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Anthony Barbier8140e1e2017-12-14 23:48:46 +00001/*
Jenkins514be652019-02-28 12:25:18 +00002 * Copyright (c) 2017-2019 ARM Limited.
Anthony Barbier8140e1e2017-12-14 23:48:46 +00003 *
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 */
Jenkinsb3a371b2018-05-23 11:36:53 +010024#include "arm_compute/graph.h"
Anthony Barbier8140e1e2017-12-14 23:48:46 +000025#include "support/ToolchainSupport.h"
Jenkins52ba29e2018-08-29 15:32:11 +000026#include "utils/CommonGraphOptions.h"
Anthony Barbier8140e1e2017-12-14 23:48:46 +000027#include "utils/GraphUtils.h"
28#include "utils/Utils.h"
29
Anthony Barbierf45d5a92018-01-24 16:23:15 +000030using namespace arm_compute::utils;
Jenkinsb3a371b2018-05-23 11:36:53 +010031using namespace arm_compute::graph::frontend;
Anthony Barbier8140e1e2017-12-14 23:48:46 +000032using namespace arm_compute::graph_utils;
33
Jenkinsb9abeae2018-11-22 11:58:08 +000034/** Example demonstrating how to implement Googlenet's network using the Compute Library's graph API */
Anthony Barbierf45d5a92018-01-24 16:23:15 +000035class GraphGooglenetExample : public Example
Anthony Barbier8140e1e2017-12-14 23:48:46 +000036{
Anthony Barbierf45d5a92018-01-24 16:23:15 +000037public:
Jenkins52ba29e2018-08-29 15:32:11 +000038 GraphGooglenetExample()
39 : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "GoogleNet")
Anthony Barbierf45d5a92018-01-24 16:23:15 +000040 {
Jenkins52ba29e2018-08-29 15:32:11 +000041 }
42 bool do_setup(int argc, char **argv) override
43 {
44 // Parse arguments
45 cmd_parser.parse(argc, argv);
46
47 // Consume common parameters
48 common_params = consume_common_graph_parameters(common_opts);
49
50 // Return when help menu is requested
51 if(common_params.help)
52 {
53 cmd_parser.print_help(argv[0]);
54 return false;
55 }
56
57 // Checks
58 ARM_COMPUTE_EXIT_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type), "QASYMM8 not supported for this graph");
Jenkins52ba29e2018-08-29 15:32:11 +000059
60 // Print parameter values
61 std::cout << common_params << std::endl;
62
63 // Get trainable parameters data path
64 std::string data_path = common_params.data_path;
Anthony Barbier8140e1e2017-12-14 23:48:46 +000065
Anthony Barbier06ea0482018-02-22 15:45:35 +000066 // Create a preprocessor object
67 const std::array<float, 3> mean_rgb{ { 122.68f, 116.67f, 104.01f } };
68 std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb);
Anthony Barbier8140e1e2017-12-14 23:48:46 +000069
Jenkins52ba29e2018-08-29 15:32:11 +000070 // Create input descriptor
71 const TensorShape tensor_shape = permute_shape(TensorShape(224U, 224U, 3U, 1U), DataLayout::NCHW, common_params.data_layout);
72 TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout);
Anthony Barbier8140e1e2017-12-14 23:48:46 +000073
Jenkins52ba29e2018-08-29 15:32:11 +000074 // Set weights trained layout
75 const DataLayout weights_layout = DataLayout::NCHW;
Anthony Barbierf45d5a92018-01-24 16:23:15 +000076
Jenkins52ba29e2018-08-29 15:32:11 +000077 graph << common_params.target
78 << common_params.fast_math_hint
79 << InputLayer(input_descriptor, get_input_accessor(common_params, std::move(preprocessor)))
Anthony Barbierf45d5a92018-01-24 16:23:15 +000080 << ConvolutionLayer(
81 7U, 7U, 64U,
Jenkins52ba29e2018-08-29 15:32:11 +000082 get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv1/conv1_7x7_s2_w.npy", weights_layout),
Anthony Barbierf45d5a92018-01-24 16:23:15 +000083 get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv1/conv1_7x7_s2_b.npy"),
