Add nn.padding to docs fixes pytorch#1127 (pytorch#1808)

* exposed nn.padding modules

* using functional

Author: nrbrd

docs/source/nn.rst

@@ -175,6 +175,40 @@ Pooling Layers
     :members:
 
 
+Padding Layers
+--------------
+
+:hidden:`ReflectionPad2d`
+~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: ReflectionPad2d
+    :members:
+
+:hidden:`ReplicationPad2d`
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: ReplicationPad2d
+    :members:
+
+:hidden:`ReplicationPad3d`
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: ReplicationPad3d
+    :members:
+
+:hidden:`ZeroPad2d`
+~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: ZeroPad2d
+    :members:
+
+:hidden:`ConstantPad2d`
+~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: ConstantPad2d
+    :members:
+
+
 Non-linear Activations
 ----------------------------------
 

torch/nn/modules/padding.py

@@ -1,71 +1,183 @@
 from .module import Module
 from .utils import _quadruple, _ntuple
-from .._functions.padding import ConstantPad2d as F_ConstantPad2d
+from .. import functional as F
 
 # TODO: grad_output size asserts in THNN
 
 
 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 self._backend.ReflectionPad2d(*self.padding)(input)
+        return F.pad(input, self.padding, 'reflect')
 
     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 self._backend.ReplicationPad2d(*self.padding)(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 self._backend.ReplicationPad3d(*self.padding)(input)
+        return F.pad(input, self.padding, 'replicate')
 
     def __repr__(self):
         return self.__class__.__name__ + ' ' + str(self.padding)
 
 
 class ZeroPad2d(Module):
+    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__()
         self.padding = _quadruple(padding)
 
     def forward(self, input):
-        return F_ConstantPad2d(pad=self.padding, value=0)(input)
+        return F.pad(input, self.padding, 'constant', 0)
 
     def __repr__(self):
         return self.__class__.__name__ + ' ' + str(self.padding)
 
 
 class ConstantPad2d(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 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_ConstantPad2d(pad=self.padding, value=self.value)(input)
+        return F.pad(input, self.padding, 'constant', self.value)
 
     def __repr__(self):
         return self.__class__.__name__ + ' ' + str(self.padding)