arm_compute v17.06
diff --git a/examples/SConscript b/examples/SConscript
new file mode 100644
index 0000000..748f771
--- /dev/null
+++ b/examples/SConscript
@@ -0,0 +1,70 @@
+# Copyright (c) 2017 ARM Limited.
+#
+# SPDX-License-Identifier: MIT
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to
+# deal in the Software without restriction, including without limitation the
+# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+# sell copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+import SCons
+import os.path
+
+Import('env')
+Import('arm_compute_a')
+Import('arm_compute_so')
+
+if env['opencl']:
+ Import('opencl')
+
+examples_env = env.Clone()
+
+examples_env.Append(CPPPATH = ["#"])
+examples_env.Append(LIBPATH = ["#build/%s" % env['build_dir']])
+examples_env.Append(LIBPATH = ["#build/%s/opencl-1.2-stubs" % env['build_dir']])
+
+# Build examples
+utils = examples_env.Object("../utils/Utils.cpp")
+
+if env['os'] in ['android', 'bare_metal']:
+ arm_compute_lib = arm_compute_a
+ arm_compute_dependency = arm_compute_a
+else:
+ arm_compute_lib = "arm_compute"
+ arm_compute_dependency = arm_compute_so
+
+if env['opencl'] and env['neon']:
+ for file in Glob("./neoncl_*.cpp"):
+ example = os.path.basename(os.path.splitext(str(file))[0])
+ prog = examples_env.Program(example, ["{}.cpp".format(example), utils], LIBS = [arm_compute_lib, "OpenCL"])
+ Depends(prog, [arm_compute_dependency, opencl])
+ alias = examples_env.Alias(example, prog)
+ Default(alias)
+
+if env['opencl']:
+ for file in Glob("./cl_*.cpp"):
+ example = os.path.basename(os.path.splitext(str(file))[0])
+ prog = examples_env.Program(example, ["{}.cpp".format(example), utils], LIBS = [arm_compute_lib, "OpenCL"])
+ Depends(prog, [arm_compute_dependency, opencl])
+ alias = examples_env.Alias(example, prog)
+ Default(alias)
+
+if env['neon']:
+ for file in Glob("./neon_*.cpp"):
+ example = os.path.basename(os.path.splitext(str(file))[0])
+ prog = examples_env.Program(example, ["{}.cpp".format(example), utils], LIBS = [arm_compute_lib])
+ Depends(prog, arm_compute_dependency)
+ alias = examples_env.Alias(example, prog)
+ Default(alias)
diff --git a/examples/cl_convolution.cpp b/examples/cl_convolution.cpp
index a021cdb..06f6f14 100644
--- a/examples/cl_convolution.cpp
+++ b/examples/cl_convolution.cpp
@@ -25,10 +25,10 @@
#include "arm_compute/core/Types.h"
#include "arm_compute/runtime/CL/CLFunctions.h"
#include "arm_compute/runtime/CL/CLScheduler.h"
-#include "test_helpers/Utils.h"
+#include "utils/Utils.h"
using namespace arm_compute;
-using namespace test_helpers;
+using namespace utils;
/** Gaussian 3x3 matrix
*/
@@ -114,5 +114,5 @@
*/
int main(int argc, const char **argv)
{
- return test_helpers::run_example(argc, argv, main_cl_convolution);
+ return utils::run_example(argc, argv, main_cl_convolution);
}
diff --git a/examples/cl_events.cpp b/examples/cl_events.cpp
index 5c39788..768f620 100644
--- a/examples/cl_events.cpp
+++ b/examples/cl_events.cpp
@@ -25,10 +25,10 @@
#include "arm_compute/core/Types.h"
#include "arm_compute/runtime/CL/CLFunctions.h"
#include "arm_compute/runtime/CL/CLScheduler.h"
-#include "test_helpers/Utils.h"
+#include "utils/Utils.h"
using namespace arm_compute;
-using namespace test_helpers;
+using namespace utils;
void main_cl_events(int argc, const char **argv)
{
@@ -110,5 +110,5 @@
*/
int main(int argc, const char **argv)
{
