| /* |
| * 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/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 <cstdlib> |
| #include <iostream> |
| #include <memory> |
| |
| 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<ITensorAccessor> get_accessor(const std::string &path, const std::string &data_file) |
| { |
| if(path.empty()) |
| { |
| return arm_compute::support::cpp14::make_unique<DummyAccessor>(); |
| } |
| else |
| { |
| return arm_compute::support::cpp14::make_unique<NumPyBinLoader>(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 |
| TargetHint hint = TargetHint::NEON; |
| if(arm_compute::opencl_is_available()) |
| { |
| arm_compute::CLScheduler::get().default_init(); |
| hint = TargetHint::OPENCL; |
| } |
| |
| Graph graph; |
| arm_compute::Logger::get().set_logger(std::cout, arm_compute::LoggerVerbosity::INFO); |
| |
| 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 |
| << ConvolutionMethodHint::DIRECT |
| << 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); |
| } |
