python、numpy、pytorch中的浅拷贝和深拷贝
1、Python中的浅拷贝和深拷贝
import copya = [1, 2, 3, 4, [11, 22, 33, [111, 222]]]
b = a
c = a.copy()
d = copy.deepcopy(a)print('before modify\r\n a=\r\n', a, '\r\n','b = a=\r\n', b, '\r\n','c = a.copy()=\r\n', c, '\r\n','d = copy.deepcopy(a)\r\n', d, '\r\n')
| before modify a= [1, 2, 3, 4, [11, 22, 33, [111, 222]]] b = a= [1, 2, 3, 4, [11, 22, 33, [111, 222]]] c = a.copy()= [1, 2, 3, 4, [11, 22, 33, [111, 222]]] d = copy.deepcopy(a) [1, 2, 3, 4, [11, 22, 33, [111, 222]]] |  |
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a[0] = 10
print('after a[0] = 10\r\n a=\r\n', a, '\r\n','b = a=\r\n', b, '\r\n','c = a.copy()=\r\n', c, '\r\n','d = copy.deepcopy(a)\r\n', d, '\r\n')| after a[0] = 10 a= [10, 2, 3, 4, [11, 22, 33, [111, 222]]] b = a= [10, 2, 3, 4, [11, 22, 33, [111, 222]]] c = a.copy()= [1, 2, 3, 4, [11, 22, 33, [111, 222]]] d = copy.deepcopy(a) [1, 2, 3, 4, [11, 22, 33, [111, 222]]] |  |
a[4][0] = 100
print('after a[4][0] = 100\r\n a=\r\n', a, '\r\n','b = a=\r\n', b, '\r\n','c = a.copy()=\r\n', c, '\r\n','d = copy.deepcopy(a)\r\n', d, '\r\n')| after a[4][0] = 100 a= [10, 2, 3, 4, [100, 22, 33, [111, 222]]] b = a= [10, 2, 3, 4, [100, 22, 33, [111, 222]]] c = a.copy()= [1, 2, 3, 4, [100, 22, 33, [111, 222]]] d = copy.deepcopy(a) [1, 2, 3, 4, [11, 22, 33, [111, 222]]] |  |
a[4][3][0] = 1000
print('after a[4][3][0] = 1000\r\n a=\r\n', a, '\r\n','b = a=\r\n', b, '\r\n','c = a.copy()=\r\n', c, '\r\n','d = copy.deepcopy(a)\r\n', d, '\r\n')| after a[4][3][0] = 1000 a= [10, 2, 3, 4, [100, 22, 33, [1000, 222]]] b = a= [10, 2, 3, 4, [100, 22, 33, [1000, 222]]] c = a.copy()= [1, 2, 3, 4, [100, 22, 33, [1000, 222]]] d = copy.deepcopy(a) [1, 2, 3, 4, [11, 22, 33, [111, 222]]] |  |
2、numpy中的浅拷贝和深拷贝
a1 = np.random.randn(2, 3)
b1 = a1
c1 = a1.copy()
d1 = copy.deepcopy(a1)print('before modify\r\n a1=\r\n', a1, '\r\n','b1 = a1=\r\n', b1, '\r\n','c1 = a1.copy()=\r\n', c1, '\r\n','d1 = copy.deepcopy(a1)=\r\n', d1, '\r\n')a1[0] = 10
print('after a1[0] = 10\r\n a1=\r\n', a1, '\r\n','b1 = a1=\r\n', b1, '\r\n','c1 = a1.copy()=\r\n', c1, '\r\n','d1 = copy.deepcopy(a1)=\r\n', d1, '\r\n')before modify
a1=
[[ 1.48618757 -0.90409826 2.05529475]
[ 0.14232255 2.93331428 0.88511785]]
b1 = a1=
[[ 1.48618757 -0.90409826 2.05529475]
[ 0.14232255 2.93331428 0.88511785]]
c1 = a1.copy()=
[[ 1.48618757 -0.90409826 2.05529475]
[ 0.14232255 2.93331428 0.88511785]]
d1 = copy.deepcopy(a1)=
[[ 1.48618757 -0.90409826 2.05529475]
[ 0.14232255 2.93331428 0.88511785]]
after a1[0] = 10
a1=
[[10. 10. 10. ]
[ 0.14232255 2.93331428 0.88511785]]
b1 = a1=
[[10. 10. 10. ]
[ 0.14232255 2.93331428 0.88511785]]
c1 = a1.copy()=
[[ 1.48618757 -0.90409826 2.05529475]
[ 0.14232255 2.93331428 0.88511785]]
d1 = copy.deepcopy(a1)=
[[ 1.48618757 -0.90409826 2.05529475]
[ 0.14232255 2.93331428 0.88511785]]
3、pytorch中的浅拷贝和深拷贝
a2 = torch.randn(2, 3)
b2 = torch.Tensor(a2)
bb2 = torch.tensor(a2)
c2 = a2.detach()
cc2 = a2.clone()
ccc2 = a2.clone().detach()
print('before modify\r\n a2=\r\n', a2, '\r\n','b2 = torch.Tensor(a2)=\r\n', b2, '\r\n','bb2 = torch.tensor(a2)=\r\n', bb2, '\r\n','c2 = a2.detach()=\r\n', c2, '\r\n','cc2 = a2.clone()=\r\n', cc2, '\r\n','ccc2 = a2.clone().detach()=\r\n', ccc2)
a2[0] = 0
