mshymyr

@VSainteuf in that case I will have an array with shape 1xC, right?

@VSainteuf i think I figured out (I got 8x1048576, because my imgae 1024 by 1024). So for each pixel of each channel, I have mean and std. Is it okay...

> > @VSainteuf in that case I will have an array with shape 1xC, right? > > Yes > > > @VSainteuf i think I figured out (I got 8x1048576,...

I am still confused regarding the normalization shapes. Now I have samples with different sizes in terms of the temporal, while the same for channel and pixel size (they are...

> yes if you have time series of varying length the best option is to compute the channelwise mean across all samples and dates. So you end up with mean...

I am encountering this error during the validation step (I would not ask this if I got this during the training too...). Error: `File "/home/adminko/PycharmProjects/pytorch-psetae/models/pse.py", line 134, in masked_mean out...

> are you giving different arguments to the train and val data loaders ? Any idea why the behaviour is different between train and val ? They are exactly the...

Any idea how to work with varying sizes of unordered temporal data?

> Any idea how to work with varying sizes of unordered temporal data? @VSainteuf any suggestions?

I executed following your comments and getting this error: ``` Traceback (most recent call last): File "/agrospace/lightweight-temporal-attention-pytorch/train.py", line 363, in main(config) File "/agrospace/lightweight-temporal-attention-pytorch/train.py", line 256, in main train_metrics = train_epoch(model,...