├── doc
    └── image
    │   ├── paddle名称.drawio.png
    │   └── paddleinferencesharp.drawio.png
├── paddle_infrer_api
    ├── dll
    │   ├── PaddleInferAPI.dll
    │   ├── PaddleInferAPI.exp
    │   └── PaddleInferAPI.lib
    ├── src
    │   └── paddle_infer_cpp_api.cpp
    ├── paddle_infrer_api.vcxproj.filters
    └── paddle_infrer_api.vcxproj
├── PaddleInferenceSharp
    ├── PaddleInferenceSharp.csproj
    ├── NativeMethods.cs
    └── PaddleInfer.cs
├── text_paddle_infer
    ├── Program.cs
    ├── text_paddle_infer.csproj
    ├── ResNet50.cs
    └── PP-Yoloe.cs
├── .gitattributes
├── PaddleInferenceSharp.sln
├── README.md
└── .gitignore


/doc/image/paddle名称.drawio.png:
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https://raw.githubusercontent.com/guojin-yan/PaddleInferenceSharp/HEAD/doc/image/paddle名称.drawio.png


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/paddle_infrer_api/dll/PaddleInferAPI.dll:
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https://raw.githubusercontent.com/guojin-yan/PaddleInferenceSharp/HEAD/paddle_infrer_api/dll/PaddleInferAPI.dll


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/paddle_infrer_api/dll/PaddleInferAPI.exp:
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https://raw.githubusercontent.com/guojin-yan/PaddleInferenceSharp/HEAD/paddle_infrer_api/dll/PaddleInferAPI.exp


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/paddle_infrer_api/dll/PaddleInferAPI.lib:
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https://raw.githubusercontent.com/guojin-yan/PaddleInferenceSharp/HEAD/paddle_infrer_api/dll/PaddleInferAPI.lib


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/doc/image/paddleinferencesharp.drawio.png:
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https://raw.githubusercontent.com/guojin-yan/PaddleInferenceSharp/HEAD/doc/image/paddleinferencesharp.drawio.png


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/paddle_infrer_api/src/paddle_infer_cpp_api.cpp:
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https://raw.githubusercontent.com/guojin-yan/PaddleInferenceSharp/HEAD/paddle_infrer_api/src/paddle_infer_cpp_api.cpp


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/PaddleInferenceSharp/PaddleInferenceSharp.csproj:
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 1 | <Project Sdk="Microsoft.NET.Sdk">
 2 | 
 3 |   <PropertyGroup>
 4 |     <TargetFramework>net6.0</TargetFramework>
 5 |     <ImplicitUsings>enable</ImplicitUsings>
 6 |     <Nullable>enable</Nullable>
 7 |     <AllowUnsafeBlocks>true</AllowUnsafeBlocks>
 8 |   </PropertyGroup>
 9 | 
10 |   <ItemGroup>
11 |     <PackageReference Include="System.Collections" Version="4.3.0" />
12 |   </ItemGroup>
13 | 
14 | </Project>
15 | 


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/text_paddle_infer/Program.cs:
--------------------------------------------------------------------------------
 1 | using System;
 2 | using OpenCvSharp;
 3 | using PaddleInferenceSharp;
 4 | namespace text_paddle_infer // Note: actual namespace depends on the project name.
 5 | {
 6 |     internal class Program
 7 |     {
 8 |         static void Main(string[] args)
 9 |         {
10 |             Console.WriteLine("Hello World!");
11 |             //ResNet50.paddle_deploy_resnet50();
12 |             PP_Yoloe.paddle_deploy_ppyoloe();
13 |         }
14 |     }
15 | }


--------------------------------------------------------------------------------
/text_paddle_infer/text_paddle_infer.csproj:
--------------------------------------------------------------------------------
 1 | <Project Sdk="Microsoft.NET.Sdk">
 2 | 
 3 |   <PropertyGroup>
 4 |     <OutputType>Exe</OutputType>
 5 |     <TargetFramework>net6.0</TargetFramework>
 6 |     <ImplicitUsings>enable</ImplicitUsings>
 7 |     <Nullable>enable</Nullable>
 8 |     <AllowUnsafeBlocks>true</AllowUnsafeBlocks>
 9 |   </PropertyGroup>
10 | 
11 |   <ItemGroup>
12 |     <PackageReference Include="OpenCvSharp4" Version="4.6.0.20220608" />
13 |     <PackageReference Include="OpenCvSharp4.runtime.win" Version="4.6.0.20220608" />
14 |     <PackageReference Include="OpenCvSharp4.Windows" Version="4.6.0.20220608" />
15 |   </ItemGroup>
16 | 
17 |   <ItemGroup>
18 |     <ProjectReference Include="..\PaddleInferenceSharp\PaddleInferenceSharp.csproj" />
19 |   </ItemGroup>
20 | 
21 | </Project>
22 | 


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10 |       <Extensions>h;hh;hpp;hxx;h++;hm;inl;inc;ipp;xsd</Extensions>
11 |     </Filter>
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13 |       <UniqueIdentifier>{67DA6AB6-F800-4c08-8B7A-83BB121AAD01}</UniqueIdentifier>
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15 |     </Filter>
16 |   </ItemGroup>
17 |   <ItemGroup>
18 |     <ClCompile Include="src\paddle_infer_cpp_api.cpp">
19 |       <Filter>源文件</Filter>
20 |     </ClCompile>
21 |   </ItemGroup>
22 | </Project>


