Tensor of input and output dimesionality different
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- Python
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Description
Hello, I am attempting to fine tune the Aurora model, when I came across a dimensionality issue when using the predicted data for loss. Here is what the input tensor dimension looks for me:
batch.surf_vars['10u'].size()
torch.Size([1, 2, 721, 1440])
However with the predicted output by Aurora, I noticed the tensors are now dimension of
torch.Size([1, 1, 720, 1440])
I was wondering if this in part by design for the model, and If it is, how you finetuned Aurora when considering the different dimensions?
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Research direction
Start by reproducing the fine-tuning case using batch.surf_vars['10u'] and compare its torch.Size([1, 2, 721, 1440]) shape with the predicted torch.Size([1, 1, 720, 1440]) shape. Trace the Aurora input and output tensors to determine whether the mismatch is intentional; done means the expected dimensionality and a compatible loss path are established.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100