Note
Click here to download the full example code
Tensors¶
Tensors behave almost exactly the same way in PyTorch as they do in Torch.
Create a tensor of size (5 x 7) with uninitialized memory:
import torch
a = torch.empty(5, 7, dtype=torch.float)
Initialize a double tensor randomized with a normal distribution with mean=0, var=1:
a = torch.randn(5, 7, dtype=torch.double)
print(a)
print(a.size())
Note
torch.Size
is in fact a tuple, so it supports the same operations
Inplace / Out-of-place¶
The first difference is that ALL operations on the tensor that operate
in-place on it will have an _
postfix. For example, add
is the
out-of-place version, and add_
is the in-place version.
a.fill_(3.5)
# a has now been filled with the value 3.5
b = a.add(4.0)
# a is still filled with 3.5
# new tensor b is returned with values 3.5 + 4.0 = 7.5
print(a, b)
Some operations like narrow
do not have in-place versions, and
hence, .narrow_
does not exist. Similarly, some operations like
fill_
do not have an out-of-place version, so .fill
does not
exist.
Zero Indexing¶
Another difference is that Tensors are zero-indexed. (In lua, tensors are one-indexed)
b = a[0, 3] # select 1st row, 4th column from a
Tensors can be also indexed with Python’s slicing
b = a[:, 3:5] # selects all rows, 4th column and 5th column from a
No camel casing¶
The next small difference is that all functions are now NOT camelCase
anymore. For example indexAdd
is now called index_add_
x = torch.ones(5, 5)
print(x)
z = torch.empty(5, 2)
z[:, 0] = 10
z[:, 1] = 100
print(z)
x.index_add_(1, torch.tensor([4, 0], dtype=torch.long), z)
print(x)
Numpy Bridge¶
Converting a torch Tensor to a numpy array and vice versa is a breeze. The torch Tensor and numpy array will share their underlying memory locations, and changing one will change the other.
Converting torch Tensor to numpy Array¶
a = torch.ones(5)
print(a)
b = a.numpy()
print(b)
a.add_(1)
print(a)
print(b) # see how the numpy array changed in value
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) # see how changing the np array changed the torch Tensor automatically
All the Tensors on the CPU except a CharTensor support converting to NumPy and back.
CUDA Tensors¶
CUDA Tensors are nice and easy in pytorch, and transfering a CUDA tensor from the CPU to GPU will retain its underlying type.
# let us run this cell only if CUDA is available
if torch.cuda.is_available():
# creates a LongTensor and transfers it
# to GPU as torch.cuda.LongTensor
a = torch.full((10,), 3, device=torch.device("cuda"))
print(type(a))
b = a.to(torch.device("cpu"))
# transfers it to CPU, back to
# being a torch.LongTensor
Total running time of the script: ( 0 minutes 0.000 seconds)