Pytorch 基础

Pytorch 1.0.0 学习笔记：

Pytorch 的学习可以参考：Welcome to PyTorch Tutorials

Pytorch 是什么？

快速上手 Pytorch!

Tensors(张量)

from __future__ import print_function

import torch

创建一个没有初始化的 \(5\times 3\) 矩阵：

x = torch.empty(5, 3)

print(x)

tensor([[0.0000e+00, 0.0000e+00, 0.0000e+00],

        [0.0000e+00, 0.0000e+00, 0.0000e+00],

        [0.0000e+00, 0.0000e+00, 0.0000e+00],

        [0.0000e+00, 1.9730e-42, 0.0000e+00],

        [0.0000e+00, 7.3909e+22, 0.0000e+00]])

创建一个已经初始化的 \(5\times 3\) 的随机矩阵：

x = torch.rand(5, 3)

print(x)

tensor([[0.2496, 0.8405, 0.7555],

        [0.9820, 0.9988, 0.5419],

        [0.6570, 0.4990, 0.4165],

        [0.6985, 0.9972, 0.4234],

        [0.0096, 0.6374, 0.8520]])

给定数据类型为 long 的 \(5\times 3\) 的全零矩阵：

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]])

直接从 list 中创建张量：

x = torch.tensor([5.5, 3])  # list

print(x)

tensor([5.5000, 3.0000])

直接从 Numpy 中创建张量：

import numpy as np

a = np.array([2, 3, 5], dtype='B')

x = torch.tensor(a)  # numpy

print(x)

x.numel()  # Tensor 中元素的个数

tensor([2, 3, 5], dtype=torch.uint8)

3

x = torch.rand(5, 3)

size = x.size()

print(size)

h, w = size

h, w

torch.Size([5, 3])

(5, 3)

Operations(运算)

Tensor 的运算大都与 Numpy 相同，下面仅仅介绍一些特殊的运算方式：

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)

tensor([[1., 1., 1.],

        [1., 1., 1.],

        [1., 1., 1.],

        [1., 1., 1.],

        [1., 1., 1.]], dtype=torch.float64)

tensor([[-0.9367, -0.1121,  1.9103],

        [ 0.2284,  0.3823,  1.0877],

        [-0.2797,  0.7217, -0.7032],

        [ 0.9047,  1.7789,  0.4215],

        [-1.0368, -0.2644, -0.7948]])

result = torch.empty(5, 3)  # 创建一个为初始化的矩阵

y = torch.rand(5, 3)

torch.add(x, y, out=result) # 计算 x + y 并将结果赋值给 result

print(result)

tensor([[-0.0202,  0.6110,  2.8150],

        [ 1.0288,  1.2454,  1.7464],

        [-0.1786,  0.8212, -0.2493],

        [ 1.5294,  2.2713,  0.8383],

        [-0.9292,  0.5749, -0.1146]])

任何一个 可变的 tensor 所对应的运算在其适当的位置后加上 _ ， 便会修改原 tensor 的值：

x = torch.tensor([7])

y = torch.tensor([2])

print(y, y.add(x))

print(y, y.add_(x))

y

tensor([2]) tensor([9])

tensor([9]) tensor([9])

tensor([9])

x = torch.tensor(7)

x.item()  # 转换为 python 的 number

7

reshape tensor： veiw()

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])

NumPy Bridge(与 Numpy 交互)

Tensor 转换为 Numpy

a = torch.ones(5)

print(a)

tensor([1., 1., 1., 1., 1.])

Tensor 转换为 Numpy

b = a.numpy()

print(b)

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

_ 的作用依然存在：

a.add_(1)

print(a)

print(b)

tensor([2., 2., 2., 2., 2.])

[2. 2. 2. 2. 2.]

Numpy 转换为 Tensor

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)

CUDA

使用 .to 方法，Tensors 可被移动到任何 device：

# 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!

tensor(8, device='cuda:0')

tensor(8., dtype=torch.float64)

更多内容参考：我的github: https://github.com/XNoteW/Studying/tree/master/PyTorch_beginner