Comments (4)
I don't understand why the authors transform the radar echo dBZ to rain rate using Z-R relationship? Why don't they directly use radar echo reflectivity?
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Thanks for your implementation of dgmr! It's organization is clear,and easy to read.
When I run the test_model.py,I also found the nan values in sampler's output. I check the ConvGRU.py, found that the bug may be aroused by spectral_norm
function.
According to PyTorch spectral_norm doc , I use torch.nn.utils.parametrizations.spectral_norm()
instead, also spectral_norm
in common.py, and it works fine now. I Think you should have a try.
Though I don't know why, hopefully it helps. Thanks.
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Oh, thanks for figuring that out! I'll update it to use that now then too
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It worked! Thanks so much
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Related Issues (20)
- Wrong dimension to split when training Discriminator HOT 2
- Issue with README instructions HOT 1
- About the metrics used in the original paper, e.g., CSI, PSD, CPRS? HOT 5
- TypeError: __init__() missing 2 required positional arguments: 'node_def' and 'op' HOT 6
- Training on other dataset + Error on using run.py HOT 8
- Loading BOM data into ram HOT 3
- How to load the old model to new
- Training is fine, but i can not load the trained model
- Training
- Trying to execute run.py in train folder renders an error HOT 4
- pretrained model prediction result HOT 2
- A problem occurs when I run distributed training HOT 8
- Extra ReLU in L Block HOT 3
- Different image resolutions train/evaluate HOT 5
- Question about model prediction results HOT 1
- try to run train.py but get the bug HOT 1
- how to train using multiple gpus? HOT 2
- Problems with running `./train/run.py` and Concerns with dependency versions HOT 8
- 11111111
- try to run train.py but get TypeError: AbortedError.__init__() missing 2 required positional arguments: 'node_def' and 'op' HOT 2
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