torch.nn.functional.pad — PyTorch 2.7 documentation (original) (raw)

torch.nn.functional.pad(input, pad, mode='constant', value=None) → Tensor[source][source]¶

Pads tensor.

Padding size:

The padding size by which to pad some dimensions of inputare described starting from the last dimension and moving forward.⌊len(pad)2⌋\left\lfloor\frac{\text{len(pad)}}{2}\right\rfloor dimensions of input will be padded. For example, to pad only the last dimension of the input tensor, thenpad has the form(padding_left,padding_right)(\text{padding\_left}, \text{padding\_right}); to pad the last 2 dimensions of the input tensor, then use(padding_left,padding_right,(\text{padding\_left}, \text{padding\_right}, padding_top,padding_bottom)\text{padding\_top}, \text{padding\_bottom}); to pad the last 3 dimensions, use(padding_left,padding_right,(\text{padding\_left}, \text{padding\_right}, padding_top,padding_bottom\text{padding\_top}, \text{padding\_bottom} padding_front,padding_back)\text{padding\_front}, \text{padding\_back}).

Padding mode:

See torch.nn.CircularPad2d, torch.nn.ConstantPad2d,torch.nn.ReflectionPad2d, and torch.nn.ReplicationPad2dfor concrete examples on how each of the padding modes works. Constant padding is implemented for arbitrary dimensions. Circular, replicate and reflection padding are implemented for padding the last 3 dimensions of a 4D or 5D input tensor, the last 2 dimensions of a 3D or 4D input tensor, or the last dimension of a 2D or 3D input tensor.

Note

When using the CUDA backend, this operation may induce nondeterministic behaviour in its backward pass that is not easily switched off. Please see the notes on Reproducibility for background.

Parameters

• input (Tensor) – N-dimensional tensor
• pad (tuple) – m-elements tuple, wherem2≤\frac{m}{2} \leq input dimensions and mm is even.
• mode (str) – 'constant', 'reflect', 'replicate' or 'circular'. Default: 'constant'
• value (Optional_[_float]) – fill value for 'constant' padding. Default: 0

Return type

Tensor

Examples:

t4d = torch.empty(3, 3, 4, 2) p1d = (1, 1) # pad last dim by 1 on each side out = F.pad(t4d, p1d, "constant", 0) # effectively zero padding print(out.size()) torch.Size([3, 3, 4, 4]) p2d = (1, 1, 2, 2) # pad last dim by (1, 1) and 2nd to last by (2, 2) out = F.pad(t4d, p2d, "constant", 0) print(out.size()) torch.Size([3, 3, 8, 4]) t4d = torch.empty(3, 3, 4, 2) p3d = (0, 1, 2, 1, 3, 3) # pad by (0, 1), (2, 1), and (3, 3) out = F.pad(t4d, p3d, "constant", 0) print(out.size()) torch.Size([3, 9, 7, 3])