| /* |
| * Copyright (c) 2019-2021 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 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<std::pair<QuantizationInfo, QuantizationInfo>> 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; |
| 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; |
| }; |
| |
| /** 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<GraphMnistExample>(argc, argv); |
| } |