/* * Copyright (c) 2019 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/CL/CLHelpers.h" #include "arm_compute/runtime/CL/CLScheduler.h" #include "arm_compute/runtime/CL/functions/CLCropResize.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], abs(end[0] - start[0]) + 1, abs(end[1] - start[1]) + 1); output->info()->set_tensor_shape(out_shape); } inline void run_crop(const ICLTensor *input, ICLTensor *output, uint32_t batch_index, Coordinates start, Coordinates end, float extrapolation_value) { 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 output. */ std::array rows_out_of_bounds{ 0 }; /** The number of columns out of bounds at the start and end of output. */ 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, output->info()->dimension(2)) : 0; rows_out_of_bounds[1] = end[1] < 0 ? std::min(-end[1], static_cast(output->info()->dimension(2))) : 0; } else { rows_out_of_bounds[0] = start[1] < 0 ? std::min(-start[1], static_cast(output->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, output->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, output->info()->dimension(1)) : 0; cols_out_of_bounds[1] = end[0] < 0 ? std::min(-end[0], static_cast(output->info()->dimension(1))) : 0; } else { cols_out_of_bounds[0] = start[0] < 0 ? std::min(-start[0], static_cast(output->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, output->info()->dimension(1)) : 0; } Window full_window = calculate_max_window(*output->info()); // Full output 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 output window for each section of the full output window. // Fill all output 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 = arm_compute::support::cpp14::make_unique(); kernel->configure(output, extrapolation_value, &slice_fill_rows_before); CLScheduler::get().enqueue(*kernel); } Window slice_in(full_window); slice_in.set(2, Window::Dimension(rows_out_of_bounds[0], output->info()->dimension(2) - rows_out_of_bounds[1], 1)); slice_in.set(1, Window::Dimension(cols_out_of_bounds[0], output->info()->dimension(1) - cols_out_of_bounds[1], 1)); int rows_in_bounds = static_cast(output->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 = arm_compute::support::cpp14::make_unique(); kernel->configure(output, extrapolation_value, &slice_fill_cols_before); CLScheduler::get().enqueue(*kernel); } if(cols_out_of_bounds[1] > 0) { Window slice_fill_cols_after(slice_in); slice_fill_cols_after.set(1, Window::Dimension(output->info()->dimension(1) - cols_out_of_bounds[1], output->info()->dimension(1), 1)); auto kernel = arm_compute::support::cpp14::make_unique(); kernel->configure(output, extrapolation_value, &slice_fill_cols_after); CLScheduler::get().enqueue(*kernel); } // Copy all elements within the input bounds from the input tensor. int cols_in_bounds = static_cast(output->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 = arm_compute::support::cpp14::make_unique(); kernel->configure(input, output, start_in, end_in, batch_index, extrapolation_value, &slice_in); CLScheduler::get().enqueue(*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(output->info()->dimension(2) - rows_out_of_bounds[1], output->info()->dimension(2), 1)); auto kernel = arm_compute::support::cpp14::make_unique(); kernel->configure(output, extrapolation_value, &slice_fill_rows_after); CLScheduler::get().enqueue(*kernel); } } } // 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() { } 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(CLCropKernel::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) { ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); ARM_COMPUTE_ERROR_THROW_ON(CLCropResize::validate(input->info(), boxes->info(), box_ind->info(), output->info(), crop_size, method, extrapolation_value)); _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 CLCropKernel and up to four CLMemsetKernels. // - 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 CLCopyKernel. for(unsigned int i = 0; i < _num_boxes; ++i) { auto crop_tensor = support::cpp14::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 = support::cpp14::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)); } } void CLCropResize::run() { ARM_COMPUTE_ERROR_ON_MSG(_output == nullptr, "Unconfigured function"); // 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 i = 0; i < _num_boxes; ++i) { // Size of the crop box in _boxes and thus the shape of _crop_results[i] // may not be known until run-time and so the kernels cannot be configured until then. uint32_t batch_index; Coordinates start{}; Coordinates end{}; configure_crop(_input, _boxes, _box_ind, _crop_results[i].get(), i, start, end, batch_index); auto scale_kernel = support::cpp14::make_unique(); scale_kernel->configure(_crop_results[i].get(), _scaled_results[i].get(), _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(i, i + 1, 1)); auto copy_kernel = support::cpp14::make_unique(); copy_kernel->configure(_scaled_results[i].get(), _output, PaddingList(), &win); _copy.emplace_back(std::move(copy_kernel)); _crop_results[i]->allocator()->allocate(); _scaled_results[i]->allocator()->allocate(); run_crop(_input, _crop_results[i].get(), batch_index, start, end, _extrapolation_value); } _boxes->unmap(CLScheduler::get().queue()); _box_ind->unmap(CLScheduler::get().queue()); CLScheduler::get().sync(); for(auto &kernel : _scale) { kernel->run(); } CLScheduler::get().sync(); for(auto &kernel : _copy) { CLScheduler::get().enqueue(*kernel, true); } CLScheduler::get().sync(); } } // namespace arm_compute