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author | John Richardson <john.richardson@arm.com> | 2018-01-09 11:17:00 +0000 |
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committer | Anthony Barbier <anthony.barbier@arm.com> | 2018-11-02 16:49:16 +0000 |
commit | 684cb0f29d23fbe418e5e5347234abf9eccef363 (patch) | |
tree | 60731b6bb63b1a0dc997107d3bd55d8b4b82626b /tests/validation/reference/HOGDetector.cpp | |
parent | 7da29b6b12ff319ed2b6e2c46588dfa1991556fb (diff) | |
download | ComputeLibrary-684cb0f29d23fbe418e5e5347234abf9eccef363.tar.gz |
COMPMID-596: Port HOGDetector to new validation
Change-Id: I73231fc71c5166268e6c909b7930b7e034f3794e
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/118876
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Tested-by: Jenkins <bsgcomp@arm.com>
Diffstat (limited to 'tests/validation/reference/HOGDetector.cpp')
-rw-r--r-- | tests/validation/reference/HOGDetector.cpp | 132 |
1 files changed, 132 insertions, 0 deletions
diff --git a/tests/validation/reference/HOGDetector.cpp b/tests/validation/reference/HOGDetector.cpp new file mode 100644 index 0000000000..5a5ae3700d --- /dev/null +++ b/tests/validation/reference/HOGDetector.cpp @@ -0,0 +1,132 @@ +/* + * Copyright (c) 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 "HOGDetector.h" + +namespace arm_compute +{ +namespace test +{ +namespace validation +{ +namespace reference +{ +namespace +{ +/** Computes the number of detection windows to iterate over in the feature vector. */ +Size2D num_detection_windows(const TensorShape &shape, const Size2D &window_step, const HOGInfo &hog_info) +{ + const size_t num_block_strides_width = hog_info.detection_window_size().width / hog_info.block_stride().width; + const size_t num_block_strides_height = hog_info.detection_window_size().height / hog_info.block_stride().height; + + return Size2D(floor_to_multiple(shape.x() - num_block_strides_width, window_step.width) + window_step.width, + floor_to_multiple(shape.y() - num_block_strides_height, window_step.height) + window_step.height); +} +} // namespace + +template <typename T> +std::vector<DetectionWindow> hog_detector(const SimpleTensor<T> &src, const std::vector<T> &descriptor, unsigned int max_num_detection_windows, + const HOGInfo &hog_info, const Size2D &detection_window_stride, float threshold, uint16_t idx_class) +{ + ARM_COMPUTE_ERROR_ON_MSG((detection_window_stride.width % hog_info.block_stride().width != 0), + "Detection window stride width must be multiple of block stride width"); + ARM_COMPUTE_ERROR_ON_MSG((detection_window_stride.height % hog_info.block_stride().height != 0), + "Detection window stride height must be multiple of block stride height"); + + // Create vector for identifying each detection window + std::vector<DetectionWindow> windows; + + // Calculate detection window step + const Size2D window_step(detection_window_stride.width / hog_info.block_stride().width, + detection_window_stride.height / hog_info.block_stride().height); + + // Calculate number of detection windows + const Size2D num_windows = num_detection_windows(src.shape(), window_step, hog_info); + + // Calculate detection window and row offsets in feature vector + const size_t src_offset_x = window_step.width * hog_info.num_bins() * hog_info.num_cells_per_block().area(); + const size_t src_offset_y = window_step.height * hog_info.num_bins() * hog_info.num_cells_per_block().area() * src.shape().x(); + const size_t src_offset_row = src.num_channels() * src.shape().x(); + + // Calculate detection window attributes + const Size2D num_block_positions_per_detection_window = hog_info.num_block_positions_per_image(hog_info.detection_window_size()); + const unsigned int num_bins_per_descriptor_x = num_block_positions_per_detection_window.width * src.num_channels(); + const unsigned int num_blocks_per_descriptor_y = num_block_positions_per_detection_window.height; + + ARM_COMPUTE_ERROR_ON((num_bins_per_descriptor_x * num_blocks_per_descriptor_y + 1) != hog_info.descriptor_size()); + + size_t win_id = 0; + + // Traverse feature vector in detection window steps + for(auto win_y = 0u, offset_y = 0u; win_y < num_windows.height; win_y += window_step.height, offset_y += src_offset_y) + { + for(auto win_x = 0u, offset_x = 0u; win_x < num_windows.width; win_x += window_step.width, offset_x += src_offset_x) + { + // Reset the score + float score = 0.0f; + + // Traverse detection window + for(auto y = 0u, offset_row = 0u; y < num_blocks_per_descriptor_y; ++y, offset_row += src_offset_row) + { + const int bin_offset = y * num_bins_per_descriptor_x; + + for(auto x = 0u; x < num_bins_per_descriptor_x; ++x) + { + // Compute Linear SVM + const float a = src[x + offset_x + offset_y + offset_row]; + const float b = descriptor[x + bin_offset]; + score += a * b; + } + } + + // Add the bias. The bias is located at the position (descriptor_size() - 1) + score += descriptor[num_bins_per_descriptor_x * num_blocks_per_descriptor_y]; + + if(score > threshold) + { + DetectionWindow window; + + if(win_id++ < max_num_detection_windows) + { + window.x = win_x * hog_info.block_stride().width; + window.y = win_y * hog_info.block_stride().height; + window.width = hog_info.detection_window_size().width; + window.height = hog_info.detection_window_size().height; + window.idx_class = idx_class; + window.score = score; + + windows.push_back(window); + } + } + } + } + + return windows; +} + +template std::vector<DetectionWindow> hog_detector(const SimpleTensor<float> &src, const std::vector<float> &descriptor, unsigned int max_num_detection_windows, + const HOGInfo &hog_info, const Size2D &detection_window_stride, float threshold, uint16_t idx_class); +} // namespace reference +} // namespace validation +} // namespace test +} // namespace arm_compute |