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_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