From 766b70ce570b8f837e530213dcac752dde0182b3 Mon Sep 17 00:00:00 2001 From: Georgios Pinitas Date: Mon, 13 Aug 2018 17:50:34 +0100 Subject: COMPMID-1456: Create mobilenet v2 1.0 224 graph example Change-Id: I26533af88aebe4bd9692ee1cdcd24eca34acea32 Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/143984 Tested-by: Jenkins Reviewed-by: Pablo Tello --- examples/graph_mobilenet_v2.cpp | 244 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 244 insertions(+) create mode 100644 examples/graph_mobilenet_v2.cpp (limited to 'examples') diff --git a/examples/graph_mobilenet_v2.cpp b/examples/graph_mobilenet_v2.cpp new file mode 100644 index 0000000000..1e50947c67 --- /dev/null +++ b/examples/graph_mobilenet_v2.cpp @@ -0,0 +1,244 @@ +/* + * Copyright (c) 2018 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 MobileNetV2's network using the Compute Library's graph API */ +class GraphMobilenetV2Example : public Example +{ +public: + GraphMobilenetV2Example() + : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "MobileNetV2") + { + } + GraphMobilenetV2Example(const GraphMobilenetV2Example &) = delete; + GraphMobilenetV2Example &operator=(const GraphMobilenetV2Example &) = delete; + GraphMobilenetV2Example(GraphMobilenetV2Example &&) = default; // NOLINT + GraphMobilenetV2Example &operator=(GraphMobilenetV2Example &&) = default; // NOLINT + ~GraphMobilenetV2Example() override = default; + + bool do_setup(int argc, char **argv) override + { + // Parse arguments + cmd_parser.parse(argc, argv); + + // 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; + + // Create core of graph + std::string model_path = "/mobilenet_v2_1.0_224_model/"; + + // Create input descriptor + const TensorShape tensor_shape = permute_shape(TensorShape(224U, 224U, 3U, 1U), DataLayout::NCHW, common_params.data_layout); + TensorDescriptor input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout); + + // Create a preprocessor object + std::unique_ptr preprocessor = arm_compute::support::cpp14::make_unique(); + + // Get trainable parameters data path + std::string data_path = common_params.data_path; + + // Add model path to data path + if(!data_path.empty()) + { + data_path += model_path; + } + + // Create graph + graph << common_params.target + << DepthwiseConvolutionMethod::Optimized3x3 // FIXME(COMPMID-1073): Add heuristics to automatically call the optimized 3x3 method + << common_params.fast_math_hint + << InputLayer(input_descriptor, get_input_accessor(common_params, std::move(preprocessor), false)) + << ConvolutionLayer(3U, 3U, 32U, + get_weights_accessor(data_path, "Conv_weights.npy", DataLayout::NCHW), + std::unique_ptr(nullptr), + PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL)) + .set_name("Conv") + << BatchNormalizationLayer(get_weights_accessor(data_path, "Conv_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, "Conv_BatchNorm_moving_variance.npy"), + get_weights_accessor(data_path, "Conv_BatchNorm_gamma.npy"), + get_weights_accessor(data_path, "Conv_BatchNorm_beta.npy"), + 0.0010000000474974513f) + .set_name("Conv/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)) + .set_name("Conv/Relu6"); + + get_expanded_conv(data_path, "expanded_conv", 32U, 16U, PadStrideInfo(1, 1, 1, 1)); + get_expanded_conv(data_path, "expanded_conv_1", 16U, 24U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), true); + get_expanded_conv(data_path, "expanded_conv_2", 24U, 24U, PadStrideInfo(1, 1, 1, 1), true, true); + get_expanded_conv(data_path, "expanded_conv_3", 24U, 32U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), true); + get_expanded_conv(data_path, "expanded_conv_4", 32U, 32U, PadStrideInfo(1, 1, 1, 1), true, true); + get_expanded_conv(data_path, "expanded_conv_5", 32U, 32U, PadStrideInfo(1, 1, 1, 1), true, true); + get_expanded_conv(data_path, "expanded_conv_6", 32U, 64U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), true); + get_expanded_conv(data_path, "expanded_conv_7", 64U, 64U, PadStrideInfo(1, 1, 1, 1), true, true); + get_expanded_conv(data_path, "expanded_conv_8", 64U, 64U, PadStrideInfo(1, 1, 1, 1), true, true); + get_expanded_conv(data_path, "expanded_conv_9", 64U, 64U, PadStrideInfo(1, 1, 1, 1), true, true); + get_expanded_conv(data_path, "expanded_conv_10", 64U, 96U, PadStrideInfo(1, 1, 1, 1), true); + get_expanded_conv(data_path, "expanded_conv_11", 96U, 96U, PadStrideInfo(1, 1, 1, 1), true, true); + get_expanded_conv(data_path, "expanded_conv_12", 96U, 96U, PadStrideInfo(1, 1, 1, 