from typing import Tuple
from ..functional import nn
from .module import Module
[docs]class Pad(Module):
r"""Pads the input tensor.
Args:
pad_width(Tuple[Tuple[int, int], ...]): A tuple. Each element in the tuple is the tuple of 2-elements,
the 2 elements represent the padding size on both sides of the current dimension, ``(front_offset, back_offset)``
mode(str): One of the following string values. Default: ``'constant'``
* ``'constant'``: Pads with a constant value.
* ``'reflect'``: Pads with the edge values of tensor.
* ``'replicate'``: Pads with the reflection of the tensor mirrored on the first and last values of the tensor along each axis.
constant_val(float): Fill value for ``'constant'`` padding. Default: 0
Returns:
Return type: module. The instance of the ``Pad`` module.
Examples:
>>> import numpy as np
>>> inp = Tensor([[1., 2., 3.],[4., 5., 6.]])
>>> inp
Tensor([[1. 2. 3.]
[4. 5. 6.]], device=xpux:0)
>>> m = M.Pad(pad_width=((1, 1),), mode="constant")
>>> m(inp)
Tensor([[0. 0. 0.]
[1. 2. 3.]
[4. 5. 6.]
[0. 0. 0.]], device=xpux:0)
>>> m = M.Pad(pad_width=((1, 1),), mode="constant", constant_val=9)
>>> m(inp)
Tensor([[9. 9. 9.]
[1. 2. 3.]
[4. 5. 6.]
[9. 9. 9.]], device=xpux:0)
>>> m = M.Pad(pad_width=((1, 1), (1, 2)), mode="reflect")
>>> m(inp)
Tensor([[5. 4. 5. 6. 5. 4.]
[2. 1. 2. 3. 2. 1.]
[5. 4. 5. 6. 5. 4.]
[2. 1. 2. 3. 2. 1.]], device=xpux:0)
>>> m = M.Pad(pad_width=((1, 1), (1, 2)), mode="replicate")
>>> m(inp)
Tensor([[1. 1. 2. 3. 3. 3.]
[1. 1. 2. 3. 3. 3.]
[4. 4. 5. 6. 6. 6.]
[4. 4. 5. 6. 6. 6.]], device=xpux:0)
"""
def __init__(
self,
pad_width: Tuple[Tuple[int, int], ...],
mode: str = "constant",
constant_val: float = 0.0,
):
super().__init__()
self.pad_width = pad_width
self.mode = mode
self.pad_val = constant_val
def forward(self, src):
return nn.pad(
src, pad_width=self.pad_width, mode=self.mode, constant_value=self.pad_val
)