view size not compatible with input tensor — Fix
Fix the PyTorch error 'view size is not compatible with input tensor's size and stride'. Learn why this happens and when to use reshape vs view vs contiguous.
Built by Michael Lip
Frequently Asked Questions
What does 'view size is not compatible' mean?
tensor.view() requires the tensor to be contiguous in memory. After operations like transpose, permute, or narrow, the tensor may no longer be contiguous. The fix is to call .contiguous() before .view(), or use .reshape() which handles non-contiguous tensors automatically.
What is the difference between view and reshape?
view() requires contiguous memory and never copies data. reshape() works on any tensor — it returns a view if possible, or copies the data if needed. reshape() is safer but view() makes memory guarantees explicit.
When does a tensor become non-contiguous?
After transpose(), permute(), expand(), narrow(), or indexing operations. These create a new view with different strides without copying data. Check with tensor.is_contiguous(). Call tensor.contiguous() to create a contiguous copy.
About This Tool
This tool is part of HeyTensor, a free suite of PyTorch and deep learning utilities. All calculations run entirely in your browser — no data is sent to any server. The source code is open on GitHub.
Contact
HeyTensor is built and maintained by Michael Lip. For questions or feedback, email [email protected].