--------------------------------------------------------------------------------
/.gitattributes:
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 1 | ###############################################################################
 2 | # Set default behavior to automatically normalize line endings.
 3 | ###############################################################################
 4 | * text=auto
 5 | 
 6 | ###############################################################################
 7 | # Set default behavior for command prompt diff.
 8 | #
 9 | # This is need for earlier builds of msysgit that does not have it on by
10 | # default for csharp files.
11 | # Note: This is only used by command line
12 | ###############################################################################
13 | #*.cs     diff=csharp
14 | 
15 | ###############################################################################
16 | # Set the merge driver for project and solution files
17 | #
18 | # Merging from the command prompt will add diff markers to the files if there
19 | # are conflicts (Merging from VS is not affected by the settings below, in VS
20 | # the diff markers are never inserted). Diff markers may cause the following 
21 | # file extensions to fail to load in VS. An alternative would be to treat
22 | # these files as binary and thus will always conflict and require user
23 | # intervention with every merge. To do so, just uncomment the entries below
24 | ###############################################################################
25 | #*.sln       merge=binary
26 | #*.csproj    merge=binary
27 | #*.vbproj    merge=binary
28 | #*.vcxproj   merge=binary
29 | #*.vcproj    merge=binary
30 | #*.dbproj    merge=binary
31 | #*.fsproj    merge=binary
32 | #*.lsproj    merge=binary
33 | #*.wixproj   merge=binary
34 | #*.modelproj merge=binary
35 | #*.sqlproj   merge=binary
36 | #*.wwaproj   merge=binary
37 | 
38 | ###############################################################################
39 | # behavior for image files
40 | #
41 | # image files are treated as binary by default.
42 | ###############################################################################
43 | #*.jpg   binary
44 | #*.png   binary
45 | #*.gif   binary
46 | 
47 | ###############################################################################
48 | # diff behavior for common document formats
49 | # 
50 | # Convert binary document formats to text before diffing them. This feature
51 | # is only available from the command line. Turn it on by uncommenting the 
52 | # entries below.
53 | ###############################################################################
54 | #*.doc   diff=astextplain
55 | #*.DOC   diff=astextplain
56 | #*.docx  diff=astextplain
57 | #*.DOCX  diff=astextplain
58 | #*.dot   diff=astextplain
59 | #*.DOT   diff=astextplain
60 | #*.pdf   diff=astextplain
61 | #*.PDF   diff=astextplain
62 | #*.rtf   diff=astextplain
63 | #*.RTF   diff=astextplain
64 | 


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/text_paddle_infer/ResNet50.cs:
--------------------------------------------------------------------------------
 1 | using System;
 2 | using System.Collections.Generic;
 3 | using System.Linq;
 4 | using System.Text;
 5 | using System.Threading.Tasks;
 6 | using PaddleInferenceSharp;
 7 | using OpenCvSharp;
 8 | 
 9 | namespace text_paddle_infer
10 | {
11 |     internal class ResNet50
12 |     {
13 |         public static void paddle_deploy_resnet50() 
14 |         {
15 |             //----------------------1.模型相关信息----------------------//
16 |             string model_path = "E:/Text_Model/flowerclas/inference.pdmodel";
17 |             string params_path = "E:/Text_Model/flowerclas/inference.pdiparams";
18 |             string image_path = "E:/Text_dataset/flowers102/jpg/image_00005.jpg";
19 |             DateTime begin, end;
20 |             TimeSpan t0, t1, t2, t3;
21 |             //----------------------2. 创建并配置预测器------------------//
22 |             begin = DateTime.Now;
23 |             PaddleInfer predictor = new PaddleInfer(model_path, params_path);
24 |             // 设置设备类型
25 |             predictor.set_divice(Divice.CPU, 10); // CPU
26 |             //predictor.set_divice(Divice.GPU, 0, 500, 30); // GPU
27 |             //predictor.set_divice(Divice.ONNX_runtime, 10); // ONNX_runtime
28 |             //predictor.set_divice(Divice.oneDNN, 1); // oneDNN
29 |             // 获取输入节点
30 |             List<string> input_name = predictor.get_input_names();
31 |             for (int i = 0; i < input_name.Count; i++)
32 |             {
33 |                 Console.WriteLine("模型输入 {0}  {1}", i, input_name[i]);
34 |             }
35 |             // 设置输入形状
36 |             int[] input_size = new int[4] { 1, 3, 224, 224 };
37 |             predictor.set_input_shape(input_size, input_name[0]);
38 |             end = DateTime.Now;
39 |             t0 = end - begin;
40 |             //----------------------3. 加载推理数据------------------//
41 |             begin = DateTime.Now;
42 |             Mat image = Cv2.ImRead(image_path);
43 |             byte[] input_image_data = image.ImEncode(".bmp");
44 |             // 数据长度
45 |             ulong input_image_length = Convert.ToUInt64(input_image_data.Length);
46 |             predictor.load_input_data(input_name[0], input_image_data, input_image_length, 0);
47 |             end = DateTime.Now;
48 |             t1 = end - begin;
49 |             //----------------------4. 模型推理------------------//
50 |             begin = DateTime.Now;
51 |             predictor.infer();
52 |             end = DateTime.Now;
53 |             t2 = end - begin;
54 |             //----------------------5. 模型推理结果------------------//
55 |             begin = DateTime.Now;
56 |             int[] leng = new int[4];
57 |             List<string> output_name = predictor.get_output_names();
58 |             for (int i = 0; i < input_name.Count; i++)
59 |             {
60 |                 Console.WriteLine("模型输出 {0}  {1}", i, output_name[i]);
61 |             }
62 |             float[] output = predictor.read_infer_result<float>(output_name[0], 102);
63 |             float max;
64 |             int index = max_indax<float>(output, out max);
65 |             end = DateTime.Now;
66 |             t3 = end - begin;
67 |             Console.WriteLine("最大类别为：{0}，分数：{1}。", index, max);
68 |             Console.WriteLine("模型加载时间：{0}", t0.TotalMilliseconds);
69 |             Console.WriteLine("推理数据加载时间：{0}", t1.TotalMilliseconds);
70 |             Console.WriteLine("模型推理时间：{0}", t2.TotalMilliseconds);
71 |             Console.WriteLine("结果处理时间：{0}", t3.TotalMilliseconds);
72 | 
73 | 
74 |         }
75 | 
76 |         static int max_indax<T>(T[] data, out T max) where T : IComparable<T>
77 |         { 
78 |             int index = 0;
79 |             max = data[0];
80 |             for (int i = 0; i < data.Length; i++) 
81 |             {
82 |                 if (data[i].CompareTo(max) > 0)
83 |                 {
84 |                     index = i;
85 |                     max = data[i];
86 |                 }
87 |             }
88 |             
89 | 
90 | 
91 | 
92 |             return index;
93 |         }
94 |     }
95 | }
96 | 


