872 lines
57 KiB
C++
872 lines
57 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 InceptionV3's network using the Compute Library's graph API */
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class InceptionV3Example : public Example
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{
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public:
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InceptionV3Example()
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: cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "InceptionV3")
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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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std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>();
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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(299U, 299U, 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))
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<< ConvolutionLayer(3U, 3U, 32U,
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get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_1a_3x3_weights.npy", weights_layout),
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std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(2, 2, 0, 0))
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.set_name("Conv2d_1a_3x3/convolution")
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<< BatchNormalizationLayer(get_weights_accessor(data_path,
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"/cnn_data/inceptionv3_model/Conv2d_1a_3x3_BatchNorm_moving_mean.npy"),
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get_weights_accessor(data_path,
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"/cnn_data/inceptionv3_model/Conv2d_1a_3x3_BatchNorm_moving_variance.npy"),
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nullptr, get_weights_accessor(data_path,
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"/cnn_data/inceptionv3_model/Conv2d_1a_3x3_BatchNorm_beta.npy"),
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0.001f)
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.set_name("Conv2d_1a_3x3/BatchNorm/batchnorm")
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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_1a_3x3/Relu")
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<< ConvolutionLayer(3U, 3U, 32U,
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get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_2a_3x3_weights.npy", weights_layout),
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std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
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.set_name("Conv2d_2a_3x3/convolution")
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<< BatchNormalizationLayer(get_weights_accessor(data_path,
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"/cnn_data/inceptionv3_model/Conv2d_2a_3x3_BatchNorm_moving_mean.npy"),
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get_weights_accessor(data_path,
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"/cnn_data/inceptionv3_model/Conv2d_2a_3x3_BatchNorm_moving_variance.npy"),
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nullptr, get_weights_accessor(data_path,
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"/cnn_data/inceptionv3_model/Conv2d_2a_3x3_BatchNorm_beta.npy"),
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0.001f)
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.set_name("Conv2d_2a_3x3/BatchNorm/batchnorm")
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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_2a_3x3/Relu")
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<< ConvolutionLayer(3U, 3U, 64U,
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get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_2b_3x3_weights.npy", weights_layout),
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std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1))
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.set_name("Conv2d_2b_3x3/convolution")
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<< BatchNormalizationLayer(get_weights_accessor(data_path,
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"/cnn_data/inceptionv3_model/Conv2d_2b_3x3_BatchNorm_moving_mean.npy"),
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get_weights_accessor(data_path,
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"/cnn_data/inceptionv3_model/Conv2d_2b_3x3_BatchNorm_moving_variance.npy"),
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nullptr, get_weights_accessor(data_path,
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"/cnn_data/inceptionv3_model/Conv2d_2b_3x3_BatchNorm_beta.npy"),
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0.001f)
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.set_name("Conv2d_2b_3x3/BatchNorm/batchnorm")
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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_2b_3x3/Relu")
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<< PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("MaxPool_3a_3x3/MaxPool")
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<< ConvolutionLayer(1U, 1U, 80U,
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get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_3b_1x1_weights.npy", weights_layout),
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std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
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.set_name("Conv2d_3b_1x1/convolution")
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<< BatchNormalizationLayer(get_weights_accessor(data_path,
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"/cnn_data/inceptionv3_model/Conv2d_3b_1x1_BatchNorm_moving_mean.npy"),
