464 lines
27 KiB
C++
464 lines
27 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;
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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 MobileNetV2's network using the Compute Library's graph API */
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class GraphMobilenetV2Example : public Example
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{
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public:
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GraphMobilenetV2Example()
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: cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "MobileNetV2")
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{
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}
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GraphMobilenetV2Example(const GraphMobilenetV2Example &) = delete;
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GraphMobilenetV2Example &operator=(const GraphMobilenetV2Example &) = delete;
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~GraphMobilenetV2Example() 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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// Print parameter values
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std::cout << common_params << std::endl;
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// Create input descriptor
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const TensorShape tensor_shape = permute_shape(TensorShape(224U, 224U, 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 graph hints
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graph << common_params.target
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<< common_params.fast_math_hint;
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// Create core graph
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if(arm_compute::is_data_type_float(common_params.data_type))
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{
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create_graph_float(input_descriptor);
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}
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else
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{
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create_graph_qasymm8(input_descriptor);
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}
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// Create common tail
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graph << 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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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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private:
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enum class IsResidual
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{
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Yes,
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No
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};
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enum class HasExpand
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{
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Yes,
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No
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};
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private:
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void create_graph_float(TensorDescriptor &input_descriptor)
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{
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// Create model path
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const std::string model_path = "/cnn_data/mobilenet_v2_1.0_224_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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// Get trainable parameters data path
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std::string data_path = common_params.data_path;
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// Add model path to data path
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if(!data_path.empty())
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{
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data_path += model_path;
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}
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graph << 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, "Conv_weights.npy", DataLayout::NCHW),
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std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
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PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL))
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.set_name("Conv")
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<< BatchNormalizationLayer(get_weights_accessor(data_path, "Conv_BatchNorm_moving_mean.npy"),
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get_weights_accessor(data_path, "Conv_BatchNorm_moving_variance.npy"),
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get_weights_accessor(data_path, "Conv_BatchNorm_gamma.npy"),
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get_weights_accessor(data_path, "Conv_BatchNorm_beta.npy"),
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0.0010000000474974513f)
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.set_name("Conv/BatchNorm")
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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f))
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.set_name("Conv/Relu6");
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get_expanded_conv_float(data_path, "expanded_conv", 32U, 16U, PadStrideInfo(1, 1, 1, 1));
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get_expanded_conv_float(data_path, "expanded_conv_1", 16U, 24U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), HasExpand::Yes);
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get_expanded_conv_float(data_path, "expanded_conv_2", 24U, 24U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes);
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get_expanded_conv_float(data_path, "expanded_conv_3", 24U, 32U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), HasExpand::Yes);
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get_expanded_conv_float(data_path, "expanded_conv_4", 32U, 32U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes);
