diff options
Diffstat (limited to 'src/runtime/CL/functions/CLHOGMultiDetection.cpp')
| -rw-r--r-- | src/runtime/CL/functions/CLHOGMultiDetection.cpp | 274 |
1 files changed, 0 insertions, 274 deletions
diff --git a/src/runtime/CL/functions/CLHOGMultiDetection.cpp b/src/runtime/CL/functions/CLHOGMultiDetection.cpp deleted file mode 100644 index 248f7307e6..0000000000 --- a/src/runtime/CL/functions/CLHOGMultiDetection.cpp +++ /dev/null @@ -1,274 +0,0 @@ -/* - * Copyright (c) 2017-2020 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/CLHOGMultiDetection.h" - -#include "arm_compute/core/CL/OpenCL.h" -#include "arm_compute/core/Error.h" -#include "arm_compute/core/TensorInfo.h" -#include "arm_compute/runtime/CL/CLArray.h" -#include "arm_compute/runtime/CL/CLScheduler.h" -#include "arm_compute/runtime/CL/CLTensor.h" -#include "arm_compute/runtime/Scheduler.h" - -using namespace arm_compute; - -CLHOGMultiDetection::CLHOGMultiDetection(std::shared_ptr<IMemoryManager> memory_manager) // NOLINT - : _memory_group(std::move(memory_manager)), - _gradient_kernel(), - _orient_bin_kernel(), - _block_norm_kernel(), - _hog_detect_kernel(), - _non_maxima_kernel(), - _hog_space(), - _hog_norm_space(), - _detection_windows(), - _mag(), - _phase(), - _non_maxima_suppression(false), - _num_orient_bin_kernel(0), - _num_block_norm_kernel(0), - _num_hog_detect_kernel(0) -{ -} - -void CLHOGMultiDetection::configure(ICLTensor *input, const ICLMultiHOG *multi_hog, ICLDetectionWindowArray *detection_windows, ICLSize2DArray *detection_window_strides, BorderMode border_mode, - uint8_t constant_border_value, float threshold, bool non_maxima_suppression, float min_distance) -{ - configure(CLKernelLibrary::get().get_compile_context(), input, multi_hog, detection_windows, detection_window_strides, border_mode, constant_border_value, threshold, non_maxima_suppression, - min_distance); -} - -void CLHOGMultiDetection::configure(const CLCompileContext &compile_context, ICLTensor *input, const ICLMultiHOG *multi_hog, ICLDetectionWindowArray *detection_windows, - ICLSize2DArray *detection_window_strides, BorderMode border_mode, - uint8_t constant_border_value, float threshold, bool non_maxima_suppression, float min_distance) -{ - ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8); - ARM_COMPUTE_ERROR_ON_INVALID_MULTI_HOG(multi_hog); - ARM_COMPUTE_ERROR_ON(nullptr == detection_windows); - ARM_COMPUTE_ERROR_ON(detection_window_strides->num_values() != multi_hog->num_models()); - - const size_t width = input->info()->dimension(Window::DimX); - const size_t height = input->info()->dimension(Window::DimY); - const TensorShape &shape_img = input->info()->tensor_shape(); - const size_t num_models = multi_hog->num_models(); - PhaseType phase_type = multi_hog->model(0)->info()->phase_type(); - - size_t prev_num_bins = multi_hog->model(0)->info()->num_bins(); - Size2D prev_cell_size = multi_hog->model(0)->info()->cell_size(); - Size2D prev_block_size = multi_hog->model(0)->info()->block_size(); - Size2D prev_block_stride = multi_hog->model(0)->info()->block_stride(); - - /* Check if CLHOGOrientationBinningKernel and CLHOGBlockNormalizationKernel kernels can be skipped for a specific HOG data-object - * - * 1) CLHOGOrientationBinningKernel and CLHOGBlockNormalizationKernel are skipped if the cell size and the number of bins don't change. - * Since "multi_hog" is sorted,it is enough to check the HOG descriptors at level "ith" and level "(i-1)th - * 2) CLHOGBlockNormalizationKernel is skipped if the cell