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common.cpp
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common.cpp
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#include "common.h"
std::string locateFile(const std::string& input, const std::vector<std::string> & directories)
{
std::string file;
const int MAX_DEPTH{10};
bool found{false};
for (auto &dir : directories)
{
file = dir + input;
std::cout << file << std::endl;
for (int i = 0; i < MAX_DEPTH && !found; i++)
{
std::ifstream checkFile(file);
found = checkFile.is_open();
if (found) break;
file = "../" + file;
}
if (found) break;
file.clear();
}
std::cout << file << std::endl;
assert(!file.empty() && "Could not find a file due to it not existing in the data directory.");
return file;
}
void readPGMFile(const std::string& fileName, uint8_t *buffer, int inH, int inW)
{
std::ifstream infile(fileName, std::ifstream::binary);
assert(infile.is_open() && "Attempting to read from a file that is not open.");
std::string magic, h, w, max;
infile >> magic >> h >> w >> max;
infile.seekg(1, infile.cur);
infile.read(reinterpret_cast<char*>(buffer), inH*inW);
}
/*********************************/
/* Updated date: 2018.3.7
/*This is my own implementation of the detectout layer code, because I met a mistake with the detectout api of
/*tensorrt3.0 a few months ago. You can use the detectout api of tensorrt3.0 correctly by adding an extra output
/*in the deploy prototxt file. Please refer to my deploy prototxt.
/********************************/
// Retrieve all location predictions.
void GetLocPredictions(const float* loc_data,
const int num_preds_per_class, const int num_loc_classes,
std::vector<std::vector<float> >* loc_preds) {
for (int p = 0; p < num_preds_per_class; ++p) {
int start_idx = p * num_loc_classes * 4;
vector<float> labelbbox;
for (int c = 0; c < num_loc_classes; ++c) {
labelbbox.push_back(loc_data[start_idx + c * 4]);
labelbbox.push_back(loc_data[start_idx + c * 4 + 1]);
labelbbox.push_back(loc_data[start_idx + c * 4 + 2]);
labelbbox.push_back(loc_data[start_idx + c * 4 + 3]);
loc_preds->push_back(labelbbox);
}
}
}
// Retrieve all confidences.
void GetConfidenceScores(const float* conf_data,
const int num_preds_per_class, const int num_classes,
vector<vector<float> >* conf_preds) {
for (int p = 0; p < num_preds_per_class; ++p) {
int start_idx = p * num_classes;
vector<float> conf_classes;
for (int c = 0; c < num_classes; ++c) {
conf_classes.push_back(conf_data[start_idx + c]);
}
conf_preds->push_back(conf_classes);
}
}
// Retrieve all prior bboxes. bboxes and variances
void GetPriorBBoxes(const float* prior_data, const int num_priors,
vector<vector<float> >* prior_bboxes,
vector<vector<float> >* prior_variances) {
for (int i = 0; i < num_priors; ++i) {
int start_idx = i * 4;
vector<float> prior_bbox;
prior_bbox.push_back(prior_data[start_idx]);
prior_bbox.push_back(prior_data[start_idx + 1]);
prior_bbox.push_back(prior_data[start_idx + 2]);
prior_bbox.push_back(prior_data[start_idx + 3]);
prior_bboxes->push_back(prior_bbox);
}
for (int i = 0; i < num_priors; ++i) {
int start_idx = (num_priors + i) * 4;
vector<float> prior_variance;
vector<float> var;
for (int j = 0; j < 4; ++j) {
prior_variance.push_back(prior_data[start_idx + j]);
}
prior_variances->push_back(prior_variance);
}
}
/* code_type: 0 = CORNER; 1 = CENTER_SIZE; 2 = CORNER_SIZE
*
*/
void DecodeBBox(
const vector<float>& prior_bbox, const vector<float>& prior_variance,
const int code_type, const bool variance_encoded_in_target,
const bool clip_bbox, const vector<float>& bbox,
vector<float>* decode_bbox) {
if (0 == code_type) {
if (variance_encoded_in_target) {
// variance is encoded in target, we simply need to add the offset
// predictions.