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/PaddleInferenceSharp/NativeMethods.cs:
--------------------------------------------------------------------------------
 1 | using System;
 2 | using System.Collections.Generic;
 3 | using System.Linq;
 4 | using System.Runtime.InteropServices;
 5 | using System.Text;
 6 | using System.Threading.Tasks;
 7 | 
 8 | namespace PaddleInferenceSharp
 9 | {
10 |     public class NativeMethods
11 |     {
12 |         private const string paddle_infer_path = @"E:\Git_space\PaddleInferenceSharp\paddle_infrer_api\dll\PaddleInferAPI.dll";
13 |         [DllImport(paddle_infer_path, CharSet = CharSet.Unicode, CallingConvention = CallingConvention.Cdecl)]
14 |         public static extern IntPtr paddle_infer_init();
15 |         [DllImport(paddle_infer_path, CharSet = CharSet.Unicode, CallingConvention = CallingConvention.Cdecl)]
16 |         public static extern IntPtr set_model(IntPtr paddle_infer, string model_path, string params_path);
17 |         [DllImport(paddle_infer_path, CharSet = CharSet.Unicode, CallingConvention = CallingConvention.Cdecl)]
18 |         public static extern IntPtr predictor_device_cpu(IntPtr paddle_infer, int cpu_math_library_num_threads);
19 |         [DllImport(paddle_infer_path, CharSet = CharSet.Unicode, CallingConvention = CallingConvention.Cdecl)]
20 |         public static extern IntPtr predictor_device_gpu(IntPtr paddle_infer, ulong memory_init_size, int device_id, int workspace_size);
21 |         [DllImport(paddle_infer_path, CharSet = CharSet.Unicode, CallingConvention = CallingConvention.Cdecl)]
22 |         public static extern IntPtr predictor_device_ONNX_runtime(IntPtr paddle_infer, int cpu_math_library_num_threads);
23 |         [DllImport(paddle_infer_path, CharSet = CharSet.Unicode, CallingConvention = CallingConvention.Cdecl)]
24 |         public static extern IntPtr predictor_device_oneDNN(IntPtr paddle_infer, int capacity);
25 |         [DllImport(paddle_infer_path, CharSet = CharSet.Unicode, CallingConvention = CallingConvention.Cdecl)]
26 |         public static extern string get_input_names(IntPtr paddle_infer, ref int length);
27 |         [DllImport(paddle_infer_path, CharSet = CharSet.Unicode, CallingConvention = CallingConvention.Cdecl)]
28 |         public static extern IntPtr set_input_shape(IntPtr paddle_infer, string input_name, ref int input_shape, int length);
29 |         [DllImport(paddle_infer_path, CharSet = CharSet.Unicode, CallingConvention = CallingConvention.Cdecl)]
30 |         public static extern IntPtr load_input_image_data(IntPtr paddle_infer, string input_name, ref byte image_data, ulong image_size, int type);
31 |         [DllImport(paddle_infer_path, CharSet = CharSet.Unicode, CallingConvention = CallingConvention.Cdecl)]
32 |         public static extern IntPtr load_input_data(IntPtr paddle_infer, string input_name, ref float data);
33 |         [DllImport(paddle_infer_path, CharSet = CharSet.Unicode, CallingConvention = CallingConvention.Cdecl)]
34 |         public static extern IntPtr infer(IntPtr paddle_infer);
35 |         [DllImport(paddle_infer_path, CharSet = CharSet.Unicode, CallingConvention = CallingConvention.Cdecl)]
36 |         public static extern string get_output_names(IntPtr paddle_infer, ref int length);
37 |         [DllImport(paddle_infer_path, CharSet = CharSet.Unicode, CallingConvention = CallingConvention.Cdecl)]
38 |         public static extern void get_node_shape(IntPtr paddle_infer_ptr, string node_name, ref int shape, ref int dimension);
39 |         [DllImport(paddle_infer_path, CharSet = CharSet.Unicode, CallingConvention = CallingConvention.Cdecl)]
40 |         public static extern void read_result_data_F32(IntPtr paddle_infer, string output_name, ref float result);
41 |         [DllImport(paddle_infer_path, CharSet = CharSet.Unicode, CallingConvention = CallingConvention.Cdecl)]
42 |         public static extern void read_result_data_I32(IntPtr paddle_infer, string output_name, ref int result);
43 |         [DllImport(paddle_infer_path, CharSet = CharSet.Unicode, CallingConvention = CallingConvention.Cdecl)]
44 |         public static extern void read_result_data_I64(IntPtr paddle_infer, string output_name, ref long result);
45 |         [DllImport(paddle_infer_path, CharSet = CharSet.Unicode, CallingConvention = CallingConvention.Cdecl)]
46 |         public static extern void dispose(IntPtr paddle_infer);
47 |            
48 | 
49 |     }
50 | }
51 | 


