Bug: rotate_iou CUDA mode

🐛 Bug

from maskrcnn_benchmark.layers.rotate_nms import rotate_iou
a=torch.tensor([[50.7325,50.7325,100.7325,300.7325,-3.4]]).float().to('cuda')
b=torch.tensor([[50.7325,50.7325,100.7325,300.7325,-3.4]]).float().to('cuda')
rotate_iou(a,b)     # get the result 0.3333
a=torch.tensor([[50.7325,50.7325,100.7325,300.7325,-3.4]]).float()
b=torch.tensor([[50.7325,50.7325,100.7325,300.7325,-3.4]]).float()
rotate_iou(a,b)   # get the result 1.0

however, when I test on the cpu mode, It return the result, tensor=1.0 Quite surprised to debug the result, maybe the rotate_iou cuda version is wrong? Whats more, I test the rotated ops file (maskrcnn_benchmark/modeling/rotate_ops.py) the rotated version used in GPU mode is slower than CPU mode. It’s because the test box array is small? @mrlooi , hope for your updates, Thanks

[mrlooi]

1. augmented gt_proposals - I did this to augment the shape of the rotated boxes. The motivation was to increase segmentation robustness to differences in rotated box width + height, since there is more variability in rotated boxes than bounding boxes
2. cos function - I found a very slight AP improvement using cos (+0.02 AP). But this may vary. Bottom line: I found somewhat negligible impact, and marginal improvement at best
3. Comments: those are just there for reference

[WeihongM]

@mrlooi Thanks for your reply. I have used rotated iou script in detectron2 project. successfully ported to your project.

excuse me, may I ask one more question.

• I have read your code, and it seems you have do some tricks in this project. such as dir maskrcnn_benchmark/rrpn/inference.py function add_gt_proposals, you defind aug_rrect, rather than directly use gt_rrect, do you find this tricks matters?

• Also, the way you compute angle loss is cos function, rather than smooth loss, which seems different from RRPN paper. Do you find this also matters?

• Also, in the file maskrcnn_benchmark/modeling/rrpn/loss.py, the call function has the comments old and new. Do you forget to uncomment this, or maybe these comments is redundant.

looking forward to your reply. thanks