"); |
Georgios Pinitas | be2772a | 2018-08-17 15:33:39 +0100 | [diff] [blame] | 161 | |
| 162 | block_mixed_5b(data_path, weights_layout); |
| 163 | block35_repeat(data_path, weights_layout, 10); |
| 164 | block_mixed_6a(data_path, weights_layout); |
| 165 | block17_repeat(data_path, weights_layout, 20); |
| 166 | block_mixed_7a(data_path, weights_layout); |
| 167 | block8_repeat(data_path, weights_layout, 9, 0.2f, true); |
| 168 | block8_repeat(data_path, weights_layout, 1, 1.f, false); |
| 169 | |
| 170 | // Conv2d_7b_1x1 |
| 171 | graph << ConvolutionLayer(1U, 1U, 1536U, |
| 172 | get_weights_accessor(data_path, "Conv2d_7b_1x1_weights.npy", weights_layout), |
| 173 | std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| 174 | PadStrideInfo(1, 1, 0, 0)) |
| 175 | .set_name("Conv2d_7b_1x1/convolution") |
| 176 | << BatchNormalizationLayer(get_weights_accessor(data_path, "Conv2d_7b_1x1_BatchNorm_moving_mean.npy"), |
| 177 | get_weights_accessor(data_path, "Conv2d_7b_1x1_BatchNorm_moving_variance.npy"), |
| 178 | get_random_accessor(1.f, 1.f), |
| 179 | get_weights_accessor(data_path, "Conv2d_7b_1x1_BatchNorm_beta.npy"), |
| 180 | 0.0010000000474974513f) |
| 181 | .set_name("Conv2d_7b_1x1/BatchNorm") |
| 182 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_7b_1x1/Relu") |
Sang-Hoon Park | 11fedda | 2020-01-15 14:44:04 +0000 | [diff] [blame] | 183 | << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, operation_layout)).set_name("Logits/AvgPool_1a_8x8") |
Georgios Pinitas | be2772a | 2018-08-17 15:33:39 +0100 | [diff] [blame] | 184 | << FlattenLayer().set_name("Logits/Flatten") |
| 185 | << FullyConnectedLayer( |
| 186 | 1001U, |
| 187 | get_weights_accessor(data_path, "Logits_Logits_weights.npy", weights_layout), |
| 188 | get_weights_accessor(data_path, "Logits_Logits_biases.npy")) |
| 189 | .set_name("Logits/Logits") |
| 190 | << SoftmaxLayer().set_name("Logits/Predictions") |
| 191 | << OutputLayer(get_output_accessor(common_params, 5)); |
| 192 | |
| 193 | // Finalize graph |
| 194 | GraphConfig config; |
| 195 | config.num_threads = common_params.threads; |
| 196 | config.use_tuner = common_params.enable_tuner; |
Vidhya Sudhan Loganathan | 050471e | 2019-04-25 09:27:24 +0100 | [diff] [blame] | 197 | config.tuner_mode = common_params.tuner_mode; |
Georgios Pinitas | be2772a | 2018-08-17 15:33:39 +0100 | [diff] [blame] | 198 | config.tuner_file = common_params.tuner_file; |
| 199 | |
| 200 | graph.finalize(common_params.target, config); |
| 201 | |
| 202 | return true; |
| 203 | } |
| 204 | |
| 205 | void do_run() override |
| 206 | { |
| 207 | graph.run(); |
| 208 | } |
| 209 | |
| 210 | private: |
| 211 | CommandLineParser cmd_parser; |
| 212 | CommonGraphOptions common_opts; |
| 213 | CommonGraphParams common_params; |
| 214 | Stream graph; |
| 215 | |
| 216 | private: |
| 217 | void block_mixed_5b(const std::string &data_path, DataLayout weights_layout) |
| 218 | { |
| 219 | // Branch 0 |
| 220 | SubStream i_a(graph); |
| 221 | i_a << ConvolutionLayer(1U, 1U, 96U, |
| 222 | get_weights_accessor(data_path, "Mixed_5b_Branch_0_Conv2d_1x1_weights.npy", weights_layout), |
| 223 | std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| 224 | PadStrideInfo(1, 1, 0, 0)) |
| 225 | .set_name("Mixed_5b/Branch_0/Conv2d_1x1/convolution") |
| 226 | << BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_5b_Branch_0_Conv2d_1x1_BatchNorm_moving_mean.npy"), |
| 227 | get_weights_accessor(data_path, "Mixed_5b_Branch_0_Conv2d_1x1_BatchNorm_moving_variance.npy"), |
| 228 | get_random_accessor(1.f, 1.f), |
| 229 | get_weights_accessor(data_path, "Mixed_5b_Branch_0_Conv2d_1x1_BatchNorm_beta.npy"), |
| 230 | 0.0010000000474974513f) |
| 231 | .set_name("Mixed_5b/Branch_0/Conv2d_1x1/BatchNorm") |
| 232 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_5b/Branch_0/Conv2d_1x1/Relu"); |
| 233 | |
| 234 | // Branch 1 |
| 235 | SubStream i_b(graph); |
| 236 | i_b << ConvolutionLayer(1U, 1U, 48U, |
| 237 | get_weights_accessor(data_path, "Mixed_5b_Branch_1_Conv2d_0a_1x1_weights.npy", weights_layout), |
| 238 | std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| 239 | PadStrideInfo(1, 1, 0, 0)) |
| 240 | .set_name("Mixed_5b/Branch_1/Conv2d_0a_1x1/convolution") |
| 241 | << BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_5b_Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), |
| 242 | get_weights_accessor(data_path, "Mixed_5b_Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), |
| 243 | get_random_accessor(1.f, 1.f), |
| 244 | get_weights_accessor(data_path, "Mixed_5b_Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"), |
| 245 | 0.0010000000474974513f) |
| 246 | .set_name("Mixed_5b/Branch_1/Conv2d_0a_1x1/BatchNorm") |
| 247 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_5b/Branch_1/Conv2d_0a_1x1/Relu") |
| 248 | << ConvolutionLayer(5U, 5U, 64U, |
| 249 | get_weights_accessor(data_path, "Mixed_5b_Branch_1_Conv2d_0b_5x5_weights.npy", weights_layout), |
| 250 | std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| 251 | PadStrideInfo(1, 1, 2, 2)) |
