COMPMID-925: Enabling OpenCL tuner in the graph examples

Change-Id: I4fe501281f527e20e8fdd0253d59ea2c4629056b
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/120354
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>

10 files changed, 75 insertions, 33 deletions

diff --git a/examples/graph_alexnet.cpp b/examples/graph_alexnet.cpp
index 2f2c8bd182..bd620574b8 100644
--- a/examples/graph_alexnet.cpp
+++ b/examples/graph_alexnet.cpp
@@ -38,7 +38,7 @@ using namespace arm_compute::graph_utils;
 /** Example demonstrating how to implement AlexNet'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] image, [optional] labels )
+ * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] image, [optional] labels )
  */
 class GraphAlexnetExample : public Example
 {
@@ -53,8 +53,9 @@ public:
         constexpr float mean_g = 116.67f; /* Mean value to subtract from green channel */
         constexpr float mean_b = 104.01f; /* Mean value to subtract from blue channel */
 
-        // 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);
+        // 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 bool            is_gemm_convolution5x5 = Graph::gpu_target() == arm_compute::GPUTarget::MIDGARD || target_hint == TargetHint::NEON;
         ConvolutionMethodHint convolution_5x5_hint   = is_gemm_convolution5x5 ? ConvolutionMethodHint::GEMM : ConvolutionMethodHint::DIRECT;
@@ -91,6 +92,9 @@ public:
             label     = argv[4];
         }
 
+        // Initialize the graph
+        graph.graph_init(int_target_hint == 2);
+
         graph << target_hint
               << Tensor(TensorInfo(TensorShape(227U, 227U, 3U, 1U), 1, DataType::F32),
                         get_input_accessor(image, mean_r, mean_g, mean_b))
diff --git a/examples/graph_googlenet.cpp b/examples/graph_googlenet.cpp
index b2e2f1bf8f..13f6543ef6 100644
--- a/examples/graph_googlenet.cpp
+++ b/examples/graph_googlenet.cpp
@@ -53,8 +53,9 @@ public:
         constexpr float mean_g = 116.67f; /* Mean value to subtract from green channel */
         constexpr float mean_b = 104.01f; /* Mean value to subtract from blue channel */
 
-        // 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);
+        // 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);
         ConvolutionMethodHint convolution_hint = ConvolutionMethodHint::GEMM;
 
         // Parse arguments
@@ -89,6 +90,9 @@ public:
             label     = argv[4];
         }
 
+        // Initialize graph
+        graph.graph_init(int_target_hint == 2);
+
         graph << target_hint
               << Tensor(TensorInfo(TensorShape(224U, 224U, 3U, 1U), 1, DataType::F32),
                         get_input_accessor(image, mean_r, mean_g, mean_b))
diff --git a/examples/graph_inception_v3.cpp b/examples/graph_inception_v3.cpp
index 88a0325b63..f2423eb4bd 100644
--- a/examples/graph_inception_v3.cpp
+++ b/examples/graph_inception_v3.cpp
@@ -52,8 +52,9 @@ public:
         constexpr float mean = 0.f;   /* Mean value to subtract from the channels */
         constexpr float std  = 255.f; /* Standard deviation value to divide from the channels */
 
-        // 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);
+        // 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);
 
         // Parse arguments
         if(argc < 2)
@@ -87,6 +88,9 @@ public:
             label     = argv[4];
         }
 
+        // Initialize graph
+        graph.graph_init(int_target_hint == 2);
+
         graph << target_hint << Tensor(TensorInfo(TensorShape(299U, 299U, 3U, 1U), 1, DataType::F32),
                                        get_input_accessor(image,
                                                           mean, mean, mean,
diff --git a/examples/graph_lenet.cpp b/examples/graph_lenet.cpp
index 1d4fc33357..863efeafbf 100644
--- a/examples/graph_lenet.cpp
+++ b/examples/graph_lenet.cpp
@@ -46,8 +46,9 @@ public:
         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);
+        // 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);
 
         // Parse arguments
         if(argc < 2)
@@ -75,6 +76,9 @@ public:
             batches   = std::strtol(argv[3], nullptr, 0);
         }
 