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get_expanded_conv_float(data_path, "expanded_conv_5", 32U, 32U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes);
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get_expanded_conv_float(data_path, "expanded_conv_6", 32U, 64U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), HasExpand::Yes);
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get_expanded_conv_float(data_path, "expanded_conv_7", 64U, 64U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes);
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get_expanded_conv_float(data_path, "expanded_conv_8", 64U, 64U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes);
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get_expanded_conv_float(data_path, "expanded_conv_9", 64U, 64U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes);
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get_expanded_conv_float(data_path, "expanded_conv_10", 64U, 96U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes);
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get_expanded_conv_float(data_path, "expanded_conv_11", 96U, 96U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes);
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get_expanded_conv_float(data_path, "expanded_conv_12", 96U, 96U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes);
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get_expanded_conv_float(data_path, "expanded_conv_13", 96U, 160U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), HasExpand::Yes);
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get_expanded_conv_float(data_path, "expanded_conv_14", 160U, 160U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes);
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get_expanded_conv_float(data_path, "expanded_conv_15", 160U, 160U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes, IsResidual::Yes);
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get_expanded_conv_float(data_path, "expanded_conv_16", 160U, 320U, PadStrideInfo(1, 1, 1, 1), HasExpand::Yes);
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graph << ConvolutionLayer(1U, 1U, 1280U,
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get_weights_accessor(data_path, "Conv_1_weights.npy", DataLayout::NCHW),
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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("Conv_1")
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<< BatchNormalizationLayer(get_weights_accessor(data_path, "Conv_1_BatchNorm_moving_mean.npy"),
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get_weights_accessor(data_path, "Conv_1_BatchNorm_moving_variance.npy"),
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get_weights_accessor(data_path, "Conv_1_BatchNorm_gamma.npy"),
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get_weights_accessor(data_path, "Conv_1_BatchNorm_beta.npy"),
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0.0010000000474974513f)
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.set_name("Conv_1/BatchNorm")
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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f))
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.set_name("Conv_1/Relu6")
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<< PoolingLayer(PoolingLayerInfo(PoolingType::AVG, common_params.data_layout)).set_name("Logits/AvgPool")
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<< ConvolutionLayer(1U, 1U, 1001U,
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get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_weights.npy", DataLayout::NCHW),
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get_weights_accessor(data_path, "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");
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}
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void get_expanded_conv_float(const std::string &data_path, std::string &¶m_path,
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unsigned int input_channels, unsigned int output_channels,
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PadStrideInfo dwc_pad_stride_info,
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HasExpand has_expand = HasExpand::No, IsResidual is_residual = IsResidual::No,
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unsigned int expansion_size = 6)
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{
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std::string total_path = param_path + "_";
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SubStream left(graph);
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// Add expand node
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if(has_expand == HasExpand::Yes)
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{
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left << ConvolutionLayer(1U, 1U, input_channels * expansion_size,
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get_weights_accessor(data_path, total_path + "expand_weights.npy", DataLayout::NCHW),
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std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
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.set_name(param_path + "/expand/Conv2D")
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<< BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "expand_BatchNorm_moving_mean.npy"),
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get_weights_accessor(data_path, total_path + "expand_BatchNorm_moving_variance.npy"),
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get_weights_accessor(data_path, total_path + "expand_BatchNorm_gamma.npy"),
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get_weights_accessor(data_path, total_path + "expand_BatchNorm_beta.npy"),
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0.0010000000474974513f)
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.set_name(param_path + "/expand/BatchNorm")
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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f))
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.set_name(param_path + "/expand/Relu6");
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}
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// Add depthwise node
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left << DepthwiseConvolutionLayer(3U, 3U,