- return test_helpers::run_example(argc, argv, main_cl_events);
+ return utils::run_example(argc, argv, main_cl_events);
}
diff --git a/examples/neon_cnn.cpp b/examples/neon_cnn.cpp
new file mode 100644
index 0000000..952ae4d
--- /dev/null
+++ b/examples/neon_cnn.cpp
@@ -0,0 +1,230 @@
+/*
+ * Copyright (c) 2016, 2017 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/runtime/NEON/NEFunctions.h"
+
+#include "arm_compute/core/Types.h"
+#include "utils/Utils.h"
+
+using namespace arm_compute;
+using namespace utils;
+
+void main_cnn(int argc, const char **argv)
+{
+ ARM_COMPUTE_UNUSED(argc);
+ ARM_COMPUTE_UNUSED(argv);
+
+ // The src tensor should contain the input image
+ Tensor src;
+
+ // The weights and biases tensors should be initialized with the values inferred with the training
+ Tensor weights0;
+ Tensor weights1;
+ Tensor weights2;
+ Tensor biases0;
+ Tensor biases1;
+ Tensor biases2;
+
+ Tensor out_conv0;
+ Tensor out_conv1;
+ Tensor out_act0;
+ Tensor out_act1;
+ Tensor out_act2;
+ Tensor out_pool0;
+ Tensor out_pool1;
+ Tensor out_fc0;
+ Tensor out_softmax;
+
+ NEConvolutionLayer conv0;
+ NEConvolutionLayer conv1;
+ NEPoolingLayer pool0;
+ NEPoolingLayer pool1;
+ NEFullyConnectedLayer fc0;
+ NEActivationLayer act0;
+ NEActivationLayer act1;
+ NEActivationLayer act2;
+ NESoftmaxLayer softmax;
+
+ /* [Initialize tensors] */
+
+ // Initialize src tensor
+ constexpr unsigned int width_src_image = 32;
+ constexpr unsigned int height_src_image = 32;
+ constexpr unsigned int ifm_src_img = 1;
+
+ const TensorShape src_shape(width_src_image, height_src_image, ifm_src_img);
+ src.allocator()->init(TensorInfo(src_shape, 1, DataType::F32));
+
+ // Initialize tensors of conv0
+ constexpr unsigned int kernel_x_conv0 = 5;
+ constexpr unsigned int kernel_y_conv0 = 5;
+ constexpr unsigned int ofm_conv0 = 8;
+
+ const TensorShape weights_shape_conv0(kernel_x_conv0, kernel_y_conv0, src_shape.z(), ofm_conv0);
+ const TensorShape biases_shape_conv0(weights_shape_conv0[3]);
+ const TensorShape out_shape_conv0(src_shape.x(), src_shape.y(), weights_shape_conv0[3]);
+
+ weights0.allocator()->init(TensorInfo(weights_shape_conv0, 1, DataType::F32));
+ biases0.allocator()->init(TensorInfo(biases_shape_conv0, 1, DataType::F32));
+ out_conv0.allocator()->init(TensorInfo(out_shape_conv0, 1, DataType::F32));
+
+ // Initialize tensor of act0
+ out_act0.allocator()->init(TensorInfo(out_shape_conv0, 1, DataType::F32));
+
+ // Initialize tensor of pool0
+ TensorShape out_shape_pool0 = out_shape_conv0;
+ out_shape_pool0.set(0, out_shape_pool0.x() / 2);
+ out_shape_pool0.set(1, out_shape_pool0.y() / 2);
+ out_pool0.allocator()->init(TensorInfo(out_shape_pool0, 1, DataType::F32));
+
+ // Initialize tensors of conv1
+ constexpr unsigned int kernel_x_conv1 = 3;
+ constexpr unsigned int kernel_y_conv1 = 3;
+ constexpr unsigned int ofm_conv1 = 16;
+
+ const TensorShape weights_shape_conv1(kernel_x_conv1, kernel_y_conv1, out_shape_pool0.z(), ofm_conv1);
+
+ const TensorShape biases_shape_conv1(weights_shape_conv1[3]);
+ const TensorShape out_shape_conv1(out_shape_pool0.x(), out_shape_pool0.y(), weights_shape_conv1[3]);
+
+ weights1.allocator()->init(TensorInfo(weights_shape_conv1, 1, DataType::F32));
+ biases1.allocator()->init(TensorInfo(biases_shape_conv1, 1, DataType::F32));
+ out_conv1.allocator()->init(TensorInfo(out_shape_conv1, 1, DataType::F32));
+
+ // Initialize tensor of act1