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get_weights_accessor(data_path,
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"/cnn_data/inceptionv3_model/Conv2d_3b_1x1_BatchNorm_moving_variance.npy"),
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nullptr, get_weights_accessor(data_path,
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"/cnn_data/inceptionv3_model/Conv2d_3b_1x1_BatchNorm_beta.npy"),
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0.001f)
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.set_name("Conv2d_3b_1x1/BatchNorm/batchnorm")
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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_3b_1x1/Relu")
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<< ConvolutionLayer(3U, 3U, 192U,
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get_weights_accessor(data_path, "/cnn_data/inceptionv3_model/Conv2d_4a_3x3_weights.npy", weights_layout),
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std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
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.set_name("Conv2d_4a_3x3/convolution")
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<< BatchNormalizationLayer(get_weights_accessor(data_path,
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"/cnn_data/inceptionv3_model/Conv2d_4a_3x3_BatchNorm_moving_mean.npy"),
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get_weights_accessor(data_path,
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"/cnn_data/inceptionv3_model/Conv2d_4a_3x3_BatchNorm_moving_variance.npy"),
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nullptr, get_weights_accessor(data_path,
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"/cnn_data/inceptionv3_model/Conv2d_4a_3x3_BatchNorm_beta.npy"),
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0.001f)
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.set_name("Conv2d_4a_3x3/BatchNorm/batchnorm")
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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_4a_3x3/Relu")
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<< PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name("MaxPool_5a_3x3/MaxPool");
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graph << get_inception_node_A(data_path, "Mixed_5b", weights_layout, 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U),
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32U)
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.set_name("Mixed_5b/concat");
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graph << get_inception_node_A(data_path, "Mixed_5c", weights_layout, 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U),
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64U, true)
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.set_name("Mixed_5c/concat");
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graph << get_inception_node_A(data_path, "Mixed_5d", weights_layout, 64U, std::make_tuple(48U, 64U), std::make_tuple(64U, 96U, 96U),
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64U)
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.set_name("Mixed_5d/concat");
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graph << get_inception_node_B(data_path, "Mixed_6a", weights_layout, 384U, std::make_tuple(64U, 96U, 96U)).set_name("Mixed_6a/concat");
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graph << get_inception_node_C(data_path, "Mixed_6b", weights_layout, 192U, std::make_tuple(128U, 128U, 192U),
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std::make_tuple(128U, 128U, 128U, 128U, 192U), 192U)
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.set_name("Mixed_6b/concat");
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graph << get_inception_node_C(data_path, "Mixed_6c", weights_layout, 192U, std::make_tuple(160U, 160U, 192U),
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std::make_tuple(160U, 160U, 160U, 160U, 192U), 192U)
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.set_name("Mixed_6c/concat");
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graph << get_inception_node_C(data_path, "Mixed_6d", weights_layout, 192U, std::make_tuple(160U, 160U, 192U),
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std::make_tuple(160U, 160U, 160U, 160U, 192U), 192U)
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.set_name("Mixed_6d/concat");
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graph << get_inception_node_C(data_path, "Mixed_6e", weights_layout, 192U, std::make_tuple(192U, 192U, 192U),
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std::make_tuple(192U, 192U, 192U, 192U, 192U), 192U)
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.set_name("Mixed_6e/concat");
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graph << get_inception_node_D(data_path, "Mixed_7a", weights_layout, std::make_tuple(192U, 320U),
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std::make_tuple(192U, 192U, 192U, 192U))
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.set_name("Mixed_7a/concat");
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graph << get_inception_node_E(data_path, "Mixed_7b", weights_layout, 320U, std::make_tuple(384U, 384U, 384U),
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std::make_tuple(448U, 384U, 384U, 384U), 192U)
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.set_name("Mixed_7b/concat");
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graph << get_inception_node_E(data_path, "Mixed_7c", weights_layout, 320U, std::make_tuple(384U, 384U, 384U),
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std::make_tuple(448U, 384U, 384U, 384U), 192U, true)
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.set_name("Mixed_7c/concat");
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graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 8, operation_layout, PadStrideInfo(1, 1, 0, 0, DimensionRoundingType::CEIL))).set_name("Logits/AvgPool_1a_8x8/AvgPool")