+        // Initialize graph
+        graph.graph_init(int_target_hint == 2);
+
         //conv1 << pool1 << conv2 << pool2 << fc1 << act1 << fc2 << smx
         graph << target_hint
               << Tensor(TensorInfo(TensorShape(28U, 28U, 1U, batches), 1, DataType::F32), DummyAccessor())
diff --git a/examples/graph_mobilenet.cpp b/examples/graph_mobilenet.cpp
index d3d4774eaa..0cc636a07d 100644
--- a/examples/graph_mobilenet.cpp
+++ b/examples/graph_mobilenet.cpp
@@ -36,7 +36,7 @@ using namespace arm_compute::graph_utils;
 /** Example demonstrating how to implement MobileNet'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] image, [optional] labels )
+ * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] image, [optional] labels )
  */
 class GraphMobilenetExample : public Example
 {
@@ -50,8 +50,9 @@ public:
         constexpr float mean = 0.f;   /* Mean value to subtract from the channels */
         constexpr float std  = 255.f; /* Standard deviation value to divide from the channels */
 
-        // 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);
+        // 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);
         ConvolutionMethodHint convolution_hint = ConvolutionMethodHint::GEMM;
 
         // Set model to execute. 0 (MobileNetV1_1.0_224), 1 (MobileNetV1_0.75_160)
@@ -106,6 +107,9 @@ public:
             data_path += model_path;
         }
 
+        // Initialize graph
+        graph.graph_init(int_target_hint == 2);
+
         graph << target_hint
               << convolution_hint
               << Tensor(TensorInfo(TensorShape(spatial_size, spatial_size, 3U, 1U), 1, DataType::F32),
diff --git a/examples/graph_cl_mobilenet_qasymm8.cpp b/examples/graph_mobilenet_qasymm8.cpp
index 046c7779b1..29daeffeac 100644
--- a/examples/graph_cl_mobilenet_qasymm8.cpp
+++ b/examples/graph_mobilenet_qasymm8.cpp
@@ -34,7 +34,7 @@ using namespace arm_compute::graph_utils;
 /** Example demonstrating how to implement QASYMM8 MobileNet'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] npy_input, [optional] labels )
+ * @param[in] argv Arguments ( [optional] Target (0 = NEON, 1 = OpenCL, 2 = OpenCL with Tuner), [optional] Path to the weights folder, [optional] npy_input, [optional] labels )
  */
 class GraphMobileNetQASYMM8Example : public utils::Example
 {
@@ -89,34 +89,40 @@ public:
             QuantizationInfo(0.0338749065995f, 140)   // dwsc13
         };
 
+        // 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);
+
         // Parse arguments
         if(argc < 2)
         {
             // Print help
-            std::cout << "Usage: " << argv[0] << " [path_to_data] [npy_input] [labels]\n\n";
+            std::cout << "Usage: " << argv[0] << " [target] [path_to_data] [npy_input] [labels]\n\n";
             std::cout << "No data folder provided: using random values\n\n";
         }
         else if(argc == 2)
         {
-            data_path = argv[1];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " [npy_input] [labels]\n\n";
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " [path_to_data] [npy_input] [labels]\n\n";
             std::cout << "No input provided: using random values\n\n";
         }
-        else if(argc == 3)
+        else if(argc == 4)
         {
-            data_path = argv[1];
-            input     = argv[2];
-            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " [labels]\n\n";
+            data_path = argv[2];
+            input     = argv[3];
+            std::cout << "Usage: " << argv[0] << " " << argv[1] << " " << argv[2] << " " << argv[3] << " [labels]\n\n";
             std::cout << "No text file with labels provided: skipping output accessor\n\n";
         }
         else
         {
-            data_path = argv[1];
-            input     = argv[2];
-            label     = argv[3];
+            data_path = argv[2];
+            input     = argv[3];
+            label     = argv[4];
         }
 
