Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

multi threading issue #5

Open
toiyeumayhoc opened this issue Dec 23, 2018 · 0 comments
Open

multi threading issue #5

toiyeumayhoc opened this issue Dec 23, 2018 · 0 comments

Comments

@toiyeumayhoc
Copy link

i really appreciate your github. Thanks a lot for your works on fine-tuning models.
When i tried to run your code locally, i faced a issue and it looks like a multi-threading issue. This is the log:

C:\Users\dk12a7\Desktop\New folder\fine-tuning.pytorch>python main.py --finetune

[Phase 1] : Data Preperation
C:\Users\dk12a7\Anaconda3\lib\site-packages\torchvision-0.2.1-py3.7.egg\torchvision\transforms\transforms.py:199: UserWarning: The use of the transforms.Scale transform is deprecated, please use transforms.Resize instead.
C:\Users\dk12a7\Anaconda3\lib\site-packages\torchvision-0.2.1-py3.7.egg\torchvision\transforms\transforms.py:604: UserWarning: The use of the transforms.RandomSizedCrop transform is deprecated, please use transforms.RandomResizedCrop instead.
| Preparing model trained on hymenoptera_data dataset...

[Phase 2] : Model setup
| Downloading ImageNet fine-tuned ResNet-50...

[Phase 3] : Training Model
| Training Epochs = 50
| Initial Learning Rate = 0.001000
| Optimizer = SGD

=> Training Epoch #1, LR=0.001000

[Phase 1] : Data Preperation
C:\Users\dk12a7\Anaconda3\lib\site-packages\torchvision-0.2.1-py3.7.egg\torchvision\transforms\transforms.py:199: UserWarning: The use of the transforms.Scale transform is deprecated, please use transforms.Resize instead.
C:\Users\dk12a7\Anaconda3\lib\site-packages\torchvision-0.2.1-py3.7.egg\torchvision\transforms\transforms.py:604: UserWarning: The use of the transforms.RandomSizedCrop transform is deprecated, please use transforms.RandomResizedCrop instead.
| Preparing model trained on hymenoptera_data dataset...

[Phase 2] : Model setup
| Downloading ImageNet fine-tuned ResNet-50...

[Phase 1] : Data Preperation
C:\Users\dk12a7\Anaconda3\lib\site-packages\torchvision-0.2.1-py3.7.egg\torchvision\transforms\transforms.py:199: UserWarning: The use of the transforms.Scale transform is deprecated, please use transforms.Resize instead.
C:\Users\dk12a7\Anaconda3\lib\site-packages\torchvision-0.2.1-py3.7.egg\torchvision\transforms\transforms.py:604: UserWarning: The use of the transforms.RandomSizedCrop transform is deprecated, please use transforms.RandomResizedCrop instead.
| Preparing model trained on hymenoptera_data dataset...

[Phase 2] : Model setup
| Downloading ImageNet fine-tuned ResNet-50...

[Phase 1] : Data Preperation
C:\Users\dk12a7\Anaconda3\lib\site-packages\torchvision-0.2.1-py3.7.egg\torchvision\transforms\transforms.py:199: UserWarning: The use of the transforms.Scale transform is deprecated, please use transforms.Resize instead.
C:\Users\dk12a7\Anaconda3\lib\site-packages\torchvision-0.2.1-py3.7.egg\torchvision\transforms\transforms.py:604: UserWarning: The use of the transforms.RandomSizedCrop transform is deprecated, please use transforms.RandomResizedCrop instead.
| Preparing model trained on hymenoptera_data dataset...

[Phase 2] : Model setup
| Downloading ImageNet fine-tuned ResNet-50...

[Phase 1] : Data Preperation
C:\Users\dk12a7\Anaconda3\lib\site-packages\torchvision-0.2.1-py3.7.egg\torchvision\transforms\transforms.py:199: UserWarning: The use of the transforms.Scale transform is deprecated, please use transforms.Resize instead.
C:\Users\dk12a7\Anaconda3\lib\site-packages\torchvision-0.2.1-py3.7.egg\torchvision\transforms\transforms.py:604: UserWarning: The use of the transforms.RandomSizedCrop transform is deprecated, please use transforms.RandomResizedCrop instead.
| Preparing model trained on hymenoptera_data dataset...

After get into the training phase for the first time, the code keep repeating the phase 1 and phase 2. Do you have any idea about this issue. Thank you

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

1 participant