from .module import Module
from .utils import _pair, _quadruple, _ntuple
from .. import functional as F
# TODO: grad_output size asserts in THNN
class ConstantPad1d(Module):
r"""Pads the input tensor boundaries with a constant value.
Args:
padding (int, tuple): the size of the padding. If is int, uses the same
padding in both boundaries. If a 2-tuple, uses (paddingLeft, paddingRight)
Shape:
- Input: :math:`(N, C, W_{in})`
- Output: :math:`(N, C, W_{out})` where
:math:`W_{out} = W_{in} + paddingLeft + paddingRight`
Examples::
>>> m = nn.ConstantPad1d(3, 3.5)
>>> input = autograd.Variable(torch.randn(16, 2, 480))
>>> output = m(input)
>>> # using different paddings
>>> m = nn.ConstantPad1d((3, 5), 3.5)
>>> output = m(input)
"""
def __init__(self, padding, value):
super(ConstantPad1d, self).__init__()
self.padding = _pair(padding)
self.value = value
def forward(self, input):
return F.pad(input, self.padding, 'constant', self.value)
def __repr__(self):
return self.__class__.__name__ + '(' \
+ str(self.padding) + ')'
[docs]class ConstantPad2d(Module):
r"""Pads the input tensor boundaries with a constant value.
For Nd-padding, use 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 (paddingLeft, paddingRight, paddingTop, paddingBottom)
Shape:
- Input: :math:`(N, C, H_{in}, W_{in})`
- Output: :math:`(N, C, H_{out}, W_{out})` where
:math:`H_{out} = H_{in} + paddingTop + paddingBottom`
:math:`W_{out} = W_{in} + paddingLeft + paddingRight`
Examples::
>>> m = nn.ConstantPad2d(3, 3.5)
>>> input = autograd.Variable(torch.randn(16, 3, 320, 480))
>>> output = m(input)
>>> # using different paddings
>>> m = nn.ConstantPad2d((3, 3, 6, 6), 3.5)
>>> output = m(input)
"""
def __init__(self, padding, value):
super(ConstantPad2d, self).__init__()
self.padding = _quadruple(padding)
self.value = value
def forward(self, input):
return F.pad(input, self.padding, 'constant', self.value)
def __repr__(self):
return self.__class__.__name__ + '(' \
+ str(self.padding) + ')'
class ConstantPad3d(Module):
r"""Pads the input tensor boundaries with a constant value.
Args:
padding (int, tuple): the size of the padding. If is int, uses the same
padding in all boundaries. If a 6-tuple, uses
(paddingLeft, paddingRight, paddingTop, paddingBottom, paddingFront, paddingBack)
Shape:
- Input: :math:`(N, C, D_{in}, H_{in}, W_{in})`
- Output: :math:`(N, C, D_{out}, H_{out}, W_{out})` where
:math:`D_{out} = D_{in} + paddingFront + paddingBack`
:math:`H_{out} = H_{in} + paddingTop + paddingBottom`
:math:`W_{out} = W_{in} + paddingLeft + paddingRight`
Examples::
>>> m = nn.ConstantPad3d(3, 3.5)
>>> input = autograd.Variable(torch.randn(16, 3, 10, 20, 30))
>>> output = m(input)
>>> # using different paddings
>>> m = nn.ConstantPad3d((3, 3, 6, 6, 0, 1), 3.5)
>>> output = m(input)
"""
def __init__(self, padding, value):
super(ConstantPad3d, self).__init__()
self.padding = _ntuple(6)(padding)
self.value = value
def forward(self, input):
return F.pad(input, self.padding, 'constant', self.value)
def __repr__(self):
return self.__class__.__name__ + '(' \
+ str(self.padding) + ')'
class ReflectionPad1d(Module):
r"""Pads the input tensor using the reflection of the input boundary.
Args:
padding (int, tuple): the size of the padding. If is int, uses the same
padding in all boundaries. If a 2-tuple, uses (paddingLeft, paddingRight)
Shape:
- Input: :math:`(N, C, W_{in})`
- Output: :math:`(N, C, W_{out})` where
:math:`W_{out} = W_{in} + paddingLeft + paddingRight`
Examples::
>>> m = nn.ReflectionPad1d(3)
>>> input = autograd.Variable(torch.randn(16, 3, 480))
>>> output = m(input)
>>> # using different paddings
>>> m = nn.ReflectionPad1d((3, 6))
>>> output = m(input)
"""
def __init__(self, padding):
super(ReflectionPad1d, self).__init__()
self.padding = _pair(padding)
def forward(self, input):
return F.pad(input, self.padding, 'reflect')
def __repr__(self):
return self.__class__.__name__ + '(' \
+ str(self.padding) + ')'
[docs]class ReflectionPad2d(Module):
r"""Pads the input tensor using the reflection of the input boundary.
