torch.nn.functional.pad(input, pad, mode=‘constant‘, value=None)记录

torch.nn.functional.pad该函数用来填充tensor

其中参数pad定义了四个参数，表示对输入矩阵的后两个维度（w，h–与正常的h，w相反）进行扩充：
(左边填充数， 右边填充数， 上边填充数， 下边填充数)
如果仅写两个参数，则填充的是w：
(左边填充数, 右边填充数)
如果写六个参数，则填充的是（w，h，c）三个维度：
(左边填充数， 右边填充数， 上边填充数， 下边填充数，通道填充数1，通道填充数2)

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.size())
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.size())
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.size())

注意：
上述经常使用填充数是正数，但实际应用中也可使用负数，来缩小tensor的size，比如：

x = torch.rand((8,3,57,57))

up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)

xx = up(x)

xx.shape
Out[8]: torch.Size([8, 3, 114, 114])

import torch.nn.functional as F

xxx = F.pad(xx, [0, -1, 0, -1])              

xxx.shape
Out[18]: torch.Size([8, 3, 113, 113])

xxxx = F.pad(xxx,[-2,-2,-3,-3,-1,-1])

xxxx.shape
Out[20]: torch.Size([8, 1, 107, 109])