1 /*
2 * Copyright (c) 2018-2021 Arm Limited.
3 *
4 * SPDX-License-Identifier: MIT
5 *
6 * Permission is hereby granted, free of charge, to any person obtaining a copy
7 * of this software and associated documentation files (the "Software"), to
8 * deal in the Software without restriction, including without limitation the
9 * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
10 * sell copies of the Software, and to permit persons to whom the Software is
11 * furnished to do so, subject to the following conditions:
12 *
13 * The above copyright notice and this permission notice shall be included in all
14 * copies or substantial portions of the Software.
15 *
16 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
17 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
18 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
19 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
20 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
21 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
22 * SOFTWARE.
23 */
24 #include "arm_compute/graph.h"
25 #ifdef ARM_COMPUTE_CL
26 #include "arm_compute/runtime/CL/Utils.h"
27 #endif /* ARM_COMPUTE_CL */
28 #include "support/ToolchainSupport.h"
29 #include "utils/CommonGraphOptions.h"
30 #include "utils/GraphUtils.h"
31 #include "utils/Utils.h"
32
33 using namespace arm_compute;
34 using namespace arm_compute::utils;
35 using namespace arm_compute::graph::frontend;
36 using namespace arm_compute::graph_utils;
37
38 /** Example demonstrating how to implement InceptionV4's network using the Compute Library's graph API */
39 class InceptionV4Example final : public Example
40 {
41 public:
InceptionV4Example()42 InceptionV4Example()
43 : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "InceptionV4")
44 {
45 }
do_setup(int argc,char ** argv)46 bool do_setup(int argc, char **argv) override
47 {
48 // Parse arguments
49 cmd_parser.parse(argc, argv);
50 cmd_parser.validate();
51
52 // Consume common parameters
53 common_params = consume_common_graph_parameters(common_opts);
54
55 // Return when help menu is requested
56 if(common_params.help)
57 {
58 cmd_parser.print_help(argv[0]);
59 return false;
60 }
61
62 // Print parameter values
63 std::cout << common_params << std::endl;
64
65 // Get trainable parameters data path
66 std::string data_path = common_params.data_path;
67
68 // Create a preprocessor object
69 std::unique_ptr<IPreprocessor> preprocessor = std::make_unique<TFPreproccessor>();
70
71 // Create input descriptor
72 const auto operation_layout = common_params.data_layout;
73 const TensorShape tensor_shape = permute_shape(TensorShape(299U, 299U, 3U, common_params.batches), DataLayout::NCHW, operation_layout);
74 TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_layout);
75
76 // Set weights trained layout
77 const DataLayout weights_layout = DataLayout::NCHW;
78
79 graph << common_params.target
80 << common_params.fast_math_hint
81 << InputLayer(input_descriptor, get_input_accessor(common_params, std::move(preprocessor), false))
82 // Conv2d_1a_3x3
83 << ConvolutionLayer(3U, 3U, 32U,
84 get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_1a_3x3_weights.npy", weights_layout),
85 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(2, 2, 0, 0))
86 .set_name("Conv2d_1a_3x3/Conv2D")
87 << BatchNormalizationLayer(get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_1a_3x3_BatchNorm_moving_mean.npy"),
88 get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_1a_3x3_BatchNorm_moving_variance.npy"),
89 get_random_accessor(1.f, 1.f),
90 get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_1a_3x3_BatchNorm_beta.npy"),
91 0.001f)
92 .set_name("Conv2d_1a_3x3/BatchNorm")
93 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_1a_3x3/Relu")
94 // Conv2d_2a_3x3
95 << ConvolutionLayer(3U, 3U, 32U,
96 get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_2a_3x3_weights.npy", weights_layout),
97 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
98 .set_name("Conv2d_2a_3x3/Conv2D")
99 << BatchNormalizationLayer(get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_2a_3x3_BatchNorm_moving_mean.npy"),
100 get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_2a_3x3_BatchNorm_moving_variance.npy"),
101 get_random_accessor(1.f, 1.f),
102 get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_2a_3x3_BatchNorm_beta.npy"),
103 0.001f)
104 .set_name("Conv2d_2a_3x3/BatchNorm")
105 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_2a_3x3/Relu")
106 // Conv2d_2b_3x3
107 << ConvolutionLayer(3U, 3U, 64U,
108 get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_2b_3x3_weights.npy", weights_layout),
109 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1))
110 .set_name("Conv2d_2b_3x3/Conv2D")
111 << BatchNormalizationLayer(get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_2b_3x3_BatchNorm_moving_mean.npy"),
112 get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_2b_3x3_BatchNorm_moving_variance.npy"),
113 get_random_accessor(1.f, 1.f),
114 get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Conv2d_2b_3x3_BatchNorm_beta.npy"),
115 0.001f)
116 .set_name("Conv2d_2b_3x3/BatchNorm")
117 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_2b_3x3/Relu");
118
119 graph << get_mixed_3a(data_path, weights_layout).set_name("Mixed_3a/concat");
120 graph << get_mixed_4a(data_path, weights_layout).set_name("Mixed_4a/concat");
121 graph << get_mixed_5a(data_path, weights_layout).set_name("Mixed_5a/concat");
122 // 4 inception A blocks
123 graph << get_inceptionA_block(data_path, weights_layout, "Mixed_5b").set_name("Mixed_5b/concat");
