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87d2536971
pytorch/torch/nn/modules/padding.py
Mikayla Gawarecki 87d2536971 Add nn.CircularPad{*}d for consistency + fix no_batch_dim support (#106148) 
Fixes #105749 https://github.com/pytorch/pytorch/issues/95320

(tldr is that input should always be `[N, C, H, (W, D])` where only H, W and D dimensions get circular padding, so the 2D case where user wants both dimensions to be padded --> they should `.unsqueeze(0)` (as is the case for `Reflection/ReplicationPad`) but we didn't document this for circular padding. [This seems to be the old docstring](277b05014a/torch/nn/functional.py (L4689)) that was somehow lost.

Fixes no_batch_dim support https://github.com/pytorch/pytorch/issues/104860

- Adds missing documentation for circular padding
- Adds missing CircularPad modules
- Migrates legacy test_nn tests from circular padding to ModuleInfo
- Adds no_batch_dim support + sample inputs that test this

Pull Request resolved: https://github.com/pytorch/pytorch/pull/106148
Approved by: https://github.com/albanD
ghstack dependencies: #106325, #106147
2023-08-01 12:49:58 +00:00

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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__ = ['CircularPad1d', 'CircularPad2d', 'CircularPad3d', 'ConstantPad1d', 'ConstantPad2d',
'ConstantPad3d', 'ReflectionPad1d', 'ReflectionPad2d', 'ReflectionPad3d',
'ReplicationPad1d', 'ReplicationPad2d', 'ReplicationPad3d', 'ZeroPad1d', 'ZeroPad2d', 'ZeroPad3d']
class _CircularPadNd(Module):
__constants__ = ['padding']
padding: Sequence[int]
def _check_input_dim(self, input):
raise NotImplementedError
def forward(self, input: Tensor) -> Tensor:
self._check_input_dim(input)
return F.pad(input, self.padding, 'circular')
def extra_repr(self) -> str:
return f'{self.padding}'
class CircularPad1d(_CircularPadNd):
r"""Pads the input tensor using circular padding of the input boundary.
Tensor values at the beginning of the dimension are used to pad the end,
and values at the end are used to pad the beginning. If negative padding is
applied then the ends of the tensor get removed.
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::
>>> # xdoctest: +IGNORE_WANT("not sure why xdoctest is choking on this")
>>> m = nn.CircularPad1d(2)
>>> 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., 3., 0., 1., 2., 3., 0., 1.],
[6., 7., 4., 5., 6., 7., 4., 5.]]])
>>> # using different paddings for different sides
>>> m = nn.CircularPad1d((3, 1))
>>> m(input)
tensor([[[1., 2., 3., 0., 1., 2., 3., 0.],
[5., 6., 7., 4., 5., 6., 7., 4.]]])
"""
padding: Tuple[int, int]
def __init__(self, padding: _size_2_t) -> None:
super().__init__()
self.padding = _pair(padding)
def _check_input_dim(self, input):
if input.dim() != 2 and input.dim() != 3:
raise ValueError(
f"expected 2D or 3D input (got {input.dim()}D input)"
)
class CircularPad2d(_CircularPadNd):
r"""Pads the input tensor using circular padding of the input boundary.
Tensor values at the beginning of the dimension are used to pad the end,
and values at the end are used to pad the beginning. If negative padding is
applied then the ends of the tensor get removed.
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::
>>> m = nn.CircularPad2d(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([[[[4., 5., 3., 4., 5., 3., 4.],
[7., 8., 6., 7., 8., 6., 7.],
[1., 2., 0., 1., 2., 0., 1.],
[4., 5., 3., 4., 5., 3., 4.],
[7., 8., 6., 7., 8., 6., 7.],
[1., 2., 0., 1., 2., 0., 1.],
[4., 5., 3., 4., 5., 3., 4.]]]])
>>> # using different paddings for different sides
>>> m = nn.CircularPad2d((1, 1, 2, 0))
>>> m(input)
tensor([[[[5., 3., 4., 5., 3.],
[8., 6., 7., 8., 6.],
[2., 0., 1., 2., 0.],
[5., 3., 4., 5., 3.],
[8., 6., 7., 8., 6.]]]])
"""
padding: Tuple[int, int, int, int]
def __init__(self, padding: _size_4_t) -> None:
super().__init__()
self.padding = _quadruple(padding)
def _check_input_dim(self, input):
if input.dim() != 3 and input.dim() != 4:
raise ValueError(
f"expected 3D or 4D input (got {input.dim()}D input)"
)
class CircularPad3d(_CircularPadNd):
r"""Pads the input tensor using circular padding of the input boundary.
Tensor values at the beginning of the dimension are used to pad the end,
and values at the end are used to pad the beginning. If negative padding is
applied then the ends of the tensor get removed.
