Tensor and numpy convert "suggested collection" to each other

Hello everyone , I meet you again , I'm your friend, Quan Jun .

Tensor and NumPy transformation

We're easy to use numpy() and from_numpy() take Tensor and NumPy Arrays in convert to each other . But one thing to note is ： Generated by these two functions Tensor and NumPy Arrays in share the same memory （ So the transition between them is very fast ）, When you change one of them, the other will change ！

1. Tensor turn NumPy

a = torch.ones(6)
b = a.numpy()
print(a, b)

a += 1
print(a, b)
b += 1
print(a, b)

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tensor([1., 1., 1., 1., 1., 1.]) [1. 1. 1. 1. 1. 1.]
tensor([2., 2., 2., 2., 2., 2.]) [2. 2. 2. 2. 2. 2.]
tensor([3., 3., 3., 3., 3., 3.]) [3. 3. 3. 3. 3. 3.]

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2. NumPy Array rotation Tensor

import numpy as np
a = np.ones(7)
b = torch.from_numpy(a)
print(a, b)

a += 1
print(a, b)
b += 1
print(a, b)

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[1. 1. 1. 1. 1. 1. 1.] tensor([1., 1., 1., 1., 1., 1., 1.], dtype=torch.float64)
[2. 2. 2. 2. 2. 2. 2.] tensor([2., 2., 2., 2., 2., 2., 2.], dtype=torch.float64)
[3. 3. 3. 3. 3. 3. 3.] tensor([3., 3., 3., 3., 3., 3., 3.], dtype=torch.float64)

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3. torch.tensor() take NumPy Array to Tensor

Direct use torch.tensor() take NumPy Array to Tensor, This method always copies the data , Back to Tensor No longer share memory with the original data .

import numpy as np
a = np.ones((2,3))
c = torch.tensor(a)
a += 1
print('a:',a)
print('c:',c)
print(id(a)==id(c))

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a: [[2. 2. 2.]
 [2. 2. 2.]]
c: tensor([[1., 1., 1.],
        [1., 1., 1.]], dtype=torch.float64)
False

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