mirror of
https://github.com/zebrajr/pytorch.git
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32b3841553
* Improvize documentation 1. Add formula for erf, erfinv 2. Make exp, expm1 similar to log, log1p 3. Symbol change in ge, le, ne, isnan * Fix minor nit in the docstring * More doc improvements 1. Added some formulae 2. Complete scanning till "Other Operations" in Tensor docs * Add more changes 1. Modify all torch.Tensor wherever required * Fix Conv docs 1. Fix minor nits in the references for LAPACK routines * Improve Pooling docs 1. Fix lint error * Improve docs for RNN, Normalization and Padding 1. Fix flake8 error for pooling * Final fixes for torch.nn.* docs. 1. Improve Loss Function documentation 2. Improve Vision Layers documentation * Fix lint error * Improve docstrings in torch.nn.init * Fix lint error * Fix minor error in torch.nn.init.sparse * Fix Activation and Utils Docs 1. Fix Math Errors 2. Add explicit clean to Makefile in docs to prevent running graph generation script while cleaning 3. Fix utils docs * Make PYCMD a Makefile argument, clear up prints in the build_activation_images.py * Fix batch norm doc error
333 lines
10 KiB
Python
333 lines
10 KiB
Python
from .module import Module
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from .utils import _pair, _quadruple, _ntuple
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from .. import functional as F
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# TODO: grad_output size asserts in THNN
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class ConstantPad1d(Module):
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r"""Pads the input tensor boundaries with a constant value.
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Args:
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padding (int, tuple): the size of the padding. If is `int`, uses the same
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padding in both boundaries. If a 2-`tuple`, uses (`paddingLeft`, `paddingRight`)
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Shape:
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- Input: :math:`(N, C, W_{in})`
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- Output: :math:`(N, C, W_{out})` where
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:math:`W_{out} = W_{in} + \textit{paddingLeft} + \textit{paddingRight}`
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Examples::
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>>> m = nn.ConstantPad1d(3, 3.5)
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>>> input = torch.randn(16, 2, 480)
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>>> output = m(input)
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>>> # using different paddings
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>>> m = nn.ConstantPad1d((3, 5), 3.5)
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>>> output = m(input)
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"""
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def __init__(self, padding, value):
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super(ConstantPad1d, self).__init__()
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self.padding = _pair(padding)
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self.value = value
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def forward(self, input):
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return F.pad(input, self.padding, 'constant', self.value)
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def __repr__(self):
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return self.__class__.__name__ + '(' \
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+ str(self.padding) + ')'
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class ConstantPad2d(Module):
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r"""Pads the input tensor boundaries with a constant value.
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For Nd-padding, use :meth:`nn.functional.pad()`.
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Args:
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padding (int, tuple): the size of the padding. If is `int`, uses the same
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padding in all boundaries. If a 4-`tuple`, uses (`paddingLeft`, `paddingRight`,
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`paddingTop`, `paddingBottom`)
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Shape:
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- Input: :math:`(N, C, H_{in}, W_{in})`
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- Output: :math:`(N, C, H_{out}, W_{out})` where
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:math:`H_{out} = H_{in} + \textit{paddingTop} + \textit{paddingBottom}`
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:math:`W_{out} = W_{in} + \textit{paddingLeft} + \textit{paddingRight}`
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Examples::
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>>> m = nn.ConstantPad2d(3, 3.5)
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>>> input = torch.randn(16, 3, 320, 480)
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>>> output = m(input)
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>>> # using different paddings
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>>> m = nn.ConstantPad2d((3, 3, 6, 6), 3.5)
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>>> output = m(input)
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"""
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def __init__(self, padding, value):
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super(ConstantPad2d, self).__init__()
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self.padding = _quadruple(padding)
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self.value = value
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def forward(self, input):
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return F.pad(input, self.padding, 'constant', self.value)
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def __repr__(self):
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return self.__class__.__name__ + '(' \
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+ str(self.padding) + ')'
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class ConstantPad3d(Module):
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r"""Pads the input tensor boundaries with a constant value.
