| /* |
| * 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 <cstddef> |
| |
| 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<int32_t *>(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<const float *>(crop_boxes->ptr_to_element(Coordinates(1, crop_box_ind))); |
| const float y0 = *reinterpret_cast<const float *>(crop_boxes->ptr_to_element(Coordinates(0, crop_box_ind))); |
| const float x1 = *reinterpret_cast<const float *>(crop_boxes->ptr_to_element(Coordinates(3, crop_box_ind))); |
| const float y1 = *reinterpret_cast<const float *>(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. */ |
| int32_t rows_out_of_bounds[2]; |
| /** The number of columns out of bounds at the start and end of output. */ |
| int32_t cols_out_of_bounds[2]; |
| if(is_height_flipped) |
| { |
| rows_out_of_bounds[0] = start[1] >= static_cast<int32_t>(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<int32_t>(output->info()->dimension(2))) : 0; |
| } |
| else |
| { |
| rows_out_of_bounds[0] = start[1] < 0 ? std::min(-start[1], static_cast<int32_t>(output->info()->dimension(2))) : 0; |
| rows_out_of_bounds[1] = end[1] >= static_cast<int32_t>(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<int32_t>(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<int32_t>(output->info()->dimension(1))) : 0; |
| } |
| else |
| { |
| cols_out_of_bounds[0] = start[0] < 0 ? std::min(-start[0], static_cast<int32_t>(output->info()->dimension(1))) : 0; |
| cols_out_of_bounds[1] = end[0] >= static_cast<int32_t>(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<CLMemsetKernel>(); |
| 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<int32_t>(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<CLMemsetKernel>(); |
| 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<CLMemsetKernel>(); |
| 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<int32_t>(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<CLCropKernel>(); |
| |
| 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<CLMemsetKernel>(); |
| 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() |
| { |
| } |
| |
| 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. |
| _crop_results = arm_compute::support::cpp14::make_unique<CLTensor[]>(_num_boxes); |
| _scale = arm_compute::support::cpp14::make_unique<CLScale[]>(_num_boxes); |
| _scaled_results = arm_compute::support::cpp14::make_unique<CLTensor[]>(_num_boxes); |
| _copy = arm_compute::support::cpp14::make_unique<CLCopyKernel[]>(_num_boxes); |
| |
| for(unsigned int i = 0; i < _num_boxes; ++i) |
| { |
| TensorInfo crop_result_info(1, DataType::F32); |
| crop_result_info.set_data_layout(DataLayout::NHWC); |
| _crop_results[i].allocator()->init(crop_result_info); |
| |
| TensorInfo scaled_result_info(out_shape, 1, DataType::F32); |
| scaled_result_info.set_data_layout(DataLayout::NHWC); |
| _scaled_results[i].allocator()->init(scaled_result_info); |
| } |
| } |
| |
| 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, end; |
| configure_crop(_input, _boxes, _box_ind, &_crop_results[i], i, start, end, batch_index); |
| _scale[i].configure(&_crop_results[i], &_scaled_results[i], _method, BorderMode::CONSTANT, PixelValue(_extrapolation_value), SamplingPolicy::TOP_LEFT); |
| |
| Window win = calculate_max_window(*_output->info()); |
| win.set(3, Window::Dimension(i, i + 1, 1)); |
| _copy[i].configure(&_scaled_results[i], _output, PaddingList(), &win); |
| |
| _crop_results[i].allocator()->allocate(); |
| _scaled_results[i].allocator()->allocate(); |
| |
| run_crop(_input, &_crop_results[i], batch_index, start, end, _extrapolation_value); |
| } |
| _boxes->unmap(CLScheduler::get().queue()); |
| _box_ind->unmap(CLScheduler::get().queue()); |
| CLScheduler::get().sync(); |
| for(unsigned int i = 0; i < _num_boxes; ++i) |
| { |
| // Scale the cropped image |
| _scale[i].run(); |
| } |
| CLScheduler::get().sync(); |
| for(unsigned int i = 0; i < _num_boxes; ++i) |
| { |
| // Copy scaled image into output. |
| CLScheduler::get().enqueue(_copy[i]); |
| } |
| CLScheduler::get().sync(); |
| } |
| } // namespace arm_compute |