720 lines
49 KiB
C++
720 lines
49 KiB
C++
/*
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* Copyright (c) 2018-2020 Arm Limited.
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*
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* SPDX-License-Identifier: MIT
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to
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* deal in the Software without restriction, including without limitation the
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* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
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* sell copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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#include "arm_compute/graph.h"
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#include "support/ToolchainSupport.h"
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#include "utils/CommonGraphOptions.h"
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#include "utils/GraphUtils.h"
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#include "utils/Utils.h"
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using namespace arm_compute::utils;
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using namespace arm_compute::graph::frontend;
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using namespace arm_compute::graph_utils;
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const float batch_norm_epsilon = 0.0010000000474974513f;
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/** Example demonstrating how to implement Inception ResNet V1 network using the Compute Library's graph API */
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class InceptionResNetV1Example final : public Example
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{
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public:
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InceptionResNetV1Example()
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: cmd_parser(), common_opts(cmd_parser), common_params(), model_input_width(nullptr), model_input_height(nullptr), graph(0, "InceptionResNetV1")
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{
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model_input_width = cmd_parser.add_option<SimpleOption<unsigned int>>("image-width", 512);
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model_input_height = cmd_parser.add_option<SimpleOption<unsigned int>>("image-height", 512);
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// Add model id option
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model_input_width->set_help("Input image width.");
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model_input_height->set_help("Input image height.");
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}
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InceptionResNetV1Example(const InceptionResNetV1Example &) = delete;
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InceptionResNetV1Example &operator=(const InceptionResNetV1Example &) = delete;
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~InceptionResNetV1Example() override = default;
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bool do_setup(int argc, char **argv) override
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{
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// Parse arguments
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cmd_parser.parse(argc, argv);
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cmd_parser.validate();
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// Consume common parameters
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common_params = consume_common_graph_parameters(common_opts);
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// Return when help menu is requested
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if(common_params.help)
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{
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cmd_parser.print_help(argv[0]);
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return false;
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}
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// Get input image width and height
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const unsigned int image_width = model_input_width->value();
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const unsigned int image_height = model_input_height->value();
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// Set default layout if needed
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if(!common_opts.data_layout->is_set() && common_params.target == Target::NEON)
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{
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common_params.data_layout = DataLayout::NCHW;
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}
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// Checks
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ARM_COMPUTE_EXIT_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type), "QASYMM8 not supported for this graph");
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// Print parameter values
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std::cout << common_params << std::endl;
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std::cout << "Image width: " << image_width << std::endl;
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std::cout << "Image height: " << image_height << std::endl;
