230 lines
13 KiB
C++
230 lines
13 KiB
C++
/*
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* Copyright (c) 2017-2020 Arm Limited.
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*
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* SPDX-License-Identifier: MIT
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to
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* deal in the Software without restriction, including without limitation the
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* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
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* sell copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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#include "arm_compute/graph.h"
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#include "support/ToolchainSupport.h"
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#include "utils/CommonGraphOptions.h"
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#include "utils/GraphUtils.h"
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#include "utils/Utils.h"
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using namespace arm_compute::utils;
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using namespace arm_compute::graph::frontend;
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using namespace arm_compute::graph_utils;
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/** Example demonstrating how to implement Googlenet's network using the Compute Library's graph API */
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class GraphGooglenetExample : public Example
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{
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public:
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GraphGooglenetExample()
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: cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "GoogleNet")
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{
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}
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bool do_setup(int argc, char **argv) override
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{
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// Parse arguments
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cmd_parser.parse(argc, argv);
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cmd_parser.validate();
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// Consume common parameters
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common_params = consume_common_graph_parameters(common_opts);
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// Return when help menu is requested
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if(common_params.help)
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{
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cmd_parser.print_help(argv[0]);
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return false;
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}
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// Checks
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ARM_COMPUTE_EXIT_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type), "QASYMM8 not supported for this graph");
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// Print parameter values
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std::cout << common_params << std::endl;
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// Get trainable parameters data path
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std::string data_path = common_params.data_path;
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// Create a preprocessor object
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const std::array<float, 3> mean_rgb{ { 122.68f, 116.67f, 104.01f } };
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std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb);
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// Create input descriptor
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const auto operation_layout = common_params.data_layout;
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const TensorShape tensor_shape = permute_shape(TensorShape(224U, 224U, 3U, 1U), DataLayout::NCHW, operation_layout);
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TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_layout);
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// Set weights trained layout
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const DataLayout weights_layout = DataLayout::NCHW;
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graph << common_params.target
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<< common_params.fast_math_hint
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<< InputLayer(input_descriptor, get_input_accessor(common_params, std::move(preprocessor)))
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<< ConvolutionLayer(
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7U, 7U, 64U,
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get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv1/conv1_7x7_s2_w.npy", weights_layout),
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get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv1/conv1_7x7_s2_b.npy"),
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PadStrideInfo(2, 2, 3, 3))
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.set_name("conv1/7x7_s2")
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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv1/relu_7x7")
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<< PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool1/3x3_s2")
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<< NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f)).set_name("pool1/norm1")
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<< ConvolutionLayer(
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1U, 1U, 64U,
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get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_reduce_w.npy", weights_layout),
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get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_reduce_b.npy"),
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PadStrideInfo(1, 1, 0, 0))
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.set_name("conv2/3x3_reduce")
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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv2/relu_3x3_reduce")
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<< ConvolutionLayer(
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3U, 3U, 192U,
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get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_w.npy", weights_layout),
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get_weights_accessor(data_path, "/cnn_data/googlenet_model/conv2/conv2_3x3_b.npy"),
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PadStrideInfo(1, 1, 1, 1))
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.set_name("conv2/3x3")
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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv2/relu_3x3")
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<< NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f)).set_name("conv2/norm2")
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<< PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool2/3x3_s2");
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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");
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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");
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graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool3/3x3_s2");
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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");
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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");
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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");
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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");
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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");
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graph << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("pool4/3x3_s2");
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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");
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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");
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graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 7, operation_layout, PadStrideInfo(1, 1, 0, 0, DimensionRoundingType::CEIL))).set_name("pool5/7x7_s1")
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<< FullyConnectedLayer(
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1000U,