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Args:
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padding (int, tuple): the size of the padding. If is `int`, uses the same
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padding in all boundaries. If a 6-`tuple`, uses
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(`paddingLeft`, `paddingRight`, `paddingTop`, `paddingBottom`, `paddingFront`, `paddingBack`)
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Shape:
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- Input: :math:`(N, C, D_{in}, H_{in}, W_{in})`
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- Output: :math:`(N, C, D_{out}, H_{out}, W_{out})` where
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:math:`D_{out} = D_{in} + \textit{paddingFront} + \textit{paddingBack}`
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:math:`H_{out} = H_{in} + \textit{paddingTop} + \textit{paddingBottom}`
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:math:`W_{out} = W_{in} + \textit{paddingLeft} + \textit{paddingRight}`
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Examples::
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>>> m = nn.ConstantPad3d(3, 3.5)
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>>> input = torch.randn(16, 3, 10, 20, 30)
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>>> output = m(input)
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>>> # using different paddings
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>>> m = nn.ConstantPad3d((3, 3, 6, 6, 0, 1), 3.5)
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>>> output = m(input)
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"""
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def __init__(self, padding, value):
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super(ConstantPad3d, self).__init__()
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self.padding = _ntuple(6)(padding)
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self.value = value
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def forward(self, input):
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return F.pad(input, self.padding, 'constant', self.value)
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def __repr__(self):
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return self.__class__.__name__ + '(' \
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+ str(self.padding) + ')'
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class ReflectionPad1d(Module):
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r"""Pads the input tensor using the reflection of the input boundary.
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Args:
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padding (int, tuple): the size of the padding. If is `int`, uses the same
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padding in all boundaries. If a 2-`tuple`, uses (`paddingLeft`, `paddingRight`)
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Shape:
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- Input: :math:`(N, C, W_{in})`
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- Output: :math:`(N, C, W_{out})` where
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:math:`W_{out} = W_{in} + \textit{paddingLeft} + \textit{paddingRight}`
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Examples::
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>>> m = nn.ReflectionPad1d(3)
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>>> input = torch.randn(16, 3, 480)
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>>> output = m(input)
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>>> # using different paddings
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>>> m = nn.ReflectionPad1d((3, 6))
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>>> output = m(input)
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"""
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def __init__(self, padding):
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super(ReflectionPad1d, self).__init__()
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self.padding = _pair(padding)
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def forward(self, input):
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return F.pad(input, self.padding, 'reflect')
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def __repr__(self):
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return self.__class__.__name__ + '(' \
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+ str(self.padding) + ')'
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class ReflectionPad2d(Module):
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r"""Pads the input tensor using the reflection of the input boundary.
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Args:
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padding (int, tuple): the size of the padding. If is `int`, uses the same
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padding in all boundaries. If a 4-`tuple`, uses (`paddingLeft`, `paddingRight`,
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`paddingTop`, `paddingBottom`)
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Shape:
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- Input: :math:`(N, C, H_{in}, W_{in})`
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- Output: :math:`(N, C, H_{out}, W_{out})` where
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:math:`H_{out} = H_{in} + \textit{paddingTop} + \textit{paddingBottom}`
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:math:`W_{out} = W_{in} + \textit{paddingLeft} + \textit{paddingRight}`
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Examples::
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>>> m = nn.ReflectionPad2d(3)
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>>> input = torch.randn(16, 3, 320, 480)
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>>> output = m(input)
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>>> # using different paddings
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>>> m = nn.ReflectionPad2d((3, 3, 6, 6))
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>>> output = m(input)
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"""
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def __init__(self, padding):
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super(ReflectionPad2d, self).__init__()
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self.padding = _quadruple(padding)
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def forward(self, input):
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return F.pad(input, self.padding, 'reflect')
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def __repr__(self):
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return self.__class__.__name__ + '(' \
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+ str(self.padding) + ')'
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class ReplicationPad1d(Module):
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r"""Pads the input tensor using replication of the input boundary.
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Args:
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padding (int, tuple): the size of the padding. If is `int`, uses the same
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padding in all boundaries. If a 2-`tuple`, uses (`paddingLeft`, `paddingRight`)
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Shape:
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- Input: :math:`(N, C, W_{in})`
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- Output: :math:`(N, C, W_{out})` where
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:math:`W_{out} = W_{in} + \textit{paddingLeft} + \textit{paddingRight}`
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Examples::
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>>> m = nn.ReplicationPad1d(3)
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>>> input = torch.randn(16, 3, 480)
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>>> output = m(input)
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>>> # using different paddings
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>>> m = nn.ReplicationPad1d((3, 6))
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>>> output = m(input)
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"""
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def __init__(self, padding):
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super(ReplicationPad1d, self).__init__()
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self.padding = _pair(padding)
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def forward(self, input):
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return F.pad(input, self.padding, 'replicate')
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def __repr__(self):
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return self.__class__.__name__ + '(' \
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+ str(self.padding) + ')'
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class ReplicationPad2d(Module):
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r"""Pads the input tensor using replication of the input boundary.
