From 177a9a54d6b556edf39bb3ec968c074d1b865964 Mon Sep 17 00:00:00 2001 From: Michalis Spyrou Date: Thu, 6 Sep 2018 15:10:22 +0100 Subject: COMPMID-1460 Create Yolo v3 graph example Adding darknet53, still need to add YOLO layer. Change-Id: I0feba46ea850c9107f9e7ea456336e87a0bf9b27 Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/147610 Tested-by: Jenkins Reviewed-by: Georgios Pinitas --- examples/graph_yolov3.cpp | 267 ++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 267 insertions(+) create mode 100644 examples/graph_yolov3.cpp (limited to 'examples') diff --git a/examples/graph_yolov3.cpp b/examples/graph_yolov3.cpp new file mode 100644 index 0000000000..d2f7ae5e59 --- /dev/null +++ b/examples/graph_yolov3.cpp @@ -0,0 +1,267 @@ +/* + * Copyright (c) 2018 ARM Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#include "arm_compute/graph.h" +#include "support/ToolchainSupport.h" +#include "utils/CommonGraphOptions.h" +#include "utils/GraphUtils.h" +#include "utils/Utils.h" + +using namespace arm_compute::utils; +using namespace arm_compute::graph::frontend; +using namespace arm_compute::graph_utils; + +/** Example demonstrating how to implement YOLOv3 network using the Compute Library's graph API + * + * @param[in] argc Number of arguments + * @param[in] argv Arguments + */ +class GraphYOLOv3Example : public Example +{ +public: + GraphYOLOv3Example() + : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "YOLOv3") + { + } + bool do_setup(int argc, char **argv) override + { + // Parse arguments + cmd_parser.parse(argc, argv); + + // Consume common parameters + common_params = consume_common_graph_parameters(common_opts); + + // Return when help menu is requested + if(common_params.help) + { + cmd_parser.print_help(argv[0]); + return false; + } + + // Checks + ARM_COMPUTE_EXIT_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type), "QASYMM8 not supported for this graph"); + + // Print parameter values + std::cout << common_params << std::endl; + + // Get trainable parameters data path + std::string data_path = common_params.data_path; + + // Create a preprocessor object + std::unique_ptr preprocessor = arm_compute::support::cpp14::make_unique(0.f); + + // Create input descriptor + const TensorShape tensor_shape = permute_shape(TensorShape(608U, 608U, 3U, 1U), DataLayout::NCHW, common_params.data_layout); + TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout); + + // Set weights trained layout + const DataLayout weights_layout = DataLayout::NCHW; + + graph << common_params.target + << common_params.fast_math_hint + << InputLayer(input_descriptor, get_input_accessor(common_params, std::move(preprocessor), false)) + // Layer 1 + << ConvolutionLayer( + 3U, 3U, 32U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_1_w.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 1, 1)) + .set_name("conv2d_1") + << BatchNormalizationLayer( + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_1_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_1_var.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_1_gamma.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_1_beta.npy"), + 0.000001f) + .set_name("conv2d_1/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_1/LeakyRelu") + + // Layer 2 + << ConvolutionLayer( + 3U, 3U, 64U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_2_w.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(2, 2, 1, 1)) + .set_name("conv2d_2") + << BatchNormalizationLayer( + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_2_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_2_var.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_2_gamma.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_2_beta.npy"), + 0.000001f) + .set_name("conv2d_2/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_2/LeakyRelu"); + darknet53_block(data_path, "3", weights_layout, 32U); + graph << ConvolutionLayer( + 3U, 3U, 128U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_5_w.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(2, 2, 1, 1)) + .set_name("conv2d_5") + << BatchNormalizationLayer( + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_5_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_5_var.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_5_gamma.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_5_beta.npy"), + 0.000001f) + .set_name("conv2d_5/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_5/LeakyRelu"); + darknet53_block(data_path, "6", weights_layout, 64U); + darknet53_block(data_path, "8", weights_layout, 64U); + graph << ConvolutionLayer( + 3U, 3U, 256U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_10_w.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(2, 2, 1, 1)) + .set_name("conv2d_10") + << BatchNormalizationLayer( + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_10_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_10_var.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_10_gamma.