217 lines
9.5 KiB
C++
217 lines
9.5 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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#ifdef ARM_COMPUTE_CL
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#include "arm_compute/runtime/CL/Utils.h"
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#endif /* ARM_COMPUTE_CL */
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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;
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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 AlexNet's network using the Compute Library's graph API */
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class GraphAlexnetExample : public Example
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{
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public:
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GraphAlexnetExample()
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: cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "AlexNet")
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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(227U, 227U, 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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// Layer 1
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<< ConvolutionLayer(
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11U, 11U, 96U,
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get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv1_w.npy", weights_layout),
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get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv1_b.npy"),
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PadStrideInfo(4, 4, 0, 0))
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.set_name("conv1")
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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu1")
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<< NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f)).set_name("norm1")
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<< PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool1")
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// Layer 2
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<< ConvolutionLayer(
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5U, 5U, 256U,
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get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv2_w.npy", weights_layout),
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get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv2_b.npy"),
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PadStrideInfo(1, 1, 2, 2), 2)
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.set_name("conv2")
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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu2")
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<< NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f)).set_name("norm2")
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<< PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool2")
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// Layer 3
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<< ConvolutionLayer(
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3U, 3U, 384U,
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get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv3_w.npy", weights_layout),
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get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv3_b.npy"),
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PadStrideInfo(1, 1, 1, 1))
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.set_name("conv3")
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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu3")
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// Layer 4
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<< ConvolutionLayer(
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3U, 3U, 384U,
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get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv4_w.npy", weights_layout),
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get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv4_b.npy"),
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PadStrideInfo(1, 1, 1, 1), 2)
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.set_name("conv4")
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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu4")
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// Layer 5
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<< ConvolutionLayer(
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3U, 3U, 256U,
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get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv5_w.npy", weights_layout),
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get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv5_b.npy"),
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PadStrideInfo(1, 1, 1, 1), 2)
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.set_name("conv5")
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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu5")
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<< PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("pool5")
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// Layer 6
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<< FullyConnectedLayer(
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4096U,
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get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc6_w.npy", weights_layout),
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get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc6_b.npy"))
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.set_name("fc6")
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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu6")
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// Layer 7
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<< FullyConnectedLayer(
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4096U,
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get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc7_w.npy", weights_layout),
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get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc7_b.npy"))
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.set_name("fc7")
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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu7")
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// Layer 8
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<< FullyConnectedLayer(
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1000U,
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get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc8_w.npy", weights_layout),
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get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc8_b.npy"))
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.set_name("fc8")
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// Softmax
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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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// Load the precompiled kernels from a file into the kernel library, in this way the next time they are needed
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// compilation won't be required.
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if(common_params.enable_cl_cache)
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{
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#ifdef ARM_COMPUTE_CL
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restore_program_cache_from_file();
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#endif /* ARM_COMPUTE_CL */
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}
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graph.finalize(common_params.target, config);
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// Save the opencl kernels to a file
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if(common_opts.enable_cl_cache)
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{
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#ifdef ARM_COMPUTE_CL
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save_program_cache_to_file();
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#endif /* ARM_COMPUTE_CL */
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}
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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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};
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/** Main program for AlexNet
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*
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* Model is based on:
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* https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks
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* "ImageNet Classification with Deep Convolutional Neural Networks"
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* Alex Krizhevsky and Sutskever, Ilya and Hinton, Geoffrey E
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*
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* Provenance: https://github.com/BVLC/caffe/tree/master/models/bvlc_alexnet
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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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* @return Return code
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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<GraphAlexnetExample>(argc, argv);
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}
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