124 graph << get_inceptionA_block(data_path, weights_layout, "Mixed_5c").set_name("Mixed_5c/concat");
125 graph << get_inceptionA_block(data_path, weights_layout, "Mixed_5d").set_name("Mixed_5d/concat");
126 graph << get_inceptionA_block(data_path, weights_layout, "Mixed_5e").set_name("Mixed_5e/concat");
127 // reduction A block
128 graph << get_reductionA_block(data_path, weights_layout).set_name("Mixed_6a/concat");
129 // 7 inception B blocks
130 graph << get_inceptionB_block(data_path, weights_layout, "Mixed_6b").set_name("Mixed_6b/concat");
131 graph << get_inceptionB_block(data_path, weights_layout, "Mixed_6c").set_name("Mixed_6c/concat");
132 graph << get_inceptionB_block(data_path, weights_layout, "Mixed_6d").set_name("Mixed_6d/concat");
133 graph << get_inceptionB_block(data_path, weights_layout, "Mixed_6e").set_name("Mixed_6e/concat");
134 graph << get_inceptionB_block(data_path, weights_layout, "Mixed_6f").set_name("Mixed_6f/concat");
135 graph << get_inceptionB_block(data_path, weights_layout, "Mixed_6g").set_name("Mixed_6g/concat");
136 graph << get_inceptionB_block(data_path, weights_layout, "Mixed_6h").set_name("Mixed_6h/concat");
137 // reduction B block
138 graph << get_reductionB_block(data_path, weights_layout).set_name("Mixed_7a/concat");
139 // 3 inception C blocks
140 graph << get_inceptionC_block(data_path, weights_layout, "Mixed_7b").set_name("Mixed_7b/concat");
141 graph << get_inceptionC_block(data_path, weights_layout, "Mixed_7c").set_name("Mixed_7c/concat");
142 graph << get_inceptionC_block(data_path, weights_layout, "Mixed_7d").set_name("Mixed_7d/concat");
143 graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, operation_layout)).set_name("Logits/AvgPool_1a/AvgPool")
144 << FlattenLayer().set_name("Logits/Flatten")
145 << FullyConnectedLayer(
146 1001U,
147 get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Logits_Logits_weights.npy", weights_layout),
148 get_weights_accessor(data_path, "/cnn_data/inceptionv4_model/Logits_Logits_biases.npy"))
149 .set_name("Logits/MatMul")
150 << SoftmaxLayer().set_name("Logits/Predictions")
151 << OutputLayer(get_output_accessor(common_params, 5));
152
153 // Finalize graph
154 GraphConfig config;
155 config.num_threads = common_params.threads;
156 config.use_tuner = common_params.enable_tuner;
157 config.tuner_mode = common_params.tuner_mode;
158 config.tuner_file = common_params.tuner_file;
159 config.mlgo_file = common_params.mlgo_file;
160 config.use_synthetic_type = arm_compute::is_data_type_quantized(common_params.data_type);
161 config.synthetic_type = common_params.data_type;
162
163 // Load the precompiled kernels from a file into the kernel library, in this way the next time they are needed
164 // compilation won't be required.
165 if(common_params.enable_cl_cache)
166 {
167 #ifdef ARM_COMPUTE_CL
168 restore_program_cache_from_file();
169 #endif /* ARM_COMPUTE_CL */
170 }
171
172 graph.finalize(common_params.target, config);
173
174 // Save the opencl kernels to a file
175 if(common_opts.enable_cl_cache)
176 {
177 #ifdef ARM_COMPUTE_CL
178 save_program_cache_to_file();
179 #endif /* ARM_COMPUTE_CL */
180 }
181
182 return true;
183 }
184
do_run()185 void do_run() override
186 {
187 graph.run();
188 }
189
190 private:
191 CommandLineParser cmd_parser;
192 CommonGraphOptions common_opts;
193 CommonGraphParams common_params;
194 Stream graph;
195
196 private:
get_mixed_3a(const std::string & data_path,DataLayout weights_layout)197 ConcatLayer get_mixed_3a(const std::string &data_path, DataLayout weights_layout)
198 {
199 std::string total_path = "/cnn_data/inceptionv4_model/Mixed_3a_";
200
201 SubStream i_a(graph);
202 i_a << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, common_params.data_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL),
203 true))
204 .set_name("Mixed_3a/Branch_0/MaxPool_0a_3x3/MaxPool");
205
206 SubStream i_b(graph);
207 i_b << ConvolutionLayer(3U, 3U, 96U,
208 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_3x3_weights.npy", weights_layout),
209 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(2, 2, 0, 0))
210 .set_name("Mixed_3a/Branch_1/Conv2d_0a_3x3/Conv2D")
211 << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_3x3_BatchNorm_moving_mean.npy"),
212 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_3x3_BatchNorm_moving_variance.npy"),
213 get_random_accessor(1.f, 1.f),
214 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_3x3_BatchNorm_beta.npy"),
215 0.001f)
216 .set_name("Mixed_3a/Branch_1/Conv2d_0a_3x3/BatchNorm")
217 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_3a/Branch_1/Conv2d_0a_3x3/Relu");
218
219 return ConcatLayer(std::move(i_a), std::move(i_b));
220 }
221
get_mixed_4a(const std::string & data_path,DataLayout weights_layout)222 ConcatLayer get_mixed_4a(const std::string &data_path, DataLayout weights_layout)
223 {
224 std::string total_path = "/cnn_data/inceptionv4_model/Mixed_4a_";
225
226 SubStream i_a(graph);
227 i_a << ConvolutionLayer(1U, 1U, 64U,
228 get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy", weights_layout),
229 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
230 .set_name("Mixed_4a/Branch_0/Conv2d_0a_1x1/Conv2D")
231 << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
232 get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
233 get_random_accessor(1.f, 1.f),
234 get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy"),
235 0.001f)
236 .set_name("Mixed_4a/Branch_0/Conv2d_0a_1x1/BatchNorm")
237 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_4a/Branch_0/Conv2d_0a_1x1/Relu")
238 << ConvolutionLayer(3U, 3U, 96U,