decode_bbox->push_back(prior_bbox[0] + bbox[0]);
decode_bbox->push_back(prior_bbox[1] + bbox[1]);
decode_bbox->push_back(prior_bbox[2] + bbox[2]);
decode_bbox->push_back(prior_bbox[3] + bbox[3]);
} else {
// variance is encoded in bbox, we need to scale the offset accordingly.
decode_bbox->push_back(
prior_bbox[0]+ prior_variance[0] * bbox[0]);
decode_bbox->push_back(
prior_bbox[1] + prior_variance[1] * bbox[1]);
decode_bbox->push_back(
prior_bbox[2] + prior_variance[2] * bbox[2]);
decode_bbox->push_back(
prior_bbox[3] + prior_variance[3] * bbox[3]);
}
} else if (1 == code_type) {
float prior_width = prior_bbox[2] - prior_bbox[0];
//CHECK_GT(prior_width, 0);
float prior_height = prior_bbox[3] - prior_bbox[1];
//CHECK_GT(prior_height, 0);
float prior_center_x = (prior_bbox[0] + prior_bbox[2]) / 2.;
float prior_center_y = (prior_bbox[1] + prior_bbox[3]) / 2.;
float decode_bbox_center_x, decode_bbox_center_y;
float decode_bbox_width, decode_bbox_height;
if (variance_encoded_in_target) {
// variance is encoded in target, we simply need to retore the offset
// predictions.
decode_bbox_center_x = bbox[0] * prior_width + prior_center_x;
decode_bbox_center_y = bbox[1] * prior_height + prior_center_y;
decode_bbox_width = exp(bbox[2]) * prior_width;
decode_bbox_height = exp(bbox[3]) * prior_height;
} else {
// variance is encoded in bbox, we need to scale the offset accordingly.
decode_bbox_center_x =
prior_variance[0] * bbox[0] * prior_width + prior_center_x;
decode_bbox_center_y =
prior_variance[1] * bbox[1] * prior_height + prior_center_y;
decode_bbox_width =
exp(prior_variance[2] * bbox[2]) * prior_width;
decode_bbox_height =
exp(prior_variance[3] * bbox[3]) * prior_height;
}
decode_bbox->push_back(decode_bbox_center_x - decode_bbox_width / 2.);
decode_bbox->push_back(decode_bbox_center_y - decode_bbox_height / 2.);
decode_bbox->push_back(decode_bbox_center_x + decode_bbox_width / 2.);
decode_bbox->push_back(decode_bbox_center_y + decode_bbox_height / 2.);
} else if (2 == code_type) {
float prior_width = prior_bbox[2] - prior_bbox[0];
//CHECK_GT(prior_width, 0);
float prior_height = prior_bbox[3] - prior_bbox[1];
//CHECK_GT(prior_height, 0);
if (variance_encoded_in_target) {
// variance is encoded in target, we simply need to add the offset
// predictions.
decode_bbox->push_back(prior_bbox[0] + bbox[0] * prior_width);
decode_bbox->push_back(prior_bbox[1] + bbox[1] * prior_height);
decode_bbox->push_back(prior_bbox[2] + bbox[2] * prior_width);
decode_bbox->push_back(prior_bbox[3] + bbox[3] * prior_height);
} else {
// variance is encoded in bbox, we need to scale the offset accordingly.
