mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-07 12:21:27 +01:00
acb73c729b
* - Remove spaces in `__repr__` of layers - Replace `size` by `kernel_size` in `__repr__` of a pooling layer * Fix flake8 errors
329 lines
10 KiB
Python
329 lines
10 KiB
Python
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) + ')'
|
|
|
|
|
|
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) + ')'
|
|
|
|
|
|
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) + ')'
|
|
|
|
|
|
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) + ')'
|
|
|
|
|
|
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) + ')'
|
|
|
|
|
|
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)
|