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from .module import Module
from .utils import _pair, _quadruple, _ntuple
from .. import functional as F
from torch import Tensor
from ..common_types import _size_2_t, _size_4_t, _size_6_t
from typing import Sequence, Tuple
# TODO: grad_output size asserts in THNN
__all__ = ['ConstantPad1d', 'ConstantPad2d', 'ConstantPad3d', 'ReflectionPad1d', 'ReflectionPad2d',
'ReflectionPad3d', 'ReplicationPad1d', 'ReplicationPad2d', 'ReplicationPad3d', 'ZeroPad2d']
class _ConstantPadNd(Module):
__constants__ = ['padding', 'value']
value: float
padding: Sequence[int]
def __init__(self, value: float) -> None:
super().__init__()
self.value = value
def forward(self, input: Tensor) -> Tensor:
return F.pad(input, self.padding, 'constant', self.value)
def extra_repr(self) -> str:
return 'padding={}, value={}'.format(self.padding, self.value)
class ConstantPad1d(_ConstantPadNd):
r"""Pads the input tensor boundaries with a constant value.
For `N`-dimensional padding, use :func:`torch.nn.functional.pad()`.
Args:
padding (int, tuple): the size of the padding. If is `int`, uses the same
padding in both boundaries. If a 2-`tuple`, uses
(:math:`\text{padding\_left}`, :math:`\text{padding\_right}`)
Shape:
- Input: :math:`(C, W_{in})` or :math:`(N, C, W_{in})`.
- Output: :math:`(C, W_{out})` or :math:`(N, C, W_{out})`, where
:math:`W_{out} = W_{in} + \text{padding\_left} + \text{padding\_right}`
Examples::
>>> # xdoctest: +IGNORE_WANT("non-deterministic")
>>> m = nn.ConstantPad1d(2, 3.5)
>>> input = torch.randn(1, 2, 4)
>>> input
tensor([[[-1.0491, -0.7152, -0.0749, 0.8530],
[-1.3287, 1.8966, 0.1466, -0.2771]]])
>>> m(input)
tensor([[[ 3.5000, 3.5000, -1.0491, -0.7152, -0.0749, 0.8530, 3.5000,
3.5000],
[ 3.5000, 3.5000, -1.3287, 1.8966, 0.1466, -0.2771, 3.5000,
3.5000]]])
>>> m = nn.ConstantPad1d(2, 3.5)
>>> input = torch.randn(1, 2, 3)
>>> input
tensor([[[ 1.6616, 1.4523, -1.1255],
[-3.6372, 0.1182, -1.8652]]])
>>> m(input)
tensor([[[ 3.5000, 3.5000, 1.6616, 1.4523, -1.1255, 3.5000, 3.5000],
[ 3.5000, 3.5000, -3.6372, 0.1182, -1.8652, 3.5000, 3.5000]]])
>>> # using different paddings for different sides
>>> m = nn.ConstantPad1d((3, 1), 3.5)
>>> m(input)
tensor([[[ 3.5000, 3.5000, 3.5000, 1.6616, 1.4523, -1.1255, 3.5000],
[ 3.5000, 3.5000, 3.5000, -3.6372, 0.1182, -1.8652, 3.5000]]])
"""
padding: Tuple[int, int]
def __init__(self, padding: _size_2_t, value: float):
super().__init__(value)
self.padding = _pair(padding)
class ConstantPad2d(_ConstantPadNd):
r"""Pads the input tensor boundaries with a constant value.
For `N`-dimensional padding, use :func:`torch.nn.functional.pad()`.
Args:
padding (int, tuple): the size of the padding. If is `int`, uses the same
padding in all boundaries. If a 4-`tuple`, uses (:math:`\text{padding\_left}`,
:math:`\text{padding\_right}`, :math:`\text{padding\_top}`, :math:`\text{padding\_bottom}`)
Shape:
- Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`.
- Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(C, H_{out}, W_{out})`, where
:math:`H_{out} = H_{in} + \text{padding\_top} + \text{padding\_bottom}`
:math:`W_{out} = W_{in} + \text{padding\_left} + \text{padding\_right}`
Examples::
>>> # xdoctest: +IGNORE_WANT("non-deterministic")
>>> m = nn.ConstantPad2d(2, 3.5)
>>> input = torch.randn(1, 2, 2)
>>> input
tensor([[[ 1.6585, 0.4320],
[-0.8701, -0.4649]]])
>>> m(input)
tensor([[[ 3.5000, 3.5000, 3.5000, 3.5000, 3.5000, 3.5000],
[ 3.5000, 3.5000, 3.5000, 3.5000, 3.5000, 3.5000],
[ 3.5000, 3.5000, 1.6585, 0.4320, 3.5000, 3.5000],
[ 3.5000, 3.5000, -0.8701, -0.4649, 3.5000, 3.5000],
[ 3.5000, 3.5000, 3.5000, 3.5000, 3.5000, 3.5000],
[ 3.5000, 3.5000, 3.5000, 3.5000, 3.5000, 3.5000]]])
>>> # using different paddings for different sides
>>> m = nn.ConstantPad2d((3, 0, 2, 1), 3.5)
>>> m(input)
tensor([[[ 3.5000, 3.5000, 3.5000, 3.5000, 3.5000],
[ 3.5000, 3.5000, 3.5000, 3.5000, 3.5000],
[ 3.5000, 3.5000, 3.5000, 1.6585, 0.4320],
[ 3.5000, 3.5000, 3.5000, -0.8701, -0.4649],
[ 3.5000, 3.5000, 3.5000, 3.5000, 3.5000]]])
"""
__constants__ = ['padding', 'value']
padding: Tuple[int, int, int, int]
def __init__(self, padding: _size_4_t, value: float) -> None:
super().__init__(value)
self.padding = _quadruple(padding)
class ConstantPad3d(_ConstantPadNd):
r"""Pads the input tensor boundaries with a constant value.
For `N`-dimensional padding, use :func:`torch.nn.functional.pad()`.
Args:
padding (int, tuple): the size of the padding. If is `int`, uses the same
padding in all boundaries. If a 6-`tuple`, uses
(:math:`\text{padding\_left}`, :math:`\text{padding\_right}`,
:math:`\text{padding\_top}`, :math:`\text{padding\_bottom}`,
:math:`\text{padding\_front}`, :math:`\text{padding\_back}`)
Shape:
- Input: :math:`(N, C, D_{in}, H_{in}, W_{in})` or :math:`(C, D_{in}, H_{in}, W_{in})`.
- Output: :math:`(N, C, D_{out}, H_{out}, W_{out})` or
:math:`(C, D_{out}, H_{out}, W_{out})`, where
:math:`D_{out} = D_{in} + \text{padding\_front} + \text{padding\_back}`
:math:`H_{out} = H_{in} + \text{padding\_top} + \text{padding\_bottom}`
:math:`W_{out} = W_{in} + \text{padding\_left} + \text{padding\_right}`
Examples::
>>> m = nn.ConstantPad3d(3, 3.5)
>>> input = torch.randn(16, 3, 10, 20, 30)
>>> output = m(input)
>>> # using different paddings for different sides
>>> m = nn.ConstantPad3d((3, 3, 6, 6, 0, 1), 3.5)
>>> output = m(input)
"""
padding: Tuple[int, int, int, int, int, int]
def __init__(self, padding: _size_6_t, value: float) -> None:
super().__init__(value)
self.padding = _ntuple(6)(padding)
class _ReflectionPadNd(Module):
__constants__ = ['padding']
padding: Sequence[int]
def forward(self, input: Tensor) -> Tensor:
return F.pad(input, self.padding, 'reflect')
def extra_repr(self) -> str:
return '{}'.format(self.padding)
class ReflectionPad1d(_ReflectionPadNd):
r"""Pads the input tensor using the reflection of the input boundary.
For `N`-dimensional padding, use :func:`torch.nn.functional.pad()`.
Args:
padding (int, tuple): the size of the padding. If is `int`, uses the same
padding in all boundaries. If a 2-`tuple`, uses
(:math:`\text{padding\_left}`, :math:`\text{padding\_right}`)
Shape:
- Input: :math:`(C, W_{in})` or :math:`(N, C, W_{in})`.
- Output: :math:`(C, W_{out})` or :math:`(N, C, W_{out})`, where
:math:`W_{out} = W_{in} + \text{padding\_left} + \text{padding\_right}`
Examples::
>>> m = nn.ReflectionPad1d(2)
>>> # xdoctest: +IGNORE_WANT("other tests seem to modify printing styles")
>>> input = torch.arange(8, dtype=torch.float).reshape(1, 2, 4)
>>> input
tensor([[[0., 1., 2., 3.],
[4., 5., 6., 7.]]])
>>> m(input)
tensor([[[2., 1., 0., 1., 2., 3., 2., 1.],
[6., 5., 4., 5., 6., 7., 6., 5.]]])
>>> # using different paddings for different sides
>>> m = nn.ReflectionPad1d((3, 1))
>>> m(input)
tensor([[[3., 2., 1., 0., 1., 2., 3., 2.],
[7., 6., 5., 4., 5., 6., 7., 6.]]])
"""
padding: Tuple[int, int]
def __init__(self, padding: _size_2_t) -> None:
super().__init__()
self.padding = _pair(padding)
class ReflectionPad2d(_ReflectionPadNd):
r"""Pads the input tensor using the reflection of the input boundary.
For `N`-dimensional padding, use :func:`torch.nn.functional.pad()`.
