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Home Page: https://pomme.readthedocs.io
License: GNU General Public License v3.0
Bayesian model reconstruction based on astronomical spectral line observations.
Home Page: https://pomme.readthedocs.io
License: GNU General Public License v3.0
The following code:
import torch
from torch.optim import Adam
m = torch.arange(5.0, requires_grad=True)
optimizer = Adam([m], lr=0.1)
f = 1 / m
f[m == 0] = 1.0
loss = torch.mean(f**2)
optimizer.zero_grad()
loss.backward()
optimizer.step()
will cause the tensor m to evaluate to:
tensor([ nan, 1.1000, 2.1000, 3.1000, 4.1000], requires_grad=True)
Profile the scaling behaviour for larger models (e.g. 256 x 256 x 256).
Memory consumption seems rather large (50 GB), and computations seem unreasonably slow (~10 minutes).
Profile seems to suggest that a nonzero
function is the culprit.
from p3droslo.forward import image_along_last_axis as forward_image_along_last_axis
at line 379-399, the function lines.image_along_last_axis is defined
This is confusing, so should be changed
Given the still unresolved bug in PyTorch:
pytorch/pytorch#22013
Please ensure that each torch.Tensor is indexed by a torch.Tensor.
Just to be safe, it is probably best to cast all indices always to torch.Tensor.
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