PyTorch之图像和Tensor填充的实例

在PyTorch中可以对图像和Tensor进行填充，如常量值填充，镜像填充和复制填充等。在图像预

在PyTorch中可以对图像和Tensor进行填充，如常量值填充，镜像填充和复制填充等。在图像预处理阶段设置图像边界填充的方式如下：

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

对Tensor进行填充的方式如下：

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

这里需要注意一点的是，transforms.Pad只能对PIL图像格式进行填充，而F.pad可以对Tensor进行填充，目前F.pad不支持对2D Tensor进行填充，可以通过unsqueeze扩展为4D Tensor进行填充。

F.pad的部分源码如下：

@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")
 return ret

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