code: def _eval_seq_sde(
      self,
      dataloader,
      save_dir=None,
      show_image=False,
      frame_rate=30,
      seq_name="",
      scaled=False,
      det_file="",
      draw_threshold=0,
  ):
      if save_dir:
          if not os.path.exists(save_dir):
              os.makedirs(save_dir)
      use_detector = False if not self.model.detector else True
      use_reid = hasattr(self.model, "reid")
      if use_reid and self.model.reid is not None:
          use_reid = True
      else:
          use_reid = False
  
      timer = MOTTimer()
      results = defaultdict(list)
      frame_id = 0
      self.status["mode"] = "track"
      self.model.eval()
      if use_reid:
          self.model.reid.eval()
      if not use_detector:
          dets_list = load_det_results(det_file, len(dataloader))
          logger.info(
              "Finish loading detection results file {}.".format(
                  det_file
              )
          )
  
      tracker = self.model.tracker
      for step_id, data in enumerate(tqdm(dataloader)):
          self.status["step_id"] = step_id
          ori_image = data["ori_image"]  # [bs, H, W, 3]
          ori_image_shape = data["ori_image"].shape[1:3]
          # ori_image_shape: [H, W]
  
          input_shape = data["image"].shape[2:]
          # input_shape: [h, w], before data transforms, set in model config
  
          im_shape = data["im_shape"][0].numpy()
          # im_shape: [new_h, new_w], after data transforms
          scale_factor = data["scale_factor"][0].numpy()
  
          empty_detections = False
          # when it has no detected bboxes, will not inference reid model
          # and if visualize, use original image instead
  
          # forward
          timer.tic()
          if not use_detector:
              dets = dets_list[frame_id]
              bbox_tlwh = np.array(dets["bbox"], dtype="float32")
              if bbox_tlwh.shape[0] > 0:
                  # detector outputs: pred_cls_ids, pred_scores, pred_bboxes
                  pred_cls_ids = np.array(
                      dets["cls_id"], dtype="float32"
                  )
                  pred_scores = np.array(dets["score"], dtype="float32")
                  pred_bboxes = np.concatenate(
                      (
                          bbox_tlwh[:, 0:2],
                          bbox_tlwh[:, 2:4] + bbox_tlwh[:, 0:2],
                      ),
                      axis=1,
                  )
              else:
                  logger.warning(
                      "Frame {} has not object, try to modify score threshold.".format(
                          frame_id
                      )
                  )
                  empty_detections = True
          else:
              outs = self.model.detector(data)
              outs["bbox"] = outs["bbox"].numpy()
              outs["bbox_num"] = outs["bbox_num"].numpy()
  
              if len(outs["bbox"]) > 0 and empty_detections == False:
                  # detector outputs: pred_cls_ids, pred_scores, pred_bboxes
                  pred_cls_ids = outs["bbox"][:, 0:1]
                  pred_scores = outs["bbox"][:, 1:2]
                  if not scaled:
                      # Note: scaled=False only in JDE YOLOv3 or other detectors
                      # with LetterBoxResize and JDEBBoxPostProcess.
                      #
                      # 'scaled' means whether the coords after detector outputs
                      # have been scaled back to the original image, set True
                      # in general detector, set False in JDE YOLOv3.
                      pred_bboxes = scale_coords(
                          outs["bbox"][:, 2:],
                          input_shape,
                          im_shape,
                          scale_factor,
                      )
                  else:
                      pred_bboxes = outs["bbox"][:, 2:]
                  pred_dets_old = np.concatenate(
                      (pred_cls_ids, pred_scores, pred_bboxes), axis=1
                  )
              else:
                  logger.warning(
                      "Frame {} has not detected object, try to modify score threshold.".format(
                          frame_id
                      )
                  )
                  empty_detections = True
  
          if not empty_detections:
              pred_xyxys, keep_idx = clip_box(
                  pred_bboxes, ori_image_shape
              )
              if len(keep_idx[0]) == 0:
                  logger.warning(
                      "Frame {} has not detected object left after clip_box.".format(
                          frame_id
                      )
                  )
                  empty_detections = True
  
          if empty_detections:
              timer.toc()
              # if visualize, use original image instead
              online_ids, online_tlwhs, online_scores = None, None, None
              save_vis_results(
                  data,
                  frame_id,
                  online_ids,
                  online_tlwhs,
                  online_scores,
                  timer.average_time,
                  show_image,
                  save_dir,
                  self.cfg.num_classes,
                  self.ids2names,
              )
              frame_id += 1
              # thus will not inference reid model
              continue
  
          pred_cls_ids = pred_cls_ids[keep_idx[0]]
          pred_scores = pred_scores[keep_idx[0]]
          pred_dets = np.concatenate(
              (pred_cls_ids, pred_scores, pred_xyxys), axis=1
          )
  
          if use_reid:
              crops = get_crops(
                  pred_xyxys,
                  ori_image,
                  w=tracker.input_size[0],
                  h=tracker.input_size[1],
              )
              crops = paddle.to_tensor(crops)
  
              data.update({"crops": crops})
              pred_embs = self.model(data)["embeddings"].numpy()
          else:
              pred_embs = None
  
          if isinstance(tracker, DeepSORTTracker):
              online_tlwhs, online_scores, online_ids = [], [], []
              tracker.predict()
              online_targets = tracker.update(pred_dets, pred_embs)
              for t in online_targets:
                  if not t.is_confirmed() or t.time_since_update > 1:
                      continue
                  tlwh = t.to_tlwh()
                  tscore = t.score
                  tid = t.track_id
                  if tscore < draw_threshold:
                      continue
                  if tlwh[2] * tlwh[3] <= tracker.min_box_area:
                      continue
                  if (
                      tracker.vertical_ratio > 0
                      and tlwh[2] / tlwh[3] > tracker.vertical_ratio
                  ):
                      continue
                  online_tlwhs.append(tlwh)
                  online_scores.append(tscore)
                  online_ids.append(tid)
              timer.toc()
  