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<< ConvolutionLayer(1U, 1U, 1001U, get_weights_accessor(data_path,
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"/cnn_data/inceptionv3_model/Logits_Conv2d_1c_1x1_weights.npy", weights_layout),
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get_weights_accessor(data_path,
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"/cnn_data/inceptionv3_model/Logits_Conv2d_1c_1x1_biases.npy"),
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PadStrideInfo(1, 1, 0, 0))
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.set_name("Logits/Conv2d_1c_1x1/convolution")
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<< ReshapeLayer(TensorShape(1001U)).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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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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private:
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ConcatLayer get_inception_node_A(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, unsigned int> c_filters,
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unsigned int d_filt,
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bool is_name_different = false)
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{
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std::string total_path = "/cnn_data/inceptionv3_model/" + param_path + "_";
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// This is due to a naming issue in the tf model
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std::string conv_id0 = "_0a_";
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std::string conv_id1 = "2d_0b_";
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if(is_name_different)
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{
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conv_id0 = "_0b_";
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conv_id1 = "_1_0c_";
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}
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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 + "Branch_0_Conv2d_0a_1x1_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(param_path + "/Branch_0/Conv2d_0a_1x1/convolution")
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<< BatchNormalizationLayer(
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get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
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get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
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nullptr,
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get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy"),
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0.001f)
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.set_name(param_path + "/Branch_0/Conv2d_0a_1x1/BatchNorm/batchnorm")
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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_0a_1x1/Relu");
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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 + "Branch_1_Conv2d" + conv_id0 + "1x1_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(param_path + "/Branch_1/Conv2d" + conv_id0 + "1x1/convolution")
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<< BatchNormalizationLayer(
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get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id0 + "1x1_BatchNorm_moving_mean.npy"),
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get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id0 + "1x1_BatchNorm_moving_variance.npy"),
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nullptr,
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get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id0 + "1x1_BatchNorm_beta.npy"),
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0.001f)
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.set_name(param_path + "/Branch_1/Conv2d" + conv_id0 + "1x1/BatchNorm/batchnorm")
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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d" + conv_id0 + "1x1/Relu")
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<< ConvolutionLayer(
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5U, 5U, std::get<1>(b_filters),
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get_weights_accessor(data_path, total_path + "Branch_1_Conv" + conv_id1 + "5x5_weights.npy", weights_layout),
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std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
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PadStrideInfo(1, 1, 2, 2))
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.set_name(param_path + "/Branch_1/Conv2d" + conv_id1 + "5x5/convolution")
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<< BatchNormalizationLayer(
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get_weights_accessor(data_path, total_path + "Branch_1_Conv" + conv_id1 + "5x5_BatchNorm_moving_mean.npy"),
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get_weights_accessor(data_path, total_path + "Branch_1_Conv" + conv_id1 + "5x5_BatchNorm_moving_variance.npy"),
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nullptr,
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get_weights_accessor(data_path, total_path + "Branch_1_Conv" + conv_id1 + "5x5_BatchNorm_beta.npy"),
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0.001f)
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.set_name(param_path + "/Branch_1/Conv2d" + conv_id1 + "5x5/BatchNorm/batchnorm")
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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d" + conv_id1 + "5x5/Relu");