239 get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_weights.npy", weights_layout),
240 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
241 .set_name("Mixed_4a/Branch_0/Conv2d_1a_3x3/Conv2D")
242 << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"),
243 get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"),
244 get_random_accessor(1.f, 1.f),
245 get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_beta.npy"),
246 0.001f)
247 .set_name("Mixed_4a/Branch_0/Conv2d_1a_3x3/BatchNorm")
248 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_4a/Branch_0/Conv2d_1a_3x3/Relu");
249
250 SubStream i_b(graph);
251 i_b << ConvolutionLayer(1U, 1U, 64U,
252 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy", weights_layout),
253 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
254 .set_name("Mixed_4a/Branch_1/Conv2d_0a_1x1/Conv2D")
255 << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
256 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
257 get_random_accessor(1.f, 1.f),
258 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"),
259 0.001f)
260 .set_name("Mixed_4a/Branch_1/Conv2d_0a_1x1/BatchNorm")
261 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_4a/Branch_1/Conv2d_0a_1x1/Relu")
262 << ConvolutionLayer(7U, 1U, 64U,
263 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_weights.npy", weights_layout),
264 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 3, 0))
265 .set_name("Mixed_4a/Branch_1/Conv2d_0b_1x7/Conv2D")
266 << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_mean.npy"),
267 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_variance.npy"),
268 get_random_accessor(1.f, 1.f),
269 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_beta.npy"),
270 0.001f)
271 .set_name("Mixed_4a/Branch_1/Conv2d_0b_1x7/BatchNorm")
272 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_4a/Branch_1/Conv2d_0b_1x7/Relu")
273 << ConvolutionLayer(1U, 7U, 64U,
274 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_weights.npy", weights_layout),
275 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 3))
276 .set_name("Mixed_4a/Branch_1/Conv2d_0c_7x1/Conv2D")
277 << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_mean.npy"),
278 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_variance.npy"),
279 get_random_accessor(1.f, 1.f),
280 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_beta.npy"),
281 0.001f)
282 .set_name("Mixed_4a/Branch_1/Conv2d_0c_7x1/BatchNorm")
283 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_4a/Branch_1/Conv2d_0c_7x1/Relu")
284 << ConvolutionLayer(3U, 3U, 96U,
285 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_weights.npy", weights_layout),
286 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
287 .set_name("Mixed_4a/Branch_1/Conv2d_1a_3x3/Conv2D")
288 << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"),
289 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"),
290 get_random_accessor(1.f, 1.f),
291 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_beta.npy"),
292 0.001f)
293 .set_name("Mixed_4a/Branch_1/Conv2d_1a_3x3/BatchNorm")
294 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_4a/Branch_1/Conv2d_1a_3x3/Relu");
295
296 return ConcatLayer(std::move(i_a), std::move(i_b));
297 }
298
get_mixed_5a(const std::string & data_path,DataLayout weights_layout)299 ConcatLayer get_mixed_5a(const std::string &data_path, DataLayout weights_layout)
300 {
301 std::string total_path = "/cnn_data/inceptionv4_model/Mixed_5a_";
302
303 SubStream i_a(graph);
304 i_a << ConvolutionLayer(3U, 3U, 192U,
305 get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_weights.npy", weights_layout),
306 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(2, 2, 0, 0))
307 .set_name("Mixed_5a/Branch_0/Conv2d_1a_3x3/Conv2D")
308 << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"),
309 get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"),
310 get_random_accessor(1.f, 1.f),
311 get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_beta.npy"),
312 0.001f)
313 .set_name("Mixed_5a/Branch_0/Conv2d_1a_3x3/BatchNorm")
314 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_5a/Branch_0/Conv2d_1a_3x3/Relu");
315
316 SubStream i_b(graph);
317 i_b << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, common_params.data_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL),
318 true))
319 .set_name("Mixed_5a/Branch_1/MaxPool_1a_3x3/MaxPool");
320
321 return ConcatLayer(std::move(i_a), std::move(i_b));
322 }
323
get_inceptionA_block(const std::string & data_path,DataLayout weights_layout,std::string && param_path)324 ConcatLayer get_inceptionA_block(const std::string &data_path, DataLayout weights_layout, std::string &¶m_path)
325 {
326 std::string total_path = "/cnn_data/inceptionv4_model/" + param_path + "_";
327
328 SubStream i_a(graph);
329 i_a << ConvolutionLayer(1U, 1U, 96U,
330 get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy", weights_layout),
331 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
332 .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/Conv2D")
333 << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
334 get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
335 get_random_accessor(1.f, 1.f),
336 get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy"),
337 0.001f)
338 .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/BatchNorm")
339 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_0a_1x1/Relu");
340
341 SubStream i_b(graph);
342 i_b << ConvolutionLayer(1U, 1U, 64U,
343 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy", weights_layout),
344 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