| 252 | .set_name("Mixed_5b/Branch_1/Conv2d_0b_5x5/convolution") |
| 253 | << BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_5b_Branch_1_Conv2d_0b_5x5_BatchNorm_moving_mean.npy"), |
| 254 | get_weights_accessor(data_path, "Mixed_5b_Branch_1_Conv2d_0b_5x5_BatchNorm_moving_variance.npy"), |
| 255 | get_random_accessor(1.f, 1.f), |
| 256 | get_weights_accessor(data_path, "Mixed_5b_Branch_1_Conv2d_0b_5x5_BatchNorm_beta.npy"), |
| 257 | 0.0010000000474974513f) |
| 258 | .set_name("Mixed_5b/Branch_1/Conv2d_0b_5x5/BatchNorm") |
| 259 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_5b/Branch_1/Conv2d_0b_5x5/Relu"); |
| 260 | |
| 261 | // Branch 2 |
| 262 | SubStream i_c(graph); |
| 263 | i_c << ConvolutionLayer(1U, 1U, 64U, |
| 264 | get_weights_accessor(data_path, "Mixed_5b_Branch_2_Conv2d_0a_1x1_weights.npy", weights_layout), |
| 265 | std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| 266 | PadStrideInfo(1, 1, 0, 0)) |
| 267 | .set_name("Mixed_5b/Branch_2/Conv2d_0a_1x1/convolution") |
| 268 | << BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_5b_Branch_2_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), |
| 269 | get_weights_accessor(data_path, "Mixed_5b_Branch_2_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), |
| 270 | get_random_accessor(1.f, 1.f), |
| 271 | get_weights_accessor(data_path, "Mixed_5b_Branch_2_Conv2d_0a_1x1_BatchNorm_beta.npy"), |
| 272 | 0.0010000000474974513f) |
| 273 | .set_name("Mixed_5b/Branch_2/Conv2d_0a_1x1/BatchNorm") |
| 274 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_5b/Branch_2/Conv2d_0a_1x1/Relu") |
| 275 | << ConvolutionLayer(3U, 3U, 96U, |
| 276 | get_weights_accessor(data_path, "Mixed_5b_Branch_2_Conv2d_0b_3x3_weights.npy", weights_layout), |
| 277 | std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| 278 | PadStrideInfo(1, 1, 1, 1)) |
| 279 | .set_name("Mixed_5b/Branch_2/Conv2d_0b_3x3/convolution") |
| 280 | << BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_5b_Branch_2_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"), |
| 281 | get_weights_accessor(data_path, "Mixed_5b_Branch_2_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"), |
| 282 | get_random_accessor(1.f, 1.f), |
| 283 | get_weights_accessor(data_path, "Mixed_5b_Branch_2_Conv2d_0b_3x3_BatchNorm_beta.npy"), |
| 284 | 0.0010000000474974513f) |
| 285 | .set_name("Mixed_5b/Branch_2/Conv2d_0b_3x3/BatchNorm") |
| 286 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_5b/Branch_2/Conv2d_0b_3x3/Relu") |
| 287 | << ConvolutionLayer(3U, 3U, 96U, |
| 288 | get_weights_accessor(data_path, "Mixed_5b_Branch_2_Conv2d_0c_3x3_weights.npy", weights_layout), |
| 289 | std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| 290 | PadStrideInfo(1, 1, 1, 1)) |
| 291 | .set_name("Mixed_5b/Branch_2/Conv2d_0c_3x3/convolution") |
| 292 | << BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_5b_Branch_2_Conv2d_0c_3x3_BatchNorm_moving_mean.npy"), |
| 293 | get_weights_accessor(data_path, "Mixed_5b_Branch_2_Conv2d_0c_3x3_BatchNorm_moving_variance.npy"), |
| 294 | get_random_accessor(1.f, 1.f), |
| 295 | get_weights_accessor(data_path, "Mixed_5b_Branch_2_Conv2d_0c_3x3_BatchNorm_beta.npy"), |
| 296 | 0.0010000000474974513f) |
| 297 | .set_name("Mixed_5b/Branch_2/Conv2d_0c_3x3/BatchNorm") |
| 298 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_5b/Branch_2/Conv2d_0c_3x3/Relu"); |
| 299 | |
| 300 | // Branch 3 |
| 301 | SubStream i_d(graph); |
Sang-Hoon Park | 11fedda | 2020-01-15 14:44:04 +0000 | [diff] [blame] | 302 | i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, common_params.data_layout, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL), true)).set_name("Mixed_5b/Branch_3/AvgPool_0a_3x3") |
Georgios Pinitas | be2772a | 2018-08-17 15:33:39 +0100 | [diff] [blame] | 303 | << ConvolutionLayer(1U, 1U, 64U, |
| 304 | get_weights_accessor(data_path, "Mixed_5b_Branch_3_Conv2d_0b_1x1_weights.npy", weights_layout), |
| 305 | std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| 306 | PadStrideInfo(1, 1, 0, 0)) |
| 307 | .set_name("Mixed_5b/Branch_3/Conv2d_0b_1x1/convolution") |
| 308 | << BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_5b_Branch_3_Conv2d_0b_1x1_BatchNorm_moving_mean.npy"), |
| 309 | get_weights_accessor(data_path, "Mixed_5b_Branch_3_Conv2d_0b_1x1_BatchNorm_moving_variance.npy"), |
| 310 | get_random_accessor(1.f, 1.f), |
| 311 | get_weights_accessor(data_path, "Mixed_5b_Branch_3_Conv2d_0b_1x1_BatchNorm_beta.npy"), |
| 312 | 0.0010000000474974513f) |
| 313 | .set_name("Mixed_5b/Branch_3/Conv2d_0b_1x1/BatchNorm") |
| 314 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_5b/Branch_3/Conv2d_0b_1x1/Relu"); |
| 315 | |
| 316 | // Concatenate |
Georgios Pinitas | 427bbbf | 2018-08-28 13:32:02 +0100 | [diff] [blame] | 317 | graph << ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d)).set_name("Mixed_5a/concat"); |
Georgios Pinitas | be2772a | 2018-08-17 15:33:39 +0100 | [diff] [blame] | 318 | } |
| 319 | |
| 320 | void block_mixed_6a(const std::string &data_path, DataLayout weights_layout) |
