目录
第二个参数输入列表，则按照列表和翻转规则，依次翻转列表内的维度
方法描述
torch.flip()函数是PyTorch中用于翻转张量的函数。它可以用于在指定维度上对张量进行翻转操作
torch.flip(input,dim):第一个参数是tensor输入，第二个参数是输入的第几维度，按照维度对输入进行翻转, 反转后shape不变
参数
-
input：输入张量，可以是任意形状的张量。 -
dims：一个整数或整数列表，表示要翻转的维度。
函数签名
torch.flip(input, dims) → Tensor第二个参数输入单维度，翻转列表内的维度
python
import torch
x = torch.arange(120).view(2, 3, 4,5)
print('x=\n',x)
a = torch.flip(x, [0])
b = torch.flip(x, [1])
c = torch.flip(x, [2])
d = torch.flip(x, [3])
print('a=\n',a)
print('b=\n',b)
print('c=\n',c)
print('d=\n',d)原张量显示
x=
tensor([[[[ 0, 1, 2, 3, 4],
5, 6, 7, 8, 9\], \[ 10, 11, 12, 13, 14\], \[ 15, 16, 17, 18, 19\]\], \[\[ 20, 21, 22, 23, 24\], \[ 25, 26, 27, 28, 29\], \[ 30, 31, 32, 33, 34\], \[ 35, 36, 37, 38, 39\]\], \[\[ 40, 41, 42, 43, 44\], \[ 45, 46, 47, 48, 49\], \[ 50, 51, 52, 53, 54\], \[ 55, 56, 57, 58, 59\]\]\], \[\[\[ 60, 61, 62, 63, 64\], \[ 65, 66, 67, 68, 69\], \[ 70, 71, 72, 73, 74\], \[ 75, 76, 77, 78, 79\]\], \[\[ 80, 81, 82, 83, 84\], \[ 85, 86, 87, 88, 89\], \[ 90, 91, 92, 93, 94\], \[ 95, 96, 97, 98, 99\]\], \[\[100, 101, 102, 103, 104\], \[105, 106, 107, 108, 109\], \[110, 111, 112, 113, 114\], \[115, 116, 117, 118, 119\]\]\]\])
a=
tensor([[[[ 60, 61, 62, 63, 64],
65, 66, 67, 68, 69\], \[ 70, 71, 72, 73, 74\], \[ 75, 76, 77, 78, 79\]\], \[\[ 80, 81, 82, 83, 84\], \[ 85, 86, 87, 88, 89\], \[ 90, 91, 92, 93, 94\], \[ 95, 96, 97, 98, 99\]\], \[\[100, 101, 102, 103, 104\], \[105, 106, 107, 108, 109\], \[110, 111, 112, 113, 114\], \[115, 116, 117, 118, 119\]\]\], \[\[\[ 0, 1, 2, 3, 4\], \[ 5, 6, 7, 8, 9\], \[ 10, 11, 12, 13, 14\], \[ 15, 16, 17, 18, 19\]\], \[\[ 20, 21, 22, 23, 24\], \[ 25, 26, 27, 28, 29\], \[ 30, 31, 32, 33, 34\], \[ 35, 36, 37, 38, 39\]\], \[\[ 40, 41, 42, 43, 44\], \[ 45, 46, 47, 48, 49\], \[ 50, 51, 52, 53, 54\], \[ 55, 56, 57, 58, 59\]\]\]\])
按照1维翻转结果（在两个方括号"[[ ]]"内的内容为一体 在三括号"[[[ ]]]"内进行倒叙排列）
b=
tensor([[[[ 40, 41, 42, 43, 44],
45, 46, 47, 48, 49\], \[ 50, 51, 52, 53, 54\], \[ 55, 56, 57, 58, 59\]\], \[\[ 20, 21, 22, 23, 24\], \[ 25, 26, 27, 28, 29\], \[ 30, 31, 32, 33, 34\], \[ 35, 36, 37, 38, 39\]\], \[\[ 0, 1, 2, 3, 4\], \[ 5, 6, 7, 8, 9\], \[ 10, 11, 12, 13, 14\], \[ 15, 16, 17, 18, 19\]\]\], \[\[\[100, 101, 102, 103, 104\], \[105, 106, 107, 108, 109\], \[110, 111, 112, 113, 114\], \[115, 116, 117, 118, 119\]\], \[\[ 80, 81, 82, 83, 84\], \[ 85, 86, 87, 88, 89\], \[ 90, 91, 92, 93, 94\], \[ 95, 96, 97, 98, 99\]\], \[\[ 60, 61, 62, 63, 64\], \[ 65, 66, 67, 68, 69\], \[ 70, 71, 72, 73, 74\], \[ 75, 76, 77, 78, 79\]\]\]\])
c=
tensor([[[[ 15, 16, 17, 18, 19],
10, 11, 12, 13, 14\], \[ 5, 6, 7, 8, 9\], \[ 0, 1, 2, 3, 4\]\], \[\[ 35, 36, 37, 38, 39\], \[ 30, 31, 32, 33, 34\], \[ 25, 26, 27, 28, 29\], \[ 20, 21, 22, 23, 24\]\], \[\[ 55, 56, 57, 58, 59\], \[ 50, 51, 52, 53, 54\], \[ 45, 46, 47, 48, 49\], \[ 40, 41, 42, 43, 44\]\]\], \[\[\[ 75, 76, 77, 78, 79\], \[ 70, 71, 72, 73, 74\], \[ 65, 66, 67, 68, 69\], \[ 60, 61, 62, 63, 64\]\], \[\[ 95, 96, 97, 98, 99\], \[ 90, 91, 92, 93, 94\], \[ 85, 86, 87, 88, 89\], \[ 80, 81, 82, 83, 84\]\], \[\[115, 116, 117, 118, 119\], \[110, 111, 112, 113, 114\], \[105, 106, 107, 108, 109\], \[100, 101, 102, 103, 104\]\]\]\])
d=
tensor([[[[ 4, 3, 2, 1, 0],
9, 8, 7, 6, 5\], \[ 14, 13, 12, 11, 10\], \[ 19, 18, 17, 16, 15\]\], \[\[ 24, 23, 22, 21, 20\], \[ 29, 28, 27, 26, 25\], \[ 34, 33, 32, 31, 30\], \[ 39, 38, 37, 36, 35\]\], \[\[ 44, 43, 42, 41, 40\], \[ 49, 48, 47, 46, 45\], \[ 54, 53, 52, 51, 50\], \[ 59, 58, 57, 56, 55\]\]\], \[\[\[ 64, 63, 62, 61, 60\], \[ 69, 68, 67, 66, 65\], \[ 74, 73, 72, 71, 70\], \[ 79, 78, 77, 76, 75\]\], \[\[ 84, 83, 82, 81, 80\], \[ 89, 88, 87, 86, 85\], \[ 94, 93, 92, 91, 90\], \[ 99, 98, 97, 96, 95\]\], \[\[104, 103, 102, 101, 100\], \[109, 108, 107, 106, 105\], \[114, 113, 112, 111, 110\], \[119, 118, 117, 116, 115\]\]\]\])
python
import torch
# 翻转张量
a = torch.tensor([[1, 2, 3], [4, 5, 6]])
b = torch.flip(a, dims=[0, 1])
print(b)结果输出
tensor([[6, 5, 4],
3, 2, 1\]\])
翻转逻辑
先变成
tensor([[4,5,6],
1,2,3\]\]) 再变成 tensor(\[\[6,5,4\],\[\]\])