345 .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/Conv2D")
346 << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
347 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
348 get_random_accessor(1.f, 1.f),
349 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"),
350 0.001f)
351 .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/BatchNorm")
352 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0a_1x1/Relu")
353 << ConvolutionLayer(3U, 3U, 96U,
354 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_weights.npy", weights_layout),
355 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1))
356 .set_name(param_path + "/Branch_1/Conv2d_0b_3x3/Conv2D")
357 << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"),
358 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"),
359 get_random_accessor(1.f, 1.f),
360 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_beta.npy"),
361 0.001f)
362 .set_name(param_path + "/Branch_1/Conv2d_0b_3x3/BatchNorm")
363 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0b_3x3/Relu");
364
365 SubStream i_c(graph);
366 i_c << ConvolutionLayer(1U, 1U, 64U,
367 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_weights.npy", weights_layout),
368 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
369 .set_name(param_path + "/Branch_2/Conv2d_0a_1x1/Conv2D")
370 << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
371 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
372 get_random_accessor(1.f, 1.f),
373 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_beta.npy"),
374 0.001f)
375 .set_name(param_path + "/Branch_2/Conv2d_0a_1x1/BatchNorm")
376 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0a_1x1/Relu")
377 << ConvolutionLayer(3U, 3U, 96U,
378 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_weights.npy", weights_layout),
379 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1))
380 .set_name(param_path + "/Branch_2/Conv2d_0b_3x3/Conv2D")
381 << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"),
382 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"),
383 get_random_accessor(1.f, 1.f),
384 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_beta.npy"),
385 0.001f)
386 .set_name(param_path + "/Branch_2/Conv2d_0b_3x3/BatchNorm")
387 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0b_3x3/Relu")
388 << ConvolutionLayer(3U, 3U, 96U,
389 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_3x3_weights.npy", weights_layout),
390 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1))
391 .set_name(param_path + "/Branch_2/Conv2d_0c_3x3/Conv2D")
392 << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_3x3_BatchNorm_moving_mean.npy"),
393 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_3x3_BatchNorm_moving_variance.npy"),
394 get_random_accessor(1.f, 1.f),
395 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_3x3_BatchNorm_beta.npy"),
396 0.001f)
397 .set_name(param_path + "/Branch_2/Conv2d_0c_3x3/BatchNorm")
398 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0c_3x3/Relu");
399
400 SubStream i_d(graph);
401 i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, common_params.data_layout, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL),
402 true))
403 .set_name(param_path + "/Branch_3/AvgPool_0a_3x3/AvgPool")
404 << ConvolutionLayer(1U, 1U, 96U,
405 get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy", weights_layout),
406 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
407 .set_name(param_path + "/Branch_3/Conv2d_0b_1x1/Conv2D")
408 << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_mean.npy"),
409 get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_variance.npy"),
410 get_random_accessor(1.f, 1.f),
411 get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_beta.npy"),
412 0.001f)
413 .set_name(param_path + "/Branch_3/Conv2d_0b_1x1/BatchNorm")
414 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_3/Conv2d_0b_1x1/Relu");
415
416 return ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d));
417 }
418
get_reductionA_block(const std::string & data_path,DataLayout weights_layout)419 ConcatLayer get_reductionA_block(const std::string &data_path, DataLayout weights_layout)
420 {
421 std::string total_path = "/cnn_data/inceptionv4_model/Mixed_6a_";
422
423 SubStream i_a(graph);
424 i_a << ConvolutionLayer(3U, 3U, 384U,
425 get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_weights.npy", weights_layout),
426 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(2, 2, 0, 0))
427 .set_name("Mixed_6a/Branch_0/Conv2d_1a_3x3/Conv2D")
428 << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"),
429 get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"),
430 get_random_accessor(1.f, 1.f),
431 get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_beta.npy"),
432 0.001f)
433 .set_name("Mixed_6a/Branch_0/Conv2d_1a_3x3/BatchNorm")
434 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_6a/Branch_0/Conv2d_1a_3x3/Relu");
435
436 SubStream i_b(graph);
437 i_b << ConvolutionLayer(1U, 1U, 192U,
438 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy", weights_layout),
439 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
440 .set_name("Mixed_6a/Branch_1/Conv2d_0a_1x1/Conv2D")
441 << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
442 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
443 get_random_accessor(1.f, 1.f),
444 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"),
445 0.001f)
446 .set_name("Mixed_6a/Branch_1/Conv2d_0a_1x1/BatchNorm")
447 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_6a/Branch_1/Conv2d_0a_1x1/Relu")
