I am a Ph.D. student majored in Data Science at NUS.
yu-rp / distribution-shift-iverson Goto Github PK
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License: Apache License 2.0
Hi,
Thanks for the good work.
I am trying to reproduce your results and I have 2 questions.
Thanks
Hi,
I am trying to replicate your experiments. I was able to successfully follow the Finetuning of Diffusion Model
step mentioned here. However, when I tried running the code provided for the Transformation step, I get the following error:
Traceback (most recent call last):
File "scripts/augment.py", line 444, in <module>
main()
File "scripts/augment.py", line 355, in main
print(outstr)
UnboundLocalError: local variable 'outstr' referenced before assignment
When I commented out: print(outstr)
and changed the --dataset PACS_Augmentation to PACS, I got the following error:
File "scripts/augment.py", line 444, in <module>
main()
File "scripts/augment.py", line 353, in main
dataset = onedomainPACS_withlabel(root = "../../Data/", test_envs = opt.domain_index, data_augmentation = False, image_size = 256)
NameError: name 'onedomainPACS_withlabel' is not defined
Therefore, I would greatly appreciate your clarification on how to run this step.
Thanks.
Sara A. Al-Emadi
Hi,
I want to reproduce your results via your provided codes. But I was stuck in the fine-tuning section. No matter how I reduce the batch size and input image size, it still says cuda out of memory. I run the codes by your instructions on a nvidia3090 gpu with 24g memory. But it seems that before the program loads the images, all the memory has already been allocated. So although I reduce the batch size to 1 and input size to 64, the cuda out of memory problem is still there. Does the memory be allocated to the models? Do you have any idea on how to solve this problem? I guess 24g is enough to tune a diffusion model.
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