lizhaoliu-lec / cpcm Goto Github PK

Hi.
Thank you for releasing the code.
However, I cannot find the training script for the 0.02% setting on the S3DIS dataset.
I modified the script for 0.01% as 0.02% and tried it.
Unfortunately, this setting fails to reproduce the reported performance.
Could you provide the official training script for it?
Any helpful comment will be greatly appreciated.

I want to know the data type of percentage0.001evenc and how to open it，thank you

Thanks for your code! I have encountered an error when training:

Traceback (most recent call last):
File "ddp_train.py", line 116, in
main()
File "ddp_train.py", line 107, in main
trainer.train()
File "/home/Point_Cloud/CPCM/trainer/base.py", line 190, in train
self.train_one_epoch()
File "/home/Point_Cloud/CPCM/trainer/fully_supervised_trainer.py", line 332, in train_one_epoch
step_ret = self.step(batch)
File "/home/Point_Cloud/CPCM/trainer/fully_supervised_trainer.py", line 1232, in step
return self._step_two_and_mask_stream(batch=batch)
File "/home/Point_Cloud/CPCM/trainer/fully_supervised_trainer.py", line 1199, in _step_two_and_mask_stream
loss.backward()
File "/home/.conda/envs/seg/lib/python3.8/site-packages/torch/_tensor.py", line 307, in backward
torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs)
File "/home/.conda/envs/seg/lib/python3.8/site-packages/torch/autograd/init.py", line 154, in backward
Variable._execution_engine.run_backward(
RuntimeError: merge_sort: failed to synchronize: cudaErrorIllegalAddress: an illegal memory access was encountered
terminate called after throwing an instance of 'c10::Error'

How can I fix this? I tried this taesungp/contrastive-unpaired-translation#83 but did not success.

Hi? Could you please share scripts for preparing weak labels of the S3DIS dataset?
Thanks.

Thank you for your codes. I have found that the setting of two_stream_mask_self_loss in default.yaml is "False", which means that the calculation of mask loss for feats_aux and feats_masked_aux (Z2, Zm loss in your paper) is not involved. I also found that two_stream_mask_mode was set to "random", while it should be "grid". I have corrected them and test under 0.1% setting on S3DIS, while the mIoU is only 63.5, not 66.3.

Thank you for such a good job. I'm a little confused. How do you visualise your test results?Looking forward to your reply.

Hi! I wonder how many GPUs and what kind of GPUs are required in general weakly-supervised semantic segmentation task including your work? Plus as introduced in your paper, 1 or 2 TITAN 3090 GPU is enough, so how long does it take for a single group of experiment? Thanks!

Thank you for such a good job. I have a small question. Trained, how to test. Which parameters should be changed?

thank you for your excellent work. my hardware are 2080ti *5,may be I can't use MinkowskiEngine 0.5.4.
Dose your code only can be train on MinkowskiEngine 0.4.3 Env now,beause I see this:

thank you for your excellent work.
because my sigle gpu's memory isn't enough, I want to know if your code can be modified use multi-gpus to train.

Thanks for the great research, I'm having some issues running this. MinkowskiEngine 0.5.4 is used. During the process of preparing S3DIS data, an error occurred and the return value was incorrect. However, it can run normally after subsequent modifications. However, when running CPCM's 0.1S3DIS data alone, val_miou is 56%, and both the script and default.yaml are the latest in the project. Looking forward to your reply。