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get_weights_accessor(data_path, total_path + "depthwise_depthwise_weights.npy", DataLayout::NCHW),
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std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
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dwc_pad_stride_info)
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.set_name(param_path + "/depthwise/depthwise")
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<< BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_moving_mean.npy"),
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get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_moving_variance.npy"),
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get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_gamma.npy"),
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get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_beta.npy"),
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0.0010000000474974513f)
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.set_name(param_path + "/depthwise/BatchNorm")
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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f))
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.set_name(param_path + "/depthwise/Relu6");
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// Add project node
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left << ConvolutionLayer(1U, 1U, output_channels,
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get_weights_accessor(data_path, total_path + "project_weights.npy", DataLayout::NCHW),
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std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
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.set_name(param_path + "/project/Conv2D")
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<< BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "project_BatchNorm_moving_mean.npy"),
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get_weights_accessor(data_path, total_path + "project_BatchNorm_moving_variance.npy"),
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get_weights_accessor(data_path, total_path + "project_BatchNorm_gamma.npy"),
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get_weights_accessor(data_path, total_path + "project_BatchNorm_beta.npy"),
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0.0010000000474974513)
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.set_name(param_path + "/project/BatchNorm");
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if(is_residual == IsResidual::Yes)
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{
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// Add residual node
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SubStream right(graph);
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graph << EltwiseLayer(std::move(left), std::move(right), EltwiseOperation::Add).set_name(param_path + "/add");
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}
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else
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{
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graph.forward_tail(left.tail_node());
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}
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}
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void create_graph_qasymm8(TensorDescriptor &input_descriptor)
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{
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// Create model path
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const std::string model_path = "/cnn_data/mobilenet_v2_1.0_224_quantized_model/";
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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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// Add model path to data path
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if(!data_path.empty())
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{
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data_path += model_path;
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}
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const QuantizationInfo in_quant_info = QuantizationInfo(0.0078125f, 128);
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const QuantizationInfo mid_quant_info = QuantizationInfo(0.023528477177023888f, 128);
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const std::vector<QuantizationInfo> conv_weights_quant_info =
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{
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QuantizationInfo(0.03396892547607422f, 122), // Conv
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QuantizationInfo(0.005167067516595125f, 125), // Conv1
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QuantizationInfo(0.0016910821432247758f, 113) // Conv2d_1c_1x1
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};
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// Pointwise expand convolution quantization info
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const std::vector<QuantizationInfo> pwc_q =
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{
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QuantizationInfo(0.254282623529f, 129), // expand_0 (Dummy)
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QuantizationInfo(0.009758507832884789f, 127), // expand_1
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QuantizationInfo(0.0036556976847350597f, 144), // expand_2
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QuantizationInfo(0.0029988749884068966f, 104), // expand_3
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QuantizationInfo(0.0019244228024035692f, 128), // expand_4
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QuantizationInfo(0.0013649158645421267f, 135), // expand_5
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QuantizationInfo(0.0019170437008142471f, 127), // expand_6
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QuantizationInfo(0.0015538912266492844f, 125), // expand_7
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QuantizationInfo(0.0014702979242429137f, 134), // expand_8
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QuantizationInfo(0.0013733493397012353f, 127), // expand_9
