[
  {
    "path": "LICENSE",
    "content": "MIT License\n\nCopyright (c) 2020 Garrick Brazil\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n"
  },
  {
    "path": "data/kitti_split1/devkit/cpp/CMakeLists.txt",
    "content": "cmake_minimum_required (VERSION 2.6)\nproject(devkit_object)\n\nadd_executable(evaluate_object evaluate_object.cpp)"
  },
  {
    "path": "data/kitti_split1/devkit/cpp/build.sh",
    "content": "cd data/kitti_split1/devkit/cpp/\ng++ -O3 -DNDEBUG -o evaluate_object evaluate_object.cpp\ncd ../../../../\n\n"
  },
  {
    "path": "data/kitti_split1/devkit/cpp/evaluate_object.cpp",
    "content": "#include <iostream>\n#include <algorithm>\n#include <stdio.h>\n#include <math.h>\n#include <vector>\n#include <numeric>\n#include <strings.h>\n#include <assert.h>\n\n#include <dirent.h>\n\n#include <boost/numeric/ublas/matrix.hpp>\n#include <boost/numeric/ublas/io.hpp>\n\n#include <boost/geometry.hpp>\n#include <boost/geometry/geometries/point_xy.hpp>\n#include <boost/geometry/geometries/polygon.hpp>\n#include <boost/geometry/geometries/adapted/c_array.hpp>\n\n#include \"mail.h\"\n\nBOOST_GEOMETRY_REGISTER_C_ARRAY_CS(cs::cartesian)\n\ntypedef boost::geometry::model::polygon<boost::geometry::model::d2::point_xy<double> > Polygon;\n\n\nusing namespace std;\n\n/*=======================================================================\nSTATIC EVALUATION PARAMETERS\n=======================================================================*/\n\n// holds the number of test images on the server\n//const int32_t N_TESTIMAGES = 7518;\n//const int32_t N_TESTIMAGES = 7480;\nconst int32_t N_TESTIMAGES = 3769;\n//const int32_t N_TESTIMAGES = 3712;\n\n\n// easy, moderate and hard evaluation level\nenum DIFFICULTY{EASY=0, MODERATE=1, HARD=2};\n\n// evaluation metrics: image, ground or 3D\nenum METRIC{IMAGE=0, GROUND=1, BOX3D=2};\n\n// evaluation parameter\nconst int32_t MIN_HEIGHT[3]     = {40, 25, 25};     // minimum height for evaluated groundtruth/detections\nconst int32_t MAX_OCCLUSION[3]  = {0, 1, 2};        // maximum occlusion level of the groundtruth used for evaluation\nconst double  MAX_TRUNCATION[3] = {0.15, 0.3, 0.5}; // maximum truncation level of the groundtruth used for evaluation\n\n// evaluated object classes\nenum CLASSES{CAR=0, PEDESTRIAN=1, CYCLIST=2};\nconst int NUM_CLASS = 3;\n\n// parameters varying per class\nvector<string> CLASS_NAMES;\nvector<string> CLASS_NAMES_CAP;\n// the minimum overlap required for 2D evaluation on the image/ground plane and 3D evaluation\nconst double MIN_OVERLAP[3][3] = {{0.7, 0.5, 0.5}, {0.7, 0.5, 0.5}, {0.7, 0.5, 0.5}};\n\n// no. of recall steps that should be evaluated (discretized)\nconst double N_SAMPLE_PTS = 41;\n\n// initialize class names\nvoid initGlobals () {\n  CLASS_NAMES.push_back(\"car\");\n  CLASS_NAMES.push_back(\"pedestrian\");\n  CLASS_NAMES.push_back(\"cyclist\");\n  CLASS_NAMES_CAP.push_back(\"Car\");\n  CLASS_NAMES_CAP.push_back(\"Pedestrian\");\n  CLASS_NAMES_CAP.push_back(\"Cyclist\");\n}\n\n/*=======================================================================\nDATA TYPES FOR EVALUATION\n=======================================================================*/\n\n// holding data needed for precision-recall and precision-aos\nstruct tPrData {\n  vector<double> v;           // detection score for computing score thresholds\n  double         similarity;  // orientation similarity\n  int32_t        tp;          // true positives\n  int32_t        fp;          // false positives\n  int32_t        fn;          // false negatives\n  tPrData () :\n    similarity(0), tp(0), fp(0), fn(0) {}\n};\n\n// holding bounding boxes for ground truth and detections\nstruct tBox {\n  string  type;     // object type as car, pedestrian or cyclist,...\n  double   x1;      // left corner\n  double   y1;      // top corner\n  double   x2;      // right corner\n  double   y2;      // bottom corner\n  double   alpha;   // image orientation\n  tBox (string type, double x1,double y1,double x2,double y2,double alpha) :\n    type(type),x1(x1),y1(y1),x2(x2),y2(y2),alpha(alpha) {}\n};\n\n// holding ground truth data\nstruct tGroundtruth {\n  tBox    box;        // object type, box, orientation\n  double  truncation; // truncation 0..1\n  int32_t occlusion;  // occlusion 0,1,2 (non, partly, fully)\n  double ry;\n  double  t1, t2, t3;\n  double h, w, l;\n  tGroundtruth () :\n    box(tBox(\"invalild\",-1,-1,-1,-1,-10)),truncation(-1),occlusion(-1) {}\n  tGroundtruth (tBox box,double truncation,int32_t occlusion) :\n    box(box),truncation(truncation),occlusion(occlusion) {}\n  tGroundtruth (string type,double x1,double y1,double x2,double y2,double alpha,double truncation,int32_t occlusion) :\n    box(tBox(type,x1,y1,x2,y2,alpha)),truncation(truncation),occlusion(occlusion) {}\n};\n\n// holding detection data\nstruct tDetection {\n  tBox    box;    // object type, box, orientation\n  double  thresh; // detection score\n  double  ry;\n  double  t1, t2, t3;\n  double  h, w, l;\n  tDetection ():\n    box(tBox(\"invalid\",-1,-1,-1,-1,-10)),thresh(-1000) {}\n  tDetection (tBox box,double thresh) :\n    box(box),thresh(thresh) {}\n  tDetection (string type,double x1,double y1,double x2,double y2,double alpha,double thresh) :\n    box(tBox(type,x1,y1,x2,y2,alpha)),thresh(thresh) {}\n};\n\n\n/*=======================================================================\nFUNCTIONS TO LOAD DETECTION AND GROUND TRUTH DATA ONCE, SAVE RESULTS\n=======================================================================*/\nvector<tDetection> loadDetections(string file_name, bool &compute_aos,\n        vector<bool> &eval_image, vector<bool> &eval_ground,\n        vector<bool> &eval_3d, bool &success) {\n\n  // holds all detections (ignored detections are indicated by an index vector\n  vector<tDetection> detections;\n  FILE *fp = fopen(file_name.c_str(),\"r\");\n  if (!fp) {\n    success = false;\n    return detections;\n  }\n  while (!feof(fp)) {\n    tDetection d;\n    double trash;\n    char str[255];\n    if (fscanf(fp, \"%s %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf\",\n                   str, &trash, &trash, &d.box.alpha, &d.box.x1, &d.box.y1,\n                   &d.box.x2, &d.box.y2, &d.h, &d.w, &d.l, &d.t1, &d.t2, &d.t3,\n                   &d.ry, &d.thresh)==16) {\n\n        // d.thresh = 1;\n      d.box.type = str;\n      detections.push_back(d);\n\n      // orientation=-10 is invalid, AOS is not evaluated if at least one orientation is invalid\n      if(d.box.alpha == -10)\n        compute_aos = false;\n\n      // a class is only evaluated if it is detected at least once\n      for (int c = 0; c < NUM_CLASS; c++) {\n        if (!strcasecmp(d.box.type.c_str(), CLASS_NAMES[c].c_str()) || !strcasecmp(d.box.type.c_str(), CLASS_NAMES_CAP[c].c_str())) {\n          if (!eval_image[c] && d.box.x1 >= 0)\n            eval_image[c] = true;\n          if (!eval_ground[c] && d.t1 != -1000 && d.t3 != -1000 && d.w > 0 && d.l > 0)\n            eval_ground[c] = true;\n          if (!eval_3d[c] && d.t1 != -1000 && d.t2 != -1000 && d.t3 != -1000 && d.h > 0 && d.w > 0 && d.l > 0) \n            eval_3d[c] = true;\n          break;\n        }\n      }\n    }\n  }\n  \n  fclose(fp);\n  success = true;\n  return detections;\n}\n\nvector<tGroundtruth> loadGroundtruth(string file_name,bool &success) {\n\n  // holds all ground truth (ignored ground truth is indicated by an index vector\n  vector<tGroundtruth> groundtruth;\n  FILE *fp = fopen(file_name.c_str(),\"r\");\n  if (!fp) {\n    success = false;\n    return groundtruth;\n  }\n  while (!feof(fp)) {\n    tGroundtruth g;\n    char str[255];\n    if (fscanf(fp, \"%s %lf %d %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf\",\n                   str, &g.truncation, &g.occlusion, &g.box.alpha,\n                   &g.box.x1,   &g.box.y1,     &g.box.x2,    &g.box.y2,\n                   &g.h,      &g.w,        &g.l,       &g.t1,\n                   &g.t2,      &g.t3,        &g.ry )==15) {\n      g.box.type = str;\n      groundtruth.push_back(g);\n    }\n  }\n  fclose(fp);\n  success = true;\n  return groundtruth;\n}\n\nvoid saveStats (const vector<double> &precision, const vector<double> &aos, FILE *fp_det, FILE *fp_ori) {\n\n  // save precision to file\n  if(precision.empty())\n    return;\n  for (int32_t i=0; i<precision.size(); i++)\n    fprintf(fp_det,\"%f \",precision[i]);\n  fprintf(fp_det,\"\\n\");\n\n  // save orientation similarity, only if there were no invalid orientation entries in submission (alpha=-10)\n  if(aos.empty())\n    return;\n  for (int32_t i=0; i<aos.size(); i++)\n    fprintf(fp_ori,\"%f \",aos[i]);\n  fprintf(fp_ori,\"\\n\");\n}\n\n/*=======================================================================\nEVALUATION HELPER FUNCTIONS\n=======================================================================*/\n\n// criterion defines whether the overlap is computed with respect to both areas (ground truth and detection)\n// or with respect to box a or b (detection and \"dontcare\" areas)\ninline double imageBoxOverlap(tBox a, tBox b, int32_t criterion=-1){\n\n  // overlap is invalid in the beginning\n  double o = -1;\n\n  // get overlapping area\n  double x1 = max(a.x1, b.x1);\n  double y1 = max(a.y1, b.y1);\n  double x2 = min(a.x2, b.x2);\n  double y2 = min(a.y2, b.y2);\n\n  // compute width and height of overlapping area\n  double w = x2-x1;\n  double h = y2-y1;\n\n  // set invalid entries to 0 overlap\n  if(w<=0 || h<=0)\n    return 0;\n\n  // get overlapping areas\n  double inter = w*h;\n  double a_area = (a.x2-a.x1) * (a.y2-a.y1);\n  double b_area = (b.x2-b.x1) * (b.y2-b.y1);\n\n  // intersection over union overlap depending on users choice\n  if(criterion==-1)     // union\n    o = inter / (a_area+b_area-inter);\n  else if(criterion==0) // bbox_a\n    o = inter / a_area;\n  else if(criterion==1) // bbox_b\n    o = inter / b_area;\n\n  // overlap\n  return o;\n}\n\ninline double imageBoxOverlap(tDetection a, tGroundtruth b, int32_t criterion=-1){\n  return imageBoxOverlap(a.box, b.box, criterion);\n}\n\n// compute polygon of an oriented bounding box\ntemplate <typename T>\nPolygon toPolygon(const T& g) {\n    using namespace boost::numeric::ublas;\n    using namespace boost::geometry;\n    matrix<double> mref(2, 2);\n    mref(0, 0) = cos(g.ry); mref(0, 1) = sin(g.ry);\n    mref(1, 0) = -sin(g.ry); mref(1, 1) = cos(g.ry);\n\n    static int count = 0;\n    matrix<double> corners(2, 4);\n    double data[] = {g.l / 2, g.l / 2, -g.l / 2, -g.l / 2,\n                     g.w / 2, -g.w / 2, -g.w / 2, g.w / 2};\n    std::copy(data, data + 8, corners.data().begin());\n    matrix<double> gc = prod(mref, corners);\n    for (int i = 0; i < 4; ++i) {\n        gc(0, i) += g.t1;\n        gc(1, i) += g.t3;\n    }\n\n    double points[][2] = {{gc(0, 0), gc(1, 0)},{gc(0, 1), gc(1, 1)},{gc(0, 2), gc(1, 2)},{gc(0, 3), gc(1, 3)},{gc(0, 0), gc(1, 0)}};\n    Polygon poly;\n    append(poly, points);\n    return poly;\n}\n\n// measure overlap between bird's eye view bounding boxes, parametrized by (ry, l, w, tx, tz)\ninline double groundBoxOverlap(tDetection d, tGroundtruth g, int32_t criterion = -1) {\n    using namespace boost::geometry;\n    Polygon gp = toPolygon(g);\n    Polygon dp = toPolygon(d);\n\n    std::vector<Polygon> in, un;\n    intersection(gp, dp, in);\n    union_(gp, dp, un);\n\n    double inter_area = in.empty() ? 0 : area(in.front());\n    double union_area = area(un.front());\n    double o;\n    if(criterion==-1)     // union\n        o = inter_area / union_area;\n    else if(criterion==0) // bbox_a\n        o = inter_area / area(dp);\n    else if(criterion==1) // bbox_b\n        o = inter_area / area(gp);\n\n    return o;\n}\n\n// measure overlap between 3D bounding boxes, parametrized by (ry, h, w, l, tx, ty, tz)\ninline double box3DOverlap(tDetection d, tGroundtruth g, int32_t criterion = -1) {\n    using namespace boost::geometry;\n    Polygon gp = toPolygon(g);\n    Polygon dp = toPolygon(d);\n\n    std::vector<Polygon> in, un;\n    intersection(gp, dp, in);\n    union_(gp, dp, un);\n\n    double ymax = min(d.t2, g.t2);\n    double ymin = max(d.t2 - d.h, g.t2 - g.h);\n\n    double inter_area = in.empty() ? 0 : area(in.front());\n    double inter_vol = inter_area * max(0.0, ymax - ymin);\n\n    double det_vol = d.h * d.l * d.w;\n    double gt_vol = g.h * g.l * g.w;\n\n    double o;\n    if(criterion==-1)     // union\n        o = inter_vol / (det_vol + gt_vol - inter_vol);\n    else if(criterion==0) // bbox_a\n        o = inter_vol / det_vol;\n    else if(criterion==1) // bbox_b\n        o = inter_vol / gt_vol;\n\n    return o;\n}\n\nvector<double> getThresholds(vector<double> &v, double n_groundtruth){\n\n  // holds scores needed to compute N_SAMPLE_PTS recall values\n  vector<double> t;\n\n  // sort scores in descending order\n  // (highest score is assumed to give best/most confident detections)\n  sort(v.begin(), v.end(), greater<double>());\n\n  // get scores for linearly spaced recall\n  double current_recall = 0;\n  for(int32_t i=0; i<v.size(); i++){\n\n    // check if right-hand-side recall with respect to current recall is close than left-hand-side one\n    // in this case, skip the current detection score\n    double l_recall, r_recall, recall;\n    l_recall = (double)(i+1)/n_groundtruth;\n    if(i<(v.size()-1))\n      r_recall = (double)(i+2)/n_groundtruth;\n    else\n      r_recall = l_recall;\n\n    if( (r_recall-current_recall) < (current_recall-l_recall) && i<(v.size()-1))\n      continue;\n\n    // left recall is the best approximation, so use this and goto next recall step for approximation\n    recall = l_recall;\n\n    // the next recall step was reached\n    t.push_back(v[i]);\n    current_recall += 1.0/(N_SAMPLE_PTS-1.0);\n  }\n  return t;\n}\n\nvoid cleanData(CLASSES current_class, const vector<tGroundtruth> &gt, const vector<tDetection> &det, vector<int32_t> &ignored_gt, vector<tGroundtruth> &dc, vector<int32_t> &ignored_det, int32_t &n_gt, DIFFICULTY difficulty){\n\n  // extract ground truth bounding boxes for current evaluation class\n  for(int32_t i=0;i<gt.size(); i++){\n\n    // only bounding boxes with a minimum height are used for evaluation\n    double height = gt[i].box.y2 - gt[i].box.y1;\n\n    // neighboring classes are ignored (\"van\" for \"car\" and \"person_sitting\" for \"pedestrian\")\n    // (lower/upper cases are ignored)\n    int32_t valid_class;\n\n    // all classes without a neighboring class\n    if(!strcasecmp(gt[i].box.type.c_str(), CLASS_NAMES[current_class].c_str()))\n      valid_class = 1;\n\n    // classes with a neighboring class\n    else if(!strcasecmp(CLASS_NAMES[current_class].c_str(), \"Pedestrian\") && !strcasecmp(\"Person_sitting\", gt[i].box.type.c_str()))\n      valid_class = 0;\n    else if(!strcasecmp(CLASS_NAMES[current_class].c_str(), \"Car\") && !strcasecmp(\"Van\", gt[i].box.type.c_str()))\n      valid_class = 0;\n\n    // classes not used for evaluation\n    else\n      valid_class = -1;\n\n    // ground truth is ignored, if occlusion, truncation exceeds the difficulty or ground truth is too small\n    // (doesn't count as FN nor TP, although detections may be assigned)\n    bool ignore = false;\n    if(gt[i].occlusion>MAX_OCCLUSION[difficulty] || gt[i].truncation>MAX_TRUNCATION[difficulty] || height<=MIN_HEIGHT[difficulty])\n      ignore = true;\n\n    // set ignored vector for ground truth\n    // current class and not ignored (total no. of ground truth is detected for recall denominator)\n    if(valid_class==1 && !ignore){\n      ignored_gt.push_back(0);\n      n_gt++;\n    }\n\n    // neighboring class, or current class but ignored\n    else if(valid_class==0 || (ignore && valid_class==1))\n      ignored_gt.push_back(1);\n\n    // all other classes which are FN in the evaluation\n    else\n      ignored_gt.push_back(-1);\n  }\n\n  // extract dontcare areas\n  for(int32_t i=0;i<gt.size(); i++)\n    if(!strcasecmp(\"DontCare\", gt[i].box.type.c_str()))\n      dc.push_back(gt[i]);\n\n  // extract detections bounding boxes of the current class\n  for(int32_t i=0;i<det.size(); i++){\n\n    // neighboring classes are not evaluated\n    int32_t valid_class;\n    if(!strcasecmp(det[i].box.type.c_str(), CLASS_NAMES[current_class].c_str()))\n      valid_class = 1;\n    else\n      valid_class = -1;\n    \n    int32_t height = fabs(det[i].box.y1 - det[i].box.y2);\n    \n    // set ignored vector for detections\n    if(height<MIN_HEIGHT[difficulty])\n      ignored_det.push_back(1);\n    else if(valid_class==1)\n      ignored_det.push_back(0);\n    else\n      ignored_det.push_back(-1);\n  }\n}\n\ntPrData computeStatistics(CLASSES current_class, const vector<tGroundtruth> &gt,\n        const vector<tDetection> &det, const vector<tGroundtruth> &dc,\n        const vector<int32_t> &ignored_gt, const vector<int32_t>  &ignored_det,\n        bool compute_fp, double (*boxoverlap)(tDetection, tGroundtruth, int32_t),\n        METRIC metric, bool compute_aos=false, double thresh=0, bool debug=false){\n\n  tPrData stat = tPrData();\n  const double NO_DETECTION = -10000000;\n  vector<double> delta;            // holds angular difference for TPs (needed for AOS evaluation)\n  vector<bool> assigned_detection; // holds wether a detection was assigned to a valid or ignored ground truth\n  assigned_detection.assign(det.size(), false);\n  vector<bool> ignored_threshold;\n  ignored_threshold.assign(det.size(), false); // holds detections with a threshold lower than thresh if FP are computed\n\n  // detections with a low score are ignored for computing precision (needs FP)\n  if(compute_fp)\n    for(int32_t i=0; i<det.size(); i++)\n      if(det[i].thresh<thresh)\n        ignored_threshold[i] = true;\n\n  // evaluate all ground truth boxes\n  for(int32_t i=0; i<gt.size(); i++){\n\n    // this ground truth is not of the current or a neighboring class and therefore ignored\n    if(ignored_gt[i]==-1)\n      continue;\n\n    /*=======================================================================\n    find candidates (overlap with ground truth > 0.5) (logical len(det))\n    =======================================================================*/\n    int32_t det_idx          = -1;\n    double valid_detection = NO_DETECTION;\n    double max_overlap     = 0;\n\n    // search for a possible detection\n    bool assigned_ignored_det = false;\n    for(int32_t j=0; j<det.size(); j++){\n\n      // detections not of the current class, already assigned or with a low threshold are ignored\n      if(ignored_det[j]==-1)\n        continue;\n      if(assigned_detection[j])\n        continue;\n      if(ignored_threshold[j])\n        continue;\n\n      // find the maximum score for the candidates and get idx of respective detection\n      double overlap = boxoverlap(det[j], gt[i], -1);\n\n      // for computing recall thresholds, the candidate with highest score is considered\n      if(!compute_fp && overlap>MIN_OVERLAP[metric][current_class] && det[j].thresh>valid_detection){\n        det_idx         = j;\n        valid_detection = det[j].thresh;\n      }\n\n      // for computing pr curve values, the candidate with the greatest overlap is considered\n      // if the greatest overlap is an ignored detection (min_height), the overlapping detection is used\n      else if(compute_fp && overlap>MIN_OVERLAP[metric][current_class] && (overlap>max_overlap || assigned_ignored_det) && ignored_det[j]==0){\n        max_overlap     = overlap;\n        det_idx         = j;\n        valid_detection = 1;\n        assigned_ignored_det = false;\n      }\n      else if(compute_fp && overlap>MIN_OVERLAP[metric][current_class] && valid_detection==NO_DETECTION && ignored_det[j]==1){\n        det_idx              = j;\n        valid_detection      = 1;\n        assigned_ignored_det = true;\n      }\n    }\n\n    /*=======================================================================\n    compute TP, FP and FN\n    =======================================================================*/\n\n    // nothing was assigned to this valid ground truth\n    if(valid_detection==NO_DETECTION && ignored_gt[i]==0) {\n      stat.fn++;\n    }\n\n    // only evaluate valid ground truth <=> detection assignments (considering difficulty level)\n    else if(valid_detection!=NO_DETECTION && (ignored_gt[i]==1 || ignored_det[det_idx]==1))\n      assigned_detection[det_idx] = true;\n\n    // found a valid true positive\n    else if(valid_detection!=NO_DETECTION){\n\n      // write highest score to threshold vector\n      stat.tp++;\n      stat.v.push_back(det[det_idx].thresh);\n\n      // compute angular difference of detection and ground truth if valid detection orientation was provided\n      if(compute_aos)\n        delta.push_back(gt[i].box.alpha - det[det_idx].box.alpha);\n\n      // clean up\n      assigned_detection[det_idx] = true;\n    }\n  }\n\n  // if FP are requested, consider stuff area\n  if(compute_fp){\n\n    // count fp\n    for(int32_t i=0; i<det.size(); i++){\n\n      // count false positives if required (height smaller than required is ignored (ignored_det==1)\n      if(!(assigned_detection[i] || ignored_det[i]==-1 || ignored_det[i]==1 || ignored_threshold[i]))\n        stat.fp++;\n    }\n\n    // do not consider detections overlapping with stuff area\n    int32_t nstuff = 0;\n    for(int32_t i=0; i<dc.size(); i++){\n      for(int32_t j=0; j<det.size(); j++){\n\n        // detections not of the current class, already assigned, with a low threshold or a low minimum height are ignored\n        if(assigned_detection[j])\n          continue;\n        if(ignored_det[j]==-1 || ignored_det[j]==1)\n          continue;\n        if(ignored_threshold[j])\n          continue;\n\n        // compute overlap and assign to stuff area, if overlap exceeds class specific value\n        double overlap = boxoverlap(det[j], dc[i], 0);\n        if(overlap>MIN_OVERLAP[metric][current_class]){\n          assigned_detection[j] = true;\n          nstuff++;\n        }\n      }\n    }\n\n    // FP = no. of all not to ground truth assigned detections - detections assigned to stuff areas\n    stat.fp -= nstuff;\n\n    // if all orientation values are valid, the AOS is computed\n    if(compute_aos){\n      vector<double> tmp;\n\n      // FP have a similarity of 0, for all TP compute AOS\n      tmp.assign(stat.fp, 0);\n      for(int32_t i=0; i<delta.size(); i++)\n        tmp.push_back((1.0+cos(delta[i]))/2.0);\n\n      // be sure, that all orientation deltas are computed\n      assert(tmp.size()==stat.fp+stat.tp);\n      assert(delta.size()==stat.tp);\n\n      // get the mean orientation similarity for this image\n      if(stat.tp>0 || stat.fp>0)\n        stat.similarity = accumulate(tmp.begin(), tmp.end(), 0.0);\n\n      // there was neither a FP nor a TP, so the similarity is ignored in the evaluation\n      else\n        stat.similarity = -1;\n    }\n  }\n  return stat;\n}\n\n/*=======================================================================\nEVALUATE CLASS-WISE\n=======================================================================*/\n\nbool eval_class (FILE *fp_det, FILE *fp_ori, CLASSES current_class,\n        const vector< vector<tGroundtruth> > &groundtruth,\n        const vector< vector<tDetection> > &detections, bool compute_aos,\n        double (*boxoverlap)(tDetection, tGroundtruth, int32_t),\n        vector<double> &precision, vector<double> &aos,\n        DIFFICULTY difficulty, METRIC metric) {\n    assert(groundtruth.size() == detections.size());\n\n  // init\n  int32_t n_gt=0;                                     // total no. of gt (denominator of recall)\n  vector<double> v, thresholds;                       // detection scores, evaluated for recall discretization\n  vector< vector<int32_t> > ignored_gt, ignored_det;  // index of ignored gt detection for current class/difficulty\n  vector< vector<tGroundtruth> > dontcare;            // index of dontcare areas, included in ground truth\n\n  // for all test images do\n  for (int32_t i=0; i<groundtruth.size(); i++){\n\n    // holds ignored ground truth, ignored detections and dontcare areas for current frame\n    vector<int32_t> i_gt, i_det;\n    vector<tGroundtruth> dc;\n\n    // only evaluate objects of current class and ignore occluded, truncated objects\n    cleanData(current_class, groundtruth[i], detections[i], i_gt, dc, i_det, n_gt, difficulty);\n    ignored_gt.push_back(i_gt);\n    ignored_det.push_back(i_det);\n    dontcare.push_back(dc);\n\n    // compute statistics to get recall values\n    tPrData pr_tmp = tPrData();\n    pr_tmp = computeStatistics(current_class, groundtruth[i], detections[i], dc, i_gt, i_det, false, boxoverlap, metric);\n\n    // add detection scores to vector over all images\n    for(int32_t j=0; j<pr_tmp.v.size(); j++)\n      v.push_back(pr_tmp.v[j]);\n  }\n\n  // get scores that must be evaluated for recall discretization\n  thresholds = getThresholds(v, n_gt);\n\n  // compute TP,FP,FN for relevant scores\n  vector<tPrData> pr;\n  pr.assign(thresholds.size(),tPrData());\n  for (int32_t i=0; i<groundtruth.size(); i++){\n\n    // for all scores/recall thresholds do:\n    for(int32_t t=0; t<thresholds.size(); t++){\n      tPrData tmp = tPrData();\n      tmp = computeStatistics(current_class, groundtruth[i], detections[i], dontcare[i],\n                              ignored_gt[i], ignored_det[i], true, boxoverlap, metric,\n                              compute_aos, thresholds[t], t==38);\n\n      // add no. of TP, FP, FN, AOS for current frame to total evaluation for current threshold\n      pr[t].tp += tmp.tp;\n      pr[t].fp += tmp.fp;\n      pr[t].fn += tmp.fn;\n      if(tmp.similarity!=-1)\n        pr[t].similarity += tmp.similarity;\n    }\n  }\n\n  // compute recall, precision and AOS\n  vector<double> recall;\n  precision.assign(N_SAMPLE_PTS, 0);\n  if(compute_aos)\n    aos.assign(N_SAMPLE_PTS, 0);\n  double r=0;\n  for (int32_t i=0; i<thresholds.size(); i++){\n    r = pr[i].tp/(double)(pr[i].tp + pr[i].fn);\n    recall.push_back(r);\n    precision[i] = pr[i].tp/(double)(pr[i].tp + pr[i].fp);\n    if(compute_aos)\n      aos[i] = pr[i].similarity/(double)(pr[i].tp + pr[i].fp);\n  }\n\n  // filter precision and AOS using max_{i..end}(precision)\n  for (int32_t i=0; i<thresholds.size(); i++){\n    precision[i] = *max_element(precision.begin()+i, precision.end());\n    if(compute_aos)\n      aos[i] = *max_element(aos.begin()+i, aos.end());\n  }\n\n  // save statisics and finish with success\n  saveStats(precision, aos, fp_det, fp_ori);\n    return true;\n}\n\nvoid saveAndPlotPlots(string dir_name,string file_name,string obj_type,vector<double> vals[],bool is_aos){\n\n  char command[1024];\n\n  // save plot data to file\n  FILE *fp = fopen((dir_name + \"/\" + file_name + \".txt\").c_str(),\"w\");\n  printf(\"save %s\\n\", (dir_name + \"/\" + file_name + \".txt\").c_str());\n  for (int32_t i=0; i<(int)N_SAMPLE_PTS; i++)\n    fprintf(fp,\"%f %f %f %f\\n\",(double)i/(N_SAMPLE_PTS-1.0),vals[0][i],vals[1][i],vals[2][i]);\n  fclose(fp);\n\n  // create png + eps\n  for (int32_t j=0; j<2; j++) {\n\n    // open file\n    FILE *fp = fopen((dir_name + \"/\" + file_name + \".gp\").c_str(),\"w\");\n\n    // save gnuplot instructions\n    if (j==0) {\n      fprintf(fp,\"set term png size 450,315 font \\\"Helvetica\\\" 11\\n\");\n      fprintf(fp,\"set output \\\"%s.png\\\"\\n\",file_name.c_str());\n    } else {\n      fprintf(fp,\"set term postscript eps enhanced color font \\\"Helvetica\\\" 20\\n\");\n      fprintf(fp,\"set output \\\"%s.eps\\\"\\n\",file_name.c_str());\n    }\n\n    // set labels and ranges\n    fprintf(fp,\"set size ratio 0.7\\n\");\n    fprintf(fp,\"set xrange [0:1]\\n\");\n    fprintf(fp,\"set yrange [0:1]\\n\");\n    fprintf(fp,\"set xlabel \\\"Recall\\\"\\n\");\n    if (!is_aos) fprintf(fp,\"set ylabel \\\"Precision\\\"\\n\");\n    else         fprintf(fp,\"set ylabel \\\"Orientation Similarity\\\"\\n\");\n    obj_type[0] = toupper(obj_type[0]);\n    fprintf(fp,\"set title \\\"%s\\\"\\n\",obj_type.c_str());\n\n    // line width\n    int32_t   lw = 5;\n    if (j==0) lw = 3;\n\n    // plot error curve\n    fprintf(fp,\"plot \");\n    fprintf(fp,\"\\\"%s.txt\\\" using 1:2 title 'Easy' with lines ls 1 lw %d,\",file_name.c_str(),lw);\n    fprintf(fp,\"\\\"%s.txt\\\" using 1:3 title 'Moderate' with lines ls 2 lw %d,\",file_name.c_str(),lw);\n    fprintf(fp,\"\\\"%s.txt\\\" using 1:4 title 'Hard' with lines ls 3 lw %d\",file_name.c_str(),lw);\n\n    // close file\n    fclose(fp);\n\n    // run gnuplot => create png + eps\n    sprintf(command,\"cd %s; gnuplot %s\",dir_name.c_str(),(file_name + \".gp\").c_str());\n    system(command);\n  }\n\n  // create pdf and crop\n  sprintf(command,\"cd %s; ps2pdf %s.eps %s_large.pdf\",dir_name.c_str(),file_name.c_str(),file_name.c_str());\n  system(command);\n  sprintf(command,\"cd %s; pdfcrop %s_large.pdf %s.pdf\",dir_name.c_str(),file_name.c_str(),file_name.c_str());\n  system(command);\n  sprintf(command,\"cd %s; rm %s_large.pdf\",dir_name.c_str(),file_name.c_str());\n  system(command);\n}\n\nbool eval(string result_sha,Mail* mail){\n\n  // set some global parameters\n  initGlobals();\n\n  // ground truth and result directories\n  string gt_dir         = \"data/kitti_split1/validation/label_2\";\n  string result_dir     = result_sha;\n  string plot_dir       = result_dir + \"/plot\";\n\n  // create output directories\n  system((\"mkdir \" + plot_dir).c_str());\n\n  // hold detections and ground truth in memory\n  vector< vector<tGroundtruth> > groundtruth;\n  vector< vector<tDetection> >   detections;\n\n  // holds wether orientation similarity shall be computed (might be set to false while loading detections)\n  // and which labels where provided by this submission\n  bool compute_aos=true;\n  vector<bool> eval_image(NUM_CLASS, false);\n  vector<bool> eval_ground(NUM_CLASS, false);\n  vector<bool> eval_3d(NUM_CLASS, false);\n\n  // for all images read groundtruth and detections\n  mail->msg(\"Loading detections...\");\n  for (int32_t i=0; i<N_TESTIMAGES; i++) {\n\n    // file name\n    char file_name[256];\n    sprintf(file_name,\"%06d.txt\",i);\n\n    // read ground truth and result poses\n    bool gt_success,det_success;\n    vector<tGroundtruth> gt   = loadGroundtruth(gt_dir + \"/\" + file_name,gt_success);\n    vector<tDetection>   det  = loadDetections(result_dir + \"/data/\" + file_name,\n            compute_aos, eval_image, eval_ground, eval_3d, det_success);\n    groundtruth.push_back(gt);\n    detections.push_back(det);\n\n    // check for errors\n    if (!gt_success) {\n      mail->msg(\"ERROR: Couldn't read: %s of ground truth. Please write me an email!\", file_name);\n      return false;\n    }\n    if (!det_success) {\n      mail->msg(\"ERROR: Couldn't read: %s\", file_name);\n      return false;\n    }\n  }\n  mail->msg(\"  done.\");\n\n  // holds pointers for result files\n  FILE *fp_det=0, *fp_ori=0;\n\n  // eval image 2D bounding boxes\n  for (int c = 0; c < NUM_CLASS; c++) {\n    CLASSES cls = (CLASSES)c;\n    //mail->msg(\"Checking 2D evaluation (%s) ...\", CLASS_NAMES[c].c_str());\n    if (eval_image[c]) {\n      mail->msg(\"Starting 2D evaluation (%s) ...\", CLASS_NAMES[c].c_str());\n      fp_det = fopen((result_dir + \"/stats_\" + CLASS_NAMES[c] + \"_detection.txt\").c_str(), \"w\");\n      if(compute_aos)\n        fp_ori = fopen((result_dir + \"/stats_\" + CLASS_NAMES[c] + \"_orientation.txt\").c_str(),\"w\");\n      vector<double> precision[3], aos[3];\n      if(   !eval_class(fp_det, fp_ori, cls, groundtruth, detections, compute_aos, imageBoxOverlap, precision[0], aos[0], EASY, IMAGE)\n         || !eval_class(fp_det, fp_ori, cls, groundtruth, detections, compute_aos, imageBoxOverlap, precision[1], aos[1], MODERATE, IMAGE)\n         || !eval_class(fp_det, fp_ori, cls, groundtruth, detections, compute_aos, imageBoxOverlap, precision[2], aos[2], HARD, IMAGE)) {\n        mail->msg(\"%s evaluation failed.\", CLASS_NAMES[c].c_str());\n        return false;\n      }\n      fclose(fp_det);\n      saveAndPlotPlots(plot_dir, CLASS_NAMES[c] + \"_detection\", CLASS_NAMES[c], precision, 0);\n      if(compute_aos){\n        saveAndPlotPlots(plot_dir, CLASS_NAMES[c] + \"_orientation\", CLASS_NAMES[c], aos, 1);\n        fclose(fp_ori);\n      }\n      mail->msg(\"  done.\");\n    }\n  }\n\n  // don't evaluate AOS for birdview boxes and 3D boxes\n  compute_aos = false;\n\n  // eval bird's eye view bounding boxes\n  for (int c = 0; c < NUM_CLASS; c++) {\n    CLASSES cls = (CLASSES)c;\n    //mail->msg(\"Checking bird's eye evaluation (%s) ...\", CLASS_NAMES[c].c_str());\n    if (eval_ground[c]) {\n      mail->msg(\"Starting bird's eye evaluation (%s) ...\", CLASS_NAMES[c].c_str());\n      fp_det = fopen((result_dir + \"/stats_\" + CLASS_NAMES[c] + \"_detection_ground.txt\").c_str(), \"w\");\n      vector<double> precision[3], aos[3];\n      if(   !eval_class(fp_det, fp_ori, cls, groundtruth, detections, compute_aos, groundBoxOverlap, precision[0], aos[0], EASY, GROUND)\n         || !eval_class(fp_det, fp_ori, cls, groundtruth, detections, compute_aos, groundBoxOverlap, precision[1], aos[1], MODERATE, GROUND)\n         || !eval_class(fp_det, fp_ori, cls, groundtruth, detections, compute_aos, groundBoxOverlap, precision[2], aos[2], HARD, GROUND)) {\n        mail->msg(\"%s evaluation failed.\", CLASS_NAMES[c].c_str());\n        return false;\n      }\n      fclose(fp_det);\n      saveAndPlotPlots(plot_dir, CLASS_NAMES[c] + \"_detection_ground\", CLASS_NAMES[c], precision, 0);\n      mail->msg(\"  done.\");\n    }\n  }\n\n  // eval 3D bounding boxes\n  for (int c = 0; c < NUM_CLASS; c++) {\n    CLASSES cls = (CLASSES)c;\n    //mail->msg(\"Checking 3D evaluation (%s) ...\", CLASS_NAMES[c].c_str());\n    if (eval_3d[c]) {\n      mail->msg(\"Starting 3D evaluation (%s) ...\", CLASS_NAMES[c].c_str());\n      fp_det = fopen((result_dir + \"/stats_\" + CLASS_NAMES[c] + \"_detection_3d.txt\").c_str(), \"w\");\n      vector<double> precision[3], aos[3];\n      if(   !eval_class(fp_det, fp_ori, cls, groundtruth, detections, compute_aos, box3DOverlap, precision[0], aos[0], EASY, BOX3D)\n         || !eval_class(fp_det, fp_ori, cls, groundtruth, detections, compute_aos, box3DOverlap, precision[1], aos[1], MODERATE, BOX3D)\n         || !eval_class(fp_det, fp_ori, cls, groundtruth, detections, compute_aos, box3DOverlap, precision[2], aos[2], HARD, BOX3D)) {\n        mail->msg(\"%s evaluation failed.\", CLASS_NAMES[c].c_str());\n        return false;\n      }\n      fclose(fp_det);\n      saveAndPlotPlots(plot_dir, CLASS_NAMES[c] + \"_detection_3d\", CLASS_NAMES[c], precision, 0);\n      mail->msg(\"  done.\");\n    }\n  }\n\n  // success\n  return true;\n}\n\nint32_t main (int32_t argc,char *argv[]) {\n\n  // we need 2 or 4 arguments!\n  if (argc!=2 && argc!=4) {\n    cout << \"Usage: ./eval_detection result_sha [user_sha email]\" << endl;\n    return 1;\n  }\n\n  // read arguments\n  string result_sha = argv[1];\n\n  // init notification mail\n  Mail *mail;\n  if (argc==4) mail = new Mail(argv[3]);\n  else         mail = new Mail();\n  mail->msg(\"Thank you for participating in our evaluation!\");\n\n  // run evaluation\n  if (eval(result_sha,mail)) {\n    mail->msg(\"Your evaluation results are available at:\");\n    mail->msg(\"http://www.cvlibs.net/datasets/kitti/user_submit_check_login.php?benchmark=object&user=%s&result=%s\",argv[2], result_sha.c_str());\n  } else {\n    system((\"rm -r results/\" + result_sha).c_str());\n    mail->msg(\"An error occured while processing your results.\");\n    mail->msg(\"Please make sure that the data in your zip archive has the right format!\");\n  }\n\n  // send mail and exit\n  delete mail;\n\n  return 0;\n}\n"
  },
  {
    "path": "data/kitti_split1/devkit/cpp/mail.h",
    "content": "#ifndef MAIL_H\n#define MAIL_H\n\n#include <stdio.h>\n#include <stdarg.h>\n#include <string.h>\n\nclass Mail {\n\npublic:\n\n  Mail (std::string email = \"\") {\n    if (email.compare(\"\")) {\n      mail = popen(\"/usr/lib/sendmail -t -f noreply@cvlibs.net\",\"w\");\n      fprintf(mail,\"To: %s\\n\", email.c_str());\n      fprintf(mail,\"From: noreply@cvlibs.net\\n\");\n      fprintf(mail,\"Subject: KITTI Evaluation Benchmark\\n\");\n      fprintf(mail,\"\\n\\n\");\n    } else {\n      mail = 0;\n    }\n  }\n  \n  ~Mail() {\n    if (mail) {\n      pclose(mail);\n    }\n  }\n  \n  void msg (const char *format, ...) {\n    va_list args;\n    va_start(args,format);\n    if (mail) {\n      vfprintf(mail,format,args);\n      fprintf(mail,\"\\n\");\n    }\n    vprintf(format,args);\n    printf(\"\\n\");\n    va_end(args);\n  }\n    \nprivate:\n\n  FILE *mail;\n  \n};\n\n#endif\n"
  },
  {
    "path": "data/kitti_split1/devkit/mapping/train_mapping.txt",
    "content": "2011_09_26 2011_09_26_drive_0005_sync 0000000109\n2011_09_26 2011_09_26_drive_0005_sync 0000000125\n2011_09_26 2011_09_26_drive_0005_sync 0000000127\n2011_09_26 2011_09_26_drive_0005_sync 0000000130\n2011_09_26 2011_09_26_drive_0005_sync 0000000133\n2011_09_26 2011_09_26_drive_0005_sync 0000000136\n2011_09_26 2011_09_26_drive_0005_sync 0000000139\n2011_09_26 2011_09_26_drive_0005_sync 0000000142\n2011_09_26 2011_09_26_drive_0005_sync 0000000145\n2011_09_26 2011_09_26_drive_0005_sync 0000000151\n2011_09_26 2011_09_26_drive_0009_sync 0000000001\n2011_09_26 2011_09_26_drive_0009_sync 0000000002\n2011_09_26 2011_09_26_drive_0009_sync 0000000003\n2011_09_26 2011_09_26_drive_0009_sync 0000000004\n2011_09_26 2011_09_26_drive_0009_sync 0000000005\n2011_09_26 2011_09_26_drive_0009_sync 0000000006\n2011_09_26 2011_09_26_drive_0009_sync 0000000007\n2011_09_26 2011_09_26_drive_0009_sync 0000000008\n2011_09_26 2011_09_26_drive_0009_sync 0000000009\n2011_09_26 2011_09_26_drive_0009_sync 0000000010\n2011_09_26 2011_09_26_drive_0009_sync 0000000011\n2011_09_26 2011_09_26_drive_0009_sync 0000000012\n2011_09_26 2011_09_26_drive_0009_sync 0000000013\n2011_09_26 2011_09_26_drive_0009_sync 0000000014\n2011_09_26 2011_09_26_drive_0009_sync 0000000015\n2011_09_26 2011_09_26_drive_0009_sync 0000000016\n2011_09_26 2011_09_26_drive_0009_sync 0000000017\n2011_09_26 2011_09_26_drive_0009_sync 0000000018\n2011_09_26 2011_09_26_drive_0009_sync 0000000019\n2011_09_26 2011_09_26_drive_0009_sync 0000000020\n2011_09_26 2011_09_26_drive_0009_sync 0000000021\n2011_09_26 2011_09_26_drive_0009_sync 0000000022\n2011_09_26 2011_09_26_drive_0009_sync 0000000023\n2011_09_26 2011_09_26_drive_0009_sync 0000000024\n2011_09_26 2011_09_26_drive_0009_sync 0000000025\n2011_09_26 2011_09_26_drive_0009_sync 0000000026\n2011_09_26 2011_09_26_drive_0009_sync 0000000027\n2011_09_26 2011_09_26_drive_0009_sync 0000000028\n2011_09_26 2011_09_26_drive_0009_sync 0000000029\n2011_09_26 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2011_09_26_drive_0101_sync 0000000413\n2011_09_26 2011_09_26_drive_0101_sync 0000000414\n2011_09_26 2011_09_26_drive_0101_sync 0000000415\n2011_09_26 2011_09_26_drive_0101_sync 0000000418\n2011_09_26 2011_09_26_drive_0101_sync 0000000419\n2011_09_26 2011_09_26_drive_0101_sync 0000000420\n2011_09_26 2011_09_26_drive_0101_sync 0000000421\n2011_09_26 2011_09_26_drive_0101_sync 0000000422\n2011_09_26 2011_09_26_drive_0101_sync 0000000423\n2011_09_26 2011_09_26_drive_0101_sync 0000000424\n2011_09_26 2011_09_26_drive_0101_sync 0000000425\n2011_09_26 2011_09_26_drive_0101_sync 0000000426\n2011_09_26 2011_09_26_drive_0101_sync 0000000427\n2011_09_26 2011_09_26_drive_0101_sync 0000000429\n2011_09_26 2011_09_26_drive_0101_sync 0000000430\n2011_09_26 2011_09_26_drive_0101_sync 0000000431\n2011_09_26 2011_09_26_drive_0101_sync 0000000432\n2011_09_26 2011_09_26_drive_0101_sync 0000000433\n2011_09_26 2011_09_26_drive_0101_sync 0000000434\n2011_09_26 2011_09_26_drive_0101_sync 0000000435\n2011_09_26 2011_09_26_drive_0101_sync 0000000436\n2011_09_26 2011_09_26_drive_0101_sync 0000000437\n2011_09_26 2011_09_26_drive_0101_sync 0000000438\n2011_09_26 2011_09_26_drive_0101_sync 0000000439\n2011_09_26 2011_09_26_drive_0101_sync 0000000440\n2011_09_26 2011_09_26_drive_0101_sync 0000000441\n2011_09_26 2011_09_26_drive_0101_sync 0000000442\n2011_09_26 2011_09_26_drive_0101_sync 0000000443\n2011_09_26 2011_09_26_drive_0101_sync 0000000444\n2011_09_26 2011_09_26_drive_0101_sync 0000000445\n2011_09_26 2011_09_26_drive_0101_sync 0000000446\n2011_09_26 2011_09_26_drive_0101_sync 0000000447\n2011_09_26 2011_09_26_drive_0101_sync 0000000448\n2011_09_26 2011_09_26_drive_0101_sync 0000000449\n2011_09_26 2011_09_26_drive_0101_sync 0000000450\n2011_09_26 2011_09_26_drive_0101_sync 0000000451\n2011_09_26 2011_09_26_drive_0101_sync 0000000452\n2011_09_26 2011_09_26_drive_0101_sync 0000000453\n2011_09_26 2011_09_26_drive_0101_sync 0000000454\n2011_09_26 2011_09_26_drive_0101_sync 0000000455\n2011_09_26 2011_09_26_drive_0101_sync 0000000456\n2011_09_26 2011_09_26_drive_0101_sync 0000000459\n2011_09_26 2011_09_26_drive_0101_sync 0000000495\n2011_09_26 2011_09_26_drive_0101_sync 0000000496\n2011_09_26 2011_09_26_drive_0101_sync 0000000497\n2011_09_26 2011_09_26_drive_0101_sync 0000000498\n2011_09_26 2011_09_26_drive_0101_sync 0000000499\n2011_09_26 2011_09_26_drive_0101_sync 0000000500\n2011_09_26 2011_09_26_drive_0101_sync 0000000501\n2011_09_26 2011_09_26_drive_0101_sync 0000000502\n2011_09_26 2011_09_26_drive_0101_sync 0000000503\n2011_09_26 2011_09_26_drive_0101_sync 0000000504\n2011_09_26 2011_09_26_drive_0101_sync 0000000505\n2011_09_26 2011_09_26_drive_0101_sync 0000000506\n2011_09_26 2011_09_26_drive_0101_sync 0000000507\n2011_09_26 2011_09_26_drive_0101_sync 0000000524\n2011_09_26 2011_09_26_drive_0101_sync 0000000525\n2011_09_26 2011_09_26_drive_0101_sync 0000000526\n2011_09_26 2011_09_26_drive_0101_sync 0000000527\n2011_09_26 2011_09_26_drive_0101_sync 0000000528\n2011_09_26 2011_09_26_drive_0101_sync 0000000529\n2011_09_26 2011_09_26_drive_0101_sync 0000000530\n2011_09_26 2011_09_26_drive_0101_sync 0000000531\n2011_09_26 2011_09_26_drive_0101_sync 0000000532\n2011_09_26 2011_09_26_drive_0101_sync 0000000533\n2011_09_26 2011_09_26_drive_0101_sync 0000000534\n2011_09_26 2011_09_26_drive_0101_sync 0000000535\n2011_09_26 2011_09_26_drive_0101_sync 0000000536\n2011_09_26 2011_09_26_drive_0101_sync 0000000537\n2011_09_26 2011_09_26_drive_0101_sync 0000000538\n2011_09_26 2011_09_26_drive_0101_sync 0000000539\n2011_09_26 2011_09_26_drive_0101_sync 0000000540\n2011_09_26 2011_09_26_drive_0101_sync 0000000541\n2011_09_26 2011_09_26_drive_0101_sync 0000000542\n2011_09_26 2011_09_26_drive_0101_sync 0000000543\n2011_09_26 2011_09_26_drive_0101_sync 0000000544\n2011_09_26 2011_09_26_drive_0101_sync 0000000545\n2011_09_26 2011_09_26_drive_0101_sync 0000000546\n2011_09_26 2011_09_26_drive_0101_sync 0000000547\n2011_09_26 2011_09_26_drive_0101_sync 0000000548\n2011_09_26 2011_09_26_drive_0101_sync 0000000549\n2011_09_26 2011_09_26_drive_0101_sync 0000000550\n2011_09_26 2011_09_26_drive_0101_sync 0000000551\n2011_09_26 2011_09_26_drive_0101_sync 0000000552\n2011_09_26 2011_09_26_drive_0101_sync 0000000553\n2011_09_26 2011_09_26_drive_0101_sync 0000000554\n2011_09_26 2011_09_26_drive_0101_sync 0000000555\n2011_09_26 2011_09_26_drive_0101_sync 0000000556\n2011_09_26 2011_09_26_drive_0101_sync 0000000557\n2011_09_26 2011_09_26_drive_0101_sync 0000000558\n2011_09_26 2011_09_26_drive_0101_sync 0000000559\n2011_09_26 2011_09_26_drive_0101_sync 0000000560\n2011_09_26 2011_09_26_drive_0101_sync 0000000561\n2011_09_26 2011_09_26_drive_0101_sync 0000000562\n2011_09_26 2011_09_26_drive_0101_sync 0000000563\n2011_09_26 2011_09_26_drive_0101_sync 0000000564\n2011_09_26 2011_09_26_drive_0101_sync 0000000565\n2011_09_26 2011_09_26_drive_0101_sync 0000000566\n2011_09_26 2011_09_26_drive_0101_sync 0000000567\n2011_09_26 2011_09_26_drive_0101_sync 0000000568\n2011_09_26 2011_09_26_drive_0101_sync 0000000569\n2011_09_26 2011_09_26_drive_0101_sync 0000000570\n2011_09_26 2011_09_26_drive_0101_sync 0000000571\n2011_09_26 2011_09_26_drive_0101_sync 0000000572\n2011_09_26 2011_09_26_drive_0101_sync 0000000573\n2011_09_26 2011_09_26_drive_0101_sync 0000000574\n2011_09_26 2011_09_26_drive_0101_sync 0000000575\n2011_09_26 2011_09_26_drive_0101_sync 0000000576\n2011_09_26 2011_09_26_drive_0101_sync 0000000577\n2011_09_26 2011_09_26_drive_0101_sync 0000000578\n2011_09_26 2011_09_26_drive_0101_sync 0000000579\n2011_09_26 2011_09_26_drive_0101_sync 0000000580\n2011_09_26 2011_09_26_drive_0101_sync 0000000581\n2011_09_26 2011_09_26_drive_0101_sync 0000000582\n2011_09_26 2011_09_26_drive_0101_sync 0000000583\n2011_09_26 2011_09_26_drive_0101_sync 0000000584\n2011_09_26 2011_09_26_drive_0101_sync 0000000585\n2011_09_26 2011_09_26_drive_0101_sync 0000000586\n2011_09_26 2011_09_26_drive_0101_sync 0000000587\n2011_09_26 2011_09_26_drive_0101_sync 0000000588\n2011_09_26 2011_09_26_drive_0101_sync 0000000589\n2011_09_26 2011_09_26_drive_0101_sync 0000000590\n2011_09_26 2011_09_26_drive_0101_sync 0000000591\n2011_09_26 2011_09_26_drive_0101_sync 0000000592\n2011_09_26 2011_09_26_drive_0101_sync 0000000593\n2011_09_26 2011_09_26_drive_0101_sync 0000000594\n2011_09_26 2011_09_26_drive_0101_sync 0000000595\n2011_09_26 2011_09_26_drive_0101_sync 0000000596\n2011_09_26 2011_09_26_drive_0101_sync 0000000597\n2011_09_26 2011_09_26_drive_0101_sync 0000000598\n2011_09_26 2011_09_26_drive_0101_sync 0000000599\n2011_09_26 2011_09_26_drive_0101_sync 0000000600\n2011_09_26 2011_09_26_drive_0101_sync 0000000601\n2011_09_26 2011_09_26_drive_0101_sync 0000000602\n2011_09_26 2011_09_26_drive_0101_sync 0000000603\n2011_09_26 2011_09_26_drive_0101_sync 0000000604\n2011_09_26 2011_09_26_drive_0101_sync 0000000605\n2011_09_26 2011_09_26_drive_0101_sync 0000000606\n2011_09_26 2011_09_26_drive_0101_sync 0000000607\n2011_09_26 2011_09_26_drive_0101_sync 0000000608\n2011_09_26 2011_09_26_drive_0101_sync 0000000609\n2011_09_26 2011_09_26_drive_0101_sync 0000000620\n2011_09_26 2011_09_26_drive_0101_sync 0000000621\n2011_09_26 2011_09_26_drive_0101_sync 0000000622\n2011_09_26 2011_09_26_drive_0101_sync 0000000623\n2011_09_26 2011_09_26_drive_0101_sync 0000000624\n2011_09_26 2011_09_26_drive_0101_sync 0000000625\n2011_09_26 2011_09_26_drive_0101_sync 0000000626\n2011_09_26 2011_09_26_drive_0101_sync 0000000627\n2011_09_26 2011_09_26_drive_0101_sync 0000000628\n2011_09_26 2011_09_26_drive_0101_sync 0000000629\n2011_09_26 2011_09_26_drive_0101_sync 0000000630\n2011_09_26 2011_09_26_drive_0101_sync 0000000631\n2011_09_26 2011_09_26_drive_0101_sync 0000000632\n2011_09_26 2011_09_26_drive_0101_sync 0000000633\n2011_09_26 2011_09_26_drive_0101_sync 0000000634\n2011_09_26 2011_09_26_drive_0101_sync 0000000635\n2011_09_26 2011_09_26_drive_0101_sync 0000000636\n2011_09_26 2011_09_26_drive_0101_sync 0000000637\n2011_09_26 2011_09_26_drive_0101_sync 0000000649\n2011_09_26 2011_09_26_drive_0101_sync 0000000650\n2011_09_26 2011_09_26_drive_0101_sync 0000000651\n2011_09_26 2011_09_26_drive_0101_sync 0000000652\n2011_09_26 2011_09_26_drive_0101_sync 0000000653\n2011_09_26 2011_09_26_drive_0101_sync 0000000654\n2011_09_26 2011_09_26_drive_0101_sync 0000000655\n2011_09_26 2011_09_26_drive_0101_sync 0000000656\n2011_09_26 2011_09_26_drive_0101_sync 0000000657\n2011_09_26 2011_09_26_drive_0101_sync 0000000658\n2011_09_26 2011_09_26_drive_0101_sync 0000000659\n2011_09_26 2011_09_26_drive_0101_sync 0000000660\n2011_09_26 2011_09_26_drive_0101_sync 0000000661\n2011_09_26 2011_09_26_drive_0101_sync 0000000687\n2011_09_26 2011_09_26_drive_0101_sync 0000000688\n2011_09_26 2011_09_26_drive_0101_sync 0000000689\n2011_09_26 2011_09_26_drive_0101_sync 0000000690\n2011_09_26 2011_09_26_drive_0101_sync 0000000691\n2011_09_26 2011_09_26_drive_0101_sync 0000000692\n2011_09_26 2011_09_26_drive_0101_sync 0000000693\n2011_09_26 2011_09_26_drive_0101_sync 0000000694\n2011_09_26 2011_09_26_drive_0101_sync 0000000695\n2011_09_26 2011_09_26_drive_0101_sync 0000000696\n2011_09_26 2011_09_26_drive_0101_sync 0000000697\n2011_09_26 2011_09_26_drive_0101_sync 0000000698\n2011_09_26 2011_09_26_drive_0101_sync 0000000699\n2011_09_26 2011_09_26_drive_0101_sync 0000000700\n2011_09_26 2011_09_26_drive_0101_sync 0000000701\n2011_09_26 2011_09_26_drive_0101_sync 0000000702\n2011_09_26 2011_09_26_drive_0101_sync 0000000709\n2011_09_26 2011_09_26_drive_0101_sync 0000000710\n2011_09_26 2011_09_26_drive_0101_sync 0000000711\n2011_09_26 2011_09_26_drive_0101_sync 0000000712\n2011_09_26 2011_09_26_drive_0101_sync 0000000713\n2011_09_26 2011_09_26_drive_0101_sync 0000000714\n2011_09_26 2011_09_26_drive_0101_sync 0000000715\n2011_09_26 2011_09_26_drive_0101_sync 0000000716\n2011_09_26 2011_09_26_drive_0101_sync 0000000717\n2011_09_26 2011_09_26_drive_0101_sync 0000000718\n2011_09_26 2011_09_26_drive_0101_sync 0000000719\n2011_09_26 2011_09_26_drive_0101_sync 0000000720\n2011_09_26 2011_09_26_drive_0101_sync 0000000721\n2011_09_26 2011_09_26_drive_0101_sync 0000000722\n2011_09_26 2011_09_26_drive_0101_sync 0000000723\n2011_09_26 2011_09_26_drive_0101_sync 0000000724\n2011_09_26 2011_09_26_drive_0101_sync 0000000725\n2011_09_26 2011_09_26_drive_0101_sync 0000000726\n2011_09_26 2011_09_26_drive_0101_sync 0000000727\n2011_09_26 2011_09_26_drive_0101_sync 0000000728\n2011_09_26 2011_09_26_drive_0101_sync 0000000729\n2011_09_26 2011_09_26_drive_0101_sync 0000000730\n2011_09_26 2011_09_26_drive_0101_sync 0000000731\n2011_09_26 2011_09_26_drive_0101_sync 0000000732\n2011_09_26 2011_09_26_drive_0101_sync 0000000733\n2011_09_26 2011_09_26_drive_0101_sync 0000000734\n2011_09_26 2011_09_26_drive_0101_sync 0000000735\n2011_09_26 2011_09_26_drive_0101_sync 0000000736\n2011_09_26 2011_09_26_drive_0101_sync 0000000737\n2011_09_26 2011_09_26_drive_0101_sync 0000000738\n2011_09_26 2011_09_26_drive_0101_sync 0000000739\n2011_09_26 2011_09_26_drive_0101_sync 0000000740\n2011_09_26 2011_09_26_drive_0101_sync 0000000741\n2011_09_26 2011_09_26_drive_0101_sync 0000000742\n2011_09_26 2011_09_26_drive_0101_sync 0000000743\n2011_09_26 2011_09_26_drive_0101_sync 0000000744\n2011_09_26 2011_09_26_drive_0101_sync 0000000745\n2011_09_26 2011_09_26_drive_0101_sync 0000000746\n2011_09_26 2011_09_26_drive_0101_sync 0000000747\n2011_09_26 2011_09_26_drive_0101_sync 0000000748\n2011_09_26 2011_09_26_drive_0101_sync 0000000749\n2011_09_26 2011_09_26_drive_0101_sync 0000000755\n2011_09_26 2011_09_26_drive_0101_sync 0000000756\n2011_09_26 2011_09_26_drive_0101_sync 0000000757\n2011_09_26 2011_09_26_drive_0101_sync 0000000758\n2011_09_26 2011_09_26_drive_0101_sync 0000000759\n2011_09_26 2011_09_26_drive_0101_sync 0000000760\n2011_09_26 2011_09_26_drive_0101_sync 0000000761\n2011_09_26 2011_09_26_drive_0101_sync 0000000762\n2011_09_26 2011_09_26_drive_0101_sync 0000000763\n2011_09_26 2011_09_26_drive_0101_sync 0000000764\n2011_09_26 2011_09_26_drive_0101_sync 0000000765\n2011_09_26 2011_09_26_drive_0101_sync 0000000766\n2011_09_26 2011_09_26_drive_0101_sync 0000000767\n2011_09_26 2011_09_26_drive_0101_sync 0000000768\n2011_09_26 2011_09_26_drive_0101_sync 0000000769\n2011_09_26 2011_09_26_drive_0101_sync 0000000770\n2011_09_26 2011_09_26_drive_0101_sync 0000000771\n2011_09_26 2011_09_26_drive_0101_sync 0000000772\n2011_09_26 2011_09_26_drive_0101_sync 0000000773\n2011_09_26 2011_09_26_drive_0101_sync 0000000774\n2011_09_26 2011_09_26_drive_0101_sync 0000000775\n2011_09_26 2011_09_26_drive_0101_sync 0000000776\n2011_09_26 2011_09_26_drive_0101_sync 0000000777\n2011_09_26 2011_09_26_drive_0101_sync 0000000778\n2011_09_26 2011_09_26_drive_0101_sync 0000000779\n2011_09_26 2011_09_26_drive_0101_sync 0000000780\n2011_09_26 2011_09_26_drive_0101_sync 0000000781\n2011_09_26 2011_09_26_drive_0101_sync 0000000786\n2011_09_26 2011_09_26_drive_0101_sync 0000000787\n2011_09_26 2011_09_26_drive_0101_sync 0000000788\n2011_09_26 2011_09_26_drive_0101_sync 0000000789\n2011_09_26 2011_09_26_drive_0101_sync 0000000790\n2011_09_26 2011_09_26_drive_0101_sync 0000000791\n2011_09_26 2011_09_26_drive_0101_sync 0000000792\n2011_09_26 2011_09_26_drive_0101_sync 0000000793\n2011_09_26 2011_09_26_drive_0101_sync 0000000794\n2011_09_26 2011_09_26_drive_0101_sync 0000000795\n2011_09_26 2011_09_26_drive_0101_sync 0000000796\n2011_09_26 2011_09_26_drive_0101_sync 0000000797\n2011_09_26 2011_09_26_drive_0101_sync 0000000798\n2011_09_26 2011_09_26_drive_0101_sync 0000000799\n2011_09_26 2011_09_26_drive_0101_sync 0000000800\n2011_09_26 2011_09_26_drive_0101_sync 0000000801\n2011_09_26 2011_09_26_drive_0101_sync 0000000802\n2011_09_26 2011_09_26_drive_0101_sync 0000000803\n2011_09_26 2011_09_26_drive_0101_sync 0000000804\n2011_09_26 2011_09_26_drive_0101_sync 0000000805\n2011_09_26 2011_09_26_drive_0101_sync 0000000806\n2011_09_26 2011_09_26_drive_0101_sync 0000000807\n2011_09_26 2011_09_26_drive_0101_sync 0000000808\n2011_09_26 2011_09_26_drive_0101_sync 0000000809\n2011_09_26 2011_09_26_drive_0101_sync 0000000810\n2011_09_26 2011_09_26_drive_0101_sync 0000000811\n2011_09_26 2011_09_26_drive_0101_sync 0000000812\n2011_09_26 2011_09_26_drive_0101_sync 0000000813\n2011_09_26 2011_09_26_drive_0101_sync 0000000814\n2011_09_26 2011_09_26_drive_0101_sync 0000000815\n2011_09_26 2011_09_26_drive_0101_sync 0000000816\n2011_09_26 2011_09_26_drive_0101_sync 0000000817\n2011_09_26 2011_09_26_drive_0101_sync 0000000818\n2011_09_26 2011_09_26_drive_0101_sync 0000000819\n2011_09_26 2011_09_26_drive_0101_sync 0000000820\n2011_09_26 2011_09_26_drive_0101_sync 0000000821\n2011_09_26 2011_09_26_drive_0101_sync 0000000822\n2011_09_26 2011_09_26_drive_0101_sync 0000000823\n2011_09_26 2011_09_26_drive_0101_sync 0000000824\n2011_09_26 2011_09_26_drive_0101_sync 0000000825\n2011_09_26 2011_09_26_drive_0101_sync 0000000826\n2011_09_26 2011_09_26_drive_0101_sync 0000000827\n2011_09_26 2011_09_26_drive_0101_sync 0000000828\n2011_09_26 2011_09_26_drive_0101_sync 0000000829\n2011_09_26 2011_09_26_drive_0101_sync 0000000830\n2011_09_26 2011_09_26_drive_0101_sync 0000000831\n2011_09_26 2011_09_26_drive_0101_sync 0000000833\n2011_09_26 2011_09_26_drive_0101_sync 0000000841\n2011_09_26 2011_09_26_drive_0101_sync 0000000843\n2011_09_26 2011_09_26_drive_0101_sync 0000000844\n2011_09_26 2011_09_26_drive_0101_sync 0000000845\n2011_09_26 2011_09_26_drive_0101_sync 0000000846\n2011_09_26 2011_09_26_drive_0101_sync 0000000847\n2011_09_26 2011_09_26_drive_0101_sync 0000000848\n2011_09_26 2011_09_26_drive_0101_sync 0000000849\n2011_09_26 2011_09_26_drive_0101_sync 0000000850\n2011_09_26 2011_09_26_drive_0101_sync 0000000851\n2011_09_26 2011_09_26_drive_0101_sync 0000000852\n2011_09_26 2011_09_26_drive_0101_sync 0000000853\n2011_09_26 2011_09_26_drive_0101_sync 0000000854\n2011_09_26 2011_09_26_drive_0101_sync 0000000880\n2011_09_26 2011_09_26_drive_0101_sync 0000000883\n2011_09_26 2011_09_26_drive_0101_sync 0000000884\n2011_09_26 2011_09_26_drive_0101_sync 0000000885\n2011_09_26 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2011_09_26_drive_0061_sync 0000000021\n2011_09_26 2011_09_26_drive_0061_sync 0000000023\n2011_09_26 2011_09_26_drive_0061_sync 0000000025\n2011_09_26 2011_09_26_drive_0061_sync 0000000027\n2011_09_26 2011_09_26_drive_0061_sync 0000000029\n2011_09_26 2011_09_26_drive_0061_sync 0000000031\n2011_09_26 2011_09_26_drive_0061_sync 0000000033\n2011_09_26 2011_09_26_drive_0061_sync 0000000035\n2011_09_26 2011_09_26_drive_0061_sync 0000000037\n2011_09_26 2011_09_26_drive_0061_sync 0000000039\n2011_09_26 2011_09_26_drive_0061_sync 0000000041\n2011_09_26 2011_09_26_drive_0061_sync 0000000043\n2011_09_26 2011_09_26_drive_0061_sync 0000000045\n2011_09_26 2011_09_26_drive_0061_sync 0000000047\n2011_09_26 2011_09_26_drive_0061_sync 0000000049\n2011_09_26 2011_09_26_drive_0061_sync 0000000051\n2011_09_26 2011_09_26_drive_0061_sync 0000000053\n2011_09_26 2011_09_26_drive_0061_sync 0000000055\n2011_09_26 2011_09_26_drive_0061_sync 0000000057\n2011_09_26 2011_09_26_drive_0061_sync 0000000059\n2011_09_26 2011_09_26_drive_0061_sync 0000000061\n2011_09_26 2011_09_26_drive_0061_sync 0000000063\n2011_09_26 2011_09_26_drive_0061_sync 0000000065\n2011_09_26 2011_09_26_drive_0061_sync 0000000067\n2011_09_26 2011_09_26_drive_0061_sync 0000000069\n2011_09_26 2011_09_26_drive_0061_sync 0000000071\n2011_09_26 2011_09_26_drive_0061_sync 0000000073\n2011_09_26 2011_09_26_drive_0061_sync 0000000075\n2011_09_26 2011_09_26_drive_0061_sync 0000000077\n2011_09_26 2011_09_26_drive_0061_sync 0000000079\n2011_09_26 2011_09_26_drive_0061_sync 0000000081\n2011_09_26 2011_09_26_drive_0061_sync 0000000083\n2011_09_26 2011_09_26_drive_0061_sync 0000000085\n2011_09_26 2011_09_26_drive_0061_sync 0000000087\n2011_09_26 2011_09_26_drive_0061_sync 0000000089\n2011_09_26 2011_09_26_drive_0061_sync 0000000091\n2011_09_26 2011_09_26_drive_0061_sync 0000000093\n2011_09_26 2011_09_26_drive_0061_sync 0000000095\n2011_09_26 2011_09_26_drive_0061_sync 0000000097\n2011_09_26 2011_09_26_drive_0061_sync 0000000099\n2011_09_26 2011_09_26_drive_0061_sync 0000000101\n2011_09_26 2011_09_26_drive_0061_sync 0000000103\n2011_09_26 2011_09_26_drive_0061_sync 0000000105\n2011_09_26 2011_09_26_drive_0061_sync 0000000107\n2011_09_26 2011_09_26_drive_0061_sync 0000000109\n2011_09_26 2011_09_26_drive_0061_sync 0000000111\n2011_09_26 2011_09_26_drive_0061_sync 0000000113\n2011_09_26 2011_09_26_drive_0061_sync 0000000115\n2011_09_26 2011_09_26_drive_0061_sync 0000000117\n2011_09_26 2011_09_26_drive_0061_sync 0000000119\n2011_09_26 2011_09_26_drive_0061_sync 0000000121\n2011_09_26 2011_09_26_drive_0061_sync 0000000123\n2011_09_26 2011_09_26_drive_0061_sync 0000000125\n2011_09_26 2011_09_26_drive_0061_sync 0000000127\n2011_09_26 2011_09_26_drive_0061_sync 0000000129\n2011_09_26 2011_09_26_drive_0061_sync 0000000131\n2011_09_26 2011_09_26_drive_0061_sync 0000000133\n2011_09_26 2011_09_26_drive_0061_sync 0000000135\n2011_09_26 2011_09_26_drive_0061_sync 0000000137\n2011_09_26 2011_09_26_drive_0061_sync 0000000139\n2011_09_26 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2011_09_28_drive_0185_sync 0000000028\n2011_09_28 2011_09_28_drive_0185_sync 0000000035\n2011_09_28 2011_09_28_drive_0185_sync 0000000041\n2011_09_28 2011_09_28_drive_0185_sync 0000000048\n2011_09_28 2011_09_28_drive_0185_sync 0000000054\n2011_09_28 2011_09_28_drive_0185_sync 0000000061\n2011_09_28 2011_09_28_drive_0185_sync 0000000068\n2011_09_28 2011_09_28_drive_0185_sync 0000000076\n2011_09_28 2011_09_28_drive_0191_sync 0000000007\n2011_09_28 2011_09_28_drive_0191_sync 0000000014\n2011_09_28 2011_09_28_drive_0191_sync 0000000021\n2011_09_28 2011_09_28_drive_0191_sync 0000000028\n2011_09_28 2011_09_28_drive_0195_sync 0000000009\n2011_09_28 2011_09_28_drive_0195_sync 0000000016\n2011_09_28 2011_09_28_drive_0195_sync 0000000025\n2011_09_28 2011_09_28_drive_0199_sync 0000000008\n2011_09_28 2011_09_28_drive_0199_sync 0000000015\n2011_09_28 2011_09_28_drive_0199_sync 0000000023\n2011_09_28 2011_09_28_drive_0199_sync 0000000031\n2011_09_28 2011_09_28_drive_0201_sync 0000000007\n2011_09_28 2011_09_28_drive_0201_sync 0000000014\n2011_09_28 2011_09_28_drive_0201_sync 0000000021\n2011_09_28 2011_09_28_drive_0201_sync 0000000028\n2011_09_28 2011_09_28_drive_0201_sync 0000000035\n2011_09_28 2011_09_28_drive_0201_sync 0000000042\n2011_09_28 2011_09_28_drive_0201_sync 0000000049\n2011_09_28 2011_09_28_drive_0201_sync 0000000056\n2011_09_28 2011_09_28_drive_0201_sync 0000000063\n2011_09_28 2011_09_28_drive_0201_sync 0000000070\n2011_09_28 2011_09_28_drive_0201_sync 0000000078\n2011_09_28 2011_09_28_drive_0205_sync 0000000008\n2011_09_28 2011_09_28_drive_0205_sync 0000000015\n2011_09_28 2011_09_28_drive_0205_sync 0000000027\n2011_09_28 2011_09_28_drive_0209_sync 0000000007\n2011_09_28 2011_09_28_drive_0209_sync 0000000014\n2011_09_28 2011_09_28_drive_0209_sync 0000000021\n2011_09_28 2011_09_28_drive_0209_sync 0000000028\n2011_09_28 2011_09_28_drive_0209_sync 0000000035\n2011_09_28 2011_09_28_drive_0209_sync 0000000042\n2011_09_28 2011_09_28_drive_0209_sync 0000000050\n2011_09_28 2011_09_28_drive_0209_sync 0000000057\n2011_09_28 2011_09_28_drive_0209_sync 0000000065\n2011_09_28 2011_09_28_drive_0209_sync 0000000072\n2011_09_28 2011_09_28_drive_0209_sync 0000000079\n2011_09_28 2011_09_28_drive_0216_sync 0000000008\n2011_09_28 2011_09_28_drive_0216_sync 0000000015\n2011_09_28 2011_09_28_drive_0216_sync 0000000022\n2011_09_28 2011_09_28_drive_0216_sync 0000000030\n2011_09_28 2011_09_28_drive_0216_sync 0000000038\n2011_09_28 2011_09_28_drive_0220_sync 0000000028\n2011_09_28 2011_09_28_drive_0220_sync 0000000035\n2011_09_28 2011_09_28_drive_0220_sync 0000000042\n2011_09_28 2011_09_28_drive_0220_sync 0000000049\n2011_09_28 2011_09_28_drive_0220_sync 0000000057\n2011_09_28 2011_09_28_drive_0220_sync 0000000066\n2011_09_28 2011_09_28_drive_0222_sync 0000000007\n2011_09_28 2011_09_28_drive_0222_sync 0000000014\n2011_09_28 2011_09_28_drive_0222_sync 0000000021\n2011_09_28 2011_09_28_drive_0222_sync 0000000027\n2011_09_28 2011_09_28_drive_0222_sync 0000000033\n2011_09_28 2011_09_28_drive_0222_sync 0000000040\n2011_09_28 2011_09_28_drive_0222_sync 0000000053\n"
  },
  {
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    "content": 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  },
  {
    "path": "data/kitti_split1/devkit/matlab/computeBox3D.m",
    "content": "function [corners_2D,face_idx] = computeBox3D(object,P)\n% takes an object and a projection matrix (P) and projects the 3D\n% bounding box into the image plane.\n\n% index for 3D bounding box faces\nface_idx = [ 1,2,6,5   % front face\n             2,3,7,6   % left face\n             3,4,8,7   % back face\n             4,1,5,8]; % right face\n\n% compute rotational matrix around yaw axis\nR = [+cos(object.ry), 0, +sin(object.ry);\n                   0, 1,               0;\n     -sin(object.ry), 0, +cos(object.ry)];\n\n% 3D bounding box dimensions\nl = object.l;\nw = object.w;\nh = object.h;\n\n% 3D bounding box corners\nx_corners = [l/2, l/2, -l/2, -l/2, l/2, l/2, -l/2, -l/2];\ny_corners = [0,0,0,0,-h,-h,-h,-h];\nz_corners = [w/2, -w/2, -w/2, w/2, w/2, -w/2, -w/2, w/2];\n\n% rotate and translate 3D bounding box\ncorners_3D = R*[x_corners;y_corners;z_corners];\ncorners_3D(1,:) = corners_3D(1,:) + object.t(1);\ncorners_3D(2,:) = corners_3D(2,:) + object.t(2);\ncorners_3D(3,:) = corners_3D(3,:) + object.t(3);\n\n% only draw 3D bounding box for objects in front of the camera\nif any(corners_3D(3,:)<0.1) \n  corners_2D = [];\n  return;\nend\n\n% project the 3D bounding box into the image plane\ncorners_2D = projectToImage(corners_3D, P);\n"
  },
  {
    "path": "data/kitti_split1/devkit/matlab/computeOrientation3D.m",
    "content": "function [orientation_2D] = computeOrientation3D(object,P)\n% takes an object and a projection matrix (P) and projects the 3D\n% object orientation vector into the image plane.\n\n% compute rotational matrix around yaw axis\nR = [cos(object.ry),  0, sin(object.ry);\n     0,               1,              0;\n     -sin(object.ry), 0, cos(object.ry)];\n\n% orientation in object coordinate system\norientation_3D = [0.0, object.l\n                  0.0, 0.0\n                  0.0, 0.0];\n\n% rotate and translate in camera coordinate system, project in image\norientation_3D      = R*orientation_3D;\norientation_3D(1,:) = orientation_3D(1,:) + object.t(1);\norientation_3D(2,:) = orientation_3D(2,:) + object.t(2);\norientation_3D(3,:) = orientation_3D(3,:) + object.t(3);\n\n% vector behind image plane?\nif any(orientation_3D(3,:)<0.1)\n  orientation_2D = [];\n  return;\nend\n\n% project orientation into the image plane\norientation_2D = projectToImage(orientation_3D,P);\n"
  },
  {
    "path": "data/kitti_split1/devkit/matlab/drawBox2D.m",
    "content": "function drawBox2D(h,object)\n\n% set styles for occlusion and truncation\nocc_col    = {'g','y','r','w'};\ntrun_style = {'-','--'};\n\n% draw regular objects\nif ~strcmp(object.type,'DontCare')\n\n  % show rectangular bounding boxes\n  pos = [object.x1,object.y1,object.x2-object.x1+1,object.y2-object.y1+1];\n  trc = double(object.truncation>0.1)+1;\n  rectangle('Position',pos,'EdgeColor',occ_col{object.occlusion+1},...\n            'LineWidth',3,'LineStyle',trun_style{trc},'parent',h(1).axes)\n  rectangle('Position',pos,'EdgeColor','b', 'parent', h(1).axes)\n\n  % draw label\n  label_text = sprintf('%s\\n%1.1f rad',object.type,object.alpha);\n  x = (object.x1+object.x2)/2;\n  y = object.y1;\n  text(x,max(y-5,40),label_text,'color',occ_col{object.occlusion+1},...\n       'BackgroundColor','k','HorizontalAlignment','center',...\n       'VerticalAlignment','bottom','FontWeight','bold',...\n       'FontSize',8,'parent',h(1).axes);\n     \n% draw don't care regions\nelse\n  \n  % draw dotted rectangle\n  pos = [object.x1,object.y1,object.x2-object.x1+1,object.y2-object.y1+1];\n  rectangle('Position',pos,'EdgeColor','c',...\n            'LineWidth',2,'LineStyle','-','parent',h(1).axes)\nend\n"
  },
  {
    "path": "data/kitti_split1/devkit/matlab/drawBox3D.m",
    "content": "function drawBox3D(h,object,corners,face_idx,orientation)\n\n  % set styles for occlusion and truncation\n  occ_col    = {'g','y','r','w'};\n  trun_style = {'-','--'};\n  trc        = double(object.truncation>0.1)+1;\n  \n  % draw projected 3D bounding boxes\n  if ~isempty(corners)\n    for f=1:4\n      line([corners(1,face_idx(f,:)),corners(1,face_idx(f,1))]+1,...\n           [corners(2,face_idx(f,:)),corners(2,face_idx(f,1))]+1,...\n           'parent',h(2).axes, 'color',occ_col{object.occlusion+1},...\n           'LineWidth',3,'LineStyle',trun_style{trc});\n      line([corners(1,face_idx(f,:)),corners(1,face_idx(f,1))]+1,...\n           [corners(2,face_idx(f,:)),corners(2,face_idx(f,1))]+1,...\n           'parent',h(2).axes,'color','b','LineWidth',1);\n    end\n  end\n  \n  % draw orientation vector\n  if ~isempty(orientation)\n    line([orientation(1,:),orientation(1,:)]+1,...\n         [orientation(2,:),orientation(2,:)]+1,...\n         'parent',h(2).axes,'color','w','LineWidth',4);\n    line([orientation(1,:),orientation(1,:)]+1,...\n         [orientation(2,:),orientation(2,:)]+1,...\n         'parent',h(2).axes,'color','k','LineWidth',2);\n  end\nend\n"
  },
  {
    "path": "data/kitti_split1/devkit/matlab/projectToImage.m",
    "content": "function pts_2D = projectToImage(pts_3D, P)\n% PROJECTTOIMAGE projects 3D points in given coordinate system in the image\n% plane using the given projection matrix P.\n%\n% Usage: pts_2D = projectToImage(pts_3D, P)\n%   input: pts_3D: 3xn matrix\n%          P:      3x4 projection matrix\n%   output: pts_2D: 2xn matrix\n%\n% last edited on: 2012-02-27\n% Philip Lenz - lenz@kit.edu\n\n\n  % project in image\n  pts_2D = P * [pts_3D; ones(1,size(pts_3D,2))];\n  % scale projected points\n  pts_2D(1,:) = pts_2D(1,:)./pts_2D(3,:);\n  pts_2D(2,:) = pts_2D(2,:)./pts_2D(3,:);\n  pts_2D(3,:) = [];\nend"
  },
  {
    "path": "data/kitti_split1/devkit/matlab/readCalibration.m",
    "content": "function P = readCalibration(calib_dir,img_idx,cam)\n\n  % load 3x4 projection matrix\n  P = dlmread(sprintf('%s/%06d.txt',calib_dir,img_idx),' ',0,1);\n  P = P(cam+1,:);\n  P = reshape(P ,[4,3])';\n  \nend\n"
  },
  {
    "path": "data/kitti_split1/devkit/matlab/readLabels.m",
    "content": "function objects = readLabels(label_dir,img_idx)\n\n% parse input file\nfid = fopen(sprintf('%s/%06d.txt',label_dir,img_idx),'r');\nC   = textscan(fid,'%s %f %d %f %f %f %f %f %f %f %f %f %f %f %f','delimiter', ' ');\nfclose(fid);\n\n% for all objects do\nobjects = [];\nfor o = 1:numel(C{1})\n\n  % extract label, truncation, occlusion\n  lbl = C{1}(o);                   % for converting: cell -> string\n  objects(o).type       = lbl{1};  % 'Car', 'Pedestrian', ...\n  objects(o).truncation = C{2}(o); % truncated pixel ratio ([0..1])\n  objects(o).occlusion  = C{3}(o); % 0 = visible, 1 = partly occluded, 2 = fully occluded, 3 = unknown\n  objects(o).alpha      = C{4}(o); % object observation angle ([-pi..pi])\n\n  % extract 2D bounding box in 0-based coordinates\n  objects(o).x1 = C{5}(o); % left\n  objects(o).y1 = C{6}(o); % top\n  objects(o).x2 = C{7}(o); % right\n  objects(o).y2 = C{8}(o); % bottom\n\n  % extract 3D bounding box information\n  objects(o).h    = C{9} (o); % box width\n  objects(o).w    = C{10}(o); % box height\n  objects(o).l    = C{11}(o); % box length\n  objects(o).t(1) = C{12}(o); % location (x)\n  objects(o).t(2) = C{13}(o); % location (y)\n  objects(o).t(3) = C{14}(o); % location (z)\n  objects(o).ry   = C{15}(o); % yaw angle\nend\n"
  },
  {
    "path": "data/kitti_split1/devkit/matlab/run_demo.m",
    "content": "% KITTI OBJECT DETECTION AND ORIENTATION ESTIMATION BENCHMARK DEMONSTRATION\n% \n% This tool displays the images and the object labels for the benchmark and\n% provides an entry point for writing your own interface to the data set.\n% Before running this tool, set root_dir to the directory where you have\n% downloaded the dataset. 'root_dir' must contain the subdirectory\n% 'training', which in turn contains 'image_2', 'label_2' and 'calib'.\n% For more information about the data format, please look into readme.txt.\n%\n% Usage:\n%   SPACE: next frame\n%   '-':   last frame\n%   'x':   +10 frames\n%   'y':   -10 frames\n%   q:     quit\n%\n% Occlusion Coding:\n%   green:  not occluded\n%   yellow: partly occluded\n%   red:    fully occluded\n%   white:  unknown\n%\n% Truncation Coding:\n%   solid:  not truncated\n%   dashed: truncated\n\n% clear and close everything\nclear all; close all;\ndisp('======= KITTI DevKit Demo =======');\n\n% options\nroot_dir = '/media/data/kitti/2012_object';\ndata_set = 'training';\n\n% get sub-directories\ncam = 2; % 2 = left color camera\nimage_dir = fullfile(root_dir,[data_set '/image_' num2str(cam)]);\nlabel_dir = fullfile(root_dir,[data_set '/label_' num2str(cam)]);\ncalib_dir = fullfile(root_dir,[data_set '/calib']);\n\n% get number of images for this dataset\nnimages = length(dir(fullfile(image_dir, '*.png')));\n\n% set up figure\nh = visualization('init',image_dir);\n\n% main loop\nimg_idx=0;\nwhile 1\n\n  % load projection matrix\n  P = readCalibration(calib_dir,img_idx,cam);\n  \n  % load labels\n  objects = readLabels(label_dir,img_idx);\n  \n  % visualization update for next frame\n  visualization('update',image_dir,h,img_idx,nimages,data_set);\n \n  % for all annotated objects do\n  for obj_idx=1:numel(objects)\n   \n    % plot 2D bounding box\n    drawBox2D(h,objects(obj_idx));\n    \n    % plot 3D bounding box\n    [corners,face_idx] = computeBox3D(objects(obj_idx),P);\n    orientation = computeOrientation3D(objects(obj_idx),P);\n    drawBox3D(h, objects(obj_idx),corners,face_idx,orientation);\n    \n  end\n\n  % force drawing and tiny user interface\n  waitforbuttonpress; \n  key = get(gcf,'CurrentCharacter');\n  switch lower(key)                         \n    case 'q',  break;                                 % quit\n    case '-',  img_idx = max(img_idx-1,  0);          % previous frame\n    case 'x',  img_idx = min(img_idx+1000,nimages-1); % +100 frames\n    case 'y',  img_idx = max(img_idx-1000,0);         % -100 frames\n    otherwise, img_idx = min(img_idx+1,  nimages-1);  % next frame\n  end\n\nend\n\n% clean up\nclose all;\n"
  },
  {
    "path": "data/kitti_split1/devkit/matlab/run_readWriteDemo.m",
    "content": "% clear and close everything\nclear all; close all;\ndisp('======= KITTI DevKit Demo =======');\n\nroot_dir  = '/media/karlsruhe_data/kitti/2012_object';\ntrain_dir = fullfile(root_dir,'/training/label_2');\ntest_dir  = '.'; % location of your testing dir\n\n% read objects of first training image\ntrain_objects = readLabels(train_dir,0);\n\n% loop over all images\n% ... YOUR TRAINING CODE HERE ...\n% ... YOUR TESTING CODE HERE ...\n\n% detect one object (car) in first test image\ntest_objects(1).type  = 'Car';\ntest_objects(1).x1    = 10;\ntest_objects(1).y1    = 10;\ntest_objects(1).x2    = 100;\ntest_objects(1).y2    = 100;\ntest_objects(1).alpha = pi/2;\ntest_objects(1).score = 0.5;\n\n% write object to file\nwriteLabels(test_objects,test_dir,0);\ndisp('Test label file written!');\n"
  },
  {
    "path": "data/kitti_split1/devkit/matlab/run_statistics.m",
    "content": "% KITTI OBJECT DETECTION AND ORIENTATION ESTIMATION BENCHMARK STATISTICS\nclear all; close all; clc;\ndisp('======= KITTI DevKit Statistics =======');\ndisp('Computing statistics of Cars, Pedestrians and Cyclists in training set.');\ndisp('Please wait ...');\n\n% options\nroot_dir = '/media/karlsruhe_data/kitti/2012_object';\n  \n% get label directory and number of images\nlabel_dir = fullfile(root_dir,'training/label_2');\nnimages = length(dir(fullfile(label_dir, '*.txt')));\n\n% init statistics\ncars.occ = zeros(1,4);\npeds.occ = zeros(1,4);\ncycs.occ = zeros(1,4);\n\n% compute statistics\nfor j=1:nimages\n  objects = readLabels(label_dir,j-1);\n  for k=1:length(objects)\n    if strcmp(objects(k).type,'Car')\n      cars.occ(objects(k).occlusion+1)  = cars.occ(objects(k).occlusion+1)  + 1;\n    end\n    if strcmp(objects(k).type,'Pedestrian')\n      peds.occ(objects(k).occlusion+1)  = peds.occ(objects(k).occlusion+1)  + 1;\n    end\n    if strcmp(objects(k).type,'Cyclist')\n      cycs.occ(objects(k).occlusion+1)  = cycs.occ(objects(k).occlusion+1)  + 1;\n    end\n  end    \nend\n\n% plot statistics\nfprintf('Cars: Not occluded: %d, partly occluded: %d, largely occluded: %d, unknown: %d\\n',cars.occ);\nfprintf('Pedestrians: Not occluded: %d, partly occluded: %d, largely occluded: %d, unknown: %d\\n',peds.occ);\nfprintf('Cyclists: Not occluded: %d, partly occluded: %d, largely occluded: %d, unknown: %d\\n',cycs.occ);\n"
  },
  {
    "path": "data/kitti_split1/devkit/matlab/visualization.m",
    "content": "function varargout = visualization(mode,image_dir,varargin)\n\nswitch mode\n  \n  % init figure\n  case 'init'\n    \n    % create figure using size of first image in repository\n    fig = figure(1);\n    img = imread(sprintf('%s/%06d.png',image_dir,0));\n    set(fig,'position',[0,30,0.8*size(img,2),0.8*2*size(img,1)]);\n    h(1).axes = axes('position',[0,0.5,1,0.5]);\n    h(2).axes = axes('position',[0,0,1,0.5]);\n    varargout{1} = h;\n    \n  % update figure\n  case 'update'\n    \n    % unpack input arguments\n    h        = varargin{1};\n    img_idx  = varargin{2};\n    nimages  = varargin{3};\n    data_set = varargin{4};\n    \n    % read image\n    img = imread(sprintf('%s/%06d.png',image_dir,img_idx));\n    \n    % clear axes, draw image\n    cla(h(1).axes); cla(h(2).axes);\n    imshow(img,'parent',h(1).axes); axis(h(1).axes,'image','off'); hold(h(1).axes, 'on');\n    imshow(img,'parent',h(2).axes); axis(h(2).axes,'image','off'); hold(h(2).axes, 'on');\n    \n    % title\n    text(size(img,2)/2,3,sprintf('2D Bounding Boxes'),'parent',h(1).axes,'color','g','HorizontalAlignment','center','VerticalAlignment','top','FontSize',14,'FontWeight','bold','BackgroundColor','black');\n    text(size(img,2)/2,3,sprintf('3D Bounding Boxes'),'parent',h(2).axes,'color','g','HorizontalAlignment','center','VerticalAlignment','top','FontSize',14,'FontWeight','bold','BackgroundColor','black');\n    \n    % legend\n    text(0,00,'Not occluded','parent',h(1).axes,'color','g','HorizontalAlignment','left','VerticalAlignment','top','FontSize',14,'FontWeight','bold','BackgroundColor','black');\n    text(0,30,'Partly occluded','parent',h(1).axes,'color','y','HorizontalAlignment','left','VerticalAlignment','top','FontSize',14,'FontWeight','bold','BackgroundColor','black');\n    text(0,60,'Fully occluded','parent',h(1).axes,'color','r','HorizontalAlignment','left','VerticalAlignment','top','FontSize',14,'FontWeight','bold','BackgroundColor','black');\n    text(0,90,'Unknown','parent',h(1).axes,'color','w','HorizontalAlignment','left','VerticalAlignment','top','FontSize',14,'FontWeight','bold','BackgroundColor','black');\n    text(0,120,'Don''t care region','parent',h(1).axes,'color','c','HorizontalAlignment','left','VerticalAlignment','top','FontSize',14,'FontWeight','bold','BackgroundColor','black');\n\n    % frame number\n    text(size(img,2),0,sprintf('%s set frame %d/%d',data_set,img_idx,nimages-1), 'parent', h(1).axes,'color','g','HorizontalAlignment','right','VerticalAlignment','top','FontSize',14,'FontWeight','bold','BackgroundColor','black');\n    \n    % usage instructions\n    text(size(img,2)/2,size(img,1),sprintf('''SPACE'': Next Image  |  ''-'': Previous Image  |  ''x'': +1000  |  ''y'': -1000 | ''q'': quit'), 'parent', h(2).axes,'color','g','HorizontalAlignment','center','VerticalAlignment','bottom','FontSize',14,'FontWeight','bold', 'BackgroundColor','black');\nend\n"
  },
  {
    "path": "data/kitti_split1/devkit/matlab/writeLabels.m",
    "content": "function writeLabels(objects,label_dir,img_idx)\n\n% parse input file\nfid = fopen(sprintf('%s/%06d.txt',label_dir,img_idx),'w');\n\n% for all objects do\nfor o = 1:numel(objects)\n\n  % set label, truncation, occlusion\n  if isfield(objects(o),'type'),         fprintf(fid,'%s ',objects(o).type);\n  else                                   error('ERROR: type not specified!'), end;\n  if isfield(objects(o),'truncation'),   fprintf(fid,'%.2f ',objects(o).truncation);\n  else                                   fprintf(fid,'-1 '); end; % default\n  if isfield(objects(o),'occlusion'),    fprintf(fid,'%.d ',objects(o).occlusion);\n  else                                   fprintf(fid,'-1 '); end; % default\n  if isfield(objects(o),'alpha'),        fprintf(fid,'%.2f ',wrapToPi(objects(o).alpha));\n  else                                   fprintf(fid,'-10 '); end; % default\n\n  % set 2D bounding box in 0-based C++ coordinates\n  if isfield(objects(o),'x1'),           fprintf(fid,'%.2f ',objects(o).x1);\n  else                                   error('ERROR: x1 not specified!'); end;\n  if isfield(objects(o),'y1'),           fprintf(fid,'%.2f ',objects(o).y1);\n  else                                   error('ERROR: y1 not specified!'); end;\n  if isfield(objects(o),'x2'),           fprintf(fid,'%.2f ',objects(o).x2);\n  else                                   error('ERROR: x2 not specified!'); end;\n  if isfield(objects(o),'y2'),           fprintf(fid,'%.2f ',objects(o).y2);\n  else                                   error('ERROR: y2 not specified!'); end;\n\n  % set 3D bounding box\n  if isfield(objects(o),'h'),            fprintf(fid,'%.2f ',objects(o).h);\n  else                                   fprintf(fid,'-1 '); end; % default\n  if isfield(objects(o),'w'),            fprintf(fid,'%.2f ',objects(o).w);\n  else                                   fprintf(fid,'-1 '); end; % default\n  if isfield(objects(o),'l'),            fprintf(fid,'%.2f ',objects(o).l);\n  else                                   fprintf(fid,'-1 '); end; % default\n  if isfield(objects(o),'t'),            fprintf(fid,'%.2f %.2f %.2f ',objects(o).t);\n  else                                   fprintf(fid,'-1000 -1000 -1000 '); end; % default\n  if isfield(objects(o),'ry'),           fprintf(fid,'%.2f ',wrapToPi(objects(o).ry));\n  else                                   fprintf(fid,'-10 '); end; % default\n\n  % set score\n  if isfield(objects(o),'score'),        fprintf(fid,'%.2f ',objects(o).score);\n  else                                   error('ERROR: score not specified!'); end;\n\n  % next line\n  fprintf(fid,'\\n');\nend\n\n% close file\nfclose(fid);\n\nfunction alpha = wrapToPi(alpha)\n\n% wrap to [0..2*pi]\nalpha = mod(alpha,2*pi);\n\n% wrap to [-pi..pi]\nidx = alpha>pi;\nalpha(idx) = alpha(idx)-2*pi;\n\n"
  },
  {
    "path": "data/kitti_split1/devkit/readme.txt",
    "content": "###########################################################################\n#            THE KITTI VISION BENCHMARK SUITE: OBJECT BENCHMARK           #\n#              Andreas Geiger    Philip Lenz    Raquel Urtasun            #\n#                    Karlsruhe Institute of Technology                    #\n#                Toyota Technological Institute at Chicago                #\n#                             www.cvlibs.net                              #\n###########################################################################\n\nFor recent updates see http://www.cvlibs.net/datasets/kitti/eval_object.php.\n\nThis file describes the KITTI 2D object detection and orientation estimation\nbenchmark, the 3D object detection benchmark and the bird's eye view benchmark.\nThe benchmarks consist of 7481 training images (and point clouds) \nand 7518 test images (and point clouds) for each task.\nDespite the fact that we have labeled 8 different classes, only the\nclasses 'Car' and 'Pedestrian' are evaluated in our benchmark, as only for\nthose classes enough instances for a comprehensive evaluation have been\nlabeled. The labeling process has been performed in two steps: First we\nhired a set of annotators, to label 3D bounding boxe tracklets in point\nclouds. Since for a pedestrian tracklet, a single 3D bounding box tracklet\n(dimensions have been fixed) often fits badly, we additionally labeled the\nleft/right boundaries of each object by making use of Mechanical Turk. We\nalso collected labels of the object's occlusion state, and computed the\nobject's truncation via backprojecting a car/pedestrian model into the\nimage plane.\n\nNOTE: WHEN SUBMITTING RESULTS, PLEASE STORE THEM IN THE SAME DATA FORMAT IN\nWHICH THE GROUND TRUTH DATA IS PROVIDED (SEE BELOW), USING THE FILE NAMES\n000000.txt 000001.txt ... CREATE A ZIP ARCHIVE OF THEM AND STORE YOUR\nRESULTS (ONLY THE RESULTS OF THE TEST SET) IN ITS ROOT FOLDER.\n\nNOTE2: Please read the bottom of this file carefully if you plan to evaluate\nresults yourself on the training set.\n\nNOTE3: WHEN SUBMITTING RESULTS FOR THE 3D OBJECT DETECTION BENCHMARK OR THE\nBIRD'S EYE VIEW BENCHMARK (AS OF 2017), READ THE INSTRUCTIONS BELOW CAREFULLY.\nIN PARTICULAR, MAKE SURE TO ALWAYS SUBMIT BOTH THE 2D BOUNDING BOXES AND THE\n3D BOUNDING BOXES AND FILTER BOUNDING BOXES NOT VISIBLE ON THE IMAGE PLANE.\n\nData Format Description\n=======================\n\nThe data for training and testing can be found in the corresponding folders.\nThe sub-folders are structured as follows:\n\n  - image_02/ contains the left color camera images (png)\n  - label_02/ contains the left color camera label files (plain text files)\n  - calib/ contains the calibration for all four cameras (plain text file)\n\nThe label files contain the following information, which can be read and\nwritten using the matlab tools (readLabels.m, writeLabels.m) provided within\nthis devkit. All values (numerical or strings) are separated via spaces,\neach row corresponds to one object. The 15 columns represent:\n\n#Values    Name      Description\n----------------------------------------------------------------------------\n   1    type         Describes the type of object: 'Car', 'Van', 'Truck',\n                     'Pedestrian', 'Person_sitting', 'Cyclist', 'Tram',\n                     'Misc' or 'DontCare'\n   1    truncated    Float from 0 (non-truncated) to 1 (truncated), where\n                     truncated refers to the object leaving image boundaries\n   1    occluded     Integer (0,1,2,3) indicating occlusion state:\n                     0 = fully visible, 1 = partly occluded\n                     2 = largely occluded, 3 = unknown\n   1    alpha        Observation angle of object, ranging [-pi..pi]\n   4    bbox         2D bounding box of object in the image (0-based index):\n                     contains left, top, right, bottom pixel coordinates\n   3    dimensions   3D object dimensions: height, width, length (in meters)\n   3    location     3D object location x,y,z in camera coordinates (in meters)\n   1    rotation_y   Rotation ry around Y-axis in camera coordinates [-pi..pi]\n   1    score        Only for results: Float, indicating confidence in\n                     detection, needed for p/r curves, higher is better.\n\nHere, 'DontCare' labels denote regions in which objects have not been labeled,\nfor example because they have been too far away from the laser scanner. To\nprevent such objects from being counted as false positives our evaluation\nscript will ignore objects detected in don't care regions of the test set.\nYou can use the don't care labels in the training set to avoid that your object\ndetector is harvesting hard negatives from those areas, in case you consider\nnon-object regions from the training images as negative examples.\n\nThe coordinates in the camera coordinate system can be projected in the image\nby using the 3x4 projection matrix in the calib folder, where for the left\ncolor camera for which the images are provided, P2 must be used. The\ndifference between rotation_y and alpha is, that rotation_y is directly\ngiven in camera coordinates, while alpha also considers the vector from the\ncamera center to the object center, to compute the relative orientation of\nthe object with respect to the camera. For example, a car which is facing\nalong the X-axis of the camera coordinate system corresponds to rotation_y=0,\nno matter where it is located in the X/Z plane (bird's eye view), while\nalpha is zero only, when this object is located along the Z-axis of the\ncamera. When moving the car away from the Z-axis, the observation angle\nwill change.\n\nTo project a point from Velodyne coordinates into the left color image,\nyou can use this formula: x = P2 * R0_rect * Tr_velo_to_cam * y\nFor the right color image: x = P3 * R0_rect * Tr_velo_to_cam * y\n\nNote: All matrices are stored row-major, i.e., the first values correspond\nto the first row. R0_rect contains a 3x3 matrix which you need to extend to\na 4x4 matrix by adding a 1 as the bottom-right element and 0's elsewhere.\nTr_xxx is a 3x4 matrix (R|t), which you need to extend to a 4x4 matrix \nin the same way!\n\nNote, that while all this information is available for the training data,\nonly the data which is actually needed for the particular benchmark must\nbe provided to the evaluation server. However, all 15 values must be provided\nat all times, with the unused ones set to their default values (=invalid) as\nspecified in writeLabels.m. Additionally a 16'th value must be provided\nwith a floating value of the score for a particular detection, where higher\nindicates higher confidence in the detection. The range of your scores will\nbe automatically determined by our evaluation server, you don't have to\nnormalize it, but it should be roughly linear. If you use writeLabels.m for\nwriting your results, this function will take care of storing all required\ndata correctly.\n\n2D Object Detection Benchmark\n=============================\n\nThe goal in the 2D object detection task is to train object detectors for the\nclasses 'Car', 'Pedestrian', and 'Cyclist'. The object detectors must\nprovide as output the 2D 0-based bounding box in the image using the format\nspecified above, as well as a detection score, indicating the confidence\nin the detection. All other values must be set to their default values\n(=invalid), see above. One text file per image must be provided in a zip\narchive, where each file can contain many detections, depending on the \nnumber of objects per image. In our evaluation we only evaluate detections/\nobjects larger than 25 pixel (height) in the image and do not count 'Van' as\nfalse positives for 'Car' or 'Sitting Person' as false positive for 'Pedestrian'\ndue to their similarity in appearance. As evaluation criterion we follow\nPASCAL and require the intersection-over-union of bounding boxes to be\nlarger than 50% for an object to be detected correctly.\n\nObject Orientation Estimation Benchmark\n=======================================\n\nThis benchmark is similar as the previous one, except that you have to\nprovide additionally the most likely relative object observation angle\n(=alpha) for each detection. As described in our paper, our score here\nconsiders both, the detection performance as well as the orientation\nestimation performance of the algorithm jointly.\n\n3D Object Detection Benchmark\n=============================\n\nThe goal in the 3D object detection task is to train object detectors for\nthe classes 'Car', 'Pedestrian', and 'Cyclist'. The object detectors\nmust provide BOTH the 2D 0-based bounding box in the image as well as the 3D\nbounding box (in the format specified above, i.e. 3D dimensions and 3D locations)\nand the detection score/confidence. Note that the 2D bounding box should correspond\nto the projection of the 3D bounding box - this is required to filter objects\nlarger than 25 pixel (height). We also note that not all objects in the point clouds\nhave been labeled. To avoid false positives, detections not visible on the image plane\nshould be filtered (the evaluation does not take care of this, see \n'cpp/evaluate_object.cpp'). Similar to the 2D object detection benchmark,\nwe do not count 'Van' as false positives for 'Car' or 'Sitting Person'\nas false positive for 'Pedestrian'. Evaluation criterion follows the 2D\nobject detection benchmark (using 3D bounding box overlap).\n\nBird's Eye View Benchmark\n=========================\n\nThe goal in the bird's eye view detection task is to train object detectors\nfor the classes 'Car', 'Pedestrian', and 'Cyclist' where the detectors must provide\nBOTH the 2D 0-based bounding box in the image as well as the 3D bounding box\nin bird's eye view and the detection score/confidence. This means that the 3D\nbounding box does not have to include information on the height axis, i.e.\nthe height of the bounding box and the bounding box location along the height axis.\nFor example, when evaluating the bird's eye view benchmark only (without the\n3D object detection benchmark), the height of the bounding box can be set to\na value equal to or smaller than zero. Similarly, the y-axis location of the\nbounding box can be set to -1000 (note that an arbitrary negative value will\nnot work). As above, we note that the 2D bounding boxes are required to filter\nobjects larger than 25 pixel (height) and that - to avoid false positives - detections\nnot visible on the image plane should be filtered. As in all benchmarks, we do\nnot count 'Van' as false positives for 'Car' or 'Sitting Person' as false positive\nfor 'Pedestrian'. Evaluation criterion follows the above benchmarks using\na bird's eye view bounding box overlap.\n\nMapping to Raw Data\n===================\n\nNote that this section is additional to the benchmark, and not required for\nsolving the object detection task.\n\nIn order to allow the usage of the laser point clouds, gps data, the right\ncamera image and the grayscale images for the TRAINING data as well, we\nprovide the mapping of the training set to the raw data of the KITTI dataset.\n\nThis information is saved in mapping/train_mapping.txt and train_rand.txt:\n\ntrain_rand.txt: Random permutation, assigning a unique index to each image\nfrom the object detection training set. The index is 1-based.\n\ntrain_mapping.txt: Maps each unique index (= 1-based line numbers) to a zip\nfile of the KITTI raw data set files. Note that those files are split into\nseveral categories on the website!\n\nExample: Image 0 from the training set has index 7282 and maps to date\n2011_09_28, drive 106 and frame 48. Drives and frames are 0-based.\n\nEvaluation Protocol:\n====================\n\nFor transparency we have included the KITTI evaluation code in the\nsubfolder 'cpp' of this development kit. It can be compiled via:\n\ng++ -O3 -DNDEBUG -o evaluate_object evaluate_object.cpp\n\nor using CMake and the provided 'CMakeLists.txt'.\n\nIMPORTANT NOTE:\n\nThis code will result in 41 values (41 recall discretization steps). However,\nnote that in order to compute average precision, we follow the PASCAL protocol\nand average by summing in 10% recall steps. The pseudocode for computing average\nprecision or orientation similarity is given as follows:\n\nsum = 0;\nfor (i=0; i<=40; i+=4)\n  sum += vals[i];\naverage = sum/11.0;\n\n"