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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 + "Branch_2_Conv2d_0a_1x1_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(param_path + "/Branch_2/Conv2d_0a_1x1/convolution")
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<< BatchNormalizationLayer(
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get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
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get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
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nullptr,
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get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_beta.npy"),
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0.001f)
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.set_name(param_path + "/Branch_2/Conv2d_0a_1x1/BatchNorm/batchnorm")
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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0a_1x1/Relu")
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<< ConvolutionLayer(
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3U, 3U, std::get<1>(c_filters),
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get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_weights.npy", weights_layout),
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std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
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PadStrideInfo(1, 1, 1, 1))
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.set_name(param_path + "/Branch_2/Conv2d_0b_3x3/convolution")
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<< BatchNormalizationLayer(
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get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"),
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get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"),
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nullptr,
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get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_beta.npy"),
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0.001f)
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.set_name(param_path + "/Branch_2/Conv2d_0b_3x3/BatchNorm/batchnorm")
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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0b_3x3/Relu")
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<< ConvolutionLayer(
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3U, 3U, std::get<2>(c_filters),
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get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_3x3_weights.npy", weights_layout),
|
|
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
|
|
PadStrideInfo(1, 1, 1, 1))
|
|
.set_name(param_path + "/Branch_2/Conv2d_0c_3x3/convolution")
|
|
<< BatchNormalizationLayer(
|
|
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_3x3_BatchNorm_moving_mean.npy"),
|
|
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_3x3_BatchNorm_moving_variance.npy"),
|
|
nullptr,
|
|
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_3x3_BatchNorm_beta.npy"),
|
|
0.001f)
|
|
.set_name(param_path + "/Branch_2/Conv2d_0c_3x3/BatchNorm/batcnorm")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0c_3x3/Relu");
|
|
|
|
SubStream i_d(graph);
|
|
i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, common_params.data_layout, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL),
|
|
true))
|
|
.set_name(param_path + "/Branch_3/AvgPool_0a_3x3/AvgPool")
|
|
<< ConvolutionLayer(
|
|
1U, 1U, d_filt,
|
|
get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy", weights_layout),
|
|
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
|
|
PadStrideInfo(1, 1, 0, 0))
|
|
.set_name(param_path + "/Branch_3/Conv2d_0b_1x1/convolution")
|
|
<< BatchNormalizationLayer(
|
|
get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_mean.npy"),
|
|
get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_variance.npy"),
|
|
nullptr,
|
|
get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_beta.npy"),
|
|
0.001f)
|
|
.set_name(param_path + "/Branch_3/Conv2d_0b_1x1/BatchNorm/batchnorm")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_3/Conv2d_0b_1x1/Relu");
|
|
|
|
return ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d));
|
|
}
|
|
|
|
ConcatLayer get_inception_node_B(const std::string &data_path, std::string &¶m_path, DataLayout weights_layout,
|
|
unsigned int a_filt,
|
|
std::tuple<unsigned int, unsigned int, unsigned int> b_filters)
|
|
{
|
|
std::string total_path = "/cnn_data/inceptionv3_model/" + param_path + "_";
|
|
SubStream i_a(graph);
|
|
i_a << ConvolutionLayer(
|
|
3U, 3U, a_filt,
|
|
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_1x1_weights.npy", weights_layout),
|
|
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
|
|
PadStrideInfo(2, 2, 0, 0))
|
|
.set_name(param_path + "/Branch_0/Conv2d_1a_1x1/convolution")
|
|
<< BatchNormalizationLayer(
|
|
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_1x1_BatchNorm_moving_mean.npy"),
|
|
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_1x1_BatchNorm_moving_variance.npy"),
|
|
nullptr,
|
|
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_1x1_BatchNorm_beta.npy"),
|
|
0.001f)
|
|
.set_name(param_path + "/Branch_0/Conv2d_1a_1x1/BatchNorm/batchnorm")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_1a_1x1/Relu");
|
|
|
|
SubStream i_b(graph);
|
|
i_b << ConvolutionLayer(
|
|
1U, 1U, std::get<0>(b_filters),
|
|
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy", weights_layout),
|
|
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
|
|
PadStrideInfo(1, 1, 0, 0))
|
|
.set_name(param_path + "/Branch_1/Conv2d_0a_1x1/convolution")
|
|
<< BatchNormalizationLayer(
|
|
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
|
|
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
