Thanks for your error report and we appreciate it a lot.
Checklist
- I have searched related issues but cannot get the expected help. yes
- I have read the FAQ documentation but cannot get the expected help. yes
- The bug has not been fixed in the latest version. yes , as far as i know
not display correct value for train time ,see bellow image.
Describe the bug
A clear and concise description of what the bug is.
i am trying to train CenterNet in my own custom dataset ,which has the same format of COCO dataset.
i got the results of the training as (.log.json) and got images tested by a desired (epoch.pth) file . but when i try to analysis the training and find the time of training i got a nan value
here is the error massage:

Reproduction
- What command or script did you run?
python tools/analysis_tools/analyze_logs.py cal_train_time train_results/centernet/20220309_152747.log.json
- Did you make any modifications on the code or config? Did you understand what you have modified?
yes , i made same modification on other models ,for example fasterRCNN , CentriapetalNet , and it works ,and i got the results of training time.
- What dataset did you use?
my custom dataset ,with a same format of COCO one.
Environment
- Please run
python mmdet/utils/collect_env.py to collect necessary environment information and paste it here.
TorchVision: 0.8.0a0
OpenCV: 4.3.0
MMCV: 1.3.8
MMCV Compiler: GCC 7.5
MMCV CUDA Compiler: 11.0
MMDetection: 2.18.1+393c376
- You may add addition that may be helpful for locating the problem, such as
- How you installed PyTorch [e.g., pip, conda, source]
it was installed before in the server (GPU) using some docker files.
- Other environment variables that may be related (such as
$PATH, $LD_LIBRARY_PATH, $PYTHONPATH, etc.)
i install seaborn using this instruction pip install seaborn as the system asked me.
Error traceback
If applicable, paste the error trackback here.
tools/analysis_tools/analyze_logs.py:21: RuntimeWarning: Mean of empty slice.
epoch_ave_time = all_times.mean(-1)
/opt/conda/lib/python3.6/site-packages/numpy/core/_methods.py:163: RuntimeWarning: invalid value encountered in true_divide
ret, rcount, out=ret, casting='unsafe', subok=False)
/opt/conda/lib/python3.6/site-packages/numpy/core/fromnumeric.py:3373: RuntimeWarning: Mean of empty slice.
out=out, **kwargs)
/opt/conda/lib/python3.6/site-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars
ret = ret.dtype.type(ret / rcount)
A placeholder for trackback.
Bug fix
If you have already identified the reason, you can provide the information here. If you are willing to create a PR to fix it, please also leave a comment here and that would be much appreciated!
Thanks for your error report and we appreciate it a lot.
Checklist
not display correct value for train time ,see bellow image.
Describe the bug
A clear and concise description of what the bug is.
i am trying to train CenterNet in my own custom dataset ,which has the same format of COCO dataset.
i got the results of the training as (.log.json) and got images tested by a desired (epoch.pth) file . but when i try to analysis the training and find the time of training i got a nan value
here is the error massage:
Reproduction
yes , i made same modification on other models ,for example fasterRCNN , CentriapetalNet , and it works ,and i got the results of training time.
my custom dataset ,with a same format of COCO one.
Environment
python mmdet/utils/collect_env.pyto collect necessary environment information and paste it here.TorchVision: 0.8.0a0
OpenCV: 4.3.0
MMCV: 1.3.8
MMCV Compiler: GCC 7.5
MMCV CUDA Compiler: 11.0
MMDetection: 2.18.1+393c376
it was installed before in the server (GPU) using some docker files.
$PATH,$LD_LIBRARY_PATH,$PYTHONPATH, etc.)i install seaborn using this instruction
pip install seabornas the system asked me.Error traceback
If applicable, paste the error trackback here.
Bug fix
If you have already identified the reason, you can provide the information here. If you are willing to create a PR to fix it, please also leave a comment here and that would be much appreciated!