decode_bbox->push_back(
prior_bbox[0] + prior_variance[0] * bbox[0] * prior_width);
decode_bbox->push_back(
prior_bbox[1] + prior_variance[1] * bbox[1] * prior_height);
decode_bbox->push_back(
prior_bbox[2] + prior_variance[2] * bbox[2] * prior_width);
decode_bbox->push_back(
prior_bbox[3] + prior_variance[3] * bbox[3] * prior_height);
}
} else {
std::cout<< "Unknown LocLossType."<<std::endl;
}
//clip_bbox = false, 所以没实现
/*if (clip_bbox) {
ClipBBox(*decode_bbox, decode_bbox);
}*/
}
void DecodeBBoxes(
const vector<vector<float> >& prior_bboxes,
const vector<vector<float> >& prior_variances,
const int code_type, const bool variance_encoded_in_target,
const bool clip_bbox, const vector<vector<float> >& bboxes,
vector<vector<float> >* decode_bboxes) {
//CHECK_EQ(prior_bboxes.size(), prior_variances.size());
//CHECK_EQ(prior_bboxes.size(), bboxes.size());
int num_bboxes = prior_bboxes.size();
for (int i = 0; i < num_bboxes; ++i) {
vector<float> decode_bbox;
DecodeBBox(prior_bboxes[i], prior_variances[i], code_type,
variance_encoded_in_target, clip_bbox, bboxes[i], &decode_bbox);
decode_bboxes->push_back(decode_bbox);
}
}
//
void ConfData(const float* data, const int num_classes, const int num_prior, float* new_data) {
int idx = 0;
for (int c = 0; c < num_classes; ++c) {
for (int p = 0; p < num_prior; ++p) {
new_data[idx] = data[p*num_classes + c];
idx++;
}
}
//softmax
for (int p = 0; p < num_prior; ++p) {
int sum = 0;
float _max = new_data[p];//new_data[0*num_prior + p]
for (int c = 1; c < num_classes; ++c) {
_max = std::max(_max, new_data[c*num_prior + p]);
}
for (int c = 0; c < num_classes; ++c) {
sum += exp(new_data[c*num_prior + p]-_max);
}
for (int j = 0; j < num_classes; ++j) {
new_data[j*num_prior + p] = exp(new_data[j*num_prior + p]-_max)/sum;
}
}
}
template <typename Dtype>
void DecodeBBoxes_2(const Dtype* loc_data, const Dtype* prior_data,
const int code_type, const bool variance_encoded_in_target,
const int num_priors, const bool share_location,
const int num_loc_classes, const int background_label_id,
const bool clip_bbox, Dtype* bbox_data) {
if(code_type == 0){
for(int p = 0; p < num_priors; p++) {
if (variance_encoded_in_target) {
for (int i = 0; i < 4; i++) {
bbox_data[4 * p + i] = prior_data[4 * p + i] + loc_data[4 * p + i];
}
} else {
for (int i = 0; i < 4; i++) {
bbox_data[4 * p + i] = prior_data[4 * p + i] + prior_data[4 * num_priors + 4 * p + i] + loc_data[4 * p + i];
}
}
}
}else if(code_type == 1){
for(int p = 0; p < num_priors; p++) {
float prior_width = prior_data[4 * p + 2] - prior_data[4 * p + 0];
float prior_height = prior_data[4 * p + 3] - prior_data[4 * p + 1];
float prior_center_x = (prior_data[4 * p + 0] + prior_data[4 * p + 2]) / 2.;
float prior_center_y = (prior_data[4 * p + 1] + prior_data[4 * p + 3]) / 2.;
float decode_bbox_center_x, decode_bbox_center_y;
float decode_bbox_width, decode_bbox_height;;
if (variance_encoded_in_target) {
decode_bbox_center_x = loc_data[4 * p + 0] * prior_width + prior_center_x;
decode_bbox_center_y = loc_data[4 * p + 1] * prior_height + prior_center_y;
decode_bbox_width = exp(loc_data[4 * p + 2]) * prior_width;
decode_bbox_height = exp(loc_data[4 * p + 3]) * prior_height;
}else{
decode_bbox_center_x = prior_data[4 * num_priors + 4 * p + 0] * loc_data[4 * p + 0] * prior_width + prior_center_x;
