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I tried to run the code. In data preprocessing step2 a 4.23G sets.pkl file is generated,when reading this file 【sets = pickle.load()】in step3, a memory error is reported. 【My computer: GeForce RTX 3090, 3TB.】how can I fix this? ~👀
I'm sorry to bother you due to a question I don't understand, the value of "samples_y" in the "forecast" function has been changed and is no longer equal to the original ground truth【pic 1】, why is gt=samples_y[:,2]【pic 2】. In that case is the MSE calculation no longer correct?
Hello @PingChang818 , thank you so much for providing the source code for the interesting paper. I've been trying to reproduce the results from the paper, but haven't been able to do so.
So I followed the readme file, downloaded the data, ran through preprocessing, and did training and evaluation. There wasn't any problem aside from getting the paths right for the dataset.
I trained the model twice with python main.py
, and evaluated each checkpoint twice. That gave us 4 results:
model 1 run 1
CRPS: 0.7520603882639032
MSE: 622.348388671875
model 1 run 2
CRPS: 0.5939008311221474
MSE: 1333.6546630859375
model 2 run 1
CRPS: 0.830156125520405
MSE: 232.1817626953125
model 2 run 2
CRPS: 0.7796238849037572
MSE: 774.9886474609375
I understand that there is some inevitable randomness in diffusion models, but these results seem far away from Table 2. Is it correct to use hyperparameters in config/base.yaml
? Is there something else that I'm missing? Thanks!
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