%matplotlib inline
67. Pytorch Tensors#
It’s a Python-based scientific computing package targeted at two sets of audiences:
A replacement for NumPy to use the power of GPUs
a deep learning research platform that provides maximum flexibility and speed
Tensors are similar to NumPy’s ndarrays, with the addition being that Tensors can also be used on a GPU to accelerate computing.
from __future__ import print_function
import torch
Construct a 5x3 matrix, uninitialized:
x = torch.empty(5, 3)
print(x)
tensor([[-1484854811542562013184.0000, 0.0000,
-1484854811542562013184.0000],
[ 0.0000, 0.0000,
0.0000],
[ 0.0000, 0.0000,
0.0000],
[ 0.0000, 0.0000,
0.0000],
[ 0.0000, 0.0000,
0.0000]])
Construct a randomly initialized matrix:
x = torch.rand(5, 3)
print(x)
tensor([[0.4742, 0.4538, 0.4304],
[0.1801, 0.4597, 0.2645],
[0.4020, 0.2434, 0.2058],
[0.6396, 0.7139, 0.3221],
[0.1281, 0.3521, 0.9752]])
Construct a matrix filled zeros and of dtype long:
x = torch.zeros(5, 3, dtype=torch.long)
print(x)
tensor([[0, 0, 0],
[0, 0, 0],
[0, 0, 0],
[0, 0, 0],
[0, 0, 0]])
Construct a tensor directly from data:
x = torch.tensor([5.5, 3])
print(x)
tensor([5.5000, 3.0000])
or create a tensor based on an existing tensor. These methods will reuse properties of the input tensor, e.g. dtype, unless new values are provided by user
x = x.new_ones(5, 3, dtype=torch.double) # new_* methods take in sizes
print(x)
x = torch.randn_like(x, dtype=torch.float) # override dtype!
print(x) # result has the same size
tensor([[1., 1., 1.],
[1., 1., 1.],
[1., 1., 1.],
[1., 1., 1.],
[1., 1., 1.]], dtype=torch.float64)
tensor([[-0.5785, 0.6964, -1.4768],
[-0.0999, 0.1288, -0.7034],
[-0.0728, -2.1770, 0.4147],
[ 1.0561, -0.7525, 0.7957],
[-0.2872, -1.4618, -0.7413]])
Get its size:
print(x.size())
torch.Size([5, 3])
Note
``torch.Size`` is in fact a tuple, so it supports all tuple operations.
Operations ^^^^^^^^^^ There are multiple syntaxes for operations. In the following example, we will take a look at the addition operation.
Addition: syntax 1
y = torch.rand(5, 3)
print(x + y)
tensor([[-0.5261, 1.0870, -0.9739],
[ 0.5831, 0.5069, -0.5522],
[ 0.3222, -1.4902, 0.8171],
[ 1.5093, -0.5091, 1.5999],
[ 0.5226, -0.8918, 0.0173]])
Addition: syntax 2
print(torch.add(x, y))
tensor([[-0.5261, 1.0870, -0.9739],
[ 0.5831, 0.5069, -0.5522],
[ 0.3222, -1.4902, 0.8171],
[ 1.5093, -0.5091, 1.5999],
[ 0.5226, -0.8918, 0.0173]])
Addition: providing an output tensor as argument
result = torch.empty(5, 3)
torch.add(x, y, out=result)
print(result)
tensor([[-0.5261, 1.0870, -0.9739],
[ 0.5831, 0.5069, -0.5522],
[ 0.3222, -1.4902, 0.8171],
[ 1.5093, -0.5091, 1.5999],
[ 0.5226, -0.8918, 0.0173]])
Addition: in-place
# adds x to y
y.add_(x)
print(y)
tensor([[-0.5261, 1.0870, -0.9739],
[ 0.5831, 0.5069, -0.5522],
[ 0.3222, -1.4902, 0.8171],
[ 1.5093, -0.5091, 1.5999],
[ 0.5226, -0.8918, 0.0173]])
Note
Any operation that mutates a tensor in-place is post-fixed with an ``_``. For example: ``x.copy_(y)``, ``x.t_()``, will change ``x``.
You can use standard NumPy-like indexing with all bells and whistles!
print(x[:, 1])
tensor([ 0.6964, 0.1288, -2.1770, -0.7525, -1.4618])
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())
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())
tensor([-1.0914])
-1.0913640260696411
Read later:
100+ Tensor operations, including transposing, indexing, slicing,
mathematical operations, linear algebra, random numbers, etc.,
are described
here <http://pytorch.org/docs/torch>_.
67.1. 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 a Torch Tensor to a NumPy Array ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
a = torch.ones(5)
print(a)
tensor([1., 1., 1., 1., 1.])
b = a.numpy()
print(b)
[1. 1. 1. 1. 1.]
See how the numpy array changed in value.
a.add_(1)
print(a)
print(b)
tensor([2., 2., 2., 2., 2.])
[2. 2. 2. 2. 2.]
Converting NumPy Array to Torch Tensor ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ See how changing the np array changed the Torch Tensor automatically
import numpy as np
a = np.ones(5)
b = torch.from_numpy(a)
np.add(a, 1, out=a)
print(a)
print(b)
[2. 2. 2. 2. 2.]
tensor([2., 2., 2., 2., 2.], dtype=torch.float64)
All the Tensors on the CPU except a CharTensor support converting to NumPy and back.
67.2. CUDA Tensors#
Tensors can be moved onto any device using the .to method.
# let us run this cell only if CUDA is available
# We will use ``torch.device`` objects to move tensors in and out of GPU
if torch.cuda.is_available():
device = torch.device("cuda") # a CUDA device object
y = torch.ones_like(x, device=device) # directly create a tensor on GPU
x = x.to(device) # or just use strings ``.to("cuda")``
z = x + y
print(z)
print(z.to("cpu", torch.double)) # ``.to`` can also change dtype together!