|
|
nullptr,
|
|
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"),
|
|
0.001f)
|
|
.set_name(param_path + "/Branch_1/Conv2d_0a_1x1/BatchNorm/batchnorm")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0a_1x1/Relu")
|
|
<< ConvolutionLayer(
|
|
3U, 3U, std::get<1>(b_filters),
|
|
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_weights.npy", weights_layout),
|
|
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
|
|
PadStrideInfo(1, 1, 1, 1))
|
|
.set_name(param_path + "/Branch_1/Conv2d_0b_3x3/convolution")
|
|
<< BatchNormalizationLayer(
|
|
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"),
|
|
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"),
|
|
nullptr,
|
|
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_beta.npy"),
|
|
0.001f)
|
|
.set_name(param_path + "/Branch_1/Conv2d_0b_3x3/BatchNorm/batchnorm")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0b_3x3/Relu")
|
|
<< ConvolutionLayer(
|
|
3U, 3U, std::get<2>(b_filters),
|
|
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_1x1_weights.npy", weights_layout),
|
|
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
|
|
PadStrideInfo(2, 2, 0, 0))
|
|
.set_name(param_path + "/Branch_1/Conv2d_1a_1x1/convolution")
|
|
<< BatchNormalizationLayer(
|
|
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_1x1_BatchNorm_moving_mean.npy"),
|
|
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_1x1_BatchNorm_moving_variance.npy"),
|
|
nullptr,
|
|
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_1x1_BatchNorm_beta.npy"),
|
|
0.001f)
|
|
.set_name(param_path + "/Branch_1/Conv2d_1a_1x1/BatchNorm/batchnorm")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_1a_1x1/Relu");
|
|
|
|
SubStream i_c(graph);
|
|
i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, common_params.data_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name(param_path + "/Branch_2/MaxPool_1a_3x3/MaxPool");
|
|
|
|
return ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c));
|
|
}
|
|
|
|
ConcatLayer get_inception_node_C(const std::string &data_path, std::string &¶m_path, DataLayout weights_layout,
|
|
unsigned int a_filt,
|
|
std::tuple<unsigned int, unsigned int, unsigned int> b_filters,
|
|
std::tuple<unsigned int, unsigned int, unsigned int, unsigned int, unsigned int> c_filters,
|
|
unsigned int d_filt)
|
|
{
|
|
std::string total_path = "/cnn_data/inceptionv3_model/" + param_path + "_";
|
|
SubStream i_a(graph);
|
|
i_a << ConvolutionLayer(
|
|
1U, 1U, a_filt,
|
|
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy", weights_layout),
|
|
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
|
|
PadStrideInfo(1, 1, 0, 0))
|
|
.set_name(param_path + "/Branch_0/Conv2d_0a_1x1/convolution")
|
|
<< BatchNormalizationLayer(
|
|
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
|
|
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
|
|
nullptr,
|
|
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy"),
|
|
0.001f)
|
|
.set_name(param_path + "/Branch_0/Conv2d_0a_1x1/BatchNorm/batchnorm")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_0a_1x1/Relu");
|
|
|
|
SubStream i_b(graph);
|
|
i_b << ConvolutionLayer(
|
|
1U, 1U, std::get<0>(b_filters),
|
|
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy", weights_layout),
|
|
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
|
|
PadStrideInfo(1, 1, 0, 0))
|
|
.set_name(param_path + "/Branch_1/Conv2d_0a_1x1/convolution")
|
|
<< BatchNormalizationLayer(
|
|
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
|
|
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
|
|
nullptr,
|
|
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"),
|
|
0.001f)
|
|
.set_name(param_path + "/Branch_1/Conv2d_0a_1x1/BatchNorm/batchnorm")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0a_1x1/Relu")
|
|
<< ConvolutionLayer(
|
|
7U, 1U, std::get<1>(b_filters),
|
|
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_weights.npy", weights_layout),
|
|
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
|
|
PadStrideInfo(1, 1, 3, 0))
|
|
.set_name(param_path + "/Branch_1/Conv2d_0b_1x7/convolution")
|
|
<< BatchNormalizationLayer(
|
|
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_mean.npy"),
|
|
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_variance.npy"),
|
|
nullptr,
|
|
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_beta.npy"),
|
|
0.001f)
|
|
.set_name(param_path + "/Branch_1/Conv2d_0b_1x7/BatchNorm/batchnorm")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0b_1x7/Relu")
|
|
<< ConvolutionLayer(
|
|
1U, 7U, std::get<2>(b_filters),
|
|
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_weights.npy", weights_layout),
|
|
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
|
|
PadStrideInfo(1, 1, 0, 3))
|
|
.set_name(param_path + "/Branch_1/Conv2d_0c_7x1/convolution")
|
|
<< BatchNormalizationLayer(
|
|
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_mean.npy"),
|
|
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_variance.npy"),
|
|
nullptr,
|
|
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_beta.npy"),
|
|
0.001f)
|
|
.set_name(param_path + "/Branch_1/Conv2d_0c_7x1/BatchNorm/batchnorm")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_0c_7x1/Relu");
|
|
|
|
SubStream i_c(graph);
|
|
i_c << ConvolutionLayer(
|
|
1U, 1U, std::get<0>(c_filters),
|
|
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_weights.npy", weights_layout),
|
|
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
|
|
PadStrideInfo(1, 1, 0, 0))
|
|
.set_name(param_path + "/Branch_2/Conv2d_0a_1x1/convolution")
|
|
<< BatchNormalizationLayer(
|
|
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
|
|
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
|
|
nullptr,
|
|
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_beta.npy"),
|
|
0.001f)
|
|
.set_name(param_path + "/Branch_2/Conv2d_0a_1x1/BatchNorm/batchnorm")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0a_1x1/Relu")
