arm_compute v17.12
diff --git a/examples/graph_lenet.cpp b/examples/graph_lenet.cpp
index 1427abe..d4a4438 100644
--- a/examples/graph_lenet.cpp
+++ b/examples/graph_lenet.cpp
@@ -21,26 +21,19 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
-#ifndef ARM_COMPUTE_CL /* Needed by Utils.cpp to handle OpenCL exceptions properly */
-#error "This example needs to be built with -DARM_COMPUTE_CL"
-#endif /* ARM_COMPUTE_CL */
-
-#include "arm_compute/core/Logger.h"
#include "arm_compute/graph/Graph.h"
#include "arm_compute/graph/Nodes.h"
-#include "arm_compute/runtime/CL/CLScheduler.h"
-#include "arm_compute/runtime/Scheduler.h"
#include "support/ToolchainSupport.h"
#include "utils/GraphUtils.h"
#include "utils/Utils.h"
#include <cstdlib>
-#include <iostream>
-#include <memory>
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
@@ -61,51 +54,51 @@
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(arm_compute::opencl_is_available())
- {
- arm_compute::CLScheduler::get().default_init();
- hint = TargetHint::OPENCL;
+ data_path = argv[2];
+ batches = std::strtol(argv[3], nullptr, 0);
}
Graph graph;
- arm_compute::Logger::get().set_logger(std::cout, arm_compute::LoggerVerbosity::INFO);
//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,
@@ -137,7 +130,7 @@
/** 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)
{