| /* |
| * Copyright (c) 2017-2018 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/GraphUtils.h" |
| #include "utils/Utils.h" |
| |
| #include <cstdlib> |
| #include <iostream> |
| #include <memory> |
| |
| using namespace arm_compute::utils; |
| using namespace arm_compute::graph::frontend; |
| using namespace arm_compute::graph_utils; |
| |
| /** 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] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] image, [optional] labels ) |
| */ |
| class GraphAlexnetExample : public Example |
| { |
| public: |
| void do_setup(int argc, char **argv) override |
| { |
| std::string data_path; /* Path to the trainable data */ |
| std::string image; /* Image data */ |
| std::string label; /* Label data */ |
| |
| // Create a preprocessor object |
| const std::array<float, 3> mean_rgb{ { 122.68f, 116.67f, 104.01f } }; |
| std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb); |
| |
| // Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON |
| const int target = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0; |
| Target target_hint = set_target_hint(target); |
| |
| // TODO (geopin01) : Get GPU target somehow and set gemm also for midgard ? |
| const bool is_gemm_convolution5x5 = (target_hint == Target::NEON); |
| const bool is_winograd_convolution3x3 = target_hint == Target::CL; |
| ConvolutionMethod convolution_5x5_hint = is_gemm_convolution5x5 ? ConvolutionMethod::GEMM : ConvolutionMethod::DIRECT; |
| ConvolutionMethod convolution_3x3_hint = is_winograd_convolution3x3 ? ConvolutionMethod::WINOGRAD : ConvolutionMethod::GEMM; |
| |
| // Parse arguments |
| if(argc < 2) |
| { |
| // Print help |
| std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [image] [labels]\n\n"; |
| std::cout << "No data folder provided: using random values\n\n"; |
| } |
| else if(argc == 2) |
| { |
| std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [image] [labels]\n\n"; |
| std::cout << "No data folder provided: using random values\n\n"; |
| } |
| else if(argc == 3) |
| { |
| data_path = argv[2]; |
| std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [image] [labels]\n\n"; |
| std::cout << "No image provided: using random values\n\n"; |
| } |
| else if(argc == 4) |
| { |
| data_path = argv[2]; |
| image = argv[3]; |
| std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels]\n\n"; |
| std::cout << "No text file with labels provided: skipping output accessor\n\n"; |
| } |
| else |
| { |
| data_path = argv[2]; |
| image = argv[3]; |
| label = argv[4]; |
| } |
| |
| graph << target_hint |
| << InputLayer(TensorDescriptor(TensorShape(227U, 227U, 3U, 1U), DataType::F32), |
| get_input_accessor(image, std::move(preprocessor))) |
| // Layer 1 |
| << ConvolutionLayer( |
| 11U, 11U, 96U, |
| get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv1_w.npy"), |
| get_weights_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 |
| << convolution_5x5_hint |
| << ConvolutionLayer( |
| 5U, 5U, 256U, |
| get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv2_w.npy"), |
| get_weights_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))) |
| << convolution_3x3_hint |
| // Layer 3 |
| << ConvolutionLayer( |
| 3U, 3U, 384U, |
| get_weights_accessor(data_path, "/cnn_data/alexnet_model/conv3_w.npy"), |
| get_weights_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_weights_accessor(data_path, "/cnn_data/alexnet_model/conv4_w.npy"), |
| get_weights_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_weights_accessor(data_path, "/cnn_data/alexnet_model/conv5_w.npy"), |
| get_weights_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_weights_accessor(data_path, "/cnn_data/alexnet_model/fc6_w.npy"), |
| get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc6_b.npy")) |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) |
| // Layer 7 |
| << FullyConnectedLayer( |
| 4096U, |
| get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc7_w.npy"), |
| get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc7_b.npy")) |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) |
| // Layer 8 |
| << FullyConnectedLayer( |
| 1000U, |
| get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc8_w.npy"), |
| get_weights_accessor(data_path, "/cnn_data/alexnet_model/fc8_b.npy")) |
| // Softmax |
| << SoftmaxLayer() |
| << OutputLayer(get_output_accessor(label, 5)); |
| |
| // Finalize graph |
| GraphConfig config; |
| config.use_function_memory_manager = true; |
| config.use_tuner = (target == 2); |
| graph.finalize(target_hint, config); |
| } |
| void do_run() override |
| { |
| // Run graph |
| graph.run(); |
| } |
| |
| private: |
| Stream graph{ 0, "AlexNet" }; |
| }; |
| |
| /** Main program for AlexNet |
| * |
| * @param[in] argc Number of arguments |
| * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] image, [optional] labels ) |
| */ |
| int main(int argc, char **argv) |
| { |
| return arm_compute::utils::run_example<GraphAlexnetExample>(argc, argv); |
| } |