ARTICLE AD BOX
I have an .h5 file that contains only model weights, not the model architecture. I want to use these weights in a PyTorch model and convert them into a .pth file.
Some context:
The .h5 file does not include the architecture (it’s not a full saved model, just weights).
I already have a model defined in PyTorch that I believe matches the original architecture.
I cannot retrain the model.
I attempted manually mapping weights between the .h5 file and my PyTorch model (by iterating over layers and assigning values), but the results were incorrect — likely due to mismatched layer ordering, naming, or tensor shapes.
What I want to achieve:
- Get a .pth file corresponding to the .h5 file I have.
My questions:
Is it possible to reliably convert .h5 weights (without architecture) into .pth?
How can I ensure correct mapping between the .h5 weights and PyTorch model layers?
Are there any tools or best practices for handling this kind of cross-framework weight transfer (especially if the .h5 file originated from Keras/TensorFlow)?
Any guidance or examples would be greatly appreciated.