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Args:
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padding (int, tuple): the size of the padding. If is `int`, uses the same
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padding in all boundaries. If a 4-`tuple`, uses (`paddingLeft`, `paddingRight`,
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`paddingTop`, `paddingBottom`)
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Shape:
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- Input: :math:`(N, C, H_{in}, W_{in})`
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- Output: :math:`(N, C, H_{out}, W_{out})` where
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:math:`H_{out} = H_{in} + \textit{paddingTop} + \textit{paddingBottom}`
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:math:`W_{out} = W_{in} + \textit{paddingLeft} + \textit{paddingRight}`
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Examples::
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>>> m = nn.ReplicationPad2d(3)
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>>> input = torch.randn(16, 3, 320, 480)
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>>> output = m(input)
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>>> # using different paddings
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>>> m = nn.ReplicationPad2d((3, 3, 6, 6))
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>>> output = m(input)
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"""
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def __init__(self, padding):
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super(ReplicationPad2d, self).__init__()
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self.padding = _quadruple(padding)
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def forward(self, input):
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return F.pad(input, self.padding, 'replicate')
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def __repr__(self):
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return self.__class__.__name__ + '(' \
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+ str(self.padding) + ')'
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class ReplicationPad3d(Module):
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r"""Pads the input tensor using replication of the input boundary.
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Args:
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padding (int, tuple): the size of the padding. If is `int`, uses the same
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padding in all boundaries. If a 6-`tuple`, uses (`paddingLeft`, `paddingRight`,
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`paddingTop`, `paddingBottom`, `paddingFront`, `paddingBack`)
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Shape:
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- Input: :math:`(N, C, D_{in}, H_{in}, W_{in})`
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- Output: :math:`(N, C, D_{out}, H_{out}, W_{out})` where
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:math:`D_{out} = D_{in} + \textit{paddingFront} + \textit{paddingBack}`
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:math:`H_{out} = H_{in} + \textit{paddingTop} + \textit{paddingBottom}`
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:math:`W_{out} = W_{in} + \textit{paddingLeft} + \textit{paddingRight}`
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Examples::
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>>> m = nn.ReplicationPad3d(3)
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>>> input = torch.randn(16, 3, 8, 320, 480)
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>>> output = m(input)
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>>> # using different paddings
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>>> m = nn.ReplicationPad3d((3, 3, 6, 6, 1, 1))
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>>> output = m(input)
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"""
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def __init__(self, padding):
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super(ReplicationPad3d, self).__init__()
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self.padding = _ntuple(6)(padding)
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def forward(self, input):
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return F.pad(input, self.padding, 'replicate')
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def __repr__(self):
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return self.__class__.__name__ + '(' \
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+ str(self.padding) + ')'
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class ZeroPad2d(ConstantPad2d):
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r"""Pads the input tensor boundaries with zero.
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Args:
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padding (int, tuple): the size of the padding. If is `int`, uses the same
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padding in all boundaries. If a 4-`tuple`, uses (`paddingLeft`, `paddingRight`,
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`paddingTop`, `paddingBottom`)
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Shape:
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- Input: :math:`(N, C, H_{in}, W_{in})`
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- Output: :math:`(N, C, H_{out}, W_{out})` where
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:math:`H_{out} = H_{in} + \textit{paddingTop} + \textit{paddingBottom}`
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:math:`W_{out} = W_{in} + \textit{paddingLeft} + \textit{paddingRight}`
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Examples::
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>>> m = nn.ZeroPad2d(3)
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>>> input = torch.randn(16, 3, 320, 480)
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>>> output = m(input)
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>>> # using different paddings
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>>> m = nn.ZeroPad2d((3, 3, 6, 6))
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>>> output = m(input)
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"""
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def __init__(self, padding):
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super(ZeroPad2d, self).__init__(padding, 0)
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