upd yolox link

README.md

@@ -47,7 +47,7 @@ We adopt yolox as the detection method. We used stronger data augmentation and r
 
 ### Training 
 
-Download the pretrained model at [Onedrive](https://mailstsinghuaeducn-my.sharepoint.com/:f:/g/personal/liuxy21_mails_tsinghua_edu_cn/EkCTrRfHUZVEtD7eHwLkYSkBCTXlh9ekDteSzK6jM4oo-A?e=6TCh8y) (password: groupji) and put it in the folder `pretrained_models/yolox`. Then use the following command:
+Download the pretrained model at [Onedrive](https://mailstsinghuaeducn-my.sharepoint.com/:f:/g/personal/liuxy21_mails_tsinghua_edu_cn/EkCTrRfHUZVEtD7eHwLkYSkBCTXlh9ekDteSzK6jM4oo-A?e=m0aNCy) (password: groupji) and put it in the folder `pretrained_models/yolox`. Then use the following command:
 
 `./det/yolox/tools/train_yolox.sh <config_path> <gpu_ids> (other args)`
 

core/gdrn_modeling/engine/gdrn_evaluator.py

@@ -518,8 +518,6 @@ class GDRN_Evaluator(DatasetEvaluator):
                 net_cfg = cfg.MODEL.POSE_NET
                 crop_res = net_cfg.OUTPUT_RES
 
-
-
                 for _ in range(cfg.TEST.DEPTH_REFINE_ITER):
                     self.ren.clear()
                     self.ren.set_cam(K_crop)