How to resize a tensor in PyTorch?

How to resize a tensor in PyTorch?

In PyTorch, you can resize or reshape a tensor using the view() method, the reshape() method, or the resize_() method. The one you choose depends on the specific requirement and the shape of the input tensor.

  1. Using view():

    • This method returns a new tensor with the specified shape.
    • The original tensor and the new tensor share the same underlying data, so modifying one will modify the other.
    • You must ensure that the requested size is compatible with the original size.
    import torch tensor = torch.randn(2, 3) reshaped = tensor.view(6)

  2. Using reshape():

    • It's similar to view(), but it can also return a copy if the requested shape is not contiguous in memory.
    • This method is more versatile but potentially less efficient than view().
    tensor = torch.randn(2, 3) reshaped = tensor.reshape(6)

  3. Using resize_():

    • This method is an in-place version and can resize the tensor to a larger or smaller size.
    • If you resize to a larger size, new elements will be uninitialized. If you resize to a smaller size, some elements will be removed.
    • It's important to note the underscore in the method name, indicating the operation is in-place.
    tensor = torch.randn(2, 3) tensor.resize_(3, 2)

In general, if you want to ensure a new reshaped tensor without any possible side effects, use reshape(). If you're sure about the memory layout and want potentially faster reshaping, use view(). Use resize_() with caution, as it modifies the tensor in place.

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