Comments (4)
i think the randomness is caused by the usage of nondeterministic-algorithms , which means even you set all the random seed ,the result will still have some randomness. check it out by add torch.use_deterministic_algorithms(True)
into the main function .
from lanegcn.
Hello, Thanks for your nice project.
I tried to train the model several times without code editing, then I found that the difference in performance of each trial was quite large. There is no big difference when testing multiple times with one checkpoint, so randomness seems to occur during the learning process. Do you have any idea what could be the reason?
Thank you!
btw,have you try to submit the test result to eval AI ? how is the outcome? i try to submit it result using the pretrained model provided by the author and my own training result , it turns out very bad....
from lanegcn.
Thank you for you answer. I'll check it later.
I didn't submit the evaluation, just checked the validation result. I am sorry for not being helpful 😅
from lanegcn.
Hello, Thanks for your nice project.
I tried to train the model several times without code editing, then I found that the difference in performance of each trial was quite large. There is no big difference when testing multiple times with one checkpoint, so randomness seems to occur during the learning process. Do you have any idea what could be the reason?
Thank you!btw,have you try to submit the test result to eval AI ? how is the outcome? i try to submit it result using the pretrained model provided by the author and my own training result , it turns out very bad....
I recently obtained the h5 file using the pretrained model provided by the author, but the results are much worse than what the author described in the paper. Do you know the reason? Thank you
from lanegcn.
Related Issues (20)
- Pretrain Model HOT 1
- Learning Rate Drop
- Meet error when installing mpi4py
- cannot run preprocess data(OSError: [Errno 24] Too many open files) HOT 5
- Does it really generate graph['node_idcs']? HOT 1
- AttributeError: 'RandomSampler' object has no attribute 'set_epoch' HOT 2
- train.py
- How can I get the results about test dataset HOT 2
- ValueError: need at least one array to concatenate
- get someting wrong in training
- Prediction error
- Cannot download the pretrained model HOT 2
- "left" and "right" not in gragh
- left and right in the lane graph
- visualization HOT 2
- Getting data Forbidden HOT 1
- IndexError: list index out of range HOT 2
- I used the pretrained model you provided, but the accuracy is much worse than that in Table 1 HOT 1
- ### Has anyone encountered the same issue below?
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