From 6f669f039fb74675b858bc3703295609a6a3e122 Mon Sep 17 00:00:00 2001 From: Georgios Pinitas Date: Tue, 26 Sep 2017 12:32:57 +0100 Subject: COMPMID-417: Add grouping in convolution layer -Adds grouping support in convolution layer -Adds Normalization layer node in graph -Adds alexnet example -Fixes FullyConnectedLayer output autoconfigure (works only for 1d batch space) Change-Id: I5bd75f9a8b08cfd68f7c34745150266c2bc4221f Reviewed-on: http://mpd-gerrit.cambridge.arm.com/89518 Tested-by: Kaizen Reviewed-by: Anthony Barbier --- examples/graph_alexnet.cpp | 182 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 182 insertions(+) create mode 100644 examples/graph_alexnet.cpp (limited to 'examples/graph_alexnet.cpp') diff --git a/examples/graph_alexnet.cpp b/examples/graph_alexnet.cpp new file mode 100644 index 0000000000..cf5f635d33 --- /dev/null +++ b/examples/graph_alexnet.cpp @@ -0,0 +1,182 @@ +/* + * 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/graph/Graph.h" +#include "arm_compute/graph/Nodes.h" +#include "arm_compute/runtime/CL/CLScheduler.h" +#include "arm_compute/runtime/CPP/CPPScheduler.h" +#include "arm_compute/runtime/Scheduler.h" +#include "support/ToolchainSupport.h" +#include "utils/GraphUtils.h" +#include "utils/Utils.h" + +#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[in] path Path to the data files + * @param[in] 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); + } +} + +/** Example demonstrating how to implement AlexNet'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_alexnet(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 + Hint hint = Hint::NEON; + if(arm_compute::opencl_is_available()) + { + arm_compute::CLScheduler::get().default_init(); + hint = Hint::OPENCL; + } + + Graph graph; + graph.set_info_enablement(true); + + graph << hint + << Tensor(TensorInfo(TensorShape(227U, 227U, 3U, batches), 1, DataType::F32), DummyAccessor()) + // Layer 1 + << ConvolutionLayer( + 11U, 11U, 96U, + get_accessor(data_path, "/cnn_data/alexnet_model/conv1_w.npy"), + get_accessor(data_path, "/cnn_data/alexnet_model/conv1_b.npy"), + PadStrideInfo(4, 4, 0, 0)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f)) + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0))) + // Layer 2 + << ConvolutionLayer( + 5U, 5U, 256U, + get_accessor(data_path, "/cnn_data/alexnet_model/conv2_w.npy"), + get_accessor(data_path, "/cnn_data/alexnet_model/conv2_b.npy"), + PadStrideInfo(1, 1, 2, 2), 2) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << NormalizationLayer(NormalizationLayerInfo(NormType::CROSS_MAP, 5, 0.0001f, 0.75f)) + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0))) + // Layer 3 + << ConvolutionLayer( + 3U, 3U, 384U, + get_accessor(data_path, "/cnn_data/alexnet_model/conv3_w.npy"), + get_accessor(data_path, "/cnn_data/alexnet_model/conv3_b.npy"), + PadStrideInfo(1, 1, 1, 1)) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + // Layer 4 + << ConvolutionLayer( + 3U, 3U, 384U, + get_accessor(data_path, "/cnn_data/alexnet_model/conv4_w.npy"), + get_accessor(data_path, "/cnn_data/alexnet_model/conv4_b.npy"), + PadStrideInfo(1, 1, 1, 1), 2) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + // Layer 5 + << ConvolutionLayer( + 3U, 3U, 256U, + get_accessor(data_path, "/cnn_data/alexnet_model/conv5_w.npy"), + get_accessor(data_path, "/cnn_data/alexnet_model/conv5_b.npy"), + PadStrideInfo(1, 1, 1, 1), 2) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 3, PadStrideInfo(2, 2, 0, 0))) + // Layer 6 + << FullyConnectedLayer( + 4096U, + get_accessor(data_path, "/cnn_data/alexnet_model/fc6_w.npy"), + get_accessor(data_path, "/cnn_data/alexnet_model/fc6_b.npy")) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + // Layer 7 + << FullyConnectedLayer( + 4096U, + get_accessor(data_path, "/cnn_data/alexnet_model/fc7_w.npy"), + get_accessor(data_path, "/cnn_data/alexnet_model/fc7_b.npy")) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) + // Layer 8 + << FullyConnectedLayer( + 1000U, + get_accessor(data_path, "/cnn_data/alexnet_model/fc8_w.npy"), + get_accessor(data_path, "/cnn_data/alexnet_model/fc8_b.npy")) + // Softmax + << SoftmaxLayer() + << Tensor(DummyAccessor()); + + // Run graph + graph.run(); +} + +/** Main program for AlexNet + * + * @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_alexnet); +} -- cgit v1.2.1