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get_weights_accessor(data_path, "/cnn_data/googlenet_model/loss3/loss3_classifier_w.npy", weights_layout),
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get_weights_accessor(data_path, "/cnn_data/googlenet_model/loss3/loss3_classifier_b.npy"))
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.set_name("loss3/classifier")
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<< SoftmaxLayer().set_name("prob")
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<< OutputLayer(get_output_accessor(common_params, 5));
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// Finalize graph
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GraphConfig config;
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config.num_threads = common_params.threads;
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config.use_tuner = common_params.enable_tuner;
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config.tuner_mode = common_params.tuner_mode;
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config.tuner_file = common_params.tuner_file;
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graph.finalize(common_params.target, config);
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return true;
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}
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void do_run() override
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{
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// Run graph
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graph.run();
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}
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private:
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CommandLineParser cmd_parser;
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CommonGraphOptions common_opts;
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CommonGraphParams common_params;
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Stream graph;
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ConcatLayer get_inception_node(const std::string &data_path, std::string &¶m_path, DataLayout weights_layout,
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unsigned int a_filt,
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std::tuple<unsigned int, unsigned int> b_filters,
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std::tuple<unsigned int, unsigned int> c_filters,
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unsigned int d_filt)
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{
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std::string total_path = "/cnn_data/googlenet_model/" + param_path + "/" + param_path + "_";
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SubStream i_a(graph);
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i_a << ConvolutionLayer(
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1U, 1U, a_filt,
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get_weights_accessor(data_path, total_path + "1x1_w.npy", weights_layout),
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get_weights_accessor(data_path, total_path + "1x1_b.npy"),
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PadStrideInfo(1, 1, 0, 0))
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.set_name(param_path + "/1x1")
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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/relu_1x1");
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SubStream i_b(graph);
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i_b << ConvolutionLayer(
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1U, 1U, std::get<0>(b_filters),
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get_weights_accessor(data_path, total_path + "3x3_reduce_w.npy", weights_layout),
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get_weights_accessor(data_path, total_path + "3x3_reduce_b.npy"),
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PadStrideInfo(1, 1, 0, 0))
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.set_name(param_path + "/3x3_reduce")
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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/relu_3x3_reduce")
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<< ConvolutionLayer(
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3U, 3U, std::get<1>(b_filters),
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get_weights_accessor(data_path, total_path + "3x3_w.npy", weights_layout),
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get_weights_accessor(data_path, total_path + "3x3_b.npy"),
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PadStrideInfo(1, 1, 1, 1))
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.set_name(param_path + "/3x3")
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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/relu_3x3");
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SubStream i_c(graph);
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i_c << ConvolutionLayer(
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1U, 1U, std::get<0>(c_filters),
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get_weights_accessor(data_path, total_path + "5x5_reduce_w.npy", weights_layout),
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get_weights_accessor(data_path, total_path + "5x5_reduce_b.npy"),
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PadStrideInfo(1, 1, 0, 0))
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.set_name(param_path + "/5x5_reduce")
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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/relu_5x5_reduce")
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<< ConvolutionLayer(
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5U, 5U, std::get<1>(c_filters),
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get_weights_accessor(data_path, total_path + "5x5_w.npy", weights_layout),
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get_weights_accessor(data_path, total_path + "5x5_b.npy"),
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PadStrideInfo(1, 1, 2, 2))
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.set_name(param_path + "/5x5")
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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/relu_5x5");
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SubStream i_d(graph);
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i_d << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, common_params.data_layout, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL))).set_name(param_path + "/pool")
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<< ConvolutionLayer(
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1U, 1U, d_filt,
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get_weights_accessor(data_path, total_path + "pool_proj_w.npy", weights_layout),
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get_weights_accessor(data_path, total_path + "pool_proj_b.npy"),
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PadStrideInfo(1, 1, 0, 0))
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.set_name(param_path + "/pool_proj")
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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/relu_pool_proj");
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return ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d));
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}
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};
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/** Main program for Googlenet
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*
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* Model is based on:
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* https://arxiv.org/abs/1409.4842
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* "Going deeper with convolutions"
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* Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich
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*
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* Provenance: https://github.com/BVLC/caffe/tree/master/models/bvlc_googlenet
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*
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* @note To list all the possible arguments execute the binary appended with the --help option
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*
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* @param[in] argc Number of arguments
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* @param[in] argv Arguments
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*/
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int main(int argc, char **argv)
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{
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return arm_compute::utils::run_example<GraphGooglenetExample>(argc, argv);
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}
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