Args:
padding (int, tuple): the size of the padding. If is `int`, uses the same
padding in all boundaries. If a 4-`tuple`, uses (:math:`\text{padding\_left}`,
:math:`\text{padding\_right}`, :math:`\text{padding\_top}`, :math:`\text{padding\_bottom}`)
Shape:
- Input: :math:`(N, C, H_{in}, W_{in})` or :math:`(C, H_{in}, W_{in})`.
- Output: :math:`(N, C, H_{out}, W_{out})` or :math:`(C, H_{out}, W_{out})` where
:math:`H_{out} = H_{in} + \text{padding\_top} + \text{padding\_bottom}`
:math:`W_{out} = W_{in} + \text{padding\_left} + \text{padding\_right}`
Examples::
>>> # xdoctest: +IGNORE_WANT("not sure why xdoctest is choking on this")
>>> m = nn.ReflectionPad2d(2)
>>> input = torch.arange(9, dtype=torch.float).reshape(1, 1, 3, 3)
>>> input
tensor([[[[0., 1., 2.],
[3., 4., 5.],
[6., 7., 8.]]]])
>>> m(input)
tensor([[[[8., 7., 6., 7., 8., 7., 6.],
[5., 4., 3., 4., 5., 4., 3.],
[2., 1., 0., 1., 2., 1., 0.],
[5., 4., 3., 4., 5., 4., 3.],
[8., 7., 6., 7., 8., 7., 6.],
[5., 4., 3., 4., 5., 4., 3.],
[2., 1., 0., 1., 2., 1., 0.]]]])
>>> # using different paddings for different sides
>>> m = nn.ReflectionPad2d((1, 1, 2, 0))
>>> m(input)
tensor([[[[7., 6., 7., 8., 7.],
[4., 3., 4., 5., 4.],
[1., 0., 1., 2., 1.],
[4., 3., 4., 5., 4.],
[7., 6., 7., 8., 7.]]]])
"""
padding: Tuple[int, int, int, int]
def __init__(self, padding: _size_4_t) -> None:
super().__init__()
self.padding = _quadruple(padding)
class ReflectionPad3d(_ReflectionPadNd):
r"""Pads the input tensor using the reflection of the input boundary.
For `N`-dimensional padding, use :func:`torch.nn.functional.pad()`.
Args:
padding (int, tuple): the size of the padding. If is `int`, uses the same
padding in all boundaries. If a 6-`tuple`, uses
(:math:`\text{padding\_left}`, :math:`\text{padding\_right}`,
:math:`\text{padding\_top}`, :math:`\text{padding\_bottom}`,
:math:`\text{padding\_front}`, :math:`\text{padding\_back}`)
Shape:
- Input: :math:`(N, C, D_{in}, H_{in}, W_{in})` or :math:`(C, D_{in}, H_{in}, W_{in})`.
- Output: :math:`(N, C, D_{out}, H_{out}, W_{out})` or :math:`(C, D_{out}, H_{out}, W_{out})`,
where
:math:`D_{out} = D_{in} + \text{padding\_front} + \text{padding\_back}`
:math:`H_{out} = H_{in} + \text{padding\_top} + \text{padding\_bottom}`
:math:`W_{out} = W_{in} + \text{padding\_left} + \text{padding\_right}`
Examples::
>>> # xdoctest: +IGNORE_WANT("not sure why xdoctest is choking on this")
>>> m = nn.ReflectionPad3d(1)
>>> input = torch.arange(8, dtype=torch.float).reshape(1, 1, 2, 2, 2)
>>> m(input)
tensor([[[[[7., 6., 7., 6.],
[5., 4., 5., 4.],
[7., 6., 7., 6.],
[5., 4., 5., 4.]],
[[3., 2., 3., 2.],
[1., 0., 1., 0.],
[3., 2., 3., 2.],
[1., 0., 1., 0.]],
[[7., 6., 7., 6.],
[5., 4., 5., 4.],
[7., 6., 7., 6.],
[5., 4., 5., 4.]],
[[3., 2., 3., 2.],
[1., 0., 1., 0.],
[3., 2., 3., 2.],
[1., 0., 1., 0.]]]]])
"""
padding: Tuple[int, int, int, int, int, int]
def __init__(self, padding: _size_6_t) -> None:
super().__init__()
self.padding = _ntuple(6)(padding)
class _ReplicationPadNd(Module):
__constants__ = ['padding']
padding: Sequence[int]
def forward(self, input: Tensor) -> Tensor:
return F.pad(input, self.padding, 'replicate')
def extra_repr(self) -> str:
return '{}'.format(self.padding)
class ReplicationPad1d(_ReplicationPadNd):
r"""Pads the input tensor using replication of the input boundary.
For `N`-dimensional padding, use :func:`torch.nn.functional.pad()`.