|
|
<< ConvolutionLayer(
|
|
1U, 7U, std::get<1>(c_filters),
|
|
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_7x1_weights.npy", weights_layout),
|
|
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
|
|
PadStrideInfo(1, 1, 0, 3))
|
|
.set_name(param_path + "/Branch_2/Conv2d_0b_7x1/convolution")
|
|
<< BatchNormalizationLayer(
|
|
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_7x1_BatchNorm_moving_mean.npy"),
|
|
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_7x1_BatchNorm_moving_variance.npy"),
|
|
nullptr,
|
|
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_7x1_BatchNorm_beta.npy"),
|
|
0.001f)
|
|
.set_name(param_path + "/Branch_2/Conv2d_0b_7x1/BatchNorm/batchnorm")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0b_7x1/Relu")
|
|
<< ConvolutionLayer(
|
|
7U, 1U, std::get<2>(c_filters),
|
|
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x7_weights.npy", weights_layout),
|
|
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
|
|
PadStrideInfo(1, 1, 3, 0))
|
|
.set_name(param_path + "/Branch_2/Conv2d_0c_1x7/convolution")
|
|
<< BatchNormalizationLayer(
|
|
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x7_BatchNorm_moving_mean.npy"),
|
|
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x7_BatchNorm_moving_variance.npy"),
|
|
nullptr,
|
|
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x7_BatchNorm_beta.npy"),
|
|
0.001f)
|
|
.set_name(param_path + "/Branch_2/Conv2d_0c_1x7/BatchNorm/batchnorm")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0c_1x7/Relu")
|
|
<< ConvolutionLayer(
|
|
1U, 7U, std::get<3>(c_filters),
|
|
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_7x1_weights.npy", weights_layout),
|
|
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
|
|
PadStrideInfo(1, 1, 0, 3))
|
|
.set_name(param_path + "/Branch_2/Conv2d_0d_7x1/convolution")
|
|
<< BatchNormalizationLayer(
|
|
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_7x1_BatchNorm_moving_mean.npy"),
|
|
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_7x1_BatchNorm_moving_variance.npy"),
|
|
nullptr,
|
|
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_7x1_BatchNorm_beta.npy"),
|
|
0.001f)
|
|
.set_name(param_path + "/Branch_2/Conv2d_0d_7x1/BatchNorm/batchnorm")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0d_7x1/Relu")
|
|
<< ConvolutionLayer(
|
|
7U, 1U, std::get<4>(c_filters),
|
|
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_1x7_weights.npy", weights_layout),
|
|
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
|
|
PadStrideInfo(1, 1, 3, 0))
|
|
.set_name(param_path + "/Branch_2/Conv2d_0e_1x7/convolution")
|
|
<< BatchNormalizationLayer(
|
|
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_1x7_BatchNorm_moving_mean.npy"),
|
|
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_1x7_BatchNorm_moving_variance.npy"),
|
|
nullptr,
|
|
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0e_1x7_BatchNorm_beta.npy"),
|
|
0.001f)
|
|
.set_name(param_path + "/Branch_2/Conv2d_0e_1x7/BatchNorm/batchnorm")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0e_1x7/Relu");
|
|
|
|
SubStream i_d(graph);
|
|
i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, common_params.data_layout, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL),
|
|
true))
|
|
.set_name(param_path + "/Branch_3/AvgPool_0a_3x3/AvgPool")
|
|
<< ConvolutionLayer(
|
|
1U, 1U, d_filt,
|
|
get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy", weights_layout),
|
|
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
|
|
PadStrideInfo(1, 1, 0, 0))
|
|
.set_name(param_path + "/Branch_3/Conv2d_0b_1x1/convolution")
|
|
<< BatchNormalizationLayer(
|
|
get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_mean.npy"),
|
|
get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_variance.npy"),
|
|
nullptr,
|
|
get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_beta.npy"),
|
|
0.001f)
|
|
.set_name(param_path + "/Branch_3/Conv2d_0b_1x1/BatchNorm/batchnorm")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_3/Conv2d_0b_1x1/Relu");
|
|
|
|
return ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d));
|
|
}
|
|
|
|
ConcatLayer get_inception_node_D(const std::string &data_path, std::string &¶m_path, DataLayout weights_layout,
|
|
std::tuple<unsigned int, unsigned int> a_filters,
|
|
std::tuple<unsigned int, unsigned int, unsigned int, unsigned int> b_filters)
|
|
{
|
|
std::string total_path = "/cnn_data/inceptionv3_model/" + param_path + "_";
|
|
SubStream i_a(graph);
|
|
i_a << ConvolutionLayer(
|
|
1U, 1U, std::get<0>(a_filters),
|
|
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy", weights_layout),
|
|
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
|
|
PadStrideInfo(1, 1, 0, 0))
|
|
.set_name(param_path + "/Branch_0/Conv2d_0a_1x1/convolution")
|
|
<< BatchNormalizationLayer(
|
|
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
|
|
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
|
|
nullptr,
|
|
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy"),
|
|
0.001f)
|
|
.set_name(param_path + "/Branch_0/Conv2d_0a_1x1/BatchNorm/batchnorm")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_0a_1x1/Relu")
|
|
<< ConvolutionLayer(
|
|
3U, 3U, std::get<1>(a_filters),
|
|
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_weights.npy", weights_layout),
|
|
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
|
|
PadStrideInfo(2, 2, 0, 0))
|
|
.set_name(param_path + "/Branch_0/Conv2d_1a_3x3/convolution")
|
|
<< BatchNormalizationLayer(
|
|
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"),
|
|
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"),
|
|
nullptr,
|
|
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_1a_3x3_BatchNorm_beta.npy"),
|
|
0.001f)
|
|
.set_name(param_path + "/Branch_0/Conv2d_1a_3x3/BatchNorm/batchnorm")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_1a_3x3/Relu");
|
|
|
|
SubStream i_b(graph);
|
|
i_b << ConvolutionLayer(
