From 8fe103c35b351f2f2028782c74f0b619a744595e Mon Sep 17 00:00:00 2001 From: Georgios Pinitas Date: Tue, 4 Dec 2018 14:26:31 +0000 Subject: COMPMID-1805: Implement SRCNN 9-5-5 Change-Id: I2463c44e79e8df3dc081c645b2aa37468d5b9f0b Reviewed-on: https://review.mlplatform.org/346 Tested-by: Arm Jenkins Reviewed-by: Anthony Barbier --- examples/graph_srcnn955.cpp | 159 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 159 insertions(+) create mode 100644 examples/graph_srcnn955.cpp (limited to 'examples') diff --git a/examples/graph_srcnn955.cpp b/examples/graph_srcnn955.cpp new file mode 100644 index 0000000000..f03e8fe287 --- /dev/null +++ b/examples/graph_srcnn955.cpp @@ -0,0 +1,159 @@ +/* + * 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 SRCNN 9-5-5 network using the Compute Library's graph API */ +class GraphSRCNN955Example : public Example +{ +public: + GraphSRCNN955Example() + : cmd_parser(), common_opts(cmd_parser), model_input_width(nullptr), model_input_height(nullptr), common_params(), graph(0, "SRCNN955") + { + model_input_width = cmd_parser.add_option>("image-width", 300); + model_input_height = cmd_parser.add_option>("image-height", 300); + + // Add model id option + model_input_width->set_help("Input image width."); + model_input_height->set_help("Input image height."); + } + GraphSRCNN955Example(const GraphSRCNN955Example &) = delete; + GraphSRCNN955Example &operator=(const GraphSRCNN955Example &) = delete; + GraphSRCNN955Example(GraphSRCNN955Example &&) = default; // NOLINT + GraphSRCNN955Example &operator=(GraphSRCNN955Example &&) = default; // NOLINT + ~GraphSRCNN955Example() override = default; + 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; + } + + // Get input image width and height + const unsigned int image_width = model_input_width->value(); + const unsigned int image_height = model_input_height->value(); + + // Print parameter values + std::cout << common_params << std::endl; + std::cout << "Image width: " << image_width << std::endl; + std::cout << "Image height: " << image_height << std::endl; + + // Checks + ARM_COMPUTE_EXIT_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type), "QASYMM8 not supported for this graph"); + + // Get trainable parameters data path + const std::string data_path = common_params.data_path; + const std::string model_path = "/cnn_data/srcnn955_model/"; + + // Create a preprocessor object + std::unique_ptr preprocessor = arm_compute::support::cpp14::make_unique(); + + // Create input descriptor + const TensorShape tensor_shape = permute_shape(TensorShape(image_width, image_height, 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 /* Do not convert to BGR */)) + << ConvolutionLayer( + 9U, 9U, 64U, + get_weights_accessor(data_path, "conv1_weights.npy", weights_layout), + get_weights_accessor(data_path, "conv1_biases.npy"), + PadStrideInfo(1, 1, 4, 4)) + .set_name("conv1/convolution") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv1/Relu") + << ConvolutionLayer( + 5U, 5U, 32U, + get_weights_accessor(data_path, "conv2_weights.npy", weights_layout), + get_weights_accessor(data_path, "conv2_biases.npy"), + PadStrideInfo(1, 1, 2, 2)) + .set_name("conv2/convolution") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv2/Relu") + << ConvolutionLayer( + 5U, 5U, 3U, + get_weights_accessor(data_path, "conv3_weights.npy", weights_layout), + get_weights_accessor(data_path, "conv3_biases.npy"), + PadStrideInfo(1, 1, 2, 2)) + .set_name("conv3/convolution") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv3/Relu") + << OutputLayer(arm_compute::support::cpp14::make_unique(0)); + + // Finalize graph + GraphConfig config; + config.num_threads = common_params.threads; + config.use_tuner = common_params.enable_tuner; + graph.finalize(common_params.target, config); + + return true; + } + + void do_run() override + { + // Run graph + graph.run(); + } + +private: + CommandLineParser cmd_parser; + CommonGraphOptions common_opts; + SimpleOption *model_input_width{ nullptr }; + SimpleOption *model_input_height{ nullptr }; + CommonGraphParams common_params; + Stream graph; +}; + +/** Main program for SRCNN 9-5-5 + * + * Model is based on: + * http://mmlab.ie.cuhk.edu.hk/projects/SRCNN.html + * "Image Super-Resolution Using Deep Convolutional Networks" + * Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang + * + * @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(argc, argv); +} -- cgit v1.2.1