From 3756186e54d77639564e999082f4bbd1ceec5a2f Mon Sep 17 00:00:00 2001 From: Georgios Pinitas Date: Thu, 19 Oct 2017 10:51:03 +0100 Subject: COMPMID-553: Add squeezenet example Change-Id: Id233f0c1c329ee0d5ee93166d4aa0718f7d629b7 Reviewed-on: http://mpd-gerrit.cambridge.arm.com/92337 Reviewed-by: Michalis Spyrou Tested-by: Kaizen Reviewed-by: Anthony Barbier --- examples/graph_squeezenet.cpp | 217 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 217 insertions(+) create mode 100644 examples/graph_squeezenet.cpp (limited to 'examples/graph_squeezenet.cpp') diff --git a/examples/graph_squeezenet.cpp b/examples/graph_squeezenet.cpp new file mode 100644 index 0000000000..c550df2c0f --- /dev/null +++ b/examples/graph_squeezenet.cpp @@ -0,0 +1,217 @@ +/* + * Copyright (c) 2017 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. + */ +#ifndef ARM_COMPUTE_CL /* Needed by Utils.cpp to handle OpenCL exceptions properly */ +#error "This example needs to be built with -DARM_COMPUTE_CL" +#endif /* ARM_COMPUTE_CL */ + +#include "arm_compute/core/Logger.h" +#include "arm_compute/graph/Graph.h" +#include "arm_compute/graph/Nodes.h" +#include "arm_compute/graph/SubGraph.h" +#include "arm_compute/runtime/CL/CLScheduler.h" +#include "arm_compute/runtime/Scheduler.h" +#include "support/ToolchainSupport.h" +#include "utils/GraphUtils.h" +#include "utils/Utils.h" + +#include +#include +#include +#include + +using namespace arm_compute::graph; +using namespace arm_compute::graph_utils; + +/** Generates appropriate accessor according to the specified path + * + * @note If path is empty will generate a DummyAccessor else will generate a NumPyBinLoader + * + * @param path Path to the data files + * @param data_file Relative path to the data files from path + * + * @return An appropriate tensor accessor + */ +std::unique_ptr get_accessor(const std::string &path, const std::string &data_file) +{ + if(path.empty()) + { + return arm_compute::support::cpp14::make_unique(); + } + else + { + return arm_compute::support::cpp14::make_unique(path + data_file); + } +} + +BranchLayer get_expand_fire_node(const std::string &data_path, std::string &¶m_path, unsigned int expand1_filt, unsigned int expand3_filt) +{ + std::string total_path = "/cnn_data/squeezenet_v1.0_model/" + param_path + "_"; + SubGraph i_a; + i_a << ConvolutionLayer( + 1U, 1U, expand1_filt, + get_accessor(data_path, total_path + "expand1x1_w.npy"), + get_accessor(data_path, total_path + "expand1x1_b.npy"), + PadStrideInfo(1, 1, 0, 0)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); + + SubGraph i_b; + i_b << ConvolutionLayer( + 3U, 3U, expand3_filt, + get_accessor(data_path, total_path + "expand3x3_w.npy"), + get_accessor(data_path, total_path + "expand3x3_b.npy"), + PadStrideInfo(1, 1, 1, 1)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)); + + return BranchLayer(BranchMergeMethod::DEPTH_CONCATENATE, std::move(i_a), std::move(i_b)); +} + +/** Example demonstrating how to implement Squeezenet's network using the Compute Library's graph API + * + * @param[in] argc Number of arguments + * @param[in] argv Arguments ( [optional] Path to the weights folder, [optional] batches ) + */ +void main_graph_squeezenet(int argc, const char **argv) +{ + std::string data_path; /** Path to the trainable data */ + unsigned int batches = 4; /** Number of batches */ + + // Parse arguments + if(argc < 2) + { + // Print help + std::cout << "Usage: " << argv[0] << " [path_to_data] [batches]\n\n"; + std::cout << "No data folder provided: using random values\n\n"; + } + else if(argc == 2) + { + //Do something with argv[1] + data_path = argv[1]; + std::cout << "Usage: " << argv[0] << " [path_to_data] [batches]\n\n"; + std::cout << "No number of batches where specified, thus will use the default : " << batches << "\n\n"; + } + else + { + //Do something with argv[1] and argv[2] + data_path = argv[1]; + batches = std::strtol(argv[2], nullptr, 0); + } + + // Check if OpenCL is available and initialize the scheduler + if(arm_compute::opencl_is_available()) + { + arm_compute::CLScheduler::get().default_init(); + } + + Graph