Args:
padding (int, tuple): the size of the padding. If is int, uses the same
padding in all boundaries. If a 4-tuple, uses (paddingLeft, paddingRight, paddingTop, paddingBottom)
Shape:
- Input: :math:`(N, C, H_{in}, W_{in})`
- Output: :math:`(N, C, H_{out}, W_{out})` where
:math:`H_{out} = H_{in} + paddingTop + paddingBottom`
:math:`W_{out} = W_{in} + paddingLeft + paddingRight`
Examples::
>>> m = nn.ReflectionPad2d(3)
>>> input = autograd.Variable(torch.randn(16, 3, 320, 480))
>>> output = m(input)
>>> # using different paddings
>>> m = nn.ReflectionPad2d((3, 3, 6, 6))
>>> output = m(input)
"""
def __init__(self, padding):
super(ReflectionPad2d, self).__init__()
self.padding = _quadruple(padding)
def forward(self, input):
return F.pad(input, self.padding, 'reflect')
def __repr__(self):
return self.__class__.__name__ + '(' \
+ str(self.padding) + ')'
class ReplicationPad1d(Module):
r"""Pads the input tensor using replication of the input boundary.
Args:
padding (int, tuple): the size of the padding. If is int, uses the same
padding in all boundaries. If a 2-tuple, uses (paddingLeft, paddingRight)
Shape:
- Input: :math:`(N, C, W_{in})`
- Output: :math:`(N, C, W_{out})` where
:math:`W_{out} = W_{in} + paddingLeft + paddingRight`
Examples::
>>> m = nn.ReplicationPad1d(3)
>>> input = autograd.Variable(torch.randn(16, 3, 480))
>>> output = m(input)
>>> # using different paddings
>>> m = nn.ReplicationPad1d((3, 6))
>>> output = m(input)
"""
def __init__(self, padding):
super(ReplicationPad1d, self).__init__()
self.padding = _pair(padding)
def forward(self, input):
return F.pad(input, self.padding, 'replicate')
def __repr__(self):
return self.__class__.__name__ + '(' \
+ str(self.padding) + ')'
[docs]class ReplicationPad2d(Module):
r"""Pads the input tensor using replication of the input boundary.
Args:
padding (int, tuple): the size of the padding. If is int, uses the same
padding in all boundaries. If a 4-tuple, uses (paddingLeft, paddingRight, paddingTop, paddingBottom)
Shape:
- Input: :math:`(N, C, H_{in}, W_{in})`
- Output: :math:`(N, C, H_{out}, W_{out})` where
:math:`H_{out} = H_{in} + paddingTop + paddingBottom`
:math:`W_{out} = W_{in} + paddingLeft + paddingRight`
Examples::
>>> m = nn.ReplicationPad2d(3)
>>> input = autograd.Variable(torch.randn(16, 3, 320, 480))
>>> output = m(input)
>>> # using different paddings
>>> m = nn.ReplicationPad2d((3, 3, 6, 6))
>>> output = m(input)
"""
def __init__(self, padding):
super(ReplicationPad2d, self).__init__()
self.padding = _quadruple(padding)
def forward(self, input):
return F.pad(input, self.padding, 'replicate')
def __repr__(self):
return self.__class__.__name__ + '(' \
+ str(self.padding) + ')'
[docs]class ReplicationPad3d(Module):
r"""Pads the input tensor using replication of the input boundary.
Args:
padding (int, tuple): the size of the padding. If is int, uses the same
padding in all boundaries. If a 6-tuple, uses (paddingLeft, paddingRight,
paddingTop, paddingBottom, paddingFront, paddingBack)
Shape:
- Input: :math:`(N, C, D_{in}, H_{in}, W_{in})`
- Output: :math:`(N, C, D_{out}, H_{out}, W_{out})` where
:math:`D_{out} = D_{in} + paddingFront + paddingBack`
:math:`H_{out} = H_{in} + paddingTop + paddingBottom`
:math:`W_{out} = W_{in} + paddingLeft + paddingRight`
Examples::
>>> m = nn.ReplicationPad3d(3)
>>> input = autograd.Variable(torch.randn(16, 3, 8, 320, 480))
>>> output = m(input)
>>> # using different paddings
>>> m = nn.ReplicationPad3d((3, 3, 6, 6, 1, 1))
>>> output = m(input)
"""
def __init__(self, padding):
super(ReplicationPad3d, self).__init__()
self.padding = _ntuple(6)(padding)
def forward(self, input):
return F.pad(input, self.padding, 'replicate')
def __repr__(self):
return self.__class__.__name__ + '(' \
+ str(self.padding) + ')'
[docs]class ZeroPad2d(ConstantPad2d):
r"""Pads the input tensor boundaries with zero.
Args:
padding (int, tuple): the size of the padding. If is int, uses the same
padding in all boundaries. If a 4-tuple, uses (paddingLeft, paddingRight, paddingTop, paddingBottom)
Shape:
- Input: :math:`(N, C, H_{in}, W_{in})`
- Output: :math:`(N, C, H_{out}, W_{out})` where
:math:`H_{out} = H_{in} + paddingTop + paddingBottom`
:math:`W_{out} = W_{in} + paddingLeft + paddingRight`
Examples::
>>> m = nn.ZeroPad2d(3)
>>> input = autograd.Variable(torch.randn(16, 3, 320, 480))
>>> output = m(input)
>>> # using different paddings
>>> m = nn.ZeroPad2d((3, 3, 6, 6))
>>> output = m(input)
"""
def __init__(self, padding):
super(ZeroPad2d, self).__init__(padding, 0)