diff options
Diffstat (limited to 'examples/graph_lenet.cpp')
| -rw-r--r-- | examples/graph_lenet.cpp | 25 |
1 files changed, 18 insertions, 7 deletions
diff --git a/examples/graph_lenet.cpp b/examples/graph_lenet.cpp index ea0916b317..92be2d48c1 100644 --- a/examples/graph_lenet.cpp +++ b/examples/graph_lenet.cpp @@ -36,7 +36,7 @@ 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 ) + * @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 { @@ -50,34 +50,45 @@ public: 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]\n\n"; + 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]\n\n"; + 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]\n\n"; + 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 + else if(argc == 4) { - //Do something with argv[1] and argv[2] 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, @@ -125,7 +136,7 @@ private: /** Main program for LeNet * * @param[in] argc Number of arguments - * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL), [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, [optional] Fast math for convolution layer (0 = DISABLED, 1 = ENABLED) ) */ int main(int argc, char **argv) { |