permute
Usage
Converts tensor dimensions.
Interface
1 | torchvision.ops.permute(dims:List[int]).contiguous() |
Parameters
Parameter |
Description |
Value Range |
Input/Output |
|---|---|---|---|
dims |
Sequence, which specifies the dimension order for the transpose. |
[2, 0, 1] |
Input |
Return Value
- Tensor after dimension conversion
Example
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | import numpy as np import torch from torchvision import transforms, ops # Create a 5x5 image. src = np.array([[1, 0, 0, 0, 0], [0, 1, 1, 1, 0], [0, 1, 0, 1, 0], [0, 1, 1, 1, 0], [1, 0, 0, 0, 0]], dtype=np.float32) # Convert the NumPy array into a tensor. src_tensor = torch.from_numpy(src) # Define the dimension rearrangement sequence: dims = [1, 0] # Use torchvision.ops.permute to rearrange image dimensions. permute_op = ops.Permute(dims) permuted_tensor = permute_op(src_tensor) print(permuted_tensor) |
Output:
1 2 3 4 5 | tensor([[1., 0., 0., 0., 1.], [0., 1., 1., 1., 0.], [0., 1., 0., 1., 0.], [0., 1., 1., 1., 0.], [0., 0., 0., 0., 0.]]) |
Parent topic: Interface Definition