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Installation and getting started with pytoch

2022-06-30 05:09:00 【Students who don't want to be bald】

Pytorch Installation

The goal is

Know how to install pytorch

1. Pytorch Introduction to

Pytorch Is a facebook Published in-depth learning framework , Because of its ease of use , Friendliness , Favored by the majority of users .

2. Pytorch Version of

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3. Pytorch Installation

Installation address introduction ：https://pytorch.org/get-started/locally/

belt GPU Installation steps ：

conda install pytorch torchvision cudatoolkit=9.0 -c pytorch

No GPU Installation steps

conda install pytorch-cpu torchvision-cpu -c pytorch

Open after installation ipython

Input ：

In [1]:import torch
In [2]: torch.__version__
Out[2]: '1.0.1'

Be careful ： When installing the module, you install pytorch , But in the code, we use torch

There was an article before the detailed introduction , You can go there by yourself if necessary

The first step of deep learning （anaconda、pytorch install ）

Pytorch To get started with

The goal is

Know tensor sum Pytorch The tensor in
know pytorch How to create tensors in
know pytorch in tensor Common ways to do it
know pytorch in tensor Data type of
know pytorch How to realize tensor stay cpu and cuda Medium transformation

1. tensor Tensor

Tensor is a general term , There are many types ：

0 Order tensor ： Scalar 、 constant ,0-D Tensor
1 Order tensor ： vector ,1-D Tensor
2 Order tensor ： matrix ,2-D Tensor
3 Order tensor
…
N Order tensor

2. Pytorch Create tensors in

Use python Create a list or sequence in tensor

torch.tensor([[1., -1.], [1., -1.]])
tensor([[ 1.0000, -1.0000],
        [ 1.0000, -1.0000]])

Use numpy Array creation in tensor

torch.tensor(np.array([[1, 2, 3], [4, 5, 6]]))
tensor([[ 1,  2,  3],
        [ 4,  5,  6]])

Use torch Of api establish tensor
1. torch.empty(3,4) establish 3 That's ok 4 The column is empty tensor, Will be filled with useless data
2. torch.ones([3,4]) establish 3 That's ok 4 Column All for 1 Of tensor
3. torch.zeros([3,4]) establish 3 That's ok 4 Column All for 0 Of tensor
4. torch.rand([3,4]) establish 3 That's ok 4 Column Random value Of tensor, The interval of random values is [0, 1)
```
>>> torch.rand(2, 3)
tensor([[ 0.8237,  0.5781,  0.6879],
[ 0.3816,  0.7249,  0.0998]])
```
5. torch.randint(low=0,high=10,size=[3,4]) establish 3 That's ok 4 Column Random integers Of tensor, The interval of random values is [low, high)
```
>>> torch.randint(3, 10, (2, 2))
tensor([[4, 5],
	[6, 7]])
```
6. torch.randn([3,4]) establish 3 That's ok 4 Column random number Of tensor, The distributed mean of random values is 0, The variance of 1

3. Pytorch in tensor The common method of

obtain tensor Data in ( When tensor Only one element is available in )：tensor.item()

In [10]: a = torch.tensor(np.arange(1))

In [11]: a
Out[11]: tensor([0])

In [12]: a.item()
Out[12]: 0

Turn into numpy Array

In [55]: z.numpy()
Out[55]:
array([[-2.5871205],
       [ 7.3690367],
       [-2.4918075]], dtype=float32)

Get shape ：tensor.size()

In [72]: x
Out[72]:
tensor([[    1,     2],
        [    3,     4],
        [    5,    10]], dtype=torch.int32)

In [73]: x.size()
Out[73]: torch.Size([3, 2])

Shape change ：tensor.view((3,4)). similar numpy Medium reshape, It's a shallow copy , Just a change in shape

In [76]: x.view(2,3)
Out[76]:
tensor([[    1,     2,     3],
        [    4,     5,    10]], dtype=torch.int32)

Get order ：tensor.dim()
```
In [77]: x.dim()
Out[77]: 2
```

Get the maximum ：tensor.max()

In [78]: x.max()
Out[78]: tensor(10, dtype=torch.int32)

Transposition ：tensor.t()

In [79]: x.t()
Out[79]:
tensor([[    1,     3,     5],
        [    2,     4, 	  10]], dtype=torch.int32)

tensor[1,3] obtain tensor The value of the first row and the third column in
tensor[1,3]=100 Yes tensor The position of the first row and the third column in the is assigned 100
tensor The section of