448 << ConvolutionLayer(3U, 3U, 224U,
449 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_weights.npy", weights_layout),
450 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1))
451 .set_name("Mixed_6a/Branch_1/Conv2d_0b_3x3/Conv2D")
452 << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"),
453 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"),
454 get_random_accessor(1.f, 1.f),
455 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_beta.npy"),
456 0.001f)
457 .set_name("Mixed_6a/Branch_1/Conv2d_0b_3x3/BatchNorm")
458 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_6a/Branch_1/Conv2d_0b_3x3/Relu")
459 << ConvolutionLayer(3U, 3U, 256U,
460 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_weights.npy", weights_layout),
461 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(2, 2, 0, 0))
462 .set_name("Mixed_6a/Branch_1/Conv2d_1a_3x3/Conv2D")
463 << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"),
464 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"),
465 get_random_accessor(1.f, 1.f),
466 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_beta.npy"),
467 0.001f)
468 .set_name("Mixed_6a/Branch_1/Conv2d_1a_3x3/BatchNorm")
469 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_6a/Branch_1/Conv2d_1a_3x3/Relu");
470
471 SubStream i_c(graph);
472 i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, common_params.data_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL),
473 true))
474 .set_name("Mixed_6a/Branch_2/MaxPool_1a_3x3/MaxPool");
475
476 return ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c));
477 }
478
get_inceptionB_block(const std::string & data_path,DataLayout weights_layout,std::string && param_path)479 ConcatLayer get_inceptionB_block(const std::string &data_path, DataLayout weights_layout, std::string &¶m_path)
480 {
481 std::string total_path = "/cnn_data/inceptionv4_model/" + param_path + "_";
482
483 SubStream i_a(graph);
484 i_a << ConvolutionLayer(1U, 1U, 384U,
485 get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy", weights_layout),
486 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
487 .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/Conv2D")
488 << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
489 get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
490 get_random_accessor(1.f, 1.f),
491 get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy"),
492 0.001f)
493 .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/BatchNorm")
494 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_0a_1x1/Relu");
495
496 SubStream i_b(graph);
497 i_b << ConvolutionLayer(1U, 1U, 192U,
498 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy", weights_layout),
499 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
500 .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/Conv2D")
501 << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
502 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
503 get_random_accessor(1.f, 1.f),
504 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"),
505 0.001f)
506 .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/BatchNorm")
507 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0a_1x1/Relu")
508 << ConvolutionLayer(7U, 1U, 224U,
509 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_weights.npy", weights_layout),
510 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 3, 0))
511 .set_name(param_path + "/Branch_1/Conv2d_0b_1x7/Conv2D")
512 << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_mean.npy"),
513 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_variance.npy"),
514 get_random_accessor(1.f, 1.f),
515 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_beta.npy"),
516 0.001f)
517 .set_name(param_path + "/Branch_1/Conv2d_0b_1x7/BatchNorm")
518 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0b_1x7/Relu")
519 << ConvolutionLayer(1U, 7U, 256U,
520 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_weights.npy", weights_layout),
521 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 3))
522 .set_name(param_path + "/Branch_1/Conv2d_0c_7x1/Conv2D")
523 << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_mean.npy"),
524 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_variance.npy"),
525 get_random_accessor(1.f, 1.f),
526 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_beta.npy"),
527 0.001f)
528 .set_name(param_path + "/Branch_1/Conv2d_0c_7x1/BatchNorm")
529 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0c_7x1/Relu");
530
531 SubStream i_c(graph);
532 i_c << ConvolutionLayer(1U, 1U, 192U,
533 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_weights.npy", weights_layout),
534 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
535 .set_name(param_path + "/Branch_2/Conv2d_0a_1x1/Conv2D")
536 << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
537 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
538 get_random_accessor(1.f, 1.f),
539 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_beta.npy"),
540 0.001f)
541 .set_name(param_path + "/Branch_2/Conv2d_0a_1x1/BatchNorm")
542 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0a_1x1/Relu")
543 << ConvolutionLayer(1U, 7U, 192U,
544 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_7x1_weights.npy", weights_layout),
545 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 3))
546 .set_name(param_path + "/Branch_2/Conv2d_0b_7x1/Conv2D")
547 << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_7x1_BatchNorm_moving_mean.npy"),
548 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_7x1_BatchNorm_moving_variance.npy"),
549 get_random_accessor(1.f, 1.f),