  },
  {
    "path": "data/kitti_split1/setup_split.py",
    "content": "from importlib import import_module\nfrom getopt import getopt\nimport scipy.io as sio\nimport matplotlib.pyplot as plt\nfrom matplotlib.path import Path\nimport numpy as np\nimport pprint\nimport sys\nimport os\nimport cv2\nimport math\nimport shutil\nimport re\n\n# stop python from writing so much bytecode\nsys.dont_write_bytecode = True\nsys.path.append(os.getcwd())\nnp.set_printoptions(suppress=True)\n\n# -----------------------------------------\n# custom modules\n# -----------------------------------------\nfrom lib.util import *\n\nsplit = 'kitti_split1'\n\n# base paths\nbase_data = os.path.join(os.getcwd(), 'data')\n\nkitti_raw = dict()\nkitti_raw['cal'] = os.path.join(base_data, 'kitti', 'training', 'calib')\nkitti_raw['ims'] = os.path.join(base_data, 'kitti', 'training', 'image_2')\nkitti_raw['lab'] = os.path.join(base_data, 'kitti', 'training', 'label_2')\nkitti_raw['pre'] = os.path.join(base_data, 'kitti', 'training', 'prev_2')\n\nkitti_tra = dict()\nkitti_tra['cal'] = os.path.join(base_data, split, 'training', 'calib')\nkitti_tra['ims'] = os.path.join(base_data, split, 'training', 'image_2')\nkitti_tra['lab'] = os.path.join(base_data, split, 'training', 'label_2')\nkitti_tra['pre'] = os.path.join(base_data, split, 'training', 'prev_2')\n\nkitti_val = dict()\nkitti_val['cal'] = os.path.join(base_data, split, 'validation', 'calib')\nkitti_val['ims'] = os.path.join(base_data, split, 'validation', 'image_2')\nkitti_val['lab'] = os.path.join(base_data, split, 'validation', 'label_2')\nkitti_val['pre'] = os.path.join(base_data, split, 'validation', 'prev_2')\n\ntra_file = os.path.join(base_data, split, 'train.txt')\nval_file = os.path.join(base_data, split, 'val.txt')\n\n# mkdirs\nmkdir_if_missing(kitti_tra['cal'])\nmkdir_if_missing(kitti_tra['ims'])\nmkdir_if_missing(kitti_tra['lab'])\nmkdir_if_missing(kitti_tra['pre'])\nmkdir_if_missing(kitti_val['cal'])\nmkdir_if_missing(kitti_val['ims'])\nmkdir_if_missing(kitti_val['lab'])\nmkdir_if_missing(kitti_val['pre'])\n\n\nprint('Linking train')\ntext_file = open(tra_file, 'r')\n\nimind = 0\n\nfor line in text_file:\n\n    parsed = re.search('(\\d+)', line)\n\n    if parsed is not None:\n\n        id = str(parsed[0])\n        new_id = '{:06d}'.format(imind)\n\n        if not os.path.exists(os.path.join(kitti_tra['cal'], str(new_id) + '.txt')):\n            os.symlink(os.path.join(kitti_raw['cal'], str(id) + '.txt'), os.path.join(kitti_tra['cal'], str(new_id) + '.txt'))\n\n        if not os.path.exists(os.path.join(kitti_tra['ims'], str(new_id) + '.png')):\n            os.symlink(os.path.join(kitti_raw['ims'], str(id) + '.png'), os.path.join(kitti_tra['ims'], str(new_id) + '.png'))\n\n        if not os.path.exists(os.path.join(kitti_tra['pre'], str(new_id) + '_01.png')):\n            os.symlink(os.path.join(kitti_raw['pre'], str(id) + '_01.png'), os.path.join(kitti_tra['pre'], str(new_id) + '_01.png'))\n\n        if not os.path.exists(os.path.join(kitti_tra['pre'], str(new_id) + '_02.png')):\n            os.symlink(os.path.join(kitti_raw['pre'], str(id) + '_02.png'), os.path.join(kitti_tra['pre'], str(new_id) + '_02.png'))\n\n        if not os.path.exists(os.path.join(kitti_tra['pre'], str(new_id) + '_03.png')):\n            os.symlink(os.path.join(kitti_raw['pre'], str(id) + '_03.png'), os.path.join(kitti_tra['pre'], str(new_id) + '_03.png'))\n\n        if not os.path.exists(os.path.join(kitti_tra['lab'], str(new_id) + '.txt')):\n            os.symlink(os.path.join(kitti_raw['lab'], str(id) + '.txt'), os.path.join(kitti_tra['lab'], str(new_id) + '.txt'))\n\n        imind += 1\n\ntext_file.close()\n\nprint('Linking val')\ntext_file = open(val_file, 'r')\n\nimind = 0\n\nfor line in text_file:\n\n    parsed = re.search('(\\d+)', line)\n\n    if parsed is not None:\n\n        id = str(parsed[0])\n        new_id = '{:06d}'.format(imind)\n\n        if not os.path.exists(os.path.join(kitti_val['cal'], str(new_id) + '.txt')):\n            os.symlink(os.path.join(kitti_raw['cal'], str(id) + '.txt'), os.path.join(kitti_val['cal'], str(new_id) + '.txt'))\n\n        if not os.path.exists(os.path.join(kitti_val['ims'], str(new_id) + '.png')):\n            os.symlink(os.path.join(kitti_raw['ims'], str(id) + '.png'), os.path.join(kitti_val['ims'], str(new_id) + '.png'))\n\n        if not os.path.exists(os.path.join(kitti_val['pre'], str(new_id) + '_01.png')):\n            os.symlink(os.path.join(kitti_raw['pre'], str(id) + '_01.png'), os.path.join(kitti_val['pre'], str(new_id) + '_01.png'))\n        \n        if not os.path.exists(os.path.join(kitti_val['pre'], str(new_id) + '_02.png')):\n            os.symlink(os.path.join(kitti_raw['pre'], str(id) + '_02.png'), os.path.join(kitti_val['pre'], str(new_id) + '_02.png'))\n        \n        if not os.path.exists(os.path.join(kitti_val['pre'], str(new_id) + '_03.png')):\n            os.symlink(os.path.join(kitti_raw['pre'], str(id) + '_03.png'), os.path.join(kitti_val['pre'], str(new_id) + '_03.png'))\n\n        if not os.path.exists(os.path.join(kitti_val['lab'], str(new_id) + '.txt')):\n            os.symlink(os.path.join(kitti_raw['lab'], str(id) + '.txt'), os.path.join(kitti_val['lab'], str(new_id) + '.txt'))\n\n        imind += 1\n\ntext_file.close()\n\nprint('Done')\n"
  },
  {
    "path": "data/kitti_split1/test.txt",
    "content": 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  {
    "path": "data/kitti_split2/devkit/cpp/CMakeLists.txt",
    "content": "cmake_minimum_required (VERSION 2.6)\nproject(devkit_object)\n\nadd_executable(evaluate_object evaluate_object.cpp)"
  },
  {
    "path": "data/kitti_split2/devkit/cpp/build.sh",
    "content": "cd data/kitti_split2/devkit/cpp/\ng++ -O3 -DNDEBUG -o evaluate_object evaluate_object.cpp\ncd ../../../../\n\n"
  },
  {
    "path": "data/kitti_split2/devkit/cpp/evaluate_object.cpp",
    "content": "#include <iostream>\n#include <algorithm>\n#include <stdio.h>\n#include <math.h>\n#include <vector>\n#include <numeric>\n#include <strings.h>\n#include <assert.h>\n\n#include <dirent.h>\n\n#include <boost/numeric/ublas/matrix.hpp>\n#include <boost/numeric/ublas/io.hpp>\n\n#include <boost/geometry.hpp>\n#include <boost/geometry/geometries/point_xy.hpp>\n#include <boost/geometry/geometries/polygon.hpp>\n#include <boost/geometry/geometries/adapted/c_array.hpp>\n\n#include \"mail.h\"\n\nBOOST_GEOMETRY_REGISTER_C_ARRAY_CS(cs::cartesian)\n\ntypedef boost::geometry::model::polygon<boost::geometry::model::d2::point_xy<double> > Polygon;\n\n\nusing namespace std;\n\n/*=======================================================================\nSTATIC EVALUATION PARAMETERS\n=======================================================================*/\n\n// holds the number of test images on the server\n//const int32_t N_TESTIMAGES = 7518;\n//const int32_t N_TESTIMAGES = 7480;\nconst int32_t N_TESTIMAGES = 3799;\n//const int32_t N_TESTIMAGES = 3712;\n\n\n// easy, moderate and hard evaluation level\nenum DIFFICULTY{EASY=0, MODERATE=1, HARD=2};\n\n// evaluation metrics: image, ground or 3D\nenum METRIC{IMAGE=0, GROUND=1, BOX3D=2};\n\n// evaluation parameter\nconst int32_t MIN_HEIGHT[3]     = {40, 25, 25};     // minimum height for evaluated groundtruth/detections\nconst int32_t MAX_OCCLUSION[3]  = {0, 1, 2};        // maximum occlusion level of the groundtruth used for evaluation\nconst double  MAX_TRUNCATION[3] = {0.15, 0.3, 0.5}; // maximum truncation level of the groundtruth used for evaluation\n\n// evaluated object classes\nenum CLASSES{CAR=0, PEDESTRIAN=1, CYCLIST=2};\nconst int NUM_CLASS = 3;\n\n// parameters varying per class\nvector<string> CLASS_NAMES;\nvector<string> CLASS_NAMES_CAP;\n// the minimum overlap required for 2D evaluation on the image/ground plane and 3D evaluation\nconst double MIN_OVERLAP[3][3] = {{0.7, 0.5, 0.5}, {0.7, 0.5, 0.5}, {0.7, 0.5, 0.5}};\n\n// no. of recall steps that should be evaluated (discretized)\nconst double N_SAMPLE_PTS = 41;\n\n// initialize class names\nvoid initGlobals () {\n  CLASS_NAMES.push_back(\"car\");\n  CLASS_NAMES.push_back(\"pedestrian\");\n  CLASS_NAMES.push_back(\"cyclist\");\n  CLASS_NAMES_CAP.push_back(\"Car\");\n  CLASS_NAMES_CAP.push_back(\"Pedestrian\");\n  CLASS_NAMES_CAP.push_back(\"Cyclist\");\n}\n\n/*=======================================================================\nDATA TYPES FOR EVALUATION\n=======================================================================*/\n\n// holding data needed for precision-recall and precision-aos\nstruct tPrData {\n  vector<double> v;           // detection score for computing score thresholds\n  double         similarity;  // orientation similarity\n  int32_t        tp;          // true positives\n  int32_t        fp;          // false positives\n  int32_t        fn;          // false negatives\n  tPrData () :\n    similarity(0), tp(0), fp(0), fn(0) {}\n};\n\n// holding bounding boxes for ground truth and detections\nstruct tBox {\n  string  type;     // object type as car, pedestrian or cyclist,...\n  double   x1;      // left corner\n  double   y1;      // top corner\n  double   x2;      // right corner\n  double   y2;      // bottom corner\n  double   alpha;   // image orientation\n  tBox (string type, double x1,double y1,double x2,double y2,double alpha) :\n    type(type),x1(x1),y1(y1),x2(x2),y2(y2),alpha(alpha) {}\n};\n\n// holding ground truth data\nstruct tGroundtruth {\n  tBox    box;        // object type, box, orientation\n  double  truncation; // truncation 0..1\n  int32_t occlusion;  // occlusion 0,1,2 (non, partly, fully)\n  double ry;\n  double  t1, t2, t3;\n  double h, w, l;\n  tGroundtruth () :\n    box(tBox(\"invalild\",-1,-1,-1,-1,-10)),truncation(-1),occlusion(-1) {}\n  tGroundtruth (tBox box,double truncation,int32_t occlusion) :\n    box(box),truncation(truncation),occlusion(occlusion) {}\n  tGroundtruth (string type,double x1,double y1,double x2,double y2,double alpha,double truncation,int32_t occlusion) :\n    box(tBox(type,x1,y1,x2,y2,alpha)),truncation(truncation),occlusion(occlusion) {}\n};\n\n// holding detection data\nstruct tDetection {\n  tBox    box;    // object type, box, orientation\n  double  thresh; // detection score\n  double  ry;\n  double  t1, t2, t3;\n  double  h, w, l;\n  tDetection ():\n    box(tBox(\"invalid\",-1,-1,-1,-1,-10)),thresh(-1000) {}\n  tDetection (tBox box,double thresh) :\n    box(box),thresh(thresh) {}\n  tDetection (string type,double x1,double y1,double x2,double y2,double alpha,double thresh) :\n    box(tBox(type,x1,y1,x2,y2,alpha)),thresh(thresh) {}\n};\n\n\n/*=======================================================================\nFUNCTIONS TO LOAD DETECTION AND GROUND TRUTH DATA ONCE, SAVE RESULTS\n=======================================================================*/\nvector<tDetection> loadDetections(string file_name, bool &compute_aos,\n        vector<bool> &eval_image, vector<bool> &eval_ground,\n        vector<bool> &eval_3d, bool &success) {\n\n  // holds all detections (ignored detections are indicated by an index vector\n  vector<tDetection> detections;\n  FILE *fp = fopen(file_name.c_str(),\"r\");\n  if (!fp) {\n    success = false;\n    return detections;\n  }\n  while (!feof(fp)) {\n    tDetection d;\n    double trash;\n    char str[255];\n    if (fscanf(fp, \"%s %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf\",\n                   str, &trash, &trash, &d.box.alpha, &d.box.x1, &d.box.y1,\n                   &d.box.x2, &d.box.y2, &d.h, &d.w, &d.l, &d.t1, &d.t2, &d.t3,\n                   &d.ry, &d.thresh)==16) {\n\n        // d.thresh = 1;\n      d.box.type = str;\n      detections.push_back(d);\n\n      // orientation=-10 is invalid, AOS is not evaluated if at least one orientation is invalid\n      if(d.box.alpha == -10)\n        compute_aos = false;\n\n      // a class is only evaluated if it is detected at least once\n      for (int c = 0; c < NUM_CLASS; c++) {\n        if (!strcasecmp(d.box.type.c_str(), CLASS_NAMES[c].c_str()) || !strcasecmp(d.box.type.c_str(), CLASS_NAMES_CAP[c].c_str())) {\n          if (!eval_image[c] && d.box.x1 >= 0)\n            eval_image[c] = true;\n          if (!eval_ground[c] && d.t1 != -1000 && d.t3 != -1000 && d.w > 0 && d.l > 0)\n            eval_ground[c] = true;\n          if (!eval_3d[c] && d.t1 != -1000 && d.t2 != -1000 && d.t3 != -1000 && d.h > 0 && d.w > 0 && d.l > 0) \n            eval_3d[c] = true;\n          break;\n        }\n      }\n    }\n  }\n  \n  fclose(fp);\n  success = true;\n  return detections;\n}\n\nvector<tGroundtruth> loadGroundtruth(string file_name,bool &success) {\n\n  // holds all ground truth (ignored ground truth is indicated by an index vector\n  vector<tGroundtruth> groundtruth;\n  FILE *fp = fopen(file_name.c_str(),\"r\");\n  if (!fp) {\n    success = false;\n    return groundtruth;\n  }\n  while (!feof(fp)) {\n    tGroundtruth g;\n    char str[255];\n    if (fscanf(fp, \"%s %lf %d %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf %lf\",\n                   str, &g.truncation, &g.occlusion, &g.box.alpha,\n                   &g.box.x1,   &g.box.y1,     &g.box.x2,    &g.box.y2,\n                   &g.h,      &g.w,        &g.l,       &g.t1,\n                   &g.t2,      &g.t3,        &g.ry )==15) {\n      g.box.type = str;\n      groundtruth.push_back(g);\n    }\n  }\n  fclose(fp);\n  success = true;\n  return groundtruth;\n}\n\nvoid saveStats (const vector<double> &precision, const vector<double> &aos, FILE *fp_det, FILE *fp_ori) {\n\n  // save precision to file\n  if(precision.empty())\n    return;\n  for (int32_t i=0; i<precision.size(); i++)\n    fprintf(fp_det,\"%f \",precision[i]);\n  fprintf(fp_det,\"\\n\");\n\n  // save orientation similarity, only if there were no invalid orientation entries in submission (alpha=-10)\n  if(aos.empty())\n    return;\n  for (int32_t i=0; i<aos.size(); i++)\n    fprintf(fp_ori,\"%f \",aos[i]);\n  fprintf(fp_ori,\"\\n\");\n}\n\n/*=======================================================================\nEVALUATION HELPER FUNCTIONS\n=======================================================================*/\n\n// criterion defines whether the overlap is computed with respect to both areas (ground truth and detection)\n// or with respect to box a or b (detection and \"dontcare\" areas)\ninline double imageBoxOverlap(tBox a, tBox b, int32_t criterion=-1){\n\n  // overlap is invalid in the beginning\n  double o = -1;\n\n  // get overlapping area\n  double x1 = max(a.x1, b.x1);\n  double y1 = max(a.y1, b.y1);\n  double x2 = min(a.x2, b.x2);\n  double y2 = min(a.y2, b.y2);\n\n  // compute width and height of overlapping area\n  double w = x2-x1;\n  double h = y2-y1;\n\n  // set invalid entries to 0 overlap\n  if(w<=0 || h<=0)\n    return 0;\n\n  // get overlapping areas\n  double inter = w*h;\n  double a_area = (a.x2-a.x1) * (a.y2-a.y1);\n  double b_area = (b.x2-b.x1) * (b.y2-b.y1);\n\n  // intersection over union overlap depending on users choice\n  if(criterion==-1)     // union\n    o = inter / (a_area+b_area-inter);\n  else if(criterion==0) // bbox_a\n    o = inter / a_area;\n  else if(criterion==1) // bbox_b\n    o = inter / b_area;\n\n  // overlap\n  return o;\n}\n\ninline double imageBoxOverlap(tDetection a, tGroundtruth b, int32_t criterion=-1){\n  return imageBoxOverlap(a.box, b.box, criterion);\n}\n\n// compute polygon of an oriented bounding box\ntemplate <typename T>\nPolygon toPolygon(const T& g) {\n    using namespace boost::numeric::ublas;\n    using namespace boost::geometry;\n    matrix<double> mref(2, 2);\n    mref(0, 0) = cos(g.ry); mref(0, 1) = sin(g.ry);\n    mref(1, 0) = -sin(g.ry); mref(1, 1) = cos(g.ry);\n\n    static int count = 0;\n    matrix<double> corners(2, 4);\n    double data[] = {g.l / 2, g.l / 2, -g.l / 2, -g.l / 2,\n                     g.w / 2, -g.w / 2, -g.w / 2, g.w / 2};\n    std::copy(data, data + 8, corners.data().begin());\n    matrix<double> gc = prod(mref, corners);\n    for (int i = 0; i < 4; ++i) {\n        gc(0, i) += g.t1;\n        gc(1, i) += g.t3;\n    }\n\n    double points[][2] = {{gc(0, 0), gc(1, 0)},{gc(0, 1), gc(1, 1)},{gc(0, 2), gc(1, 2)},{gc(0, 3), gc(1, 3)},{gc(0, 0), gc(1, 0)}};\n    Polygon poly;\n    append(poly, points);\n    return poly;\n}\n\n// measure overlap between bird's eye view bounding boxes, parametrized by (ry, l, w, tx, tz)\ninline double groundBoxOverlap(tDetection d, tGroundtruth g, int32_t criterion = -1) {\n    using namespace boost::geometry;\n    Polygon gp = toPolygon(g);\n    Polygon dp = toPolygon(d);\n\n    std::vector<Polygon> in, un;\n    intersection(gp, dp, in);\n    union_(gp, dp, un);\n\n    double inter_area = in.empty() ? 0 : area(in.front());\n    double union_area = area(un.front());\n    double o;\n    if(criterion==-1)     // union\n        o = inter_area / union_area;\n    else if(criterion==0) // bbox_a\n        o = inter_area / area(dp);\n    else if(criterion==1) // bbox_b\n        o = inter_area / area(gp);\n\n    return o;\n}\n\n// measure overlap between 3D bounding boxes, parametrized by (ry, h, w, l, tx, ty, tz)\ninline double box3DOverlap(tDetection d, tGroundtruth g, int32_t criterion = -1) {\n    using namespace boost::geometry;\n    Polygon gp = toPolygon(g);\n    Polygon dp = toPolygon(d);\n\n    std::vector<Polygon> in, un;\n    intersection(gp, dp, in);\n    union_(gp, dp, un);\n\n    double ymax = min(d.t2, g.t2);\n    double ymin = max(d.t2 - d.h, g.t2 - g.h);\n\n    double inter_area = in.empty() ? 0 : area(in.front());\n    double inter_vol = inter_area * max(0.0, ymax - ymin);\n\n    double det_vol = d.h * d.l * d.w;\n    double gt_vol = g.h * g.l * g.w;\n\n    double o;\n    if(criterion==-1)     // union\n        o = inter_vol / (det_vol + gt_vol - inter_vol);\n    else if(criterion==0) // bbox_a\n        o = inter_vol / det_vol;\n    else if(criterion==1) // bbox_b\n        o = inter_vol / gt_vol;\n\n    return o;\n}\n\nvector<double> getThresholds(vector<double> &v, double n_groundtruth){\n\n  // holds scores needed to compute N_SAMPLE_PTS recall values\n  vector<double> t;\n\n  // sort scores in descending order\n  // (highest score is assumed to give best/most confident detections)\n  sort(v.begin(), v.end(), greater<double>());\n\n  // get scores for linearly spaced recall\n  double current_recall = 0;\n  for(int32_t i=0; i<v.size(); i++){\n\n    // check if right-hand-side recall with respect to current recall is close than left-hand-side one\n    // in this case, skip the current detection score\n    double l_recall, r_recall, recall;\n    l_recall = (double)(i+1)/n_groundtruth;\n    if(i<(v.size()-1))\n      r_recall = (double)(i+2)/n_groundtruth;\n    else\n      r_recall = l_recall;\n\n    if( (r_recall-current_recall) < (current_recall-l_recall) && i<(v.size()-1))\n      continue;\n\n    // left recall is the best approximation, so use this and goto next recall step for approximation\n    recall = l_recall;\n\n    // the next recall step was reached\n    t.push_back(v[i]);\n    current_recall += 1.0/(N_SAMPLE_PTS-1.0);\n  }\n  return t;\n}\n\nvoid cleanData(CLASSES current_class, const vector<tGroundtruth> &gt, const vector<tDetection> &det, vector<int32_t> &ignored_gt, vector<tGroundtruth> &dc, vector<int32_t> &ignored_det, int32_t &n_gt, DIFFICULTY difficulty){\n\n  // extract ground truth bounding boxes for current evaluation class\n  for(int32_t i=0;i<gt.size(); i++){\n\n    // only bounding boxes with a minimum height are used for evaluation\n    double height = gt[i].box.y2 - gt[i].box.y1;\n\n    // neighboring classes are ignored (\"van\" for \"car\" and \"person_sitting\" for \"pedestrian\")\n    // (lower/upper cases are ignored)\n    int32_t valid_class;\n\n    // all classes without a neighboring class\n    if(!strcasecmp(gt[i].box.type.c_str(), CLASS_NAMES[current_class].c_str()))\n      valid_class = 1;\n\n    // classes with a neighboring class\n    else if(!strcasecmp(CLASS_NAMES[current_class].c_str(), \"Pedestrian\") && !strcasecmp(\"Person_sitting\", gt[i].box.type.c_str()))\n      valid_class = 0;\n    else if(!strcasecmp(CLASS_NAMES[current_class].c_str(), \"Car\") && !strcasecmp(\"Van\", gt[i].box.type.c_str()))\n      valid_class = 0;\n\n    // classes not used for evaluation\n    else\n      valid_class = -1;\n\n    // ground truth is ignored, if occlusion, truncation exceeds the difficulty or ground truth is too small\n    // (doesn't count as FN nor TP, although detections may be assigned)\n    bool ignore = false;\n    if(gt[i].occlusion>MAX_OCCLUSION[difficulty] || gt[i].truncation>MAX_TRUNCATION[difficulty] || height<=MIN_HEIGHT[difficulty])\n      ignore = true;\n\n    // set ignored vector for ground truth\n    // current class and not ignored (total no. of ground truth is detected for recall denominator)\n    if(valid_class==1 && !ignore){\n      ignored_gt.push_back(0);\n      n_gt++;\n    }\n\n    // neighboring class, or current class but ignored\n    else if(valid_class==0 || (ignore && valid_class==1))\n      ignored_gt.push_back(1);\n\n    // all other classes which are FN in the evaluation\n    else\n      ignored_gt.push_back(-1);\n  }\n\n  // extract dontcare areas\n  for(int32_t i=0;i<gt.size(); i++)\n    if(!strcasecmp(\"DontCare\", gt[i].box.type.c_str()))\n      dc.push_back(gt[i]);\n\n  // extract detections bounding boxes of the current class\n  for(int32_t i=0;i<det.size(); i++){\n\n    // neighboring classes are not evaluated\n    int32_t valid_class;\n    if(!strcasecmp(det[i].box.type.c_str(), CLASS_NAMES[current_class].c_str()))\n      valid_class = 1;\n    else\n      valid_class = -1;\n    \n    int32_t height = fabs(det[i].box.y1 - det[i].box.y2);\n    \n    // set ignored vector for detections\n    if(height<MIN_HEIGHT[difficulty])\n      ignored_det.push_back(1);\n    else if(valid_class==1)\n      ignored_det.push_back(0);\n    else\n      ignored_det.push_back(-1);\n  }\n}\n\ntPrData computeStatistics(CLASSES current_class, const vector<tGroundtruth> &gt,\n        const vector<tDetection> &det, const vector<tGroundtruth> &dc,\n        const vector<int32_t> &ignored_gt, const vector<int32_t>  &ignored_det,\n        bool compute_fp, double (*boxoverlap)(tDetection, tGroundtruth, int32_t),\n        METRIC metric, bool compute_aos=false, double thresh=0, bool debug=false){\n\n  tPrData stat = tPrData();\n  const double NO_DETECTION = -10000000;\n  vector<double> delta;            // holds angular difference for TPs (needed for AOS evaluation)\n  vector<bool> assigned_detection; // holds wether a detection was assigned to a valid or ignored ground truth\n  assigned_detection.assign(det.size(), false);\n  vector<bool> ignored_threshold;\n  ignored_threshold.assign(det.size(), false); // holds detections with a threshold lower than thresh if FP are computed\n\n  // detections with a low score are ignored for computing precision (needs FP)\n  if(compute_fp)\n    for(int32_t i=0; i<det.size(); i++)\n      if(det[i].thresh<thresh)\n        ignored_threshold[i] = true;\n\n  // evaluate all ground truth boxes\n  for(int32_t i=0; i<gt.size(); i++){\n\n    // this ground truth is not of the current or a neighboring class and therefore ignored\n    if(ignored_gt[i]==-1)\n      continue;\n\n    /*=======================================================================\n    find candidates (overlap with ground truth > 0.5) (logical len(det))\n    =======================================================================*/\n    int32_t det_idx          = -1;\n    double valid_detection = NO_DETECTION;\n    double max_overlap     = 0;\n\n    // search for a possible detection\n    bool assigned_ignored_det = false;\n    for(int32_t j=0; j<det.size(); j++){\n\n      // detections not of the current class, already assigned or with a low threshold are ignored\n      if(ignored_det[j]==-1)\n        continue;\n      if(assigned_detection[j])\n        continue;\n      if(ignored_threshold[j])\n        continue;\n\n      // find the maximum score for the candidates and get idx of respective detection\n      double overlap = boxoverlap(det[j], gt[i], -1);\n\n      // for computing recall thresholds, the candidate with highest score is considered\n      if(!compute_fp && overlap>MIN_OVERLAP[metric][current_class] && det[j].thresh>valid_detection){\n        det_idx         = j;\n        valid_detection = det[j].thresh;\n      }\n\n      // for computing pr curve values, the candidate with the greatest overlap is considered\n      // if the greatest overlap is an ignored detection (min_height), the overlapping detection is used\n      else if(compute_fp && overlap>MIN_OVERLAP[metric][current_class] && (overlap>max_overlap || assigned_ignored_det) && ignored_det[j]==0){\n        max_overlap     = overlap;\n        det_idx         = j;\n        valid_detection = 1;\n        assigned_ignored_det = false;\n      }\n      else if(compute_fp && overlap>MIN_OVERLAP[metric][current_class] && valid_detection==NO_DETECTION && ignored_det[j]==1){\n        det_idx              = j;\n        valid_detection      = 1;\n        assigned_ignored_det = true;\n      }\n    }\n\n    /*=======================================================================\n    compute TP, FP and FN\n    =======================================================================*/\n\n    // nothing was assigned to this valid ground truth\n    if(valid_detection==NO_DETECTION && ignored_gt[i]==0) {\n      stat.fn++;\n    }\n\n    // only evaluate valid ground truth <=> detection assignments (considering difficulty level)\n    else if(valid_detection!=NO_DETECTION && (ignored_gt[i]==1 || ignored_det[det_idx]==1))\n      assigned_detection[det_idx] = true;\n\n    // found a valid true positive\n    else if(valid_detection!=NO_DETECTION){\n\n      // write highest score to threshold vector\n      stat.tp++;\n      stat.v.push_back(det[det_idx].thresh);\n\n      // compute angular difference of detection and ground truth if valid detection orientation was provided\n      if(compute_aos)\n        delta.push_back(gt[i].box.alpha - det[det_idx].box.alpha);\n\n      // clean up\n      assigned_detection[det_idx] = true;\n    }\n  }\n\n  // if FP are requested, consider stuff area\n  if(compute_fp){\n\n    // count fp\n    for(int32_t i=0; i<det.size(); i++){\n\n      // count false positives if required (height smaller than required is ignored (ignored_det==1)\n      if(!(assigned_detection[i] || ignored_det[i]==-1 || ignored_det[i]==1 || ignored_threshold[i]))\n        stat.fp++;\n    }\n\n    // do not consider detections overlapping with stuff area\n    int32_t nstuff = 0;\n    for(int32_t i=0; i<dc.size(); i++){\n      for(int32_t j=0; j<det.size(); j++){\n\n        // detections not of the current class, already assigned, with a low threshold or a low minimum height are ignored\n        if(assigned_detection[j])\n          continue;\n        if(ignored_det[j]==-1 || ignored_det[j]==1)\n          continue;\n        if(ignored_threshold[j])\n          continue;\n\n        // compute overlap and assign to stuff area, if overlap exceeds class specific value\n        double overlap = boxoverlap(det[j], dc[i], 0);\n        if(overlap>MIN_OVERLAP[metric][current_class]){\n          assigned_detection[j] = true;\n          nstuff++;\n        }\n      }\n    }\n\n    // FP = no. of all not to ground truth assigned detections - detections assigned to stuff areas\n    stat.fp -= nstuff;\n\n    // if all orientation values are valid, the AOS is computed\n    if(compute_aos){\n      vector<double> tmp;\n\n      // FP have a similarity of 0, for all TP compute AOS\n      tmp.assign(stat.fp, 0);\n      for(int32_t i=0; i<delta.size(); i++)\n        tmp.push_back((1.0+cos(delta[i]))/2.0);\n\n      // be sure, that all orientation deltas are computed\n      assert(tmp.size()==stat.fp+stat.tp);\n      assert(delta.size()==stat.tp);\n\n      // get the mean orientation similarity for this image\n      if(stat.tp>0 || stat.fp>0)\n        stat.similarity = accumulate(tmp.begin(), tmp.end(), 0.0);\n\n      // there was neither a FP nor a TP, so the similarity is ignored in the evaluation\n      else\n        stat.similarity = -1;\n    }\n  }\n  return stat;\n}\n\n/*=======================================================================\nEVALUATE CLASS-WISE\n=======================================================================*/\n\nbool eval_class (FILE *fp_det, FILE *fp_ori, CLASSES current_class,\n        const vector< vector<tGroundtruth> > &groundtruth,\n        const vector< vector<tDetection> > &detections, bool compute_aos,\n        double (*boxoverlap)(tDetection, tGroundtruth, int32_t),\n        vector<double> &precision, vector<double> &aos,\n        DIFFICULTY difficulty, METRIC metric) {\n    assert(groundtruth.size() == detections.size());\n\n  // init\n  int32_t n_gt=0;                                     // total no. of gt (denominator of recall)\n  vector<double> v, thresholds;                       // detection scores, evaluated for recall discretization\n  vector< vector<int32_t> > ignored_gt, ignored_det;  // index of ignored gt detection for current class/difficulty\n  vector< vector<tGroundtruth> > dontcare;            // index of dontcare areas, included in ground truth\n\n  // for all test images do\n  for (int32_t i=0; i<groundtruth.size(); i++){\n\n    // holds ignored ground truth, ignored detections and dontcare areas for current frame\n    vector<int32_t> i_gt, i_det;\n    vector<tGroundtruth> dc;\n\n    // only evaluate objects of current class and ignore occluded, truncated objects\n    cleanData(current_class, groundtruth[i], detections[i], i_gt, dc, i_det, n_gt, difficulty);\n    ignored_gt.push_back(i_gt);\n    ignored_det.push_back(i_det);\n    dontcare.push_back(dc);\n\n    // compute statistics to get recall values\n    tPrData pr_tmp = tPrData();\n    pr_tmp = computeStatistics(current_class, groundtruth[i], detections[i], dc, i_gt, i_det, false, boxoverlap, metric);\n\n    // add detection scores to vector over all images\n    for(int32_t j=0; j<pr_tmp.v.size(); j++)\n      v.push_back(pr_tmp.v[j]);\n  }\n\n  // get scores that must be evaluated for recall discretization\n  thresholds = getThresholds(v, n_gt);\n\n  // compute TP,FP,FN for relevant scores\n  vector<tPrData> pr;\n  pr.assign(thresholds.size(),tPrData());\n  for (int32_t i=0; i<groundtruth.size(); i++){\n\n    // for all scores/recall thresholds do:\n    for(int32_t t=0; t<thresholds.size(); t++){\n      tPrData tmp = tPrData();\n      tmp = computeStatistics(current_class, groundtruth[i], detections[i], dontcare[i],\n                              ignored_gt[i], ignored_det[i], true, boxoverlap, metric,\n                              compute_aos, thresholds[t], t==38);\n\n      // add no. of TP, FP, FN, AOS for current frame to total evaluation for current threshold\n      pr[t].tp += tmp.tp;\n      pr[t].fp += tmp.fp;\n      pr[t].fn += tmp.fn;\n      if(tmp.similarity!=-1)\n        pr[t].similarity += tmp.similarity;\n    }\n  }\n\n  // compute recall, precision and AOS\n  vector<double> recall;\n  precision.assign(N_SAMPLE_PTS, 0);\n  if(compute_aos)\n    aos.assign(N_SAMPLE_PTS, 0);\n  double r=0;\n  for (int32_t i=0; i<thresholds.size(); i++){\n    r = pr[i].tp/(double)(pr[i].tp + pr[i].fn);\n    recall.push_back(r);\n    precision[i] = pr[i].tp/(double)(pr[i].tp + pr[i].fp);\n    if(compute_aos)\n      aos[i] = pr[i].similarity/(double)(pr[i].tp + pr[i].fp);\n  }\n\n  // filter precision and AOS using max_{i..end}(precision)\n  for (int32_t i=0; i<thresholds.size(); i++){\n    precision[i] = *max_element(precision.begin()+i, precision.end());\n    if(compute_aos)\n      aos[i] = *max_element(aos.begin()+i, aos.end());\n  }\n\n  // save statisics and finish with success\n  saveStats(precision, aos, fp_det, fp_ori);\n    return true;\n}\n\nvoid saveAndPlotPlots(string dir_name,string file_name,string obj_type,vector<double> vals[],bool is_aos){\n\n  char command[1024];\n\n  // save plot data to file\n  FILE *fp = fopen((dir_name + \"/\" + file_name + \".txt\").c_str(),\"w\");\n  printf(\"save %s\\n\", (dir_name + \"/\" + file_name + \".txt\").c_str());\n  for (int32_t i=0; i<(int)N_SAMPLE_PTS; i++)\n    fprintf(fp,\"%f %f %f %f\\n\",(double)i/(N_SAMPLE_PTS-1.0),vals[0][i],vals[1][i],vals[2][i]);\n  fclose(fp);\n\n  // create png + eps\n  for (int32_t j=0; j<2; j++) {\n\n    // open file\n    FILE *fp = fopen((dir_name + \"/\" + file_name + \".gp\").c_str(),\"w\");\n\n    // save gnuplot instructions\n    if (j==0) {\n      fprintf(fp,\"set term png size 450,315 font \\\"Helvetica\\\" 11\\n\");\n      fprintf(fp,\"set output \\\"%s.png\\\"\\n\",file_name.c_str());\n    } else {\n      fprintf(fp,\"set term postscript eps enhanced color font \\\"Helvetica\\\" 20\\n\");\n      fprintf(fp,\"set output \\\"%s.eps\\\"\\n\",file_name.c_str());\n    }\n\n    // set labels and ranges\n    fprintf(fp,\"set size ratio 0.7\\n\");\n    fprintf(fp,\"set xrange [0:1]\\n\");\n    fprintf(fp,\"set yrange [0:1]\\n\");\n    fprintf(fp,\"set xlabel \\\"Recall\\\"\\n\");\n    if (!is_aos) fprintf(fp,\"set ylabel \\\"Precision\\\"\\n\");\n    else         fprintf(fp,\"set ylabel \\\"Orientation Similarity\\\"\\n\");\n    obj_type[0] = toupper(obj_type[0]);\n    fprintf(fp,\"set title \\\"%s\\\"\\n\",obj_type.c_str());\n\n    // line width\n    int32_t   lw = 5;\n    if (j==0) lw = 3;\n\n    // plot error curve\n    fprintf(fp,\"plot \");\n    fprintf(fp,\"\\\"%s.txt\\\" using 1:2 title 'Easy' with lines ls 1 lw %d,\",file_name.c_str(),lw);\n    fprintf(fp,\"\\\"%s.txt\\\" using 1:3 title 'Moderate' with lines ls 2 lw %d,\",file_name.c_str(),lw);\n    fprintf(fp,\"\\\"%s.txt\\\" using 1:4 title 'Hard' with lines ls 3 lw %d\",file_name.c_str(),lw);\n\n    // close file\n    fclose(fp);\n\n    // run gnuplot => create png + eps\n    sprintf(command,\"cd %s; gnuplot %s\",dir_name.c_str(),(file_name + \".gp\").c_str());\n    system(command);\n  }\n\n  // create pdf and crop\n  sprintf(command,\"cd %s; ps2pdf %s.eps %s_large.pdf\",dir_name.c_str(),file_name.c_str(),file_name.c_str());\n  system(command);\n  sprintf(command,\"cd %s; pdfcrop %s_large.pdf %s.pdf\",dir_name.c_str(),file_name.c_str(),file_name.c_str());\n  system(command);\n  sprintf(command,\"cd %s; rm %s_large.pdf\",dir_name.c_str(),file_name.c_str());\n  system(command);\n}\n\nbool eval(string result_sha,Mail* mail){\n\n  // set some global parameters\n  initGlobals();\n\n  // ground truth and result directories\n  string gt_dir         = \"data/kitti_split2/validation/label_2\";\n  string result_dir     = result_sha;\n  string plot_dir       = result_dir + \"/plot\";\n\n  // create output directories\n  system((\"mkdir \" + plot_dir).c_str());\n\n  // hold detections and ground truth in memory\n  vector< vector<tGroundtruth> > groundtruth;\n  vector< vector<tDetection> >   detections;\n\n  // holds wether orientation similarity shall be computed (might be set to false while loading detections)\n  // and which labels where provided by this submission\n  bool compute_aos=true;\n  vector<bool> eval_image(NUM_CLASS, false);\n  vector<bool> eval_ground(NUM_CLASS, false);\n  vector<bool> eval_3d(NUM_CLASS, false);\n\n  // for all images read groundtruth and detections\n  mail->msg(\"Loading detections...\");\n  for (int32_t i=0; i<N_TESTIMAGES; i++) {\n\n    // file name\n    char file_name[256];\n    sprintf(file_name,\"%06d.txt\",i);\n\n    // read ground truth and result poses\n    bool gt_success,det_success;\n    vector<tGroundtruth> gt   = loadGroundtruth(gt_dir + \"/\" + file_name,gt_success);\n    vector<tDetection>   det  = loadDetections(result_dir + \"/data/\" + file_name,\n            compute_aos, eval_image, eval_ground, eval_3d, det_success);\n    groundtruth.push_back(gt);\n    detections.push_back(det);\n\n    // check for errors\n    if (!gt_success) {\n      mail->msg(\"ERROR: Couldn't read: %s of ground truth. Please write me an email!\", file_name);\n      return false;\n    }\n    if (!det_success) {\n      mail->msg(\"ERROR: Couldn't read: %s\", file_name);\n      return false;\n    }\n  }\n  mail->msg(\"  done.\");\n\n  // holds pointers for result files\n  FILE *fp_det=0, *fp_ori=0;\n\n  // eval image 2D bounding boxes\n  for (int c = 0; c < NUM_CLASS; c++) {\n    CLASSES cls = (CLASSES)c;\n    //mail->msg(\"Checking 2D evaluation (%s) ...\", CLASS_NAMES[c].c_str());\n    if (eval_image[c]) {\n      mail->msg(\"Starting 2D evaluation (%s) ...\", CLASS_NAMES[c].c_str());\n      fp_det = fopen((result_dir + \"/stats_\" + CLASS_NAMES[c] + \"_detection.txt\").c_str(), \"w\");\n      if(compute_aos)\n        fp_ori = fopen((result_dir + \"/stats_\" + CLASS_NAMES[c] + \"_orientation.txt\").c_str(),\"w\");\n      vector<double> precision[3], aos[3];\n      if(   !eval_class(fp_det, fp_ori, cls, groundtruth, detections, compute_aos, imageBoxOverlap, precision[0], aos[0], EASY, IMAGE)\n         || !eval_class(fp_det, fp_ori, cls, groundtruth, detections, compute_aos, imageBoxOverlap, precision[1], aos[1], MODERATE, IMAGE)\n         || !eval_class(fp_det, fp_ori, cls, groundtruth, detections, compute_aos, imageBoxOverlap, precision[2], aos[2], HARD, IMAGE)) {\n        mail->msg(\"%s evaluation failed.\", CLASS_NAMES[c].c_str());\n        return false;\n      }\n      fclose(fp_det);\n      saveAndPlotPlots(plot_dir, CLASS_NAMES[c] + \"_detection\", CLASS_NAMES[c], precision, 0);\n      if(compute_aos){\n        saveAndPlotPlots(plot_dir, CLASS_NAMES[c] + \"_orientation\", CLASS_NAMES[c], aos, 1);\n        fclose(fp_ori);\n      }\n      mail->msg(\"  done.\");\n    }\n  }\n\n  // don't evaluate AOS for birdview boxes and 3D boxes\n  compute_aos = false;\n\n  // eval bird's eye view bounding boxes\n  for (int c = 0; c < NUM_CLASS; c++) {\n    CLASSES cls = (CLASSES)c;\n    //mail->msg(\"Checking bird's eye evaluation (%s) ...\", CLASS_NAMES[c].c_str());\n    if (eval_ground[c]) {\n      mail->msg(\"Starting bird's eye evaluation (%s) ...\", CLASS_NAMES[c].c_str());\n      fp_det = fopen((result_dir + \"/stats_\" + CLASS_NAMES[c] + \"_detection_ground.txt\").c_str(), \"w\");\n      vector<double> precision[3], aos[3];\n      if(   !eval_class(fp_det, fp_ori, cls, groundtruth, detections, compute_aos, groundBoxOverlap, precision[0], aos[0], EASY, GROUND)\n         || !eval_class(fp_det, fp_ori, cls, groundtruth, detections, compute_aos, groundBoxOverlap, precision[1], aos[1], MODERATE, GROUND)\n         || !eval_class(fp_det, fp_ori, cls, groundtruth, detections, compute_aos, groundBoxOverlap, precision[2], aos[2], HARD, GROUND)) {\n        mail->msg(\"%s evaluation failed.\", CLASS_NAMES[c].c_str());\n        return false;\n      }\n      fclose(fp_det);\n      saveAndPlotPlots(plot_dir, CLASS_NAMES[c] + \"_detection_ground\", CLASS_NAMES[c], precision, 0);\n      mail->msg(\"  done.\");\n    }\n  }\n\n  // eval 3D bounding boxes\n  for (int c = 0; c < NUM_CLASS; c++) {\n    CLASSES cls = (CLASSES)c;\n    //mail->msg(\"Checking 3D evaluation (%s) ...\", CLASS_NAMES[c].c_str());\n    if (eval_3d[c]) {\n      mail->msg(\"Starting 3D evaluation (%s) ...\", CLASS_NAMES[c].c_str());\n      fp_det = fopen((result_dir + \"/stats_\" + CLASS_NAMES[c] + \"_detection_3d.txt\").c_str(), \"w\");\n      vector<double> precision[3], aos[3];\n      if(   !eval_class(fp_det, fp_ori, cls, groundtruth, detections, compute_aos, box3DOverlap, precision[0], aos[0], EASY, BOX3D)\n         || !eval_class(fp_det, fp_ori, cls, groundtruth, detections, compute_aos, box3DOverlap, precision[1], aos[1], MODERATE, BOX3D)\n         || !eval_class(fp_det, fp_ori, cls, groundtruth, detections, compute_aos, box3DOverlap, precision[2], aos[2], HARD, BOX3D)) {\n        mail->msg(\"%s evaluation failed.\", CLASS_NAMES[c].c_str());\n        return false;\n      }\n      fclose(fp_det);\n      saveAndPlotPlots(plot_dir, CLASS_NAMES[c] + \"_detection_3d\", CLASS_NAMES[c], precision, 0);\n      mail->msg(\"  done.\");\n    }\n  }\n\n  // success\n  return true;\n}\n\nint32_t main (int32_t argc,char *argv[]) {\n\n  // we need 2 or 4 arguments!\n  if (argc!=2 && argc!=4) {\n    cout << \"Usage: ./eval_detection result_sha [user_sha email]\" << endl;\n    return 1;\n  }\n\n  // read arguments\n  string result_sha = argv[1];\n\n  // init notification mail\n  Mail *mail;\n  if (argc==4) mail = new Mail(argv[3]);\n  else         mail = new Mail();\n  mail->msg(\"Thank you for participating in our evaluation!\");\n\n  // run evaluation\n  if (eval(result_sha,mail)) {\n    mail->msg(\"Your evaluation results are available at:\");\n    mail->msg(\"http://www.cvlibs.net/datasets/kitti/user_submit_check_login.php?benchmark=object&user=%s&result=%s\",argv[2], result_sha.c_str());\n  } else {\n    system((\"rm -r results/\" + result_sha).c_str());\n    mail->msg(\"An error occured while processing your results.\");\n    mail->msg(\"Please make sure that the data in your zip archive has the right format!\");\n  }\n\n  // send mail and exit\n  delete mail;\n\n  return 0;\n}\n"
  },
  {
    "path": "data/kitti_split2/devkit/cpp/mail.h",
    "content": "#ifndef MAIL_H\n#define MAIL_H\n\n#include <stdio.h>\n#include <stdarg.h>\n#include <string.h>\n\nclass Mail {\n\npublic:\n\n  Mail (std::string email = \"\") {\n    if (email.compare(\"\")) {\n      mail = popen(\"/usr/lib/sendmail -t -f noreply@cvlibs.net\",\"w\");\n      fprintf(mail,\"To: %s\\n\", email.c_str());\n      fprintf(mail,\"From: noreply@cvlibs.net\\n\");\n      fprintf(mail,\"Subject: KITTI Evaluation Benchmark\\n\");\n      fprintf(mail,\"\\n\\n\");\n    } else {\n      mail = 0;\n    }\n  }\n  \n  ~Mail() {\n    if (mail) {\n      pclose(mail);\n    }\n  }\n  \n  void msg (const char *format, ...) {\n    va_list args;\n    va_start(args,format);\n    if (mail) {\n      vfprintf(mail,format,args);\n      fprintf(mail,\"\\n\");\n    }\n    vprintf(format,args);\n    printf(\"\\n\");\n    va_end(args);\n  }\n    \nprivate:\n\n  FILE *mail;\n  \n};\n\n#endif\n"
  },
  {
    "path": "data/kitti_split2/devkit/mapping/train_mapping.txt",
    "content": "2011_09_26 2011_09_26_drive_0005_sync 0000000109\n2011_09_26 2011_09_26_drive_0005_sync 0000000125\n2011_09_26 2011_09_26_drive_0005_sync 0000000127\n2011_09_26 2011_09_26_drive_0005_sync 0000000130\n2011_09_26 2011_09_26_drive_0005_sync 0000000133\n2011_09_26 2011_09_26_drive_0005_sync 0000000136\n2011_09_26 2011_09_26_drive_0005_sync 0000000139\n2011_09_26 2011_09_26_drive_0005_sync 0000000142\n2011_09_26 2011_09_26_drive_0005_sync 0000000145\n2011_09_26 2011_09_26_drive_0005_sync 0000000151\n2011_09_26 2011_09_26_drive_0009_sync 0000000001\n2011_09_26 2011_09_26_drive_0009_sync 0000000002\n2011_09_26 2011_09_26_drive_0009_sync 0000000003\n2011_09_26 2011_09_26_drive_0009_sync 0000000004\n2011_09_26 2011_09_26_drive_0009_sync 0000000005\n2011_09_26 2011_09_26_drive_0009_sync 0000000006\n2011_09_26 2011_09_26_drive_0009_sync 0000000007\n2011_09_26 2011_09_26_drive_0009_sync 0000000008\n2011_09_26 2011_09_26_drive_0009_sync 0000000009\n2011_09_26 2011_09_26_drive_0009_sync 0000000010\n2011_09_26 2011_09_26_drive_0009_sync 0000000011\n2011_09_26 2011_09_26_drive_0009_sync 0000000012\n2011_09_26 2011_09_26_drive_0009_sync 0000000013\n2011_09_26 2011_09_26_drive_0009_sync 0000000014\n2011_09_26 2011_09_26_drive_0009_sync 0000000015\n2011_09_26 2011_09_26_drive_0009_sync 0000000016\n2011_09_26 2011_09_26_drive_0009_sync 0000000017\n2011_09_26 2011_09_26_drive_0009_sync 0000000018\n2011_09_26 2011_09_26_drive_0009_sync 0000000019\n2011_09_26 2011_09_26_drive_0009_sync 0000000020\n2011_09_26 2011_09_26_drive_0009_sync 0000000021\n2011_09_26 2011_09_26_drive_0009_sync 0000000022\n2011_09_26 2011_09_26_drive_0009_sync 0000000023\n2011_09_26 2011_09_26_drive_0009_sync 0000000024\n2011_09_26 2011_09_26_drive_0009_sync 0000000025\n2011_09_26 2011_09_26_drive_0009_sync 0000000026\n2011_09_26 2011_09_26_drive_0009_sync 0000000027\n2011_09_26 2011_09_26_drive_0009_sync 0000000028\n2011_09_26 2011_09_26_drive_0009_sync 0000000029\n2011_09_26 2011_09_26_drive_0009_sync 0000000030\n2011_09_26 2011_09_26_drive_0009_sync 0000000031\n2011_09_26 2011_09_26_drive_0009_sync 0000000032\n2011_09_26 2011_09_26_drive_0009_sync 0000000033\n2011_09_26 2011_09_26_drive_0009_sync 0000000034\n2011_09_26 2011_09_26_drive_0009_sync 0000000035\n2011_09_26 2011_09_26_drive_0009_sync 0000000036\n2011_09_26 2011_09_26_drive_0009_sync 0000000037\n2011_09_26 2011_09_26_drive_0009_sync 0000000038\n2011_09_26 2011_09_26_drive_0009_sync 0000000039\n2011_09_26 2011_09_26_drive_0009_sync 0000000040\n2011_09_26 2011_09_26_drive_0009_sync 0000000041\n2011_09_26 2011_09_26_drive_0009_sync 0000000042\n2011_09_26 2011_09_26_drive_0009_sync 0000000043\n2011_09_26 2011_09_26_drive_0009_sync 0000000044\n2011_09_26 2011_09_26_drive_0009_sync 0000000045\n2011_09_26 2011_09_26_drive_0009_sync 0000000046\n2011_09_26 2011_09_26_drive_0009_sync 0000000047\n2011_09_26 2011_09_26_drive_0009_sync 0000000048\n2011_09_26 2011_09_26_drive_0009_sync 0000000049\n2011_09_26 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2011_09_26_drive_0101_sync 0000000413\n2011_09_26 2011_09_26_drive_0101_sync 0000000414\n2011_09_26 2011_09_26_drive_0101_sync 0000000415\n2011_09_26 2011_09_26_drive_0101_sync 0000000418\n2011_09_26 2011_09_26_drive_0101_sync 0000000419\n2011_09_26 2011_09_26_drive_0101_sync 0000000420\n2011_09_26 2011_09_26_drive_0101_sync 0000000421\n2011_09_26 2011_09_26_drive_0101_sync 0000000422\n2011_09_26 2011_09_26_drive_0101_sync 0000000423\n2011_09_26 2011_09_26_drive_0101_sync 0000000424\n2011_09_26 2011_09_26_drive_0101_sync 0000000425\n2011_09_26 2011_09_26_drive_0101_sync 0000000426\n2011_09_26 2011_09_26_drive_0101_sync 0000000427\n2011_09_26 2011_09_26_drive_0101_sync 0000000429\n2011_09_26 2011_09_26_drive_0101_sync 0000000430\n2011_09_26 2011_09_26_drive_0101_sync 0000000431\n2011_09_26 2011_09_26_drive_0101_sync 0000000432\n2011_09_26 2011_09_26_drive_0101_sync 0000000433\n2011_09_26 2011_09_26_drive_0101_sync 0000000434\n2011_09_26 2011_09_26_drive_0101_sync 0000000435\n2011_09_26 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2011_09_26_drive_0101_sync 0000000456\n2011_09_26 2011_09_26_drive_0101_sync 0000000459\n2011_09_26 2011_09_26_drive_0101_sync 0000000495\n2011_09_26 2011_09_26_drive_0101_sync 0000000496\n2011_09_26 2011_09_26_drive_0101_sync 0000000497\n2011_09_26 2011_09_26_drive_0101_sync 0000000498\n2011_09_26 2011_09_26_drive_0101_sync 0000000499\n2011_09_26 2011_09_26_drive_0101_sync 0000000500\n2011_09_26 2011_09_26_drive_0101_sync 0000000501\n2011_09_26 2011_09_26_drive_0101_sync 0000000502\n2011_09_26 2011_09_26_drive_0101_sync 0000000503\n2011_09_26 2011_09_26_drive_0101_sync 0000000504\n2011_09_26 2011_09_26_drive_0101_sync 0000000505\n2011_09_26 2011_09_26_drive_0101_sync 0000000506\n2011_09_26 2011_09_26_drive_0101_sync 0000000507\n2011_09_26 2011_09_26_drive_0101_sync 0000000524\n2011_09_26 2011_09_26_drive_0101_sync 0000000525\n2011_09_26 2011_09_26_drive_0101_sync 0000000526\n2011_09_26 2011_09_26_drive_0101_sync 0000000527\n2011_09_26 2011_09_26_drive_0101_sync 0000000528\n2011_09_26 2011_09_26_drive_0101_sync 0000000529\n2011_09_26 2011_09_26_drive_0101_sync 0000000530\n2011_09_26 2011_09_26_drive_0101_sync 0000000531\n2011_09_26 2011_09_26_drive_0101_sync 0000000532\n2011_09_26 2011_09_26_drive_0101_sync 0000000533\n2011_09_26 2011_09_26_drive_0101_sync 0000000534\n2011_09_26 2011_09_26_drive_0101_sync 0000000535\n2011_09_26 2011_09_26_drive_0101_sync 0000000536\n2011_09_26 2011_09_26_drive_0101_sync 0000000537\n2011_09_26 2011_09_26_drive_0101_sync 0000000538\n2011_09_26 2011_09_26_drive_0101_sync 0000000539\n2011_09_26 2011_09_26_drive_0101_sync 0000000540\n2011_09_26 2011_09_26_drive_0101_sync 0000000541\n2011_09_26 2011_09_26_drive_0101_sync 0000000542\n2011_09_26 2011_09_26_drive_0101_sync 0000000543\n2011_09_26 2011_09_26_drive_0101_sync 0000000544\n2011_09_26 2011_09_26_drive_0101_sync 0000000545\n2011_09_26 2011_09_26_drive_0101_sync 0000000546\n2011_09_26 2011_09_26_drive_0101_sync 0000000547\n2011_09_26 2011_09_26_drive_0101_sync 0000000548\n2011_09_26 2011_09_26_drive_0101_sync 0000000549\n2011_09_26 2011_09_26_drive_0101_sync 0000000550\n2011_09_26 2011_09_26_drive_0101_sync 0000000551\n2011_09_26 2011_09_26_drive_0101_sync 0000000552\n2011_09_26 2011_09_26_drive_0101_sync 0000000553\n2011_09_26 2011_09_26_drive_0101_sync 0000000554\n2011_09_26 2011_09_26_drive_0101_sync 0000000555\n2011_09_26 2011_09_26_drive_0101_sync 0000000556\n2011_09_26 2011_09_26_drive_0101_sync 0000000557\n2011_09_26 2011_09_26_drive_0101_sync 0000000558\n2011_09_26 2011_09_26_drive_0101_sync 0000000559\n2011_09_26 2011_09_26_drive_0101_sync 0000000560\n2011_09_26 2011_09_26_drive_0101_sync 0000000561\n2011_09_26 2011_09_26_drive_0101_sync 0000000562\n2011_09_26 2011_09_26_drive_0101_sync 0000000563\n2011_09_26 2011_09_26_drive_0101_sync 0000000564\n2011_09_26 2011_09_26_drive_0101_sync 0000000565\n2011_09_26 2011_09_26_drive_0101_sync 0000000566\n2011_09_26 2011_09_26_drive_0101_sync 0000000567\n2011_09_26 2011_09_26_drive_0101_sync 0000000568\n2011_09_26 2011_09_26_drive_0101_sync 0000000569\n2011_09_26 2011_09_26_drive_0101_sync 0000000570\n2011_09_26 2011_09_26_drive_0101_sync 0000000571\n2011_09_26 2011_09_26_drive_0101_sync 0000000572\n2011_09_26 2011_09_26_drive_0101_sync 0000000573\n2011_09_26 2011_09_26_drive_0101_sync 0000000574\n2011_09_26 2011_09_26_drive_0101_sync 0000000575\n2011_09_26 2011_09_26_drive_0101_sync 0000000576\n2011_09_26 2011_09_26_drive_0101_sync 0000000577\n2011_09_26 2011_09_26_drive_0101_sync 0000000578\n2011_09_26 2011_09_26_drive_0101_sync 0000000579\n2011_09_26 2011_09_26_drive_0101_sync 0000000580\n2011_09_26 2011_09_26_drive_0101_sync 0000000581\n2011_09_26 2011_09_26_drive_0101_sync 0000000582\n2011_09_26 2011_09_26_drive_0101_sync 0000000583\n2011_09_26 2011_09_26_drive_0101_sync 0000000584\n2011_09_26 2011_09_26_drive_0101_sync 0000000585\n2011_09_26 2011_09_26_drive_0101_sync 0000000586\n2011_09_26 2011_09_26_drive_0101_sync 0000000587\n2011_09_26 2011_09_26_drive_0101_sync 0000000588\n2011_09_26 2011_09_26_drive_0101_sync 0000000589\n2011_09_26 2011_09_26_drive_0101_sync 0000000590\n2011_09_26 2011_09_26_drive_0101_sync 0000000591\n2011_09_26 2011_09_26_drive_0101_sync 0000000592\n2011_09_26 2011_09_26_drive_0101_sync 0000000593\n2011_09_26 2011_09_26_drive_0101_sync 0000000594\n2011_09_26 2011_09_26_drive_0101_sync 0000000595\n2011_09_26 2011_09_26_drive_0101_sync 0000000596\n2011_09_26 2011_09_26_drive_0101_sync 0000000597\n2011_09_26 2011_09_26_drive_0101_sync 0000000598\n2011_09_26 2011_09_26_drive_0101_sync 0000000599\n2011_09_26 2011_09_26_drive_0101_sync 0000000600\n2011_09_26 2011_09_26_drive_0101_sync 0000000601\n2011_09_26 2011_09_26_drive_0101_sync 0000000602\n2011_09_26 2011_09_26_drive_0101_sync 0000000603\n2011_09_26 2011_09_26_drive_0101_sync 0000000604\n2011_09_26 2011_09_26_drive_0101_sync 0000000605\n2011_09_26 2011_09_26_drive_0101_sync 0000000606\n2011_09_26 2011_09_26_drive_0101_sync 0000000607\n2011_09_26 2011_09_26_drive_0101_sync 0000000608\n2011_09_26 2011_09_26_drive_0101_sync 0000000609\n2011_09_26 2011_09_26_drive_0101_sync 0000000620\n2011_09_26 2011_09_26_drive_0101_sync 0000000621\n2011_09_26 2011_09_26_drive_0101_sync 0000000622\n2011_09_26 2011_09_26_drive_0101_sync 0000000623\n2011_09_26 2011_09_26_drive_0101_sync 0000000624\n2011_09_26 2011_09_26_drive_0101_sync 0000000625\n2011_09_26 2011_09_26_drive_0101_sync 0000000626\n2011_09_26 2011_09_26_drive_0101_sync 0000000627\n2011_09_26 2011_09_26_drive_0101_sync 0000000628\n2011_09_26 2011_09_26_drive_0101_sync 0000000629\n2011_09_26 2011_09_26_drive_0101_sync 0000000630\n2011_09_26 2011_09_26_drive_0101_sync 0000000631\n2011_09_26 2011_09_26_drive_0101_sync 0000000632\n2011_09_26 2011_09_26_drive_0101_sync 0000000633\n2011_09_26 2011_09_26_drive_0101_sync 0000000634\n2011_09_26 2011_09_26_drive_0101_sync 0000000635\n2011_09_26 2011_09_26_drive_0101_sync 0000000636\n2011_09_26 2011_09_26_drive_0101_sync 0000000637\n2011_09_26 2011_09_26_drive_0101_sync 0000000649\n2011_09_26 2011_09_26_drive_0101_sync 0000000650\n2011_09_26 2011_09_26_drive_0101_sync 0000000651\n2011_09_26 2011_09_26_drive_0101_sync 0000000652\n2011_09_26 2011_09_26_drive_0101_sync 0000000653\n2011_09_26 2011_09_26_drive_0101_sync 0000000654\n2011_09_26 2011_09_26_drive_0101_sync 0000000655\n2011_09_26 2011_09_26_drive_0101_sync 0000000656\n2011_09_26 2011_09_26_drive_0101_sync 0000000657\n2011_09_26 2011_09_26_drive_0101_sync 0000000658\n2011_09_26 2011_09_26_drive_0101_sync 0000000659\n2011_09_26 2011_09_26_drive_0101_sync 0000000660\n2011_09_26 2011_09_26_drive_0101_sync 0000000661\n2011_09_26 2011_09_26_drive_0101_sync 0000000687\n2011_09_26 2011_09_26_drive_0101_sync 0000000688\n2011_09_26 2011_09_26_drive_0101_sync 0000000689\n2011_09_26 2011_09_26_drive_0101_sync 0000000690\n2011_09_26 2011_09_26_drive_0101_sync 0000000691\n2011_09_26 2011_09_26_drive_0101_sync 0000000692\n2011_09_26 2011_09_26_drive_0101_sync 0000000693\n2011_09_26 2011_09_26_drive_0101_sync 0000000694\n2011_09_26 2011_09_26_drive_0101_sync 0000000695\n2011_09_26 2011_09_26_drive_0101_sync 0000000696\n2011_09_26 2011_09_26_drive_0101_sync 0000000697\n2011_09_26 2011_09_26_drive_0101_sync 0000000698\n2011_09_26 2011_09_26_drive_0101_sync 0000000699\n2011_09_26 2011_09_26_drive_0101_sync 0000000700\n2011_09_26 2011_09_26_drive_0101_sync 0000000701\n2011_09_26 2011_09_26_drive_0101_sync 0000000702\n2011_09_26 2011_09_26_drive_0101_sync 0000000709\n2011_09_26 2011_09_26_drive_0101_sync 0000000710\n2011_09_26 2011_09_26_drive_0101_sync 0000000711\n2011_09_26 2011_09_26_drive_0101_sync 0000000712\n2011_09_26 2011_09_26_drive_0101_sync 0000000713\n2011_09_26 2011_09_26_drive_0101_sync 0000000714\n2011_09_26 2011_09_26_drive_0101_sync 0000000715\n2011_09_26 2011_09_26_drive_0101_sync 0000000716\n2011_09_26 2011_09_26_drive_0101_sync 0000000717\n2011_09_26 2011_09_26_drive_0101_sync 0000000718\n2011_09_26 2011_09_26_drive_0101_sync 0000000719\n2011_09_26 2011_09_26_drive_0101_sync 0000000720\n2011_09_26 2011_09_26_drive_0101_sync 0000000721\n2011_09_26 2011_09_26_drive_0101_sync 0000000722\n2011_09_26 2011_09_26_drive_0101_sync 0000000723\n2011_09_26 2011_09_26_drive_0101_sync 0000000724\n2011_09_26 2011_09_26_drive_0101_sync 0000000725\n2011_09_26 2011_09_26_drive_0101_sync 0000000726\n2011_09_26 2011_09_26_drive_0101_sync 0000000727\n2011_09_26 2011_09_26_drive_0101_sync 0000000728\n2011_09_26 2011_09_26_drive_0101_sync 0000000729\n2011_09_26 2011_09_26_drive_0101_sync 0000000730\n2011_09_26 2011_09_26_drive_0101_sync 0000000731\n2011_09_26 2011_09_26_drive_0101_sync 0000000732\n2011_09_26 2011_09_26_drive_0101_sync 0000000733\n2011_09_26 2011_09_26_drive_0101_sync 0000000734\n2011_09_26 2011_09_26_drive_0101_sync 0000000735\n2011_09_26 2011_09_26_drive_0101_sync 0000000736\n2011_09_26 2011_09_26_drive_0101_sync 0000000737\n2011_09_26 2011_09_26_drive_0101_sync 0000000738\n2011_09_26 2011_09_26_drive_0101_sync 0000000739\n2011_09_26 2011_09_26_drive_0101_sync 0000000740\n2011_09_26 2011_09_26_drive_0101_sync 0000000741\n2011_09_26 2011_09_26_drive_0101_sync 0000000742\n2011_09_26 2011_09_26_drive_0101_sync 0000000743\n2011_09_26 2011_09_26_drive_0101_sync 0000000744\n2011_09_26 2011_09_26_drive_0101_sync 0000000745\n2011_09_26 2011_09_26_drive_0101_sync 0000000746\n2011_09_26 2011_09_26_drive_0101_sync 0000000747\n2011_09_26 2011_09_26_drive_0101_sync 0000000748\n2011_09_26 2011_09_26_drive_0101_sync 0000000749\n2011_09_26 2011_09_26_drive_0101_sync 0000000755\n2011_09_26 2011_09_26_drive_0101_sync 0000000756\n2011_09_26 2011_09_26_drive_0101_sync 0000000757\n2011_09_26 2011_09_26_drive_0101_sync 0000000758\n2011_09_26 2011_09_26_drive_0101_sync 0000000759\n2011_09_26 2011_09_26_drive_0101_sync 0000000760\n2011_09_26 2011_09_26_drive_0101_sync 0000000761\n2011_09_26 2011_09_26_drive_0101_sync 0000000762\n2011_09_26 2011_09_26_drive_0101_sync 0000000763\n2011_09_26 2011_09_26_drive_0101_sync 0000000764\n2011_09_26 2011_09_26_drive_0101_sync 0000000765\n2011_09_26 2011_09_26_drive_0101_sync 0000000766\n2011_09_26 2011_09_26_drive_0101_sync 0000000767\n2011_09_26 2011_09_26_drive_0101_sync 0000000768\n2011_09_26 2011_09_26_drive_0101_sync 0000000769\n2011_09_26 2011_09_26_drive_0101_sync 0000000770\n2011_09_26 2011_09_26_drive_0101_sync 0000000771\n2011_09_26 2011_09_26_drive_0101_sync 0000000772\n2011_09_26 2011_09_26_drive_0101_sync 0000000773\n2011_09_26 2011_09_26_drive_0101_sync 0000000774\n2011_09_26 2011_09_26_drive_0101_sync 0000000775\n2011_09_26 2011_09_26_drive_0101_sync 0000000776\n2011_09_26 2011_09_26_drive_0101_sync 0000000777\n2011_09_26 2011_09_26_drive_0101_sync 0000000778\n2011_09_26 2011_09_26_drive_0101_sync 0000000779\n2011_09_26 2011_09_26_drive_0101_sync 0000000780\n2011_09_26 2011_09_26_drive_0101_sync 0000000781\n2011_09_26 2011_09_26_drive_0101_sync 0000000786\n2011_09_26 2011_09_26_drive_0101_sync 0000000787\n2011_09_26 2011_09_26_drive_0101_sync 0000000788\n2011_09_26 2011_09_26_drive_0101_sync 0000000789\n2011_09_26 2011_09_26_drive_0101_sync 0000000790\n2011_09_26 2011_09_26_drive_0101_sync 0000000791\n2011_09_26 2011_09_26_drive_0101_sync 0000000792\n2011_09_26 2011_09_26_drive_0101_sync 0000000793\n2011_09_26 2011_09_26_drive_0101_sync 0000000794\n2011_09_26 2011_09_26_drive_0101_sync 0000000795\n2011_09_26 2011_09_26_drive_0101_sync 0000000796\n2011_09_26 2011_09_26_drive_0101_sync 0000000797\n2011_09_26 2011_09_26_drive_0101_sync 0000000798\n2011_09_26 2011_09_26_drive_0101_sync 0000000799\n2011_09_26 2011_09_26_drive_0101_sync 0000000800\n2011_09_26 2011_09_26_drive_0101_sync 0000000801\n2011_09_26 2011_09_26_drive_0101_sync 0000000802\n2011_09_26 2011_09_26_drive_0101_sync 0000000803\n2011_09_26 2011_09_26_drive_0101_sync 0000000804\n2011_09_26 2011_09_26_drive_0101_sync 0000000805\n2011_09_26 2011_09_26_drive_0101_sync 0000000806\n2011_09_26 2011_09_26_drive_0101_sync 0000000807\n2011_09_26 2011_09_26_drive_0101_sync 0000000808\n2011_09_26 2011_09_26_drive_0101_sync 0000000809\n2011_09_26 2011_09_26_drive_0101_sync 0000000810\n2011_09_26 2011_09_26_drive_0101_sync 0000000811\n2011_09_26 2011_09_26_drive_0101_sync 0000000812\n2011_09_26 2011_09_26_drive_0101_sync 0000000813\n2011_09_26 2011_09_26_drive_0101_sync 0000000814\n2011_09_26 2011_09_26_drive_0101_sync 0000000815\n2011_09_26 2011_09_26_drive_0101_sync 0000000816\n2011_09_26 2011_09_26_drive_0101_sync 0000000817\n2011_09_26 2011_09_26_drive_0101_sync 0000000818\n2011_09_26 2011_09_26_drive_0101_sync 0000000819\n2011_09_26 2011_09_26_drive_0101_sync 0000000820\n2011_09_26 2011_09_26_drive_0101_sync 0000000821\n2011_09_26 2011_09_26_drive_0101_sync 0000000822\n2011_09_26 2011_09_26_drive_0101_sync 0000000823\n2011_09_26 2011_09_26_drive_0101_sync 0000000824\n2011_09_26 2011_09_26_drive_0101_sync 0000000825\n2011_09_26 2011_09_26_drive_0101_sync 0000000826\n2011_09_26 2011_09_26_drive_0101_sync 0000000827\n2011_09_26 2011_09_26_drive_0101_sync 0000000828\n2011_09_26 2011_09_26_drive_0101_sync 0000000829\n2011_09_26 2011_09_26_drive_0101_sync 0000000830\n2011_09_26 2011_09_26_drive_0101_sync 0000000831\n2011_09_26 2011_09_26_drive_0101_sync 0000000833\n2011_09_26 2011_09_26_drive_0101_sync 0000000841\n2011_09_26 2011_09_26_drive_0101_sync 0000000843\n2011_09_26 2011_09_26_drive_0101_sync 0000000844\n2011_09_26 2011_09_26_drive_0101_sync 0000000845\n2011_09_26 2011_09_26_drive_0101_sync 0000000846\n2011_09_26 2011_09_26_drive_0101_sync 0000000847\n2011_09_26 2011_09_26_drive_0101_sync 0000000848\n2011_09_26 2011_09_26_drive_0101_sync 0000000849\n2011_09_26 2011_09_26_drive_0101_sync 0000000850\n2011_09_26 2011_09_26_drive_0101_sync 0000000851\n2011_09_26 2011_09_26_drive_0101_sync 0000000852\n2011_09_26 2011_09_26_drive_0101_sync 0000000853\n2011_09_26 2011_09_26_drive_0101_sync 0000000854\n2011_09_26 2011_09_26_drive_0101_sync 0000000880\n2011_09_26 2011_09_26_drive_0101_sync 0000000883\n2011_09_26 2011_09_26_drive_0101_sync 0000000884\n2011_09_26 2011_09_26_drive_0101_sync 0000000885\n2011_09_26 2011_09_26_drive_0101_sync 0000000887\n2011_09_26 2011_09_26_drive_0101_sync 0000000888\n2011_09_26 2011_09_26_drive_0101_sync 0000000889\n2011_09_26 2011_09_26_drive_0101_sync 0000000893\n2011_09_26 2011_09_26_drive_0101_sync 0000000894\n2011_09_26 2011_09_26_drive_0101_sync 0000000895\n2011_09_26 2011_09_26_drive_0101_sync 0000000896\n2011_09_26 2011_09_26_drive_0101_sync 0000000897\n2011_09_26 2011_09_26_drive_0101_sync 0000000898\n2011_09_26 2011_09_26_drive_0101_sync 0000000899\n2011_09_26 2011_09_26_drive_0101_sync 0000000900\n2011_09_26 2011_09_26_drive_0101_sync 0000000901\n2011_09_26 2011_09_26_drive_0101_sync 0000000902\n2011_09_26 2011_09_26_drive_0101_sync 0000000903\n2011_09_26 2011_09_26_drive_0101_sync 0000000904\n2011_09_26 2011_09_26_drive_0101_sync 0000000906\n2011_09_26 2011_09_26_drive_0101_sync 0000000907\n2011_09_26 2011_09_26_drive_0101_sync 0000000908\n2011_09_26 2011_09_26_drive_0101_sync 0000000909\n2011_09_26 2011_09_26_drive_0101_sync 0000000910\n2011_09_26 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2011_09_26_drive_0061_sync 0000000101\n2011_09_26 2011_09_26_drive_0061_sync 0000000103\n2011_09_26 2011_09_26_drive_0061_sync 0000000105\n2011_09_26 2011_09_26_drive_0061_sync 0000000107\n2011_09_26 2011_09_26_drive_0061_sync 0000000109\n2011_09_26 2011_09_26_drive_0061_sync 0000000111\n2011_09_26 2011_09_26_drive_0061_sync 0000000113\n2011_09_26 2011_09_26_drive_0061_sync 0000000115\n2011_09_26 2011_09_26_drive_0061_sync 0000000117\n2011_09_26 2011_09_26_drive_0061_sync 0000000119\n2011_09_26 2011_09_26_drive_0061_sync 0000000121\n2011_09_26 2011_09_26_drive_0061_sync 0000000123\n2011_09_26 2011_09_26_drive_0061_sync 0000000125\n2011_09_26 2011_09_26_drive_0061_sync 0000000127\n2011_09_26 2011_09_26_drive_0061_sync 0000000129\n2011_09_26 2011_09_26_drive_0061_sync 0000000131\n2011_09_26 2011_09_26_drive_0061_sync 0000000133\n2011_09_26 2011_09_26_drive_0061_sync 0000000135\n2011_09_26 2011_09_26_drive_0061_sync 0000000137\n2011_09_26 2011_09_26_drive_0061_sync 0000000139\n2011_09_26 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2011_09_28_drive_0209_sync 0000000057\n2011_09_28 2011_09_28_drive_0209_sync 0000000065\n2011_09_28 2011_09_28_drive_0209_sync 0000000072\n2011_09_28 2011_09_28_drive_0209_sync 0000000079\n2011_09_28 2011_09_28_drive_0216_sync 0000000008\n2011_09_28 2011_09_28_drive_0216_sync 0000000015\n2011_09_28 2011_09_28_drive_0216_sync 0000000022\n2011_09_28 2011_09_28_drive_0216_sync 0000000030\n2011_09_28 2011_09_28_drive_0216_sync 0000000038\n2011_09_28 2011_09_28_drive_0220_sync 0000000028\n2011_09_28 2011_09_28_drive_0220_sync 0000000035\n2011_09_28 2011_09_28_drive_0220_sync 0000000042\n2011_09_28 2011_09_28_drive_0220_sync 0000000049\n2011_09_28 2011_09_28_drive_0220_sync 0000000057\n2011_09_28 2011_09_28_drive_0220_sync 0000000066\n2011_09_28 2011_09_28_drive_0222_sync 0000000007\n2011_09_28 2011_09_28_drive_0222_sync 0000000014\n2011_09_28 2011_09_28_drive_0222_sync 0000000021\n2011_09_28 2011_09_28_drive_0222_sync 0000000027\n2011_09_28 2011_09_28_drive_0222_sync 0000000033\n2011_09_28 2011_09_28_drive_0222_sync 0000000040\n2011_09_28 2011_09_28_drive_0222_sync 0000000053\n"
  },
  {
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  },
  {
    "path": "data/kitti_split2/devkit/matlab/computeBox3D.m",
    "content": "function [corners_2D,face_idx] = computeBox3D(object,P)\n% takes an object and a projection matrix (P) and projects the 3D\n% bounding box into the image plane.\n\n% index for 3D bounding box faces\nface_idx = [ 1,2,6,5   % front face\n             2,3,7,6   % left face\n             3,4,8,7   % back face\n             4,1,5,8]; % right face\n\n% compute rotational matrix around yaw axis\nR = [+cos(object.ry), 0, +sin(object.ry);\n                   0, 1,               0;\n     -sin(object.ry), 0, +cos(object.ry)];\n\n% 3D bounding box dimensions\nl = object.l;\nw = object.w;\nh = object.h;\n\n% 3D bounding box corners\nx_corners = [l/2, l/2, -l/2, -l/2, l/2, l/2, -l/2, -l/2];\ny_corners = [0,0,0,0,-h,-h,-h,-h];\nz_corners = [w/2, -w/2, -w/2, w/2, w/2, -w/2, -w/2, w/2];\n\n% rotate and translate 3D bounding box\ncorners_3D = R*[x_corners;y_corners;z_corners];\ncorners_3D(1,:) = corners_3D(1,:) + object.t(1);\ncorners_3D(2,:) = corners_3D(2,:) + object.t(2);\ncorners_3D(3,:) = corners_3D(3,:) + object.t(3);\n\n% only draw 3D bounding box for objects in front of the camera\nif any(corners_3D(3,:)<0.1) \n  corners_2D = [];\n  return;\nend\n\n% project the 3D bounding box into the image plane\ncorners_2D = projectToImage(corners_3D, P);\n"
  },
  {
    "path": "data/kitti_split2/devkit/matlab/computeOrientation3D.m",
    "content": "function [orientation_2D] = computeOrientation3D(object,P)\n% takes an object and a projection matrix (P) and projects the 3D\n% object orientation vector into the image plane.\n\n% compute rotational matrix around yaw axis\nR = [cos(object.ry),  0, sin(object.ry);\n     0,               1,              0;\n     -sin(object.ry), 0, cos(object.ry)];\n\n% orientation in object coordinate system\norientation_3D = [0.0, object.l\n                  0.0, 0.0\n                  0.0, 0.0];\n\n% rotate and translate in camera coordinate system, project in image\norientation_3D      = R*orientation_3D;\norientation_3D(1,:) = orientation_3D(1,:) + object.t(1);\norientation_3D(2,:) = orientation_3D(2,:) + object.t(2);\norientation_3D(3,:) = orientation_3D(3,:) + object.t(3);\n\n% vector behind image plane?\nif any(orientation_3D(3,:)<0.1)\n  orientation_2D = [];\n  return;\nend\n\n% project orientation into the image plane\norientation_2D = projectToImage(orientation_3D,P);\n"
  },
  {
    "path": "data/kitti_split2/devkit/matlab/drawBox2D.m",
    "content": "function drawBox2D(h,object)\n\n% set styles for occlusion and truncation\nocc_col    = {'g','y','r','w'};\ntrun_style = {'-','--'};\n\n% draw regular objects\nif ~strcmp(object.type,'DontCare')\n\n  % show rectangular bounding boxes\n  pos = [object.x1,object.y1,object.x2-object.x1+1,object.y2-object.y1+1];\n  trc = double(object.truncation>0.1)+1;\n  rectangle('Position',pos,'EdgeColor',occ_col{object.occlusion+1},...\n            'LineWidth',3,'LineStyle',trun_style{trc},'parent',h(1).axes)\n  rectangle('Position',pos,'EdgeColor','b', 'parent', h(1).axes)\n\n  % draw label\n  label_text = sprintf('%s\\n%1.1f rad',object.type,object.alpha);\n  x = (object.x1+object.x2)/2;\n  y = object.y1;\n  text(x,max(y-5,40),label_text,'color',occ_col{object.occlusion+1},...\n       'BackgroundColor','k','HorizontalAlignment','center',...\n       'VerticalAlignment','bottom','FontWeight','bold',...\n       'FontSize',8,'parent',h(1).axes);\n     \n% draw don't care regions\nelse\n  \n  % draw dotted rectangle\n  pos = [object.x1,object.y1,object.x2-object.x1+1,object.y2-object.y1+1];\n  rectangle('Position',pos,'EdgeColor','c',...\n            'LineWidth',2,'LineStyle','-','parent',h(1).axes)\nend\n"
  },
  {
    "path": "data/kitti_split2/devkit/matlab/drawBox3D.m",
    "content": "function drawBox3D(h,object,corners,face_idx,orientation)\n\n  % set styles for occlusion and truncation\n  occ_col    = {'g','y','r','w'};\n  trun_style = {'-','--'};\n  trc        = double(object.truncation>0.1)+1;\n  \n  % draw projected 3D bounding boxes\n  if ~isempty(corners)\n    for f=1:4\n      line([corners(1,face_idx(f,:)),corners(1,face_idx(f,1))]+1,...\n           [corners(2,face_idx(f,:)),corners(2,face_idx(f,1))]+1,...\n           'parent',h(2).axes, 'color',occ_col{object.occlusion+1},...\n           'LineWidth',3,'LineStyle',trun_style{trc});\n      line([corners(1,face_idx(f,:)),corners(1,face_idx(f,1))]+1,...\n           [corners(2,face_idx(f,:)),corners(2,face_idx(f,1))]+1,...\n           'parent',h(2).axes,'color','b','LineWidth',1);\n    end\n  end\n  \n  % draw orientation vector\n  if ~isempty(orientation)\n    line([orientation(1,:),orientation(1,:)]+1,...\n         [orientation(2,:),orientation(2,:)]+1,...\n         'parent',h(2).axes,'color','w','LineWidth',4);\n    line([orientation(1,:),orientation(1,:)]+1,...\n         [orientation(2,:),orientation(2,:)]+1,...\n         'parent',h(2).axes,'color','k','LineWidth',2);\n  end\nend\n"
  },
  {
    "path": "data/kitti_split2/devkit/matlab/projectToImage.m",
    "content": "function pts_2D = projectToImage(pts_3D, P)\n% PROJECTTOIMAGE projects 3D points in given coordinate system in the image\n% plane using the given projection matrix P.\n%\n% Usage: pts_2D = projectToImage(pts_3D, P)\n%   input: pts_3D: 3xn matrix\n%          P:      3x4 projection matrix\n%   output: pts_2D: 2xn matrix\n%\n% last edited on: 2012-02-27\n% Philip Lenz - lenz@kit.edu\n\n\n  % project in image\n  pts_2D = P * [pts_3D; ones(1,size(pts_3D,2))];\n  % scale projected points\n  pts_2D(1,:) = pts_2D(1,:)./pts_2D(3,:);\n  pts_2D(2,:) = pts_2D(2,:)./pts_2D(3,:);\n  pts_2D(3,:) = [];\nend"
  },
  {
    "path": "data/kitti_split2/devkit/matlab/readCalibration.m",
    "content": "function P = readCalibration(calib_dir,img_idx,cam)\n\n  % load 3x4 projection matrix\n  P = dlmread(sprintf('%s/%06d.txt',calib_dir,img_idx),' ',0,1);\n  P = P(cam+1,:);\n  P = reshape(P ,[4,3])';\n  \nend\n"
  },
  {
    "path": "data/kitti_split2/devkit/matlab/readLabels.m",
    "content": "function objects = readLabels(label_dir,img_idx)\n\n% parse input file\nfid = fopen(sprintf('%s/%06d.txt',label_dir,img_idx),'r');\nC   = textscan(fid,'%s %f %d %f %f %f %f %f %f %f %f %f %f %f %f','delimiter', ' ');\nfclose(fid);\n\n% for all objects do\nobjects = [];\nfor o = 1:numel(C{1})\n\n  % extract label, truncation, occlusion\n  lbl = C{1}(o);                   % for converting: cell -> string\n  objects(o).type       = lbl{1};  % 'Car', 'Pedestrian', ...\n  objects(o).truncation = C{2}(o); % truncated pixel ratio ([0..1])\n  objects(o).occlusion  = C{3}(o); % 0 = visible, 1 = partly occluded, 2 = fully occluded, 3 = unknown\n  objects(o).alpha      = C{4}(o); % object observation angle ([-pi..pi])\n\n  % extract 2D bounding box in 0-based coordinates\n  objects(o).x1 = C{5}(o); % left\n  objects(o).y1 = C{6}(o); % top\n  objects(o).x2 = C{7}(o); % right\n  objects(o).y2 = C{8}(o); % bottom\n\n  % extract 3D bounding box information\n  objects(o).h    = C{9} (o); % box width\n  objects(o).w    = C{10}(o); % box height\n  objects(o).l    = C{11}(o); % box length\n  objects(o).t(1) = C{12}(o); % location (x)\n  objects(o).t(2) = C{13}(o); % location (y)\n  objects(o).t(3) = C{14}(o); % location (z)\n  objects(o).ry   = C{15}(o); % yaw angle\nend\n"
  },
  {
    "path": "data/kitti_split2/devkit/matlab/run_demo.m",
    "content": "% KITTI OBJECT DETECTION AND ORIENTATION ESTIMATION BENCHMARK DEMONSTRATION\n% \n% This tool displays the images and the object labels for the benchmark and\n% provides an entry point for writing your own interface to the data set.\n% Before running this tool, set root_dir to the directory where you have\n% downloaded the dataset. 'root_dir' must contain the subdirectory\n% 'training', which in turn contains 'image_2', 'label_2' and 'calib'.\n% For more information about the data format, please look into readme.txt.\n%\n% Usage:\n%   SPACE: next frame\n%   '-':   last frame\n%   'x':   +10 frames\n%   'y':   -10 frames\n%   q:     quit\n%\n% Occlusion Coding:\n%   green:  not occluded\n%   yellow: partly occluded\n%   red:    fully occluded\n%   white:  unknown\n%\n% Truncation Coding:\n%   solid:  not truncated\n%   dashed: truncated\n\n% clear and close everything\nclear all; close all;\ndisp('======= KITTI DevKit Demo =======');\n\n% options\nroot_dir = '/media/data/kitti/2012_object';\ndata_set = 'training';\n\n% get sub-directories\ncam = 2; % 2 = left color camera\nimage_dir = fullfile(root_dir,[data_set '/image_' num2str(cam)]);\nlabel_dir = fullfile(root_dir,[data_set '/label_' num2str(cam)]);\ncalib_dir = fullfile(root_dir,[data_set '/calib']);\n\n% get number of images for this dataset\nnimages = length(dir(fullfile(image_dir, '*.png')));\n\n% set up figure\nh = visualization('init',image_dir);\n\n% main loop\nimg_idx=0;\nwhile 1\n\n  % load projection matrix\n  P = readCalibration(calib_dir,img_idx,cam);\n  \n  % load labels\n  objects = readLabels(label_dir,img_idx);\n  \n  % visualization update for next frame\n  visualization('update',image_dir,h,img_idx,nimages,data_set);\n \n  % for all annotated objects do\n  for obj_idx=1:numel(objects)\n   \n    % plot 2D bounding box\n    drawBox2D(h,objects(obj_idx));\n    \n    % plot 3D bounding box\n    [corners,face_idx] = computeBox3D(objects(obj_idx),P);\n    orientation = computeOrientation3D(objects(obj_idx),P);\n    drawBox3D(h, objects(obj_idx),corners,face_idx,orientation);\n    \n  end\n\n  % force drawing and tiny user interface\n  waitforbuttonpress; \n  key = get(gcf,'CurrentCharacter');\n  switch lower(key)                         \n    case 'q',  break;                                 % quit\n    case '-',  img_idx = max(img_idx-1,  0);          % previous frame\n    case 'x',  img_idx = min(img_idx+1000,nimages-1); % +100 frames\n    case 'y',  img_idx = max(img_idx-1000,0);         % -100 frames\n    otherwise, img_idx = min(img_idx+1,  nimages-1);  % next frame\n  end\n\nend\n\n% clean up\nclose all;\n"
  },
  {
    "path": "data/kitti_split2/devkit/matlab/run_readWriteDemo.m",
    "content": "% clear and close everything\nclear all; close all;\ndisp('======= KITTI DevKit Demo =======');\n\nroot_dir  = '/media/karlsruhe_data/kitti/2012_object';\ntrain_dir = fullfile(root_dir,'/training/label_2');\ntest_dir  = '.'; % location of your testing dir\n\n% read objects of first training image\ntrain_objects = readLabels(train_dir,0);\n\n% loop over all images\n% ... YOUR TRAINING CODE HERE ...\n% ... YOUR TESTING CODE HERE ...\n\n% detect one object (car) in first test image\ntest_objects(1).type  = 'Car';\ntest_objects(1).x1    = 10;\ntest_objects(1).y1    = 10;\ntest_objects(1).x2    = 100;\ntest_objects(1).y2    = 100;\ntest_objects(1).alpha = pi/2;\ntest_objects(1).score = 0.5;\n\n% write object to file\nwriteLabels(test_objects,test_dir,0);\ndisp('Test label file written!');\n"
  },
  {
    "path": "data/kitti_split2/devkit/matlab/run_statistics.m",
    "content": "% KITTI OBJECT DETECTION AND ORIENTATION ESTIMATION BENCHMARK STATISTICS\nclear all; close all; clc;\ndisp('======= KITTI DevKit Statistics =======');\ndisp('Computing statistics of Cars, Pedestrians and Cyclists in training set.');\ndisp('Please wait ...');\n\n% options\nroot_dir = '/media/karlsruhe_data/kitti/2012_object';\n  \n% get label directory and number of images\nlabel_dir = fullfile(root_dir,'training/label_2');\nnimages = length(dir(fullfile(label_dir, '*.txt')));\n\n% init statistics\ncars.occ = zeros(1,4);\npeds.occ = zeros(1,4);\ncycs.occ = zeros(1,4);\n\n% compute statistics\nfor j=1:nimages\n  objects = readLabels(label_dir,j-1);\n  for k=1:length(objects)\n    if strcmp(objects(k).type,'Car')\n      cars.occ(objects(k).occlusion+1)  = cars.occ(objects(k).occlusion+1)  + 1;\n    end\n    if strcmp(objects(k).type,'Pedestrian')\n      peds.occ(objects(k).occlusion+1)  = peds.occ(objects(k).occlusion+1)  + 1;\n    end\n    if strcmp(objects(k).type,'Cyclist')\n      cycs.occ(objects(k).occlusion+1)  = cycs.occ(objects(k).occlusion+1)  + 1;\n    end\n  end    \nend\n\n% plot statistics\nfprintf('Cars: Not occluded: %d, partly occluded: %d, largely occluded: %d, unknown: %d\\n',cars.occ);\nfprintf('Pedestrians: Not occluded: %d, partly occluded: %d, largely occluded: %d, unknown: %d\\n',peds.occ);\nfprintf('Cyclists: Not occluded: %d, partly occluded: %d, largely occluded: %d, unknown: %d\\n',cycs.occ);\n"
  },
  {
    "path": "data/kitti_split2/devkit/matlab/visualization.m",
    "content": "function varargout = visualization(mode,image_dir,varargin)\n\nswitch mode\n  \n  % init figure\n  case 'init'\n    \n    % create figure using size of first image in repository\n    fig = figure(1);\n    img = imread(sprintf('%s/%06d.png',image_dir,0));\n    set(fig,'position',[0,30,0.8*size(img,2),0.8*2*size(img,1)]);\n    h(1).axes = axes('position',[0,0.5,1,0.5]);\n    h(2).axes = axes('position',[0,0,1,0.5]);\n    varargout{1} = h;\n    \n  % update figure\n  case 'update'\n    \n    % unpack input arguments\n    h        = varargin{1};\n    img_idx  = varargin{2};\n    nimages  = varargin{3};\n    data_set = varargin{4};\n    \n    % read image\n    img = imread(sprintf('%s/%06d.png',image_dir,img_idx));\n    \n    % clear axes, draw image\n    cla(h(1).axes); cla(h(2).axes);\n    imshow(img,'parent',h(1).axes); axis(h(1).axes,'image','off'); hold(h(1).axes, 'on');\n    imshow(img,'parent',h(2).axes); axis(h(2).axes,'image','off'); hold(h(2).axes, 'on');\n    \n    % title\n    text(size(img,2)/2,3,sprintf('2D Bounding Boxes'),'parent',h(1).axes,'color','g','HorizontalAlignment','center','VerticalAlignment','top','FontSize',14,'FontWeight','bold','BackgroundColor','black');\n    text(size(img,2)/2,3,sprintf('3D Bounding Boxes'),'parent',h(2).axes,'color','g','HorizontalAlignment','center','VerticalAlignment','top','FontSize',14,'FontWeight','bold','BackgroundColor','black');\n    \n    % legend\n    text(0,00,'Not occluded','parent',h(1).axes,'color','g','HorizontalAlignment','left','VerticalAlignment','top','FontSize',14,'FontWeight','bold','BackgroundColor','black');\n    text(0,30,'Partly occluded','parent',h(1).axes,'color','y','HorizontalAlignment','left','VerticalAlignment','top','FontSize',14,'FontWeight','bold','BackgroundColor','black');\n    text(0,60,'Fully occluded','parent',h(1).axes,'color','r','HorizontalAlignment','left','VerticalAlignment','top','FontSize',14,'FontWeight','bold','BackgroundColor','black');\n    text(0,90,'Unknown','parent',h(1).axes,'color','w','HorizontalAlignment','left','VerticalAlignment','top','FontSize',14,'FontWeight','bold','BackgroundColor','black');\n    text(0,120,'Don''t care region','parent',h(1).axes,'color','c','HorizontalAlignment','left','VerticalAlignment','top','FontSize',14,'FontWeight','bold','BackgroundColor','black');\n\n    % frame number\n    text(size(img,2),0,sprintf('%s set frame %d/%d',data_set,img_idx,nimages-1), 'parent', h(1).axes,'color','g','HorizontalAlignment','right','VerticalAlignment','top','FontSize',14,'FontWeight','bold','BackgroundColor','black');\n    \n    % usage instructions\n    text(size(img,2)/2,size(img,1),sprintf('''SPACE'': Next Image  |  ''-'': Previous Image  |  ''x'': +1000  |  ''y'': -1000 | ''q'': quit'), 'parent', h(2).axes,'color','g','HorizontalAlignment','center','VerticalAlignment','bottom','FontSize',14,'FontWeight','bold', 'BackgroundColor','black');\nend\n"
  },
  {
    "path": "data/kitti_split2/devkit/matlab/writeLabels.m",
    "content": "function writeLabels(objects,label_dir,img_idx)\n\n% parse input file\nfid = fopen(sprintf('%s/%06d.txt',label_dir,img_idx),'w');\n\n% for all objects do\nfor o = 1:numel(objects)\n\n  % set label, truncation, occlusion\n  if isfield(objects(o),'type'),         fprintf(fid,'%s ',objects(o).type);\n  else                                   error('ERROR: type not specified!'), end;\n  if isfield(objects(o),'truncation'),   fprintf(fid,'%.2f ',objects(o).truncation);\n  else                                   fprintf(fid,'-1 '); end; % default\n  if isfield(objects(o),'occlusion'),    fprintf(fid,'%.d ',objects(o).occlusion);\n  else                                   fprintf(fid,'-1 '); end; % default\n  if isfield(objects(o),'alpha'),        fprintf(fid,'%.2f ',wrapToPi(objects(o).alpha));\n  else                                   fprintf(fid,'-10 '); end; % default\n\n  % set 2D bounding box in 0-based C++ coordinates\n  if isfield(objects(o),'x1'),           fprintf(fid,'%.2f ',objects(o).x1);\n  else                                   error('ERROR: x1 not specified!'); end;\n  if isfield(objects(o),'y1'),           fprintf(fid,'%.2f ',objects(o).y1);\n  else                                   error('ERROR: y1 not specified!'); end;\n  if isfield(objects(o),'x2'),           fprintf(fid,'%.2f ',objects(o).x2);\n  else                                   error('ERROR: x2 not specified!'); end;\n  if isfield(objects(o),'y2'),           fprintf(fid,'%.2f ',objects(o).y2);\n  else                                   error('ERROR: y2 not specified!'); end;\n\n  % set 3D bounding box\n  if isfield(objects(o),'h'),            fprintf(fid,'%.2f ',objects(o).h);\n  else                                   fprintf(fid,'-1 '); end; % default\n  if isfield(objects(o),'w'),            fprintf(fid,'%.2f ',objects(o).w);\n  else                                   fprintf(fid,'-1 '); end; % default\n  if isfield(objects(o),'l'),            fprintf(fid,'%.2f ',objects(o).l);\n  else                                   fprintf(fid,'-1 '); end; % default\n  if isfield(objects(o),'t'),            fprintf(fid,'%.2f %.2f %.2f ',objects(o).t);\n  else                                   fprintf(fid,'-1000 -1000 -1000 '); end; % default\n  if isfield(objects(o),'ry'),           fprintf(fid,'%.2f ',wrapToPi(objects(o).ry));\n  else                                   fprintf(fid,'-10 '); end; % default\n\n  % set score\n  if isfield(objects(o),'score'),        fprintf(fid,'%.2f ',objects(o).score);\n  else                                   error('ERROR: score not specified!'); end;\n\n  % next line\n  fprintf(fid,'\\n');\nend\n\n% close file\nfclose(fid);\n\nfunction alpha = wrapToPi(alpha)\n\n% wrap to [0..2*pi]\nalpha = mod(alpha,2*pi);\n\n% wrap to [-pi..pi]\nidx = alpha>pi;\nalpha(idx) = alpha(idx)-2*pi;\n\n"
  },
  {
    "path": "data/kitti_split2/devkit/readme.txt",
    "content": "###########################################################################\n#            THE KITTI VISION BENCHMARK SUITE: OBJECT BENCHMARK           #\n#              Andreas Geiger    Philip Lenz    Raquel Urtasun            #\n#                    Karlsruhe Institute of Technology                    #\n#                Toyota Technological Institute at Chicago                #\n#                             www.cvlibs.net                              #\n###########################################################################\n\nFor recent updates see http://www.cvlibs.net/datasets/kitti/eval_object.php.\n\nThis file describes the KITTI 2D object detection and orientation estimation\nbenchmark, the 3D object detection benchmark and the bird's eye view benchmark.\nThe benchmarks consist of 7481 training images (and point clouds) \nand 7518 test images (and point clouds) for each task.\nDespite the fact that we have labeled 8 different classes, only the\nclasses 'Car' and 'Pedestrian' are evaluated in our benchmark, as only for\nthose classes enough instances for a comprehensive evaluation have been\nlabeled. The labeling process has been performed in two steps: First we\nhired a set of annotators, to label 3D bounding boxe tracklets in point\nclouds. Since for a pedestrian tracklet, a single 3D bounding box tracklet\n(dimensions have been fixed) often fits badly, we additionally labeled the\nleft/right boundaries of each object by making use of Mechanical Turk. We\nalso collected labels of the object's occlusion state, and computed the\nobject's truncation via backprojecting a car/pedestrian model into the\nimage plane.\n\nNOTE: WHEN SUBMITTING RESULTS, PLEASE STORE THEM IN THE SAME DATA FORMAT IN\nWHICH THE GROUND TRUTH DATA IS PROVIDED (SEE BELOW), USING THE FILE NAMES\n000000.txt 000001.txt ... CREATE A ZIP ARCHIVE OF THEM AND STORE YOUR\nRESULTS (ONLY THE RESULTS OF THE TEST SET) IN ITS ROOT FOLDER.\n\nNOTE2: Please read the bottom of this file carefully if you plan to evaluate\nresults yourself on the training set.\n\nNOTE3: WHEN SUBMITTING RESULTS FOR THE 3D OBJECT DETECTION BENCHMARK OR THE\nBIRD'S EYE VIEW BENCHMARK (AS OF 2017), READ THE INSTRUCTIONS BELOW CAREFULLY.\nIN PARTICULAR, MAKE SURE TO ALWAYS SUBMIT BOTH THE 2D BOUNDING BOXES AND THE\n3D BOUNDING BOXES AND FILTER BOUNDING BOXES NOT VISIBLE ON THE IMAGE PLANE.\n\nData Format Description\n=======================\n\nThe data for training and testing can be found in the corresponding folders.\nThe sub-folders are structured as follows:\n\n  - image_02/ contains the left color camera images (png)\n  - label_02/ contains the left color camera label files (plain text files)\n  - calib/ contains the calibration for all four cameras (plain text file)\n\nThe label files contain the following information, which can be read and\nwritten using the matlab tools (readLabels.m, writeLabels.m) provided within\nthis devkit. All values (numerical or strings) are separated via spaces,\neach row corresponds to one object. The 15 columns represent:\n\n#Values    Name      Description\n----------------------------------------------------------------------------\n   1    type         Describes the type of object: 'Car', 'Van', 'Truck',\n                     'Pedestrian', 'Person_sitting', 'Cyclist', 'Tram',\n                     'Misc' or 'DontCare'\n   1    truncated    Float from 0 (non-truncated) to 1 (truncated), where\n                     truncated refers to the object leaving image boundaries\n   1    occluded     Integer (0,1,2,3) indicating occlusion state:\n                     0 = fully visible, 1 = partly occluded\n                     2 = largely occluded, 3 = unknown\n   1    alpha        Observation angle of object, ranging [-pi..pi]\n   4    bbox         2D bounding box of object in the image (0-based index):\n                     contains left, top, right, bottom pixel coordinates\n   3    dimensions   3D object dimensions: height, width, length (in meters)\n   3    location     3D object location x,y,z in camera coordinates (in meters)\n   1    rotation_y   Rotation ry around Y-axis in camera coordinates [-pi..pi]\n   1    score        Only for results: Float, indicating confidence in\n                     detection, needed for p/r curves, higher is better.\n\nHere, 'DontCare' labels denote regions in which objects have not been labeled,\nfor example because they have been too far away from the laser scanner. To\nprevent such objects from being counted as false positives our evaluation\nscript will ignore objects detected in don't care regions of the test set.\nYou can use the don't care labels in the training set to avoid that your object\ndetector is harvesting hard negatives from those areas, in case you consider\nnon-object regions from the training images as negative examples.\n\nThe coordinates in the camera coordinate system can be projected in the image\nby using the 3x4 projection matrix in the calib folder, where for the left\ncolor camera for which the images are provided, P2 must be used. The\ndifference between rotation_y and alpha is, that rotation_y is directly\ngiven in camera coordinates, while alpha also considers the vector from the\ncamera center to the object center, to compute the relative orientation of\nthe object with respect to the camera. For example, a car which is facing\nalong the X-axis of the camera coordinate system corresponds to rotation_y=0,\nno matter where it is located in the X/Z plane (bird's eye view), while\nalpha is zero only, when this object is located along the Z-axis of the\ncamera. When moving the car away from the Z-axis, the observation angle\nwill change.\n\nTo project a point from Velodyne coordinates into the left color image,\nyou can use this formula: x = P2 * R0_rect * Tr_velo_to_cam * y\nFor the right color image: x = P3 * R0_rect * Tr_velo_to_cam * y\n\nNote: All matrices are stored row-major, i.e., the first values correspond\nto the first row. R0_rect contains a 3x3 matrix which you need to extend to\na 4x4 matrix by adding a 1 as the bottom-right element and 0's elsewhere.\nTr_xxx is a 3x4 matrix (R|t), which you need to extend to a 4x4 matrix \nin the same way!\n\nNote, that while all this information is available for the training data,\nonly the data which is actually needed for the particular benchmark must\nbe provided to the evaluation server. However, all 15 values must be provided\nat all times, with the unused ones set to their default values (=invalid) as\nspecified in writeLabels.m. Additionally a 16'th value must be provided\nwith a floating value of the score for a particular detection, where higher\nindicates higher confidence in the detection. The range of your scores will\nbe automatically determined by our evaluation server, you don't have to\nnormalize it, but it should be roughly linear. If you use writeLabels.m for\nwriting your results, this function will take care of storing all required\ndata correctly.\n\n2D Object Detection Benchmark\n=============================\n\nThe goal in the 2D object detection task is to train object detectors for the\nclasses 'Car', 'Pedestrian', and 'Cyclist'. The object detectors must\nprovide as output the 2D 0-based bounding box in the image using the format\nspecified above, as well as a detection score, indicating the confidence\nin the detection. All other values must be set to their default values\n(=invalid), see above. One text file per image must be provided in a zip\narchive, where each file can contain many detections, depending on the \nnumber of objects per image. In our evaluation we only evaluate detections/\nobjects larger than 25 pixel (height) in the image and do not count 'Van' as\nfalse positives for 'Car' or 'Sitting Person' as false positive for 'Pedestrian'\ndue to their similarity in appearance. As evaluation criterion we follow\nPASCAL and require the intersection-over-union of bounding boxes to be\nlarger than 50% for an object to be detected correctly.\n\nObject Orientation Estimation Benchmark\n=======================================\n\nThis benchmark is similar as the previous one, except that you have to\nprovide additionally the most likely relative object observation angle\n(=alpha) for each detection. As described in our paper, our score here\nconsiders both, the detection performance as well as the orientation\nestimation performance of the algorithm jointly.\n\n3D Object Detection Benchmark\n=============================\n\nThe goal in the 3D object detection task is to train object detectors for\nthe classes 'Car', 'Pedestrian', and 'Cyclist'. The object detectors\nmust provide BOTH the 2D 0-based bounding box in the image as well as the 3D\nbounding box (in the format specified above, i.e. 3D dimensions and 3D locations)\nand the detection score/confidence. Note that the 2D bounding box should correspond\nto the projection of the 3D bounding box - this is required to filter objects\nlarger than 25 pixel (height). We also note that not all objects in the point clouds\nhave been labeled. To avoid false positives, detections not visible on the image plane\nshould be filtered (the evaluation does not take care of this, see \n'cpp/evaluate_object.cpp'). Similar to the 2D object detection benchmark,\nwe do not count 'Van' as false positives for 'Car' or 'Sitting Person'\nas false positive for 'Pedestrian'. Evaluation criterion follows the 2D\nobject detection benchmark (using 3D bounding box overlap).\n\nBird's Eye View Benchmark\n=========================\n\nThe goal in the bird's eye view detection task is to train object detectors\nfor the classes 'Car', 'Pedestrian', and 'Cyclist' where the detectors must provide\nBOTH the 2D 0-based bounding box in the image as well as the 3D bounding box\nin bird's eye view and the detection score/confidence. This means that the 3D\nbounding box does not have to include information on the height axis, i.e.\nthe height of the bounding box and the bounding box location along the height axis.\nFor example, when evaluating the bird's eye view benchmark only (without the\n3D object detection benchmark), the height of the bounding box can be set to\na value equal to or smaller than zero. Similarly, the y-axis location of the\nbounding box can be set to -1000 (note that an arbitrary negative value will\nnot work). As above, we note that the 2D bounding boxes are required to filter\nobjects larger than 25 pixel (height) and that - to avoid false positives - detections\nnot visible on the image plane should be filtered. As in all benchmarks, we do\nnot count 'Van' as false positives for 'Car' or 'Sitting Person' as false positive\nfor 'Pedestrian'. Evaluation criterion follows the above benchmarks using\na bird's eye view bounding box overlap.\n\nMapping to Raw Data\n===================\n\nNote that this section is additional to the benchmark, and not required for\nsolving the object detection task.\n\nIn order to allow the usage of the laser point clouds, gps data, the right\ncamera image and the grayscale images for the TRAINING data as well, we\nprovide the mapping of the training set to the raw data of the KITTI dataset.\n\nThis information is saved in mapping/train_mapping.txt and train_rand.txt:\n\ntrain_rand.txt: Random permutation, assigning a unique index to each image\nfrom the object detection training set. The index is 1-based.\n\ntrain_mapping.txt: Maps each unique index (= 1-based line numbers) to a zip\nfile of the KITTI raw data set files. Note that those files are split into\nseveral categories on the website!\n\nExample: Image 0 from the training set has index 7282 and maps to date\n2011_09_28, drive 106 and frame 48. Drives and frames are 0-based.\n\nEvaluation Protocol:\n====================\n\nFor transparency we have included the KITTI evaluation code in the\nsubfolder 'cpp' of this development kit. It can be compiled via:\n\ng++ -O3 -DNDEBUG -o evaluate_object evaluate_object.cpp\n\nor using CMake and the provided 'CMakeLists.txt'.\n\nIMPORTANT NOTE:\n\nThis code will result in 41 values (41 recall discretization steps). However,\nnote that in order to compute average precision, we follow the PASCAL protocol\nand average by summing in 10% recall steps. The pseudocode for computing average\nprecision or orientation similarity is given as follows:\n\nsum = 0;\nfor (i=0; i<=40; i+=4)\n  sum += vals[i];\naverage = sum/11.0;\n\n"