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("non-deterministic")
>>> m = nn.CircularPad3d(3)
>>> input = torch.randn(16, 3, 8, 320, 480)
>>> output = m(input)
>>> # using different paddings for different sides
>>> m = nn.CircularPad3d((3, 3, 6, 6, 1, 1))
>>> output = m(input)
"""
padding: Tuple[int, int, int, int, int, int]
def __init__(self, padding: _size_6_t) -> None:
super().__init__()
self.padding = _ntuple(6)(padding)
def _check_input_dim(self, input):
if input.dim() != 4 and input.dim() != 5:
raise ValueError(
f"expected 4D or 5D input (got {input.dim()}D input)"
)
class _ConstantPadNd(Module):
__constants__ = ['padding', 'value']
value: float
padding: Sequence[int]
def __init__(self, value: float) -> None:
super().__init__()
self.value = value
def _check_input_dim(self, input):
raise NotImplementedError
def forward(self, input: Tensor) -> Tensor:
self._check_input_dim(input)
return F.pad(input, self.padding, 'constant', self.value)
def extra_repr(self) -> str:
return f'padding={self.padding}, value={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)
def _check_input_dim(self, input):
if input.dim() != 2 and input.dim() != 3:
raise ValueError(
f"expected 2D or 3D input (got {input.dim()}D input)"
)
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)
def _check_input_dim(self, input):
if input.dim() != 3 and input.dim() != 4:
raise ValueError(
f"expected 3D or 4D input (got {input.dim()}D input)"
)
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)
def _check_input_dim(self, input):
if input.dim() != 4 and input.dim() != 5:
raise ValueError(
f"expected 4D or 5D input (got {input.dim()}D input)"
)
class _ReflectionPadNd(Module):
__constants__ = ['padding']
padding: Sequence[int]
def _check_input_dim(self, input):
raise NotImplementedError
def forward(self, input: Tensor) -> Tensor:
self._check_input_dim(input)
return F.pad(input, self.padding, 'reflect')
def extra_repr(self) -> str:
return f'{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)
def _check_input_dim(self, input):
if input.dim() != 2 and input.dim() != 3:
raise ValueError(
f"expected 2D or 3D input (got {input.dim()}D input)"
)
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)
def _check_input_dim(self, input):
if input.dim() != 3 and input.dim() != 4:
raise ValueError(
f"expected 3D or 4D input (got {input.dim()}D input)"
)
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)
def _check_input_dim(self, input):
if input.dim() != 4 and input.dim() != 5:
raise ValueError(
f"expected 4D or 5D input (got {input.dim()}D input)"
)
class _ReplicationPadNd(Module):
__constants__ = ['padding']
padding: Sequence[int]
def _check_input_dim(self, input):
raise NotImplementedError
def forward(self, input: Tensor) -> Tensor:
self._check_input_dim(input)
return F.pad(input, self.padding, 'replicate')
def extra_repr(self) -> str:
return f'{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()`.
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::
>>> # xdoctest: +IGNORE_WANT("not sure why xdoctest is choking on this")
>>> m = nn.ReplicationPad1d(2)
>>> input = torch.arange(8, dtype=torch.float).reshape(1, 2, 4)
>>> input
tensor([[[0., 1., 2., 3.],
[4., 5., 6., 7.]]])
>>> m(input)
tensor([[[0., 0., 0., 1., 2., 3., 3., 3.],
[4., 4., 4., 5., 6., 7., 7., 7.]]])
>>> # using different paddings for different sides
>>> m = nn.ReplicationPad1d((3, 1))
>>> m(input)
tensor([[[0., 0., 0., 0., 1., 2., 3., 3.],
[4., 4., 4., 4., 5., 6., 7., 7.]]])
"""
padding: Tuple[int, int]
def __init__(self, padding: _size_2_t) -> None:
super().__init__()
self.padding = _pair(padding)
def _check_input_dim(self, input):
if input.dim() != 2 and input.dim() != 3:
raise ValueError(
f"expected 2D or 3D input (got {input.dim()}D input)"
)
class ReplicationPad2d(_ReplicationPadNd):
r"""Pads the input tensor using replication 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::
>>> m = nn.ReplicationPad2d(2)
>>> # xdoctest: +IGNORE_WANT("non-deterministic")
>>> 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([[[[0., 0., 0., 1., 2., 2., 2.],
[0., 0., 0., 1., 2., 2., 2.],
[0., 0., 0., 1., 2., 2., 2.],
[3., 3., 3., 4., 5., 5., 5.],
[6., 6., 6., 7., 8., 8., 8.],
[6., 6., 6., 7., 8., 8., 8.],
[6., 6., 6., 7., 8., 8., 8.]]]])
>>> # using different paddings for different sides
>>> m = nn.ReplicationPad2d((1, 1, 2, 0))
>>> m(input)
