aboutsummaryrefslogtreecommitdiff
path: root/examples/graph_alexnet.cpp
diff options
context:
space:
mode:
Diffstat (limited to 'examples/graph_alexnet.cpp')
-rw-r--r--examples/graph_alexnet.cpp10
1 files changed, 7 insertions, 3 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))