@torch._jit_internal.weak_script
def pad(input, pad, mode='constant', value=0):
# type: (Tensor, List[int], str, float) -> Tensor
r"""Pads tensor.
Pading size:
The number of dimensions to pad is :math:`\left\lfloor\frac{\text{len(pad)}}{2}\right\rfloor`
and the dimensions that get padded begins with the last dimension and moves forward.
For example, to pad the last dimension of the input tensor, then `pad` has form
`(padLeft, padRight)`; to pad the last 2 dimensions of the input tensor, then use
`(padLeft, padRight, padTop, padBottom)`; to pad the last 3 dimensions, use
`(padLeft, padRight, padTop, padBottom, padFront, padBack)`.
Padding mode:
See :class:`torch.nn.ConstantPad2d`, :class:`torch.nn.ReflectionPad2d`, and
:class:`torch.nn.ReplicationPad2d` for concrete examples on how each of the
padding modes works. Constant padding is implemented for arbitrary dimensions.
Replicate padding is implemented for padding the last 3 dimensions of 5D input
tensor, or the last 2 dimensions of 4D input tensor, or the last dimension of
3D input tensor. Reflect padding is only implemented for padding the last 2
dimensions of 4D input tensor, or the last dimension of 3D input tensor.
.. include:: cuda_deterministic_backward.rst
Args:
input (Tensor): `Nd` tensor
pad (tuple): m-elem tuple, where :math:`\frac{m}{2} \leq` input dimensions and :math:`m` is even.
mode: 'constant', 'reflect' or 'replicate'. Default: 'constant'
value: fill value for 'constant' padding. Default: 0
Examples::
>>> t4d = torch.empty(3, 3, 4, 2)
>>> p1d = (1, 1) # pad last dim by 1 on each side
>>> out = F.pad(t4d, p1d, "constant", 0) # effectively zero padding
>>> print(out.data.size())
torch.Size([3, 3, 4, 4])
>>> p2d = (1, 1, 2, 2) # pad last dim by (1, 1) and 2nd to last by (2, 2)
>>> out = F.pad(t4d, p2d, "constant", 0)
>>> print(out.data.size())
torch.Size([3, 3, 8, 4])
>>> t4d = torch.empty(3, 3, 4, 2)
>>> p3d = (0, 1, 2, 1, 3, 3) # pad by (0, 1), (2, 1), and (3, 3)
>>> out = F.pad(t4d, p3d, "constant", 0)
>>> print(out.data.size())
torch.Size([3, 9, 7, 3])
"""
assert len(pad) % 2 == 0, 'Padding length must be divisible by 2'
assert len(pad) // 2 <= input.dim(), 'Padding length too large'
if mode == 'constant':
ret = _VF.constant_pad_nd(input, pad, value)
else:
assert value == 0, 'Padding mode "{}"" doesn\'t take in value argument'.format(mode)
if input.dim() == 3:
assert len(pad) == 2, '3D tensors expect 2 values for padding'
if mode == 'reflect':
ret = torch._C._nn.reflection_pad1d(input, pad)
elif mode == 'replicate':
ret = torch._C._nn.replication_pad1d(input, pad)
else:
ret = input # TODO: remove this when jit raise supports control flow
raise NotImplementedError
elif input.dim() == 4:
assert len(pad) == 4, '4D tensors expect 4 values for padding'
if mode == 'reflect':
ret = torch._C._nn.reflection_pad2d(input, pad)
elif mode == 'replicate':
ret = torch._C._nn.replication_pad2d(input, pad)
else:
ret = input # TODO: remove this when jit raise supports control flow
raise NotImplementedError
elif input.dim() == 5:
assert len(pad) == 6, '5D tensors expect 6 values for padding'
if mode == 'reflect':
ret = input # TODO: remove this when jit raise supports control flow
raise NotImplementedError
elif mode == 'replicate':
ret = torch._C._nn.replication_pad3d(input, pad)
else:
ret = input # TODO: remove this when jit raise supports control flow
raise NotImplementedError
else:
ret = input # TODO: remove this when jit raise supports control flow
raise NotImplementedError("Only 3D, 4D, 5D padding with non-constant padding are supported for now")
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