257 lines
12 KiB
C++
257 lines
12 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 ResNetV1_50 network using the Compute Library's graph API */
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class GraphResNetV1_50Example : public Example
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{
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public:
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GraphResNetV1_50Example()
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: cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "ResNetV1_50")
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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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// 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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false /* Do not convert to BGR */);
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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), false /* Do not convert to BGR */))
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<< ConvolutionLayer(
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7U, 7U, 64U,
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get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_weights.npy", weights_layout),
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std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
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PadStrideInfo(2, 2, 3, 3))
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.set_name("conv1/convolution")
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<< BatchNormalizationLayer(
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get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_BatchNorm_moving_mean.npy"),
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get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_BatchNorm_moving_variance.npy"),
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get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_BatchNorm_gamma.npy"),
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get_weights_accessor(data_path, "/cnn_data/resnet50_model/conv1_BatchNorm_beta.npy"),
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0.0000100099996416f)
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.set_name("conv1/BatchNorm")
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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv1/Relu")
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<< PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR))).set_name("pool1/MaxPool");
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add_residual_block(data_path, "block1", weights_layout, 64, 3, 2);
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add_residual_block(data_path, "block2", weights_layout, 128, 4, 2);
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add_residual_block(data_path, "block3", weights_layout, 256, 6, 2);
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add_residual_block(data_path, "block4", weights_layout, 512, 3, 1);
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graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, operation_layout)).set_name("pool5")
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<< ConvolutionLayer(
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1U, 1U, 1000U,
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get_weights_accessor(data_path, "/cnn_data/resnet50_model/logits_weights.npy", weights_layout),
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get_weights_accessor(data_path, "/cnn_data/resnet50_model/logits_biases.npy"),
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PadStrideInfo(1, 1, 0, 0))
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.set_name("logits/convolution")
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<< FlattenLayer().set_name("predictions/Reshape")
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<< SoftmaxLayer().set_name("predictions/Softmax")
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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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config.convert_to_uint8 = (common_params.data_type == DataType::QASYMM8);
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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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void add_residual_block(const std::string &data_path, const std::string &name, DataLayout weights_layout,
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unsigned int base_depth, unsigned int num_units, unsigned int stride)
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{
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for(unsigned int i = 0; i < num_units; ++i)
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{
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std::stringstream unit_path_ss;
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unit_path_ss << "/cnn_data/resnet50_model/" << name << "_unit_" << (i + 1) << "_bottleneck_v1_";
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std::stringstream unit_name_ss;
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unit_name_ss << name << "/unit" << (i + 1) << "/bottleneck_v1/";
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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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unsigned int middle_stride = 1;
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if(i == (num_units - 1))
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{
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middle_stride = stride;
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}
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SubStream right(graph);
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right << ConvolutionLayer(
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1U, 1U, base_depth,
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get_weights_accessor(data_path, unit_path + "conv1_weights.npy", weights_layout),
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std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
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PadStrideInfo(1, 1, 0, 0))
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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, base_depth,
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get_weights_accessor(data_path, unit_path + "conv2_weights.npy", weights_layout),
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std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
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PadStrideInfo(middle_stride, middle_stride, 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 + "conv1/Relu")
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<< ConvolutionLayer(
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1U, 1U, base_depth * 4,
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get_weights_accessor(data_path, unit_path + "conv3_weights.npy", weights_layout),
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std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
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PadStrideInfo(1, 1, 0, 0))
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.set_name(unit_name + "conv3/convolution")
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<< BatchNormalizationLayer(
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get_weights_accessor(data_path, unit_path + "conv3_BatchNorm_moving_mean.npy"),
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get_weights_accessor(data_path, unit_path + "conv3_BatchNorm_moving_variance.npy"),
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get_weights_accessor(data_path, unit_path + "conv3_BatchNorm_gamma.npy"),
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get_weights_accessor(data_path, unit_path + "conv3_BatchNorm_beta.npy"),
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0.0000100099996416f)
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.set_name(unit_name + "conv2/BatchNorm");
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if(i == 0)
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{
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SubStream left(graph);
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left << ConvolutionLayer(
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1U, 1U, base_depth * 4,
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get_weights_accessor(data_path, unit_path + "shortcut_weights.npy", weights_layout),
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std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
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PadStrideInfo(1, 1, 0, 0))
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.set_name(unit_name + "shortcut/convolution")
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<< BatchNormalizationLayer(
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get_weights_accessor(data_path, unit_path + "shortcut_BatchNorm_moving_mean.npy"),
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get_weights_accessor(data_path, unit_path + "shortcut_BatchNorm_moving_variance.npy"),
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get_weights_accessor(data_path, unit_path + "shortcut_BatchNorm_gamma.npy"),
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get_weights_accessor(data_path, unit_path + "shortcut_BatchNorm_beta.npy"),
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0.0000100099996416f)
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.set_name(unit_name + "shortcut/BatchNorm");
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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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else if(middle_stride > 1)
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{
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SubStream left(graph);
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left << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 1, common_params.data_layout, PadStrideInfo(middle_stride, middle_stride, 0, 0), true)).set_name(unit_name + "shortcut/MaxPool");
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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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else
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{
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SubStream left(graph);
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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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graph << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Relu");
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}
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}
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};
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/** Main program for ResNetV1_50
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*
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* Model is based on:
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* https://arxiv.org/abs/1512.03385
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* "Deep Residual Learning for Image Recognition"
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* Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
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*
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* Provenance: download.tensorflow.org/models/resnet_v1_50_2016_08_28.tar.gz
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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<GraphResNetV1_50Example>(argc, argv);
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}
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