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View Code? Open in Web Editor NEWNetwork-to-Network Translation with Conditional Invertible Neural Networks
Home Page: https://compvis.github.io/net2net/
Network-to-Network Translation with Conditional Invertible Neural Networks
Home Page: https://compvis.github.io/net2net/
thank you for shareing your code and pretrained model.
If I want to retrain the unpaired - traslation task Oil-Portrait ⟷ Photography what is dataset I need ?
such as , how many Oil portrait images? and how many real human image photograph?
I want to train a pretrained model by myself, thank you
Hi,if I have a new dataset with source domain x and target domain y , how I train the model like creativity/portrait-to-photo
as your paper said, it should be train two autoencoder (Resnet101-as encoder, bigGAN as decoder)。
is right ??
and would you provide an tutorial for how to apply on new datasets? thank you
In readme about how to train unpaired translations task ;
you said :
python translation.py --base configs/translation/<task-of-interest>.yaml -t --gpus 0,
but in translation folder it has only faces32-to-faces256.yaml not any other, so I think it maybe :
python translation.py --base configs/creativty/<task-of-interest>.yaml -t --gpus 0,
Thank you for your surprising work.
During the SBERT-to-BigGAN, SBERT-to-BigBiGAN and SBERT-to-AE (COCO) execution, I received the following error:
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "translation.py", line 531, in
melk()
NameError: name 'melk' is not defined
I'd appreciate it if you could check.
Hi, thanks for your interesting work. When i run the anime to photography task: python translation.py --base configs/creativity/anime_photography_256.yaml -t --gpus0, i receive the following error:
Traceback (most recent call last):
File "translation.py", line 522, in
trainer . fit(model, data)
File " /home /projects /miniconda3/ envs/net2net/lib/python3.7/site- packages/pytorch lightning/ trainer /states.py", line 48, in wrapped_ fn
result = fn(self , *args, **kwargs
File " /home/projects/miniconda3/envs /net2net/lib/python3.7/site-packages/pytorch_ lightning/ trainer/trainer .py", line 1058, in fit
results = self . accelerator_ backend. spawn_ ddp_ children( model )
File "/home/projects/miniconda3/envs /net2net/lib/python3 .7/site - packages /pytorch_ lightning/ accelerators/ddp_ backend.py", line 123, in spawn_ ddp_ childrenresults = self .ddp_ train(local_ rank, mp_ queue=None, model=model, is_ master=True )
File " /home / projects /miniconda3/envs /net2net/ lib/ python3.7/site- packages / pytorch_ lightning/ accelerators/ddp_ backend.py", line 224, in ddp_ train
results = self . trainer .run_ pretrain_ routine( model )
File " /home/projects/miniconda3/ envs /ne t2net/ lib/py thon3.7/site - packages/py torch_ lightning/trainer/trainer .py", line 1224, in run_ pretrain_ routineself._ run_ sanity check(ref_ model,model)
File " /home/projects/miniconda3/envs /net2net/lib/python3.7/site - packages/pytorch_ lightning/trainer/trainer .py", line 1257, in run_ sanity check
eval_ results = self._ evaluate(model, self .val_ dataloaders, max_ batches, False )
File " /home / projects /miniconda3 /envs /net2net/lib/python3.7/site- packages/ pytorch_ lightning/trainer /evaluation_ loop.py", line 369, in_ evaluate
self . on_ validation_ batch_ end( batch, batch_ idx, dataloader_ idx
File " /home /projects/miniconda3/envs /net2net/lib/py thon3.7/site packages/pytorch lightning/trainer/callback_ hook.py", line 156, in on_ validation_ batch_ endcallback. on validation batch end(self, self . getdell0batch, batch_ idx, dataloader idx)
File " /home /projects/net2net/translation.py", line 297, in on_ validation_ batch_ end
self.log_ img(pl_ module, batch, batch_ idx, split="val"
File " /home /projects /net2net/ translation.py", line 265, in log_ img
images = pl_ module. log_ images(batch, split=split)
File " /home /projects/miniconda3/envs /net2net/lib/python3. 7/site- packages/ torch/ autograd/grad_ mode.py", line 28, in decorate_ context
return func(*args, **kwargs)
File " /home /projects/net2net/net2net/models/flows/flow.py", line 157, in log_ images
log[" conditioning"] = log_ txt_ as_ img((w,h), xc)
File " /home /projects/ net2net /net2net/modules/util.py", line 18, in log_ txt_ as_ img
lines = "In" . join(xc[bi][start:start+nc] for start in range(0, len(xc[bi]), nc))
File " /home /projects /miniconda3/envs /net2nelib/python3.7/site-ackages/torch/_ tensor.py", line 589, in_ len__
raise TypeError("len() of a 0-d tensor "
TypeError: len() of a 0-d tensor
I don't know what causes this error. I would greatly appreciate it if you could help me find out the problem. Thanks for your time.
Hi,
Thank you for your amazing work!
I am trying to replicate your results and training using
python translation.py --base configs/translation/sbert-to-biggan256.yaml -t --gpus 0,
I was wondering what gpu was used to train your model and what batch size did you use? I am only able to fit batch_size=2 on a TITAN XP, the default batch_size in the config was 16 but I am not able to launch it using 4 TITANs XP without running into memory issues. Is the BigGan or Sentence Transformer fine-tuned during the training (from your paper it seems like it was not), do you have any insight on what am I missing?
Thank you in advance
Hi,
Thanks for the interesting work. I am trying to reproduce the results bu running faces32-to-faces256.yaml
. However, I am confused about how to prepare the corresponding dataset.
I have downloaded celebahq dataset from https://drive.google.com/drive/folders/11Vz0fqHS2rXDb5pprgTjpD7S2BAJhi1P, and put them into the data/celebahq folder. And there are 4 subfolders corresponding to different resolutions: 128 x 128, 256 x 256, 512 x 512 and 1024 x 1024, the images are in .jpg format.
My questions are:
First and foremost, I would like to express my sincere gratitude and respect for your work on this repository. The progress and innovations shared here have been immensely insightful and valuable to the community.
I am currently exploring the concept of fission in invertible neural networks, where a single latent representation 'x' can be decomposed into two distinct components 'y' and 'z'. My objective is to parameterize 'z' with a tractable distribution while ensuring that the combination of 'y' and 'z' can be accurately recombined to reconstruct 'x' using the reverse of the model.
Given your expertise in this field, I would greatly appreciate any guidance or suggestions you could provide on the following aspects:
Any insights, references, or examples you could share would be extremely helpful.
Thank you for your time and for the impactful contributions you've made to the field.
Best regards
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