Resolving RuntimeError: expected scalar type Double but found Float in PyTorch GCNN

Learn how to fix the `RuntimeError: expected scalar type Double but found Float` error in PyTorch when working with GCNN, ensuring compatibility between your model and data types.
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Resolving RuntimeError: expected scalar type Double but found Float in PyTorch GCNN

When working with Graph Convolutional Neural Networks (GCNN) in PyTorch, you might stumble upon a frustrating error message: RuntimeError: expected scalar type Double but found Float. This error typically surfaces when there is a mismatch between the data types of your model parameters and input tensors, specifically when your input data is in float64 (double) format while your model expects float32 (single) format.

In this guide, we'll break down this issue, examine the underlying components, and provide a clear solution to resolve it effectively.

Understanding the Problem

In the context of your problem:

Your input data, which includes edge indices and node features, is being converted from NumPy arrays to PyTorch tensors in float64 format.

This discrepancy manifests when you attempt to train your GCNN model. As the error suggests, the model encountered a Float (which refers to float32 in PyTorch) but expected a Double (or float64).

The Data Type Issue

Let's reflect on the data types:

Input Data:

Node features (x): float64

Edge indices (edge_index): int64

Model Expectation:

Models in PyTorch, under default conditions, use float32 for their parameters and computations unless specified otherwise.

Steps to Resolve the Issue

To address this issue, we can convert the model's parameters to match the float64 format of our input data. Here’s how to do it:

1. Convert the Model to Double

You can simply call the double() method on your model after instantiation. This method converts all the parameters of the model to DoubleTensor. Here’s how to do it:

[[See Video to Reveal this Text or Code Snippet]]

2. Confirm All Data Types

Make sure to check that all relevant tensors passed into the model are of the appropriate type:

[[See Video to Reveal this Text or Code Snippet]]

3. Additional Considerations

Remember that while float64 provides higher precision, it usually comes at a performance cost compared to float32. If speed is a concern, consider converting your data to float32 instead, bearing in mind how this will affect the precision and numerical stability of your training.

You can convert your tensors to float32 using:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

Happy coding, and enjoy the exploration of GCNN with PyTorch!