omerbt / splice Goto Github PK

Hello, very cool project! Is there a parameter to change the batch size? It seems that 8GB of VRAM isn't enough and I'm getting CUDA OOM errors. I've tried lowering the image resolutions but I still get the same issue.

Pytorch version 1.10.1
CUDA version 11.3

When running the model, the time per iteration increases with each epoch.
When printing
import os, psutil; process = psutil.Process(os.getpid()); print(process.memory_info().rss),
the memory used by the CPU increases with each epoch.
After digging a bit, I think the issue is with the handlers, that are removed but aren't deleted from the hook_handlers list (the length of the hook_handlers list keeps increasing), so python's garbage collector does not collect them.

Hi!

I love your work and have a question regarding multiple target images.
Have you experimented with multiple target images?
For example:
Source Domain - Horses
Target Domain - Zebras
What happens if we pick a new zebra image as the target for every epoch?

Thanks!

How can I evaluate this model after training with my datasets?

Hi, thanks for your great work.
when I try to training model, meet this error.
self.model = torch.hub.load('facebookresearch/dino:main', model_name).to(device) -- > URLError:
It maybe my machine is no internet, but I try using "torch.save()" , and "torch.load" to replace "torch.hub.load " , it was error again.
what shoud I fix this? Thanks again , waiting for your reply.

I can't find a way to delete an issue, sorry for the spam. Awesome work though.

Dear authors:

Thanks for sharing your interesting work! Can you share the codes of inversion? Thanks!

Hi, thank you for your work. How to save the generated model? Can you provide the test code?
Looking forward to your reply.
@tnarek @shaibagon

I'm trying to recreate the results in figure 9 in your paper. Specifically, disabling L_structure. Are both lambda_global_ssim and lambda_entire_ssim taken together form the entirety of L_struct or am I misreading the code?

So far tinkering with the various lambda terms, it seems the result always retains very high structural fidelity to the structure image.

Trying your code in Colab, everything goes well for a few minutes (~300 epochs) and then it gets a CUDA memory error. Is there a leak somewhere?
https://colab.research.google.com/drive/17UgzBmKtqRXniuG6fHqMGh3xIaVIJnd2?usp=sharing

Traceback (most recent call last):
  File "train.py", line 87, in <module>
    train_model(dataroot)
  File "train.py", line 58, in train_model
    losses = criterion(outputs, inputs)
  File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
  File "/content/Splice/util/losses.py", line 64, in forward
    losses['loss_global_cls'] = self.calculate_crop_cls_loss(outputs['x_global'], inputs['B_global'])
  File "/content/Splice/util/losses.py", line 90, in calculate_crop_cls_loss
    cls_token = self.extractor.get_feature_from_input(a)[-1][0, 0, :]
  File "/content/Splice/models/extractor.py", line 84, in get_feature_from_input
    self.model(input_img)
  File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
  File "/root/.cache/torch/hub/facebookresearch_dino_main/vision_transformer.py", line 212, in forward
    x = blk(x)
  File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 1071, in _call_impl
    result = forward_call(*input, **kwargs)
  File "/root/.cache/torch/hub/facebookresearch_dino_main/vision_transformer.py", line 108, in forward
    y, attn = self.attn(self.norm1(x))
  File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 1071, in _call_impl
    result = forward_call(*input, **kwargs)
  File "/root/.cache/torch/hub/facebookresearch_dino_main/vision_transformer.py", line 85, in forward
    attn = (q @ k.transpose(-2, -1)) * self.scale
RuntimeError: CUDA out of memory. Tried to allocate 30.00 MiB (GPU 0; 11.17 GiB total capacity; 10.35 GiB already allocated; 23.81 MiB free; 10.68 GiB reserved in total by PyTorch)

请问跑这个模型的配置是啥

In the beginning, the seed is being created as int32 for some reason.
Maybe pick a lower seed?

Traceback (most recent call last):
  File "train.py", line 88, in <module>
    train_model(dataroot)
  File "train.py", line 28, in train_model
    seed = np.random.randint(2 ** 32)
  File "mtrand.pyx", line 745, in numpy.random.mtrand.RandomState.randint
  File "_bounded_integers.pyx", line 1343, in numpy.random._bounded_integers._rand_int32
ValueError: high is out of bounds for int32

Hi，
Could you please guide how to modify your code such that I could run it using a GPU with 8GB RAM?