Tensor

Resizing

If you want to resize/reshape tensor, you can use torch.view:

x = torch.randn(4, 4)
y = x.view(16)
z = x.view(-1, 8)  # the size -1 is inferred from other dimensions
print(x.size(), y.size(), z.size())

Out:

torch.Size([4, 4]) torch.Size([16]) torch.Size([2, 8])

If you have a one element tensor, use .item to get the value as a Python number

x = torch.randn(1)
print(x)
print(x.item())

Out

tensor([-0.2028])
-0.20277611911296844

NumPy Bridge

The Torch Tensor and NumPy array will share their underlying memory locations(if the Torch Tensor is on CPU), and changing one will change the other.

a = torch.ones(5)
b = a.numpy()
print(b)
a.add_(1)
print(a)
print(b)

Out

array([1.,1.,1.,1.,1.], dtype=float32)
tensor([2., 2., 2., 2., 2.])
array([2.,2.,2.,2.,2.], dtype=float32)

Converting NumPy Array to Torch Tensor

import numpy as np
a = np.ones(5)
b = torch.from_numpy(a)
np.add(a, 1, out=a)
print(a)
print(b)

Out

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