• The data should be prepared in the following structure (one example):

data/
   |———  mot_pkl
   |        └——————train
   |        |        └——————Crowdhuman_train.pkl
   |        |        └——————Crowdhuman_val.pkl
   |———  mot17_pkl
   |        └——————train
   |        |        └——————MOT17_train.pkl
   |        |

• Some XXX_pkl dirs: MOT_data .

• OUTPUT_ROOT: Save model name & saved work dir name under work_dirs/
• DATASET.FILE & DATASET.NAME: Dataset file name and corresponding Class module name
• DATASET.ROOT: root path for the anno files
• DATASET.PREFIX: Root of the image dir, i.e. prefix of the img path in the anno files
• MODEL.FILE & MODEL.NAME: Model file name and corresponding Class module name
• MODEL.RESUME_PATH & TRAIN.RESUME: Set TRAIN.RESUME to False to load the single model from resume path, or load the optimizer and train state too
• TRAINER.FILE & TRAINER.NAME: Trainer file name and corresponding Class module name

• libs.dataset.match_ch.py (crowdhuman) line 33 - line 45 (create image pair using affine transform)

crowdhuman_idnum = 0 (do not invovle the id of crowdhuman to self-supervise the id appearance learning or rs module training)

crowdhuman_idnum = 6000 (invovle 6000 human boxes to self-supervise the id appearance learning and rs module training)

commented (involve all the crowdhuman boxes to self-supervise the id appearance learning and rs module training)

id_base = 0
cur_id = 0
for i, anno in enumerate(self.annotations):
    anno['ann']['extra_anns'] = np.array(anno['ann']['extra_anns']).reshape(-1, )
    if len(anno['ann']['extra_anns']) == 0:
        anno['ann']['extra_anns'] = -np.ones(len(anno['ann']['bboxes'])).reshape(-1, )
    for j, single_id in enumerate(anno['ann']['extra_anns']):
        if cur_id >= $crowdhuman_idnum$:
            anno['ann']['extra_anns'][j] = -1
        else:
            anno['ann']['extra_anns'][j] = cur_id
            cur_id += 1

• libs.dataset.customtask.py (MOT17) line 33 - line 45 (create image pair using two frames adjacent in temporal line)

id_base = $crowdhuman_idnum$ + 1
max_id = $crowdhuman_idnum$ + 1

• configs.deformable_track.yaml RS.NIDS = total_idnum + 1, total_idnum = crowdhuman_idnum + mot17_idnum

srun --partition=$PARTITION --mpi=pmi2 -n 12 --gres=gpu:6 --ntasks-per-node=8 --job-name=TRAIN --kill-on-bad-exit=1  python3 tools/train.py --cfg configs/deformable_macthtrack_ch.yaml

• Involve all crowdhuman boxes or most of them for id appearance supervision and rs module training. The invovled box num is up to the GPU memory.
• DATASET.IMG_NUM_PER_GPU = 1
• 20 epoch training without lr drop
• TRAIN.LR: 0.0001
• My pretrained weight: full_chid_rs_pretrain.pth .
• Deformable DETR pretrained weight: r50_deformable_detr_plus_iterative_bbox_refinement_plus_plus_two_stage-checkpoint.pth .

srun --partition=$PARTITION --mpi=pmi2 -n 12 --gres=gpu:6 --ntasks-per-node=8 --job-name=TRAIN --kill-on-bad-exit=1  python3 tools/train.py --cfg configs/deformable_track.yaml

• Involve 0 crowdhuman boxes (crowdhuman_idnum = 0) for id appearance training or rs module training
• DATASET.IMG_NUM_PER_GPU = 1
• 30 epoch training with lr drop by 0.1 in epoch 20
• TRAIN.LR: 0.0001
• MODEL.RESUME_PATH: full_chid_rs_pretrain.pth . TRAIN.RESUM = False.

• tools.offset_track.py line 174 thr = 0.38

def process_img(img_path, model, postprocessors, device, threshold=$thr$, references=None):

• tools.offset_track.py line 490-

data_dir =  # your own data dir path (e.g. '/mnt/lustre/chenmingfei/data/MOT_data/')
# test mot17 (you can uncomment the val mot17 or any else, set seqs_str and data_root correspondingly)
seqs_str = '''MOT17-01-SDP
              MOT17-03-SDP
              MOT17-06-SDP
              MOT17-07-SDP
              MOT17-08-SDP
              MOT17-12-SDP
              MOT17-14-SDP'''
data_root = os.path.join(data_dir, 'MOT17/test')

• test

PARTITION = pat_largescale
srun --partition=$PARTITION --mpi=pmi2 -n 1 --gres=gpu:1 --ntasks-per-node=8 --job-name=EVAL python3 tools/offset_track.py --cfg [config_path](e.g. configs/deformable_track.yaml) \
     MODEL.RESUME_PATH [resume_path](e.g. output/matchtrack_epoch025_checkpoint.pth)

• Results saved in work_dirs/$output_root$/results/

• Test results on MOT17, thr=0.38-0.42 (0.38 will be fine), our results is FRT (emphasized in red) in the following result picture.

srun --partition=$PARTITION --mpi=pmi2 -n 12 --gres=gpu:6 --ntasks-per-node=8 --job-name=TRAIN --kill-on-bad-exit=1  python3 tools/train.py --cfg configs/YOLOv5m_matchtrack.yaml

Pending check to this part...

If you get any address already in use errror, please modify the port number of the returned ip address from the function get_ip_address in train.py or offset_track.py. Or you can just rewrite this function to generate ip address with a random port.