--------------------------------------------------------------------------------
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--------------------------------------------------------------------------------
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/text_paddle_infer/PP-Yoloe.cs:
--------------------------------------------------------------------------------
  1 | using System;
  2 | using System.Collections.Generic;
  3 | using System.Linq;
  4 | using System.Text;
  5 | using System.Threading.Tasks;
  6 | using PaddleInferenceSharp;
  7 | using OpenCvSharp;
  8 | 
  9 | namespace text_paddle_infer
 10 | {
 11 |     internal class PP_Yoloe
 12 |     {
 13 |         public static void paddle_deploy_ppyoloe() 
 14 |         {
 15 |             //----------------------1.模型相关信息----------------------//
 16 |             string model_path = @"E:\Text_Model\ppyoloe_plus_crn_l_80e_coco\model.pdmodel";
 17 |             string params_path = @"E:\Text_Model\ppyoloe_plus_crn_l_80e_coco\model.pdiparams";
 18 |             string image_path = @"E:\Text_dataset\YOLOv5\0003.jpg";
 19 |             DateTime begin, end;
 20 |             TimeSpan t0, t1, t2, t3;
 21 |             //----------------------2. 创建并配置预测器------------------//
 22 |             begin = DateTime.Now;
 23 |             PaddleInfer predictor = new PaddleInfer(model_path, params_path);
 24 |             // 设置设备类型
 25 |             //predictor.set_divice(Divice.CPU, 10); // CPU
 26 |             //predictor.set_divice(Divice.GPU, 0, 500, 30); // GPU
 27 |             predictor.set_divice(Divice.ONNX_runtime, 10); // ONNX_runtime
 28 |             //predictor.set_divice(Divice.oneDNN, 1); // oneDNN
 29 |             // 获取输入节点
 30 |             List<string> input_name = predictor.get_input_names();
 31 |             for (int i = 0; i < input_name.Count; i++)
 32 |             {
 33 |                 Console.WriteLine("模型输入 {0}  {1}", i, input_name[i]);
 34 |             }
 35 |             // 设置输入形状
 36 |             int[] input_size1 = new int[4] { 1, 3, 640, 640 };
 37 |             predictor.set_input_shape(input_size1, input_name[0]);
 38 |             int[] input_size2 = new int[2] { 1,2};
 39 |             predictor.set_input_shape(input_size2, input_name[1]);
 40 |             end = DateTime.Now;
 41 |             t0 = end - begin;
 42 |             //----------------------3. 加载推理数据------------------//
 43 |             begin = DateTime.Now;
 44 |             Mat image = Cv2.ImRead(image_path);
 45 |             // 将图片放在矩形背景下
 46 |             int max_image_length = image.Cols > image.Rows ? image.Cols : image.Rows;
 47 |             Mat max_image = Mat.Zeros(new Size(max_image_length, max_image_length), MatType.CV_8UC3);
 48 |             Rect roi = new Rect(0, 0, image.Cols, image.Rows);
 49 |             image.CopyTo(new Mat(max_image, roi));
 50 |             byte[] input_image_data = max_image.ImEncode(".bmp");
 51 |             // 数据长度
 52 |             ulong input_image_length = Convert.ToUInt64(input_image_data.Length);
 53 |             predictor.load_input_data(input_name[0], input_image_data, input_image_length, 2);
 54 |             float scale_factor = 640.0f/ max_image_length;
 55 |             float[] input_scale = new float[] { scale_factor, scale_factor };
 56 |             predictor.load_input_data(input_name[1], input_scale);
 57 |             end = DateTime.Now;
 58 |             t1 = end - begin;
 59 |             //----------------------4. 模型推理------------------//
 60 |             begin = DateTime.Now;
 61 |             predictor.infer();
 62 |             end = DateTime.Now;
 63 |             t2 = end - begin;
 64 |             //----------------------5. 模型推理结果------------------//
 65 |             begin = DateTime.Now;
 66 |             int[] leng = new int[4];
 67 |             List<string> output_name = predictor.get_output_names();
 68 |             for (int i = 0; i < input_name.Count; i++)
 69 |             {
 70 |                 Console.WriteLine("模型输出 {0}  {1}", i, output_name[i]);
 71 |             }
 72 |             List<int> output_shape = predictor.get_shape(output_name[0]);
 73 |             Console.WriteLine("output_shape：{0} × {1}", output_shape[0], output_shape[1]);
 74 |             int[] output_num = predictor.read_infer_result<int>(output_name[1], 1);
 75 |             Console.WriteLine(output_num[0]);
 76 |             float[] output = predictor.read_infer_result<float>(output_name[0], output_num[0] * 6);
 77 |             List<Rect> boxes = new List<Rect>();
 78 |             List<int> classes = new List<int>();
 79 |             List<float> scores = new List<float>();
 80 |             for (int i = 0; i < output_num[0]; i++)
 81 |             {
 82 |                 if (output[6 * i + 1] > 0.4)
 83 |                 { 
 84 |                     scores.Add(output[6 * i + 1]);
 85 |                     classes.Add((int)output[6 * i]);
 86 |                     Rect rect = new Rect((int)output[6 * i + 2], (int)output[6 * i + 3],
 87 |                         (int)(output[6 * i + 4] - output[6 * i + 2]), (int)(output[6 * i + 5] - output[6 * i + 3]));
 88 |                     boxes.Add(rect);
 89 |                 }
 90 |                 
 91 |             }
 92 |             end = DateTime.Now;
 93 |             t3 = end - begin;
 94 |             Console.WriteLine("模型加载时间：{0}", t0.TotalMilliseconds);
 95 |             Console.WriteLine("推理数据加载时间：{0}", t1.TotalMilliseconds);
 96 |             Console.WriteLine("模型推理时间：{0}", t2.TotalMilliseconds);
 97 |             Console.WriteLine("结果处理时间：{0}", t3.TotalMilliseconds);
 98 | 
 99 |             for (int i = 0; i < classes.Count; i++) 
100 |             {
101 |                 Cv2.Rectangle(image, boxes[i], new Scalar(0, 0, 255), 1, LineTypes.Link8);
102 |                 Cv2.PutText(image, scores[i].ToString(), new Point(boxes[i].X, boxes[i].Y - 5),
103 |                     HersheyFonts.HersheySimplex, 0.5, new Scalar(0, 255, 0));
104 |             }
105 |             Cv2.ImShow("result", image);
106 |             Cv2.WaitKey(0);
107 |         }
108 |     }
109 | }
110 | 


