PyTorch之图像和Tensor填充的实例

import vision.torchvision.transforms as transforms img_to_pad = transforms.Compose([    transforms.Pad(padding=2, padding_mode='symmetric'),    transforms.ToTensor(),   ])

import torch.nn.functional as F feature = feature.unsqueeze(0).unsqueeze(0)avg_feature = F.pad(feature, pad = [1, 1, 1, 1], mode='replicate')

@torch._jit_internal.weak_scriptdef 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") return ret