File size: 3,686 Bytes
36c95ba |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 |
import torch
import torch.nn as nn
__all__ = [
"Vflip",
"Hflip",
"Rot180",
"rot180",
"hflip",
"vflip",
]
class Vflip(nn.Module):
r"""Vertically flip a tensor image or a batch of tensor images.
Input must be a tensor of shape (C, H, W) or a batch of tensors :math:`(*, C, H, W)`.
Args:
input: input tensor.
Returns:
The vertically flipped image tensor.
Examples:
>>> vflip = Vflip()
>>> input = torch.tensor([[[
... [0., 0., 0.],
... [0., 0., 0.],
... [0., 1., 1.]
... ]]])
>>> vflip(input)
tensor([[[[0., 1., 1.],
[0., 0., 0.],
[0., 0., 0.]]]])
"""
def forward(self, input: torch.Tensor) -> torch.Tensor: # type: ignore
return vflip(input)
def __repr__(self):
return self.__class__.__name__
class Hflip(nn.Module):
r"""Horizontally flip a tensor image or a batch of tensor images.
Input must be a tensor of shape (C, H, W) or a batch of tensors :math:`(*, C, H, W)`.
Args:
input: input tensor.
Returns:
The horizontally flipped image tensor.
Examples:
>>> hflip = Hflip()
>>> input = torch.tensor([[[
... [0., 0., 0.],
... [0., 0., 0.],
... [0., 1., 1.]
... ]]])
>>> hflip(input)
tensor([[[[0., 0., 0.],
[0., 0., 0.],
[1., 1., 0.]]]])
"""
def forward(self, input: torch.Tensor) -> torch.Tensor: # type: ignore
return hflip(input)
def __repr__(self):
return self.__class__.__name__
class Rot180(nn.Module):
r"""Rotate a tensor image or a batch of tensor images 180 degrees.
Input must be a tensor of shape (C, H, W) or a batch of tensors :math:`(*, C, H, W)`.
Args:
input: input tensor.
Examples:
>>> rot180 = Rot180()
>>> input = torch.tensor([[[
... [0., 0., 0.],
... [0., 0., 0.],
... [0., 1., 1.]
... ]]])
>>> rot180(input)
tensor([[[[1., 1., 0.],
[0., 0., 0.],
[0., 0., 0.]]]])
"""
def forward(self, input: torch.Tensor) -> torch.Tensor: # type: ignore
return rot180(input)
def __repr__(self):
return self.__class__.__name__
def rot180(input: torch.Tensor) -> torch.Tensor:
r"""Rotate a tensor image or a batch of tensor images 180 degrees.
.. image:: _static/img/rot180.png
Input must be a tensor of shape (C, H, W) or a batch of tensors :math:`(*, C, H, W)`.
Args:
input: input tensor.
Returns:
The rotated image tensor.
"""
return torch.flip(input, [-2, -1])
def hflip(input: torch.Tensor) -> torch.Tensor:
r"""Horizontally flip a tensor image or a batch of tensor images.
.. image:: _static/img/hflip.png
Input must be a tensor of shape (C, H, W) or a batch of tensors :math:`(*, C, H, W)`.
Args:
input: input tensor.
Returns:
The horizontally flipped image tensor.
"""
w = input.shape[-1]
return input[..., torch.arange(w - 1, -1, -1, device=input.device)]
def vflip(input: torch.Tensor) -> torch.Tensor:
r"""Vertically flip a tensor image or a batch of tensor images.
.. image:: _static/img/vflip.png
Input must be a tensor of shape (C, H, W) or a batch of tensors :math:`(*, C, H, W)`.
Args:
input: input tensor.
Returns:
The vertically flipped image tensor.
"""
h = input.shape[-2]
return input[..., torch.arange(h - 1, -1, -1, device=input.device), :]
|