decode_bbox_center_y = prior_data[4 * num_priors + 4 * p + 1] * loc_data[4 * p + 1] * prior_height + prior_center_y;
decode_bbox_width = exp(prior_data[4 * num_priors + 4 * p + 2] * loc_data[4 * p + 2]) * prior_width;
decode_bbox_height = exp(prior_data[4 * num_priors + 4 * p + 3] * loc_data[4 * p + 3]) * prior_height;
}
bbox_data[4 * p + 0] = (decode_bbox_center_x - decode_bbox_width / 2.);
bbox_data[4 * p + 1] = (decode_bbox_center_y - decode_bbox_height / 2.);
bbox_data[4 * p + 2] = (decode_bbox_center_x + decode_bbox_width / 2.);
bbox_data[4 * p + 3] = (decode_bbox_center_y + decode_bbox_height / 2.);
}
}else if(code_type == 2){
for(int p = 0; p < num_priors; p++) {
float prior_width = prior_data[4 * p + 2] - prior_data[4 * p + 0];
float prior_height = prior_data[4 * p + 3] - prior_data[4 * p + 1];
if (variance_encoded_in_target) {
bbox_data[4 * p + 0] = prior_data[4 * p + 0] + loc_data[4 * p + 0] * prior_width;
bbox_data[4 * p + 1] = prior_data[4 * p + 1] + loc_data[4 * p + 1] * prior_height;
bbox_data[4 * p + 2] = exp(prior_data[4 * p + 2]) + loc_data[4 * p + 2] * prior_width;
bbox_data[4 * p + 3] = exp(prior_data[4 * p + 3]) + loc_data[4 * p + 3] * prior_height;
}else {
bbox_data[4 * p + 0] = prior_data[4 * p + 0] +
prior_data[4 * num_priors + 4 * p + 0] * loc_data[4 * p + 0] * prior_width;
bbox_data[4 * p + 1] = prior_data[4 * p + 1] +
prior_data[4 * num_priors + 4 * p + 1] * loc_data[4 * p + 1] * prior_height;
bbox_data[4 * p + 2] = prior_data[4 * p + 2] +
prior_data[4 * num_priors + 4 * p + 2] * loc_data[4 * p + 2] * prior_width;
bbox_data[4 * p + 3] = prior_data[4 * p + 3] +
prior_data[4 * num_priors + 4 * p + 3] * loc_data[4 * p + 3] * prior_height;
}
}
}else{
std::cout << "Unknown LocLossType." << std::endl;
}
}
template <typename Dtype>
Dtype BBoxSize(const Dtype* bbox, const bool normalized = true) {
if (bbox[2] < bbox[0] || bbox[3] < bbox[1]) {
// If bbox is invalid (e.g. xmax < xmin or ymax < ymin), return 0.
return Dtype(0.);
} else {
const Dtype width = bbox[2] - bbox[0];
const Dtype height = bbox[3] - bbox[1];
if (normalized) {
return width * height;
} else {
// If bbox is not within range [0, 1].
return (width + 1) * (height + 1);
}
}
}
template <typename Dtype>
Dtype JaccardOverlap(const Dtype* bbox1, const Dtype* bbox2) {
if (bbox2[0] > bbox1[2] || bbox2[2] < bbox1[0] ||
bbox2[1] > bbox1[3] || bbox2[3] < bbox1[1]) {
return Dtype(0.);
} else {
const Dtype inter_xmin = std::max(bbox1[0], bbox2[0]);
const Dtype inter_ymin = std::max(bbox1[1], bbox2[1]);
const Dtype inter_xmax = std::min(bbox1[2], bbox2[2]);
const Dtype inter_ymax = std::min(bbox1[3], bbox2[3]);
const Dtype inter_width = inter_xmax - inter_xmin;
const Dtype inter_height = inter_ymax - inter_ymin;
const Dtype inter_size = inter_width * inter_height;
const Dtype bbox1_size = BBoxSize(bbox1);
const Dtype bbox2_size = BBoxSize(bbox2);
return inter_size / (bbox1_size + bbox2_size - inter_size);
}
}
template <typename T>
bool SortScorePairDescend(const pair<float, T>& pair1,
const pair<float, T>& pair2) {
return pair1.first > pair2.first;
}
template <typename Dtype>
void GetMaxScoreIndex(const Dtype* scores, const int num, const float threshold,
const int top_k, vector<pair<Dtype, int> >* score_index_vec) {
// Generate index score pairs.
