什么是 PyTorch?
PyTorch 是一个基于 Python 的科学计算包，主要定位两类人群：
- NumPy 的替代品，可以利用 GPU 的性能进行计算。
- 深度学习研究平台拥有足够的灵活性和速度
开始学习
Tensors (张量)
Tensors 类似于 NumPy 的 ndarrays ，同时 Tensors 可以使用 GPU 进行计算。
from __future__ import print_function
import torch
构造一个5×3矩阵，不初始化。
x = torch.empty(5, 3)
print(x)
输出:
tensor(1.00000e-04 *
[[-0.0000, 0.0000, 1.5135],
[ 0.0000, 0.0000, 0.0000],
[ 0.0000, 0.0000, 0.0000],
[ 0.0000, 0.0000, 0.0000],
[ 0.0000, 0.0000, 0.0000]])
构造一个随机初始化的矩阵：
x = torch.rand(5, 3)
print(x)
输出:
tensor([[ 0.6291, 0.2581, 0.6414],
[ 0.9739, 0.8243, 0.2276],
[ 0.4184, 0.1815, 0.5131],
[ 0.5533, 0.5440, 0.0718],
[ 0.2908, 0.1850, 0.5297]])
构造一个矩阵全为 0，而且数据类型是 long.
Construct a matrix filled zeros and of dtype long:
x = torch.zeros(5, 3, dtype=torch.long)
print(x)
输出:
tensor([[ 0, 0, 0],
[ 0, 0, 0],
[ 0, 0, 0],
[ 0, 0, 0],
[ 0, 0, 0]])
构造一个张量，直接使用数据：
x = torch.tensor([5.5, 3])
print(x)
输出:
tensor([ 5.5000, 3.0000])
创建一个 tensor 基于已经存在的 tensor。
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
输出:
tensor([[ 1., 1., 1.],
[ 1., 1., 1.],
[ 1., 1., 1.],
[ 1., 1., 1.],
[ 1., 1., 1.]], dtype=torch.float64)
tensor([[-0.2183, 0.4477, -0.4053],
[ 1.7353, -0.0048, 1.2177],
[-1.1111, 1.0878, 0.9722],
[-0.7771, -0.2174, 0.0412],
[-2.1750, 1.3609, -0.3322]])
获取它的维度信息:
print(x.size())
输出:
torch.Size([5, 3])
Note
torch.Size is in fact a tuple, so it supports all tuple operations.
操作
在接下来的例子中，我们将会看到加法操作。
Addition: 方式 1
y = torch.rand(5, 3)
print(x + y)
Out:
tensor([[-0.1859, 1.3970, 0.5236],
[ 2.3854, 0.0707, 2.1970],
[-0.3587, 1.2359, 1.8951],
[-0.1189, -0.1376, 0.4647],
[-1.8968, 2.0164, 0.1092]])
Addition: 方式2
print(torch.add(x, y))
Out:
tensor([[-0.1859, 1.3970, 0.5236],
[ 2.3854, 0.0707, 2.1970],
[-0.3587, 1.2359, 1.8951],
[-0.1189, -0.1376, 0.4647],
[-1.8968, 2.0164, 0.1092]])
Addition: providing an output tensor as argument
result = torch.empty(5, 3)
torch.add(x, y, out=result)
print(result)
Out:
tensor([[-0.1859, 1.3970, 0.5236],
[ 2.3854, 0.0707, 2.1970],
[-0.3587, 1.2359, 1.8951],
[-0.1189, -0.1376, 0.4647],
[-1.8968, 2.0164, 0.1092]])
Addition: in-place
# adds x to y
y.add_(x)
print(y)
Out:
tensor([[-0.1859, 1.3970, 0.5236],
[ 2.3854, 0.0707, 2.1970],
[-0.3587, 1.2359, 1.8951],
[-0.1189, -0.1376, 0.4647],
[-1.8968, 2.0164, 0.1092]])
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([ 0.4477, -0.0048, 1.0878, -0.2174, 1.3609])
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([ 0.9422])
0.9422121644020081
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, 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([ 1.9422], device='cuda:0')
tensor([ 1.9422], dtype=torch.float64)
Total running time of the script: ( 0 minutes 0.057 seconds)