  },
  {
    "path": "data/kitti_split2/setup_split.py",
    "content": "from importlib import import_module\nfrom getopt import getopt\nimport scipy.io as sio\nimport matplotlib.pyplot as plt\nfrom matplotlib.path import Path\nimport numpy as np\nimport pprint\nimport sys\nimport os\nimport cv2\nimport math\nimport shutil\nimport re\nfrom scipy.io import loadmat\n\n# stop python from writing so much bytecode\nsys.dont_write_bytecode = True\nsys.path.append(os.getcwd())\nnp.set_printoptions(suppress=True)\n\n# -----------------------------------------\n# custom modules\n# -----------------------------------------\nfrom lib.util import *\n\nsplit = 'kitti_split2'\n\n# base paths\nbase_data = os.path.join(os.getcwd(), 'data')\n\nkitti_raw = dict()\nkitti_raw['cal'] = os.path.join(base_data, 'kitti', 'training', 'calib')\nkitti_raw['ims'] = os.path.join(base_data, 'kitti', 'training', 'image_2')\nkitti_raw['lab'] = os.path.join(base_data, 'kitti', 'training', 'label_2')\n\nkitti_tra = dict()\nkitti_tra['cal'] = os.path.join(base_data, split, 'training', 'calib')\nkitti_tra['ims'] = os.path.join(base_data, split, 'training', 'image_2')\nkitti_tra['lab'] = os.path.join(base_data, split, 'training', 'label_2')\n\nkitti_val = dict()\nkitti_val['cal'] = os.path.join(base_data, split, 'validation', 'calib')\nkitti_val['ims'] = os.path.join(base_data, split, 'validation', 'image_2')\nkitti_val['lab'] = os.path.join(base_data, split, 'validation', 'label_2')\n\nsplit_data = loadmat(os.path.join(base_data, split, 'kitti_ids_new.mat'))\n\n# mkdirs\nmkdir_if_missing(kitti_tra['cal'])\nmkdir_if_missing(kitti_tra['ims'])\nmkdir_if_missing(kitti_tra['lab'])\nmkdir_if_missing(kitti_val['cal'])\nmkdir_if_missing(kitti_val['ims'])\nmkdir_if_missing(kitti_val['lab'])\n\n\nprint('Linking {} train'.format(split_data['ids_train'][0].shape[0]))\n\nimind = 0\n\nfor id_num in split_data['ids_train'][0]:\n\n    id = '{:06d}'.format(id_num)\n    new_id = '{:06d}'.format(imind)\n\n    if not os.path.exists(os.path.join(kitti_tra['cal'], str(new_id) + '.txt')):\n        os.symlink(os.path.join(kitti_raw['cal'], str(id) + '.txt'), os.path.join(kitti_tra['cal'], str(new_id) + '.txt'))\n\n    if not os.path.exists(os.path.join(kitti_tra['ims'], str(new_id) + '.png')):\n        os.symlink(os.path.join(kitti_raw['ims'], str(id) + '.png'), os.path.join(kitti_tra['ims'], str(new_id) + '.png'))\n\n    if not os.path.exists(os.path.join(kitti_tra['lab'], str(new_id) + '.txt')):\n        os.symlink(os.path.join(kitti_raw['lab'], str(id) + '.txt'), os.path.join(kitti_tra['lab'], str(new_id) + '.txt'))\n\n    imind += 1\n\nprint('Linking {} val'.format(split_data['ids_val'][0].shape[0]))\n\nimind = 0\n\nfor id_num in split_data['ids_val'][0]:\n\n    id = '{:06d}'.format(id_num)\n    new_id = '{:06d}'.format(imind)\n\n    if not os.path.exists(os.path.join(kitti_val['cal'], str(new_id) + '.txt')):\n        os.symlink(os.path.join(kitti_raw['cal'], str(id) + '.txt'), os.path.join(kitti_val['cal'], str(new_id) + '.txt'))\n\n    if not os.path.exists(os.path.join(kitti_val['ims'], str(new_id) + '.png')):\n        os.symlink(os.path.join(kitti_raw['ims'], str(id) + '.png'), os.path.join(kitti_val['ims'], str(new_id) + '.png'))\n\n    if not os.path.exists(os.path.join(kitti_val['lab'], str(new_id) + '.txt')):\n        os.symlink(os.path.join(kitti_raw['lab'], str(id) + '.txt'), os.path.join(kitti_val['lab'], str(new_id) + '.txt'))\n\n        imind += 1\n\nprint('Done')\n"
  },
  {
    "path": "lib/augmentations.py",
    "content": "\"\"\"\nThis file contains all PyTroch data augmentation functions.\n\nEvery transform should have a __call__ function which takes in (self, image, imobj)\nwhere imobj is an arbitary dict containing relevant information to the image.\n\nIn many cases the imobj can be None, which enables the same augmentations to be used\nduring testing as they are in training.\n\nOptionally, most transforms should have an __init__ function as well, if needed.\n\"\"\"\n\nimport numpy as np\nfrom numpy import random\nimport cv2\nimport math\nimport os\nimport sys\n\nfrom lib.util import *\n\n\nclass Compose(object):\n    \"\"\"\n    Composes a set of functions which take in an image and an object, into a single transform\n    \"\"\"\n    def __init__(self, transforms):\n        self.transforms = transforms\n\n    def __call__(self, img, imobj=None):\n        for t in self.transforms:\n            img, imobj = t(img, imobj)\n        return img, imobj\n\n\nclass ConvertToFloat(object):\n    \"\"\"\n    Converts image data type to float.\n    \"\"\"\n    def __call__(self, image, imobj=None):\n        return image.astype(np.float32), imobj\n\n\nclass Normalize(object):\n    \"\"\"\n    Normalize the image\n    \"\"\"\n    def __init__(self, mean, stds):\n        self.mean = np.array(mean, dtype=np.float32)\n        self.stds = np.array(stds, dtype=np.float32)\n\n    def __call__(self, image, imobj=None):\n        image = image.astype(np.float32)\n        image /= 255.0\n        image -= np.tile(self.mean, int(image.shape[2]/self.mean.shape[0]))\n        image /= np.tile(self.stds, int(image.shape[2]/self.stds.shape[0]))\n        return image.astype(np.float32), imobj\n\n\nclass Resize(object):\n    \"\"\"\n    Resize the image according to the target size height and the image height.\n    If the image needs to be cropped after the resize, we crop it to self.size,\n    otherwise we pad it with zeros along the right edge\n\n    If the object has ground truths we also scale the (known) box coordinates.\n    \"\"\"\n    def __init__(self, size):\n        self.size = size\n\n    def __call__(self, image, imobj=None):\n\n        scale_factor = self.size[0] / image.shape[0]\n\n        h = np.round(image.shape[0] * scale_factor).astype(int)\n        w = np.round(image.shape[1] * scale_factor).astype(int)\n\n        # resize\n        image = cv2.resize(image, (w, h))\n\n        if len(self.size) > 1:\n\n            # crop in\n            if image.shape[1] > self.size[1]:\n                image = image[:, 0:self.size[1], :]\n\n            # pad out\n            elif image.shape[1] < self.size[1]:\n                padW = self.size[1] - image.shape[1]\n                image = np.pad(image, [(0, 0), (0, padW), (0, 0)], 'constant')\n\n        if imobj:\n\n            # store scale factor, just in case\n            imobj.scale_factor = scale_factor\n\n            if 'gts' in imobj:\n\n                # scale all coordinates\n                for gtind, gt in enumerate(imobj.gts):\n\n                    if 'bbox_full' in imobj.gts[gtind]:\n                        imobj.gts[gtind].bbox_full *= scale_factor\n\n                    if 'bbox_vis' in imobj.gts[gtind]:\n                        imobj.gts[gtind].bbox_vis *= scale_factor\n\n                    if 'bbox_3d' in imobj.gts[gtind]:\n\n                        # only scale x/y center locations (in 2D space!)\n                        imobj.gts[gtind].bbox_3d[0] *= scale_factor\n                        imobj.gts[gtind].bbox_3d[1] *= scale_factor\n\n            if 'gts_pre' in imobj:\n\n                # scale all coordinates\n                for gtind, gt in enumerate(imobj.gts_pre):\n\n                    if 'bbox_full' in imobj.gts_pre[gtind]:\n                        imobj.gts_pre[gtind].bbox_full *= scale_factor\n\n                    if 'bbox_vis' in imobj.gts_pre[gtind]:\n                        imobj.gts_pre[gtind].bbox_vis *= scale_factor\n\n                    if 'bbox_3d' in imobj.gts_pre[gtind]:\n\n                        # only scale x/y center locations (in 2D space!)\n                        imobj.gts_pre[gtind].bbox_3d[0] *= scale_factor\n                        imobj.gts_pre[gtind].bbox_3d[1] *= scale_factor\n\n        return image, imobj\n\n\nclass RandomSaturation(object):\n    \"\"\"\n    Randomly adjust the saturation of an image given a lower and upper bound,\n    and a distortion probability.\n\n    This function assumes the image is in HSV!!\n    \"\"\"\n    def __init__(self, distort_prob, lower=0.5, upper=1.5):\n\n        self.distort_prob = distort_prob\n        self.lower = lower\n        self.upper = upper\n\n        assert self.upper >= self.lower, \"contrast upper must be >= lower.\"\n        assert self.lower >= 0, \"contrast lower must be non-negative.\"\n\n    def __call__(self, image, imobj=None):\n        if random.rand() <= self.distort_prob:\n            image[:, :, 1] *= random.uniform(self.lower, self.upper)\n\n        return image, imobj\n\n\nclass RandomHue(object):\n    \"\"\"\n    Randomly adjust the hue of an image given a delta degree to rotate by,\n    and a distortion probability.\n\n    This function assumes the image is in HSV!!\n    \"\"\"\n    def __init__(self, distort_prob, delta=18.0):\n        assert delta >= 0.0 and delta <= 360.0\n        self.delta = delta\n        self.distort_prob = distort_prob\n\n    def __call__(self, image, imobj=None):\n        if random.rand() <= self.distort_prob:\n            image[:, :, 0] += random.uniform(-self.delta, self.delta)\n            image[:, :, 0][image[:, :, 0] > 360.0] -= 360.0\n            image[:, :, 0][image[:, :, 0] < 0.0] += 360.0\n        return image, imobj\n\n\nclass ConvertColor(object):\n    \"\"\"\n    Converts color spaces to/from HSV and BGR\n    \"\"\"\n    def __init__(self, current='BGR', transform='HSV'):\n        self.transform = transform\n        self.current = current\n\n    def __call__(self, image, imobj=None):\n\n        # BGR --> HSV\n        if self.current == 'BGR' and self.transform == 'HSV':\n            image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)\n\n        # HSV --> BGR\n        elif self.current == 'HSV' and self.transform == 'BGR':\n            image = cv2.cvtColor(image, cv2.COLOR_HSV2BGR)\n\n        else:\n            raise NotImplementedError\n\n        return image, imobj\n\n\nclass RandomContrast(object):\n    \"\"\"\n    Randomly adjust contrast of an image given lower and upper bound,\n    and a distortion probability.\n    \"\"\"\n    def __init__(self, distort_prob, lower=0.5, upper=1.5):\n\n        self.lower = lower\n        self.upper = upper\n        self.distort_prob = distort_prob\n\n        assert self.upper >= self.lower, \"contrast upper must be >= lower.\"\n        assert self.lower >= 0, \"contrast lower must be non-negative.\"\n\n    def __call__(self, image, imobj=None):\n        if random.rand() <= self.distort_prob:\n            alpha = random.uniform(self.lower, self.upper)\n            image *= alpha\n        return image, imobj\n\n\nclass RandomMirror(object):\n    \"\"\"\n    Randomly mirror an image horzontially, given a mirror probabilty.\n\n    Also, adjust all box cordinates accordingly.\n    \"\"\"\n    def __init__(self, mirror_prob):\n        self.mirror_prob = mirror_prob\n\n    def __call__(self, image, imobj):\n\n        _, width, _ = image.shape\n\n        if random.rand() <= self.mirror_prob:\n\n            image = image[:, ::-1, :]\n            image = np.ascontiguousarray(image)\n\n            # flip the coordinates w.r.t the horizontal flip (only adjust X)\n            for gtind, gt in enumerate(imobj.gts):\n\n                if 'bbox_full' in imobj.gts[gtind]:\n                    imobj.gts[gtind].bbox_full[0] = image.shape[1] - gt.bbox_full[0] - gt.bbox_full[2]\n\n                if 'bbox_vis' in imobj.gts[gtind]:\n                    imobj.gts[gtind].bbox_vis[0] = image.shape[1] - gt.bbox_vis[0] - gt.bbox_vis[2]\n\n                if 'bbox_3d' in imobj.gts[gtind]:\n                    imobj.gts[gtind].bbox_3d[0] = image.shape[1] - gt.bbox_3d[0] - 1\n                    rotY = gt.bbox_3d[10]\n\n                    rotY = (-math.pi - rotY) if rotY < 0 else (math.pi - rotY)\n\n                    while rotY > math.pi: rotY -= math.pi * 2\n                    while rotY < (-math.pi): rotY += math.pi * 2\n\n                    cx2d = gt.bbox_3d[0]\n                    cy2d = gt.bbox_3d[1]\n                    cz2d = gt.bbox_3d[2]\n\n                    coord3d = imobj.p2_inv.dot(np.array([cx2d * cz2d, cy2d * cz2d, cz2d, 1]))\n\n                    alpha = convertRot2Alpha(rotY, coord3d[2], coord3d[0])\n\n                    imobj.gts[gtind].bbox_3d[10] = rotY\n                    imobj.gts[gtind].bbox_3d[6] = alpha\n\n\n        return image, imobj\n\n\nclass RandomBrightness(object):\n    \"\"\"\n    Randomly adjust the brightness of an image given given a +- delta range,\n    and a distortion probability.\n    \"\"\"\n    def __init__(self, distort_prob, delta=32):\n        assert delta >= 0.0\n        assert delta <= 255.0\n        self.delta = delta\n        self.distort_prob = distort_prob\n\n    def __call__(self, image, imobj=None):\n        if random.rand() <= self.distort_prob:\n            delta = random.uniform(-self.delta, self.delta)\n            image += delta\n        return image, imobj\n\n\nclass PhotometricDistort(object):\n    \"\"\"\n    Packages all photometric distortions into a single transform.\n    \"\"\"\n    def __init__(self, distort_prob):\n\n        self.distort_prob = distort_prob\n\n        # contrast is duplicated because it may happen before or after\n        # the other transforms with equal probability.\n        self.transforms = [\n            RandomContrast(distort_prob),\n            ConvertColor(transform='HSV'),\n            RandomSaturation(distort_prob),\n            RandomHue(distort_prob),\n            ConvertColor(current='HSV', transform='BGR'),\n            RandomContrast(distort_prob)\n        ]\n\n        self.rand_brightness = RandomBrightness(distort_prob)\n\n    def __call__(self, image, imobj):\n\n        # do contrast first\n        if random.rand() <= 0.5:\n            distortion = self.transforms[:-1]\n\n        # do contrast last\n        else:\n            distortion = self.transforms[1:]\n\n        # add random brightness\n        distortion.insert(0, self.rand_brightness)\n\n        # compose transformation\n        distortion = Compose(distortion)\n\n        return distortion(image.copy(), imobj)\n\n\nclass Augmentation(object):\n    \"\"\"\n    Data Augmentation class which packages the typical pre-processing\n    and all data augmentation transformations (mirror and photometric distort)\n    into a single transform.\n    \"\"\"\n    def __init__(self, conf):\n\n        self.mean = conf.image_means\n        self.stds = conf.image_stds\n        self.size = conf.crop_size\n        self.mirror_prob = conf.mirror_prob\n        self.distort_prob = conf.distort_prob\n\n        if conf.distort_prob <= 0:\n            self.augment = Compose([\n                ConvertToFloat(),\n                RandomMirror(self.mirror_prob),\n                Resize(self.size),\n                Normalize(self.mean, self.stds)\n            ])\n        else:\n            self.augment = Compose([\n                ConvertToFloat(),\n                PhotometricDistort(self.distort_prob),\n                RandomMirror(self.mirror_prob),\n                Resize(self.size),\n                Normalize(self.mean, self.stds)\n            ])\n\n    def __call__(self, img, imobj):\n        return self.augment(img, imobj)\n\n\nclass Preprocess(object):\n    \"\"\"\n    Preprocess function which ONLY does the basic pre-processing of an image,\n    meant to be used during the testing/eval stages.\n    \"\"\"\n    def __init__(self, size, mean, stds):\n\n        self.mean = mean\n        self.stds = stds\n        self.size = size\n\n        self.preprocess = Compose([\n            ConvertToFloat(),\n            Resize(self.size),\n            Normalize(self.mean, self.stds)\n        ])\n\n    def __call__(self, img):\n\n        img = self.preprocess(img)[0]\n\n        for i in range(int(img.shape[2]/3)):\n\n            # convert to RGB then permute to be [B C H W]\n            img[:, :, (i*3):(i*3) + 3] = img[:, :, (i*3+2, i*3+1, i*3)]\n\n        img = np.transpose(img, [2, 0, 1])\n\n        return img\n"
  },
  {
    "path": "lib/core.py",
    "content": "\"\"\"\nThis file is meant to contain all functions of the detective framework\nwhich are \"specific\" to the framework but generic among experiments.\n\nFor example, all the experiments need to initialize configs, training models,\nlog stats, display stats, and etc. However, these functions are generally fixed\nto this framework and cannot be easily transferred in other projects.\n\"\"\"\n\n# -----------------------------------------\n# python modules\n# -----------------------------------------\nfrom easydict import EasyDict as edict\nfrom shapely.geometry import Polygon\nimport matplotlib.pyplot as plt\nfrom copy import copy\nimport importlib\nimport random\nimport visdom\nimport torch\nimport shutil\nimport sys\nimport os\nimport cv2\nimport math\n\n# stop python from writing so much bytecode\nsys.dont_write_bytecode = True\n\n# -----------------------------------------\n# custom modules\n# -----------------------------------------\nfrom lib.util import *\n\n\ndef init_config(conf_name):\n    \"\"\"\n    Loads configuration file, by checking for the conf_name.py configuration file as\n    ./config/<conf_name>.py which must have function \"Config\".\n\n    This function must return a configuration dictionary with any necessary variables for the experiment.\n    \"\"\"\n\n    conf = importlib.import_module('config.' + conf_name).Config()\n\n    return conf\n\n\ndef init_training_model(conf, cache_folder):\n    \"\"\"\n    This function is meant to load the training model and optimizer, which expects\n    ./model/<conf.model>.py to be the pytorch model file.\n\n    The function copies the model file into the cache BEFORE loading, for easy reproducibility.\n    \"\"\"\n\n    src_path = os.path.join('.', 'models', conf.model + '.py')\n    dst_path = os.path.join(cache_folder, conf.model + '.py')\n\n    # (re-) copy the pytorch model file\n    if os.path.exists(dst_path): os.remove(dst_path)\n    shutil.copyfile(src_path, dst_path)\n\n    # load and build\n    network = absolute_import(dst_path)\n    network = network.build(conf, 'train')\n\n    # multi-gpu\n    network = torch.nn.DataParallel(network)\n\n    # load SGD\n    if conf.solver_type.lower() == 'sgd':\n\n        lr = conf.lr\n        mo = conf.momentum\n        wd = conf.weight_decay\n\n        optimizer = torch.optim.SGD(network.parameters(), lr=lr, momentum=mo, weight_decay=wd)\n\n    # load adam\n    elif conf.solver_type.lower() == 'adam':\n\n        lr = conf.lr\n        wd = conf.weight_decay\n\n        optimizer = torch.optim.Adam(network.parameters(), lr=lr, weight_decay=wd)\n\n    # load adamax\n    elif conf.solver_type.lower() == 'adamax':\n\n        lr = conf.lr\n        wd = conf.weight_decay\n\n        optimizer = torch.optim.Adamax(network.parameters(), lr=lr, weight_decay=wd)\n\n\n    return network, optimizer\n\n\ndef adjust_lr(conf, optimizer, iter):\n    \"\"\"\n    Adjusts the learning rate of an optimizer according to iteration and configuration,\n    primarily regarding regular SGD learning rate policies.\n\n    Args:\n        conf (dict): configuration dictionary\n        optimizer (object): pytorch optim object\n        iter (int): current iteration\n    \"\"\"\n\n    if 'batch_skip' in conf and ((iter + 1) % conf.batch_skip) > 0: return\n\n    if conf.solver_type.lower() == 'sgd':\n\n        lr = conf.lr\n        lr_steps = conf.lr_steps\n        max_iter = conf.max_iter\n        lr_policy = conf.lr_policy\n        lr_target = conf.lr_target\n\n        if lr_steps:\n            steps = np.array(lr_steps) * max_iter\n            total_steps = steps.shape[0]\n            step_count = np.sum((steps - iter) <= 0)\n\n        else:\n            total_steps = max_iter\n            step_count = iter\n\n        # perform the exact number of steps needed to get to lr_target\n        if lr_policy.lower() == 'step':\n            scale = (lr_target / lr) ** (1 / total_steps)\n            lr *= scale ** step_count\n\n        # compute the scale needed to go from lr --> lr_target\n        # using a polynomial function instead.\n        elif lr_policy.lower() == 'poly':\n\n            power = 0.9\n            scale = total_steps / (1 - (lr_target / lr) ** (1 / power))\n            lr *= (1 - step_count / scale) ** power\n\n        else:\n            raise ValueError('{} lr_policy not understood'.format(lr_policy))\n\n        # update the actual learning rate\n        for gind, g in enumerate(optimizer.param_groups):\n            g['lr'] = lr\n\n\ndef intersect(box_a, box_b, mode='combinations', data_type=None):\n    \"\"\"\n    Computes the amount of intersect between two different sets of boxes.\n\n    Args:\n        box_a (nparray): Mx4 boxes, defined by [x1, y1, x2, y2]\n        box_a (nparray): Nx4 boxes, defined by [x1, y1, x2, y2]\n        mode (str): either 'combinations' or 'list', where combinations will check all combinations of box_a and\n                    box_b hence MxN array, and list expects the same size list M == N, hence returns Mx1 array.\n        data_type (type): either torch.Tensor or np.ndarray, we automatically determine otherwise\n    \"\"\"\n\n    # determine type\n    if data_type is None: data_type = type(box_a)\n\n    # this mode computes the intersect in the sense of combinations.\n    # i.e., box_a = M x 4, box_b = N x 4 then the output is M x N\n    if mode == 'combinations':\n\n        # np.ndarray\n        if data_type == np.ndarray:\n            max_xy = np.minimum(box_a[:, 2:4], np.expand_dims(box_b[:, 2:4], axis=1))\n            min_xy = np.maximum(box_a[:, 0:2], np.expand_dims(box_b[:, 0:2], axis=1))\n            inter = np.clip((max_xy - min_xy), a_min=0, a_max=None)\n\n        # unknown type\n        else:\n            raise ValueError('type {} is not implemented'.format(data_type))\n\n        return inter[:, :, 0] * inter[:, :, 1]\n\n    # this mode computes the intersect in the sense of list_a vs. list_b.\n    # i.e., box_a = M x 4, box_b = M x 4 then the output is Mx1\n    elif mode == 'list':\n\n        # torch.Tesnor\n        if data_type == torch.Tensor:\n            max_xy = torch.min(box_a[:, 2:], box_b[:, 2:])\n            min_xy = torch.max(box_a[:, :2], box_b[:, :2])\n            inter = torch.clamp((max_xy - min_xy), 0)\n\n        # np.ndarray\n        elif data_type == np.ndarray:\n            max_xy = np.min(box_a[:, 2:], box_b[:, 2:])\n            min_xy = np.max(box_a[:, :2], box_b[:, :2])\n            inter = np.clip((max_xy - min_xy), a_min=0, a_max=None)\n\n        # unknown type\n        else:\n            raise ValueError('unknown data type {}'.format(data_type))\n\n        return inter[:, 0] * inter[:, 1]\n\n    else:\n        raise ValueError('unknown mode {}'.format(mode))\n\n\ndef iou3d(corners_3d_b1, corners_3d_b2, vol):\n\n    corners_3d_b1 = copy.copy(corners_3d_b1)\n    corners_3d_b2 = copy.copy(corners_3d_b2)\n\n    corners_3d_b1 = corners_3d_b1.T\n    corners_3d_b2 = corners_3d_b2.T\n\n    y_min_b1 = np.min(corners_3d_b1[:, 1])\n    y_max_b1 = np.max(corners_3d_b1[:, 1])\n    y_min_b2 = np.min(corners_3d_b2[:, 1])\n    y_max_b2 = np.max(corners_3d_b2[:, 1])\n    y_intersect = np.max([0, np.min([y_max_b1, y_max_b2]) - np.max([y_min_b1, y_min_b2])])\n\n    # set Z as Y\n    corners_3d_b1[:, 1] = corners_3d_b1[:, 2]\n    corners_3d_b2[:, 1] = corners_3d_b2[:, 2]\n\n    polygon_order = [7, 2, 3, 6, 7]\n    box_b1_bev = Polygon([list(corners_3d_b1[i][0:2]) for i in polygon_order])\n    box_b2_bev = Polygon([list(corners_3d_b2[i][0:2]) for i in polygon_order])\n\n    intersect_bev = box_b2_bev.intersection(box_b1_bev).area\n    intersect_3d = y_intersect * intersect_bev\n\n    iou_bev = intersect_bev / (box_b2_bev.area + box_b1_bev.area - intersect_bev)\n    iou_3d = intersect_3d / (vol - intersect_3d)\n\n    return iou_bev, iou_3d\n\n\ndef iou(box_a, box_b, mode='combinations', data_type=None):\n    \"\"\"\n    Computes the amount of Intersection over Union (IoU) between two different sets of boxes.\n\n    Args:\n        box_a (nparray): Mx4 boxes, defined by [x1, y1, x2, y2]\n        box_a (nparray): Nx4 boxes, defined by [x1, y1, x2, y2]\n        mode (str): either 'combinations' or 'list', where combinations will check all combinations of box_a and\n                    box_b hence MxN array, and list expects the same size list M == N, hence returns Mx1 array.\n        data_type (type): either torch.Tensor or np.ndarray, we automatically determine otherwise\n    \"\"\"\n\n    # determine type\n    if data_type is None: data_type = type(box_a)\n\n    # this mode computes the IoU in the sense of combinations.\n    # i.e., box_a = M x 4, box_b = N x 4 then the output is M x N\n    if mode == 'combinations':\n\n        inter = intersect(box_a, box_b, data_type=data_type)\n        area_a = ((box_a[:, 2] - box_a[:, 0]) *\n                  (box_a[:, 3] - box_a[:, 1]))\n        area_b = ((box_b[:, 2] - box_b[:, 0]) *\n                  (box_b[:, 3] - box_b[:, 1]))\n        union = np.expand_dims(area_a, 0) + np.expand_dims(area_b, 1) - inter\n\n        # torch.Tensor\n        if data_type == torch.Tensor:\n            return (inter / union).permute(1, 0)\n\n        # np.ndarray\n        elif data_type == np.ndarray:\n            return (inter / union).T\n\n        # unknown type\n        else:\n            raise ValueError('unknown data type {}'.format(data_type))\n\n\n    # this mode compares every box in box_a with target in box_b\n    # i.e., box_a = M x 4 and box_b = M x 4 then output is M x 1\n    elif mode == 'list':\n\n        inter = intersect(box_a, box_b, mode=mode)\n        area_a = (box_a[:, 2] - box_a[:, 0]) * (box_a[:, 3] - box_a[:, 1])\n        area_b = (box_b[:, 2] - box_b[:, 0]) * (box_b[:, 3] - box_b[:, 1])\n        union = area_a + area_b - inter\n\n        return inter / union\n\n    else:\n        raise ValueError('unknown mode {}'.format(mode))\n\n\ndef iou_ign(box_a, box_b, mode='combinations', data_type=None):\n    \"\"\"\n    Computes the amount of overap of box_b has within box_a, which is handy for dealing with ignore regions.\n    Hence, assume that box_b are ignore regions and box_a are anchor boxes, then we may want to know how\n    much overlap the anchors have inside of the ignore regions (hence ignore area_b!)\n\n    Args:\n        box_a (nparray): Mx4 boxes, defined by [x1, y1, x2, y2]\n        box_a (nparray): Nx4 boxes, defined by [x1, y1, x2, y2]\n        mode (str): either 'combinations' or 'list', where combinations will check all combinations of box_a and\n                    box_b hence MxN array, and list expects the same size list M == N, hence returns Mx1 array.\n        data_type (type): either torch.Tensor or np.ndarray, we automatically determine otherwise\n    \"\"\"\n\n    if data_type is None: data_type = type(box_a)\n\n    # this mode computes the IoU in the sense of combinations.\n    # i.e., box_a = M x 4, box_b = N x 4 then the output is M x N\n    if mode == 'combinations':\n\n        inter = intersect(box_a, box_b, data_type=data_type)\n        area_a = ((box_a[:, 2] - box_a[:, 0]) *\n                  (box_a[:, 3] - box_a[:, 1]))\n        area_b = ((box_b[:, 2] - box_b[:, 0]) *\n                  (box_b[:, 3] - box_b[:, 1]))\n        union = np.expand_dims(area_a, 0) + np.expand_dims(area_b, 1) * 0 - inter * 0\n\n        # torch and numpy have different calls for transpose\n        if data_type == torch.Tensor:\n            return (inter / union).permute(1, 0)\n        elif data_type == np.ndarray:\n            return (inter / union).T\n\n        # unknown type\n        else:\n            raise ValueError('unknown data type {}'.format(data_type))\n\n    else:\n        raise ValueError('unknown mode {}'.format(mode))\n\n\ndef freeze_layers(network, blacklist=None, whitelist=None, verbose=False):\n\n    if blacklist is not None:\n\n        for name, param in network.named_parameters():\n\n            if not any([allowed in name for allowed in blacklist]):\n                if verbose:\n                    logging.info('freezing {}'.format(name))\n                param.requires_grad = False\n\n        for name, module in network.named_modules():\n            if not any([allowed in name for allowed in blacklist]):\n                if isinstance(module, torch.nn.BatchNorm2d):\n                    module.eval()\n\n    if whitelist is not None:\n\n        for name, param in network.named_parameters():\n\n            if any([banned in name for banned in whitelist]):\n                if verbose:\n                    logging.info('freezing {}'.format(name))\n                param.requires_grad = False\n            #else:\n            #    logging.info('NOT freezing {}'.format(name))\n\n        for name, module in network.named_modules():\n            if any([banned in name for banned in whitelist]):\n                if isinstance(module, torch.nn.BatchNorm2d):\n                    module.eval()\n\n\ndef load_weights(model, path, remove_module=False):\n    \"\"\"\n    Simply loads a pytorch models weights from a given path.\n    \"\"\"\n    dst_weights = model.state_dict()\n    src_weights = torch.load(path)\n\n    dst_keys = list(dst_weights.keys())\n    src_keys = list(src_weights.keys())\n\n    if remove_module:\n\n        # copy keys without module\n        for key in src_keys:\n            src_weights[key.replace('module.', '')] = src_weights[key]\n            del src_weights[key]\n        src_keys = list(src_weights.keys())\n\n        # remove keys not in dst\n        for key in src_keys:\n            if key not in dst_keys: del src_weights[key]\n\n    else:\n\n        # remove keys not in dst\n        for key in src_keys:\n            if key not in dst_keys: del src_weights[key]\n\n        # add keys not in src\n        for key in dst_keys:\n            if key not in src_keys: src_weights[key] = dst_weights[key]\n\n    model.load_state_dict(src_weights)\n\n\ndef log_stats(tracker, iteration, start_time, start_iter, max_iter, skip=1):\n    \"\"\"\n    This function writes the given stats to the log / prints to the screen.\n    Also, computes the estimated time arrival (eta) for completion and (dt) delta time per iteration.\n\n    Args:\n        tracker (array): dictionary array tracker objects. See below.\n        iteration (int): the current iteration\n        start_time (float): starting time of whole experiment\n        start_iter (int): starting iteration of whole experiment\n        max_iter (int): maximum iteration to go to\n\n    A tracker object is a dictionary with the following:\n        \"name\": the name of the statistic being tracked, e.g., 'fg_acc', 'abs_z'\n        \"group\": an arbitrary group key, e.g., 'loss', 'acc', 'misc'\n        \"format\": the python string format to use (see official str format function in python), e.g., '{:.2f}' for\n                  a float with 2 decimal places.\n    \"\"\"\n\n    display_str = 'iter: {}'.format((int((iteration + 1)/skip)))\n\n    # compute eta\n    time_str, dt = compute_eta(start_time, iteration - start_iter, max_iter - start_iter)\n\n    # cycle through all tracks\n    last_group = ''\n    for key in sorted(tracker.keys()):\n\n        if type(tracker[key]) == list:\n\n            # compute mean\n            meanval = np.mean(tracker[key])\n\n            # get properties\n            format = tracker[key + '_obj'].format\n            group = tracker[key + '_obj'].group\n            name = tracker[key + '_obj'].name\n\n            # logic to have the string formatted nicely\n            # basically roughly this format:\n            #   iter: {}, group_1 (name: val, name: val), group_2 (name: val), dt: val, eta: val\n            if last_group != group and last_group == '':\n                display_str += (', {} ({}: ' + format).format(group, name, meanval)\n\n            elif last_group != group:\n                display_str += ('), {} ({}: ' + format).format(group, name, meanval)\n\n            else:\n                display_str += (', {}: ' + format).format(name, meanval)\n\n            last_group = group\n\n    # append dt and eta\n    display_str += '), dt: {:0.2f}, eta: {}'.format(dt, time_str)\n\n    # log\n    logging.info(display_str)\n\n\ndef display_stats(vis, tracker, iteration, start_time, start_iter, max_iter, conf_name, conf_pretty, skip=1):\n    \"\"\"\n    This function plots the statistics using visdom package, similar to the log_stats function.\n    Also, computes the estimated time arrival (eta) for completion and (dt) delta time per iteration.\n\n    Args:\n        vis (visdom): the main visdom session object\n        tracker (array): dictionary array tracker objects. See below.\n        iteration (int): the current iteration\n        start_time (float): starting time of whole experiment\n        start_iter (int): starting iteration of whole experiment\n        max_iter (int): maximum iteration to go to\n        conf_name (str): experiment name used for visdom display\n        conf_pretty (str): pretty string with ALL configuration params to display\n\n    A tracker object is a dictionary with the following:\n        \"name\": the name of the statistic being tracked, e.g., 'fg_acc', 'abs_z'\n        \"group\": an arbitrary group key, e.g., 'loss', 'acc', 'misc'\n        \"format\": the python string format to use (see official str format function in python), e.g., '{:.2f}' for\n                  a float with 2 decimal places.\n    \"\"\"\n\n    # compute eta\n    time_str, dt = compute_eta(start_time, iteration - start_iter, max_iter - start_iter)\n\n    # general info\n    info = 'Experiment: <b>{}</b>, Eta: <b>{}</b>, Time/it: {:0.2f}s\\n'.format(conf_name, time_str, dt)\n    info += conf_pretty\n\n    # replace all newlines and spaces with line break <br> and non-breaking spaces &nbsp\n    info = info.replace('\\n', '<br>')\n    info = info.replace(' ', '&nbsp')\n\n    # pre-formatted html tag\n    info = '<pre>' + info + '</pre'\n\n    # update the info window\n    vis.text(info, win='info', opts={'title': 'info', 'width': 500, 'height': 350})\n\n    # draw graphs for each track\n    for key in sorted(tracker.keys()):\n\n        if type(tracker[key]) == list:\n            meanval = np.mean(tracker[key])\n            group = tracker[key + '_obj'].group\n            name = tracker[key + '_obj'].name\n\n            # new data point\n            vis.line(X=np.array([(iteration + 1)]), Y=np.array([meanval]), win=group, name=name, update='append',\n                     opts={'showlegend': True, 'title': group, 'width': 500, 'height': 350,\n                           'xlabel': 'iteration'})\n\n\ndef compute_stats(tracker, stats):\n    \"\"\"\n    Copies any arbitary statistics which appear in 'stats' into 'tracker'.\n    Also, for each new object to track we will secretly store the objects information\n    into 'tracker' with the key as (group + name + '_obj'). This way we can retrieve these properties later.\n\n    Args:\n        tracker (array): dictionary array tracker objects. See below.\n        stats (array): dictionary array tracker objects. See below.\n\n    A tracker object is a dictionary with the following:\n        \"name\": the name of the statistic being tracked, e.g., 'fg_acc', 'abs_z'\n        \"group\": an arbitrary group key, e.g., 'loss', 'acc', 'misc'\n        \"format\": the python string format to use (see official str format function in python), e.g., '{:.2f}' for\n                  a float with 2 decimal places.\n    \"\"\"\n\n    # through all stats\n    for stat in stats:\n\n        # get properties\n        name = stat['name']\n        group = stat['group']\n        val = stat['val']\n\n        # convention for identificaiton\n        id = group + name\n\n        # init if not exist?\n        if not (id in tracker): tracker[id] = []\n\n        # convert tensor to numpy\n        if type(val) == torch.Tensor:\n            val = val.cpu().detach().numpy()\n\n        # store\n        tracker[id].append(val)\n\n        # store object info\n        obj_id = id + '_obj'\n        if not (obj_id in tracker):\n            stat.pop('val', None)\n            tracker[id + '_obj'] = stat\n\n\ndef next_iteration(loader, iterator):\n    \"\"\"\n    Loads the next iteration of 'iterator' OR makes a new epoch using 'loader'.\n\n    Args:\n        loader (object): PyTorch DataLoader object\n        iterator (object): python in-built iter(loader) object\n    \"\"\"\n\n    # create if none\n    if iterator == None: iterator = iter(loader)\n\n    # next batch\n    try:\n        images, imobjs = next(iterator)\n\n    # new epoch / shuffle\n    except StopIteration:\n        iterator = iter(loader)\n        images, imobjs = next(iterator)\n\n    return iterator, images, imobjs\n\n\ndef init_training_paths(conf_name, use_tmp_folder=None):\n    \"\"\"\n    Simple function to store and create the relevant paths for the project,\n    based on the base = current_working_dir (cwd). For this reason, we expect\n    that the experiments are run from the root folder.\n\n    data    =  ./data\n    output  =  ./output/<conf_name>\n    weights =  ./output/<conf_name>/weights\n    results =  ./output/<conf_name>/results\n    logs    =  ./output/<conf_name>/log\n\n    Args:\n        conf_name (str): configuration experiment name (used for storage into ./output/<conf_name>)\n    \"\"\"\n\n    # make paths\n    paths = edict()\n    paths.base = os.getcwd()\n    paths.data = os.path.join(paths.base, 'data')\n    paths.output = os.path.join(os.getcwd(), 'output', conf_name)\n    paths.weights = os.path.join(paths.output, 'weights')\n    paths.logs = os.path.join(paths.output, 'log')\n\n    if use_tmp_folder: paths.results = os.path.join(paths.base, '.tmp_results', conf_name, 'results')\n    else: paths.results = os.path.join(paths.output, 'results')\n\n    # make directories\n    mkdir_if_missing(paths.output)\n    mkdir_if_missing(paths.logs)\n    mkdir_if_missing(paths.weights)\n    mkdir_if_missing(paths.results)\n\n    return paths\n\n\ndef init_torch(rng_seed, cuda_seed):\n    \"\"\"\n    Initializes the seeds for ALL potential randomness, including torch, numpy, and random packages.\n\n    Args:\n        rng_seed (int): the shared random seed to use for numpy and random\n        cuda_seed (int): the random seed to use for pytorch's torch.cuda.manual_seed_all function\n    \"\"\"\n\n    # default tensor\n    torch.set_default_tensor_type('torch.cuda.FloatTensor')\n\n    # seed everything\n    torch.manual_seed(rng_seed)\n    np.random.seed(rng_seed)\n    random.seed(rng_seed)\n    torch.cuda.manual_seed_all(cuda_seed)\n\n    # make the code deterministic\n    torch.backends.cudnn.deterministic = True\n    torch.backends.cudnn.benchmark = False\n\n\ndef init_visdom(conf_name, visdom_port):\n    \"\"\"\n    Simply initializes a visdom session (if possible) then closes all windows within it.\n    If there is no visdom server running (externally), then function will return 'None'.\n    \"\"\"\n    try:\n        vis = visdom.Visdom(port=visdom_port, env=conf_name)\n        vis.close(env=conf_name, win=None)\n\n        if vis.socket_alive:\n            return vis\n        else:\n            return None\n\n    except:\n        return None\n\n\ndef check_tensors():\n    \"\"\"\n    Checks on tensors currently loaded within PyTorch\n    for debugging purposes only (esp memory leaks).\n    \"\"\"\n    for obj in gc.get_objects():\n        try:\n            if torch.is_tensor(obj) or (hasattr(obj, 'data') and torch.is_tensor(obj.data)):\n                print(type(obj), obj.size(), obj.device, obj.shape)\n        except:\n            pass\n\n\ndef resume_checkpoint(optim, model, weights_dir, iteration):\n    \"\"\"\n    Loads the optimizer and model pair given the current iteration\n    and the weights storage directory.\n    \"\"\"\n\n    optimpath, modelpath = checkpoint_names(weights_dir, iteration)\n\n    optim.load_state_dict(torch.load(optimpath))\n    model.load_state_dict(torch.load(modelpath))\n\n\ndef save_checkpoint(optim, model, weights_dir, iteration):\n    \"\"\"\n    Saves the optimizer and model pair given the current iteration\n    and the weights storage directory.\n    \"\"\"\n\n    optimpath, modelpath = checkpoint_names(weights_dir, iteration)\n\n    torch.save(optim.state_dict(), optimpath)\n    torch.save(model.state_dict(), modelpath)\n\n    return modelpath, optimpath\n\n\ndef checkpoint_names(weights_dir, iteration):\n    \"\"\"\n    Single function to determine the saving format for\n    resuming and saving models/optim.\n    \"\"\"\n\n    optimpath = os.path.join(weights_dir, 'optim_{}_pkl'.format(iteration))\n    modelpath = os.path.join(weights_dir, 'model_{}_pkl'.format(iteration))\n\n    return optimpath, modelpath\n\n\ndef print_weights(model):\n    \"\"\"\n    Simply prints the weights for the model using the mean weight.\n    This helps keep track of frozen weights, and to make sure\n    they initialize as non-zero, although there are edge cases to\n    be weary of.\n    \"\"\"\n\n    # find max length\n    max_len = 0\n    for name, param in model.named_parameters():\n        name = str(name).replace('module.', '')\n        if (len(name) + 4) > max_len: max_len = (len(name) + 4)\n\n    # print formatted mean weights\n    for name, param in model.named_parameters():\n        mdata = np.abs(torch.mean(param.data).item())\n        name = str(name).replace('module.', '')\n\n        logging.info(('{0:' + str(max_len) + '} {1:6} {2:6}')\n                     .format(name, 'mean={:.4f}'.format(mdata), '    grad={}'.format(param.requires_grad)))\n\n"
  },
  {
    "path": "lib/imdb_util.py",
    "content": "\"\"\"\nThis file contains all image database (imdb) functionality,\nsuch as loading and reading information from a dataset.\n\nGenerally, this file is meant to read in a dataset from disk into a\nsimple custom format for the detetive framework.\n\"\"\"\n\n# -----------------------------------------\n# modules\n# -----------------------------------------\nimport torch\nimport torch.utils.data as data\nimport sys\nimport re\nfrom PIL import Image\nfrom copy import deepcopy\n\nsys.dont_write_bytecode = True\n\n# -----------------------------------------\n# custom\n# -----------------------------------------\nfrom lib.rpn_util import *\nfrom lib.util import *\nfrom lib.augmentations import *\nfrom lib.core import *\n\nclass Dataset(torch.utils.data.Dataset):\n    \"\"\"\n    A single Dataset class is used for the whole project,\n    which implements the __init__ and __get__ functions from PyTorch.\n    \"\"\"\n\n    def __init__(self, conf, root, cache_folder=None):\n        \"\"\"\n        This function reads in all datasets to be used in training and stores ANY relevant\n        information which may be needed during training as a list of edict()\n        (referred to commonly as 'imobj').\n\n        The function also optionally stores the image database (imdb) file into a cache.\n        \"\"\"\n\n        imdb = []\n\n        self.video_det = False if not ('video_det' in conf) else conf.video_det\n        self.video_count = 1 if not ('video_count' in conf) else conf.video_count\n        self.use_3d_for_2d = ('use_3d_for_2d' in conf) and conf.use_3d_for_2d\n\n        # use cache?\n        if (cache_folder is not None) and os.path.exists(os.path.join(cache_folder, 'imdb.pkl')):\n            logging.info('Preloading imdb.')\n            imdb = pickle_read(os.path.join(cache_folder, 'imdb.pkl'))\n\n        else:\n\n            # cycle through each dataset\n            for dbind, db in enumerate(conf.datasets_train):\n\n                logging.info('Loading imdb {}'.format(db['name']))\n\n                # single imdb\n                imdb_single_db = []\n\n                # kitti formatting\n                if db['anno_fmt'].lower() == 'kitti_det':\n\n                    train_folder = os.path.join(root, db['name'], 'training')\n\n                    ann_folder = os.path.join(train_folder, 'label_2', '')\n                    cal_folder = os.path.join(train_folder, 'calib', '')\n                    im_folder = os.path.join(train_folder, 'image_2', '')\n\n                    # get sorted filepaths\n                    annlist = sorted(glob(ann_folder + '*.txt'))\n\n                    imdb_start = time()\n\n                    self.affine_size = None if not ('affine_size' in conf) else conf.affine_size\n\n                    for annind, annpath in enumerate(annlist):\n\n                        # get file parts\n                        base = os.path.basename(annpath)\n                        id, ext = os.path.splitext(base)\n\n                        calpath = os.path.join(cal_folder, id + '.txt')\n                        impath = os.path.join(im_folder, id + db['im_ext'])\n                        impath_pre = os.path.join(train_folder, 'prev_2', id + '_01' + db['im_ext'])\n                        impath_pre2 = os.path.join(train_folder, 'prev_2', id + '_02' + db['im_ext'])\n                        impath_pre3 = os.path.join(train_folder, 'prev_2', id + '_03' + db['im_ext'])\n\n                        # read gts\n                        p2 = read_kitti_cal(calpath)\n                        p2_inv = np.linalg.inv(p2)\n\n                        gts = read_kitti_label(annpath, p2, self.use_3d_for_2d)\n\n                        if not self.affine_size is None:\n\n                            # filter relevant classes\n                            gts_plane = [deepcopy(gt) for gt in gts if gt.cls in conf.lbls and not gt.ign]\n\n                            if len(gts_plane) > 0:\n\n                                KITTI_H = 1.65\n\n                                # compute ray traces for default projection\n                                for gtind in range(len(gts_plane)):\n                                    gt = gts_plane[gtind]\n\n                                    #cx2d = gt.bbox_3d[0]\n                                    #cy2d = gt.bbox_3d[1]\n                                    cy2d = gt.bbox_full[1] + gt.bbox_full[3]\n                                    cx2d = gt.bbox_full[0] + gt.bbox_full[2] / 2\n\n                                    z2d, coord3d = projection_ray_trace(p2, p2_inv, cx2d, cy2d, KITTI_H)\n\n                                    gts_plane[gtind].center_in = coord3d[0:3, 0]\n                                    gts_plane[gtind].center_3d = np.array(gt.center_3d)\n\n\n                                prelim_tra = np.array([gt.center_in for gtind, gt in enumerate(gts_plane)])\n                                target_tra = np.array([gt.center_3d for gtind, gt in enumerate(gts_plane)])\n\n                                if self.affine_size == 4:\n                                    prelim_tra = np.pad(prelim_tra, [(0, 0), (0, 1)], mode='constant', constant_values=1)\n                                    target_tra = np.pad(target_tra, [(0, 0), (0, 1)], mode='constant', constant_values=1)\n\n                                affine_gt, err = solve_transform(prelim_tra, target_tra, compute_error=True)\n\n                                a = 1\n\n                        obj = edict()\n\n                        # did not compute transformer\n                        if (self.affine_size is None) or len(gts_plane) < 1:\n                            obj.affine_gt = None\n                        else:\n                            obj.affine_gt = affine_gt\n\n                        # store gts\n                        obj.id = id\n                        obj.gts = gts\n                        obj.p2 = p2\n                        obj.p2_inv = p2_inv\n\n                        # im properties\n                        im = Image.open(impath)\n                        obj.path = impath\n                        obj.path_pre = impath_pre\n                        obj.path_pre2 = impath_pre2\n                        obj.path_pre3 = impath_pre3\n                        obj.imW, obj.imH = im.size\n\n                        # database properties\n                        obj.dbname = db.name\n                        obj.scale = db.scale\n                        obj.dbind = dbind\n\n                        # store\n                        imdb_single_db.append(obj)\n\n                        if (annind % 1000) == 0 and annind > 0:\n                            time_str, dt = compute_eta(imdb_start, annind, len(annlist))\n                            logging.info('{}/{}, dt: {:0.4f}, eta: {}'.format(annind, len(annlist), dt, time_str))\n\n\n                # concatenate single imdb into full imdb\n                imdb += imdb_single_db\n\n            imdb = np.array(imdb)\n\n            # cache off the imdb?\n            if cache_folder is not None:\n                pickle_write(os.path.join(cache_folder, 'imdb.pkl'), imdb)\n\n        # store more information\n        self.datasets_train = conf.datasets_train\n        self.len = len(imdb)\n        self.imdb = imdb\n\n        # setup data augmentation transforms\n        self.transform = Augmentation(conf)\n\n        # setup sampler and data loader for this dataset\n        self.sampler = torch.utils.data.sampler.WeightedRandomSampler(balance_samples(conf, imdb), self.len)\n        self.loader = torch.utils.data.DataLoader(self, conf.batch_size, sampler=self.sampler, collate_fn=self.collate)\n\n        # check classes\n        cls_not_used = []\n        for imobj in imdb:\n\n            for gt in imobj.gts:\n                cls = gt.cls\n                if not(cls in conf.lbls or cls in conf.ilbls) and (cls not in cls_not_used):\n                    cls_not_used.append(cls)\n\n        if len(cls_not_used) > 0:\n            logging.info('Labels not used in training.. {}'.format(cls_not_used))\n\n\n    def __getitem__(self, index):\n        \"\"\"\n        Grabs the item at the given index. Specifically,\n          - read the image from disk\n          - read the imobj from RAM\n          - applies data augmentation to (im, imobj)\n          - converts image to RGB and [B C W H]\n        \"\"\"\n\n        if not self.video_det:\n\n            # read image\n            im = cv2.imread(self.imdb[index].path)\n\n        else:\n\n            # read images\n            im = cv2.imread(self.imdb[index].path)\n\n            video_count = 1 if self.video_count is None else self.video_count\n\n            if video_count >= 2:\n                im_pre = cv2.imread(self.imdb[index].path_pre)\n\n                if not im_pre.shape == im.shape:\n                    im_pre = cv2.resize(im_pre, (im.shape[1], im.shape[0]))\n\n                im = np.concatenate((im, im_pre), axis=2)\n\n            if video_count >= 3:\n\n                im_pre2 = cv2.imread(self.imdb[index].path_pre2)\n\n                if im_pre2 is None:\n                    im_pre2 = im_pre\n\n                if not im_pre2.shape == im.shape:\n                    im_pre2 = cv2.resize(im_pre2, (im.shape[1], im.shape[0]))\n\n                im = np.concatenate((im, im_pre2), axis=2)\n\n            if video_count >= 4:\n\n                im_pre3 = cv2.imread(self.imdb[index].path_pre3)\n\n                if im_pre3 is None:\n                    im_pre3 = im_pre2\n\n                if not im_pre3.shape == im.shape:\n                    im_pre3 = cv2.resize(im_pre3, (im.shape[1], im.shape[0]))\n\n                im = np.concatenate((im, im_pre3), axis=2)\n\n\n        # transform / data augmentation\n        im, imobj = self.transform(im, deepcopy(self.imdb[index]))\n\n        for i in range(int(im.shape[2]/3)):\n            # convert to RGB then permute to be [B C H W]\n            im[:, :, (i*3):(i*3) + 3] = im[:, :, (i*3+2, i*3+1, i*3)]\n        im = np.transpose(im, [2, 0, 1])\n\n        return im, imobj\n\n    @staticmethod\n    def collate(batch):\n        \"\"\"\n        Defines the methodology for PyTorch to collate the objects\n        of a batch together, for some reason PyTorch doesn't function\n        this way by default.\n        \"\"\"\n\n        imgs = []\n        imobjs = []\n\n        # go through each batch\n        for sample in batch:\n            \n            # append images and object dictionaries\n            imgs.append(sample[0])\n            imobjs.append(sample[1])\n\n        # stack images\n        imgs = np.array(imgs)\n        imgs = torch.from_numpy(imgs).cuda()\n\n        return imgs, imobjs\n\n    def __len__(self):\n        \"\"\"\n        Simply return the length of the dataset.\n        \"\"\"\n        return self.len\n\n\ndef read_kitti_cal(calfile):\n    \"\"\"\n    Reads the kitti calibration projection matrix (p2) file from disc.\n\n    Args:\n        calfile (str): path to single calibration file\n    \"\"\"\n\n    text_file = open(calfile, 'r')\n\n    p2pat = re.compile(('(P2:)\\s+(fpat)\\s+(fpat)\\s+(fpat)\\s+(fpat)\\s+(fpat)\\s+(fpat)\\s+(fpat)\\s+(fpat)\\s+(fpat)' +\n                        '\\s+(fpat)\\s+(fpat)\\s+(fpat)\\s*\\n').replace('fpat', '[-+]?[\\d]+\\.?[\\d]*[Ee](?:[-+]?[\\d]+)?'))\n\n    for line in text_file:\n\n        parsed = p2pat.fullmatch(line)\n\n        # bbGt annotation in text format of:\n        # cls x y w h occ x y w h ign ang\n        if parsed is not None:\n            p2 = np.zeros([4, 4], dtype=float)\n            p2[0, 0] = parsed.group(2)\n            p2[0, 1] = parsed.group(3)\n            p2[0, 2] = parsed.group(4)\n            p2[0, 3] = parsed.group(5)\n            p2[1, 0] = parsed.group(6)\n            p2[1, 1] = parsed.group(7)\n            p2[1, 2] = parsed.group(8)\n            p2[1, 3] = parsed.group(9)\n            p2[2, 0] = parsed.group(10)\n            p2[2, 1] = parsed.group(11)\n            p2[2, 2] = parsed.group(12)\n            p2[2, 3] = parsed.group(13)\n\n            p2[3, 3] = 1\n\n    text_file.close()\n\n    return p2\n\n\ndef read_kitti_poses(posefile):\n\n    text_file = open(posefile, 'r')\n\n    ppat1 = re.compile(('(fpat)\\s+(fpat)\\s+(fpat)\\s+(fpat)\\s+(fpat)\\s+(fpat)\\s+(fpat)\\s+(fpat)\\s+(fpat)' +\n                        '\\s+(fpat)\\s+(fpat)\\s+(fpat)\\s*\\n').replace('fpat', '[-+]?[\\d]+\\.?[\\d]*[Ee](?:[-+]?[\\d]+)?'))\n\n    ppat2 = re.compile(('(fpat)\\s+(fpat)\\s+(fpat)\\s+(fpat)\\s+(fpat)\\s+(fpat)\\s+(fpat)\\s+(fpat)\\s+(fpat)' +\n                       '\\s+(fpat)\\s+(fpat)\\s+(fpat)\\s*\\n').replace('fpat', '[-+]?[\\d]+\\.?[\\d]*'));\n\n    ps = []\n\n    for line in text_file:\n\n        parsed1 = ppat1.fullmatch(line)\n        parsed2 = ppat2.fullmatch(line)\n\n        if parsed1 is not None:\n            p = np.zeros([4, 4], dtype=float)\n            p[0, 0] = parsed1.group(1)\n            p[0, 1] = parsed1.group(2)\n            p[0, 2] = parsed1.group(3)\n            p[0, 3] = parsed1.group(4)\n            p[1, 0] = parsed1.group(5)\n            p[1, 1] = parsed1.group(6)\n            p[1, 2] = parsed1.group(7)\n            p[1, 3] = parsed1.group(8)\n            p[2, 0] = parsed1.group(9)\n            p[2, 1] = parsed1.group(10)\n            p[2, 2] = parsed1.group(11)\n            p[2, 3] = parsed1.group(12)\n\n            p[3, 3] = 1\n\n            ps.append(p)\n\n        elif parsed2 is not None:\n\n            p = np.zeros([4, 4], dtype=float)\n            p[0, 0] = parsed2.group(1)\n            p[0, 1] = parsed2.group(2)\n            p[0, 2] = parsed2.group(3)\n            p[0, 3] = parsed2.group(4)\n            p[1, 0] = parsed2.group(5)\n            p[1, 1] = parsed2.group(6)\n            p[1, 2] = parsed2.group(7)\n            p[1, 3] = parsed2.group(8)\n            p[2, 0] = parsed2.group(9)\n            p[2, 1] = parsed2.group(10)\n            p[2, 2] = parsed2.group(11)\n            p[2, 3] = parsed2.group(12)\n\n            p[3, 3] = 1\n\n            ps.append(p)\n\n    text_file.close()\n\n    return ps\n\n\ndef read_kitti_label(file, p2, use_3d_for_2d=False):\n    \"\"\"\n    Reads the kitti label file from disc.\n\n    Args:\n        file (str): path to single label file for an image\n        p2 (ndarray): projection matrix for the given image\n    \"\"\"\n\n    gts = []\n\n    text_file = open(file, 'r')\n\n    '''\n     Values    Name      Description\n    ----------------------------------------------------------------------------\n       1    type         Describes the type of object: 'Car', 'Van', 'Truck',\n                         'Pedestrian', 'Person_sitting', 'Cyclist', 'Tram',\n                         'Misc' or 'DontCare'\n       1    truncated    Float from 0 (non-truncated) to 1 (truncated), where\n                         truncated refers to the object leaving image boundaries\n       1    occluded     Integer (0,1,2,3) indicating occlusion state:\n                         0 = fully visible, 1 = partly occluded\n                         2 = largely occluded, 3 = unknown\n       1    alpha        Observation angle of object, ranging [-pi..pi]\n       4    bbox         2D bounding box of object in the image (0-based index):\n                         contains left, top, right, bottom pixel coordinates\n       3    dimensions   3D object dimensions: height, width, length (in meters)\n       3    location     3D object location x,y,z in camera coordinates (in meters)\n       1    rotation_y   Rotation ry around Y-axis in camera coordinates [-pi..pi]\n       1    score        Only for results: Float, indicating confidence in\n                         detection, needed for p/r curves, higher is better.\n    '''\n\n    pattern = re.compile(('([a-zA-Z\\-\\?\\_]+)\\s+(fpat)\\s+(fpat)\\s+(fpat)\\s+(fpat)\\s+(fpat)\\s+(fpat)\\s+(fpat)\\s+'\n                          + '(fpat)\\s+(fpat)\\s+(fpat)\\s+(fpat)\\s+(fpat)\\s+(fpat)\\s+(fpat)\\s*((fpat)?)\\n')\n                         .replace('fpat', '[-+]?\\d*\\.\\d+|[-+]?\\d+'))\n\n\n    for line in text_file:\n\n        parsed = pattern.fullmatch(line)\n\n        # bbGt annotation in text format of:\n        # cls x y w h occ x y w h ign ang\n        if parsed is not None:\n\n            obj = edict()\n\n            ign = False\n\n            cls = parsed.group(1)\n            trunc = float(parsed.group(2))\n            occ = float(parsed.group(3))\n            alpha = float(parsed.group(4))\n\n            x = float(parsed.group(5))\n            y = float(parsed.group(6))\n            x2 = float(parsed.group(7))\n            y2 = float(parsed.group(8))\n\n            width = x2 - x + 1\n            height = y2 - y + 1\n\n            h3d = float(parsed.group(9))\n            w3d = float(parsed.group(10))\n            l3d = float(parsed.group(11))\n\n            cx3d = float(parsed.group(12)) # center of car in 3d\n            cy3d = float(parsed.group(13)) # bottom of car in 3d\n            cz3d = float(parsed.group(14)) # center of car in 3d\n            rotY = float(parsed.group(15))\n\n            # actually center the box\n            cy3d -= (h3d / 2)\n\n            elevation = (1.65 - cy3d)\n\n            if use_3d_for_2d and h3d > 0 and w3d > 0 and l3d > 0:\n\n                # re-compute the 2D box using 3D (finally, avoids clipped boxes)\n                verts3d, corners_3d = project_3d(p2, cx3d, cy3d, cz3d, w3d, h3d, l3d, rotY, return_3d=True)\n\n                # any boxes behind camera plane?\n                if np.any(corners_3d[2, :] <= 0):\n                    ign = True\n\n                else:\n                    x = min(verts3d[:, 0])\n                    y = min(verts3d[:, 1])\n                    x2 = max(verts3d[:, 0])\n                    y2 = max(verts3d[:, 1])\n\n                    width = x2 - x + 1\n                    height = y2 - y + 1\n\n            # project cx, cy, cz\n            coord3d = p2.dot(np.array([cx3d, cy3d, cz3d, 1]))\n\n            # store the projected instead\n            cx3d_2d = coord3d[0]\n            cy3d_2d = coord3d[1]\n            cz3d_2d = coord3d[2]\n\n            cx = cx3d_2d / cz3d_2d\n            cy = cy3d_2d / cz3d_2d\n\n            # encode occlusion with range estimation\n            # 0 = fully visible, 1 = partly occluded\n            # 2 = largely occluded, 3 = unknown\n            if occ == 0: vis = 1\n            elif occ == 1: vis = 0.66\n            elif occ == 2: vis = 0.33\n            else: vis = 0.0\n\n            while rotY > math.pi: rotY -= math.pi * 2\n            while rotY < (-math.pi): rotY += math.pi * 2\n\n            # recompute alpha\n            alpha = convertRot2Alpha(rotY, cz3d, cx3d)\n\n            obj.elevation = elevation\n            obj.cls = cls\n            obj.occ = occ > 0\n            obj.ign = ign\n            obj.visibility = vis\n            obj.trunc = trunc\n            obj.alpha = alpha\n            obj.rotY = rotY\n\n            # is there an extra field? (assume to be track)\n            if len(parsed.groups()) >= 16 and parsed.group(16).isdigit(): obj.track = int(parsed.group(16))\n\n            obj.bbox_full = np.array([x, y, width, height])\n            obj.bbox_3d = [cx, cy, cz3d_2d, w3d, h3d, l3d, alpha, cx3d, cy3d, cz3d, rotY]\n            obj.center_3d = [cx3d, cy3d, cz3d]\n\n            gts.append(obj)\n\n    text_file.close()\n\n    return gts\n\n\ndef balance_samples(conf, imdb):\n    \"\"\"\n    Balances the samples in an image dataset according to the given configuration.\n    Basically we check which images have relevant foreground samples and which are empty,\n    then we compute the sampling weights according to a desired fg_image_ratio.\n\n    This is primarily useful in datasets which have a lot of empty (background) images, which may\n    cause instability during training if not properly balanced against.\n    \"\"\"\n\n    sample_weights = np.ones(len(imdb))\n\n    if conf.fg_image_ratio >= 0:\n\n        empty_inds = []\n        valid_inds = []\n\n        for imind, imobj in enumerate(imdb):\n\n            valid = 0\n\n            scale = conf.test_scale / imobj.imH\n            igns, rmvs = determine_ignores(imobj.gts, conf.lbls, conf.ilbls, conf.min_gt_vis,\n                                           conf.min_gt_h, conf.max_gt_h, scale)\n\n            for gtind, gt in enumerate(imobj.gts):\n\n                if (not igns[gtind]) and (not rmvs[gtind]):\n                    valid += 1\n\n            sample_weights[imind] = valid\n\n            if valid>0:\n                valid_inds.append(imind)\n            else:\n                empty_inds.append(imind)\n\n        if not (conf.fg_image_ratio == 2):\n            fg_weight = len(imdb) * conf.fg_image_ratio / len(valid_inds)\n            bg_weight = len(imdb) * (1 - conf.fg_image_ratio) / len(empty_inds)\n            sample_weights[valid_inds] = fg_weight\n            sample_weights[empty_inds] = bg_weight\n\n            logging.info('weighted respectively as {:.2f} and {:.2f}'.format(fg_weight, bg_weight))\n\n        logging.info('Found {} foreground and {} empty images'.format(np.sum(sample_weights > 0), np.sum(sample_weights <= 0)))\n\n    # force sampling weights to sum to 1\n    sample_weights /= np.sum(sample_weights)\n\n    return sample_weights\n    \n"
  },
  {
    "path": "lib/loss/rpn_3d.py",
    "content": "import torch.nn as nn\nimport torch.nn.functional as F\nimport sys\n\n# stop python from writing so much bytecode\nsys.dont_write_bytecode = True\n\n# -----------------------------------------\n# custom modules\n# -----------------------------------------\nfrom lib.rpn_util import *\n\n\nclass RPN_3D_loss(nn.Module):\n\n    def __init__(self, conf):\n\n        super(RPN_3D_loss, self).__init__()\n\n        self.num_classes = len(conf.lbls) + 1\n        self.num_anchors = conf.anchors.shape[0]\n        self.anchors = conf.anchors\n        self.bbox_means = conf.bbox_means\n        self.bbox_stds = conf.bbox_stds\n        self.feat_stride = conf.feat_stride\n        self.fg_fraction = conf.fg_fraction\n        self.box_samples = conf.box_samples\n        self.ign_thresh = conf.ign_thresh\n        self.nms_thres = conf.nms_thres\n        self.fg_thresh = conf.fg_thresh\n        self.bg_thresh_lo = conf.bg_thresh_lo\n        self.bg_thresh_hi = conf.bg_thresh_hi\n        self.best_thresh = conf.best_thresh\n        self.hard_negatives = conf.hard_negatives\n        self.focal_loss = conf.focal_loss\n\n        self.crop_size = conf.crop_size\n\n        self.cls_2d_lambda = conf.cls_2d_lambda\n        self.iou_2d_lambda = conf.iou_2d_lambda\n        self.bbox_2d_lambda = conf.bbox_2d_lambda\n        self.bbox_3d_lambda = conf.bbox_3d_lambda\n        self.bbox_3d_proj_lambda = conf.bbox_3d_proj_lambda\n\n        self.lbls = conf.lbls\n        self.ilbls = conf.ilbls\n\n        self.min_gt_vis = conf.min_gt_vis\n        self.min_gt_h = conf.min_gt_h\n        self.max_gt_h = conf.max_gt_h\n\n\n    def forward(self, cls, prob, bbox_2d, bbox_3d, imobjs, feat_size):\n\n        stats = []\n        loss = torch.tensor(0).type(torch.cuda.FloatTensor)\n\n        FG_ENC = 1000\n        BG_ENC = 2000\n\n        IGN_FLAG = 3000\n\n        batch_size = cls.shape[0]\n\n        prob_detach = prob.cpu().detach().numpy()\n\n        bbox_x = bbox_2d[:, :, 0]\n        bbox_y = bbox_2d[:, :, 1]\n        bbox_w = bbox_2d[:, :, 2]\n        bbox_h = bbox_2d[:, :, 3]\n\n        bbox_x3d = bbox_3d[:, :, 0]\n        bbox_y3d = bbox_3d[:, :, 1]\n        bbox_z3d = bbox_3d[:, :, 2]\n        bbox_w3d = bbox_3d[:, :, 3]\n        bbox_h3d = bbox_3d[:, :, 4]\n        bbox_l3d = bbox_3d[:, :, 5]\n        bbox_ry3d = bbox_3d[:, :, 6]\n\n        bbox_x3d_proj = torch.zeros(bbox_x3d.shape)\n        bbox_y3d_proj = torch.zeros(bbox_x3d.shape)\n        bbox_z3d_proj = torch.zeros(bbox_x3d.shape)\n\n        labels = np.zeros(cls.shape[0:2])\n        labels_weight = np.zeros(cls.shape[0:2])\n\n        labels_scores = np.zeros(cls.shape[0:2])\n\n        bbox_x_tar = np.zeros(cls.shape[0:2])\n        bbox_y_tar = np.zeros(cls.shape[0:2])\n        bbox_w_tar = np.zeros(cls.shape[0:2])\n        bbox_h_tar = np.zeros(cls.shape[0:2])\n\n        bbox_x3d_tar = np.zeros(cls.shape[0:2])\n        bbox_y3d_tar = np.zeros(cls.shape[0:2])\n        bbox_z3d_tar = np.zeros(cls.shape[0:2])\n        bbox_w3d_tar = np.zeros(cls.shape[0:2])\n        bbox_h3d_tar = np.zeros(cls.shape[0:2])\n        bbox_l3d_tar = np.zeros(cls.shape[0:2])\n        bbox_ry3d_tar = np.zeros(cls.shape[0:2])\n\n        bbox_x3d_proj_tar = np.zeros(cls.shape[0:2])\n        bbox_y3d_proj_tar = np.zeros(cls.shape[0:2])\n        bbox_z3d_proj_tar = np.zeros(cls.shape[0:2])\n\n        bbox_weights = np.zeros(cls.shape[0:2])\n\n        ious_2d = torch.zeros(cls.shape[0:2])\n        ious_3d = torch.zeros(cls.shape[0:2])\n        coords_abs_z = torch.zeros(cls.shape[0:2])\n        coords_abs_ry = torch.zeros(cls.shape[0:2])\n\n        # get all rois\n        rois = locate_anchors(self.anchors, feat_size, self.feat_stride, convert_tensor=True)\n        rois = rois.type(torch.cuda.FloatTensor)\n\n        bbox_x3d_dn = bbox_x3d * self.bbox_stds[:, 4][0] + self.bbox_means[:, 4][0]\n        bbox_y3d_dn = bbox_y3d * self.bbox_stds[:, 5][0] + self.bbox_means[:, 5][0]\n        bbox_z3d_dn = bbox_z3d * self.bbox_stds[:, 6][0] + self.bbox_means[:, 6][0]\n        bbox_w3d_dn = bbox_w3d * self.bbox_stds[:, 7][0] + self.bbox_means[:, 7][0]\n        bbox_h3d_dn = bbox_h3d * self.bbox_stds[:, 8][0] + self.bbox_means[:, 8][0]\n        bbox_l3d_dn = bbox_l3d * self.bbox_stds[:, 9][0] + self.bbox_means[:, 9][0]\n        bbox_ry3d_dn = bbox_ry3d * self.bbox_stds[:, 10][0] + self.bbox_means[:, 10][0]\n\n        src_anchors = self.anchors[rois[:, 4].type(torch.cuda.LongTensor), :]\n        src_anchors = torch.tensor(src_anchors, requires_grad=False).type(torch.cuda.FloatTensor)\n        if len(src_anchors.shape) == 1: src_anchors = src_anchors.unsqueeze(0)\n\n        # compute 3d transform\n        widths = rois[:, 2] - rois[:, 0] + 1.0\n        heights = rois[:, 3] - rois[:, 1] + 1.0\n        ctr_x = rois[:, 0] + 0.5 * widths\n        ctr_y = rois[:, 1] + 0.5 * heights\n\n        bbox_x3d_dn = bbox_x3d_dn * widths.unsqueeze(0) + ctr_x.unsqueeze(0)\n        bbox_y3d_dn = bbox_y3d_dn * heights.unsqueeze(0) + ctr_y.unsqueeze(0)\n        bbox_z3d_dn = src_anchors[:, 4].unsqueeze(0) + bbox_z3d_dn\n        bbox_w3d_dn = torch.exp(bbox_w3d_dn) * src_anchors[:, 5].unsqueeze(0)\n        bbox_h3d_dn = torch.exp(bbox_h3d_dn) * src_anchors[:, 6].unsqueeze(0)\n        bbox_l3d_dn = torch.exp(bbox_l3d_dn) * src_anchors[:, 7].unsqueeze(0)\n        bbox_ry3d_dn = src_anchors[:, 8].unsqueeze(0) + bbox_ry3d_dn\n\n        for bind in range(0, batch_size):\n\n            imobj = imobjs[bind]\n            gts = imobj.gts\n\n            p2_inv = torch.from_numpy(imobj.p2_inv).type(torch.cuda.FloatTensor)\n\n            # filter gts\n            igns, rmvs = determine_ignores(gts, self.lbls, self.ilbls, self.min_gt_vis, self.min_gt_h)\n\n            # accumulate boxes\n            gts_all = bbXYWH2Coords(np.array([gt.bbox_full for gt in gts]))\n            gts_3d = np.array([gt.bbox_3d for gt in gts])\n\n            if not ((rmvs == False) & (igns == False)).any():\n                continue\n\n            # filter out irrelevant cls, and ignore cls\n            gts_val = gts_all[(rmvs == False) & (igns == False), :]\n            gts_ign = gts_all[(rmvs == False) & (igns == True), :]\n            gts_3d = gts_3d[(rmvs == False) & (igns == False), :]\n\n            # accumulate labels\n            box_lbls = np.array([gt.cls for gt in gts])\n            box_lbls = box_lbls[(rmvs == False) & (igns == False)]\n            box_lbls = np.array([clsName2Ind(self.lbls, cls) for cls in box_lbls])\n\n            if gts_val.shape[0] > 0 or gts_ign.shape[0] > 0:\n\n                rois = rois.cpu()\n\n                # bbox regression\n                transforms, ols, raw_gt = compute_targets(gts_val, gts_ign, box_lbls, rois.numpy(), self.fg_thresh,\n                                                  self.ign_thresh, self.bg_thresh_lo, self.bg_thresh_hi,\n                                                  self.best_thresh, anchors=self.anchors,  gts_3d=gts_3d,\n                                                  tracker=rois[:, 4].numpy())\n\n                # normalize 2d\n                transforms[:, 0:4] -= self.bbox_means[:, 0:4]\n                transforms[:, 0:4] /= self.bbox_stds[:, 0:4]\n\n                # normalize 3d\n                transforms[:, 5:12] -= self.bbox_means[:, 4:]\n                transforms[:, 5:12] /= self.bbox_stds[:, 4:]\n\n                labels_fg = transforms[:, 4] > 0\n                labels_bg = transforms[:, 4] < 0\n                labels_ign = transforms[:, 4] == 0\n\n                fg_inds = np.flatnonzero(labels_fg)\n                bg_inds = np.flatnonzero(labels_bg)\n                ign_inds = np.flatnonzero(labels_ign)\n\n                transforms = torch.from_numpy(transforms).cuda()\n\n                labels[bind, fg_inds] = transforms[fg_inds, 4]\n                labels[bind, ign_inds] = IGN_FLAG\n                labels[bind, bg_inds] = 0\n\n                bbox_x_tar[bind, :] = transforms[:, 0]\n                bbox_y_tar[bind, :] = transforms[:, 1]\n                bbox_w_tar[bind, :] = transforms[:, 2]\n                bbox_h_tar[bind, :] = transforms[:, 3]\n\n                bbox_x3d_tar[bind, :] = transforms[:, 5]\n                bbox_y3d_tar[bind, :] = transforms[:, 6]\n                bbox_z3d_tar[bind, :] = transforms[:, 7]\n                bbox_w3d_tar[bind, :] = transforms[:, 8]\n                bbox_h3d_tar[bind, :] = transforms[:, 9]\n                bbox_l3d_tar[bind, :] = transforms[:, 10]\n                bbox_ry3d_tar[bind, :] = transforms[:, 11]\n\n                bbox_x3d_proj_tar[bind, :] = raw_gt[:, 12]\n                bbox_y3d_proj_tar[bind, :] = raw_gt[:, 13]\n                bbox_z3d_proj_tar[bind, :] = raw_gt[:, 14]\n\n                # ----------------------------------------\n                # box sampling\n                # ----------------------------------------\n\n                if self.box_samples == np.inf:\n                    fg_num = len(fg_inds)\n                    bg_num = len(bg_inds)\n\n                else:\n                    fg_num = min(round(rois.shape[0]*self.box_samples * self.fg_fraction), len(fg_inds))\n                    bg_num = min(round(rois.shape[0]*self.box_samples - fg_num), len(bg_inds))\n\n                if self.hard_negatives:\n\n                    if fg_num > 0 and fg_num != fg_inds.shape[0]:\n                        scores = prob_detach[bind, fg_inds, labels[bind, fg_inds].astype(int)]\n                        fg_score_ascend = (scores).argsort()\n                        fg_inds = fg_inds[fg_score_ascend]\n                        fg_inds = fg_inds[0:fg_num]\n\n                    if bg_num > 0 and bg_num != bg_inds.shape[0]:\n                        scores = prob_detach[bind, bg_inds, labels[bind, bg_inds].astype(int)]\n                        bg_score_ascend = (scores).argsort()\n                        bg_inds = bg_inds[bg_score_ascend]\n                        bg_inds = bg_inds[0:bg_num]\n\n                else:\n\n                    if fg_num > 0 and fg_num != fg_inds.shape[0]:\n                        fg_inds = np.random.choice(fg_inds, fg_num, replace=False)\n\n                    if bg_num > 0 and bg_num != bg_inds.shape[0]:\n                        bg_inds = np.random.choice(bg_inds, bg_num, replace=False)\n\n                labels_weight[bind, bg_inds] = BG_ENC\n                labels_weight[bind, fg_inds] = FG_ENC\n                bbox_weights[bind, fg_inds] = 1\n\n                # ----------------------------------------\n                # compute IoU stats\n                # ----------------------------------------\n\n                if fg_num > 0:\n\n                    # compile deltas pred\n                    deltas_2d = torch.cat((bbox_x[bind, :, np.newaxis ], bbox_y[bind, :, np.newaxis],\n                                           bbox_w[bind, :, np.newaxis], bbox_h[bind, :, np.newaxis]), dim=1)\n\n                    # compile deltas targets\n                    deltas_2d_tar = np.concatenate((bbox_x_tar[bind, :, np.newaxis], bbox_y_tar[bind, :, np.newaxis],\n                                                    bbox_w_tar[bind, :, np.newaxis], bbox_h_tar[bind, :, np.newaxis]),\n                                                   axis=1)\n\n                    # move to gpu\n                    deltas_2d_tar = torch.tensor(deltas_2d_tar, requires_grad=False).type(torch.cuda.FloatTensor)\n\n                    means = self.bbox_means[0, :]\n                    stds = self.bbox_stds[0, :]\n\n                    rois = rois.cuda()\n\n                    coords_2d = bbox_transform_inv(rois, deltas_2d, means=means, stds=stds)\n                    coords_2d_tar = bbox_transform_inv(rois, deltas_2d_tar, means=means, stds=stds)\n\n                    ious_2d[bind, fg_inds] = iou(coords_2d[fg_inds, :], coords_2d_tar[fg_inds, :], mode='list')\n\n                    bbox_x3d_dn_fg = bbox_x3d_dn[bind, fg_inds]\n                    bbox_y3d_dn_fg = bbox_y3d_dn[bind, fg_inds]\n\n                    src_anchors = self.anchors[rois[fg_inds, 4].type(torch.cuda.LongTensor), :]\n                    src_anchors = torch.tensor(src_anchors, requires_grad=False).type(torch.cuda.FloatTensor)\n                    if len(src_anchors.shape) == 1: src_anchors = src_anchors.unsqueeze(0)\n\n                    bbox_x3d_dn_fg = bbox_x3d_dn[bind, fg_inds]\n                    bbox_y3d_dn_fg = bbox_y3d_dn[bind, fg_inds]\n                    bbox_z3d_dn_fg = bbox_z3d_dn[bind, fg_inds]\n                    bbox_w3d_dn_fg = bbox_w3d_dn[bind, fg_inds]\n                    bbox_h3d_dn_fg = bbox_h3d_dn[bind, fg_inds]\n                    bbox_l3d_dn_fg = bbox_l3d_dn[bind, fg_inds]\n                    bbox_ry3d_dn_fg = bbox_ry3d_dn[bind, fg_inds]\n\n                    # re-scale all 2D back to original\n                    bbox_x3d_dn_fg /= imobj['scale_factor']\n                    bbox_y3d_dn_fg /= imobj['scale_factor']\n\n                    coords_2d = torch.cat((bbox_x3d_dn_fg[np.newaxis,:] * bbox_z3d_dn_fg[np.newaxis,:], bbox_y3d_dn_fg[np.newaxis,:] * bbox_z3d_dn_fg[np.newaxis,:], bbox_z3d_dn_fg[np.newaxis,:]), dim=0)\n                    coords_2d = torch.cat((coords_2d, torch.ones([1, coords_2d.shape[1]])), dim=0)\n\n                    coords_3d = torch.mm(p2_inv, coords_2d)\n\n                    bbox_x3d_proj[bind, fg_inds] = coords_3d[0, :]\n                    bbox_y3d_proj[bind, fg_inds] = coords_3d[1, :]\n                    bbox_z3d_proj[bind, fg_inds] = coords_3d[2, :]\n\n                    # absolute targets\n                    bbox_z3d_dn_tar = bbox_z3d_tar[bind, fg_inds] * self.bbox_stds[:, 6][0] + self.bbox_means[:, 6][0]\n                    bbox_z3d_dn_tar = torch.tensor(bbox_z3d_dn_tar, requires_grad=False).type(torch.cuda.FloatTensor)\n                    bbox_z3d_dn_tar = src_anchors[:, 4] + bbox_z3d_dn_tar\n\n                    bbox_ry3d_dn_tar = bbox_ry3d_tar[bind, fg_inds] * self.bbox_stds[:, 10][0] + self.bbox_means[:, 10][0]\n                    bbox_ry3d_dn_tar = torch.tensor(bbox_ry3d_dn_tar, requires_grad=False).type(torch.cuda.FloatTensor)\n                    bbox_ry3d_dn_tar = src_anchors[:, 8] + bbox_ry3d_dn_tar\n\n                    coords_abs_z[bind, fg_inds] = torch.abs(bbox_z3d_dn_tar - bbox_z3d_dn_fg)\n                    coords_abs_ry[bind, fg_inds] = torch.abs(bbox_ry3d_dn_tar - bbox_ry3d_dn_fg)\n\n            else:\n\n                bg_inds = np.arange(0, rois.shape[0])\n\n                if self.box_samples == np.inf: bg_num = len(bg_inds)\n                else: bg_num = min(round(self.box_samples * (1 - self.fg_fraction)), len(bg_inds))\n\n                if self.hard_negatives:\n\n                    if bg_num > 0 and bg_num != bg_inds.shape[0]:\n                        scores = prob_detach[bind, bg_inds, labels[bind, bg_inds].astype(int)]\n                        bg_score_ascend = (scores).argsort()\n                        bg_inds = bg_inds[bg_score_ascend]\n                        bg_inds = bg_inds[0:bg_num]\n\n                else:\n\n                    if bg_num > 0 and bg_num != bg_inds.shape[0]:\n                        bg_inds = np.random.choice(bg_inds, bg_num, replace=False)\n\n\n                labels[bind, :] = 0\n                labels_weight[bind, bg_inds] = BG_ENC\n\n\n            # grab label predictions (for weighing purposes)\n            active = labels[bind, :] != IGN_FLAG\n            labels_scores[bind, active] = prob_detach[bind, active, labels[bind, active].astype(int)]\n\n        # ----------------------------------------\n        # useful statistics\n        # ----------------------------------------\n\n        fg_inds_all = np.flatnonzero((labels > 0) & (labels != IGN_FLAG))\n        bg_inds_all = np.flatnonzero((labels == 0) & (labels != IGN_FLAG))\n\n        fg_inds_unravel = np.unravel_index(fg_inds_all, prob_detach.shape[0:2])\n        bg_inds_unravel = np.unravel_index(bg_inds_all, prob_detach.shape[0:2])\n\n        cls_pred = cls.argmax(dim=2).cpu().detach().numpy()\n\n        if self.cls_2d_lambda and len(fg_inds_all) > 0:\n            acc_fg = np.mean(cls_pred[fg_inds_unravel] == labels[fg_inds_unravel])\n            stats.append({'name': 'fg', 'val': acc_fg, 'format': '{:0.2f}', 'group': 'acc'})\n\n        if self.cls_2d_lambda and len(bg_inds_all) > 0:\n            acc_bg = np.mean(cls_pred[bg_inds_unravel] == labels[bg_inds_unravel])\n            stats.append({'name': 'bg', 'val': acc_bg, 'format': '{:0.2f}', 'group': 'acc'})\n\n        # ----------------------------------------\n        # box weighting\n        # ----------------------------------------\n\n        fg_inds = np.flatnonzero(labels_weight == FG_ENC)\n        bg_inds = np.flatnonzero(labels_weight == BG_ENC)\n        active_inds = np.concatenate((fg_inds, bg_inds), axis=0)\n\n        fg_num = len(fg_inds)\n        bg_num = len(bg_inds)\n\n        labels_weight[...] = 0.0\n        box_samples = fg_num + bg_num\n\n        fg_inds_unravel = np.unravel_index(fg_inds, labels_weight.shape)\n        bg_inds_unravel = np.unravel_index(bg_inds, labels_weight.shape)\n        active_inds_unravel = np.unravel_index(active_inds, labels_weight.shape)\n\n        labels_weight[active_inds_unravel] = 1.0\n\n        if self.fg_fraction is not None:\n\n            if fg_num > 0:\n\n                fg_weight = (self.fg_fraction /(1 - self.fg_fraction)) * (bg_num / fg_num)\n                labels_weight[fg_inds_unravel] = fg_weight\n                labels_weight[bg_inds_unravel] = 1.0\n\n            else:\n                labels_weight[bg_inds_unravel] = 1.0\n\n        # different method of doing hard negative mining\n        # use the scores to normalize the importance of each sample\n        # hence, encourages the network to get all \"correct\" rather than\n        # becoming more correct at a decision it is already good at\n        # this method is equivelent to the focal loss with additional mean scaling\n        if self.focal_loss:\n\n            weights_sum = 0\n\n            # re-weight bg\n            if bg_num > 0:\n                bg_scores = labels_scores[bg_inds_unravel]\n                bg_weights = (1 - bg_scores) ** self.focal_loss\n                weights_sum += np.sum(bg_weights)\n                labels_weight[bg_inds_unravel] *= bg_weights\n\n            # re-weight fg\n            if fg_num > 0:\n                fg_scores = labels_scores[fg_inds_unravel]\n                fg_weights = (1 - fg_scores) ** self.focal_loss\n                weights_sum += np.sum(fg_weights)\n                labels_weight[fg_inds_unravel] *= fg_weights\n\n\n        # ----------------------------------------\n        # classification loss\n        # ----------------------------------------\n        labels = torch.tensor(labels, requires_grad=False)\n        labels = labels.view(-1).type(torch.cuda.LongTensor)\n\n        labels_weight = torch.tensor(labels_weight, requires_grad=False)\n        labels_weight = labels_weight.view(-1).type(torch.cuda.FloatTensor)\n\n        cls = cls.view(-1, cls.shape[2])\n\n        if self.cls_2d_lambda:\n\n            # cls loss\n            active = labels_weight > 0\n\n            if np.any(active.cpu().numpy()):\n\n                loss_cls = F.cross_entropy(cls[active, :], labels[active], reduction='none', ignore_index=IGN_FLAG)\n                loss_cls = (loss_cls * labels_weight[active])\n\n                # simple gradient clipping\n                loss_cls = loss_cls.clamp(min=0, max=2000)\n\n                # take mean and scale lambda\n                loss_cls = loss_cls.mean()\n                loss_cls *= self.cls_2d_lambda\n\n                loss += loss_cls\n\n                stats.append({'name': 'cls', 'val': loss_cls, 'format': '{:0.4f}', 'group': 'loss'})\n\n        # ----------------------------------------\n        # bbox regression loss\n        # ----------------------------------------\n\n        if np.sum(bbox_weights) > 0:\n\n            bbox_weights = torch.tensor(bbox_weights, requires_grad=False).type(torch.cuda.FloatTensor).view(-1)\n\n            active = bbox_weights > 0\n\n            if self.bbox_2d_lambda:\n\n                # bbox loss 2d\n                bbox_x_tar = torch.tensor(bbox_x_tar, requires_grad=False).type(torch.FloatTensor).cuda().view(-1)\n                bbox_y_tar = torch.tensor(bbox_y_tar, requires_grad=False).type(torch.FloatTensor).cuda().view(-1)\n                bbox_w_tar = torch.tensor(bbox_w_tar, requires_grad=False).type(torch.FloatTensor).cuda().view(-1)\n                bbox_h_tar = torch.tensor(bbox_h_tar, requires_grad=False).type(torch.FloatTensor).cuda().view(-1)\n\n                bbox_x = bbox_x[:, :].unsqueeze(2).view(-1)\n                bbox_y = bbox_y[:, :].unsqueeze(2).view(-1)\n                bbox_w = bbox_w[:, :].unsqueeze(2).view(-1)\n                bbox_h = bbox_h[:, :].unsqueeze(2).view(-1)\n\n                loss_bbox_x = F.smooth_l1_loss(bbox_x[active], bbox_x_tar[active], reduction='none')\n                loss_bbox_y = F.smooth_l1_loss(bbox_y[active], bbox_y_tar[active], reduction='none')\n                loss_bbox_w = F.smooth_l1_loss(bbox_w[active], bbox_w_tar[active], reduction='none')\n                loss_bbox_h = F.smooth_l1_loss(bbox_h[active], bbox_h_tar[active], reduction='none')\n\n                loss_bbox_x = (loss_bbox_x * bbox_weights[active]).mean()\n                loss_bbox_y = (loss_bbox_y * bbox_weights[active]).mean()\n                loss_bbox_w = (loss_bbox_w * bbox_weights[active]).mean()\n                loss_bbox_h = (loss_bbox_h * bbox_weights[active]).mean()\n\n                bbox_2d_loss = (loss_bbox_x + loss_bbox_y + loss_bbox_w + loss_bbox_h)\n                bbox_2d_loss *= self.bbox_2d_lambda\n\n                loss += bbox_2d_loss\n                stats.append({'name': 'bbox_2d', 'val': bbox_2d_loss, 'format': '{:0.4f}', 'group': 'loss'})\n\n\n            if self.bbox_3d_lambda:\n\n                # bbox loss 3d\n                bbox_x3d_tar = torch.tensor(bbox_x3d_tar, requires_grad=False).type(torch.FloatTensor).cuda().view(-1)\n                bbox_y3d_tar = torch.tensor(bbox_y3d_tar, requires_grad=False).type(torch.FloatTensor).cuda().view(-1)\n                bbox_z3d_tar = torch.tensor(bbox_z3d_tar, requires_grad=False).type(torch.FloatTensor).cuda().view(-1)\n                bbox_w3d_tar = torch.tensor(bbox_w3d_tar, requires_grad=False).type(torch.FloatTensor).cuda().view(-1)\n                bbox_h3d_tar = torch.tensor(bbox_h3d_tar, requires_grad=False).type(torch.FloatTensor).cuda().view(-1)\n                bbox_l3d_tar = torch.tensor(bbox_l3d_tar, requires_grad=False).type(torch.FloatTensor).cuda().view(-1)\n                bbox_ry3d_tar = torch.tensor(bbox_ry3d_tar, requires_grad=False).type(torch.FloatTensor).cuda().view(-1)\n\n                bbox_x3d = bbox_x3d[:, :].view(-1)\n                bbox_y3d = bbox_y3d[:, :].view(-1)\n                bbox_z3d = bbox_z3d[:, :].view(-1)\n                bbox_w3d = bbox_w3d[:, :].view(-1)\n                bbox_h3d = bbox_h3d[:, :].view(-1)\n                bbox_l3d = bbox_l3d[:, :].view(-1)\n                bbox_ry3d = bbox_ry3d[:, :].view(-1)\n\n                loss_bbox_x3d = F.smooth_l1_loss(bbox_x3d[active], bbox_x3d_tar[active], reduction='none')\n                loss_bbox_y3d = F.smooth_l1_loss(bbox_y3d[active], bbox_y3d_tar[active], reduction='none')\n                loss_bbox_z3d = F.smooth_l1_loss(bbox_z3d[active], bbox_z3d_tar[active], reduction='none')\n                loss_bbox_w3d = F.smooth_l1_loss(bbox_w3d[active], bbox_w3d_tar[active], reduction='none')\n                loss_bbox_h3d = F.smooth_l1_loss(bbox_h3d[active], bbox_h3d_tar[active], reduction='none')\n                loss_bbox_l3d = F.smooth_l1_loss(bbox_l3d[active], bbox_l3d_tar[active], reduction='none')\n                loss_bbox_ry3d = F.smooth_l1_loss(bbox_ry3d[active], bbox_ry3d_tar[active], reduction='none')\n\n                loss_bbox_x3d = (loss_bbox_x3d * bbox_weights[active]).mean()\n                loss_bbox_y3d = (loss_bbox_y3d * bbox_weights[active]).mean()\n                loss_bbox_z3d = (loss_bbox_z3d * bbox_weights[active]).mean()\n                loss_bbox_w3d = (loss_bbox_w3d * bbox_weights[active]).mean()\n                loss_bbox_h3d = (loss_bbox_h3d * bbox_weights[active]).mean()\n                loss_bbox_l3d = (loss_bbox_l3d * bbox_weights[active]).mean()\n                loss_bbox_ry3d = (loss_bbox_ry3d * bbox_weights[active]).mean()\n\n                bbox_3d_loss = (loss_bbox_x3d + loss_bbox_y3d + loss_bbox_z3d)\n                bbox_3d_loss += (loss_bbox_w3d + loss_bbox_h3d + loss_bbox_l3d + loss_bbox_ry3d)\n\n                bbox_3d_loss *= self.bbox_3d_lambda\n\n                loss += bbox_3d_loss\n                stats.append({'name': 'bbox_3d', 'val': bbox_3d_loss, 'format': '{:0.4f}', 'group': 'loss'})\n\n            if self.bbox_3d_proj_lambda:\n\n                # bbox loss 3d\n                bbox_x3d_proj_tar = torch.tensor(bbox_x3d_proj_tar, requires_grad=False).type(torch.FloatTensor).cuda().view(-1)\n                bbox_y3d_proj_tar = torch.tensor(bbox_y3d_proj_tar, requires_grad=False).type(torch.FloatTensor).cuda().view(-1)\n                bbox_z3d_proj_tar = torch.tensor(bbox_z3d_proj_tar, requires_grad=False).type(torch.FloatTensor).cuda().view(-1)\n\n                bbox_x3d_proj = bbox_x3d_proj[:, :].view(-1)\n                bbox_y3d_proj = bbox_y3d_proj[:, :].view(-1)\n                bbox_z3d_proj = bbox_z3d_proj[:, :].view(-1)\n\n                loss_bbox_x3d_proj = F.smooth_l1_loss(bbox_x3d_proj[active], bbox_x3d_proj_tar[active], reduction='none')\n                loss_bbox_y3d_proj = F.smooth_l1_loss(bbox_y3d_proj[active], bbox_y3d_proj_tar[active], reduction='none')\n                loss_bbox_z3d_proj = F.smooth_l1_loss(bbox_z3d_proj[active], bbox_z3d_proj_tar[active], reduction='none')\n\n                loss_bbox_x3d_proj = (loss_bbox_x3d_proj * bbox_weights[active]).mean()\n                loss_bbox_y3d_proj = (loss_bbox_y3d_proj * bbox_weights[active]).mean()\n                loss_bbox_z3d_proj = (loss_bbox_z3d_proj * bbox_weights[active]).mean()\n\n                bbox_3d_proj_loss = (loss_bbox_x3d_proj + loss_bbox_y3d_proj + loss_bbox_z3d_proj)\n\n                bbox_3d_proj_loss *= self.bbox_3d_proj_lambda\n\n                loss += bbox_3d_proj_loss\n                stats.append({'name': 'bbox_3d_proj', 'val': bbox_3d_proj_loss, 'format': '{:0.4f}', 'group': 'loss'})\n\n            coords_abs_z = coords_abs_z.view(-1)\n            stats.append({'name': 'z', 'val': coords_abs_z[active].mean(), 'format': '{:0.2f}', 'group': 'misc'})\n\n            coords_abs_ry = coords_abs_ry.view(-1)\n            stats.append({'name': 'ry', 'val': coords_abs_ry[active].mean(), 'format': '{:0.2f}', 'group': 'misc'})\n\n            ious_2d = ious_2d.view(-1)\n            stats.append({'name': 'iou', 'val': ious_2d[active].mean(), 'format': '{:0.2f}', 'group': 'acc'})\n\n            # use a 2d IoU based log loss\n            if self.iou_2d_lambda:\n                iou_2d_loss = -torch.log(ious_2d[active])\n                iou_2d_loss = (iou_2d_loss * bbox_weights[active])\n                iou_2d_loss = iou_2d_loss.mean()\n\n                iou_2d_loss *= self.iou_2d_lambda\n                loss += iou_2d_loss\n\n                stats.append({'name': 'iou', 'val': iou_2d_loss, 'format': '{:0.4f}', 'group': 'loss'})\n\n\n        return loss, stats\n"