| 321 | { |
| 322 | // Branch 0 |
| 323 | SubStream i_a(graph); |
| 324 | i_a << ConvolutionLayer(3U, 3U, 384U, |
| 325 | get_weights_accessor(data_path, "Mixed_6a_Branch_0_Conv2d_1a_3x3_weights.npy", weights_layout), |
| 326 | std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| 327 | PadStrideInfo(2, 2, 0, 0)) |
| 328 | .set_name("Mixed_6a/Branch_0/Conv2d_1a_3x3/convolution") |
| 329 | << BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_6a_Branch_0_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"), |
| 330 | get_weights_accessor(data_path, "Mixed_6a_Branch_0_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"), |
| 331 | get_random_accessor(1.f, 1.f), |
| 332 | get_weights_accessor(data_path, "Mixed_6a_Branch_0_Conv2d_1a_3x3_BatchNorm_beta.npy"), |
| 333 | 0.0010000000474974513f) |
| 334 | .set_name("Mixed_6a/Branch_0/Conv2d_1a_3x3/BatchNorm") |
| 335 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_6a/Branch_0/Conv2d_1a_3x3/Relu"); |
| 336 | |
| 337 | // Branch 1 |
| 338 | SubStream i_b(graph); |
| 339 | i_b << ConvolutionLayer(1U, 1U, 256U, |
| 340 | get_weights_accessor(data_path, "Mixed_6a_Branch_1_Conv2d_0a_1x1_weights.npy", weights_layout), |
| 341 | std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| 342 | PadStrideInfo(1, 1, 0, 0)) |
| 343 | .set_name("Mixed_6a/Branch_1/Conv2d_0a_1x1/convolution") |
| 344 | << BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_6a_Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), |
| 345 | get_weights_accessor(data_path, "Mixed_6a_Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), |
| 346 | get_random_accessor(1.f, 1.f), |
| 347 | get_weights_accessor(data_path, "Mixed_6a_Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"), |
| 348 | 0.0010000000474974513f) |
| 349 | .set_name("Mixed_6a/Branch_1/Conv2d_0a_1x1/BatchNorm") |
| 350 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_6a/Branch_1/Conv2d_0a_1x1/Relu") |
| 351 | << ConvolutionLayer(3U, 3U, 256U, |
| 352 | get_weights_accessor(data_path, "Mixed_6a_Branch_1_Conv2d_0b_3x3_weights.npy", weights_layout), |
| 353 | std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| 354 | PadStrideInfo(1, 1, 1, 1)) |
| 355 | .set_name("Mixed_6a/Branch_1/Conv2d_0b_3x3/convolution") |
| 356 | << BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_6a_Branch_1_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"), |
| 357 | get_weights_accessor(data_path, "Mixed_6a_Branch_1_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"), |
| 358 | get_random_accessor(1.f, 1.f), |
| 359 | get_weights_accessor(data_path, "Mixed_6a_Branch_1_Conv2d_0b_3x3_BatchNorm_beta.npy"), |
| 360 | 0.0010000000474974513f) |
| 361 | .set_name("Mixed_6a/Branch_1/Conv2d_0b_3x3/BatchNorm") |
| 362 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_6a/Branch_1/Conv2d_0b_3x3/Relu") |
| 363 | << ConvolutionLayer(3U, 3U, 384U, |
| 364 | get_weights_accessor(data_path, "Mixed_6a_Branch_1_Conv2d_1a_3x3_weights.npy", weights_layout), |
| 365 | std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| 366 | PadStrideInfo(2, 2, 0, 0)) |
| 367 | .set_name("Mixed_6a/Branch_1/Conv2d_1a_3x3/convolution") |
| 368 | << BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_6a_Branch_1_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"), |
| 369 | get_weights_accessor(data_path, "Mixed_6a_Branch_1_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"), |
| 370 | get_random_accessor(1.f, 1.f), |
| 371 | get_weights_accessor(data_path, "Mixed_6a_Branch_1_Conv2d_1a_3x3_BatchNorm_beta.npy"), |
| 372 | 0.0010000000474974513f) |
| 373 | .set_name("Mixed_6a/Branch_1/Conv2d_1a_3x3/BatchNorm") |
| 374 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_6a/Branch_1/Conv2d_1a_3x3/Relu"); |
| 375 | |
| 376 | // Branch 2 |
| 377 | SubStream i_c(graph); |
Sang-Hoon Park | 11fedda | 2020-01-15 14:44:04 +0000 | [diff] [blame] | 378 | i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, common_params.data_layout, PadStrideInfo(2, 2, 0, 0), true)).set_name("Mixed_6a/Branch_2/MaxPool_1a_3x3"); |
Georgios Pinitas | be2772a | 2018-08-17 15:33:39 +0100 | [diff] [blame] | 379 | |
| 380 | // Concatenate |
Georgios Pinitas | 427bbbf | 2018-08-28 13:32:02 +0100 | [diff] [blame] | 381 | graph << ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c)).set_name("Mixed_6a/concat"); |
Georgios Pinitas | be2772a | 2018-08-17 15:33:39 +0100 | [diff] [blame] | 382 | } |
| 383 | |
| 384 | void block_mixed_7a(const std::string &data_path, DataLayout weights_layout) |
| 385 | { |
| 386 | // Branch 0 |
| 387 | SubStream i_a(graph); |
| 388 | i_a << ConvolutionLayer(1U, 1U, 256U, |
| 389 | get_weights_accessor(data_path, "Mixed_7a_Branch_0_Conv2d_0a_1x1_weights.npy", weights_layout), |
| 390 | std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| 391 | PadStrideInfo(1, 1, 0, 0)) |
| 392 | .set_name("Mixed_7a/Branch_0/Conv2d_0a_1x1/convolution") |
| 393 | << BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_7a_Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), |
