commit d8734b55d89f05901ba9a75349761a9c955d9243
author Georgios Pinitas <georgios.pinitas@arm.com> Fri Dec 22 15:27:52 2017 +0000
committer Anthony Barbier <anthony.barbier@arm.com> Fri Nov 02 16:49:16 2018 +0000
tree e23d53a0fb73251f7416993e4d3a7241e533e79e
parent 7390e05561a5c49306ebbf2eb2dcb1848546f201

COMPMID-793 : Add graph intermediate representation

Change-Id: Ic1685de4e19e0ac79669ef2da64e1dc96c7ea0bf
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/115248
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>

examples/graph_lenet.cpp

diff --git a/examples/graph_lenet.cpp b/examples/graph_lenet.cpp
index 61bc7bd..e4b8eff 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/graph2.h"
+
 #include "support/ToolchainSupport.h"
 #include "utils/GraphUtils.h"
 #include "utils/Utils.h"
@@ -30,7 +30,7 @@
 #include <cstdlib>
 
 using namespace arm_compute::utils;
-using namespace arm_compute::graph;
+using namespace arm_compute::graph2::frontend;
 using namespace arm_compute::graph_utils;
 
 /** Example demonstrating how to implement LeNet's network using the Compute Library's graph API
@@ -47,8 +47,10 @@
         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_hint2(target);
+        bool      enable_tuning            = (target == 2);
+        bool      enable_memory_management = true;
 
         // Parse arguments
         if(argc < 2)
@@ -78,7 +80,7 @@
 
         //conv1 << pool1 << conv2 << pool2 << fc1 << act1 << fc2 << smx
         graph << target_hint
-              << Tensor(TensorInfo(TensorShape(28U, 28U, 1U, batches), 1, DataType::F32), DummyAccessor())
+              << 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"),
@@ -101,10 +103,10 @@
                   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));
+              << 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
+        graph.finalize(target_hint, enable_tuning, enable_memory_management);
     }
     void do_run() override
     {
@@ -113,7 +115,7 @@
     }
 
 private:
-    Graph graph{};
+    Stream graph{ 0, "LeNet" };
 };
 
 /** Main program for LeNet