+ out_act1.allocator()->init(TensorInfo(out_shape_conv1, 1, DataType::F32));
+
+ // Initialize tensor of pool1
+ TensorShape out_shape_pool1 = out_shape_conv1;
+ out_shape_pool1.set(0, out_shape_pool1.x() / 2);
+ out_shape_pool1.set(1, out_shape_pool1.y() / 2);
+ out_pool1.allocator()->init(TensorInfo(out_shape_pool1, 1, DataType::F32));
+
+ // Initialize tensor of fc0
+ constexpr unsigned int num_labels = 128;
+
+ const TensorShape weights_shape_fc0(out_shape_pool1.x() * out_shape_pool1.y() * out_shape_pool1.z(), num_labels);
+ const TensorShape biases_shape_fc0(num_labels);
+ const TensorShape out_shape_fc0(num_labels);
+
+ weights2.allocator()->init(TensorInfo(weights_shape_fc0, 1, DataType::F32));
+ biases2.allocator()->init(TensorInfo(biases_shape_fc0, 1, DataType::F32));
+ out_fc0.allocator()->init(TensorInfo(out_shape_fc0, 1, DataType::F32));
+
+ // Initialize tensor of act2
+ out_act2.allocator()->init(TensorInfo(out_shape_fc0, 1, DataType::F32));
+
+ // Initialize tensor of softmax
+ const TensorShape out_shape_softmax(out_shape_fc0.x());
+ out_softmax.allocator()->init(TensorInfo(out_shape_softmax, 1, DataType::F32));
+
+ /* -----------------------End: [Initialize tensors] */
+
+ /* [Configure functions] */
+
+ // in:32x32x1: 5x5 convolution, 8 output features maps (OFM)
+ conv0.configure(&src, &weights0, &biases0, &out_conv0, PadStrideInfo());
+
+ // in:32x32x8, out:32x32x8, Activation function: relu
+ act0.configure(&out_conv0, &out_act0, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+
+ // in:32x32x8, out:16x16x8 (2x2 pooling), Pool type function: Max
+ pool0.configure(&out_act0, &out_pool0, PoolingLayerInfo(PoolingType::MAX, 2));
+
+ // in:16x16x8: 3x3 convolution, 16 output features maps (OFM)
+ conv1.configure(&out_pool0, &weights1, &biases1, &out_conv1, PadStrideInfo());
+
+ // in:16x16x16, out:16x16x16, Activation function: relu
+ act1.configure(&out_conv1, &out_act1, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+
+ // in:16x16x16, out:8x8x16 (2x2 pooling), Pool type function: Average
+ pool1.configure(&out_act1, &out_pool1, PoolingLayerInfo(PoolingType::AVG, 2));
+
+ // in:8x8x16, out:128
+ fc0.configure(&out_pool1, &weights2, &biases2, &out_fc0);
+
+ // in:128, out:128, Activation function: relu
+ act2.configure(&out_fc0, &out_act2, ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU));
+
+ // in:128, out:128
+ softmax.configure(&out_act2, &out_softmax);
+
+ /* -----------------------End: [Configure functions] */
+
+ /* [Allocate tensors] */
+
+ // Now that the padding requirements are known we can allocate the images:
+ src.allocator()->allocate();
+ weights0.allocator()->allocate();
+ weights1.allocator()->allocate();
+ weights2.allocator()->allocate();
+ biases0.allocator()->allocate();
+ biases1.allocator()->allocate();
+ biases2.allocator()->allocate();
+ out_conv0.allocator()->allocate();
+ out_conv1.allocator()->allocate();
+ out_act0.allocator()->allocate();
+ out_act1.allocator()->allocate();
+ out_act2.allocator()->allocate();
+ out_pool0.allocator()->allocate();
+ out_pool1.allocator()->allocate();
+ out_fc0.allocator()->allocate();
+ out_softmax.allocator()->allocate();
+
+ /* -----------------------End: [Allocate tensors] */
+
+ /* [Initialize weights and biases tensors] */
+
+ // Once the tensors have been allocated, the src, weights and biases tensors can be initialized
+ // ...