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// Create model path
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std::string data_path = common_params.data_path;
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std::string model_path = "/cnn_data/inception_resnet_v1_model/";
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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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// Create a preprocessor object
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std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>(0.f, 1.f);
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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(image_width, image_height, 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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// Conv2d_1a_3x3
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<< ConvolutionLayer(3U, 3U, 32U,
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get_weights_accessor(data_path, "Conv2d_1a_3x3_weights.npy", weights_layout),
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std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
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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, "Conv2d_1a_3x3_BatchNorm_moving_mean.npy"),
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get_weights_accessor(data_path, "Conv2d_1a_3x3_BatchNorm_moving_variance.npy"),
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get_random_accessor(1.f, 1.f),
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get_weights_accessor(data_path, "Conv2d_1a_3x3_BatchNorm_beta.npy"),
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batch_norm_epsilon)
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.set_name("Conv2d_1a_3x3/BatchNorm")
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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_1a_3x3/Relu")
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// Conv2d_2a_3x3
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<< ConvolutionLayer(3U, 3U, 32U,
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get_weights_accessor(data_path, "Conv2d_2a_3x3_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("Conv2d_2a_3x3/convolution")
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<< BatchNormalizationLayer(get_weights_accessor(data_path, "Conv2d_2a_3x3_BatchNorm_moving_mean.npy"),
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get_weights_accessor(data_path, "Conv2d_2a_3x3_BatchNorm_moving_variance.npy"),
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get_random_accessor(1.f, 1.f),
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get_weights_accessor(data_path, "Conv2d_2a_3x3_BatchNorm_beta.npy"),
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batch_norm_epsilon)
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.set_name("Conv2d_2a_3x3/BatchNorm")
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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_2a_3x3/Relu")
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// Conv2d_2b_3x3
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<< ConvolutionLayer(3U, 3U, 64U,
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get_weights_accessor(data_path, "Conv2d_2b_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("Conv2d_2b_3x3/convolution")
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<< BatchNormalizationLayer(get_weights_accessor(data_path, "Conv2d_2b_3x3_BatchNorm_moving_mean.npy"),
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get_weights_accessor(data_path, "Conv2d_2b_3x3_BatchNorm_moving_variance.npy"),
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get_random_accessor(1.f, 1.f),
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get_weights_accessor(data_path, "Conv2d_2b_3x3_BatchNorm_beta.npy"),
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batch_norm_epsilon)
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.set_name("Conv2d_2b_3x3/BatchNorm")
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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_2b_3x3/Relu")
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// MaxPool_3a_3x3
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<< PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL), true)).set_name("MaxPool_3a_3x3/MaxPool")
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// Conv2d_3b_1x1
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<< ConvolutionLayer(1U, 1U, 80U,
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get_weights_accessor(data_path, "Conv2d_3b_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("Conv2d_3b_1x1/convolution")
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<< BatchNormalizationLayer(get_weights_accessor(data_path, "Conv2d_3b_1x1_BatchNorm_moving_mean.npy"),
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get_weights_accessor(data_path, "Conv2d_3b_1x1_BatchNorm_moving_variance.npy"),
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get_random_accessor(1.f, 1.f),
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get_weights_accessor(data_path, "Conv2d_3b_1x1_BatchNorm_beta.npy"),
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batch_norm_epsilon)
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.set_name("Conv2d_3b_1x1/BatchNorm")
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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_3b_1x1/Relu")