graph; + arm_compute::Logger::get().set_logger(std::cout, arm_compute::LoggerVerbosity::INFO); + + graph << TargetHint::OPENCL + << Tensor(TensorInfo(TensorShape(224U, 224U, 3U, batches), 1, DataType::F32), DummyAccessor()) + << ConvolutionLayer( + 7U, 7U, 96U, + get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv1_w.npy"), + get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv1_b.npy"), + PadStrideInfo(2, 2, 0, 0)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))) + << ConvolutionLayer( + 1U, 1U, 16U, + get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire2_squeeze1x1_w.npy"), + get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire2_squeeze1x1_b.npy"), + PadStrideInfo(1, 1, 0, 0)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << get_expand_fire_node(data_path, "fire2", 64U, 64U) + << ConvolutionLayer( + 1U, 1U, 16U, + get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire3_squeeze1x1_w.npy"), + get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire3_squeeze1x1_b.npy"), + PadStrideInfo(1, 1, 0, 0)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << get_expand_fire_node(data_path, "fire3", 64U, 64U) + << ConvolutionLayer( + 1U, 1U, 32U, + get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire4_squeeze1x1_w.npy"), + get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire4_squeeze1x1_b.npy"), + PadStrideInfo(1, 1, 0, 0)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << get_expand_fire_node(data_path, "fire4", 128U, 128U) + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))) + << ConvolutionLayer( + 1U, 1U, 32U, + get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire5_squeeze1x1_w.npy"), + get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire5_squeeze1x1_b.npy"), + PadStrideInfo(1, 1, 0, 0)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << get_expand_fire_node(data_path, "fire5", 128U, 128U) + << ConvolutionLayer( + 1U, 1U, 48U, + get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire6_squeeze1x1_w.npy"), + get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire6_squeeze1x1_b.npy"), + PadStrideInfo(1, 1, 0, 0)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << get_expand_fire_node(data_path, "fire6", 192U, 192U) + << ConvolutionLayer( + 1U, 1U, 48U, + get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire7_squeeze1x1_w.npy"), + get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire7_squeeze1x1_b.npy"), + PadStrideInfo(1, 1, 0, 0)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << get_expand_fire_node(data_path, "fire7", 192U, 192U) + << ConvolutionLayer( + 1U, 1U, 64U, + get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire8_squeeze1x1_w.npy"), + get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire8_squeeze1x1_b.npy"), + PadStrideInfo(1, 1, 0, 0)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << get_expand_fire_node(data_path, "fire8", 256U, 256U) + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0, DimensionRoundingType::CEIL))) + << ConvolutionLayer( + 1U, 1U, 64U, + get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire9_squeeze1x1_w.npy"), + get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/fire9_squeeze1x1_b.npy"), + PadStrideInfo(1, 1, 0, 0)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << get_expand_fire_node(data_path, "fire9", 256U, 256U) + << ConvolutionLayer( + 1U, 1U, 1000U, + get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv10_w.npy"), + get_accessor(data_path, "/cnn_data/squeezenet_v1.0_model/conv10_b.npy"), + PadStrideInfo(1, 1, 0, 0)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << PoolingLayer(PoolingLayerInfo(PoolingType::AVG, 13, PadStrideInfo(1, 1, 0, 0, DimensionRoundingType::CEIL))) + << SoftmaxLayer() + << Tensor(DummyAccessor()); + + graph.run(); +} + +/** Main program for Squeezenet v1.0 + * + * @param[in] argc Number of arguments + * @param[in] argv Arguments ( [optional] Path to the weights folder, [optional] batches ) + */ +int main(int argc, const char **argv) +{ + return arm_compute::utils::run_example(argc, argv, main_graph_squeezenet); +} -- cgit v1.2.1