  },
  {
    "path": "lib/nms/Makefile",
    "content": "all:\n\tpython setup.py build_ext --inplace\n\trm -rf build\n"
  },
  {
    "path": "lib/nms/cpu_nms.c",
    "content": "/* Generated by Cython 0.28.2 */\n\n#define PY_SSIZE_T_CLEAN\n#include \"Python.h\"\n#ifndef Py_PYTHON_H\n    #error Python headers needed to compile C extensions, please install development version of Python.\n#elif PY_VERSION_HEX < 0x02060000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03030000)\n    #error Cython requires Python 2.6+ or Python 3.3+.\n#else\n#define CYTHON_ABI \"0_28_2\"\n#define CYTHON_FUTURE_DIVISION 0\n#include <stddef.h>\n#ifndef offsetof\n  #define offsetof(type, member) ( (size_t) & ((type*)0) -> member )\n#endif\n#if !defined(WIN32) && !defined(MS_WINDOWS)\n  #ifndef __stdcall\n    #define __stdcall\n  #endif\n  #ifndef __cdecl\n    #define __cdecl\n  #endif\n  #ifndef __fastcall\n    #define __fastcall\n  #endif\n#endif\n#ifndef DL_IMPORT\n  #define DL_IMPORT(t) t\n#endif\n#ifndef DL_EXPORT\n  #define DL_EXPORT(t) t\n#endif\n#define __PYX_COMMA ,\n#ifndef HAVE_LONG_LONG\n  #if PY_VERSION_HEX >= 0x02070000\n    #define HAVE_LONG_LONG\n  #endif\n#endif\n#ifndef PY_LONG_LONG\n  #define PY_LONG_LONG LONG_LONG\n#endif\n#ifndef Py_HUGE_VAL\n  #define Py_HUGE_VAL HUGE_VAL\n#endif\n#ifdef PYPY_VERSION\n  #define CYTHON_COMPILING_IN_PYPY 1\n  #define CYTHON_COMPILING_IN_PYSTON 0\n  #define CYTHON_COMPILING_IN_CPYTHON 0\n  #undef CYTHON_USE_TYPE_SLOTS\n  #define CYTHON_USE_TYPE_SLOTS 0\n  #undef CYTHON_USE_PYTYPE_LOOKUP\n  #define CYTHON_USE_PYTYPE_LOOKUP 0\n  #if PY_VERSION_HEX < 0x03050000\n    #undef CYTHON_USE_ASYNC_SLOTS\n    #define CYTHON_USE_ASYNC_SLOTS 0\n  #elif !defined(CYTHON_USE_ASYNC_SLOTS)\n    #define CYTHON_USE_ASYNC_SLOTS 1\n  #endif\n  #undef CYTHON_USE_PYLIST_INTERNALS\n  #define CYTHON_USE_PYLIST_INTERNALS 0\n  #undef CYTHON_USE_UNICODE_INTERNALS\n  #define CYTHON_USE_UNICODE_INTERNALS 0\n  #undef CYTHON_USE_UNICODE_WRITER\n  #define CYTHON_USE_UNICODE_WRITER 0\n  #undef CYTHON_USE_PYLONG_INTERNALS\n  #define CYTHON_USE_PYLONG_INTERNALS 0\n  #undef CYTHON_AVOID_BORROWED_REFS\n  #define CYTHON_AVOID_BORROWED_REFS 1\n  #undef CYTHON_ASSUME_SAFE_MACROS\n  #define CYTHON_ASSUME_SAFE_MACROS 0\n  #undef CYTHON_UNPACK_METHODS\n  #define CYTHON_UNPACK_METHODS 0\n  #undef CYTHON_FAST_THREAD_STATE\n  #define CYTHON_FAST_THREAD_STATE 0\n  #undef CYTHON_FAST_PYCALL\n  #define CYTHON_FAST_PYCALL 0\n  #undef CYTHON_PEP489_MULTI_PHASE_INIT\n  #define CYTHON_PEP489_MULTI_PHASE_INIT 0\n  #undef CYTHON_USE_TP_FINALIZE\n  #define CYTHON_USE_TP_FINALIZE 0\n#elif defined(PYSTON_VERSION)\n  #define CYTHON_COMPILING_IN_PYPY 0\n  #define CYTHON_COMPILING_IN_PYSTON 1\n  #define CYTHON_COMPILING_IN_CPYTHON 0\n  #ifndef CYTHON_USE_TYPE_SLOTS\n    #define CYTHON_USE_TYPE_SLOTS 1\n  #endif\n  #undef CYTHON_USE_PYTYPE_LOOKUP\n  #define CYTHON_USE_PYTYPE_LOOKUP 0\n  #undef CYTHON_USE_ASYNC_SLOTS\n  #define CYTHON_USE_ASYNC_SLOTS 0\n  #undef CYTHON_USE_PYLIST_INTERNALS\n  #define CYTHON_USE_PYLIST_INTERNALS 0\n  #ifndef CYTHON_USE_UNICODE_INTERNALS\n    #define CYTHON_USE_UNICODE_INTERNALS 1\n  #endif\n  #undef CYTHON_USE_UNICODE_WRITER\n  #define CYTHON_USE_UNICODE_WRITER 0\n  #undef CYTHON_USE_PYLONG_INTERNALS\n  #define CYTHON_USE_PYLONG_INTERNALS 0\n  #ifndef CYTHON_AVOID_BORROWED_REFS\n    #define CYTHON_AVOID_BORROWED_REFS 0\n  #endif\n  #ifndef CYTHON_ASSUME_SAFE_MACROS\n    #define CYTHON_ASSUME_SAFE_MACROS 1\n  #endif\n  #ifndef CYTHON_UNPACK_METHODS\n    #define CYTHON_UNPACK_METHODS 1\n  #endif\n  #undef CYTHON_FAST_THREAD_STATE\n  #define CYTHON_FAST_THREAD_STATE 0\n  #undef CYTHON_FAST_PYCALL\n  #define CYTHON_FAST_PYCALL 0\n  #undef CYTHON_PEP489_MULTI_PHASE_INIT\n  #define CYTHON_PEP489_MULTI_PHASE_INIT 0\n  #undef CYTHON_USE_TP_FINALIZE\n  #define CYTHON_USE_TP_FINALIZE 0\n#else\n  #define CYTHON_COMPILING_IN_PYPY 0\n  #define CYTHON_COMPILING_IN_PYSTON 0\n  #define CYTHON_COMPILING_IN_CPYTHON 1\n  #ifndef CYTHON_USE_TYPE_SLOTS\n    #define CYTHON_USE_TYPE_SLOTS 1\n  #endif\n  #if PY_VERSION_HEX < 0x02070000\n    #undef CYTHON_USE_PYTYPE_LOOKUP\n    #define CYTHON_USE_PYTYPE_LOOKUP 0\n  #elif !defined(CYTHON_USE_PYTYPE_LOOKUP)\n    #define CYTHON_USE_PYTYPE_LOOKUP 1\n  #endif\n  #if PY_MAJOR_VERSION < 3\n    #undef CYTHON_USE_ASYNC_SLOTS\n    #define CYTHON_USE_ASYNC_SLOTS 0\n  #elif !defined(CYTHON_USE_ASYNC_SLOTS)\n    #define CYTHON_USE_ASYNC_SLOTS 1\n  #endif\n  #if PY_VERSION_HEX < 0x02070000\n    #undef CYTHON_USE_PYLONG_INTERNALS\n    #define CYTHON_USE_PYLONG_INTERNALS 0\n  #elif !defined(CYTHON_USE_PYLONG_INTERNALS)\n    #define CYTHON_USE_PYLONG_INTERNALS 1\n  #endif\n  #ifndef CYTHON_USE_PYLIST_INTERNALS\n    #define CYTHON_USE_PYLIST_INTERNALS 1\n  #endif\n  #ifndef CYTHON_USE_UNICODE_INTERNALS\n    #define CYTHON_USE_UNICODE_INTERNALS 1\n  #endif\n  #if PY_VERSION_HEX < 0x030300F0\n    #undef CYTHON_USE_UNICODE_WRITER\n    #define CYTHON_USE_UNICODE_WRITER 0\n  #elif !defined(CYTHON_USE_UNICODE_WRITER)\n    #define CYTHON_USE_UNICODE_WRITER 1\n  #endif\n  #ifndef CYTHON_AVOID_BORROWED_REFS\n    #define CYTHON_AVOID_BORROWED_REFS 0\n  #endif\n  #ifndef CYTHON_ASSUME_SAFE_MACROS\n    #define CYTHON_ASSUME_SAFE_MACROS 1\n  #endif\n  #ifndef CYTHON_UNPACK_METHODS\n    #define CYTHON_UNPACK_METHODS 1\n  #endif\n  #ifndef CYTHON_FAST_THREAD_STATE\n    #define CYTHON_FAST_THREAD_STATE 1\n  #endif\n  #ifndef CYTHON_FAST_PYCALL\n    #define CYTHON_FAST_PYCALL 1\n  #endif\n  #ifndef CYTHON_PEP489_MULTI_PHASE_INIT\n    #define CYTHON_PEP489_MULTI_PHASE_INIT (0 && PY_VERSION_HEX >= 0x03050000)\n  #endif\n  #ifndef CYTHON_USE_TP_FINALIZE\n    #define CYTHON_USE_TP_FINALIZE (PY_VERSION_HEX >= 0x030400a1)\n  #endif\n#endif\n#if !defined(CYTHON_FAST_PYCCALL)\n#define CYTHON_FAST_PYCCALL  (CYTHON_FAST_PYCALL && PY_VERSION_HEX >= 0x030600B1)\n#endif\n#if CYTHON_USE_PYLONG_INTERNALS\n  #include \"longintrepr.h\"\n  #undef SHIFT\n  #undef BASE\n  #undef MASK\n#endif\n#ifndef __has_attribute\n  #define __has_attribute(x) 0\n#endif\n#ifndef __has_cpp_attribute\n  #define __has_cpp_attribute(x) 0\n#endif\n#ifndef CYTHON_RESTRICT\n  #if defined(__GNUC__)\n    #define CYTHON_RESTRICT __restrict__\n  #elif defined(_MSC_VER) && _MSC_VER >= 1400\n    #define CYTHON_RESTRICT __restrict\n  #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L\n    #define CYTHON_RESTRICT restrict\n  #else\n    #define CYTHON_RESTRICT\n  #endif\n#endif\n#ifndef CYTHON_UNUSED\n# if defined(__GNUC__)\n#   if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4))\n#     define CYTHON_UNUSED __attribute__ ((__unused__))\n#   else\n#     define CYTHON_UNUSED\n#   endif\n# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER))\n#   define CYTHON_UNUSED __attribute__ ((__unused__))\n# else\n#   define CYTHON_UNUSED\n# endif\n#endif\n#ifndef CYTHON_MAYBE_UNUSED_VAR\n#  if defined(__cplusplus)\n     template<class T> void CYTHON_MAYBE_UNUSED_VAR( const T& ) { }\n#  else\n#    define CYTHON_MAYBE_UNUSED_VAR(x) (void)(x)\n#  endif\n#endif\n#ifndef CYTHON_NCP_UNUSED\n# if CYTHON_COMPILING_IN_CPYTHON\n#  define CYTHON_NCP_UNUSED\n# else\n#  define CYTHON_NCP_UNUSED CYTHON_UNUSED\n# endif\n#endif\n#define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None)\n#ifdef _MSC_VER\n    #ifndef _MSC_STDINT_H_\n        #if _MSC_VER < 1300\n           typedef unsigned char     uint8_t;\n           typedef unsigned int      uint32_t;\n        #else\n           typedef unsigned __int8   uint8_t;\n           typedef unsigned __int32  uint32_t;\n        #endif\n    #endif\n#else\n   #include <stdint.h>\n#endif\n#ifndef CYTHON_FALLTHROUGH\n  #if defined(__cplusplus) && __cplusplus >= 201103L\n    #if __has_cpp_attribute(fallthrough)\n      #define CYTHON_FALLTHROUGH [[fallthrough]]\n    #elif __has_cpp_attribute(clang::fallthrough)\n      #define CYTHON_FALLTHROUGH [[clang::fallthrough]]\n    #elif __has_cpp_attribute(gnu::fallthrough)\n      #define CYTHON_FALLTHROUGH [[gnu::fallthrough]]\n    #endif\n  #endif\n  #ifndef CYTHON_FALLTHROUGH\n    #if __has_attribute(fallthrough)\n      #define CYTHON_FALLTHROUGH __attribute__((fallthrough))\n    #else\n      #define CYTHON_FALLTHROUGH\n    #endif\n  #endif\n  #if defined(__clang__ ) && defined(__apple_build_version__)\n    #if __apple_build_version__ < 7000000\n      #undef  CYTHON_FALLTHROUGH\n      #define CYTHON_FALLTHROUGH\n    #endif\n  #endif\n#endif\n\n#ifndef CYTHON_INLINE\n  #if defined(__clang__)\n    #define CYTHON_INLINE __inline__ __attribute__ ((__unused__))\n  #elif defined(__GNUC__)\n    #define CYTHON_INLINE __inline__\n  #elif defined(_MSC_VER)\n    #define CYTHON_INLINE __inline\n  #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L\n    #define CYTHON_INLINE inline\n  #else\n    #define CYTHON_INLINE\n  #endif\n#endif\n\n#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 && !defined(Py_OptimizeFlag)\n  #define Py_OptimizeFlag 0\n#endif\n#define __PYX_BUILD_PY_SSIZE_T \"n\"\n#define CYTHON_FORMAT_SSIZE_T \"z\"\n#if PY_MAJOR_VERSION < 3\n  #define __Pyx_BUILTIN_MODULE_NAME \"__builtin__\"\n  #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\\\n          PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\n  #define __Pyx_DefaultClassType PyClass_Type\n#else\n  #define __Pyx_BUILTIN_MODULE_NAME \"builtins\"\n  #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\\\n          PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\n  #define __Pyx_DefaultClassType PyType_Type\n#endif\n#ifndef Py_TPFLAGS_CHECKTYPES\n  #define Py_TPFLAGS_CHECKTYPES 0\n#endif\n#ifndef Py_TPFLAGS_HAVE_INDEX\n  #define Py_TPFLAGS_HAVE_INDEX 0\n#endif\n#ifndef Py_TPFLAGS_HAVE_NEWBUFFER\n  #define Py_TPFLAGS_HAVE_NEWBUFFER 0\n#endif\n#ifndef Py_TPFLAGS_HAVE_FINALIZE\n  #define Py_TPFLAGS_HAVE_FINALIZE 0\n#endif\n#if PY_VERSION_HEX <= 0x030700A3 || !defined(METH_FASTCALL)\n  #ifndef METH_FASTCALL\n     #define METH_FASTCALL 0x80\n  #endif\n  typedef PyObject *(*__Pyx_PyCFunctionFast) (PyObject *self, PyObject *const *args, Py_ssize_t nargs);\n  typedef PyObject *(*__Pyx_PyCFunctionFastWithKeywords) (PyObject *self, PyObject *const *args,\n                                                          Py_ssize_t nargs, PyObject *kwnames);\n#else\n  #define __Pyx_PyCFunctionFast _PyCFunctionFast\n  #define __Pyx_PyCFunctionFastWithKeywords _PyCFunctionFastWithKeywords\n#endif\n#if CYTHON_FAST_PYCCALL\n#define __Pyx_PyFastCFunction_Check(func)\\\n    ((PyCFunction_Check(func) && (METH_FASTCALL == (PyCFunction_GET_FLAGS(func) & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS)))))\n#else\n#define __Pyx_PyFastCFunction_Check(func) 0\n#endif\n#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Malloc)\n  #define PyObject_Malloc(s)   PyMem_Malloc(s)\n  #define PyObject_Free(p)     PyMem_Free(p)\n  #define PyObject_Realloc(p)  PyMem_Realloc(p)\n#endif\n#if CYTHON_COMPILING_IN_PYSTON\n  #define __Pyx_PyCode_HasFreeVars(co)  PyCode_HasFreeVars(co)\n  #define __Pyx_PyFrame_SetLineNumber(frame, lineno) PyFrame_SetLineNumber(frame, lineno)\n#else\n  #define __Pyx_PyCode_HasFreeVars(co)  (PyCode_GetNumFree(co) > 0)\n  #define __Pyx_PyFrame_SetLineNumber(frame, lineno)  (frame)->f_lineno = (lineno)\n#endif\n#if !CYTHON_FAST_THREAD_STATE || PY_VERSION_HEX < 0x02070000\n  #define __Pyx_PyThreadState_Current PyThreadState_GET()\n#elif PY_VERSION_HEX >= 0x03060000\n  #define __Pyx_PyThreadState_Current _PyThreadState_UncheckedGet()\n#elif PY_VERSION_HEX >= 0x03000000\n  #define __Pyx_PyThreadState_Current PyThreadState_GET()\n#else\n  #define __Pyx_PyThreadState_Current _PyThreadState_Current\n#endif\n#if PY_VERSION_HEX < 0x030700A2 && !defined(PyThread_tss_create) && !defined(Py_tss_NEEDS_INIT)\n#include \"pythread.h\"\n#define Py_tss_NEEDS_INIT 0\ntypedef int Py_tss_t;\nstatic CYTHON_INLINE int PyThread_tss_create(Py_tss_t *key) {\n  *key = PyThread_create_key();\n  return 0; // PyThread_create_key reports success always\n}\nstatic CYTHON_INLINE Py_tss_t * PyThread_tss_alloc(void) {\n  Py_tss_t *key = (Py_tss_t *)PyObject_Malloc(sizeof(Py_tss_t));\n  *key = Py_tss_NEEDS_INIT;\n  return key;\n}\nstatic CYTHON_INLINE void PyThread_tss_free(Py_tss_t *key) {\n  PyObject_Free(key);\n}\nstatic CYTHON_INLINE int PyThread_tss_is_created(Py_tss_t *key) {\n  return *key != Py_tss_NEEDS_INIT;\n}\nstatic CYTHON_INLINE void PyThread_tss_delete(Py_tss_t *key) {\n  PyThread_delete_key(*key);\n  *key = Py_tss_NEEDS_INIT;\n}\nstatic CYTHON_INLINE int PyThread_tss_set(Py_tss_t *key, void *value) {\n  return PyThread_set_key_value(*key, value);\n}\nstatic CYTHON_INLINE void * PyThread_tss_get(Py_tss_t *key) {\n  return PyThread_get_key_value(*key);\n}\n#endif // TSS (Thread Specific Storage) API\n#if CYTHON_COMPILING_IN_CPYTHON || defined(_PyDict_NewPresized)\n#define __Pyx_PyDict_NewPresized(n)  ((n <= 8) ? PyDict_New() : _PyDict_NewPresized(n))\n#else\n#define __Pyx_PyDict_NewPresized(n)  PyDict_New()\n#endif\n#if PY_MAJOR_VERSION >= 3 || CYTHON_FUTURE_DIVISION\n  #define __Pyx_PyNumber_Divide(x,y)         PyNumber_TrueDivide(x,y)\n  #define __Pyx_PyNumber_InPlaceDivide(x,y)  PyNumber_InPlaceTrueDivide(x,y)\n#else\n  #define __Pyx_PyNumber_Divide(x,y)         PyNumber_Divide(x,y)\n  #define __Pyx_PyNumber_InPlaceDivide(x,y)  PyNumber_InPlaceDivide(x,y)\n#endif\n#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1 && CYTHON_USE_UNICODE_INTERNALS\n#define __Pyx_PyDict_GetItemStr(dict, name)  _PyDict_GetItem_KnownHash(dict, name, ((PyASCIIObject *) name)->hash)\n#else\n#define __Pyx_PyDict_GetItemStr(dict, name)  PyDict_GetItem(dict, name)\n#endif\n#if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND)\n  #define CYTHON_PEP393_ENABLED 1\n  #define __Pyx_PyUnicode_READY(op)       (likely(PyUnicode_IS_READY(op)) ?\\\n                                              0 : _PyUnicode_Ready((PyObject *)(op)))\n  #define __Pyx_PyUnicode_GET_LENGTH(u)   PyUnicode_GET_LENGTH(u)\n  #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i)\n  #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u)   PyUnicode_MAX_CHAR_VALUE(u)\n  #define __Pyx_PyUnicode_KIND(u)         PyUnicode_KIND(u)\n  #define __Pyx_PyUnicode_DATA(u)         PyUnicode_DATA(u)\n  #define __Pyx_PyUnicode_READ(k, d, i)   PyUnicode_READ(k, d, i)\n  #define __Pyx_PyUnicode_WRITE(k, d, i, ch)  PyUnicode_WRITE(k, d, i, ch)\n  #define __Pyx_PyUnicode_IS_TRUE(u)      (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : PyUnicode_GET_SIZE(u)))\n#else\n  #define CYTHON_PEP393_ENABLED 0\n  #define PyUnicode_1BYTE_KIND  1\n  #define PyUnicode_2BYTE_KIND  2\n  #define PyUnicode_4BYTE_KIND  4\n  #define __Pyx_PyUnicode_READY(op)       (0)\n  #define __Pyx_PyUnicode_GET_LENGTH(u)   PyUnicode_GET_SIZE(u)\n  #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i]))\n  #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u)   ((sizeof(Py_UNICODE) == 2) ? 65535 : 1114111)\n  #define __Pyx_PyUnicode_KIND(u)         (sizeof(Py_UNICODE))\n  #define __Pyx_PyUnicode_DATA(u)         ((void*)PyUnicode_AS_UNICODE(u))\n  #define __Pyx_PyUnicode_READ(k, d, i)   ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i]))\n  #define __Pyx_PyUnicode_WRITE(k, d, i, ch)  (((void)(k)), ((Py_UNICODE*)d)[i] = ch)\n  #define __Pyx_PyUnicode_IS_TRUE(u)      (0 != PyUnicode_GET_SIZE(u))\n#endif\n#if CYTHON_COMPILING_IN_PYPY\n  #define __Pyx_PyUnicode_Concat(a, b)      PyNumber_Add(a, b)\n  #define __Pyx_PyUnicode_ConcatSafe(a, b)  PyNumber_Add(a, b)\n#else\n  #define __Pyx_PyUnicode_Concat(a, b)      PyUnicode_Concat(a, b)\n  #define __Pyx_PyUnicode_ConcatSafe(a, b)  ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ?\\\n      PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b))\n#endif\n#if CYTHON_COMPILING_IN_PYPY && !defined(PyUnicode_Contains)\n  #define PyUnicode_Contains(u, s)  PySequence_Contains(u, s)\n#endif\n#if CYTHON_COMPILING_IN_PYPY && !defined(PyByteArray_Check)\n  #define PyByteArray_Check(obj)  PyObject_TypeCheck(obj, &PyByteArray_Type)\n#endif\n#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Format)\n  #define PyObject_Format(obj, fmt)  PyObject_CallMethod(obj, \"__format__\", \"O\", fmt)\n#endif\n#define __Pyx_PyString_FormatSafe(a, b)   ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b))\n#define __Pyx_PyUnicode_FormatSafe(a, b)  ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b))\n#if PY_MAJOR_VERSION >= 3\n  #define __Pyx_PyString_Format(a, b)  PyUnicode_Format(a, b)\n#else\n  #define __Pyx_PyString_Format(a, b)  PyString_Format(a, b)\n#endif\n#if PY_MAJOR_VERSION < 3 && !defined(PyObject_ASCII)\n  #define PyObject_ASCII(o)            PyObject_Repr(o)\n#endif\n#if PY_MAJOR_VERSION >= 3\n  #define PyBaseString_Type            PyUnicode_Type\n  #define PyStringObject               PyUnicodeObject\n  #define PyString_Type                PyUnicode_Type\n  #define PyString_Check               PyUnicode_Check\n  #define PyString_CheckExact          PyUnicode_CheckExact\n#endif\n#if PY_MAJOR_VERSION >= 3\n  #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj)\n  #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj)\n#else\n  #define __Pyx_PyBaseString_Check(obj) (PyString_Check(obj) || PyUnicode_Check(obj))\n  #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj))\n#endif\n#ifndef PySet_CheckExact\n  #define PySet_CheckExact(obj)        (Py_TYPE(obj) == &PySet_Type)\n#endif\n#if CYTHON_ASSUME_SAFE_MACROS\n  #define __Pyx_PySequence_SIZE(seq)  Py_SIZE(seq)\n#else\n  #define __Pyx_PySequence_SIZE(seq)  PySequence_Size(seq)\n#endif\n#if PY_MAJOR_VERSION >= 3\n  #define PyIntObject                  PyLongObject\n  #define PyInt_Type                   PyLong_Type\n  #define PyInt_Check(op)              PyLong_Check(op)\n  #define PyInt_CheckExact(op)         PyLong_CheckExact(op)\n  #define PyInt_FromString             PyLong_FromString\n  #define PyInt_FromUnicode            PyLong_FromUnicode\n  #define PyInt_FromLong               PyLong_FromLong\n  #define PyInt_FromSize_t             PyLong_FromSize_t\n  #define PyInt_FromSsize_t            PyLong_FromSsize_t\n  #define PyInt_AsLong                 PyLong_AsLong\n  #define PyInt_AS_LONG                PyLong_AS_LONG\n  #define PyInt_AsSsize_t              PyLong_AsSsize_t\n  #define PyInt_AsUnsignedLongMask     PyLong_AsUnsignedLongMask\n  #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask\n  #define PyNumber_Int                 PyNumber_Long\n#endif\n#if PY_MAJOR_VERSION >= 3\n  #define PyBoolObject                 PyLongObject\n#endif\n#if PY_MAJOR_VERSION >= 3 && CYTHON_COMPILING_IN_PYPY\n  #ifndef PyUnicode_InternFromString\n    #define PyUnicode_InternFromString(s) PyUnicode_FromString(s)\n  #endif\n#endif\n#if PY_VERSION_HEX < 0x030200A4\n  typedef long Py_hash_t;\n  #define __Pyx_PyInt_FromHash_t PyInt_FromLong\n  #define __Pyx_PyInt_AsHash_t   PyInt_AsLong\n#else\n  #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t\n  #define __Pyx_PyInt_AsHash_t   PyInt_AsSsize_t\n#endif\n#if PY_MAJOR_VERSION >= 3\n  #define __Pyx_PyMethod_New(func, self, klass) ((self) ? PyMethod_New(func, self) : (Py_INCREF(func), func))\n#else\n  #define __Pyx_PyMethod_New(func, self, klass) PyMethod_New(func, self, klass)\n#endif\n#if CYTHON_USE_ASYNC_SLOTS\n  #if PY_VERSION_HEX >= 0x030500B1\n    #define __Pyx_PyAsyncMethodsStruct PyAsyncMethods\n    #define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async)\n  #else\n    #define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved))\n  #endif\n#else\n  #define __Pyx_PyType_AsAsync(obj) NULL\n#endif\n#ifndef __Pyx_PyAsyncMethodsStruct\n    typedef struct {\n        unaryfunc am_await;\n        unaryfunc am_aiter;\n        unaryfunc am_anext;\n    } __Pyx_PyAsyncMethodsStruct;\n#endif\n\n#if defined(WIN32) || defined(MS_WINDOWS)\n  #define _USE_MATH_DEFINES\n#endif\n#include <math.h>\n#ifdef NAN\n#define __PYX_NAN() ((float) NAN)\n#else\nstatic CYTHON_INLINE float __PYX_NAN() {\n  float value;\n  memset(&value, 0xFF, sizeof(value));\n  return value;\n}\n#endif\n#if defined(__CYGWIN__) && defined(_LDBL_EQ_DBL)\n#define __Pyx_truncl trunc\n#else\n#define __Pyx_truncl truncl\n#endif\n\n\n#define __PYX_ERR(f_index, lineno, Ln_error) \\\n{ \\\n  __pyx_filename = __pyx_f[f_index]; __pyx_lineno = lineno; __pyx_clineno = __LINE__; goto Ln_error; \\\n}\n\n#ifndef __PYX_EXTERN_C\n  #ifdef __cplusplus\n    #define __PYX_EXTERN_C extern \"C\"\n  #else\n    #define __PYX_EXTERN_C extern\n  #endif\n#endif\n\n#define __PYX_HAVE__cpu_nms\n#define __PYX_HAVE_API__cpu_nms\n/* Early includes */\n#include <string.h>\n#include <stdio.h>\n#include \"numpy/arrayobject.h\"\n#include \"numpy/ufuncobject.h\"\n#ifdef _OPENMP\n#include <omp.h>\n#endif /* _OPENMP */\n\n#if defined(PYREX_WITHOUT_ASSERTIONS) && !defined(CYTHON_WITHOUT_ASSERTIONS)\n#define CYTHON_WITHOUT_ASSERTIONS\n#endif\n\ntypedef struct {PyObject **p; const char *s; const Py_ssize_t n; const char* encoding;\n                const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry;\n\n#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0\n#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT 0\n#define __PYX_DEFAULT_STRING_ENCODING \"\"\n#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString\n#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize\n#define __Pyx_uchar_cast(c) ((unsigned char)c)\n#define __Pyx_long_cast(x) ((long)x)\n#define __Pyx_fits_Py_ssize_t(v, type, is_signed)  (\\\n    (sizeof(type) < sizeof(Py_ssize_t))  ||\\\n    (sizeof(type) > sizeof(Py_ssize_t) &&\\\n          likely(v < (type)PY_SSIZE_T_MAX ||\\\n                 v == (type)PY_SSIZE_T_MAX)  &&\\\n          (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||\\\n                                v == (type)PY_SSIZE_T_MIN)))  ||\\\n    (sizeof(type) == sizeof(Py_ssize_t) &&\\\n          (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\\\n                               v == (type)PY_SSIZE_T_MAX)))  )\n#if defined (__cplusplus) && __cplusplus >= 201103L\n    #include <cstdlib>\n    #define __Pyx_sst_abs(value) std::abs(value)\n#elif SIZEOF_INT >= SIZEOF_SIZE_T\n    #define __Pyx_sst_abs(value) abs(value)\n#elif SIZEOF_LONG >= SIZEOF_SIZE_T\n    #define __Pyx_sst_abs(value) labs(value)\n#elif defined (_MSC_VER)\n    #define __Pyx_sst_abs(value) ((Py_ssize_t)_abs64(value))\n#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L\n    #define __Pyx_sst_abs(value) llabs(value)\n#elif defined (__GNUC__)\n    #define __Pyx_sst_abs(value) __builtin_llabs(value)\n#else\n    #define __Pyx_sst_abs(value) ((value<0) ? -value : value)\n#endif\nstatic CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject*);\nstatic CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length);\n#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s))\n#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l)\n#define __Pyx_PyBytes_FromString        PyBytes_FromString\n#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize\nstatic CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*);\n#if PY_MAJOR_VERSION < 3\n    #define __Pyx_PyStr_FromString        __Pyx_PyBytes_FromString\n    #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize\n#else\n    #define __Pyx_PyStr_FromString        __Pyx_PyUnicode_FromString\n    #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize\n#endif\n#define __Pyx_PyBytes_AsWritableString(s)     ((char*) PyBytes_AS_STRING(s))\n#define __Pyx_PyBytes_AsWritableSString(s)    ((signed char*) PyBytes_AS_STRING(s))\n#define __Pyx_PyBytes_AsWritableUString(s)    ((unsigned char*) PyBytes_AS_STRING(s))\n#define __Pyx_PyBytes_AsString(s)     ((const char*) PyBytes_AS_STRING(s))\n#define __Pyx_PyBytes_AsSString(s)    ((const signed char*) PyBytes_AS_STRING(s))\n#define __Pyx_PyBytes_AsUString(s)    ((const unsigned char*) PyBytes_AS_STRING(s))\n#define __Pyx_PyObject_AsWritableString(s)    ((char*) __Pyx_PyObject_AsString(s))\n#define __Pyx_PyObject_AsWritableSString(s)    ((signed char*) __Pyx_PyObject_AsString(s))\n#define __Pyx_PyObject_AsWritableUString(s)    ((unsigned char*) __Pyx_PyObject_AsString(s))\n#define __Pyx_PyObject_AsSString(s)    ((const signed char*) __Pyx_PyObject_AsString(s))\n#define __Pyx_PyObject_AsUString(s)    ((const unsigned char*) __Pyx_PyObject_AsString(s))\n#define __Pyx_PyObject_FromCString(s)  __Pyx_PyObject_FromString((const char*)s)\n#define __Pyx_PyBytes_FromCString(s)   __Pyx_PyBytes_FromString((const char*)s)\n#define __Pyx_PyByteArray_FromCString(s)   __Pyx_PyByteArray_FromString((const char*)s)\n#define __Pyx_PyStr_FromCString(s)     __Pyx_PyStr_FromString((const char*)s)\n#define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s)\nstatic CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) {\n    const Py_UNICODE *u_end = u;\n    while (*u_end++) ;\n    return (size_t)(u_end - u - 1);\n}\n#define __Pyx_PyUnicode_FromUnicode(u)       PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u))\n#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode\n#define __Pyx_PyUnicode_AsUnicode            PyUnicode_AsUnicode\n#define __Pyx_NewRef(obj) (Py_INCREF(obj), obj)\n#define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None)\n#define __Pyx_PyBool_FromLong(b) ((b) ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False))\nstatic CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*);\nstatic CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x);\n#define __Pyx_PySequence_Tuple(obj)\\\n    (likely(PyTuple_CheckExact(obj)) ? __Pyx_NewRef(obj) : PySequence_Tuple(obj))\nstatic CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*);\nstatic CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t);\n#if CYTHON_ASSUME_SAFE_MACROS\n#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x))\n#else\n#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x)\n#endif\n#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x))\n#if PY_MAJOR_VERSION >= 3\n#define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x))\n#else\n#define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x))\n#endif\n#define __Pyx_PyNumber_Float(x) (PyFloat_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Float(x))\n#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII\nstatic int __Pyx_sys_getdefaultencoding_not_ascii;\nstatic int __Pyx_init_sys_getdefaultencoding_params(void) {\n    PyObject* sys;\n    PyObject* default_encoding = NULL;\n    PyObject* ascii_chars_u = NULL;\n    PyObject* ascii_chars_b = NULL;\n    const char* default_encoding_c;\n    sys = PyImport_ImportModule(\"sys\");\n    if (!sys) goto bad;\n    default_encoding = PyObject_CallMethod(sys, (char*) \"getdefaultencoding\", NULL);\n    Py_DECREF(sys);\n    if (!default_encoding) goto bad;\n    default_encoding_c = PyBytes_AsString(default_encoding);\n    if (!default_encoding_c) goto bad;\n    if (strcmp(default_encoding_c, \"ascii\") == 0) {\n        __Pyx_sys_getdefaultencoding_not_ascii = 0;\n    } else {\n        char ascii_chars[128];\n        int c;\n        for (c = 0; c < 128; c++) {\n            ascii_chars[c] = c;\n        }\n        __Pyx_sys_getdefaultencoding_not_ascii = 1;\n        ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL);\n        if (!ascii_chars_u) goto bad;\n        ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL);\n        if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) {\n            PyErr_Format(\n                PyExc_ValueError,\n                \"This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.\",\n                default_encoding_c);\n            goto bad;\n        }\n        Py_DECREF(ascii_chars_u);\n        Py_DECREF(ascii_chars_b);\n    }\n    Py_DECREF(default_encoding);\n    return 0;\nbad:\n    Py_XDECREF(default_encoding);\n    Py_XDECREF(ascii_chars_u);\n    Py_XDECREF(ascii_chars_b);\n    return -1;\n}\n#endif\n#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3\n#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL)\n#else\n#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL)\n#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT\nstatic char* __PYX_DEFAULT_STRING_ENCODING;\nstatic int __Pyx_init_sys_getdefaultencoding_params(void) {\n    PyObject* sys;\n    PyObject* default_encoding = NULL;\n    char* default_encoding_c;\n    sys = PyImport_ImportModule(\"sys\");\n    if (!sys) goto bad;\n    default_encoding = PyObject_CallMethod(sys, (char*) (const char*) \"getdefaultencoding\", NULL);\n    Py_DECREF(sys);\n    if (!default_encoding) goto bad;\n    default_encoding_c = PyBytes_AsString(default_encoding);\n    if (!default_encoding_c) goto bad;\n    __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c));\n    if (!__PYX_DEFAULT_STRING_ENCODING) goto bad;\n    strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c);\n    Py_DECREF(default_encoding);\n    return 0;\nbad:\n    Py_XDECREF(default_encoding);\n    return -1;\n}\n#endif\n#endif\n\n\n/* Test for GCC > 2.95 */\n#if defined(__GNUC__)     && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95)))\n  #define likely(x)   __builtin_expect(!!(x), 1)\n  #define unlikely(x) __builtin_expect(!!(x), 0)\n#else /* !__GNUC__ or GCC < 2.95 */\n  #define likely(x)   (x)\n  #define unlikely(x) (x)\n#endif /* __GNUC__ */\nstatic CYTHON_INLINE void __Pyx_pretend_to_initialize(void* ptr) { (void)ptr; }\n\nstatic PyObject *__pyx_m = NULL;\nstatic PyObject *__pyx_d;\nstatic PyObject *__pyx_b;\nstatic PyObject *__pyx_cython_runtime;\nstatic PyObject *__pyx_empty_tuple;\nstatic PyObject *__pyx_empty_bytes;\nstatic PyObject *__pyx_empty_unicode;\nstatic int __pyx_lineno;\nstatic int __pyx_clineno = 0;\nstatic const char * __pyx_cfilenm= __FILE__;\nstatic const char *__pyx_filename;\n\n/* Header.proto */\n#if !defined(CYTHON_CCOMPLEX)\n  #if defined(__cplusplus)\n    #define CYTHON_CCOMPLEX 1\n  #elif defined(_Complex_I)\n    #define CYTHON_CCOMPLEX 1\n  #else\n    #define CYTHON_CCOMPLEX 0\n  #endif\n#endif\n#if CYTHON_CCOMPLEX\n  #ifdef __cplusplus\n    #include <complex>\n  #else\n    #include <complex.h>\n  #endif\n#endif\n#if CYTHON_CCOMPLEX && !defined(__cplusplus) && defined(__sun__) && defined(__GNUC__)\n  #undef _Complex_I\n  #define _Complex_I 1.0fj\n#endif\n\n\nstatic const char *__pyx_f[] = {\n  \"cpu_nms.pyx\",\n  \"__init__.pxd\",\n  \"type.pxd\",\n};\n/* BufferFormatStructs.proto */\n#define IS_UNSIGNED(type) (((type) -1) > 0)\nstruct __Pyx_StructField_;\n#define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0)\ntypedef struct {\n  const char* name;\n  struct __Pyx_StructField_* fields;\n  size_t size;\n  size_t arraysize[8];\n  int ndim;\n  char typegroup;\n  char is_unsigned;\n  int flags;\n} __Pyx_TypeInfo;\ntypedef struct __Pyx_StructField_ {\n  __Pyx_TypeInfo* type;\n  const char* name;\n  size_t offset;\n} __Pyx_StructField;\ntypedef struct {\n  __Pyx_StructField* field;\n  size_t parent_offset;\n} __Pyx_BufFmt_StackElem;\ntypedef struct {\n  __Pyx_StructField root;\n  __Pyx_BufFmt_StackElem* head;\n  size_t fmt_offset;\n  size_t new_count, enc_count;\n  size_t struct_alignment;\n  int is_complex;\n  char enc_type;\n  char new_packmode;\n  char enc_packmode;\n  char is_valid_array;\n} __Pyx_BufFmt_Context;\n\n\n/* \"../../../../anaconda3/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd\":730\n * # in Cython to enable them only on the right systems.\n * \n * ctypedef npy_int8       int8_t             # <<<<<<<<<<<<<<\n * ctypedef npy_int16      int16_t\n * ctypedef npy_int32      int32_t\n */\ntypedef npy_int8 __pyx_t_5numpy_int8_t;\n\n/* \"../../../../anaconda3/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd\":731\n * \n * ctypedef npy_int8       int8_t\n * ctypedef npy_int16      int16_t             # <<<<<<<<<<<<<<\n * ctypedef npy_int32      int32_t\n * ctypedef npy_int64      int64_t\n */\ntypedef npy_int16 __pyx_t_5numpy_int16_t;\n\n/* \"../../../../anaconda3/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd\":732\n * ctypedef npy_int8       int8_t\n * ctypedef npy_int16      int16_t\n * ctypedef npy_int32      int32_t             # <<<<<<<<<<<<<<\n * ctypedef npy_int64      int64_t\n * #ctypedef npy_int96      int96_t\n */\ntypedef npy_int32 __pyx_t_5numpy_int32_t;\n\n/* \"../../../../anaconda3/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd\":733\n * ctypedef npy_int16      int16_t\n * ctypedef npy_int32      int32_t\n * ctypedef npy_int64      int64_t             # <<<<<<<<<<<<<<\n * #ctypedef npy_int96      int96_t\n * #ctypedef npy_int128     int128_t\n */\ntypedef npy_int64 __pyx_t_5numpy_int64_t;\n\n/* \"../../../../anaconda3/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd\":737\n * #ctypedef npy_int128     int128_t\n * \n * ctypedef npy_uint8      uint8_t             # <<<<<<<<<<<<<<\n * ctypedef npy_uint16     uint16_t\n * ctypedef npy_uint32     uint32_t\n */\ntypedef npy_uint8 __pyx_t_5numpy_uint8_t;\n\n/* \"../../../../anaconda3/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd\":738\n * \n * ctypedef npy_uint8      uint8_t\n * ctypedef npy_uint16     uint16_t             # <<<<<<<<<<<<<<\n * ctypedef npy_uint32     uint32_t\n * ctypedef npy_uint64     uint64_t\n */\ntypedef npy_uint16 __pyx_t_5numpy_uint16_t;\n\n/* \"../../../../anaconda3/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd\":739\n * ctypedef npy_uint8      uint8_t\n * ctypedef npy_uint16     uint16_t\n * ctypedef npy_uint32     uint32_t             # <<<<<<<<<<<<<<\n * ctypedef npy_uint64     uint64_t\n * #ctypedef npy_uint96     uint96_t\n */\ntypedef npy_uint32 __pyx_t_5numpy_uint32_t;\n\n/* \"../../../../anaconda3/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd\":740\n * ctypedef npy_uint16     uint16_t\n * ctypedef npy_uint32     uint32_t\n * ctypedef npy_uint64     uint64_t             # <<<<<<<<<<<<<<\n * #ctypedef npy_uint96     uint96_t\n * #ctypedef npy_uint128    uint128_t\n */\ntypedef npy_uint64 __pyx_t_5numpy_uint64_t;\n\n/* \"../../../../anaconda3/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd\":744\n * #ctypedef npy_uint128    uint128_t\n * \n * ctypedef npy_float32    float32_t             # <<<<<<<<<<<<<<\n * ctypedef npy_float64    float64_t\n * #ctypedef npy_float80    float80_t\n */\ntypedef npy_float32 __pyx_t_5numpy_float32_t;\n\n/* \"../../../../anaconda3/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd\":745\n * \n * ctypedef npy_float32    float32_t\n * ctypedef npy_float64    float64_t             # <<<<<<<<<<<<<<\n * #ctypedef npy_float80    float80_t\n * #ctypedef npy_float128   float128_t\n */\ntypedef npy_float64 __pyx_t_5numpy_float64_t;\n\n/* \"../../../../anaconda3/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd\":754\n * # The int types are mapped a bit surprising --\n * # numpy.int corresponds to 'l' and numpy.long to 'q'\n * ctypedef npy_long       int_t             # <<<<<<<<<<<<<<\n * ctypedef npy_longlong   long_t\n * ctypedef npy_longlong   longlong_t\n */\ntypedef npy_long __pyx_t_5numpy_int_t;\n\n/* \"../../../../anaconda3/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd\":755\n * # numpy.int corresponds to 'l' and numpy.long to 'q'\n * ctypedef npy_long       int_t\n * ctypedef npy_longlong   long_t             # <<<<<<<<<<<<<<\n * ctypedef npy_longlong   longlong_t\n * \n */\ntypedef npy_longlong __pyx_t_5numpy_long_t;\n\n/* \"../../../../anaconda3/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd\":756\n * ctypedef npy_long       int_t\n * ctypedef npy_longlong   long_t\n * ctypedef npy_longlong   longlong_t             # <<<<<<<<<<<<<<\n * \n * ctypedef npy_ulong      uint_t\n */\ntypedef npy_longlong __pyx_t_5numpy_longlong_t;\n\n/* \"../../../../anaconda3/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd\":758\n * ctypedef npy_longlong   longlong_t\n * \n * ctypedef npy_ulong      uint_t             # <<<<<<<<<<<<<<\n * ctypedef npy_ulonglong  ulong_t\n * ctypedef npy_ulonglong  ulonglong_t\n */\ntypedef npy_ulong __pyx_t_5numpy_uint_t;\n\n/* \"../../../../anaconda3/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd\":759\n * \n * ctypedef npy_ulong      uint_t\n * ctypedef npy_ulonglong  ulong_t             # <<<<<<<<<<<<<<\n * ctypedef npy_ulonglong  ulonglong_t\n * \n 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0)\n#else\nstatic int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line);\n#endif\n\n/* CodeObjectCache.proto */\ntypedef struct {\n    PyCodeObject* code_object;\n    int code_line;\n} __Pyx_CodeObjectCacheEntry;\nstruct __Pyx_CodeObjectCache {\n    int count;\n    int max_count;\n    __Pyx_CodeObjectCacheEntry* entries;\n};\nstatic struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL};\nstatic int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line);\nstatic PyCodeObject *__pyx_find_code_object(int code_line);\nstatic void __pyx_insert_code_object(int code_line, PyCodeObject* code_object);\n\n/* AddTraceback.proto */\nstatic void __Pyx_AddTraceback(const char *funcname, int c_line,\n                               int py_line, const char *filename);\n\n/* BufferStructDeclare.proto */\ntypedef struct {\n  Py_ssize_t shape, strides, suboffsets;\n} __Pyx_Buf_DimInfo;\ntypedef struct {\n  size_t refcount;\n  Py_buffer pybuffer;\n} __Pyx_Buffer;\ntypedef struct {\n  __Pyx_Buffer *rcbuffer;\n  char *data;\n  __Pyx_Buf_DimInfo diminfo[8];\n} __Pyx_LocalBuf_ND;\n\n#if PY_MAJOR_VERSION < 3\n    static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags);\n    static void __Pyx_ReleaseBuffer(Py_buffer *view);\n#else\n    #define __Pyx_GetBuffer PyObject_GetBuffer\n    #define __Pyx_ReleaseBuffer PyBuffer_Release\n#endif\n\n\n/* CIntToPy.proto */\nstatic CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value);\n\n/* CIntToPy.proto */\nstatic CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value);\n\n/* RealImag.proto */\n#if CYTHON_CCOMPLEX\n  #ifdef __cplusplus\n    #define __Pyx_CREAL(z) ((z).real())\n    #define __Pyx_CIMAG(z) ((z).imag())\n  #else\n    #define __Pyx_CREAL(z) (__real__(z))\n    #define __Pyx_CIMAG(z) (__imag__(z))\n  #endif\n#else\n    #define __Pyx_CREAL(z) ((z).real)\n    #define __Pyx_CIMAG(z) ((z).imag)\n#endif\n#if defined(__cplusplus) && CYTHON_CCOMPLEX\\\n        && (defined(_WIN32) || defined(__clang__) || (defined(__GNUC__) && (__GNUC__ >= 5 || __GNUC__ == 4 && __GNUC_MINOR__ >= 4 )) || __cplusplus >= 201103)\n    #define __Pyx_SET_CREAL(z,x) ((z).real(x))\n    #define __Pyx_SET_CIMAG(z,y) ((z).imag(y))\n#else\n    #define __Pyx_SET_CREAL(z,x) __Pyx_CREAL(z) = (x)\n    #define __Pyx_SET_CIMAG(z,y) __Pyx_CIMAG(z) = (y)\n#endif\n\n/* Arithmetic.proto */\n#if CYTHON_CCOMPLEX\n    #define __Pyx_c_eq_float(a, b)   ((a)==(b))\n    #define __Pyx_c_sum_float(a, b)  ((a)+(b))\n    #define __Pyx_c_diff_float(a, b) ((a)-(b))\n    #define __Pyx_c_prod_float(a, b) ((a)*(b))\n    #define __Pyx_c_quot_float(a, b) ((a)/(b))\n    #define __Pyx_c_neg_float(a)     (-(a))\n  #ifdef __cplusplus\n    #define __Pyx_c_is_zero_float(z) ((z)==(float)0)\n    #define __Pyx_c_conj_float(z)    (::std::conj(z))\n    #if 1\n        #define __Pyx_c_abs_float(z)     (::std::abs(z))\n        #define __Pyx_c_pow_float(a, b)  (::std::pow(a, b))\n    #endif\n  #else\n    #define __Pyx_c_is_zero_float(z) ((z)==0)\n    #define __Pyx_c_conj_float(z)    (conjf(z))\n    #if 1\n        #define __Pyx_c_abs_float(z)     (cabsf(z))\n        #define __Pyx_c_pow_float(a, b)  (cpowf(a, b))\n    #endif\n #endif\n#else\n    static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex, __pyx_t_float_complex);\n    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex, __pyx_t_float_complex);\n    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex, __pyx_t_float_complex);\n    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex, __pyx_t_float_complex);\n    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex, __pyx_t_float_complex);\n    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex);\n    static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex);\n    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex);\n    #if 1\n        static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex);\n        static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex, __pyx_t_float_complex);\n    #endif\n#endif\n\n/* Arithmetic.proto */\n#if CYTHON_CCOMPLEX\n    #define __Pyx_c_eq_double(a, b)   ((a)==(b))\n    #define __Pyx_c_sum_double(a, b)  ((a)+(b))\n    #define __Pyx_c_diff_double(a, b) ((a)-(b))\n    #define __Pyx_c_prod_double(a, b) ((a)*(b))\n    #define __Pyx_c_quot_double(a, b) ((a)/(b))\n    #define __Pyx_c_neg_double(a)     (-(a))\n  #ifdef __cplusplus\n    #define __Pyx_c_is_zero_double(z) ((z)==(double)0)\n    #define __Pyx_c_conj_double(z)    (::std::conj(z))\n    #if 1\n        #define __Pyx_c_abs_double(z)     (::std::abs(z))\n        #define __Pyx_c_pow_double(a, b)  (::std::pow(a, b))\n    #endif\n  #else\n    #define __Pyx_c_is_zero_double(z) ((z)==0)\n    #define __Pyx_c_conj_double(z)    (conj(z))\n    #if 1\n        #define __Pyx_c_abs_double(z)     (cabs(z))\n        #define __Pyx_c_pow_double(a, b)  (cpow(a, b))\n    #endif\n #endif\n#else\n    static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex, __pyx_t_double_complex);\n    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex, __pyx_t_double_complex);\n    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex, __pyx_t_double_complex);\n    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex, __pyx_t_double_complex);\n    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex, __pyx_t_double_complex);\n    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex);\n    static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex);\n    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex);\n    #if 1\n        static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex);\n        static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex, __pyx_t_double_complex);\n    #endif\n#endif\n\n/* CIntToPy.proto */\nstatic CYTHON_INLINE PyObject* __Pyx_PyInt_From_enum__NPY_TYPES(enum NPY_TYPES value);\n\n/* CIntFromPy.proto */\nstatic CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *);\n\n/* CIntFromPy.proto */\nstatic CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *);\n\n/* FastTypeChecks.proto */\n#if CYTHON_COMPILING_IN_CPYTHON\n#define __Pyx_TypeCheck(obj, type) __Pyx_IsSubtype(Py_TYPE(obj), (PyTypeObject *)type)\nstatic CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b);\nstatic CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject *type);\nstatic CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *type1, PyObject *type2);\n#else\n#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type)\n#define __Pyx_PyErr_GivenExceptionMatches(err, type) PyErr_GivenExceptionMatches(err, type)\n#define __Pyx_PyErr_GivenExceptionMatches2(err, type1, type2) (PyErr_GivenExceptionMatches(err, type1) || PyErr_GivenExceptionMatches(err, type2))\n#endif\n#define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception)\n\n/* CheckBinaryVersion.proto */\nstatic int __Pyx_check_binary_version(void);\n\n/* PyIdentifierFromString.proto */\n#if !defined(__Pyx_PyIdentifier_FromString)\n#if PY_MAJOR_VERSION < 3\n  #define __Pyx_PyIdentifier_FromString(s) PyString_FromString(s)\n#else\n  #define __Pyx_PyIdentifier_FromString(s) PyUnicode_FromString(s)\n#endif\n#endif\n\n/* ModuleImport.proto */\nstatic PyObject *__Pyx_ImportModule(const char *name);\n\n/* TypeImport.proto */\nstatic PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, size_t size, int strict);\n\n/* InitStrings.proto */\nstatic int __Pyx_InitStrings(__Pyx_StringTabEntry *t);\n\n\n/* Module declarations from 'cpython.buffer' */\n\n/* Module declarations from 'libc.string' */\n\n/* Module declarations from 'libc.stdio' */\n\n/* Module declarations from '__builtin__' */\n\n/* Module declarations from 'cpython.type' */\nstatic PyTypeObject *__pyx_ptype_7cpython_4type_type = 0;\n\n/* Module declarations from 'cpython' */\n\n/* Module declarations from 'cpython.object' */\n\n/* Module declarations from 'cpython.ref' */\n\n/* Module declarations from 'cpython.mem' */\n\n/* Module declarations from 'numpy' */\n\n/* Module declarations from 'numpy' */\nstatic PyTypeObject *__pyx_ptype_5numpy_dtype = 0;\nstatic PyTypeObject *__pyx_ptype_5numpy_flatiter = 0;\nstatic PyTypeObject *__pyx_ptype_5numpy_broadcast = 0;\nstatic PyTypeObject *__pyx_ptype_5numpy_ndarray = 0;\nstatic PyTypeObject *__pyx_ptype_5numpy_ufunc = 0;\nstatic CYTHON_INLINE char *__pyx_f_5numpy__util_dtypestring(PyArray_Descr *, char *, char *, int *); /*proto*/\n\n/* Module declarations from 'cpu_nms' */\nstatic CYTHON_INLINE __pyx_t_5numpy_float32_t __pyx_f_7cpu_nms_max(__pyx_t_5numpy_float32_t, __pyx_t_5numpy_float32_t); /*proto*/\nstatic CYTHON_INLINE __pyx_t_5numpy_float32_t __pyx_f_7cpu_nms_min(__pyx_t_5numpy_float32_t, __pyx_t_5numpy_float32_t); /*proto*/\nstatic __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_5numpy_float32_t = { \"float32_t\", NULL, sizeof(__pyx_t_5numpy_float32_t), { 0 }, 0, 'R', 0, 0 };\nstatic __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_5numpy_int_t = { \"int_t\", NULL, sizeof(__pyx_t_5numpy_int_t), { 0 }, 0, IS_UNSIGNED(__pyx_t_5numpy_int_t) ? 'U' : 'I', IS_UNSIGNED(__pyx_t_5numpy_int_t), 0 };\n#define __Pyx_MODULE_NAME \"cpu_nms\"\nextern int __pyx_module_is_main_cpu_nms;\nint __pyx_module_is_main_cpu_nms = 0;\n\n/* Implementation of 'cpu_nms' */\nstatic PyObject *__pyx_builtin_range;\nstatic PyObject *__pyx_builtin_ValueError;\nstatic PyObject *__pyx_builtin_RuntimeError;\nstatic PyObject *__pyx_builtin_ImportError;\nstatic const char __pyx_k_h[] = \"h\";\nstatic const char __pyx_k_i[] = \"_i\";\nstatic const char __pyx_k_j[] = \"_j\";\nstatic const char __pyx_k_w[] = \"w\";\nstatic const char __pyx_k_np[] = \"np\";\nstatic const char __pyx_k_x1[] = \"x1\";\nstatic const char __pyx_k_x2[] = \"x2\";\nstatic const char __pyx_k_y1[] = \"y1\";\nstatic const char __pyx_k_y2[] = \"y2\";\nstatic const char __pyx_k_i_2[] = \"i\";\nstatic const char __pyx_k_int[] = \"int\";\nstatic const char __pyx_k_ix1[] = \"ix1\";\nstatic const char __pyx_k_ix2[] = \"ix2\";\nstatic const char __pyx_k_iy1[] = \"iy1\";\nstatic const char __pyx_k_iy2[] = \"iy2\";\nstatic const char __pyx_k_j_2[] = \"j\";\nstatic const char __pyx_k_ovr[] = \"ovr\";\nstatic const char __pyx_k_xx1[] = \"xx1\";\nstatic const char __pyx_k_xx2[] = \"xx2\";\nstatic const char __pyx_k_yy1[] = \"yy1\";\nstatic const char __pyx_k_yy2[] = \"yy2\";\nstatic const char __pyx_k_dets[] = \"dets\";\nstatic const char __pyx_k_keep[] = \"keep\";\nstatic const char __pyx_k_main[] = \"__main__\";\nstatic const char __pyx_k_test[] = \"__test__\";\nstatic const char __pyx_k_areas[] = \"areas\";\nstatic const char __pyx_k_dtype[] = \"dtype\";\nstatic const char __pyx_k_iarea[] = \"iarea\";\nstatic const char __pyx_k_inter[] = \"inter\";\nstatic const char __pyx_k_ndets[] = \"ndets\";\nstatic const char __pyx_k_numpy[] = \"numpy\";\nstatic const char __pyx_k_order[] = \"order\";\nstatic const char __pyx_k_range[] = \"range\";\nstatic const char __pyx_k_zeros[] = \"zeros\";\nstatic const char __pyx_k_import[] = \"__import__\";\nstatic const char __pyx_k_scores[] = \"scores\";\nstatic const char __pyx_k_thresh[] = \"thresh\";\nstatic const char __pyx_k_argsort[] = \"argsort\";\nstatic const char __pyx_k_cpu_nms[] = \"cpu_nms\";\nstatic const char __pyx_k_ValueError[] = \"ValueError\";\nstatic const char __pyx_k_suppressed[] = \"suppressed\";\nstatic const char __pyx_k_ImportError[] = \"ImportError\";\nstatic const char __pyx_k_cpu_nms_pyx[] = \"cpu_nms.pyx\";\nstatic const char __pyx_k_RuntimeError[] = \"RuntimeError\";\nstatic const char __pyx_k_cline_in_traceback[] = \"cline_in_traceback\";\nstatic const char __pyx_k_ndarray_is_not_C_contiguous[] = \"ndarray is not C contiguous\";\nstatic const char __pyx_k_numpy_core_multiarray_failed_to[] = \"numpy.core.multiarray failed to import\";\nstatic const char __pyx_k_unknown_dtype_code_in_numpy_pxd[] = \"unknown dtype code in numpy.pxd (%d)\";\nstatic const char __pyx_k_Format_string_allocated_too_shor[] = \"Format string allocated too short, see comment in numpy.pxd\";\nstatic const char __pyx_k_Non_native_byte_order_not_suppor[] = \"Non-native byte order not supported\";\nstatic const char __pyx_k_ndarray_is_not_Fortran_contiguou[] = \"ndarray is not Fortran contiguous\";\nstatic const char __pyx_k_numpy_core_umath_failed_to_impor[] = \"numpy.core.umath failed to import\";\nstatic const char __pyx_k_Format_string_allocated_too_shor_2[] = \"Format string allocated too short.\";\nstatic PyObject *__pyx_kp_u_Format_string_allocated_too_shor;\nstatic PyObject *__pyx_kp_u_Format_string_allocated_too_shor_2;\nstatic PyObject *__pyx_n_s_ImportError;\nstatic PyObject *__pyx_kp_u_Non_native_byte_order_not_suppor;\nstatic PyObject *__pyx_n_s_RuntimeError;\nstatic PyObject *__pyx_n_s_ValueError;\nstatic PyObject *__pyx_n_s_areas;\nstatic PyObject *__pyx_n_s_argsort;\nstatic PyObject *__pyx_n_s_cline_in_traceback;\nstatic PyObject *__pyx_n_s_cpu_nms;\nstatic PyObject *__pyx_kp_s_cpu_nms_pyx;\nstatic PyObject *__pyx_n_s_dets;\nstatic PyObject *__pyx_n_s_dtype;\nstatic PyObject *__pyx_n_s_h;\nstatic PyObject *__pyx_n_s_i;\nstatic PyObject *__pyx_n_s_i_2;\nstatic PyObject *__pyx_n_s_iarea;\nstatic PyObject *__pyx_n_s_import;\nstatic PyObject *__pyx_n_s_int;\nstatic PyObject *__pyx_n_s_inter;\nstatic PyObject *__pyx_n_s_ix1;\nstatic PyObject *__pyx_n_s_ix2;\nstatic PyObject *__pyx_n_s_iy1;\nstatic PyObject *__pyx_n_s_iy2;\nstatic PyObject *__pyx_n_s_j;\nstatic PyObject *__pyx_n_s_j_2;\nstatic PyObject *__pyx_n_s_keep;\nstatic PyObject *__pyx_n_s_main;\nstatic PyObject *__pyx_kp_u_ndarray_is_not_C_contiguous;\nstatic PyObject *__pyx_kp_u_ndarray_is_not_Fortran_contiguou;\nstatic PyObject *__pyx_n_s_ndets;\nstatic PyObject *__pyx_n_s_np;\nstatic PyObject *__pyx_n_s_numpy;\nstatic PyObject *__pyx_kp_s_numpy_core_multiarray_failed_to;\nstatic PyObject *__pyx_kp_s_numpy_core_umath_failed_to_impor;\nstatic PyObject *__pyx_n_s_order;\nstatic PyObject *__pyx_n_s_ovr;\nstatic PyObject *__pyx_n_s_range;\nstatic PyObject *__pyx_n_s_scores;\nstatic PyObject *__pyx_n_s_suppressed;\nstatic PyObject *__pyx_n_s_test;\nstatic PyObject *__pyx_n_s_thresh;\nstatic PyObject *__pyx_kp_u_unknown_dtype_code_in_numpy_pxd;\nstatic PyObject *__pyx_n_s_w;\nstatic PyObject *__pyx_n_s_x1;\nstatic PyObject *__pyx_n_s_x2;\nstatic PyObject *__pyx_n_s_xx1;\nstatic PyObject *__pyx_n_s_xx2;\nstatic PyObject *__pyx_n_s_y1;\nstatic PyObject *__pyx_n_s_y2;\nstatic PyObject *__pyx_n_s_yy1;\nstatic PyObject *__pyx_n_s_yy2;\nstatic PyObject *__pyx_n_s_zeros;\nstatic PyObject *__pyx_pf_7cpu_nms_cpu_nms(CYTHON_UNUSED PyObject *__pyx_self, PyArrayObject *__pyx_v_dets, PyObject *__pyx_v_thresh); /* proto */\nstatic int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */\nstatic void __pyx_pf_5numpy_7ndarray_2__releasebuffer__(PyArrayObject *__pyx_v_self, Py_buffer *__pyx_v_info); /* proto */\nstatic PyObject *__pyx_int_0;\nstatic PyObject *__pyx_int_1;\nstatic PyObject *__pyx_int_2;\nstatic PyObject 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result;\n}\n\n/* RaiseArgTupleInvalid */\nstatic void __Pyx_RaiseArgtupleInvalid(\n    const char* func_name,\n    int exact,\n    Py_ssize_t num_min,\n    Py_ssize_t num_max,\n    Py_ssize_t num_found)\n{\n    Py_ssize_t num_expected;\n    const char *more_or_less;\n    if (num_found < num_min) {\n        num_expected = num_min;\n        more_or_less = \"at least\";\n    } else {\n        num_expected = num_max;\n        more_or_less = \"at most\";\n    }\n    if (exact) {\n        more_or_less = \"exactly\";\n    }\n    PyErr_Format(PyExc_TypeError,\n                 \"%.200s() takes %.8s %\" CYTHON_FORMAT_SSIZE_T \"d positional argument%.1s (%\" CYTHON_FORMAT_SSIZE_T \"d given)\",\n                 func_name, more_or_less, num_expected,\n                 (num_expected == 1) ? \"\" : \"s\", num_found);\n}\n\n/* RaiseDoubleKeywords */\nstatic void __Pyx_RaiseDoubleKeywordsError(\n    const char* func_name,\n    PyObject* kw_name)\n{\n    PyErr_Format(PyExc_TypeError,\n        #if PY_MAJOR_VERSION >= 3\n        \"%s() got multiple values for keyword argument '%U'\", func_name, kw_name);\n        #else\n        \"%s() got multiple values for keyword argument '%s'\", func_name,\n        PyString_AsString(kw_name));\n        #endif\n}\n\n/* ParseKeywords */\nstatic int __Pyx_ParseOptionalKeywords(\n    PyObject *kwds,\n    PyObject **argnames[],\n    PyObject *kwds2,\n    PyObject *values[],\n    Py_ssize_t num_pos_args,\n    const char* function_name)\n{\n    PyObject *key = 0, *value = 0;\n    Py_ssize_t pos = 0;\n    PyObject*** name;\n    PyObject*** first_kw_arg = argnames + num_pos_args;\n    while (PyDict_Next(kwds, &pos, &key, &value)) {\n        name = first_kw_arg;\n        while (*name && (**name != key)) name++;\n        if (*name) {\n            values[name-argnames] = value;\n            continue;\n        }\n        name = first_kw_arg;\n        #if PY_MAJOR_VERSION < 3\n        if (likely(PyString_CheckExact(key)) || likely(PyString_Check(key))) {\n 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:\n                #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3\n                    (PyUnicode_GET_SIZE(**name) != PyUnicode_GET_SIZE(key)) ? 1 :\n                #endif\n                    PyUnicode_Compare(**name, key);\n                if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad;\n                if (cmp == 0) {\n                    values[name-argnames] = value;\n                    break;\n                }\n                name++;\n            }\n            if (*name) continue;\n            else {\n                PyObject*** argname = argnames;\n                while (argname != first_kw_arg) {\n                    int cmp = (**argname == key) ? 0 :\n                    #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3\n                        (PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(key)) ? 1 :\n                    #endif\n                        PyUnicode_Compare(**argname, key);\n                    if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad;\n                    if (cmp == 0) goto arg_passed_twice;\n                    argname++;\n                }\n            }\n        } else\n            goto invalid_keyword_type;\n        if (kwds2) {\n            if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad;\n        } else {\n            goto invalid_keyword;\n        }\n    }\n    return 0;\narg_passed_twice:\n    __Pyx_RaiseDoubleKeywordsError(function_name, key);\n    goto bad;\ninvalid_keyword_type:\n    PyErr_Format(PyExc_TypeError,\n        \"%.200s() keywords must be strings\", function_name);\n    goto bad;\ninvalid_keyword:\n    PyErr_Format(PyExc_TypeError,\n    #if PY_MAJOR_VERSION < 3\n        \"%.200s() got an unexpected keyword argument '%.200s'\",\n        function_name, PyString_AsString(key));\n    #else\n        \"%s() got an unexpected keyword argument '%U'\",\n        function_name, key);\n    #endif\nbad:\n    return -1;\n}\n\n/* ArgTypeTest */\nstatic int __Pyx__ArgTypeTest(PyObject *obj, PyTypeObject *type, const char *name, int exact)\n{\n    if (unlikely(!type)) {\n        PyErr_SetString(PyExc_SystemError, \"Missing type object\");\n        return 0;\n    }\n    else if (exact) {\n        #if PY_MAJOR_VERSION == 2\n        if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1;\n        #endif\n    }\n    else {\n        if (likely(__Pyx_TypeCheck(obj, type))) return 1;\n    }\n    PyErr_Format(PyExc_TypeError,\n        \"Argument '%.200s' has incorrect type (expected %.200s, got %.200s)\",\n        name, type->tp_name, Py_TYPE(obj)->tp_name);\n    return 0;\n}\n\n/* IsLittleEndian */\nstatic CYTHON_INLINE int __Pyx_Is_Little_Endian(void)\n{\n  union {\n    uint32_t u32;\n    uint8_t u8[4];\n  } S;\n  S.u32 = 0x01020304;\n  return S.u8[0] == 4;\n}\n\n/* BufferFormatCheck */\nstatic void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx,\n                              __Pyx_BufFmt_StackElem* stack,\n       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*ts = t;\n    return count;\n}\nstatic int __Pyx_BufFmt_ExpectNumber(const char **ts) {\n    int number = __Pyx_BufFmt_ParseNumber(ts);\n    if (number == -1)\n        PyErr_Format(PyExc_ValueError,\\\n                     \"Does not understand character buffer dtype format string ('%c')\", **ts);\n    return number;\n}\nstatic void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) {\n  PyErr_Format(PyExc_ValueError,\n               \"Unexpected format string character: '%c'\", ch);\n}\nstatic const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) {\n  switch (ch) {\n    case 'c': return \"'char'\";\n    case 'b': return \"'signed char'\";\n    case 'B': return \"'unsigned char'\";\n    case 'h': return \"'short'\";\n    case 'H': return \"'unsigned short'\";\n    case 'i': return \"'int'\";\n    case 'I': return \"'unsigned int'\";\n    case 'l': return \"'long'\";\n    case 'L': return \"'unsigned long'\";\n    case 'q': return \"'long long'\";\n    case 'Q': return \"'unsigned long long'\";\n    case 'f': return (is_complex ? \"'complex float'\" : \"'float'\");\n    case 'd': return (is_complex ? \"'complex double'\" : \"'double'\");\n    case 'g': return (is_complex ? \"'complex long double'\" : \"'long double'\");\n    case 'T': return \"a struct\";\n    case 'O': return \"Python object\";\n    case 'P': return \"a pointer\";\n    case 's': case 'p': return \"a string\";\n    case 0: return \"end\";\n    default: return \"unparseable format string\";\n  }\n}\nstatic size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) {\n  switch (ch) {\n    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;\n    case 'h': case 'H': return 2;\n    case 'i': case 'I': case 'l': case 'L': return 4;\n    case 'q': case 'Q': return 8;\n    case 'f': return (is_complex ? 8 : 4);\n    case 'd': return (is_complex ? 16 : 8);\n    case 'g': {\n      PyErr_SetString(PyExc_ValueError, \"Python does not define a standard format string size for long double ('g')..\");\n      return 0;\n    }\n    case 'O': case 'P': return sizeof(void*);\n    default:\n      __Pyx_BufFmt_RaiseUnexpectedChar(ch);\n      return 0;\n    }\n}\nstatic size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) {\n  switch (ch) {\n    case 'c': case 'b': case 'B': case 's': case 'p': return 1;\n    case 'h': case 'H': return sizeof(short);\n    case 'i': case 'I': return sizeof(int);\n    case 'l': case 'L': return sizeof(long);\n    #ifdef HAVE_LONG_LONG\n    case 'q': case 'Q': return sizeof(PY_LONG_LONG);\n    #endif\n    case 'f': return sizeof(float) * (is_complex ? 2 : 1);\n    case 'd': return sizeof(double) * (is_complex ? 2 : 1);\n    case 'g': return sizeof(long double) * (is_complex ? 2 : 1);\n    case 'O': case 'P': return sizeof(void*);\n    default: {\n      __Pyx_BufFmt_RaiseUnexpectedChar(ch);\n      return 0;\n    }\n  }\n}\ntypedef struct { char c; short x; } __Pyx_st_short;\ntypedef struct { char c; int x; } __Pyx_st_int;\ntypedef struct { char c; long x; } __Pyx_st_long;\ntypedef struct { char c; float x; } __Pyx_st_float;\ntypedef struct { char c; double x; } __Pyx_st_double;\ntypedef struct { char c; long double x; } __Pyx_st_longdouble;\ntypedef struct { char c; void *x; } __Pyx_st_void_p;\n#ifdef HAVE_LONG_LONG\ntypedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong;\n#endif\nstatic size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, CYTHON_UNUSED int is_complex) {\n  switch (ch) {\n    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;\n    case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short);\n    case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int);\n    case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long);\n#ifdef HAVE_LONG_LONG\n    case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG);\n#endif\n    case 'f': return sizeof(__Pyx_st_float) - sizeof(float);\n    case 'd': return sizeof(__Pyx_st_double) - sizeof(double);\n    case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double);\n    case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*);\n    default:\n      __Pyx_BufFmt_RaiseUnexpectedChar(ch);\n      return 0;\n    }\n}\n/* These are for computing the padding at the end of the struct to align\n   on the first member of the struct. This will probably the same as above,\n   but we don't have any guarantees.\n */\ntypedef struct { short x; char c; } __Pyx_pad_short;\ntypedef struct { int x; char c; } __Pyx_pad_int;\ntypedef struct { long x; char c; } __Pyx_pad_long;\ntypedef struct { float x; char c; } __Pyx_pad_float;\ntypedef struct { double x; char c; } __Pyx_pad_double;\ntypedef struct { long double x; char c; } __Pyx_pad_longdouble;\ntypedef struct { void *x; char c; } __Pyx_pad_void_p;\n#ifdef HAVE_LONG_LONG\ntypedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong;\n#endif\nstatic size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) {\n  switch (ch) {\n    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;\n    case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short);\n    case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int);\n    case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long);\n#ifdef HAVE_LONG_LONG\n    case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG);\n#endif\n    case 'f': return sizeof(__Pyx_pad_float) - sizeof(float);\n    case 'd': return sizeof(__Pyx_pad_double) - sizeof(double);\n    case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double);\n    case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*);\n    default:\n      __Pyx_BufFmt_RaiseUnexpectedChar(ch);\n      return 0;\n    }\n}\nstatic char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) {\n  switch (ch) {\n    case 'c':\n        return 'H';\n    case 'b': case 'h': case 'i':\n    case 'l': case 'q': case 's': case 'p':\n        return 'I';\n    case 'B': case 'H': case 'I': case 'L': case 'Q':\n        return 'U';\n    case 'f': case 'd': case 'g':\n        return (is_complex ? 'C' : 'R');\n    case 'O':\n        return 'O';\n    case 'P':\n        return 'P';\n    default: {\n      __Pyx_BufFmt_RaiseUnexpectedChar(ch);\n      return 0;\n    }\n  }\n}\nstatic void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) {\n  if (ctx->head == NULL || ctx->head->field == &ctx->root) {\n    const char* expected;\n    const char* quote;\n    if (ctx->head == NULL) {\n      expected = \"end\";\n      quote = \"\";\n    } else {\n      expected = ctx->head->field->type->name;\n      quote = \"'\";\n    }\n    PyErr_Format(PyExc_ValueError,\n                 \"Buffer dtype mismatch, expected %s%s%s but got %s\",\n                 quote, expected, quote,\n                 __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex));\n  } else {\n    __Pyx_StructField* field = ctx->head->field;\n    __Pyx_StructField* parent = (ctx->head - 1)->field;\n    PyErr_Format(PyExc_ValueError,\n                 \"Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'\",\n                 field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex),\n                 parent->type->name, field->name);\n  }\n}\nstatic int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) {\n  char group;\n  size_t size, offset, arraysize = 1;\n  if (ctx->enc_type == 0) return 0;\n  if (ctx->head->field->type->arraysize[0]) {\n    int i, ndim = 0;\n    if (ctx->enc_type == 's' || ctx->enc_type == 'p') {\n        ctx->is_valid_array = ctx->head->field->type->ndim == 1;\n        ndim = 1;\n        if (ctx->enc_count != ctx->head->field->type->arraysize[0]) {\n            PyErr_Format(PyExc_ValueError,\n                         \"Expected a dimension of size %zu, got %zu\",\n                         ctx->head->field->type->arraysize[0], ctx->enc_count);\n            return -1;\n        }\n    }\n    if (!ctx->is_valid_array) {\n      PyErr_Format(PyExc_ValueError, \"Expected %d dimensions, got %d\",\n                   ctx->head->field->type->ndim, ndim);\n      return -1;\n    }\n    for (i = 0; i < ctx->head->field->type->ndim; i++) {\n      arraysize *= ctx->head->field->type->arraysize[i];\n    }\n    ctx->is_valid_array = 0;\n    ctx->enc_count = 1;\n  }\n  group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex);\n  do {\n    __Pyx_StructField* field = ctx->head->field;\n    __Pyx_TypeInfo* type = field->type;\n    if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') {\n      size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex);\n    } else {\n      size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex);\n    }\n    if (ctx->enc_packmode == '@') {\n      size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex);\n      size_t align_mod_offset;\n      if (align_at == 0) return -1;\n      align_mod_offset = ctx->fmt_offset % align_at;\n      if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset;\n      if (ctx->struct_alignment == 0)\n          ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type,\n                                                                 ctx->is_complex);\n    }\n    if (type->size != size || type->typegroup != group) {\n      if (type->typegroup == 'C' && type->fields != NULL) {\n        size_t parent_offset = ctx->head->parent_offset + field->offset;\n        ++ctx->head;\n        ctx->head->field = type->fields;\n        ctx->head->parent_offset = parent_offset;\n        continue;\n      }\n      if ((type->typegroup == 'H' || group == 'H') && type->size == size) {\n      } else {\n          __Pyx_BufFmt_RaiseExpected(ctx);\n          return -1;\n      }\n    }\n    offset = ctx->head->parent_offset + field->offset;\n    if (ctx->fmt_offset != offset) {\n      PyErr_Format(PyExc_ValueError,\n                   \"Buffer dtype mismatch; next field is at offset %\" CYTHON_FORMAT_SSIZE_T \"d but %\" CYTHON_FORMAT_SSIZE_T \"d expected\",\n                   (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset);\n      return -1;\n    }\n    ctx->fmt_offset += size;\n    if (arraysize)\n      ctx->fmt_offset += (arraysize - 1) * size;\n    --ctx->enc_count;\n    while (1) {\n      if (field == &ctx->root) {\n        ctx->head = NULL;\n        if (ctx->enc_count != 0) {\n          __Pyx_BufFmt_RaiseExpected(ctx);\n          return -1;\n        }\n        break;\n      }\n      ctx->head->field = ++field;\n      if (field->type == NULL) {\n        --ctx->head;\n        field = ctx->head->field;\n        continue;\n      } else if (field->type->typegroup == 'S') {\n        size_t parent_offset = ctx->head->parent_offset + field->offset;\n        if (field->type->fields->type == NULL) continue;\n        field = field->type->fields;\n        ++ctx->head;\n        ctx->head->field = field;\n        ctx->head->parent_offset = parent_offset;\n        break;\n      } else {\n        break;\n      }\n    }\n  } while (ctx->enc_count);\n  ctx->enc_type = 0;\n  ctx->is_complex = 0;\n  return 0;\n}\nstatic PyObject *\n__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp)\n{\n    const char *ts = *tsp;\n    int i = 0, number;\n    int ndim = ctx->head->field->type->ndim;\n;\n    ++ts;\n    if (ctx->new_count != 1) {\n        PyErr_SetString(PyExc_ValueError,\n                        \"Cannot handle repeated arrays in format string\");\n        return NULL;\n    }\n    if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;\n    while (*ts && *ts != ')') {\n        switch (*ts) {\n            case ' ': case '\\f': case '\\r': case '\\n': case '\\t': case '\\v':  continue;\n            default:  break;\n        }\n        number = __Pyx_BufFmt_ExpectNumber(&ts);\n        if (number == -1) return NULL;\n        if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i])\n            return PyErr_Format(PyExc_ValueError,\n                        \"Expected a dimension of size %zu, got %d\",\n                        ctx->head->field->type->arraysize[i], number);\n        if (*ts != ',' && *ts != ')')\n            return PyErr_Format(PyExc_ValueError,\n                                \"Expected a comma in format string, got '%c'\", *ts);\n        if (*ts == ',') ts++;\n        i++;\n    }\n    if (i != ndim)\n        return PyErr_Format(PyExc_ValueError, \"Expected %d dimension(s), got %d\",\n                            ctx->head->field->type->ndim, i);\n    if (!*ts) {\n        PyErr_SetString(PyExc_ValueError,\n                        \"Unexpected end of format string, expected ')'\");\n        return NULL;\n    }\n    ctx->is_valid_array = 1;\n    ctx->new_count = 1;\n    *tsp = ++ts;\n    return Py_None;\n}\nstatic const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) {\n  int got_Z = 0;\n  while (1) {\n    switch(*ts) {\n      case 0:\n        if (ctx->enc_type != 0 && ctx->head == NULL) {\n          __Pyx_BufFmt_RaiseExpected(ctx);\n          return NULL;\n        }\n        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;\n        if (ctx->head != NULL) {\n          __Pyx_BufFmt_RaiseExpected(ctx);\n          return NULL;\n        }\n        return ts;\n      case ' ':\n      case '\\r':\n      case '\\n':\n        ++ts;\n        break;\n      case '<':\n        if (!__Pyx_Is_Little_Endian()) {\n          PyErr_SetString(PyExc_ValueError, \"Little-endian buffer not supported on big-endian compiler\");\n          return NULL;\n        }\n        ctx->new_packmode = '=';\n        ++ts;\n        break;\n      case '>':\n      case '!':\n        if (__Pyx_Is_Little_Endian()) {\n          PyErr_SetString(PyExc_ValueError, \"Big-endian buffer not supported on little-endian compiler\");\n          return NULL;\n        }\n        ctx->new_packmode = '=';\n        ++ts;\n        break;\n      case '=':\n      case '@':\n      case '^':\n        ctx->new_packmode = *ts++;\n        break;\n      case 'T':\n        {\n          const char* ts_after_sub;\n          size_t i, struct_count = ctx->new_count;\n          size_t struct_alignment = ctx->struct_alignment;\n          ctx->new_count = 1;\n          ++ts;\n          if (*ts != '{') {\n            PyErr_SetString(PyExc_ValueError, \"Buffer acquisition: Expected '{' after 'T'\");\n            return NULL;\n          }\n          if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;\n          ctx->enc_type = 0;\n          ctx->enc_count = 0;\n          ctx->struct_alignment = 0;\n          ++ts;\n          ts_after_sub = ts;\n          for (i = 0; i != struct_count; ++i) {\n            ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts);\n            if (!ts_after_sub) return NULL;\n          }\n          ts = ts_after_sub;\n          if (struct_alignment) ctx->struct_alignment = struct_alignment;\n        }\n        break;\n      case '}':\n        {\n          size_t alignment = ctx->struct_alignment;\n          ++ts;\n          if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;\n          ctx->enc_type = 0;\n          if (alignment && ctx->fmt_offset % alignment) {\n            ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment);\n          }\n        }\n        return ts;\n      case 'x':\n        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;\n        ctx->fmt_offset += ctx->new_count;\n        ctx->new_count = 1;\n        ctx->enc_count = 0;\n        ctx->enc_type = 0;\n        ctx->enc_packmode = ctx->new_packmode;\n        ++ts;\n        break;\n      case 'Z':\n        got_Z = 1;\n        ++ts;\n        if (*ts != 'f' && *ts != 'd' && *ts != 'g') {\n          __Pyx_BufFmt_RaiseUnexpectedChar('Z');\n          return NULL;\n        }\n        CYTHON_FALLTHROUGH;\n      case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I':\n      case 'l': case 'L': case 'q': case 'Q':\n      case 'f': case 'd': case 'g':\n      case 'O': case 'p':\n        if (ctx->enc_type == *ts && got_Z == ctx->is_complex &&\n            ctx->enc_packmode == ctx->new_packmode) {\n          ctx->enc_count += ctx->new_count;\n          ctx->new_count = 1;\n          got_Z = 0;\n          ++ts;\n          break;\n        }\n        CYTHON_FALLTHROUGH;\n      case 's':\n        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;\n        ctx->enc_count = ctx->new_count;\n        ctx->enc_packmode = ctx->new_packmode;\n        ctx->enc_type = *ts;\n        ctx->is_complex = got_Z;\n        ++ts;\n        ctx->new_count = 1;\n        got_Z = 0;\n        break;\n      case ':':\n        ++ts;\n        while(*ts != ':') ++ts;\n        ++ts;\n        break;\n      case '(':\n        if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL;\n        break;\n      default:\n        {\n          int number = __Pyx_BufFmt_ExpectNumber(&ts);\n          if (number == -1) return NULL;\n          ctx->new_count = (size_t)number;\n        }\n    }\n  }\n}\n\n/* BufferGetAndValidate */\n  static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info) {\n  if (unlikely(info->buf == NULL)) return;\n  if (info->suboffsets == __Pyx_minusones) info->suboffsets = NULL;\n  __Pyx_ReleaseBuffer(info);\n}\nstatic void __Pyx_ZeroBuffer(Py_buffer* buf) {\n  buf->buf = NULL;\n  buf->obj = NULL;\n  buf->strides = __Pyx_zeros;\n  buf->shape = __Pyx_zeros;\n  buf->suboffsets = __Pyx_minusones;\n}\nstatic int __Pyx__GetBufferAndValidate(\n        Py_buffer* buf, PyObject* obj,  __Pyx_TypeInfo* dtype, int flags,\n        int nd, int cast, __Pyx_BufFmt_StackElem* stack)\n{\n  buf->buf = NULL;\n  if (unlikely(__Pyx_GetBuffer(obj, buf, flags) == -1)) {\n    __Pyx_ZeroBuffer(buf);\n    return -1;\n  }\n  if (unlikely(buf->ndim != nd)) {\n    PyErr_Format(PyExc_ValueError,\n                 \"Buffer has wrong number of dimensions (expected %d, got %d)\",\n                 nd, buf->ndim);\n    goto fail;\n  }\n  if (!cast) {\n    __Pyx_BufFmt_Context ctx;\n    __Pyx_BufFmt_Init(&ctx, stack, dtype);\n    if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail;\n  }\n  if (unlikely((unsigned)buf->itemsize != dtype->size)) {\n    PyErr_Format(PyExc_ValueError,\n      \"Item size of buffer (%\" CYTHON_FORMAT_SSIZE_T \"d byte%s) does not match size of '%s' (%\" CYTHON_FORMAT_SSIZE_T \"d byte%s)\",\n      buf->itemsize, (buf->itemsize > 1) ? \"s\" : \"\",\n      dtype->name, (Py_ssize_t)dtype->size, (dtype->size > 1) ? \"s\" : \"\");\n    goto fail;\n  }\n  if (buf->suboffsets == NULL) buf->suboffsets = __Pyx_minusones;\n  return 0;\nfail:;\n  __Pyx_SafeReleaseBuffer(buf);\n  return -1;\n}\n\n/* GetItemInt */\n  static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) {\n    PyObject *r;\n    if (!j) return NULL;\n    r = PyObject_GetItem(o, j);\n    Py_DECREF(j);\n    return r;\n}\nstatic CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i,\n                                                              CYTHON_NCP_UNUSED int wraparound,\n                                                              CYTHON_NCP_UNUSED int boundscheck) {\n#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS\n    Py_ssize_t wrapped_i = i;\n    if (wraparound & unlikely(i < 0)) {\n        wrapped_i += PyList_GET_SIZE(o);\n    }\n    if ((!boundscheck) || likely((0 <= wrapped_i) & (wrapped_i < PyList_GET_SIZE(o)))) {\n        PyObject *r = PyList_GET_ITEM(o, wrapped_i);\n        Py_INCREF(r);\n        return r;\n    }\n    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));\n#else\n    return PySequence_GetItem(o, i);\n#endif\n}\nstatic CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i,\n                                                              CYTHON_NCP_UNUSED int wraparound,\n                                                              CYTHON_NCP_UNUSED int boundscheck) {\n#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS\n    Py_ssize_t wrapped_i = i;\n    if (wraparound & unlikely(i < 0)) {\n        wrapped_i += PyTuple_GET_SIZE(o);\n    }\n    if ((!boundscheck) || likely((0 <= wrapped_i) & (wrapped_i < PyTuple_GET_SIZE(o)))) {\n        PyObject *r = PyTuple_GET_ITEM(o, wrapped_i);\n        Py_INCREF(r);\n        return r;\n    }\n    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));\n#else\n    return PySequence_GetItem(o, i);\n#endif\n}\nstatic CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list,\n                                                     CYTHON_NCP_UNUSED int wraparound,\n                                                     CYTHON_NCP_UNUSED int boundscheck) {\n#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS\n    if (is_list || PyList_CheckExact(o)) {\n        Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o);\n        if ((!boundscheck) || (likely((n >= 0) & (n < PyList_GET_SIZE(o))))) {\n            PyObject *r = PyList_GET_ITEM(o, n);\n            Py_INCREF(r);\n            return r;\n        }\n    }\n    else if (PyTuple_CheckExact(o)) {\n        Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o);\n        if ((!boundscheck) || likely((n >= 0) & (n < PyTuple_GET_SIZE(o)))) {\n            PyObject *r = PyTuple_GET_ITEM(o, n);\n            Py_INCREF(r);\n            return r;\n        }\n    } else {\n        PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence;\n        if (likely(m && m->sq_item)) {\n            if (wraparound && unlikely(i < 0) && likely(m->sq_length)) {\n                Py_ssize_t l = m->sq_length(o);\n                if (likely(l >= 0)) {\n                    i += l;\n                } else {\n                    if (!PyErr_ExceptionMatches(PyExc_OverflowError))\n                        return NULL;\n                    PyErr_Clear();\n                }\n            }\n            return m->sq_item(o, i);\n        }\n    }\n#else\n    if (is_list || PySequence_Check(o)) {\n        return PySequence_GetItem(o, i);\n    }\n#endif\n    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));\n}\n\n/* ObjectGetItem */\n  #if CYTHON_USE_TYPE_SLOTS\nstatic PyObject *__Pyx_PyObject_GetIndex(PyObject *obj, PyObject* index) {\n    PyObject *runerr;\n    Py_ssize_t key_value;\n    PySequenceMethods *m = Py_TYPE(obj)->tp_as_sequence;\n    if (unlikely(!(m && m->sq_item))) {\n        PyErr_Format(PyExc_TypeError, \"'%.200s' object is not subscriptable\", Py_TYPE(obj)->tp_name);\n        return NULL;\n    }\n    key_value = __Pyx_PyIndex_AsSsize_t(index);\n    if (likely(key_value != -1 || !(runerr = PyErr_Occurred()))) {\n        return __Pyx_GetItemInt_Fast(obj, key_value, 0, 1, 1);\n    }\n    if (PyErr_GivenExceptionMatches(runerr, PyExc_OverflowError)) {\n        PyErr_Clear();\n        PyErr_Format(PyExc_IndexError, \"cannot fit '%.200s' into an index-sized integer\", Py_TYPE(index)->tp_name);\n    }\n    return NULL;\n}\nstatic PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject* key) {\n    PyMappingMethods *m = Py_TYPE(obj)->tp_as_mapping;\n    if (likely(m && m->mp_subscript)) {\n        return m->mp_subscript(obj, key);\n    }\n    return __Pyx_PyObject_GetIndex(obj, key);\n}\n#endif\n\n/* ExtTypeTest */\n  static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) {\n    if (unlikely(!type)) {\n        PyErr_SetString(PyExc_SystemError, \"Missing type object\");\n        return 0;\n    }\n    if (likely(__Pyx_TypeCheck(obj, type)))\n        return 1;\n    PyErr_Format(PyExc_TypeError, \"Cannot convert %.200s to %.200s\",\n                 Py_TYPE(obj)->tp_name, type->tp_name);\n    return 0;\n}\n\n/* PyIntBinop */\n  #if !CYTHON_COMPILING_IN_PYPY\nstatic PyObject* __Pyx_PyInt_AddObjC(PyObject *op1, PyObject *op2, CYTHON_UNUSED long intval, CYTHON_UNUSED int inplace) {\n    #if PY_MAJOR_VERSION < 3\n    if (likely(PyInt_CheckExact(op1))) {\n        const long b = intval;\n        long x;\n        long a = PyInt_AS_LONG(op1);\n            x = (long)((unsigned long)a + b);\n            if (likely((x^a) >= 0 || (x^b) >= 0))\n                return PyInt_FromLong(x);\n            return PyLong_Type.tp_as_number->nb_add(op1, op2);\n    }\n    #endif\n    #if CYTHON_USE_PYLONG_INTERNALS\n    if (likely(PyLong_CheckExact(op1))) {\n        const long b = intval;\n        long a, x;\n#ifdef HAVE_LONG_LONG\n        const PY_LONG_LONG llb = intval;\n        PY_LONG_LONG lla, llx;\n#endif\n        const digit* digits = ((PyLongObject*)op1)->ob_digit;\n        const Py_ssize_t size = Py_SIZE(op1);\n        if (likely(__Pyx_sst_abs(size) <= 1)) {\n            a = likely(size) ? digits[0] : 0;\n            if (size == -1) a = -a;\n        } else {\n            switch (size) {\n                case -2:\n                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {\n                        a = -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));\n                        break;\n#ifdef HAVE_LONG_LONG\n                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) {\n                        lla = -(PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));\n                        goto long_long;\n#endif\n                    }\n                    CYTHON_FALLTHROUGH;\n                case 2:\n                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {\n                        a = (long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));\n                        break;\n#ifdef HAVE_LONG_LONG\n                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 2 * PyLong_SHIFT) {\n                        lla = (PY_LONG_LONG) (((((unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));\n                        goto long_long;\n#endif\n                    }\n                    CYTHON_FALLTHROUGH;\n                case -3:\n                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {\n                        a = -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));\n                        break;\n#ifdef HAVE_LONG_LONG\n                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) {\n                        lla = -(PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));\n                        goto long_long;\n#endif\n                    }\n                    CYTHON_FALLTHROUGH;\n                case 3:\n                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {\n                        a = (long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));\n                        break;\n#ifdef HAVE_LONG_LONG\n                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 3 * PyLong_SHIFT) {\n                        lla = (PY_LONG_LONG) (((((((unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));\n                        goto long_long;\n#endif\n                    }\n                    CYTHON_FALLTHROUGH;\n                case -4:\n                    if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {\n                        a = -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));\n                        break;\n#ifdef HAVE_LONG_LONG\n                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) {\n                        lla = -(PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));\n                        goto long_long;\n#endif\n                    }\n                    CYTHON_FALLTHROUGH;\n                case 4:\n                    if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {\n                        a = (long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]));\n                        break;\n#ifdef HAVE_LONG_LONG\n                    } else if (8 * sizeof(PY_LONG_LONG) - 1 > 4 * PyLong_SHIFT) {\n                        lla = (PY_LONG_LONG) (((((((((unsigned PY_LONG_LONG)digits[3]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[2]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[1]) << PyLong_SHIFT) | (unsigned PY_LONG_LONG)digits[0]));\n                        goto long_long;\n#endif\n                    }\n                    CYTHON_FALLTHROUGH;\n                default: return PyLong_Type.tp_as_number->nb_add(op1, op2);\n            }\n        }\n                x = a + b;\n            return PyLong_FromLong(x);\n#ifdef HAVE_LONG_LONG\n        long_long:\n                llx = lla + llb;\n            return PyLong_FromLongLong(llx);\n#endif\n        \n        \n    }\n    #endif\n    if (PyFloat_CheckExact(op1)) {\n        const long b = intval;\n        double a = PyFloat_AS_DOUBLE(op1);\n            double result;\n            PyFPE_START_PROTECT(\"add\", return NULL)\n            result = ((double)a) + (double)b;\n            PyFPE_END_PROTECT(result)\n            return PyFloat_FromDouble(result);\n    }\n    return (inplace ? PyNumber_InPlaceAdd : PyNumber_Add)(op1, op2);\n}\n#endif\n\n/* PyCFunctionFastCall */\n  #if CYTHON_FAST_PYCCALL\nstatic CYTHON_INLINE PyObject * __Pyx_PyCFunction_FastCall(PyObject *func_obj, PyObject **args, Py_ssize_t nargs) {\n    PyCFunctionObject *func = (PyCFunctionObject*)func_obj;\n    PyCFunction meth = PyCFunction_GET_FUNCTION(func);\n    PyObject *self = PyCFunction_GET_SELF(func);\n    int flags = PyCFunction_GET_FLAGS(func);\n    assert(PyCFunction_Check(func));\n    assert(METH_FASTCALL == (flags & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS)));\n    assert(nargs >= 0);\n    assert(nargs == 0 || args != NULL);\n    /* _PyCFunction_FastCallDict() must not be called with an exception set,\n       because it may clear it (directly or indirectly) and so the\n       caller loses its exception */\n    assert(!PyErr_Occurred());\n    if ((PY_VERSION_HEX < 0x030700A0) || unlikely(flags & METH_KEYWORDS)) {\n        return (*((__Pyx_PyCFunctionFastWithKeywords)meth)) (self, args, nargs, NULL);\n    } else {\n        return (*((__Pyx_PyCFunctionFast)meth)) (self, args, nargs);\n    }\n}\n#endif\n\n/* PyFunctionFastCall */\n  #if CYTHON_FAST_PYCALL\n#include \"frameobject.h\"\nstatic PyObject* __Pyx_PyFunction_FastCallNoKw(PyCodeObject *co, PyObject **args, Py_ssize_t na,\n                                               PyObject *globals) {\n    PyFrameObject *f;\n    PyThreadState *tstate = __Pyx_PyThreadState_Current;\n    PyObject **fastlocals;\n    Py_ssize_t i;\n    PyObject *result;\n    assert(globals != NULL);\n    /* XXX Perhaps we should create a specialized\n       PyFrame_New() that doesn't take locals, but does\n       take builtins without sanity checking them.