C# SwinV2 Stable Diffusion 提示词反推 Onnx Demo

介绍

效果

模型信息

项目

代码

下载

C# SwinV2 Stable Diffusion 提示词反推 Onnx Demo

介绍

模型出处github地址：https://github.com/SmilingWolf/SW-CV-ModelZoo

模型下载地址：https://huggingface.co/SmilingWolf/wd-v1-4-swinv2-tagger-v2

效果

模型信息

Model Properties
-------------------------
---------------------------------------------------------------

Inputs
-------------------------
name：input_1:0
tensor：Float[1, 448, 448, 3]
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Outputs
-------------------------
name：predictions_sigmoid
tensor：Float[1, 9083]
---------------------------------------------------------------

项目

代码

using Microsoft.ML.OnnxRuntime;
using Microsoft.ML.OnnxRuntime.Tensors;
using OpenCvSharp;
using System;
using System.Collections.Generic;
using System.Drawing;
using System.IO;
using System.Linq;
using System.Text;
using System.Windows.Forms;

namespace Onnx_Demo
{
public partial class Form1 : Form
{
public Form1()
{
InitializeComponent();
}

string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png";
string image_path = "";
DateTime dt1 = DateTime.Now;
DateTime dt2 = DateTime.Now;
string model_path;
Mat image;

SessionOptions options;
InferenceSession onnx_session;
Tensor<float> input_tensor;
List<NamedOnnxValue> input_container;
IDisposableReadOnlyCollection<DisposableNamedOnnxValue> result_infer;
DisposableNamedOnnxValue[] results_onnxvalue;

Tensor<float> result_tensors;

StringBuilder sb = new StringBuilder();

public string[] class_names;

private void button1_Click(object sender, EventArgs e)
{
OpenFileDialog ofd = new OpenFileDialog();
ofd.Filter = fileFilter;
if (ofd.ShowDialog() != DialogResult.OK) return;
pictureBox1.Image = null;
image_path = ofd.FileName;
pictureBox1.Image = new Bitmap(image_path);
textBox1.Text = "";
image = new Mat(image_path);
}

private void button2_Click(object sender, EventArgs e)
{
if (image_path == "")
{
return;
}

button2.Enabled = false;
textBox1.Text = "";
sb.Clear();
Application.DoEvents();

//图片缩放
image = new Mat(image_path);
int max_image_length = image.Cols > image.Rows ? image.Cols : image.Rows;
Mat max_image = Mat.Zeros(new OpenCvSharp.Size(max_image_length, max_image_length), MatType.CV_8UC3);
Rect roi = new Rect(0, 0, image.Cols, image.Rows);
image.CopyTo(new Mat(max_image, roi));

float[] result_array;

// 将图片转为RGB通道
Mat image_rgb = new Mat();
Cv2.CvtColor(max_image, image_rgb, ColorConversionCodes.BGR2RGB);
Mat resize_image = new Mat();
Cv2.Resize(max_image, resize_image, new OpenCvSharp.Size(448, 448));

// 输入Tensor
for (int y = 0; y < resize_image.Height; y++)
{
for (int x = 0; x < resize_image.Width; x++)
{
input_tensor[0, y, x, 0] = resize_image.At<Vec3b>(y, x)[0];
input_tensor[0, y, x, 1] = resize_image.At<Vec3b>(y, x)[1];
input_tensor[0, y, x, 2] = resize_image.At<Vec3b>(y, x)[2];
}
}

//将 input_tensor 放入一个输入参数的容器，并指定名称
input_container.Add(NamedOnnxValue.CreateFromTensor("input_1:0", input_tensor));

dt1 = DateTime.Now;
//运行 Inference 并获取结果
result_infer = onnx_session.Run(input_container);
dt2 = DateTime.Now;

// 将输出结果转为DisposableNamedOnnxValue数组
results_onnxvalue = result_infer.ToArray();

// 读取第一个节点输出并转为Tensor数据
result_tensors = results_onnxvalue[0].AsTensor<float>();

result_array = result_tensors.ToArray();

List<ScoreIndex> ltResult = new List<ScoreIndex>();
ScoreIndex temp;
for (int i = 0; i < result_array.Length; i++)
{
temp = new ScoreIndex(i, result_array[i]);
ltResult.Add(temp);
}

//根据分数倒序排序，取前14个
var SortedByScore = ltResult.OrderByDescending(p => p.Score).ToList().Take(14);

foreach (var item in SortedByScore)
{
sb.Append(class_names[item.Index] + ",");
}
sb.Length--; // 将长度减1来移除最后一个字符

sb.AppendLine("");
sb.AppendLine("------------------");

// 只取分数最高的
// float max = result_array.Max();
// int maxIndex = Array.IndexOf(result_array, max);
// sb.AppendLine(class_names[maxIndex]+" "+ max.ToString("P2"));

sb.AppendLine("推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms");
textBox1.Text = sb.ToString();
button2.Enabled = true;
}

private void Form1_Load(object sender, EventArgs e)
{
model_path = "model/model.onnx";

// 创建输出会话，用于输出模型读取信息
options = new SessionOptions();
options.LogSeverityLevel = OrtLoggingLevel.ORT_LOGGING_LEVEL_INFO;
options.AppendExecutionProvider_CPU(0);// 设置为CPU上运行

// 创建推理模型类，读取本地模型文件
onnx_session = new InferenceSession(model_path, options);//model_path 为onnx模型文件的路径