for (int i = 0; i < num; ++i) {
if (scores[i] > threshold) {
score_index_vec->push_back(std::make_pair(scores[i], i));
}
}
// Sort the score pair according to the scores in descending order
std::sort(score_index_vec->begin(), score_index_vec->end(),
SortScorePairDescend<int>);
// Keep top_k scores if needed.
if (top_k > -1 && top_k < score_index_vec->size()) {
score_index_vec->resize(top_k);
}
}
template <typename Dtype>
void ApplyNMSFast(const Dtype* bboxes, const Dtype* scores, const int num,
const float score_threshold, const float nms_threshold,
const float eta, const int top_k, vector<int>* indices) {
// Get top_k scores (with corresponding indices).
vector<pair<Dtype, int> > score_index_vec;
//float n1 = cv::getTickCount();
GetMaxScoreIndex(scores, num, score_threshold, top_k, &score_index_vec);
// n1 = (cv::getTickCount()-n1) / cv::getTickFrequency();
//printf("======n==1 Forward_DetectionOutputLayer time is %f \n", n1);
// Do nms.
float adaptive_threshold = nms_threshold;
indices->clear();
//float n2 = cv::getTickCount();
std::cout<<"======n==n" <<score_index_vec.size()<<std::endl;
while (score_index_vec.size() != 0) {
const int idx = score_index_vec.front().second;
bool keep = true;
for (int k = 0; k < indices->size(); ++k) {
if (keep) {
const int kept_idx = (*indices)[k];
float overlap = JaccardOverlap(bboxes + idx * 4, bboxes + kept_idx * 4);
keep = overlap <= adaptive_threshold;
} else {
break;
}
}
if (keep) {
indices->push_back(idx);
}
score_index_vec.erase(score_index_vec.begin());
if (keep && eta < 1 && adaptive_threshold > 0.5) {
adaptive_threshold *= eta;
}
}
//n2 = (cv::getTickCount()-n2) / cv::getTickFrequency();
//printf("======n==2 Forward_DetectionOutputLayer time is %f \n", n2);
}
void Forward_DetectionOutputLayer(float* loc_data, float* conf_data, float* prior_data, int num_priors_, int num_classes_, vector<vector<float> >* detecions) {
// Retrieve all location predictions.
/*vector<vector<float>> all_loc_preds;
GetLocPredictions(loc_data, num_priors_, num_loc_classes_, &all_loc_preds);
// Retrieve all confidences.
vector <vector<float>> all_conf_scores;
GetConfidenceScores(conf_data, num_priors_, num_classes_,
&all_conf_scores);
// Retrieve all prior bboxes.
vector<vector<float>> prior_bboxes;
vector<vector<float>> prior_variances;
GetPriorBBoxes(prior_data, num_priors_, &prior_bboxes, &prior_variances);
// Decode all loc predictions to bboxes.