\n       */\n    assert(tstate != NULL);\n    f = PyFrame_New(tstate, co, globals, NULL);\n    if (f == NULL) {\n        return NULL;\n    }\n    fastlocals = f->f_localsplus;\n    for (i = 0; i < na; i++) {\n        Py_INCREF(*args);\n        fastlocals[i] = *args++;\n    }\n    result = PyEval_EvalFrameEx(f,0);\n    ++tstate->recursion_depth;\n    Py_DECREF(f);\n    --tstate->recursion_depth;\n    return result;\n}\n#if 1 || PY_VERSION_HEX < 0x030600B1\nstatic PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, int nargs, PyObject *kwargs) {\n    PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func);\n    PyObject *globals = PyFunction_GET_GLOBALS(func);\n    PyObject *argdefs = PyFunction_GET_DEFAULTS(func);\n    PyObject *closure;\n#if PY_MAJOR_VERSION >= 3\n    PyObject *kwdefs;\n#endif\n    PyObject *kwtuple, **k;\n    PyObject **d;\n    Py_ssize_t nd;\n    Py_ssize_t nk;\n    PyObject *result;\n    assert(kwargs == NULL || PyDict_Check(kwargs));\n    nk = kwargs ? PyDict_Size(kwargs) : 0;\n    if (Py_EnterRecursiveCall((char*)\" while calling a Python object\")) {\n        return NULL;\n    }\n    if (\n#if PY_MAJOR_VERSION >= 3\n            co->co_kwonlyargcount == 0 &&\n#endif\n            likely(kwargs == NULL || nk == 0) &&\n            co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) {\n        if (argdefs == NULL && co->co_argcount == nargs) {\n            result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals);\n            goto done;\n        }\n        else if (nargs == 0 && argdefs != NULL\n                 && co->co_argcount == Py_SIZE(argdefs)) {\n            /* function called with no arguments, but all parameters have\n               a default value: use default values as arguments .*/\n            args = &PyTuple_GET_ITEM(argdefs, 0);\n            result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals);\n            goto done;\n        }\n    }\n    if (kwargs != NULL) {\n        Py_ssize_t pos, i;\n        kwtuple = PyTuple_New(2 * nk);\n        if (kwtuple == NULL) {\n            result = NULL;\n            goto done;\n        }\n        k = &PyTuple_GET_ITEM(kwtuple, 0);\n        pos = i = 0;\n        while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) {\n            Py_INCREF(k[i]);\n            Py_INCREF(k[i+1]);\n            i += 2;\n        }\n        nk = i / 2;\n    }\n    else {\n        kwtuple = NULL;\n        k = NULL;\n    }\n    closure = PyFunction_GET_CLOSURE(func);\n#if PY_MAJOR_VERSION >= 3\n    kwdefs = PyFunction_GET_KW_DEFAULTS(func);\n#endif\n    if (argdefs != NULL) {\n        d = &PyTuple_GET_ITEM(argdefs, 0);\n        nd = Py_SIZE(argdefs);\n    }\n    else {\n        d = NULL;\n        nd = 0;\n    }\n#if PY_MAJOR_VERSION >= 3\n    result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL,\n                               args, nargs,\n                               k, (int)nk,\n                               d, (int)nd, kwdefs, closure);\n#else\n    result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL,\n                               args, nargs,\n                               k, (int)nk,\n                               d, (int)nd, closure);\n#endif\n    Py_XDECREF(kwtuple);\ndone:\n    Py_LeaveRecursiveCall();\n    return result;\n}\n#endif\n#endif\n\n/* PyObjectCall */\n  #if CYTHON_COMPILING_IN_CPYTHON\nstatic CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) {\n    PyObject *result;\n    ternaryfunc call = func->ob_type->tp_call;\n    if (unlikely(!call))\n        return PyObject_Call(func, arg, kw);\n    if (unlikely(Py_EnterRecursiveCall((char*)\" while calling a Python object\")))\n        return NULL;\n    result = (*call)(func, arg, kw);\n    Py_LeaveRecursiveCall();\n    if (unlikely(!result) && unlikely(!PyErr_Occurred())) {\n        PyErr_SetString(\n            PyExc_SystemError,\n            \"NULL result without error in PyObject_Call\");\n    }\n    return result;\n}\n#endif\n\n/* PyObjectCallMethO */\n  #if CYTHON_COMPILING_IN_CPYTHON\nstatic CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) {\n    PyObject *self, *result;\n    PyCFunction cfunc;\n    cfunc = PyCFunction_GET_FUNCTION(func);\n    self = PyCFunction_GET_SELF(func);\n    if (unlikely(Py_EnterRecursiveCall((char*)\" while calling a Python object\")))\n        return NULL;\n    result = cfunc(self, arg);\n    Py_LeaveRecursiveCall();\n    if (unlikely(!result) && unlikely(!PyErr_Occurred())) {\n        PyErr_SetString(\n            PyExc_SystemError,\n            \"NULL result without error in PyObject_Call\");\n    }\n    return result;\n}\n#endif\n\n/* PyObjectCallOneArg */\n  #if CYTHON_COMPILING_IN_CPYTHON\nstatic PyObject* __Pyx__PyObject_CallOneArg(PyObject *func, PyObject *arg) {\n    PyObject *result;\n    PyObject *args = PyTuple_New(1);\n    if (unlikely(!args)) return NULL;\n    Py_INCREF(arg);\n    PyTuple_SET_ITEM(args, 0, arg);\n    result = __Pyx_PyObject_Call(func, args, NULL);\n    Py_DECREF(args);\n    return result;\n}\nstatic CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) {\n#if CYTHON_FAST_PYCALL\n    if (PyFunction_Check(func)) {\n        return __Pyx_PyFunction_FastCall(func, &arg, 1);\n    }\n#endif\n    if (likely(PyCFunction_Check(func))) {\n        if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) {\n            return __Pyx_PyObject_CallMethO(func, arg);\n#if CYTHON_FAST_PYCCALL\n        } else if (PyCFunction_GET_FLAGS(func) & METH_FASTCALL) {\n            return __Pyx_PyCFunction_FastCall(func, &arg, 1);\n#endif\n        }\n    }\n    return __Pyx__PyObject_CallOneArg(func, arg);\n}\n#else\nstatic CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) {\n    PyObject *result;\n    PyObject *args = PyTuple_Pack(1, arg);\n    if (unlikely(!args)) return NULL;\n    result = __Pyx_PyObject_Call(func, args, NULL);\n    Py_DECREF(args);\n    return result;\n}\n#endif\n\n/* PyObjectCallNoArg */\n  #if CYTHON_COMPILING_IN_CPYTHON\nstatic CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func) {\n#if CYTHON_FAST_PYCALL\n    if (PyFunction_Check(func)) {\n        return __Pyx_PyFunction_FastCall(func, NULL, 0);\n    }\n#endif\n#ifdef __Pyx_CyFunction_USED\n    if (likely(PyCFunction_Check(func) || __Pyx_TypeCheck(func, __pyx_CyFunctionType))) {\n#else\n    if (likely(PyCFunction_Check(func))) {\n#endif\n        if (likely(PyCFunction_GET_FLAGS(func) & METH_NOARGS)) {\n            return __Pyx_PyObject_CallMethO(func, NULL);\n        }\n    }\n    return __Pyx_PyObject_Call(func, __pyx_empty_tuple, NULL);\n}\n#endif\n\n/* GetModuleGlobalName */\n    static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name) {\n    PyObject *result;\n#if !CYTHON_AVOID_BORROWED_REFS\n#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1\n    result = _PyDict_GetItem_KnownHash(__pyx_d, name, ((PyASCIIObject *) name)->hash);\n    if (likely(result)) {\n        Py_INCREF(result);\n    } else if (unlikely(PyErr_Occurred())) {\n        result = NULL;\n    } else {\n#else\n    result = PyDict_GetItem(__pyx_d, name);\n    if (likely(result)) {\n        Py_INCREF(result);\n    } else {\n#endif\n#else\n    result = PyObject_GetItem(__pyx_d, name);\n    if (!result) {\n        PyErr_Clear();\n#endif\n        result = __Pyx_GetBuiltinName(name);\n    }\n    return result;\n}\n\n/* BufferIndexError */\n        static void __Pyx_RaiseBufferIndexError(int axis) {\n  PyErr_Format(PyExc_IndexError,\n     \"Out of bounds on buffer access (axis %d)\", axis);\n}\n\n/* PyErrFetchRestore */\n        #if CYTHON_FAST_THREAD_STATE\nstatic CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) {\n    PyObject *tmp_type, *tmp_value, *tmp_tb;\n    tmp_type = tstate->curexc_type;\n    tmp_value = tstate->curexc_value;\n    tmp_tb = tstate->curexc_traceback;\n    tstate->curexc_type = type;\n    tstate->curexc_value = value;\n    tstate->curexc_traceback = tb;\n    Py_XDECREF(tmp_type);\n    Py_XDECREF(tmp_value);\n    Py_XDECREF(tmp_tb);\n}\nstatic CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {\n    *type = tstate->curexc_type;\n    *value = tstate->curexc_value;\n    *tb = tstate->curexc_traceback;\n    tstate->curexc_type = 0;\n    tstate->curexc_value = 0;\n    tstate->curexc_traceback = 0;\n}\n#endif\n\n/* RaiseException */\n        #if PY_MAJOR_VERSION < 3\nstatic void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb,\n                        CYTHON_UNUSED PyObject *cause) {\n    __Pyx_PyThreadState_declare\n    Py_XINCREF(type);\n    if (!value || value == Py_None)\n        value = NULL;\n    else\n        Py_INCREF(value);\n    if (!tb || tb == Py_None)\n        tb = NULL;\n    else {\n        Py_INCREF(tb);\n        if (!PyTraceBack_Check(tb)) {\n            PyErr_SetString(PyExc_TypeError,\n                \"raise: arg 3 must be a traceback or None\");\n            goto raise_error;\n        }\n    }\n    if (PyType_Check(type)) {\n#if CYTHON_COMPILING_IN_PYPY\n        if (!value) {\n            Py_INCREF(Py_None);\n            value = Py_None;\n        }\n#endif\n        PyErr_NormalizeException(&type, &value, &tb);\n    } else {\n        if (value) {\n            PyErr_SetString(PyExc_TypeError,\n                \"instance exception may not have a separate value\");\n            goto raise_error;\n        }\n        value = type;\n        type = (PyObject*) Py_TYPE(type);\n        Py_INCREF(type);\n        if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) {\n            PyErr_SetString(PyExc_TypeError,\n                \"raise: exception class must be a subclass of BaseException\");\n            goto raise_error;\n        }\n    }\n    __Pyx_PyThreadState_assign\n    __Pyx_ErrRestore(type, value, tb);\n    return;\nraise_error:\n    Py_XDECREF(value);\n    Py_XDECREF(type);\n    Py_XDECREF(tb);\n    return;\n}\n#else\nstatic void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) {\n    PyObject* owned_instance = NULL;\n    if (tb == Py_None) {\n        tb = 0;\n    } else if (tb && !PyTraceBack_Check(tb)) {\n        PyErr_SetString(PyExc_TypeError,\n            \"raise: arg 3 must be a traceback or None\");\n        goto bad;\n    }\n    if (value == Py_None)\n        value = 0;\n    if (PyExceptionInstance_Check(type)) {\n        if (value) {\n            PyErr_SetString(PyExc_TypeError,\n                \"instance exception may not have a separate value\");\n            goto bad;\n        }\n        value = type;\n        type = (PyObject*) Py_TYPE(value);\n    } else if (PyExceptionClass_Check(type)) {\n        PyObject *instance_class = NULL;\n        if (value && PyExceptionInstance_Check(value)) {\n            instance_class = (PyObject*) Py_TYPE(value);\n            if (instance_class != type) {\n                int is_subclass = PyObject_IsSubclass(instance_class, type);\n                if (!is_subclass) {\n                    instance_class = NULL;\n                } else if (unlikely(is_subclass == -1)) {\n                    goto bad;\n                } else {\n                    type = instance_class;\n                }\n            }\n        }\n        if (!instance_class) {\n            PyObject *args;\n            if (!value)\n                args = PyTuple_New(0);\n            else if (PyTuple_Check(value)) {\n                Py_INCREF(value);\n                args = value;\n            } else\n                args = PyTuple_Pack(1, value);\n            if (!args)\n                goto bad;\n            owned_instance = PyObject_Call(type, args, NULL);\n            Py_DECREF(args);\n            if (!owned_instance)\n                goto bad;\n            value = owned_instance;\n            if (!PyExceptionInstance_Check(value)) {\n                PyErr_Format(PyExc_TypeError,\n                             \"calling %R should have returned an instance of \"\n                             \"BaseException, not %R\",\n                             type, Py_TYPE(value));\n                goto bad;\n            }\n        }\n    } else {\n        PyErr_SetString(PyExc_TypeError,\n            \"raise: exception class must be a subclass of BaseException\");\n        goto bad;\n    }\n    if (cause) {\n        PyObject *fixed_cause;\n        if (cause == Py_None) {\n            fixed_cause = NULL;\n        } else if (PyExceptionClass_Check(cause)) {\n            fixed_cause = PyObject_CallObject(cause, NULL);\n            if (fixed_cause == NULL)\n                goto bad;\n        } else if (PyExceptionInstance_Check(cause)) {\n            fixed_cause = cause;\n            Py_INCREF(fixed_cause);\n        } else {\n            PyErr_SetString(PyExc_TypeError,\n                            \"exception causes must derive from \"\n                            \"BaseException\");\n            goto bad;\n        }\n        PyException_SetCause(value, fixed_cause);\n    }\n    PyErr_SetObject(type, value);\n    if (tb) {\n#if CYTHON_COMPILING_IN_PYPY\n        PyObject *tmp_type, *tmp_value, *tmp_tb;\n        PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb);\n        Py_INCREF(tb);\n        PyErr_Restore(tmp_type, tmp_value, tb);\n        Py_XDECREF(tmp_tb);\n#else\n        PyThreadState *tstate = __Pyx_PyThreadState_Current;\n        PyObject* tmp_tb = tstate->curexc_traceback;\n        if (tb != tmp_tb) {\n            Py_INCREF(tb);\n            tstate->curexc_traceback = tb;\n            Py_XDECREF(tmp_tb);\n        }\n#endif\n    }\nbad:\n    Py_XDECREF(owned_instance);\n    return;\n}\n#endif\n\n/* DictGetItem */\n        #if PY_MAJOR_VERSION >= 3 && !CYTHON_COMPILING_IN_PYPY\nstatic PyObject *__Pyx_PyDict_GetItem(PyObject *d, PyObject* key) {\n    PyObject *value;\n    value = PyDict_GetItemWithError(d, key);\n    if (unlikely(!value)) {\n        if (!PyErr_Occurred()) {\n            PyObject* args = PyTuple_Pack(1, key);\n            if (likely(args))\n                PyErr_SetObject(PyExc_KeyError, args);\n            Py_XDECREF(args);\n        }\n        return NULL;\n    }\n    Py_INCREF(value);\n    return value;\n}\n#endif\n\n/* RaiseTooManyValuesToUnpack */\n        static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) {\n    PyErr_Format(PyExc_ValueError,\n                 \"too many values to unpack (expected %\" CYTHON_FORMAT_SSIZE_T \"d)\", expected);\n}\n\n/* RaiseNeedMoreValuesToUnpack */\n        static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) {\n    PyErr_Format(PyExc_ValueError,\n                 \"need more than %\" CYTHON_FORMAT_SSIZE_T \"d value%.1s to unpack\",\n                 index, (index == 1) ? \"\" : \"s\");\n}\n\n/* RaiseNoneIterError */\n        static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) {\n    PyErr_SetString(PyExc_TypeError, \"'NoneType' object is not iterable\");\n}\n\n/* SaveResetException */\n        #if CYTHON_FAST_THREAD_STATE\nstatic CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {\n    #if PY_VERSION_HEX >= 0x030700A2\n    *type = tstate->exc_state.exc_type;\n    *value = tstate->exc_state.exc_value;\n    *tb = tstate->exc_state.exc_traceback;\n    #else\n    *type = tstate->exc_type;\n    *value = tstate->exc_value;\n    *tb = tstate->exc_traceback;\n    #endif\n    Py_XINCREF(*type);\n    Py_XINCREF(*value);\n    Py_XINCREF(*tb);\n}\nstatic CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) {\n    PyObject *tmp_type, *tmp_value, *tmp_tb;\n    #if PY_VERSION_HEX >= 0x030700A2\n    tmp_type = tstate->exc_state.exc_type;\n    tmp_value = tstate->exc_state.exc_value;\n    tmp_tb = tstate->exc_state.exc_traceback;\n    tstate->exc_state.exc_type = type;\n    tstate->exc_state.exc_value = value;\n    tstate->exc_state.exc_traceback = tb;\n    #else\n    tmp_type = tstate->exc_type;\n    tmp_value = tstate->exc_value;\n    tmp_tb = tstate->exc_traceback;\n    tstate->exc_type = type;\n    tstate->exc_value = value;\n    tstate->exc_traceback = tb;\n    #endif\n    Py_XDECREF(tmp_type);\n    Py_XDECREF(tmp_value);\n    Py_XDECREF(tmp_tb);\n}\n#endif\n\n/* PyErrExceptionMatches */\n        #if CYTHON_FAST_THREAD_STATE\nstatic int __Pyx_PyErr_ExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) {\n    Py_ssize_t i, n;\n    n = PyTuple_GET_SIZE(tuple);\n#if PY_MAJOR_VERSION >= 3\n    for (i=0; i<n; i++) {\n        if (exc_type == PyTuple_GET_ITEM(tuple, i)) return 1;\n    }\n#endif\n    for (i=0; i<n; i++) {\n        if (__Pyx_PyErr_GivenExceptionMatches(exc_type, PyTuple_GET_ITEM(tuple, i))) return 1;\n    }\n    return 0;\n}\nstatic CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err) {\n    PyObject *exc_type = tstate->curexc_type;\n    if (exc_type == err) return 1;\n    if (unlikely(!exc_type)) return 0;\n    if (unlikely(PyTuple_Check(err)))\n        return __Pyx_PyErr_ExceptionMatchesTuple(exc_type, err);\n    return __Pyx_PyErr_GivenExceptionMatches(exc_type, err);\n}\n#endif\n\n/* GetException */\n        #if CYTHON_FAST_THREAD_STATE\nstatic int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {\n#else\nstatic int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) {\n#endif\n    PyObject *local_type, *local_value, *local_tb;\n#if CYTHON_FAST_THREAD_STATE\n    PyObject *tmp_type, *tmp_value, *tmp_tb;\n    local_type = tstate->curexc_type;\n    local_value = tstate->curexc_value;\n    local_tb = tstate->curexc_traceback;\n    tstate->curexc_type = 0;\n    tstate->curexc_value = 0;\n    tstate->curexc_traceback = 0;\n#else\n    PyErr_Fetch(&local_type, &local_value, &local_tb);\n#endif\n    PyErr_NormalizeException(&local_type, &local_value, &local_tb);\n#if CYTHON_FAST_THREAD_STATE\n    if (unlikely(tstate->curexc_type))\n#else\n    if (unlikely(PyErr_Occurred()))\n#endif\n        goto bad;\n    #if PY_MAJOR_VERSION >= 3\n    if (local_tb) {\n        if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0))\n            goto bad;\n    }\n    #endif\n    Py_XINCREF(local_tb);\n    Py_XINCREF(local_type);\n    Py_XINCREF(local_value);\n    *type = local_type;\n    *value = local_value;\n    *tb = local_tb;\n#if CYTHON_FAST_THREAD_STATE\n    #if PY_VERSION_HEX >= 0x030700A2\n    tmp_type = tstate->exc_state.exc_type;\n    tmp_value = tstate->exc_state.exc_value;\n    tmp_tb = tstate->exc_state.exc_traceback;\n    tstate->exc_state.exc_type = local_type;\n    tstate->exc_state.exc_value = local_value;\n    tstate->exc_state.exc_traceback = local_tb;\n    #else\n    tmp_type = tstate->exc_type;\n    tmp_value = tstate->exc_value;\n    tmp_tb = tstate->exc_traceback;\n    tstate->exc_type = local_type;\n    tstate->exc_value = local_value;\n    tstate->exc_traceback = local_tb;\n    #endif\n    Py_XDECREF(tmp_type);\n    Py_XDECREF(tmp_value);\n    Py_XDECREF(tmp_tb);\n#else\n    PyErr_SetExcInfo(local_type, local_value, local_tb);\n#endif\n    return 0;\nbad:\n    *type = 0;\n    *value = 0;\n    *tb = 0;\n    Py_XDECREF(local_type);\n    Py_XDECREF(local_value);\n    Py_XDECREF(local_tb);\n    return -1;\n}\n\n/* Import */\n          static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) {\n    PyObject *empty_list = 0;\n    PyObject *module = 0;\n    PyObject *global_dict = 0;\n    PyObject *empty_dict = 0;\n    PyObject *list;\n    #if PY_MAJOR_VERSION < 3\n    PyObject *py_import;\n    py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import);\n    if (!py_import)\n        goto bad;\n    #endif\n    if (from_list)\n        list = from_list;\n    else {\n        empty_list = PyList_New(0);\n        if (!empty_list)\n            goto bad;\n        list = empty_list;\n    }\n    global_dict = PyModule_GetDict(__pyx_m);\n    if (!global_dict)\n        goto bad;\n    empty_dict = PyDict_New();\n    if (!empty_dict)\n        goto bad;\n    {\n        #if PY_MAJOR_VERSION >= 3\n        if (level == -1) {\n            if (strchr(__Pyx_MODULE_NAME, '.')) {\n                module = PyImport_ImportModuleLevelObject(\n                    name, global_dict, empty_dict, list, 1);\n                if (!module) {\n                    if (!PyErr_ExceptionMatches(PyExc_ImportError))\n                        goto bad;\n                    PyErr_Clear();\n                }\n            }\n            level = 0;\n        }\n        #endif\n        if (!module) {\n            #if PY_MAJOR_VERSION < 3\n            PyObject *py_level = PyInt_FromLong(level);\n            if (!py_level)\n                goto bad;\n            module = PyObject_CallFunctionObjArgs(py_import,\n                name, global_dict, empty_dict, list, py_level, NULL);\n            Py_DECREF(py_level);\n            #else\n            module = PyImport_ImportModuleLevelObject(\n                name, global_dict, empty_dict, list, level);\n            #endif\n        }\n    }\nbad:\n    #if PY_MAJOR_VERSION < 3\n    Py_XDECREF(py_import);\n    #endif\n    Py_XDECREF(empty_list);\n    Py_XDECREF(empty_dict);\n    return module;\n}\n\n/* CLineInTraceback */\n          #ifndef CYTHON_CLINE_IN_TRACEBACK\nstatic int __Pyx_CLineForTraceback(CYTHON_UNUSED PyThreadState *tstate, int c_line) {\n    PyObject *use_cline;\n    PyObject *ptype, *pvalue, *ptraceback;\n#if CYTHON_COMPILING_IN_CPYTHON\n    PyObject **cython_runtime_dict;\n#endif\n    __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback);\n#if CYTHON_COMPILING_IN_CPYTHON\n    cython_runtime_dict = _PyObject_GetDictPtr(__pyx_cython_runtime);\n    if (likely(cython_runtime_dict)) {\n      use_cline = __Pyx_PyDict_GetItemStr(*cython_runtime_dict, __pyx_n_s_cline_in_traceback);\n    } else\n#endif\n    {\n      PyObject *use_cline_obj = __Pyx_PyObject_GetAttrStr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback);\n      if (use_cline_obj) {\n        use_cline = PyObject_Not(use_cline_obj) ? Py_False : Py_True;\n        Py_DECREF(use_cline_obj);\n      } else {\n        PyErr_Clear();\n        use_cline = NULL;\n      }\n    }\n    if (!use_cline) {\n        c_line = 0;\n        PyObject_SetAttr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback, Py_False);\n    }\n    else if (PyObject_Not(use_cline) != 0) {\n        c_line = 0;\n    }\n    __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback);\n    return c_line;\n}\n#endif\n\n/* CodeObjectCache */\n          static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) {\n    int start = 0, mid = 0, end = count - 1;\n    if (end >= 0 && code_line > entries[end].code_line) {\n        return count;\n    }\n    while (start < end) {\n        mid = start + (end - start) / 2;\n        if (code_line < entries[mid].code_line) {\n            end = mid;\n        } else if (code_line > entries[mid].code_line) {\n             start = mid + 1;\n        } else {\n            return mid;\n        }\n    }\n    if (code_line <= entries[mid].code_line) {\n        return mid;\n    } else {\n        return mid + 1;\n    }\n}\nstatic PyCodeObject *__pyx_find_code_object(int code_line) {\n    PyCodeObject* code_object;\n    int pos;\n    if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) {\n        return NULL;\n    }\n    pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);\n    if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) {\n        return NULL;\n    }\n    code_object = __pyx_code_cache.entries[pos].code_object;\n    Py_INCREF(code_object);\n    return code_object;\n}\nstatic void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) {\n    int pos, i;\n    __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries;\n    if (unlikely(!code_line)) {\n        return;\n    }\n    if (unlikely(!entries)) {\n        entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry));\n        if (likely(entries)) {\n            __pyx_code_cache.entries = entries;\n            __pyx_code_cache.max_count = 64;\n            __pyx_code_cache.count = 1;\n            entries[0].code_line = code_line;\n            entries[0].code_object = code_object;\n            Py_INCREF(code_object);\n        }\n        return;\n    }\n    pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);\n    if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) {\n        PyCodeObject* tmp = entries[pos].code_object;\n        entries[pos].code_object = code_object;\n        Py_DECREF(tmp);\n        return;\n    }\n    if (__pyx_code_cache.count == __pyx_code_cache.max_count) {\n        int new_max = __pyx_code_cache.max_count + 64;\n        entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc(\n            __pyx_code_cache.entries, (size_t)new_max*sizeof(__Pyx_CodeObjectCacheEntry));\n        if (unlikely(!entries)) {\n            return;\n        }\n        __pyx_code_cache.entries = entries;\n        __pyx_code_cache.max_count = new_max;\n    }\n    for (i=__pyx_code_cache.count; i>pos; i--) {\n        entries[i] = entries[i-1];\n    }\n    entries[pos].code_line = code_line;\n    entries[pos].code_object = code_object;\n    __pyx_code_cache.count++;\n    Py_INCREF(code_object);\n}\n\n/* AddTraceback */\n          #include \"compile.h\"\n#include \"frameobject.h\"\n#include \"traceback.h\"\nstatic PyCodeObject* __Pyx_CreateCodeObjectForTraceback(\n            const char *funcname, int c_line,\n            int py_line, const char *filename) {\n    PyCodeObject *py_code = 0;\n    PyObject *py_srcfile = 0;\n    PyObject *py_funcname = 0;\n    #if PY_MAJOR_VERSION < 3\n    py_srcfile = PyString_FromString(filename);\n    #else\n    py_srcfile = PyUnicode_FromString(filename);\n    #endif\n    if (!py_srcfile) goto bad;\n    if (c_line) {\n        #if PY_MAJOR_VERSION < 3\n        py_funcname = PyString_FromFormat( \"%s (%s:%d)\", funcname, __pyx_cfilenm, c_line);\n        #else\n        py_funcname = PyUnicode_FromFormat( \"%s (%s:%d)\", funcname, __pyx_cfilenm, c_line);\n        #endif\n    }\n    else {\n        #if PY_MAJOR_VERSION < 3\n        py_funcname = PyString_FromString(funcname);\n        #else\n        py_funcname = PyUnicode_FromString(funcname);\n        #endif\n    }\n    if (!py_funcname) goto bad;\n    py_code = __Pyx_PyCode_New(\n        0,\n        0,\n        0,\n        0,\n        0,\n        __pyx_empty_bytes, /*PyObject *code,*/\n        __pyx_empty_tuple, /*PyObject *consts,*/\n        __pyx_empty_tuple, /*PyObject *names,*/\n        __pyx_empty_tuple, /*PyObject *varnames,*/\n        __pyx_empty_tuple, /*PyObject *freevars,*/\n        __pyx_empty_tuple, /*PyObject *cellvars,*/\n        py_srcfile,   /*PyObject *filename,*/\n        py_funcname,  /*PyObject *name,*/\n        py_line,\n        __pyx_empty_bytes  /*PyObject *lnotab*/\n    );\n    Py_DECREF(py_srcfile);\n    Py_DECREF(py_funcname);\n    return py_code;\nbad:\n    Py_XDECREF(py_srcfile);\n    Py_XDECREF(py_funcname);\n    return NULL;\n}\nstatic void __Pyx_AddTraceback(const char *funcname, int c_line,\n                               int py_line, const char *filename) {\n    PyCodeObject *py_code = 0;\n    PyFrameObject *py_frame = 0;\n    PyThreadState *tstate = __Pyx_PyThreadState_Current;\n    if (c_line) {\n        c_line = __Pyx_CLineForTraceback(tstate, c_line);\n    }\n    py_code = __pyx_find_code_object(c_line ? -c_line : py_line);\n    if (!py_code) {\n        py_code = __Pyx_CreateCodeObjectForTraceback(\n            funcname, c_line, py_line, filename);\n        if (!py_code) goto bad;\n        __pyx_insert_code_object(c_line ? -c_line : py_line, py_code);\n    }\n    py_frame = PyFrame_New(\n        tstate,            /*PyThreadState *tstate,*/\n        py_code,           /*PyCodeObject *code,*/\n        __pyx_d,    /*PyObject *globals,*/\n        0                  /*PyObject *locals*/\n    );\n    if (!py_frame) goto bad;\n    __Pyx_PyFrame_SetLineNumber(py_frame, py_line);\n    PyTraceBack_Here(py_frame);\nbad:\n    Py_XDECREF(py_code);\n    Py_XDECREF(py_frame);\n}\n\n#if PY_MAJOR_VERSION < 3\nstatic int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) {\n    if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags);\n        if (__Pyx_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) return __pyx_pw_5numpy_7ndarray_1__getbuffer__(obj, view, flags);\n    PyErr_Format(PyExc_TypeError, \"'%.200s' does not have the buffer interface\", Py_TYPE(obj)->tp_name);\n    return -1;\n}\nstatic void __Pyx_ReleaseBuffer(Py_buffer *view) {\n    PyObject *obj = view->obj;\n    if (!obj) return;\n    if (PyObject_CheckBuffer(obj)) {\n        PyBuffer_Release(view);\n        return;\n    }\n    if ((0)) {}\n        else if (__Pyx_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) __pyx_pw_5numpy_7ndarray_3__releasebuffer__(obj, view);\n    view->obj = NULL;\n    Py_DECREF(obj);\n}\n#endif\n\n\n          /* CIntToPy */\n          static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) {\n    const int neg_one = (int) -1, const_zero = (int) 0;\n    const int is_unsigned = neg_one > const_zero;\n    if (is_unsigned) {\n        if (sizeof(int) < sizeof(long)) {\n            return PyInt_FromLong((long) value);\n        } else if (sizeof(int) <= sizeof(unsigned long)) {\n            return PyLong_FromUnsignedLong((unsigned long) value);\n#ifdef HAVE_LONG_LONG\n        } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) {\n            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);\n#endif\n        }\n    } else {\n        if (sizeof(int) <= sizeof(long)) {\n            return PyInt_FromLong((long) value);\n#ifdef HAVE_LONG_LONG\n        } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) {\n            return PyLong_FromLongLong((PY_LONG_LONG) value);\n#endif\n        }\n    }\n    {\n        int one = 1; int little = (int)*(unsigned char *)&one;\n        unsigned char *bytes = (unsigned char *)&value;\n        return _PyLong_FromByteArray(bytes, sizeof(int),\n                                     little, !is_unsigned);\n    }\n}\n\n/* CIntFromPyVerify */\n          #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\\\n    __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0)\n#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\\\n    __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1)\n#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\\\n    {\\\n        func_type value = func_value;\\\n        if (sizeof(target_type) < sizeof(func_type)) {\\\n            if (unlikely(value != (func_type) (target_type) value)) {\\\n                func_type zero = 0;\\\n                if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\\\n                    return (target_type) -1;\\\n                if (is_unsigned && unlikely(value < zero))\\\n                    goto raise_neg_overflow;\\\n                else\\\n                    goto raise_overflow;\\\n            }\\\n        }\\\n        return (target_type) value;\\\n    }\n\n/* CIntToPy */\n          static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) {\n    const long neg_one = (long) -1, const_zero = (long) 0;\n    const int is_unsigned = neg_one > const_zero;\n    if (is_unsigned) {\n        if (sizeof(long) < sizeof(long)) {\n            return PyInt_FromLong((long) value);\n        } else if (sizeof(long) <= sizeof(unsigned long)) {\n            return PyLong_FromUnsignedLong((unsigned long) value);\n#ifdef HAVE_LONG_LONG\n        } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) {\n            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);\n#endif\n        }\n    } else {\n        if (sizeof(long) <= sizeof(long)) {\n            return PyInt_FromLong((long) value);\n#ifdef HAVE_LONG_LONG\n        } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) {\n            return PyLong_FromLongLong((PY_LONG_LONG) value);\n#endif\n        }\n    }\n    {\n        int one = 1; int little = (int)*(unsigned char *)&one;\n        unsigned char *bytes = (unsigned char *)&value;\n        return _PyLong_FromByteArray(bytes, sizeof(long),\n                                     little, !is_unsigned);\n    }\n}\n\n/* Declarations */\n          #if CYTHON_CCOMPLEX\n  #ifdef __cplusplus\n    static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {\n      return ::std::complex< float >(x, y);\n    }\n  #else\n    static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {\n      return x + y*(__pyx_t_float_complex)_Complex_I;\n    }\n  #endif\n#else\n    static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {\n      __pyx_t_float_complex z;\n      z.real = x;\n      z.imag = y;\n      return z;\n    }\n#endif\n\n/* Arithmetic */\n          #if CYTHON_CCOMPLEX\n#else\n    static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {\n       return (a.real == b.real) && (a.imag == b.imag);\n    }\n    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {\n        __pyx_t_float_complex z;\n        z.real = a.real + b.real;\n        z.imag = a.imag + b.imag;\n        return z;\n    }\n    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {\n        __pyx_t_float_complex z;\n        z.real = a.real - b.real;\n        z.imag = a.imag - b.imag;\n        return z;\n    }\n    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {\n        __pyx_t_float_complex z;\n        z.real = a.real * b.real - a.imag * b.imag;\n        z.imag = a.real * b.imag + a.imag * b.real;\n        return z;\n    }\n    #if 1\n    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {\n        if (b.imag == 0) {\n            return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real);\n        } else if (fabsf(b.real) >= fabsf(b.imag)) {\n            if (b.real == 0 && b.imag == 0) {\n                return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.imag);\n            } else {\n                float r = b.imag / b.real;\n                float s = 1.0 / (b.real + b.imag * r);\n                return __pyx_t_float_complex_from_parts(\n                    (a.real + a.imag * r) * s, (a.imag - a.real * r) * s);\n            }\n        } else {\n            float r = b.real / b.imag;\n            float s = 1.0 / (b.imag + b.real * r);\n            return __pyx_t_float_complex_from_parts(\n                (a.real * r + a.imag) * s, (a.imag * r - a.real) * s);\n        }\n    }\n    #else\n    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {\n        if (b.imag == 0) {\n            return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real);\n        } else {\n            float denom = b.real * b.real + b.imag * b.imag;\n            return __pyx_t_float_complex_from_parts(\n                (a.real * b.real + a.imag * b.imag) / denom,\n                (a.imag * b.real - a.real * b.imag) / denom);\n        }\n    }\n    #endif\n    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex a) {\n        __pyx_t_float_complex z;\n        z.real = -a.real;\n        z.imag = -a.imag;\n        return z;\n    }\n    static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex a) {\n       return (a.real == 0) && (a.imag == 0);\n    }\n    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex a) {\n        __pyx_t_float_complex z;\n        z.real =  a.real;\n        z.imag = -a.imag;\n        return z;\n    }\n    #if 1\n        static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex z) {\n          #if !defined(HAVE_HYPOT) || defined(_MSC_VER)\n            return sqrtf(z.real*z.real + z.imag*z.imag);\n          #else\n            return hypotf(z.real, z.imag);\n          #endif\n        }\n        static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {\n            __pyx_t_float_complex z;\n            float r, lnr, theta, z_r, z_theta;\n            if (b.imag == 0 && b.real == (int)b.real) {\n                if (b.real < 0) {\n                    float denom = a.real * a.real + a.imag * a.imag;\n                    a.real = a.real / denom;\n                    a.imag = -a.imag / denom;\n                    b.real = -b.real;\n                }\n                switch ((int)b.real) {\n                    case 0:\n                        z.real = 1;\n                        z.imag = 0;\n                        return z;\n                    case 1:\n                        return a;\n                    case 2:\n                        z = __Pyx_c_prod_float(a, a);\n                        return __Pyx_c_prod_float(a, a);\n                    case 3:\n                        z = __Pyx_c_prod_float(a, a);\n                        return __Pyx_c_prod_float(z, a);\n                    case 4:\n                        z = __Pyx_c_prod_float(a, a);\n                        return __Pyx_c_prod_float(z, z);\n                }\n            }\n            if (a.imag == 0) {\n                if (a.real == 0) {\n                    return a;\n                } else if (b.imag == 0) {\n                    z.real = powf(a.real, b.real);\n                    z.imag = 0;\n                    return z;\n                } else if (a.real > 0) {\n                    r = a.real;\n                    theta = 0;\n                } else {\n                    r = -a.real;\n                    theta = atan2f(0, -1);\n                }\n            } else {\n                r = __Pyx_c_abs_float(a);\n                theta = atan2f(a.imag, a.real);\n            }\n            lnr = logf(r);\n            z_r = expf(lnr * b.real - theta * b.imag);\n            z_theta = theta * b.real + lnr * b.imag;\n            z.real = z_r * cosf(z_theta);\n            z.imag = z_r * sinf(z_theta);\n            return z;\n        }\n    #endif\n#endif\n\n/* Declarations */\n          #if CYTHON_CCOMPLEX\n  #ifdef __cplusplus\n    static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {\n      return ::std::complex< double >(x, y);\n    }\n  #else\n    static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {\n      return x + y*(__pyx_t_double_complex)_Complex_I;\n    }\n  #endif\n#else\n    static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {\n      __pyx_t_double_complex z;\n      z.real = x;\n      z.imag = y;\n      return z;\n    }\n#endif\n\n/* Arithmetic */\n          #if CYTHON_CCOMPLEX\n#else\n    static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {\n       return (a.real == b.real) && (a.imag == b.imag);\n    }\n    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {\n        __pyx_t_double_complex z;\n        z.real = a.real + b.real;\n        z.imag = a.imag + b.imag;\n        return z;\n    }\n    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {\n        __pyx_t_double_complex z;\n        z.real = a.real - b.real;\n        z.imag = a.imag - b.imag;\n        return z;\n    }\n    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {\n        __pyx_t_double_complex z;\n        z.real = a.real * b.real - a.imag * b.imag;\n        z.imag = a.real * b.imag + a.imag * b.real;\n        return z;\n    }\n    #if 1\n    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {\n        if (b.imag == 0) {\n            return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real);\n        } else if (fabs(b.real) >= fabs(b.imag)) {\n            if (b.real == 0 && b.imag == 0) {\n                return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.imag);\n            } else {\n                double r = b.imag / b.real;\n                double s = 1.0 / (b.real + b.imag * r);\n                return __pyx_t_double_complex_from_parts(\n                    (a.real + a.imag * r) * s, (a.imag - a.real * r) * s);\n            }\n        } else {\n            double r = b.real / b.imag;\n            double s = 1.0 / (b.imag + b.real * r);\n            return __pyx_t_double_complex_from_parts(\n                (a.real * r + a.imag) * s, (a.imag * r - a.real) * s);\n        }\n    }\n    #else\n    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {\n        if (b.imag == 0) {\n            return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real);\n        } else {\n            double denom = b.real * b.real + b.imag * b.imag;\n            return __pyx_t_double_complex_from_parts(\n                (a.real * b.real + a.imag * b.imag) / denom,\n                (a.imag * b.real - a.real * b.imag) / denom);\n        }\n    }\n    #endif\n    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex a) {\n        __pyx_t_double_complex z;\n        z.real = -a.real;\n        z.imag = -a.imag;\n        return z;\n    }\n    static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex a) {\n       return (a.real == 0) && (a.imag == 0);\n    }\n    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex a) {\n        __pyx_t_double_complex z;\n        z.real =  a.real;\n        z.imag = -a.imag;\n        return z;\n    }\n    #if 1\n        static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex z) {\n          #if !defined(HAVE_HYPOT) || defined(_MSC_VER)\n            return sqrt(z.real*z.real + z.imag*z.imag);\n          #else\n            return hypot(z.real, z.imag);\n          #endif\n        }\n        static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {\n            __pyx_t_double_complex z;\n            double r, lnr, theta, z_r, z_theta;\n            if (b.imag == 0 && b.real == (int)b.real) {\n                if (b.real < 0) {\n                    double denom = a.real * a.real + a.imag * a.imag;\n                    a.real = a.real / denom;\n                    a.imag = -a.imag / denom;\n                    b.real = -b.real;\n                }\n                switch ((int)b.real) {\n                    case 0:\n                        z.real = 1;\n                        z.imag = 0;\n                        return z;\n                    case 1:\n                        return a;\n                    case 2:\n                        z = __Pyx_c_prod_double(a, a);\n                        return __Pyx_c_prod_double(a, a);\n                    case 3:\n                        z = __Pyx_c_prod_double(a, a);\n                        return __Pyx_c_prod_double(z, a);\n                    case 4:\n                        z = __Pyx_c_prod_double(a, a);\n                        return __Pyx_c_prod_double(z, z);\n                }\n            }\n            if (a.imag == 0) {\n                if (a.real == 0) {\n                    return a;\n                } else if (b.imag == 0) {\n                    z.real = pow(a.real, b.real);\n                    z.imag = 0;\n                    return z;\n                } else if (a.real > 0) {\n                    r = a.real;\n                    theta = 0;\n                } else {\n                    r = -a.real;\n                    theta = atan2(0, -1);\n                }\n            } else {\n                r = __Pyx_c_abs_double(a);\n                theta = atan2(a.imag, a.real);\n            }\n            lnr = log(r);\n            z_r = exp(lnr * b.real - theta * b.imag);\n            z_theta = theta * b.real + lnr * b.imag;\n            z.real = z_r * cos(z_theta);\n            z.imag = z_r * sin(z_theta);\n            return z;\n        }\n    #endif\n#endif\n\n/* CIntToPy */\n          static CYTHON_INLINE PyObject* __Pyx_PyInt_From_enum__NPY_TYPES(enum NPY_TYPES value) {\n    const enum NPY_TYPES neg_one = (enum NPY_TYPES) -1, const_zero = (enum NPY_TYPES) 0;\n    const int is_unsigned = neg_one > const_zero;\n    if (is_unsigned) {\n        if (sizeof(enum NPY_TYPES) < sizeof(long)) {\n            return PyInt_FromLong((long) value);\n        } else if (sizeof(enum NPY_TYPES) <= sizeof(unsigned long)) {\n            return PyLong_FromUnsignedLong((unsigned long) value);\n#ifdef HAVE_LONG_LONG\n        } else if (sizeof(enum NPY_TYPES) <= sizeof(unsigned PY_LONG_LONG)) {\n            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);\n#endif\n        }\n    } else {\n        if (sizeof(enum NPY_TYPES) <= sizeof(long)) {\n            return PyInt_FromLong((long) value);\n#ifdef HAVE_LONG_LONG\n        } else if (sizeof(enum NPY_TYPES) <= sizeof(PY_LONG_LONG)) {\n            return PyLong_FromLongLong((PY_LONG_LONG) value);\n#endif\n        }\n    }\n    {\n        int one = 1; int little = (int)*(unsigned char *)&one;\n        unsigned char *bytes = (unsigned char *)&value;\n        return _PyLong_FromByteArray(bytes, sizeof(enum NPY_TYPES),\n                                     little, !is_unsigned);\n    }\n}\n\n/* CIntFromPy */\n          static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) {\n    const int neg_one = (int) -1, const_zero = (int) 0;\n    const int is_unsigned = neg_one > const_zero;\n#if PY_MAJOR_VERSION < 3\n    if (likely(PyInt_Check(x))) {\n        if (sizeof(int) < sizeof(long)) {\n            __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x))\n        } else {\n            long val = PyInt_AS_LONG(x);\n            if (is_unsigned && unlikely(val < 0)) {\n                goto raise_neg_overflow;\n            }\n            return (int) val;\n        }\n    } else\n#endif\n    if (likely(PyLong_Check(x))) {\n        if (is_unsigned) {\n#if CYTHON_USE_PYLONG_INTERNALS\n            const digit* digits = ((PyLongObject*)x)->ob_digit;\n            switch (Py_SIZE(x)) {\n                case  0: return (int) 0;\n                case  1: __PYX_VERIFY_RETURN_INT(int, digit, digits[0])\n                case 2:\n                    if (8 * sizeof(int) > 1 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(int) >= 2 * PyLong_SHIFT) {\n                            return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));\n                        }\n                    }\n                    break;\n                case 3:\n                    if (8 * sizeof(int) > 2 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(int) >= 3 * PyLong_SHIFT) {\n                            return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));\n                        }\n                    }\n                    break;\n                case 4:\n                    if (8 * sizeof(int) > 3 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(int) >= 4 * PyLong_SHIFT) {\n                            return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));\n                        }\n                    }\n                    break;\n            }\n#endif\n#if CYTHON_COMPILING_IN_CPYTHON\n            if (unlikely(Py_SIZE(x) < 0)) {\n                goto raise_neg_overflow;\n            }\n#else\n            {\n                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);\n                if (unlikely(result < 0))\n                    return (int) -1;\n                if (unlikely(result == 1))\n                    goto raise_neg_overflow;\n            }\n#endif\n            if (sizeof(int) <= sizeof(unsigned long)) {\n                __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x))\n#ifdef HAVE_LONG_LONG\n            } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) {\n                __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))\n#endif\n            }\n        } else {\n#if CYTHON_USE_PYLONG_INTERNALS\n            const digit* digits = ((PyLongObject*)x)->ob_digit;\n            switch (Py_SIZE(x)) {\n                case  0: return (int) 0;\n                case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, (sdigit) (-(sdigit)digits[0]))\n                case  1: __PYX_VERIFY_RETURN_INT(int,  digit, +digits[0])\n                case -2:\n                    if (8 * sizeof(int) - 1 > 1 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {\n                            return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));\n                        }\n                    }\n                    break;\n                case 2:\n                    if (8 * sizeof(int) > 1 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {\n                            return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));\n                        }\n                    }\n                    break;\n                case -3:\n                    if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {\n                            return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));\n                        }\n                    }\n                    break;\n                case 3:\n                    if (8 * sizeof(int) > 2 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {\n                            return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));\n                        }\n                    }\n                    break;\n                case -4:\n                    if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) {\n                            return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));\n                        }\n                    }\n                    break;\n                case 4:\n                    if (8 * sizeof(int) > 3 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) {\n                            return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));\n                        }\n                    }\n                    break;\n            }\n#endif\n            if (sizeof(int) <= sizeof(long)) {\n                __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x))\n#ifdef HAVE_LONG_LONG\n            } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) {\n                __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x))\n#endif\n            }\n        }\n        {\n#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)\n            PyErr_SetString(PyExc_RuntimeError,\n                            \"_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers\");\n#else\n            int val;\n            PyObject *v = __Pyx_PyNumber_IntOrLong(x);\n #if PY_MAJOR_VERSION < 3\n            if (likely(v) && !PyLong_Check(v)) {\n                PyObject *tmp = v;\n                v = PyNumber_Long(tmp);\n                Py_DECREF(tmp);\n            }\n #endif\n            if (likely(v)) {\n                int one = 1; int is_little = (int)*(unsigned char *)&one;\n                unsigned char *bytes = (unsigned char *)&val;\n                int ret = _PyLong_AsByteArray((PyLongObject *)v,\n                                              bytes, sizeof(val),\n                                              is_little, !is_unsigned);\n                Py_DECREF(v);\n                if (likely(!ret))\n                    return val;\n            }\n#endif\n            return (int) -1;\n        }\n    } else {\n        int val;\n        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);\n        if (!tmp) return (int) -1;\n        val = __Pyx_PyInt_As_int(tmp);\n        Py_DECREF(tmp);\n        return val;\n    }\nraise_overflow:\n    PyErr_SetString(PyExc_OverflowError,\n        \"value too large to convert to int\");\n    return (int) -1;\nraise_neg_overflow:\n    PyErr_SetString(PyExc_OverflowError,\n        \"can't convert negative value to int\");\n    return (int) -1;\n}\n\n/* CIntFromPy */\n          static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) {\n    const long neg_one = (long) -1, const_zero = (long) 0;\n    const int is_unsigned = neg_one > const_zero;\n#if PY_MAJOR_VERSION < 3\n    if (likely(PyInt_Check(x))) {\n        if (sizeof(long) < sizeof(long)) {\n            __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x))\n        } else {\n            long val = PyInt_AS_LONG(x);\n            if (is_unsigned && unlikely(val < 0)) {\n                goto raise_neg_overflow;\n            }\n            return (long) val;\n        }\n    } else\n#endif\n    if (likely(PyLong_Check(x))) {\n        if (is_unsigned) {\n#if CYTHON_USE_PYLONG_INTERNALS\n            const digit* digits = ((PyLongObject*)x)->ob_digit;\n            switch (Py_SIZE(x)) {\n                case  0: return (long) 0;\n                case  1: __PYX_VERIFY_RETURN_INT(long, digit, digits[0])\n                case 2:\n                    if (8 * sizeof(long) > 1 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(long) >= 2 * PyLong_SHIFT) {\n                            return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));\n                        }\n                    }\n                    break;\n                case 3:\n                    if (8 * sizeof(long) > 2 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(long) >= 3 * PyLong_SHIFT) {\n                            return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));\n                        }\n                    }\n                    break;\n                case 4:\n                    if (8 * sizeof(long) > 3 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(long) >= 4 * PyLong_SHIFT) {\n                            return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));\n                        }\n                    }\n                    break;\n            }\n#endif\n#if CYTHON_COMPILING_IN_CPYTHON\n            if (unlikely(Py_SIZE(x) < 0)) {\n                goto raise_neg_overflow;\n            }\n#else\n            {\n                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);\n                if (unlikely(result < 0))\n                    return (long) -1;\n                if (unlikely(result == 1))\n                    goto raise_neg_overflow;\n            }\n#endif\n            if (sizeof(long) <= sizeof(unsigned long)) {\n                __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x))\n#ifdef HAVE_LONG_LONG\n            } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) {\n                __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))\n#endif\n            }\n        } else {\n#if CYTHON_USE_PYLONG_INTERNALS\n            const digit* digits = ((PyLongObject*)x)->ob_digit;\n            switch (Py_SIZE(x)) {\n                case  0: return (long) 0;\n                case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, (sdigit) (-(sdigit)digits[0]))\n                case  1: __PYX_VERIFY_RETURN_INT(long,  digit, +digits[0])\n                case -2:\n                    if (8 * sizeof(long) - 1 > 1 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {\n                            return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));\n                        }\n                    }\n                    break;\n                case 2:\n                    if (8 * sizeof(long) > 1 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {\n                            return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));\n                        }\n                    }\n                    break;\n                case -3:\n                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {\n                            return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));\n                        }\n                    }\n                    break;\n                case 3:\n                    if (8 * sizeof(long) > 2 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {\n                            return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));\n                        }\n                    }\n                    break;\n                case -4:\n                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {\n                            return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));\n                        }\n                    }\n                    break;\n                case 4:\n                    if (8 * sizeof(long) > 3 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {\n                            return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));\n                        }\n                    }\n                    break;\n            }\n#endif\n            if (sizeof(long) <= sizeof(long)) {\n                __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x))\n#ifdef HAVE_LONG_LONG\n            } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) {\n                __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x))\n#endif\n            }\n        }\n        {\n#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)\n            PyErr_SetString(PyExc_RuntimeError,\n                            \"_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers\");\n#else\n            long val;\n            PyObject *v = __Pyx_PyNumber_IntOrLong(x);\n #if PY_MAJOR_VERSION < 3\n            if (likely(v) && !PyLong_Check(v)) {\n                PyObject *tmp = v;\n                v = PyNumber_Long(tmp);\n                Py_DECREF(tmp);\n            }\n #endif\n            if (likely(v)) {\n                int one = 1; int is_little = (int)*(unsigned char *)&one;\n                unsigned char *bytes = (unsigned char *)&val;\n                int ret = _PyLong_AsByteArray((PyLongObject *)v,\n                                              bytes, sizeof(val),\n                                              is_little, !is_unsigned);\n                Py_DECREF(v);\n                if (likely(!ret))\n                    return val;\n            }\n#endif\n            return (long) -1;\n        }\n    } else {\n        long val;\n        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);\n        if (!tmp) return (long) -1;\n        val = __Pyx_PyInt_As_long(tmp);\n        Py_DECREF(tmp);\n        return val;\n    }\nraise_overflow:\n    PyErr_SetString(PyExc_OverflowError,\n        \"value too large to convert to long\");\n    return (long) -1;\nraise_neg_overflow:\n    PyErr_SetString(PyExc_OverflowError,\n        \"can't convert negative value to long\");\n    return (long) -1;\n}\n\n/* FastTypeChecks */\n          #if CYTHON_COMPILING_IN_CPYTHON\nstatic int __Pyx_InBases(PyTypeObject *a, PyTypeObject *b) {\n    while (a) {\n        a = a->tp_base;\n        if (a == b)\n            return 1;\n    }\n    return b == &PyBaseObject_Type;\n}\nstatic CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b) {\n    PyObject *mro;\n    if (a == b) return 1;\n    mro = a->tp_mro;\n    if (likely(mro)) {\n        Py_ssize_t i, n;\n        n = PyTuple_GET_SIZE(mro);\n        for (i = 0; i < n; i++) {\n            if (PyTuple_GET_ITEM(mro, i) == (PyObject *)b)\n                return 1;\n        }\n        return 0;\n    }\n    return __Pyx_InBases(a, b);\n}\n#if PY_MAJOR_VERSION == 2\nstatic int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject* exc_type2) {\n    PyObject *exception, *value, *tb;\n    int res;\n    __Pyx_PyThreadState_declare\n    __Pyx_PyThreadState_assign\n    __Pyx_ErrFetch(&exception, &value, &tb);\n    res = exc_type1 ? PyObject_IsSubclass(err, exc_type1) : 0;\n    if (unlikely(res == -1)) {\n        PyErr_WriteUnraisable(err);\n        res = 0;\n    }\n    if (!res) {\n        res = PyObject_IsSubclass(err, exc_type2);\n        if (unlikely(res == -1)) {\n            PyErr_WriteUnraisable(err);\n            res = 0;\n        }\n    }\n    __Pyx_ErrRestore(exception, value, tb);\n    return res;\n}\n#else\nstatic CYTHON_INLINE int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject *exc_type2) {\n    int res = exc_type1 ? __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type1) : 0;\n    if (!res) {\n        res = __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type2);\n    }\n    return res;\n}\n#endif\nstatic CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject* exc_type) {\n    if (likely(err == exc_type)) return 1;\n    if (likely(PyExceptionClass_Check(err))) {\n        return __Pyx_inner_PyErr_GivenExceptionMatches2(err, NULL, exc_type);\n    }\n    return PyErr_GivenExceptionMatches(err, exc_type);\n}\nstatic CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *exc_type1, PyObject *exc_type2) {\n    if (likely(err == exc_type1 || err == exc_type2)) return 1;\n    if (likely(PyExceptionClass_Check(err))) {\n        return __Pyx_inner_PyErr_GivenExceptionMatches2(err, exc_type1, exc_type2);\n    }\n    return (PyErr_GivenExceptionMatches(err, exc_type1) || PyErr_GivenExceptionMatches(err, exc_type2));\n}\n#endif\n\n/* CheckBinaryVersion */\n          static int __Pyx_check_binary_version(void) {\n    char ctversion[4], rtversion[4];\n    PyOS_snprintf(ctversion, 4, \"%d.%d\", PY_MAJOR_VERSION, PY_MINOR_VERSION);\n    PyOS_snprintf(rtversion, 4, \"%s\", Py_GetVersion());\n    if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) {\n        char message[200];\n        PyOS_snprintf(message, sizeof(message),\n                      \"compiletime version %s of module '%.100s' \"\n                      \"does not match runtime version %s\",\n                      ctversion, __Pyx_MODULE_NAME, rtversion);\n        return PyErr_WarnEx(NULL, message, 1);\n    }\n    return 0;\n}\n\n/* ModuleImport */\n          #ifndef __PYX_HAVE_RT_ImportModule\n#define __PYX_HAVE_RT_ImportModule\nstatic PyObject *__Pyx_ImportModule(const char *name) {\n    PyObject *py_name = 0;\n    PyObject *py_module = 0;\n    py_name = __Pyx_PyIdentifier_FromString(name);\n    if (!py_name)\n        goto bad;\n    py_module = PyImport_Import(py_name);\n    Py_DECREF(py_name);\n    return py_module;\nbad:\n    Py_XDECREF(py_name);\n    return 0;\n}\n#endif\n\n/* TypeImport */\n          #ifndef __PYX_HAVE_RT_ImportType\n#define __PYX_HAVE_RT_ImportType\nstatic PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name,\n    size_t size, int strict)\n{\n    PyObject *py_module = 0;\n    PyObject *result = 0;\n    PyObject *py_name = 0;\n    char warning[200];\n    Py_ssize_t basicsize;\n#ifdef Py_LIMITED_API\n    PyObject *py_basicsize;\n#endif\n    py_module = __Pyx_ImportModule(module_name);\n    if (!py_module)\n        goto bad;\n    py_name = __Pyx_PyIdentifier_FromString(class_name);\n    if (!py_name)\n        goto bad;\n    result = PyObject_GetAttr(py_module, py_name);\n    Py_DECREF(py_name);\n    py_name = 0;\n    Py_DECREF(py_module);\n    py_module = 0;\n    if (!result)\n        goto bad;\n    if (!PyType_Check(result)) {\n        PyErr_Format(PyExc_TypeError,\n            \"%.200s.%.200s is not a type object\",\n            module_name, class_name);\n        goto bad;\n    }\n#ifndef Py_LIMITED_API\n    basicsize = ((PyTypeObject *)result)->tp_basicsize;\n#else\n    py_basicsize = PyObject_GetAttrString(result, \"__basicsize__\");\n    if (!py_basicsize)\n        goto bad;\n    basicsize = PyLong_AsSsize_t(py_basicsize);\n    Py_DECREF(py_basicsize);\n    py_basicsize = 0;\n    if (basicsize == (Py_ssize_t)-1 && PyErr_Occurred())\n        goto bad;\n#endif\n    if (!strict && (size_t)basicsize > size) {\n        PyOS_snprintf(warning, sizeof(warning),\n            \"%s.%s size changed, may indicate binary incompatibility. Expected %zd, got %zd\",\n            module_name, class_name, basicsize, size);\n        if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad;\n    }\n    else if ((size_t)basicsize != size) {\n        PyErr_Format(PyExc_ValueError,\n            \"%.200s.%.200s has the wrong size, try recompiling. Expected %zd, got %zd\",\n            module_name, class_name, basicsize, size);\n        goto bad;\n    }\n    return (PyTypeObject *)result;\nbad:\n    Py_XDECREF(py_module);\n    Py_XDECREF(result);\n    return NULL;\n}\n#endif\n\n/* InitStrings */\n          static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) {\n    while (t->p) {\n        #if PY_MAJOR_VERSION < 3\n        if (t->is_unicode) {\n            *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL);\n        } else if (t->intern) {\n            *t->p = PyString_InternFromString(t->s);\n        } else {\n            *t->p = PyString_FromStringAndSize(t->s, t->n - 1);\n        }\n        #else\n        if (t->is_unicode | t->is_str) {\n            if (t->intern) {\n                *t->p = PyUnicode_InternFromString(t->s);\n            } else if (t->encoding) {\n                *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL);\n            } else {\n                *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1);\n            }\n        } else {\n            *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1);\n        }\n        #endif\n        if (!*t->p)\n            return -1;\n        if (PyObject_Hash(*t->p) == -1)\n            return -1;\n        ++t;\n    }\n    return 0;\n}\n\nstatic CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) {\n    return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str));\n}\nstatic CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject* o) {\n    Py_ssize_t ignore;\n    return __Pyx_PyObject_AsStringAndSize(o, &ignore);\n}\n#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT\n#if !CYTHON_PEP393_ENABLED\nstatic const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) {\n    char* defenc_c;\n    PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL);\n    if (!defenc) return NULL;\n    defenc_c = PyBytes_AS_STRING(defenc);\n#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII\n    {\n        char* end = defenc_c + PyBytes_GET_SIZE(defenc);\n        char* c;\n        for (c = defenc_c; c < end; c++) {\n            if ((unsigned char) (*c) >= 128) {\n                PyUnicode_AsASCIIString(o);\n                return NULL;\n            }\n        }\n    }\n#endif\n    *length = PyBytes_GET_SIZE(defenc);\n    return defenc_c;\n}\n#else\nstatic CYTHON_INLINE const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) {\n    if (unlikely(__Pyx_PyUnicode_READY(o) == -1)) return NULL;\n#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII\n    if (likely(PyUnicode_IS_ASCII(o))) {\n        *length = PyUnicode_GET_LENGTH(o);\n        return PyUnicode_AsUTF8(o);\n    } else {\n        PyUnicode_AsASCIIString(o);\n        return NULL;\n    }\n#else\n    return PyUnicode_AsUTF8AndSize(o, length);\n#endif\n}\n#endif\n#endif\nstatic CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) {\n#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT\n    if (\n#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII\n            __Pyx_sys_getdefaultencoding_not_ascii &&\n#endif\n            PyUnicode_Check(o)) {\n        return __Pyx_PyUnicode_AsStringAndSize(o, length);\n    } else\n#endif\n#if (!CYTHON_COMPILING_IN_PYPY) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE))\n    if (PyByteArray_Check(o)) {\n        *length = PyByteArray_GET_SIZE(o);\n        return PyByteArray_AS_STRING(o);\n    } else\n#endif\n    {\n        char* result;\n        int r = PyBytes_AsStringAndSize(o, &result, length);\n        if (unlikely(r < 0)) {\n            return NULL;\n        } else {\n            return result;\n        }\n    }\n}\nstatic CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) {\n   int is_true = x == Py_True;\n   if (is_true | (x == Py_False) | (x == Py_None)) return is_true;\n   else return PyObject_IsTrue(x);\n}\nstatic PyObject* __Pyx_PyNumber_IntOrLongWrongResultType(PyObject* result, const char* type_name) {\n#if PY_MAJOR_VERSION >= 3\n    if (PyLong_Check(result)) {\n        if (PyErr_WarnFormat(PyExc_DeprecationWarning, 1,\n                \"__int__ returned non-int (type %.200s).  \"\n                \"The ability to return an instance of a strict subclass of int \"\n                \"is deprecated, and may be removed in a future version of Python.\",\n                Py_TYPE(result)->tp_name)) {\n            Py_DECREF(result);\n            return NULL;\n        }\n        return result;\n    }\n#endif\n    PyErr_Format(PyExc_TypeError,\n                 \"__%.4s__ returned non-%.4s (type %.200s)\",\n                 type_name, type_name, Py_TYPE(result)->tp_name);\n    Py_DECREF(result);\n    return NULL;\n}\nstatic CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) {\n#if CYTHON_USE_TYPE_SLOTS\n  PyNumberMethods *m;\n#endif\n  const char *name = NULL;\n  PyObject *res = NULL;\n#if PY_MAJOR_VERSION < 3\n  if (likely(PyInt_Check(x) || PyLong_Check(x)))\n#else\n  if (likely(PyLong_Check(x)))\n#endif\n    return __Pyx_NewRef(x);\n#if CYTHON_USE_TYPE_SLOTS\n  m = Py_TYPE(x)->tp_as_number;\n  #if PY_MAJOR_VERSION < 3\n  if (m && m->nb_int) {\n    name = \"int\";\n    res = m->nb_int(x);\n  }\n  else if (m && m->nb_long) {\n    name = \"long\";\n    res = m->nb_long(x);\n  }\n  #else\n  if (likely(m && m->nb_int)) {\n    name = \"int\";\n    res = m->nb_int(x);\n  }\n  #endif\n#else\n  if (!PyBytes_CheckExact(x) && !PyUnicode_CheckExact(x)) {\n    res = PyNumber_Int(x);\n  }\n#endif\n  if (likely(res)) {\n#if PY_MAJOR_VERSION < 3\n    if (unlikely(!PyInt_Check(res) && !PyLong_Check(res))) {\n#else\n    if (unlikely(!PyLong_CheckExact(res))) {\n#endif\n        return __Pyx_PyNumber_IntOrLongWrongResultType(res, name);\n    }\n  }\n  else if (!PyErr_Occurred()) {\n    PyErr_SetString(PyExc_TypeError,\n                    \"an integer is required\");\n  }\n  return res;\n}\nstatic CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) {\n  Py_ssize_t ival;\n  PyObject *x;\n#if PY_MAJOR_VERSION < 3\n  if (likely(PyInt_CheckExact(b))) {\n    if (sizeof(Py_ssize_t) >= sizeof(long))\n        return PyInt_AS_LONG(b);\n    else\n        return PyInt_AsSsize_t(x);\n  }\n#endif\n  if (likely(PyLong_CheckExact(b))) {\n    #if CYTHON_USE_PYLONG_INTERNALS\n    const digit* digits = ((PyLongObject*)b)->ob_digit;\n    const Py_ssize_t size = Py_SIZE(b);\n    if (likely(__Pyx_sst_abs(size) <= 1)) {\n        ival = likely(size) ? digits[0] : 0;\n        if (size == -1) ival = -ival;\n        return ival;\n    } else {\n      switch (size) {\n         case 2:\n           if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) {\n             return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));\n           }\n           break;\n         case -2:\n           if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) {\n             return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));\n           }\n           break;\n         case 3:\n           if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) {\n             return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));\n           }\n           break;\n         case -3:\n           if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) {\n             return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));\n           }\n           break;\n         case 4:\n           if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) {\n             return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));\n           }\n           break;\n         case -4:\n           if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) {\n             return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));\n           }\n           break;\n      }\n    }\n    #endif\n    return PyLong_AsSsize_t(b);\n  }\n  x = PyNumber_Index(b);\n  if (!x) return -1;\n  ival = PyInt_AsSsize_t(x);\n  Py_DECREF(x);\n  return ival;\n}\nstatic CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) {\n    return PyInt_FromSize_t(ival);\n}\n\n\n#endif /* Py_PYTHON_H */\n"
  },
  {
    "path": "lib/nms/cpu_nms.pyx",
    "content": "# --------------------------------------------------------\n# Fast R-CNN\n# Copyright (c) 2015 Microsoft\n# Licensed under The MIT License [see LICENSE for details]\n# Written by Ross Girshick\n# --------------------------------------------------------\n\nimport numpy as np\ncimport numpy as np\n\ncdef inline np.float32_t max(np.float32_t a, np.float32_t b):\n    return a if a >= b else b\n\ncdef inline np.float32_t min(np.float32_t a, np.float32_t b):\n    return a if a <= b else b\n\ndef cpu_nms(np.ndarray[np.float32_t, ndim=2] dets, np.float thresh):\n    cdef np.ndarray[np.float32_t, ndim=1] x1 = dets[:, 0]\n    cdef np.ndarray[np.float32_t, ndim=1] y1 = dets[:, 1]\n    cdef np.ndarray[np.float32_t, ndim=1] x2 = dets[:, 2]\n    cdef np.ndarray[np.float32_t, ndim=1] y2 = dets[:, 3]\n    cdef np.ndarray[np.float32_t, ndim=1] scores = dets[:, 4]\n\n    cdef np.ndarray[np.float32_t, ndim=1] areas = (x2 - x1 + 1) * (y2 - y1 + 1)\n    cdef np.ndarray[np.int_t, ndim=1] order = scores.argsort()[::-1]\n\n    cdef int ndets = dets.shape[0]\n    cdef np.ndarray[np.int_t, ndim=1] suppressed = \\\n            np.zeros((ndets), dtype=np.int)\n\n    # nominal indices\n    cdef int _i, _j\n    # sorted indices\n    cdef int i, j\n    # temp variables for box i's (the box currently under consideration)\n    cdef np.float32_t ix1, iy1, ix2, iy2, iarea\n    # variables for computing overlap with box j (lower scoring box)\n    cdef np.float32_t xx1, yy1, xx2, yy2\n    cdef np.float32_t w, h\n    cdef np.float32_t inter, ovr\n\n    keep = []\n    for _i in range(ndets):\n        i = order[_i]\n        if suppressed[i] == 1:\n            continue\n        keep.append(i)\n        ix1 = x1[i]\n        iy1 = y1[i]\n        ix2 = x2[i]\n        iy2 = y2[i]\n        iarea = areas[i]\n        for _j in range(_i + 1, ndets):\n            j = order[_j]\n            if suppressed[j] == 1:\n                continue\n            xx1 = max(ix1, x1[j])\n            yy1 = max(iy1, y1[j])\n            xx2 = min(ix2, x2[j])\n            yy2 = min(iy2, y2[j])\n            w = max(0.0, xx2 - xx1 + 1)\n            h = max(0.0, yy2 - yy1 + 1)\n            inter = w * h\n            ovr = inter / (iarea + areas[j] - inter)\n            if ovr >= thresh:\n                suppressed[j] = 1\n\n    return keep\n"
  },
  {
    "path": "lib/nms/gpu_nms.cpp",
    "content": "/* Generated by Cython 0.28.2 */\n\n#define PY_SSIZE_T_CLEAN\n#include \"Python.h\"\n#ifndef Py_PYTHON_H\n    #error Python headers needed to compile C extensions, please install development version of Python.\n#elif PY_VERSION_HEX < 0x02060000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03030000)\n    #error Cython requires Python 2.6+ or Python 3.3+.\n#else\n#define CYTHON_ABI \"0_28_2\"\n#define CYTHON_FUTURE_DIVISION 0\n#include <stddef.h>\n#ifndef offsetof\n  #define offsetof(type, member) ( (size_t) & ((type*)0) -> member )\n#endif\n#if !defined(WIN32) && !defined(MS_WINDOWS)\n  #ifndef __stdcall\n    #define __stdcall\n  #endif\n  #ifndef __cdecl\n    #define __cdecl\n  #endif\n  #ifndef __fastcall\n    #define __fastcall\n  #endif\n#endif\n#ifndef DL_IMPORT\n  #define DL_IMPORT(t) t\n#endif\n#ifndef DL_EXPORT\n  #define DL_EXPORT(t) t\n#endif\n#define __PYX_COMMA ,\n#ifndef HAVE_LONG_LONG\n  #if PY_VERSION_HEX >= 0x02070000\n    #define 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PyUnicode_GET_LENGTH(u) : PyUnicode_GET_SIZE(u)))\n#else\n  #define CYTHON_PEP393_ENABLED 0\n  #define PyUnicode_1BYTE_KIND  1\n  #define PyUnicode_2BYTE_KIND  2\n  #define PyUnicode_4BYTE_KIND  4\n  #define __Pyx_PyUnicode_READY(op)       (0)\n  #define __Pyx_PyUnicode_GET_LENGTH(u)   PyUnicode_GET_SIZE(u)\n  #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i]))\n  #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u)   ((sizeof(Py_UNICODE) == 2) ? 65535 : 1114111)\n  #define __Pyx_PyUnicode_KIND(u)         (sizeof(Py_UNICODE))\n  #define __Pyx_PyUnicode_DATA(u)         ((void*)PyUnicode_AS_UNICODE(u))\n  #define __Pyx_PyUnicode_READ(k, d, i)   ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i]))\n  #define __Pyx_PyUnicode_WRITE(k, d, i, ch)  (((void)(k)), ((Py_UNICODE*)d)[i] = ch)\n  #define __Pyx_PyUnicode_IS_TRUE(u)      (0 != PyUnicode_GET_SIZE(u))\n#endif\n#if CYTHON_COMPILING_IN_PYPY\n  #define __Pyx_PyUnicode_Concat(a, b)      PyNumber_Add(a, b)\n  #define __Pyx_PyUnicode_ConcatSafe(a, b)  PyNumber_Add(a, b)\n#else\n  #define __Pyx_PyUnicode_Concat(a, b)      PyUnicode_Concat(a, b)\n  #define __Pyx_PyUnicode_ConcatSafe(a, b)  ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ?\\\n      PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b))\n#endif\n#if CYTHON_COMPILING_IN_PYPY && !defined(PyUnicode_Contains)\n  #define PyUnicode_Contains(u, s)  PySequence_Contains(u, s)\n#endif\n#if CYTHON_COMPILING_IN_PYPY && !defined(PyByteArray_Check)\n  #define PyByteArray_Check(obj)  PyObject_TypeCheck(obj, &PyByteArray_Type)\n#endif\n#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Format)\n  #define PyObject_Format(obj, fmt)  PyObject_CallMethod(obj, \"__format__\", \"O\", fmt)\n#endif\n#define __Pyx_PyString_FormatSafe(a, b)   ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b))\n#define __Pyx_PyUnicode_FormatSafe(a, b)  ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b))\n#if PY_MAJOR_VERSION >= 3\n  #define __Pyx_PyString_Format(a, b)  PyUnicode_Format(a, b)\n#else\n  #define __Pyx_PyString_Format(a, b)  PyString_Format(a, b)\n#endif\n#if PY_MAJOR_VERSION < 3 && !defined(PyObject_ASCII)\n  #define PyObject_ASCII(o)            PyObject_Repr(o)\n#endif\n#if PY_MAJOR_VERSION >= 3\n  #define PyBaseString_Type            PyUnicode_Type\n  #define PyStringObject               PyUnicodeObject\n  #define PyString_Type                PyUnicode_Type\n  #define PyString_Check               PyUnicode_Check\n  #define PyString_CheckExact          PyUnicode_CheckExact\n#endif\n#if PY_MAJOR_VERSION >= 3\n  #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj)\n  #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj)\n#else\n  #define __Pyx_PyBaseString_Check(obj) (PyString_Check(obj) || PyUnicode_Check(obj))\n  #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj))\n#endif\n#ifndef PySet_CheckExact\n  #define PySet_CheckExact(obj)        (Py_TYPE(obj) == &PySet_Type)\n#endif\n#if CYTHON_ASSUME_SAFE_MACROS\n  #define __Pyx_PySequence_SIZE(seq)  Py_SIZE(seq)\n#else\n  #define __Pyx_PySequence_SIZE(seq)  PySequence_Size(seq)\n#endif\n#if PY_MAJOR_VERSION >= 3\n  #define PyIntObject                  PyLongObject\n  #define PyInt_Type                   PyLong_Type\n  #define PyInt_Check(op)              PyLong_Check(op)\n  #define PyInt_CheckExact(op)         PyLong_CheckExact(op)\n  #define PyInt_FromString             PyLong_FromString\n  #define PyInt_FromUnicode            PyLong_FromUnicode\n  #define PyInt_FromLong               PyLong_FromLong\n  #define PyInt_FromSize_t             PyLong_FromSize_t\n  #define PyInt_FromSsize_t            PyLong_FromSsize_t\n  #define PyInt_AsLong                 PyLong_AsLong\n  #define PyInt_AS_LONG                PyLong_AS_LONG\n  #define PyInt_AsSsize_t              PyLong_AsSsize_t\n  #define PyInt_AsUnsignedLongMask     PyLong_AsUnsignedLongMask\n  #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask\n  #define PyNumber_Int                 PyNumber_Long\n#endif\n#if PY_MAJOR_VERSION >= 3\n  #define PyBoolObject                 PyLongObject\n#endif\n#if PY_MAJOR_VERSION >= 3 && CYTHON_COMPILING_IN_PYPY\n  #ifndef PyUnicode_InternFromString\n    #define PyUnicode_InternFromString(s) PyUnicode_FromString(s)\n  #endif\n#endif\n#if PY_VERSION_HEX < 0x030200A4\n  typedef long Py_hash_t;\n  #define __Pyx_PyInt_FromHash_t PyInt_FromLong\n  #define __Pyx_PyInt_AsHash_t   PyInt_AsLong\n#else\n  #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t\n  #define __Pyx_PyInt_AsHash_t   PyInt_AsSsize_t\n#endif\n#if PY_MAJOR_VERSION >= 3\n  #define __Pyx_PyMethod_New(func, self, klass) ((self) ? PyMethod_New(func, self) : (Py_INCREF(func), func))\n#else\n  #define __Pyx_PyMethod_New(func, self, klass) PyMethod_New(func, self, klass)\n#endif\n#if CYTHON_USE_ASYNC_SLOTS\n  #if PY_VERSION_HEX >= 0x030500B1\n    #define __Pyx_PyAsyncMethodsStruct PyAsyncMethods\n    #define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async)\n  #else\n    #define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved))\n  #endif\n#else\n  #define __Pyx_PyType_AsAsync(obj) NULL\n#endif\n#ifndef __Pyx_PyAsyncMethodsStruct\n    typedef struct {\n        unaryfunc am_await;\n        unaryfunc am_aiter;\n        unaryfunc am_anext;\n    } __Pyx_PyAsyncMethodsStruct;\n#endif\n\n#if defined(WIN32) || defined(MS_WINDOWS)\n  #define _USE_MATH_DEFINES\n#endif\n#include <math.h>\n#ifdef NAN\n#define __PYX_NAN() ((float) NAN)\n#else\nstatic CYTHON_INLINE float __PYX_NAN() {\n  float value;\n  memset(&value, 0xFF, sizeof(value));\n  return value;\n}\n#endif\n#if defined(__CYGWIN__) && defined(_LDBL_EQ_DBL)\n#define __Pyx_truncl trunc\n#else\n#define __Pyx_truncl truncl\n#endif\n\n\n#define __PYX_ERR(f_index, lineno, Ln_error) \\\n{ \\\n  __pyx_filename = __pyx_f[f_index]; __pyx_lineno = lineno; __pyx_clineno = __LINE__; goto Ln_error; \\\n}\n\n#ifndef __PYX_EXTERN_C\n  #ifdef __cplusplus\n    #define __PYX_EXTERN_C extern \"C\"\n  #else\n    #define __PYX_EXTERN_C extern\n  #endif\n#endif\n\n#define __PYX_HAVE__gpu_nms\n#define __PYX_HAVE_API__gpu_nms\n/* Early includes */\n#include <string.h>\n#include <stdio.h>\n#include \"numpy/arrayobject.h\"\n#include \"numpy/ufuncobject.h\"\n#include \"gpu_nms.hpp\"\n#ifdef _OPENMP\n#include <omp.h>\n#endif /* _OPENMP */\n\n#if defined(PYREX_WITHOUT_ASSERTIONS) && !defined(CYTHON_WITHOUT_ASSERTIONS)\n#define CYTHON_WITHOUT_ASSERTIONS\n#endif\n\ntypedef struct {PyObject **p; const char *s; const Py_ssize_t n; const char* encoding;\n                const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry;\n\n#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0\n#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT 0\n#define __PYX_DEFAULT_STRING_ENCODING \"\"\n#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString\n#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize\n#define __Pyx_uchar_cast(c) ((unsigned char)c)\n#define __Pyx_long_cast(x) ((long)x)\n#define __Pyx_fits_Py_ssize_t(v, type, is_signed)  (\\\n    (sizeof(type) < sizeof(Py_ssize_t))  ||\\\n    (sizeof(type) > sizeof(Py_ssize_t) &&\\\n          likely(v < (type)PY_SSIZE_T_MAX ||\\\n                 v == (type)PY_SSIZE_T_MAX)  &&\\\n          (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||\\\n                                v == (type)PY_SSIZE_T_MIN)))  ||\\\n    (sizeof(type) == sizeof(Py_ssize_t) &&\\\n          (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\\\n                               v == (type)PY_SSIZE_T_MAX)))  )\n#if defined (__cplusplus) && __cplusplus >= 201103L\n    #include <cstdlib>\n    #define __Pyx_sst_abs(value) std::abs(value)\n#elif SIZEOF_INT >= SIZEOF_SIZE_T\n    #define __Pyx_sst_abs(value) abs(value)\n#elif SIZEOF_LONG >= SIZEOF_SIZE_T\n    #define __Pyx_sst_abs(value) labs(value)\n#elif defined (_MSC_VER)\n    #define __Pyx_sst_abs(value) ((Py_ssize_t)_abs64(value))\n#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L\n    #define __Pyx_sst_abs(value) llabs(value)\n#elif defined (__GNUC__)\n    #define __Pyx_sst_abs(value) __builtin_llabs(value)\n#else\n    #define __Pyx_sst_abs(value) ((value<0) ? -value : value)\n#endif\nstatic CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject*);\nstatic CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length);\n#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s))\n#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l)\n#define __Pyx_PyBytes_FromString        PyBytes_FromString\n#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize\nstatic CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*);\n#if PY_MAJOR_VERSION < 3\n    #define __Pyx_PyStr_FromString        __Pyx_PyBytes_FromString\n    #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize\n#else\n    #define __Pyx_PyStr_FromString        __Pyx_PyUnicode_FromString\n    #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize\n#endif\n#define __Pyx_PyBytes_AsWritableString(s)     ((char*) PyBytes_AS_STRING(s))\n#define __Pyx_PyBytes_AsWritableSString(s)    ((signed char*) PyBytes_AS_STRING(s))\n#define __Pyx_PyBytes_AsWritableUString(s)    ((unsigned char*) PyBytes_AS_STRING(s))\n#define __Pyx_PyBytes_AsString(s)     ((const char*) PyBytes_AS_STRING(s))\n#define __Pyx_PyBytes_AsSString(s)    ((const signed char*) PyBytes_AS_STRING(s))\n#define __Pyx_PyBytes_AsUString(s)    ((const unsigned char*) PyBytes_AS_STRING(s))\n#define __Pyx_PyObject_AsWritableString(s)    ((char*) __Pyx_PyObject_AsString(s))\n#define __Pyx_PyObject_AsWritableSString(s)    ((signed char*) __Pyx_PyObject_AsString(s))\n#define __Pyx_PyObject_AsWritableUString(s)    ((unsigned char*) __Pyx_PyObject_AsString(s))\n#define __Pyx_PyObject_AsSString(s)    ((const signed char*) __Pyx_PyObject_AsString(s))\n#define __Pyx_PyObject_AsUString(s)    ((const unsigned char*) __Pyx_PyObject_AsString(s))\n#define __Pyx_PyObject_FromCString(s)  __Pyx_PyObject_FromString((const char*)s)\n#define __Pyx_PyBytes_FromCString(s)   __Pyx_PyBytes_FromString((const char*)s)\n#define __Pyx_PyByteArray_FromCString(s)   __Pyx_PyByteArray_FromString((const char*)s)\n#define __Pyx_PyStr_FromCString(s)     __Pyx_PyStr_FromString((const char*)s)\n#define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s)\nstatic CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) {\n    const Py_UNICODE *u_end = u;\n    while (*u_end++) ;\n    return (size_t)(u_end - u - 1);\n}\n#define __Pyx_PyUnicode_FromUnicode(u)       PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u))\n#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode\n#define __Pyx_PyUnicode_AsUnicode            PyUnicode_AsUnicode\n#define __Pyx_NewRef(obj) (Py_INCREF(obj), obj)\n#define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None)\n#define __Pyx_PyBool_FromLong(b) ((b) ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False))\nstatic CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*);\nstatic CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x);\n#define __Pyx_PySequence_Tuple(obj)\\\n    (likely(PyTuple_CheckExact(obj)) ? __Pyx_NewRef(obj) : PySequence_Tuple(obj))\nstatic CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*);\nstatic CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t);\n#if CYTHON_ASSUME_SAFE_MACROS\n#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x))\n#else\n#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x)\n#endif\n#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x))\n#if PY_MAJOR_VERSION >= 3\n#define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x))\n#else\n#define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x))\n#endif\n#define __Pyx_PyNumber_Float(x) (PyFloat_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Float(x))\n#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII\nstatic int __Pyx_sys_getdefaultencoding_not_ascii;\nstatic int __Pyx_init_sys_getdefaultencoding_params(void) {\n    PyObject* sys;\n    PyObject* default_encoding = NULL;\n    PyObject* ascii_chars_u = NULL;\n    PyObject* ascii_chars_b = NULL;\n    const char* default_encoding_c;\n    sys = PyImport_ImportModule(\"sys\");\n    if (!sys) goto bad;\n    default_encoding = PyObject_CallMethod(sys, (char*) \"getdefaultencoding\", NULL);\n    Py_DECREF(sys);\n    if (!default_encoding) goto bad;\n    default_encoding_c = PyBytes_AsString(default_encoding);\n    if (!default_encoding_c) goto bad;\n    if (strcmp(default_encoding_c, \"ascii\") == 0) {\n        __Pyx_sys_getdefaultencoding_not_ascii = 0;\n    } else {\n        char ascii_chars[128];\n        int c;\n        for (c = 0; c < 128; c++) {\n            ascii_chars[c] = c;\n        }\n        __Pyx_sys_getdefaultencoding_not_ascii = 1;\n        ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL);\n        if (!ascii_chars_u) goto bad;\n        ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL);\n        if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) {\n            PyErr_Format(\n                PyExc_ValueError,\n                \"This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.\",\n                default_encoding_c);\n            goto bad;\n        }\n        Py_DECREF(ascii_chars_u);\n        Py_DECREF(ascii_chars_b);\n    }\n    Py_DECREF(default_encoding);\n    return 0;\nbad:\n    Py_XDECREF(default_encoding);\n    Py_XDECREF(ascii_chars_u);\n    Py_XDECREF(ascii_chars_b);\n    return -1;\n}\n#endif\n#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3\n#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL)\n#else\n#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL)\n#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT\nstatic char* __PYX_DEFAULT_STRING_ENCODING;\nstatic int __Pyx_init_sys_getdefaultencoding_params(void) {\n    PyObject* sys;\n    PyObject* default_encoding = NULL;\n    char* default_encoding_c;\n    sys = PyImport_ImportModule(\"sys\");\n    if (!sys) goto bad;\n    default_encoding = PyObject_CallMethod(sys, (char*) (const char*) \"getdefaultencoding\", NULL);\n    Py_DECREF(sys);\n    if (!default_encoding) goto bad;\n    default_encoding_c = PyBytes_AsString(default_encoding);\n    if (!default_encoding_c) goto bad;\n    __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c));\n    if (!__PYX_DEFAULT_STRING_ENCODING) goto bad;\n    strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c);\n    Py_DECREF(default_encoding);\n    return 0;\nbad:\n    Py_XDECREF(default_encoding);\n    return -1;\n}\n#endif\n#endif\n\n\n/* Test for GCC > 2.95 */\n#if defined(__GNUC__)     && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95)))\n  #define likely(x)   __builtin_expect(!!(x), 1)\n  #define unlikely(x) __builtin_expect(!!(x), 0)\n#else /* !__GNUC__ or GCC < 2.95 */\n  #define likely(x)   (x)\n  #define unlikely(x) (x)\n#endif /* __GNUC__ */\nstatic CYTHON_INLINE void __Pyx_pretend_to_initialize(void* ptr) { (void)ptr; }\n\nstatic PyObject *__pyx_m = NULL;\nstatic PyObject *__pyx_d;\nstatic PyObject *__pyx_b;\nstatic PyObject *__pyx_cython_runtime;\nstatic PyObject *__pyx_empty_tuple;\nstatic PyObject *__pyx_empty_bytes;\nstatic PyObject *__pyx_empty_unicode;\nstatic int __pyx_lineno;\nstatic int __pyx_clineno = 0;\nstatic const char * __pyx_cfilenm= __FILE__;\nstatic const char *__pyx_filename;\n\n/* Header.proto */\n#if !defined(CYTHON_CCOMPLEX)\n  #if defined(__cplusplus)\n    #define CYTHON_CCOMPLEX 1\n  #elif defined(_Complex_I)\n    #define CYTHON_CCOMPLEX 1\n  #else\n    #define CYTHON_CCOMPLEX 0\n  #endif\n#endif\n#if CYTHON_CCOMPLEX\n  #ifdef __cplusplus\n    #include <complex>\n  #else\n    #include <complex.h>\n  #endif\n#endif\n#if CYTHON_CCOMPLEX && !defined(__cplusplus) && defined(__sun__) && defined(__GNUC__)\n  #undef _Complex_I\n  #define _Complex_I 1.0fj\n#endif\n\n\nstatic const char *__pyx_f[] = {\n  \"gpu_nms.pyx\",\n  \"__init__.pxd\",\n  \"type.pxd\",\n};\n/* BufferFormatStructs.proto */\n#define IS_UNSIGNED(type) (((type) -1) > 0)\nstruct __Pyx_StructField_;\n#define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0)\ntypedef struct {\n  const char* name;\n  struct __Pyx_StructField_* fields;\n  size_t size;\n  size_t arraysize[8];\n  int ndim;\n  char typegroup;\n  char is_unsigned;\n  int flags;\n} __Pyx_TypeInfo;\ntypedef struct __Pyx_StructField_ {\n  __Pyx_TypeInfo* type;\n  const char* name;\n  size_t offset;\n} __Pyx_StructField;\ntypedef struct {\n  __Pyx_StructField* field;\n  size_t parent_offset;\n} __Pyx_BufFmt_StackElem;\ntypedef struct {\n  __Pyx_StructField root;\n  __Pyx_BufFmt_StackElem* head;\n  size_t fmt_offset;\n  size_t new_count, enc_count;\n  size_t struct_alignment;\n  int is_complex;\n  char enc_type;\n  char new_packmode;\n  char enc_packmode;\n  char is_valid_array;\n} __Pyx_BufFmt_Context;\n\n\n/* \"../../../../anaconda3/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd\":730\n * # in Cython to enable them only on the right systems.