-        graph << TargetHint::OPENCL
+        // Initialize graph
+        graph.graph_init(int_target_hint == 2);
+
+        graph << target_hint
               << arm_compute::graph::Tensor(TensorInfo(TensorShape(224U, 224U, 3U, 1U), 1, DataType::QASYMM8, in_quant_info),
                                             get_weights_accessor(data_path, "/cnn_data/mobilenet_qasymm8_model/" + input))
               << ConvolutionLayer(
diff --git a/examples/graph_squeezenet.cpp b/examples/graph_squeezenet.cpp
index e85108702d..517d0cc127 100644
--- a/examples/graph_squeezenet.cpp
+++ b/examples/graph_squeezenet.cpp
@@ -58,8 +58,9 @@ public:
         constexpr float mean_g = 116.67f; /* Mean value to subtract from green channel */
         constexpr float mean_b = 104.01f; /* Mean value to subtract from blue channel */
 
-        // 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);
+        // 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);
 
         // Parse arguments
         if(argc < 2)
@@ -93,6 +94,9 @@ public:
             label     = argv[4];
         }
 
+        // Initialize graph
+        graph.graph_init(int_target_hint == 2);
+
         graph << target_hint
               << Tensor(TensorInfo(TensorShape(224U, 224U, 3U, 1U), 1, DataType::F32),
                         get_input_accessor(image, mean_r, mean_g, mean_b))
diff --git a/examples/graph_squeezenet_v1_1.cpp b/examples/graph_squeezenet_v1_1.cpp
index fad07e5043..3c6be742fa 100644
--- a/examples/graph_squeezenet_v1_1.cpp
+++ b/examples/graph_squeezenet_v1_1.cpp
@@ -58,8 +58,9 @@ public:
         constexpr float mean_g = 116.67f; /* Mean value to subtract from green channel */
         constexpr float mean_b = 104.01f; /* Mean value to subtract from blue channel */
 
-        // 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);
+        // 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);
 
         // Parse arguments
         if(argc < 2)
@@ -93,6 +94,9 @@ public:
             label     = argv[4];
         }
 
+        // Initialize graph
+        graph.graph_init(int_target_hint == 2);
+
         graph << target_hint
               << Tensor(TensorInfo(TensorShape(227U, 227U, 3U, 1U), 1, DataType::F32),
                         get_input_accessor(image, mean_r, mean_g, mean_b))
diff --git a/examples/graph_vgg16.cpp b/examples/graph_vgg16.cpp
index c3eb922f0e..ccb9dbb19d 100644
--- a/examples/graph_vgg16.cpp
+++ b/examples/graph_vgg16.cpp
@@ -65,8 +65,9 @@ public:
         constexpr float mean_g = 116.779f; /* Mean value to subtract from green channel */
         constexpr float mean_b = 103.939f; /* Mean value to subtract from blue channel */
 
-        // 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);
+        // 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);
 
         // Check if we can use GEMM-based convolutions evaluating if the platform has at least 1.8 GB of available memory
         const size_t          memory_required  = 1932735283L;
@@ -104,6 +105,9 @@ public:
             label     = argv[4];
         }
 
+        // Initialize graph
+        graph.graph_init(int_target_hint == 2);
+
         graph << target_hint
               << convolution_hint
               << Tensor(TensorInfo(TensorShape(224U, 224U, 3U, 1U), 1, DataType::F32),
diff --git a/examples/graph_vgg19.cpp b/examples/graph_vgg19.cpp
index 5214438d7f..c940c4ef73 100644
--- a/examples/graph_vgg19.cpp
+++ b/examples/graph_vgg19.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017, 2018 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -51,8 +51,9 @@ public:
         constexpr float mean_g = 116.779f; /* Mean value to subtract from green channel */
         constexpr float mean_b = 103.939f; /* Mean value to subtract from blue channel */
 
-        // 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);
+        // 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);
         ConvolutionMethodHint convolution_hint = ConvolutionMethodHint::DIRECT;
 
         // Parse arguments
@@ -87,6 +88,9 @@ public:
             label     = argv[4];
         }
 
+        // Initialize graph
+        graph.graph_init(int_target_hint == 2);
+
         graph << target_hint
               << convolution_hint
               << Tensor(TensorInfo(TensorShape(224U, 224U, 3U, 1U), 1, DataType::F32),