| /* |
| * Copyright (c) 2018-2020 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 ResNet12 network using the Compute Library's graph API */ |
| class GraphResNet12Example : public Example |
| { |
| public: |
| GraphResNet12Example() |
| : cmd_parser(), common_opts(cmd_parser), model_input_width(nullptr), model_input_height(nullptr), common_params(), graph(0, "ResNet12") |
| { |
| 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", 128); |
| |
| // Add model id option |
| model_input_width->set_help("Input image width."); |
| model_input_height->set_help("Input image height."); |
| } |
| GraphResNet12Example(const GraphResNet12Example &) = delete; |
| GraphResNet12Example &operator=(const GraphResNet12Example &) = delete; |
| ~GraphResNet12Example() 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(); |
| |
| // 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; |
| 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/resnet12_model/"; |
| |
| // Create a preprocessor object |
| std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<TFPreproccessor>(); |
| |
| // 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", weights_layout), |
| PadStrideInfo(1, 1, 4, 4)) |
| .set_name("conv1/convolution") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv1/Relu"); |
| |
| add_residual_block(data_path, "block1", weights_layout); |
| add_residual_block(data_path, "block2", weights_layout); |
| add_residual_block(data_path, "block3", weights_layout); |
| add_residual_block(data_path, "block4", weights_layout); |
| |
| graph << ConvolutionLayer( |
| 3U, 3U, 64U, |
| get_weights_accessor(data_path, "conv10_weights.npy", weights_layout), |
| get_weights_accessor(data_path, "conv10_biases.npy"), |
| PadStrideInfo(1, 1, 1, 1)) |
| .set_name("conv10/convolution") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv10/Relu") |
| << ConvolutionLayer( |
| 3U, 3U, 64U, |
| get_weights_accessor(data_path, "conv11_weights.npy", weights_layout), |
| get_weights_accessor(data_path, "conv11_biases.npy"), |
| PadStrideInfo(1, 1, 1, 1)) |
| .set_name("conv11/convolution") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv11/Relu") |
| << ConvolutionLayer( |
| 9U, 9U, 3U, |
| get_weights_accessor(data_path, "conv12_weights.npy", weights_layout), |
| get_weights_accessor(data_path, "conv12_biases.npy"), |
| PadStrideInfo(1, 1, 4, 4)) |
| .set_name("conv12/convolution") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH)).set_name("conv12/Tanh") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 0.58f, 0.5f)).set_name("conv12/Linear") |
| << OutputLayer(arm_compute::support::cpp14::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; |
| |
| 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; |
| |
| void add_residual_block(const std::string &data_path, const std::string &name, DataLayout weights_layout) |
| { |
| std::stringstream unit_path_ss; |
| unit_path_ss << data_path << name << "_"; |
| std::stringstream unit_name_ss; |
| unit_name_ss << name << "/"; |
| |
| std::string unit_path = unit_path_ss.str(); |
| std::string unit_name = unit_name_ss.str(); |
| |
| SubStream left(graph); |
| SubStream right(graph); |
| |
| right << ConvolutionLayer( |
| 3U, 3U, 64U, |
| get_weights_accessor(data_path, unit_path + "conv1_weights.npy", weights_layout), |
| get_weights_accessor(data_path, unit_path + "conv1_biases.npy", weights_layout), |
| PadStrideInfo(1, 1, 1, 1)) |
| .set_name(unit_name + "conv1/convolution") |
| << BatchNormalizationLayer( |
| get_weights_accessor(data_path, unit_path + "conv1_BatchNorm_moving_mean.npy"), |
| get_weights_accessor(data_path, unit_path + "conv1_BatchNorm_moving_variance.npy"), |
| get_weights_accessor(data_path, unit_path + "conv1_BatchNorm_gamma.npy"), |
| get_weights_accessor(data_path, unit_path + "conv1_BatchNorm_beta.npy"), |
| 0.0000100099996416f) |
| .set_name(unit_name + "conv1/BatchNorm") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "conv1/Relu") |
| |
| << ConvolutionLayer( |
| 3U, 3U, 64U, |
| get_weights_accessor(data_path, unit_path + "conv2_weights.npy", weights_layout), |
| get_weights_accessor(data_path, unit_path + "conv2_biases.npy", weights_layout), |
| PadStrideInfo(1, 1, 1, 1)) |
| .set_name(unit_name + "conv2/convolution") |
| << BatchNormalizationLayer( |
| get_weights_accessor(data_path, unit_path + "conv2_BatchNorm_moving_mean.npy"), |
| get_weights_accessor(data_path, unit_path + "conv2_BatchNorm_moving_variance.npy"), |
| get_weights_accessor(data_path, unit_path + "conv2_BatchNorm_gamma.npy"), |
| get_weights_accessor(data_path, unit_path + "conv2_BatchNorm_beta.npy"), |
| 0.0000100099996416f) |
| .set_name(unit_name + "conv2/BatchNorm") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "conv2/Relu"); |
| |
| graph << EltwiseLayer(std::move(left), std::move(right), EltwiseOperation::Add).set_name(unit_name + "add"); |
| } |
| }; |
| |
| /** Main program for ResNet12 |
| * |
| * Model is based on: |
| * https://arxiv.org/pdf/1709.01118.pdf |
| * "WESPE: Weakly Supervised Photo Enhancer for Digital Cameras" |
| * Andrey Ignatov, Nikolay Kobyshev, Kenneth Vanhoey, Radu Timofte, Luc Van Gool |
| * |
| * @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<GraphResNet12Example>(argc, argv); |
| } |