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// Conv2d_4a_3x3
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<< ConvolutionLayer(3U, 3U, 192U,
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get_weights_accessor(data_path, "Conv2d_4a_3x3_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("Conv2d_4a_3x3/convolution")
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<< BatchNormalizationLayer(get_weights_accessor(data_path, "Conv2d_4a_3x3_BatchNorm_moving_mean.npy"),
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get_weights_accessor(data_path, "Conv2d_4a_3x3_BatchNorm_moving_variance.npy"),
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get_random_accessor(1.f, 1.f),
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get_weights_accessor(data_path, "Conv2d_4a_3x3_BatchNorm_beta.npy"),
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batch_norm_epsilon)
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.set_name("Conv2d_4a_3x3/BatchNorm")
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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_4a_3x3/Relu")
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// Conv2d_4b_3x3
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<< ConvolutionLayer(3U, 3U, 256U,
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get_weights_accessor(data_path, "Conv2d_4b_3x3_weights.npy", weights_layout),
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std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
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PadStrideInfo(2, 2, 0, 0))
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.set_name("Conv2d_4a_3x3/convolution")
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<< BatchNormalizationLayer(get_weights_accessor(data_path, "Conv2d_4b_3x3_BatchNorm_moving_mean.npy"),
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get_weights_accessor(data_path, "Conv2d_4b_3x3_BatchNorm_moving_variance.npy"),
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get_random_accessor(1.f, 1.f),
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get_weights_accessor(data_path, "Conv2d_4b_3x3_BatchNorm_beta.npy"),
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batch_norm_epsilon)
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.set_name("Conv2d_4b_3x3/BatchNorm")
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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Conv2d_4b_3x3/Relu");
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// 5 x Inception-resnet-A
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block35_repeat(data_path, weights_layout, 5);
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// Reduction-A
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reduction_a(data_path, weights_layout);
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// 10 x Inception-Resnet-B
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block17_repeat(data_path, weights_layout, 10);
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// Reduction-B
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reduction_b(data_path, weights_layout);
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// 5 x Inception-resnet-C
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block8_repeat(data_path, weights_layout, 5, 0.2f, true);
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block8_repeat(data_path, weights_layout, 1, 1.f, false);
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// Logits tail
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graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, operation_layout)).set_name("Logits/AvgPool_1a_8x8")
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<< FlattenLayer().set_name("Logits/Flatten")
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<< FullyConnectedLayer(
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128U,
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get_weights_accessor(data_path, "Logits_Logits_weights.npy", weights_layout),
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get_weights_accessor(data_path, "Logits_Logits_biases.npy"))
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.set_name("Logits/Logits")
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<< OutputLayer(arm_compute::support::cpp14::make_unique<DummyAccessor>(0));
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// Finalize graph
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GraphConfig config;
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config.num_threads = common_params.threads;
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config.use_tuner = common_params.enable_tuner;
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config.tuner_mode = common_params.tuner_mode;
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config.tuner_file = common_params.tuner_file;
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graph.finalize(common_params.target, config);
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return true;
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}
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void do_run() override
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{
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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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SimpleOption<unsigned int> *model_input_width{ nullptr };
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SimpleOption<unsigned int> *model_input_height{ nullptr };
