mlpc-ucsd / boundaryformer Goto Github PK

Hi, author!
Sorry to bother you, but I found that the checkpoint link has expired. Can you provide it again?

Excellent work! Thanks for the release.
When I ran the test file after compiling the diff ras, I found the GT rasterization agreement percentage is around 60, while the masks produced by Pytorch version and CUDA version are almost same. And the whole training process works fine.

GT Rasterization agreement: 0.594480990116706
Rasterized agreement (tau = 1.0): 0.9998779296875
Gradient agreement (tau = 1.0): 1.0

Does this implicates there is a gap between the mask produced by HARD_CUDA_RASTERIZER and the gt mask ? And what causes this gap?
Thanks in advance.

First of all congrats for the great work!! Thanks for release.

I run your model using the demo.py file. Yet I got segmentation result in pixel format.
I made following changes to demo.py file to get it run succesfully (inside setup_cfg(args) function):

from boundary_former.config import add_boundaryformer_config
add_boundaryformer_config(cfg)

Could you explain how should I update demo file or the code to get segmentation predictions in polygon format?

Note*
I used the following config file and model respectively:
-> config file link, model.

Best

Hi Thanks for your great work! I loaded your pretrained model on COCO, and ran the inference. The results are as follows:

[06/29 21:55:25 d2.engine.defaults]: Evaluation results for coco_2017_val in csv format:
[06/29 21:55:25 d2.evaluation.testing]: copypaste: Task: bbox
[06/29 21:55:25 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl
[06/29 21:55:25 d2.evaluation.testing]: copypaste: 38.4919,59.4363,41.7748,22.3543,41.2834,50.4875
[06/29 21:55:25 d2.evaluation.testing]: copypaste: Task: segm
[06/29 21:55:25 d2.evaluation.testing]: copypaste: AP,AP50,AP75,APs,APm,APl
[06/29 21:55:25 d2.evaluation.testing]: copypaste: 7.8351,29.3128,1.0689,4.7795,8.2153,10.8173

You can see the AP results of segm is much lower than the number in your paper. I also visualize the results using tools/visualize_json_results.py . The visualized results are also not good. I have followed your default hyperparameters except that I set num-gpus as 1 and BATCH_SIZE = 8. Could you please give some hints on what may be wrong? Thanks in advance!