264 lines
5.6 KiB
Markdown
264 lines
5.6 KiB
Markdown
Pytorch 备忘清单
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===
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Pytorch 是一种开源机器学习框架，可加速从研究原型设计到生产部署的过程，备忘单是 官网
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备忘清单为您提供了 [Pytorch](https://pytorch.org/) 基本语法和初步应用参考
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入门
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-----
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### 介绍
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- [Pytorch 官网](https://pytorch.org/) _(pytorch.org)_
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- [Pytorch 官方备忘清单](https://pytorch.org/tutorials/beginner/ptcheat.html) _(pytorch.org)_
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### 认识 Pytorch
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```python
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from __future__ import print_function
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import torch
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x = torch.empty(5, 3)
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>>> print(x)
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tensor([
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[2.4835e+27, 2.5428e+30, 1.0877e-19],
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[1.5163e+23, 2.2012e+12, 3.7899e+22],
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[5.2480e+05, 1.0175e+31, 9.7056e+24],
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[1.6283e+32, 3.7913e+22, 3.9653e+28],
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[1.0876e-19, 6.2027e+26, 2.3685e+21]
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])
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```
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<!--rehype:className=wrap-text-->
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Tensors 张量: 张量的概念类似于Numpy中的ndarray数据结构, 最大的区别在于Tensor可以利用GPU的加速功能.
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### 创建一个全零矩阵
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```python
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x = torch.zeros(5, 3, dtype=torch.long)
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>>> print(x)
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tensor([[0, 0, 0],
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[0, 0, 0],
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[0, 0, 0],
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[0, 0, 0],
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[0, 0, 0]])
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```
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创建一个全零矩阵并可指定数据元素的类型为long
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### 数据创建张量
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```python
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x = torch.tensor([2.5, 3.5])
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>>> print(x)
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tensor([2.5000, 3.3000])
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```
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Pytorch 的基本语法
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---------------
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### 加法操作(1)
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```python
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y = torch.rand(5, 3)
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>>> print(x + y)
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tensor([[ 1.6978, -1.6979, 0.3093],
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[ 0.4953, 0.3954, 0.0595],
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[-0.9540, 0.3353, 0.1251],
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[ 0.6883, 0.9775, 1.1764],
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[ 2.6784, 0.1209, 1.5542]])
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```
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### 加法操作(2)
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```python
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>>> print(torch.add(x, y))
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tensor([[ 1.6978, -1.6979, 0.3093],
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[ 0.4953, 0.3954, 0.0595],
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[-0.9540, 0.3353, 0.1251],
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[ 0.6883, 0.9775, 1.1764],
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[ 2.6784, 0.1209, 1.5542]])
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```
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### 加法操作(3)
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```python
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# 提前设定一个空的张量
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result = torch.empty(5, 3)
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# 将空的张量作为加法的结果存储张量
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torch.add(x, y, out=result)
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>>> print(result)
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tensor([[ 1.6978, -1.6979, 0.3093],
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[ 0.4953, 0.3954, 0.0595],
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[-0.9540, 0.3353, 0.1251],
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[ 0.6883, 0.9775, 1.1764],
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[ 2.6784, 0.1209, 1.5542]])
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```
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### 加法操作(4)
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```python
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y.add_(x)
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>>> print(y)
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tensor([[ 1.6978, -1.6979, 0.3093],
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[ 0.4953, 0.3954, 0.0595],
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[-0.9540, 0.3353, 0.1251],
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[ 0.6883, 0.9775, 1.1764],
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[ 2.6784, 0.1209, 1.5542]])
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```
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注意: 所有 `in-place` 的操作函数都有一个下划线的后缀。
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比如 `x.copy_(y)`, `x.add_(y)`, 都会直接改变x的值
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### 张量操作
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```python
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>>> print(x[:, 1])
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tensor([-2.0902, -0.4489, -0.1441, 0.8035, -0.8341])
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```
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<!--rehype:className=wrap-text-->
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### 张量形状
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```python
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x = torch.randn(4, 4)
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# tensor.view()操作需要保证数据元素的总数量不变
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y = x.view(16)
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# -1代表自动匹配个数
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z = x.view(-1, 8)
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>>> print(x.size(), y.size(), z.size())
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torch.Size([4, 4]) torch.Size([16]) torch.Size([2, 8])
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```
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<!--rehype:className=wrap-text-->
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### 取张量元素
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```python
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x = torch.randn(1)
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>>> print(x)
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>>> print(x.item())
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tensor([-0.3531])
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-0.3530771732330322
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```
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### Torch Tensor 和 Numpy array互换
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```python
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a = torch.ones(5)
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>>> print(a)
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tensor([1., 1., 1., 1., 1.])
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```
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Torch Tensor和Numpy array共享底层的内存空间, 因此改变其中一个的值, 另一个也会随之被改变
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### Torch Tensor 转换为 Numpy array
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```python
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b = a.numpy()
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>>> print(b)
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[1. 1. 1. 1. 1.]
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```
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### Numpy array转换为Torch Tensor
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```python
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import numpy as np
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a = np.ones(5)
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b = torch.from_numpy(a)
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np.add(a, 1, out=a)
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>>> print(a)
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>>> print(b)
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[2. 2. 2. 2. 2.]
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tensor([2., 2., 2., 2., 2.], dtype=torch.float64)
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```
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<!--rehype:className=wrap-text-->
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注意: 所有在CPU上的Tensors, 除了CharTensor, 都可以转换为Numpy array并可以反向转换.
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导入 Imports
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---
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### 一般
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```python
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# 根包
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import torch
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# 数据集表示和加载
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from torch.utils.data import Dataset, DataLoader
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```
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<!--rehype:className=wrap-text-->
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### 神经网络 API
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```python
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# 计算图
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import torch.autograd as autograd
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# 计算图中的张量节点
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from torch import Tensor
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# 神经网络
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import torch.nn as nn
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# 层、激活等
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import torch.nn.functional as F
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# 优化器，例如 梯度下降、ADAM等
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import torch.optim as optim
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# 混合前端装饰器和跟踪 jit
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from torch.jit import script, trace
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```
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### Torchscript 和 JIT
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```python
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torch.jit.trace()
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```
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使用你的模块或函数和一个例子，数据输入，并追溯计算步骤，数据在模型中前进时遇到的情况
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```python
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@script
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```
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装饰器用于指示被跟踪代码中的数据相关控制流
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### ONNX
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```python
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torch.onnx.export(model, dummy data, xxxx.proto)
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# 导出 ONNX 格式
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# 使用经过训练的模型模型，dummy
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# 数据和所需的文件名
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model = onnx.load("alexnet.proto")
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# 加载 ONNX 模型
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onnx.checker.check_model(model)
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# 检查模型，IT 是否结构良好
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onnx.helper.printable_graph(model.graph)
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# 打印一个人类可读的，图的表示
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```
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<!--rehype:className=wrap-text-->
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### Vision
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```python
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# 视觉数据集，架构 & 变换
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from torchvision import datasets, models, transforms
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# 组合转换
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import torchvision.transforms as transforms
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```
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<!--rehype:className=wrap-text-->
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### 分布式训练
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```python
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# 分布式通信
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import torch.distributed as dist
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# 内存共享进程
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from torch.multiprocessing import Process
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```
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<!--rehype:className=wrap-text-->
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另见
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---
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- [Pytorch 官网](https://pytorch.org/) _(pytorch.org)_
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- [Pytorch 官方备忘清单](https://pytorch.org/tutorials/beginner/ptcheat.html) _(pytorch.org)_
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