size, the number of bins and block size do not change. - * Since "multi_hog" is sorted,it is enough to check the HOG descriptors at level "ith" and level "(i-1)th - * - * @note Since the orientation binning and block normalization kernels can be skipped, we need to keep track of the input to process for each kernel - * with "input_orient_bin", "input_hog_detect" and "input_block_norm" - */ - std::vector<size_t> input_orient_bin; - std::vector<size_t> input_hog_detect; - std::vector<std::pair<size_t, size_t>> input_block_norm; - - input_orient_bin.push_back(0); - input_hog_detect.push_back(0); - input_block_norm.emplace_back(0, 0); - - for(size_t i = 1; i < num_models; ++i) - { - size_t cur_num_bins = multi_hog->model(i)->info()->num_bins(); - Size2D cur_cell_size = multi_hog->model(i)->info()->cell_size(); - Size2D cur_block_size = multi_hog->model(i)->info()->block_size(); - Size2D cur_block_stride = multi_hog->model(i)->info()->block_stride(); - - if((cur_num_bins != prev_num_bins) || (cur_cell_size.width != prev_cell_size.width) || (cur_cell_size.height != prev_cell_size.height)) - { - prev_num_bins = cur_num_bins; - prev_cell_size = cur_cell_size; - prev_block_size = cur_block_size; - prev_block_stride = cur_block_stride; - - // Compute orientation binning and block normalization kernels. Update input to process - input_orient_bin.push_back(i); - input_block_norm.emplace_back(i, input_orient_bin.size() - 1); - } - else if((cur_block_size.width != prev_block_size.width) || (cur_block_size.height != prev_block_size.height) || (cur_block_stride.width != prev_block_stride.width) - || (cur_block_stride.height != prev_block_stride.height)) - { - prev_block_size = cur_block_size; - prev_block_stride = cur_block_stride; - - // Compute block normalization kernel. Update input to process - input_block_norm.emplace_back(i, input_orient_bin.size() - 1); - } - - // Update input to process for hog detector kernel - input_hog_detect.push_back(input_block_norm.size() - 1); - } - - _detection_windows = detection_windows; - _non_maxima_suppression = non_maxima_suppression; - _num_orient_bin_kernel = input_orient_bin.size(); // Number of CLHOGOrientationBinningKernel kernels to compute - _num_block_norm_kernel = input_block_norm.size(); // Number of CLHOGBlockNormalizationKernel kernels to compute - _num_hog_detect_kernel = input_hog_detect.size(); // Number of CLHOGDetector functions to compute - - _orient_bin_kernel.resize(_num_orient_bin_kernel); - _block_norm_kernel.resize(_num_block_norm_kernel); - _hog_detect_kernel.resize(_num_hog_detect_kernel); - _hog_space.resize(_num_orient_bin_kernel); - _hog_norm_space.resize(_num_block_norm_kernel); - - // Allocate tensors for magnitude and phase - TensorInfo info_mag(shape_img, Format::S16); - _mag.allocator()->init(info_mag); - - TensorInfo info_phase(shape_img, Format::U8); - _phase.allocator()->init(info_phase); - - // Manage intermediate buffers - _memory_group.manage(&_mag); - _memory_group.manage(&_phase); - - // Initialise gradient kernel - _gradient_kernel.configure(compile_context, input, &_mag, &_phase, phase_type, border_mode, constant_border_value); - - // Configure NETensor for the HOG space and orientation binning kernel - for(size_t i = 0; i < _num_orient_bin_kernel; ++i) - { - const size_t idx_multi_hog = input_orient_bin[i]; - - // Get the corresponding cell size and number of bins - const Size2D &cell = multi_hog->model(idx_multi_hog)->info()->cell_size(); - const size_t num_bins = multi_hog->model(idx_multi_hog)->info()->num_bins(); - - // Calculate