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/*
* Copyright (c) 2017-2018 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/WindowIterator.h"
#include "tests/Utils.h"
#include "tests/framework/Asserts.h"
#include "tests/framework/Macros.h"
#include "tests/framework/datasets/Datasets.h"
#include "tests/validation/Validation.h"
#include "utils/TypePrinter.h"
#include <stdexcept>
using namespace arm_compute;
using namespace arm_compute::test;
using namespace arm_compute::test::validation;
TEST_SUITE(UNIT)
TEST_SUITE(WindowIterator)
template <typename Dim, typename... Dims>
Window create_window(Dim &&dim0, Dims &&... dims)
{
Window win;
const std::array < Dim, 1 + sizeof...(Dims) > dimensions{ { dim0, std::forward<Dims>(dims)... } };
for(size_t i = 0; i < dimensions.size(); i++)
{
win.set(i, dimensions[i]);
}
return win;
}
template <typename T>
std::vector<T> create_vector(std::initializer_list<T> list_objs)
{
std::vector<T> vec_objs;
for(auto it : list_objs)
{
vec_objs.push_back(it);
}
return vec_objs;
}
DATA_TEST_CASE(WholeWindow, framework::DatasetMode::ALL, zip(framework::dataset::make("Window", { create_window(Window::Dimension(0, 1)),
create_window(Window::Dimension(1, 5, 2), Window::Dimension(3, 5)),
create_window(Window::Dimension(4, 16, 4), Window::Dimension(3, 13, 5), Window::Dimension(1, 3, 2))
}),
framework::dataset::make("Expected", { create_vector({ Coordinates(0, 0) }),
create_vector({ Coordinates(1, 3), Coordinates(3, 3), Coordinates(1, 4), Coordinates(3, 4) }),
create_vector({ Coordinates(4, 3, 1), Coordinates(8, 3, 1), Coordinates(12, 3, 1), Coordinates(4, 8, 1), Coordinates(8, 8, 1), Coordinates(12, 8, 1) })
})),
window, expected)
{
unsigned int i = 0;
int row_size = 0;
TensorShape window_shape = window.shape();
Coordinates start_offset = index2coords(window_shape, 0);
Coordinates end_offset = index2coords(window_shape, window.num_iterations_total() - 1);
auto window_iterator = create_window_iterator(window, start_offset, end_offset, [&](const Coordinates & id)
{
ARM_COMPUTE_EXPECT_EQUAL(row_size, (window[0].end() - window[0].start()), framework::LogLevel::ERRORS);
ARM_COMPUTE_ASSERT(i < expected.size());
Coordinates expected_coords = expected[i++];
//Set number of dimensions to the maximum (To match the number of dimensions used by the id passed to the lambda function)
expected_coords.set_num_dimensions(Coordinates::num_max_dimensions);
ARM_COMPUTE_EXPECT_EQUAL(id, expected_coords, framework::LogLevel::ERRORS);
});
window_iterator.iterate_3D([&](int start, int end)
{
ARM_COMPUTE_EXPECT_EQUAL(window[0].start(), start, framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT_EQUAL(window[0].end(), end, framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(end > start, framework::LogLevel::ERRORS);
row_size = end - start;
});
ARM_COMPUTE_EXPECT_EQUAL(i, expected.size(), framework::LogLevel::ERRORS);
}
DATA_TEST_CASE(PartialWindow2D, framework::DatasetMode::ALL, zip(zip(zip(combine(framework::dataset::make("Window",
create_window(Window::Dimension(4, 20, 4), Window::Dimension(3, 32, 5), Window::Dimension(1, 2, 1))),
framework::dataset::make("Start", { 0, 1, 3, 2, 4 })),
framework::dataset::make("End", { 0, 2, 5, 8, 7 })),
framework::dataset::make("RowSize",
{
create_vector({ 4 }),
create_vector({ 8, 8 }),
create_vector({ 4, 8, 8 }),
create_vector({ 8, 8, 16, 16, 16, 16, 4 }),
create_vector({ 16, 16, 16, 16 }),
})),
framework::dataset::make("Expected", { create_vector({ Coordinates(4, 3, 1) }), create_vector({ Coordinates(8, 3, 1), Coordinates(12, 3, 1) }), create_vector({ Coordinates(16, 3, 1), Coordinates(4, 8, 1), Coordinates(8, 8, 1) }), create_vector({ Coordinates(12, 3, 1), Coordinates(16, 3, 1), Coordinates(4, 8, 1), Coordinates(8, 8, 1), Coordinates(12, 8, 1), Coordinates(16, 8, 1), Coordinates(4, 13, 1) }), create_vector({ Coordinates(4, 8, 1), Coordinates(8, 8, 1), Coordinates(12, 8, 1), Coordinates(16, 8, 1) }) })),
window, start, end, expected_row_size, expected)
{
unsigned int i = 0;
int row_size = 0;
TensorShape window_shape = window.shape();
Coordinates start_offset = index2coords(window_shape, start);
Coordinates end_offset = index2coords(window_shape, end);
auto window_iterator = create_window_iterator(window, start_offset, end_offset, [&](const Coordinates & id)
{
ARM_COMPUTE_ASSERT(i < expected.size());
ARM_COMPUTE_EXPECT_EQUAL(expected_row_size[i], row_size, framework::LogLevel::ERRORS);
Coordinates expected_coords = expected[i++];
//Set number of dimensions to the maximum (To match the number of dimensions used by the id passed to the lambda function)
expected_coords.set_num_dimensions(Coordinates::num_max_dimensions);
ARM_COMPUTE_EXPECT_EQUAL(id, expected_coords, framework::LogLevel::ERRORS);
});
window_iterator.iterate_3D([&](int start, int end)
{
ARM_COMPUTE_EXPECT(start >= window[0].start(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(end <= window[0].end(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(end > start, framework::LogLevel::ERRORS);
row_size = end - start;
});
ARM_COMPUTE_EXPECT_EQUAL(i, expected.size(), framework::LogLevel::ERRORS);
}
TEST_SUITE_END()
TEST_SUITE_END()
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