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Stream graph;
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private:
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void block35_repeat(const std::string &data_path, DataLayout weights_layout, unsigned int num_blocks)
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{
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for(unsigned int i = 0; i < num_blocks; ++i)
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{
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std::stringstream unit_path_ss;
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unit_path_ss << "Repeat_block35_" << (i + 1) << "_";
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std::stringstream unit_name_ss;
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unit_name_ss << "Repeat/block35_" << (i + 1) << "/";
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std::string unit_path = unit_path_ss.str();
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std::string unit_name = unit_name_ss.str();
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// Create left and write substreams
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SubStream i_l(graph);
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SubStream i_r(graph);
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// Branch 0
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SubStream i_la(i_l);
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i_la << ConvolutionLayer(1U, 1U, 32U,
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get_weights_accessor(data_path, unit_path + "Branch_0_Conv2d_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(unit_name + "Branch_0/Conv2d_1x1/convolution")
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<< BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_0_Conv2d_1x1_BatchNorm_moving_mean.npy"),
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get_weights_accessor(data_path, unit_path + "Branch_0_Conv2d_1x1_BatchNorm_moving_variance.npy"),
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get_random_accessor(1.f, 1.f),
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get_weights_accessor(data_path, unit_path + "Branch_0_Conv2d_1x1_BatchNorm_beta.npy"),
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batch_norm_epsilon)
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.set_name(unit_name + "Branch_0/Conv2d_1x1/BatchNorm")
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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_0/Conv2d_1x1/Relu");
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// Branch 1
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SubStream i_lb(i_l);
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i_lb << ConvolutionLayer(1U, 1U, 32U,
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get_weights_accessor(data_path, unit_path + "Branch_1_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(unit_name + "Branch_1/Conv2d_0a_1x1/convolution")
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<< BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
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get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
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get_random_accessor(1.f, 1.f),
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get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"),
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batch_norm_epsilon)
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.set_name(unit_name + "Branch_1/Conv2d_0a_1x1/BatchNorm")
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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_1/Conv2d_0a_1x1/Relu")
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<< ConvolutionLayer(3U, 3U, 32U,
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get_weights_accessor(data_path, unit_path + "Branch_1_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(unit_name + "Branch_1/Conv2d_0b_3x3/convolution")
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<< BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"),
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get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"),
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get_random_accessor(1.f, 1.f),
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get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0b_3x3_BatchNorm_beta.npy"),
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batch_norm_epsilon)
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.set_name(unit_name + "Branch_1/Conv2d_0b_3x3/BatchNorm")
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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_1/Conv2d_0b_3x3/Relu");
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// Branch 2
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SubStream i_lc(i_l);
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i_lc << ConvolutionLayer(1U, 1U, 32U,
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get_weights_accessor(data_path, unit_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(unit_name + "Branch_2/Conv2d_0a_1x1/convolution")
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<< BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
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get_weights_accessor(data_path, unit_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
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get_random_accessor(1.f, 1.f),