84 PadStrideInfo(2, 2, 3, 3))
Jenkins514be652019-02-28 12:25:18 +000085 .set_name("conv1/7x7_s2")
86 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv1/relu_7x7")
87 << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool1/3x3_s2")
88 << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f)).set_name("pool1/norm1")
Anthony Barbierf45d5a92018-01-24 16:23:15 +000089 << ConvolutionLayer(
90 1U, 1U, 64U,
Jenkins52ba29e2018-08-29 15:32:11 +000091 get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_reduce_w.npy", weights_layout),
Anthony Barbierf45d5a92018-01-24 16:23:15 +000092 get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_reduce_b.npy"),
93 PadStrideInfo(1, 1, 0, 0))
Jenkins514be652019-02-28 12:25:18 +000094 .set_name("conv2/3x3_reduce")
95 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv2/relu_3x3_reduce")
Anthony Barbierf45d5a92018-01-24 16:23:15 +000096 << ConvolutionLayer(
97 3U, 3U, 192U,
Jenkins52ba29e2018-08-29 15:32:11 +000098 get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_w.npy", weights_layout),
Anthony Barbierf45d5a92018-01-24 16:23:15 +000099 get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_b.npy"),
100 PadStrideInfo(1, 1, 1, 1))
Jenkins514be652019-02-28 12:25:18 +0000101 .set_name("conv2/3x3")
102 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv2/relu_3x3")
103 << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f)).set_name("conv2/norm2")
104 << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool2/3x3_s2");
105 graph << get_inception_node(data_path, "inception_3a", weights_layout, 64, std::make_tuple(96U, 128U), std::make_tuple(16U, 32U), 32U).set_name("inception_3a/concat");
106 graph << get_inception_node(data_path, "inception_3b", weights_layout, 128, std::make_tuple(128U, 192U), std::make_tuple(32U, 96U), 64U).set_name("inception_3b/concat");
107 graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool3/3x3_s2");
108 graph << get_inception_node(data_path, "inception_4a", weights_layout, 192, std::make_tuple(96U, 208U), std::make_tuple(16U, 48U), 64U).set_name("inception_4a/concat");
109 graph << get_inception_node(data_path, "inception_4b", weights_layout, 160, std::make_tuple(112U, 224U), std::make_tuple(24U, 64U), 64U).set_name("inception_4b/concat");
110 graph << get_inception_node(data_path, "inception_4c", weights_layout, 128, std::make_tuple(128U, 256U), std::make_tuple(24U, 64U), 64U).set_name("inception_4c/concat");
111 graph << get_inception_node(data_path, "inception_4d", weights_layout, 112, std::make_tuple(144U, 288U), std::make_tuple(32U, 64U), 64U).set_name("inception_4d/concat");
112 graph << get_inception_node(data_path, "inception_4e", weights_layout, 256, std::make_tuple(160U, 320U), std::make_tuple(32U, 128U), 128U).set_name("inception_4e/concat");
113 graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool4/3x3_s2");
114 graph << get_inception_node(data_path, "inception_5a", weights_layout, 256, std::make_tuple(160U, 320U), std::make_tuple(32U, 128U), 128U).set_name("inception_5a/concat");
115 graph << get_inception_node(data_path, "inception_5b", weights_layout, 384, std::make_tuple(192U, 384U), std::make_tuple(48U, 128U), 128U).set_name("inception_5b/concat");
116 graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 7, PadStrideInfo(1, 1, 0, 0, DimensionRoundingType::CEIL))).set_name("pool5/7x7_s1")
Anthony Barbierf45d5a92018-01-24 16:23:15 +0000117 << FullyConnectedLayer(
118 1000U,
Jenkins52ba29e2018-08-29 15:32:11 +0000119 get_weights_accessor(data_path, "/cnn_data/googlenet_model/loss3/loss3_classifier_w.npy", weights_layout),
