Comments (10)
It seems like the error is in your label. Maybe you should check your label, or you could provide more evidence about how this error comes up.
from pytorch-deeplab-xception.
Thanks! But I have altered the number of label, but the error is same.
Train loss: 0.193: 2%|β | 7/398 [00:06<06:18, 1.03it/s]Traceback (most recent call last): File "train.py", line 305, in <module> main() File "train.py", line 298, in main trainer.training(epoch) File "train.py", line 109, in training loss.backward() File "/home/image/anaconda3/envs/ajy/lib/python3.6/site-packages/torch/tensor.py", line 93, in backward torch.autograd.backward(self, gradient, retain_graph, create_graph) File "/home/image/anaconda3/envs/ajy/lib/python3.6/site-packages/torch/autograd/__init__.py", line 90, in backward allow_unreachable=True) # allow_unreachable flag RuntimeError: CuDNN error: CUDNN_STATUS_EXECUTION_FAILED
from pytorch-deeplab-xception.
I did not encounter such problem before. Could you successfully run my default training code in VOC dataset?
from pytorch-deeplab-xception.
I have a same problem when I run the default training code in VOC dataset. Have you solved it?
from pytorch-deeplab-xception.
Have the same issue.
Using poly LR Scheduler! Starting Epoch: 0 Total Epoches: 50 0%| | 0/4179 [00:00<?, ?it/s] =>Epoches 0, learning rate = 0.0070, previous best = 0.0000 /home/ubuntu/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/nn/functional.py:52: UserWarning: size_average and reduce args will be deprecated, please use reduction='elementwise_mean' instead. warnings.warn(warning.format(ret)) Traceback (most recent call last): File "train.py", line 301, in <module> main() File "train.py", line 294, in main trainer.training(epoch) File "train.py", line 106, in training loss.backward() File "/home/ubuntu/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/tensor.py", line 93, in backward torch.autograd.backward(self, gradient, retain_graph, create_graph) File "/home/ubuntu/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/autograd/__init__.py", line 90, in backward allow_unreachable=True) # allow_unreachable flag RuntimeError: CuDNN error: CUDNN_STATUS_EXECUTION_FAILED
Any suggestions?
from pytorch-deeplab-xception.
maybe try smaller batch size if your GPU memory is not enough.
from pytorch-deeplab-xception.
recently I meet the same issue, any suggestions?
from pytorch-deeplab-xception.
Thanks! But I have altered the number of label, but the error is same.
Train loss: 0.193: 2%|β | 7/398 [00:06<06:18, 1.03it/s]Traceback (most recent call last): File "train.py", line 305, in main() File "train.py", line 298, in main trainer.training(epoch) File "train.py", line 109, in training loss.backward() File "/home/image/anaconda3/envs/ajy/lib/python3.6/site-packages/torch/tensor.py", line 93, in backward torch.autograd.backward(self, gradient, retain_graph, create_graph) File "/home/image/anaconda3/envs/ajy/lib/python3.6/site-packages/torch/autograd/init.py", line 90, in backward allow_unreachable=True) # allow_unreachable flag RuntimeError: CuDNN error: CUDNN_STATUS_EXECUTION_FAILED
did you solve this problem, can give some suggestions? thank you.
from pytorch-deeplab-xception.
In my case, I solve the same issue by fixing the error labels of my own dataset.
from pytorch-deeplab-xception.
In my case, I solve the same issue by fixing the error labels of my own dataset.
Can you be more specific as to where did you made those changes?
from pytorch-deeplab-xception.
Related Issues (20)
- TypeError: cannot pickle 'module' object HOT 1
- using drn-105: Missing key(s) in state_dict
- my data are images and its 0,255 masks,,the masks are 0 255 images, it is voc format? HOT 1
- how to chnage the input h w of image?
- @stonessss yes, i write test code. HOT 1
- run inference.py success HOT 2
- self.last_conv() input_channels in file decoder.py HOT 1
- self = reduction.pickle.load(from_parent) error Ran out of input HOT 1
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- validation loss always lower than train loss
- Target 10 out of bounds while training on complete dataset HOT 1
- Could you give us some training details about your Pretrained Model
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- xception backbone url doesn't exist HOT 1
- Params And FLOPs HOT 1
- Why do I keep the imou and other parameter values obtained from each epoch unchanged during the training process
- Why force to set the output stride for DRN to 8?
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from pytorch-deeplab-xception.