Comments (5)
Principally, you can do it by replacing device = torch.device('cuda')
to device = torch.device('cpu')
, but it's extremely slow especially in training the model.
https://github.com/naoto0804/pytorch-inpainting-with-partial-conv/blob/master/train.py#L62
from pytorch-inpainting-with-partial-conv.
Ahh thank you so much. Thank you for sharing the code btw. This is by far the best and easiest to use out of all repos!
from pytorch-inpainting-with-partial-conv.
Principally, you can do it by replacing
device = torch.device('cuda')
todevice = torch.device('cpu')
, but it's extremely slow especially in training the model.
https://github.com/naoto0804/pytorch-inpainting-with-partial-conv/blob/master/train.py#L62
Traceback (most recent call last):
File "E:\Programs\Miniconda3\envs\py37\lib\site-packages\torch\utils\data\
dataloader.py", line 511, in _try_get_batch
data = self.data_queue.get(timeout=timeout)
File "E:\Programs\Miniconda3\envs\py37\lib\multiprocessing\queues.py", lin
e 105, in get
raise Empty
_queue.Empty
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "train.py", line 110, in <module>
image, mask, gt = [x.to(device) for x in next(iterator_train)]
File "E:\Programs\Miniconda3\envs\py37\lib\site-packages\torch\utils\data\
dataloader.py", line 576, in __next__
idx, batch = self._get_batch()
File "E:\Programs\Miniconda3\envs\py37\lib\site-packages\torch\utils\data\
dataloader.py", line 553, in _get_batch
success, data = self._try_get_batch()
File "E:\Programs\Miniconda3\envs\py37\lib\site-packages\torch\utils\data\
dataloader.py", line 519, in _try_get_batch
raise RuntimeError('DataLoader worker (pid(s) {}) exited unexpectedly'.forma
t(pids_str))
RuntimeError: DataLoader worker (pid(s) 13708, 13816, 4588, 14068, 6768, 13380,
9600, 10924, 13540, 13924, 14024, 13776, 13796, 4104, 13832, 11968) exited unexp
ectedly
I tried running the train.py and this is what I got @naoto0804
from pytorch-inpainting-with-partial-conv.
Could you check if the dataset is correctly loaded? (ref: preprocess)
from pytorch-inpainting-with-partial-conv.
I'm not using places 2 dataset, but I edited the yml file and I've made sure that data_large, val_large, and test_large are inside the folder. I also changed the mask root to ./mask
because the mask generator saved the mask in that folder instead
from pytorch-inpainting-with-partial-conv.
Related Issues (20)
- A problem in net.py
- Blurry problem in training HOT 1
- can not find the dataset Places2 HOT 2
- Problem while using net.py HOT 1
- Inquiry about LICENSE HOT 2
- test.py uses Places2 Class incorrectly
- Hi, where are the trained models in your project? HOT 1
- About Model Size HOT 1
- bad examples HOT 1
- Slight scaling issue in PartialConv function
- Blurry results HOT 3
- Generate Mask HOT 1
- 索引 HOT 2
- 版本不匹配 HOT 1
- 类型错误
- 类型错误
- Why the mask is convolved in partial conv?
- Loss values vary a lot
- http://places2.csail.mit.edu/ が開けません HOT 1
- Your Partial Conv is new in computer vision. However, if you use a ground truth image in your loss function for your model trining, your paper is worthless for image inpainting. In most cases, we only have a deteriorated image, and the the ground truth is an unknown target. HOT 3
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from pytorch-inpainting-with-partial-conv.