padding操作是给图像外围加像素点。
为了实际说明操作过程，这里我们使用一张实际的图片来做一下处理。
这张图片是大小是(256,256)，使用pad来给它加上一个黑色的边框。具体代码如下：
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import
torch.nn,functional as F
import
torch
from
PIL
import
Image
im
=
Image.
open
(
"heibai.jpg"
,
'r'
)
X
=
torch.Tensor(np.asarray(im))
print
(
"shape:"
,X.shape)
dim
=
(
10
,
10
,
10
,
10
)
X
=
F.pad(X,dim,
"constant"
,value
=
0
)
padX
=
X.data.numpy()
padim
=
Image.fromarray(padX)
padim
=
padim.convert(
"RGB"
)
#这里必须转为RGB不然会
padim.save(
"padded.jpg"
,
"jpeg"
)
padim.show()
print
(
"shape:"
,padX.shape)
|
输出：
|
1
2
|
shape: torch.Size([
256
,
256
])
shape: (
276
,
276
)
|
可以看出给原图四个方向给加上10维度的0，维度变为256+10+10得到的图像如下：
再举几个简单例子：
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x
=
np.asarray([[[
1
,
2
],[
1
,
2
]]])
X
=
torch.Tensor(x)
print
(X.shape)
pad_dims
=
(
2
,
2
,
2
,
2
,
1
,
1
,
)
X
=
F.pad(X,pad_dims,
"constant"
)
print
(X.shape)
print
(X)
|
输出：
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torch.Size([
1
,
2
,
2
])
torch.Size([
3
,
6
,
6
])
tensor([[[
0.
,
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
,
0.
]],
[[
0.
,
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
1.
,
2.
,
0.
,
0.
],
[
0.
,
0.
,
1.
,
2.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
,
0.
]],
[[
0.
,
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
,
0.
]]])
|
可以知若pad_dims为(2,2,2,2,1,1)则原维度变化是2+2+2=6,1+1+1=3.也就是第一个(2,2) pad的是最后一个维度，第二个(2,2) pad是倒数第二个维度，第三个(1,1) pad是第一个维度。
再举一个四维度的，但是只pad三个维度：
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x
=
np.asarray([[[[
1
,
2
],[
1
,
2
]]]])
X
=
torch.Tensor(x)
#(1,2,2)
print
(X.shape)
pad_dims
=
(
2
,
2
,
2
,
2
,
1
,
1
,
)
X
=
F.pad(X,pad_dims,
"constant"
)
#(1,1,12,12)
print
(X.shape)
print
(X)
|
输出：
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torch.Size([
1
,
1
,
2
,
2
])
torch.Size([
1
,
3
,
6
,
6
])
tensor([[[[
0.
,
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
,
0.
]],
[[
0.
,
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
1.
,
2.
,
0.
,
0.
],
[
0.
,
0.
,
1.
,
2.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
,
0.
]],
[[
0.
,
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
,
0.
]]]])
|
再举一个四维度的，pad四个维度：
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x
=
np.asarray([[[[
1
,
2
],[
1
,
2
]]]])
X
=
torch.Tensor(x)
#(1,2,2)
print
(X.shape)
pad_dims
=
(
2
,
2
,
2
,
2
,
1
,
1
,
2
,
2
)
X
=
F.pad(X,pad_dims,
"constant"
)
#(1,1,12,12)
print
(X.shape)
print
(X)
|
输出：
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torch.Size([
1
,
1
,
2
,
2
])
torch.Size([
5
,
3
,
6
,
6
])
tensor([[[[
0.
,
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
,
0.
]],
[[
0.
,
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
,
0.
]],
[[
0.
,
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
,
0.
],
[
0.
,
0.
,
0.
,
0.
,
0.
,
0.
]]],
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