/* * 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::utils; using namespace arm_compute::graph::frontend; using namespace arm_compute::graph_utils; /** Example demonstrating how to implement ResNeXt50 network using the Compute Library's graph API */ class GraphResNeXt50Example : public Example { public: GraphResNeXt50Example() : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "ResNeXt50") { } 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; } // 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 input descriptor const auto operation_layout = common_params.data_layout; const TensorShape tensor_shape = permute_shape(TensorShape(224U, 224U, 3U, common_params.batches), DataLayout::NCHW, operation_layout); TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_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)) << ScaleLayer(get_weights_accessor(data_path, "/cnn_data/resnext50_model/bn_data_mul.npy"), get_weights_accessor(data_path, "/cnn_data/resnext50_model/bn_data_add.npy")) .set_name("bn_data/Scale") << ConvolutionLayer( 7U, 7U, 64U, get_weights_accessor(data_path, "/cnn_data/resnext50_model/conv0_weights.npy", weights_layout), get_weights_accessor(data_path, "/cnn_data/resnext50_model/conv0_biases.npy"), PadStrideInfo(2, 2, 2, 3, 2, 3, DimensionRoundingType::FLOOR)) .set_name("conv0/Convolution") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("conv0/Relu") << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, operation_layout, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR))).set_name("pool0"); add_residual_block(data_path, weights_layout, /*ofm*/ 256, /*stage*/ 1, /*num_unit*/ 3, /*stride_conv_unit1*/ 1); add_residual_block(data_path, weights_layout, 512, 2, 4, 2); add_residual_block(data_path, weights_layout, 1024, 3, 6, 2); add_residual_block(data_path, weights_layout, 2048, 4, 3, 2); graph << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, operation_layout)).set_name("pool1") << FlattenLayer().set_name("predictions/Reshape") << OutputLayer(get_npy_output_accessor(common_params.labels, TensorShape(2048U), DataType::F32)); // 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; 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 add_residual_block(const std::string &data_path, DataLayout weights_layout, unsigned int base_depth, unsigned int stage, unsigned int num_units, unsigned int stride_conv_unit1) { for(unsigned int i = 0; i < num_units; ++i) { std::stringstream unit_path_ss; unit_path_ss << "/cnn_data/resnext50_model/stage" << stage << "_unit" << (i + 1) << "_"; std::string unit_path = unit_path_ss.str(); std::stringstream unit_name_ss; unit_name_ss << "stage" << stage << "/unit" << (i + 1) << "/"; std::string unit_name = unit_name_ss.str(); PadStrideInfo pad_grouped_conv(1, 1, 1, 1); if(i == 0) { pad_grouped_conv = (stage == 1) ? PadStrideInfo(stride_conv_unit1, stride_conv_unit1, 1, 1) : PadStrideInfo(stride_conv_unit1, stride_conv_unit1, 0, 1, 0, 1, DimensionRoundingType::FLOOR); } SubStream right(graph); right << ConvolutionLayer( 1U, 1U, base_depth / 2, get_weights_accessor(data_path, unit_path + "conv1_weights.npy", weights_layout), get_weights_accessor(data_path, unit_path + "conv1_biases.npy"), PadStrideInfo(1, 1, 0, 0)) .set_name(unit_name + "conv1/convolution") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "conv1/Relu") << ConvolutionLayer( 3U, 3U, base_depth / 2, get_weights_accessor(data_path, unit_path + "conv2_weights.npy", weights_layout), std::unique_ptr(nullptr), pad_grouped_conv, 32) .set_name(unit_name + "conv2/convolution") << ScaleLayer(get_weights_accessor(data_path, unit_path + "bn2_mul.npy"), get_weights_accessor(data_path, unit_path + "bn2_add.npy")) .set_name(unit_name + "conv1/Scale") << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "conv2/Relu") << ConvolutionLayer( 1U, 1U, base_depth, get_weights_accessor(data_path, unit_path + "conv3_weights.npy", weights_layout), get_weights_accessor(data_path, unit_path + "conv3_biases.npy"), PadStrideInfo(1, 1, 0, 0)) .set_name(unit_name + "conv3/convolution"); SubStream left(graph); if(i == 0) { left << ConvolutionLayer( 1U, 1U, base_depth, get_weights_accessor(data_path, unit_path + "sc_weights.npy", weights_layout), std::unique_ptr(nullptr), PadStrideInfo(stride_conv_unit1, stride_conv_unit1, 0, 0)) .set_name(unit_name + "sc/convolution") << ScaleLayer(get_weights_accessor(data_path, unit_path + "sc_bn_mul.npy"), get_weights_accessor(data_path, unit_path + "sc_bn_add.npy")) .set_name(unit_name + "sc/scale"); } graph << EltwiseLayer(std::move(left), std::move(right), EltwiseOperation::Add).set_name(unit_name + "add"); graph << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name(unit_name + "Relu"); } } }; /** Main program for ResNeXt50 * * Model is based on: * https://arxiv.org/abs/1611.05431 * "Aggregated Residual Transformations for Deep Neural Networks" * Saining Xie, Ross Girshick, Piotr Dollar, Zhuowen Tu, Kaiming He. * * @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); }