|
|
1U, 1U, std::get<0>(b_filters),
|
|
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy", weights_layout),
|
|
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
|
|
PadStrideInfo(1, 1, 0, 0))
|
|
.set_name(param_path + "/Branch_1/Conv2d_0a_1x1/convolution")
|
|
<< BatchNormalizationLayer(
|
|
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
|
|
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
|
|
nullptr,
|
|
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"),
|
|
0.001f)
|
|
.set_name(param_path + "/Branch_1/Conv2d_0a_1x1/BatchNorm/batchnorm")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0a_1x1/Relu")
|
|
<< ConvolutionLayer(
|
|
7U, 1U, std::get<1>(b_filters),
|
|
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_weights.npy", weights_layout),
|
|
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
|
|
PadStrideInfo(1, 1, 3, 0))
|
|
.set_name(param_path + "/Branch_1/Conv2d_0b_1x7/convolution")
|
|
<< BatchNormalizationLayer(
|
|
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_mean.npy"),
|
|
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_variance.npy"),
|
|
nullptr,
|
|
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_beta.npy"),
|
|
0.001f)
|
|
.set_name(param_path + "/Branch_1/Conv2d_0b_1x7/BatchNorm/batchnorm")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0b_1x7/Relu")
|
|
<< ConvolutionLayer(
|
|
1U, 7U, std::get<2>(b_filters),
|
|
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_weights.npy", weights_layout),
|
|
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
|
|
PadStrideInfo(1, 1, 0, 3))
|
|
.set_name(param_path + "/Branch_1/Conv2d_0c_7x1/convolution")
|
|
<< BatchNormalizationLayer(
|
|
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_mean.npy"),
|
|
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_variance.npy"),
|
|
nullptr,
|
|
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_beta.npy"),
|
|
0.001f)
|
|
.set_name(param_path + "/Branch_1/Conv2d_0c_7x1/BatchNorm/batchnorm")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0c_7x1/Relu")
|
|
<< ConvolutionLayer(
|
|
3U, 3U, std::get<3>(b_filters),
|
|
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_weights.npy", weights_layout),
|
|
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
|
|
PadStrideInfo(2, 2, 0, 0))
|
|
.set_name(param_path + "/Branch_1/Conv2d_1a_3x3/convolution")
|
|
<< BatchNormalizationLayer(
|
|
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"),
|
|
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"),
|
|
nullptr,
|
|
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_1a_3x3_BatchNorm_beta.npy"),
|
|
0.001f)
|
|
.set_name(param_path + "/Branch_1/Conv2d_1a_3x3/BatchNorm/batchnorm")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_1a_3x3/Relu");
|
|
|
|
SubStream i_c(graph);
|
|
i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, common_params.data_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))).set_name(param_path + "/Branch_2/MaxPool_1a_3x3/MaxPool");
|
|
|
|
return ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c));
|
|
}
|
|
|
|
ConcatLayer get_inception_node_E(const std::string &data_path, std::string &¶m_path, DataLayout weights_layout,
|
|
unsigned int a_filt,
|
|
std::tuple<unsigned int, unsigned int, unsigned int> b_filters,
|
|
std::tuple<unsigned int, unsigned int, unsigned int, unsigned int> c_filters,
|
|
unsigned int d_filt,
|
|
bool is_name_different = false)
|
|
{
|
|
// This is due to a naming issue in the tf model
|
|
std::string conv_id = "_0b_";
|
|
if(is_name_different)
|
|
{
|
|
conv_id = "_0c_";
|
|
}
|
|
|
|
std::string total_path = "/cnn_data/inceptionv3_model/" + param_path + "_";
|
|
SubStream i_a(graph);
|
|
i_a << ConvolutionLayer(
|
|
1U, 1U, a_filt,
|
|
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_weights.npy", weights_layout),
|
|
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
|
|
PadStrideInfo(1, 1, 0, 0))
|
|
.set_name(param_path + "/Branch_0/Conv2d_0a_1x1/convolution")
|
|
<< BatchNormalizationLayer(
|
|
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
|
|
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
|
|
nullptr,
|
|
get_weights_accessor(data_path, total_path + "Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy"),
|
|
0.001f)
|
|
.set_name(param_path + "/Branch_0/Conv2d_0a_1x1/BatchNorm/batchnorm")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_0/Conv2d_0a_1x1/Relu");
|
|
|
|
SubStream i_b(graph);
|
|
i_b << ConvolutionLayer(
|
|
1U, 1U, std::get<0>(b_filters),
|
|
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_weights.npy", weights_layout),
|
|
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
|
|
PadStrideInfo(1, 1, 0, 0))
|
|
.set_name(param_path + "/Branch_1/Conv2d_0a_1x1/convolution")
|
|
<< BatchNormalizationLayer(
|
|
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
|
|
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
|
|
nullptr,
|
|
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"),
|
|
0.001f)
|
|
.set_name(param_path + "/Branch_1/Conv2d_0a_1x1/BatchNorm/batchnorm")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0a_1x1/Relu");
|
|
|
|
SubStream i_b1(i_b);
|
|
i_b1 << ConvolutionLayer(
|
|
3U, 1U, std::get<1>(b_filters),
|
|
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x3_weights.npy", weights_layout),
|
|
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
|
|
PadStrideInfo(1, 1, 1, 0))
|
|
.set_name(param_path + "/Branch_1/Conv2d_0b_1x3/convolution")
|
|
<< BatchNormalizationLayer(
|
|
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x3_BatchNorm_moving_mean.npy"),
|
|
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x3_BatchNorm_moving_variance.npy"),
|
|
nullptr,
|
|
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d_0b_1x3_BatchNorm_beta.npy"),
|
|
0.001f)
|
|
.set_name(param_path + "/Branch_1/Conv2d_0b_1x3/BatchNorm/batchnorm")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d_0b_1x3/Relu");