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get_weights_accessor(data_path, unit_path + "Branch_2_Conv2d_0a_1x1_BatchNorm_beta.npy"),
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batch_norm_epsilon)
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.set_name(unit_name + "Branch_2/Conv2d_0a_1x1/BatchNorm")
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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_2/Conv2d_0a_1x1/Relu")
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<< ConvolutionLayer(3U, 3U, 32U,
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get_weights_accessor(data_path, unit_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(unit_name + "Branch_2/Conv2d_0b_3x3/convolution")
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<< BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"),
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get_weights_accessor(data_path, unit_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"),
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get_random_accessor(1.f, 1.f),
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get_weights_accessor(data_path, unit_path + "Branch_2_Conv2d_0b_3x3_BatchNorm_beta.npy"),
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batch_norm_epsilon)
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.set_name(unit_name + "Branch_2/Conv2d_0b_3x3/BatchNorm")
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<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_2/Conv2d_0b_3x3/Relu")
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<< ConvolutionLayer(3U, 3U, 32U,
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get_weights_accessor(data_path, unit_path + "Branch_2_Conv2d_0c_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(unit_name + "Branch_2/Conv2d_0c_3x3/convolution")
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<< BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_2_Conv2d_0c_3x3_BatchNorm_moving_mean.npy"),
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get_weights_accessor(data_path, unit_path + "Branch_2_Conv2d_0c_3x3_BatchNorm_moving_variance.npy"),
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get_random_accessor(1.f, 1.f),
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get_weights_accessor(data_path, unit_path + "Branch_2_Conv2d_0c_3x3_BatchNorm_beta.npy"),
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batch_norm_epsilon)
|
|
.set_name(unit_name + "Branch_2/Conv2d_0c_3x3/BatchNorm")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_2/Conv2d_0c_3x3/Relu");
|
|
|
|
// Concatenate
|
|
i_l << ConcatLayer(std::move(i_la), std::move(i_lb), std::move(i_lc)).set_name(unit_name + "concat")
|
|
<< ConvolutionLayer(1U, 1U, 256U,
|
|
get_weights_accessor(data_path, unit_path + "Conv2d_1x1_weights.npy", weights_layout),
|
|
get_weights_accessor(data_path, unit_path + "Conv2d_1x1_biases.npy", weights_layout),
|
|
PadStrideInfo(1, 1, 0, 0))
|
|
.set_name(unit_name + "Conv2d_1x1/convolution")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 0.17f, 0.f)).set_name(unit_name + "mul");
|
|
|
|
graph << EltwiseLayer(std::move(i_l), std::move(i_r), EltwiseOperation::Add).set_name(unit_name + "add")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Relu");
|
|
}
|
|
}
|
|
|
|
void block17_repeat(const std::string &data_path, DataLayout weights_layout, unsigned int num_blocks)
|
|
{
|
|
for(unsigned int i = 0; i < num_blocks; ++i)
|
|
{
|
|
std::stringstream unit_path_ss;
|
|
unit_path_ss << "Repeat_1_block17_" << (i + 1) << "_";
|
|
std::stringstream unit_name_ss;
|
|
unit_name_ss << "Repeat_1/block17_" << (i + 1) << "/";
|
|
|
|
std::string unit_path = unit_path_ss.str();
|
|
std::string unit_name = unit_name_ss.str();
|
|
|
|
// Create left and write substreams
|
|
SubStream i_l(graph);
|
|
SubStream i_r(graph);
|
|
|
|
// Branch 0
|
|
SubStream i_la(i_l);
|
|
i_la << ConvolutionLayer(1U, 1U, 128U,
|
|
get_weights_accessor(data_path, unit_path + "Branch_0_Conv2d_1x1_weights.npy", weights_layout),
|
|
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
|
|
PadStrideInfo(1, 1, 0, 0))
|
|
.set_name(unit_name + "Branch_0/Conv2d_1x1/convolution")
|
|
<< BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_0_Conv2d_1x1_BatchNorm_moving_mean.npy"),
|
|
get_weights_accessor(data_path, unit_path + "Branch_0_Conv2d_1x1_BatchNorm_moving_variance.npy"),
|
|
get_random_accessor(1.f, 1.f),
|
|
get_weights_accessor(data_path, unit_path + "Branch_0_Conv2d_1x1_BatchNorm_beta.npy"),
|
|
batch_norm_epsilon)
|
|
.set_name(unit_name + "Branch_0/Conv2d_1x1/BatchNorm")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_0/Conv2d_1x1/Relu");
|
|
|
|
// Branch 1
|
|
SubStream i_lb(i_l);
|
|
i_lb << ConvolutionLayer(1U, 1U, 128U,
|
|
get_weights_accessor(data_path, unit_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(unit_name + "Branch_1/Conv2d_0a_1x1/convolution")
|
|
<< BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
|
|
get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
|
|
get_random_accessor(1.f, 1.f),
|
|
get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"),
|
|
batch_norm_epsilon)
|
|
.set_name(unit_name + "Branch_1/Conv2d_0a_1x1/BatchNorm")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_1/Conv2d_0a_1x1/Relu")
|
|
<< ConvolutionLayer(7U, 1U, 128U,
|
|
get_weights_accessor(data_path, unit_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(unit_name + "Branch_1/Conv2d_0b_1x7/convolution")
|
|
<< BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_mean.npy"),
|
|
get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_moving_variance.npy"),