| 394 | get_weights_accessor(data_path, "Mixed_7a_Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), |
| 395 | get_random_accessor(1.f, 1.f), |
| 396 | get_weights_accessor(data_path, "Mixed_7a_Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy"), |
| 397 | 0.0010000000474974513f) |
| 398 | .set_name("Mixed_7a/Branch_0/Conv2d_0a_1x1/BatchNorm") |
| 399 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_7a/Branch_0/Conv2d_0a_1x1/Relu") |
| 400 | << ConvolutionLayer(3U, 3U, 384U, |
| 401 | get_weights_accessor(data_path, "Mixed_7a_Branch_0_Conv2d_1a_3x3_weights.npy", weights_layout), |
| 402 | std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| 403 | PadStrideInfo(2, 2, 0, 0)) |
| 404 | .set_name("Mixed_7a/Branch_0/Conv2d_1a_3x3/convolution") |
| 405 | << BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_7a_Branch_0_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"), |
| 406 | get_weights_accessor(data_path, "Mixed_7a_Branch_0_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"), |
| 407 | get_random_accessor(1.f, 1.f), |
| 408 | get_weights_accessor(data_path, "Mixed_7a_Branch_0_Conv2d_1a_3x3_BatchNorm_beta.npy"), |
| 409 | 0.0010000000474974513f) |
| 410 | .set_name("Mixed_7a/Branch_0/Conv2d_1a_3x3/BatchNorm") |
| 411 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_7a/Branch_0/Conv2d_1a_3x3/Relu"); |
| 412 | |
| 413 | // Branch 1 |
| 414 | SubStream i_b(graph); |
| 415 | i_b << ConvolutionLayer(1U, 1U, 256U, |
| 416 | get_weights_accessor(data_path, "Mixed_7a_Branch_1_Conv2d_0a_1x1_weights.npy", weights_layout), |
| 417 | std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| 418 | PadStrideInfo(1, 1, 0, 0)) |
| 419 | .set_name("Mixed_7a/Branch_1/Conv2d_0a_1x1/convolution") |
| 420 | << BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_7a_Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), |
| 421 | get_weights_accessor(data_path, "Mixed_7a_Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), |
| 422 | get_random_accessor(1.f, 1.f), |
| 423 | get_weights_accessor(data_path, "Mixed_7a_Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"), |
| 424 | 0.0010000000474974513f) |
| 425 | .set_name("Mixed_7a/Branch_1/Conv2d_0a_1x1/BatchNorm") |
| 426 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_7a/Branch_1/Conv2d_0a_1x1/Relu") |
| 427 | << ConvolutionLayer(3U, 3U, 288U, |
| 428 | get_weights_accessor(data_path, "Mixed_7a_Branch_1_Conv2d_1a_3x3_weights.npy", weights_layout), |
| 429 | std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| 430 | PadStrideInfo(2, 2, 0, 0)) |
| 431 | .set_name("Mixed_7a/Branch_1/Conv2d_1a_3x3/convolution") |
| 432 | << BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_7a_Branch_1_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"), |
| 433 | get_weights_accessor(data_path, "Mixed_7a_Branch_1_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"), |
| 434 | get_random_accessor(1.f, 1.f), |
| 435 | get_weights_accessor(data_path, "Mixed_7a_Branch_1_Conv2d_1a_3x3_BatchNorm_beta.npy"), |
| 436 | 0.0010000000474974513f) |
| 437 | .set_name("Mixed_7a/Branch_1/Conv2d_1a_3x3/BatchNorm") |
| 438 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_7a/Branch_1/Conv2d_1a_3x3/Relu"); |
| 439 | |
| 440 | // Branch 2 |
| 441 | SubStream i_c(graph); |
| 442 | i_c << ConvolutionLayer(1U, 1U, 256U, |
| 443 | get_weights_accessor(data_path, "Mixed_7a_Branch_2_Conv2d_0a_1x1_weights.npy", weights_layout), |
| 444 | std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| 445 | PadStrideInfo(1, 1, 0, 0)) |
| 446 | .set_name("Mixed_7a/Branch_2/Conv2d_0a_1x1/convolution") |
| 447 | << BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_7a_Branch_2_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), |
| 448 | get_weights_accessor(data_path, "Mixed_7a_Branch_2_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), |
| 449 | get_random_accessor(1.f, 1.f), |
| 450 | get_weights_accessor(data_path, "Mixed_7a_Branch_2_Conv2d_0a_1x1_BatchNorm_beta.npy"), |
| 451 | 0.0010000000474974513f) |
| 452 | .set_name("Mixed_7a/Branch_2/Conv2d_0a_1x1/BatchNorm") |
| 453 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_7a/Branch_2/Conv2d_0a_1x1/Relu") |
| 454 | << ConvolutionLayer(3U, 3U, 288U, |
| 455 | get_weights_accessor(data_path, "Mixed_7a_Branch_2_Conv2d_0b_3x3_weights.npy", weights_layout), |
| 456 | std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| 457 | PadStrideInfo(1, 1, 1, 1)) |
| 458 | .set_name("Mixed_7a/Branch_2/Conv2d_0b_3x3/convolution") |
| 459 | << BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_7a_Branch_2_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"), |
| 460 | get_weights_accessor(data_path, "Mixed_7a_Branch_2_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"), |
| 461 | get_random_accessor(1.f, 1.f), |
| 462 | get_weights_accessor(data_path, "Mixed_7a_Branch_2_Conv2d_0b_3x3_BatchNorm_beta.npy"), |
| 463 | 0.0010000000474974513f) |
| 464 | .set_name("Mixed_7a/Branch_2/Conv2d_0b_3x3/BatchNorm") |
