/* * Copyright (c) 2019-2020 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; using namespace arm_compute::utils; using namespace arm_compute::graph::frontend; using namespace arm_compute::graph_utils; /** Example demonstrating how to implement Mnist's network using the Compute Library's graph API */ class GraphMnistExample : public Example { public: GraphMnistExample() : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "LeNet") { } 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; } // Print parameter values std::cout << common_params << std::endl; // Get trainable parameters data path std::string data_path = common_params.data_path; // Add model path to data path if(!data_path.empty() && arm_compute::is_data_type_quantized_asymmetric(common_params.data_type)) { data_path += "/cnn_data/mnist_qasymm8_model/"; } // Create input descriptor const auto operation_layout = common_params.data_layout; const TensorShape tensor_shape = permute_shape(TensorShape(28U, 28U, 1U), DataLayout::NCHW, operation_layout); TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(operation_layout); const QuantizationInfo in_quant_info = QuantizationInfo(0.003921568859368563f, 0); const std::vector> conv_quant_info = { { QuantizationInfo(0.004083447158336639f, 138), QuantizationInfo(0.0046257381327450275f, 0) }, // conv0 { QuantizationInfo(0.0048590428195893764f, 149), QuantizationInfo(0.03558270260691643f, 0) }, // conv1 { QuantizationInfo(0.004008443560451269f, 146), QuantizationInfo(0.09117382764816284f, 0) }, // conv2 { QuantizationInfo(0.004344311077147722f, 160), QuantizationInfo(0.5494495034217834f, 167) }, // fc }; // Set weights trained layout const DataLayout weights_layout = DataLayout::NHWC; FullyConnectedLayerInfo fc_info = FullyConnectedLayerInfo(); fc_info.set_weights_trained_layout(weights_layout); graph << common_params.target << common_params.fast_math_hint << InputLayer(input_descriptor.set_quantization_info(in_quant_info), get_input_accessor(common_params)) << ConvolutionLayer( 3U, 3U, 32U, get_weights_accessor(data_path, "conv2d_weights_quant_FakeQuantWithMinMaxVars.npy", weights_layout), get_weights_accessor(data_path, "conv2d_Conv2D_bias.npy"), PadStrideInfo(1U, 1U, 1U, 1U), 1, conv_quant_info.at(0).first, conv_quant_info.at(0).second) .set_name("Conv0") << ConvolutionLayer( 3U, 3U, 32U, get_weights_accessor(data_path, "conv2d_1_weights_quant_FakeQuantWithMinMaxVars.npy", weights_layout), get_weights_accessor(data_path, "conv2d_1_Conv2D_bias.npy"), PadStrideInfo(1U, 1U, 1U, 1U), 1, conv_quant_info.at(1).first, conv_quant_info.at(1).second) .set_name("conv1") << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("maxpool1") << ConvolutionLayer( 3U, 3U, 32U, get_weights_accessor(data_path, "conv2d_2_weights_quant_FakeQuantWithMinMaxVars.npy", weights_layout), get_weights_accessor(data_path, "conv2d_2_Conv2D_bias.npy"), PadStrideInfo(1U, 1U, 1U, 1U), 1, conv_quant_info.at(2).first, conv_quant_info.at(2).second) .set_name("conv2") << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, operation_layout, PadStrideInfo(2, 2, 0, 0))).set_name("maxpool2") << FullyConnectedLayer( 10U, get_weights_accessor(data_path, "dense_weights_quant_FakeQuantWithMinMaxVars_transpose.npy", weights_layout), get_weights_accessor(data_path, "dense_MatMul_bias.npy"), fc_info, conv_quant_info.at(3).first, conv_quant_info.at(3).second) .set_name("fc") << SoftmaxLayer().set_name("prob"); if(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type)) { graph << DequantizationLayer().set_name("dequantize"); } 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_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; CommonGraphParams common_params; Stream graph; }; /** Main program for Mnist Example * * @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); }