| /* |
| * 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() |