222 lines
10 KiB
C++
222 lines
10 KiB
C++
/*
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* Copyright (c) 2018-2020 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 ResNet12 network using the Compute Library's graph API */
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class GraphResNet12Example : public Example
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{
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public:
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GraphResNet12Example()
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: cmd_parser(), common_opts(cmd_parser), model_input_width(nullptr), model_input_height(nullptr), common_params(), graph(0, "ResNet12")
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{
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model_input_width = cmd_parser.add_option<SimpleOption<unsigned int>>("image-width", 192);
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model_input_height = cmd_parser.add_option<SimpleOption<unsigned int>>("image-height", 128);
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// Add model id option
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model_input_width->set_help("Input image width.");
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model_input_height->set_help("Input image height.");
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}
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GraphResNet12Example(const GraphResNet12Example &) = delete;
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GraphResNet12Example &operator=(const GraphResNet12Example &) = delete;
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~GraphResNet12Example() override = default;
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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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// Get input image width and height
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const unsigned int image_width = model_input_width->value();
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const unsigned int image_height = model_input_height->value();
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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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std::cout << "Image width: " << image_width << std::endl;
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std::cout << "Image height: " << image_height << std::endl;
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// Get trainable parameters data path
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const std::string data_path = common_params.data_path;
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const std::string model_path = "/cnn_data/resnet12_model/";
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// Create a preprocessor object
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std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>();
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// Create input descriptor
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const TensorShape tensor_shape = permute_shape(TensorShape(image_width, image_height, 3U, 1U), DataLayout::NCHW, common_params.data_layout);
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TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_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), false /* Do not convert to BGR */))
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<< ConvolutionLayer(
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9U, 9U, 64U,
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get_weights_accessor(data_path, "conv1_weights.npy", weights_layout),
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get_weights_accessor(data_path, "conv1_biases.npy", weights_layout),
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PadStrideInfo(1, 1, 4, 4))
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.set_name("conv1/convolution")
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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv1/Relu");
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add_residual_block(data_path, "block1", weights_layout);
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add_residual_block(data_path, "block2", weights_layout);
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add_residual_block(data_path, "block3", weights_layout);
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add_residual_block(data_path, "block4", weights_layout);
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graph << ConvolutionLayer(
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3U, 3U, 64U,
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get_weights_accessor(data_path, "conv10_weights.npy", weights_layout),
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get_weights_accessor(data_path, "conv10_biases.npy"),
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PadStrideInfo(1, 1, 1, 1))
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.set_name("conv10/convolution")
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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv10/Relu")
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<< ConvolutionLayer(
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3U, 3U, 64U,
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get_weights_accessor(data_path, "conv11_weights.npy", weights_layout),
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get_weights_accessor(data_path, "conv11_biases.npy"),
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PadStrideInfo(1, 1, 1, 1))
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.set_name("conv11/convolution")
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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv11/Relu")
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<< ConvolutionLayer(
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9U, 9U, 3U,
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get_weights_accessor(data_path, "conv12_weights.npy", weights_layout),
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get_weights_accessor(data_path, "conv12_biases.npy"),
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PadStrideInfo(1, 1, 4, 4))
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.set_name("conv12/convolution")
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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH)).set_name("conv12/Tanh")
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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 0.58f, 0.5f)).set_name("conv12/Linear")
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<< OutputLayer(arm_compute::support::cpp14::make_unique<DummyAccessor>(0));
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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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SimpleOption<unsigned int> *model_input_width{ nullptr };
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SimpleOption<unsigned int> *model_input_height{ nullptr };
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CommonGraphParams common_params;
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Stream graph;
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void add_residual_block(const std::string &data_path, const std::string &name, DataLayout weights_layout)
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{
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std::stringstream unit_path_ss;
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unit_path_ss << data_path << name << "_";
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std::stringstream unit_name_ss;
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unit_name_ss << name << "/";
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std::string unit_path = unit_path_ss.str();
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std::string unit_name = unit_name_ss.str();
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SubStream left(graph);
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SubStream right(graph);
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right << ConvolutionLayer(
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3U, 3U, 64U,
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get_weights_accessor(data_path, unit_path + "conv1_weights.npy", weights_layout),
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get_weights_accessor(data_path, unit_path + "conv1_biases.npy", weights_layout),
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PadStrideInfo(1, 1, 1, 1))
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.set_name(unit_name + "conv1/convolution")
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<< BatchNormalizationLayer(
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get_weights_accessor(data_path, unit_path + "conv1_BatchNorm_moving_mean.npy"),
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get_weights_accessor(data_path, unit_path + "conv1_BatchNorm_moving_variance.npy"),
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get_weights_accessor(data_path, unit_path + "conv1_BatchNorm_gamma.npy"),
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get_weights_accessor(data_path, unit_path + "conv1_BatchNorm_beta.npy"),
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0.0000100099996416f)
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.set_name(unit_name + "conv1/BatchNorm")
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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "conv1/Relu")
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<< ConvolutionLayer(
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3U, 3U, 64U,
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get_weights_accessor(data_path, unit_path + "conv2_weights.npy", weights_layout),
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get_weights_accessor(data_path, unit_path + "conv2_biases.npy", weights_layout),
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PadStrideInfo(1, 1, 1, 1))
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.set_name(unit_name + "conv2/convolution")
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<< BatchNormalizationLayer(
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get_weights_accessor(data_path, unit_path + "conv2_BatchNorm_moving_mean.npy"),
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get_weights_accessor(data_path, unit_path + "conv2_BatchNorm_moving_variance.npy"),
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get_weights_accessor(data_path, unit_path + "conv2_BatchNorm_gamma.npy"),
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get_weights_accessor(data_path, unit_path + "conv2_BatchNorm_beta.npy"),
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0.0000100099996416f)
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.set_name(unit_name + "conv2/BatchNorm")
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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "conv2/Relu");
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graph << EltwiseLayer(std::move(left), std::move(right), EltwiseOperation::Add).set_name(unit_name + "add");
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}
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};
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/** Main program for ResNet12
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*
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* Model is based on:
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* https://arxiv.org/pdf/1709.01118.pdf
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* "WESPE: Weakly Supervised Photo Enhancer for Digital Cameras"
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* Andrey Ignatov, Nikolay Kobyshev, Kenneth Vanhoey, Radu Timofte, Luc Van Gool
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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<GraphResNet12Example>(argc, argv);
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}
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