+
+ /* -----------------------[Initialize weights and biases tensors] */
+
+ /* [Execute the functions] */
+
+ conv0.run();
+ act0.run();
+ pool0.run();
+ conv1.run();
+ act1.run();
+ pool1.run();
+ fc0.run();
+ act2.run();
+ softmax.run();
+
+ /* -----------------------End: [Execute the functions] */
+}
+
+/** Main program for cnn test
+ *
+ * The example implements the following CNN architecture:
+ *
+ * Input -> conv0:5x5 -> act0:relu -> pool:2x2 -> conv1:3x3 -> act1:relu -> pool:2x2 -> fc0 -> act2:relu -> softmax
+ *
+ * @param[in] argc Number of arguments
+ * @param[in] argv Arguments
+ */
+int main(int argc, const char **argv)
+{
+ return utils::run_example(argc, argv, main_cnn);
+}
\ No newline at end of file
diff --git a/examples/neon_convolution.cpp b/examples/neon_convolution.cpp
index fc68aa2..222c8f9 100644
--- a/examples/neon_convolution.cpp
+++ b/examples/neon_convolution.cpp
@@ -24,10 +24,10 @@
#include "arm_compute/runtime/NEON/NEFunctions.h"
#include "arm_compute/core/Types.h"
-#include "test_helpers/Utils.h"
+#include "utils/Utils.h"
using namespace arm_compute;
-using namespace test_helpers;
+using namespace utils;
/** Gaussian 3x3 matrix
*/
@@ -113,5 +113,5 @@
*/
int main(int argc, const char **argv)
{
- return test_helpers::run_example(argc, argv, main_neon_convolution);
+ return utils::run_example(argc, argv, main_neon_convolution);
}
diff --git a/examples/neon_copy_objects.cpp b/examples/neon_copy_objects.cpp
index 3f53939..191f455 100644
--- a/examples/neon_copy_objects.cpp
+++ b/examples/neon_copy_objects.cpp
@@ -25,7 +25,7 @@
#include "arm_compute/runtime/NEON/NEFunctions.h"
#include "arm_compute/core/Types.h"
-#include "test_helpers/Utils.h"
+#include "utils/Utils.h"
#include <cstring>
#include <iostream>
@@ -148,5 +148,5 @@
*/
int main(int argc, const char **argv)
{
- return test_helpers::run_example(argc, argv, main_neon_copy_objects);
+ return utils::run_example(argc, argv, main_neon_copy_objects);
}
diff --git a/examples/neon_scale.cpp b/examples/neon_scale.cpp
index c1435af..75780c9 100644
--- a/examples/neon_scale.cpp
+++ b/examples/neon_scale.cpp
@@ -24,10 +24,10 @@
#include "arm_compute/runtime/NEON/NEFunctions.h"
#include "arm_compute/core/Types.h"
-#include "test_helpers/Utils.h"
+#include "utils/Utils.h"
using namespace arm_compute;
-using namespace test_helpers;
+using namespace utils;
void main_neon_scale(int argc, const char **argv)
{
@@ -86,5 +86,5 @@
*/
int main(int argc, const char **argv)
{
- return test_helpers::run_example(argc, argv, main_neon_scale);
+ return utils::run_example(argc, argv, main_neon_scale);
}
diff --git a/examples/neoncl_scale_median_gaussian.cpp b/examples/neoncl_scale_median_gaussian.cpp
index a4e4414..a32ba6d 100644
--- a/examples/neoncl_scale_median_gaussian.cpp
+++ b/examples/neoncl_scale_median_gaussian.cpp
@@ -26,10 +26,10 @@
#include "arm_compute/runtime/CL/CLFunctions.h"
#include "arm_compute/runtime/CL/CLScheduler.h"
#include "arm_compute/runtime/NEON/NEFunctions.h"
-#include "test_helpers/Utils.h"
+#include "utils/Utils.h"
using namespace arm_compute;
-using namespace test_helpers;
+using namespace utils;
/** Example demonstrating how to use both CL and NEON functions in the same pipeline
*
@@ -122,5 +122,5 @@
*/
int main(int argc, const char **argv)
{
- return test_helpers::run_example(argc, argv, main_neoncl_scale_median_gaussian);
+ return utils::run_example(argc, argv, main_neoncl_scale_median_gaussian);
}