Anthony Barbierf45d5a92018-01-24 16:23:15 +0000120 get_weights_accessor(data_path, "/cnn_data/googlenet_model/loss3/loss3_classifier_b.npy"))
Jenkins514be652019-02-28 12:25:18 +0000121 .set_name("loss3/classifier")
122 << SoftmaxLayer().set_name("prob")
Jenkins52ba29e2018-08-29 15:32:11 +0000123 << OutputLayer(get_output_accessor(common_params, 5));
Anthony Barbier06ea0482018-02-22 15:45:35 +0000124
Jenkinsb3a371b2018-05-23 11:36:53 +0100125 // Finalize graph
126 GraphConfig config;
Jenkins52ba29e2018-08-29 15:32:11 +0000127 config.num_threads = common_params.threads;
128 config.use_tuner = common_params.enable_tuner;
Jenkins4ba87db2019-05-23 17:11:51 +0100129 config.tuner_mode = common_params.tuner_mode;
Jenkins52ba29e2018-08-29 15:32:11 +0000130 config.tuner_file = common_params.tuner_file;
131
132 graph.finalize(common_params.target, config);
133
134 return true;
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000135 }
Anthony Barbierf45d5a92018-01-24 16:23:15 +0000136 void do_run() override
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000137 {
Anthony Barbierf45d5a92018-01-24 16:23:15 +0000138 // Run graph
139 graph.run();
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000140 }
141
Anthony Barbierf45d5a92018-01-24 16:23:15 +0000142private:
Jenkins52ba29e2018-08-29 15:32:11 +0000143 CommandLineParser cmd_parser;
144 CommonGraphOptions common_opts;
145 CommonGraphParams common_params;
146 Stream graph;
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000147
Jenkinsb9abeae2018-11-22 11:58:08 +0000148 ConcatLayer get_inception_node(const std::string &data_path, std::string &&param_path, DataLayout weights_layout,
Anthony Barbierf45d5a92018-01-24 16:23:15 +0000149 unsigned int a_filt,
150 std::tuple<unsigned int, unsigned int> b_filters,
151 std::tuple<unsigned int, unsigned int> c_filters,
152 unsigned int d_filt)
153 {
154 std::string total_path = "/cnn_data/googlenet_model/" + param_path + "/" + param_path + "_";
Jenkinsb3a371b2018-05-23 11:36:53 +0100155 SubStream i_a(graph);
Anthony Barbierf45d5a92018-01-24 16:23:15 +0000156 i_a << ConvolutionLayer(
157 1U, 1U, a_filt,
Jenkins52ba29e2018-08-29 15:32:11 +0000158 get_weights_accessor(data_path, total_path + "1x1_w.npy", weights_layout),
Anthony Barbierf45d5a92018-01-24 16:23:15 +0000159 get_weights_accessor(data_path, total_path + "1x1_b.npy"),
160 PadStrideInfo(1, 1, 0, 0))
Jenkins514be652019-02-28 12:25:18 +0000161 .set_name(param_path + "/1x1")
162 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/relu_1x1");
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000163
Jenkinsb3a371b2018-05-23 11:36:53 +0100164 SubStream i_b(graph);
Anthony Barbierf45d5a92018-01-24 16:23:15 +0000165 i_b << ConvolutionLayer(
166 1U, 1U, std::get<0>(b_filters),
Jenkins52ba29e2018-08-29 15:32:11 +0000167 get_weights_accessor(data_path, total_path + "3x3_reduce_w.npy", weights_layout),
Anthony Barbierf45d5a92018-01-24 16:23:15 +0000168 get_weights_accessor(data_path, total_path + "3x3_reduce_b.npy"),
169 PadStrideInfo(1, 1, 0, 0))
Jenkins514be652019-02-28 12:25:18 +0000170 .set_name(param_path + "/3x3_reduce")
171 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/relu_3x3_reduce")
Anthony Barbierf45d5a92018-01-24 16:23:15 +0000172 << ConvolutionLayer(
173 3U, 3U, std::get<1>(b_filters),
Jenkins52ba29e2018-08-29 15:32:11 +0000174 get_weights_accessor(data_path, total_path + "3x3_w.npy", weights_layout),
