Hi, thanks for your nice work and open source project. I have the following questions about the implementation details on teacher part:

1. How did you get the inference performance (e.g. 42.5 mAP in Table 2(a)) of the trained C-FF(Crossing Feature-level Fusion) teacher? Is it an ensemble of two inference parts(high/low resolution inputs), or ensemble of three parts(low/high/fusion), or only from the fusion part?

2. Does the teacher's detection head for the fused features (returned by C-FF module) share exactly the same learnable weights with the head used for high/low resolution inputs?

Thanks a lot for your reply.

Thanks for your excellent work! I encountered the following problems during training: Environment: 2080Ti x 4 + cuda 10.2 + pytorch 1.6 +detectron2 The issue is described as follows: RuntimeError: Expected to have finished reduction in the prior iteration before starting a new one. This error indicates that your module has parameters that were not used in producing loss. You can enable unused parameter detection by (1) passing the keyword argument `find_unused_parameters=True` to `torch.nn.parallel.DistributedDataParallel`; (2) making sure all `forward` function outputs participate in calculating loss. If you already have done the above two steps, then the distributed data parallel module wasn't able to locate the output tensors in the return value of your module's `forward` function. Please include the loss function and the structure of the return value of `forward` of your module when reporting this issue (e.g. list, dict, iterable). It seems that there exists residuary parameters, and then I add find_unused_parameters=True to the class DefaultTrainer(TrainerBase): in detectron2, the code runs normally. However, when I train SLR025-50-S.yamlwith the teacher weights you provide, the training loss seems abnormal compared to the provided log:

[04/13 17:33:38] d2.engine.train_loop INFO: Starting training from iteration 0
[04/13 17:34:33] d2.utils.events INFO:  eta: 15:20:23  iter: 19  total_loss: 2.931  loss_fcos_cls_st: 1.145  loss_fcos_loc_st: 0.9628  loss_fcos_ctr_st: 0.7237  loss_kd: 0.1189  time: 0.6158  data_time: 2.0520  lr: 0.00019981  max_mem: 5806M
[04/13 17:34:45] d2.utils.events INFO:  eta: 15:20:54  iter: 39  total_loss: 2.825  loss_fcos_cls_st: 0.9993  loss_fcos_loc_st: 0.7467  loss_fcos_ctr_st: 0.6875  loss_kd: 0.3699  time: 0.6174  data_time: 0.0149  lr: 0.00039961  max_mem: 5806M
[04/13 17:34:57] d2.utils.events INFO:  eta: 15:20:42  iter: 59  total_loss: 2.879  loss_fcos_cls_st: 0.9837  loss_fcos_loc_st: 0.6015  loss_fcos_ctr_st: 0.6765  loss_kd: 0.6186  time: 0.6161  data_time: 0.0160  lr: 0.00059941  max_mem: 5806M
[04/13 17:35:10] d2.utils.events INFO:  eta: 15:25:02  iter: 79  total_loss: 3.017  loss_fcos_cls_st: 0.9883  loss_fcos_loc_st: 0.5809  loss_fcos_ctr_st: 0.6759  loss_kd: 0.7888  time: 0.6172  data_time: 0.0161  lr: 0.00079921  max_mem: 5806M
[04/13 17:35:22] d2.utils.events INFO:  eta: 15:23:48  iter: 99  total_loss: 3.152  loss_fcos_cls_st: 0.9831  loss_fcos_loc_st: 0.5779  loss_fcos_ctr_st: 0.6779  loss_kd: 0.9303  time: 0.6178  data_time: 0.0148  lr: 0.00099901  max_mem: 5806M
[04/13 17:35:35] d2.utils.events INFO:  eta: 15:27:29  iter: 119  total_loss: 3.209  loss_fcos_cls_st: 0.9703  loss_fcos_loc_st: 0.5653  loss_fcos_ctr_st: 0.6757  loss_kd: 1.001  time: 0.6205  data_time: 0.0162  lr: 0.0011988  max_mem: 5807M
[04/13 17:35:48] d2.utils.events INFO:  eta: 15:30:00  iter: 139  total_loss: 3.268  loss_fcos_cls_st: 0.9778  loss_fcos_loc_st: 0.5575  loss_fcos_ctr_st: 0.6778  loss_kd: 1.052  time: 0.6219  data_time: 0.0153  lr: 0.0013986  max_mem: 5807M
[04/13 17:36:00] d2.utils.events INFO:  eta: 15:29:48  iter: 159  total_loss: 3.273  loss_fcos_cls_st: 0.9318  loss_fcos_loc_st: 0.5516  loss_fcos_ctr_st: 0.6738  loss_kd: 1.123  time: 0.6228  data_time: 0.0152  lr: 0.0015984  max_mem: 5807M
[04/13 17:36:12] d2.utils.events INFO:  eta: 15:24:32  iter: 179  total_loss: 3.388  loss_fcos_cls_st: 0.9453  loss_fcos_loc_st: 0.5504  loss_fcos_ctr_st: 0.6754  loss_kd: 1.195  time: 0.6191  data_time: 0.0154  lr: 0.0017982  max_mem: 5807M

Hi, This is a good work, the idea is very novel! The Student Model is ok, but when I training on Teacher Model , its mAp is only around 20, the config files are R50-T.yaml and SLR100-50-T.yaml. the batchsize was reduce to 4 ,and learning rate also reduce by 50%. Machine is 2x 3090.

I want to know do you have similar problems when you training? Thanks

作者你好，我的电脑没有gpu，因此执行 python3 projects/MSAD/train_net_T.py --config-file <config.yaml> --num-gpus 8 --eval-only MODEL.WEIGHTS model_checkpoint 时会报错 。请问怎么用cpu来evaluation呢？