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_squeezenet_v1_1.cpp

diff --git a/examples/graph_squeezenet_v1_1.cpp b/examples/graph_squeezenet_v1_1.cpp
index 189cc02..4ebfd3f 100644
--- a/examples/graph_squeezenet_v1_1.cpp
+++ b/examples/graph_squeezenet_v1_1.cpp
@@ -21,9 +21,7 @@
  * 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/SubGraph.h"
+#include "arm_compute/graph2.h"
 #include "support/ToolchainSupport.h"
 #include "utils/GraphUtils.h"
 #include "utils/Utils.h"
@@ -32,9 +30,8 @@
 #include <tuple>
 
 using namespace arm_compute::utils;
-using namespace arm_compute::graph;
+using namespace arm_compute::graph2::frontend;
 using namespace arm_compute::graph_utils;
-using namespace arm_compute::logging;
 
 namespace
 {
@@ -59,8 +56,10 @@
         std::unique_ptr<IPreprocessor> preprocessor = arm_compute::support::cpp14::make_unique<CaffePreproccessor>(mean_rgb);
 
         // 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)
@@ -95,8 +94,8 @@
         }
 
         graph << target_hint
-              << Tensor(TensorInfo(TensorShape(227U, 227U, 3U, 1U), 1, DataType::F32),
-                        get_input_accessor(image, std::move(preprocessor)))
+              << InputLayer(TensorDescriptor(TensorShape(227U, 227U, 3U, 1U), DataType::F32),
+                            get_input_accessor(image, std::move(preprocessor)))
               << ConvolutionLayer(
                   3U, 3U, 64U,
                   get_weights_accessor(data_path, "/cnn_data/squeezenet_v1_1_model/conv1_w.npy"),
@@ -171,10 +170,10 @@
               << PoolingLayer(PoolingLayerInfo(PoolingType::AVG))
               << FlattenLayer()
               << SoftmaxLayer()
-              << Tensor(get_output_accessor(label, 5));
+              << OutputLayer(get_output_accessor(label, 5));
 
-        // 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
     {
@@ -183,12 +182,12 @@
     }
 
 private:
-    Graph graph{};
+    Stream graph{ 0, "SqueezeNetV1.1" };
 
     BranchLayer get_expand_fire_node(const std::string &data_path, std::string &&param_path, unsigned int expand1_filt, unsigned int expand3_filt)
     {
         std::string total_path = "/cnn_data/squeezenet_v1_1_model/" + param_path + "_";
-        SubGraph    i_a;
+        SubStream   i_a(graph);
         i_a << ConvolutionLayer(
                 1U, 1U, expand1_filt,
                 get_weights_accessor(data_path, total_path + "expand1x1_w.npy"),
@@ -196,7 +195,7 @@
                 PadStrideInfo(1, 1, 0, 0))
             << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
 
-        SubGraph i_b;
+        SubStream i_b(graph);
         i_b << ConvolutionLayer(
                 3U, 3U, expand3_filt,
                 get_weights_accessor(data_path, total_path + "expand3x3_w.npy"),