We read every piece of feedback, and take your input very seriously.
To see all available qualifiers, see our documentation.
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
Hi, @lukemelas ~ I'm so excited about your code, but today, after i upgrade the pip package today, a new problem appeared:
C:\ProgramData\Anaconda3\lib\site-packages\efficientnet_pytorch\utils.py in drop_connect(inputs, p, training) 72 random_tensor += torch.rand([batch_size, 1, 1, 1], dtype=inputs.dtype) # uniform [0,1) 73 binary_tensor = torch.floor(random_tensor) ---> 74 output = inputs / keep_prob * binary_tensor 75 return output 76 RuntimeError: expected type torch.cuda.FloatTensor but got torch.FloatTensor
I sure that i don't change any of my code, just upgrade the pip package "efficientNet-Pytroch" in my code, i use your model like:
from efficientnet_pytorch import EfficientNet modelPre = EfficientNet.from_pretrained('efficientnet-b0')
The text was updated successfully, but these errors were encountered:
See #32
This is fixed in master :)
Sorry, something went wrong.
See #32 This is fixed in master :)
Thanks~
No branches or pull requests
Hi, @lukemelas ~
I'm so excited about your code, but today, after i upgrade the pip package today, a new problem appeared:
C:\ProgramData\Anaconda3\lib\site-packages\efficientnet_pytorch\utils.py in drop_connect(inputs, p, training)
72 random_tensor += torch.rand([batch_size, 1, 1, 1], dtype=inputs.dtype) # uniform [0,1)
73 binary_tensor = torch.floor(random_tensor)
---> 74 output = inputs / keep_prob * binary_tensor
75 return output
76
RuntimeError: expected type torch.cuda.FloatTensor but got torch.FloatTensor
I sure that i don't change any of my code, just upgrade the pip package "efficientNet-Pytroch"
in my code, i use your model like:
from efficientnet_pytorch import EfficientNet
modelPre = EfficientNet.from_pretrained('efficientnet-b0')
The text was updated successfully, but these errors were encountered: