#pragma once
#include <limits>
#include "../C/detection_output.h"
+#include "../C/detection_output_sort.h"
#include "primitive.hpp"
namespace cldnn
/// @brief Generates a list of detections based on location and confidence predictions by doing non maximum suppression.
/// @details Each row is a 7 dimension vector, which stores: [image_id, label, confidence, xmin, ymin, xmax, ymax].
-/// If number of detections per image is lower than keep_top_k, will write dummy results at the end with image_id=-1.
+/// If number of detections per image is lower than keep_top_k, will write dummy results at the end with image_id=-1.
struct detection_output : public primitive_base<detection_output, CLDNN_PRIMITIVE_DESC(detection_output)>
{
CLDNN_DECLARE_PRIMITIVE(detection_output)
const int32_t input_width = -1,
const int32_t input_height = -1,
const bool decrease_label_id = false,
- const bool clip = false,
+ const bool clip_before_nms = false,
+ const bool clip_after_nms = false,
const padding& output_padding = padding()
)
: primitive_base(id, { input_location, input_confidence, input_prior_box }, output_padding)
, input_width(input_width)
, input_height(input_height)
, decrease_label_id(decrease_label_id)
- , clip(clip)
+ , clip_before_nms(clip_before_nms)
+ , clip_after_nms(clip_after_nms)
{
if (decrease_label_id && background_label_id != 0)
throw std::invalid_argument("Cannot use decrease_label_id and background_label_id parameter simultaneously.");
, input_width(dto->input_width)
, input_height(dto->input_height)
, decrease_label_id(dto->decrease_label_id != 0)
- , clip(dto->clip != 0)
+ , clip_before_nms(dto->clip_before_nms != 0)
+ , clip_after_nms(dto->clip_after_nms != 0)
{
if (decrease_label_id && background_label_id != 0)
throw std::invalid_argument("Cannot use decrease_label_id and background_label_id parameter simultaneously.");
const int32_t input_height;
/// @brief Decrease label id to skip background label equal to 0. Can't be used simultaneously with background_label_id.
const bool decrease_label_id;
- /// @brief Clip decoded boxes
- const bool clip;
+ /// @brief Clip decoded boxes right after decoding
+ const bool clip_before_nms;
+ /// @brief Clip decoded boxes after nms step
+ const bool clip_after_nms;
protected:
void update_dto(dto& dto) const override
dto.input_width = input_width;
dto.input_height = input_height;
dto.decrease_label_id = decrease_label_id;
- dto.clip = clip;
+ dto.clip_before_nms = clip_before_nms;
+ dto.clip_after_nms = clip_after_nms;
+ }
+};
+
+/// @brief Generates a list of detections based on location and confidence predictions by doing non maximum suppression.
+/// @details Each row is a 7 dimension vector, which stores: [image_id, label, confidence, xmin, ymin, xmax, ymax].
+/// If number of detections per image is lower than keep_top_k, will write dummy results at the end with image_id=-1.
+struct detection_output_sort : public primitive_base<detection_output_sort, CLDNN_PRIMITIVE_DESC(detection_output_sort)>
+{
+ CLDNN_DECLARE_PRIMITIVE(detection_output_sort)
+
+ /// @brief Constructs detection output primitive.
+ /// @param id This primitive id.
+ /// @param input_bboxes Input bounding boxes primitive id.
+ /// @param num_images Number of images to be predicted.
+ /// @param num_classes Number of classes to be predicted.
+ /// @param keep_top_k Number of total bounding boxes to be kept per image after NMS step.
+ /// @param share_location If true bounding box are shared among different classes.
+ /// @param top_k Maximum number of results to be kept in NMS.
+ /// @param output_padding Output padding.
+ detection_output_sort(
+ const primitive_id& id,
+ const primitive_id& input_bboxes,
+ const uint32_t num_images,
+ const uint32_t num_classes,
+ const uint32_t keep_top_k,
+ const bool share_location = true,
+ const int top_k = -1,
+ const int background_label_id = -1,
+ const padding& output_padding = padding()
+ )
+ : primitive_base(id, { input_bboxes }, output_padding)
+ , num_images(num_images)
+ , num_classes(num_classes)
+ , keep_top_k(keep_top_k)
+ , share_location(share_location)
+ , top_k(top_k)
+ , background_label_id(background_label_id)
+ {}
+
+ /// @brief Constructs a copy from C API @CLDNN_PRIMITIVE_DESC{detection_output}
+ detection_output_sort(const dto* dto)
+ : primitive_base(dto)
+ , num_images(dto->num_images)
+ , num_classes(dto->num_classes)
+ , keep_top_k(dto->keep_top_k)
+ , share_location(dto->share_location != 0)
+ , top_k(dto->top_k)
+ , background_label_id(dto->background_label_id)
+ {}
+
+ /// @brief Number of classes to be predicted.
+ const uint32_t num_images;
+ /// @brief Number of classes to be predicted.
+ const uint32_t num_classes;
+ /// @brief Number of total bounding boxes to be kept per image after NMS step.
+ const int keep_top_k;
+ /// @brief If true, bounding box are shared among different classes.
+ const bool share_location;
+ /// @brief Maximum number of results to be kept in NMS.
+ const int top_k;
+ /// @brief Background label id (-1 if there is no background class).
+ const int background_label_id;
+
+
+protected:
+ void update_dto(dto& dto) const override
+ {
+ dto.num_classes = num_classes;
+ dto.num_images = num_images;
+ dto.keep_top_k = keep_top_k;
+ dto.share_location = share_location;
+ dto.top_k = top_k;
+ dto.background_label_id = background_label_id;
}
};
/// @}
projects / platform / upstream / dldt.git / blobdiff