about the base model result

[robotzheng]

plan_L2_1s:0.3348474009348846
plan_L2_2s:0.6005098198601633
plan_L2_3s:0.9474086193724374
plan_obj_col_1s:0.0
plan_obj_col_2s:0.0
plan_obj_col_3s:3.255844337443258e-05
plan_obj_box_col_1s:0.0019535065442469234
plan_obj_box_col_2s:0.002979097479976558
plan_obj_box_col_3s:0.006088428835334152

projects/configs/VAD/VAD_base_e2e.py

L2 is better， but collasion is worse than the paper‘s. Why?

[robotzheng]

loss_plan_col=dict(type='PlanCollisionLoss', loss_weight=1.0),

can I make the above "loss_weight" bigger?

[Halo5082]

Did you train the model yourself, or use the pretrained model? If using the pretrained one, I think VAD_base_stage_2.py should be used instead of VAD_base_e2e.py
Additionally, I think that they use another image normalization config in the paper and pretrained model. See #18 (comment) and #9 (comment)

[robotzheng]

I train the model from resnet50-19c8e357.pth by the configs of "VAD_base_e2e.py“. Is the "loss_weight" not important?

[StevenJ308]

I was in a similar situation

-------------- Motion Prediction --------------
EPA_car: 0.6096603478939834
EPA_pedestrian: 0.3235923685435086
ADE_car: 0.8396922945976257
ADE_pedestrian: 0.8586953282356262
FDE_car: 1.1747483015060425
FDE_pedestrian: 1.189048409461975
MR_car: 0.12827822120866592
MR_pedestrian: 0.16290983606557377

-------------- Planning --------------
gt_car:4.503418636452432
gt_pedestrian:2.099042781793319
cnt_ade_car:3.6200429771439735
cnt_ade_pedestrian:1.077358859152178
cnt_fde_car:3.4264504786091035
cnt_fde_pedestrian:0.9533111935924985
hit_car:2.9869115061535454
hit_pedestrian:0.7980074233248682
fp_car:0.40378980269583903
fp_pedestrian:0.20296932994725533
ADE_car:3.1434736251831055
ADE_pedestrian:0.9509602189064026
FDE_car:4.025217056274414
FDE_pedestrian:1.1335331201553345
MR_car:0.43953897245555773
MR_pedestrian:0.1553037702676304
plan_L2_1s:0.30277927277533684
plan_L2_2s:0.5726086170295752
plan_L2_3s:0.949615497207263
plan_obj_col_1s:0.0
plan_obj_col_2s:0.0
plan_obj_col_3s:3.255844337443258e-05
plan_obj_box_col_1s:0.0034186364524321157
plan_obj_box_col_2s:0.005176792342254347
plan_obj_box_col_3s:0.011851273242745881
fut_valid_flag:1.0

projects//configs/VAD/VAD_tiny_e2e.py

[robotzheng]

@StevenJ308, I have checked my log file, not find redownload the resnet50 model. Maybe, some superparameters are not same as the paper's.

[yuyuyuyuyuty]

I would like to ask why I can not use my own training pth file to test? I still get errors when I use my own pth:result_dict['ADE_'+cls] = all_metric_dict['ADE_'+cls] / all_metric_dict['cnt_ade_'+cls]
ZeroDivisionError: float division by zero

[yuyuyuyuyuty]

您是自己训练模型，还是使用预训练模型？如果使用预训练模型，我认为VAD_base_stage_2.py应该使用而不是VAD_base_e2e.py 此外，我认为他们在论文和预训练模型中使用了另一个图像规范化配置。请参阅#18（评论）和#9（评论）
Hello, I would like to ask what is the difference between the pth trained by myself and the pth provided by the paper?