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fix picodet cpp infer (PaddlePaddle#5061)
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* fix picodet cpp infer
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@yghstill
yghstill authored Jan 5, 2022
1 parent 2711a60 commit b3a7649
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Showing 7 changed files with 863 additions and 974 deletions.
27 changes: 13 additions & 14 deletions deploy/cpp/include/picodet_postprocess.h
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Original file line number Diff line number Diff line change
Expand Up @@ -14,25 +14,24 @@

#pragma once

#include <string>
#include <vector>
#include <memory>
#include <utility>
#include <cmath>
#include <ctime>
#include <memory>
#include <numeric>
#include <string>
#include <utility>
#include <vector>

#include "include/utils.h"

namespace PaddleDetection {

void PicoDetPostProcess(std::vector<PaddleDetection::ObjectResult>* results,
std::vector<const float *> outs,
std::vector<int> fpn_stride,
std::vector<float> im_shape,
std::vector<float> scale_factor,
float score_threshold = 0.3,
float nms_threshold = 0.5,
int num_class = 80,
int reg_max = 7);
void PicoDetPostProcess(std::vector<PaddleDetection::ObjectResult> *results,
std::vector<const float *> outs,
std::vector<int> fpn_stride,
std::vector<float> im_shape,
std::vector<float> scale_factor,
float score_threshold = 0.3, float nms_threshold = 0.5,
int num_class = 80, int reg_max = 7);

} // namespace PaddleDetection
} // namespace PaddleDetection
126 changes: 62 additions & 64 deletions deploy/cpp/src/picodet_postprocess.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -20,79 +20,76 @@
namespace PaddleDetection {

float fast_exp(float x) {
union {
uint32_t i;
float f;
} v{};
v.i = (1 << 23) * (1.4426950409 * x + 126.93490512f);
return v.f;
union {
uint32_t i;
float f;
} v{};
v.i = (1 << 23) * (1.4426950409 * x + 126.93490512f);
return v.f;
}

template <typename _Tp>
int activation_function_softmax(const _Tp *src, _Tp *dst, int length) {
const _Tp alpha = *std::max_element(src, src + length);
_Tp denominator{0};
const _Tp alpha = *std::max_element(src, src + length);
_Tp denominator{0};

for (int i = 0; i < length; ++i) {
dst[i] = fast_exp(src[i] - alpha);
denominator += dst[i];
}
for (int i = 0; i < length; ++i) {
dst[i] = fast_exp(src[i] - alpha);
denominator += dst[i];
}

for (int i = 0; i < length; ++i) {
dst[i] /= denominator;
}
for (int i = 0; i < length; ++i) {
dst[i] /= denominator;
}

return 0;
return 0;
}

// PicoDet decode
PaddleDetection::ObjectResult disPred2Bbox(const float *&dfl_det, int label, float score,
int x, int y, int stride, std::vector<float> im_shape,
int reg_max) {
float ct_x = (x + 0.5) * stride;
float ct_y = (y + 0.5) * stride;
std::vector<float> dis_pred;
dis_pred.resize(4);
for (int i = 0; i < 4; i++) {
float dis = 0;
float* dis_after_sm = new float[reg_max + 1];
activation_function_softmax(dfl_det + i * (reg_max + 1), dis_after_sm, reg_max + 1);
for (int j = 0; j < reg_max + 1; j++) {
dis += j * dis_after_sm[j];
}
dis *= stride;
dis_pred[i] = dis;
delete[] dis_after_sm;
PaddleDetection::ObjectResult
disPred2Bbox(const float *&dfl_det, int label, float score, int x, int y,
int stride, std::vector<float> im_shape, int reg_max) {
float ct_x = (x + 0.5) * stride;
float ct_y = (y + 0.5) * stride;
std::vector<float> dis_pred;
dis_pred.resize(4);
for (int i = 0; i < 4; i++) {
float dis = 0;
float *dis_after_sm = new float[reg_max + 1];
activation_function_softmax(dfl_det + i * (reg_max + 1), dis_after_sm,
reg_max + 1);
for (int j = 0; j < reg_max + 1; j++) {
dis += j * dis_after_sm[j];
}
int xmin = (int)(std::max)(ct_x - dis_pred[0], .0f);
int ymin = (int)(std::max)(ct_y - dis_pred[1], .0f);
int xmax = (int)(std::min)(ct_x + dis_pred[2], (float)im_shape[0]);
int ymax = (int)(std::min)(ct_y + dis_pred[3], (float)im_shape[1]);
dis *= stride;
dis_pred[i] = dis;
delete[] dis_after_sm;
}
int xmin = (int)(std::max)(ct_x - dis_pred[0], .0f);
int ymin = (int)(std::max)(ct_y - dis_pred[1], .0f);
int xmax = (int)(std::min)(ct_x + dis_pred[2], (float)im_shape[0]);
int ymax = (int)(std::min)(ct_y + dis_pred[3], (float)im_shape[1]);