\n * \n * ctypedef npy_int8       int8_t             # <<<<<<<<<<<<<<\n * ctypedef npy_int16      int16_t\n * ctypedef npy_int32      int32_t\n */\ntypedef npy_int8 __pyx_t_5numpy_int8_t;\n\n/* \"../../../../anaconda3/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd\":731\n * \n * ctypedef npy_int8       int8_t\n * ctypedef npy_int16      int16_t             # <<<<<<<<<<<<<<\n * ctypedef npy_int32      int32_t\n * ctypedef npy_int64      int64_t\n */\ntypedef npy_int16 __pyx_t_5numpy_int16_t;\n\n/* \"../../../../anaconda3/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd\":732\n * ctypedef npy_int8       int8_t\n * ctypedef npy_int16      int16_t\n * ctypedef npy_int32      int32_t             # <<<<<<<<<<<<<<\n * ctypedef npy_int64      int64_t\n * #ctypedef npy_int96      int96_t\n */\ntypedef npy_int32 __pyx_t_5numpy_int32_t;\n\n/* \"../../../../anaconda3/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd\":733\n * ctypedef npy_int16      int16_t\n * ctypedef npy_int32      int32_t\n * ctypedef npy_int64      int64_t             # <<<<<<<<<<<<<<\n * #ctypedef npy_int96      int96_t\n * #ctypedef npy_int128     int128_t\n */\ntypedef npy_int64 __pyx_t_5numpy_int64_t;\n\n/* \"../../../../anaconda3/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd\":737\n * #ctypedef npy_int128     int128_t\n * \n * ctypedef npy_uint8      uint8_t             # <<<<<<<<<<<<<<\n * ctypedef npy_uint16     uint16_t\n * ctypedef npy_uint32     uint32_t\n */\ntypedef npy_uint8 __pyx_t_5numpy_uint8_t;\n\n/* \"../../../../anaconda3/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd\":738\n * \n * ctypedef npy_uint8      uint8_t\n * ctypedef npy_uint16     uint16_t             # <<<<<<<<<<<<<<\n * ctypedef npy_uint32     uint32_t\n * ctypedef npy_uint64     uint64_t\n */\ntypedef npy_uint16 __pyx_t_5numpy_uint16_t;\n\n/* \"../../../../anaconda3/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd\":739\n * ctypedef npy_uint8      uint8_t\n * ctypedef npy_uint16     uint16_t\n * ctypedef npy_uint32     uint32_t             # <<<<<<<<<<<<<<\n * ctypedef npy_uint64     uint64_t\n * #ctypedef npy_uint96     uint96_t\n */\ntypedef npy_uint32 __pyx_t_5numpy_uint32_t;\n\n/* \"../../../../anaconda3/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd\":740\n * ctypedef npy_uint16     uint16_t\n * ctypedef npy_uint32     uint32_t\n * ctypedef npy_uint64     uint64_t             # <<<<<<<<<<<<<<\n * #ctypedef npy_uint96     uint96_t\n * #ctypedef npy_uint128    uint128_t\n */\ntypedef npy_uint64 __pyx_t_5numpy_uint64_t;\n\n/* \"../../../../anaconda3/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd\":744\n * #ctypedef npy_uint128    uint128_t\n * \n * ctypedef npy_float32    float32_t             # <<<<<<<<<<<<<<\n * ctypedef npy_float64    float64_t\n * #ctypedef npy_float80    float80_t\n */\ntypedef npy_float32 __pyx_t_5numpy_float32_t;\n\n/* 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*/\nstatic CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index);\n\n/* RaiseNoneIterError.proto */\nstatic CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void);\n\n/* SaveResetException.proto */\n#if CYTHON_FAST_THREAD_STATE\n#define __Pyx_ExceptionSave(type, value, tb)  __Pyx__ExceptionSave(__pyx_tstate, type, value, tb)\nstatic CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);\n#define __Pyx_ExceptionReset(type, value, tb)  __Pyx__ExceptionReset(__pyx_tstate, type, value, tb)\nstatic CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb);\n#else\n#define __Pyx_ExceptionSave(type, value, tb)   PyErr_GetExcInfo(type, value, tb)\n#define __Pyx_ExceptionReset(type, value, tb)  PyErr_SetExcInfo(type, value, tb)\n#endif\n\n/* PyErrExceptionMatches.proto */\n#if CYTHON_FAST_THREAD_STATE\n#define __Pyx_PyErr_ExceptionMatches(err) __Pyx_PyErr_ExceptionMatchesInState(__pyx_tstate, err)\nstatic CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err);\n#else\n#define __Pyx_PyErr_ExceptionMatches(err)  PyErr_ExceptionMatches(err)\n#endif\n\n/* GetException.proto */\n#if CYTHON_FAST_THREAD_STATE\n#define __Pyx_GetException(type, value, tb)  __Pyx__GetException(__pyx_tstate, type, value, tb)\nstatic int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb);\n#else\nstatic int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb);\n#endif\n\n/* Import.proto */\nstatic PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level);\n\n/* CLineInTraceback.proto */\n#ifdef CYTHON_CLINE_IN_TRACEBACK\n#define __Pyx_CLineForTraceback(tstate, c_line)  (((CYTHON_CLINE_IN_TRACEBACK)) ? c_line : 0)\n#else\nstatic int __Pyx_CLineForTraceback(PyThreadState *tstate, int c_line);\n#endif\n\n/* CodeObjectCache.proto */\ntypedef struct {\n    PyCodeObject* code_object;\n    int code_line;\n} __Pyx_CodeObjectCacheEntry;\nstruct __Pyx_CodeObjectCache {\n    int count;\n    int max_count;\n    __Pyx_CodeObjectCacheEntry* entries;\n};\nstatic struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL};\nstatic int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line);\nstatic PyCodeObject *__pyx_find_code_object(int code_line);\nstatic void __pyx_insert_code_object(int code_line, PyCodeObject* code_object);\n\n/* AddTraceback.proto */\nstatic void __Pyx_AddTraceback(const char *funcname, int c_line,\n                               int py_line, const char *filename);\n\n/* BufferStructDeclare.proto */\ntypedef struct {\n  Py_ssize_t shape, strides, suboffsets;\n} __Pyx_Buf_DimInfo;\ntypedef struct {\n  size_t refcount;\n  Py_buffer pybuffer;\n} __Pyx_Buffer;\ntypedef struct {\n  __Pyx_Buffer *rcbuffer;\n  char *data;\n  __Pyx_Buf_DimInfo diminfo[8];\n} __Pyx_LocalBuf_ND;\n\n#if PY_MAJOR_VERSION < 3\n    static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags);\n    static void __Pyx_ReleaseBuffer(Py_buffer *view);\n#else\n    #define __Pyx_GetBuffer PyObject_GetBuffer\n    #define __Pyx_ReleaseBuffer PyBuffer_Release\n#endif\n\n\n/* CIntToPy.proto */\nstatic CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value);\n\n/* RealImag.proto */\n#if CYTHON_CCOMPLEX\n  #ifdef __cplusplus\n    #define __Pyx_CREAL(z) ((z).real())\n    #define __Pyx_CIMAG(z) ((z).imag())\n  #else\n    #define __Pyx_CREAL(z) (__real__(z))\n    #define __Pyx_CIMAG(z) (__imag__(z))\n  #endif\n#else\n    #define __Pyx_CREAL(z) ((z).real)\n    #define __Pyx_CIMAG(z) ((z).imag)\n#endif\n#if defined(__cplusplus) && CYTHON_CCOMPLEX\\\n        && (defined(_WIN32) || defined(__clang__) || (defined(__GNUC__) && (__GNUC__ >= 5 || __GNUC__ == 4 && __GNUC_MINOR__ >= 4 )) || __cplusplus >= 201103)\n    #define __Pyx_SET_CREAL(z,x) ((z).real(x))\n    #define __Pyx_SET_CIMAG(z,y) ((z).imag(y))\n#else\n    #define __Pyx_SET_CREAL(z,x) __Pyx_CREAL(z) = (x)\n    #define __Pyx_SET_CIMAG(z,y) __Pyx_CIMAG(z) = (y)\n#endif\n\n/* Arithmetic.proto */\n#if CYTHON_CCOMPLEX\n    #define __Pyx_c_eq_float(a, b)   ((a)==(b))\n    #define __Pyx_c_sum_float(a, b)  ((a)+(b))\n    #define __Pyx_c_diff_float(a, b) ((a)-(b))\n    #define __Pyx_c_prod_float(a, b) ((a)*(b))\n    #define __Pyx_c_quot_float(a, b) ((a)/(b))\n    #define __Pyx_c_neg_float(a)     (-(a))\n  #ifdef __cplusplus\n    #define __Pyx_c_is_zero_float(z) ((z)==(float)0)\n    #define __Pyx_c_conj_float(z)    (::std::conj(z))\n    #if 1\n        #define __Pyx_c_abs_float(z)     (::std::abs(z))\n        #define __Pyx_c_pow_float(a, b)  (::std::pow(a, b))\n    #endif\n  #else\n    #define __Pyx_c_is_zero_float(z) ((z)==0)\n    #define __Pyx_c_conj_float(z)    (conjf(z))\n    #if 1\n        #define __Pyx_c_abs_float(z)     (cabsf(z))\n        #define __Pyx_c_pow_float(a, b)  (cpowf(a, b))\n    #endif\n #endif\n#else\n    static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex, __pyx_t_float_complex);\n    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex, __pyx_t_float_complex);\n    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex, __pyx_t_float_complex);\n    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex, __pyx_t_float_complex);\n    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex, __pyx_t_float_complex);\n    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex);\n    static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex);\n    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex);\n    #if 1\n        static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex);\n        static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex, __pyx_t_float_complex);\n    #endif\n#endif\n\n/* Arithmetic.proto */\n#if CYTHON_CCOMPLEX\n    #define __Pyx_c_eq_double(a, b)   ((a)==(b))\n    #define __Pyx_c_sum_double(a, b)  ((a)+(b))\n    #define __Pyx_c_diff_double(a, b) ((a)-(b))\n    #define __Pyx_c_prod_double(a, b) ((a)*(b))\n    #define __Pyx_c_quot_double(a, b) ((a)/(b))\n    #define __Pyx_c_neg_double(a)     (-(a))\n  #ifdef __cplusplus\n    #define __Pyx_c_is_zero_double(z) ((z)==(double)0)\n    #define __Pyx_c_conj_double(z)    (::std::conj(z))\n    #if 1\n        #define __Pyx_c_abs_double(z)     (::std::abs(z))\n        #define __Pyx_c_pow_double(a, b)  (::std::pow(a, b))\n    #endif\n  #else\n    #define __Pyx_c_is_zero_double(z) ((z)==0)\n    #define __Pyx_c_conj_double(z)    (conj(z))\n    #if 1\n        #define __Pyx_c_abs_double(z)     (cabs(z))\n        #define __Pyx_c_pow_double(a, b)  (cpow(a, b))\n    #endif\n #endif\n#else\n    static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex, __pyx_t_double_complex);\n    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex, __pyx_t_double_complex);\n    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex, __pyx_t_double_complex);\n    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex, __pyx_t_double_complex);\n    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex, __pyx_t_double_complex);\n    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex);\n    static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex);\n    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex);\n    #if 1\n        static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex);\n        static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex, __pyx_t_double_complex);\n    #endif\n#endif\n\n/* CIntToPy.proto */\nstatic CYTHON_INLINE PyObject* __Pyx_PyInt_From_enum__NPY_TYPES(enum NPY_TYPES value);\n\n/* CIntFromPy.proto */\nstatic CYTHON_INLINE npy_int32 __Pyx_PyInt_As_npy_int32(PyObject *);\n\n/* CIntFromPy.proto */\nstatic CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *);\n\n/* CIntToPy.proto */\nstatic CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value);\n\n/* CIntFromPy.proto */\nstatic CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *);\n\n/* FastTypeChecks.proto */\n#if CYTHON_COMPILING_IN_CPYTHON\n#define __Pyx_TypeCheck(obj, type) __Pyx_IsSubtype(Py_TYPE(obj), (PyTypeObject *)type)\nstatic CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b);\nstatic CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject *type);\nstatic CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *type1, PyObject *type2);\n#else\n#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type)\n#define __Pyx_PyErr_GivenExceptionMatches(err, type) PyErr_GivenExceptionMatches(err, type)\n#define __Pyx_PyErr_GivenExceptionMatches2(err, type1, type2) (PyErr_GivenExceptionMatches(err, type1) || PyErr_GivenExceptionMatches(err, type2))\n#endif\n#define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception)\n\n/* CheckBinaryVersion.proto */\nstatic int __Pyx_check_binary_version(void);\n\n/* PyIdentifierFromString.proto */\n#if !defined(__Pyx_PyIdentifier_FromString)\n#if PY_MAJOR_VERSION < 3\n  #define __Pyx_PyIdentifier_FromString(s) PyString_FromString(s)\n#else\n  #define __Pyx_PyIdentifier_FromString(s) PyUnicode_FromString(s)\n#endif\n#endif\n\n/* ModuleImport.proto */\nstatic PyObject *__Pyx_ImportModule(const char *name);\n\n/* TypeImport.proto */\nstatic PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, size_t size, int strict);\n\n/* InitStrings.proto */\nstatic int __Pyx_InitStrings(__Pyx_StringTabEntry *t);\n\n\n/* Module declarations from 'cpython.buffer' */\n\n/* Module declarations from 'libc.string' */\n\n/* Module declarations from 'libc.stdio' */\n\n/* Module declarations from '__builtin__' */\n\n/* Module declarations from 'cpython.type' */\nstatic PyTypeObject *__pyx_ptype_7cpython_4type_type = 0;\n\n/* Module declarations from 'cpython' */\n\n/* Module declarations from 'cpython.object' */\n\n/* Module declarations from 'cpython.ref' */\n\n/* Module declarations from 'cpython.mem' */\n\n/* Module declarations from 'numpy' */\n\n/* Module declarations from 'numpy' */\nstatic PyTypeObject *__pyx_ptype_5numpy_dtype = 0;\nstatic PyTypeObject *__pyx_ptype_5numpy_flatiter = 0;\nstatic PyTypeObject *__pyx_ptype_5numpy_broadcast = 0;\nstatic PyTypeObject *__pyx_ptype_5numpy_ndarray = 0;\nstatic PyTypeObject *__pyx_ptype_5numpy_ufunc = 0;\nstatic CYTHON_INLINE char *__pyx_f_5numpy__util_dtypestring(PyArray_Descr *, char *, char *, int *); /*proto*/\n\n/* Module declarations from 'gpu_nms' */\nstatic __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_5numpy_float32_t = { \"float32_t\", NULL, sizeof(__pyx_t_5numpy_float32_t), { 0 }, 0, 'R', 0, 0 };\nstatic __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_5numpy_int32_t = { \"int32_t\", NULL, sizeof(__pyx_t_5numpy_int32_t), { 0 }, 0, IS_UNSIGNED(__pyx_t_5numpy_int32_t) ? 'U' : 'I', IS_UNSIGNED(__pyx_t_5numpy_int32_t), 0 };\nstatic __Pyx_TypeInfo __Pyx_TypeInfo_nn___pyx_t_5numpy_int_t = { \"int_t\", NULL, sizeof(__pyx_t_5numpy_int_t), { 0 }, 0, IS_UNSIGNED(__pyx_t_5numpy_int_t) ? 'U' : 'I', IS_UNSIGNED(__pyx_t_5numpy_int_t), 0 };\n#define __Pyx_MODULE_NAME \"gpu_nms\"\nextern int __pyx_module_is_main_gpu_nms;\nint __pyx_module_is_main_gpu_nms = 0;\n\n/* Implementation of 'gpu_nms' */\nstatic PyObject *__pyx_builtin_ValueError;\nstatic PyObject *__pyx_builtin_range;\nstatic PyObject *__pyx_builtin_RuntimeError;\nstatic PyObject *__pyx_builtin_ImportError;\nstatic const char __pyx_k_np[] = \"np\";\nstatic const char __pyx_k_dets[] = \"dets\";\nstatic const char __pyx_k_keep[] = \"keep\";\nstatic const char __pyx_k_main[] = \"__main__\";\nstatic const char __pyx_k_test[] = \"__test__\";\nstatic const char __pyx_k_dtype[] = \"dtype\";\nstatic const char __pyx_k_int32[] = \"int32\";\nstatic const char __pyx_k_numpy[] = \"numpy\";\nstatic const char __pyx_k_order[] = \"order\";\nstatic const char __pyx_k_range[] = \"range\";\nstatic const char __pyx_k_zeros[] = \"zeros\";\nstatic const char __pyx_k_import[] = \"__import__\";\nstatic const char __pyx_k_scores[] = \"scores\";\nstatic const char __pyx_k_thresh[] = \"thresh\";\nstatic const char __pyx_k_argsort[] = \"argsort\";\nstatic const char __pyx_k_gpu_nms[] = \"gpu_nms\";\nstatic const char __pyx_k_num_out[] = \"num_out\";\nstatic const char __pyx_k_boxes_dim[] = \"boxes_dim\";\nstatic const char __pyx_k_boxes_num[] = \"boxes_num\";\nstatic const char __pyx_k_device_id[] = \"device_id\";\nstatic const char __pyx_k_ValueError[] = \"ValueError\";\nstatic const char __pyx_k_ImportError[] = \"ImportError\";\nstatic const char __pyx_k_gpu_nms_pyx[] = \"gpu_nms.pyx\";\nstatic const char __pyx_k_sorted_dets[] = \"sorted_dets\";\nstatic const char __pyx_k_RuntimeError[] = \"RuntimeError\";\nstatic const char __pyx_k_cline_in_traceback[] = \"cline_in_traceback\";\nstatic const char __pyx_k_ndarray_is_not_C_contiguous[] = \"ndarray is not C contiguous\";\nstatic const char __pyx_k_numpy_core_multiarray_failed_to[] = \"numpy.core.multiarray failed to import\";\nstatic const char __pyx_k_unknown_dtype_code_in_numpy_pxd[] = \"unknown dtype code in numpy.pxd (%d)\";\nstatic const char __pyx_k_Format_string_allocated_too_shor[] = \"Format string allocated too short, see comment in numpy.pxd\";\nstatic const char __pyx_k_Non_native_byte_order_not_suppor[] = \"Non-native byte order not supported\";\nstatic const char __pyx_k_ndarray_is_not_Fortran_contiguou[] = \"ndarray is not Fortran contiguous\";\nstatic const char __pyx_k_numpy_core_umath_failed_to_impor[] = \"numpy.core.umath failed to import\";\nstatic const char __pyx_k_Format_string_allocated_too_shor_2[] = \"Format string allocated too short.\";\nstatic PyObject *__pyx_kp_u_Format_string_allocated_too_shor;\nstatic PyObject *__pyx_kp_u_Format_string_allocated_too_shor_2;\nstatic PyObject *__pyx_n_s_ImportError;\nstatic PyObject *__pyx_kp_u_Non_native_byte_order_not_suppor;\nstatic PyObject *__pyx_n_s_RuntimeError;\nstatic PyObject *__pyx_n_s_ValueError;\nstatic PyObject *__pyx_n_s_argsort;\nstatic PyObject *__pyx_n_s_boxes_dim;\nstatic PyObject *__pyx_n_s_boxes_num;\nstatic PyObject *__pyx_n_s_cline_in_traceback;\nstatic PyObject *__pyx_n_s_dets;\nstatic PyObject *__pyx_n_s_device_id;\nstatic PyObject *__pyx_n_s_dtype;\nstatic PyObject *__pyx_n_s_gpu_nms;\nstatic PyObject *__pyx_kp_s_gpu_nms_pyx;\nstatic PyObject *__pyx_n_s_import;\nstatic PyObject *__pyx_n_s_int32;\nstatic PyObject *__pyx_n_s_keep;\nstatic PyObject *__pyx_n_s_main;\nstatic PyObject *__pyx_kp_u_ndarray_is_not_C_contiguous;\nstatic PyObject *__pyx_kp_u_ndarray_is_not_Fortran_contiguou;\nstatic PyObject *__pyx_n_s_np;\nstatic PyObject *__pyx_n_s_num_out;\nstatic PyObject *__pyx_n_s_numpy;\nstatic PyObject *__pyx_kp_s_numpy_core_multiarray_failed_to;\nstatic PyObject *__pyx_kp_s_numpy_core_umath_failed_to_impor;\nstatic PyObject *__pyx_n_s_order;\nstatic PyObject *__pyx_n_s_range;\nstatic PyObject *__pyx_n_s_scores;\nstatic PyObject *__pyx_n_s_sorted_dets;\nstatic PyObject *__pyx_n_s_test;\nstatic PyObject 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info.strides = <Py_ssize_t*>PyObject_Malloc(sizeof(Py_ssize_t) * 2 * <size_t>ndim)             # <<<<<<<<<<<<<<\n *                 info.shape = info.strides + ndim\n *                 for i in range(ndim):\n */\n    __pyx_v_info->strides = ((Py_ssize_t *)PyObject_Malloc((((sizeof(Py_ssize_t)) * 2) * ((size_t)__pyx_v_ndim))));\n\n    /* \"../../../../anaconda3/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd\":241\n *                 # This is allocated as one block, strides first.\n *                 info.strides = <Py_ssize_t*>PyObject_Malloc(sizeof(Py_ssize_t) * 2 * <size_t>ndim)\n *                 info.shape = info.strides + ndim             # <<<<<<<<<<<<<<\n *                 for i in range(ndim):\n *                     info.strides[i] = PyArray_STRIDES(self)[i]\n */\n    __pyx_v_info->shape = (__pyx_v_info->strides + __pyx_v_ndim);\n\n    /* \"../../../../anaconda3/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd\":242\n *                 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\"../../../../anaconda3/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd\":244\n *                 for i in range(ndim):\n *                     info.strides[i] = PyArray_STRIDES(self)[i]\n *                     info.shape[i] = PyArray_DIMS(self)[i]             # <<<<<<<<<<<<<<\n *             else:\n *                 info.strides = <Py_ssize_t*>PyArray_STRIDES(self)\n */\n      (__pyx_v_info->shape[__pyx_v_i]) = (PyArray_DIMS(__pyx_v_self)[__pyx_v_i]);\n    }\n\n    /* \"../../../../anaconda3/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd\":237\n *             info.buf = PyArray_DATA(self)\n *             info.ndim = ndim\n *             if sizeof(npy_intp) != sizeof(Py_ssize_t):             # <<<<<<<<<<<<<<\n *                 # Allocate new buffer for strides and shape info.\n *                 # This is allocated as one block, strides first.\n */\n    goto __pyx_L9;\n  }\n\n  /* \"../../../../anaconda3/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd\":246\n *                     info.shape[i] = PyArray_DIMS(self)[i]\n *             else:\n *                 info.strides = <Py_ssize_t*>PyArray_STRIDES(self)             # <<<<<<<<<<<<<<\n *                 info.shape = <Py_ssize_t*>PyArray_DIMS(self)\n *             info.suboffsets = NULL\n */\n  /*else*/ {\n    __pyx_v_info->strides = ((Py_ssize_t *)PyArray_STRIDES(__pyx_v_self));\n\n    /* \"../../../../anaconda3/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd\":247\n *             else:\n *                 info.strides = <Py_ssize_t*>PyArray_STRIDES(self)\n *                 info.shape = <Py_ssize_t*>PyArray_DIMS(self)             # <<<<<<<<<<<<<<\n *             info.suboffsets = NULL\n *             info.itemsize = PyArray_ITEMSIZE(self)\n */\n    __pyx_v_info->shape = ((Py_ssize_t *)PyArray_DIMS(__pyx_v_self));\n  }\n  __pyx_L9:;\n\n  /* \"../../../../anaconda3/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd\":248\n *                 info.strides = <Py_ssize_t*>PyArray_STRIDES(self)\n *                 info.shape = <Py_ssize_t*>PyArray_DIMS(self)\n *             info.suboffsets = NULL             # <<<<<<<<<<<<<<\n *             info.itemsize = PyArray_ITEMSIZE(self)\n *             info.readonly = not PyArray_ISWRITEABLE(self)\n */\n  __pyx_v_info->suboffsets = NULL;\n\n  /* \"../../../../anaconda3/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd\":249\n *                 info.shape = <Py_ssize_t*>PyArray_DIMS(self)\n *             info.suboffsets = NULL\n *             info.itemsize = PyArray_ITEMSIZE(self)             # <<<<<<<<<<<<<<\n *             info.readonly = not PyArray_ISWRITEABLE(self)\n * \n */\n  __pyx_v_info->itemsize = PyArray_ITEMSIZE(__pyx_v_self);\n\n  /* \"../../../../anaconda3/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd\":250\n *             info.suboffsets = NULL\n *             info.itemsize = PyArray_ITEMSIZE(self)\n *             info.readonly = not PyArray_ISWRITEABLE(self)             # <<<<<<<<<<<<<<\n * \n *             cdef int t\n */\n  __pyx_v_info->readonly = (!(PyArray_ISWRITEABLE(__pyx_v_self) != 0));\n\n  /* \"../../../../anaconda3/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd\":253\n * \n *             cdef int t\n *             cdef char* f = NULL             # <<<<<<<<<<<<<<\n *             cdef dtype descr = self.descr\n *             cdef int offset\n */\n  __pyx_v_f = NULL;\n\n  /* \"../../../../anaconda3/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd\":254\n *             cdef int t\n *             cdef char* f = NULL\n *             cdef dtype descr = self.descr             # <<<<<<<<<<<<<<\n *             cdef int offset\n * \n */\n  __pyx_t_3 = ((PyObject *)__pyx_v_self->descr);\n  __Pyx_INCREF(__pyx_t_3);\n  __pyx_v_descr = ((PyArray_Descr *)__pyx_t_3);\n  __pyx_t_3 = 0;\n\n  /* \"../../../../anaconda3/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd\":257\n *             cdef int offset\n * \n *             info.obj = self             # <<<<<<<<<<<<<<\n * \n *             if not PyDataType_HASFIELDS(descr):\n */\n  __Pyx_INCREF(((PyObject *)__pyx_v_self));\n  __Pyx_GIVEREF(((PyObject *)__pyx_v_self));\n  __Pyx_GOTREF(__pyx_v_info->obj);\n  __Pyx_DECREF(__pyx_v_info->obj);\n  __pyx_v_info->obj = ((PyObject *)__pyx_v_self);\n\n  /* \"../../../../anaconda3/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd\":259\n *             info.obj = self\n * \n *             if not PyDataType_HASFIELDS(descr):             # <<<<<<<<<<<<<<\n *                 t = descr.type_num\n *                 if ((descr.byteorder == c'>' and little_endian) or\n */\n  __pyx_t_1 = ((!(PyDataType_HASFIELDS(__pyx_v_descr) != 0)) != 0);\n  if (__pyx_t_1) {\n\n    /* \"../../../../anaconda3/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd\":260\n * \n *             if not PyDataType_HASFIELDS(descr):\n *                 t = descr.type_num             # <<<<<<<<<<<<<<\n *                 if ((descr.byteorder == c'>' and little_endian) or\n *                     (descr.byteorder == c'<' and not little_endian)):\n */\n    __pyx_t_4 = __pyx_v_descr->type_num;\n    __pyx_v_t = __pyx_t_4;\n\n    /* \"../../../../anaconda3/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd\":261\n *             if not PyDataType_HASFIELDS(descr):\n *                 t = descr.type_num\n *                 if ((descr.byteorder == c'>' and little_endian) or             # <<<<<<<<<<<<<<\n *                     (descr.byteorder == c'<' and not little_endian)):\n *                     raise ValueError(u\"Non-native byte order not supported\")\n */\n    __pyx_t_2 = ((__pyx_v_descr->byteorder == '>') != 0);\n    if (!__pyx_t_2) {\n      goto __pyx_L15_next_or;\n    } else {\n    }\n    __pyx_t_2 = (__pyx_v_little_endian != 0);\n    if (!__pyx_t_2) {\n    } else {\n      __pyx_t_1 = __pyx_t_2;\n      goto __pyx_L14_bool_binop_done;\n    }\n    __pyx_L15_next_or:;\n\n    /* \"../../../../anaconda3/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd\":262\n *                 t = descr.type_num\n *                 if ((descr.byteorder == c'>' and little_endian) or\n *                     (descr.byteorder == c'<' and not little_endian)):             # <<<<<<<<<<<<<<\n *                     raise ValueError(u\"Non-native byte order not supported\")\n *                 if   t == NPY_BYTE:        f = \"b\"\n */\n    __pyx_t_2 = ((__pyx_v_descr->byteorder == '<') != 0);\n    if (__pyx_t_2) {\n    } else {\n      __pyx_t_1 = __pyx_t_2;\n      goto __pyx_L14_bool_binop_done;\n    }\n    __pyx_t_2 = ((!(__pyx_v_little_endian != 0)) != 0);\n    __pyx_t_1 = __pyx_t_2;\n    __pyx_L14_bool_binop_done:;\n\n    /* 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__Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__7, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 263, __pyx_L1_error)\n      __Pyx_GOTREF(__pyx_t_3);\n      __Pyx_Raise(__pyx_t_3, 0, 0, 0);\n      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;\n      __PYX_ERR(1, 263, __pyx_L1_error)\n\n      /* \"../../../../anaconda3/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd\":261\n *             if not PyDataType_HASFIELDS(descr):\n *                 t = descr.type_num\n *                 if ((descr.byteorder == c'>' and little_endian) or             # <<<<<<<<<<<<<<\n *                     (descr.byteorder == c'<' and not little_endian)):\n *                     raise ValueError(u\"Non-native byte order not supported\")\n */\n    }\n\n    /* \"../../../../anaconda3/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd\":264\n *                     (descr.byteorder == c'<' and not little_endian)):\n *                     raise ValueError(u\"Non-native byte order not supported\")\n *                 if   t == NPY_BYTE:        f = \"b\"             # <<<<<<<<<<<<<<\n *                 elif t == NPY_UBYTE:       f = \"B\"\n *                 elif t == NPY_SHORT:       f = \"h\"\n */\n    switch (__pyx_v_t) {\n      case NPY_BYTE:\n      __pyx_v_f = ((char *)\"b\");\n      break;\n\n      /* \"../../../../anaconda3/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd\":265\n *                     raise ValueError(u\"Non-native byte order not supported\")\n *                 if   t == NPY_BYTE:        f = \"b\"\n *                 elif t == NPY_UBYTE:       f = \"B\"             # <<<<<<<<<<<<<<\n *                 elif t == NPY_SHORT:       f = \"h\"\n *                 elif t == NPY_USHORT:      f = \"H\"\n */\n      case NPY_UBYTE:\n      __pyx_v_f = ((char *)\"B\");\n      break;\n\n      /* \"../../../../anaconda3/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd\":266\n *                 if   t == NPY_BYTE:        f = 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                elif t == NPY_UINT:        f = \"I\"\n *                 elif t == NPY_LONG:        f = \"l\"             # <<<<<<<<<<<<<<\n *                 elif t == NPY_ULONG:       f = \"L\"\n *                 elif t == NPY_LONGLONG:    f = \"q\"\n */\n      case NPY_LONG:\n      __pyx_v_f = ((char *)\"l\");\n      break;\n\n      /* \"../../../../anaconda3/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd\":271\n *                 elif t == NPY_UINT:        f = \"I\"\n *                 elif t == NPY_LONG:        f = \"l\"\n *                 elif t == NPY_ULONG:       f = \"L\"             # <<<<<<<<<<<<<<\n *                 elif t == NPY_LONGLONG:    f = \"q\"\n *                 elif t == NPY_ULONGLONG:   f = \"Q\"\n */\n      case NPY_ULONG:\n      __pyx_v_f = ((char *)\"L\");\n      break;\n\n      /* \"../../../../anaconda3/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd\":272\n *                 elif t == NPY_LONG:        f = \"l\"\n *                 elif t == NPY_ULONG:       f = \"L\"\n *                 elif t == NPY_LONGLONG:    f = \"q\"             # <<<<<<<<<<<<<<\n *                 elif t == NPY_ULONGLONG:   f = \"Q\"\n *                 elif t == NPY_FLOAT:       f = \"f\"\n */\n      case NPY_LONGLONG:\n      __pyx_v_f = ((char *)\"q\");\n      break;\n\n      /* \"../../../../anaconda3/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd\":273\n *                 elif t == NPY_ULONG:       f = \"L\"\n *                 elif t == NPY_LONGLONG:    f = \"q\"\n *                 elif t == NPY_ULONGLONG:   f = \"Q\"             # <<<<<<<<<<<<<<\n *                 elif t == NPY_FLOAT:       f = \"f\"\n *                 elif t == NPY_DOUBLE:      f = \"d\"\n */\n      case NPY_ULONGLONG:\n      __pyx_v_f = ((char *)\"Q\");\n      break;\n\n      /* \"../../../../anaconda3/lib/python3.6/site-packages/Cython/Includes/numpy/__init__.pxd\":274\n *                 elif t == NPY_LONGLONG:    f = \"q\"\n *         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<<<<<<<<<<<<<<\n *     try:\n *         _import_umath()\n */\n\n  /*--- Wrapped vars code ---*/\n\n  goto __pyx_L0;\n  __pyx_L1_error:;\n  __Pyx_XDECREF(__pyx_t_1);\n  if (__pyx_m) {\n    if (__pyx_d) {\n      __Pyx_AddTraceback(\"init gpu_nms\", 0, __pyx_lineno, __pyx_filename);\n    }\n    Py_DECREF(__pyx_m); __pyx_m = 0;\n  } else if (!PyErr_Occurred()) {\n    PyErr_SetString(PyExc_ImportError, \"init gpu_nms\");\n  }\n  __pyx_L0:;\n  __Pyx_RefNannyFinishContext();\n  #if CYTHON_PEP489_MULTI_PHASE_INIT\n  return (__pyx_m != NULL) ? 0 : -1;\n  #elif PY_MAJOR_VERSION >= 3\n  return __pyx_m;\n  #else\n  return;\n  #endif\n}\n\n/* --- Runtime support code --- */\n/* Refnanny */\n#if CYTHON_REFNANNY\nstatic __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname) {\n    PyObject *m = NULL, *p = NULL;\n    void *r = NULL;\n    m = PyImport_ImportModule((char *)modname);\n    if (!m) goto end;\n    p = PyObject_GetAttrString(m, (char *)\"RefNannyAPI\");\n    if (!p) goto 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__Pyx_RaiseDoubleKeywordsError(\n    const char* func_name,\n    PyObject* kw_name)\n{\n    PyErr_Format(PyExc_TypeError,\n        #if PY_MAJOR_VERSION >= 3\n        \"%s() got multiple values for keyword argument '%U'\", func_name, kw_name);\n        #else\n        \"%s() got multiple values for keyword argument '%s'\", func_name,\n        PyString_AsString(kw_name));\n        #endif\n}\n\n/* ParseKeywords */\nstatic int __Pyx_ParseOptionalKeywords(\n    PyObject *kwds,\n    PyObject **argnames[],\n    PyObject *kwds2,\n    PyObject *values[],\n    Py_ssize_t num_pos_args,\n    const char* function_name)\n{\n    PyObject *key = 0, *value = 0;\n    Py_ssize_t pos = 0;\n    PyObject*** name;\n    PyObject*** first_kw_arg = argnames + num_pos_args;\n    while (PyDict_Next(kwds, &pos, &key, &value)) {\n        name = first_kw_arg;\n        while (*name && (**name != key)) name++;\n        if (*name) {\n            values[name-argnames] = value;\n            continue;\n        }\n        name = first_kw_arg;\n        #if PY_MAJOR_VERSION < 3\n        if (likely(PyString_CheckExact(key)) || likely(PyString_Check(key))) {\n            while (*name) {\n                if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key))\n                        && _PyString_Eq(**name, key)) {\n                    values[name-argnames] = value;\n                    break;\n                }\n                name++;\n            }\n            if (*name) continue;\n            else {\n                PyObject*** argname = argnames;\n                while (argname != first_kw_arg) {\n                    if ((**argname == key) || (\n                            (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key))\n                             && _PyString_Eq(**argname, key))) {\n                        goto arg_passed_twice;\n                    }\n                    argname++;\n                }\n            }\n        } else\n 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__Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx,\n                              __Pyx_BufFmt_StackElem* stack,\n                              __Pyx_TypeInfo* type) {\n  stack[0].field = &ctx->root;\n  stack[0].parent_offset = 0;\n  ctx->root.type = type;\n  ctx->root.name = \"buffer dtype\";\n  ctx->root.offset = 0;\n  ctx->head = stack;\n  ctx->head->field = &ctx->root;\n  ctx->fmt_offset = 0;\n  ctx->head->parent_offset = 0;\n  ctx->new_packmode = '@';\n  ctx->enc_packmode = '@';\n  ctx->new_count = 1;\n  ctx->enc_count = 0;\n  ctx->enc_type = 0;\n  ctx->is_complex = 0;\n  ctx->is_valid_array = 0;\n  ctx->struct_alignment = 0;\n  while (type->typegroup == 'S') {\n    ++ctx->head;\n    ctx->head->field = type->fields;\n    ctx->head->parent_offset = 0;\n    type = type->fields->type;\n  }\n}\nstatic int __Pyx_BufFmt_ParseNumber(const char** ts) {\n    int count;\n    const char* t = *ts;\n    if (*t < '0' || *t > '9') {\n      return -1;\n    } else {\n        count = *t++ - '0';\n        while (*t >= '0' && *t < '9') {\n            count *= 10;\n            count += *t++ - '0';\n        }\n    }\n    *ts = t;\n    return count;\n}\nstatic int __Pyx_BufFmt_ExpectNumber(const char **ts) {\n    int number = __Pyx_BufFmt_ParseNumber(ts);\n    if (number == -1)\n        PyErr_Format(PyExc_ValueError,\\\n                     \"Does not understand character buffer dtype format string ('%c')\", **ts);\n    return number;\n}\nstatic void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) {\n  PyErr_Format(PyExc_ValueError,\n               \"Unexpected format string character: '%c'\", ch);\n}\nstatic const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) {\n  switch (ch) {\n    case 'c': return \"'char'\";\n    case 'b': return \"'signed char'\";\n    case 'B': return \"'unsigned char'\";\n    case 'h': return \"'short'\";\n    case 'H': return \"'unsigned short'\";\n    case 'i': return \"'int'\";\n    case 'I': return \"'unsigned int'\";\n    case 'l': return \"'long'\";\n    case 'L': return \"'unsigned long'\";\n    case 'q': return \"'long long'\";\n    case 'Q': return \"'unsigned long long'\";\n    case 'f': return (is_complex ? \"'complex float'\" : \"'float'\");\n    case 'd': return (is_complex ? \"'complex double'\" : \"'double'\");\n    case 'g': return (is_complex ? \"'complex long double'\" : \"'long double'\");\n    case 'T': return \"a struct\";\n    case 'O': return \"Python object\";\n    case 'P': return \"a pointer\";\n    case 's': case 'p': return \"a string\";\n    case 0: return \"end\";\n    default: return \"unparseable format string\";\n  }\n}\nstatic size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) {\n  switch (ch) {\n    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;\n    case 'h': case 'H': return 2;\n    case 'i': case 'I': case 'l': case 'L': return 4;\n    case 'q': case 'Q': return 8;\n    case 'f': return (is_complex ? 8 : 4);\n    case 'd': return (is_complex ? 16 : 8);\n    case 'g': {\n      PyErr_SetString(PyExc_ValueError, \"Python does not define a standard format string size for long double ('g')..\");\n      return 0;\n    }\n    case 'O': case 'P': return sizeof(void*);\n    default:\n      __Pyx_BufFmt_RaiseUnexpectedChar(ch);\n      return 0;\n    }\n}\nstatic size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) {\n  switch (ch) {\n    case 'c': case 'b': case 'B': case 's': case 'p': return 1;\n    case 'h': case 'H': return sizeof(short);\n    case 'i': case 'I': return sizeof(int);\n    case 'l': case 'L': return sizeof(long);\n    #ifdef HAVE_LONG_LONG\n    case 'q': case 'Q': return sizeof(PY_LONG_LONG);\n    #endif\n    case 'f': return sizeof(float) * (is_complex ? 2 : 1);\n    case 'd': return sizeof(double) * (is_complex ? 2 : 1);\n    case 'g': return sizeof(long double) * (is_complex ? 2 : 1);\n    case 'O': case 'P': return sizeof(void*);\n    default: {\n      __Pyx_BufFmt_RaiseUnexpectedChar(ch);\n      return 0;\n    }\n  }\n}\ntypedef struct { char c; short x; } __Pyx_st_short;\ntypedef struct { char c; int x; } __Pyx_st_int;\ntypedef struct { char c; long x; } __Pyx_st_long;\ntypedef struct { char c; float x; } __Pyx_st_float;\ntypedef struct { char c; double x; } __Pyx_st_double;\ntypedef struct { char c; long double x; } __Pyx_st_longdouble;\ntypedef struct { char c; void *x; } __Pyx_st_void_p;\n#ifdef HAVE_LONG_LONG\ntypedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong;\n#endif\nstatic size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, CYTHON_UNUSED int is_complex) {\n  switch (ch) {\n    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;\n    case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short);\n    case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int);\n    case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long);\n#ifdef HAVE_LONG_LONG\n    case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG);\n#endif\n    case 'f': return sizeof(__Pyx_st_float) - sizeof(float);\n    case 'd': return sizeof(__Pyx_st_double) - sizeof(double);\n    case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double);\n    case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*);\n    default:\n      __Pyx_BufFmt_RaiseUnexpectedChar(ch);\n      return 0;\n    }\n}\n/* These are for computing the padding at the end of the struct to align\n   on the first member of the struct. This will probably the same as above,\n   but we don't have any guarantees.\n */\ntypedef struct { short x; char c; } __Pyx_pad_short;\ntypedef struct { int x; char c; } __Pyx_pad_int;\ntypedef struct { long x; char c; } __Pyx_pad_long;\ntypedef struct { float x; char c; } __Pyx_pad_float;\ntypedef struct { double x; char c; } __Pyx_pad_double;\ntypedef struct { long double x; char c; } __Pyx_pad_longdouble;\ntypedef struct { void *x; char c; } __Pyx_pad_void_p;\n#ifdef HAVE_LONG_LONG\ntypedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong;\n#endif\nstatic size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) {\n  switch (ch) {\n    case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1;\n    case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short);\n    case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int);\n    case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long);\n#ifdef HAVE_LONG_LONG\n    case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG);\n#endif\n    case 'f': return sizeof(__Pyx_pad_float) - sizeof(float);\n    case 'd': return sizeof(__Pyx_pad_double) - sizeof(double);\n    case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double);\n    case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*);\n    default:\n      __Pyx_BufFmt_RaiseUnexpectedChar(ch);\n      return 0;\n    }\n}\nstatic char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) {\n  switch (ch) {\n    case 'c':\n        return 'H';\n    case 'b': case 'h': case 'i':\n    case 'l': case 'q': case 's': case 'p':\n        return 'I';\n    case 'B': case 'H': case 'I': case 'L': case 'Q':\n        return 'U';\n    case 'f': case 'd': case 'g':\n        return (is_complex ? 'C' : 'R');\n    case 'O':\n        return 'O';\n    case 'P':\n        return 'P';\n    default: {\n      __Pyx_BufFmt_RaiseUnexpectedChar(ch);\n      return 0;\n    }\n  }\n}\nstatic void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) {\n  if (ctx->head == NULL || ctx->head->field == &ctx->root) {\n    const char* expected;\n    const char* quote;\n    if (ctx->head == NULL) {\n      expected = \"end\";\n      quote = \"\";\n    } else {\n      expected = ctx->head->field->type->name;\n      quote = \"'\";\n    }\n    PyErr_Format(PyExc_ValueError,\n                 \"Buffer dtype mismatch, expected %s%s%s but got %s\",\n                 quote, expected, quote,\n                 __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex));\n  } else {\n    __Pyx_StructField* field = ctx->head->field;\n    __Pyx_StructField* parent = (ctx->head - 1)->field;\n    PyErr_Format(PyExc_ValueError,\n                 \"Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'\",\n                 field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex),\n                 parent->type->name, field->name);\n  }\n}\nstatic int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) {\n  char group;\n  size_t size, offset, arraysize = 1;\n  if (ctx->enc_type == 0) return 0;\n  if (ctx->head->field->type->arraysize[0]) {\n    int i, ndim = 0;\n    if (ctx->enc_type == 's' || ctx->enc_type == 'p') {\n        ctx->is_valid_array = ctx->head->field->type->ndim == 1;\n        ndim = 1;\n        if (ctx->enc_count != ctx->head->field->type->arraysize[0]) {\n            PyErr_Format(PyExc_ValueError,\n                         \"Expected a dimension of size %zu, got %zu\",\n                         ctx->head->field->type->arraysize[0], ctx->enc_count);\n            return -1;\n        }\n    }\n    if (!ctx->is_valid_array) {\n      PyErr_Format(PyExc_ValueError, \"Expected %d dimensions, got %d\",\n                   ctx->head->field->type->ndim, ndim);\n      return -1;\n    }\n    for (i = 0; i < ctx->head->field->type->ndim; i++) {\n      arraysize *= ctx->head->field->type->arraysize[i];\n    }\n    ctx->is_valid_array = 0;\n    ctx->enc_count = 1;\n  }\n  group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex);\n  do {\n    __Pyx_StructField* field = ctx->head->field;\n    __Pyx_TypeInfo* type = field->type;\n    if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') {\n      size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex);\n    } else {\n      size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex);\n    }\n    if (ctx->enc_packmode == '@') {\n      size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex);\n      size_t align_mod_offset;\n      if (align_at == 0) return -1;\n      align_mod_offset = ctx->fmt_offset % align_at;\n      if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset;\n      if (ctx->struct_alignment == 0)\n          ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type,\n                                                                 ctx->is_complex);\n    }\n    if (type->size != size || type->typegroup != group) {\n      if (type->typegroup == 'C' && type->fields != NULL) {\n        size_t parent_offset = ctx->head->parent_offset + field->offset;\n        ++ctx->head;\n        ctx->head->field = type->fields;\n        ctx->head->parent_offset = parent_offset;\n        continue;\n      }\n      if ((type->typegroup == 'H' || group == 'H') && type->size == size) {\n      } else {\n          __Pyx_BufFmt_RaiseExpected(ctx);\n          return -1;\n      }\n    }\n    offset = ctx->head->parent_offset + field->offset;\n    if (ctx->fmt_offset != offset) {\n      PyErr_Format(PyExc_ValueError,\n                   \"Buffer dtype mismatch; next field is at offset %\" CYTHON_FORMAT_SSIZE_T \"d but %\" CYTHON_FORMAT_SSIZE_T \"d expected\",\n                   (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset);\n      return -1;\n    }\n    ctx->fmt_offset += size;\n    if (arraysize)\n      ctx->fmt_offset += (arraysize - 1) * size;\n    --ctx->enc_count;\n    while (1) {\n      if (field == &ctx->root) {\n        ctx->head = NULL;\n        if (ctx->enc_count != 0) {\n          __Pyx_BufFmt_RaiseExpected(ctx);\n          return -1;\n        }\n        break;\n      }\n      ctx->head->field = ++field;\n      if (field->type == NULL) {\n        --ctx->head;\n        field = ctx->head->field;\n        continue;\n      } else if (field->type->typegroup == 'S') {\n        size_t parent_offset = ctx->head->parent_offset + field->offset;\n        if (field->type->fields->type == NULL) continue;\n        field = field->type->fields;\n        ++ctx->head;\n        ctx->head->field = field;\n        ctx->head->parent_offset = parent_offset;\n        break;\n      } else {\n        break;\n      }\n    }\n  } while (ctx->enc_count);\n  ctx->enc_type = 0;\n  ctx->is_complex = 0;\n  return 0;\n}\nstatic PyObject *\n__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp)\n{\n    const char *ts = *tsp;\n    int i = 0, number;\n    int ndim = ctx->head->field->type->ndim;\n;\n    ++ts;\n    if (ctx->new_count != 1) {\n        PyErr_SetString(PyExc_ValueError,\n                        \"Cannot handle repeated arrays in format string\");\n        return NULL;\n    }\n    if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;\n    while (*ts && *ts != ')') {\n        switch (*ts) {\n            case ' ': case '\\f': case '\\r': case '\\n': case '\\t': case '\\v':  continue;\n            default:  break;\n        }\n        number = __Pyx_BufFmt_ExpectNumber(&ts);\n        if (number == -1) return NULL;\n        if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i])\n            return PyErr_Format(PyExc_ValueError,\n                        \"Expected a dimension of size %zu, got %d\",\n                        ctx->head->field->type->arraysize[i], number);\n        if (*ts != ',' && *ts != ')')\n            return PyErr_Format(PyExc_ValueError,\n                                \"Expected a comma in format string, got '%c'\", *ts);\n        if (*ts == ',') ts++;\n        i++;\n    }\n    if (i != ndim)\n        return PyErr_Format(PyExc_ValueError, \"Expected %d dimension(s), got %d\",\n                            ctx->head->field->type->ndim, i);\n    if (!*ts) {\n        PyErr_SetString(PyExc_ValueError,\n                        \"Unexpected end of format string, expected ')'\");\n        return NULL;\n    }\n    ctx->is_valid_array = 1;\n    ctx->new_count = 1;\n    *tsp = ++ts;\n    return Py_None;\n}\nstatic const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) {\n  int got_Z = 0;\n  while (1) {\n    switch(*ts) {\n      case 0:\n        if (ctx->enc_type != 0 && ctx->head == NULL) {\n          __Pyx_BufFmt_RaiseExpected(ctx);\n          return NULL;\n        }\n        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;\n        if (ctx->head != NULL) {\n          __Pyx_BufFmt_RaiseExpected(ctx);\n          return NULL;\n        }\n        return ts;\n      case ' ':\n      case '\\r':\n      case '\\n':\n        ++ts;\n        break;\n      case '<':\n        if (!__Pyx_Is_Little_Endian()) {\n          PyErr_SetString(PyExc_ValueError, \"Little-endian buffer not supported on big-endian compiler\");\n          return NULL;\n        }\n        ctx->new_packmode = '=';\n        ++ts;\n        break;\n      case '>':\n      case '!':\n        if (__Pyx_Is_Little_Endian()) {\n          PyErr_SetString(PyExc_ValueError, \"Big-endian buffer not supported on little-endian compiler\");\n          return NULL;\n        }\n        ctx->new_packmode = '=';\n        ++ts;\n        break;\n      case '=':\n      case '@':\n      case '^':\n        ctx->new_packmode = *ts++;\n        break;\n      case 'T':\n        {\n          const char* ts_after_sub;\n          size_t i, struct_count = ctx->new_count;\n          size_t struct_alignment = ctx->struct_alignment;\n          ctx->new_count = 1;\n          ++ts;\n          if (*ts != '{') {\n            PyErr_SetString(PyExc_ValueError, \"Buffer acquisition: Expected '{' after 'T'\");\n            return NULL;\n          }\n          if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;\n          ctx->enc_type = 0;\n          ctx->enc_count = 0;\n          ctx->struct_alignment = 0;\n          ++ts;\n          ts_after_sub = ts;\n          for (i = 0; i != struct_count; ++i) {\n            ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts);\n            if (!ts_after_sub) return NULL;\n          }\n          ts = ts_after_sub;\n          if (struct_alignment) ctx->struct_alignment = struct_alignment;\n        }\n        break;\n      case '}':\n        {\n          size_t alignment = ctx->struct_alignment;\n          ++ts;\n          if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;\n          ctx->enc_type = 0;\n          if (alignment && ctx->fmt_offset % alignment) {\n            ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment);\n          }\n        }\n        return ts;\n      case 'x':\n        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;\n        ctx->fmt_offset += ctx->new_count;\n        ctx->new_count = 1;\n        ctx->enc_count = 0;\n        ctx->enc_type = 0;\n        ctx->enc_packmode = ctx->new_packmode;\n        ++ts;\n        break;\n      case 'Z':\n        got_Z = 1;\n        ++ts;\n        if (*ts != 'f' && *ts != 'd' && *ts != 'g') {\n          __Pyx_BufFmt_RaiseUnexpectedChar('Z');\n          return NULL;\n        }\n        CYTHON_FALLTHROUGH;\n      case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I':\n      case 'l': case 'L': case 'q': case 'Q':\n      case 'f': case 'd': case 'g':\n      case 'O': case 'p':\n        if (ctx->enc_type == *ts && got_Z == ctx->is_complex &&\n            ctx->enc_packmode == ctx->new_packmode) {\n          ctx->enc_count += ctx->new_count;\n          ctx->new_count = 1;\n          got_Z = 0;\n          ++ts;\n          break;\n        }\n        CYTHON_FALLTHROUGH;\n      case 's':\n        if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL;\n        ctx->enc_count = ctx->new_count;\n        ctx->enc_packmode = ctx->new_packmode;\n        ctx->enc_type = *ts;\n        ctx->is_complex = got_Z;\n        ++ts;\n        ctx->new_count = 1;\n        got_Z = 0;\n        break;\n      case ':':\n        ++ts;\n        while(*ts != ':') ++ts;\n        ++ts;\n        break;\n      case '(':\n        if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL;\n        break;\n      default:\n        {\n          int number = __Pyx_BufFmt_ExpectNumber(&ts);\n          if (number == -1) return NULL;\n          ctx->new_count = (size_t)number;\n        }\n    }\n  }\n}\n\n/* BufferGetAndValidate */\n  static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info) {\n  if (unlikely(info->buf == NULL)) return;\n  if (info->suboffsets == __Pyx_minusones) info->suboffsets = NULL;\n  __Pyx_ReleaseBuffer(info);\n}\nstatic void __Pyx_ZeroBuffer(Py_buffer* buf) {\n  buf->buf = NULL;\n  buf->obj = NULL;\n  buf->strides = __Pyx_zeros;\n  buf->shape = __Pyx_zeros;\n  buf->suboffsets = __Pyx_minusones;\n}\nstatic int __Pyx__GetBufferAndValidate(\n        Py_buffer* buf, PyObject* obj,  __Pyx_TypeInfo* dtype, int flags,\n        int nd, int cast, __Pyx_BufFmt_StackElem* stack)\n{\n  buf->buf = NULL;\n  if (unlikely(__Pyx_GetBuffer(obj, buf, flags) == -1)) {\n    __Pyx_ZeroBuffer(buf);\n    return -1;\n  }\n  if (unlikely(buf->ndim != nd)) {\n    PyErr_Format(PyExc_ValueError,\n                 \"Buffer has wrong number of dimensions (expected %d, got %d)\",\n                 nd, buf->ndim);\n    goto fail;\n  }\n  if (!cast) {\n    __Pyx_BufFmt_Context ctx;\n    __Pyx_BufFmt_Init(&ctx, stack, dtype);\n    if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail;\n  }\n  if (unlikely((unsigned)buf->itemsize != dtype->size)) {\n    PyErr_Format(PyExc_ValueError,\n      \"Item size of buffer (%\" CYTHON_FORMAT_SSIZE_T \"d byte%s) does not match size of '%s' (%\" CYTHON_FORMAT_SSIZE_T \"d byte%s)\",\n      buf->itemsize, (buf->itemsize > 1) ? \"s\" : \"\",\n      dtype->name, (Py_ssize_t)dtype->size, (dtype->size > 1) ? \"s\" : \"\");\n    goto fail;\n  }\n  if (buf->suboffsets == NULL) buf->suboffsets = __Pyx_minusones;\n  return 0;\nfail:;\n  __Pyx_SafeReleaseBuffer(buf);\n  return -1;\n}\n\n/* PyObjectGetAttrStr */\n  #if CYTHON_USE_TYPE_SLOTS\nstatic CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name) {\n    PyTypeObject* tp = Py_TYPE(obj);\n    if (likely(tp->tp_getattro))\n        return tp->tp_getattro(obj, attr_name);\n#if PY_MAJOR_VERSION < 3\n    if (likely(tp->tp_getattr))\n        return tp->tp_getattr(obj, PyString_AS_STRING(attr_name));\n#endif\n    return PyObject_GetAttr(obj, attr_name);\n}\n#endif\n\n/* GetBuiltinName */\n  static PyObject *__Pyx_GetBuiltinName(PyObject *name) {\n    PyObject* result = __Pyx_PyObject_GetAttrStr(__pyx_b, name);\n    if (unlikely(!result)) {\n        PyErr_Format(PyExc_NameError,\n#if PY_MAJOR_VERSION >= 3\n            \"name '%U' is not defined\", name);\n#else\n            \"name '%.200s' is not defined\", PyString_AS_STRING(name));\n#endif\n    }\n    return result;\n}\n\n/* GetModuleGlobalName */\n  static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name) {\n    PyObject *result;\n#if !CYTHON_AVOID_BORROWED_REFS\n#if CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x030500A1\n    result = _PyDict_GetItem_KnownHash(__pyx_d, name, ((PyASCIIObject *) name)->hash);\n    if (likely(result)) {\n        Py_INCREF(result);\n    } else if (unlikely(PyErr_Occurred())) {\n        result = NULL;\n    } else {\n#else\n    result = PyDict_GetItem(__pyx_d, name);\n    if (likely(result)) {\n        Py_INCREF(result);\n    } else {\n#endif\n#else\n    result = PyObject_GetItem(__pyx_d, name);\n    if (!result) {\n        PyErr_Clear();\n#endif\n        result = __Pyx_GetBuiltinName(name);\n    }\n    return result;\n}\n\n/* PyObjectCall */\n      #if CYTHON_COMPILING_IN_CPYTHON\nstatic CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) {\n    PyObject *result;\n    ternaryfunc call = func->ob_type->tp_call;\n    if (unlikely(!call))\n        return PyObject_Call(func, arg, kw);\n    if (unlikely(Py_EnterRecursiveCall((char*)\" while calling a Python object\")))\n        return NULL;\n    result = (*call)(func, arg, kw);\n    Py_LeaveRecursiveCall();\n    if (unlikely(!result) && unlikely(!PyErr_Occurred())) {\n        PyErr_SetString(\n            PyExc_SystemError,\n            \"NULL result without error in PyObject_Call\");\n    }\n    return result;\n}\n#endif\n\n/* ExtTypeTest */\n      static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) {\n    if (unlikely(!type)) {\n        PyErr_SetString(PyExc_SystemError, \"Missing type object\");\n        return 0;\n    }\n    if (likely(__Pyx_TypeCheck(obj, type)))\n        return 1;\n    PyErr_Format(PyExc_TypeError, \"Cannot convert %.200s to %.200s\",\n                 Py_TYPE(obj)->tp_name, type->tp_name);\n    return 0;\n}\n\n/* GetItemInt */\n      static PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) {\n    PyObject *r;\n    if (!j) return NULL;\n    r = PyObject_GetItem(o, j);\n    Py_DECREF(j);\n    return r;\n}\nstatic CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i,\n                                                              CYTHON_NCP_UNUSED int wraparound,\n                                                              CYTHON_NCP_UNUSED int boundscheck) {\n#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS\n    Py_ssize_t wrapped_i = i;\n    if (wraparound & unlikely(i < 0)) {\n        wrapped_i += PyList_GET_SIZE(o);\n    }\n    if ((!boundscheck) || likely((0 <= wrapped_i) & (wrapped_i < PyList_GET_SIZE(o)))) {\n        PyObject *r = PyList_GET_ITEM(o, wrapped_i);\n        Py_INCREF(r);\n        return r;\n    }\n    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));\n#else\n    return PySequence_GetItem(o, i);\n#endif\n}\nstatic CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i,\n                                                              CYTHON_NCP_UNUSED int wraparound,\n                                                              CYTHON_NCP_UNUSED int boundscheck) {\n#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS\n    Py_ssize_t wrapped_i = i;\n    if (wraparound & unlikely(i < 0)) {\n        wrapped_i += PyTuple_GET_SIZE(o);\n    }\n    if ((!boundscheck) || likely((0 <= wrapped_i) & (wrapped_i < PyTuple_GET_SIZE(o)))) {\n        PyObject *r = PyTuple_GET_ITEM(o, wrapped_i);\n        Py_INCREF(r);\n        return r;\n    }\n    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));\n#else\n    return PySequence_GetItem(o, i);\n#endif\n}\nstatic CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i, int is_list,\n                                                     CYTHON_NCP_UNUSED int wraparound,\n                                                     CYTHON_NCP_UNUSED int boundscheck) {\n#if CYTHON_ASSUME_SAFE_MACROS && !CYTHON_AVOID_BORROWED_REFS && CYTHON_USE_TYPE_SLOTS\n    if (is_list || PyList_CheckExact(o)) {\n        Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyList_GET_SIZE(o);\n        if ((!boundscheck) || (likely((n >= 0) & (n < PyList_GET_SIZE(o))))) {\n            PyObject *r = PyList_GET_ITEM(o, n);\n            Py_INCREF(r);\n            return r;\n        }\n    }\n    else if (PyTuple_CheckExact(o)) {\n        Py_ssize_t n = ((!wraparound) | likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o);\n        if ((!boundscheck) || likely((n >= 0) & (n < PyTuple_GET_SIZE(o)))) {\n            PyObject *r = PyTuple_GET_ITEM(o, n);\n            Py_INCREF(r);\n            return r;\n        }\n    } else {\n        PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence;\n        if (likely(m && m->sq_item)) {\n            if (wraparound && unlikely(i < 0) && likely(m->sq_length)) {\n                Py_ssize_t l = m->sq_length(o);\n                if (likely(l >= 0)) {\n                    i += l;\n                } else {\n                    if (!PyErr_ExceptionMatches(PyExc_OverflowError))\n                        return NULL;\n                    PyErr_Clear();\n                }\n            }\n            return m->sq_item(o, i);\n        }\n    }\n#else\n    if (is_list || PySequence_Check(o)) {\n        return PySequence_GetItem(o, i);\n    }\n#endif\n    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));\n}\n\n/* ObjectGetItem */\n      #if CYTHON_USE_TYPE_SLOTS\nstatic PyObject *__Pyx_PyObject_GetIndex(PyObject *obj, PyObject* index) {\n    PyObject *runerr;\n    Py_ssize_t key_value;\n    PySequenceMethods *m = Py_TYPE(obj)->tp_as_sequence;\n    if (unlikely(!(m && m->sq_item))) {\n        PyErr_Format(PyExc_TypeError, \"'%.200s' object is not subscriptable\", Py_TYPE(obj)->tp_name);\n        return NULL;\n    }\n    key_value = __Pyx_PyIndex_AsSsize_t(index);\n    if (likely(key_value != -1 || !(runerr = PyErr_Occurred()))) {\n        return __Pyx_GetItemInt_Fast(obj, key_value, 0, 1, 1);\n    }\n    if (PyErr_GivenExceptionMatches(runerr, PyExc_OverflowError)) {\n        PyErr_Clear();\n        PyErr_Format(PyExc_IndexError, \"cannot fit '%.200s' into an index-sized integer\", Py_TYPE(index)->tp_name);\n    }\n    return NULL;\n}\nstatic PyObject *__Pyx_PyObject_GetItem(PyObject *obj, PyObject* key) {\n    PyMappingMethods *m = Py_TYPE(obj)->tp_as_mapping;\n    if (likely(m && m->mp_subscript)) {\n        return m->mp_subscript(obj, key);\n    }\n    return __Pyx_PyObject_GetIndex(obj, key);\n}\n#endif\n\n/* PyCFunctionFastCall */\n      #if CYTHON_FAST_PYCCALL\nstatic CYTHON_INLINE PyObject * __Pyx_PyCFunction_FastCall(PyObject *func_obj, PyObject **args, Py_ssize_t nargs) {\n    PyCFunctionObject *func = (PyCFunctionObject*)func_obj;\n    PyCFunction meth = PyCFunction_GET_FUNCTION(func);\n    PyObject *self = PyCFunction_GET_SELF(func);\n    int flags = PyCFunction_GET_FLAGS(func);\n    assert(PyCFunction_Check(func));\n    assert(METH_FASTCALL == (flags & ~(METH_CLASS | METH_STATIC | METH_COEXIST | METH_KEYWORDS)));\n    assert(nargs >= 0);\n    assert(nargs == 0 || args != NULL);\n    /* _PyCFunction_FastCallDict() must not be called with an exception set,\n       because it may clear it (directly or indirectly) and so the\n       caller loses its exception */\n    assert(!PyErr_Occurred());\n    if ((PY_VERSION_HEX < 0x030700A0) || unlikely(flags & METH_KEYWORDS)) {\n        return (*((__Pyx_PyCFunctionFastWithKeywords)meth)) (self, args, nargs, NULL);\n    } else {\n        return (*((__Pyx_PyCFunctionFast)meth)) (self, args, nargs);\n    }\n}\n#endif\n\n/* PyFunctionFastCall */\n      #if CYTHON_FAST_PYCALL\n#include \"frameobject.h\"\nstatic PyObject* __Pyx_PyFunction_FastCallNoKw(PyCodeObject *co, PyObject **args, Py_ssize_t na,\n                                               PyObject *globals) {\n    PyFrameObject *f;\n    PyThreadState *tstate = __Pyx_PyThreadState_Current;\n    PyObject **fastlocals;\n    Py_ssize_t i;\n    PyObject *result;\n    assert(globals != NULL);\n    /* XXX Perhaps we should create a specialized\n       PyFrame_New() that doesn't take locals, but does\n       take builtins without sanity checking them.\n       */\n    assert(tstate != NULL);\n    f = PyFrame_New(tstate, co, globals, NULL);\n    if (f == NULL) {\n        return NULL;\n    }\n    fastlocals = f->f_localsplus;\n    for (i = 0; i < na; i++) {\n        Py_INCREF(*args);\n        fastlocals[i] = *args++;\n    }\n    result = PyEval_EvalFrameEx(f,0);\n    ++tstate->recursion_depth;\n    Py_DECREF(f);\n    --tstate->recursion_depth;\n    return result;\n}\n#if 1 || PY_VERSION_HEX < 0x030600B1\nstatic PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, int nargs, PyObject *kwargs) {\n    PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func);\n    PyObject *globals = PyFunction_GET_GLOBALS(func);\n    PyObject *argdefs = PyFunction_GET_DEFAULTS(func);\n    PyObject *closure;\n#if PY_MAJOR_VERSION >= 3\n    PyObject *kwdefs;\n#endif\n    PyObject *kwtuple, **k;\n    PyObject **d;\n    Py_ssize_t nd;\n    Py_ssize_t nk;\n    PyObject *result;\n    assert(kwargs == NULL || PyDict_Check(kwargs));\n    nk = kwargs ? PyDict_Size(kwargs) : 0;\n    if (Py_EnterRecursiveCall((char*)\" while calling a Python object\")) {\n        return NULL;\n    }\n    if (\n#if PY_MAJOR_VERSION >= 3\n            co->co_kwonlyargcount == 0 &&\n#endif\n            likely(kwargs == NULL || nk == 0) &&\n            co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) {\n        if (argdefs == NULL && co->co_argcount == nargs) {\n            result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals);\n            goto done;\n        }\n        else if (nargs == 0 && argdefs != NULL\n                 && co->co_argcount == Py_SIZE(argdefs)) {\n            /* function called with no arguments, but all parameters have\n               a default value: use default values as arguments .*/\n            args = &PyTuple_GET_ITEM(argdefs, 0);\n            result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals);\n            goto done;\n        }\n    }\n    if (kwargs != NULL) {\n        Py_ssize_t pos, i;\n        kwtuple = PyTuple_New(2 * nk);\n        if (kwtuple == NULL) {\n            result = NULL;\n            goto done;\n        }\n        k = &PyTuple_GET_ITEM(kwtuple, 0);\n        pos = i = 0;\n        while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) {\n            Py_INCREF(k[i]);\n            Py_INCREF(k[i+1]);\n            i += 2;\n        }\n        nk = i / 2;\n    }\n    else {\n        kwtuple = NULL;\n        k = NULL;\n    }\n    closure = PyFunction_GET_CLOSURE(func);\n#if PY_MAJOR_VERSION >= 3\n    kwdefs = PyFunction_GET_KW_DEFAULTS(func);\n#endif\n    if (argdefs != NULL) {\n        d = &PyTuple_GET_ITEM(argdefs, 0);\n        nd = Py_SIZE(argdefs);\n    }\n    else {\n        d = NULL;\n        nd = 0;\n    }\n#if PY_MAJOR_VERSION >= 3\n    result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL,\n                               args, nargs,\n                               k, (int)nk,\n                               d, (int)nd, kwdefs, closure);\n#else\n    result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL,\n                               args, nargs,\n                               k, (int)nk,\n                               d, (int)nd, closure);\n#endif\n    Py_XDECREF(kwtuple);\ndone:\n    Py_LeaveRecursiveCall();\n    return result;\n}\n#endif\n#endif\n\n/* PyObjectCallMethO */\n      #if CYTHON_COMPILING_IN_CPYTHON\nstatic CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) {\n    PyObject *self, *result;\n    PyCFunction cfunc;\n    cfunc = PyCFunction_GET_FUNCTION(func);\n    self = PyCFunction_GET_SELF(func);\n    if (unlikely(Py_EnterRecursiveCall((char*)\" while calling a Python object\")))\n        return NULL;\n    result = cfunc(self, arg);\n    Py_LeaveRecursiveCall();\n    if (unlikely(!result) && unlikely(!PyErr_Occurred())) {\n        PyErr_SetString(\n            PyExc_SystemError,\n            \"NULL result without error in PyObject_Call\");\n    }\n    return result;\n}\n#endif\n\n/* PyObjectCallOneArg */\n      #if CYTHON_COMPILING_IN_CPYTHON\nstatic PyObject* __Pyx__PyObject_CallOneArg(PyObject *func, PyObject *arg) {\n    PyObject *result;\n    PyObject *args = PyTuple_New(1);\n    if (unlikely(!args)) return NULL;\n    Py_INCREF(arg);\n    PyTuple_SET_ITEM(args, 0, arg);\n    result = __Pyx_PyObject_Call(func, args, NULL);\n    Py_DECREF(args);\n    return result;\n}\nstatic CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) {\n#if CYTHON_FAST_PYCALL\n    if (PyFunction_Check(func)) {\n        return __Pyx_PyFunction_FastCall(func, &arg, 1);\n    }\n#endif\n    if (likely(PyCFunction_Check(func))) {\n        if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) {\n            return __Pyx_PyObject_CallMethO(func, arg);\n#if CYTHON_FAST_PYCCALL\n        } else if (PyCFunction_GET_FLAGS(func) & METH_FASTCALL) {\n            return __Pyx_PyCFunction_FastCall(func, &arg, 1);\n#endif\n        }\n    }\n    return __Pyx__PyObject_CallOneArg(func, arg);\n}\n#else\nstatic CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) {\n    PyObject *result;\n    PyObject *args = PyTuple_Pack(1, arg);\n    if (unlikely(!args)) return NULL;\n    result = __Pyx_PyObject_Call(func, args, NULL);\n    Py_DECREF(args);\n    return result;\n}\n#endif\n\n/* PyObjectCallNoArg */\n      #if CYTHON_COMPILING_IN_CPYTHON\nstatic CYTHON_INLINE PyObject* __Pyx_PyObject_CallNoArg(PyObject *func) {\n#if CYTHON_FAST_PYCALL\n    if (PyFunction_Check(func)) {\n        return __Pyx_PyFunction_FastCall(func, NULL, 0);\n    }\n#endif\n#ifdef __Pyx_CyFunction_USED\n    if (likely(PyCFunction_Check(func) || __Pyx_TypeCheck(func, __pyx_CyFunctionType))) {\n#else\n    if (likely(PyCFunction_Check(func))) {\n#endif\n        if (likely(PyCFunction_GET_FLAGS(func) & METH_NOARGS)) {\n            return __Pyx_PyObject_CallMethO(func, NULL);\n        }\n    }\n    return __Pyx_PyObject_Call(func, __pyx_empty_tuple, NULL);\n}\n#endif\n\n/* BufferIndexError */\n        static void __Pyx_RaiseBufferIndexError(int axis) {\n  PyErr_Format(PyExc_IndexError,\n     \"Out of bounds on buffer access (axis %d)\", axis);\n}\n\n/* BufferFallbackError */\n        static void __Pyx_RaiseBufferFallbackError(void) {\n  PyErr_SetString(PyExc_ValueError,\n     \"Buffer acquisition failed on assignment; and then reacquiring the old buffer failed too!\");\n}\n\n/* PyErrFetchRestore */\n        #if CYTHON_FAST_THREAD_STATE\nstatic CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) {\n    PyObject *tmp_type, *tmp_value, *tmp_tb;\n    tmp_type = tstate->curexc_type;\n    tmp_value = tstate->curexc_value;\n    tmp_tb = tstate->curexc_traceback;\n    tstate->curexc_type = type;\n    tstate->curexc_value = value;\n    tstate->curexc_traceback = tb;\n    Py_XDECREF(tmp_type);\n    Py_XDECREF(tmp_value);\n    Py_XDECREF(tmp_tb);\n}\nstatic CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {\n    *type = tstate->curexc_type;\n    *value = tstate->curexc_value;\n    *tb = tstate->curexc_traceback;\n    tstate->curexc_type = 0;\n    tstate->curexc_value = 0;\n    tstate->curexc_traceback = 0;\n}\n#endif\n\n/* RaiseException */\n        #if PY_MAJOR_VERSION < 3\nstatic void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb,\n                        CYTHON_UNUSED PyObject *cause) {\n    __Pyx_PyThreadState_declare\n    Py_XINCREF(type);\n    if (!value || value == Py_None)\n        value = NULL;\n    else\n        Py_INCREF(value);\n    if (!tb || tb == Py_None)\n        tb = NULL;\n    else {\n        Py_INCREF(tb);\n        if (!PyTraceBack_Check(tb)) {\n            PyErr_SetString(PyExc_TypeError,\n                \"raise: arg 3 must be a traceback or None\");\n            goto raise_error;\n        }\n    }\n    if (PyType_Check(type)) {\n#if CYTHON_COMPILING_IN_PYPY\n        if (!value) {\n            Py_INCREF(Py_None);\n            value = Py_None;\n        }\n#endif\n        PyErr_NormalizeException(&type, &value, &tb);\n    } else {\n        if (value) {\n            PyErr_SetString(PyExc_TypeError,\n                \"instance exception may not have a separate value\");\n            goto raise_error;\n        }\n        value = type;\n        type = (PyObject*) Py_TYPE(type);\n        Py_INCREF(type);\n        if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) {\n            PyErr_SetString(PyExc_TypeError,\n                \"raise: exception class must be a subclass of BaseException\");\n            goto raise_error;\n        }\n    }\n    __Pyx_PyThreadState_assign\n    __Pyx_ErrRestore(type, value, tb);\n    return;\nraise_error:\n    Py_XDECREF(value);\n    Py_XDECREF(type);\n    Py_XDECREF(tb);\n    return;\n}\n#else\nstatic void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) {\n    PyObject* owned_instance = NULL;\n    if (tb == Py_None) {\n        tb = 0;\n    } else if (tb && !PyTraceBack_Check(tb)) {\n        PyErr_SetString(PyExc_TypeError,\n            \"raise: arg 3 must be a traceback or None\");\n        goto bad;\n    }\n    if (value == Py_None)\n        value = 0;\n    if (PyExceptionInstance_Check(type)) {\n        if (value) {\n            PyErr_SetString(PyExc_TypeError,\n                \"instance exception may not have a separate value\");\n            goto bad;\n        }\n        value = type;\n        type = (PyObject*) Py_TYPE(value);\n    } else if (PyExceptionClass_Check(type)) {\n        PyObject *instance_class = NULL;\n        if (value && PyExceptionInstance_Check(value)) {\n            instance_class = (PyObject*) Py_TYPE(value);\n            if (instance_class != type) {\n                int is_subclass = PyObject_IsSubclass(instance_class, type);\n                if (!is_subclass) {\n                    instance_class = NULL;\n                } else if (unlikely(is_subclass == -1)) {\n                    goto bad;\n                } else {\n                    type = instance_class;\n                }\n            }\n        }\n        if (!instance_class) {\n            PyObject *args;\n            if (!value)\n                args = PyTuple_New(0);\n            else if (PyTuple_Check(value)) {\n                Py_INCREF(value);\n                args = value;\n            } else\n                args = PyTuple_Pack(1, value);\n            if (!args)\n                goto bad;\n            owned_instance = PyObject_Call(type, args, NULL);\n            Py_DECREF(args);\n            if (!owned_instance)\n                goto bad;\n            value = owned_instance;\n            if (!PyExceptionInstance_Check(value)) {\n                PyErr_Format(PyExc_TypeError,\n                             \"calling %R should have returned an instance of \"\n                             \"BaseException, not %R\",\n                             type, Py_TYPE(value));\n                goto bad;\n            }\n        }\n    } else {\n        PyErr_SetString(PyExc_TypeError,\n            \"raise: exception class must be a subclass of BaseException\");\n        goto bad;\n    }\n    if (cause) {\n        PyObject *fixed_cause;\n        if (cause == Py_None) {\n            fixed_cause = NULL;\n        } else if (PyExceptionClass_Check(cause)) {\n            fixed_cause = PyObject_CallObject(cause, NULL);\n            if (fixed_cause == NULL)\n                goto bad;\n        } else if (PyExceptionInstance_Check(cause)) {\n            fixed_cause = cause;\n            Py_INCREF(fixed_cause);\n        } else {\n            PyErr_SetString(PyExc_TypeError,\n                            \"exception causes must derive from \"\n                            \"BaseException\");\n            goto bad;\n        }\n        PyException_SetCause(value, fixed_cause);\n    }\n    PyErr_SetObject(type, value);\n    if (tb) {\n#if CYTHON_COMPILING_IN_PYPY\n        PyObject *tmp_type, *tmp_value, *tmp_tb;\n        PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb);\n        Py_INCREF(tb);\n        PyErr_Restore(tmp_type, tmp_value, tb);\n        Py_XDECREF(tmp_tb);\n#else\n        PyThreadState *tstate = __Pyx_PyThreadState_Current;\n        PyObject* tmp_tb = tstate->curexc_traceback;\n        if (tb != tmp_tb) {\n            Py_INCREF(tb);\n            tstate->curexc_traceback = tb;\n            Py_XDECREF(tmp_tb);\n        }\n#endif\n    }\nbad:\n    Py_XDECREF(owned_instance);\n    return;\n}\n#endif\n\n/* DictGetItem */\n        #if PY_MAJOR_VERSION >= 3 && !CYTHON_COMPILING_IN_PYPY\nstatic PyObject *__Pyx_PyDict_GetItem(PyObject *d, PyObject* key) {\n    PyObject *value;\n    value = PyDict_GetItemWithError(d, key);\n    if (unlikely(!value)) {\n        if (!PyErr_Occurred()) {\n            PyObject* args = PyTuple_Pack(1, key);\n            if (likely(args))\n                PyErr_SetObject(PyExc_KeyError, args);\n            Py_XDECREF(args);\n        }\n        return NULL;\n    }\n    Py_INCREF(value);\n    return value;\n}\n#endif\n\n/* RaiseTooManyValuesToUnpack */\n        static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) {\n    PyErr_Format(PyExc_ValueError,\n                 \"too many values to unpack (expected %\" CYTHON_FORMAT_SSIZE_T \"d)\", expected);\n}\n\n/* RaiseNeedMoreValuesToUnpack */\n        static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) {\n    PyErr_Format(PyExc_ValueError,\n                 \"need more than %\" CYTHON_FORMAT_SSIZE_T \"d value%.1s to unpack\",\n                 index, (index == 1) ? \"\" : \"s\");\n}\n\n/* RaiseNoneIterError */\n        static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) {\n    PyErr_SetString(PyExc_TypeError, \"'NoneType' object is not iterable\");\n}\n\n/* SaveResetException */\n        #if CYTHON_FAST_THREAD_STATE\nstatic CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {\n    #if PY_VERSION_HEX >= 0x030700A2\n    *type = tstate->exc_state.exc_type;\n    *value = tstate->exc_state.exc_value;\n    *tb = tstate->exc_state.exc_traceback;\n    #else\n    *type = tstate->exc_type;\n    *value = tstate->exc_value;\n    *tb = tstate->exc_traceback;\n    #endif\n    Py_XINCREF(*type);\n    Py_XINCREF(*value);\n    Py_XINCREF(*tb);\n}\nstatic CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) {\n    PyObject *tmp_type, *tmp_value, *tmp_tb;\n    #if PY_VERSION_HEX >= 0x030700A2\n    tmp_type = tstate->exc_state.exc_type;\n    tmp_value = tstate->exc_state.exc_value;\n    tmp_tb = tstate->exc_state.exc_traceback;\n    tstate->exc_state.exc_type = type;\n    tstate->exc_state.exc_value = value;\n    tstate->exc_state.exc_traceback = tb;\n    #else\n    tmp_type = tstate->exc_type;\n    tmp_value = tstate->exc_value;\n    tmp_tb = tstate->exc_traceback;\n    tstate->exc_type = type;\n    tstate->exc_value = value;\n    tstate->exc_traceback = tb;\n    #endif\n    Py_XDECREF(tmp_type);\n    Py_XDECREF(tmp_value);\n    Py_XDECREF(tmp_tb);\n}\n#endif\n\n/* PyErrExceptionMatches */\n        #if CYTHON_FAST_THREAD_STATE\nstatic int __Pyx_PyErr_ExceptionMatchesTuple(PyObject *exc_type, PyObject *tuple) {\n    Py_ssize_t i, n;\n    n = PyTuple_GET_SIZE(tuple);\n#if PY_MAJOR_VERSION >= 3\n    for (i=0; i<n; i++) {\n        if (exc_type == PyTuple_GET_ITEM(tuple, i)) return 1;\n    }\n#endif\n    for (i=0; i<n; i++) {\n        if (__Pyx_PyErr_GivenExceptionMatches(exc_type, PyTuple_GET_ITEM(tuple, i))) return 1;\n    }\n    return 0;\n}\nstatic CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err) {\n    PyObject *exc_type = tstate->curexc_type;\n    if (exc_type == err) return 1;\n    if (unlikely(!exc_type)) return 0;\n    if (unlikely(PyTuple_Check(err)))\n        return __Pyx_PyErr_ExceptionMatchesTuple(exc_type, err);\n    return __Pyx_PyErr_GivenExceptionMatches(exc_type, err);\n}\n#endif\n\n/* GetException */\n        #if CYTHON_FAST_THREAD_STATE\nstatic int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) {\n#else\nstatic int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) {\n#endif\n    PyObject *local_type, *local_value, *local_tb;\n#if CYTHON_FAST_THREAD_STATE\n    PyObject *tmp_type, *tmp_value, *tmp_tb;\n    local_type = tstate->curexc_type;\n    local_value = tstate->curexc_value;\n    local_tb = tstate->curexc_traceback;\n    tstate->curexc_type = 0;\n    tstate->curexc_value = 0;\n    tstate->curexc_traceback = 0;\n#else\n    PyErr_Fetch(&local_type, &local_value, &local_tb);\n#endif\n    PyErr_NormalizeException(&local_type, &local_value, &local_tb);\n#if CYTHON_FAST_THREAD_STATE\n    if (unlikely(tstate->curexc_type))\n#else\n    if (unlikely(PyErr_Occurred()))\n#endif\n        goto bad;\n    #if PY_MAJOR_VERSION >= 3\n    if (local_tb) {\n        if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0))\n            goto bad;\n    }\n    #endif\n    Py_XINCREF(local_tb);\n    Py_XINCREF(local_type);\n    Py_XINCREF(local_value);\n    *type = local_type;\n    *value = local_value;\n    *tb = local_tb;\n#if CYTHON_FAST_THREAD_STATE\n    #if PY_VERSION_HEX >= 0x030700A2\n    tmp_type = tstate->exc_state.exc_type;\n    tmp_value = tstate->exc_state.exc_value;\n    tmp_tb = tstate->exc_state.exc_traceback;\n    tstate->exc_state.exc_type = local_type;\n    tstate->exc_state.exc_value = local_value;\n    tstate->exc_state.exc_traceback = local_tb;\n    #else\n    tmp_type = tstate->exc_type;\n    tmp_value = tstate->exc_value;\n    tmp_tb = tstate->exc_traceback;\n    tstate->exc_type = local_type;\n    tstate->exc_value = local_value;\n    tstate->exc_traceback = local_tb;\n    #endif\n    Py_XDECREF(tmp_type);\n    Py_XDECREF(tmp_value);\n    Py_XDECREF(tmp_tb);\n#else\n    PyErr_SetExcInfo(local_type, local_value, local_tb);\n#endif\n    return 0;\nbad:\n    *type = 0;\n    *value = 0;\n    *tb = 0;\n    Py_XDECREF(local_type);\n    Py_XDECREF(local_value);\n    Py_XDECREF(local_tb);\n    return -1;\n}\n\n/* Import */\n          static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) {\n    PyObject *empty_list = 0;\n    PyObject *module = 0;\n    PyObject *global_dict = 0;\n    PyObject *empty_dict = 0;\n    PyObject *list;\n    #if PY_MAJOR_VERSION < 3\n    PyObject *py_import;\n    py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import);\n    if (!py_import)\n        goto bad;\n    #endif\n    if (from_list)\n        list = from_list;\n    else {\n        empty_list = PyList_New(0);\n        if (!empty_list)\n            goto bad;\n        list = empty_list;\n    }\n    global_dict = PyModule_GetDict(__pyx_m);\n    if (!global_dict)\n        goto bad;\n    empty_dict = PyDict_New();\n    if (!empty_dict)\n        goto bad;\n    {\n        #if PY_MAJOR_VERSION >= 3\n        if (level == -1) {\n            if (strchr(__Pyx_MODULE_NAME, '.')) {\n                module = PyImport_ImportModuleLevelObject(\n                    name, global_dict, empty_dict, list, 1);\n                if (!module) {\n                    if (!PyErr_ExceptionMatches(PyExc_ImportError))\n                        goto bad;\n                    PyErr_Clear();\n                }\n            }\n            level = 0;\n        }\n        #endif\n        if (!module) {\n            #if PY_MAJOR_VERSION < 3\n            PyObject *py_level = PyInt_FromLong(level);\n            if (!py_level)\n                goto bad;\n            module = PyObject_CallFunctionObjArgs(py_import,\n                name, global_dict, empty_dict, list, py_level, NULL);\n            Py_DECREF(py_level);\n            #else\n            module = PyImport_ImportModuleLevelObject(\n                name, global_dict, empty_dict, list, level);\n            #endif\n        }\n    }\nbad:\n    #if PY_MAJOR_VERSION < 3\n    Py_XDECREF(py_import);\n    #endif\n    Py_XDECREF(empty_list);\n    Py_XDECREF(empty_dict);\n    return module;\n}\n\n/* CLineInTraceback */\n          #ifndef CYTHON_CLINE_IN_TRACEBACK\nstatic int __Pyx_CLineForTraceback(CYTHON_UNUSED PyThreadState *tstate, int c_line) {\n    PyObject *use_cline;\n    PyObject *ptype, *pvalue, *ptraceback;\n#if CYTHON_COMPILING_IN_CPYTHON\n    PyObject **cython_runtime_dict;\n#endif\n    __Pyx_ErrFetchInState(tstate, &ptype, &pvalue, &ptraceback);\n#if CYTHON_COMPILING_IN_CPYTHON\n    cython_runtime_dict = _PyObject_GetDictPtr(__pyx_cython_runtime);\n    if (likely(cython_runtime_dict)) {\n      use_cline = __Pyx_PyDict_GetItemStr(*cython_runtime_dict, __pyx_n_s_cline_in_traceback);\n    } else\n#endif\n    {\n      PyObject *use_cline_obj = __Pyx_PyObject_GetAttrStr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback);\n      if (use_cline_obj) {\n        use_cline = PyObject_Not(use_cline_obj) ? Py_False : Py_True;\n        Py_DECREF(use_cline_obj);\n      } else {\n        PyErr_Clear();\n        use_cline = NULL;\n      }\n    }\n    if (!use_cline) {\n        c_line = 0;\n        PyObject_SetAttr(__pyx_cython_runtime, __pyx_n_s_cline_in_traceback, Py_False);\n    }\n    else if (PyObject_Not(use_cline) != 0) {\n        c_line = 0;\n    }\n    __Pyx_ErrRestoreInState(tstate, ptype, pvalue, ptraceback);\n    return c_line;\n}\n#endif\n\n/* CodeObjectCache */\n          static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) {\n    int start = 0, mid = 0, end = count - 1;\n    if (end >= 0 && code_line > entries[end].code_line) {\n        return count;\n    }\n    while (start < end) {\n        mid = start + (end - start) / 2;\n        if (code_line < entries[mid].code_line) {\n            end = mid;\n        } else if (code_line > entries[mid].code_line) {\n             start = mid + 1;\n        } else {\n            return mid;\n        }\n    }\n    if (code_line <= entries[mid].code_line) {\n        return mid;\n    } else {\n        return mid + 1;\n    }\n}\nstatic PyCodeObject *__pyx_find_code_object(int code_line) {\n    PyCodeObject* code_object;\n    int pos;\n    if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) {\n        return NULL;\n    }\n    pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);\n    if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) {\n        return NULL;\n    }\n    code_object = __pyx_code_cache.entries[pos].code_object;\n    Py_INCREF(code_object);\n    return code_object;\n}\nstatic void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) {\n    int pos, i;\n    __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries;\n    if (unlikely(!code_line)) {\n        return;\n    }\n    if (unlikely(!entries)) {\n        entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry));\n        if (likely(entries)) {\n            __pyx_code_cache.entries = entries;\n            __pyx_code_cache.max_count = 64;\n            __pyx_code_cache.count = 1;\n            entries[0].code_line = code_line;\n            entries[0].code_object = code_object;\n            Py_INCREF(code_object);\n        }\n        return;\n    }\n    pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);\n    if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) {\n        PyCodeObject* tmp = entries[pos].code_object;\n        entries[pos].code_object = code_object;\n        Py_DECREF(tmp);\n        return;\n    }\n    if (__pyx_code_cache.count == __pyx_code_cache.max_count) {\n        int new_max = __pyx_code_cache.max_count + 64;\n        entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc(\n            __pyx_code_cache.entries, (size_t)new_max*sizeof(__Pyx_CodeObjectCacheEntry));\n        if (unlikely(!entries)) {\n            return;\n        }\n        __pyx_code_cache.entries = entries;\n        __pyx_code_cache.max_count = new_max;\n    }\n    for (i=__pyx_code_cache.count; i>pos; i--) {\n        entries[i] = entries[i-1];\n    }\n    entries[pos].code_line = code_line;\n    entries[pos].code_object = code_object;\n    __pyx_code_cache.count++;\n    Py_INCREF(code_object);\n}\n\n/* AddTraceback */\n          #include \"compile.h\"\n#include \"frameobject.h\"\n#include \"traceback.h\"\nstatic PyCodeObject* __Pyx_CreateCodeObjectForTraceback(\n            const char *funcname, int c_line,\n            int py_line, const char *filename) {\n    PyCodeObject *py_code = 0;\n    PyObject *py_srcfile = 0;\n    PyObject *py_funcname = 0;\n    #if PY_MAJOR_VERSION < 3\n    py_srcfile = PyString_FromString(filename);\n    #else\n    py_srcfile = PyUnicode_FromString(filename);\n    #endif\n    if (!py_srcfile) goto bad;\n    if (c_line) {\n        #if PY_MAJOR_VERSION < 3\n        py_funcname = PyString_FromFormat( \"%s (%s:%d)\", funcname, __pyx_cfilenm, c_line);\n        #else\n        py_funcname = PyUnicode_FromFormat( \"%s (%s:%d)\", funcname, __pyx_cfilenm, c_line);\n        #endif\n    }\n    else {\n        #if PY_MAJOR_VERSION < 3\n        py_funcname = PyString_FromString(funcname);\n        #else\n        py_funcname = PyUnicode_FromString(funcname);\n        #endif\n    }\n    if (!py_funcname) goto bad;\n    py_code = __Pyx_PyCode_New(\n        0,\n        0,\n        0,\n        0,\n        0,\n        __pyx_empty_bytes, /*PyObject *code,*/\n        __pyx_empty_tuple, /*PyObject *consts,*/\n        __pyx_empty_tuple, /*PyObject *names,*/\n        __pyx_empty_tuple, /*PyObject *varnames,*/\n        __pyx_empty_tuple, /*PyObject *freevars,*/\n        __pyx_empty_tuple, /*PyObject *cellvars,*/\n        py_srcfile,   /*PyObject *filename,*/\n        py_funcname,  /*PyObject *name,*/\n        py_line,\n        __pyx_empty_bytes  /*PyObject *lnotab*/\n    );\n    Py_DECREF(py_srcfile);\n    Py_DECREF(py_funcname);\n    return py_code;\nbad:\n    Py_XDECREF(py_srcfile);\n    Py_XDECREF(py_funcname);\n    return NULL;\n}\nstatic void __Pyx_AddTraceback(const char *funcname, int c_line,\n                               int py_line, const char *filename) {\n    PyCodeObject *py_code = 0;\n    PyFrameObject *py_frame = 0;\n    PyThreadState *tstate = __Pyx_PyThreadState_Current;\n    if (c_line) {\n        c_line = __Pyx_CLineForTraceback(tstate, c_line);\n    }\n    py_code = __pyx_find_code_object(c_line ? -c_line : py_line);\n    if (!py_code) {\n        py_code = __Pyx_CreateCodeObjectForTraceback(\n            funcname, c_line, py_line, filename);\n        if (!py_code) goto bad;\n        __pyx_insert_code_object(c_line ? -c_line : py_line, py_code);\n    }\n    py_frame = PyFrame_New(\n        tstate,            /*PyThreadState *tstate,*/\n        py_code,           /*PyCodeObject *code,*/\n        __pyx_d,    /*PyObject *globals,*/\n        0                  /*PyObject *locals*/\n    );\n    if (!py_frame) goto bad;\n    __Pyx_PyFrame_SetLineNumber(py_frame, py_line);\n    PyTraceBack_Here(py_frame);\nbad:\n    Py_XDECREF(py_code);\n    Py_XDECREF(py_frame);\n}\n\n#if PY_MAJOR_VERSION < 3\nstatic int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) {\n    if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags);\n        if (__Pyx_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) return __pyx_pw_5numpy_7ndarray_1__getbuffer__(obj, view, flags);\n    PyErr_Format(PyExc_TypeError, \"'%.200s' does not have the buffer interface\", Py_TYPE(obj)->tp_name);\n    return -1;\n}\nstatic void __Pyx_ReleaseBuffer(Py_buffer *view) {\n    PyObject *obj = view->obj;\n    if (!obj) return;\n    if (PyObject_CheckBuffer(obj)) {\n        PyBuffer_Release(view);\n        return;\n    }\n    if ((0)) {}\n        else if (__Pyx_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) __pyx_pw_5numpy_7ndarray_3__releasebuffer__(obj, view);\n    view->obj = NULL;\n    Py_DECREF(obj);\n}\n#endif\n\n\n          /* CIntFromPyVerify */\n          #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\\\n    __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0)\n#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\\\n    __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1)\n#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\\\n    {\\\n        func_type value = func_value;\\\n        if (sizeof(target_type) < sizeof(func_type)) {\\\n            if (unlikely(value != (func_type) (target_type) value)) {\\\n                func_type zero = 0;\\\n                if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\\\n                    return (target_type) -1;\\\n                if (is_unsigned && unlikely(value < zero))\\\n                    goto raise_neg_overflow;\\\n                else\\\n                    goto raise_overflow;\\\n            }\\\n        }\\\n        return (target_type) value;\\\n    }\n\n/* CIntToPy */\n          static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) {\n    const int neg_one = (int) -1, const_zero = (int) 0;\n    const int is_unsigned = neg_one > const_zero;\n    if (is_unsigned) {\n        if (sizeof(int) < sizeof(long)) {\n            return PyInt_FromLong((long) value);\n        } else if (sizeof(int) <= sizeof(unsigned long)) {\n            return PyLong_FromUnsignedLong((unsigned long) value);\n#ifdef HAVE_LONG_LONG\n        } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) {\n            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);\n#endif\n        }\n    } else {\n        if (sizeof(int) <= sizeof(long)) {\n            return PyInt_FromLong((long) value);\n#ifdef HAVE_LONG_LONG\n        } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) {\n            return PyLong_FromLongLong((PY_LONG_LONG) value);\n#endif\n        }\n    }\n    {\n        int one = 1; int little = (int)*(unsigned char *)&one;\n        unsigned char *bytes = (unsigned char *)&value;\n        return _PyLong_FromByteArray(bytes, sizeof(int),\n                                     little, !is_unsigned);\n    }\n}\n\n/* Declarations */\n          #if CYTHON_CCOMPLEX\n  #ifdef __cplusplus\n    static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {\n      return ::std::complex< float >(x, y);\n    }\n  #else\n    static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {\n      return x + y*(__pyx_t_float_complex)_Complex_I;\n    }\n  #endif\n#else\n    static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) {\n      __pyx_t_float_complex z;\n      z.real = x;\n      z.imag = y;\n      return z;\n    }\n#endif\n\n/* Arithmetic */\n          #if CYTHON_CCOMPLEX\n#else\n    static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {\n       return (a.real == b.real) && (a.imag == b.imag);\n    }\n    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {\n        __pyx_t_float_complex z;\n        z.real = a.real + b.real;\n        z.imag = a.imag + b.imag;\n        return z;\n    }\n    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {\n        __pyx_t_float_complex z;\n        z.real = a.real - b.real;\n        z.imag = a.imag - b.imag;\n        return z;\n    }\n    