|
|
get_random_accessor(1.f, 1.f),
|
|
get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0b_1x7_BatchNorm_beta.npy"),
|
|
batch_norm_epsilon)
|
|
.set_name(unit_name + "Branch_1/Conv2d_0b_1x7/BatchNorm")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_1/Conv2d_0b_1x7/Relu")
|
|
<< ConvolutionLayer(1U, 7U, 128U,
|
|
get_weights_accessor(data_path, unit_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(unit_name + "Branch_1/Conv2d_0c_7x1/convolution")
|
|
<< BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_mean.npy"),
|
|
get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_moving_variance.npy"),
|
|
get_random_accessor(1.f, 1.f),
|
|
get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0c_7x1_BatchNorm_beta.npy"),
|
|
batch_norm_epsilon)
|
|
.set_name(unit_name + "Branch_1/Conv2d_0c_7x1/BatchNorm")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_1/Conv2d_0c_7x1/Relu");
|
|
|
|
// Concatenate
|
|
i_l << ConcatLayer(std::move(i_la), std::move(i_lb)).set_name(unit_name + "concat")
|
|
<< ConvolutionLayer(1U, 1U, 896U,
|
|
get_weights_accessor(data_path, unit_path + "Conv2d_1x1_weights.npy", weights_layout),
|
|
get_weights_accessor(data_path, unit_path + "Conv2d_1x1_biases.npy", weights_layout),
|
|
PadStrideInfo(1, 1, 0, 0))
|
|
.set_name(unit_name + "Conv2d_1x1/convolution")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 0.10f, 0.f)).set_name(unit_name + "mul");
|
|
|
|
graph << EltwiseLayer(std::move(i_l), std::move(i_r), EltwiseOperation::Add).set_name(unit_name + "add")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Relu");
|
|
}
|
|
}
|
|
|
|
void block8_repeat(const std::string &data_path, DataLayout weights_layout, unsigned int num_blocks, float scale, bool has_activation)
|
|
{
|
|
for(unsigned int i = 0; i < num_blocks; ++i)
|
|
{
|
|
std::stringstream unit_path_ss;
|
|
std::stringstream unit_name_ss;
|
|
if(num_blocks != 1)
|
|
{
|
|
unit_path_ss << "Repeat_2_block8_" << (i + 1) << "_";
|
|
unit_name_ss << "Repeat_2/block8_" << (i + 1) << "/";
|
|
}
|
|
else
|
|
{
|
|
unit_path_ss << "Block8_";
|
|
unit_name_ss << "Block8/";
|
|
}
|
|
|
|
std::string unit_path = unit_path_ss.str();
|
|
std::string unit_name = unit_name_ss.str();
|
|
|
|
// Create left and write substreams
|
|
SubStream i_l(graph);
|
|
SubStream i_r(graph);
|
|
|
|
// Branch 0
|
|
SubStream i_la(i_l);
|
|
i_la << ConvolutionLayer(1U, 1U, 192U,
|
|
get_weights_accessor(data_path, unit_path + "Branch_0_Conv2d_1x1_weights.npy", weights_layout),
|
|
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
|
|
PadStrideInfo(1, 1, 0, 0))
|
|
.set_name(unit_name + "Branch_0/Conv2d_1x1/convolution")
|
|
<< BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_0_Conv2d_1x1_BatchNorm_moving_mean.npy"),
|
|
get_weights_accessor(data_path, unit_path + "Branch_0_Conv2d_1x1_BatchNorm_moving_variance.npy"),
|
|
get_random_accessor(1.f, 1.f),
|
|
get_weights_accessor(data_path, unit_path + "Branch_0_Conv2d_1x1_BatchNorm_beta.npy"),
|
|
batch_norm_epsilon)
|
|
.set_name(unit_name + "Branch_0/Conv2d_1x1/BatchNorm")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_0/Conv2d_1x1/Relu");
|
|
|
|
// Branch 1
|
|
SubStream i_lb(i_l);
|
|
i_lb << ConvolutionLayer(1U, 1U, 192U,
|
|
get_weights_accessor(data_path, unit_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(unit_name + "Branch_1/Conv2d_0a_1x1/convolution")
|
|
<< BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
|
|
get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
|
|
get_random_accessor(1.f, 1.f),
|
|
get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"),
|
|
batch_norm_epsilon)
|
|
.set_name(unit_name + "Branch_1/Conv2d_0a_1x1/BatchNorm")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_1/Conv2d_0a_1x1/Relu")
|
|
<< ConvolutionLayer(3U, 1U, 192U,
|
|
get_weights_accessor(data_path, unit_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(unit_name + "Branch_1/Conv2d_0b_1x3/convolution")
|
|
<< BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0b_1x3_BatchNorm_moving_mean.npy"),
|
|
get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0b_1x3_BatchNorm_moving_variance.npy"),
|
|
get_random_accessor(1.f, 1.f),
|
|
get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0b_1x3_BatchNorm_beta.npy"),
|
|
batch_norm_epsilon)
|
|
.set_name(unit_name + "Branch_1/Conv2d_0b_1x3/BatchNorm")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_1/Conv2d_0b_1x3/Relu")
|
|
<< ConvolutionLayer(1U, 3U, 192U,
|
|
get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0c_3x1_weights.npy", weights_layout),
|
|
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
|
|
PadStrideInfo(1, 1, 0, 1))
|
|
.set_name(unit_name + "Branch_1/Conv2d_0c_3x1/convolution")
|
|
<< BatchNormalizationLayer(get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0c_3x1_BatchNorm_moving_mean.npy"),
|
|
get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0c_3x1_BatchNorm_moving_variance.npy"),
|
|
get_random_accessor(1.f, 1.f),
|
|
get_weights_accessor(data_path, unit_path + "Branch_1_Conv2d_0c_3x1_BatchNorm_beta.npy"),
|
|
batch_norm_epsilon)
|
|
.set_name(unit_name + "Branch_1/Conv2d_0c_3x1/BatchNorm")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Branch_1/Conv2d_0c_3x1/Relu");
|
|
|
|
// Concatenate
|
|
i_l << ConcatLayer(std::move(i_la), std::move(i_lb)).set_name(unit_name + "concat")
|
|
<< ConvolutionLayer(1U, 1U, 1792U,
|
|
get_weights_accessor(data_path, unit_path + "Conv2d_1x1_weights.npy", weights_layout),
|
|
get_weights_accessor(data_path, unit_path + "Conv2d_1x1_biases.npy", weights_layout),