PaddleDetection::ObjectResult result_item;
result_item.rect = {xmin, ymin, xmax, ymax};
result_item.class_id = label;
result_item.confidence = score;
PaddleDetection::ObjectResult result_item;
result_item.rect = {xmin, ymin, xmax, ymax};
result_item.class_id = label;
result_item.confidence = score;

return result_item;
return result_item;
}


void PicoDetPostProcess(std::vector<PaddleDetection::ObjectResult>* results,
std::vector<const float *> outs,
std::vector<int> fpn_stride,
std::vector<float> im_shape,
std::vector<float> scale_factor,
float score_threshold,
float nms_threshold,
int num_class,
int reg_max) {
void PicoDetPostProcess(std::vector<PaddleDetection::ObjectResult> *results,
std::vector<const float *> outs,
std::vector<int> fpn_stride,
std::vector<float> im_shape,
std::vector<float> scale_factor, float score_threshold,
float nms_threshold, int num_class, int reg_max) {
std::vector<std::vector<PaddleDetection::ObjectResult>> bbox_results;
bbox_results.resize(num_class);
int in_h = im_shape[0], in_w = im_shape[1];
for (int i = 0; i < fpn_stride.size(); ++i) {
int feature_h = in_h / fpn_stride[i];
int feature_w = in_w / fpn_stride[i];
int feature_h = std::ceil((float)in_h / fpn_stride[i]);
int feature_w = std::ceil((float)in_w / fpn_stride[i]);
for (int idx = 0; idx < feature_h * feature_w; idx++) {
const float *scores = outs[i] + (idx * num_class);

Expand All @@ -107,24 +104,25 @@ void PicoDetPostProcess(std::vector<PaddleDetection::ObjectResult>* results,
}
}
if (score > score_threshold) {
const float *bbox_pred = outs[i + fpn_stride.size()]
+ (idx * 4 * (reg_max + 1));
bbox_results[cur_label].push_back(disPred2Bbox(bbox_pred,
cur_label, score, col, row, fpn_stride[i], im_shape, reg_max));
const float *bbox_pred =
outs[i + fpn_stride.size()] + (idx * 4 * (reg_max + 1));
bbox_results[cur_label].push_back(
disPred2Bbox(bbox_pred, cur_label, score, col, row, fpn_stride[i],
im_shape, reg_max));
}
}
}
for (int i = 0; i < (int)bbox_results.size(); i++) {
PaddleDetection::nms(bbox_results[i], nms_threshold);

for (auto box : bbox_results[i]) {
box.rect[0] = box.rect[0] / scale_factor[1];
box.rect[2] = box.rect[2] / scale_factor[1];
box.rect[1] = box.rect[1] / scale_factor[0];
box.rect[3] = box.rect[3] / scale_factor[0];
results->push_back(box);
box.rect[0] = box.rect[0] / scale_factor[1];
box.rect[2] = box.rect[2] / scale_factor[1];
box.rect[1] = box.rect[1] / scale_factor[0];
box.rect[3] = box.rect[3] / scale_factor[0];
results->push_back(box);
}
}
}

} // namespace PaddleDetection
} // namespace PaddleDetection
126 changes: 62 additions & 64 deletions deploy/lite/src/picodet_postprocess.cc
View file
Original file line number Diff line number Diff line change
Expand Up @@ -20,79 +20,76 @@
namespace PaddleDetection {

float fast_exp(float x) {
union {
uint32_t i;
float f;
} v{};
v.i = (1 << 23) * (1.4426950409 * x + 126.93490512f);
return v.f;
union {
uint32_t i;
float f;
} v{};
v.i = (1 << 23) * (1.4426950409 * x + 126.93490512f);
return v.f;
}

template <typename _Tp>
int activation_function_softmax(const _Tp *src, _Tp *dst, int length) {
const _Tp alpha = *std::max_element(src, src + length);
_Tp denominator{0};
const _Tp alpha = *std::max_element(src, src + length);
_Tp denominator{0};

for (int i = 0; i < length; ++i) {
dst[i] = fast_exp(src[i] - alpha);
denominator += dst[i];
}
for (int i = 0; i < length; ++i) {
dst[i] = fast_exp(src[i] - alpha);
denominator += dst[i];
}

for (int i = 0; i < length; ++i) {
dst[i] /= denominator;
}
for (int i = 0; i < length; ++i) {
dst[i] /= denominator;
}

