From 2b5f0f2574551f59970bb9d710bafad2bc4bbd4a Mon Sep 17 00:00:00 2001 From: Michalis Spyrou Date: Wed, 10 Jan 2018 14:08:50 +0000 Subject: COMPMID-782 Port examples to the new format Change-Id: Ib178a97c080ff650094d02ee49e2a0aa22376dd0 Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/115717 Reviewed-by: Anthony Barbier Tested-by: Jenkins --- examples/graph_lenet.cpp | 151 +++++++++++++++++++++-------------------------- 1 file changed, 68 insertions(+), 83 deletions(-) (limited to 'examples/graph_lenet.cpp') diff --git a/examples/graph_lenet.cpp b/examples/graph_lenet.cpp index 3e4727f189..2442cffe08 100644 --- a/examples/graph_lenet.cpp +++ b/examples/graph_lenet.cpp @@ -29,103 +29,88 @@ #include +using namespace arm_compute::utils; using namespace arm_compute::graph; using namespace arm_compute::graph_utils; -namespace -{ -/** 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); - } -} -} // namespace - /** 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 ) */ -void main_graph_lenet(int argc, char **argv) +class GraphLenetExample : public Example { - std::string data_path; /** Path to the trainable data */ - unsigned int batches = 4; /** Number of batches */ +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). By default it is NEON - TargetHint target_hint = set_target_hint(argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0); + // Set target. 0 (NEON), 1 (OpenCL). By default it is NEON + TargetHint target_hint = set_target_hint(argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0); - // Parse arguments - if(argc < 2) - { - // Print help - std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [batches]\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]\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]\n\n"; - std::cout << "No number of batches where specified, thus will use the default : " << batches << "\n\n"; + // Parse arguments + if(argc < 2) + { + // Print help + std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [batches]\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]\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]\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[2]; + batches = std::strtol(argv[3], nullptr, 0); + } + + //conv1 << pool1 << conv2 << pool2 << fc1 << act1 << fc2 << smx + graph << target_hint + << Tensor(TensorInfo(TensorShape(28U, 28U, 1U, batches), 1, DataType::F32), DummyAccessor()) + << 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)) + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))) + << 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)) + << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))) + << 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")) + << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::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")) + << SoftmaxLayer() + << Tensor(DummyAccessor()); } - else + void do_run() override { - //Do something with argv[1] and argv[2] - data_path = argv[2]; - batches = std::strtol(argv[3], nullptr, 0); + // Run graph + graph.run(); } - Graph graph; - - //conv1 << pool1 << conv2 << pool2 << fc1 << act1 << fc2 << smx - graph << target_hint - << Tensor(TensorInfo(TensorShape(28U, 28U, 1U, batches), 1, DataType::F32), DummyAccessor()) - << ConvolutionLayer( - 5U, 5U, 20U, - get_accessor(data_path, "/cnn_data/lenet_model/conv1_w.npy"), - get_accessor(data_path, "/cnn_data/lenet_model/conv1_b.npy"), - PadStrideInfo(1, 1, 0, 0)) - << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))) - << ConvolutionLayer( - 5U, 5U, 50U, - get_accessor(data_path, "/cnn_data/lenet_model/conv2_w.npy"), - get_accessor(data_path, "/cnn_data/lenet_model/conv2_b.npy"), - PadStrideInfo(1, 1, 0, 0)) - << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 0, 0))) - << FullyConnectedLayer( - 500U, - get_accessor(data_path, "/cnn_data/lenet_model/ip1_w.npy"), - get_accessor(data_path, "/cnn_data/lenet_model/ip1_b.npy")) - << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU)) - << FullyConnectedLayer( - 10U, - get_accessor(data_path, "/cnn_data/lenet_model/ip2_w.npy"), - get_accessor(data_path, "/cnn_data/lenet_model/ip2_b.npy")) - << SoftmaxLayer() - << Tensor(DummyAccessor()); - - graph.run(); -} +private: + Graph graph{}; +}; /** Main program for LeNet * @@ -134,5 +119,5 @@ void main_graph_lenet(int argc, char **argv) */ int main(int argc, char **argv) { - return arm_compute::utils::run_example(argc, argv, main_graph_lenet); + return arm_compute::utils::run_example(argc, argv); } -- cgit v1.2.1