| /* |
| * Copyright (c) 2018-2021 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; |
| using namespace arm_compute::utils; |
| using namespace arm_compute::graph::frontend; |
| using namespace arm_compute::graph_utils; |
| |
| /** Example demonstrating how to implement VGG based VDSR network using the Compute Library's graph API */ |
| class GraphVDSRExample : public Example |
| { |
| public: |
| GraphVDSRExample() |
| : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "VDSR") |
| { |
| model_input_width = cmd_parser.add_option<SimpleOption<unsigned int>>("image-width", 192); |
| model_input_height = cmd_parser.add_option<SimpleOption<unsigned int>>("image-height", 192); |
| |
| // Add model id option |
| model_input_width->set_help("Input image width."); |
| model_input_height->set_help("Input image height."); |
| } |
| GraphVDSRExample(const GraphVDSRExample &) = delete; |
| GraphVDSRExample &operator=(const GraphVDSRExample &) = delete; |
| ~GraphVDSRExample() override = default; |
| bool do_setup(int argc, char **argv) override |
| { |
| // Parse arguments |
| cmd_parser.parse(argc, argv); |
| cmd_parser.validate(); |
| |
| // 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; |
| |
| // Get trainable parameters data path |
| const std::string data_path = common_params.data_path; |
| const std::string model_path = "/cnn_data/vdsr_model/"; |
| |
| // Create a preprocessor object |
| std::unique_ptr<IPreprocessor> preprocessor = std::make_unique<TFPreproccessor>(); |
| |
| // Create input descriptor |
| const TensorShape tensor_shape = permute_shape(TensorShape(image_width, image_height, 1U, common_params.batches), 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; |
| |
| // Note: Quantization info are random and used only for benchmarking purposes |
| graph << common_params.target |
| << common_params.fast_math_hint |
| << InputLayer(input_descriptor.set_quantization_info(QuantizationInfo(0.0078125f, 128)), |
| get_input_accessor(common_params, std::move(preprocessor), false)); |
| |
| SubStream left(graph); |
| SubStream right(graph); |
| |
| // Layer 1 |
| right << ConvolutionLayer( |
| 3U, 3U, 64U, |
| get_weights_accessor(data_path, "conv0_w.npy", weights_layout), |
| get_weights_accessor(data_path, "conv0_b.npy"), |
| PadStrideInfo(1, 1, 1, 1), 1, QuantizationInfo(0.031778190285f, 156), QuantizationInfo(0.0784313753247f, 128)) |
| .set_name("conv0") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv0/Relu"); |
| |
| // Rest 17 layers |
| for(unsigned int i = 1; i < 19; ++i) |
| { |
| const std::string conv_w_path = "conv" + arm_compute::support::cpp11::to_string(i) + "_w.npy"; |
| const std::string conv_b_path = "conv" + arm_compute::support::cpp11::to_string(i) + "_b.npy"; |
| const std::string conv_name = "conv" + arm_compute::support::cpp11::to_string(i); |
| right << ConvolutionLayer( |
| 3U, 3U, 64U, |
| get_weights_accessor(data_path, conv_w_path, weights_layout), |
| get_weights_accessor(data_path, conv_b_path), |
| PadStrideInfo(1, 1, 1, 1), 1, QuantizationInfo(0.015851572156f, 93)) |
| .set_name(conv_name) |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(conv_name + "/Relu"); |
| } |
| |
| // Final layer |
| right << ConvolutionLayer( |
| 3U, 3U, 1U, |
| get_weights_accessor(data_path, "conv20_w.npy", weights_layout), |
| get_weights_accessor(data_path, "conv20_b.npy"), |
| PadStrideInfo(1, 1, 1, 1), 1, QuantizationInfo(0.015851572156f, 93)) |
| .set_name("conv20") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv20/Relu"); |
| |
| // Add residual to input |
| graph << EltwiseLayer(std::move(left), std::move(right), EltwiseOperation::Add).set_name("add") |
| << OutputLayer(std::make_unique<DummyAccessor>(0)); |
| |
| // Finalize graph |
| GraphConfig config; |
| config.num_threads = common_params.threads; |
| config.use_tuner = common_params.enable_tuner; |
| config.tuner_mode = common_params.tuner_mode; |
| config.tuner_file = common_params.tuner_file; |
| config.mlgo_file = common_params.mlgo_file; |
| config.use_synthetic_type = arm_compute::is_data_type_quantized(common_params.data_type); |
| config.synthetic_type = common_params.data_type; |
| |
| graph.finalize(common_params.target, config); |
| |
| return true; |
| } |
| void do_run() override |
| { |
| // Run graph |
| graph.run(); |
| } |
| |
| private: |
| CommandLineParser cmd_parser; |
| CommonGraphOptions common_opts; |
| SimpleOption<unsigned int> *model_input_width{ nullptr }; |
| SimpleOption<unsigned int> *model_input_height{ nullptr }; |
| CommonGraphParams common_params; |
| Stream graph; |
| }; |
| |
| /** Main program for VGG-based VDSR |
| * |
| * Model is based on: |
| * https://arxiv.org/pdf/1511.04587.pdf |
| * "Accurate Image Super-Resolution Using Very Deep Convolutional Networks" |
| * Jiwon Kim, Jung Kwon Lee and Kyoung Mu Lee |
| * |
| * @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<GraphVDSRExample>(argc, argv); |
| } |