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What is PyTorch?

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

Getting Started

Tensors

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)

Out:

tensor([[0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.],
        [0., 0., 0.]])

Construct a randomly initialized matrix:

x = torch.rand(5, 3)
print(x)

Out:

tensor([[0.3750, 0.9301, 0.9839],
        [0.6945, 0.8202, 0.6852],
        [0.1580, 0.9943, 0.3490],
        [0.9965, 0.9281, 0.8554],
        [0.6517, 0.7697, 0.9365]])

Construct a matrix filled zeros and of dtype long:

x = torch.zeros(5, 3, dtype=torch.long)
print(x)

Out:

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)

Out:

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

Out:

tensor([[1., 1., 1.],
        [1., 1., 1.],
        [1., 1., 1.],
        [1., 1., 1.],
        [1., 1., 1.]], dtype=torch.float64)
tensor([[-1.2677, -0.7443, -1.8391],
        [-0.1863,  0.4972, -0.2146],
        [ 0.6553,  0.9298, -0.1348],
        [ 0.3347, -0.0263, -1.9030],
        [ 0.6216,  0.2751,  0.5796]])

Get its size:

print(x.size())

Out:

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)

Out:

tensor([[-0.4532,  0.0666, -1.7779],
        [-0.0779,  1.3306,  0.6896],
        [ 1.4601,  1.2675,  0.5450],
        [ 0.7894,  0.8313, -1.6973],
        [ 1.4154,  0.8531,  1.4813]])

Addition: syntax 2

print(torch.add(x, y))

Out:

tensor([[-0.4532,  0.0666, -1.7779],
        [-0.0779,  1.3306,  0.6896],
        [ 1.4601,  1.2675,  0.5450],
        [ 0.7894,  0.8313, -1.6973],
        [ 1.4154,  0.8531,  1.4813]])

Addition: providing an output tensor as argument

result = torch.empty(5, 3)
torch.add(x, y, out=result)
print(result)

Out:

tensor([[-0.4532,  0.0666, -1.7779],
        [-0.0779,  1.3306,  0.6896],
        [ 1.4601,  1.2675,  0.5450],
        [ 0.7894,  0.8313, -1.6973],
        [ 1.4154,  0.8531,  1.4813]])

Addition: in-place

# adds x to y
y.add_(x)
print(y)

Out:

tensor([[-0.4532,  0.0666, -1.7779],
        [-0.0779,  1.3306,  0.6896],
        [ 1.4601,  1.2675,  0.5450],
        [ 0.7894,  0.8313, -1.6973],
        [ 1.4154,  0.8531,  1.4813]])

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])

Out:

tensor([-0.7443,  0.4972,  0.9298, -0.0263,  0.2751])

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([-0.8032])
-0.803248405456543

Read later:

100+ Tensor operations, including transposing, indexing, slicing, mathematical operations, linear algebra, random numbers, etc., are described here.

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 (if the Torch Tensor is on CPU), and changing one will change the other.

Converting a Torch Tensor to a NumPy Array

a = torch.ones(5)
print(a)

Out:

tensor([1., 1., 1., 1., 1.])
b = a.numpy()
print(b)

Out:

[1. 1. 1. 1. 1.]

See how the numpy array changed in value.

a.add_(1)
print(a)
print(b)

Out:

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)

Out:

[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.

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!

Out:

tensor([0.1968], device='cuda:0')
tensor([0.1968], dtype=torch.float64)

Total running time of the script: ( 0 minutes 6.693 seconds)

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