vector<vector<float>> all_decode_bboxes;
//const bool clip_bbox = false;
DecodeBBoxes(prior_bboxes, prior_variances, code_type_,
variance_encoded_in_target_, clip_bbox, all_loc_preds,
&all_decode_bboxes);*/
int num_kept = 0;
vector<map<int, vector<int> > > all_indices;
map<int , vector<int>> indices;
int num_det = 0;
const int conf_idx = num_classes_ * num_priors_;
const bool share_location_ = true;
const int num_loc_classes = 1;
int background_label_id_ = 0;
float confidence_threshold_ = 0.1;
float nms_threshold_ = 0.45;
float eta_ = 1.0;//默认1.0
int top_k_ = 400;
int keep_top_k_ = 200;
const int code_type = 1;//center
const bool variance_encoded_in_target = false;//default
const bool clip_bbox = false;
float* decode_bboxes = new float[4 * num_priors_];
float t = cv::getTickCount();
DecodeBBoxes_2<float>(loc_data, prior_data, code_type, variance_encoded_in_target, num_priors_, share_location_, num_loc_classes,background_label_id_, clip_bbox, decode_bboxes);
t = (cv::getTickCount()-t) / cv::getTickFrequency();
printf("======1 Forward_DetectionOutputLayer time is %f \n", t);
float* new_conf_data = new float[num_priors_ * num_classes_];
float t1 = cv::getTickCount();
ConfData(conf_data, num_classes_, num_priors_, new_conf_data);
t1 = (cv::getTickCount()-t1) / cv::getTickFrequency();
printf("======2 Forward_DetectionOutputLayer time is %f \n", t1);
float t2 = cv::getTickCount();
for(int c = 0; c < num_classes_; c++){
if(c == background_label_id_){
continue;
}
float* cur_conf_data = new_conf_data + c * num_priors_;
//float* cur_bbox_data = all_decode_bboxes
float tt = cv::getTickCount();
ApplyNMSFast<float>(decode_bboxes, cur_conf_data, num_priors_,
confidence_threshold_, nms_threshold_, eta_, top_k_, &(indices[c]));
tt = (cv::getTickCount()-tt) / cv::getTickFrequency();
std::cout<<"===nms==="<<c<<"==nms=="<<std::endl;
printf("======nms Forward_DetectionOutputLayer time is %f \n", tt);
num_det += indices[c].size();
}
t2 = (cv::getTickCount()-t2) / cv::getTickFrequency();
printf("======3 Forward_DetectionOutputLayer time is %f \n", t2);
float t3 = cv::getTickCount();
if(keep_top_k_ > -1 && num_det > keep_top_k_){
vector<pair<float, pair<int, int> > > score_index_pairs;
for(map<int, vector<int> >::iterator it = indices.begin(); it != indices.end(); ++it){
int label = it->first;
const vector<int>& label_indices = it->second;
for(int j = 0; j < label_indices.size(); ++j){
int idx = label_indices[j];
float score = new_conf_data[label * num_priors_ + idx];
score_index_pairs.push_back(std::make_pair(score, std::make_pair(label, idx)));
}
}
// Keep top k results per image.
std::sort(score_index_pairs.begin(), score_index_pairs.end(), SortScorePairDescend<pair<int, int> >);
score_index_pairs.resize(keep_top_k_);
// Store the new indices.
map<int, vector<int> > new_indices;
for(int j = 0; j < score_index_pairs.size(); ++j){
int label = score_index_pairs[j].second.first;
int idx = score_index_pairs[j].second.second;
new_indices[label].push_back(idx);
}
all_indices.push_back(new_indices);
num_kept += keep_top_k_;
}else{
all_indices.push_back(indices);
num_kept += num_det;
}
if(num_kept == 0){
printf("Couldn't find any detections");
}else{
for(map<int, vector<int> >::iterator it = all_indices[0].begin(); it != all_indices[0].end(); ++it){
int label = it->first;
vector<int>& _indices = it->second;
const float* _cur_conf_data = new_conf_data + label * num_priors_;
for(int j = 0; j < _indices.size(); ++j){
int idx = _indices[j];
vector<float> detect;
for(int k = 0; k < 4; ++k){
detect.push_back(decode_bboxes[idx * 4 + k]);
}
detect.push_back(_cur_conf_data[idx]);
detect.push_back(label);
detecions->push_back(detect);
}
}
}
t3 = (cv::getTickCount()-t3) / cv::getTickFrequency();
printf("======4 Forward_DetectionOutputLayer time is %f \n", t3);
delete[] decode_bboxes;
delete[] new_conf_data;
}