550 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_7x1_BatchNorm_beta.npy"),
551 0.001f)
552 .set_name(param_path + "/Branch_2/Conv2d_0b_7x1/BatchNorm")
553 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0b_7x1/Relu")
554 << ConvolutionLayer(7U, 1U, 224U,
555 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x7_weights.npy", weights_layout),
556 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 3, 0))
557 .set_name(param_path + "/Branch_2/Conv2d_0c_1x7/Conv2D")
558 << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x7_BatchNorm_moving_mean.npy"),
559 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x7_BatchNorm_moving_variance.npy"),
560 get_random_accessor(1.f, 1.f),
561 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x7_BatchNorm_beta.npy"),
562 0.001f)
563 .set_name(param_path + "/Branch_2/Conv2d_0c_1x7/BatchNorm")
564 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0c_1x7/Relu")
565 << ConvolutionLayer(1U, 7U, 224U,
566 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_7x1_weights.npy", weights_layout),
567 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 3))
568 .set_name(param_path + "/Branch_2/Conv2d_0d_7x1/Conv2D")
569 << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_7x1_BatchNorm_moving_mean.npy"),
570 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_7x1_BatchNorm_moving_variance.npy"),
571 get_random_accessor(1.f, 1.f),
572 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_7x1_BatchNorm_beta.npy"),
573 0.001f)
574 .set_name(param_path + "/Branch_2/Conv2d_0d_7x1/BatchNorm")
575 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0d_7x1/Relu")
576 << ConvolutionLayer(7U, 1U, 256U,
577 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_1x7_weights.npy", weights_layout),
578 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 3, 0))
579 .set_name(param_path + "/Branch_2/Conv2d_0e_1x7/Conv2D")
580 << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_1x7_BatchNorm_moving_mean.npy"),
581 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_1x7_BatchNorm_moving_variance.npy"),
582 get_random_accessor(1.f, 1.f),
583 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_1x7_BatchNorm_beta.npy"),
584 0.001f)
585 .set_name(param_path + "/Branch_2/Conv2d_0e_1x7/BatchNorm")
586 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0e_1x7/Relu");
587
588 SubStream i_d(graph);
589 i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, common_params.data_layout, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL),
590 true))
591 .set_name(param_path + "/Branch_3/AvgPool_0a_3x3/AvgPool")
592 << ConvolutionLayer(1U, 1U, 128U,
593 get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy", weights_layout),
594 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
595 .set_name(param_path + "/Branch_3/Conv2d_0b_1x1/Conv2D")
596 << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_mean.npy"),
597 get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_variance.npy"),
598 get_random_accessor(1.f, 1.f),
599 get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_beta.npy"),
600 0.001f)
601 .set_name(param_path + "/Branch_3/Conv2d_0b_1x1/BatchNorm")
602 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_3/Conv2d_0b_1x1/Relu");
603
604 return ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d));
605 }
606
get_reductionB_block(const std::string & data_path,DataLayout weights_layout)607 ConcatLayer get_reductionB_block(const std::string &data_path, DataLayout weights_layout)
608 {
609 std::string total_path = "/cnn_data/inceptionv4_model/Mixed_7a_";
610
611 SubStream i_a(graph);
612 i_a << ConvolutionLayer(1U, 1U, 192U,
613 get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy", weights_layout),
614 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
615 .set_name("Mixed_7a/Branch_1/Conv2d_0a_1x1/Conv2D")
616 << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
617 get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
618 get_random_accessor(1.f, 1.f),
619 get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy"),
620 0.001f)
621 .set_name("Mixed_7a/Branch_1/Conv2d_0a_1x1/BatchNorm")
622 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_7a/Branch_1/Conv2d_0a_1x1/Relu")
623 << ConvolutionLayer(3U, 3U, 192U,
624 get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_weights.npy", weights_layout),
625 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(2, 2, 0, 0))
626 .set_name("Mixed_7a/Branch_0/Conv2d_1a_3x3/Conv2D")
627 << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"),
628 get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"),
629 get_random_accessor(1.f, 1.f),
630 get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_beta.npy"),
631 0.001f)
632 .set_name("Mixed_7a/Branch_0/Conv2d_1a_3x3/BatchNorm")
633 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_7a/Branch_0/Conv2d_1a_3x3/Relu");
634
635 SubStream i_b(graph);
636 i_b << ConvolutionLayer(1U, 1U, 256U,
637 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy", weights_layout),
638 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
639 .set_name("Mixed_7a/Branch_1/Conv2d_0a_1x1/Conv2D")
640 << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
641 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
642 get_random_accessor(1.f, 1.f),
643 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"),
644 0.001f)
645 .set_name("Mixed_7a/Branch_1/Conv2d_0a_1x1/BatchNorm")
646 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_7a/Branch_1/Conv2d_0a_1x1/Relu")
647 << ConvolutionLayer(7U, 1U, 256U,