| 465 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_7a/Branch_2/Conv2d_0b_3x3/Relu") |
| 466 | << ConvolutionLayer(3U, 3U, 320U, |
| 467 | get_weights_accessor(data_path, "Mixed_7a_Branch_2_Conv2d_1a_3x3_weights.npy", weights_layout), |
| 468 | std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| 469 | PadStrideInfo(2, 2, 0, 0)) |
| 470 | .set_name("Mixed_7a/Branch_2/Conv2d_1a_3x3/convolution") |
| 471 | << BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_7a_Branch_2_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"), |
| 472 | get_weights_accessor(data_path, "Mixed_7a_Branch_2_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"), |
| 473 | get_random_accessor(1.f, 1.f), |
| 474 | get_weights_accessor(data_path, "Mixed_7a_Branch_2_Conv2d_1a_3x3_BatchNorm_beta.npy"), |
| 475 | 0.0010000000474974513f) |
| 476 | .set_name("Mixed_7a/Branch_2/Conv2d_1a_3x3/BatchNorm") |
| 477 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_7a/Branch_2/Conv2d_1a_3x3/Relu"); |
| 478 | |
| 479 | // Branch 3 |
| 480 | SubStream i_d(graph); |
Sang-Hoon Park | 11fedda | 2020-01-15 14:44:04 +0000 | [diff] [blame] | 481 | i_d << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, common_params.data_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL), true)).set_name("Mixed_7a/Branch_3/MaxPool_1a_3x3"); |
Georgios Pinitas | be2772a | 2018-08-17 15:33:39 +0100 | [diff] [blame] | 482 | |
| 483 | // Concatenate |
Georgios Pinitas | 427bbbf | 2018-08-28 13:32:02 +0100 | [diff] [blame] | 484 | graph << ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d)).set_name("Mixed_7a/concat"); |
Georgios Pinitas | be2772a | 2018-08-17 15:33:39 +0100 | [diff] [blame] | 485 | } |
| 486 | |
| 487 | void block35_repeat(const std::string &data_path, DataLayout weights_layout, unsigned int num_blocks) |
| 488 | { |
| 489 | for(unsigned int i = 0; i < num_blocks; ++i) |
| 490 | { |
| 491 | std::stringstream unit_path_ss; |
| 492 | unit_path_ss << "Repeat_block35_" << (i + 1) << "_"; |
| 493 | std::stringstream unit_name_ss; |
| 494 | unit_name_ss << "Repeat/block35_" << (i + 1) << "/"; |
| 495 | |
| 496 | std::string unit_path = unit_path_ss.str(); |
| 497 | std::string unit_name = unit_name_ss.str(); |
| 498 | |
| 499 | // Create left and write substreams |
| 500 | SubStream i_l(graph); |
| 501 | SubStream i_r(graph); |
| 502 | |
| 503 | // Branch 0 |
| 504 | SubStream i_la(i_l); |
| 505 | i_la << ConvolutionLayer(1U, 1U, 32U, |
| 506 | get_weights_accessor(data_path, unit_path + "Branch_0_Conv2d_1x1_weights.npy", weights_layout), |
| 507 | std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| 508 | PadStrideInfo(1, 1, 0, 0)) |
| 509 | .set_name(unit_name + "Branch_0/Conv2d_1x1/convolution") |
| 510 | << BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_0_Conv2d_1x1_BatchNorm_moving_mean.npy"), |
| 511 | get_weights_accessor(data_path, unit_path + "Branch_0_Conv2d_1x1_BatchNorm_moving_variance.npy"), |
| 512 | get_random_accessor(1.f, 1.f), |
| 513 | get_weights_accessor(data_path, unit_path + "Branch_0_Conv2d_1x1_BatchNorm_beta.npy"), |
| 514 | 0.0010000000474974513f) |
| 515 | .set_name(unit_name + "Branch_0/Conv2d_1x1/BatchNorm") |
| 516 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_0/Conv2d_1x1/Relu"); |
| 517 | |
| 518 | // Branch 1 |
| 519 | SubStream i_lb(i_l); |
| 520 | i_lb << ConvolutionLayer(1U, 1U, 32U, |
| 521 | get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0a_1x1_weights.npy", weights_layout), |
| 522 | std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| 523 | PadStrideInfo(1, 1, 0, 0)) |
| 524 | .set_name(unit_name + "Branch_1/Conv2d_0a_1x1/convolution") |
| 525 | << BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), |
| 526 | get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), |
| 527 | get_random_accessor(1.f, 1.f), |
| 528 | get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"), |
| 529 | 0.0010000000474974513f) |
| 530 | .set_name(unit_name + "Branch_1/Conv2d_0a_1x1/BatchNorm") |
| 531 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_1/Conv2d_0a_1x1/Relu") |
| 532 | << ConvolutionLayer(3U, 3U, 32U, |
| 533 | get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0b_3x3_weights.npy", weights_layout), |
| 534 | std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| 535 | PadStrideInfo(1, 1, 1, 1)) |
| 536 | .set_name(unit_name + "Branch_1/Conv2d_0b_3x3/convolution") |
| 537 | << BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"), |
| 538 | get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"), |
| 539 | get_random_accessor(1.f, 1.f), |
| 540 | get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_beta.npy"), |
| 541 | 0.0010000000474974513f) |
| 542 | .set_name(unit_name + "Branch_1/Conv2d_0b_3x3/BatchNorm") |
| 543 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_1/Conv2d_0b_3x3/Relu"); |
| 544 | |
| 545 | // Branch 2 |
| 546 | SubStream i_lc(i_l); |
| 547 | i_lc << ConvolutionLayer(1U, 1U, 32U, |
| 548 | get_weights_accessor(data_path, unit_path + "Branch_2_Conv2d_0a_1x1_weights.npy", weights_layout), |
