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>
diff --git a/examples/graph_alexnet.cpp b/examples/graph_alexnet.cpp
index 2f2c8bd..bd62057 100644
--- a/examples/graph_alexnet.cpp
+++ b/examples/graph_alexnet.cpp
@@ -38,7 +38,7 @@
/** 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 @@
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 @@
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 b2e2f1b..13f6543 100644
--- a/examples/graph_googlenet.cpp
+++ b/examples/graph_googlenet.cpp
@@ -53,8 +53,9 @@
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 @@
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 88a0325..f2423eb 100644
--- a/examples/graph_inception_v3.cpp
+++ b/examples/graph_inception_v3.cpp
@@ -52,8 +52,9 @@
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 @@
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 1d4fc33..863efea 100644
--- a/examples/graph_lenet.cpp
+++ b/examples/graph_lenet.cpp
@@ -46,8 +46,9 @@
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 @@
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 d3d4774..0cc636a 100644
--- a/examples/graph_mobilenet.cpp
+++ b/examples/graph_mobilenet.cpp
@@ -36,7 +36,7 @@
/** 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 @@
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 @@
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
similarity index 92%
rename from examples/graph_cl_mobilenet_qasymm8.cpp
rename to examples/graph_mobilenet_qasymm8.cpp
index 046c777..29daeff 100644
--- a/examples/graph_cl_mobilenet_qasymm8.cpp
+++ b/examples/graph_mobilenet_qasymm8.cpp
@@ -34,7 +34,7 @@
/** 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 @@
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 e851087..517d0cc 100644
--- a/examples/graph_squeezenet.cpp
+++ b/examples/graph_squeezenet.cpp
@@ -58,8 +58,9 @@
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 @@
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 fad07e5..3c6be74 100644
--- a/examples/graph_squeezenet_v1_1.cpp
+++ b/examples/graph_squeezenet_v1_1.cpp
@@ -58,8 +58,9 @@
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 @@
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 c3eb922..ccb9dbb 100644
--- a/examples/graph_vgg16.cpp
+++ b/examples/graph_vgg16.cpp
@@ -65,8 +65,9 @@
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 @@
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 5214438..c940c4e 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 @@
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 @@
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),