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/*
* Copyright (c) 2016-2021 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/core/Types.h"
#include "arm_compute/runtime/NEON/NEFunctions.h"
#include "utils/Utils.h"
#include <cstring>
#include <iostream>
using namespace arm_compute;
using namespace utils;
class NEONCopyObjectsExample : public Example
{
public:
bool do_setup(int argc, char **argv) override
{
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 pseudo(meaningless) values:
for (unsigned int b = 0; b < batch; b++)
{
for (unsigned int h = 0; h < height; h++)
{
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] */
return true;
}
void do_run() override
{
// Run softmax:
softmax.run();
}
void do_teardown() override
{
delete[] src_data;
delete[] dst_data;
}
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, char **argv)
{
return utils::run_example<NEONCopyObjectsExample>(argc, argv);
}
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