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/README.md:
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  1 | # 基于C#和Paddle Inference 部署PaddlePaddle模型——PaddleInferenceSharp
  2 | 
  3 | ![paddle名称.drawio](./doc/image/paddle名称.drawio.png)
  4 | 
  5 | # 1. 项目介绍
  6 | 
  7 | &emsp; Paddle Inference 是飞桨的原生推理库， 提供服务器端的高性能推理能力，直接基于飞桨的训练算子，因此它支持飞桨训练出的所有模型的推理；Paddle Inference 功能特性丰富，性能优异，针对不同平台不同的应用场景进行了深度的适配优化，做到高吞吐、低时延，保证了飞桨模型在服务器端即训即用，快速部署。
  8 | 
  9 | &emsp; 但由于Paddle Inference目前只提供了Python、C++、C、Go语言方法接口，C#无法直接使用。近几年以来 ，C#语言快速发展，并跻身于编程语言排行榜前几名的位置，为了能够在C#语言中调用Paddle Inference模型推理库，根据C++动态链接库的原理，推出PaddleInferenceSharp，实现直接在C#平台调用Paddle Inference部署深度学习模型。其实现原理可以参考下图：
 10 | 
 11 | ![paddleinferencesharp.drawio](./doc/image/paddleinferencesharp.drawio.png)
 12 | 
 13 | 
 14 | 
 15 | # 2.项目环境配置
 16 | 
 17 | &emsp;为了防止复现代码出现问题，列出以下代码开发环境，可以根据自己需求设置。
 18 | 
 19 | -  操作系统：Windows 11
 20 | -  CUDA：11.4
 21 | -  cuDNN：8.2.4
 22 | -  TensorRT：8.4.0.6
 23 | -  OpenCV：4.5.5
 24 | -  Visual Studio：2022
 25 | -  C#框架：.NET 6.0
 26 | -  OpenCvSharp：OpenCvSharp4
 27 | 
 28 | &emsp;此处最重要的是需要安装Paddle Inference C++版，具体安装方式可以参考下面的连接：[Paddle Inference C++ 依赖库安装(Windows)](https://blog.csdn.net/grape_yan/article/details/127322648?spm=1001.2014.3001.5501)。对于其他依赖项安装可以参考以下链接：[NVIDIA TensorR 安装 (Windows C++)](https://blog.csdn.net/grape_yan/article/details/127320959?spm=1001.2014.3001.5501)、[OpenCV C++安装和配置](https://blog.csdn.net/grape_yan/article/details/126954261?spm=1001.2014.3001.5501)。
 29 | 
 30 | # 3.项目下载方式
 31 | 
 32 | &emsp;项目所使用的源码均已经在Github和Gitee上开源，
 33 | 
 34 | ```shell
 35 | Github:
 36 | 
 37 | git clone https://github.com/guojin-yan/PaddleInferenceSharp.git
 38 | 
 39 | Gitee:
 40 | 
 41 | git clone https://gitee.com/guojin-yan/PaddleInferenceSharp.git
 42 | ```
 43 | 
 44 | 
 45 | 
 46 | # 4. PaddleInfer类 
 47 | 
 48 | ## 4.1 API 方法
 49 | 
 50 | <table>
 51 | 	<tr>
 52 | 	    <th width="7%" align="center" bgcolor=#FF7A68>序号</th>
 53 | 	    <th width="35%" colspan="2" align="center" bgcolor=#FF7A68>API</th>
 54 | 	    <th width="43%" align="center" bgcolor=#FF7A68>参数解释</th>  
 55 |         <th width="15%" align="center" bgcolor=#FF7A68>说明</th>
 56 | 	</tr >
 57 | 	<tr >
 58 | 	    <td rowspan="3" align="center">1</td>
 59 | 	    <td align="center">方法</td>
 60 |         <td>PaddleInfer()</td>
 61 |         <td>构造函数，初始化推理核心，读取本地模型</td>
 62 |         <td rowspan="3"></td>
 63 | 	</tr>
 64 |     <tr >
 65 | 	    <td rowspan="2" align="center">参数</td>
 66 |         <td><font color=blue>string</font> model_path</td>
 67 |         <td>静态图模型文件</td>
 68 | 	</tr>
 69 |     <tr >
 70 |         <td><font color=blue>string</font> params_path</td>
 71 |         <td>模型配置文件信息，默认为空</td>
 72 | 	</tr>
 73 |     <tr >
 74 | 	    <td rowspan="5" align="center">2</td>
 75 | 	    <td align="center">方法</td>
 76 |         <td><font color=blue>void</font> set_divice()</td>
 77 |         <td>设置推理设备</td>
 78 |         <td rowspan="5">支持 CPU、GPU、ONNX runtime、oneDNN</td>
 79 | 	</tr>
 80 |     <tr >
 81 | 	    <td rowspan="4" align="center">参数</td>
 82 |         <td>Divice divice</td>
 83 |         <td>设备名称选择</td>
 84 | 	</tr>
 85 |     <tr >
 86 |         <td><font color=blue>int</font> num </td>
 87 |         <td>对于CPU、ONNX runtime代表线程数，默认为10；<br>对于GPU代表显卡编号，默认为0；<br>对于oneDNN代表cache数量，默认为1</br></td>
 88 | 	</tr>
 89 |     <tr >
 90 |         <td><font color=blue>ulong</font> memory_init_size</td>
 91 |         <td>显存分配空间(在使用GPU时作用)，默认为500</td>
 92 | 	</tr>
 93 |     <tr >
 94 |         <td><font color=blue>int</font> workspace_size</td>
 95 |         <td>显存工作空间(在使用GPU时作用)，默认为30</td>
 96 | 	</tr>
 97 | 	<tr >
 98 | 	    <td rowspan="1" align="center">3</td>
 99 | 	    <td align="center">方法</td>
100 |         <td><font color=green>List</font>  &lt<font color=blue>string</font>&gt get_input_names()</td>
101 |         <td>获取输入节点名字</td>
102 |         <td rowspan="1"></td>
103 | 	</tr>
104 | 	<tr >
105 | 	    <td rowspan="3" align="center">4</td>
106 | 	    <td align="center">方法</td>
107 |         <td><font color=blue>void</font> set_input_shape()</td>
108 |         <td>设置输入节点形状</td>
109 |         <td rowspan="3">根据节点维度设置</td>
110 | 	</tr>
111 |     <tr >
112 | 	    <td rowspan="2" align="center">参数</td>
113 |         <td><font color=blue>int[]</font> input_shape</td>
114 |         <td>形状数组</td>
115 | 	</tr>
116 |     <tr >
117 |         <td><font color=blue>string</font> input_name</td>
118 |         <td>节点名称</td>
119 | 	</tr>
120 | 	<tr >
121 | 	    <td rowspan="7" align="center">5</td>
122 | 	    <td align="center">方法</td>
123 |         <td><font color=blue>void</font> load_input_data()</td>
124 |         <td>设置图片/普通输入数据</td>
125 |         <td rowspan="7">方法重载</td>
126 | 	</tr>
127 |     <tr >
128 | 	    <td rowspan="2" align="center">参数</td>
129 |         <td><font color=blue>string</font> input_name</td>
130 |         <td>输入节点名称</td>
131 | 	</tr>
132 |     <tr >
133 |         <td><font color=blue>float[]</font> input_data</td>
134 |         <td>输入数据</td>
135 | 	</tr>
136 |     <tr >
137 | 	    <td rowspan="4" align="center">参数</td>
138 |         <td><font color=blue>string</font> input_name</td>
139 |         <td>输入节点名称</td>
140 | 	</tr>
141 |     <tr >
142 |         <td><font color=blue>byte[]</font> image_data</td>
143 |         <td>图片数据</td>
144 | 	</tr>
145 |     <tr >
146 |         <td><font color=blue>ulong</font> image_size</td>
147 |         <td>图片长度</td>
148 | 	</tr>
149 |     <tr >
150 |         <td><font color=blue>int</font> type</td>
151 |         <td>数据处理类型：<br>type == 0: 均值方差归一化、直接缩放<br>type == 1: 普通归一化、直接缩放<br>type == 2: 均值方差归一化、仿射变换</td>
152 | 	</tr>
153 | 	<tr >
154 | 	    <td rowspan="1" align="center">6</td>
155 | 	    <td align="center">方法</td>
156 |         <td><font color=blue>void</font> infer()</td>
157 |         <td>模型推理</td>
158 |         <td rowspan="1"></td>
159 | 	</tr>
160 | 	<tr >
161 | 	    <td rowspan="1" align="center">7</td>
162 | 	    <td align="center">方法</td>
163 |         <td><font color=green>List</font>  &lt<font color=blue>string</font>&gt get_output_names()</td>
164 |         <td>获取输出节点名字</td>
165 |         <td rowspan="1"></td>
166 | 	</tr>
167 | 	<tr >
168 | 	    <td rowspan="2" align="center">8</td>
169 | 	    <td align="center">方法</td>
170 |         <td><font color=green>List</font>  &lt<font color=blue>int</font>&gt get_shape()</td>
171 |         <td>获取指定节点形状</td>
172 |         <td rowspan="2"></td>
173 | 	</tr>
174 |     <tr >
175 | 	    <td rowspan="1" align="center">参数</td>
176 |         <td><font color=blue>string</font> node_name</td>
177 |         <td>节点名称</td>
178 | 	</tr>
179 | 	<tr >
180 | 	    <td rowspan="3" align="center">9</td>
181 | 	    <td align="center">方法</td>
182 |         <td><font color=blue>void</font> <font color=green>T</font>[] read_infer_result &lt<font color=green>T</font>&gt()</td>
183 |         <td>读取推理结果数据</td>
184 |         <td rowspan="3">支持读取Float32、Int32、Int64格式数据</td>
185 | 	</tr>
186 |     <tr >
187 | 	    <td rowspan="2" align="center">参数</td>
188 |         <td><font color=blue>string</font> output_name</td>
189 |         <td>输出节点名</td>
190 | 	</tr>
191 |     <tr >
192 |         <td><font color=blue>int</font> data_size</td>
193 |         <td>输出数据长度</td>
194 | 	</tr>
195 | 	<tr >
196 | 	    <td rowspan="1" align="center">10</td>
197 | 	    <td align="center">方法</td>
198 |         <td><font color=blue>void</font> delet()</td>
199 |         <td>删除内存地址</td>
200 |         <td rowspan="1"></td>
201 | 	</tr>
202 | 
203 | 
204 | 
205 | 
206 | ## 4.2 枚举
207 | 
208 | <table>
209 | 	<tr>
210 | 	    <th width="10%" align="center" bgcolor=#FF7A68>序号</th>
211 | 	    <th width="20%"  align="center" bgcolor=#FF7A68>枚举名</th>
212 | 	    <th width="30%" align="center" bgcolor=#FF7A68>枚举变量</th>  
213 |         <th width="40%" align="center" bgcolor=#FF7A68>含义</th>
214 | 	</tr >
215 | 	<tr >
216 | 	    <td rowspan="4" align="center">1</td>
217 |         <td rowspan="4" align="center">Divice<br>设备名称</br></td>
218 |         <td align="center">CPU</td>
219 |         <td>使用CPU推理</td>
220 | 	</tr>
221 | 	<tr >
222 |         <td align="center">GPU</td>
223 |         <td>使用GPU推理</td>
224 | 	</tr>
225 | 	<tr >
226 |         <td align="center">ONNX_runtime</td>
227 |         <td>使用ONNX_runtime推理</td>
228 | 	</tr>
229 | 	<tr >
230 |         <td align="center">oneDNN</td>
231 |         <td>使用oneDNN推理</td>
232 | 	</tr>
233 | 
234 | &emsp;关于上述方法的使用，后续会更新县官的案例教程以及详细的技术文档，敬请期待。
235 | 


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/PaddleInferenceSharp/PaddleInfer.cs:
--------------------------------------------------------------------------------
  1 | using System;
  2 | using System.Collections.Generic;
  3 | using System.Linq;
  4 | using System.Text;
  5 | using System.Threading.Tasks;
  6 | 
  7 | namespace PaddleInferenceSharp
  8 | {
  9 |     /// <summary>
 10 |     /// PaddlePaddle Inference 模型推理类
 11 |     /// </summary>
 12 |     public class PaddleInfer
 13 |     {
 14 |         // 推理核心地址
 15 |         private IntPtr paddle_infer;
 16 | 
 17 |         /// <summary>
 18 |         /// 构造函数
 19 |         /// </summary>
 20 |         /// <param name="model_path">模型地址</param>
 21 |         /// <param name="params_path">模型参数地址</param>
 22 |         public PaddleInfer(string model_path, string params_path=" ") 
 23 |         {
 24 |             paddle_infer = NativeMethods.paddle_infer_init();
 25 |             paddle_infer = NativeMethods.set_model(paddle_infer, model_path, params_path);
 26 |         }
 27 |         /// <summary>
 28 |         /// 设置推理设备
 29 |         /// 0.CPU，1.GPU，2.ONNX runtime，3.oneDNN
 30 |         /// </summary>
 31 |         /// <param name="divice">设备选择</param>
 32 |         /// <param name="num">对于CPU、ONNX runtime代表线程数，默认为10；对于GPU代表显卡编号，默认为0；对于oneDNN代表cache数量，默认为1</param>
 33 |         /// <param name="memory_init_size">显存分配空间(尽在使用GPU时作用)</param>
 34 |         /// <param name="workspace_size">显存工作空间(尽在使用GPU时作用)</param>
 35 |         public void set_divice(Divice divice, int num = 0, ulong memory_init_size = 500, int workspace_size = 30)
 36 |         {
 37 |             if (divice == Divice.CPU)
 38 |             {
 39 |                 if (num == 0)
 40 |                 {
 41 |                     num = 10;
 42 |                 }
 43 |                 paddle_infer = NativeMethods.predictor_device_cpu(paddle_infer, num);
 44 |             }
 45 |             else if (divice == Divice.ONNX_runtime)
 46 |             {
 47 |                 if (num == 0)
 48 |                 {
 49 |                     num = 10;
 50 |                 }
 51 |                 paddle_infer = NativeMethods.predictor_device_ONNX_runtime(paddle_infer, num);
 52 |             }
 53 |             else if (divice == Divice.oneDNN)
 54 |             {
 55 |                 if (num == 0)
 56 |                 {
 57 |                     num = 1;
 58 |                 }
 59 |                 paddle_infer = NativeMethods.predictor_device_oneDNN(paddle_infer, num);
 60 |             }
 61 |             else if (divice == Divice.GPU) 
 62 |             {
 63 |                 paddle_infer = NativeMethods.predictor_device_gpu(paddle_infer, memory_init_size, num, workspace_size);
 64 |             }
 65 |         }
 66 |         /// <summary>
 67 |         /// 获取输入节点名字
 68 |         /// </summary>
 69 |         /// <returns>输入节点列表</returns>
 70 |         public List<string> get_input_names()
 71 |         {
 72 |             int[] length = new int[5];
 73 |             string input = NativeMethods.get_input_names(paddle_infer,ref length[0]);
 74 |             List<string> input_name = new List<string>();
 75 |             int l = 0;
 76 |             for (int i = 0; i < length.Length; i++) 
 77 |             {
 78 |                 string str = input.Substring(l, length[i]);
 79 |                 input_name.Add(str);
 80 |                 l += length[i];
 81 |                 if (l >= input.Length) 
 82 |                 {
 83 |                     break;
 84 |                 }
 85 |             }
 86 |             return input_name;
 87 |         }
 88 |         /// <summary>
 89 |         /// 设置输入节点形状
 90 |         /// </summary>
 91 |         /// <param name="input_shape">形状数组</param>
 92 |         /// <param name="input_name">节点名称</param>
 93 |         public void set_input_shape(int[] input_shape, string input_name) 
 94 |         {
 95 |             paddle_infer = NativeMethods.set_input_shape(paddle_infer, input_name, ref input_shape[0], input_shape.Length);
 96 |         }
 97 | 
 98 |         /// <summary>
 99 |         /// 加载普通数据
100 |         /// </summary>
101 |         /// <param name="input_name">输入节点名称</param>
102 |         /// <param name="input_data">输入数据</param>
103 |         public void load_input_data(string input_name, float[] input_data)
104 |         {
105 |             paddle_infer = NativeMethods.load_input_data(paddle_infer, input_name, ref input_data[0]);
106 |         }
107 |         /// <summary>
108 |         ///  加载图片数据
109 |         /// </summary>
110 |         /// <param name="input_name">输入节点名称</param>
111 |         /// <param name="image_data">图片数据</param>
112 |         /// <param name="image_size">图片长度</param>
113 |         /// <param name="type">数据处理类型</param>
114 |         public void load_input_data(string input_name, byte[] image_data, ulong image_size, int type)
115 |         {
116 |             paddle_infer = NativeMethods.load_input_image_data(paddle_infer, input_name, ref image_data[0], image_size, type);
117 |         }
118 |         /// <summary>
119 |         /// 模型推理
120 |         /// </summary>
121 |         public void infer()
122 |         {
123 |             paddle_infer = NativeMethods.infer(paddle_infer);
124 |         }
125 |         /// <summary>
126 |         /// 获取输出节点名字
127 |         /// </summary>
128 |         /// <returns>输出节点列表</returns>
129 |         public List<string> get_output_names()
130 |         {
131 |             int[] length = new int[5];
132 |             string output = NativeMethods.get_output_names(paddle_infer, ref length[0]);
133 |             List<string> output_name = new List<string>();
134 |             int l = 0;
135 |             for (int i = 0; i < length.Length; i++)
136 |             {
137 |                 string str = output.Substring(l, length[i]);
138 |                 output_name.Add(str);
139 |                 l += length[i];
140 |                 if (l >= output.Length)
141 |                 {
142 |                     break;
143 |                 }
144 |             }
145 |             return output_name;
146 |         }
147 |         /// <summary>
148 |         /// 获取指定节点形状
149 |         /// </summary>
150 |         /// <param name="node_name">节点名称</param>
151 |         /// <returns></returns>
152 |         public List<int> get_shape(string node_name) 
153 |         {
154 |             int[] shape = new int[5];
155 |             int dimension = 0;
156 |             List<int> shape_out = new List<int>();
157 |             NativeMethods.get_node_shape(paddle_infer, node_name, ref shape[0], ref dimension);
158 |             for (int i = 0; i < dimension; i++) 
159 |             {
160 |                 shape_out.Add(shape[i]);
161 |             }
162 |             return shape_out;
163 |         }
164 | 
165 |         /// <summary>
166 |         /// 读取推理结果数据
167 |         /// </summary>
168 |         /// <typeparam name="T">数据类型</typeparam>
169 |         /// <param name="output_name">输出节点名</param>
170 |         /// <param name="data_size">输出数据长度</param>
171 |         /// <returns>推理结果数组</returns>
172 |         public T[] read_infer_result<T>(string output_name, int data_size)
173 |         {
174 |             // 获取设定类型
175 |             string t = typeof(T).ToString();
176 |             // 新建返回值数组
177 |             T[] result = new T[data_size];
178 |             if (t == "System.Int32")
179 |             { // 读取数据类型为整形数据
180 |                 int[] inference_result = new int[data_size];
181 |                 NativeMethods.read_result_data_I32(paddle_infer, output_name, ref inference_result[0]);
182 |                 result = (T[])Convert.ChangeType(inference_result, typeof(T[]));
183 |                 return result;
184 |             }
185 |             else if (t == "System.Int64")
186 |             {
187 |                 long[] inference_result = new long[data_size];
188 |                 NativeMethods.read_result_data_I64(paddle_infer, output_name, ref inference_result[0]);
189 |                 result = (T[])Convert.ChangeType(inference_result, typeof(T[]));
190 |                 return result;
191 |             }
192 |             else
193 |             { // 读取数据类型为浮点型数据
194 |                 float[] inference_result = new float[data_size];
195 |                 NativeMethods.read_result_data_F32(paddle_infer, output_name, ref inference_result[0]);
196 |                 result = (T[])Convert.ChangeType(inference_result, typeof(T[]));
197 |                 return result;
198 |             }
199 |         }
200 |         /// <summary>
201 |         /// 删除内存地址
202 |         /// </summary>
203 |         public void delet()
204 |         {
205 |             NativeMethods.dispose(paddle_infer);
206 |         }
207 | 
208 |     }
209 | 
210 | 
211 |     /// <summary>
212 |     /// 设备名称
213 |     /// </summary>
214 |     public enum Divice { 
215 |         CPU,
216 |         GPU,
217 |         ONNX_runtime,
218 |         oneDNN
219 |     }
220 | }


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