|
|
PadStrideInfo(1, 1, 0, 0))
|
|
.set_name(unit_name + "Conv2d_1x1/convolution");
|
|
|
|
// Scale result
|
|
if(scale != 1.f)
|
|
{
|
|
i_l << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, scale, 0.f)).set_name(unit_name + "mul");
|
|
}
|
|
|
|
// Residual add
|
|
graph << EltwiseLayer(std::move(i_l), std::move(i_r), EltwiseOperation::Add).set_name(unit_name + "add");
|
|
|
|
// Apply activation if needed
|
|
if(has_activation)
|
|
{
|
|
graph << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Relu");
|
|
}
|
|
}
|
|
}
|
|
|
|
void reduction_a(const std::string &data_path, DataLayout weights_layout)
|
|
{
|
|
// Branch 0
|
|
SubStream i_a(graph);
|
|
i_a << ConvolutionLayer(3U, 3U, 384U,
|
|
get_weights_accessor(data_path, "Mixed_6a_Branch_0_Conv2d_1a_3x3_weights.npy", weights_layout),
|
|
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
|
|
PadStrideInfo(2, 2, 0, 0))
|
|
.set_name("Mixed_6a/Branch_0/Conv2d_1a_3x3/convolution")
|
|
<< BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_6a_Branch_0_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"),
|
|
get_weights_accessor(data_path, "Mixed_6a_Branch_0_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"),
|
|
get_random_accessor(1.f, 1.f),
|
|
get_weights_accessor(data_path, "Mixed_6a_Branch_0_Conv2d_1a_3x3_BatchNorm_beta.npy"),
|
|
batch_norm_epsilon)
|
|
.set_name("Mixed_6a/Branch_0/Conv2d_1a_3x3/BatchNorm")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_6a/Branch_0/Conv2d_1a_3x3/Relu");
|
|
|
|
// Branch 1
|
|
SubStream i_b(graph);
|
|
i_b << ConvolutionLayer(1U, 1U, 192U,
|
|
get_weights_accessor(data_path, "Mixed_6a_Branch_1_Conv2d_0a_1x1_weights.npy", weights_layout),
|
|
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
|
|
PadStrideInfo(1, 1, 0, 0))
|
|
.set_name("Mixed_6a/Branch_1/Conv2d_0a_1x1/convolution")
|
|
<< BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_6a_Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
|
|
get_weights_accessor(data_path, "Mixed_6a_Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
|
|
get_random_accessor(1.f, 1.f),
|
|
get_weights_accessor(data_path, "Mixed_6a_Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"),
|
|
batch_norm_epsilon)
|
|
.set_name("Mixed_6a/Branch_1/Conv2d_0a_1x1/BatchNorm")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_6a/Branch_1/Conv2d_0a_1x1/Relu")
|
|
<< ConvolutionLayer(3U, 3U, 192U,
|
|
get_weights_accessor(data_path, "Mixed_6a_Branch_1_Conv2d_0b_3x3_weights.npy", weights_layout),
|
|
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
|
|
PadStrideInfo(1, 1, 1, 1))
|
|
.set_name("Mixed_6a/Branch_1/Conv2d_0b_3x3/convolution")
|
|
<< BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_6a_Branch_1_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"),
|
|
get_weights_accessor(data_path, "Mixed_6a_Branch_1_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"),
|
|
get_random_accessor(1.f, 1.f),
|
|
get_weights_accessor(data_path, "Mixed_6a_Branch_1_Conv2d_0b_3x3_BatchNorm_beta.npy"),
|
|
batch_norm_epsilon)
|
|
.set_name("Mixed_6a/Branch_1/Conv2d_0b_3x3/BatchNorm")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_6a/Branch_1/Conv2d_0b_3x3/Relu")
|
|
<< ConvolutionLayer(3U, 3U, 256U,
|
|
get_weights_accessor(data_path, "Mixed_6a_Branch_1_Conv2d_1a_3x3_weights.npy", weights_layout),
|
|
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
|
|
PadStrideInfo(2, 2, 0, 0))
|
|
.set_name("Mixed_6a/Branch_1/Conv2d_1a_3x3/convolution")
|
|
<< BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_6a_Branch_1_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"),
|
|
get_weights_accessor(data_path, "Mixed_6a_Branch_1_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"),
|
|
get_random_accessor(1.f, 1.f),
|
|
get_weights_accessor(data_path, "Mixed_6a_Branch_1_Conv2d_1a_3x3_BatchNorm_beta.npy"),
|
|
batch_norm_epsilon)
|
|
.set_name("Mixed_6a/Branch_1/Conv2d_1a_3x3/BatchNorm")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_6a/Branch_1/Conv2d_1a_3x3/Relu");
|
|
|
|
// Branch 2
|
|
SubStream i_c(graph);
|
|
i_c << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, common_params.data_layout, PadStrideInfo(2, 2, 0, 0), true)).set_name("Mixed_6a/Branch_2/MaxPool_1a_3x3");
|
|
|
|
// Concatenate
|
|
graph << ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c)).set_name("Mixed_6a/concat");
|
|
}
|
|
|
|
void reduction_b(const std::string &data_path, DataLayout weights_layout)
|
|
{
|
|
// Branch 0
|
|
SubStream i_a(graph);
|
|
i_a << ConvolutionLayer(1U, 1U, 256U,
|
|
get_weights_accessor(data_path, "Mixed_7a_Branch_0_Conv2d_0a_1x1_weights.npy", weights_layout),
|
|
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
|
|
PadStrideInfo(1, 1, 0, 0))
|
|
.set_name("Mixed_7a/Branch_0/Conv2d_0a_1x1/convolution")
|
|
<< BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_7a_Branch_0_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
|
|
get_weights_accessor(data_path, "Mixed_7a_Branch_0_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
|
|
get_random_accessor(1.f, 1.f),
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get_weights_accessor(data_path, "Mixed_7a_Branch_0_Conv2d_0a_1x1_BatchNorm_beta.npy"),
|
|
batch_norm_epsilon)
|
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.set_name("Mixed_7a/Branch_0/Conv2d_0a_1x1/BatchNorm")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_7a/Branch_0/Conv2d_0a_1x1/Relu")
|
|
<< ConvolutionLayer(3U, 3U, 384U,
|
|
get_weights_accessor(data_path, "Mixed_7a_Branch_0_Conv2d_1a_3x3_weights.npy", weights_layout),
|
|
