ml.net例子笔记4-ml.net v2版本例子运行

1 Ml.NET版本更新

当前的Microsoft.ML的软件版本如下：

https://gitee.com/mirrors_feiyun0112/machinelearning-samples.zh-cn 例子使用版本为1.6.0
例子工程更换版本的办法：
1 Directory.Build.props nuget.config
修改samples目录下文件Directory.Build.props的内容

~~ ~~
** 2.0.1**
0.18.0

2 打开samples\csharp\All-Samples.sln解决方案
VisualStudio就会加载新的版本的Microsoft.ML库

如以前的工程的引用ml.net库的地方类似如下：

2 例子更新版本到ml.net2.0.1

3 情绪分析例子 [SentimentAnalysis]

SentimentAnalysisConsoleApp.csproj工程的设置修改为：

Exe ** netcoreapp2.1** 变更为如下： Exe ** .net6.0** latest 最终编译结果的差别如下： 使用ml.net v2后指定.net6的编译文件

运行程序

4 垃圾信息检测 SpamDetectionConsoleApp设置后运行

5 ML.NET2官方的例子 [https://github.com/dotnet/machinelearning-

samples/tree/main/samples/csharp/getting- started/MLNET2](https://github.com/dotnet/machinelearning- samples/tree/main/samples/csharp/getting-started/MLNET2) https://gitee.com/mirrors_dotnet/machinelearning- samples 这是gitee中国镜像站，1.8G，很大的文件 目前这个是英文的

6 AutoML

• AutoMLQuickStart - C# console application that shows how to get started with the AutoML API.
• AutoMLAdvanced - C# console application that shows the following concepts:
  • Modifying column inference results
  • Excluding trainers
  • Configuring monitoring
  • Choosing tuners
  • Cancelling experiments
• AutoMLEstimators - C# console application that shows how to:
  • Customize search spaces
  • Create sweepable estimators
• AutoMLTrialRunner - C# console application that shows how to create your own trial runner for the Text Classification API.

7 Natural Language Processing (NLP)

• TextClassification - C# console app that shows how to use the Text Classification API for inferencing using code generated by Model Builder. The model is trained using Model Builder.
• TextClassification_Sentiment_Razor - ASP.NET Core Razor Pages application for sentiment analysis. Code sample for Analyze sentiment of website comments in a web application using ML.NET Model Builder tutorial. Model is trained using Model Builder.
• SentenceSimilarity - C# console app that shows how to use the Sentence Similarity API. Like the Text Classification API, the Sentence Similarity API uses a NAS-BERT transformer-based deep learning model built with TorchSharp to compare how similar two pieces of text are.

8 例子解析

数据来源，从这个地址下载

• Taxi Fare
• Yelp Reviews (yelp_labelled.txt)
• Home Depot

9 句子相似度 SentenceSimilarity

【测试时机器没有cuda环境，使用cpu进行训练】
train.csv - 训练集，包含产品、搜索和相关性分数

id	product_uid	product_title	search_term	relevance
2	100001	Simpson Strong-Tie 12-Gauge Angle	angle bracket	3
3	100001	Simpson Strong-Tie 12-Gauge Angle	l bracket	2.5
9	100002	BEHR Premium Textured DeckOver 1-gal. #SC-141 Tugboat Wood and Concrete Coating	deck over	3