| 549 | std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| 550 | PadStrideInfo(1, 1, 0, 0)) |
| 551 | .set_name(unit_name + "Branch_2/Conv2d_0a_1x1/convolution") |
| 552 | << BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), |
| 553 | get_weights_accessor(data_path, unit_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), |
| 554 | get_random_accessor(1.f, 1.f), |
| 555 | get_weights_accessor(data_path, unit_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_beta.npy"), |
| 556 | 0.0010000000474974513f) |
| 557 | .set_name(unit_name + "Branch_2/Conv2d_0a_1x1/BatchNorm") |
| 558 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_2/Conv2d_0a_1x1/Relu") |
| 559 | << ConvolutionLayer(3U, 3U, 48U, |
| 560 | get_weights_accessor(data_path, unit_path + "Branch_2_Conv2d_0b_3x3_weights.npy", weights_layout), |
| 561 | std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| 562 | PadStrideInfo(1, 1, 1, 1)) |
| 563 | .set_name(unit_name + "Branch_2/Conv2d_0b_3x3/convolution") |
| 564 | << BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"), |
| 565 | get_weights_accessor(data_path, unit_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"), |
| 566 | get_random_accessor(1.f, 1.f), |
| 567 | get_weights_accessor(data_path, unit_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_beta.npy"), |
| 568 | 0.0010000000474974513f) |
| 569 | .set_name(unit_name + "Branch_2/Conv2d_0b_3x3/BatchNorm") |
| 570 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_2/Conv2d_0b_3x3/Relu") |
| 571 | << ConvolutionLayer(3U, 3U, 64U, |
| 572 | get_weights_accessor(data_path, unit_path + "Branch_2_Conv2d_0c_3x3_weights.npy", weights_layout), |
| 573 | std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| 574 | PadStrideInfo(1, 1, 1, 1)) |
| 575 | .set_name(unit_name + "Branch_2/Conv2d_0c_3x3/convolution") |
| 576 | << BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_2_Conv2d_0c_3x3_BatchNorm_moving_mean.npy"), |
| 577 | get_weights_accessor(data_path, unit_path + "Branch_2_Conv2d_0c_3x3_BatchNorm_moving_variance.npy"), |
| 578 | get_random_accessor(1.f, 1.f), |
| 579 | get_weights_accessor(data_path, unit_path + "Branch_2_Conv2d_0c_3x3_BatchNorm_beta.npy"), |
| 580 | 0.0010000000474974513f) |
| 581 | .set_name(unit_name + "Branch_2/Conv2d_0c_3x3/BatchNorm") |
| 582 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_2/Conv2d_0c_3x3/Relu"); |
| 583 | |
| 584 | // Concatenate |
Georgios Pinitas | 427bbbf | 2018-08-28 13:32:02 +0100 | [diff] [blame] | 585 | i_l << ConcatLayer(std::move(i_la), std::move(i_lb), std::move(i_lc)).set_name(unit_name + "concat") |
Georgios Pinitas | be2772a | 2018-08-17 15:33:39 +0100 | [diff] [blame] | 586 | << ConvolutionLayer(1U, 1U, 320U, |
| 587 | get_weights_accessor(data_path, unit_path + "Conv2d_1x1_weights.npy", weights_layout), |
| 588 | get_weights_accessor(data_path, unit_path + "Conv2d_1x1_biases.npy", weights_layout), |
| 589 | PadStrideInfo(1, 1, 0, 0)) |
| 590 | .set_name(unit_name + "Conv2d_1x1/convolution") |
| 591 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 0.17f, 0.f)).set_name(unit_name + "mul"); |
| 592 | |
Georgios Pinitas | 427bbbf | 2018-08-28 13:32:02 +0100 | [diff] [blame] | 593 | graph << EltwiseLayer(std::move(i_l), std::move(i_r), EltwiseOperation::Add).set_name(unit_name + "add") |
Georgios Pinitas | be2772a | 2018-08-17 15:33:39 +0100 | [diff] [blame] | 594 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Relu"); |
| 595 | } |
| 596 | } |
| 597 | |
| 598 | void block17_repeat(const std::string &data_path, DataLayout weights_layout, unsigned int num_blocks) |
| 599 | { |
| 600 | for(unsigned int i = 0; i < num_blocks; ++i) |
| 601 | { |
| 602 | std::stringstream unit_path_ss; |
| 603 | unit_path_ss << "Repeat_1_block17_" << (i + 1) << "_"; |
| 604 | std::stringstream unit_name_ss; |
| 605 | unit_name_ss << "Repeat_1/block17_" << (i + 1) << "/"; |
| 606 | |
| 607 | std::string unit_path = unit_path_ss.str(); |
| 608 | std::string unit_name = unit_name_ss.str(); |
| 609 | |
| 610 | // Create left and write substreams |
| 611 | SubStream i_l(graph); |
| 612 | SubStream i_r(graph); |
| 613 | |
| 614 | // Branch 0 |
| 615 | SubStream i_la(i_l); |
| 616 | i_la << ConvolutionLayer(1U, 1U, 192U, |
| 617 | get_weights_accessor(data_path, unit_path + "Branch_0_Conv2d_1x1_weights.npy", weights_layout), |
| 618 | std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| 619 | PadStrideInfo(1, 1, 0, 0)) |
| 620 | .set_name(unit_name + "Branch_0/Conv2d_1x1/convolution") |
| 621 | << BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_0_Conv2d_1x1_BatchNorm_moving_mean.npy"), |
| 622 | get_weights_accessor(data_path, unit_path + "Branch_0_Conv2d_1x1_BatchNorm_moving_variance.npy"), |
| 623 | get_random_accessor(1.f, 1.f), |
| 624 | get_weights_accessor(data_path, unit_path + "Branch_0_Conv2d_1x1_BatchNorm_beta.npy"), |
| 625 | 0.0010000000474974513f) |
| 626 | .set_name(unit_name + "Branch_0/Conv2d_1x1/BatchNorm") |
| 627 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_0/Conv2d_1x1/Relu"); |
