arm_compute v18.01
Change-Id: I9bfa178c2e38bfd5fc812e62aab6760d87748e05
diff --git a/examples/neon_copy_objects.cpp b/examples/neon_copy_objects.cpp
index 0402453..9409cf3 100644
--- a/examples/neon_copy_objects.cpp
+++ b/examples/neon_copy_objects.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2016, 2017 ARM Limited.
+ * Copyright (c) 2016, 2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -31,122 +31,134 @@
#include <iostream>
using namespace arm_compute;
+using namespace utils;
-void main_neon_copy_objects(int argc, const char **argv)
+class NEONCopyObjectsExample : public Example
{
- ARM_COMPUTE_UNUSED(argc);
- ARM_COMPUTE_UNUSED(argv);
-
- /** [Copy objects example] */
- constexpr unsigned int width = 4;
- constexpr unsigned int height = 3;
- constexpr unsigned int batch = 2;
-
- auto *src_data = new float[width * height * batch];
- auto *dst_data = new float[width * height * batch];
-
- // Fill src_data with dummy values:
- for(unsigned int b = 0; b < batch; b++)
+public:
+ void do_setup(int argc, char **argv) override
{
- for(unsigned int h = 0; h < height; h++)
+ ARM_COMPUTE_UNUSED(argc);
+ ARM_COMPUTE_UNUSED(argv);
+
+ /** [Copy objects example] */
+ constexpr unsigned int width = 4;
+ constexpr unsigned int height = 3;
+ constexpr unsigned int batch = 2;
+
+ src_data = new float[width * height * batch];
+ dst_data = new float[width * height * batch];
+
+ // Fill src_data with dummy values:
+ for(unsigned int b = 0; b < batch; b++)
{
- for(unsigned int w = 0; w < width; w++)
+ for(unsigned int h = 0; h < height; h++)
{
- src_data[b * (width * height) + h * width + w] = static_cast<float>(100 * b + 10 * h + w);
+ for(unsigned int w = 0; w < width; w++)
+ {
+ src_data[b * (width * height) + h * width + w] = static_cast<float>(100 * b + 10 * h + w);
+ }
}
}
+
+ // Initialize the tensors dimensions and type:
+ const TensorShape shape(width, height, batch);
+ input.allocator()->init(TensorInfo(shape, 1, DataType::F32));
+ output.allocator()->init(TensorInfo(shape, 1, DataType::F32));
+
+ // Configure softmax:
+ softmax.configure(&input, &output);
+
+ // Allocate the input / output tensors:
+ input.allocator()->allocate();
+ output.allocator()->allocate();
+
+ // Fill the input tensor:
+ // Simplest way: create an iterator to iterate through each element of the input tensor:
+ Window input_window;
+ input_window.use_tensor_dimensions(input.info()->tensor_shape());
+ std::cout << " Dimensions of the input's iterator:\n";
+ std::cout << " X = [start=" << input_window.x().start() << ", end=" << input_window.x().end() << ", step=" << input_window.x().step() << "]\n";
+ std::cout << " Y = [start=" << input_window.y().start() << ", end=" << input_window.y().end() << ", step=" << input_window.y().step() << "]\n";
+ std::cout << " Z = [start=" << input_window.z().start() << ", end=" << input_window.z().end() << ", step=" << input_window.z().step() << "]\n";
+
+ // Create an iterator:
+ Iterator input_it(&input, input_window);
+
+ // Iterate through the elements of src_data and copy them one by one to the input tensor:
+ // This is equivalent to:
+ // for( unsigned int z = 0; z < batch; ++z)
+ // {
+ // for( unsigned int y = 0; y < height; ++y)
+ // {
+ // for( unsigned int x = 0; x < width; ++x)
+ // {
+ // *reinterpret_cast<float*>( input.buffer() + input.info()->offset_element_in_bytes(Coordinates(x,y,z))) = src_data[ z * (width*height) + y * width + x];
+ // }
+ // }
+ // }
+ // Except it works for an arbitrary number of dimensions
+ execute_window_loop(input_window, [&](const Coordinates & id)
+ {
+ std::cout << "Setting item [" << id.x() << "," << id.y() << "," << id.z() << "]\n";
+ *reinterpret_cast<float *>(input_it.ptr()) = src_data[id.z() * (width * height) + id.y() * width + id.x()];
+ },
+ input_it);
+
+ // More efficient way: create an iterator to iterate through each row (instead of each element) of the output tensor:
+ Window output_window;
+ output_window.use_tensor_dimensions(output.info()->tensor_shape(), /* first_dimension =*/Window::DimY); // Iterate through the rows (not each element)
+ std::cout << " Dimensions of the output's iterator:\n";
+ std::cout << " X = [start=" << output_window.x().start() << ", end=" << output_window.x().end() << ", step=" << output_window.x().step() << "]\n";
+ std::cout << " Y = [start=" << output_window.y().start() << ", end=" << output_window.y().end() << ", step=" << output_window.y().step() << "]\n";
+ std::cout << " Z = [start=" << output_window.z().start() << ", end=" << output_window.z().end() << ", step=" << output_window.z().step() << "]\n";
+
+ // Create an iterator:
+ Iterator output_it(&output, output_window);
+
+ // Iterate through the rows of the output tensor and copy them to dst_data:
+ // This is equivalent to:
+ // for( unsigned int z = 0; z < batch; ++z)
+ // {
+ // for( unsigned int y = 0; y < height; ++y)
+ // {