home-depot-sentence-similarity.csv数据代码库没有，原始的train.csv 和 home-depot-sentence- similarity.csv关系，可以参考如下下载和生成
https://github.com/dotnet/machinelearning-samples/issues/982 【我按照代码定义的格式写了合并 csv的数据预处理，如下
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Transforms;
namespace SentenceSimilarity
{
internal class GenData
{
// id product_uid product_title search_term relevance
// 2 100001 Simpson Strong-Tie 12-Gauge Angle angle bracket 3
public class HomeDepot
{
[LoadColumn(0)]
public int id { get; set; }
[LoadColumn(1)]
public int product_uid { get; set; }
[LoadColumn(2)]
public string product_title { get; set; }
[LoadColumn(3)]
public string search_term { get; set; }
[LoadColumn(4)]
public string relevance { get; set; }
}
// https://learn.microsoft.com/en- us/dotnet/api/microsoft.ml.custommappingcatalog.custommapping?view=ml-dotnet
[CustomMappingFactoryAttribute(“product_description”)]
private class ProdDescCustomAction : CustomMappingFactory
{
// We define the custom mapping between input and output rows that will
// be applied by the transformation.
public static void CustomAction(HomeDepot input, CustomMappingOutput
output) => output.product_description = prodDesc[input.product_uid.ToString()];
public override Action GetMapping()
=> CustomAction;
}
// Defines only the column to be generated by the custom mapping
// transformation in addition to the columns already present.
private class CustomMappingOutput
{
public string product_description { get; set; }
}
static Dictionary prodDesc = new Dictionary();
static void Main(string[] args)
{
var mlContext = new MLContext(seed: 1);
var DataPath = Path.GetFullPath(@“……..\Data\product_descriptions.csv”);
{
IDataView dv = mlContext.Data.LoadFromTextFile(DataPath, hasHeader: true, separatorChar: ‘,’, allowQuoting: true,
columns: new[] {
new TextLoader.Column(“product_uid”,DataKind.String,0),
new TextLoader.Column(“product_description”,DataKind.String,1)
}
);
foreach (var row in dv.Preview(maxRows: 15_0000).RowView)
{
string uid=“”, desc=“”;
foreach (KeyValuePair column in row.Values)
{
if (column.Key == “product_uid”)
{
uid = column.Value.ToString();
}
else
{
desc= column.Value.ToString();
}
}
prodDesc[uid] = desc;
}
}
DataPath = Path.GetFullPath(@“……..\Data\train.csv”);
IDataView dataView = mlContext.Data.LoadFromTextFile(DataPath, hasHeader: true, separatorChar: ‘,’, allowQuoting: true);
var preViewTransformedData = dataView.Preview(maxRows: 5);
foreach (var row in preViewTransformedData.RowView)
{
var ColumnCollection = row.Values;
string lineToPrint = “Row–> “;
foreach (KeyValuePair column in ColumnCollection)
{
lineToPrint += $”| {column.Key}:{column.Value}“;
}
Console.WriteLine(lineToPrint + “\n”);
}
var pipeline = mlContext.Transforms.CustomMapping(new ProdDescCustomAction().GetMapping(), contractName: “product_description”);
var transformedData = pipeline.Fit(dataView).Transform(dataView);
//mlContext.ComponentCatalog.RegisterAssembly(typeof(IsUnderThirtyCustomAction).Assembly);
Console.WriteLine(“save file”);
using FileStream fs = new FileStream(Path.GetFullPath(@“……..\Data\home- depot-sentence-similarity.csv”), FileMode.Create);
mlContext.Data.SaveAsText(transformedData, fs, schema: false, separatorChar:‘,’);
}
}
}
具体参考 https://gitee.com/iamops/x-unix- dotnet/blob/main/ml.net2/SentenceSimilarity/GenData.cs
】

数据放好后运行时，会类似如下下载模型文件：
[Source=NasBertTrainer; TrainModel, Kind=Trace] Channel started
[Source=NasBertTrainer; Ensuring model file is present., Kind=Trace] Channel started
[Source=NasBertTrainer; Ensuring model file is present., Kind=Info] Downloading NasBert2000000.tsm from https://aka.ms/mlnet- resources/models/NasBert2000000.tsm to C:\Users\homelap\AppData\Local\Temp\mlnet\NasBert2000000.tsm
[Source=NasBertTrainer; Ensuring model file is present., Kind=Info] NasBert2000000.tsm: Downloaded 3620 bytes out of 17907563
…

TorchSharp目前版本没有正式发布，例子运行问题多多，如上步骤放好数据文件后，直接运行出现

Fatal error. System.AccessViolationException: Attempted to read or write protected memory. This is often an indication that other memory is corrupt.
Repeat 2 times:

at TorchSharp.torch+random.THSGenerator_manual_seed(Int64)

at TorchSharp.torch+random.manual_seed(Int64) …错误

https://github.com/dotnet/machinelearning/issues/6669 按照这个说明设置也不对

ML.NET Version	TorchSharp Package Version
2.0.0	0.98.1
2.0.1	0.98.1
3.0.0-preview	0.98.3
Next preview	0.99.5

按如上的设置版本也不对，仍是运行异常，那就进入TorchSharp的源码看看吧。

10 TorchSharp

初步的软件库结构

如上可见，在Microsoft.ML的大框架下【Microsoft.ML.Core.dll Microsoft.ML.dll Microsoft.ML.PCA.dll Microsoft.ML.Transforms.dll Microsoft.ML.Data.dll Microsoft.ML.KMeansClustering.dll Microsoft.ML.StandardTrainers.dll】
针对Torch，按照ML的框架结构，扩展了Microsoft.ML.TorchSharp这层【Microsoft.ML.TorchSharp.dll】，如下是其扩展的概览图