648 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_weights.npy", weights_layout),
649 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 3, 0))
650 .set_name("Mixed_7a/Branch_1/Conv2d_0b_1x7/Conv2D")
651 << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_mean.npy"),
652 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_variance.npy"),
653 get_random_accessor(1.f, 1.f),
654 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_beta.npy"),
655 0.001f)
656 .set_name("Mixed_7a/Branch_1/Conv2d_0b_1x7/BatchNorm")
657 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_7a/Branch_1/Conv2d_0b_1x7/Relu")
658 << ConvolutionLayer(1U, 7U, 320U,
659 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_weights.npy", weights_layout),
660 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 3))
661 .set_name("Mixed_7a/Branch_1/Conv2d_0c_7x1/Conv2D")
662 << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_mean.npy"),
663 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_variance.npy"),
664 get_random_accessor(1.f, 1.f),
665 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_beta.npy"),
666 0.001f)
667 .set_name("Mixed_7a/Branch_1/Conv2d_0c_7x1/BatchNorm")
668 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_7a/Branch_1/Conv2d_0c_7x1/Relu")
669 << ConvolutionLayer(3U, 3U, 320U,
670 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_weights.npy", weights_layout),
671 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(2, 2, 0, 0))
672 .set_name("Mixed_7a/Branch_1/Conv2d_1a_3x3/Conv2D")
673 << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"),
674 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"),
675 get_random_accessor(1.f, 1.f),
676 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_beta.npy"),
677 0.001f)
678 .set_name("Mixed_7a/Branch_1/Conv2d_1a_3x3/BatchNorm")
679 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_7a/Branch_1/Conv2d_1a_3x3/Relu");
680
681 SubStream i_c(graph);
682 i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, common_params.data_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL),
683 true))
684 .set_name("Mixed_7a/Branch_2/MaxPool_1a_3x3/MaxPool");
685
686 return ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c));
687 }
688
get_inceptionC_block(const std::string & data_path,DataLayout weights_layout,std::string && param_path)689 ConcatLayer get_inceptionC_block(const std::string &data_path, DataLayout weights_layout, std::string &¶m_path)
690 {
691 std::string total_path = "/cnn_data/inceptionv4_model/" + param_path + "_";
692
693 SubStream i_a(graph);
694 i_a << ConvolutionLayer(1U, 1U, 256U,
695 get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy", weights_layout),
696 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
697 .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/Conv2D")
698 << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
699 get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
700 get_random_accessor(1.f, 1.f),
701 get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy"),
702 0.001f)
703 .set_name(param_path + "/Branch_0/Conv2d_0a_1x1/BatchNorm")
704 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_0a_1x1/Relu");
705
706 SubStream i_b(graph);
707 i_b << ConvolutionLayer(
708 1U, 1U, 384U,
709 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy", weights_layout),
710 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
711 PadStrideInfo(1, 1, 0, 0))
712 .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/Conv2D")
713 << BatchNormalizationLayer(
714 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
715 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
716 get_random_accessor(1.f, 1.f),
717 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"),
718 0.001f)
719 .set_name(param_path + "/Branch_1/Conv2d_0a_1x1/BatchNorm")
720 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0a_1x1/Relu");
721
722 SubStream i_b1(i_b);
723 i_b1 << ConvolutionLayer(
724 3U, 1U, 256U,
725 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x3_weights.npy", weights_layout),
726 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
727 PadStrideInfo(1, 1, 1, 0))
728 .set_name(param_path + "/Branch_1/Conv2d_0b_1x3/Conv2D")
729 << BatchNormalizationLayer(
730 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x3_BatchNorm_moving_mean.npy"),
731 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x3_BatchNorm_moving_variance.npy"),
732 get_random_accessor(1.f, 1.f),
733 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x3_BatchNorm_beta.npy"),
734 0.001f)
735 .set_name(param_path + "/Branch_1/Conv2d_0b_1x3/BatchNorm")
736 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0b_1x3/Relu");
737
738 SubStream i_b2(i_b);
739 i_b2 << ConvolutionLayer(
740 1U, 3U, 256U,
741 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_3x1_weights.npy", weights_layout),
742 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
743 PadStrideInfo(1, 1, 0, 1))
744 .set_name(param_path + "/Branch_1/Conv2d_0c_3x1/Conv2D")
745 << BatchNormalizationLayer(
746 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_3x1_BatchNorm_moving_mean.npy"),
747 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_3x1_BatchNorm_moving_variance.npy"),
748 get_random_accessor(1.f, 1.f),
749 get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_3x1_BatchNorm_beta.npy"),
750 0.001f)
751 .set_name(param_path + "/Branch_1/Conv2d_0c_3x1/BatchNorm")
752 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0c_3x1/Relu");
753
754 // Merge b1 and b2
755 i_b << ConcatLayer(std::move(i_b1), std::move(i_b2)).set_name(param_path + "/Branch_1/concat");
756
757 SubStream i_c(graph);
