PyTorch RuntimeError: Input and Weight Type Mismatch - Solution Guide

This error arises when the data types of your model parameters and input data are incompatible. The discrepancy usually stems from type conversions happening during model definition or data processing. To fix this, you need to ensure that both your model parameters and input data share the same data type.

There are two primary ways to address this issue:

1. Convert Input Data to Match Model Parameter Type:

data = data.to(device, dtype=torch.float)

In your training and validation code, convert the input data to torch.float to align with the model parameters' data type.

2. Convert Model Parameters to Match Input Data Type:

model = model.to(device, dtype=torch.float)

After defining your model, convert the entire model to torch.float, mirroring the input data's type.

Choose the approach that best suits your scenario and ensure that your model parameters and input data types match. This will prevent runtime errors caused by type incompatibility.