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QuantizationInfo(0.0016282502328976989f, 131), // expand_10
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QuantizationInfo(0.0016309921629726887f, 134), // expand_11
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QuantizationInfo(0.0018258779309689999f, 138), // expand_12
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QuantizationInfo(0.0013828007504343987f, 123), // expand_13
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QuantizationInfo(0.0020222084131091833f, 135), // expand_14
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QuantizationInfo(0.04281935095787048f, 102), // expand_15
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QuantizationInfo(0.002046825597062707f, 135) // expand_16
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};
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// Depthwise expand convolution quantization info
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const std::vector<QuantizationInfo> dwc_q =
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{
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QuantizationInfo(0.3436955213546753f, 165), // expand_0
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QuantizationInfo(0.020969120785593987f, 109), // expand_1
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QuantizationInfo(0.16981913149356842f, 52), // expand_2
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QuantizationInfo(0.017202870920300484f, 143), // expand_3
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QuantizationInfo(0.06525065749883652f, 118), // expand_4
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QuantizationInfo(0.07909784466028214f, 95), // expand_5
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QuantizationInfo(0.010087885893881321f, 127), // expand_6
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QuantizationInfo(0.06092711538076401f, 110), // expand_7
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QuantizationInfo(0.052407849580049515f, 133), // expand_8
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QuantizationInfo(0.04077887907624245f, 155), // expand_9
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QuantizationInfo(0.031107846647500992f, 143), // expand_10
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QuantizationInfo(0.07080810517072678f, 66), // expand_11
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QuantizationInfo(0.07448793947696686f, 159), // expand_12
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QuantizationInfo(0.01525793131440878f, 92), // expand_13
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QuantizationInfo(0.04166752099990845f, 147), // expand_14
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QuantizationInfo(0.04281935095787048f, 102), // expand_15
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QuantizationInfo(0.16456253826618195, 201) // expand_16
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};
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// Project convolution quantization info
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const std::vector<QuantizationInfo> prwc_q =
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{
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QuantizationInfo(0.03737175464630127f, 140), // expand_0
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QuantizationInfo(0.0225360207259655f, 156), // expand_1
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QuantizationInfo(0.02740888111293316f, 122), // expand_2
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QuantizationInfo(0.016844693571329117f, 111), // expand_3
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QuantizationInfo(0.019062912091612816f, 146), // expand_4
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QuantizationInfo(0.018293123692274094f, 128), // expand_5
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QuantizationInfo(0.014601286500692368f, 147), // expand_6
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QuantizationInfo(0.016782939434051514f, 124), // expand_7
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QuantizationInfo(0.012898261658847332f, 125), // expand_8
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QuantizationInfo(0.019561484456062317f, 144), // expand_9
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QuantizationInfo(0.007436311338096857f, 129), // expand_10
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QuantizationInfo(0.00838223285973072f, 136), // expand_11
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QuantizationInfo(0.023982593789696693f, 154), // expand_12
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QuantizationInfo(0.009447949007153511f, 140), // expand_13
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QuantizationInfo(0.00789870135486126f, 139), // expand_14
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QuantizationInfo(0.03697410225868225f, 131), // expand_15
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QuantizationInfo(0.008009289391338825f, 111) // expand_16
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};
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graph << InputLayer(input_descriptor.set_quantization_info(in_quant_info),
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get_weights_accessor(data_path, common_params.image))
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<< ConvolutionLayer(
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3U, 3U, 32U,
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get_weights_accessor(data_path, "Conv_weights.npy"),
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get_weights_accessor(data_path, "Conv_bias.npy"),
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PadStrideInfo(2U, 2U, 0U, 1U, 0U, 1U, DimensionRoundingType::FLOOR),
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1, conv_weights_quant_info.at(0), mid_quant_info)
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.set_name("Conv")
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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)).set_name("Conv/Relu6")
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<< DepthwiseConvolutionLayer(3U, 3U,
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get_weights_accessor(data_path, "expanded_conv_depthwise_depthwise_weights.npy"),
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get_weights_accessor(data_path, "expanded_conv_depthwise_depthwise_biases.npy"),
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PadStrideInfo(1, 1, 1, 1), 1, dwc_q.at(0))
|
|
.set_name("expanded_conv/depthwise/depthwise")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)).set_name("expanded_conv/depthwise/Relu6")
|
|
<< ConvolutionLayer(1U, 1U, 16U,
|
|
get_weights_accessor(data_path, "expanded_conv_project_weights.npy"),
|
|
get_weights_accessor(data_path, "expanded_conv_project_biases.npy"),
|
|
PadStrideInfo(1, 1, 0, 0), 1, prwc_q.at(0))
|
|
.set_name("expanded_conv/project/Conv2D");
|
|
|
|
get_expanded_conv_qasymm8(data_path, "expanded_conv_1", IsResidual::No, 96U, 24U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL),
|
|
pwc_q.at(1), dwc_q.at(1), prwc_q.at(1));
|
|
get_expanded_conv_qasymm8(data_path, "expanded_conv_2", IsResidual::Yes, 144U, 24U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(2), dwc_q.at(2), prwc_q.at(2));
|
|
get_expanded_conv_qasymm8(data_path, "expanded_conv_3", IsResidual::No, 144U, 32U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL),
|
|
pwc_q.at(3), dwc_q.at(3), prwc_q.at(3));
|
|
get_expanded_conv_qasymm8(data_path, "expanded_conv_4", IsResidual::Yes, 192U, 32U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(4), dwc_q.at(4), prwc_q.at(4));
|
|
get_expanded_conv_qasymm8(data_path, "expanded_conv_5", IsResidual::Yes, 192U, 32U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(5), dwc_q.at(5), prwc_q.at(5));
|
|
get_expanded_conv_qasymm8(data_path, "expanded_conv_6", IsResidual::No, 192U, 64U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL),
|
|
pwc_q.at(6), dwc_q.at(6), prwc_q.at(6));
|
|
get_expanded_conv_qasymm8(data_path, "expanded_conv_7", IsResidual::Yes, 384U, 64U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(7), dwc_q.at(7), prwc_q.at(7));
|
|
get_expanded_conv_qasymm8(data_path, "expanded_conv_8", IsResidual::Yes, 384U, 64U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(8), dwc_q.at(8), prwc_q.at(8));
|
|
get_expanded_conv_qasymm8(data_path, "expanded_conv_9", IsResidual::Yes, 384U, 64U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(9), dwc_q.at(9), prwc_q.at(9));
|
|
get_expanded_conv_qasymm8(data_path, "expanded_conv_10", IsResidual::No, 384U, 96U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(10), dwc_q.at(10), prwc_q.at(10));
|
|
get_expanded_conv_qasymm8(data_path, "expanded_conv_11", IsResidual::Yes, 576U, 96U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(11), dwc_q.at(11), prwc_q.at(11));
|
|
get_expanded_conv_qasymm8(data_path, "expanded_conv_12", IsResidual::Yes, 576U, 96U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(12), dwc_q.at(12), prwc_q.at(12));
|
|
get_expanded_conv_qasymm8(data_path, "expanded_conv_13", IsResidual::No, 576U, 160U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL),
|
|
pwc_q.at(13), dwc_q.at(13), prwc_q.at(13));
|
|
get_expanded_conv_qasymm8(data_path, "expanded_conv_14", IsResidual::Yes, 960U, 160U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(14), dwc_q.at(14), prwc_q.at(14));
|
|
get_expanded_conv_qasymm8(data_path, "expanded_conv_15", IsResidual::Yes, 960U, 160U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(15), dwc_q.at(15), prwc_q.at(15));
|
|
get_expanded_conv_qasymm8(data_path, "expanded_conv_16", IsResidual::No, 960U, 320U, PadStrideInfo(1, 1, 1, 1), pwc_q.at(16), dwc_q.at(16), prwc_q.at(16));
|
|
|
|
graph << ConvolutionLayer(1U, 1U, 1280U,
|
|
get_weights_accessor(data_path, "Conv_1_weights.npy"),
|
|
get_weights_accessor(data_path, "Conv_1_biases.npy"),
|
|
PadStrideInfo(1, 1, 0, 0), 1, conv_weights_quant_info.at(1))
|
|
.set_name("Conv_1")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)).set_name("Conv_1/Relu6")
|
|
<< PoolingLayer(PoolingLayerInfo(PoolingType::AVG, common_params.data_layout)).set_name("Logits/AvgPool")
|
|
<< ConvolutionLayer(1U, 1U, 1001U,
|
|
get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_weights.npy"),
|
|
get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_biases.npy"),
|
|
PadStrideInfo(1, 1, 0, 0), 1, conv_weights_quant_info.at(2))
|
|
.set_name("Logits/Conv2d_1c_1x1");
|
|
}
|
|
|
|
void get_expanded_conv_qasymm8(const std::string &data_path, std::string &¶m_path, IsResidual is_residual,
|
|
unsigned int input_channels, unsigned int output_channels,
|
|
PadStrideInfo dwc_pad_stride_info,
|
|
const QuantizationInfo &pwi, const QuantizationInfo &dwi, const QuantizationInfo &pji)
|
|
{
|
|
std::string total_path = param_path + "_";
|
|
|
|
SubStream left(graph);
|
|
left << ConvolutionLayer(1U, 1U, input_channels,
|
|
get_weights_accessor(data_path, total_path + "project_weights.npy"),
|
|
get_weights_accessor(data_path, total_path + "project_biases.npy"),
|
|
PadStrideInfo(1, 1, 0, 0), 1, pwi)
|
|
.set_name(param_path + "/Conv2D")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)).set_name(param_path + "/Conv2D/Relu6")
|
|
<< DepthwiseConvolutionLayer(3U, 3U,
|
|
get_weights_accessor(data_path, total_path + "depthwise_depthwise_weights.npy"),
|
|
get_weights_accessor(data_path, total_path + "depthwise_depthwise_biases.npy"),
|
|
dwc_pad_stride_info, 1, dwi)
|
|
.set_name(param_path + "/depthwise/depthwise")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)).set_name(param_path + "/depthwise/Relu6")
|
|
<< ConvolutionLayer(1U, 1U, output_channels,
|
|
get_weights_accessor(data_path, total_path + "project_weights.npy"),
|
|
get_weights_accessor(data_path, total_path + "project_biases.npy"),
|
|
PadStrideInfo(1, 1, 0, 0), 1, pji)
|
|
.set_name(param_path + "/project/Conv2D");
|
|
|
|
if(is_residual == IsResidual::Yes)
|
|
{
|
|
// Add residual node
|
|
SubStream right(graph);
|
|
graph << EltwiseLayer(std::move(left), std::move(right), EltwiseOperation::Add).set_name(param_path + "/add");
|
|
}
|
|
else
|
|
{
|
|
graph.forward_tail(left.tail_node());
|
|
}
|
|
}
|
|
};
|
|
|
|
/** Main program for MobileNetV2
|
|
*
|
|
* Model is based on:
|
|
* https://arxiv.org/abs/1801.04381
|
|
* "MobileNetV2: Inverted Residuals and Linear Bottlenecks"
|
|
* Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen
|
|
*
|
|
* Provenance: https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_1.0_224.tgz
|
|
*
|
|
* @note To list all the possible arguments execute the binary appended with the --help option
|
|
*
|
|
* @param[in] argc Number of arguments
|
|
* @param[in] argv Arguments
|
|
*/
|
|
int main(int argc, char **argv)
|
|
{
|
|
return arm_compute::utils::run_example<GraphMobilenetV2Example>(argc, argv);
|
|
}
|