针对CPU/GPU 的场景，分别提供不同的库支持
TorchSharp TorchAudio TorchVision是使用C#语言实现的不同业务类别的库，这个库在pytorch的C语言库的基础上进行成抽象封装，为Microsoft.ML.TorchSharp提供服务；该工程使用C++语言提供了LibTorchSharp库【

】，供TorchSharp/TorchAudio/TorchVision来调用
LibTorchSharp最后使用P/Invoke的模式来调用 pytorch的c语言库
如下就是TorchSharp 工程封装C语言和打包的配置

相关工程的参考地址：
https://github.com/dotnet/TorchSharp
https://github.com/dotnet/TorchSharpExamples
https://www.nuget.org/packages/TorchSharp/
As we build up to a v1.0 release, we will continue to make breaking changes, but only when we consider it necessary for usability. Similarity to the PyTorch experience is a primary design tenet, and we will continue on that path.
如上可见TorchSharp由于未发布1.0,因此接口变化很快，而且官方仓库在nuget发布的的也没有分支和tag，兼容性问题较大
比如找到这个tag 如 https://github.com/dotnet/TorchSharpExamples/releases/tag/v0.95.4 这个的官方例子运行都有问题【
TorchSharpExamples-0.95.4.tar\TorchSharpExamples-0.95.4\src\CSharp\CSharpExamples

random.manual_seed(1); 这个直接不能访问，报异常
但取main分支，0.100.5的版本运行正常

】

回到当前的例子：

TorchSharp.dll!TorchSharp.torch.random.manual_seed(long seed) Line 21623
at TorchSharp\torch.cs(21623)

0.98.1的版本在 https://github.com/dotnet/TorchSharp 这个仓库没有分支和标签，这代码够乱的， https://www.nuget.org/packages/TorchSharp-cpu 不知道这个仓库发布的版本的代码来自哪里？
0.99.3的版本 https://github.com/dotnet/TorchSharp/releases/tag/v0.99.3 和Microsoft.ML.TorchSharp版本直接不兼容，运行缺少部分实现，估计版本迭代节奏差别大
找了个有分支的代码0.98.2,分析下

11 TorchSharp 0.98.2版本调试

https://github.com/dotnet/TorchSharp/tree/0.98.2 整个这个分支跟踪下，按照其DEVGUIDE.md的说明，使用devenv构建

msbuild TorchSharp.sln

msbuild TorchSharp.sln /p:Configuration=Release /p:Platform=x64

libtorch 的版本和 pytorch 是对应的，比如 libtorch 1.6.0 对应于 pytorch 1.6.0。

• DEBUG模式版本

https://download.pytorch.org/libtorch/cpu/
https://download.pytorch.org/libtorch/cu113
https://download.pytorch.org/libtorch/cu113/libtorch-win-shared-with-deps- debug-1.11.0%2Bcu113.zip
https://download.pytorch.org/libtorch/cpu/libtorch-win-shared-with-deps- debug-1.11.0%2Bcpu.zip
下载后会将这些编译结果自动下载下来

libtorch-cpu\libtorch-win-shared-with-deps-debug-1.11.0%2Bcpu.zip 650M
libtorch-cuda-11.3\libtorch-win-shared-with-deps-debug-1.11.0%2Bcu113.zip 2.7G
文件很大
libtorch-cpu\libtorch-win-shared-with-deps-1.11.0%2Bcpu.zip 143M
libtorch-cuda-11.3\libtorch-win-shared-with-deps-1.11.0%2Bcu113.zip 2G
准备好后，直接vs中调试出现问题的函数，正常没问题

将例子代码拿过来，运行，也正常

• Release模式版本

https://download.pytorch.org/libtorch/cu113/libtorch-win-shared-with- deps-1.11.0%2Bcu113.zip
https://download.pytorch.org/libtorch/cpu/libtorch-win-shared-with- deps-1.11.0%2Bcpu.zip

12 例子正常运行

经尝试，只要将如下libtorch的相关库：

例子默认的发布文件不能工作，更换后如下

初步估计是nuget中发布0.98.2版本可能哪里有不一致的地方
【具体的工程参考
https://gitee.com/iamops/x-unix- dotnet/blob/main/ml.net2/SentenceSimilarity/SentenceSimilarity.csproj 】
运行过程

13 小结

Torch的数据训练使用cpu进行速度的确很慢

SentenceSimilarity -官方的这个例子由于混合了c#和pytorch的c版本，由于torch这块的版本不稳定，使用比较麻烦。具体原因上述已分析。