arm_compute v18.05
diff --git a/examples/graph_lenet.cpp b/examples/graph_lenet.cpp
index 61bc7bd..32c7582 100644
--- a/examples/graph_lenet.cpp
+++ b/examples/graph_lenet.cpp
@@ -21,8 +21,8 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
-#include "arm_compute/graph/Graph.h"
-#include "arm_compute/graph/Nodes.h"
+#include "arm_compute/graph.h"
+
#include "support/ToolchainSupport.h"
#include "utils/GraphUtils.h"
#include "utils/Utils.h"
@@ -30,13 +30,13 @@
#include <cstdlib>
using namespace arm_compute::utils;
-using namespace arm_compute::graph;
+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 )
+ * @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
{
@@ -47,64 +47,81 @@
unsigned int batches = 4; /** Number of batches */
// Set target. 0 (NEON), 1 (OpenCL), 2 (OpenCL with Tuner). By default it is NEON
- const int int_target_hint = argc > 1 ? std::strtol(argv[1], nullptr, 10) : 0;
- TargetHint target_hint = set_target_hint(int_target_hint);
+ 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 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);
+ 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
- << Tensor(TensorInfo(TensorShape(28U, 28U, 1U, batches), 1, DataType::F32), DummyAccessor())
+ << 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))
- << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 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))
- << PoolingLayer(PoolingLayerInfo(PoolingType::MAX, 2, PadStrideInfo(2, 2, 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"))
- << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU))
+ .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"))
- << SoftmaxLayer()
- << Tensor(DummyAccessor(0));
+ .set_name("ip2")
+ << SoftmaxLayer().set_name("prob")
+ << OutputLayer(get_output_accessor(""));
- // In order to enable the OpenCL tuner, graph_init() has to be called only when all nodes have been instantiated
- graph.graph_init(int_target_hint == 2);
+ // Finalize graph
+ GraphConfig config;
+ config.use_tuner = (target == 2);
+ graph.finalize(target_hint, config);
}
void do_run() override
{
@@ -113,13 +130,13 @@
}
private:
- Graph graph{};
+ Stream graph{ 0, "LeNet" };
};
/** 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, 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)
{