Comments (12)
@Hesene Hello Hesene, in my lab I only have one single 2080Ti, therefore I cannot replicate this issue. I'm sorry about it!
from efficientunet-pytorch.
@Hesene Hello Hesene, in my lab I only have one single 2080Ti, therefore I cannot replicate this issue. I'm sorry about it!
Ok, thank you for your code, it help me a lot
from efficientunet-pytorch.
I face the same problem.
Which part is the cause?
from efficientunet-pytorch.
did you use torch.nn.DataParallel()?
from efficientunet-pytorch.
did you use torch.nn.DataParallel()?
no I didn't, but I think it may work
from efficientunet-pytorch.
I face the same problem.
Which part is the cause?
I'm not sure, but I think you can try to integrate nn.DataParallel()
into the source code
from efficientunet-pytorch.
I face the same problem.
Which part is the cause?I'm not sure, but I think you can try to integrate
nn.DataParallel()
into the source code
I use efficientnet as backbone to trian a object detection model, and the nn.DataParallel() works fine, the only issue is the speed of multi gpu is quit slow
from efficientunet-pytorch.
I'm seeing a similar issue when running with nn.DataParallel:
RuntimeError: Caught RuntimeError in replica 0 on device 0.
Original Traceback (most recent call last):
File "/home/ryanstout/.local/share/virtualenvs/arsenal_train2-TlJZ47AR/lib/python3.7/site-packages/torch/nn/parallel/parallel_apply.py", line 60, in _worker
output = module(*input, **kwargs)
File "/home/ryanstout/.local/share/virtualenvs/arsenal_train2-TlJZ47AR/lib/python3.7/site-packages/torch/nn/modules/module.py", line 550, in __call__
result = self.forward(*input, **kwargs)
File "/home/ryanstout/.local/share/virtualenvs/arsenal_train2-TlJZ47AR/lib/python3.7/site-packages/efficientunet/efficientunet.py", line 106, in forward
x = torch.cat([x, blocks.popitem()[1]], dim=1)
RuntimeError: All input tensors must be on the same device. Received cuda:0 and cuda:1
Any ideas?
Thanks!
from efficientunet-pytorch.
I'm seeing a similar issue when running with nn.DataParallel:
RuntimeError: Caught RuntimeError in replica 0 on device 0. Original Traceback (most recent call last): File "/home/ryanstout/.local/share/virtualenvs/arsenal_train2-TlJZ47AR/lib/python3.7/site-packages/torch/nn/parallel/parallel_apply.py", line 60, in _worker output = module(*input, **kwargs) File "/home/ryanstout/.local/share/virtualenvs/arsenal_train2-TlJZ47AR/lib/python3.7/site-packages/torch/nn/modules/module.py", line 550, in __call__ result = self.forward(*input, **kwargs) File "/home/ryanstout/.local/share/virtualenvs/arsenal_train2-TlJZ47AR/lib/python3.7/site-packages/efficientunet/efficientunet.py", line 106, in forward x = torch.cat([x, blocks.popitem()[1]], dim=1) RuntimeError: All input tensors must be on the same device. Received cuda:0 and cuda:1
Any ideas?
Thanks!
Hi, bro.
Are you solved the problem?
from efficientunet-pytorch.
I suspect that this problem is due to the sharing of a certain module in Efficientunet, which results in this module being only on one GPU, perhaps the encoder……
from efficientunet-pytorch.
I suspect that this problem is due to the sharing of a certain module in Efficientunet, which results in this module being only on one GPU, perhaps the encoder……
I agree, I'm now facing the same problem.
from efficientunet-pytorch.
@NPU-Franklin Franklin created a PR (#11 ) to support multi GPUs. I do not have multi cards therefore I cannot test it. But maybe you can give it a try.
from efficientunet-pytorch.
Related Issues (11)
- why fixed decoder channels
- block parameter
- Can't Pickle or Save resulting Models HOT 1
- Can we change last layer input from 32 to 64 HOT 1
- issue solved
- cannot import name 'load_state_dict_from_url' HOT 4
- multi gpu error HOT 1
- 4-th channel input HOT 1
- Cannot download pretrained weights HOT 1
- Please help me solve the problem,Thank you HOT 1
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from efficientunet-pytorch.