tensor([[[[0., 0., 1., 2., 2.],
[0., 0., 1., 2., 2.],
[0., 0., 1., 2., 2.],
[3., 3., 4., 5., 5.],
[6., 6., 7., 8., 8.]]]])
"""
padding: Tuple[int, int, int, int]
def __init__(self, padding: _size_4_t) -> None:
super().__init__()
self.padding = _quadruple(padding)
def _check_input_dim(self, input):
if input.dim() != 3 and input.dim() != 4:
raise ValueError(
f"expected 3D or 4D input (got {input.dim()}D input)"
)
class ReplicationPad3d(_ReplicationPadNd):
r"""Pads the input tensor using replication 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("non-deterministic")
>>> m = nn.ReplicationPad3d(3)
>>> input = torch.randn(16, 3, 8, 320, 480)
>>> output = m(input)
>>> # using different paddings for different sides
>>> m = nn.ReplicationPad3d((3, 3, 6, 6, 1, 1))
>>> output = m(input)
"""
padding: Tuple[int, int, int, int, int, int]
def __init__(self, padding: _size_6_t) -> None:
super().__init__()
self.padding = _ntuple(6)(padding)
def _check_input_dim(self, input):
if input.dim() != 4 and input.dim() != 5:
raise ValueError(
f"expected 4D or 5D input (got {input.dim()}D input)"
)
class ZeroPad1d(ConstantPad1d):
r"""Pads the input tensor boundaries with zero.
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.ZeroPad1d(2)
>>> 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([[[ 0.0000, 0.0000, -1.0491, -0.7152, -0.0749, 0.8530, 0.0000,
0.0000],
[ 0.0000, 0.0000, -1.3287, 1.8966, 0.1466, -0.2771, 0.0000,
0.0000]]])
>>> m = nn.ZeroPad1d(2)
>>> input = torch.randn(1, 2, 3)
>>> input
tensor([[[ 1.6616, 1.4523, -1.1255],
[-3.6372, 0.1182, -1.8652]]])
>>> m(input)
tensor([[[ 0.0000, 0.0000, 1.6616, 1.4523, -1.1255, 0.0000, 0.0000],
[ 0.0000, 0.0000, -3.6372, 0.1182, -1.8652, 0.0000, 0.0000]]])
>>> # using different paddings for different sides
>>> m = nn.ZeroPad1d((3, 1))
>>> m(input)
tensor([[[ 0.0000, 0.0000, 0.0000, 1.6616, 1.4523, -1.1255, 0.0000],
[ 0.0000, 0.0000, 0.0000, -3.6372, 0.1182, -1.8652, 0.0000]]])
"""
padding: Tuple[int, int]
def __init__(self, padding: _size_2_t) -> None:
super().__init__(padding, 0.)
def extra_repr(self) -> str:
return f'{self.padding}'
def _check_input_dim(self, input):
if input.dim() != 2 and input.dim() != 3:
raise ValueError(
f"expected 2D or 3D input (got {input.dim()}D input)"
)
class ZeroPad2d(ConstantPad2d):
r"""Pads the input tensor boundaries with zero.
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.ZeroPad2d(2)
>>> input = torch.randn(1, 1, 3, 3)
>>> input
tensor([[[[-0.1678, -0.4418, 1.9466],
[ 0.9604, -0.4219, -0.5241],
[-0.9162, -0.5436, -0.6446]]]])
>>> m(input)
tensor([[[[ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
[ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
[ 0.0000, 0.0000, -0.1678, -0.4418, 1.9466, 0.0000, 0.0000],
[ 0.0000, 0.0000, 0.9604, -0.4219, -0.5241, 0.0000, 0.0000],
[ 0.0000, 0.0000, -0.9162, -0.5436, -0.6446, 0.0000, 0.0000],
[ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
[ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000]]]])
>>> # using different paddings for different sides
>>> m = nn.ZeroPad2d((1, 1, 2, 0))
>>> m(input)
tensor([[[[ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
[ 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
[ 0.0000, -0.1678, -0.4418, 1.9466, 0.0000],
[ 0.0000, 0.9604, -0.4219, -0.5241, 0.0000],
[ 0.0000, -0.9162, -0.5436, -0.6446, 0.0000]]]])
"""
padding: Tuple[int, int, int, int]
def __init__(self, padding: _size_4_t) -> None:
super().__init__(padding, 0.)
def extra_repr(self) -> str:
return f'{self.padding}'
def _check_input_dim(self, input):
if input.dim() != 3 and input.dim() != 4:
raise ValueError(
f"expected 3D or 4D input (got {input.dim()}D input)"
)
class ZeroPad3d(ConstantPad3d):
r"""Pads the input tensor boundaries with zero.
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.ZeroPad3d(3)
>>> input = torch.randn(16, 3, 10, 20, 30)
>>> output = m(input)
>>> # using different paddings for different sides
>>> m = nn.ZeroPad3d((3, 3, 6, 6, 0, 1))
>>> output = m(input)
"""
padding: Tuple[int, int, int, int, int, int]
def __init__(self, padding: _size_6_t) -> None:
super().__init__(padding, 0.)
def extra_repr(self) -> str:
return f'{self.padding}'
def _check_input_dim(self, input):
if input.dim() != 4 and input.dim() != 5:
raise ValueError(
f"expected 4D or 5D input (got {input.dim()}D input)"
)
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