              # save results
              results[0].append(
                  (
                      frame_id + 1,
                      online_tlwhs,
                      online_scores,
                      online_ids,
                  )
              )
              save_vis_results(
                  data,
                  frame_id,
                  online_ids,
                  online_tlwhs,
                  online_scores,
                  timer.average_time,
                  show_image,
                  save_dir,
                  self.cfg.num_classes,
                  self.ids2names,
              )
  
          elif isinstance(tracker, JDETracker):
              # trick hyperparams only used for MOTChallenge (MOT17, MOT20) Test-set
              (
                  tracker.track_buffer,
                  tracker.conf_thres,
              ) = get_trick_hyperparams(
                  seq_name, tracker.track_buffer, tracker.conf_thres
              )
  
              online_targets_dict = tracker.update(
                  pred_dets_old, pred_embs
              )
              online_tlwhs = defaultdict(list)
              online_scores = defaultdict(list)
              online_ids = defaultdict(list)
              for cls_id in range(self.cfg.num_classes):
                  online_targets = online_targets_dict[cls_id]
                  for t in online_targets:
                      tlwh = t.tlwh
                      tid = t.track_id
                      tscore = t.score
                      if tlwh[2] * tlwh[3] <= tracker.min_box_area:
                          continue
                      if (
                          tracker.vertical_ratio > 0
                          and tlwh[2] / tlwh[3] > tracker.vertical_ratio
                      ):
                          continue
                      online_tlwhs[cls_id].append(tlwh)
                      online_ids[cls_id].append(tid)
                      online_scores[cls_id].append(tscore)
                  # save results
                  results[cls_id].append(
                      (
                          frame_id + 1,
                          online_tlwhs[cls_id],
                          online_scores[cls_id],
                          online_ids[cls_id],
                      )
                  )
              timer.toc()
              save_vis_results(
                  data,
                  frame_id,
                  online_ids,
                  online_tlwhs,
                  online_scores,
                  timer.average_time,
                  show_image,
                  save_dir,
                  self.cfg.num_classes,
                  self.ids2names,
              )
  
          elif isinstance(tracker, OCSORTTracker):
              # OC_SORT Tracker
              online_targets = tracker.update(pred_dets_old, pred_embs)
              online_tlwhs = []
              online_ids = []
              online_scores = []
              for t in online_targets:
                  tlwh = [t[0], t[1], t[2] - t[0], t[3] - t[1]]
                  tscore = float(t[4])
                  tid = int(t[5])
                  if tlwh[2] * tlwh[3] > 0:
                      online_tlwhs.append(tlwh)
                      online_ids.append(tid)
                      online_scores.append(tscore)
              timer.toc()
              # save results
              results[0].append(
                  (
                      frame_id + 1,
                      online_tlwhs,
                      online_scores,
                      online_ids,
                  )
              )
              save_vis_results(
                  data,
                  frame_id,
                  online_ids,
                  online_tlwhs,
                  online_scores,
                  timer.average_time,
                  show_image,
                  save_dir,
                  self.cfg.num_classes,
                  self.ids2names,
              )
  
          elif isinstance(tracker, BOTSORTTracker):
              # BOTSORT Tracker
              online_targets = tracker.update(
                  pred_dets_old, img=ori_image.numpy()
              )
              online_tlwhs = []
              online_ids = []
              online_scores = []
              for t in online_targets:
                  tlwh = t.tlwh
                  tid = t.track_id
                  tscore = t.score
                  if tlwh[2] * tlwh[3] > 0:
                      online_tlwhs.append(tlwh)
                      online_ids.append(tid)
                      online_scores.append(tscore)
              timer.toc()
              # save results
              results[0].append(
                  (
                      frame_id + 1,
                      online_tlwhs,
                      online_scores,
                      online_ids,
                  )
              )
              save_vis_results(
                  data,
                  frame_id,
                  online_ids,
                  online_tlwhs,
                  online_scores,
                  timer.average_time,
                  show_image,
                  save_dir,
                  self.cfg.num_classes,
                  self.ids2names,
              )
  
          else:
              raise ValueError(tracker)
          frame_id += 1
  
      return results, frame_id, timer.average_time, timer.calls
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import glob
  import re
  import paddle
  import paddle.nn
  import numpy
  from tqdm import tqdm
  from collections import defaultdict
  from ppdet.core.workspace import create
  from ppdet.utils.checkpoint import load_weight, load_pretrain_weight
  from ppdet.modeling.mot.utils import Detection, get_crops, scale_coords, clip_box
  from ppdet.modeling.mot.utils import MOTTimer, load_det_results, write_mot_results, save_vis_results
  from ppdet.modeling.mot.tracker import JDETracker, CenterTracker
  from ppdet.modeling.mot.tracker import DeepSORTTracker, OCSORTTracker, BOTSORTTracker
  from ppdet.modeling.architectures import YOLOX
  from ppdet.metrics import Metric, MOTMetric, KITTIMOTMetric, MCMOTMetric
  from ppdet.data.source.category import get_categories
  import ppdet.utils.stats
  from .callbacks import Callback, ComposeCallback
  from ppdet.utils.logger import setup_logger
label_desc: Use image node_id 11 for calling the Tracker obj's underlying member method code with example usage: obj._eval_seq_sde(dataloader, save_dir, show_image, frame_rate, seq_name, scaled, det_file, draw_threshold) and returns: results,  frame_id,  timer,  timer