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {\n        __pyx_t_float_complex z;\n        z.real = a.real * b.real - a.imag * b.imag;\n        z.imag = a.real * b.imag + a.imag * b.real;\n        return z;\n    }\n    #if 1\n    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {\n        if (b.imag == 0) {\n            return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real);\n        } else if (fabsf(b.real) >= fabsf(b.imag)) {\n            if (b.real == 0 && b.imag == 0) {\n                return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.imag);\n            } else {\n                float r = b.imag / b.real;\n                float s = 1.0 / (b.real + b.imag * r);\n                return __pyx_t_float_complex_from_parts(\n                    (a.real + a.imag * r) * s, (a.imag - a.real * r) * s);\n            }\n        } else {\n            float r = b.real / b.imag;\n            float s = 1.0 / (b.imag + b.real * r);\n            return __pyx_t_float_complex_from_parts(\n                (a.real * r + a.imag) * s, (a.imag * r - a.real) * s);\n        }\n    }\n    #else\n    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {\n        if (b.imag == 0) {\n            return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real);\n        } else {\n            float denom = b.real * b.real + b.imag * b.imag;\n            return __pyx_t_float_complex_from_parts(\n                (a.real * b.real + a.imag * b.imag) / denom,\n                (a.imag * b.real - a.real * b.imag) / denom);\n        }\n    }\n    #endif\n    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex a) {\n        __pyx_t_float_complex z;\n        z.real = -a.real;\n        z.imag = -a.imag;\n        return z;\n    }\n    static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex a) {\n       return (a.real == 0) && (a.imag == 0);\n    }\n    static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex a) {\n        __pyx_t_float_complex z;\n        z.real =  a.real;\n        z.imag = -a.imag;\n        return z;\n    }\n    #if 1\n        static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex z) {\n          #if !defined(HAVE_HYPOT) || defined(_MSC_VER)\n            return sqrtf(z.real*z.real + z.imag*z.imag);\n          #else\n            return hypotf(z.real, z.imag);\n          #endif\n        }\n        static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex a, __pyx_t_float_complex b) {\n            __pyx_t_float_complex z;\n            float r, lnr, theta, z_r, z_theta;\n            if (b.imag == 0 && b.real == (int)b.real) {\n                if (b.real < 0) {\n                    float denom = a.real * a.real + a.imag * a.imag;\n                    a.real = a.real / denom;\n                    a.imag = -a.imag / denom;\n                    b.real = -b.real;\n                }\n                switch ((int)b.real) {\n                    case 0:\n                        z.real = 1;\n                        z.imag = 0;\n                        return z;\n                    case 1:\n                        return a;\n                    case 2:\n                        z = __Pyx_c_prod_float(a, a);\n                        return __Pyx_c_prod_float(a, a);\n                    case 3:\n                        z = __Pyx_c_prod_float(a, a);\n                        return __Pyx_c_prod_float(z, a);\n                    case 4:\n                        z = __Pyx_c_prod_float(a, a);\n                        return __Pyx_c_prod_float(z, z);\n                }\n            }\n            if (a.imag == 0) {\n                if (a.real == 0) {\n                    return a;\n                } else if (b.imag == 0) {\n                    z.real = powf(a.real, b.real);\n                    z.imag = 0;\n                    return z;\n                } else if (a.real > 0) {\n                    r = a.real;\n                    theta = 0;\n                } else {\n                    r = -a.real;\n                    theta = atan2f(0, -1);\n                }\n            } else {\n                r = __Pyx_c_abs_float(a);\n                theta = atan2f(a.imag, a.real);\n            }\n            lnr = logf(r);\n            z_r = expf(lnr * b.real - theta * b.imag);\n            z_theta = theta * b.real + lnr * b.imag;\n            z.real = z_r * cosf(z_theta);\n            z.imag = z_r * sinf(z_theta);\n            return z;\n        }\n    #endif\n#endif\n\n/* Declarations */\n          #if CYTHON_CCOMPLEX\n  #ifdef __cplusplus\n    static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {\n      return ::std::complex< double >(x, y);\n    }\n  #else\n    static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {\n      return x + y*(__pyx_t_double_complex)_Complex_I;\n    }\n  #endif\n#else\n    static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) {\n      __pyx_t_double_complex z;\n      z.real = x;\n      z.imag = y;\n      return z;\n    }\n#endif\n\n/* Arithmetic */\n          #if CYTHON_CCOMPLEX\n#else\n    static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {\n       return (a.real == b.real) && (a.imag == b.imag);\n    }\n    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {\n        __pyx_t_double_complex z;\n        z.real = a.real + b.real;\n        z.imag = a.imag + b.imag;\n        return z;\n    }\n    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {\n        __pyx_t_double_complex z;\n        z.real = a.real - b.real;\n        z.imag = a.imag - b.imag;\n        return z;\n    }\n    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {\n        __pyx_t_double_complex z;\n        z.real = a.real * b.real - a.imag * b.imag;\n        z.imag = a.real * b.imag + a.imag * b.real;\n        return z;\n    }\n    #if 1\n    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {\n        if (b.imag == 0) {\n            return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real);\n        } else if (fabs(b.real) >= fabs(b.imag)) {\n            if (b.real == 0 && b.imag == 0) {\n                return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.imag);\n            } else {\n                double r = b.imag / b.real;\n                double s = 1.0 / (b.real + b.imag * r);\n                return __pyx_t_double_complex_from_parts(\n                    (a.real + a.imag * r) * s, (a.imag - a.real * r) * s);\n            }\n        } else {\n            double r = b.real / b.imag;\n            double s = 1.0 / (b.imag + b.real * r);\n            return __pyx_t_double_complex_from_parts(\n                (a.real * r + a.imag) * s, (a.imag * r - a.real) * s);\n        }\n    }\n    #else\n    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {\n        if (b.imag == 0) {\n            return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real);\n        } else {\n            double denom = b.real * b.real + b.imag * b.imag;\n            return __pyx_t_double_complex_from_parts(\n                (a.real * b.real + a.imag * b.imag) / denom,\n                (a.imag * b.real - a.real * b.imag) / denom);\n        }\n    }\n    #endif\n    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex a) {\n        __pyx_t_double_complex z;\n        z.real = -a.real;\n        z.imag = -a.imag;\n        return z;\n    }\n    static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex a) {\n       return (a.real == 0) && (a.imag == 0);\n    }\n    static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex a) {\n        __pyx_t_double_complex z;\n        z.real =  a.real;\n        z.imag = -a.imag;\n        return z;\n    }\n    #if 1\n        static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex z) {\n          #if !defined(HAVE_HYPOT) || defined(_MSC_VER)\n            return sqrt(z.real*z.real + z.imag*z.imag);\n          #else\n            return hypot(z.real, z.imag);\n          #endif\n        }\n        static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex a, __pyx_t_double_complex b) {\n            __pyx_t_double_complex z;\n            double r, lnr, theta, z_r, z_theta;\n            if (b.imag == 0 && b.real == (int)b.real) {\n                if (b.real < 0) {\n                    double denom = a.real * a.real + a.imag * a.imag;\n                    a.real = a.real / denom;\n                    a.imag = -a.imag / denom;\n                    b.real = -b.real;\n                }\n                switch ((int)b.real) {\n                    case 0:\n                        z.real = 1;\n                        z.imag = 0;\n                        return z;\n                    case 1:\n                        return a;\n                    case 2:\n                        z = __Pyx_c_prod_double(a, a);\n                        return __Pyx_c_prod_double(a, a);\n                    case 3:\n                        z = __Pyx_c_prod_double(a, a);\n                        return __Pyx_c_prod_double(z, a);\n                    case 4:\n                        z = __Pyx_c_prod_double(a, a);\n                        return __Pyx_c_prod_double(z, z);\n                }\n            }\n            if (a.imag == 0) {\n                if (a.real == 0) {\n                    return a;\n                } else if (b.imag == 0) {\n                    z.real = pow(a.real, b.real);\n                    z.imag = 0;\n                    return z;\n                } else if (a.real > 0) {\n                    r = a.real;\n                    theta = 0;\n                } else {\n                    r = -a.real;\n                    theta = atan2(0, -1);\n                }\n            } else {\n                r = __Pyx_c_abs_double(a);\n                theta = atan2(a.imag, a.real);\n            }\n            lnr = log(r);\n            z_r = exp(lnr * b.real - theta * b.imag);\n            z_theta = theta * b.real + lnr * b.imag;\n            z.real = z_r * cos(z_theta);\n            z.imag = z_r * sin(z_theta);\n            return z;\n        }\n    #endif\n#endif\n\n/* CIntToPy */\n          static CYTHON_INLINE PyObject* __Pyx_PyInt_From_enum__NPY_TYPES(enum NPY_TYPES value) {\n    const enum NPY_TYPES neg_one = (enum NPY_TYPES) -1, const_zero = (enum NPY_TYPES) 0;\n    const int is_unsigned = neg_one > const_zero;\n    if (is_unsigned) {\n        if (sizeof(enum NPY_TYPES) < sizeof(long)) {\n            return PyInt_FromLong((long) value);\n        } else if (sizeof(enum NPY_TYPES) <= sizeof(unsigned long)) {\n            return PyLong_FromUnsignedLong((unsigned long) value);\n#ifdef HAVE_LONG_LONG\n        } else if (sizeof(enum NPY_TYPES) <= sizeof(unsigned PY_LONG_LONG)) {\n            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);\n#endif\n        }\n    } else {\n        if (sizeof(enum NPY_TYPES) <= sizeof(long)) {\n            return PyInt_FromLong((long) value);\n#ifdef HAVE_LONG_LONG\n        } else if (sizeof(enum NPY_TYPES) <= sizeof(PY_LONG_LONG)) {\n            return PyLong_FromLongLong((PY_LONG_LONG) value);\n#endif\n        }\n    }\n    {\n        int one = 1; int little = (int)*(unsigned char *)&one;\n        unsigned char *bytes = (unsigned char *)&value;\n        return _PyLong_FromByteArray(bytes, sizeof(enum NPY_TYPES),\n                                     little, !is_unsigned);\n    }\n}\n\n/* CIntFromPy */\n          static CYTHON_INLINE npy_int32 __Pyx_PyInt_As_npy_int32(PyObject *x) {\n    const npy_int32 neg_one = (npy_int32) -1, const_zero = (npy_int32) 0;\n    const int is_unsigned = neg_one > const_zero;\n#if PY_MAJOR_VERSION < 3\n    if (likely(PyInt_Check(x))) {\n        if (sizeof(npy_int32) < sizeof(long)) {\n            __PYX_VERIFY_RETURN_INT(npy_int32, long, PyInt_AS_LONG(x))\n        } else {\n            long val = PyInt_AS_LONG(x);\n            if (is_unsigned && unlikely(val < 0)) {\n                goto raise_neg_overflow;\n            }\n            return (npy_int32) val;\n        }\n    } else\n#endif\n    if (likely(PyLong_Check(x))) {\n        if (is_unsigned) {\n#if CYTHON_USE_PYLONG_INTERNALS\n            const digit* digits = ((PyLongObject*)x)->ob_digit;\n            switch (Py_SIZE(x)) {\n                case  0: return (npy_int32) 0;\n                case  1: __PYX_VERIFY_RETURN_INT(npy_int32, digit, digits[0])\n                case 2:\n                    if (8 * sizeof(npy_int32) > 1 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(npy_int32, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(npy_int32) >= 2 * PyLong_SHIFT) {\n                            return (npy_int32) (((((npy_int32)digits[1]) << PyLong_SHIFT) | (npy_int32)digits[0]));\n                        }\n                    }\n                    break;\n                case 3:\n                    if (8 * sizeof(npy_int32) > 2 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(npy_int32, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(npy_int32) >= 3 * PyLong_SHIFT) {\n                            return (npy_int32) (((((((npy_int32)digits[2]) << PyLong_SHIFT) | (npy_int32)digits[1]) << PyLong_SHIFT) | (npy_int32)digits[0]));\n                        }\n                    }\n                    break;\n                case 4:\n                    if (8 * sizeof(npy_int32) > 3 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(npy_int32, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(npy_int32) >= 4 * PyLong_SHIFT) {\n                            return (npy_int32) (((((((((npy_int32)digits[3]) << PyLong_SHIFT) | (npy_int32)digits[2]) << PyLong_SHIFT) | (npy_int32)digits[1]) << PyLong_SHIFT) | (npy_int32)digits[0]));\n                        }\n                    }\n                    break;\n            }\n#endif\n#if CYTHON_COMPILING_IN_CPYTHON\n            if (unlikely(Py_SIZE(x) < 0)) {\n                goto raise_neg_overflow;\n            }\n#else\n            {\n                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);\n                if (unlikely(result < 0))\n                    return (npy_int32) -1;\n                if (unlikely(result == 1))\n                    goto raise_neg_overflow;\n            }\n#endif\n            if (sizeof(npy_int32) <= sizeof(unsigned long)) {\n                __PYX_VERIFY_RETURN_INT_EXC(npy_int32, unsigned long, PyLong_AsUnsignedLong(x))\n#ifdef HAVE_LONG_LONG\n            } else if (sizeof(npy_int32) <= sizeof(unsigned PY_LONG_LONG)) {\n                __PYX_VERIFY_RETURN_INT_EXC(npy_int32, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))\n#endif\n            }\n        } else {\n#if CYTHON_USE_PYLONG_INTERNALS\n            const digit* digits = ((PyLongObject*)x)->ob_digit;\n            switch (Py_SIZE(x)) {\n                case  0: return (npy_int32) 0;\n                case -1: __PYX_VERIFY_RETURN_INT(npy_int32, sdigit, (sdigit) (-(sdigit)digits[0]))\n                case  1: __PYX_VERIFY_RETURN_INT(npy_int32,  digit, +digits[0])\n                case -2:\n                    if (8 * sizeof(npy_int32) - 1 > 1 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(npy_int32, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(npy_int32) - 1 > 2 * PyLong_SHIFT) {\n                            return (npy_int32) (((npy_int32)-1)*(((((npy_int32)digits[1]) << PyLong_SHIFT) | (npy_int32)digits[0])));\n                        }\n                    }\n                    break;\n                case 2:\n                    if (8 * sizeof(npy_int32) > 1 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(npy_int32, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(npy_int32) - 1 > 2 * PyLong_SHIFT) {\n                            return (npy_int32) ((((((npy_int32)digits[1]) << PyLong_SHIFT) | (npy_int32)digits[0])));\n                        }\n                    }\n                    break;\n                case -3:\n                    if (8 * sizeof(npy_int32) - 1 > 2 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(npy_int32, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(npy_int32) - 1 > 3 * PyLong_SHIFT) {\n                            return (npy_int32) (((npy_int32)-1)*(((((((npy_int32)digits[2]) << PyLong_SHIFT) | (npy_int32)digits[1]) << PyLong_SHIFT) | (npy_int32)digits[0])));\n                        }\n                    }\n                    break;\n                case 3:\n                    if (8 * sizeof(npy_int32) > 2 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(npy_int32, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(npy_int32) - 1 > 3 * PyLong_SHIFT) {\n                            return (npy_int32) ((((((((npy_int32)digits[2]) << PyLong_SHIFT) | (npy_int32)digits[1]) << PyLong_SHIFT) | (npy_int32)digits[0])));\n                        }\n                    }\n                    break;\n                case -4:\n                    if (8 * sizeof(npy_int32) - 1 > 3 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(npy_int32, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(npy_int32) - 1 > 4 * PyLong_SHIFT) {\n                            return (npy_int32) (((npy_int32)-1)*(((((((((npy_int32)digits[3]) << PyLong_SHIFT) | (npy_int32)digits[2]) << PyLong_SHIFT) | (npy_int32)digits[1]) << PyLong_SHIFT) | (npy_int32)digits[0])));\n                        }\n                    }\n                    break;\n                case 4:\n                    if (8 * sizeof(npy_int32) > 3 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(npy_int32, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(npy_int32) - 1 > 4 * PyLong_SHIFT) {\n                            return (npy_int32) ((((((((((npy_int32)digits[3]) << PyLong_SHIFT) | (npy_int32)digits[2]) << PyLong_SHIFT) | (npy_int32)digits[1]) << PyLong_SHIFT) | (npy_int32)digits[0])));\n                        }\n                    }\n                    break;\n            }\n#endif\n            if (sizeof(npy_int32) <= sizeof(long)) {\n                __PYX_VERIFY_RETURN_INT_EXC(npy_int32, long, PyLong_AsLong(x))\n#ifdef HAVE_LONG_LONG\n            } else if (sizeof(npy_int32) <= sizeof(PY_LONG_LONG)) {\n                __PYX_VERIFY_RETURN_INT_EXC(npy_int32, PY_LONG_LONG, PyLong_AsLongLong(x))\n#endif\n            }\n        }\n        {\n#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)\n            PyErr_SetString(PyExc_RuntimeError,\n                            \"_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers\");\n#else\n            npy_int32 val;\n            PyObject *v = __Pyx_PyNumber_IntOrLong(x);\n #if PY_MAJOR_VERSION < 3\n            if (likely(v) && !PyLong_Check(v)) {\n                PyObject *tmp = v;\n                v = PyNumber_Long(tmp);\n                Py_DECREF(tmp);\n            }\n #endif\n            if (likely(v)) {\n                int one = 1; int is_little = (int)*(unsigned char *)&one;\n                unsigned char *bytes = (unsigned char *)&val;\n                int ret = _PyLong_AsByteArray((PyLongObject *)v,\n                                              bytes, sizeof(val),\n                                              is_little, !is_unsigned);\n                Py_DECREF(v);\n                if (likely(!ret))\n                    return val;\n            }\n#endif\n            return (npy_int32) -1;\n        }\n    } else {\n        npy_int32 val;\n        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);\n        if (!tmp) return (npy_int32) -1;\n        val = __Pyx_PyInt_As_npy_int32(tmp);\n        Py_DECREF(tmp);\n        return val;\n    }\nraise_overflow:\n    PyErr_SetString(PyExc_OverflowError,\n        \"value too large to convert to npy_int32\");\n    return (npy_int32) -1;\nraise_neg_overflow:\n    PyErr_SetString(PyExc_OverflowError,\n        \"can't convert negative value to npy_int32\");\n    return (npy_int32) -1;\n}\n\n/* CIntFromPy */\n          static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) {\n    const int neg_one = (int) -1, const_zero = (int) 0;\n    const int is_unsigned = neg_one > const_zero;\n#if PY_MAJOR_VERSION < 3\n    if (likely(PyInt_Check(x))) {\n        if (sizeof(int) < sizeof(long)) {\n            __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x))\n        } else {\n            long val = PyInt_AS_LONG(x);\n            if (is_unsigned && unlikely(val < 0)) {\n                goto raise_neg_overflow;\n            }\n            return (int) val;\n        }\n    } else\n#endif\n    if (likely(PyLong_Check(x))) {\n        if (is_unsigned) {\n#if CYTHON_USE_PYLONG_INTERNALS\n            const digit* digits = ((PyLongObject*)x)->ob_digit;\n            switch (Py_SIZE(x)) {\n                case  0: return (int) 0;\n                case  1: __PYX_VERIFY_RETURN_INT(int, digit, digits[0])\n                case 2:\n                    if (8 * sizeof(int) > 1 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(int) >= 2 * PyLong_SHIFT) {\n                            return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));\n                        }\n                    }\n                    break;\n                case 3:\n                    if (8 * sizeof(int) > 2 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(int) >= 3 * PyLong_SHIFT) {\n                            return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));\n                        }\n                    }\n                    break;\n                case 4:\n                    if (8 * sizeof(int) > 3 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(int) >= 4 * PyLong_SHIFT) {\n                            return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]));\n                        }\n                    }\n                    break;\n            }\n#endif\n#if CYTHON_COMPILING_IN_CPYTHON\n            if (unlikely(Py_SIZE(x) < 0)) {\n                goto raise_neg_overflow;\n            }\n#else\n            {\n                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);\n                if (unlikely(result < 0))\n                    return (int) -1;\n                if (unlikely(result == 1))\n                    goto raise_neg_overflow;\n            }\n#endif\n            if (sizeof(int) <= sizeof(unsigned long)) {\n                __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x))\n#ifdef HAVE_LONG_LONG\n            } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) {\n                __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))\n#endif\n            }\n        } else {\n#if CYTHON_USE_PYLONG_INTERNALS\n            const digit* digits = ((PyLongObject*)x)->ob_digit;\n            switch (Py_SIZE(x)) {\n                case  0: return (int) 0;\n                case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, (sdigit) (-(sdigit)digits[0]))\n                case  1: __PYX_VERIFY_RETURN_INT(int,  digit, +digits[0])\n                case -2:\n                    if (8 * sizeof(int) - 1 > 1 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {\n                            return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));\n                        }\n                    }\n                    break;\n                case 2:\n                    if (8 * sizeof(int) > 1 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {\n                            return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));\n                        }\n                    }\n                    break;\n                case -3:\n                    if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {\n                            return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));\n                        }\n                    }\n                    break;\n                case 3:\n                    if (8 * sizeof(int) > 2 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {\n                            return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));\n                        }\n                    }\n                    break;\n                case -4:\n                    if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) {\n                            return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));\n                        }\n                    }\n                    break;\n                case 4:\n                    if (8 * sizeof(int) > 3 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) {\n                            return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])));\n                        }\n                    }\n                    break;\n            }\n#endif\n            if (sizeof(int) <= sizeof(long)) {\n                __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x))\n#ifdef HAVE_LONG_LONG\n            } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) {\n                __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x))\n#endif\n            }\n        }\n        {\n#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)\n            PyErr_SetString(PyExc_RuntimeError,\n                            \"_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers\");\n#else\n            int val;\n            PyObject *v = __Pyx_PyNumber_IntOrLong(x);\n #if PY_MAJOR_VERSION < 3\n            if (likely(v) && !PyLong_Check(v)) {\n                PyObject *tmp = v;\n                v = PyNumber_Long(tmp);\n                Py_DECREF(tmp);\n            }\n #endif\n            if (likely(v)) {\n                int one = 1; int is_little = (int)*(unsigned char *)&one;\n                unsigned char *bytes = (unsigned char *)&val;\n                int ret = _PyLong_AsByteArray((PyLongObject *)v,\n                                              bytes, sizeof(val),\n                                              is_little, !is_unsigned);\n                Py_DECREF(v);\n                if (likely(!ret))\n                    return val;\n            }\n#endif\n            return (int) -1;\n        }\n    } else {\n        int val;\n        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);\n        if (!tmp) return (int) -1;\n        val = __Pyx_PyInt_As_int(tmp);\n        Py_DECREF(tmp);\n        return val;\n    }\nraise_overflow:\n    PyErr_SetString(PyExc_OverflowError,\n        \"value too large to convert to int\");\n    return (int) -1;\nraise_neg_overflow:\n    PyErr_SetString(PyExc_OverflowError,\n        \"can't convert negative value to int\");\n    return (int) -1;\n}\n\n/* CIntToPy */\n          static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) {\n    const long neg_one = (long) -1, const_zero = (long) 0;\n    const int is_unsigned = neg_one > const_zero;\n    if (is_unsigned) {\n        if (sizeof(long) < sizeof(long)) {\n            return PyInt_FromLong((long) value);\n        } else if (sizeof(long) <= sizeof(unsigned long)) {\n            return PyLong_FromUnsignedLong((unsigned long) value);\n#ifdef HAVE_LONG_LONG\n        } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) {\n            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value);\n#endif\n        }\n    } else {\n        if (sizeof(long) <= sizeof(long)) {\n            return PyInt_FromLong((long) value);\n#ifdef HAVE_LONG_LONG\n        } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) {\n            return PyLong_FromLongLong((PY_LONG_LONG) value);\n#endif\n        }\n    }\n    {\n        int one = 1; int little = (int)*(unsigned char *)&one;\n        unsigned char *bytes = (unsigned char *)&value;\n        return _PyLong_FromByteArray(bytes, sizeof(long),\n                                     little, !is_unsigned);\n    }\n}\n\n/* CIntFromPy */\n          static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) {\n    const long neg_one = (long) -1, const_zero = (long) 0;\n    const int is_unsigned = neg_one > const_zero;\n#if PY_MAJOR_VERSION < 3\n    if (likely(PyInt_Check(x))) {\n        if (sizeof(long) < sizeof(long)) {\n            __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x))\n        } else {\n            long val = PyInt_AS_LONG(x);\n            if (is_unsigned && unlikely(val < 0)) {\n                goto raise_neg_overflow;\n            }\n            return (long) val;\n        }\n    } else\n#endif\n    if (likely(PyLong_Check(x))) {\n        if (is_unsigned) {\n#if CYTHON_USE_PYLONG_INTERNALS\n            const digit* digits = ((PyLongObject*)x)->ob_digit;\n            switch (Py_SIZE(x)) {\n                case  0: return (long) 0;\n                case  1: __PYX_VERIFY_RETURN_INT(long, digit, digits[0])\n                case 2:\n                    if (8 * sizeof(long) > 1 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(long) >= 2 * PyLong_SHIFT) {\n                            return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));\n                        }\n                    }\n                    break;\n                case 3:\n                    if (8 * sizeof(long) > 2 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(long) >= 3 * PyLong_SHIFT) {\n                            return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));\n                        }\n                    }\n                    break;\n                case 4:\n                    if (8 * sizeof(long) > 3 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(long) >= 4 * PyLong_SHIFT) {\n                            return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]));\n                        }\n                    }\n                    break;\n            }\n#endif\n#if CYTHON_COMPILING_IN_CPYTHON\n            if (unlikely(Py_SIZE(x) < 0)) {\n                goto raise_neg_overflow;\n            }\n#else\n            {\n                int result = PyObject_RichCompareBool(x, Py_False, Py_LT);\n                if (unlikely(result < 0))\n                    return (long) -1;\n                if (unlikely(result == 1))\n                    goto raise_neg_overflow;\n            }\n#endif\n            if (sizeof(long) <= sizeof(unsigned long)) {\n                __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x))\n#ifdef HAVE_LONG_LONG\n            } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) {\n                __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x))\n#endif\n            }\n        } else {\n#if CYTHON_USE_PYLONG_INTERNALS\n            const digit* digits = ((PyLongObject*)x)->ob_digit;\n            switch (Py_SIZE(x)) {\n                case  0: return (long) 0;\n                case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, (sdigit) (-(sdigit)digits[0]))\n                case  1: __PYX_VERIFY_RETURN_INT(long,  digit, +digits[0])\n                case -2:\n                    if (8 * sizeof(long) - 1 > 1 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {\n                            return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));\n                        }\n                    }\n                    break;\n                case 2:\n                    if (8 * sizeof(long) > 1 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {\n                            return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));\n                        }\n                    }\n                    break;\n                case -3:\n                    if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {\n                            return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));\n                        }\n                    }\n                    break;\n                case 3:\n                    if (8 * sizeof(long) > 2 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {\n                            return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));\n                        }\n                    }\n                    break;\n                case -4:\n                    if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {\n                            return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));\n                        }\n                    }\n                    break;\n                case 4:\n                    if (8 * sizeof(long) > 3 * PyLong_SHIFT) {\n                        if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) {\n                            __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0])))\n                        } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) {\n                            return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])));\n                        }\n                    }\n                    break;\n            }\n#endif\n            if (sizeof(long) <= sizeof(long)) {\n                __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x))\n#ifdef HAVE_LONG_LONG\n            } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) {\n                __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x))\n#endif\n            }\n        }\n        {\n#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)\n            PyErr_SetString(PyExc_RuntimeError,\n                            \"_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers\");\n#else\n            long val;\n            PyObject *v = __Pyx_PyNumber_IntOrLong(x);\n #if PY_MAJOR_VERSION < 3\n            if (likely(v) && !PyLong_Check(v)) {\n                PyObject *tmp = v;\n                v = PyNumber_Long(tmp);\n                Py_DECREF(tmp);\n            }\n #endif\n            if (likely(v)) {\n                int one = 1; int is_little = (int)*(unsigned char *)&one;\n                unsigned char *bytes = (unsigned char *)&val;\n                int ret = _PyLong_AsByteArray((PyLongObject *)v,\n                                              bytes, sizeof(val),\n                                              is_little, !is_unsigned);\n                Py_DECREF(v);\n                if (likely(!ret))\n                    return val;\n            }\n#endif\n            return (long) -1;\n        }\n    } else {\n        long val;\n        PyObject *tmp = __Pyx_PyNumber_IntOrLong(x);\n        if (!tmp) return (long) -1;\n        val = __Pyx_PyInt_As_long(tmp);\n        Py_DECREF(tmp);\n        return val;\n    }\nraise_overflow:\n    PyErr_SetString(PyExc_OverflowError,\n        \"value too large to convert to long\");\n    return (long) -1;\nraise_neg_overflow:\n    PyErr_SetString(PyExc_OverflowError,\n        \"can't convert negative value to long\");\n    return (long) -1;\n}\n\n/* FastTypeChecks */\n          #if CYTHON_COMPILING_IN_CPYTHON\nstatic int __Pyx_InBases(PyTypeObject *a, PyTypeObject *b) {\n    while (a) {\n        a = a->tp_base;\n        if (a == b)\n            return 1;\n    }\n    return b == &PyBaseObject_Type;\n}\nstatic CYTHON_INLINE int __Pyx_IsSubtype(PyTypeObject *a, PyTypeObject *b) {\n    PyObject *mro;\n    if (a == b) return 1;\n    mro = a->tp_mro;\n    if (likely(mro)) {\n        Py_ssize_t i, n;\n        n = PyTuple_GET_SIZE(mro);\n        for (i = 0; i < n; i++) {\n            if (PyTuple_GET_ITEM(mro, i) == (PyObject *)b)\n                return 1;\n        }\n        return 0;\n    }\n    return __Pyx_InBases(a, b);\n}\n#if PY_MAJOR_VERSION == 2\nstatic int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject* exc_type2) {\n    PyObject *exception, *value, *tb;\n    int res;\n    __Pyx_PyThreadState_declare\n    __Pyx_PyThreadState_assign\n    __Pyx_ErrFetch(&exception, &value, &tb);\n    res = exc_type1 ? PyObject_IsSubclass(err, exc_type1) : 0;\n    if (unlikely(res == -1)) {\n        PyErr_WriteUnraisable(err);\n        res = 0;\n    }\n    if (!res) {\n        res = PyObject_IsSubclass(err, exc_type2);\n        if (unlikely(res == -1)) {\n            PyErr_WriteUnraisable(err);\n            res = 0;\n        }\n    }\n    __Pyx_ErrRestore(exception, value, tb);\n    return res;\n}\n#else\nstatic CYTHON_INLINE int __Pyx_inner_PyErr_GivenExceptionMatches2(PyObject *err, PyObject* exc_type1, PyObject *exc_type2) {\n    int res = exc_type1 ? __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type1) : 0;\n    if (!res) {\n        res = __Pyx_IsSubtype((PyTypeObject*)err, (PyTypeObject*)exc_type2);\n    }\n    return res;\n}\n#endif\nstatic CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches(PyObject *err, PyObject* exc_type) {\n    if (likely(err == exc_type)) return 1;\n    if (likely(PyExceptionClass_Check(err))) {\n        return __Pyx_inner_PyErr_GivenExceptionMatches2(err, NULL, exc_type);\n    }\n    return PyErr_GivenExceptionMatches(err, exc_type);\n}\nstatic CYTHON_INLINE int __Pyx_PyErr_GivenExceptionMatches2(PyObject *err, PyObject *exc_type1, PyObject *exc_type2) {\n    if (likely(err == exc_type1 || err == exc_type2)) return 1;\n    if (likely(PyExceptionClass_Check(err))) {\n        return __Pyx_inner_PyErr_GivenExceptionMatches2(err, exc_type1, exc_type2);\n    }\n    return (PyErr_GivenExceptionMatches(err, exc_type1) || PyErr_GivenExceptionMatches(err, exc_type2));\n}\n#endif\n\n/* CheckBinaryVersion */\n          static int __Pyx_check_binary_version(void) {\n    char ctversion[4], rtversion[4];\n    PyOS_snprintf(ctversion, 4, \"%d.%d\", PY_MAJOR_VERSION, PY_MINOR_VERSION);\n    PyOS_snprintf(rtversion, 4, \"%s\", Py_GetVersion());\n    if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) {\n        char message[200];\n        PyOS_snprintf(message, sizeof(message),\n                      \"compiletime version %s of module '%.100s' \"\n                      \"does not match runtime version %s\",\n                      ctversion, __Pyx_MODULE_NAME, rtversion);\n        return PyErr_WarnEx(NULL, message, 1);\n    }\n    return 0;\n}\n\n/* ModuleImport */\n          #ifndef __PYX_HAVE_RT_ImportModule\n#define __PYX_HAVE_RT_ImportModule\nstatic PyObject *__Pyx_ImportModule(const char *name) {\n    PyObject *py_name = 0;\n    PyObject *py_module = 0;\n    py_name = __Pyx_PyIdentifier_FromString(name);\n    if (!py_name)\n        goto bad;\n    py_module = PyImport_Import(py_name);\n    Py_DECREF(py_name);\n    return py_module;\nbad:\n    Py_XDECREF(py_name);\n    return 0;\n}\n#endif\n\n/* TypeImport */\n          #ifndef __PYX_HAVE_RT_ImportType\n#define __PYX_HAVE_RT_ImportType\nstatic PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name,\n    size_t size, int strict)\n{\n    PyObject *py_module = 0;\n    PyObject *result = 0;\n    PyObject *py_name = 0;\n    char warning[200];\n    Py_ssize_t basicsize;\n#ifdef Py_LIMITED_API\n    PyObject *py_basicsize;\n#endif\n    py_module = __Pyx_ImportModule(module_name);\n    if (!py_module)\n        goto bad;\n    py_name = __Pyx_PyIdentifier_FromString(class_name);\n    if (!py_name)\n        goto bad;\n    result = PyObject_GetAttr(py_module, py_name);\n    Py_DECREF(py_name);\n    py_name = 0;\n    Py_DECREF(py_module);\n    py_module = 0;\n    if (!result)\n        goto bad;\n    if (!PyType_Check(result)) {\n        PyErr_Format(PyExc_TypeError,\n            \"%.200s.%.200s is not a type object\",\n            module_name, class_name);\n        goto bad;\n    }\n#ifndef Py_LIMITED_API\n    basicsize = ((PyTypeObject *)result)->tp_basicsize;\n#else\n    py_basicsize = PyObject_GetAttrString(result, \"__basicsize__\");\n    if (!py_basicsize)\n        goto bad;\n    basicsize = PyLong_AsSsize_t(py_basicsize);\n    Py_DECREF(py_basicsize);\n    py_basicsize = 0;\n    if (basicsize == (Py_ssize_t)-1 && PyErr_Occurred())\n        goto bad;\n#endif\n    if (!strict && (size_t)basicsize > size) {\n        PyOS_snprintf(warning, sizeof(warning),\n            \"%s.%s size changed, may indicate binary incompatibility. Expected %zd, got %zd\",\n            module_name, class_name, basicsize, size);\n        if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad;\n    }\n    else if ((size_t)basicsize != size) {\n        PyErr_Format(PyExc_ValueError,\n            \"%.200s.%.200s has the wrong size, try recompiling. Expected %zd, got %zd\",\n            module_name, class_name, basicsize, size);\n        goto bad;\n    }\n    return (PyTypeObject *)result;\nbad:\n    Py_XDECREF(py_module);\n    Py_XDECREF(result);\n    return NULL;\n}\n#endif\n\n/* InitStrings */\n          static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) {\n    while (t->p) {\n        #if PY_MAJOR_VERSION < 3\n        if (t->is_unicode) {\n            *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL);\n        } else if (t->intern) {\n            *t->p = PyString_InternFromString(t->s);\n        } else {\n            *t->p = PyString_FromStringAndSize(t->s, t->n - 1);\n        }\n        #else\n        if (t->is_unicode | t->is_str) {\n            if (t->intern) {\n                *t->p = PyUnicode_InternFromString(t->s);\n            } else if (t->encoding) {\n                *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL);\n            } else {\n                *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1);\n            }\n        } else {\n            *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1);\n        }\n        #endif\n        if (!*t->p)\n            return -1;\n        if (PyObject_Hash(*t->p) == -1)\n            return -1;\n        ++t;\n    }\n    return 0;\n}\n\nstatic CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) {\n    return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str));\n}\nstatic CYTHON_INLINE const char* __Pyx_PyObject_AsString(PyObject* o) {\n    Py_ssize_t ignore;\n    return __Pyx_PyObject_AsStringAndSize(o, &ignore);\n}\n#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT\n#if !CYTHON_PEP393_ENABLED\nstatic const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) {\n    char* defenc_c;\n    PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL);\n    if (!defenc) return NULL;\n    defenc_c = PyBytes_AS_STRING(defenc);\n#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII\n    {\n        char* end = defenc_c + PyBytes_GET_SIZE(defenc);\n        char* c;\n        for (c = defenc_c; c < end; c++) {\n            if ((unsigned char) (*c) >= 128) {\n                PyUnicode_AsASCIIString(o);\n                return NULL;\n            }\n        }\n    }\n#endif\n    *length = PyBytes_GET_SIZE(defenc);\n    return defenc_c;\n}\n#else\nstatic CYTHON_INLINE const char* __Pyx_PyUnicode_AsStringAndSize(PyObject* o, Py_ssize_t *length) {\n    if (unlikely(__Pyx_PyUnicode_READY(o) == -1)) return NULL;\n#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII\n    if (likely(PyUnicode_IS_ASCII(o))) {\n        *length = PyUnicode_GET_LENGTH(o);\n        return PyUnicode_AsUTF8(o);\n    } else {\n        PyUnicode_AsASCIIString(o);\n        return NULL;\n    }\n#else\n    return PyUnicode_AsUTF8AndSize(o, length);\n#endif\n}\n#endif\n#endif\nstatic CYTHON_INLINE const char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) {\n#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT\n    if (\n#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII\n            __Pyx_sys_getdefaultencoding_not_ascii &&\n#endif\n            PyUnicode_Check(o)) {\n        return __Pyx_PyUnicode_AsStringAndSize(o, length);\n    } else\n#endif\n#if (!CYTHON_COMPILING_IN_PYPY) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE))\n    if (PyByteArray_Check(o)) {\n        *length = PyByteArray_GET_SIZE(o);\n        return PyByteArray_AS_STRING(o);\n    } else\n#endif\n    {\n        char* result;\n        int r = PyBytes_AsStringAndSize(o, &result, length);\n        if (unlikely(r < 0)) {\n            return NULL;\n        } else {\n            return result;\n        }\n    }\n}\nstatic CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) {\n   int is_true = x == Py_True;\n   if (is_true | (x == Py_False) | (x == Py_None)) return is_true;\n   else return PyObject_IsTrue(x);\n}\nstatic PyObject* __Pyx_PyNumber_IntOrLongWrongResultType(PyObject* result, const char* type_name) {\n#if PY_MAJOR_VERSION >= 3\n    if (PyLong_Check(result)) {\n        if (PyErr_WarnFormat(PyExc_DeprecationWarning, 1,\n                \"__int__ returned non-int (type %.200s).  \"\n                \"The ability to return an instance of a strict subclass of int \"\n                \"is deprecated, and may be removed in a future version of Python.\",\n                Py_TYPE(result)->tp_name)) {\n            Py_DECREF(result);\n            return NULL;\n        }\n        return result;\n    }\n#endif\n    PyErr_Format(PyExc_TypeError,\n                 \"__%.4s__ returned non-%.4s (type %.200s)\",\n                 type_name, type_name, Py_TYPE(result)->tp_name);\n    Py_DECREF(result);\n    return NULL;\n}\nstatic CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) {\n#if CYTHON_USE_TYPE_SLOTS\n  PyNumberMethods *m;\n#endif\n  const char *name = NULL;\n  PyObject *res = NULL;\n#if PY_MAJOR_VERSION < 3\n  if (likely(PyInt_Check(x) || PyLong_Check(x)))\n#else\n  if (likely(PyLong_Check(x)))\n#endif\n    return __Pyx_NewRef(x);\n#if CYTHON_USE_TYPE_SLOTS\n  m = Py_TYPE(x)->tp_as_number;\n  #if PY_MAJOR_VERSION < 3\n  if (m && m->nb_int) {\n    name = \"int\";\n    res = m->nb_int(x);\n  }\n  else if (m && m->nb_long) {\n    name = \"long\";\n    res = m->nb_long(x);\n  }\n  #else\n  if (likely(m && m->nb_int)) {\n    name = \"int\";\n    res = m->nb_int(x);\n  }\n  #endif\n#else\n  if (!PyBytes_CheckExact(x) && !PyUnicode_CheckExact(x)) {\n    res = PyNumber_Int(x);\n  }\n#endif\n  if (likely(res)) {\n#if PY_MAJOR_VERSION < 3\n    if (unlikely(!PyInt_Check(res) && !PyLong_Check(res))) {\n#else\n    if (unlikely(!PyLong_CheckExact(res))) {\n#endif\n        return __Pyx_PyNumber_IntOrLongWrongResultType(res, name);\n    }\n  }\n  else if (!PyErr_Occurred()) {\n    PyErr_SetString(PyExc_TypeError,\n                    \"an integer is required\");\n  }\n  return res;\n}\nstatic CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) {\n  Py_ssize_t ival;\n  PyObject *x;\n#if PY_MAJOR_VERSION < 3\n  if (likely(PyInt_CheckExact(b))) {\n    if (sizeof(Py_ssize_t) >= sizeof(long))\n        return PyInt_AS_LONG(b);\n    else\n        return PyInt_AsSsize_t(x);\n  }\n#endif\n  if (likely(PyLong_CheckExact(b))) {\n    #if CYTHON_USE_PYLONG_INTERNALS\n    const digit* digits = ((PyLongObject*)b)->ob_digit;\n    const Py_ssize_t size = Py_SIZE(b);\n    if (likely(__Pyx_sst_abs(size) <= 1)) {\n        ival = likely(size) ? digits[0] : 0;\n        if (size == -1) ival = -ival;\n        return ival;\n    } else {\n      switch (size) {\n         case 2:\n           if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) {\n             return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));\n           }\n           break;\n         case -2:\n           if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) {\n             return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));\n           }\n           break;\n         case 3:\n           if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) {\n             return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));\n           }\n           break;\n         case -3:\n           if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) {\n             return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));\n           }\n           break;\n         case 4:\n           if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) {\n             return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));\n           }\n           break;\n         case -4:\n           if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) {\n             return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0]));\n           }\n           break;\n      }\n    }\n    #endif\n    return PyLong_AsSsize_t(b);\n  }\n  x = PyNumber_Index(b);\n  if (!x) return -1;\n  ival = PyInt_AsSsize_t(x);\n  Py_DECREF(x);\n  return ival;\n}\nstatic CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) {\n    return PyInt_FromSize_t(ival);\n}\n\n\n#endif /* Py_PYTHON_H */\n"
  },
  {
    "path": "lib/nms/gpu_nms.hpp",
    "content": "void _nms(int* keep_out, int* num_out, const float* boxes_host, int boxes_num,\n          int boxes_dim, float nms_overlap_thresh, int device_id);\n"
  },
  {
    "path": "lib/nms/gpu_nms.pyx",
    "content": "# --------------------------------------------------------\n# Faster R-CNN\n# Copyright (c) 2015 Microsoft\n# Licensed under The MIT License [see LICENSE for details]\n# Written by Ross Girshick\n# --------------------------------------------------------\n\nimport numpy as np\ncimport numpy as np\n\nassert sizeof(int) == sizeof(np.int32_t)\n\ncdef extern from \"gpu_nms.hpp\":\n    void _nms(np.int32_t*, int*, np.float32_t*, int, int, float, int)\n\ndef gpu_nms(np.ndarray[np.float32_t, ndim=2] dets, np.float thresh,\n            np.int32_t device_id=0):\n    cdef int boxes_num = dets.shape[0]\n    cdef int boxes_dim = dets.shape[1]\n    cdef int num_out\n    cdef np.ndarray[np.int32_t, ndim=1] \\\n        keep = np.zeros(boxes_num, dtype=np.int32)\n    cdef np.ndarray[np.float32_t, ndim=1] \\\n        scores = dets[:, 4]\n    cdef np.ndarray[np.int_t, ndim=1] \\\n        order = scores.argsort()[::-1]\n    cdef np.ndarray[np.float32_t, ndim=2] \\\n        sorted_dets = dets[order, :]\n    _nms(&keep[0], &num_out, &sorted_dets[0, 0], boxes_num, boxes_dim, thresh, device_id)\n    keep = keep[:num_out]\n    return list(order[keep])\n"
  },
  {
    "path": "lib/nms/nms_kernel.cu",
    "content": "// ------------------------------------------------------------------\n// Faster R-CNN\n// Copyright (c) 2015 Microsoft\n// Licensed under The MIT License [see fast-rcnn/LICENSE for details]\n// Written by Shaoqing Ren\n// ------------------------------------------------------------------\n\n#include \"gpu_nms.hpp\"\n#include <vector>\n#include <iostream>\n\n#define CUDA_CHECK(condition) \\\n  /* Code block avoids redefinition of cudaError_t error */ \\\n  do { \\\n    cudaError_t error = condition; \\\n    if (error != cudaSuccess) { \\\n      std::cout << cudaGetErrorString(error) << std::endl; \\\n    } \\\n  } while (0)\n\n#define DIVUP(m,n) ((m) / (n) + ((m) % (n) > 0))\nint const threadsPerBlock = sizeof(unsigned long long) * 8;\n\n__device__ inline float devIoU(float const * const a, float const * const b) {\n  float left = max(a[0], b[0]), right = min(a[2], b[2]);\n  float top = max(a[1], b[1]), bottom = min(a[3], b[3]);\n  float width = max(right - left + 1, 0.f), height = max(bottom - top + 1, 0.f);\n  float interS = width * height;\n  float Sa = (a[2] - a[0] + 1) * (a[3] - a[1] + 1);\n  float Sb = (b[2] - b[0] + 1) * (b[3] - b[1] + 1);\n  return interS / (Sa + Sb - interS);\n}\n\n__global__ void nms_kernel(const int n_boxes, const float nms_overlap_thresh,\n                           const float *dev_boxes, unsigned long long *dev_mask) {\n  const int row_start = blockIdx.y;\n  const int col_start = blockIdx.x;\n\n  // if (row_start > col_start) return;\n\n  const int row_size =\n        min(n_boxes - row_start * threadsPerBlock, threadsPerBlock);\n  const int col_size =\n        min(n_boxes - col_start * threadsPerBlock, threadsPerBlock);\n\n  __shared__ float block_boxes[threadsPerBlock * 5];\n  if (threadIdx.x < col_size) {\n    block_boxes[threadIdx.x * 5 + 0] =\n        dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 0];\n    block_boxes[threadIdx.x * 5 + 1] =\n        dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 1];\n    block_boxes[threadIdx.x * 5 + 2] =\n        dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 2];\n    block_boxes[threadIdx.x * 5 + 3] =\n        dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 3];\n    block_boxes[threadIdx.x * 5 + 4] =\n        dev_boxes[(threadsPerBlock * col_start + threadIdx.x) * 5 + 4];\n  }\n  __syncthreads();\n\n  if (threadIdx.x < row_size) {\n    const int cur_box_idx = threadsPerBlock * row_start + threadIdx.x;\n    const float *cur_box = dev_boxes + cur_box_idx * 5;\n    int i = 0;\n    unsigned long long t = 0;\n    int start = 0;\n    if (row_start == col_start) {\n      start = threadIdx.x + 1;\n    }\n    for (i = start; i < col_size; i++) {\n      if (devIoU(cur_box, block_boxes + i * 5) > nms_overlap_thresh) {\n        t |= 1ULL << i;\n      }\n    }\n    const int col_blocks = DIVUP(n_boxes, threadsPerBlock);\n    dev_mask[cur_box_idx * col_blocks + col_start] = t;\n  }\n}\n\nvoid _set_device(int device_id) {\n  int current_device;\n  CUDA_CHECK(cudaGetDevice(&current_device));\n  if (current_device == device_id) {\n    return;\n  }\n  // The call to cudaSetDevice must come before any calls to Get, which\n  // may perform initialization using the GPU.\n  CUDA_CHECK(cudaSetDevice(device_id));\n}\n\nvoid _nms(int* keep_out, int* num_out, const float* boxes_host, int boxes_num,\n          int boxes_dim, float nms_overlap_thresh, int device_id) {\n  _set_device(device_id);\n\n  float* boxes_dev = NULL;\n  unsigned long long* mask_dev = NULL;\n\n  const int col_blocks = DIVUP(boxes_num, threadsPerBlock);\n\n  CUDA_CHECK(cudaMalloc(&boxes_dev,\n                        boxes_num * boxes_dim * sizeof(float)));\n  CUDA_CHECK(cudaMemcpy(boxes_dev,\n                        boxes_host,\n                        boxes_num * boxes_dim * sizeof(float),\n                        cudaMemcpyHostToDevice));\n\n  CUDA_CHECK(cudaMalloc(&mask_dev,\n                        boxes_num * col_blocks * sizeof(unsigned long long)));\n\n  dim3 blocks(DIVUP(boxes_num, threadsPerBlock),\n              DIVUP(boxes_num, threadsPerBlock));\n  dim3 threads(threadsPerBlock);\n  nms_kernel<<<blocks, threads>>>(boxes_num,\n                                  nms_overlap_thresh,\n                                  boxes_dev,\n                                  mask_dev);\n\n  std::vector<unsigned long long> mask_host(boxes_num * col_blocks);\n  CUDA_CHECK(cudaMemcpy(&mask_host[0],\n                        mask_dev,\n                        sizeof(unsigned long long) * boxes_num * col_blocks,\n                        cudaMemcpyDeviceToHost));\n\n  std::vector<unsigned long long> remv(col_blocks);\n  memset(&remv[0], 0, sizeof(unsigned long long) * col_blocks);\n\n  int num_to_keep = 0;\n  for (int i = 0; i < boxes_num; i++) {\n    int nblock = i / threadsPerBlock;\n    int inblock = i % threadsPerBlock;\n\n    if (!(remv[nblock] & (1ULL << inblock))) {\n      keep_out[num_to_keep++] = i;\n      unsigned long long *p = &mask_host[0] + i * col_blocks;\n      for (int j = nblock; j < col_blocks; j++) {\n        remv[j] |= p[j];\n      }\n    }\n  }\n  *num_out = num_to_keep;\n\n  CUDA_CHECK(cudaFree(boxes_dev));\n  CUDA_CHECK(cudaFree(mask_dev));\n}\n"
  },
  {
    "path": "lib/nms/py_cpu_nms.py",
    "content": "# --------------------------------------------------------\n# Fast R-CNN\n# Copyright (c) 2015 Microsoft\n# Licensed under The MIT License [see LICENSE for details]\n# Written by Ross Girshick\n# --------------------------------------------------------\n\nimport numpy as np\n\ndef py_cpu_nms(dets, thresh):\n    \"\"\"Pure Python NMS baseline.\"\"\"\n    x1 = dets[:, 0]\n    y1 = dets[:, 1]\n    x2 = dets[:, 2]\n    y2 = dets[:, 3]\n    scores = dets[:, 4]\n\n    areas = (x2 - x1 + 1) * (y2 - y1 + 1)\n    order = scores.argsort()[::-1]\n\n    keep = []\n    while order.size > 0:\n        i = order[0]\n        keep.append(i)\n        xx1 = np.maximum(x1[i], x1[order[1:]])\n        yy1 = np.maximum(y1[i], y1[order[1:]])\n        xx2 = np.minimum(x2[i], x2[order[1:]])\n        yy2 = np.minimum(y2[i], y2[order[1:]])\n\n        w = np.maximum(0.0, xx2 - xx1 + 1)\n        h = np.maximum(0.0, yy2 - yy1 + 1)\n        inter = w * h\n        ovr = inter / (areas[i] + areas[order[1:]] - inter)\n\n        inds = np.where(ovr <= thresh)[0]\n        order = order[inds + 1]\n\n    return keep\n"
  },
  {
    "path": "lib/nms/setup.py",
    "content": "# --------------------------------------------------------\n# Fast R-CNN\n# Copyright (c) 2015 Microsoft\n# Licensed under The MIT License [see LICENSE for details]\n# Written by Ross Girshick\n# --------------------------------------------------------\n\nimport os\nfrom os.path import join as pjoin\nfrom setuptools import setup\nfrom distutils.extension import Extension\nfrom Cython.Distutils import build_ext\nimport subprocess\nimport numpy as np\n\ndef find_in_path(name, path):\n    \"Find a file in a search path\"\n    # Adapted fom\n    # http://code.activestate.com/recipes/52224-find-a-file-given-a-search-path/\n    for dir in path.split(os.pathsep):\n        binpath = pjoin(dir, name)\n        if os.path.exists(binpath):\n            return os.path.abspath(binpath)\n    return None\n\n\ndef locate_cuda():\n    \"\"\"Locate the CUDA environment on the system\n\n    Returns a dict with keys 'home', 'nvcc', 'include', and 'lib64'\n    and values giving the absolute path to each directory.\n\n    Starts by looking for the CUDAHOME env variable. If not found, everything\n    is based on finding 'nvcc' in the PATH.\n    \"\"\"\n\n    # first check if the CUDAHOME env variable is in use\n    if 'CUDAHOME' in os.environ:\n        home = os.environ['CUDAHOME']\n        nvcc = pjoin(home, 'bin', 'nvcc')\n    else:\n        # otherwise, search the PATH for NVCC\n        default_path = pjoin(os.sep, 'usr', 'local', 'cuda', 'bin')\n        nvcc = find_in_path('nvcc', os.environ['PATH'] + os.pathsep + default_path)\n        if nvcc is None:\n            raise EnvironmentError('The nvcc binary could not be '\n                'located in your $PATH. Either add it to your path, or set $CUDAHOME')\n        home = os.path.dirname(os.path.dirname(nvcc))\n\n    cudaconfig = {'home':home, 'nvcc':nvcc,\n                  'include': pjoin(home, 'include'),\n                  'lib64': pjoin(home, 'lib64')}\n    for k, v in cudaconfig.items():\n        if not os.path.exists(v):\n            raise EnvironmentError('The CUDA %s path could not be located in %s' % (k, v))\n\n    return cudaconfig\nCUDA = locate_cuda()\n\n\n# Obtain the numpy include directory.  This logic works across numpy versions.\ntry:\n    numpy_include = np.get_include()\nexcept AttributeError:\n    numpy_include = np.get_numpy_include()\n\ndef customize_compiler_for_nvcc(self):\n    \"\"\"inject deep into distutils to customize how the dispatch\n    to gcc/nvcc works.\n\n    If you subclass UnixCCompiler, it's not trivial to get your subclass\n    injected in, and still have the right customizations (i.e.\n    distutils.sysconfig.customize_compiler) run on it. So instead of going\n    the OO route, I have this. Note, it's kindof like a wierd functional\n    subclassing going on.\"\"\"\n\n    # tell the compiler it can processes .cu\n    self.src_extensions.append('.cu')\n\n    # save references to the default compiler_so and _comple methods\n    default_compiler_so = self.compiler_so\n    super = self._compile\n\n    # now redefine the _compile method. This gets executed for each\n    # object but distutils doesn't have the ability to change compilers\n    # based on source extension: we add it.\n    def _compile(obj, src, ext, cc_args, extra_postargs, pp_opts):\n        if os.path.splitext(src)[1] == '.cu':\n            # use the cuda for .cu files\n            self.set_executable('compiler_so', CUDA['nvcc'])\n            # use only a subset of the extra_postargs, which are 1-1 translated\n            # from the extra_compile_args in the Extension class\n            postargs = extra_postargs['nvcc']\n        else:\n            postargs = extra_postargs['gcc']\n\n        super(obj, src, ext, cc_args, postargs, pp_opts)\n        # reset the default compiler_so, which we might have changed for cuda\n        self.compiler_so = default_compiler_so\n\n    # inject our redefined _compile method into the class\n    self._compile = _compile\n\n\n# run the customize_compiler\nclass custom_build_ext(build_ext):\n    def build_extensions(self):\n        customize_compiler_for_nvcc(self.compiler)\n        build_ext.build_extensions(self)\n\n\next_modules = [\n    Extension(\n        \"cpu_nms\",\n        [\"cpu_nms.pyx\"],\n        extra_compile_args={'gcc': [\"-Wno-cpp\", \"-Wno-unused-function\"]},\n        include_dirs = [numpy_include]\n    ),\n    Extension('gpu_nms',\n        ['nms_kernel.cu', 'gpu_nms.pyx'],\n        library_dirs=[CUDA['lib64']],\n        libraries=['cudart'],\n        language='c++',\n        runtime_library_dirs=[CUDA['lib64']],\n        # this syntax is specific to this build system\n        # we're only going to use certain compiler args with nvcc and not with\n        # gcc the implementation of this trick is in customize_compiler() below\n        extra_compile_args={'gcc': [\"-Wno-unused-function\"],\n                            'nvcc': ['-arch=sm_35',\n                                     '--ptxas-options=-v',\n                                     '-c',\n                                     '--compiler-options',\n                                     \"'-fPIC'\"]},\n        include_dirs = [numpy_include, CUDA['include']]\n    ),\n\n]\n\nsetup(\n    name='fast_rcnn',\n    ext_modules=ext_modules,\n    # inject our custom trigger\n    cmdclass={'build_ext': custom_build_ext},\n)\n"
  },
  {
    "path": "lib/rpn_util.py",
    "content": "\"\"\"\nThis file is meant to contain functions which are\nspecific to region proposal networks.\n\"\"\"\n\nimport matplotlib.pyplot as plt\nimport subprocess\nimport torch\nimport math\nimport re\nimport gc\n\nfrom lib.util import *\nfrom lib.core import *\nfrom lib.augmentations import *\nfrom lib.nms.gpu_nms import gpu_nms\nimport torch.nn.functional as F\n\nfrom copy import deepcopy\n\n\ndef generate_anchors(conf, imdb, cache_folder):\n    \"\"\"\n    Generates the anchors according to the configuration and\n    (optionally) based on the imdb properties.\n    \"\"\"\n\n    # use cache?\n    if (cache_folder is not None) and os.path.exists(os.path.join(cache_folder, 'anchors.pkl')):\n\n        anchors = pickle_read(os.path.join(cache_folder, 'anchors.pkl'))\n\n    # generate anchors\n    else:\n\n        anchors = np.zeros([len(conf.anchor_scales)*len(conf.anchor_ratios), 4], dtype=np.float32)\n\n        aind = 0\n\n        # compute simple anchors based on scale/ratios\n        for scale in conf.anchor_scales:\n\n            for ratio in conf.anchor_ratios:\n\n                h = scale\n                w = scale*ratio\n\n                anchors[aind, 0:4] = anchor_center(w, h, conf.feat_stride)\n                aind += 1\n\n\n        # optionally cluster anchors\n        if conf.cluster_anchors:\n            anchors = cluster_anchors(conf.feat_stride, anchors, conf.test_scale, imdb, conf.lbls,\n                                      conf.ilbls, conf.anchor_ratios, conf.min_gt_vis, conf.min_gt_h,\n                                      conf.max_gt_h, conf.even_anchors, conf.expand_anchors)\n\n\n        # has 3d? then need to compute stats for each new dimension\n        # presuming that anchors are initialized in \"2d\"\n        elif conf.has_3d:\n\n            # compute the default stats for each anchor\n            normalized_gts = []\n\n            # check all images\n            for imind, imobj in enumerate(imdb):\n\n                # has ground truths?\n                if len(imobj.gts) > 0:\n\n                    scale = imobj.scale * conf.test_scale / imobj.imH\n\n                    # determine ignores\n                    igns, rmvs = determine_ignores(imobj.gts, conf.lbls, conf.ilbls, conf.min_gt_vis,\n                                                   conf.min_gt_h, np.inf, scale)\n\n                    # accumulate boxes\n                    gts_all = bbXYWH2Coords(np.array([gt.bbox_full * scale for gt in imobj.gts]))\n                    gts_val = gts_all[(rmvs == False) & (igns == False), :]\n\n                    gts_3d = np.array([gt.bbox_3d for gt in imobj.gts])\n                    gts_3d = gts_3d[(rmvs == False) & (igns == False), :]\n\n                    if gts_val.shape[0] > 0:\n\n                        # center all 2D ground truths\n                        for gtind in range(0, gts_val.shape[0]):\n                            w = gts_val[gtind, 2] - gts_val[gtind, 0] + 1\n                            h = gts_val[gtind, 3] - gts_val[gtind, 1] + 1\n\n                            gts_val[gtind, 0:4] = anchor_center(w, h, conf.feat_stride)\n\n                    if gts_val.shape[0] > 0:\n                        normalized_gts += np.concatenate((gts_val, gts_3d), axis=1).tolist()\n\n            # convert to np\n            normalized_gts = np.array(normalized_gts)\n\n            # expand dimensions\n            anchors = np.concatenate((anchors, np.zeros([anchors.shape[0], 5])), axis=1)\n\n            # bbox_3d order --> [cx3d, cy3d, cz3d, w3d, h3d, l3d, rotY]\n            anchors_z3d = [[] for x in range(anchors.shape[0])]\n            anchors_w3d = [[] for x in range(anchors.shape[0])]\n            anchors_h3d = [[] for x in range(anchors.shape[0])]\n            anchors_l3d = [[] for x in range(anchors.shape[0])]\n            anchors_rotY = [[] for x in range(anchors.shape[0])]\n\n            # find best matches for each ground truth\n            ols = iou(anchors[:, 0:4], normalized_gts[:, 0:4])\n            gt_target_ols = np.amax(ols, axis=0)\n            gt_target_anchor = np.argmax(ols, axis=0)\n\n            # assign each box to an anchor\n            for gtind, gt in enumerate(normalized_gts):\n\n                anum = gt_target_anchor[gtind]\n\n                if gt_target_ols[gtind] > 0.2:\n                    anchors_z3d[anum].append(gt[6])\n                    anchors_w3d[anum].append(gt[7])\n                    anchors_h3d[anum].append(gt[8])\n                    anchors_l3d[anum].append(gt[9])\n                    anchors_rotY[anum].append(gt[10])\n\n            # compute global means\n            anchors_z3d_gl = np.empty(0)\n            anchors_w3d_gl = np.empty(0)\n            anchors_h3d_gl = np.empty(0)\n            anchors_l3d_gl = np.empty(0)\n            anchors_rotY_gl = np.empty(0)\n\n            # update anchors\n            for aind in range(0, anchors.shape[0]):\n\n                if len(np.array(anchors_z3d[aind])) > 0:\n\n                    if conf.has_3d:\n\n                        anchors_z3d_gl = np.hstack((anchors_z3d_gl, np.array(anchors_z3d[aind])))\n                        anchors_w3d_gl = np.hstack((anchors_w3d_gl, np.array(anchors_w3d[aind])))\n                        anchors_h3d_gl = np.hstack((anchors_h3d_gl, np.array(anchors_h3d[aind])))\n                        anchors_l3d_gl = np.hstack((anchors_l3d_gl, np.array(anchors_l3d[aind])))\n                        anchors_rotY_gl = np.hstack((anchors_rotY_gl, np.array(anchors_rotY[aind])))\n\n                        anchors[aind, 4] = np.mean(np.array(anchors_z3d[aind]))\n                        anchors[aind, 5] = np.mean(np.array(anchors_w3d[aind]))\n                        anchors[aind, 6] = np.mean(np.array(anchors_h3d[aind]))\n                        anchors[aind, 7] = np.mean(np.array(anchors_l3d[aind]))\n                        anchors[aind, 8] = np.mean(np.array(anchors_rotY[aind]))\n\n                else:\n                    raise ValueError('Non-used anchor #{} found'.format(aind))\n\n        if (cache_folder is not None):\n            pickle_write(os.path.join(cache_folder, 'anchors.pkl'), anchors)\n\n    conf.anchors = anchors\n\n\ndef anchor_center(w, h, stride):\n    \"\"\"\n    Centers an anchor based on a stride and the anchor shape (w, h).\n\n    center ground truths with steps of half stride\n    hence box 0 is centered at (7.5, 7.5) rather than (0, 0)\n    for a feature stride of 16 px.\n    \"\"\"\n\n    anchor = np.zeros([4], dtype=np.float32)\n\n    anchor[0] = -w / 2 + (stride - 1) / 2\n    anchor[1] = -h / 2 + (stride - 1) / 2\n    anchor[2] = w / 2 + (stride - 1) / 2\n    anchor[3] = h / 2 + (stride - 1) / 2\n\n    return anchor\n\n\ndef cluster_anchors(feat_stride, anchors, test_scale, imdb, lbls, ilbls, anchor_ratios, min_gt_vis=0.99,\n                    min_gt_h=0, max_gt_h=10e10, even_anchor_distribution=False, expand_anchors=False,\n                    expand_stop_dt=0.0025):\n    \"\"\"\n    Clusters the anchors based on the imdb boxes (in 2D and/or 3D).\n\n    Generally, this method does a custom k-means clustering using 2D IoU\n    as a distance metric.\n    \"\"\"\n\n    normalized_gts = []\n\n    # keep track if using 3d\n    has_3d = False\n\n    # check all images\n    for imind, imobj in enumerate(imdb):\n\n        # has ground truths?\n        if len(imobj.gts) > 0:\n\n            scale = imobj.scale * test_scale / imobj.imH\n\n            # determine ignores\n            igns, rmvs = determine_ignores(imobj.gts, lbls, ilbls, min_gt_vis, min_gt_h, np.inf, scale)\n\n            # check for 3d box\n            has_3d = 'bbox_3d' in imobj.gts[0]\n\n            # accumulate boxes\n            gts_all = bbXYWH2Coords(np.array([gt.bbox_full * scale for gt in imobj.gts]))\n            gts_val = gts_all[(rmvs == False) & (igns == False), :]\n\n            if has_3d:\n                gts_3d = np.array([gt.bbox_3d for gt in imobj.gts])\n                gts_3d = gts_3d[(rmvs == False) & (igns == False), :]\n\n            if gts_val.shape[0] > 0:\n\n                # center all 2D ground truths\n                for gtind in range(0, gts_val.shape[0]):\n\n                    w = gts_val[gtind, 2] - gts_val[gtind, 0] + 1\n                    h = gts_val[gtind, 3] - gts_val[gtind, 1] + 1\n\n                    gts_val[gtind, 0:4] = anchor_center(w, h, feat_stride)\n\n            if gts_val.shape[0] > 0:\n\n                # add normalized gts given 3d or 2d boxes\n                if has_3d: normalized_gts += np.concatenate((gts_val, gts_3d), axis=1).tolist()\n                else: normalized_gts += gts_val.tolist()\n\n\n    # convert to np\n    normalized_gts = np.array(normalized_gts)\n\n    # sort by height\n    sorted_inds = np.argsort((normalized_gts[:, 3] - normalized_gts[:, 1] + 1))\n    normalized_gts = normalized_gts[sorted_inds, :]\n\n    min_h = normalized_gts[0, 3] - normalized_gts[0, 1] + 1\n    max_h = normalized_gts[-1, 3] - normalized_gts[-1, 1] + 1\n\n    # for 3d, expand dimensions\n    if has_3d: anchors = np.concatenate((anchors, np.zeros([anchors.shape[0], 5])), axis=1)\n\n    # init expand\n    best_anchors = anchors\n    expand_last_iou = 0\n    expand_dif = 1\n    best_iou = 0\n    best_cov = 0\n\n    while np.round(expand_dif, 5) > expand_stop_dt:\n\n        # init cluster\n        max_rounds = 1000\n        round = 0\n        last_iou = 0\n        dif = 1\n\n        if even_anchor_distribution:\n\n            sample_num = int(np.floor(normalized_gts.shape[0] / anchors.shape[0]))\n\n            # evenly distribute the anchors\n            for aind in range(0, anchors.shape[0]):\n\n                x1 = normalized_gts[aind * sample_num:(aind * sample_num + sample_num), 0]\n                y1 = normalized_gts[aind * sample_num:(aind * sample_num + sample_num), 1]\n                x2 = normalized_gts[aind * sample_num:(aind * sample_num + sample_num), 2]\n                y2 = normalized_gts[aind * sample_num:(aind * sample_num + sample_num), 3]\n\n                w = np.mean(x2 - x1 + 1)\n                h = np.mean(y2 - y1 + 1)\n\n                anchors[aind, 0:4] = anchor_center(w, h, feat_stride)\n\n        else:\n\n            base = ((max_gt_h) / (min_gt_h)) ** (1 / (anchors.shape[0] - 1))\n            anchor_scales = np.array([(min_gt_h) * (base ** i) for i in range(0, anchors.shape[0])])\n\n            aind = 0\n\n            # compute new anchors\n            for scale in anchor_scales:\n\n                for ratio in anchor_ratios:\n\n                    h = scale\n                    w = scale * ratio\n\n                    anchors[aind, 0:4] = anchor_center(w, h, feat_stride)\n\n                    aind += 1\n\n        while round < max_rounds and dif > -0.0:\n\n            # make empty arrays for each anchor\n            anchors_h = [[] for x in range(anchors.shape[0])]\n            anchors_w = [[] for x in range(anchors.shape[0])]\n\n            if has_3d:\n\n                # bbox_3d order --> [cx3d, cy3d, cz3d, w3d, h3d, l3d, rotY]\n                anchors_z3d = [[] for x in range(anchors.shape[0])]\n                anchors_w3d = [[] for x in range(anchors.shape[0])]\n                anchors_h3d = [[] for x in range(anchors.shape[0])]\n                anchors_l3d = [[] for x in range(anchors.shape[0])]\n                anchors_rotY = [[] for x in range(anchors.shape[0])]\n\n            round_ious = []\n\n            # find best matches for each ground truth\n            ols = iou(anchors[:, 0:4], normalized_gts[:, 0:4])\n            gt_target_ols = np.amax(ols, axis=0)\n            gt_target_anchor = np.argmax(ols, axis=0)\n\n            # assign each box to an anchor\n            for gtind, gt in enumerate(normalized_gts):\n\n                anum = gt_target_anchor[gtind]\n\n                w = gt[2] - gt[0] + 1\n                h = gt[3] - gt[1] + 1\n\n                anchors_h[anum].append(h)\n                anchors_w[anum].append(w)\n\n                if has_3d:\n                    anchors_z3d[anum].append(gt[6])\n                    anchors_w3d[anum].append(gt[7])\n                    anchors_h3d[anum].append(gt[8])\n                    anchors_l3d[anum].append(gt[9])\n                    anchors_rotY[anum].append(gt[10])\n\n                round_ious.append(gt_target_ols[gtind])\n\n            # compute current iou\n            cur_iou = np.mean(np.array(round_ious))\n\n            # update anchors\n            for aind in range(0, anchors.shape[0]):\n\n                # compute mean h/w\n                if len(np.array(anchors_h[aind])) > 0:\n\n                    mean_h = np.mean(np.array(anchors_h[aind]))\n                    mean_w = np.mean(np.array(anchors_w[aind]))\n\n                    anchors[aind, 0:4] = anchor_center(mean_w, mean_h, feat_stride)\n\n                    if has_3d:\n                        anchors[aind, 4] = np.mean(np.array(anchors_z3d[aind]))\n                        anchors[aind, 5] = np.mean(np.array(anchors_w3d[aind]))\n                        anchors[aind, 6] = np.mean(np.array(anchors_h3d[aind]))\n                        anchors[aind, 7] = np.mean(np.array(anchors_l3d[aind]))\n                        anchors[aind, 8] = np.mean(np.array(anchors_rotY[aind]))\n\n                else:\n\n                    # anchor not used\n                    anchors[aind, :] = 0\n\n            anchors = np.nan_to_num(anchors)\n            valid_anchors = np.invert(np.all(anchors == 0, axis=1))\n\n            # redistribute non-valid anchors\n            valid_anchors_inds = np.flatnonzero(valid_anchors)\n\n            # determine most heavy anchors (to be split up)\n            valid_multi = np.array([len(x) for x in anchors_h])\n            valid_multi = valid_multi[valid_anchors_inds]\n            valid_multi = valid_multi / np.sum(valid_multi)\n\n            # store best configuration\n            if cur_iou > best_iou:\n                best_iou = cur_iou\n                best_anchors = anchors[valid_anchors, :]\n                best_cov = np.mean(np.array(round_ious) > 0.5)\n\n            # add random new anchors for any not used\n            for aind in range(0, anchors.shape[0]):\n\n                # make a new anchor\n                if not valid_anchors[aind]:\n                    randomness = 0.5\n                    multi = randomness*np.random.rand(len(valid_anchors_inds))\n                    multi += valid_multi\n                    multi /= np.sum(multi)\n                    anchors[aind, :] = np.dot(anchors[valid_anchors_inds, :].T, multi.T)\n\n\n            if not all(valid_anchors):\n                logging.info('warning: round {} some anchors not used during clustering'.format(round))\n\n            dif = cur_iou - last_iou\n            last_iou = cur_iou\n\n            round += 1\n\n        logging.info('anchors={}, rounds={}, mean_iou={:.4f}, gt_coverage={:.4f}'.format(anchors.shape[0], round,\n                                                                                         best_iou, best_cov))\n\n        expand_dif = best_iou - expand_last_iou\n        expand_last_iou = best_iou\n\n        # expand anchors to next size\n        if anchors.shape[0] < expand_anchors and expand_dif > expand_stop_dt:\n\n            # append blank anchor\n            if has_3d: anchors = np.vstack((anchors, [0, 0, 0, 0, 0, 0, 0, 0 ,0]))\n            else: anchors = np.vstack((anchors, [0, 0, 0, 0]))\n\n        # force stop\n        else: expand_dif = -1\n\n    logging.info('final_iou={:.4f}, final_coverage={:.4f}'.format(best_iou, best_cov))\n\n    return best_anchors\n\n\ndef compute_targets(gts_val, gts_ign, box_lbls, rois, fg_thresh, ign_thresh, bg_thresh_lo, bg_thresh_hi, best_thresh,\n                    gts_3d=None, anchors=[], tracker=[]):\n    \"\"\"\n    Computes the bbox targets of a set of rois and a set\n    of ground truth boxes, provided various ignore\n    settings in configuration\n    \"\"\"\n\n    ols = None\n    has_3d = gts_3d is not None\n\n    # init transforms which respectively hold [dx, dy, dw, dh, label]\n    # for labels bg=-1, ign=0, fg>=1\n    transforms = np.zeros([len(rois), 5], dtype=np.float32)\n    raw_gt = np.zeros([len(rois), 5], dtype=np.float32)\n\n    # if 3d, then init other terms after\n    if has_3d:\n        transforms = np.pad(transforms, [(0, 0), (0, gts_3d.shape[1])], 'constant')\n        raw_gt = np.pad(raw_gt, [(0, 0), (0, gts_3d.shape[1])], 'constant')\n\n    if gts_val.shape[0] > 0 or gts_ign.shape[0] > 0:\n\n        if gts_ign.shape[0] > 0:\n\n            # compute overlaps ign\n            ols_ign = iou_ign(rois, gts_ign)\n            ols_ign_max = np.amax(ols_ign, axis=1)\n\n        else:\n            ols_ign_max = np.zeros([rois.shape[0]], dtype=np.float32)\n\n        if gts_val.shape[0] > 0:\n\n            # compute overlaps valid\n            ols = iou(rois, gts_val)\n            ols_max = np.amax(ols, axis=1)\n            targets = np.argmax(ols, axis=1)\n\n            # find best matches for each ground truth\n            gt_best_rois = np.argmax(ols, axis=0)\n            gt_best_ols = np.amax(ols, axis=0)\n\n            gt_best_rois = gt_best_rois[gt_best_ols >= best_thresh]\n            gt_best_ols = gt_best_ols[gt_best_ols >= best_thresh]\n\n            fg_inds = np.flatnonzero(ols_max >= fg_thresh)\n            fg_inds = np.concatenate((fg_inds, gt_best_rois))\n            fg_inds = np.unique(fg_inds)\n\n            target_rois = gts_val[targets[fg_inds], :]\n            src_rois = rois[fg_inds, :]\n\n            if len(fg_inds) > 0:\n\n                # compute 2d transform\n                transforms[fg_inds, 0:4] = bbox_transform(src_rois, target_rois)\n\n                raw_gt[fg_inds, 0:4] = target_rois\n\n                if has_3d:\n\n                    tracker = tracker.astype(np.int64)\n                    src_3d = anchors[tracker[fg_inds], 4:]\n                    target_3d = gts_3d[targets[fg_inds]]\n\n                    raw_gt[fg_inds, 5:] = target_3d\n\n                    # compute 3d transform\n                    transforms[fg_inds, 5:] = bbox_transform_3d(src_rois, src_3d, target_3d)\n\n\n                # store labels\n                transforms[fg_inds, 4] = [box_lbls[x] for x in targets[fg_inds]]\n                assert (all(transforms[fg_inds, 4] >= 1))\n\n        else:\n\n            ols_max = np.zeros(rois.shape[0], dtype=int)\n            fg_inds = np.empty(shape=[0])\n            gt_best_rois = np.empty(shape=[0])\n\n        # determine ignores\n        ign_inds = np.flatnonzero(ols_ign_max >= ign_thresh)\n\n        # determine background\n        bg_inds = np.flatnonzero((ols_max >= bg_thresh_lo) & (ols_max < bg_thresh_hi))\n\n        # subtract fg and igns from background\n        bg_inds = np.setdiff1d(bg_inds, ign_inds)\n        bg_inds = np.setdiff1d(bg_inds, fg_inds)\n        bg_inds = np.setdiff1d(bg_inds, gt_best_rois)\n\n        # mark background\n        transforms[bg_inds, 4] = -1\n\n    else:\n\n        # all background\n        transforms[:, 4] = -1\n\n\n    return transforms, ols, raw_gt\n\n\ndef hill_climb(p2, p2_inv, box_2d, x2d, y2d, z2d, w3d, h3d, l3d, ry3d, step_z_init=0, step_r_init=0, z_lim=0, r_lim=0, min_ol_dif=0.0):\n\n    step_z = step_z_init\n    step_r = step_r_init\n\n    ol_best, verts_best, _, invalid = test_projection(p2, p2_inv, box_2d, x2d, y2d, z2d, w3d, h3d, l3d, ry3d)\n\n    if invalid: return z2d, ry3d, verts_best\n\n    # attempt to fit z/rot more properly\n    while (step_z > z_lim or step_r > r_lim):\n\n        if step_z > z_lim:\n\n            ol_neg, verts_neg, _, invalid_neg = test_projection(p2, p2_inv, box_2d, x2d, y2d, z2d - step_z, w3d, h3d, l3d, ry3d)\n            ol_pos, verts_pos, _, invalid_pos = test_projection(p2, p2_inv, box_2d, x2d, y2d, z2d + step_z, w3d, h3d, l3d, ry3d)\n\n            invalid = ((ol_pos - ol_best) <= min_ol_dif) and ((ol_neg - ol_best) <= min_ol_dif)\n\n            if invalid:\n                step_z = step_z * 0.5\n\n            elif (ol_pos - ol_best) > min_ol_dif and ol_pos > ol_neg and not invalid_pos:\n                z2d += step_z\n                ol_best = ol_pos\n                verts_best = verts_pos\n            elif (ol_neg - ol_best) > min_ol_dif and not invalid_neg:\n                z2d -= step_z\n                ol_best = ol_neg\n                verts_best = verts_neg\n            else:\n                step_z = step_z * 0.5\n\n        if step_r > r_lim:\n\n            ol_neg, verts_neg, _, invalid_neg = test_projection(p2, p2_inv, box_2d, x2d, y2d, z2d, w3d, h3d, l3d, ry3d - step_r)\n            ol_pos, verts_pos, _, invalid_pos = test_projection(p2, p2_inv, box_2d, x2d, y2d, z2d, w3d, h3d, l3d, ry3d + step_r)\n\n            invalid = ((ol_pos - ol_best) <= min_ol_dif) and ((ol_neg - ol_best) <= min_ol_dif)\n\n            if invalid:\n                step_r = step_r * 0.5\n\n            elif (ol_pos - ol_best) > min_ol_dif and ol_pos > ol_neg and not invalid_pos:\n                ry3d += step_r\n                ol_best = ol_pos\n                verts_best = verts_pos\n            elif (ol_neg - ol_best) > min_ol_dif and not invalid_neg:\n                ry3d -= step_r\n                ol_best = ol_neg\n                verts_best = verts_neg\n            else:\n                step_r = step_r * 0.5\n\n    while ry3d > math.pi: ry3d -= math.pi * 2\n    while ry3d < (-math.pi): ry3d += math.pi * 2\n\n    return z2d, ry3d, verts_best\n\n\ndef clsInd2Name(lbls, ind):\n    \"\"\"\n    Converts a cls ind to string name\n    \"\"\"\n\n    if ind>=0 and ind<len(lbls):\n        return lbls[ind]\n    else:\n        raise ValueError('unknown class')\n\n\ndef clsName2Ind(lbls, cls):\n    \"\"\"\n    Converts a cls name to an ind\n    \"\"\"\n    if cls in lbls:\n        return lbls.index(cls) + 1\n    else:\n        raise ValueError('unknown class')\n\n\ndef compute_bbox_stats(conf, imdb, cache_folder=''):\n    \"\"\"\n    Computes the mean and standard deviation for each regression\n    parameter (usually pertaining to [dx, dy, sw, sh] but sometimes\n    for 3d parameters too).\n\n    Once these stats are known we normalize the regression targets\n    to have 0 mean and 1 variance, to hypothetically ease training.\n    \"\"\"\n\n    if (cache_folder is not None) and os.path.exists(os.path.join(cache_folder, 'bbox_means.pkl')) \\\n            and os.path.exists(os.path.join(cache_folder, 'bbox_stds.pkl')):\n\n        means = pickle_read(os.path.join(cache_folder, 'bbox_means.pkl'))\n        stds = pickle_read(os.path.join(cache_folder, 'bbox_stds.pkl'))\n\n    else:\n\n        if conf.has_3d:\n            squared_sums = np.zeros([1, 11], dtype=np.float128)\n            sums = np.zeros([1, 11], dtype=np.float128)\n        else:\n            squared_sums = np.zeros([1, 4], dtype=np.float128)\n            sums = np.zeros([1, 4], dtype=np.float128)\n\n        class_counts = np.zeros([1], dtype=np.float128) + 1e-10\n\n        # compute the mean first\n        logging.info('Computing bbox regression mean..')\n\n        for imind, imobj in enumerate(imdb):\n\n            if len(imobj.gts) > 0:\n\n                scale_factor = imobj.scale * conf.test_scale / imobj.imH\n                feat_size = calc_output_size(np.array([imobj.imH, imobj.imW]) * scale_factor, conf.feat_stride)\n                rois = locate_anchors(conf.anchors, feat_size, conf.feat_stride)\n\n                # determine ignores\n                igns, rmvs = determine_ignores(imobj.gts, conf.lbls, conf.ilbls, conf.min_gt_vis,\n                                               conf.min_gt_h, np.inf, scale_factor)\n\n                # accumulate boxes\n                gts_all = bbXYWH2Coords(np.array([gt.bbox_full * scale_factor for gt in imobj.gts]))\n\n                # filter out irrelevant cls, and ignore cls\n                gts_val = gts_all[(rmvs == False) & (igns == False), :]\n                gts_ign = gts_all[(rmvs == False) & (igns == True), :]\n\n                # accumulate labels\n                box_lbls = np.array([gt.cls for gt in imobj.gts])\n                box_lbls = box_lbls[(rmvs == False) & (igns == False)]\n                box_lbls = np.array([clsName2Ind(conf.lbls, cls) for cls in box_lbls])\n\n                if conf.has_3d:\n\n                    # accumulate 3d boxes\n                    gts_3d = np.array([gt.bbox_3d for gt in imobj.gts])\n                    gts_3d = gts_3d[(rmvs == False) & (igns == False), :]\n\n                    # rescale centers (in 2d)\n                    for gtind, gt in enumerate(gts_3d):\n                        gts_3d[gtind, 0:2] *= scale_factor\n\n                    # compute transforms for all 3d\n                    transforms, _, _= compute_targets(gts_val, gts_ign, box_lbls, rois, conf.fg_thresh, conf.ign_thresh,\n                                                    conf.bg_thresh_lo, conf.bg_thresh_hi, conf.best_thresh, gts_3d=gts_3d,\n                                                    anchors=conf.anchors, tracker=rois[:, 4])\n                else:\n\n                    # compute transforms for 2d\n                    transforms, _, _ = compute_targets(gts_val, gts_ign, box_lbls, rois, conf.fg_thresh, conf.ign_thresh,\n                                                    conf.bg_thresh_lo, conf.bg_thresh_hi, conf.best_thresh)\n\n                gt_inds = np.flatnonzero(transforms[:, 4] > 0)\n\n                if len(gt_inds) > 0:\n\n                    if conf.has_3d:\n                        sums[:, 0:4] += np.sum(transforms[gt_inds, 0:4], axis=0)\n                        sums[:, 4:] += np.sum(transforms[gt_inds, 5:12], axis=0)\n                    else:\n                        sums += np.sum(transforms[gt_inds, 0:4], axis=0)\n\n                    class_counts += len(gt_inds)\n\n        means = sums/class_counts\n\n        logging.info('Computing bbox regression stds..')