Anthony Barbierf45d5a92018-01-24 16:23:15 +0000175 get_weights_accessor(data_path, total_path + "3x3_b.npy"),
176 PadStrideInfo(1, 1, 1, 1))
Jenkins514be652019-02-28 12:25:18 +0000177 .set_name(param_path + "/3x3")
178 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/relu_3x3");
Anthony Barbierf45d5a92018-01-24 16:23:15 +0000179
Jenkinsb3a371b2018-05-23 11:36:53 +0100180 SubStream i_c(graph);
Anthony Barbierf45d5a92018-01-24 16:23:15 +0000181 i_c << ConvolutionLayer(
182 1U, 1U, std::get<0>(c_filters),
Jenkins52ba29e2018-08-29 15:32:11 +0000183 get_weights_accessor(data_path, total_path + "5x5_reduce_w.npy", weights_layout),
Anthony Barbierf45d5a92018-01-24 16:23:15 +0000184 get_weights_accessor(data_path, total_path + "5x5_reduce_b.npy"),
185 PadStrideInfo(1, 1, 0, 0))
Jenkins514be652019-02-28 12:25:18 +0000186 .set_name(param_path + "/5x5_reduce")
187 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/relu_5x5_reduce")
Anthony Barbierf45d5a92018-01-24 16:23:15 +0000188 << ConvolutionLayer(
189 5U, 5U, std::get<1>(c_filters),
Jenkins52ba29e2018-08-29 15:32:11 +0000190 get_weights_accessor(data_path, total_path + "5x5_w.npy", weights_layout),
Anthony Barbierf45d5a92018-01-24 16:23:15 +0000191 get_weights_accessor(data_path, total_path + "5x5_b.npy"),
192 PadStrideInfo(1, 1, 2, 2))
Jenkins514be652019-02-28 12:25:18 +0000193 .set_name(param_path + "/5x5")
194 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/relu_5x5");
Anthony Barbierf45d5a92018-01-24 16:23:15 +0000195
Jenkinsb3a371b2018-05-23 11:36:53 +0100196 SubStream i_d(graph);
Jenkins514be652019-02-28 12:25:18 +0000197 i_d << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL))).set_name(param_path + "/pool")
Anthony Barbierf45d5a92018-01-24 16:23:15 +0000198 << ConvolutionLayer(
199 1U, 1U, d_filt,
Jenkins52ba29e2018-08-29 15:32:11 +0000200 get_weights_accessor(data_path, total_path + "pool_proj_w.npy", weights_layout),
Anthony Barbierf45d5a92018-01-24 16:23:15 +0000201 get_weights_accessor(data_path, total_path + "pool_proj_b.npy"),
202 PadStrideInfo(1, 1, 0, 0))
Jenkins514be652019-02-28 12:25:18 +0000203 .set_name(param_path + "/pool_proj")
204 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/relu_pool_proj");
Anthony Barbierf45d5a92018-01-24 16:23:15 +0000205
Jenkinsb9abeae2018-11-22 11:58:08 +0000206 return ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d));
Anthony Barbierf45d5a92018-01-24 16:23:15 +0000207 }
208};
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000209
210/** Main program for Googlenet
211 *
Jenkinsb9abeae2018-11-22 11:58:08 +0000212 * Model is based on:
213 * https://arxiv.org/abs/1409.4842
214 * "Going deeper with convolutions"
215 * Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich
216 *
Jenkins514be652019-02-28 12:25:18 +0000217 * Provenance: https://github.com/BVLC/caffe/tree/master/models/bvlc_googlenet
218 *
Jenkins52ba29e2018-08-29 15:32:11 +0000219 * @note To list all the possible arguments execute the binary appended with the --help option
220 *
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000221 * @param[in] argc Number of arguments
Jenkins52ba29e2018-08-29 15:32:11 +0000222 * @param[in] argv Arguments
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000223 */
Anthony Barbierf45d5a92018-01-24 16:23:15 +0000224int main(int argc, char **argv)
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000225{
Anthony Barbierf45d5a92018-01-24 16:23:15 +0000226 return arm_compute::utils::run_example<GraphGooglenetExample>(argc, argv);
Anthony Barbier8140e1e2017-12-14 23:48:46 +0000227}