|
|
|
|
SubStream i_b2(i_b);
|
|
i_b2 << ConvolutionLayer(
|
|
1U, 3U, std::get<2>(b_filters),
|
|
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id + "3x1_weights.npy", weights_layout),
|
|
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
|
|
PadStrideInfo(1, 1, 0, 1))
|
|
.set_name(param_path + "/Branch_1/Conv2d" + conv_id + "3x1/convolution")
|
|
<< BatchNormalizationLayer(
|
|
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id + "3x1_BatchNorm_moving_mean.npy"),
|
|
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id + "3x1_BatchNorm_moving_variance.npy"),
|
|
nullptr,
|
|
get_weights_accessor(data_path, total_path + "Branch_1_Conv2d" + conv_id + "3x1_BatchNorm_beta.npy"),
|
|
0.001f)
|
|
.set_name(param_path + "/Branch_1/Conv2d" + conv_id + "3x1/BatchNorm/batchnorm")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_1/Conv2d" + conv_id + "3x1/Relu");
|
|
|
|
// Merge b1 and b2
|
|
i_b << ConcatLayer(std::move(i_b1), std::move(i_b2)).set_name(param_path + "/Branch_1/concat");
|
|
|
|
SubStream i_c(graph);
|
|
i_c << ConvolutionLayer(
|
|
1U, 1U, std::get<0>(c_filters),
|
|
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_weights.npy", weights_layout),
|
|
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
|
|
PadStrideInfo(1, 1, 0, 0))
|
|
.set_name(param_path + "/Branch_2/Conv2d_0a_1x1/convolution")
|
|
<< BatchNormalizationLayer(
|
|
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
|
|
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
|
|
nullptr,
|
|
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_beta.npy"),
|
|
0.001f)
|
|
.set_name(param_path + "/Branch_2/Conv2d_0a_1x1/BatchNorm/batchnorm")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0a_1x1/Relu")
|
|
<< ConvolutionLayer(
|
|
3U, 3U, std::get<1>(c_filters),
|
|
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_weights.npy", weights_layout),
|
|
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
|
|
PadStrideInfo(1, 1, 1, 1))
|
|
.set_name(param_path + "/Branch_2/Conv2d_0b_3x3/convolution")
|
|
<< BatchNormalizationLayer(
|
|
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"),
|
|
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"),
|
|
nullptr,
|
|
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_beta.npy"),
|
|
0.001f)
|
|
.set_name(param_path + "/Branch_2/Conv2d_0b_3x3/BatchNorm/batchnorm")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0b_3x3/Relu");
|
|
|
|
SubStream i_c1(i_c);
|
|
i_c1 << ConvolutionLayer(
|
|
3U, 1U, std::get<2>(c_filters),
|
|
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x3_weights.npy", weights_layout),
|
|
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
|
|
PadStrideInfo(1, 1, 1, 0))
|
|
.set_name(param_path + "/Branch_2/Conv2d_0c_1x3/convolution")
|
|
<< BatchNormalizationLayer(
|
|
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x3_BatchNorm_moving_mean.npy"),
|
|
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x3_BatchNorm_moving_variance.npy"),
|
|
nullptr,
|
|
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0c_1x3_BatchNorm_beta.npy"),
|
|
0.001f)
|
|
.set_name(param_path + "/Branch_2/Conv2d_0c_1x3/BatchNorm/batchnorm")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0c_1x3/Relu");
|
|
|
|
SubStream i_c2(i_c);
|
|
i_c2 << ConvolutionLayer(
|
|
1U, 3U, std::get<3>(c_filters),
|
|
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_3x1_weights.npy", weights_layout),
|
|
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
|
|
PadStrideInfo(1, 1, 0, 1))
|
|
.set_name(param_path + "/Branch_2/Conv2d_0d_3x1/convolution")
|
|
<< BatchNormalizationLayer(
|
|
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_3x1_BatchNorm_moving_mean.npy"),
|
|
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_3x1_BatchNorm_moving_variance.npy"),
|
|
nullptr,
|
|
get_weights_accessor(data_path, total_path + "Branch_2_Conv2d_0d_3x1_BatchNorm_beta.npy"),
|
|
0.001f)
|
|
.set_name(param_path + "/Branch_2/Conv2d_0d_3x1/BatchNorm/batchnorm")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_2/Conv2d_0d_3x1/Relu");
|
|
|
|
// Merge i_c1 and i_c2
|
|
i_c << ConcatLayer(std::move(i_c1), std::move(i_c2)).set_name(param_path + "/Branch_2/concat");
|
|
|
|
SubStream i_d(graph);
|
|
i_d << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 3, common_params.data_layout, PadStrideInfo(1, 1, 1, 1, DimensionRoundingType::CEIL),
|
|
true))
|
|
.set_name(param_path + "/Branch_3/AvgPool_0a_3x3/AvgPool")
|
|
<< ConvolutionLayer(
|
|
1U, 1U, d_filt,
|
|
get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_weights.npy", weights_layout),
|
|
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
|
|
PadStrideInfo(1, 1, 0, 0))
|
|
.set_name(param_path + "/Branch_3/Conv2d_0b_1x1/convolution")
|
|
<< BatchNormalizationLayer(
|
|
get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_mean.npy"),
|
|
get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_moving_variance.npy"),
|
|
nullptr,
|
|
get_weights_accessor(data_path, total_path + "Branch_3_Conv2d_0b_1x1_BatchNorm_beta.npy"),
|
|
0.001f)
|
|
.set_name(param_path + "/Branch_3/Conv2d_0b_1x1/BatchNorm/batchnorm")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(param_path + "/Branch_3/Conv2d_0b_1x1/Relu");
|
|
|
|
return ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d));
|
|
}
|
|
};
|
|
|
|
/** Main program for Inception V3
|
|
*
|
|
* Model is based on:
|
|
* https://arxiv.org/abs/1512.00567
|
|
* "Rethinking the Inception Architecture for Computer Vision"
|
|
* Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, Zbigniew Wojna
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*
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* Provenance: download.tensorflow.org/models/inception_v3_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<InceptionV3Example>(argc, argv);
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}
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