pytorch学习-WHAT IS PYTORCH

红太狼 2022-01-09 09:53 472阅读 0赞

参考：https://pytorch.org/tutorials/beginner/blitz/tensor\_tutorial.html\#sphx-glr-beginner-blitz-tensor-tutorial-py

# WHAT IS PYTORCH #

这是一个基于python的实现两种功能的科学计算包：

*  用于替换NumPy去使用GPUs的算力
 *  一个提供了最大化灵活度和速度的深度学习搜索平台

## Getting Started ##

### Tensors ###

Tensors与NumPy的ndarrays相似，不同在于Tensors能够使用在GPU上去加速计算能力

from __future__ import print_function
    import torch

构造一个5\*3的矩阵，不初始化

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

输出：

(deeplearning) userdeMBP:pytorch user$ python test.py
    tensor([[ 0.0000e+00, -2.5244e-29,  0.0000e+00],
            [-2.5244e-29,  1.9618e-44,  9.2196e-41],
            [ 0.0000e+00,  7.7050e+31,  0.0000e+00],
            [ 0.0000e+00,  0.0000e+00,  0.0000e+00],
            [ 0.0000e+00,  0.0000e+00,  8.6499e-38]])

随机构造一个初始化矩阵：

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

输出：

(deeplearning) userdeMBP:pytorch user$ python test.py
    tensor([[0.4803, 0.5157, 0.9041],
            [0.1619, 0.8994, 0.4302],
            [0.6824, 0.6559, 0.9317],
            [0.5558, 0.8311, 0.2492],
            [0.8287, 0.1050, 0.7201]])

构建一个全为0的矩阵，并且设置类型为long：

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

输出：

(deeplearning) userdeMBP:pytorch user$ python test.py
    tensor([[0, 0, 0],
            [0, 0, 0],
            [0, 0, 0],
            [0, 0, 0],
            [0, 0, 0]])

直接使用数据来构造一个tensor：

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

输出：

(deeplearning) userdeMBP:pytorch user$ python test.py
    tensor([5.5000, 3.0000])

或者基于存在的tensor去创建一个tensor。这些方法将重新使用输入tensor的特性，如dtype，除非用户提供新的值。默认的dtype为torch.float

#-*- coding: utf-8 -*-
    from __future__ import print_function
    import torch
    x = torch.tensor([5.5, 3])
    print(x)
    
    x = x.new_ones(5, 3, dtype=torch.double)      # new_* methods take in sizes,就是新建一个矩阵，其与x无关
    print(x)                                      #设置dtype为torch.float64
    
    x = torch.randn_like(x, dtype=torch.float)    # override dtype!
    print(x)                                        # 会得到与x有相同大小的矩阵，dtype又从torch.float64变为torch.float

(deeplearning) userdeMBP:pytorch user$ python test.py
    tensor([5.5000, 3.0000])
    tensor([[1., 1., 1.],
            [1., 1., 1.],
            [1., 1., 1.],
            [1., 1., 1.],
            [1., 1., 1.]], dtype=torch.float64)
    tensor([[-3.0480e-01,  1.5148e+00, -1.1507e+00],
            [ 5.9181e-04, -8.0706e-01,  3.3035e-01],
            [ 1.5499e+00, -6.1708e-01,  5.8211e-01],
            [-9.1276e-02, -9.4747e-01, -1.8206e-01],
            [-8.9208e-02, -1.5132e-01,  1.2374e+00]])

得到矩阵的大小：

print(x.size())

torch.Size([5, 3])

⚠️torch.Size实际上是一个元祖，它支持所有的元祖操作

### Operations ###

这里有着多种操作的语法。如下面的例子，我们将看见的是加法操作：

#-*- coding: utf-8 -*-
    from __future__ import print_function
    import torch
    x = torch.tensor([5.5, 3])
    print(x)
    
    x = x.new_ones(5, 3)      # new_* methods take in sizes,就是新建一个矩阵，其与x无关
    print(x)                                      
    
    y = torch.rand(5, 3)
    print(y)
    print(x + y)

(deeplearning) userdeMBP:pytorch user$ python test.py
    tensor([5.5000, 3.0000])
    tensor([[1., 1., 1.],
            [1., 1., 1.],
            [1., 1., 1.],
            [1., 1., 1.],
            [1., 1., 1.]])
    tensor([[2.5123e-04, 9.8943e-01, 5.3585e-01],
            [9.4955e-01, 1.3734e-01, 4.0120e-01],
            [3.6199e-01, 1.5062e-01, 2.7033e-01],
            [9.5025e-01, 6.3539e-01, 2.3759e-01],
            [6.7833e-01, 4.3510e-01, 2.3747e-01]])
    tensor([[1.0003, 1.9894, 1.5359],
            [1.9496, 1.1373, 1.4012],
            [1.3620, 1.1506, 1.2703],
            [1.9502, 1.6354, 1.2376],
            [1.6783, 1.4351, 1.2375]])