print('after modify\r\n a2=\r\n', a2, '\r\n','b2 = torch.Tensor(a2)=\r\n', b2, '\r\n','bb2 = torch.tensor(a2)=\r\n', bb2, '\r\n','c2 = a2.detach()=\r\n', c2, '\r\n','cc2 = a2.clone()=\r\n', cc2, '\r\n','ccc2 = a2.clone().detach()=\r\n', ccc2)before modify
a2=
tensor([[-0.6472, 1.3437, -0.3386],
[ 0.8979, -0.4158, 1.1338]])
b2 = torch.Tensor(a2)=
tensor([[-0.6472, 1.3437, -0.3386],
[ 0.8979, -0.4158, 1.1338]])
bb2 = torch.tensor(a2)=
tensor([[-0.6472, 1.3437, -0.3386],
[ 0.8979, -0.4158, 1.1338]])
c2 = a2.detach()=
tensor([[-0.6472, 1.3437, -0.3386],
[ 0.8979, -0.4158, 1.1338]])
cc2 = a2.clone()=
tensor([[-0.6472, 1.3437, -0.3386],
[ 0.8979, -0.4158, 1.1338]])
ccc2 = a2.clone().detach()=
tensor([[-0.6472, 1.3437, -0.3386],
[ 0.8979, -0.4158, 1.1338]])
after modify
a2=
tensor([[ 0.0000, 0.0000, 0.0000],
[ 0.8979, -0.4158, 1.1338]])
b2 = torch.Tensor(a2)=
tensor([[ 0.0000, 0.0000, 0.0000],
[ 0.8979, -0.4158, 1.1338]])
bb2 = torch.tensor(a2)=
tensor([[-0.6472, 1.3437, -0.3386],
[ 0.8979, -0.4158, 1.1338]])
c2 = a2.detach()=
tensor([[ 0.0000, 0.0000, 0.0000],
[ 0.8979, -0.4158, 1.1338]])
cc2 = a2.clone()=
tensor([[-0.6472, 1.3437, -0.3386],
[ 0.8979, -0.4158, 1.1338]])
ccc2 = a2.clone().detach()=
tensor([[-0.6472, 1.3437, -0.3386],
[ 0.8979, -0.4158, 1.1338]])
参考
1、B站视频
十分钟！彻底弄懂Python深拷贝与浅拷贝机制_哔哩哔哩_bilibili
11、简书
NumPy之浅拷贝和深拷贝 - 简书 (jianshu.com)
2、CSDN-numpy
numpy copy(无拷贝 浅拷贝、深拷贝)类型说明_genghaihua的博客-CSDN博客
3、CSDN-pytorch
python、pytorch中的常见的浅拷贝、深拷贝问题总结_pytorch tensor的浅、复制_新嬉皮士的博客-CSDN博客
完整代码
import numpy as np
import copy
import torcha = [1, 2, 3, 4, [11, 22, 33, [111, 222]]]
b = a
c = a.copy()
d = copy.deepcopy(a)print('before modify\r\n a=\r\n', a, '\r\n','b = a=\r\n', b, '\r\n','c = a.copy()=\r\n', c, '\r\n','d = copy.deepcopy(a)\r\n', d, '\r\n')a[0] = 10
print('after a[0] = 10\r\n a=\r\n', a, '\r\n','b = a=\r\n', b, '\r\n','c = a.copy()=\r\n', c, '\r\n','d = copy.deepcopy(a)\r\n', d, '\r\n')a[4][0] = 100
print('after a[4][0] = 100\r\n a=\r\n', a, '\r\n','b = a=\r\n', b, '\r\n','c = a.copy()=\r\n', c, '\r\n','d = copy.deepcopy(a)\r\n', d, '\r\n')a[4][3][0] = 1000
print('after a[4][3][0] = 1000\r\n a=\r\n', a, '\r\n','b = a=\r\n', b, '\r\n','c = a.copy()=\r\n', c, '\r\n','d = copy.deepcopy(a)\r\n', d, '\r\n')a1 = np.random.randn(2, 3)
b1 = a1
c1 = a1.copy()
d1 = copy.deepcopy(a1)print('before modify\r\n a1=\r\n', a1, '\r\n','b1 = a1=\r\n', b1, '\r\n','c1 = a1.copy()=\r\n', c1, '\r\n','d1 = copy.deepcopy(a1)=\r\n', d1, '\r\n')a1[0] = 10
print('after a1[0] = 10\r\n a1=\r\n', a1, '\r\n','b1 = a1=\r\n', b1, '\r\n','c1 = a1.copy()=\r\n', c1, '\r\n','d1 = copy.deepcopy(a1)=\r\n', d1, '\r\n')a2 = torch.randn(2, 3)
b2 = torch.Tensor(a2)
bb2 = torch.tensor(a2)
c2 = a2.detach()
cc2 = a2.clone()
ccc2 = a2.clone().detach()
print('before modify\r\n a2=\r\n', a2, '\r\n','b2 = torch.Tensor(a2)=\r\n', b2, '\r\n','bb2 = torch.tensor(a2)=\r\n', bb2, '\r\n','c2 = a2.detach()=\r\n', c2, '\r\n','cc2 = a2.clone()=\r\n', cc2, '\r\n','ccc2 = a2.clone().detach()=\r\n', ccc2)
a2[0] = 0
print('after a2[0] = 0\r\n a2=\r\n', a2, '\r\n','b2 = torch.Tensor(a2)=\r\n', b2, '\r\n','bb2 = torch.tensor(a2)=\r\n', bb2, '\r\n','c2 = a2.detach()=\r\n', c2, '\r\n','cc2 = a2.clone()=\r\n', cc2, '\r\n','ccc2 = a2.clone().detach()=\r\n', ccc2)