| 628 | |
| 629 | // Branch 1 |
| 630 | SubStream i_lb(i_l); |
| 631 | i_lb << ConvolutionLayer(1U, 1U, 128U, |
| 632 | get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0a_1x1_weights.npy", weights_layout), |
| 633 | std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| 634 | PadStrideInfo(1, 1, 0, 0)) |
| 635 | .set_name(unit_name + "Branch_1/Conv2d_0a_1x1/convolution") |
| 636 | << BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), |
| 637 | get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), |
| 638 | get_random_accessor(1.f, 1.f), |
| 639 | get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"), |
| 640 | 0.0010000000474974513f) |
| 641 | .set_name(unit_name + "Branch_1/Conv2d_0a_1x1/BatchNorm") |
| 642 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_1/Conv2d_0a_1x1/Relu") |
| 643 | << ConvolutionLayer(7U, 1U, 160U, |
| 644 | get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0b_1x7_weights.npy", weights_layout), |
| 645 | std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| 646 | PadStrideInfo(1, 1, 3, 0)) |
| 647 | .set_name(unit_name + "Branch_1/Conv2d_0b_1x7/convolution") |
| 648 | << BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_mean.npy"), |
| 649 | get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_variance.npy"), |
| 650 | get_random_accessor(1.f, 1.f), |
| 651 | get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_beta.npy"), |
| 652 | 0.0010000000474974513f) |
| 653 | .set_name(unit_name + "Branch_1/Conv2d_0b_1x7/BatchNorm") |
| 654 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_1/Conv2d_0b_1x7/Relu") |
| 655 | << ConvolutionLayer(1U, 7U, 192U, |
| 656 | get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0c_7x1_weights.npy", weights_layout), |
| 657 | std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| 658 | PadStrideInfo(1, 1, 0, 3)) |
| 659 | .set_name(unit_name + "Branch_1/Conv2d_0c_7x1/convolution") |
| 660 | << BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_mean.npy"), |
| 661 | get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_variance.npy"), |
| 662 | get_random_accessor(1.f, 1.f), |
| 663 | get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_beta.npy"), |
| 664 | 0.0010000000474974513f) |
| 665 | .set_name(unit_name + "Branch_1/Conv2d_0c_7x1/BatchNorm") |
| 666 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_1/Conv2d_0c_7x1/Relu"); |
| 667 | |
| 668 | // Concatenate |
Georgios Pinitas | 427bbbf | 2018-08-28 13:32:02 +0100 | [diff] [blame] | 669 | i_l << ConcatLayer(std::move(i_la), std::move(i_lb)).set_name(unit_name + "concat") |
Georgios Pinitas | be2772a | 2018-08-17 15:33:39 +0100 | [diff] [blame] | 670 | << ConvolutionLayer(1U, 1U, 1088U, |
| 671 | get_weights_accessor(data_path, unit_path + "Conv2d_1x1_weights.npy", weights_layout), |
| 672 | get_weights_accessor(data_path, unit_path + "Conv2d_1x1_biases.npy", weights_layout), |
| 673 | PadStrideInfo(1, 1, 0, 0)) |
| 674 | .set_name(unit_name + "Conv2d_1x1/convolution") |
| 675 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 0.10f, 0.f)).set_name(unit_name + "mul"); |
| 676 | |
Georgios Pinitas | 427bbbf | 2018-08-28 13:32:02 +0100 | [diff] [blame] | 677 | graph << EltwiseLayer(std::move(i_l), std::move(i_r), EltwiseOperation::Add).set_name(unit_name + "add") |
Georgios Pinitas | be2772a | 2018-08-17 15:33:39 +0100 | [diff] [blame] | 678 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Relu"); |
| 679 | } |
| 680 | } |
| 681 | |
| 682 | void block8_repeat(const std::string &data_path, DataLayout weights_layout, unsigned int num_blocks, float scale, bool has_activation) |
| 683 | { |
| 684 | for(unsigned int i = 0; i < num_blocks; ++i) |
| 685 | { |
| 686 | std::stringstream unit_path_ss; |
| 687 | std::stringstream unit_name_ss; |
| 688 | if(num_blocks != 1) |
| 689 | { |
| 690 | unit_path_ss << "Repeat_2_block8_" << (i + 1) << "_"; |
| 691 | unit_name_ss << "Repeat_2/block8_" << (i + 1) << "/"; |
| 692 | } |
| 693 | else |
| 694 | { |
| 695 | unit_path_ss << "Block8_"; |
| 696 | unit_name_ss << "Block8/"; |
| 697 | } |
| 698 | |
| 699 | std::string unit_path = unit_path_ss.str(); |
| 700 | std::string unit_name = unit_name_ss.str(); |
| 701 | |
| 702 | // Create left and write substreams |
| 703 | SubStream i_l(graph); |
| 704 | SubStream i_r(graph); |
| 705 | |
| 706 | // Branch 0 |
| 707 | SubStream i_la(i_l); |
| 708 | i_la << ConvolutionLayer(1U, 1U, 192U, |
| 709 | get_weights_accessor(data_path, unit_path + "Branch_0_Conv2d_1x1_weights.npy", weights_layout), |
| 710 | std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| 711 | PadStrideInfo(1, 1, 0, 0)) |
| 712 | .set_name(unit_name + "Branch_0/Conv2d_1x1/convolution") |
| 713 | << BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_0_Conv2d_1x1_BatchNorm_moving_mean.npy"), |
| 714 | get_weights_accessor(data_path, unit_path + "Branch_0_Conv2d_1x1_BatchNorm_moving_variance.npy"), |
| 715 | get_random_accessor(1.f, 1.f), |
| 716 | get_weights_accessor(data_path, unit_path + "Branch_0_Conv2d_1x1_BatchNorm_beta.npy"), |