等价于：

print(torch.add(x,y))

还可以设置一个输出变量，然后使用变量输出：

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

还有内置加法函数：

y.add_(x)
    print(y)

⚠️任何改变张量的内置操作都使用了\_后缀。如x.copy\_(y)，x.t\_()都会改变x的值

你也可以使用标准的类似于numpy的索引：

print(x[:, 1])

调整：如果你想要调整/重塑tensor，你可以使用torch.view：

#-*- coding: utf-8 -*-
    from __future__ import print_function
    import torch
    x = torch.randn(4, 4)
    print(x)
    y = x.view(16)
    print(y)
    z = x.view(-1, 8)  # -1表示从其他维度推断，即后面设为8，那么前面就推断是2
    print(z)
    print(x.size(), y.size(), z.size())

(deeplearning) userdeMBP:pytorch user$ python test.py
    tensor([[ 1.4353, -0.7081,  1.1953, -0.1438],
            [-0.9198, -0.8695, -0.3122, -0.0882],
            [ 0.5113, -1.3449, -0.9429,  1.7962],
            [ 0.5734,  1.0710, -0.9295, -2.0507]])
    tensor([ 1.4353, -0.7081,  1.1953, -0.1438, -0.9198, -0.8695, -0.3122, -0.0882,
             0.5113, -1.3449, -0.9429,  1.7962,  0.5734,  1.0710, -0.9295, -2.0507])
    tensor([[ 1.4353, -0.7081,  1.1953, -0.1438, -0.9198, -0.8695, -0.3122, -0.0882],
            [ 0.5113, -1.3449, -0.9429,  1.7962,  0.5734,  1.0710, -0.9295, -2.0507]])
    torch.Size([4, 4]) torch.Size([16]) torch.Size([2, 8])

如果你有只有一个元素的tensor，那么就能够使用.item()去得到一个转换为python数字的值：

#-*- coding: utf-8 -*-
    from __future__ import print_function
    import torch
    x = torch.randn(1)
    print(x)
    print(x.item())

(deeplearning) userdeMBP:pytorch user$ python test.py
    tensor([-2.3159])
    -2.3158915042877197

⚠️100+的张量运算，包括转置、标引、切片、数学运算、线性代数、随机数等，都计算在[here][]

## NumPy Bridge ##

将Torch Tensor转换为NumPy数组，反之亦然，是一件轻而易举的事。  
Torch张量和NumPy数组将共享它们的底层内存位置，更改一个将更改另一个。

### Converting a Torch Tensor to a NumPy Array ###

#-*- coding: utf-8 -*-
    from __future__ import print_function
    import torch
    a = torch.ones(5)
    print(a)
    
    b = a.numpy()
    print(b)
    
    a.add_(1)
    print(a)
    print(b)

(deeplearning) userdeMBP:pytorch user$ python test.py
    tensor([1., 1., 1., 1., 1.])
    [1. 1. 1. 1. 1.]
    tensor([2., 2., 2., 2., 2.])
    [2. 2. 2. 2. 2.]

从上面的结果可以看见，仅更改tensor a也会导致b被更改

### Converting NumPy Array to Torch Tensor ###

查看如何改变np数组来自动改变torch张量

#-*- coding: utf-8 -*-
    from __future__ import print_function
    import torch
    import numpy as np
    a = np.ones(5)
    b = torch.from_numpy(a)
    np.add(a, 1, out=a)
    print(a)
    print(b)

(deeplearning) userdeMBP:pytorch user$ python test.py
    [2. 2. 2. 2. 2.]
    tensor([2., 2., 2., 2., 2.], dtype=torch.float64)

CPU上除了Char Tensor以外的所有张量都支持转换成NumPy，或者反向转换

## CUDA Tensors ##

Tensors可以被移到任意的设备，使用.to方法

#-*- coding: utf-8 -*-
    from __future__ import print_function
    import torch
    
    # 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
        x = torch.randn(4, 4)
        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!

之前使用的机器中没有CUDA，换到另一台运行：

user@home:/opt/user$ python test.py
    tensor([[0.6344, 1.7958, 2.3387, 2.0527],
            [2.1517, 2.1555, 2.1645, 0.4499],
            [2.2020, 1.7363, 3.1394, 0.1240],
            [1.9541, 1.6115, 2.0081, 1.8911]], device='cuda:0')
    tensor([[0.6344, 1.7958, 2.3387, 2.0527],
            [2.1517, 2.1555, 2.1645, 0.4499],
            [2.2020, 1.7363, 3.1394, 0.1240],
            [1.9541, 1.6115, 2.0081, 1.8911]], dtype=torch.float64)

转载于:https://www.cnblogs.com/wanghui-garcia/p/10563716.html

[here]: https://pytorch.org/docs/torch