\n\n        for imobj in imdb:\n\n            if len(imobj.gts) > 0:\n\n                scale_factor = imobj.scale * conf.test_scale / imobj.imH\n                feat_size = calc_output_size(np.array([imobj.imH, imobj.imW]) * scale_factor, conf.feat_stride)\n                rois = locate_anchors(conf.anchors, feat_size, conf.feat_stride)\n\n                # determine ignores\n                igns, rmvs = determine_ignores(imobj.gts, conf.lbls, conf.ilbls, conf.min_gt_vis, conf.min_gt_h, np.inf, scale_factor)\n\n                # accumulate boxes\n                gts_all = bbXYWH2Coords(np.array([gt.bbox_full * scale_factor for gt in imobj.gts]))\n\n                # filter out irrelevant cls, and ignore cls\n                gts_val = gts_all[(rmvs == False) & (igns == False), :]\n                gts_ign = gts_all[(rmvs == False) & (igns == True), :]\n\n                # accumulate labels\n                box_lbls = np.array([gt.cls for gt in imobj.gts])\n                box_lbls = box_lbls[(rmvs == False) & (igns == False)]\n                box_lbls = np.array([clsName2Ind(conf.lbls, cls) for cls in box_lbls])\n\n                if conf.has_3d:\n\n                    # accumulate 3d boxes\n                    gts_3d = np.array([gt.bbox_3d for gt in imobj.gts])\n                    gts_3d = gts_3d[(rmvs == False) & (igns == False), :]\n\n                    # rescale centers (in 2d)\n                    for gtind, gt in enumerate(gts_3d):\n                        gts_3d[gtind, 0:2] *= scale_factor\n\n                    # compute transforms for all 3d\n                    transforms, _, _ = compute_targets(gts_val, gts_ign, box_lbls, rois, conf.fg_thresh, conf.ign_thresh,\n                                                    conf.bg_thresh_lo, conf.bg_thresh_hi, conf.best_thresh, gts_3d=gts_3d,\n                                                    anchors=conf.anchors, tracker=rois[:, 4])\n                else:\n\n                    # compute transforms for 2d\n                    transforms, _, _ = compute_targets(gts_val, gts_ign, box_lbls, rois, conf.fg_thresh, conf.ign_thresh,\n                                                    conf.bg_thresh_lo, conf.bg_thresh_hi, conf.best_thresh)\n\n                gt_inds = np.flatnonzero(transforms[:, 4] > 0)\n\n                if len(gt_inds) > 0:\n\n                    if conf.has_3d:\n                        squared_sums[:, 0:4] += np.sum(np.power(transforms[gt_inds, 0:4] - means[:, 0:4], 2), axis=0)\n                        squared_sums[:, 4:] += np.sum(np.power(transforms[gt_inds, 5:12] - means[:, 4:], 2), axis=0)\n\n                    else:\n                        squared_sums += np.sum(np.power(transforms[gt_inds, 0:4] - means, 2), axis=0)\n\n\n        stds = np.sqrt((squared_sums/class_counts))\n\n        means = means.astype(float)\n        stds = stds.astype(float)\n\n        logging.info('used {:d} boxes with avg std {:.4f}'.format(int(class_counts[0]), np.mean(stds)))\n\n        if (cache_folder is not None):\n            pickle_write(os.path.join(cache_folder, 'bbox_means.pkl'), means)\n            pickle_write(os.path.join(cache_folder, 'bbox_stds.pkl'), stds)\n\n    conf.bbox_means = means\n    conf.bbox_stds = stds\n\n\ndef flatten_tensor(input):\n    \"\"\"\n    Flattens and permutes a tensor from size\n    [B x C x W x H] --> [B x (W x H) x C]\n    \"\"\"\n\n    bsize = input.shape[0]\n    csize = input.shape[1]\n\n    return input.permute(0, 2, 3, 1).contiguous().view(bsize, -1, csize)\n\n\ndef unflatten_tensor(input, feat_size, anchors):\n    \"\"\"\n    Un-flattens and un-permutes a tensor from size\n    [B x (W x H) x C] --> [B x C x W x H]\n    \"\"\"\n\n    bsize = input.shape[0]\n\n    if len(input.shape) >= 3: csize = input.shape[2]\n    else: csize = 1\n\n    input = input.view(bsize, feat_size[0] * anchors.shape[0], feat_size[1], csize)\n    input = input.permute(0, 3, 1, 2).contiguous()\n\n    return input\n\n\ndef project_3d(p2, x3d, y3d, z3d, w3d, h3d, l3d, ry3d, return_3d=False):\n    \"\"\"\n    Projects a 3D box into 2D vertices\n\n    Args:\n        p2 (nparray): projection matrix of size 4x3\n        x3d: x-coordinate of center of object\n        y3d: y-coordinate of center of object\n        z3d: z-cordinate of center of object\n        w3d: width of object\n        h3d: height of object\n        l3d: length of object\n        ry3d: rotation w.r.t y-axis\n    \"\"\"\n\n    # compute rotational matrix around yaw axis\n    R = np.array([[+math.cos(ry3d), 0, +math.sin(ry3d)],\n                  [0, 1, 0],\n                  [-math.sin(ry3d), 0, +math.cos(ry3d)]])\n\n    # 3D bounding box corners\n    x_corners = np.array([0, l3d, l3d, l3d, l3d,   0,   0,   0])\n    y_corners = np.array([0, 0,   h3d, h3d,   0,   0, h3d, h3d])\n    z_corners = np.array([0, 0,     0, w3d, w3d, w3d, w3d,   0])\n\n    x_corners += -l3d / 2\n    y_corners += -h3d / 2\n    z_corners += -w3d / 2\n\n    # bounding box in object co-ordinate\n    corners_3d = np.array([x_corners, y_corners, z_corners])\n\n    # rotate\n    corners_3d = R.dot(corners_3d)\n\n    # translate\n    corners_3d += np.array([x3d, y3d, z3d]).reshape((3, 1))\n\n    corners_3D_1 = np.vstack((corners_3d, np.ones((corners_3d.shape[-1]))))\n    corners_2D = p2.dot(corners_3D_1)\n    corners_2D = corners_2D / corners_2D[2]\n\n    bb3d_lines_verts_idx = [0, 1, 2, 3, 4, 5, 6, 7, 0, 5, 4, 1, 2, 7, 6, 3]\n\n    verts3d = (corners_2D[:, bb3d_lines_verts_idx][:2]).astype(float).T\n\n    if return_3d:\n        return verts3d, corners_3d\n    else:\n        return verts3d\n\n\ndef project_3d_corners(p2, x3d, y3d, z3d, w3d, h3d, l3d, ry3d):\n    \"\"\"\n    Projects a 3D box into 2D vertices\n\n    Args:\n        p2 (nparray): projection matrix of size 4x3\n        x3d: x-coordinate of center of object\n        y3d: y-coordinate of center of object\n        z3d: z-cordinate of center of object\n        w3d: width of object\n        h3d: height of object\n        l3d: length of object\n        ry3d: rotation w.r.t y-axis\n    \"\"\"\n\n    # compute rotational matrix around yaw axis\n    R = np.array([[+math.cos(ry3d), 0, +math.sin(ry3d)],\n                  [0, 1, 0],\n                  [-math.sin(ry3d), 0, +math.cos(ry3d)]])\n\n    # 3D bounding box corners\n    x_corners = np.array([0, l3d, l3d, l3d, l3d, 0, 0, 0])\n    y_corners = np.array([0, 0, h3d, h3d, 0, 0, h3d, h3d])\n    z_corners = np.array([0, 0, 0, w3d, w3d, w3d, w3d, 0])\n\n    '''\n    order of vertices\n    0  upper back right\n    1  upper front right\n    2  bottom front right\n    3  bottom front left\n    4  upper front left\n    5  upper back left\n    6  bottom back left\n    7  bottom back right\n    \n    bot_inds = np.array([2,3,6,7])\n    top_inds = np.array([0,1,4,5])\n    '''\n\n    x_corners += -l3d / 2\n    y_corners += -h3d / 2\n    z_corners += -w3d / 2\n\n    # bounding box in object co-ordinate\n    corners_3d = np.array([x_corners, y_corners, z_corners])\n\n    # rotate\n    corners_3d = R.dot(corners_3d)\n\n    # translate\n    corners_3d += np.array([x3d, y3d, z3d]).reshape((3, 1))\n\n    corners_3D_1 = np.vstack((corners_3d, np.ones((corners_3d.shape[-1]))))\n    corners_2D = p2.dot(corners_3D_1)\n    corners_2D = corners_2D / corners_2D[2]\n\n    return corners_2D, corners_3D_1\n\n\ndef bbCoords2XYWH(box):\n    \"\"\"\n    Convert from [x1, y1, x2, y2] to [x,y,w,h]\n    \"\"\"\n\n    if box.shape[0] == 0: return np.empty([0, 4], dtype=float)\n\n    box[:, 2] -= box[:, 0] + 1\n    box[:, 3] -= box[:, 1] + 1\n\n    return box\n\n\ndef bbXYWH2Coords(box):\n    \"\"\"\n    Convert from [x,y,w,h] to [x1, y1, x2, y2]\n    \"\"\"\n\n    if box.shape[0] == 0: return np.empty([0,4], dtype=float)\n\n    box[:, 2] += box[:, 0] - 1\n    box[:, 3] += box[:, 1] - 1\n\n    return box\n\n\ndef bbox_transform_3d(ex_rois_2d, ex_rois_3d, gt_rois):\n    \"\"\"\n    Compute the bbox target transforms in 3D.\n\n    Translations are done as simple difference, whereas others involving\n    scaling are done in log space (hence, log(1) = 0, log(0.8) < 0 and\n    log(1.2) > 0 which is a good property).\n    \"\"\"\n\n    ex_widths = ex_rois_2d[:, 2] - ex_rois_2d[:, 0] + 1.0\n    ex_heights = ex_rois_2d[:, 3] - ex_rois_2d[:, 1] + 1.0\n    ex_ctr_x = ex_rois_2d[:, 0] + 0.5 * (ex_widths - 1)\n    ex_ctr_y = ex_rois_2d[:, 1] + 0.5 * (ex_heights - 1)\n\n    gt_ctr_x = gt_rois[:, 0]\n    gt_ctr_y = gt_rois[:, 1]\n\n    targets_dx = (gt_ctr_x - ex_ctr_x) / ex_widths\n    targets_dy = (gt_ctr_y - ex_ctr_y) / ex_heights\n\n    delta_z = gt_rois[:, 2] - ex_rois_3d[:, 0]\n    scale_w = np.log(gt_rois[:, 3] / ex_rois_3d[:, 1])\n    scale_h = np.log(gt_rois[:, 4] / ex_rois_3d[:, 2])\n    scale_l = np.log(gt_rois[:, 5] / ex_rois_3d[:, 3])\n    deltaRotY = gt_rois[:, 6] - ex_rois_3d[:, 4]\n\n    targets = np.vstack((targets_dx, targets_dy, delta_z, scale_w, scale_h, scale_l, deltaRotY)).transpose()\n    targets = np.hstack((targets, gt_rois[:, 7:]))\n\n\n    return targets\n\n\ndef bbox_transform(ex_rois, gt_rois):\n    \"\"\"\n    Compute the bbox target transforms in 2D.\n\n    Translations are done as simple difference, whereas others involving\n    scaling are done in log space (hence, log(1) = 0, log(0.8) < 0 and\n    log(1.2) > 0 which is a good property).\n    \"\"\"\n\n    ex_widths = ex_rois[:, 2] - ex_rois[:, 0] + 1.0\n    ex_heights = ex_rois[:, 3] - ex_rois[:, 1] + 1.0\n    ex_ctr_x = ex_rois[:, 0] + 0.5 * (ex_widths - 1)\n    ex_ctr_y = ex_rois[:, 1] + 0.5 * (ex_heights - 1)\n\n    gt_widths = gt_rois[:, 2] - gt_rois[:, 0] + 1.0\n    gt_heights = gt_rois[:, 3] - gt_rois[:, 1] + 1.0\n    gt_ctr_x = gt_rois[:, 0] + 0.5 * (gt_widths - 1.0)\n    gt_ctr_y = gt_rois[:, 1] + 0.5 * (gt_heights - 1.0)\n\n    targets_dx = (gt_ctr_x - ex_ctr_x) / ex_widths\n    targets_dy = (gt_ctr_y - ex_ctr_y) / ex_heights\n    targets_dw = np.log(gt_widths / ex_widths)\n    targets_dh = np.log(gt_heights / ex_heights)\n\n    targets = np.vstack((targets_dx, targets_dy, targets_dw, targets_dh)).transpose()\n\n    return targets\n\n\ndef bbox_transform_inv(boxes, deltas, means=None, stds=None):\n    \"\"\"\n    Compute the bbox target transforms in 3D.\n\n    Translations are done as simple difference, whereas others involving\n    scaling are done in log space (hence, log(1) = 0, log(0.8) < 0 and\n    log(1.2) > 0 which is a good property).\n    \"\"\"\n\n    if boxes.shape[0] == 0:\n        return np.zeros((0, deltas.shape[1]), dtype=deltas.dtype)\n\n    # boxes = boxes.astype(deltas.dtype, copy=False)\n\n    widths = boxes[:, 2] - boxes[:, 0] + 1.0\n    heights = boxes[:, 3] - boxes[:, 1] + 1.0\n    ctr_x = boxes[:, 0] + 0.5 * widths\n    ctr_y = boxes[:, 1] + 0.5 * heights\n\n    dx = deltas[:, 0]\n    dy = deltas[:, 1]\n    dw = deltas[:, 2]\n    dh = deltas[:, 3]\n\n    if stds is not None:\n        dx *= stds[0]\n        dy *= stds[1]\n        dw *= stds[2]\n        dh *= stds[3]\n\n    if means is not None:\n        dx += means[0]\n        dy += means[1]\n        dw += means[2]\n        dh += means[3]\n\n    pred_ctr_x = dx * widths + ctr_x\n    pred_ctr_y = dy * heights + ctr_y\n    pred_w = torch.exp(dw) * widths\n    pred_h = torch.exp(dh) * heights\n\n    pred_boxes = torch.zeros(deltas.shape)\n\n    # x1, y1, x2, y2\n    pred_boxes[:, 0] = pred_ctr_x - 0.5 * pred_w\n    pred_boxes[:, 1] = pred_ctr_y - 0.5 * pred_h\n    pred_boxes[:, 2] = pred_ctr_x + 0.5 * pred_w\n    pred_boxes[:, 3] = pred_ctr_y + 0.5 * pred_h\n\n    return pred_boxes\n\n\ndef determine_ignores(gts, lbls, ilbls, min_gt_vis=0.99, min_gt_h=0, max_gt_h=10e10, scale_factor=1):\n    \"\"\"\n    Given various configuration settings, determine which ground truths\n    are ignored and which are relevant.\n    \"\"\"\n\n    igns = np.zeros([len(gts)], dtype=bool)\n    rmvs = np.zeros([len(gts)], dtype=bool)\n\n    for gtind, gt in enumerate(gts):\n\n        ign = gt.ign\n        ign |= gt.visibility < min_gt_vis\n        ign |= gt.bbox_full[3] * scale_factor < min_gt_h\n        ign |= gt.bbox_full[3] * scale_factor > max_gt_h\n        ign |= gt.cls in ilbls\n\n        rmv = not gt.cls in (lbls + ilbls)\n\n        igns[gtind] = ign\n        rmvs[gtind] = rmv\n\n    return igns, rmvs\n\n\ndef locate_anchors(anchors, feat_size, stride, convert_tensor=False):\n    \"\"\"\n    Spreads each anchor shape across a feature map of size feat_size spaced by a known stride.\n\n    Args:\n        anchors (ndarray): N x 4 array describing [x1, y1, x2, y2] displacements for N anchors\n        feat_size (ndarray): the downsampled resolution W x H to spread anchors across\n        stride (int): stride of a network\n        convert_tensor (bool, optional): whether to return a torch tensor, otherwise ndarray [default=False]\n\n    Returns:\n         ndarray: 2D array = [(W x H) x 5] array consisting of [x1, y1, x2, y2, anchor_index]\n    \"\"\"\n\n    # compute rois\n    shift_x = np.array(range(0, feat_size[1], 1)) * float(stride)\n    shift_y = np.array(range(0, feat_size[0], 1)) * float(stride)\n    [shift_x, shift_y] = np.meshgrid(shift_x, shift_y)\n\n    rois = np.expand_dims(anchors[:, 0:4], axis=1)\n    shift_x = np.expand_dims(shift_x, axis=0)\n    shift_y = np.expand_dims(shift_y, axis=0)\n\n    shift_x1 = shift_x + np.expand_dims(rois[:, :, 0], axis=2)\n    shift_y1 = shift_y + np.expand_dims(rois[:, :, 1], axis=2)\n    shift_x2 = shift_x + np.expand_dims(rois[:, :, 2], axis=2)\n    shift_y2 = shift_y + np.expand_dims(rois[:, :, 3], axis=2)\n\n    # compute anchor tracker\n    anchor_tracker = np.zeros(shift_x1.shape, dtype=float)\n    for aind in range(0, rois.shape[0]): anchor_tracker[aind, :, :] = aind\n\n    stack_size = feat_size[0] * anchors.shape[0]\n\n    # torch and numpy MAY have different calls for reshaping, although\n    # it is not very important which is used as long as it is CONSISTENT\n    if convert_tensor:\n\n        # important to unroll according to pytorch\n        shift_x1 = torch.from_numpy(shift_x1).view(1, stack_size, feat_size[1])\n        shift_y1 = torch.from_numpy(shift_y1).view(1, stack_size, feat_size[1])\n        shift_x2 = torch.from_numpy(shift_x2).view(1, stack_size, feat_size[1])\n        shift_y2 = torch.from_numpy(shift_y2).view(1, stack_size, feat_size[1])\n        anchor_tracker = torch.from_numpy(anchor_tracker).view(1, stack_size, feat_size[1])\n\n        shift_x1.requires_grad = False\n        shift_y1.requires_grad = False\n        shift_x2.requires_grad = False\n        shift_y2.requires_grad = False\n        anchor_tracker.requires_grad = False\n\n        shift_x1 = shift_x1.permute(1, 2, 0).contiguous().view(-1, 1)\n        shift_y1 = shift_y1.permute(1, 2, 0).contiguous().view(-1, 1)\n        shift_x2 = shift_x2.permute(1, 2, 0).contiguous().view(-1, 1)\n        shift_y2 = shift_y2.permute(1, 2, 0).contiguous().view(-1, 1)\n        anchor_tracker = anchor_tracker.permute(1, 2, 0).contiguous().view(-1, 1)\n\n        rois = torch.cat((shift_x1, shift_y1, shift_x2, shift_y2, anchor_tracker), 1)\n\n    else:\n\n        shift_x1 = shift_x1.reshape(1, stack_size, feat_size[1]).reshape(-1, 1)\n        shift_y1 = shift_y1.reshape(1, stack_size, feat_size[1]).reshape(-1, 1)\n        shift_x2 = shift_x2.reshape(1, stack_size, feat_size[1]).reshape(-1, 1)\n        shift_y2 = shift_y2.reshape(1, stack_size, feat_size[1]).reshape(-1, 1)\n        anchor_tracker = anchor_tracker.reshape(1, stack_size, feat_size[1]).reshape(-1, 1)\n\n        rois = np.concatenate((shift_x1, shift_y1, shift_x2, shift_y2, anchor_tracker), 1)\n\n    return rois\n\n\ndef calc_output_size(res, stride):\n    \"\"\"\n    Approximate the output size of a network\n\n    Args:\n        res (ndarray): input resolution\n        stride (int): stride of a network\n\n    Returns:\n         ndarray: output resolution\n    \"\"\"\n\n    return np.ceil(np.array(res)/stride).astype(int)\n\n\ndef im_detect_3d(im, net, rpn_conf, preprocess, p2, gpu=0, synced=False):\n    \"\"\"\n    Object detection in 3D\n    \"\"\"\n\n    imH_orig = im.shape[0]\n    imW_orig = im.shape[1]\n\n    im = preprocess(im)\n\n    # move to GPU\n    im = torch.from_numpy(im[np.newaxis, :, :, :]).cuda()\n\n    imH = im.shape[2]\n    imW = im.shape[3]\n\n    scale_factor = imH / imH_orig\n\n    cls, prob, bbox_2d, bbox_3d, feat_size, rois = net(im)\n\n    # compute feature resolution\n    num_anchors = rpn_conf.anchors.shape[0]\n\n    bbox_x = bbox_2d[:, :, 0]\n    bbox_y = bbox_2d[:, :, 1]\n    bbox_w = bbox_2d[:, :, 2]\n    bbox_h = bbox_2d[:, :, 3]\n\n    bbox_x3d = bbox_3d[:, :, 0]\n    bbox_y3d = bbox_3d[:, :, 1]\n    bbox_z3d = bbox_3d[:, :, 2]\n    bbox_w3d = bbox_3d[:, :, 3]\n    bbox_h3d = bbox_3d[:, :, 4]\n    bbox_l3d = bbox_3d[:, :, 5]\n    bbox_ry3d = bbox_3d[:, :, 6]\n\n    # detransform 3d\n    bbox_x3d = bbox_x3d * rpn_conf.bbox_stds[:, 4][0] + rpn_conf.bbox_means[:, 4][0]\n    bbox_y3d = bbox_y3d * rpn_conf.bbox_stds[:, 5][0] + rpn_conf.bbox_means[:, 5][0]\n    bbox_z3d = bbox_z3d * rpn_conf.bbox_stds[:, 6][0] + rpn_conf.bbox_means[:, 6][0]\n    bbox_w3d = bbox_w3d * rpn_conf.bbox_stds[:, 7][0] + rpn_conf.bbox_means[:, 7][0]\n    bbox_h3d = bbox_h3d * rpn_conf.bbox_stds[:, 8][0] + rpn_conf.bbox_means[:, 8][0]\n    bbox_l3d = bbox_l3d * rpn_conf.bbox_stds[:, 9][0] + rpn_conf.bbox_means[:, 9][0]\n    bbox_ry3d = bbox_ry3d * rpn_conf.bbox_stds[:, 10][0] + rpn_conf.bbox_means[:, 10][0]\n\n    # find 3d source\n    tracker = rois[:, 4].cpu().detach().numpy().astype(np.int64)\n    src_3d = torch.from_numpy(rpn_conf.anchors[tracker, 4:]).cuda().type(torch.cuda.FloatTensor)\n\n    #tracker_sca = rois_sca[:, 4].cpu().detach().numpy().astype(np.int64)\n    #src_3d_sca = torch.from_numpy(rpn_conf.anchors[tracker_sca, 4:]).cuda().type(torch.cuda.FloatTensor)\n\n    # compute 3d transform\n    widths = rois[:, 2] - rois[:, 0] + 1.0\n    heights = rois[:, 3] - rois[:, 1] + 1.0\n    ctr_x = rois[:, 0] + 0.5 * widths\n    ctr_y = rois[:, 1] + 0.5 * heights\n\n    bbox_x3d = bbox_x3d[0, :] * widths + ctr_x\n    bbox_y3d = bbox_y3d[0, :] * heights + ctr_y\n    bbox_z3d = src_3d[:, 0] + bbox_z3d[0, :]\n    bbox_w3d = torch.exp(bbox_w3d[0, :]) * src_3d[:, 1]\n    bbox_h3d = torch.exp(bbox_h3d[0, :]) * src_3d[:, 2]\n    bbox_l3d = torch.exp(bbox_l3d[0, :]) * src_3d[:, 3]\n    bbox_ry3d = src_3d[:, 4] + bbox_ry3d[0, :]\n\n    # bundle\n    coords_3d = torch.stack((bbox_x3d, bbox_y3d, bbox_z3d[:bbox_x3d.shape[0]], bbox_w3d[:bbox_x3d.shape[0]], bbox_h3d[:bbox_x3d.shape[0]], bbox_l3d[:bbox_x3d.shape[0]], bbox_ry3d[:bbox_x3d.shape[0]]), dim=1)\n\n    # compile deltas pred\n    deltas_2d = torch.cat((bbox_x[0, :, np.newaxis], bbox_y[0, :, np.newaxis], bbox_w[0, :, np.newaxis], bbox_h[0, :, np.newaxis]), dim=1)\n    coords_2d = bbox_transform_inv(rois, deltas_2d, means=rpn_conf.bbox_means[0, :], stds=rpn_conf.bbox_stds[0, :])\n\n    # detach onto cpu\n    coords_2d = coords_2d.cpu().detach().numpy()\n    coords_3d = coords_3d.cpu().detach().numpy()\n    prob = prob[0, :, :].cpu().detach().numpy()\n\n    # scale coords\n    coords_2d[:, 0:4] /= scale_factor\n    coords_3d[:, 0:2] /= scale_factor\n\n    cls_pred = np.argmax(prob[:, 1:], axis=1) + 1\n    scores = np.amax(prob[:, 1:], axis=1)\n\n    aboxes = np.hstack((coords_2d, scores[:, np.newaxis]))\n\n    sorted_inds = (-aboxes[:, 4]).argsort()\n    original_inds = (sorted_inds).argsort()\n    aboxes = aboxes[sorted_inds, :]\n    coords_3d = coords_3d[sorted_inds, :]\n    cls_pred = cls_pred[sorted_inds]\n    tracker = tracker[sorted_inds]\n\n    if synced:\n\n        # nms\n        keep_inds = gpu_nms(aboxes[:, 0:5].astype(np.float32), rpn_conf.nms_thres, device_id=gpu)\n\n        # convert to bool\n        keep = np.zeros([aboxes.shape[0], 1], dtype=bool)\n        keep[keep_inds, :] = True\n\n        # stack the keep array,\n        # sync to the original order\n        aboxes = np.hstack((aboxes, keep))\n        aboxes[original_inds, :]\n\n    else:\n\n        # pre-nms\n        cls_pred = cls_pred[0:min(rpn_conf.nms_topN_pre, cls_pred.shape[0])]\n        tracker = tracker[0:min(rpn_conf.nms_topN_pre, tracker.shape[0])]\n        aboxes = aboxes[0:min(rpn_conf.nms_topN_pre, aboxes.shape[0]), :]\n        coords_3d = coords_3d[0:min(rpn_conf.nms_topN_pre, coords_3d.shape[0])]\n\n        # nms\n        keep_inds = gpu_nms(aboxes[:, 0:5].astype(np.float32), rpn_conf.nms_thres, device_id=gpu)\n\n        # stack cls prediction\n        aboxes = np.hstack((aboxes, cls_pred[:, np.newaxis], coords_3d, tracker[:, np.newaxis]))\n\n        # suppress boxes\n        aboxes = aboxes[keep_inds, :]\n\n    # clip boxes\n    if rpn_conf.clip_boxes:\n        aboxes[:, 0] = np.clip(aboxes[:, 0], 0, imW_orig - 1)\n        aboxes[:, 1] = np.clip(aboxes[:, 1], 0, imH_orig - 1)\n        aboxes[:, 2] = np.clip(aboxes[:, 2], 0, imW_orig - 1)\n        aboxes[:, 3] = np.clip(aboxes[:, 3], 0, imH_orig - 1)\n\n    return aboxes\n\n\ndef get_2D_from_3D(p2, cx3d, cy3d, cz3d, w3d, h3d, l3d, rotY):\n\n    verts3d, corners_3d = project_3d(p2, cx3d, cy3d, cz3d, w3d, h3d, l3d, rotY, return_3d=True)\n\n    # any boxes behind camera plane?\n    if np.any(corners_3d[2, :] <= 0):\n        ign = True\n\n    else:\n        x = min(verts3d[:, 0])\n        y = min(verts3d[:, 1])\n        x2 = max(verts3d[:, 0])\n        y2 = max(verts3d[:, 1])\n\n    return np.array([x, y, x2, y2])\n\n\ndef test_kitti_3d(dataset_test, net, rpn_conf, results_path, test_path, use_log=True):\n    \"\"\"\n    Test the KITTI framework for object detection in 3D\n    \"\"\"\n\n    # import read_kitti_cal\n    from lib.imdb_util import read_kitti_cal\n\n    imlist = list_files(os.path.join(test_path, dataset_test, 'validation', 'image_2', ''), '*.png')\n\n    preprocess = Preprocess([rpn_conf.test_scale], rpn_conf.image_means, rpn_conf.image_stds)\n\n    # fix paths slightly\n    _, test_iter, _ = file_parts(results_path.replace('/data', ''))\n    test_iter = test_iter.replace('results_', '')\n\n    # init\n    test_start = time()\n\n    for imind, impath in enumerate(imlist):\n\n        im = cv2.imread(impath)\n\n        base_path, name, ext = file_parts(impath)\n\n        # read in calib\n        p2 = read_kitti_cal(os.path.join(test_path, dataset_test, 'validation', 'calib', name + '.txt'))\n        p2_inv = np.linalg.inv(p2)\n\n        # forward test batch\n        aboxes = im_detect_3d(im, net, rpn_conf, preprocess, p2)\n\n        base_path, name, ext = file_parts(impath)\n\n        file = open(os.path.join(results_path, name + '.txt'), 'w')\n        text_to_write = ''\n        \n        for boxind in range(0, min(rpn_conf.nms_topN_post, aboxes.shape[0])):\n\n            box = aboxes[boxind, :]\n            score = box[4]\n            cls = rpn_conf.lbls[int(box[5] - 1)]\n\n            if score >= 0.75:\n\n                x1 = box[0]\n                y1 = box[1]\n                x2 = box[2]\n                y2 = box[3]\n                width = (x2 - x1 + 1)\n                height = (y2 - y1 + 1)\n\n                # plot 3D box\n                x3d = box[6]\n                y3d = box[7]\n                z3d = box[8]\n                w3d = box[9]\n                h3d = box[10]\n                l3d = box[11]\n                ry3d = box[12]\n\n                # convert alpha into ry3d\n                coord3d = np.linalg.inv(p2).dot(np.array([x3d * z3d, y3d * z3d, 1 * z3d, 1]))\n                ry3d = convertAlpha2Rot(ry3d, coord3d[2], coord3d[0])\n\n                step_r = 0.3*math.pi\n                r_lim = 0.01\n                box_2d = np.array([x1, y1, width, height])\n\n                z3d, ry3d, verts_best = hill_climb(p2, p2_inv, box_2d, x3d, y3d, z3d, w3d, h3d, l3d, ry3d, step_r_init=step_r, r_lim=r_lim)\n\n                # predict a more accurate projection\n                coord3d = np.linalg.inv(p2).dot(np.array([x3d * z3d, y3d * z3d, 1 * z3d, 1]))\n                alpha = convertRot2Alpha(ry3d, coord3d[2], coord3d[0])\n\n                x3d = coord3d[0]\n                y3d = coord3d[1]\n                z3d = coord3d[2]\n\n                y3d += h3d/2\n                \n                text_to_write += ('{} -1 -1 {:.6f} {:.6f} {:.6f} {:.6f} {:.6f} {:.6f} {:.6f} {:.6f} {:.6f} {:.6f} {:.6f} '\n                           + '{:.6f} {:.6f}\\n').format(cls, alpha, x1, y1, x2, y2, h3d, w3d, l3d, x3d, y3d, z3d, ry3d, score)\n                           \n        file.write(text_to_write)\n        file.close()\n\n        # display stats\n        if (imind + 1) % 1000 == 0:\n            time_str, dt = compute_eta(test_start, imind + 1, len(imlist))\n\n            print_str = 'testing {}/{}, dt: {:0.3f}, eta: {}'.format(imind + 1, len(imlist), dt, time_str)\n\n            if use_log: logging.info(print_str)\n            else: print(print_str)\n\n\n    # evaluate\n    script = os.path.join(test_path, dataset_test, 'devkit', 'cpp', 'evaluate_object')\n    with open(os.devnull, 'w') as devnull:\n        out = subprocess.check_output([script, results_path.replace('/data', '')], stderr=devnull)\n\n    for lbl in rpn_conf.lbls:\n\n        lbl = lbl.lower()\n\n        respath_2d = os.path.join(results_path.replace('/data', ''), 'stats_{}_detection.txt'.format(lbl))\n        respath_gr = os.path.join(results_path.replace('/data', ''), 'stats_{}_detection_ground.txt'.format(lbl))\n        respath_3d = os.path.join(results_path.replace('/data', ''), 'stats_{}_detection_3d.txt'.format(lbl))\n\n        if os.path.exists(respath_2d):\n            easy, mod, hard = parse_kitti_result(respath_2d)\n\n            print_str = 'test_iter {} 2d {} --> easy: {:0.4f}, mod: {:0.4f}, hard: {:0.4f}'.format(test_iter, lbl,\n                                                                                                    easy, mod, hard)\n            if use_log: logging.info(print_str)\n            else: print(print_str)\n\n        if os.path.exists(respath_gr):\n            easy, mod, hard = parse_kitti_result(respath_gr)\n\n            print_str = 'test_iter {} gr {} --> easy: {:0.4f}, mod: {:0.4f}, hard: {:0.4f}'.format(test_iter, lbl,\n                                                                                                    easy, mod, hard)\n\n            if use_log: logging.info(print_str)\n            else: print(print_str)\n\n        if os.path.exists(respath_3d):\n            easy, mod, hard = parse_kitti_result(respath_3d)\n\n            print_str = 'test_iter {} 3d {} --> easy: {:0.4f}, mod: {:0.4f}, hard: {:0.4f}'.format(test_iter, lbl,\n                                                                                                    easy, mod, hard)\n\n            if use_log: logging.info(print_str)\n            else: print(print_str)\n\n\ndef parse_kitti_result(respath):\n\n    text_file = open(respath, 'r')\n\n    acc = np.zeros([3, 41], dtype=float)\n\n    lind = 0\n    for line in text_file:\n\n        parsed = re.findall('([\\d]+\\.?[\\d]*)', line)\n\n        for i, num in enumerate(parsed):\n            acc[lind, i] = float(num)\n\n        lind += 1\n\n    text_file.close()\n\n    easy = np.mean(acc[0, 0:41:4])\n    mod = np.mean(acc[1, 0:41:4])\n    hard = np.mean(acc[2, 0:41:4])\n\n    #easy = np.mean(acc[0, 1:41:1])\n    #mod = np.mean(acc[1, 1:41:1])\n    #hard = np.mean(acc[2, 1:41:1])\n\n    return easy, mod, hard\n\n\ndef parse_kitti_vo(respath):\n\n    text_file = open(respath, 'r')\n\n    acc = np.zeros([1, 2], dtype=float)\n\n    lind = 0\n    for line in text_file:\n\n        parsed = re.findall('([\\d]+\\.?[\\d]*)', line)\n\n        for i, num in enumerate(parsed):\n            acc[lind, i] = float(num)\n\n        lind += 1\n\n    text_file.close()\n\n    t = acc[0, 0]*100\n    r = acc[0, 1]\n\n    return t, r\n\n\ndef test_projection(p2, p2_inv, box_2d, cx, cy, z, w3d, h3d, l3d, rotY):\n    \"\"\"\n    Tests the consistency of a 3D projection compared to a 2D box\n    \"\"\"\n\n    x = box_2d[0]\n    y = box_2d[1]\n    x2 = x + box_2d[2] - 1\n    y2 = y + box_2d[3] - 1\n\n    coord3d = p2_inv.dot(np.array([cx * z, cy * z, z, 1]))\n\n    cx3d = coord3d[0]\n    cy3d = coord3d[1]\n    cz3d = coord3d[2]\n\n    # put back on ground first\n    #cy3d += h3d/2\n\n    # re-compute the 2D box using 3D (finally, avoids clipped boxes)\n    verts3d, corners_3d = project_3d(p2, cx3d, cy3d, cz3d, w3d, h3d, l3d, rotY, return_3d=True)\n\n    invalid = np.any(corners_3d[2, :] <= 0)\n\n    x_new = min(verts3d[:, 0])\n    y_new = min(verts3d[:, 1])\n    x2_new = max(verts3d[:, 0])\n    y2_new = max(verts3d[:, 1])\n\n    b1 = np.array([x, y, x2, y2])[np.newaxis, :]\n    b2 = np.array([x_new, y_new, x2_new, y2_new])[np.newaxis, :]\n\n    #ol = iou(b1, b2)[0][0]\n    ol = -(np.abs(x - x_new) + np.abs(y - y_new) + np.abs(x2 - x2_new) + np.abs(y2 - y2_new))\n\n    return ol, verts3d, b2, invalid\n\n\n"
  },
  {
    "path": "lib/util.py",
    "content": "\"\"\"\nThis file is meant to contain generic utility functions\nwhich can be easily re-used in any project, and are not\nspecific to any project or framework (except python!).\n\"\"\"\n\nimport os\nimport sys\nfrom glob import glob\nfrom time import time\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport importlib\nimport pickle\nimport logging\nimport datetime\nimport pprint\nimport shutil\nimport math\nimport copy\nimport cv2\n#from scipy.spatial.transform import Rotation as scipy_R\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas\nfrom PIL import Image\n\n\ndef copyfile(src, dst):\n    shutil.copyfile(src, dst)\n\n\ndef pretty_print(name, input, val_width=40, key_width=0):\n    \"\"\"\n    This function creates a formatted string from a given dictionary input.\n    It may not support all data types, but can probably be extended.\n\n    Args:\n        name (str): name of the variable root\n        input (dict): dictionary to print\n        val_width (int): the width of the right hand side values\n        key_width (int): the minimum key width, (always auto-defaults to the longest key!)\n\n    Example:\n        pretty_str = pretty_print('conf', conf.__dict__)\n        pretty_str = pretty_print('conf', {'key1': 'example', 'key2': [1,2,3,4,5], 'key3': np.random.rand(4,4)})\n\n        print(pretty_str)\n        or\n        logging.info(pretty_str)\n    \"\"\"\n\n    # root\n    pretty_str = name + ': {\\n'\n\n    # determine key width\n    for key in input.keys(): key_width = max(key_width, len(str(key)) + 4)\n\n    # cycle keys\n    for key in input.keys():\n\n        val = input[key]\n\n        # round values to 3 decimals..\n        if type(val) == np.ndarray: val = np.round(val, 3).tolist()\n\n        # difficult formatting\n        val_str = str(val)\n        if len(val_str) > val_width:\n            val_str = pprint.pformat(val, width=val_width, compact=True)\n            val_str = val_str.replace('\\n', '\\n{tab}')\n            tab = ('{0:' + str(4 + key_width) + '}').format('')\n            val_str = val_str.replace('{tab}', tab)\n\n        # more difficult formatting\n        format_str = '{0:' + str(4) + '}{1:' + str(key_width) + '} {2:' + str(val_width) + '}\\n'\n        pretty_str += format_str.format('', key + ':', val_str)\n\n    # close root object\n    pretty_str += '}'\n\n    return pretty_str\n\n\ndef absolute_import(file_path):\n    \"\"\"\n    Imports a python module / file given its ABSOLUTE path.\n\n    Args:\n         file_path (str): absolute path to a python file to attempt to import\n    \"\"\"\n\n    # module name\n    _, name, _ = file_parts(file_path)\n\n    # load the spec and module\n    spec = importlib.util.spec_from_file_location(name, file_path)\n    module = importlib.util.module_from_spec(spec)\n    spec.loader.exec_module(module)\n\n    return module\n\n\ndef init_log_file(folder_path, suffix=None, log_level=logging.INFO):\n    \"\"\"\n    This function inits a log file given a folder to write the log to.\n    it automatically adds a timestamp and optional suffix to the log.\n    Anything written to the log will automatically write to console too.\n\n    Example:\n        import logging\n\n        init_log_file('output/logs/')\n        logging.info('this will show up in both the log AND console!')\n    \"\"\"\n\n    timestamp = datetime.datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n    log_format = '[%(levelname)s]: %(asctime)s %(message)s'\n\n    if suffix is not None:\n        file_name = timestamp + '_' + suffix\n    else:\n        file_name = timestamp\n\n    file_path = os.path.join(folder_path, file_name)\n    logging.basicConfig(filename=file_path, level=log_level, format=log_format)\n    logging.getLogger().addHandler(logging.StreamHandler(sys.stdout))\n\n    return file_path\n\n\ndef denorm_image(im, image_means, image_stds):\n\n    im = copy.deepcopy(im)\n    im[:, :, 0] *= image_stds[0]\n    im[:, :, 1] *= image_stds[1]\n    im[:, :, 2] *= image_stds[2]\n\n    im[:, :, 0] += image_means[0]\n    im[:, :, 1] += image_means[1]\n    im[:, :, 2] += image_means[2]\n\n    return im\n\n\ndef compute_eta(start_time, idx, total):\n    \"\"\"\n    Computes the estimated time as a formatted string as well\n    as the change in delta time dt.\n\n    Example:\n        from time import time\n\n        start_time = time()\n\n        for i in range(0, total):\n            <lengthly computation>\n            time_str, dt = compute_eta(start_time, i, total)\n    \"\"\"\n\n    dt = (time() - start_time)/idx\n    timeleft = np.max([dt * (total - idx), 0])\n    if timeleft > 3600: time_str = '{:.1f}h'.format(timeleft / 3600);\n    elif timeleft > 60: time_str = '{:.1f}m'.format(timeleft / 60);\n    else: time_str = '{:.1f}s'.format(timeleft);\n\n    return time_str, dt\n\n\ndef interp_color(dist, bounds=[0, 1], color_lo=(0,0, 250), color_hi=(0, 250, 250)):\n\n    percent = (dist - bounds[0]) / (bounds[1] - bounds[0])\n    b = color_lo[0] * (1 - percent) + color_hi[0] * percent\n    g = color_lo[1] * (1 - percent) + color_hi[1] * percent\n    r = color_lo[2] * (1 - percent) + color_hi[2] * percent\n\n    return (b, g, r)\n\n\ndef create_colorbar(height, width, color_lo=(0,0, 250), color_hi=(0, 250, 250)):\n\n    im = np.zeros([height, width, 3])\n\n    for h in range(0, height):\n\n        color = interp_color(h + 0.5, [0, height], color_hi, color_lo)\n        im[h, :, 0] = (color[0])\n        im[h, :, 1] = (color[1])\n        im[h, :, 2] = (color[2])\n\n    return im.astype(np.uint8)\n\n\ndef mkdir_if_missing(directory, delete_if_exist=False):\n    \"\"\"\n    Recursively make a directory structure even if missing.\n\n    if delete_if_exist=True then we will delete it first\n    which can be useful when better control over initialization is needed.\n    \"\"\"\n\n    if delete_if_exist and os.path.exists(directory): shutil.rmtree(directory)\n\n    # check if not exist, then make\n    if not os.path.exists(directory):\n        os.makedirs(directory)\n\n\ndef list_files(base_dir, file_pattern):\n    \"\"\"\n    Returns a list of files given a directory and pattern\n    The results are sorted alphabetically\n\n    Example:\n        files = list_files('path/to/images/', '*.jpg')\n    \"\"\"\n\n    return sorted(glob(os.path.join(base_dir) + file_pattern))\n\n\ndef file_parts(file_path):\n    \"\"\"\n    Lists a files parts such as base_path, file name and extension\n\n    Example\n        base, name, ext = file_parts('path/to/file/dog.jpg')\n        print(base, name, ext) --> ('path/to/file/', 'dog', '.jpg')\n    \"\"\"\n\n    base_path, tail = os.path.split(file_path)\n    name, ext = os.path.splitext(tail)\n\n    return base_path, name, ext\n\n\ndef pickle_write(file_path, obj):\n    \"\"\"\n    Serialize an object to a provided file_path\n    \"\"\"\n\n    with open(file_path, 'wb') as file:\n        pickle.dump(obj, file)\n\n\ndef pickle_read(file_path):\n    \"\"\"\n    De-serialize an object from a provided file_path\n    \"\"\"\n\n    with open(file_path, 'rb') as file:\n        return pickle.load(file)\n\n\ndef get_color(ind, hex=False):\n\n    colors = [(111, 74, 0),\n        (81, 0, 81),\n        (128, 64, 128),\n        (244, 35, 232),\n        (250, 170, 160),\n        (230, 150, 140),\n        (70, 70, 70),\n        (102, 102, 156),\n        (190, 153, 153),\n        (180, 165, 180),\n        (150, 100, 100),\n        (150, 120, 90),\n        (153, 153, 153),\n        (250, 170, 30),\n        (220, 220, 0),\n        (107, 142, 35),\n        (152, 251, 152),\n        (70, 130, 180),\n        (220, 20, 60),\n        (255, 0, 0),\n        (0, 0, 142),\n        (0, 0, 70),\n        (0, 60, 100),\n        (0, 0, 90),\n        (0, 0, 110),\n        (0, 80, 100),\n        (0, 0, 230),\n        (119, 11, 32),\n        (0, 0, 142), ]\n\n    color = colors[ind % len(colors)]\n\n    if hex:\n        return '#%02x%02x%02x' % (color[0], color[1], color[2])\n    else:\n        return color\n\n\ndef draw_3d_box(im, verts, color=(0, 200, 200), thickness=1):\n\n    for lind in range(0, verts.shape[0] - 1):\n        v1 = verts[lind]\n        v2 = verts[lind + 1]\n        cv2.line(im, (int(v1[0]), int(v1[1])), (int(v2[0]), int(v2[1])), color, thickness)\n\n\ndef draw_bev(canvas_bev, z3d, l3d, w3d, x3d, ry3d, color=(0, 200, 200), scale=1, thickness=2):\n\n    w = l3d * scale\n    l = w3d * scale\n    x = x3d * scale\n    z = z3d * scale\n    r = ry3d*-1\n\n    corners1 = np.array([\n        [-w / 2, -l / 2, 1],\n        [+w / 2, -l / 2, 1],\n        [+w / 2, +l / 2, 1],\n        [-w / 2, +l / 2, 1]\n    ])\n\n    ry = np.array([\n        [+math.cos(r), -math.sin(r), 0],\n        [+math.sin(r), math.cos(r), 0],\n        [0, 0, 1],\n    ])\n\n    corners2 = ry.dot(corners1.T).T\n\n    corners2[:, 0] += w/2 + x + canvas_bev.shape[1] / 2\n    corners2[:, 1] += l/2 + z\n\n    draw_line(canvas_bev, corners2[0], corners2[1], color=color, thickness=thickness)\n    draw_line(canvas_bev, corners2[1], corners2[2], color=color, thickness=thickness)\n    draw_line(canvas_bev, corners2[2], corners2[3], color=color, thickness=thickness)\n    draw_line(canvas_bev, corners2[3], corners2[0], color=color, thickness=thickness)\n\n\ndef draw_line(im, v1, v2, color=(0, 200, 200), thickness=1):\n\n    cv2.line(im, (int(v1[0]), int(v1[1])), (int(v2[0]), int(v2[1])), color, thickness)\n\n\ndef draw_circle(im, pos, radius=5, thickness=1, color=(250, 100, 100), fill=True):\n\n    if fill: thickness = -1\n\n    cv2.circle(im, (int(pos[0]), int(pos[1])), radius, color=color, thickness=thickness)\n\n\ndef draw_2d_box(im, box, color=(0, 200, 200), thickness=1):\n\n    x = box[0]\n    y = box[1]\n    w = box[2]\n    h = box[3]\n    x2 = (x + w) - 1\n    y2 = (y + h) - 1\n\n    cv2.rectangle(im, (int(x), int(y)), (int(x2), int(y2)), color, thickness)\n\n\ndef imshow(im, fig_num=None):\n\n    if fig_num is not None: plt.figure(fig_num)\n\n    if len(im.shape) == 2:\n        im = np.tile(im, [3, 1, 1]).transpose([1, 2, 0])\n\n    plt.imshow(cv2.cvtColor(im.astype(np.uint8), cv2.COLOR_RGB2BGR))\n    plt.show(block=False)\n\n\ndef imwrite(im, path):\n\n    cv2.imwrite(path, im)\n\n\ndef imread(path):\n\n    return cv2.imread(path)\n\n\ndef draw_tick_marks(im, ticks):\n\n    ticks_loc = list(range(0, im.shape[0] + 1, int((im.shape[0]) / (len(ticks) - 1))))\n\n    for tind, tick in enumerate(ticks):\n        y = min(max(ticks_loc[tind], 50), im.shape[0] - 10)\n        x = im.shape[1] - 115\n\n        draw_text(im, '-{}m'.format(tick), (x, y), lineType=2, scale=1.1, bg_color=None)\n\n\ndef draw_text(im, text, pos, scale=0.4, color=(0, 0, 0), font=cv2.FONT_HERSHEY_SIMPLEX, bg_color=(0, 255, 255),\n              blend=0.33, lineType=1):\n\n    pos = [int(pos[0]), int(pos[1])]\n\n    if bg_color is not None:\n\n        text_size, _ = cv2.getTextSize(text, font, scale, lineType)\n        x_s = int(np.clip(pos[0], a_min=0, a_max=im.shape[1]))\n        x_e = int(np.clip(pos[0] + text_size[0] - 1 + 4, a_min=0, a_max=im.shape[1]))\n        y_s = int(np.clip(pos[1] - text_size[1] - 2, a_min=0, a_max=im.shape[0]))\n        y_e = int(np.clip(pos[1] + 1 - 2, a_min=0, a_max=im.shape[0]))\n\n        im[y_s:y_e + 1, x_s:x_e + 1, 0] = im[y_s:y_e + 1, x_s:x_e + 1, 0]*blend + bg_color[0] * (1 - blend)\n        im[y_s:y_e + 1, x_s:x_e + 1, 1] = im[y_s:y_e + 1, x_s:x_e + 1, 1]*blend + bg_color[1] * (1 - blend)\n        im[y_s:y_e + 1, x_s:x_e + 1, 2] = im[y_s:y_e + 1, x_s:x_e + 1, 2]*blend + bg_color[2] * (1 - blend)\n\n        pos[0] = int(np.clip(pos[0] + 2, a_min=0, a_max=im.shape[1]))\n        pos[1] = int(np.clip(pos[1] - 2, a_min=0, a_max=im.shape[0]))\n\n    cv2.putText(im, text, tuple(pos), font, scale, color, lineType)\n\n\n# Calculates rotation matrix to euler angles\n# The result is the same as MATLAB except the order\n# of the euler angles ( x and z are swapped ).\n# adopted from https://www.learnopencv.com/rotation-matrix-to-euler-angles/\ndef mat2euler(R):\n\n    sy = math.sqrt(R[0, 0] * R[0, 0] + R[1, 0] * R[1, 0])\n\n    singular = sy < 1e-6\n\n    if not singular:\n        x = math.atan2(R[2, 1], R[2, 2])\n        y = math.atan2(-R[2, 0], sy)\n        z = math.atan2(R[1, 0], R[0, 0])\n\n    else: raise ValueError('singular matrix found in mat2euler')\n\n    return np.array([x, y, z])\n\n\ndef fig_to_im(fig):\n\n    fig.canvas.draw()\n\n    # Get the RGBA buffer from the figure\n    w, h = fig.canvas.get_width_height()\n    buf = np.fromstring(fig.canvas.tostring_argb(), dtype=np.uint8)\n    buf.shape = (w, h, 4)\n\n    # canvas.tostring_argb give pixmap in ARGB mode. Roll the ALPHA channel to have it in RGBA mode\n    buf = np.roll(buf, 3, axis=2)\n\n    w, h, d = buf.shape\n\n    im_pil = Image.frombytes(\"RGBA\", (w, h), buf.tostring())\n    im_np = np.array(im_pil)[:,:,:3]\n\n    return im_np\n\n\ndef imzoom(im, zoom=0):\n\n    # single value passed in for both axis?\n    # extend same val for w, h\n    zoom = np.array(zoom)\n    if zoom.size == 1: zoom = np.array([zoom, zoom])\n\n    zoom = np.clip(zoom, a_min=0, a_max=0.99)\n\n    cx = im.shape[1]/2\n    cy = im.shape[0]/2\n\n    w = im.shape[1]*(1 - zoom[0])\n    h = im.shape[0]*(1 - zoom[-1])\n\n    x1 = int(np.clip(cx - w / 2, a_min=0, a_max=im.shape[1] - 1))\n    x2 = int(np.clip(cx + w / 2, a_min=0, a_max=im.shape[1] - 1))\n    y1 = int(np.clip(cy - h / 2, a_min=0, a_max=im.shape[0] - 1))\n    y2 = int(np.clip(cy + h / 2, a_min=0, a_max=im.shape[0] - 1))\n\n    im = im[y1:y2+1, x1:x2+1, :]\n\n    return im\n\n\ndef imhstack(im1, im2):\n\n    sf = im1.shape[0] / im2.shape[0]\n\n    if sf > 1:\n        im2 = cv2.resize(im2, (int(im2.shape[1] / sf), im1.shape[0]))\n    else:\n        im1 = cv2.resize(im1, (int(im1.shape[1] / sf), im2.shape[0]))\n\n\n    im_concat = np.hstack((im1, im2))\n\n    return im_concat\n\n\n# Calculates Rotation Matrix given euler angles.\n# adopted from https://www.learnopencv.com/rotation-matrix-to-euler-angles/\ndef euler2mat(x, y, z):\n\n    R_x = np.array([[1, 0, 0],\n                    [0, math.cos(x), -math.sin(x)],\n                    [0, math.sin(x), math.cos(x)]\n                    ])\n\n    R_y = np.array([[math.cos(y), 0, math.sin(y)],\n                    [0, 1, 0],\n                    [-math.sin(y), 0, math.cos(y)]\n                    ])\n\n    R_z = np.array([[math.cos(z), -math.sin(z), 0],\n                    [math.sin(z), math.cos(z), 0],\n                    [0, 0, 1]\n                    ])\n\n    R = np.dot(R_z, np.dot(R_y, R_x))\n\n    return R\n\n\ndef convertAlpha2Rot(alpha, z3d, x3d):\n\n    ry3d = alpha + math.atan2(-z3d, x3d) + 0.5 * math.pi\n    #ry3d = alpha + math.atan2(x3d, z3d)# + 0.5 * math.pi\n\n    while ry3d > math.pi: ry3d -= math.pi * 2\n    while ry3d < (-math.pi): ry3d += math.pi * 2\n\n    return ry3d\n\n\ndef convertRot2Alpha(ry3d, z3d, x3d):\n\n    alpha = ry3d - math.atan2(-z3d, x3d) - 0.5 * math.pi\n    #alpha = ry3d - math.atan2(x3d, z3d)# - 0.5 * math.pi\n\n    while alpha > math.pi: alpha -= math.pi * 2\n    while alpha < (-math.pi): alpha += math.pi * 2\n\n    return alpha\n"
  },
  {
    "path": "models/densenet121_3d_dilate.py",
    "content": "import torch.nn as nn\nfrom torchvision import models\nfrom lib.rpn_util import *\nimport torch\n\n\ndef dilate_layer(layer, val):\n\n    layer.dilation = val\n    layer.padding = val\n\n\nclass RPN(nn.Module):\n\n\n    def __init__(self, phase, base, conf):\n        super(RPN, self).__init__()\n\n        self.base = base\n\n        del self.base.transition3.pool\n\n        # dilate\n        dilate_layer(self.base.denseblock4.denselayer1.conv2, 2)\n        dilate_layer(self.base.denseblock4.denselayer2.conv2, 2)\n        dilate_layer(self.base.denseblock4.denselayer3.conv2, 2)\n        dilate_layer(self.base.denseblock4.denselayer4.conv2, 2)\n        dilate_layer(self.base.denseblock4.denselayer5.conv2, 2)\n        dilate_layer(self.base.denseblock4.denselayer6.conv2, 2)\n        dilate_layer(self.base.denseblock4.denselayer7.conv2, 2)\n        dilate_layer(self.base.denseblock4.denselayer8.conv2, 2)\n        dilate_layer(self.base.denseblock4.denselayer9.conv2, 2)\n        dilate_layer(self.base.denseblock4.denselayer10.conv2, 2)\n        dilate_layer(self.base.denseblock4.denselayer11.conv2, 2)\n        dilate_layer(self.base.denseblock4.denselayer12.conv2, 2)\n        dilate_layer(self.base.denseblock4.denselayer13.conv2, 2)\n        dilate_layer(self.base.denseblock4.denselayer14.conv2, 2)\n        dilate_layer(self.base.denseblock4.denselayer15.conv2, 2)\n        dilate_layer(self.base.denseblock4.denselayer16.conv2, 2)\n\n        # settings\n        self.phase = phase\n        self.num_classes = len(conf['lbls']) + 1\n        self.num_anchors = conf['anchors'].shape[0]\n\n        self.prop_feats = nn.Sequential(\n            nn.Conv2d(self.base[-1].num_features, 512, 3, padding=1),\n            nn.ReLU(inplace=True),\n        )\n\n        # outputs\n        self.cls = nn.Conv2d(self.prop_feats[0].out_channels, self.num_classes * self.num_anchors, 1)\n\n        # bbox 2d\n        self.bbox_x = nn.Conv2d(self.prop_feats[0].out_channels, self.num_anchors, 1)\n        self.bbox_y = nn.Conv2d(self.prop_feats[0].out_channels, self.num_anchors, 1)\n        self.bbox_w = nn.Conv2d(self.prop_feats[0].out_channels, self.num_anchors, 1)\n        self.bbox_h = nn.Conv2d(self.prop_feats[0].out_channels, self.num_anchors, 1)\n\n        # bbox 3d\n        self.bbox_x3d = nn.Conv2d(self.prop_feats[0].out_channels, self.num_anchors, 1)\n        self.bbox_y3d = nn.Conv2d(self.prop_feats[0].out_channels, self.num_anchors, 1)\n        self.bbox_z3d = nn.Conv2d(self.prop_feats[0].out_channels, self.num_anchors, 1)\n        self.bbox_w3d = nn.Conv2d(self.prop_feats[0].out_channels, self.num_anchors, 1)\n        self.bbox_h3d = nn.Conv2d(self.prop_feats[0].out_channels, self.num_anchors, 1)\n        self.bbox_l3d = nn.Conv2d(self.prop_feats[0].out_channels, self.num_anchors, 1)\n        self.bbox_rY3d = nn.Conv2d(self.prop_feats[0].out_channels, self.num_anchors, 1)\n\n        self.softmax = nn.Softmax(dim=1)\n\n        self.feat_stride = conf.feat_stride\n        self.feat_size = calc_output_size(np.array(conf.crop_size), self.feat_stride)\n        self.rois = locate_anchors(conf.anchors, self.feat_size, conf.feat_stride, convert_tensor=True)\n        self.rois = self.rois.type(torch.cuda.FloatTensor)\n        self.anchors = conf.anchors\n\n\n    def forward(self, x):\n\n        batch_size = x.size(0)\n\n        # resnet\n        x = self.base(x)\n\n        prop_feats = self.prop_feats(x)\n\n        cls = self.cls(prop_feats)\n\n        # bbox 2d\n        bbox_x = self.bbox_x(prop_feats)\n        bbox_y = self.bbox_y(prop_feats)\n        bbox_w = self.bbox_w(prop_feats)\n        bbox_h = self.bbox_h(prop_feats)\n\n        # bbox 3d\n        bbox_x3d = self.bbox_x3d(prop_feats)\n        bbox_y3d = self.bbox_y3d(prop_feats)\n        bbox_z3d = self.bbox_z3d(prop_feats)\n        bbox_w3d = self.bbox_w3d(prop_feats)\n        bbox_h3d = self.bbox_h3d(prop_feats)\n        bbox_l3d = self.bbox_l3d(prop_feats)\n        bbox_rY3d = self.bbox_rY3d(prop_feats)\n\n        feat_h = cls.size(2)\n        feat_w = cls.size(3)\n\n        # reshape for cross entropy\n        cls = cls.view(batch_size, self.num_classes, feat_h * self.num_anchors, feat_w)\n\n        # score probabilities\n        prob = self.softmax(cls)\n\n        # reshape for consistency\n        bbox_x = flatten_tensor(bbox_x.view(batch_size, 1, feat_h * self.num_anchors, feat_w))\n        bbox_y = flatten_tensor(bbox_y.view(batch_size, 1, feat_h * self.num_anchors, feat_w))\n        bbox_w = flatten_tensor(bbox_w.view(batch_size, 1, feat_h * self.num_anchors, feat_w))\n        bbox_h = flatten_tensor(bbox_h.view(batch_size, 1, feat_h * self.num_anchors, feat_w))\n\n        bbox_x3d = flatten_tensor(bbox_x3d.view(batch_size, 1, feat_h * self.num_anchors, feat_w))\n        bbox_y3d = flatten_tensor(bbox_y3d.view(batch_size, 1, feat_h * self.num_anchors, feat_w))\n        bbox_z3d = flatten_tensor(bbox_z3d.view(batch_size, 1, feat_h * self.num_anchors, feat_w))\n        bbox_w3d = flatten_tensor(bbox_w3d.view(batch_size, 1, feat_h * self.num_anchors, feat_w))\n        bbox_h3d = flatten_tensor(bbox_h3d.view(batch_size, 1, feat_h * self.num_anchors, feat_w))\n        bbox_l3d = flatten_tensor(bbox_l3d.view(batch_size, 1, feat_h * self.num_anchors, feat_w))\n        bbox_rY3d = flatten_tensor(bbox_rY3d.view(batch_size, 1, feat_h * self.num_anchors, feat_w))\n\n        # bundle\n        bbox_2d = torch.cat((bbox_x, bbox_y, bbox_w, bbox_h), dim=2)\n        bbox_3d = torch.cat((bbox_x3d, bbox_y3d, bbox_z3d, bbox_w3d, bbox_h3d, bbox_l3d, bbox_rY3d), dim=2)\n\n        feat_size = [feat_h, feat_w]\n\n        cls = flatten_tensor(cls)\n        prob = flatten_tensor(prob)\n\n        if self.training:\n            return cls, prob, bbox_2d, bbox_3d, feat_size\n\n        else:\n\n            if self.feat_size[0] != feat_h or self.feat_size[1] != feat_w:\n                self.feat_size = [feat_h, feat_w]\n                self.rois = locate_anchors(self.anchors, self.feat_size, self.feat_stride, convert_tensor=True)\n                self.rois = self.rois.type(torch.cuda.FloatTensor)\n\n            return cls, prob, bbox_2d, bbox_3d, feat_size, self.rois\n\n\ndef build(conf, phase='train'):\n\n    train = phase.lower() == 'train'\n\n    densenet121 = models.densenet121(pretrained=train)\n\n    rpn_net = RPN(phase, densenet121.features, conf)\n\n    if train: rpn_net.train()\n    else: rpn_net.eval()\n\n    return rpn_net\n"
  },
  {
    "path": "models/densenet121_3d_dilate_depth_aware.py",
    "content": "import torch.nn as nn\nfrom torchvision import models\nfrom lib.rpn_util import *\nimport torch.nn.functional as F\nimport torch\n\n\ndef dilate_layer(layer, val):\n    layer.dilation = val\n    layer.padding = val\n\n\nclass LocalConv2d(nn.Module):\n\n    def __init__(self, num_rows, num_feats_in, num_feats_out, kernel=1, padding=0):\n        super(LocalConv2d, self).__init__()\n\n        self.num_rows = num_rows\n        self.out_channels = num_feats_out\n        self.kernel = kernel\n        self.pad = padding\n\n        self.group_conv = nn.Conv2d(num_feats_in * num_rows, num_feats_out * num_rows, kernel, stride=1, groups=num_rows)\n\n    def forward(self, x):\n\n        b, c, h, w = x.size()\n\n        if self.pad: x = F.pad(x, (self.pad, self.pad, self.pad, self.pad), mode='constant', value=0)\n\n        t = int(h / self.num_rows)\n\n        # unfold by rows\n        x = x.unfold(2, t + self.pad*2, t)\n        x = x.permute([0, 2, 1, 4, 3]).contiguous()\n        x = x.view(b, c * self.num_rows, t + self.pad*2, (w+self.pad*2)).contiguous()\n\n        # group convolution for efficient parallel processing\n        y = self.group_conv(x)\n        y = y.view(b, self.num_rows, self.out_channels, t, w).contiguous()\n        y = y.permute([0, 2, 1, 3, 4]).contiguous()\n        y = y.view(b, self.out_channels, h, w)\n\n        return y\n\n\nclass RPN(nn.Module):\n\n    def __init__(self, phase, base, conf):\n        super(RPN, self).__init__()\n\n        self.base = base\n\n        del self.base.transition3.pool\n\n        # dilate\n        dilate_layer(self.base.denseblock4.denselayer1.conv2, 2)\n        dilate_layer(self.base.denseblock4.denselayer2.conv2, 2)\n        dilate_layer(self.base.denseblock4.denselayer3.conv2, 2)\n        dilate_layer(self.base.denseblock4.denselayer4.conv2, 2)\n        dilate_layer(self.base.denseblock4.denselayer5.conv2, 2)\n        dilate_layer(self.base.denseblock4.denselayer6.conv2, 2)\n        dilate_layer(self.base.denseblock4.denselayer7.conv2, 2)\n        dilate_layer(self.base.denseblock4.denselayer8.conv2, 2)\n        dilate_layer(self.base.denseblock4.denselayer9.conv2, 2)\n        dilate_layer(self.base.denseblock4.denselayer10.conv2, 2)\n        dilate_layer(self.base.denseblock4.denselayer11.conv2, 2)\n        dilate_layer(self.base.denseblock4.denselayer12.conv2, 2)\n        dilate_layer(self.base.denseblock4.denselayer13.conv2, 2)\n        dilate_layer(self.base.denseblock4.denselayer14.conv2, 2)\n        dilate_layer(self.base.denseblock4.denselayer15.conv2, 2)\n        dilate_layer(self.base.denseblock4.denselayer16.conv2, 2)\n\n        # settings\n        self.phase = phase\n        self.num_classes = len(conf['lbls']) + 1\n        self.num_anchors = conf['anchors'].shape[0]\n\n        self.num_rows = int(min(conf.bins, calc_output_size(conf.test_scale, conf.feat_stride)))\n\n        self.prop_feats = nn.Sequential(\n            nn.Conv2d(self.base[-1].num_features, 512, 3, padding=1),\n            nn.ReLU(inplace=True)\n        )\n        # outputs\n        self.cls = nn.Conv2d(self.prop_feats[0].out_channels, self.num_classes * self.num_anchors, 1, )\n\n        # bbox 2d\n        self.bbox_x = nn.Conv2d(self.prop_feats[0].out_channels, self.num_anchors, 1)\n        self.bbox_y = nn.Conv2d(self.prop_feats[0].out_channels, self.num_anchors, 1)\n        self.bbox_w = nn.Conv2d(self.prop_feats[0].out_channels, self.num_anchors, 1)\n        self.bbox_h = nn.Conv2d(self.prop_feats[0].out_channels, self.num_anchors, 1)\n\n        # bbox 3d\n        self.bbox_x3d = nn.Conv2d(self.prop_feats[0].out_channels, self.num_anchors, 1)\n        self.bbox_y3d = nn.Conv2d(self.prop_feats[0].out_channels, self.num_anchors, 1)\n        self.bbox_z3d = nn.Conv2d(self.prop_feats[0].out_channels, self.num_anchors, 1)\n        self.bbox_w3d = nn.Conv2d(self.prop_feats[0].out_channels, self.num_anchors, 1)\n        self.bbox_h3d = nn.Conv2d(self.prop_feats[0].out_channels, self.num_anchors, 1)\n        self.bbox_l3d = nn.Conv2d(self.prop_feats[0].out_channels, self.num_anchors, 1)\n        self.bbox_rY3d = nn.Conv2d(self.prop_feats[0].out_channels, self.num_anchors, 1)\n\n        self.prop_feats_loc = nn.Sequential(\n            LocalConv2d(self.num_rows, self.base[-1].num_features, 512, 3, padding=1),\n            nn.ReLU(inplace=True)\n        )\n\n        # outputs\n        self.cls_loc = LocalConv2d(self.num_rows, self.prop_feats[0].out_channels, self.num_classes * self.num_anchors, 1, )\n\n        # bbox 2d\n        self.bbox_x_loc = LocalConv2d(self.num_rows, self.prop_feats[0].out_channels, self.num_anchors, 1)\n        self.bbox_y_loc = LocalConv2d(self.num_rows, self.prop_feats[0].out_channels, self.num_anchors, 1)\n        self.bbox_w_loc = LocalConv2d(self.num_rows, self.prop_feats[0].out_channels, self.num_anchors, 1)\n        self.bbox_h_loc = LocalConv2d(self.num_rows, self.prop_feats[0].out_channels, self.num_anchors, 1)\n\n        # bbox 3d\n        self.bbox_x3d_loc = LocalConv2d(self.num_rows, self.prop_feats[0].out_channels, self.num_anchors, 1)\n        self.bbox_y3d_loc = LocalConv2d(self.num_rows, self.prop_feats[0].out_channels, self.num_anchors, 1)\n        self.bbox_z3d_loc = LocalConv2d(self.num_rows, self.prop_feats[0].out_channels, self.num_anchors, 1)\n        self.bbox_w3d_loc = LocalConv2d(self.num_rows, self.prop_feats[0].out_channels, self.num_anchors, 1)\n        self.bbox_h3d_loc = LocalConv2d(self.num_rows, self.prop_feats[0].out_channels, self.num_anchors, 1)\n        self.bbox_l3d_loc = LocalConv2d(self.num_rows, self.prop_feats[0].out_channels, self.num_anchors, 1)\n        self.bbox_rY3d_loc = LocalConv2d(self.num_rows, self.prop_feats[0].out_channels, self.num_anchors, 1)\n\n        self.cls_ble = nn.Parameter(torch.tensor(10e-5).type(torch.cuda.FloatTensor))\n\n        self.bbox_x_ble = nn.Parameter(torch.tensor(10e-5).type(torch.cuda.FloatTensor))\n        self.bbox_y_ble = nn.Parameter(torch.tensor(10e-5).type(torch.cuda.FloatTensor))\n        self.bbox_w_ble = nn.Parameter(torch.tensor(10e-5).type(torch.cuda.FloatTensor))\n        self.bbox_h_ble = nn.Parameter(torch.tensor(10e-5).type(torch.cuda.FloatTensor))\n\n        self.bbox_x3d_ble = nn.Parameter(torch.tensor(10e-5).type(torch.cuda.FloatTensor))\n        self.bbox_y3d_ble = nn.Parameter(torch.tensor(10e-5).type(torch.cuda.FloatTensor))\n        self.bbox_z3d_ble = nn.Parameter(torch.tensor(10e-5).type(torch.cuda.FloatTensor))\n        self.bbox_w3d_ble = nn.Parameter(torch.tensor(10e-5).type(torch.cuda.FloatTensor))\n        self.bbox_h3d_ble = nn.Parameter(torch.tensor(10e-5).type(torch.cuda.FloatTensor))\n        self.bbox_l3d_ble = nn.Parameter(torch.tensor(10e-5).type(torch.cuda.FloatTensor))\n        self.bbox_rY3d_ble = nn.Parameter(torch.tensor(10e-5).type(torch.cuda.FloatTensor))\n\n        self.sigmoid = nn.Sigmoid()\n        self.softmax = nn.Softmax(dim=1)\n\n        self.feat_stride = conf.feat_stride\n        self.feat_size = calc_output_size(np.array(conf.crop_size), self.feat_stride)\n        self.rois = locate_anchors(conf.anchors, self.feat_size, conf.feat_stride, convert_tensor=True)\n        self.rois = self.rois.type(torch.cuda.FloatTensor)\n        self.anchors = conf.anchors\n        self.bbox_means = conf.bbox_means\n        self.bbox_stds = conf.bbox_stds\n\n    def forward(self, x):\n\n        batch_size = x.size(0)\n\n        # resnet\n        x = self.base(x)\n\n        prop_feats = self.prop_feats(x)\n        prop_feats_loc = self.prop_feats_loc(x)\n\n        cls = self.cls(prop_feats)\n\n        # bbox 2d\n        bbox_x = self.bbox_x(prop_feats)\n        bbox_y = self.bbox_y(prop_feats)\n        bbox_w = self.bbox_w(prop_feats)\n        bbox_h = self.bbox_h(prop_feats)\n\n        # bbox 3d\n        bbox_x3d = self.bbox_x3d(prop_feats)\n        bbox_y3d = self.bbox_y3d(prop_feats)\n        bbox_z3d = self.bbox_z3d(prop_feats)\n        bbox_w3d = self.bbox_w3d(prop_feats)\n        bbox_h3d = self.bbox_h3d(prop_feats)\n        bbox_l3d = self.bbox_l3d(prop_feats)\n        bbox_rY3d = self.bbox_rY3d(prop_feats)\n\n        cls_loc = self.cls_loc(prop_feats_loc)\n\n        # bbox 2d\n        bbox_x_loc = self.bbox_x_loc(prop_feats_loc)\n        bbox_y_loc = self.bbox_y_loc(prop_feats_loc)\n        bbox_w_loc = self.bbox_w_loc(prop_feats_loc)\n        bbox_h_loc = self.bbox_h_loc(prop_feats_loc)\n\n        # bbox 3d\n        bbox_x3d_loc = self.bbox_x3d_loc(prop_feats_loc)\n        bbox_y3d_loc = self.bbox_y3d_loc(prop_feats_loc)\n        bbox_z3d_loc = self.bbox_z3d_loc(prop_feats_loc)\n        bbox_w3d_loc = self.bbox_w3d_loc(prop_feats_loc)\n        bbox_h3d_loc = self.bbox_h3d_loc(prop_feats_loc)\n        bbox_l3d_loc = self.bbox_l3d_loc(prop_feats_loc)\n        bbox_rY3d_loc = self.bbox_rY3d_loc(prop_feats_loc)\n\n        cls_ble = self.sigmoid(self.cls_ble)\n\n        # bbox 2d\n        bbox_x_ble = self.sigmoid(self.bbox_x_ble)\n        bbox_y_ble = self.sigmoid(self.bbox_y_ble)\n        bbox_w_ble = self.sigmoid(self.bbox_w_ble)\n        bbox_h_ble = self.sigmoid(self.bbox_h_ble)\n\n        # bbox 3d\n        bbox_x3d_ble = self.sigmoid(self.bbox_x3d_ble)\n        bbox_y3d_ble = self.sigmoid(self.bbox_y3d_ble)\n        bbox_z3d_ble = self.sigmoid(self.bbox_z3d_ble)\n        bbox_w3d_ble = self.sigmoid(self.bbox_w3d_ble)\n        bbox_h3d_ble = self.sigmoid(self.bbox_h3d_ble)\n        bbox_l3d_ble = self.sigmoid(self.bbox_l3d_ble)\n        bbox_rY3d_ble = self.sigmoid(self.bbox_rY3d_ble)\n\n        # blend\n        cls = (cls * cls_ble) + (cls_loc * (1 - cls_ble))\n\n        bbox_x = (bbox_x * bbox_x_ble) + (bbox_x_loc * (1 - bbox_x_ble))\n        bbox_y = (bbox_y * bbox_y_ble) + (bbox_y_loc * (1 - bbox_y_ble))\n        bbox_w = (bbox_w * bbox_w_ble) + (bbox_w_loc * (1 - bbox_w_ble))\n        bbox_h = (bbox_h * bbox_h_ble) + (bbox_h_loc * (1 - bbox_h_ble))\n\n        bbox_x3d = (bbox_x3d * bbox_x3d_ble) + (bbox_x3d_loc * (1 - bbox_x3d_ble))\n        bbox_y3d = (bbox_y3d * bbox_y3d_ble) + (bbox_y3d_loc * (1 - bbox_y3d_ble))\n        bbox_z3d = (bbox_z3d * bbox_z3d_ble) + (bbox_z3d_loc * (1 - bbox_z3d_ble))\n        bbox_h3d = (bbox_h3d * bbox_h3d_ble) + (bbox_h3d_loc * (1 - bbox_h3d_ble))\n        bbox_w3d = (bbox_w3d * bbox_w3d_ble) + (bbox_w3d_loc * (1 - bbox_w3d_ble))\n        bbox_l3d = (bbox_l3d * bbox_l3d_ble) + (bbox_l3d_loc * (1 - bbox_l3d_ble))\n        bbox_rY3d = (bbox_rY3d * bbox_rY3d_ble) + (bbox_rY3d_loc * (1 - bbox_rY3d_ble))\n\n        feat_h = cls.size(2)\n        feat_w = cls.size(3)\n\n        # reshape for cross entropy\n        cls = cls.view(batch_size, self.num_classes, feat_h * self.num_anchors, feat_w)\n\n        # score probabilities\n        prob = self.softmax(cls)\n\n        # reshape for consistency\n        bbox_x = flatten_tensor(bbox_x.view(batch_size, 1, feat_h * self.num_anchors, feat_w))\n        bbox_y = flatten_tensor(bbox_y.view(batch_size, 1, feat_h * self.num_anchors, feat_w))\n        bbox_w = flatten_tensor(bbox_w.view(batch_size, 1, feat_h * self.num_anchors, feat_w))\n        bbox_h = flatten_tensor(bbox_h.view(batch_size, 1, feat_h * self.num_anchors, feat_w))\n\n        bbox_x3d = flatten_tensor(bbox_x3d.view(batch_size, 1, feat_h * self.num_anchors, feat_w))\n        bbox_y3d = flatten_tensor(bbox_y3d.view(batch_size, 1, feat_h * self.num_anchors, feat_w))\n        bbox_z3d = flatten_tensor(bbox_z3d.view(batch_size, 1, feat_h * self.num_anchors, feat_w))\n        bbox_w3d = flatten_tensor(bbox_w3d.view(batch_size, 1, feat_h * self.num_anchors, feat_w))\n        bbox_h3d = flatten_tensor(bbox_h3d.view(batch_size, 1, feat_h * self.num_anchors, feat_w))\n        bbox_l3d = flatten_tensor(bbox_l3d.view(batch_size, 1, feat_h * self.num_anchors, feat_w))\n        bbox_rY3d = flatten_tensor(bbox_rY3d.view(batch_size, 1, feat_h * self.num_anchors, feat_w))\n\n        # bundle\n        bbox_2d = torch.cat((bbox_x, bbox_y, bbox_w, bbox_h), dim=2)\n        bbox_3d = torch.cat((bbox_x3d, bbox_y3d, bbox_z3d, bbox_w3d, bbox_h3d, bbox_l3d, bbox_rY3d), dim=2)\n\n        feat_size = [feat_h, feat_w]\n\n        cls = flatten_tensor(cls)\n        prob = flatten_tensor(prob)\n\n        if self.feat_size[0] != feat_h or self.feat_size[1] != feat_w:\n            self.feat_size = [feat_h, feat_w]\n            self.rois = locate_anchors(self.anchors, self.feat_size, self.feat_stride, convert_tensor=True)\n            self.rois = self.rois.type(torch.cuda.FloatTensor)\n\n        if self.training:\n            return cls, prob, bbox_2d, bbox_3d, feat_size\n\n        else:\n            return cls, prob, bbox_2d, bbox_3d, feat_size, self.rois\n\n\ndef build(conf, phase='train'):\n    train = phase.lower() == 'train'\n\n    densenet121 = models.densenet121(pretrained=train)\n\n    num_cls = len(conf['lbls']) + 1\n    num_anchors = conf['anchors'].shape[0]\n\n    # make network\n    rpn_net = RPN(phase, densenet121.features, conf)\n\n    dst_weights = rpn_net.state_dict()\n    dst_keylist = list(dst_weights.keys())\n\n    if 'pretrained' in conf and conf.pretrained is not None:\n\n        src_weights = torch.load(conf.pretrained)\n        src_keylist = list(src_weights.keys())\n\n        conv_layers = ['cls', 'bbox_x', 'bbox_y', 'bbox_w', 'bbox_h', 'bbox_x3d', 'bbox_y3d', 'bbox_w3d', 'bbox_h3d', 'bbox_l3d', 'bbox_z3d', 'bbox_rY3d']\n\n        # prop_feats\n        src_weight_key = 'module.{}.0.weight'.format('prop_feats')\n        src_bias_key = 'module.{}.0.bias'.format('prop_feats')\n\n        dst_weight_key = 'module.{}.0.group_conv.weight'.format('prop_feats_loc')\n        dst_bias_key = 'module.{}.0.group_conv.bias'.format('prop_feats_loc')\n\n        src_weights[dst_weight_key] = src_weights[src_weight_key].repeat(conf.bins, 1, 1, 1)\n        src_weights[dst_bias_key] = src_weights[src_bias_key].repeat(conf.bins, )\n\n        #del src_weights[src_weight_key]\n        #del src_weights[src_bias_key]\n\n        for layer in conv_layers:\n            src_weight_key = 'module.{}.weight'.format(layer)\n            src_bias_key = 'module.{}.bias'.format(layer)\n\n            dst_weight_key = 'module.{}.group_conv.weight'.format(layer+'_loc')\n            dst_bias_key = 'module.{}.group_conv.bias'.format(layer+'_loc')\n\n            src_weights[dst_weight_key] = src_weights[src_weight_key].repeat(conf.bins, 1, 1, 1)\n            src_weights[dst_bias_key] = src_weights[src_bias_key].repeat(conf.bins, )\n\n            #del src_weights[src_weight_key]\n            #del src_weights[src_bias_key]\n\n        src_keylist = list(src_weights.keys())\n\n        # rename layers without module..\n        for key in src_keylist:\n\n            key_new = key.replace('module.', '')\n            if key_new in dst_keylist:\n                src_weights[key_new] = src_weights[key]\n                del src_weights[key]\n\n        src_keylist = list(src_weights.keys())\n\n        # new layers?\n        for key in dst_keylist:\n            if key not in src_keylist:\n                src_weights[key] = dst_weights[key]\n\n        # remove keys not in dst\n        for key in src_keylist:\n            if key not in dst_keylist:\n                del src_weights[key]\n\n        rpn_net.load_state_dict(src_weights)\n\n    if train:\n        rpn_net.train()\n    else:\n        rpn_net.eval()\n\n    return rpn_net\n"
  },
  {
    "path": "python_packages.txt",
    "content": "# packages in environment at /home/garrick/anaconda3:\n#\n# Name                    Version                   Build  Channel\n_ipyw_jlab_nb_ext_conf    0.1.0            py36he11e457_0  \n_libgcc_mutex             0.1                        main  \nalabaster                 0.7.10           py36h306e16b_0  \nanaconda-client           1.6.14                   py36_0  \nanaconda-navigator        1.8.7                    py36_0  \nanaconda-project          0.8.2            py36h44fb852_0  \nasn1crypto                0.24.0                   py36_0  \nastroid                   1.6.3                    py36_0  \nastropy                   3.0.2            py36h3010b51_1  \natlas                     3.8.4                         0    https://repo.anaconda.com/pkgs/free\nattrs                     18.1.0                   py36_0  \nbabel                     2.5.3                    py36_0  \nbackcall                  0.1.0                    py36_0  \nbackports                 1.0              py36hfa02d7e_1  \nbackports.shutil_get_terminal_size 1.0.0            py36hfea85ff_2  \nbeautifulsoup4            4.6.0            py36h49b8c8c_1  \nbitarray                  0.8.1            py36h14c3975_1  \nbkcharts                  0.2              py36h735825a_0  \nblas                      1.0                         mkl  \nblaze                     0.11.3           py36h4e06776_0  \nbleach                    2.1.3                    py36_0  \nblosc                     1.14.3               hdbcaa40_0  \nbokeh                     0.12.16                  py36_0  \nboto                      2.48.0           py36h6e4cd66_1  \nbottleneck                1.2.1            py36haac1ea0_0  \nbzip2                     1.0.6                h14c3975_5  \nca-certificates           2019.5.15                     0  \ncairo                     1.14.12              h8948797_3  \ncertifi                   2019.6.16                py36_0  \ncffi                      1.11.5           py36h9745a5d_0  \nchardet                   3.0.4            py36h0f667ec_1  \nclick                     6.7              py36h5253387_0  \ncloudpickle               0.5.3                    py36_0  \nclyent                    1.2.2            py36h7e57e65_1  \ncolorama                  0.3.9            py36h489cec4_0  \nconda                     4.7.10                   py36_0  \nconda-build               3.10.5                   py36_0  \nconda-env                 2.6.0                h36134e3_1  \nconda-package-handling    1.3.11                   py36_0  \nconda-verify              2.0.0            py36h98955d8_0  \ncontextlib2               0.5.5            py36h6c84a62_0  \ncryptography              2.7              py36h1ba5d50_0  \ncurl                      7.65.2               hbc83047_0  \ncycler                    0.10.0           py36h93f1223_0  \ncython                    0.28.2           py36h14c3975_0  \ncytoolz                   0.9.0.1          py36h14c3975_0  \ndask                      0.17.5                   py36_0  \ndask-core                 0.17.5                   py36_0  \ndatashape                 0.5.4            py36h3ad6b5c_0  \ndbus                      1.13.2               h714fa37_1  \ndecorator                 4.3.0                    py36_0  \ndistributed               1.21.8                   py36_0  \ndocutils                  0.14             py36hb0f60f5_0  \neasydict                  1.9                        py_0    conda-forge\nentrypoints               0.2.3            py36h1aec115_2  \net_xmlfile                1.0.1            py36hd6bccc3_0  \nexpat                     2.2.6                he6710b0_0  \nfastcache                 1.0.2            py36h14c3975_2  \nfilelock                  3.0.4                    py36_0  \nflask                     1.0.2                    py36_1  \nflask-cors                3.0.4                    py36_0  \nfontconfig                2.13.0               h9420a91_0  \nfreetype                  2.9.1                h8a8886c_1  \nfribidi                   1.0.5                h7b6447c_0  \ngeos                      3.7.0                hfc679d8_0    conda-forge\nget_terminal_size         1.0.0                haa9412d_0  \ngevent                    1.3.0            py36h14c3975_0  \nglib                      2.56.2               hd408876_0  \nglob2                     0.6              py36he249c77_0  \ngmp                       6.1.2                h6c8ec71_1  \ngmpy2                     2.0.8            py36hc8893dd_2  \ngraphite2                 1.3.11               h16798f4_2  \ngreenlet                  0.4.13           py36h14c3975_0  \ngst-plugins-base          1.14.0               hbbd80ab_1  \ngstreamer                 1.14.0               hb453b48_1  \nh5py                      2.7.1            py36ha1f6525_2  \nharfbuzz                  1.8.8                hffaf4a1_0  \nhdf5                      1.10.2               hba1933b_1  \nhdf5storage               0.1.14                   py36_0    conda-forge\nheapdict                  1.0.0                    py36_2  \nhtml5lib                  1.0.1            py36h2f9c1c0_0  \nicu                       58.2                 h9c2bf20_1  \nidna                      2.6              py36h82fb2a8_1  \nimageio                   2.3.0                    py36_0  \nimagesize                 1.0.0                    py36_0  \nintel-openmp              2018.0.0                      8  \nipykernel                 4.8.2                    py36_0  \nipython                   6.4.0                    py36_0  \nipython_genutils          0.2.0            py36hb52b0d5_0  \nipywidgets                7.2.1                    py36_0  \nisort                     4.3.4                    py36_0  \nitsdangerous              0.24             py36h93cc618_1  \njbig                      2.1                  hdba287a_0  \njdcal                     1.4                      py36_0  \njedi                      0.12.0                   py36_1  \njinja2                    2.10             py36ha16c418_0  \njoblib                    0.13.2                   py36_0  \njpeg                      9b                   h024ee3a_2  \njsonschema                2.6.0            py36h006f8b5_0  \njupyter                   1.0.0                    py36_4  \njupyter_client            5.2.3                    py36_0  \njupyter_console           5.2.0            py36he59e554_1  \njupyter_core              4.4.0            py36h7c827e3_0  \njupyterlab                0.32.1                   py36_0  \njupyterlab_launcher       0.10.5                   py36_0  \nkiwisolver                1.0.1            py36h764f252_0  \nkrb5                      1.16.1               h173b8e3_7  \nlazy-object-proxy         1.3.1            py36h10fcdad_0  \nlibarchive                3.3.3                h5d8350f_5  \nlibcurl                   7.65.2               h20c2e04_0  \nlibedit                   3.1.20170329         h6b74fdf_2  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  },
  {
    "path": "readme.md",
    "content": "# M3D-RPN: Monocular 3D Region Proposal Network for Object Detection\n\nGarrick Brazil, Xiaoming Liu\n\n## Introduction\n\n\nMonocular 3D region proposal network source code as detailed in [arXiv report](https://arxiv.org/abs/1907.06038), accepted to ICCV 2019 (Oral). Please also visit our [project page](http://cvlab.cse.msu.edu/project-m3d-rpn.html).\n\nOur framework is implemented and tested with Ubuntu 16.04, CUDA 8.0, Python 3, NVIDIA 1080 Ti GPU. Unless otherwise stated the below scripts and instructions assume working directory is the project root. \n\nIf you utilize this framework, please cite our ICCV 2019 paper. \n\n    @inproceedings{brazil2019m3drpn,\n        title={M3D-RPN: Monocular 3D Region Proposal Network for Object Detection},\n        author={Brazil, Garrick and Liu, Xiaoming},\n        booktitle={Proceedings of the IEEE International Conference on Computer Vision},\n        address={Seoul, South Korea},\n        year={2019}\n    }\n    \n\n## Setup\n\n- **Cuda & Python**\n\n    In this project we utilize Pytorch with Python 3, Cuda 8, and a few Anaconda packages. Please review and follow this [installation guide](setup.md). However, feel free to try alternative versions or modes of installation. \n\n- **Data**\n\n    Download the full [KITTI](http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d) detection dataset. Then place a softlink (or the actual data) in  *M3D-RPN/data/kitti*. \n\n\t```\n    cd M3D-RPN\n\tln -s /path/to/kitti data/kitti\n\t```\n\n\tThen use the following scripts to extract the data splits, which use softlinks to the above directory for efficient storage. \n\n    ```\n    python data/kitti_split1/setup_split.py\n    python data/kitti_split2/setup_split.py\n    ```\n    \n    Next, build the KITTI devkit eval for each split.\n\n\t```\n\tsh data/kitti_split1/devkit/cpp/build.sh\n\tsh data/kitti_split2/devkit/cpp/build.sh\n\t```\n    \n    Lastly, build the nms modules\n    \n    ```\n\tcd lib/nms\n\tmake\n\t```\n\n## Training\n\nWe use [visdom](https://github.com/facebookresearch/visdom) for visualization and graphs. Optionally, start the server by command line\n\n```\npython -m visdom.server -port 8100 -readonly\n```\nThe port can be customized in *scripts/config* files. The training monitor can be viewed at [http://localhost:8100](http://localhost:8100)\n\nTraining is split into a warmup and main configurations. Review the configurations in *scripts/config* for details. \n\n``` \n// First train the warmup (without depth-aware)\npython scripts/train_rpn_3d.py --config=kitti_3d_multi_warmup\n\n// Then train the main experiment (with depth-aware)\npython scripts/train_rpn_3d.py --config=kitti_3d_multi_main\n```\n\nIf your training is accidentally stopped, you can resume at a checkpoint based on the snapshot with the *restore* flag. \nFor example to resume training starting at iteration 10k, use the following command.\n\n```\npython scripts/train_rpn_3d.py --config=kitti_3d_multi_main --restore=10000\n```\n\n## Testing\n\nWe provide models for the main experiments on val1 / val2 / test data splits available to download here [M3D-RPN-Release.zip](https://www.cse.msu.edu/computervision/M3D-RPN-Release.zip).\n\nTesting requires paths to the configuration file and model weights, exposed variables near the top *scripts/test_rpn_3d.py*. To test a configuration and model, simply update the variables and run the test file as below. \n\n```\npython scripts/test_rpn_3d.py \n```\n\n## Contact\nFor questions regarding M3D-RPN, feel free to post here or directly contact the authors {[brazilga](http://garrickbrazil.com), [liuxm](http://www.cse.msu.edu/~liuxm/index2.html)}@msu.edu.\n"
  },
  {
    "path": "scripts/config/kitti_3d_multi_main.py",
    "content": "from easydict import EasyDict as edict\nimport numpy as np\n\ndef Config():\n\n    conf = edict()\n        \n    # ----------------------------------------\n    #  general\n    # ----------------------------------------\n\n    conf.model = 'densenet121_3d_dilate_depth_aware'\n    \n    # solver settings\n    conf.solver_type = 'sgd'\n    conf.lr = 0.004\n    conf.momentum = 0.9\n    conf.weight_decay = 0.0005\n    conf.max_iter = 50000\n    conf.snapshot_iter = 10000\n    conf.display = 250\n    conf.do_test = True\n    \n    # sgd parameters\n    conf.lr_policy = 'poly'\n    conf.lr_steps = None\n    conf.lr_target = conf.lr * 0.00001\n    \n    # random\n    conf.rng_seed = 2\n    conf.cuda_seed = 2\n    \n    # misc network\n    conf.image_means = [0.485, 0.456, 0.406]\n    conf.image_stds = [0.229, 0.224, 0.225]\n    conf.feat_stride = 16\n    \n    conf.has_3d = True\n\n    # ----------------------------------------\n    #  image sampling and datasets\n    # ----------------------------------------\n\n    # scale sampling  \n    conf.test_scale = 512\n    conf.crop_size = [512, 1760]\n    conf.mirror_prob = 0.50\n    conf.distort_prob = -1\n    \n    # datasets\n    conf.dataset_test = 'kitti_split1'\n    conf.datasets_train = [{'name': 'kitti_split1', 'anno_fmt': 'kitti_det', 'im_ext': '.png', 'scale': 1}]\n    conf.use_3d_for_2d = True\n    \n    # percent expected height ranges based on test_scale\n    # used for anchor selection \n    conf.percent_anc_h = [0.0625, 0.75]\n    \n    # labels settings\n    conf.min_gt_h = conf.test_scale*conf.percent_anc_h[0]\n    conf.max_gt_h = conf.test_scale*conf.percent_anc_h[1]\n    conf.min_gt_vis = 0.65\n    conf.ilbls = ['Van', 'ignore']\n    conf.lbls = ['Car', 'Pedestrian', 'Cyclist']\n    \n    # ----------------------------------------\n    #  detection sampling\n    # ----------------------------------------\n    \n    # detection sampling\n    conf.batch_size = 2\n    conf.fg_image_ratio = 1.0\n    conf.box_samples = 0.20\n    conf.fg_fraction = 0.20\n    conf.bg_thresh_lo = 0\n    conf.bg_thresh_hi = 0.5\n    conf.fg_thresh = 0.5\n    conf.ign_thresh = 0.5\n    conf.best_thresh = 0.35\n\n    # ----------------------------------------\n    #  inference and testing\n    # ----------------------------------------\n\n    # nms\n    conf.nms_topN_pre = 3000\n    conf.nms_topN_post = 40\n    conf.nms_thres = 0.4\n    conf.clip_boxes = False\n\n    conf.test_protocol = 'kitti'\n    conf.test_db = 'kitti'\n    conf.test_min_h = 0\n    conf.min_det_scales = [0, 0]\n\n    # ----------------------------------------\n    #  anchor settings\n    # ----------------------------------------\n    \n    # clustering settings\n    conf.cluster_anchors = 0\n    conf.even_anchors = 0\n    conf.expand_anchors = 0\n                             \n    conf.anchors = None\n\n    conf.bbox_means = None\n    conf.bbox_stds = None\n    \n    # initialize anchors\n    base = (conf.max_gt_h / conf.min_gt_h) ** (1 / (12 - 1))\n    conf.anchor_scales = np.array([conf.min_gt_h * (base ** i) for i in range(0, 12)])\n    conf.anchor_ratios = np.array([0.5, 1.0, 1.5])\n    \n    # loss logic\n    conf.hard_negatives = True\n    conf.focal_loss = 0\n    conf.cls_2d_lambda = 1\n    conf.iou_2d_lambda = 1\n    conf.bbox_2d_lambda = 0\n    conf.bbox_3d_lambda = 1\n    conf.bbox_3d_proj_lambda = 0.0\n    \n    conf.hill_climbing = True\n    \n    conf.bins = 32\n    \n    # visdom\n    conf.visdom_port = 8100\n    \n    conf.pretrained = 'output/kitti_3d_multi_warmup/weights/model_50000_pkl'\n\n    return conf\n\n"
  },
  {
    "path": "scripts/config/kitti_3d_multi_warmup.py",
    "content": "from easydict import EasyDict as edict\nimport numpy as np\n\ndef Config():\n\n    conf = edict()\n        \n    # ----------------------------------------\n    #  general\n    # ----------------------------------------\n\n    conf.model = 'densenet121_3d_dilate'\n    \n    # solver settings\n    conf.solver_type = 'sgd'\n    conf.lr = 0.004\n    conf.momentum = 0.9\n    conf.weight_decay = 0.0005\n    conf.max_iter = 50000\n    conf.snapshot_iter = 10000\n    conf.display = 250\n    conf.do_test = True\n    \n    # sgd parameters\n    conf.lr_policy = 'poly'\n    conf.lr_steps = None\n    conf.lr_target = conf.lr * 0.00001\n    \n    # random\n    conf.rng_seed = 2\n    conf.cuda_seed = 2\n    \n    # misc network\n    conf.image_means = [0.485, 0.456, 0.406]\n    conf.image_stds = [0.229, 0.224, 0.225]\n    conf.feat_stride = 16\n    \n    conf.has_3d = True\n\n    # ----------------------------------------\n    #  image sampling and datasets\n    # ----------------------------------------\n\n    # scale sampling  \n    conf.test_scale = 512\n    conf.crop_size = [512, 1760]\n    conf.mirror_prob = 0.50\n    conf.distort_prob = -1\n    \n    # datasets\n    conf.dataset_test = 'kitti_split1'\n    conf.datasets_train = [{'name': 'kitti_split1', 'anno_fmt': 'kitti_det', 'im_ext': '.png', 'scale': 1}]\n    conf.use_3d_for_2d = True\n    \n    # percent expected height ranges based on test_scale\n    # used for anchor selection \n    conf.percent_anc_h = [0.0625, 0.75]\n    \n    # labels settings\n    conf.min_gt_h = conf.test_scale*conf.percent_anc_h[0]\n    conf.max_gt_h = conf.test_scale*conf.percent_anc_h[1]\n    conf.min_gt_vis = 0.65\n    conf.ilbls = ['Van', 'ignore']\n    conf.lbls = ['Car', 'Pedestrian', 'Cyclist']\n    \n    # ----------------------------------------\n    #  detection sampling\n    # ----------------------------------------\n    \n    # detection sampling\n    conf.batch_size = 2\n    conf.fg_image_ratio = 1.0\n    conf.box_samples = 0.20\n    conf.fg_fraction = 0.20\n    conf.bg_thresh_lo = 0\n    conf.bg_thresh_hi = 0.5\n    conf.fg_thresh = 0.5\n    conf.ign_thresh = 0.5\n    conf.best_thresh = 0.35\n\n    # ----------------------------------------\n    #  inference and testing\n    # ----------------------------------------\n\n    # nms\n    conf.nms_topN_pre = 3000\n    conf.nms_topN_post = 40\n    conf.nms_thres = 0.4\n    conf.clip_boxes = False\n\n    conf.test_protocol = 'kitti'\n    conf.test_db = 'kitti'\n    conf.test_min_h = 0\n    conf.min_det_scales = [0, 0]\n\n    # ----------------------------------------\n    #  anchor settings\n    # ----------------------------------------\n    \n    # clustering settings\n    conf.cluster_anchors = 0\n    conf.even_anchors = 0\n    conf.expand_anchors = 0\n                             \n    conf.anchors = None\n\n    conf.bbox_means = None\n    conf.bbox_stds = None\n    \n    # initialize anchors\n    base = (conf.max_gt_h / conf.min_gt_h) ** (1 / (12 - 1))\n    conf.anchor_scales = np.array([conf.min_gt_h * (base ** i) for i in range(0, 12)])\n    conf.anchor_ratios = np.array([0.5, 1.0, 1.5])\n    \n    # loss logic\n    conf.hard_negatives = True\n    conf.focal_loss = 0\n    conf.cls_2d_lambda = 1\n    conf.iou_2d_lambda = 1\n    conf.bbox_2d_lambda = 0\n    conf.bbox_3d_lambda = 1\n    conf.bbox_3d_proj_lambda = 0.0\n    \n    conf.hill_climbing = True\n    \n    # visdom\n    conf.visdom_port = 8100\n\n    return conf\n\n"
  },
  {
    "path": "scripts/test_rpn_3d.py",
    "content": "# -----------------------------------------\n# python modules\n# -----------------------------------------\nfrom importlib import import_module\nfrom easydict import EasyDict as edict\nimport torch.backends.cudnn as cudnn\nimport sys\nimport numpy as np\nimport os\n\n# stop python from writing so much bytecode\nsys.dont_write_bytecode = True\nsys.path.append(os.getcwd())\nnp.set_printoptions(suppress=True)\n\n# -----------------------------------------\n# custom modules\n# -----------------------------------------\nfrom lib.imdb_util import *\n\nconf_path = '/home/garrick/Desktop/M3D-RPN-Release/m3d_rpn_depth_aware_test_config.pkl'\nweights_path = '/home/garrick/Desktop/M3D-RPN-Release/m3d_rpn_depth_aware_test'\n\n# load config\nconf = edict(pickle_read(conf_path))\nconf.pretrained = None\n\ndata_path = os.path.join(os.getcwd(), 'data')\nresults_path = os.path.join('output', 'tmp_results', 'data')\n\n# make directory\nmkdir_if_missing(results_path, delete_if_exist=True)\n\n# -----------------------------------------\n# torch defaults\n# -----------------------------------------\n\n# defaults\ninit_torch(conf.rng_seed, conf.cuda_seed)\n\n# -----------------------------------------\n# setup network\n# -----------------------------------------\n\n# net\nnet = import_module('models.' + conf.model).build(conf)\n\n# load weights\nload_weights(net, weights_path, remove_module=True)\n\n# switch modes for evaluation\nnet.eval()\n\nprint(pretty_print('conf', conf))\n\n# -----------------------------------------\n# test kitti\n# -----------------------------------------\n\ntest_kitti_3d(conf.dataset_test, net, conf, results_path, data_path, use_log=False)"
  },
  {
    "path": "scripts/train_rpn_3d.py",
    "content": "# -----------------------------------------\n# python modules\n# -----------------------------------------\nfrom easydict import EasyDict as edict\nfrom getopt import getopt\nimport numpy as np\nimport sys\nimport os\n\n# stop python from writing so much bytecode\nsys.dont_write_bytecode = True\nsys.path.append(os.getcwd())\nnp.set_printoptions(suppress=True)\n\n# -----------------------------------------\n# custom modules\n# -----------------------------------------\nfrom lib.core import *\nfrom lib.imdb_util import *\nfrom lib.loss.rpn_3d import *\n\n\ndef main(argv):\n\n    # -----------------------------------------\n    # parse arguments\n    # -----------------------------------------\n    opts, args = getopt(argv, '', ['config=', 'restore='])\n\n    # defaults\n    conf_name = None\n    restore = None\n\n    # read opts\n    for opt, arg in opts:\n\n        if opt in ('--config'): conf_name = arg\n        if opt in ('--restore'): restore = int(arg)\n\n    # required opt\n    if conf_name is None:\n        raise ValueError('Please provide a configuration file name, e.g., --config=<config_name>')\n\n    # -----------------------------------------\n    # basic setup\n    # -----------------------------------------\n\n    conf = init_config(conf_name)\n    paths = init_training_paths(conf_name)\n\n    init_torch(conf.rng_seed, conf.cuda_seed)\n    init_log_file(paths.logs)\n\n    vis = init_visdom(conf_name, conf.visdom_port)\n\n    # defaults\n    start_iter = 0\n    tracker = edict()\n    iterator = None\n    has_visdom = vis is not None\n\n    dataset = Dataset(conf, paths.data, paths.output)\n\n    generate_anchors(conf, dataset.imdb, paths.output)\n    compute_bbox_stats(conf, dataset.imdb, paths.output)\n\n\n    # -----------------------------------------\n    # store config\n    # -----------------------------------------\n\n    # store configuration\n    pickle_write(os.path.join(paths.output, 'conf.pkl'), conf)\n\n    # show configuration\n    pretty = pretty_print('conf', conf)\n    logging.info(pretty)\n\n\n    # -----------------------------------------\n    # network and loss\n    # -----------------------------------------\n\n    # training network\n    rpn_net, optimizer = init_training_model(conf, paths.output)\n\n    # setup loss\n    criterion_det = RPN_3D_loss(conf)\n\n    # custom pretrained network\n    if 'pretrained' in conf:\n\n        load_weights(rpn_net, conf.pretrained)\n\n    # resume training\n    if restore:\n        start_iter = (restore - 1)\n        resume_checkpoint(optimizer, rpn_net, paths.weights, restore)\n\n    freeze_blacklist = None if 'freeze_blacklist' not in conf else conf.freeze_blacklist\n    freeze_whitelist = None if 'freeze_whitelist' not in conf else conf.freeze_whitelist\n\n    freeze_layers(rpn_net, freeze_blacklist, freeze_whitelist)\n\n    optimizer.zero_grad()\n\n    start_time = time()\n\n    # -----------------------------------------\n    # train\n    # -----------------------------------------\n\n    for iteration in range(start_iter, conf.max_iter):\n\n        # next iteration\n        iterator, images, imobjs = next_iteration(dataset.loader, iterator)\n\n        #  learning rate\n        adjust_lr(conf, optimizer, iteration)\n\n        # forward\n        cls, prob, bbox_2d, bbox_3d, feat_size = rpn_net(images)\n\n        # loss\n        det_loss, det_stats = criterion_det(cls, prob, bbox_2d, bbox_3d, imobjs, feat_size)\n\n        total_loss = det_loss\n        stats = det_stats\n\n        # backprop\n        if total_loss > 0:\n\n            total_loss.backward()\n\n            # batch skip, simulates larger batches by skipping gradient step\n            if (not 'batch_skip' in conf) or ((iteration + 1) % conf.batch_skip) == 0:\n                optimizer.step()\n                optimizer.zero_grad()\n\n        # keep track of stats\n        compute_stats(tracker, stats)\n\n        # -----------------------------------------\n        # display\n        # -----------------------------------------\n        if (iteration + 1) % conf.display == 0 and iteration > start_iter:\n\n            # log results\n            log_stats(tracker, iteration, start_time, start_iter, conf.max_iter)\n\n            # display results\n            if has_visdom:\n                display_stats(vis, tracker, iteration, start_time, start_iter, conf.max_iter, conf_name, pretty)\n\n            # reset tracker\n            tracker = edict()\n\n        # -----------------------------------------\n        # test network\n        # -----------------------------------------\n        if (iteration + 1) % conf.snapshot_iter == 0 and iteration > start_iter:\n\n            # store checkpoint\n            save_checkpoint(optimizer, rpn_net, paths.weights, (iteration + 1))\n\n            if conf.do_test:\n\n                # eval mode\n                rpn_net.eval()\n\n                # necessary paths\n                results_path = os.path.join(paths.results, 'results_{}'.format((iteration + 1)))\n\n                # -----------------------------------------\n                # test kitti\n                # -----------------------------------------\n                if conf.test_protocol.lower() == 'kitti':\n\n                    # delete and re-make\n                    results_path = os.path.join(results_path, 'data')\n                    mkdir_if_missing(results_path, delete_if_exist=True)\n\n                    test_kitti_3d(conf.dataset_test, rpn_net, conf, results_path, paths.data)\n\n                else:\n                    logging.warning('Testing protocol {} not understood.'.format(conf.test_protocol))\n\n                # train mode\n                rpn_net.train()\n\n                freeze_layers(rpn_net, freeze_blacklist, freeze_whitelist)\n\n\n# run from command line\nif __name__ == \"__main__\":\n    main(sys.argv[1:])\n"
  },
  {
    "path": "setup.md",
    "content": "\n# Setup for M3D-RPN\n\nThis document acts as a (suggested) guide for setting up cuda, Python 3, and Anaconda. If components are already setup please feel encouraged to skip sections or use any alternative methods such as pip. \n\n*Note:* there are MANY alternative methods to install all below packages. This guide is only meant to serve as an example. \n\n#### Install cuda 8\n\n1. Visit [https://developer.nvidia.com/cuda-80-ga2-download-archive]([https://developer.nvidia.com/cuda-80-ga2-download-archive)\n\n1. Download the Linux -> x86_64 -> Ubuntu -> 16.04 -> deb (local) file.\n\t\n1. Then install by\n\t```\n\tcd <download folder>\n\tsudo dpkg -i cuda-repo-ubuntu1604-8-0-local-ga2_8.0.61-1_amd64.deb\n\tsudo apt-get update\n\tsudo apt-get install cuda-8-0\n\t```\n\tDealing with multiple cuda versions?\n\tThe default cuda is a softlink at */usr/local/cuda*\n\n\tIf you want to change the default to cuda 8 follow the below lines, or update your environment variables. \n\t```\n\tsudo rm /usr/local/cuda\n\tsudo ln /usr/local/cuda-8.0 /usr/local/cuda\n    ```\n\tIf you want to see the current default version: *ls -l /usr/local/*\n\n#### Install proper cuDNN\n\t\t\nFor M3D-RPN we utilized cudnn-8.0-linux-x64-v5.1. However, please choose the appropriate package for your cuda. \n\n1. You must create an account to access this page: [https://developer.nvidia.com/rdp/cudnn-archive](https://developer.nvidia.com/rdp/cudnn-archive)\n\n1. Then download your preferred version or use this link for [cudnn-8.0-linux-x64-v5.1](https://developer.nvidia.com/compute/machine-learning/cudnn/secure/v5.1/prod_20161129/8.0/cudnn-8.0-linux-x64-v5.1-tgz).\n\n1. Extract somewhere temporary for example to downloads, e.g., *~/Downloads/cuda*.\n\n1. Then copy the files into your cuda directory as below:\n\t```\n\tsudo cp ~/Downloads/cuda/include/* /usr/local/cuda-8.0/include/\n\tsudo cp ~/Downloads/cuda/lib64/* /usr/local/cuda-8.0/lib64/\n\t```\n\n#### Install Anaconda / Python 3.6.5\n\nFor M3D-RPN we utilized Python 3.6.5 Anaconda. Other versions may also work.\n\n1. Install your preferred version of Anaconda\n\t```\n\tcd ~\n\twget https://repo.continuum.io/archive/Anaconda3-5.2.0-Linux-x86_64.sh\n\tsh Anaconda3-5.2.0-Linux-x86_64.sh\n\t```\n\n\tDefaults are usually fine. \n\tRecommend letting the installer add to your path and avoid microsoft extention (unless on windows). \n\tBefore doing any of the below make sure that the path is setup properly:\n\t\n    ```\n    python --version\n    ```\n\tHopefully you see Python 3.6.5, Anaconda Inc. \n\n1. Install python packages.\n\t\n\t```\n\tconda install -c menpo opencv3=3.1.0 openblas\n\tconda install cython scikit-image h5py nose pandas protobuf atlas libgfortran\n\t```\n    \n    If there are compatibility issues. Refer to [python_packages.txt](python_packages.txt) for specific versions known to work. \n\n1. Install pytorch\n\n\tAssuming cuda-8.0 is installed. Otherwise, refer to the official [pytorch website](https://pytorch.org/). \n\n\t```\n\tconda install pytorch torchvision cuda80 -c pytorch\n\t```\n\n1. Install visdom (optional, for graph monitoring while training)\n\t```\n\tconda install -c conda-forge visdom\n\t```\n\n\n\n"
  }
]