diff options
Diffstat (limited to 'examples/graph_lenet.cpp')
-rw-r--r-- | examples/graph_lenet.cpp | 32 |
1 files changed, 18 insertions, 14 deletions
diff --git a/examples/graph_lenet.cpp b/examples/graph_lenet.cpp index ad4a4e02c7..d4a44382b4 100644 --- a/examples/graph_lenet.cpp +++ b/examples/graph_lenet.cpp @@ -32,6 +32,8 @@ 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 @@ -52,49 +54,51 @@ std::unique_ptr<ITensorAccessor> get_accessor(const std::string &path, const std return arm_compute::support::cpp14::make_unique<NumPyBinLoader>(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] Path to the weights folder, [optional] batches ) + * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] batches ) */ void main_graph_lenet(int argc, const char **argv) { 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); + // Parse arguments if(argc < 2) { // Print help - std::cout << "Usage: " << argv[0] << " [path_to_data] [batches]\n\n"; + 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[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[1]; - batches = std::strtol(argv[2], nullptr, 0); - } - - // Check if OpenCL is available and initialize the scheduler - TargetHint hint = TargetHint::NEON; - if(Graph::opencl_is_available()) - { - hint = TargetHint::OPENCL; + data_path = argv[2]; + batches = std::strtol(argv[3], nullptr, 0); } Graph graph; //conv1 << pool1 << conv2 << pool2 << fc1 << act1 << fc2 << smx - graph << hint + graph << target_hint << Tensor(TensorInfo(TensorShape(28U, 28U, 1U, batches), 1, DataType::F32), DummyAccessor()) << ConvolutionLayer( 5U, 5U, 20U, @@ -126,7 +130,7 @@ void main_graph_lenet(int argc, const char **argv) /** Main program for LeNet * * @param[in] argc Number of arguments - * @param[in] argv Arguments ( [optional] Path to the weights folder, [optional] batches ) + * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [optional] Path to the weights folder, [optional] batches ) */ int main(int argc, const char **argv) { |