fix some bugs of reid model in bytetrack

Author: JackWoo0831

models/__pycache__/__init__.cpython-37.pyc

@@ -59,4 +59,5 @@ train:
 CUDA_VISIBLE_DEVICES=0 python train_aux.py --dataset mot17 --workers 8 --device 0 --batch-size 16 --data data/mot17.yaml --img 1280 1280 --cfg cfg/training/yolov7-w6.yaml --weights './weights/yolov7-w6_training.pt' --name yolov7-w6-custom --hyp data/hyp.scratch.custom.yaml
 
 track:
-python tracker/track.py --dataset mot17 --data_format yolo --tracker sort --model_path runs/train/yolov7-w6-custom3/weights/best.pt --save_images
+python tracker/track.py --dataset mot17 --data_format yolo --tracker sort --model_path runs/train/yolov7-w6-custom3/weights/best.pt --save_images
+python tracker/track.py --dataset mot17 --data_format yolo --tracker bytetrack --model_path weights/best.pt --save_images

models/__pycache__/common.cpython-37.pyc

@@ -303,7 +303,7 @@ def update(self, new_track, frame_id):
                 self.mean, self.cov, measurement)
 
         # update feature
-        if self.has_feature:        
+        if new_track.has_feature:        
             feature = new_track.features[0] / np.linalg.norm(new_track.features[0])  # (512, )
             if self.use_avg_of_feature:
                 smooth_feat = 0.9 * self.features[-1] + (1 - 0.9) * feature

models/__pycache__/experimental.cpython-37.pyc

@@ -400,7 +400,6 @@ def update(self, det_results, ori_img):
         IoU_dist = matching.iou_distance(strack_pool, D_high)  # IoU dist
         if self.use_apperance_model:
             Apperance_dist = 0.5*matching.embedding_distance(strack_pool, D_high, metric='cosine')
-            Dist_mat = self.gamma*IoU_dist + (1. - self.gamma)*Apperance_dist
             # equation (12)-(13) in paper
             Apperance_dist[IoU_dist > self.theta_iou] = 1
             Apperance_dist[Apperance_dist > self.theta_emb] = 1 
@@ -455,7 +454,6 @@ def update(self, det_results, ori_img):
         IoU_dist = matching.iou_distance(unconfirmed, u_dets0)
         if self.use_apperance_model:
             Apperance_dist = 0.5*matching.embedding_distance(unconfirmed, u_dets0, metric='cosine')
-            Dist_mat = self.gamma*IoU_dist + (1. - self.gamma)*Apperance_dist
             # equation (12)-(13) in paper
             Apperance_dist[IoU_dist > self.theta_iou] = 1
             Apperance_dist[Apperance_dist > self.theta_emb] = 1