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Published 20 hours ago • verified publisher icon google-deepmind

DGMR Model Code Availability

Open jacobbieker opened this issue 3 years ago • 6 comments

Is there any plans to release the model code for the nowcasting DGMR?

jacobbieker avatar Oct 05 '21 11:10 jacobbieker

FYI: the pseudocode seems to be released in gs://dm-nowcasting-example-data/pseudocode.zip (in the same bucket as the example datasets).

shmurai avatar Nov 08 '21 05:11 shmurai

Awesome! Thanks for finding that. Looks quite complete, only thing I notice is that layers.txt and latent_stack.txt are the same content, so there might be a mistake on copy/paste or something, but that does help a ton!

jacobbieker avatar Nov 08 '21 15:11 jacobbieker

The code is great, I can understand the details of the paper better, but I found a lot of code missing in layer.txt, such as SNConv3d, ApplyAlongAxis. I have finished a Pytorch version of DGMR code and uploaded it to my GitHub, https://github.com/TQRTQ/DGMR after looking at the pseudo code I found some details that I misunderstood. I want to reproduce this work, can you give me the complete code？please！

TQRTQ avatar Nov 17 '21 12:11 TQRTQ

Awesome! Thanks for finding that. Looks quite complete, only thing I notice is that layers.txt and latent_stack.txt are the same content, so there might be a mistake on copy/paste or something, but that does help a ton!

The pseudocode.zip has been updated, and this issue is fixed.

bugsuse avatar Feb 03 '22 12:02 bugsuse

Thanks for pointing it out! That helps a lot

jacobbieker avatar Feb 03 '22 13:02 jacobbieker

Thanks for updating the pseudo code! I am trying to reproduce this work, and I'm wondering if you can generally provide some training advice for me. I followed most of the settings in the paper except several settings that could shorten training period. The training dataset of mine is just simply filtered radar images in dbz (if 40% grids > dbz-> training set). I shortened the predict lead time to 6 frames and set batch size to 45 (due to the limit of my GPU). However, I always got smoothed predictions when the training epoch reached to 300~400, and the loss of discriminators and generators did not decrease. Can you provide some training tips or share your training experience? Thank you!

My code of DGMR in PyTorch version: https://github.com/hyungting/DGMR-pytorch This is my training record of DGMR: https://docs.google.com/presentation/d/13PFavy9GC7Gf6T1k9kFr8G9Xsq_Y2t3WCbgoo0Npc_4/edit?usp=sharing

hyungting avatar Mar 01 '22 03:03 hyungting