| /* |
| * 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> |
| |
| using namespace arm_compute::utils; |
| using namespace arm_compute::graph::frontend; |
| using namespace arm_compute::graph_utils; |
| |
| /** Example demonstrating how to implement LeNet'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), [optional] Path to the weights folder, [optional] batches, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) ) |
| */ |
| class GraphLenetExample : public Example |
| { |
| public: |
| void do_setup(int argc, char **argv) override |
| { |
| std::string data_path; /** Path to the trainable data */ |
| unsigned int batches = 4; /** Number of batches */ |
| |
| // 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); |
| |
| FastMathHint fast_math_hint = FastMathHint::DISABLED; |
| |
| // Parse arguments |
| if(argc < 2) |
| { |
| // Print help |
| std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [batches] [fast_math_hint]\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] [batches] [fast_math_hint]\n\n"; |
| std::cout << "No data folder provided: using random values\n\n"; |
| } |
| else if(argc == 3) |
| { |
| //Do something with argv[1] |
| data_path = argv[2]; |
| std::cout << "Usage: " << argv[0] << " [path_to_data] [batches] [fast_math_hint]\n\n"; |
| std::cout << "No number of batches where specified, thus will use the default : " << batches << "\n\n"; |
| } |
| else if(argc == 4) |
| { |
| data_path = argv[2]; |
| batches = std::strtol(argv[3], nullptr, 0); |
| std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [fast_math_hint]\n\n"; |
| std::cout << "No fast math info provided: disabling fast math\n\n"; |
| } |
| else |
| { |
| //Do something with argv[1] and argv[2] |
| data_path = argv[2]; |
| batches = std::strtol(argv[3], nullptr, 0); |
| fast_math_hint = (std::strtol(argv[4], nullptr, 1) == 0) ? FastMathHint::DISABLED : FastMathHint::ENABLED; |
| } |
| |
| //conv1 << pool1 << conv2 << pool2 << fc1 << act1 << fc2 << smx |
| graph << target_hint |
| << fast_math_hint |
| << InputLayer(TensorDescriptor(TensorShape(28U, 28U, 1U, batches), DataType::F32), get_input_accessor("")) |
| << ConvolutionLayer( |
| 5U, 5U, 20U, |
| get_weights_accessor(data_path, "/cnn_data/lenet_model/conv1_w.npy"), |
| get_weights_accessor(data_path, "/cnn_data/lenet_model/conv1_b.npy"), |
| PadStrideInfo(1, 1, 0, 0)) |
| .set_name("conv1") |
| << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("pool1") |
| << ConvolutionLayer( |
| 5U, 5U, 50U, |
| get_weights_accessor(data_path, "/cnn_data/lenet_model/conv2_w.npy"), |
| get_weights_accessor(data_path, "/cnn_data/lenet_model/conv2_b.npy"), |
| PadStrideInfo(1, 1, 0, 0)) |
| .set_name("conv2") |
| << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))).set_name("pool2") |
| << FullyConnectedLayer( |
| 500U, |
| get_weights_accessor(data_path, "/cnn_data/lenet_model/ip1_w.npy"), |
| get_weights_accessor(data_path, "/cnn_data/lenet_model/ip1_b.npy")) |
| .set_name("ip1") |
| << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)).set_name("relu") |
| << FullyConnectedLayer( |
| 10U, |
| get_weights_accessor(data_path, "/cnn_data/lenet_model/ip2_w.npy"), |
| get_weights_accessor(data_path, "/cnn_data/lenet_model/ip2_b.npy")) |
| .set_name("ip2") |
| << SoftmaxLayer().set_name("prob") |
| << OutputLayer(get_output_accessor("")); |
| |
| // Finalize graph |
| GraphConfig config; |
| config.use_tuner = (target == 2); |
| graph.finalize(target_hint, config); |
| } |
| void do_run() override |
| { |
| // Run graph |
| graph.run(); |
| } |
| |
| private: |
| Stream graph{ 0, "LeNet" }; |
| }; |
| |
| /** Main program for LeNet |
| * |
| * @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] batches, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) ) |
| */ |
| int main(int argc, char **argv) |
| { |
| return arm_compute::utils::run_example<GraphLenetExample>(argc, argv); |
| } |