// 输入Tensor
input_tensor = new DenseTensor<float>(new[] { 1, 448, 448, 3 });
// 创建输入容器
input_container = new List<NamedOnnxValue>();

image_path = "test_img/test.jpg";
pictureBox1.Image = new Bitmap(image_path);
image = new Mat(image_path);

List<string> str = new List<string>();
StreamReader sr = new StreamReader("model/lable.txt");
string line;
while ((line = sr.ReadLine()) != null)
{
str.Add(line);
}
class_names = str.ToArray();
}

}
}

using Microsoft.ML.OnnxRuntime; using Microsoft.ML.OnnxRuntime.Tensors; using OpenCvSharp; using System; using System.Collections.Generic; using System.Drawing; using System.IO; using System.Linq; using System.Text; using System.Windows.Forms; namespace Onnx_Demo { public partial class Form1 : Form { public Form1() { InitializeComponent(); } string fileFilter = "*.*|*.bmp;*.jpg;*.jpeg;*.tiff;*.tiff;*.png"; string image_path = ""; DateTime dt1 = DateTime.Now; DateTime dt2 = DateTime.Now; string model_path; Mat image; SessionOptions options; InferenceSession onnx_session; Tensor<float> input_tensor; List<NamedOnnxValue> input_container; IDisposableReadOnlyCollection<DisposableNamedOnnxValue> result_infer; DisposableNamedOnnxValue[] results_onnxvalue; Tensor<float> result_tensors; StringBuilder sb = new StringBuilder(); public string[] class_names; private void button1_Click(object sender, EventArgs e) { OpenFileDialog ofd = new OpenFileDialog(); ofd.Filter = fileFilter; if (ofd.ShowDialog() != DialogResult.OK) return; pictureBox1.Image = null; image_path = ofd.FileName; pictureBox1.Image = new Bitmap(image_path); textBox1.Text = ""; image = new Mat(image_path); } private void button2_Click(object sender, EventArgs e) { if (image_path == "") { return; } button2.Enabled = false; textBox1.Text = ""; sb.Clear(); Application.DoEvents(); //图片缩放 image = new Mat(image_path); int max_image_length = image.Cols > image.Rows ? image.Cols : image.Rows; Mat max_image = Mat.Zeros(new OpenCvSharp.Size(max_image_length, max_image_length), MatType.CV_8UC3); Rect roi = new Rect(0, 0, image.Cols, image.Rows); image.CopyTo(new Mat(max_image, roi)); float[] result_array; // 将图片转为RGB通道 Mat image_rgb = new Mat(); Cv2.CvtColor(max_image, image_rgb, ColorConversionCodes.BGR2RGB); Mat resize_image = new Mat(); Cv2.Resize(max_image, resize_image, new OpenCvSharp.Size(448, 448)); // 输入Tensor for (int y = 0; y < resize_image.Height; y++) { for (int x = 0; x < resize_image.Width; x++) { input_tensor[0, y, x, 0] = resize_image.At<Vec3b>(y, x)[0]; input_tensor[0, y, x, 1] = resize_image.At<Vec3b>(y, x)[1]; input_tensor[0, y, x, 2] = resize_image.At<Vec3b>(y, x)[2]; } } //将 input_tensor 放入一个输入参数的容器，并指定名称 input_container.Add(NamedOnnxValue.CreateFromTensor("input_1:0", input_tensor)); dt1 = DateTime.Now; //运行 Inference 并获取结果 result_infer = onnx_session.Run(input_container); dt2 = DateTime.Now; // 将输出结果转为DisposableNamedOnnxValue数组 results_onnxvalue = result_infer.ToArray(); // 读取第一个节点输出并转为Tensor数据 result_tensors = results_onnxvalue[0].AsTensor<float>(); result_array = result_tensors.ToArray(); List<ScoreIndex> ltResult = new List<ScoreIndex>(); ScoreIndex temp; for (int i = 0; i < result_array.Length; i++) { temp = new ScoreIndex(i, result_array[i]); ltResult.Add(temp); } //根据分数倒序排序，取前14个 var SortedByScore = ltResult.OrderByDescending(p => p.Score).ToList().Take(14); foreach (var item in SortedByScore) { sb.Append(class_names[item.Index] + ","); } sb.Length--; // 将长度减1来移除最后一个字符 sb.AppendLine(""); sb.AppendLine("------------------"); // 只取分数最高的 // float max = result_array.Max(); // int maxIndex = Array.IndexOf(result_array, max); // sb.AppendLine(class_names[maxIndex]+" "+ max.ToString("P2")); sb.AppendLine("推理耗时:" + (dt2 - dt1).TotalMilliseconds + "ms"); textBox1.Text = sb.ToString(); button2.Enabled = true; } private void Form1_Load(object sender, EventArgs e) { model_path = "model/model.onnx"; // 创建输出会话，用于输出模型读取信息 options = new SessionOptions(); options.LogSeverityLevel = OrtLoggingLevel.ORT_LOGGING_LEVEL_INFO; options.AppendExecutionProvider_CPU(0);// 设置为CPU上运行 // 创建推理模型类，读取本地模型文件 onnx_session = new InferenceSession(model_path, options);//model_path 为onnx模型文件的路径 // 输入Tensor input_tensor = new DenseTensor<float>(new[] { 1, 448, 448, 3 }); // 创建输入容器 input_container = new List<NamedOnnxValue>(); image_path = "test_img/test.jpg"; pictureBox1.Image = new Bitmap(image_path); image = new Mat(image_path); List<string> str = new List<string>(); StreamReader sr = new StreamReader("model/lable.txt"); string line; while ((line = sr.ReadLine()) != null) { str.Add(line); } class_names = str.ToArray(); } } }