number of cells along the x and y directions for the hog_space - const size_t num_cells_x = width / cell.width; - const size_t num_cells_y = height / cell.height; - - // TensorShape of hog space - TensorShape shape_hog_space = input->info()->tensor_shape(); - shape_hog_space.set(Window::DimX, num_cells_x); - shape_hog_space.set(Window::DimY, num_cells_y); - - // Allocate HOG space - TensorInfo info_space(shape_hog_space, num_bins, DataType::F32); - _hog_space[i].allocator()->init(info_space); - - // Manage intermediate buffers - _memory_group.manage(&_hog_space[i]); - - // Initialise orientation binning kernel - _orient_bin_kernel[i].configure(compile_context, &_mag, &_phase, &_hog_space[i], multi_hog->model(idx_multi_hog)->info()); - } - - // Allocate intermediate tensors - _mag.allocator()->allocate(); - _phase.allocator()->allocate(); - - // Configure CLTensor for the normalized HOG space and block normalization kernel - for(size_t i = 0; i < _num_block_norm_kernel; ++i) - { - const size_t idx_multi_hog = input_block_norm[i].first; - const size_t idx_orient_bin = input_block_norm[i].second; - - // Allocate normalized HOG space - TensorInfo tensor_info(*(multi_hog->model(idx_multi_hog)->info()), width, height); - _hog_norm_space[i].allocator()->init(tensor_info); - - // Manage intermediate buffers - _memory_group.manage(&_hog_norm_space[i]); - - // Initialize block normalization kernel - _block_norm_kernel[i].configure(compile_context, &_hog_space[idx_orient_bin], &_hog_norm_space[i], multi_hog->model(idx_multi_hog)->info()); - } - - // Allocate intermediate tensors - for(size_t i = 0; i < _num_orient_bin_kernel; ++i) - { - _hog_space[i].allocator()->allocate(); - } - - detection_window_strides->map(CLScheduler::get().queue(), true); - - // Configure HOG detector kernel - for(size_t i = 0; i < _num_hog_detect_kernel; ++i) - { - const size_t idx_block_norm = input_hog_detect[i]; - - _hog_detect_kernel[i].configure(compile_context, &_hog_norm_space[idx_block_norm], multi_hog->cl_model(i), detection_windows, detection_window_strides->at(i), threshold, i); - } - - detection_window_strides->unmap(CLScheduler::get().queue()); - - // Configure non maxima suppression kernel - _non_maxima_kernel.configure(_detection_windows, min_distance); - - // Allocate intermediate tensors - for(size_t i = 0; i < _num_block_norm_kernel; ++i) - { - _hog_norm_space[i].allocator()->allocate(); - } -} - -void CLHOGMultiDetection::run() -{ - ARM_COMPUTE_ERROR_ON_MSG(_detection_windows == nullptr, "Unconfigured function"); - - MemoryGroupResourceScope scope_mg(_memory_group); - - // Reset detection window - _detection_windows->clear(); - - // Run gradient - _gradient_kernel.run(); - - // Run orientation binning kernel - for(size_t i = 0; i < _num_orient_bin_kernel; ++i) - { - CLScheduler::get().enqueue(_orient_bin_kernel[i], false); - } - - // Run block normalization kernel - for(size_t i = 0; i < _num_block_norm_kernel; ++i) - { - CLScheduler::get().enqueue(_block_norm_kernel[i], false); - } - - // Run HOG detector kernel - for(size_t i = 0; i < _num_hog_detect_kernel; ++i) - { - _hog_detect_kernel[i].run(); - } - - // Run non-maxima suppression kernel if enabled - if(_non_maxima_suppression) - { - // Map detection windows array before computing non maxima suppression - _detection_windows->map(CLScheduler::get().queue(), true); - Scheduler::get().schedule(&_non_maxima_kernel, Window::DimY); - _detection_windows->unmap(CLScheduler::get().queue()); - } -} |