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_10_beta.npy"), + 0.000001f) + .set_name("conv2d_10/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_10/LeakyRelu"); + darknet53_block(data_path, "11", weights_layout, 128U); + darknet53_block(data_path, "13", weights_layout, 128U); + darknet53_block(data_path, "15", weights_layout, 128U); + darknet53_block(data_path, "17", weights_layout, 128U); + darknet53_block(data_path, "19", weights_layout, 128U); + darknet53_block(data_path, "21", weights_layout, 128U); + darknet53_block(data_path, "23", weights_layout, 128U); + darknet53_block(data_path, "25", weights_layout, 128U); + graph << ConvolutionLayer( + 3U, 3U, 512U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_27_w.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(2, 2, 1, 1)) + .set_name("conv2d_27") + << BatchNormalizationLayer( + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_27_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_27_var.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_27_gamma.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_27_beta.npy"), + 0.000001f) + .set_name("conv2d_27/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_27/LeakyRelu"); + darknet53_block(data_path, "28", weights_layout, 256U); + darknet53_block(data_path, "30", weights_layout, 256U); + darknet53_block(data_path, "32", weights_layout, 256U); + darknet53_block(data_path, "34", weights_layout, 256U); + darknet53_block(data_path, "36", weights_layout, 256U); + darknet53_block(data_path, "38", weights_layout, 256U); + darknet53_block(data_path, "40", weights_layout, 256U); + darknet53_block(data_path, "42", weights_layout, 256U); + graph << ConvolutionLayer( + 3U, 3U, 1024U, + get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_44_w.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(2, 2, 1, 1)) + .set_name("conv2d_44") + << BatchNormalizationLayer( + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_44_mean.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_44_var.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_44_gamma.npy"), + get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_44_beta.npy"), + 0.000001f) + .set_name("conv2d_44/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_44/LeakyRelu"); + darknet53_block(data_path, "45", weights_layout, 512U); + darknet53_block(data_path, "47", weights_layout, 512U); + darknet53_block(data_path, "49", weights_layout, 512U); + darknet53_block(data_path, "51", weights_layout, 512U); + graph << OutputLayer(get_output_accessor(common_params, 5)); + + // Finalize graph + GraphConfig config; + config.num_threads = common_params.threads; + config.use_tuner = common_params.enable_tuner; + config.tuner_file = common_params.tuner_file; + + graph.finalize(common_params.target, config); + + return true; + } + void do_run() override + { + // Run graph + graph.run(); + } + +private: + CommandLineParser cmd_parser; + CommonGraphOptions common_opts; + CommonGraphParams common_params; + Stream graph; + + void darknet53_block(const std::string &data_path, std::string &¶m_path, DataLayout weights_layout, + unsigned int filter_size) + { + std::string total_path = "/cnn_data/yolov3_model/"; + std::string param_path2 = std::to_string(std::stoi(param_path) + 1); + SubStream i_a(graph); + SubStream i_b(graph); + i_a << ConvolutionLayer( + 1U, 1U, filter_size, + get_weights_accessor(data_path, total_path + "conv2d_" + param_path + "_w.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 0)) + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path + "_mean.npy"), + get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path + "_var.npy"), + get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path + "_gamma.npy"), + get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path + "_beta.npy"), + 0.000001f) + .set_name("conv2d" + param_path + "/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d" + param_path + "/LeakyRelu") + << ConvolutionLayer( + 3U, 3U, filter_size * 2, + get_weights_accessor(data_path, total_path + "conv2d_" + param_path2 + "_w.npy", weights_layout), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 1, 1)) + << BatchNormalizationLayer( + get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path2 + "_mean.npy"), + get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path2 + "_var.npy"), + get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path2 + "_gamma.npy"), + get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path2 + "_beta.npy"), + 0.000001f) + .set_name("conv2d" + param_path2 + "/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d" + param_path2 + "/LeakyRelu"); + + graph << EltwiseLayer(std::move(i_a), std::move(i_b), EltwiseOperation::Add); + } +}; + +/** Main program for YOLOv3 + * + * @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 + * + * @return Return code + */ +int main(int argc, char **argv) +{ + return arm_compute::utils::run_example(argc, argv); +} -- cgit v1.2.1