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
|
|
PadStrideInfo(2, 2, 0, 0))
|
|
.set_name("Mixed_7a/Branch_0/Conv2d_1a_3x3/convolution")
|
|
<< BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_7a_Branch_0_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"),
|
|
get_weights_accessor(data_path, "Mixed_7a_Branch_0_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"),
|
|
get_random_accessor(1.f, 1.f),
|
|
get_weights_accessor(data_path, "Mixed_7a_Branch_0_Conv2d_1a_3x3_BatchNorm_beta.npy"),
|
|
batch_norm_epsilon)
|
|
.set_name("Mixed_7a/Branch_0/Conv2d_1a_3x3/BatchNorm")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_7a/Branch_0/Conv2d_1a_3x3/Relu");
|
|
|
|
// Branch 1
|
|
SubStream i_b(graph);
|
|
i_b << ConvolutionLayer(1U, 1U, 256U,
|
|
get_weights_accessor(data_path, "Mixed_7a_Branch_1_Conv2d_0a_1x1_weights.npy", weights_layout),
|
|
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
|
|
PadStrideInfo(1, 1, 0, 0))
|
|
.set_name("Mixed_7a/Branch_1/Conv2d_0a_1x1/convolution")
|
|
<< BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_7a_Branch_1_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
|
|
get_weights_accessor(data_path, "Mixed_7a_Branch_1_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
|
|
get_random_accessor(1.f, 1.f),
|
|
get_weights_accessor(data_path, "Mixed_7a_Branch_1_Conv2d_0a_1x1_BatchNorm_beta.npy"),
|
|
batch_norm_epsilon)
|
|
.set_name("Mixed_7a/Branch_1/Conv2d_0a_1x1/BatchNorm")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_7a/Branch_1/Conv2d_0a_1x1/Relu")
|
|
<< ConvolutionLayer(3U, 3U, 256U,
|
|
get_weights_accessor(data_path, "Mixed_7a_Branch_1_Conv2d_1a_3x3_weights.npy", weights_layout),
|
|
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
|
|
PadStrideInfo(2, 2, 0, 0))
|
|
.set_name("Mixed_7a/Branch_1/Conv2d_1a_3x3/convolution")
|
|
<< BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_7a_Branch_1_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"),
|
|
get_weights_accessor(data_path, "Mixed_7a_Branch_1_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"),
|
|
get_random_accessor(1.f, 1.f),
|
|
get_weights_accessor(data_path, "Mixed_7a_Branch_1_Conv2d_1a_3x3_BatchNorm_beta.npy"),
|
|
batch_norm_epsilon)
|
|
.set_name("Mixed_7a/Branch_1/Conv2d_1a_3x3/BatchNorm")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_7a/Branch_1/Conv2d_1a_3x3/Relu");
|
|
|
|
// Branch 2
|
|
SubStream i_c(graph);
|
|
i_c << ConvolutionLayer(1U, 1U, 256U,
|
|
get_weights_accessor(data_path, "Mixed_7a_Branch_2_Conv2d_0a_1x1_weights.npy", weights_layout),
|
|
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
|
|
PadStrideInfo(1, 1, 0, 0))
|
|
.set_name("Mixed_7a/Branch_2/Conv2d_0a_1x1/convolution")
|
|
<< BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_7a_Branch_2_Conv2d_0a_1x1_BatchNorm_moving_mean.npy"),
|
|
get_weights_accessor(data_path, "Mixed_7a_Branch_2_Conv2d_0a_1x1_BatchNorm_moving_variance.npy"),
|
|
get_random_accessor(1.f, 1.f),
|
|
get_weights_accessor(data_path, "Mixed_7a_Branch_2_Conv2d_0a_1x1_BatchNorm_beta.npy"),
|
|
batch_norm_epsilon)
|
|
.set_name("Mixed_7a/Branch_2/Conv2d_0a_1x1/BatchNorm")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_7a/Branch_2/Conv2d_0a_1x1/Relu")
|
|
<< ConvolutionLayer(3U, 3U, 256U,
|
|
get_weights_accessor(data_path, "Mixed_7a_Branch_2_Conv2d_0b_3x3_weights.npy", weights_layout),
|
|
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
|
|
PadStrideInfo(1, 1, 1, 1))
|
|
.set_name("Mixed_7a/Branch_2/Conv2d_0b_3x3/convolution")
|
|
<< BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_7a_Branch_2_Conv2d_0b_3x3_BatchNorm_moving_mean.npy"),
|
|
get_weights_accessor(data_path, "Mixed_7a_Branch_2_Conv2d_0b_3x3_BatchNorm_moving_variance.npy"),
|
|
get_random_accessor(1.f, 1.f),
|
|
get_weights_accessor(data_path, "Mixed_7a_Branch_2_Conv2d_0b_3x3_BatchNorm_beta.npy"),
|
|
batch_norm_epsilon)
|
|
.set_name("Mixed_7a/Branch_2/Conv2d_0b_3x3/BatchNorm")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_7a/Branch_2/Conv2d_0b_3x3/Relu")
|
|
<< ConvolutionLayer(3U, 3U, 256U,
|
|
get_weights_accessor(data_path, "Mixed_7a_Branch_2_Conv2d_1a_3x3_weights.npy", weights_layout),
|
|
std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
|
|
PadStrideInfo(2, 2, 0, 0))
|
|
.set_name("Mixed_7a/Branch_2/Conv2d_1a_3x3/convolution")
|
|
<< BatchNormalizationLayer(get_weights_accessor(data_path, "Mixed_7a_Branch_2_Conv2d_1a_3x3_BatchNorm_moving_mean.npy"),
|
|
get_weights_accessor(data_path, "Mixed_7a_Branch_2_Conv2d_1a_3x3_BatchNorm_moving_variance.npy"),
|
|
get_random_accessor(1.f, 1.f),
|
|
get_weights_accessor(data_path, "Mixed_7a_Branch_2_Conv2d_1a_3x3_BatchNorm_beta.npy"),
|
|
batch_norm_epsilon)
|
|
.set_name("Mixed_7a/Branch_2/Conv2d_1a_3x3/BatchNorm")
|
|
<< ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("Mixed_7a/Branch_2/Conv2d_1a_3x3/Relu");
|
|
|
|
// Branch 3
|
|
SubStream i_d(graph);
|
|
i_d << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, common_params.data_layout, PadStrideInfo(2, 2, 0, 0), true)).set_name("Mixed_7a/Branch_3/MaxPool_1a_3x3");
|
|
|
|
// Concatenate
|
|
graph << ConcatLayer(std::move(i_a), std::move(i_b), std::move(i_c), std::move(i_d)).set_name("Mixed_7a/concat");
|
|
}
|
|
};
|
|
|
|
/** Main program for Inception ResNet V1
|
|
*
|
|
* Model is based on:
|
|
* https://arxiv.org/abs/1602.07261
|
|
* "Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning"
|
|
* Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi
|
|
*
|
|
* @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<InceptionResNetV1Example>(argc, argv);
|
|
}
|