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.FloatTensor(5, 7)
Initialize a tensor randomized with a normal distribution with mean=0, var=1:
a = torch.randn(5, 7) print(a) print(a.size())
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
.narrow_ does not exist. Similarly, some operations like
fill_ do not have an out-of-place version, so
.fill does not
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
x = torch.ones(5, 5) print(x)
z = torch.Tensor(5, 2) z[:, 0] = 10 z[:, 1] = 100 print(z)
x.index_add_(1, torch.LongTensor([4, 0]), z) print(x)
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 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.LongTensor(10).fill_(3).cuda() print(type(a)) b = a.cpu() # transfers it to CPU, back to # being a torch.LongTensor
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