+ // memcpy( dst_data + z * (width*height) + y * width, input.buffer() + input.info()->offset_element_in_bytes(Coordinates(0,y,z)), width * sizeof(float));
+ // }
+ // }
+ // Except it works for an arbitrary number of dimensions
+ execute_window_loop(output_window, [&](const Coordinates & id)
+ {
+ std::cout << "Copying one row starting from [" << id.x() << "," << id.y() << "," << id.z() << "]\n";
+ // Copy one whole row:
+ memcpy(dst_data + id.z() * (width * height) + id.y() * width, output_it.ptr(), width * sizeof(float));
+ },
+ output_it);
+
+ /** [Copy objects example] */
+ }
+ void do_run() override
+ {
+ // Run NEON softmax:
+ softmax.run();
+ }
+ void do_teardown() override
+ {
+ delete[] src_data;
+ delete[] dst_data;
}
- Tensor input, output;
- NESoftmaxLayer softmax;
-
- // Initialize the tensors dimensions and type:
- const TensorShape shape(width, height, batch);
- input.allocator()->init(TensorInfo(shape, 1, DataType::F32));
- output.allocator()->init(TensorInfo(shape, 1, DataType::F32));
-
- // Configure softmax:
- softmax.configure(&input, &output);
-
- // Allocate the input / output tensors:
- input.allocator()->allocate();
- output.allocator()->allocate();
-
- // Fill the input tensor:
- // Simplest way: create an iterator to iterate through each element of the input tensor:
- Window input_window;
- input_window.use_tensor_dimensions(input.info()->tensor_shape());
- std::cout << " Dimensions of the input's iterator:\n";
- std::cout << " X = [start=" << input_window.x().start() << ", end=" << input_window.x().end() << ", step=" << input_window.x().step() << "]\n";
- std::cout << " Y = [start=" << input_window.y().start() << ", end=" << input_window.y().end() << ", step=" << input_window.y().step() << "]\n";
- std::cout << " Z = [start=" << input_window.z().start() << ", end=" << input_window.z().end() << ", step=" << input_window.z().step() << "]\n";
-
- // Create an iterator:
- Iterator input_it(&input, input_window);
-
- // Iterate through the elements of src_data and copy them one by one to the input tensor:
- // This is equivalent to:
- // for( unsigned int z = 0; z < batch; ++z)
- // {
- // for( unsigned int y = 0; y < height; ++y)
- // {
- // for( unsigned int x = 0; x < width; ++x)
- // {
- // *reinterpret_cast<float*>( input.buffer() + input.info()->offset_element_in_bytes(Coordinates(x,y,z))) = src_data[ z * (width*height) + y * width + x];
- // }
- // }
- // }
- // Except it works for an arbitrary number of dimensions
- execute_window_loop(input_window, [&](const Coordinates & id)
- {
- std::cout << "Setting item [" << id.x() << "," << id.y() << "," << id.z() << "]\n";
- *reinterpret_cast<float *>(input_it.ptr()) = src_data[id.z() * (width * height) + id.y() * width + id.x()];
- },
- input_it);
-
- // Run NEON softmax:
- softmax.run();
-
- // More efficient way: create an iterator to iterate through each row (instead of each element) of the output tensor:
- Window output_window;
- output_window.use_tensor_dimensions(output.info()->tensor_shape(), /* first_dimension =*/Window::DimY); // Iterate through the rows (not each element)
- std::cout << " Dimensions of the output's iterator:\n";
- std::cout << " X = [start=" << output_window.x().start() << ", end=" << output_window.x().end() << ", step=" << output_window.x().step() << "]\n";
- std::cout << " Y = [start=" << output_window.y().start() << ", end=" << output_window.y().end() << ", step=" << output_window.y().step() << "]\n";
- std::cout << " Z = [start=" << output_window.z().start() << ", end=" << output_window.z().end() << ", step=" << output_window.z().step() << "]\n";
-
- // Create an iterator:
- Iterator output_it(&output, output_window);
-
- // Iterate through the rows of the output tensor and copy them to dst_data:
- // This is equivalent to:
- // for( unsigned int z = 0; z < batch; ++z)
- // {
- // for( unsigned int y = 0; y < height; ++y)
- // {
- // memcpy( dst_data + z * (width*height) + y * width, input.buffer() + input.info()->offset_element_in_bytes(Coordinates(0,y,z)), width * sizeof(float));
- // }
- // }
- // Except it works for an arbitrary number of dimensions
- execute_window_loop(output_window, [&](const Coordinates & id)
- {
- std::cout << "Copying one row starting from [" << id.x() << "," << id.y() << "," << id.z() << "]\n";
- // Copy one whole row:
- memcpy(dst_data + id.z() * (width * height) + id.y() * width, output_it.ptr(), width * sizeof(float));
- },
- output_it);
-
- delete[] src_data;
- delete[] dst_data;
- /** [Copy objects example] */
-}
-
+private:
+ Tensor input{}, output{};
+ float *src_data{};
+ float *dst_data{};
+ NESoftmaxLayer softmax{};
+};
/** Main program for the copy objects test
*
* @param[in] argc Number of arguments
* @param[in] argv Arguments
*/
-int main(int argc, const char **argv)
+int main(int argc, char **argv)
{
- return utils::run_example(argc, argv, main_neon_copy_objects);
+ return utils::run_example<NEONCopyObjectsExample>(argc, argv);
}