In [101]: x
Out[101]:
tensor([[1.6437, 1.9439, 1.5393],
     [1.3491, 1.9575, 1.0552],
     [1.5106, 1.0123, 1.0961],
     [1.4382, 1.5939, 1.5012],
     [1.5267, 1.4858, 1.4007]])

In [102]: x[:,1]
Out[102]: tensor([1.9439, 1.9575, 1.0123, 1.5939, 1.4858])

4. tensor Data type of

tensor There are many data types in , The common types are as follows ：

Insert picture description here

In the picture above Tensor types This kind of type Of tensor Is an example

obtain tensor Data type of :tensor.dtype
```
In [80]: x.dtype
Out[80]: torch.int32
```

Specify the type when creating data

In [88]: torch.ones([2,3],dtype=torch.float32)
Out[88]:
tensor([[9.1167e+18, 0.0000e+00, 7.8796e+15],
        [8.3097e-43, 0.0000e+00, -0.0000e+00]])

Type modification

In [17]: a
Out[17]: tensor([1, 2], dtype=torch.int32)

In [18]: a.type(torch.float)
Out[18]: tensor([1., 2.])

In [19]: a.double()
Out[19]: tensor([1., 2.], dtype=torch.float64)

5. tensor Other operations of

tensor and tensor Add up

In [94]: x = x.new_ones(5, 3, dtype=torch.float)

In [95]: y = torch.rand(5, 3)

In [96]: x+y
Out[96]:
tensor([[1.6437, 1.9439, 1.5393],
        [1.3491, 1.9575, 1.0552],
        [1.5106, 1.0123, 1.0961],
        [1.4382, 1.5939, 1.5012],
        [1.5267, 1.4858, 1.4007]])
In [98]: torch.add(x,y)
Out[98]:
tensor([[1.6437, 1.9439, 1.5393],
        [1.3491, 1.9575, 1.0552],
        [1.5106, 1.0123, 1.0961],
        [1.4382, 1.5939, 1.5012],
        [1.5267, 1.4858, 1.4007]])
In [99]: x.add(y)
Out[99]:
tensor([[1.6437, 1.9439, 1.5393],
        [1.3491, 1.9575, 1.0552],
        [1.5106, 1.0123, 1.0961],
        [1.4382, 1.5939, 1.5012],
        [1.5267, 1.4858, 1.4007]])
In [100]: x.add_(y)  # The underlined method will be correct for x Make local modifications 
Out[100]:
tensor([[1.6437, 1.9439, 1.5393],
        [1.3491, 1.9575, 1.0552],
        [1.5106, 1.0123, 1.0961],
        [1.4382, 1.5939, 1.5012],
        [1.5267, 1.4858, 1.4007]])

In [101]: x #x Change 
Out[101]:
tensor([[1.6437, 1.9439, 1.5393],
        [1.3491, 1.9575, 1.0552],
        [1.5106, 1.0123, 1.0961],
        [1.4382, 1.5939, 1.5012],
        [1.5267, 1.4858, 1.4007]])

Be careful ： Underlined methods （ such as :add_) Would be right tensor Make local modifications

tensor And digital operation

In [97]: x +10
Out[97]:
tensor([[11., 11., 11.],
        [11., 11., 11.],
        [11., 11., 11.],
        [11., 11., 11.],
        [11., 11., 11.]])

CUDA Medium tensor

CUDA（Compute Unified Device Architecture）, yes NVIDIA The new computing platform . CUDA It's a kind of NVIDIA General parallel computing architecture , The architecture enables GPU Able to solve complex computing problems .

torch.cuda This module adds a pair of CUDA tensor Support for , In the cpu and gpu Use the same method on tensor

adopt .to Method can put a tensor Transfer to another device ( For instance from CPU go to GPU)

#device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
if torch.cuda.is_available():
    device = torch.device("cuda")          # cuda device object 
    y = torch.ones_like(x, device=device)  #  Create one in cuda Upper tensor
    x = x.to(device)                       #  How to use x To cuda  Of tensor
    z = x + y
    print(z)
    print(z.to("cpu", torch.double))       # .to Method can also set the type 
    
>>tensor([1.9806], device='cuda:0')
>>tensor([1.9806], dtype=torch.float64)