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andrewgordonwilson boyali makinux mjhutchinson ucalyptus2 zhehe davidko3 adeeshkolluru nishr anonymous-code-0 idrissbachali nathanfelipe mrswolf kenakai16 vz415 its-gucci lmy0217 sanmayphy ihounie 2877992943 kumarharshat ugaqun zhihaoairobotic wguo126 yonatangideoni dolphin4mi ihaeyong nc4lab rdguerrerom metaphysicist0 dweprinz anda-ralucalieconv's Issues
How to transfer a 3D voxel CNN model to your LieConv?
Thank you for your great work.
I have a 3D voxel CNN model. Because of a lack of data, I have to do data argumentation many times. And also the dataset is very sparse, so it is not efficient for memory. I want to transfer my model into a se(3) model. However, I don't know how to prepare my dataset.
In addition, how is your model difference from e3nn and se(3)cnn?
Many thanks.
Helping on testing code
Hallo, Good afternoon.
Thank you for helping me regarding the issues before.
May I know how to save the model from your training and how to run the test program?
I am very sorry for asking a dumb question since this is my first time try to code.
Error occur
Hello,
I am very interested in your code and I have it a try.
but this error happend when I am trying to run your code.
Traceback (most recent call last):
File "examples/train_springs.py", line 9, in
from lie_conv.datasets import SpringDynamics
File "/home/em308/LieConv/lie_conv/datasets.py", line 58, in
class ModelNet40(Dataset,metaclass=Named):
TypeError: metaclass conflict: the metaclass of a derived class must be a (non-strict) subclass of the metaclasses of all its bases
Since I am very new in programming, would you mind to tell me what can I do to solve this?
thank you so much for your help.
LieResNet.group is never initialised.
On line 329 in lieConv.py the group object is not instantiated, causing problems with LieResNet's forward
method. The obvious two-character fix seemed to get it working.
computational complexity
Hello,
Thank you for the very nice paper. I was wondering how is the memory and computational complexity of lift approach to building equivariant networks compared with the non-lift approach? (those used in tensor field network and se3 transformer). And what is the memory and computational bottleneck in your opinion? Thank you!
makedirs_exist_ok cannot be imported from torchvision
With torchvision version '0.8.1+cu101' and torch version '1.7.0+cu101' I get the error
ImportError: cannot import name 'makedir_exist_ok' from 'torchvision.datasets.utils'
Indeed a search in torchvision github return no results for makedir_exist_ok.
I manually changed makedir_exist_ok to os.makedirs (for Python 3.7) in lie_conv/datasets.py and it worked, but posting here for reference
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