| 717 | 0.0010000000474974513f) |
| 718 | .set_name(unit_name + "Branch_0/Conv2d_1x1/BatchNorm") |
| 719 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_0/Conv2d_1x1/Relu"); |
| 720 | |
| 721 | // Branch 1 |
| 722 | SubStream i_lb(i_l); |
| 723 | i_lb << ConvolutionLayer(1U, 1U, 192U, |
| 724 | get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0a_1x1_weights.npy", weights_layout), |
| 725 | std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| 726 | PadStrideInfo(1, 1, 0, 0)) |
| 727 | .set_name(unit_name + "Branch_1/Conv2d_0a_1x1/convolution") |
| 728 | << BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"), |
| 729 | get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"), |
| 730 | get_random_accessor(1.f, 1.f), |
| 731 | get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"), |
| 732 | 0.0010000000474974513f) |
| 733 | .set_name(unit_name + "Branch_1/Conv2d_0a_1x1/BatchNorm") |
| 734 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_1/Conv2d_0a_1x1/Relu") |
| 735 | << ConvolutionLayer(3U, 1U, 224U, |
| 736 | get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0b_1x3_weights.npy", weights_layout), |
| 737 | std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| 738 | PadStrideInfo(1, 1, 1, 0)) |
| 739 | .set_name(unit_name + "Branch_1/Conv2d_0b_1x3/convolution") |
| 740 | << BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0b_1x3_BatchNorm_moving_mean.npy"), |
| 741 | get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0b_1x3_BatchNorm_moving_variance.npy"), |
| 742 | get_random_accessor(1.f, 1.f), |
| 743 | get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0b_1x3_BatchNorm_beta.npy"), |
| 744 | 0.0010000000474974513f) |
| 745 | .set_name(unit_name + "Branch_1/Conv2d_0b_1x3/BatchNorm") |
| 746 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_1/Conv2d_0b_1x3/Relu") |
| 747 | << ConvolutionLayer(1U, 3U, 256U, |
| 748 | get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0c_3x1_weights.npy", weights_layout), |
| 749 | std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), |
| 750 | PadStrideInfo(1, 1, 0, 1)) |
| 751 | .set_name(unit_name + "Branch_1/Conv2d_0c_3x1/convolution") |
| 752 | << BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0c_3x1_BatchNorm_moving_mean.npy"), |
| 753 | get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0c_3x1_BatchNorm_moving_variance.npy"), |
| 754 | get_random_accessor(1.f, 1.f), |
| 755 | get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0c_3x1_BatchNorm_beta.npy"), |
| 756 | 0.0010000000474974513f) |
| 757 | .set_name(unit_name + "Branch_1/Conv2d_0c_3x1/BatchNorm") |
| 758 | << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_1/Conv2d_0c_3x1/Relu"); |
| 759 | |
| 760 | // Concatenate |
Georgios Pinitas | 427bbbf | 2018-08-28 13:32:02 +0100 | [diff] [blame] | 761 | i_l << ConcatLayer(std::move(i_la), std::move(i_lb)).set_name(unit_name + "concat") |
Georgios Pinitas | be2772a | 2018-08-17 15:33:39 +0100 | [diff] [blame] | 762 | << ConvolutionLayer(1U, 1U, 2080U, |
| 763 | get_weights_accessor(data_path, unit_path + "Conv2d_1x1_weights.npy", weights_layout), |
| 764 | get_weights_accessor(data_path, unit_path + "Conv2d_1x1_biases.npy", weights_layout), |
| 765 | PadStrideInfo(1, 1, 0, 0)) |
| 766 | .set_name(unit_name + "Conv2d_1x1/convolution"); |
| 767 | |
| 768 | // Scale result |
| 769 | if(scale != 1.f) |
| 770 | { |
| 771 | i_l << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, scale, 0.f)).set_name(unit_name + "mul"); |
| 772 | } |
| 773 | |
| 774 | // Residual add |
Georgios Pinitas | 427bbbf | 2018-08-28 13:32:02 +0100 | [diff] [blame] | 775 | graph << EltwiseLayer(std::move(i_l), std::move(i_r), EltwiseOperation::Add).set_name(unit_name + "add"); |
Georgios Pinitas | be2772a | 2018-08-17 15:33:39 +0100 | [diff] [blame] | 776 | |
| 777 | // Apply activation if needed |
| 778 | if(has_activation) |
| 779 | { |
| 780 | graph << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Relu"); |
| 781 | } |
| 782 | } |
| 783 | } |
| 784 | }; |
| 785 | |
| 786 | /** Main program for Inception ResNet V2 |
| 787 | * |
Georgios Pinitas | bdbbbe8 | 2018-11-07 16:06:47 +0000 | [diff] [blame] | 788 | * Model is based on: |
| 789 | * https://arxiv.org/abs/1602.07261 |
| 790 | * "Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning" |
| 791 | * Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi |
| 792 | * |
Georgios Pinitas | 588ebc5 | 2018-12-21 13:39:07 +0000 | [diff] [blame] | 793 | * Provenance: download.tensorflow.org/models/inception_resnet_v2_2016_08_30.tar.gz |
| 794 | * |
Georgios Pinitas | be2772a | 2018-08-17 15:33:39 +0100 | [diff] [blame] | 795 | * @note To list all the possible arguments execute the binary appended with the --help option |
| 796 | * |
| 797 | * @param[in] argc Number of arguments |
| 798 | * @param[in] argv Arguments |
| 799 | */ |
| 800 | int main(int argc, char **argv) |
| 801 | { |
| 802 | return arm_compute::utils::run_example<InceptionResNetV2Example>(argc, argv); |
| 803 | } |