/* * Copyright (c) 2019-2021 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/runtime/CL/functions/CLCropResize.h" #include "arm_compute/core/CL/CLHelpers.h" #include "arm_compute/runtime/CL/CLScheduler.h" #include "src/core/CL/kernels/CLFillBorderKernel.h" #include "src/core/helpers/AutoConfiguration.h" #include "src/core/helpers/WindowHelpers.h" #include "src/common/utils/Log.h" #include namespace arm_compute { namespace { inline void configure_crop(const ICLTensor *input, ICLTensor *crop_boxes, ICLTensor *box_ind, ICLTensor *output, uint32_t crop_box_ind, Coordinates &start, Coordinates &end, uint32_t &batch_index) { batch_index = *(reinterpret_cast(box_ind->ptr_to_element(Coordinates(crop_box_ind)))); // _crop_box_ind is used to index crop_boxes and retrieve the appropriate crop box. // The crop box is specified by normalized coordinates [y0, x0, y1, x1]. const float x0 = *reinterpret_cast(crop_boxes->ptr_to_element(Coordinates(1, crop_box_ind))); const float y0 = *reinterpret_cast(crop_boxes->ptr_to_element(Coordinates(0, crop_box_ind))); const float x1 = *reinterpret_cast(crop_boxes->ptr_to_element(Coordinates(3, crop_box_ind))); const float y1 = *reinterpret_cast(crop_boxes->ptr_to_element(Coordinates(2, crop_box_ind))); // The normalized coordinates are scaled to retrieve the floating point image coordinates which are rounded to integers. start = Coordinates(std::floor(x0 * (input->info()->tensor_shape()[1] - 1) + 0.5f), std::floor(y0 * (input->info()->tensor_shape()[2] - 1) + 0.5f)); end = Coordinates(std::floor(x1 * (input->info()->tensor_shape()[1] - 1) + 0.5f), std::floor(y1 * (input->info()->tensor_shape()[2] - 1) + 0.5f)); const TensorShape out_shape(input->info()->tensor_shape()[0], static_cast(abs(end[0] - start[0])) + 1, static_cast(abs(end[1] - start[1])) + 1); output->info()->set_tensor_shape(out_shape); } } // namespace CLCropResize::CLCropResize() : _input(nullptr), _boxes(nullptr), _box_ind(nullptr), _output(nullptr), _num_boxes(0), _method(), _extrapolation_value(0), _scale(), _copy(), _crop_results(), _scaled_results(), _internal_functions() { } CLCropResize::~CLCropResize() = default; Status CLCropResize::validate(const ITensorInfo *input, ITensorInfo *boxes, ITensorInfo *box_ind, const ITensorInfo *output, Coordinates2D crop_size, InterpolationPolicy method, float extrapolation_value) { ARM_COMPUTE_RETURN_ERROR_ON(crop_size.x <= 0 || crop_size.y <= 0); ARM_COMPUTE_RETURN_ERROR_ON(method == InterpolationPolicy::AREA); ARM_COMPUTE_RETURN_ERROR_ON(boxes->tensor_shape()[0] != 4); ARM_COMPUTE_RETURN_ERROR_ON(boxes->tensor_shape()[1] != box_ind->tensor_shape()[0]); TensorInfo temp_info; ARM_COMPUTE_RETURN_ON_ERROR(CLCrop::validate(input->clone().get(), &temp_info, { 0, 0 }, { 1, 1 }, input->dimension(3) - 1, extrapolation_value)); if(output->total_size() > 0) { ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_NOT_IN(output, DataType::F32); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, output); TensorShape out_shape(input->tensor_shape()[0], crop_size.x, crop_size.y, boxes->tensor_shape()[1]); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), out_shape); } return Status{}; } void CLCropResize::configure(const ICLTensor *input, ICLTensor *boxes, ICLTensor *box_ind, ICLTensor *output, Coordinates2D crop_size, InterpolationPolicy method, float extrapolation_value) { configure(CLKernelLibrary::get().get_compile_context(), input, boxes, box_ind, output, crop_size, method, extrapolation_value); } void CLCropResize::configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *boxes, ICLTensor *box_ind, ICLTensor *output, Coordinates2D crop_size, InterpolationPolicy method, float extrapolation_value) { ARM_COMPUTE_ERROR_ON_NULLPTR(input, output, boxes, box_ind); ARM_COMPUTE_ERROR_THROW_ON(CLCropResize::validate(input->info(), boxes->info(), box_ind->info(), output->info(), crop_size, method, extrapolation_value)); ARM_COMPUTE_LOG_PARAMS(input, boxes, box_ind, output, crop_size, method, extrapolation_value); TensorShape output_shape = TensorShape(input->info()->tensor_shape()[0], crop_size.x, crop_size.y, boxes->info()->tensor_shape()[1]); auto_init_if_empty(*output->info(), output_shape, 1, DataType::F32); _num_boxes = boxes->info()->tensor_shape()[1]; TensorShape out_shape(input->info()->tensor_shape()[0], crop_size.x, crop_size.y); _input = input; _boxes = boxes; _box_ind = box_ind; _output = output; _method = method; _extrapolation_value = extrapolation_value; // For each crop box: // - The initial cropped image is produced as specified by boxes[i] from the 3D image input[box_ind[i]]. // Possibly using a CLCrop and up to four CLFills. // - A tensor is required to hold this initial cropped image. // - A scale function is used to resize the cropped image to the size specified by crop_size. // - A tensor is required to hold the final scaled image before it is copied into the 4D output // that will hold all final cropped and scaled 3D images using CLCopy. // The contents of _boxes and _box_ind are required to calculate the shape // of the initial cropped image and thus are required to configure the // kernels used for cropping and scaling. _boxes->map(CLScheduler::get().queue()); _box_ind->map(CLScheduler::get().queue()); for(unsigned int num_box = 0; num_box < _num_boxes; ++num_box) { auto crop_tensor = std::make_unique(); TensorInfo crop_result_info(1, DataType::F32); crop_result_info.set_data_layout(DataLayout::NHWC); crop_tensor->allocator()->init(crop_result_info); _crop_results.emplace_back(std::move(crop_tensor)); auto scale_tensor = std::make_unique(); TensorInfo scaled_result_info(out_shape, 1, DataType::F32); scaled_result_info.set_data_layout(DataLayout::NHWC); scale_tensor->allocator()->init(scaled_result_info); _scaled_results.emplace_back(std::move(scale_tensor)); // Size of the crop box in _boxes has to be given before the configure uint32_t batch_index; Coordinates start{}; Coordinates end{}; configure_crop(_input, _boxes, _box_ind, _crop_results[num_box].get(), num_box, start, end, batch_index); auto scale_kernel = std::make_unique(); scale_kernel->configure(compile_context, _crop_results[num_box].get(), _scaled_results[num_box].get(), ScaleKernelInfo{ _method, BorderMode::CONSTANT, PixelValue(_extrapolation_value), SamplingPolicy::TOP_LEFT }); _scale.emplace_back(std::move(scale_kernel)); Window win = calculate_max_window(*_output->info()); win.set(3, Window::Dimension(num_box, num_box + 1, 1)); auto copy_kernel = std::make_unique(); copy_kernel->configure(compile_context, _scaled_results[num_box].get(), _output, &win); _copy.emplace_back(std::move(copy_kernel)); _crop_results[num_box]->allocator()->allocate(); _scaled_results[num_box]->allocator()->allocate(); bool is_width_flipped = end[0] < start[0]; bool is_height_flipped = end[1] < start[1]; /** The number of rows out of bounds at the start and end of _crop_results[num_box].get(). */ std::array rows_out_of_bounds{ 0 }; /** The number of columns out of bounds at the start and end of _crop_results[num_box].get(). */ std::array cols_out_of_bounds{ 0 }; if(is_height_flipped) { rows_out_of_bounds[0] = start[1] >= static_cast(_input->info()->dimension(2)) ? std::min(start[1] - _input->info()->dimension(2) + 1, _crop_results[num_box].get()->info()->dimension(2)) : 0; rows_out_of_bounds[1] = end[1] < 0 ? std::min(-end[1], static_cast(_crop_results[num_box].get()->info()->dimension(2))) : 0; } else { rows_out_of_bounds[0] = start[1] < 0 ? std::min(-start[1], static_cast(_crop_results[num_box].get()->info()->dimension(2))) : 0; rows_out_of_bounds[1] = end[1] >= static_cast(_input->info()->dimension(2)) ? std::min(end[1] - _input->info()->dimension(2) + 1, _crop_results[num_box].get()->info()->dimension(2)) : 0; } if(is_width_flipped) { cols_out_of_bounds[0] = start[0] >= static_cast(_input->info()->dimension(1)) ? std::min(start[0] - _input->info()->dimension(1) + 1, _crop_results[num_box].get()->info()->dimension(1)) : 0; cols_out_of_bounds[1] = end[0] < 0 ? std::min(-end[0], static_cast(_crop_results[num_box].get()->info()->dimension(1))) : 0; } else { cols_out_of_bounds[0] = start[0] < 0 ? std::min(-start[0], static_cast(_crop_results[num_box].get()->info()->dimension(1))) : 0; cols_out_of_bounds[1] = end[0] >= static_cast(_input->info()->dimension(1)) ? std::min(end[0] - _input->info()->dimension(1) + 1, _crop_results[num_box].get()->info()->dimension(1)) : 0; } Window full_window = calculate_max_window(*_crop_results[num_box].get()->info()); // Full _crop_results[num_box].get() window: // -------------------------------- // | Out of bounds | // | rows before | // |------------------------------| // | Out of | In | Out of | // | bounds | bounds | bounds | // | cols | elements | cols | // | before | copied | after | // | | from input | | // |------------------------------| // | Out of bounds | // | rows after | // |------------------------------| // Use a separate _crop_results[num_box].get() window for each section of the full _crop_results[num_box].get() window. // Fill all _crop_results[num_box].get() rows that have no elements that are within the input bounds // with the extrapolation value using memset. // First for the rows before the in bounds rows. if(rows_out_of_bounds[0] > 0) { Window slice_fill_rows_before(full_window); slice_fill_rows_before.set(2, Window::Dimension(0, rows_out_of_bounds[0], 1)); auto kernel = std::make_unique(); kernel->configure(compile_context, _crop_results[num_box].get(), extrapolation_value, &slice_fill_rows_before); //_internal_functions.emplace_back(std::move(kernel)); _internal_functions.push_back(std::move(kernel)); } Window slice_in(full_window); slice_in.set(2, Window::Dimension(rows_out_of_bounds[0], _crop_results[num_box].get()->info()->dimension(2) - rows_out_of_bounds[1], 1)); slice_in.set(1, Window::Dimension(cols_out_of_bounds[0], _crop_results[num_box].get()->info()->dimension(1) - cols_out_of_bounds[1], 1)); int rows_in_bounds = static_cast(_crop_results[num_box].get()->info()->dimension(2)) - rows_out_of_bounds[0] - rows_out_of_bounds[1]; if(rows_in_bounds > 0) { // Fill all elements that share a row with an in bounds element with the extrapolation value. if(cols_out_of_bounds[0] > 0) { Window slice_fill_cols_before(slice_in); slice_fill_cols_before.set(1, Window::Dimension(0, cols_out_of_bounds[0], 1)); auto kernel = std::make_unique(); kernel->configure(compile_context, _crop_results[num_box].get(), extrapolation_value, &slice_fill_cols_before); //_internal_functions.emplace_back(std::move(kernel)); _internal_functions.push_back(std::move(kernel)); } if(cols_out_of_bounds[1] > 0) { Window slice_fill_cols_after(slice_in); slice_fill_cols_after.set(1, Window::Dimension(_crop_results[num_box].get()->info()->dimension(1) - cols_out_of_bounds[1], _crop_results[num_box].get()->info()->dimension(1), 1)); auto kernel = std::make_unique(); kernel->configure(compile_context, _crop_results[num_box].get(), extrapolation_value, &slice_fill_cols_after); //_internal_functions.emplace_back(std::move(kernel)); _internal_functions.push_back(std::move(kernel)); } // Copy all elements within the input bounds from the input tensor. int cols_in_bounds = static_cast(_crop_results[num_box].get()->info()->dimension(1)) - cols_out_of_bounds[0] - cols_out_of_bounds[1]; if(cols_in_bounds > 0) { Coordinates2D start_in{ is_width_flipped ? start[0] - cols_out_of_bounds[0] : start[0] + cols_out_of_bounds[0], is_height_flipped ? start[1] - rows_out_of_bounds[0] : start[1] + rows_out_of_bounds[0] }; Coordinates2D end_in{ is_width_flipped ? start_in.x - cols_in_bounds + 1 : start_in.x + cols_in_bounds - 1, is_height_flipped ? start_in.y - rows_in_bounds + 1 : start_in.y + rows_in_bounds - 1 }; auto kernel = std::make_unique(); kernel->configure(compile_context, _input, _crop_results[num_box].get(), start_in, end_in, batch_index, extrapolation_value, &slice_in); //_internal_functions.emplace_back(std::move(kernel)); _internal_functions.push_back(std::move(kernel)); } } // Fill all rows after the in bounds elements with the extrapolation value. if(rows_out_of_bounds[1] > 0) { Window slice_fill_rows_after(full_window); slice_fill_rows_after.set(2, Window::Dimension(_crop_results[num_box].get()->info()->dimension(2) - rows_out_of_bounds[1], _crop_results[num_box].get()->info()->dimension(2), 1)); auto kernel = std::make_unique(); kernel->configure(compile_context, _crop_results[num_box].get(), extrapolation_value, &slice_fill_rows_after); //_internal_functions.emplace_back(std::move(kernel)); _internal_functions.push_back(std::move(kernel)); } } _boxes->unmap(CLScheduler::get().queue()); _box_ind->unmap(CLScheduler::get().queue()); CLScheduler::get().sync(); } void CLCropResize::run() { ARM_COMPUTE_ERROR_ON_MSG(_output == nullptr, "Unconfigured function"); for(unsigned int i = 0; i < _internal_functions.size(); ++i) { _internal_functions[i]->run(); } CLScheduler::get().sync(); for(auto &kernel : _scale) { kernel->run(); } CLScheduler::get().sync(); for(auto &kernel : _copy) { kernel->run(); } CLScheduler::get().sync(); } } // namespace arm_compute