758 i_c << ConvolutionLayer(
759 1U, 1U, 384U,
760 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_weights.npy", weights_layout),
761 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
762 PadStrideInfo(1, 1, 0, 0))
763 .set_name(param_path + "/Branch_2/Conv2d_0a_1x1/Conv2D")
764 << BatchNormalizationLayer(
765 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
766 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
767 get_random_accessor(1.f, 1.f),
768 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_beta.npy"),
769 0.001f)
770 .set_name(param_path + "/Branch_2/Conv2d_0a_1x1/BatchNorm")
771 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0a_1x1/Relu")
772 << ConvolutionLayer(
773 1U, 3U, 448U,
774 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x1_weights.npy", weights_layout),
775 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
776 PadStrideInfo(1, 1, 0, 1))
777 .set_name(param_path + "/Branch_2/Conv2d_0b_3x1/Conv2D")
778 << BatchNormalizationLayer(
779 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x1_BatchNorm_moving_mean.npy"),
780 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x1_BatchNorm_moving_variance.npy"),
781 get_random_accessor(1.f, 1.f),
782 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x1_BatchNorm_beta.npy"),
783 0.001f)
784 .set_name(param_path + "/Branch_2/Conv2d_0b_3x1/BatchNorm")
785 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0b_3x1/Relu")
786 << ConvolutionLayer(
787 3U, 1U, 512U,
788 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x3_weights.npy", weights_layout),
789 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
790 PadStrideInfo(1, 1, 1, 0))
791 .set_name(param_path + "/Branch_2/Conv2d_0c_1x3/Conv2D")
792 << BatchNormalizationLayer(
793 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x3_BatchNorm_moving_mean.npy"),
794 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x3_BatchNorm_moving_variance.npy"),
795 get_random_accessor(1.f, 1.f),
796 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x3_BatchNorm_beta.npy"),
797 0.001f)
798 .set_name(param_path + "/Branch_2/Conv2d_0c_1x3/BatchNorm")
799 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0c_1x3/Relu");
800
801 SubStream i_c1(i_c);
802 i_c1 << ConvolutionLayer(
803 3U, 1U, 256U,
804 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_1x3_weights.npy", weights_layout),
805 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
806 PadStrideInfo(1, 1, 1, 0))
807 .set_name(param_path + "/Branch_2/Conv2d_0d_1x3/Conv2D")
808 << BatchNormalizationLayer(
809 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_1x3_BatchNorm_moving_mean.npy"),
810 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_1x3_BatchNorm_moving_variance.npy"),
811 get_random_accessor(1.f, 1.f),
812 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_1x3_BatchNorm_beta.npy"),
813 0.001f)
814 .set_name(param_path + "/Branch_2/Conv2d_0d_1x3/BatchNorm")
815 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0d_1x3/Relu");
816
817 SubStream i_c2(i_c);
818 i_c2 << ConvolutionLayer(
819 1U, 3U, 256U,
820 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_3x1_weights.npy", weights_layout),
821 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
822 PadStrideInfo(1, 1, 0, 1))
823 .set_name(param_path + "/Branch_2/Conv2d_0e_3x1/Conv2D")
824 << BatchNormalizationLayer(
825 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_3x1_BatchNorm_moving_mean.npy"),
826 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_3x1_BatchNorm_moving_variance.npy"),
827 get_random_accessor(1.f, 1.f),
828 get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_3x1_BatchNorm_beta.npy"),
829 0.001f)
830 .set_name(param_path + "/Branch_2/Conv2d_0e_3x1/BatchNorm")
831 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0e_3x1/Relu");
832
833 // Merge i_c1 and i_c2
834 i_c << ConcatLayer(std::move(i_c1), std::move(i_c2)).set_name(param_path + "/Branch_2/concat");
835
836 SubStream i_d(graph);
837 i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, common_params.data_layout, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL),
838 true))
839 .set_name(param_path + "/Branch_3/AvgPool_0a_3x3/AvgPool")
840 << ConvolutionLayer(1U, 1U, 256U,
841 get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy", weights_layout),
842 std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
843 .set_name(param_path + "/Branch_3/Conv2d_0b_1x1/Conv2D")
844 << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_mean.npy"),
845 get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_variance.npy"),
846 get_random_accessor(1.f, 1.f),
847 get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_beta.npy"),
848 0.001f)
849 .set_name(param_path + "/Branch_3/Conv2d_0b_1x1/BatchNorm")
850 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_3/Conv2d_0b_1x1/Relu");
851
852 return ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d));
853 }
854 };
855
856 /** Main program for Inception V4
857 *
858 * Model is based on:
859 * https://arxiv.org/abs/1602.07261
860 * "Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning"
861 * Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi
862 *
863 * Provenance: download.tensorflow.org/models/inception_v4_2016_09_09.tar.gz
864 *
865 * @note To list all the possible arguments execute the binary appended with the --help option
866 *
867 * @param[in] argc Number of arguments
868 * @param[in] argv Arguments
869 */
main(int argc,char ** argv)870 int main(int argc, char **argv)
871 {
872 return arm_compute::utils::run_example<InceptionV4Example>(argc, argv);
873 }
874