-
-
Notifications
You must be signed in to change notification settings - Fork 1.6k
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
Understanding difference between TensorFlow and PyTorch implementations of Unet
#840
Comments
Hii, |
This issue is stale because it has been open 60 days with no activity. Remove stale label or comment or this will be closed in 7 days. |
This issue was closed because it has been stalled for 7 days with no activity. |
Hello, |
I am trying to convert the weights from a
segmentation-models
-based model that was trained in TensorFlow to PyTorch. I am using a U-net with anefficientnet-b0
backbone.I noticed a mismatch in the dimensions of the parameters across the PyTorch and and TensorFlow implementations in the convolutional layers of the decoder. What is the source of this discrepancy, and how can I make the architectures match exactly?
Reproducible example:
Observe the mismatch in the convolutional layers of the decoder, taking the first one as an example:
Note how the TensorFlow version has 1952 in channels, and the PyTorch version has 432 in-channels. Why is this the case?
The text was updated successfully, but these errors were encountered: