deepmr.resize

deepmr.resize(input, oshape)

Resize with zero-padding or cropping.

Adapted from SigPy [1].

Parameters:

• input (np.ndarray | torch.Tensor) – Input tensor of shape (..., ishape).

• oshape (Iterable) – Output shape.

Returns:

output – Zero-padded or cropped tensor of shape (..., oshape).

Return type:

np.ndarray | torch.Tensor

Examples

>>> import torch
>>> import deepmr

We can pad tensors to desired shape:

>>> x = torch.tensor([0, 1, 0])
>>> y = deepmr.resize(x, [5])
>>> y
tensor([0, 0, 1, 0, 0])

Batch dimensions are automatically expanded (pad will be applied starting from rightmost dimension):

>>> x = torch.tensor([0, 1, 0])[None, ...]
>>> x.shape
torch.Size([1, 3])
>>> y = deepmr.resize(x, [5]) # len(oshape) == 1
>>> y.shape
torch.Size([1, 5])

Similarly, if oshape is smaller than ishape, the tensor will be cropped:

>>> x = torch.tensor([0, 0, 1, 0, 0])
>>> y = deepmr.resize(x, [3])
>>> y
tensor([0, 1, 0])

Again, batch dimensions are automatically expanded:

>>> x = torch.tensor([0, 0, 1, 0, 0])[None, ...]
>>> x.shape
torch.Size([1, 5])
>>> y = deepmr.resize(x, [3]) # len(oshape) == 1
>>> y.shape
torch.Size([1, 3])

References

[1] mikgroup/sigpy