return 0;
return 0;
}

// PicoDet decode
PaddleDetection::ObjectResult disPred2Bbox(const float *&dfl_det, int label, float score,
int x, int y, int stride, std::vector<float> im_shape,
int reg_max) {
float ct_x = (x + 0.5) * stride;
float ct_y = (y + 0.5) * stride;
std::vector<float> dis_pred;
dis_pred.resize(4);
for (int i = 0; i < 4; i++) {
float dis = 0;
float* dis_after_sm = new float[reg_max + 1];
activation_function_softmax(dfl_det + i * (reg_max + 1), dis_after_sm, reg_max + 1);
for (int j = 0; j < reg_max + 1; j++) {
dis += j * dis_after_sm[j];
}
dis *= stride;
dis_pred[i] = dis;
delete[] dis_after_sm;
PaddleDetection::ObjectResult
disPred2Bbox(const float *&dfl_det, int label, float score, int x, int y,
int stride, std::vector<float> im_shape, int reg_max) {
float ct_x = (x + 0.5) * stride;
float ct_y = (y + 0.5) * stride;
std::vector<float> dis_pred;
dis_pred.resize(4);
for (int i = 0; i < 4; i++) {
float dis = 0;
float *dis_after_sm = new float[reg_max + 1];
activation_function_softmax(dfl_det + i * (reg_max + 1), dis_after_sm,
reg_max + 1);
for (int j = 0; j < reg_max + 1; j++) {
dis += j * dis_after_sm[j];
}
int xmin = (int)(std::max)(ct_x - dis_pred[0], .0f);
int ymin = (int)(std::max)(ct_y - dis_pred[1], .0f);
int xmax = (int)(std::min)(ct_x + dis_pred[2], (float)im_shape[0]);
int ymax = (int)(std::min)(ct_y + dis_pred[3], (float)im_shape[1]);
dis *= stride;
dis_pred[i] = dis;
delete[] dis_after_sm;
}
int xmin = (int)(std::max)(ct_x - dis_pred[0], .0f);
int ymin = (int)(std::max)(ct_y - dis_pred[1], .0f);
int xmax = (int)(std::min)(ct_x + dis_pred[2], (float)im_shape[0]);
int ymax = (int)(std::min)(ct_y + dis_pred[3], (float)im_shape[1]);

PaddleDetection::ObjectResult result_item;
result_item.rect = {xmin, ymin, xmax, ymax};
result_item.class_id = label;
result_item.confidence = score;
PaddleDetection::ObjectResult result_item;
result_item.rect = {xmin, ymin, xmax, ymax};
result_item.class_id = label;
result_item.confidence = score;

return result_item;
return result_item;
}


void PicoDetPostProcess(std::vector<PaddleDetection::ObjectResult>* results,
std::vector<const float *> outs,
std::vector<int> fpn_stride,
std::vector<float> im_shape,
std::vector<float> scale_factor,
float score_threshold,
float nms_threshold,
int num_class,
int reg_max) {
void PicoDetPostProcess(std::vector<PaddleDetection::ObjectResult> *results,
std::vector<const float *> outs,
std::vector<int> fpn_stride,
std::vector<float> im_shape,
std::vector<float> scale_factor, float score_threshold,
float nms_threshold, int num_class, int reg_max) {
std::vector<std::vector<PaddleDetection::ObjectResult>> bbox_results;
bbox_results.resize(num_class);
int in_h = im_shape[0], in_w = im_shape[1];
for (int i = 0; i < fpn_stride.size(); ++i) {
int feature_h = in_h / fpn_stride[i];
int feature_w = in_w / fpn_stride[i];
int feature_h = ceil((float)in_h / fpn_stride[i]);
int feature_w = ceil((float)in_w / fpn_stride[i]);
for (int idx = 0; idx < feature_h * feature_w; idx++) {
const float *scores = outs[i] + (idx * num_class);

Expand All @@ -107,24 +104,25 @@ void PicoDetPostProcess(std::vector<PaddleDetection::ObjectResult>* results,
}
}
if (score > score_threshold) {
const float *bbox_pred = outs[i + fpn_stride.size()]
+ (idx * 4 * (reg_max + 1));
bbox_results[cur_label].push_back(disPred2Bbox(bbox_pred,
cur_label, score, col, row, fpn_stride[i], im_shape, reg_max));
const float *bbox_pred =
outs[i + fpn_stride.size()] + (idx * 4 * (reg_max + 1));
bbox_results[cur_label].push_back(
disPred2Bbox(bbox_pred, cur_label, score, col, row, fpn_stride[i],
im_shape, reg_max));
}
}
}
for (int i = 0; i < (int)bbox_results.size(); i++) {
PaddleDetection::nms(bbox_results[i], nms_threshold);

for (auto box : bbox_results[i]) {
box.rect[0] = box.rect[0] / scale_factor[1];
box.rect[2] = box.rect[2] / scale_factor[1];
box.rect[1] = box.rect[1] / scale_factor[0];
box.rect[3] = box.rect[3] / scale_factor[0];
results->push_back(box);
box.rect[0] = box.rect[0] / scale_factor[1];
box.rect[2] = box.rect[2] / scale_factor[1];
box.rect[1] = box.rect[1] / scale_factor[0];
box.rect[3] = box.rect[3] / scale_factor[0];
results->push_back(box);
}
}
}

} // namespace PaddleDetection
} // namespace PaddleDetection
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