C# SwinV2 Stable Diffusion 提示词反推 Onnx Demo

目录

介绍

效果

CPU

GPU

模型信息

项目

代码

下载 

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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

CPU

GPU

模型信息

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

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

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();        }    }}

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