1), true, true); + get_expanded_conv(data_path, "expanded_conv_13", 96U, 160U, PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::CEIL), true); + get_expanded_conv(data_path, "expanded_conv_14", 160U, 160U, PadStrideInfo(1, 1, 1, 1), true, true); + get_expanded_conv(data_path, "expanded_conv_15", 160U, 160U, PadStrideInfo(1, 1, 1, 1), true, true); + get_expanded_conv(data_path, "expanded_conv_16", 160U, 320U, PadStrideInfo(1, 1, 1, 1), true); + + graph << ConvolutionLayer(1U, 1U, 1280U, + get_weights_accessor(data_path, "Conv_1_weights.npy", DataLayout::NCHW), + std::unique_ptr(nullptr), + PadStrideInfo(1, 1, 0, 0)) + .set_name("Conv_1") + << BatchNormalizationLayer(get_weights_accessor(data_path, "Conv_1_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, "Conv_1_BatchNorm_moving_variance.npy"), + get_weights_accessor(data_path, "Conv_1_BatchNorm_gamma.npy"), + get_weights_accessor(data_path, "Conv_1_BatchNorm_beta.npy"), + 0.0010000000474974513f) + .set_name("Conv_1/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)) + .set_name("Conv_1/Relu6") + << PoolingLayer(PoolingLayerInfo(PoolingType::AVG)).set_name("Logits/AvgPool") + << ConvolutionLayer(1U, 1U, 1001U, + get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_weights.npy", DataLayout::NCHW), + get_weights_accessor(data_path, "Logits_Conv2d_1c_1x1_biases.npy"), + PadStrideInfo(1, 1, 0, 0)) + .set_name("Logits/Conv2d_1c_1x1") + << ReshapeLayer(TensorShape(1001U)).set_name("Predictions/Reshape") + << SoftmaxLayer().set_name("Predictions/Softmax") + << 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_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; + + void get_expanded_conv(const std::string &data_path, std::string &¶m_path, + unsigned int input_channels, unsigned int output_channels, + PadStrideInfo dwc_pad_stride_info, + bool has_expand = false, bool is_residual = false, unsigned int expansion_size = 6) + { + std::string total_path = param_path + "_"; + SubStream left(graph); + + // Add expand node + if(has_expand) + { + left << ConvolutionLayer(1U, 1U, input_channels * expansion_size, + get_weights_accessor(data_path, total_path + "expand_weights.npy", DataLayout::NCHW), + std::unique_ptr(nullptr), PadStrideInfo(1, 1, 0, 0)) + .set_name(param_path + "/expand/Conv2D") + << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "expand_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "expand_BatchNorm_moving_variance.npy"), + get_weights_accessor(data_path, total_path + "expand_BatchNorm_gamma.npy"), + get_weights_accessor(data_path, total_path + "expand_BatchNorm_beta.npy"), + 0.0010000000474974513f) + .set_name(param_path + "/expand/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)) + .set_name(param_path + "/expand/Relu6"); + } + + // Add depthwise node + left << DepthwiseConvolutionLayer(3U, 3U, + get_weights_accessor(data_path, total_path + "depthwise_depthwise_weights.npy", DataLayout::NCHW), + std::unique_ptr(nullptr), + dwc_pad_stride_info) + .set_name(param_path + "/depthwise/depthwise") + << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_moving_variance.npy"), + get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_gamma.npy"), + get_weights_accessor(data_path, total_path + "depthwise_BatchNorm_beta.npy"), + 0.0010000000474974513f) + .set_name(param_path + "/depthwise/BatchNorm") + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 6.f)) + .set_name(param_path + "/depthwise/Relu6"); + + // Add project node + left << ConvolutionLayer(1U, 1U, output_channels, + get_weights_accessor(data_path, total_path + "project_weights.npy", DataLayout::NCHW), + std::unique_ptr(nullptr), PadStrideInfo(1, 1, 0, 0)) + .set_name(param_path + "/project/Conv2D") + << BatchNormalizationLayer(get_weights_accessor(data_path, total_path + "project_BatchNorm_moving_mean.npy"), + get_weights_accessor(data_path, total_path + "project_BatchNorm_moving_variance.npy"), + get_weights_accessor(data_path, total_path + "project_BatchNorm_gamma.npy"), + get_weights_accessor(data_path, total_path + "project_BatchNorm_beta.npy"), + 0.0010000000474974513) + .set_name(param_path + "/project/BatchNorm"); + + if(is_residual) + { + // Add residual node + SubStream right(graph); + graph << BranchLayer(BranchMergeMethod::ADD, std::move(left), std::move(right)).set_name(param_path + "/add"); + } + else + { + graph.forward_tail(left.tail_node()); + } + } +}; + +/** Main program for MobileNetV2 + * + * @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); +} -- cgit v1.2.1